update CITATION.cff

* v4.3 update.

* Update the cute_dsl_api changelog's doc link

* Update version to 4.3.0

* Update the example link

* Update doc to encourage user to install DSL from requirements.txt

---------

Co-authored-by: Larry Wu <larwu@nvidia.com>

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -0,0 +1,38 @@
+name: Bug Report
+description: Create a bug report to help us improve CUTLASS
+title: "[BUG] "
+labels: ["? - Needs Triage", "bug"]
+assignees: []
+
+body:
+  - type: dropdown
+    id: component
+    attributes:
+      label: Which component has the problem?
+      options:
+        - CuTe DSL
+        - CUTLASS C++
+    validations:
+      required: true
+  - type: textarea
+    id: bug-report
+    attributes:
+      label: Bug Report
+      description: Please fill out all sections below
+      value: |
+        **Describe the bug**
+        A clear and concise description of what the bug is.
+        
+        **Steps/Code to reproduce bug**
+        Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly.
+        
+        **Expected behavior**
+        A clear and concise description of what you expected to happen.
+        
+        **Environment details (please complete the following information):**
+         - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)] 
+        
+        **Additional context**
+        Add any other context about the problem here.
+    validations:
+      required: true 

.github/ISSUE_TEMPLATE/config.yml

@@ -0,0 +1,5 @@
+blank_issues_enabled: true
+contact_links:
+  - name: CUTLASS Discord
+    url: https://discord.gg/nvidiadeveloper
+    about: Come chat about using and contributing to CUTLASS!

.github/ISSUE_TEMPLATE/documentation_request.md

@@ -0,0 +1,35 @@
+---
+name: Documentation request
+about: Report incorrect or needed documentation to improve CUTLASS
+title: "[DOC]"
+labels: "? - Needs Triage, documentation"
+assignees: ''
+
+---
+
+## Report incorrect documentation
+
+**Location of incorrect documentation**
+Provide links and line numbers if applicable.
+
+**Describe the problems or issues found in the documentation**
+A clear and concise description of what you found to be incorrect.
+
+**Steps taken to verify documentation is incorrect**
+List any steps you have taken:
+
+**Suggested fix for documentation**
+Detail proposed changes to fix the documentation if you have any.
+
+---
+
+## Report needed documentation
+
+**Report needed documentation**
+A clear and concise description of what documentation you believe it is needed and why.
+
+**Describe the documentation you'd like**
+A clear and concise description of what you want to happen.
+
+**Steps taken to search for needed documentation**
+List any steps you have taken:

.github/ISSUE_TEMPLATE/feature_request.yml

@@ -0,0 +1,35 @@
+name: Feature Request
+description: Suggest an idea for CUTLASS
+title: "[FEA] "
+labels: ["? - Needs Triage", "feature request"]
+assignees: []
+
+body:
+  - type: dropdown
+    id: component
+    attributes:
+      label: Which component requires the feature?
+      options:
+        - CuTe DSL
+        - CUTLASS C++
+    validations:
+      required: true
+  - type: textarea
+    id: feature-request
+    attributes:
+      label: Feature Request
+      description: Please fill out all sections below
+      value: |
+        **Is your feature request related to a problem? Please describe.**
+        A clear and concise description of what the problem is. Ex. I wish I could use CUTLASS to do [...]
+
+        **Describe the solution you'd like**
+        A clear and concise description of what you want to happen.
+
+        **Describe alternatives you've considered**
+        A clear and concise description of any alternative solutions or features you've considered.
+
+        **Additional context**
+        Add any other context, code examples, or references to existing implementations about the feature request here.
+    validations:
+      required: true 

.github/ISSUE_TEMPLATE/submit_question.md

@@ -0,0 +1,10 @@
+---
+name: Submit question
+about: Ask a general question about CUTLASS
+title: "[QST]"
+labels: "? - Needs Triage, question"
+assignees: ''
+
+---
+
+**What is your question?**

.github/workflows/auto-label-issues.yml

@@ -0,0 +1,51 @@
+name: Auto Label Issues
+
+on:
+  issues:
+    types: [opened]
+
+jobs:
+  add-labels:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+    steps:
+      - name: Add component label
+        uses: actions/github-script@v7
+        with:
+          script: |
+            const issue = context.payload.issue;
+            const body = issue.body || '';
+            
+            // Parse the issue body to find the component selection
+            // GitHub renders dropdown selections as "### {label}\n\n{selection}"
+            // Check for both bug report and feature request dropdown labels
+            const bugComponentMatch = body.match(/### Which component has the problem\?\s*\n\s*\n\s*(.+?)(?:\n|$)/);
+            const featureComponentMatch = body.match(/### Which component requires the feature\?\s*\n\s*\n\s*(.+?)(?:\n|$)/);
+            
+            const componentMatch = bugComponentMatch || featureComponentMatch;
+            
+            if (componentMatch) {
+              const component = componentMatch[1].trim();
+              let label = '';
+              
+              // Map component selections to labels
+              switch(component) {
+                case 'CuTe DSL':
+                  label = 'CuTe DSL';
+                  break;
+                case 'CUTLASS C++':
+                  label = 'CUTLASS C++';
+                  break;
+              }
+              
+              if (label) {
+                await github.rest.issues.addLabels({
+                  owner: context.repo.owner,
+                  repo: context.repo.repo,
+                  issue_number: issue.number,
+                  labels: [label]
+                });
+                console.log(`Added label: ${label}`);
+              }
+            } 

.github/workflows/blossom-ci.yml

@@ -0,0 +1,112 @@
+#################################################################################################
+#
+# Copyright (c) 2023 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+#################################################################################################
+
+# A workflow to trigger ci on hybrid infra (github + self hosted runner)
+name: Blossom-CI
+on:
+  issue_comment:
+    types: [created]
+  workflow_dispatch:
+      inputs:
+          platform:
+            description: 'runs-on argument'
+            required: false
+          args:
+            description: 'argument'
+            required: false
+
+jobs:
+  Authorization:
+    name: Authorization
+    runs-on: blossom
+    outputs:
+      args: ${{ env.args }}
+
+    # This job only runs for pull request comments
+    if: |
+        (startsWith(github.event.comment.body, '/bot run') ||
+        startsWith(github.event.comment.body, '/bot kill')) && contains(
+        fromJson('["nv-fastkernels-cicd", "zekunf-nv", "hwu36", "IonThruster", "thakkarV", "d-k-b", "mihir-awatramani", "fengxie", "vickiw973", "Junkai-Wu", "brandon-yujie-sun", "lijingticy22", "hongw-nv", "vikgupta-nv", "IwakuraRein", "depaulmillz", "jackkosaian", "itramble", "ccecka", "sxtyzhangzk", "hbarclay", "yzhaiustc", "x86vk", "sklevtsov-nvidia", "ANIKET-SHIVAM", "Shreya-gaur", "azhurkevich", "serifyesil", "richardmcai", "lsyyy666", "Ethan-Yan27", "XiaoSong9905", "shdetect", "keithzzzzz"]'),
+        github.actor)
+    steps:
+      - name: Check if comment is issued by authorized person
+        run: blossom-ci
+        env:
+          OPERATION: 'AUTH'
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
+
+  Vulnerability-scan:
+    name: Vulnerability scan
+    needs: [Authorization]
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v2
+        with:
+          repository: ${{ fromJson(needs.Authorization.outputs.args).repo }}
+          ref: ${{ fromJson(needs.Authorization.outputs.args).ref }}
+          lfs: 'true'
+
+      - name: Run blossom action
+        uses: NVIDIA/blossom-action@main
+        env:
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          REPO_KEY_DATA: ${{ secrets.BLOSSOM_KEY }}
+        with:
+          args1: ${{ fromJson(needs.Authorization.outputs.args).args1 }}
+          args2: ${{ fromJson(needs.Authorization.outputs.args).args2 }}
+          args3: ${{ fromJson(needs.Authorization.outputs.args).args3 }}
+
+  Job-trigger:
+    name: Start ci job
+    needs: [Vulnerability-scan]
+    runs-on: blossom
+    steps:
+      - name: Start ci job
+        run: blossom-ci
+        env:
+          OPERATION: 'START-CI-JOB'
+          CI_SERVER: ${{ secrets.CI_SERVER }}
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+  Upload-Log:
+    name: Upload log
+    runs-on: blossom
+    if : github.event_name == 'workflow_dispatch'
+    steps:
+      - name: Jenkins log for pull request ${{ fromJson(github.event.inputs.args).pr }} (click here)
+        run: blossom-ci
+        env:
+          OPERATION: 'POST-PROCESSING'
+          CI_SERVER: ${{ secrets.CI_SERVER }}
+          REPO_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/labeler.yml

@@ -0,0 +1,11 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+  triage:
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/labeler@main
+      with:
+        repo-token: "${{ secrets.GITHUB_TOKEN }}"

.github/workflows/new-issues-to-triage-projects.yml

@@ -0,0 +1,35 @@
+name: Auto Assign New Issues to Triage Project
+
+on:
+  issues:
+    types: [opened]
+
+env:
+  GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+
+jobs:
+  assign_one_project:
+    runs-on: ubuntu-latest
+    name: Assign to New Issues to Triage Project
+    steps:
+    - name: Process bug issues
+      uses: docker://takanabe/github-actions-automate-projects:v0.0.1
+      if: contains(github.event.issue.labels.*.name, 'bug') && contains(github.event.issue.labels.*.name, '? - Needs Triage')
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass
+        GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing'
+    - name: Process feature issues
+      uses: docker://takanabe/github-actions-automate-projects:v0.0.1
+      if: contains(github.event.issue.labels.*.name, 'feature request') && contains(github.event.issue.labels.*.name, '? - Needs Triage')
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass
+        GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing'
+    - name: Process other issues
+      uses: docker://takanabe/github-actions-automate-projects:v0.0.1
+      if: contains(github.event.issue.labels.*.name, '? - Needs Triage') && (!contains(github.event.issue.labels.*.name, 'bug') && !contains(github.event.issue.labels.*.name, 'feature request'))
+      env:
+        GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass
+        GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing'

.github/workflows/stale.yml

@@ -0,0 +1,57 @@
+name: Mark inactive issues and pull requests
+
+on:
+  schedule:
+    - cron: "0 * * * *"
+
+jobs:
+  mark-inactive-30d:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Mark 30 day inactive issues and pull requests
+        uses: actions/stale@v3
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: >
+            This issue has been labeled `inactive-30d` due to no recent activity in the past 30 days.
+            Please close this issue if no further response or action is needed.
+            Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.
+            This issue will be labeled `inactive-90d` if there is no activity in the next 60 days.
+          stale-issue-label: "inactive-30d"
+          exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-issue-stale: 30
+          days-before-issue-close: -1
+          stale-pr-message: >
+            This PR has been labeled `inactive-30d` due to no recent activity in the past 30 days.
+            Please close this PR if it is no longer required.
+            Otherwise, please respond with a comment indicating any updates.
+            This PR will be labeled `inactive-90d` if there is no activity in the next 60 days.
+          stale-pr-label: "inactive-30d"
+          exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-pr-stale: 30
+          days-before-pr-close: -1
+          operations-per-run: 50
+  mark-inactive-90d:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Mark 90 day inactive issues and pull requests
+        uses: actions/stale@v3
+        with:
+          repo-token: ${{ secrets.GITHUB_TOKEN }}
+          stale-issue-message: >
+            This issue has been labeled `inactive-90d` due to no recent activity in the past 90 days.
+            Please close this issue if no further response or action is needed.
+            Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.
+          stale-issue-label: "inactive-90d"
+          exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-issue-stale: 90
+          days-before-issue-close: -1
+          stale-pr-message: >
+            This PR has been labeled `inactive-90d` due to no recent activity in the past 90 days.
+            Please close this PR if it is no longer required.
+            Otherwise, please respond with a comment indicating any updates.
+          stale-pr-label: "inactive-90d"
+          exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue"
+          days-before-pr-stale: 90
+          days-before-pr-close: -1
+          operations-per-run: 50

.gitignore

@@ -0,0 +1,4 @@
+# PyCache files
+__pycache__/
+cutlass_library.egg-info/
+/build*

CHANGELOG.md

@@ -0,0 +1,864 @@
+# Changelog
+
+# CUTLASS 4.x
+
+## [4.3.0](https://github.com/NVIDIA/cutlass/tree/main) (2025-10-20)
+
+### CuTe DSL
+* Debuggability improvements:
+    - Supported source location tracking for DSL APIs
+    - Supported dumping PTX and CUBIN code
+* More examples and notebooks to get started with CuTe DSL:
+    - [Kernel launch with Programmatic Dependent Launch](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/programmatic_dependent_launch.py)
+    - Improved performance of elementwise kernel (https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/elementwise_apply.py):
+        + Generalize code to handle list of input tensors
+        + Generalize TV layout computation to handle different data types
+    - Demonstrate the new Pipeline APIs in [Blackwell SM100 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py):
+        + New Pipeline API `PipelineProducer` and `PipelineConsumer` to simplify code (no more explicit pipeline state management)
+    - Separate epilogue code for non-TMA and TMA implementation
+        + Note that the updates simplifies the codes but existing APIs still work and are supported
+    - [Basic Blackwell SM100 GEMM with decent performance](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/tutorial_gemm/fp16_gemm_0.py)
+        + Simple tutorial achieves 84% SOL performance with MNK 8K
+    - Reworked [elementwise add notebook](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks/elementwise_add.ipynb) with more details and detailed explanation about TV layout
+        + Updated implementation to handle general data type and multiple inputs
+        + Updated explanation for TV layout in simpler language
+        + Added visualization of TV Layout with 3rd party utils
+    - [Benchmark and autotune demonstration](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks/benchmark_autotune.ipynb)
+* More examples of authorizing peak-performance kernels:
+    - [Blackwell SM100 mixed-input GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mixed_input_gemm.py)
+    - [Blackwell SM100 persistent blockwise dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/blockwise_gemm.py)
+    - [Blackwell SM100 persistent blockwise contiguous grouped dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/contiguous_grouped_gemm.py)
+    - [Blackwell SM100 persistent blockwise masked grouped dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/masked_grouped_gemm.py)
+    - [Blackwell SM100 fmha bwd](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/fmha_bwd.py)
+    - [Blackwell SM100 mla](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mla.py)
+    - [Hopper SM90 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/hopper/dense_gemm_persistent.py)
+    - [Blackwell GeForce batched dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell_geforce/dense_gemm.py)
+    - [Ampere HSTU Attention](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/hstu_attention.py)
+* API updates:
+    - Please refer to [DSL API changelog](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/changelog.html) for details
+* Bug fixings and improvements
+    - Add mma_tiler_n=64 and mma_tiler_n=192 support in [Blackwell SM100 persistent dense blockscaled GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_blockscaled_gemm_persistent.py).
+    - Fixed ``TensorSSA.reduce`` to support static value as initial value
+    - Updated docstring for following APIs to be more concise and easier to understand:
+        - ``make_layout_tv``
+        - ``is_static``
+        - ``PipelineAsync``
+        - ``SmemAllocator``
+    - Fixed documentation for ``pipeline``, ``utils`` and ``cute.math``
+
+### CUTLASS C++
+* Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+    - Add softmax skip correction.
+    - Fix a shared memory allocation bug where it needs to opt in maximum dynamics shared memory explicitly once it exceeds 48KB.
+    - Fix a dead hang issue caused by early return warp.
+* Add Ragged Contiguous Grouped gemm kernel in [example 92](https://github.com/NVIDIA/cutlass/tree/main/examples/92_blackwell_moe_gemm/).
+    - This kernel uses a TMA 3D load to load the weights matrix and use the tensormap update method to load activations.
+* Optimize group gemm kernels by enabling async TMA desc update.
+* Support Blackwell SM100 convolution stream-K kernel.
+    - Unit tests: [fprop_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/fprop/sm100_conv3d_fprop_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu), [dgrad_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/dgrad/sm100_conv3d_dgrad_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu), [wgrad_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/wgrad/sm100_conv2d_wgrad_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu).
+* Add profiler support for Blackwell SM100 and SM120 blockscaled sparse kernels.
+* Fix some kernel issues:
+    - Fix a race check issue of Blackwell SM103 kernels by adding missing elect one for prefetch barrier initialization.
+    - Allow user to directly specify the number of stages for Hopper sm90 mixed input gemm.
+    - Remove warnings caused by cuda vector type alignment setting in CUDA 13.
+    - Remove problematic `cutlass::int8_t` and replace it with `int8_t`.
+* Fix some profiler issues:
+    - Add some missing reference kernels.
+    - Add calculation of scale factor A and B in function `bytes_with_problem_shape` of block scaled profiler.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 13.0U1.
+
+## [4.2.1](https://github.com/NVIDIA/cutlass/releases/tag/v4.2.1) (2025-09-22)
+
+### CuTe DSL
+* Bug fixings and improvements
+    - Fixed an issue when running DSL codes with cuda-python 13.0
+    - Fixed an issue when running inductor with DSL codes
+    - Fixed an issue with unexpected logging when running DSL codes in FlashInfer
+    - Fixed the issue reported in https://github.com/NVIDIA/cutlass/issues/2647
+    - Fixed an issue when conditional define of variables outside of dynamic control flow
+
+### CUTLASS C++
+* Bypass EVT for nosmem blockwise kernels on Blackwell.
+* Rename cutlass/python/cutlass directory to cutlass/python/cutlass_cppgen.
+
+## [4.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.2.0) (2025-09-15)
+
+### CuTe DSL
+* More Python versions are now supported for both x86-64 and aarch64, including
+    - Python 3.10, 3.11, 3.12, and 3.13
+* Added new example and updated notebook to get started with CuTe DSL
+    - [Call kernels with dlpack bypassed](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/call_bypass_dlpack.py)
+    - Updates on [TensorSSA demonstration](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks/tensorssa.ipynb)
+      + Added a section for introducing the broadcast
+* API updates
+    - Please refer to [DSL API changelog](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/changelog.html) for details
+* Bug fixings and improvements
+    - Fixed ``cute.print_tensor`` for coordinate tensor
+    - Fixed `cute.print` for tuple of layouts
+    - Fixed frozen object is not properly updated after fully assigned in dynamic control flow
+    - Fixed assign tuple/list element in a dynamic control flow may cause compilation failure
+    - Improved error message when CUDA context is not initialized
+    - Improved docstring of congruent and weakly_congruent
+
+### CUTLASS C++
+* Support for Blackwell SM103 kernels for B300 GPUs.
+    - Collective mainloop codes: [Blockscaled datatypes with support for dense GEMM mainloop](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm103_blockscaled_mma_warpspecialized.hpp)
+    - New [GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/dispatch_policy.hpp) and [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
+    - Kernel codes: [Blockscaled datatypes with support for dense GEMM kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm103_blockscaled_gemm_tma_warpspecialized.hpp).
+* Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM103 architecture:
+    - [Blockscaled ultra fp4 dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/89_sm103_fp4_ultra_gemm/).
+    - [Blockscaled ultra fp4 dense grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/90_sm103_fp4_ultra_grouped_gemm).
+* Set of unit tests that demonstrate the usage of Blackwell SM103 blockscaled GEMM
+    - Unit test files with prefix name of `sm103_` under [GEMM device unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/).
+* Support for Blackwell SM121 kernels for DGX Spark GPUs.
+    - Share the major codes with Blackwell SM120 kernels.
+* Add support for heuristics-based kernel filtering and autotuning using `nvidia-matmul-heuristics` to find the best kernels for a given scenario.
+    - Details please refer to [heuristics doc](https://github.com/NVIDIA/cutlass/tree/main/media/docs/cpp/heuristics.md).
+* Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+    - Add fused reduction kernel support for cutlass MLA.
+    - Add softmax skip correction.
+    - Support for GQA in FMHA backward kernel.
+    - Fix an issue where `get_unmasked_trip_count` may return a negative value.
+    - Fix an issue where mbarriers are initialized with a zero arrival count.
+    - Fix a corner case issue where the sequence length of q is not a multiple of tile_q.
+    - Remove tma padding for forward kernel inputs.
+* Add Blackwell SM100 kernels for MoEs (focusing on Low-Latency inference performance): [example 92](https://github.com/NVIDIA/cutlass/tree/main/examples/92_blackwell_moe_gemm/).  It uses TMA (for weights) and CPASYNC (for tokens) to load input matrices and allow only one problem dimension to vary across groups/experts, unlike general Grouped GEMMs.  Note: further API simplifications and kernel improvements are upcoming. Any feedback on API is welcome.
+* Further enhance blockwise and groupwise GEMMs on Hopper and Blackwell
+    - On Blackwell SM120, a blockwise gemm kernel is added: [example 87](https://github.com/NVIDIA/cutlass/tree/main/examples/87_blackwell_geforce_gemm_blockwise/).
+    - On Hopper, add K major scale factor support for SM90 blockwise kernels.
+    - On Hopper, relax the restriction that the k dimension of the problem size has to be the multiple of the k dimension of the tile size.
+    - On Hopper, grouped version supports the case when k = 0.
+* Support for Blackwell SM100 fp4 gemv kernels.
+    - Kernel codes: [Gemv kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/gemv_blockscaled.h).
+    - Example codes: [example 91](https://github.com/NVIDIA/cutlass/tree/main/examples/91_fp4_gemv/)
+* Support for Blackwell SM100 legacy mixed input GEMM kernels.
+    - Collective mainloop codes: [Mixed input mainloop](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized_mixed_input.hpp).
+    - Kernel codes: [Mixed input kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized_mixed_input_transform.hpp).
+    - Example codes: [example 86](https://github.com/NVIDIA/cutlass/tree/main/examples/86_blackwell_mixed_dtype_gemm/).
+* Support for Blackwell SM100 cpasync kernel.
+    - Collective mainloop codes: [cpasync mainloop](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_cpasync_warpspecialized.hpp).
+    - Kernel codes: [cpasync kernel](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_cpasync_warpspecialized.hpp).
+* Support Blackwell SM120 mixed input blockscaled grouped GEMM.
+* Instantiating more Blackwell kernels in profiler.
+    - Blackwell SM100 and SM103 kernels support `CUTLASS_LIBRARY_INSTANTIATION_LEVEL` to instantiate all possible combinations.
+    - To use this feature, `CUTLASS_LIBRARY_KERNELS` must be non-empty. Profiler will combine `CUTLASS_LIBRARY_KERNELS` and `CUTLASS_LIBRARY_INSTANTIATION_LEVEL` to instantiate specific kernels.
+    - Details please check [Profiler Doc](https://github.com/NVIDIA/cutlass/tree/main/media/docs/cpp/profiler.md).
+* Fix some profiler issues:
+    - Modify default cluster callback values to none 0 to avoid profiler failure when these values are not set in command line.
+    - Fix some no output and timeout issues.
+    - Fix Pingpong Blockwise Hopper library generation.
+* From CUDA 13.0, the Blackwell SM101 for Thor GPUs is renamed to SM110.
+    - For CUDA toolkit version < 13.0, SM101 is still used for Thor GPUs.
+    - For CUDA toolkit version >= 13.0, SM110 is used for Thor GPUs and SM101 is no longer valid.
+* Rename legacy Python API package from `cutlass` to `cutlass_cppgen` and add Blackwell EVT support to legacy Python interface.
+    - Restructuring the C++ Blackwell SM100 Collective Epilogue Builder to work with the Python interface's `EpilogueDescriptors`.
+    - Added Blackwell SM100 EVT Emitter on the Python side and routed most emission through Hopper SM90 Emitter.
+    - Added some support for running SM100 kernels via the Python interface.
+* CuTe changes:
+    - Fix inaccurate GridDim calculation under [CuTe tutorial](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/blackwell/).
+    - Add [movmatrix](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-movmatrix) support.
+    - Fix smallest MMA-N allowed for Blackwell fp8 and fp16 gemm kernels.
+    - Support fp16 accmulator for sm89 fp8 mma.
+    - Shorten `nullspace` implementation.
+    - Isolate and comment on `cosize` risky changes.
+    - Important documentation correction: `E<0,1> == 1@0@1`.
+* Fix some kernel issues:
+    - Fix Hopper SM90 group gemm kernel to only use the commit group and wait group instead of also waiting on mbarriers.
+    - Fix a tiny bug when K is large for Blackwell SM103 fp4 grouped GEMM kernel.
+* Add following unit tests:
+    - [fp16 accmulator for sm89 fp8 mma](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/ampere/cooperative_gemm.cu)
+    - [movmatrix test](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/turing/movm.cu)
+    - [fp8 narrow mma n](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_tensorop_gemm/f16_f16_void_f32_narrow_mma_n.cu) and [fp16 narrow mma n](test/unit/gemm/device/sm100_tensorop_gemm/f8_f8_void_bf16_narrow_mma_n.cu)
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 13.0U1.
+
+## [4.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.1.0) (2025-07-16)
+
+### CuTe DSL
+* Add aarch64 support, you can now pip install `nvidia-cutlass-dsl` on GB200 systems!
+* More examples demonstrating how to use CuTe DSL to write peak-performance kernels
+    - [Blackwell Mamba2 SSD](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mamba2_ssd/mamba2_ssd.py)
+    - [Blackwell SM100 persistent dense blockscaled GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_blockscaled_gemm_persistent.py)
+* API updates
+    - Please refer to [DSL API changelog](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/changelog.html) for details
+
+### CUTLASS C++
+* Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+    - Add variable sequence length support for FMHA Backward kernel.
+    - Add varlen test support to Backward runner.
+    - Codes support empty batch sequences.
+* Replace `subbyte_iterator` with `cute::recast_ptr` when constructing logical iterators/arrays.
+* CuTe changes:
+    - Rewrite ArithTuple and ScaledBasis for robustness and clarity.
+    - Remove buggy and kludgy `get_layoutA|B|C_MN` and friends from Atoms/TiledX.
+    - Factor out `print_latex` and friends and rewrite.
+    - Factor out `print_svg` and friends and rewrite.
+* Support Blackwell SM100 SIMT packed fp32x2 kernels.
+* Support residual add for implicit gemm kernels.
+* Various fixes for CUTLASS C++ Python interface's EVT tracer:
+    - Add verifier for sm90 to report the invalid input.
+    - When adding an edge to the graph, if the edge already exists, add an identity compute node to avoid having multiple parallel edges.
+    - Register operations of tanh, sigmoid, exp, gelu to the python ast frontend.
+    - Replace the NotImplemented Error by packing all nodes into a single topological visitor node as a fallback.
+* Fix profiler bugs in exhaustive perf search.
+    - Fix incorrect cluster shape output issue when doing exhaustive search.
+    - Fix a bug in profiler grouped GEMM for setting tile scheduler swizzles, cluster shapes, and raster orders.
+* Fix some profiler issues.
+    - Complete the reference for Blackwell blockwise gemm kernels.
+    - Fix incorrect regex logic for L1 test.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+## [4.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v4.0.0) (2025-06-03)
+
+### CuTe DSL
+* CuTe DSL, a Python DSL centered around CuTe's abstractions
+    - [Core DSL implementation files](https://github.com/NVIDIA/cutlass/tree/main/python/CuTeDSL)
+    - [DSL quick start](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/quick_start.html)
+    - [DSL Overview](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/overview.html)
+* [Overhauled documentation with a new dedicated website](https://docs.nvidia.com/cutlass/latest)
+* Set of examples demonstrating how to use CuTe DSL to write peak-performance kernels
+    - [Blackwell SM100 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py)
+    - [Blackwell SM100 grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/grouped_gemm.py)
+    - [Blackwell SM100 fused multi-head attention forward pass](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/fmha.py)
+    - [Hopper GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/hopper/dense_gemm.py)
+    - [Ampere GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/tensorop_gemm.py)
+    - [FlashAttention-2 implementation targeting Ampere and Ada class GPUs (SM80, SM86, SM89)](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/flash_attention_v2.py)
+    - [SmemAllocator to facilitate shared memory allocation and management](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/smem_allocator.py)
+    - [C-structure based customized interface between JIT function and user codes](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/cute/ffi/jit_argument.py)
+* [Educational notebooks for getting started with CuTe DSL](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks)
+* API updates
+    - Please refer to [DSL API changelog](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/changelog.html) for details
+
+### CUTLASS C++
+* Support [Family Specific Architecture Features](https://developer.nvidia.com/blog/nvidia-blackwell-and-nvidia-cuda-12-9-introduce-family-specific-architecture-features/) which was introduced in CUDA 12.9
+  - 100f, 101f, 120f were added to support Family Specific Architecture Features which allows running the same binary on different chips belonging to the same Family (e.g. sm100) without recompiling.  Note 101a is supported since CUTLASS 3.9
+* Instruction shapes and redundant accumulation type have been removed from CUTLASS 3.x-style library kernel names to disambiguate kernels and shorten names.
+  - For example:
+    + `(old) cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma`
+    + `(new) cutlass3x_sm90_tensorop_gemm_bf16_bf16_f32_bf16_bf16_128x256x64_1x1x1_0_tnn_align8_warpspecialized_cooperative_epi_tma`
+   - If you are using the CUTLASS library kernel names directly (e.g. to compile a subset of the CUTLASS library with `-DCUTLASS_LIBRARY_KERNELS`, filter kernels in the CUTLASS profiler with `--kernels`), please update your uses accordingly, this is a breaking change.
+* Further improved [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMMs on Hopper and Blackwell.
+  - Added non-power-of-two tile sizes.
+  - Improved performance for K-major scale factors.
+  - The argument `mma_promotion_interval` has been removed from non-grouped GEMM to align with the grouped and Blackwell SM100 versions.
+* Enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+  - Support LSE output in FMHA Forward kernel.
+  - Enhance performance measurement: support of different warmup iterations; buffer rotation to keep L2 cold; separate testing of persistent and non-persistent.
+  - Enhance testing of variable sequence length.
+  - Disable B2B mode in MLA to simplify the sample.
+  - Clarify that `fmha_gen`  sample only supports head dim 128.
+  - Fixes for split-kv output in MLA.
+* Improve Blackwell and Hopper grouped GEMM performance, functionality, and profiler support.
+  - Enable runtime datatype for Blackwell SM100 grouped GEMM. Profiler support is also added.
+  - Enable kernel parameter exploration for Blackwell SM100 grouped GEMM - raster_order, swizzle.
+* Add [Blackwell SM100 implicit GEMM conv fprop/dgrad/wgrad unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/).
+* Add dynamic and preferred cluster support for convolution Blackwell SM100 kernels.
+* Fix profiler issues which cause no output or not supported error for some kernels.
+* Optimizations for Blackwell SM100 and SM120 block scaled kernels.
+* Support for Blackwell SM120 blockwise dense gemm in CUTLASS library and profiler.
+* New [Hopper SM90 FMHA example](https://github.com/NVIDIA/cutlass/tree/main/examples/88_hopper_fmha/), similar in design to the existing [Blackwell FMHA](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+* CuTe changes:
+    - Rework `cute::copy_if` so that the predicate tensor is also a true CuTe Tensor rather than a lambda and introduces transform-tensors to avoid any extra register or load/store overhead in using bool-tensors.
+    - New [CuTe tutorial](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/tiled_copy_if.cu) to show the usage of copy_if in tile copy.
+    - Add [CuTe C++ reduce op](https://github.com/NVIDIA/cutlass/tree/main/include/cute/algorithm/tensor_reduce.hpp).
+        - Add several [unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/cute/core/tensor_algs.cpp) for CuTe tensor algorithms.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+
+# CUTLASS 3.x
+
+## [3.9.2](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.2) (2025-05-03)
+* Fixed [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMM hang issue when problem size K is 128.
+* Optimal code generation with CUDA toolkit versions 12.9.
+
+## [3.9.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.1) (2025-04-30)
+* Fixed Group Gemm hang issue in CUTLASS 3.x
+* Improved Hopper [Blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) and [Groupwise](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) GEMM performance.
+
+## [3.9.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.9.0) (2025-04-24)
+
+* Support for Blackwell SM120 kernels for GeForce GPUs in CUTLASS 3.x API:
+  - Collective mainloops that target for:
+    * [Blockscaled datatypes with support for dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm120_blockscaled_mma_tma.hpp)
+    * [Blockscaled datatypes with support for sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm120_blockscaled_sparse_mma_tma.hpp)
+  - New [GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/dispatch_policy.hpp) and [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
+  - [Blackwell SM120 epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm120_visitor_store_tma_warpspecialized.hpp) and [full set of EVT fusions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm120_callbacks_tma_warpspecialized.hpp).
+* Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM120 architecture:
+  - [Blockscaled GEMM with NVFP4 input datatype and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79a_blackwell_geforce_nvfp4_bf16_gemm.cu).
+  - [Blockscaled GEMM with NVFP4 input datatype and NVFP4 output tensor with scale factor generation](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79b_blackwell_geforce_nvfp4_nvfp4_gemm.cu).
+  - [Blockscaled GEMM with mixed input datatype (MXFP8 and MXFP6) and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79c_blackwell_geforce_mixed_mxfp8_mxfp6_bf16_gemm.cu).
+  - [Grouped GEMM with nvfp4 datatype](https://github.com/NVIDIA/cutlass/tree/main/examples/79_blackwell_geforce_gemm/79d_blackwell_geforce_nvfp4_grouped_gemm.cu).
+  - [Sparse Blockscaled GEMM with mxfp8 input datatype and BF16 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/80_blackwell_geforce_sparse_gemm/80a_blackwell_geforce_mxfp8_bf16_sparse_gemm.cu).
+  - [Sparse Blockscaled GEMM with NVFP4 input datatype and NVFP4 output tensor](https://github.com/NVIDIA/cutlass/tree/main/examples/80_blackwell_geforce_sparse_gemm/80b_blackwell_geforce_nvfp4_nvfp4_sparse_gemm.cu).
+* Set of unit tests that demonstrate the usage of both [sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm120_blockscaled_sparse_tensorop_gemm/) and [dense](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm120_blockscaled_tensorop_gemm/) Blackwell SM120 blockscaled GEMM.
+* Support for Blackwell SM100 Sparse kernels:
+  - Collective mainloop that target for
+    * [SM100 Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_sparse_mma_warpspecialized.hpp)
+* Set of example that demonstrate the usage of the 3.x API for targeting Blackwell SM100 Sparse GEMM:
+  - [Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/83_blackwell_sparse_gemm/83_blackwell_sparse_gemm.cu)
+  - [Blockscaled Sparse GEMM with NVFP4 input data type](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84a_blackwell_nvfp4_bf16_sparse_gemm.cu)
+  - [Blockscaled Sparse GEMM with mixed input data type (MXFP8 and MXFP4)](https://github.com/NVIDIA/cutlass/tree/main/examples/84_blackwell_narrow_precision_sparse_gemm/84b_blackwell_mixed_mxfp8_bf16_sparse_gemm.cu)
+* Set of unit tests that demonstrate the usage of [sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_sparse_tensorop_gemm) and [blockscaled sparse](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm100_blockscaled_sparse_tensorop_gemm) Blackwell SM100 GEMM.
+* A new Multi-head Latent Attention (MLA) for SM100 Blackwell architecture in CUTLASS [example](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/) covers the flashMLA-like weight-absorbed decoding use-case.
+* A new FMHA Backward kernel for SM100 Blackwell architecture extends CUTLASS [example](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/) to show how the five backward pass MMAs can be fused into a single kernel to achieve high performance.
+* A new [distributed GEMM example](https://github.com/NVIDIA/cutlass/tree/main/examples/82_blackwell_distributed_gemm/82_blackwell_distributed_gemm.cu) for SM100 Blackwell architecture.
+* Enhancement and new support of block-wise and group-wise GEMM for Hopper and Blackwell architectures:
+  - Enhancement of [blockwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) for Hopper architecture.
+  - Enhancement of [groupwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu) for Hopper architecture.
+  - Support for [grouped GEMM with blockwise and groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/68_hopper_fp8_warp_specialized_grouped_gemm_with_blockwise_scaling/) for Hopper architecture.
+  - Support for [grouped-wise GEMM](https://github.com/NVIDIA/cutlass/tree/main/tools/profiler/src/blockwise_gemm_operation_profiler.cu) in CUTLASS profiler.
+  - Support for [blockwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_gemm_blockwise.cu) for Blackwell architecture.
+  - Support for [groupwise GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_gemm_groupwise.cu) for Blackwell architecture.
+  - Support for [grouped GEMM with blockwise](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_grouped_gemm_blockwise.cu) and [groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/81_blackwell_gemm_blockwise/81_blackwell_grouped_gemm_groupwise.cu) for Blackwell architecture.
+* Added support for enhanced kernel performance search (auto-tuning) in CUTLASS profiler:
+  - Sorting performance results by GFLOPs/second: Users can now sort the final performance report based on GFLOPs/second, making it easier to identify the most efficient kernels.
+  - Exhaustive search for best kernel performance in GFLOPs/second: The profiler now searches for the best-performing kernel across a range of problem sizes, swizzle sizes, rasterization orders, and dynamic cluster configurations to maximize performance.
+  - Performance search under a fixed GEMM shape: Enables exhaustive tuning within a fixed GEMM shape, exploring various kernel parameters to find the best configuration.
+  - More detailed introductions and examples to leverage this feature can be found in [profiler.md](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html#exhaustive-search-mode-and-top-k-output-ranking-according-to-performance-in-gflopss).
+* Support `void` as the D element in sm100 kernel epilogues.
+* Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+* Optimal code generation with CUDA toolkit versions 12.8U1.
+
+## [3.8.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.8.0) (2025-01-25)
+
+* Support for new CuTe building blocks specifically for Blackwell SM100 architecture:
+  - [5th generation Blackwell Tensor Core instructions (TCGen05)](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/mma_traits_sm100.hpp) via CuTe MMA atoms.
+  - Extensions to [Tensor Memory Accelerator](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100_tma.hpp) via CuTe Copy atoms.
+  - Exposure of Blackwell's new tensor memory (note: distinct from TMA) as [`tmem`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/pointer.hpp) across CuTe as a first class data locale.
+  - Exposure of [`tmem->rmem`, `rmem->tmem` and `smem->tmem data movement instructions`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) as copy atoms in CuTe.
+  - [`make_tmem_copy()`](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) utility method to ease creation of tiled copies for tmem copy atoms.
+  - Support for [new variants of LDSM on Blackwell](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm100.hpp) via CuTe Copy atoms.
+* Support for new CUTLASS building blocks specifically for Blackwell SM100 architecture:
+  - Various narrow precision [FP4, FP6, and FP8](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/exmy_base.h) formats as well as their [block-scaled variants NVFP4, MXFP4, MXFP6, and MXFP8](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/float_subbyte.h)
+  - [Pipelines that implement Blackwell specific synchronization](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/pipeline/sm100_pipeline.hpp).
+  - [Cluster launch control API supporting preferred and fallback cluster shapes](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/cluster_launch.hpp).
+  - Data types including NVFP4, MXFP4, MXFP6, and MXFP8 and all their supported element and scale factor types.
+  - Tile schedulers using [Blackwell's Cluster Launch Control (CLC) feature](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/blackwell_cluster_launch_control.html) to implement dynamic persistence scheduling for [GEMMs](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_tile_scheduler.hpp), and [stream-K](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_tile_scheduler_stream_k.hpp).
+  - Extensions to testbeds and reference check code for unit tests and CUTLASS profiler.
+* Full support for Blackwell SM100 kernels in CUTLASS 3.x API:
+  - [Blackwell specific kernel layers](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm100_gemm_tma_warpspecialized.hpp) that
+    + Implement a new warp-specialization recipe tuned specifically for Blackwell SM100 architecture.
+    + Leverage all the new features such as CLC based tile scheduling, preferred cluster, and TMEM based double buffering of accumulators.
+    + Support stream-K load balancing for all kernel types everywhere via composable scheduler support.
+  - Blackwell collective mainloops that target the TCGen05 MMA instructions (both SS and TS) for
+    * [Non-block scaled data types without support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_warpspecialized.hpp)
+    * [Non-block scaled data types with support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_mma_array_warpspecialized.hpp)
+    * [Block scaled data types without support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_warpspecialized.hpp)
+    * [Block scaled data types with support for pointer array and grouped GEMM with TMA](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm100_blockscaled_mma_array_warpspecialized.hpp)
+  - Blackwell [collective mainloop for convolution kernels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/collective/sm100_implicit_gemm_umma_warpspecialized.hpp) supporting non-block scaled data types for fprop, dgrad, and wgrad.
+  - New [GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/dispatch_policy.hpp), [convolution](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/dispatch_policy.hpp), and [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/dispatch_policy.hpp) dispatch policies for collectives, kernel layers, and builders.
+  - [Blackwell epilogue that supports loading accumulators from `tmem`](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm100_epilogue_tma_warpspecialized.hpp) and full set of EVT fusions.
+* CUTLASS library and profiler integration for block scaled data types for kernel emission, profiling, and verification.
+  - Support for preferred and fallback cluster shapes via profiler command line arguments parsing to set dynamic cluster shapes.
+  - Support for dynamic datatypes by parsing profiler via profiler command line arguments parsing to set dynamic datatype setting in TCGen05 MMA instruction descriptors.
+  - Support for mixed input GEMM kernels on Hopper in the profiler.
+* New CUTLASS profiler flag `use-cuda-graphs` to reduce overheads when benchmarking launch-bound kernels.
+* A new 3.x version of grouped GEMM to the CUTLASS library and generates kernels for Hopper and Blackwell. Now grouped GEMM support is enabled in the CUTLASS profiler (`./cutlass_profiler --operation=GroupedGemm --help` for details).
+* Set of examples that demonstrate the usage of the 3.x API for targeting Blackwell SM100 architecture:
+  - [Basic FP16 and FP8 GEMMs with minimal changes from Hopper examples](https://github.com/NVIDIA/cutlass/tree/main/examples/70_blackwell_gemm/), demonstrating ease of migration for off the shelf kernels using the 3.x collective builder API.
+  - GEMM with [opt-in collective builder schedules showcasing available recipes](https://github.com/NVIDIA/cutlass/tree/main/examples/71_blackwell_gemm_with_collective_builder/71_blackwell_gemm_with_collective_builder.cu) for Blackwell.
+  - Block scaled data type GEMMs targeting Blackwell's native block scaled Tensor Cores:
+    + [NVFP4 inputs with BF16 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72a_blackwell_nvfp4_bf16_gemm.cu)
+    + [NVFP4 inputs with NVFP4 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72b_blackwell_nvfp4_nvfp4_gemm.cu)
+    + [Mixed MXFP8 and MXFP6 inputs with BF16 output](https://github.com/NVIDIA/cutlass/tree/main/examples/72_blackwell_narrow_precision_gemm/72c_blackwell_mixed_mxfp8_bf16_gemm.cu)
+  - GEMM example demonstrating [Blackwell's new preferred cluster support via dynamic cluster shapes](https://github.com/NVIDIA/cutlass/tree/main/examples/73_blackwell_gemm_preferred_cluster/blackwell_gemm_preferred_cluster.cu) for increased occupancy.
+  - [GEMM with CLC based StreamK scheduler for load balancing](https://github.com/NVIDIA/cutlass/tree/main/examples/74_blackwell_gemm_streamk/blackwell_gemm_streamk.cu).
+  - Grouped GEMM for [vanilla FP8 data inputs](https://github.com/NVIDIA/cutlass/tree/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm.cu) and [NVFP4 block scaled inputs](https://github.com/NVIDIA/cutlass/tree/main/examples/75_blackwell_grouped_gemm/75_blackwell_grouped_gemm_block_scaled.cu).
+  - Convolution kernels for [fprop](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_fprop.cu), [dgrad](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_dgrad.cu), and [wgrad](https://github.com/NVIDIA/cutlass/tree/main/examples/76_blackwell_conv/76_blackwell_conv_wgrad.cu).
+  - [Fused multi-head attention fprop kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/77_blackwell_fmha.cu) supporting fp16/bf16/fp8 data types across head dims of 32,64, and 128.
+  - A new BF16x9 GEMM [kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/78_blackwell_emulated_bf16x9_gemm/78_blackwell_emulated_bf16x9_gemm.cu) that emulates FP32 GEMM (SGEMM) using BF16 operations.
+* Set of examples that demonstrate the usage of the 3.x API for targeting Hopper architecture:
+  - A set of new [Hopper grouped GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/examples/69_hopper_mixed_dtype_grouped_gemm/) that support mixed A and B datatypes.
+  - A new [Hopper FP8 GEMM with groupwise scaling](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_groupwise_scaling.cu).
+* Documentation updates:
+  - [Quickstart - instantiating a Blackwell block-scaled GEMM](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html#instantiating-a-blackwell-sm100-gemm-kernel).
+  - Detailed [Blackwell block-scaled GEMM functionality documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/blackwell_functionality.html)
+  - A new [functionality documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/functionality.html) specifically for 3.x API comprehensively documenting all supported kernel types, data types, kernel features, minimum CUDA tookit support etc for 3.x supported architectures.
+  - Updates to [compatibility](https://docs.nvidia.com/cutlass/latest/overview.html#compatibility) section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures, and [Target Architecture](https://docs.nvidia.com/cutlass/latest/overview.html#target-architecture).
+  - Updates to [profiler documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html) for testing mixed input GEMM kernels on Hopper.
+
+## [3.7.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.7.0) (2025-01-11)
+- [Hopper blockwise scaling FP8 GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling/67_hopper_fp8_warp_specialized_gemm_with_blockwise_scaling.cu) uses 2D scaling tensor, assigning one value per threadblock.  This allows a finer-grained scaling to be applied for each output tile per gemm-k iteration. The operands and scaling tensors are loaded from global memory to shared memory using TMA and cp_async, respectively. The scaling is applied inside the mainloop.  Details with figures are [here](https://github.com/NVIDIA/cutlass/pull/1932#issue-2645398439).
+- [Distributed GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/65_distributed_gemm/65_distributed_gemm.cu) is a new (experimental) API which can turn existing CUTLASS GEMM kernels into pipelined Tensor Parallel GEMMs that run efficiently on NVLink-based network of GPUs. Its pipelining schedules can hide most of the communication behind computation, and relies on point-to-point communication, which can simply use CUDA runtime's peer device access feature. It also utilizes remote TMA loads and memcopies with CUDA graphs to handle communication primarily through the Copy Engine, leaving all SMs free for Hopper's persistent kernels.  For more details you can refer to the [DistGEMM blog post](https://blog.shi-labs.com/distributed-gemm-88be6a481e2b).
+- Improved persistent grid launch for Hopper kernels with large cluster sizes (>= size of 4) using the new `make_kernel_hardware_info` API as shown in [example 48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
+- Enabled high precision accumulation for Hopper FP8 Sparse GEMM.
+- Potential API breaking changes:
+  + Fix `cute::UniversalCopy` for type safety.
+  + No longer implicitly select `cute::SM80_CP_ASYNC_*` based on input tensors. This avoids implicit downstream synchronization requirements. To use `SM80_CP_ASYNC`, users must explicitly select the appropriate CopyAtom.
+  + Fix `cute::SM80_CP_ASYNC_CACHEALWAYS`, `cute::SM80_CP_ASYNC_CACHEGLOBAL`, `cute::SM80_CP_ASYNC_CACHEALWAYS_ZFILL`, `cute::SM80_CP_ASYNC_CACHEGLOBAL_ZFILL` to avoid implicitly selecting `ZFILL` behavior on predication.
+  + Remove `cute::copy_vec<T>` in favor of `cute::copy_aligned` and `cute::copy(AutoVectorizingCopyWithAssumedAlignment<NumBits>,...)`.
+  + A refactor of default epilogue struct `DefaultEpilogue` [API](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/default_epilogue.hpp) to avoid reading non-void `ElementC` value for `ElementC = void` kernel.
+- New CUTLASS profiler flags: `profiling-duration`, `min-iterations`, and `kernels-file` documented in [profiler.md](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html#cutlass-profiler).
+- Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+- Optimal code generation with CUDA toolkit versions 12.6.
+
+## [3.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.6.0) (2024-10-03)
+
+- [Hopper structured sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/62_hopper_sparse_gemm/62_hopper_sparse_gemm.cu).
+  + [FP16](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_f16_f16_f32_tensor_op_f32.cu)
+  + [FP8](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_f8_f8_f32_tensor_op_f32.cu)
+  + [INT8](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_s8_s8_s32_tensor_op_s32.cu)
+  + [TF32](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_sparse_gemm_tf32_tf32_f32_tensor_op_f32.cu)
+- A refactor to the CUTLASS 3.x convolution `kernel::ConvUniversal` [API](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/kernel/sm90_implicit_gemm_tma_warpspecialized.hpp) to bring it in line with `gemm::GemmUniversal`. Now the 3.x convolution API is no longer considered as a beta API.
+- [An improved mixed input GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm/README.md) and a [lookup table implementation](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm/55_hopper_int4_fp8_gemm.cu) for `INT4`x`FP8` scale-only mode.
+- [EVT nodes for Top-K selection and softmax](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm90_visitor_topk_softmax.hpp) and [GEMM example using those](https://github.com/NVIDIA/cutlass/tree/main/examples/61_hopper_gemm_with_topk_and_softmax/61_hopper_gemm_with_topk_and_softmax.cu).
+- [Programmatic Dependent Launch](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/grid_dependency_control.h) (PDL) that leverages a new Hopper feature to speedup two back-to-back kernels, and its corresponding [documentations](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/dependent_kernel_launch.html).
+- [A new debugging tool, synclog](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/synclog.hpp), for dumping out all synchronization events from within a kernel to a file. Please see [synclog documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/utilities.html#debugging-asynchronous-kernels-with-cutlasss-built-in-synclog-tool) for details.
+- A new TMA-enabled [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for grouped GEMM that brings significant performance improvement, as well as its EVT support.
+- A SIMT-enabled pointer-array [epilogue](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm70_epilogue_vectorized_array.hpp).
+- A new [Ping-Pong kernel schedule for Grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_array_tma_warpspecialized_pingpong.hpp) and some other optimizations.
+- [A new instantiation strategy for CUTLASS profiler kernels](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/sm90_shapes.py) along with [improved documentation for instantiation level in CUTLASS profiler](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html#instantiating-more-kernels-with-hopper).
+- A new hardware support for comparisons and computations of [`cutlass::bfloat16_t`](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/bfloat16.h)
+- Fixed use of isnan on Windows for [`half_t`](https://github.com/NVIDIA/cutlass/tree/main/test/unit/core/functional.cu).
+- Various improvements and fixes from the community and CUTLASS team. Thanks to everyone who submitted PRs!
+- Optimal code generation with CUDA toolkit versions 12.6.
+
+## [3.5.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.5.1) (2024-07-25)
+
+- [Minimal SM90 WGMMA + TMA GEMM example in 100 lines of code](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial/wgmma_sm90.cu)
+- [Exposure of L2 `cache_hint`s in TMA copy atoms](https://github.com/NVIDIA/cutlass/tree/main/include/cute/arch/copy_sm90_tma.hpp#L48)
+- Exposure of raster order and tile swizzle extent in [CUTLASS library profiler](./media/docs/cpp/profiler.md#gemm), and
+[example 48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm/48_hopper_warp_specialized_gemm.cu).
+- [TMA store based and EVT supported epilogues](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/collective/sm90_epilogue_array_tma_warpspecialized.hpp) for [Hopper pointer array batched kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_ptr_array.cu).
+- A new [`GemmSparseUniversal` API for CUTLASS 2.x Ampere kernels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/device/gemm_sparse_universal.h) to enable serial and parallel split-k for sparse tensor cores and new tiny tile sizes to better support LLM inferrence:
+  + [FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f32t_tensor_op_f32_sparse_sm80.cu#L269-L393) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16t_f32t_tensor_op_f32_sparse_sm80.cu#L269-L411).
+  + [int8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
+  + [int4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s32t_tensor_op_s32_sparse_sm80.cu#L264-L452).
+  + [FP32 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f32t_f32n_f32t_tensor_op_f32_sparse_sm80.cu#L427-L642) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f32n_f32t_f32t_tensor_op_f32_sparse_sm80.cu#L427-L456).
+- [CUDA host adapter](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/cuda_host_adapter.hpp) extensions to support TMA descriptor construction driver APIs.
+- Inclusion of more [Hopper fprop, dgrad, and wgrad convolution kernels in CUTLASS library and profiler](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/generator.py).
+- Support for residual add (beta != 0) in convolution kernels.
+- A new convolution [epilogue](https://github.com/NVIDIA/cutlass/tree/main/examples/16_ampere_tensorop_conv2dfprop/ampere_tensorop_conv2dfprop.cu#L269) for CUTLASS 2.x to support non-packed NHWC output.
+- A refactor of [include files throughout CUTLASS core directories](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/collective_mma_decl.hpp) to reduce circular dependencies and [tests to guard against them](https://github.com/NVIDIA/cutlass/tree/main/test/self_contained_includes/CMakeLists.txt).
+- [A guide for setting up VSCode to work well with CUTLASS](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/ide_setup.html) and [expanded code style guide](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/programming_guidelines.html).
+- Better support for MSVC as a host compiler.
+- Many performance optimizations, improvements, and bug fixes including fixes for FlashAttention-2.
+- Optimal code generation with CUDA toolkit versions 12.4 and 12.5u1.
+
+## [3.5.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.5.0) (2024-04-09)
+
+- Implicit GEMM Convolutions targeting Hopper SM90A via WGMMA + [TMA im2col](https://github.com/NVIDIA/cutlass/tree/main/include/cute/atom/copy_traits_sm90_im2col.hpp)
+  + Native implementation in CUTLASS 3.x using CuTe, mirroring the [same design hierarchy as that of GEMMs](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/gemm_api_3x.html).
+  + Support for 1D, 2D, and 3D convolutions in a [rank-agnostic fashion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/convnd_problem_shape.hpp).
+  + Support for [Fprop](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/fprop/sm90_conv3d_fprop_implicit_gemm_s8_s8_s32_tensorop_s32.cu), [Dgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/dgrad/sm90_conv2d_dgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu), and [Wgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/wgrad/sm90_conv1d_wgrad_implicit_gemm_f16_f16_f32_tensorop_f16.cu) algorithms
+  + [CUTLASS profiler support](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/conv3x_emitter.py) for 2D and 3D convolutions implemented via the 3.x API.
+  + NOTE: this is a beta release. Further updates to CUTLASS will include major performance improvements, feature enablement, and possible breaking changes to the API until 3.7 release. Your feedback is welcome on the design!
+- Support for [Ada (SM89) FP8 tensor cores via the 2.x API](https://github.com/NVIDIA/cutlass/tree/main/examples/58_ada_fp8_gemm/ada_fp8_gemm.cu). Requires CUDA 12.4 or newer.
+- [Ampere gather/scatter convolution example](https://github.com/NVIDIA/cutlass/tree/main/examples/59_ampere_gather_scatter_conv/README.md) in CuTe and CUTLASS 3.x
+  + Showcasing how custom kernels can be written and optimized using CUTLASS 3.x and CuTe and the general strategy for implementing convolutions as specializations of GETTs.
+  + Implementation of a coarse grained sparse gather/scatter kernel achieving peak performance on Ampere class tensor cores.
+- 32x and 16x tile sizes are added to CUTLASS 2.x to improve the performance of narrow-tall and wide-short matrices.
+  + [Ampere FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm80.cu) and [NT](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16t_f16t_tensor_op_f32_sm80.cu#L227-L301), [Ampere INT8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm80.cu#L392-L1342), [Ampere INT4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm80.cu#L372-L934).
+  + [Turing FP16 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16t_f16n_f16t_tensor_op_f32_sm75.cu#L55-L394), [Turing INT8 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s8t_s8n_s8t_tensor_op_s32_sm75.cu#L166-L537), [Turing INT4 TN](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_s4t_s4n_s4t_tensor_op_s32_sm75.cu#L310-L564).
+- Updates to CuTe documentation for [`cute::Tensor<>`](./media/docs/cpp/cute/03_tensor.md), [MMA atoms](./media/docs/cpp/cute/0t_mma_atom.md), and an overhauled [CuTe GEMM tutorial series](https://github.com/NVIDIA/cutlass/tree/main/examples/cute/tutorial).
+- Extensions to CuTe to support [L2 prefetching](https://github.com/NVIDIA/cutlass/tree/main/include/cute/algorithm/prefetch.hpp) and [TMA store+reductions](https://github.com/NVIDIA/cutlass/tree/main/include/cute/arch/copy_sm90_tma.hpp#L1337).
+- Remove C++11 requirement on a few CUTLASS 2.x API header files. All CUTLASS files now require C++17.
+- Fixes to greatly reduce build warnings.
+- Updates and bugfixes from the community (thanks!)
+
+## [3.4.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.4.1) (2024-02-14)
+
+- Statically available [CUTLASS Version macros](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/version.h) that allow for handling API changes between CUTLASS releases on the users' side.
+- Improvements for Hopper [Group-GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/57_hopper_grouped_gemm) and [Pointer-Array Batched GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/56_hopper_ptr_array_batched_gemm).
+- Updates and bugfixes from the community (thanks!).
+
+## [3.4.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.4.0) (2024-01-12)
+* Expanded [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm) support covering {16-bit, 8-bit} x {8-bit, 4-bit} input types with fast numerical converters and group scaling factors.
+* Performance improvements to [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm)
+* Beta release of [Pointer-Array Batched GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/56_hopper_ptr_array_batched_gemm) now available on Hopper GPUs utilizing TMA and WGMMA (requires CUDA 12.3 or above).
+* Beta release of [Group-GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/57_hopper_grouped_gemm) utilizing TMA and WGMMA (requires CUDA 12.3 or above).
+* [Ampere Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm_with_visitor.cu) supports Epilogue Visitor Tree (EVT) now.
+* NamedBarriers usability improvement and list of [ReservedNamedBarriers](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/barrier.h) has been officially released.
+* Improved CuTe documentation including improved clarity and depth of [Quickstart](./media/docs/cpp/cute/00_quickstart.md), [CuTe Layout](./media/docs/cpp/cute/01_layout.md), and [CuTe Layout Algebra](./media/docs/cpp/cute/02_layout_algebra.md). Associated code comments, post-conditions, and details in [CuTe Core Unit Tests](./test/unit/cute/core/) also improved.
+
+## [3.3](https://github.com/NVIDIA/cutlass/releases/tag/v3.3.0) (2023-10-31)
+* [Mixed-input Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/55_hopper_mixed_dtype_gemm) support covering 16-bit x 8-bit input operand types.
+* [Mixed-input Ampere GEMMs](https://github.com/NVIDIA/cutlass/pull/1084) with support for canonical layouts (TN). The implementation supports upcast on operandB {fp16, bf16} x {s8, u8}, and upcast on operandA {s8, u8} x {fp16, bf16}.
+* [Copy Async based Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_bf16_bf16_bf16_alignx_tensor_op_f32_warpspecialized_cooperative.cu) - which support lower than 16B aligned input tensors.
+* Kernel schedules and Builder support for mixed precision and Copy Async GEMMs with < 16B aligned input tensors.
+* Profiler support for lower-aligned Hopper GEMMs.
+* Performance Improvements to [Scatter-Gather Hopper Example](https://github.com/NVIDIA/cutlass/tree/main/examples/52_hopper_gather_scatter_fusion).
+* Sub-Byte type fixes and improvements.
+* EVT Support for RELU with Aux bitmap tensor store (used in dRELU). See [SM90 EVT fusions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/fusion/sm90_visitor_compute_tma_warpspecialized.hpp) for details.
+* Fusion support for backprop fusions including drelu, dgelu, and dbias.
+* Support for void-C kernels and SM80 mixed-input GEMMs in the CUTLASS Python interface
+
+## [3.2.2](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.2) (2023-10-25)
+* Minor patch for issue/1138
+
+## [3.2.1](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.1) (2023-09-22)
+* Python support SM90 Epilogue Visitor Tree (EVT) on top of the C++ support released in 3.2.0.
+* SM80 EVT support in C++ and Python.
+* Other SM90 epilogue improvements.
+* Splitting CUTLASS library into smaller units based on operation, arch and datatypes. See [1105](https://github.com/NVIDIA/cutlass/discussions/1105) for details.
+* Making `tools/library/scripts` packageable - `tools/library/scripts` is now moving to `python/cutlass_library`. See the Python [README](https://github.com/NVIDIA/cutlass/tree/main/python/README.md) for details.
+* SM90 TF32 kernel improvements for all layouts.
+* SM90 rasterization direction support in the CUTLASS profiler.
+* Improvement for CUTLASS profiler build times.
+* Remove Python-C++ bindings.
+
+## [3.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.2.0) (2023-08-03)
+
+* New warp-specialized persistent FP8 GEMM kernel [kernel schedules](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) and [mainloops](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8.hpp)  targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters. An example showcasing [Hopper warp-specialized FP8 GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/54_hopper_fp8_warp_specialized_gemm). FP8 GEMMs come with a fast accumulation mode. When enabled, problem execution might be faster but at the cost of lower accuracy because intermediate results will not periodically be promoted to a higher precision.
+* New [Epilogue Visitor Tree (EVT)](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu) support for Hopper TMA epilogues. EVTs allows for user-defined customized epilogue fusion patterns without having to write a new epilogue.
+* [Stream-K](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_tile_scheduler_stream_k.hpp) feature for Hopper. Note that this is only a functional implementation of stream-K, and should not be used for performance comparison. Optimizations are expected in a future release.
+* Improved CTA rasterization and support for CTA swizzling for Hopper kernels using the [Tile Scheduler](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_tile_scheduler.hpp).
+* Improved performance for [warp-specialized TensorFloat-32 (TF32) GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
+* [Hopper GEMM+Permute](https://github.com/NVIDIA/cutlass/tree/main/examples/53_hopper_gemm_permute/53_hopper_gemm_permute.cu), an example of fusing tensor reordering (permutation) with GEMM mainloop or epilogue.
+* New CUTLASS 2D Convolution Python interface. New [example](https://github.com/NVIDIA/cutlass/tree/main/examples/python/03_basic_conv2d.ipynb) here.
+* Support for Windows (MSVC) builds. Tested with Visual Studio 2019 v16.11.27 on Windows 10.0.
+* Optimal performance using [**CUDA 12.2u1**](https://developer.nvidia.com/cuda-downloads)
+* Updates and bugfixes from the community (thanks!)
+
+## [3.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.1.0) (2023-04-14)
+* New CUTLASS Python interface that aims to provide an ease-of-use interface for instantiating, emitting, compiling, and running CUTLASS kernels via Python. More details [here](https://github.com/NVIDIA/cutlass/tree/main/python/README.md) and new [examples](https://github.com/NVIDIA/cutlass/tree/main/examples/python).
+* New [efficient epilogues](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative.cu#L783) using TMA for Hopper.
+* Support for [fused epilogues](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_f16_f16_f16_tensor_op_f32_cluster_warpspecialized_cooperative_bias_elementwise.cu), such Bias, ReLU and GELU, using the new efficient epilogues.
+* New [warp-specialized TensorFloat-32 (TF32) GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/sm90_gemm_tf32_tf32_f32_tensor_op_f32_gmma_rs_cluster_warpspecialized.cu) targeting Hopper TMA.
+* New [*warp-specialized persistent cooperative*](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_cooperative.hpp) kernel design that allows for larger tile sizes and improves performance on Hopper.
+* An [example](https://github.com/NVIDIA/cutlass/tree/main/examples/51_hopper_gett) showcasing GEMM-Like Tensor-Tensor Contraction (GETT) capability on Hopper.
+* Epilogue builders. Similar to mainloop builders (see [example 49](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_with_collective_builder/49_collective_builder.cu)), epilogue builders aim to generate the best-possible epilogue while exposing incremental opt-ins for greater customization.
+* Profiler support for overriding kernel and epilogue builder auto schedules for 3.x API kernels, allowing specific policies to be run in the CUTLASS profiler.
+* Performance optimizations for the [*warp-specialized persistent ping-pong*](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized_pingpong.hpp) kernel.
+* Changes to the [GEMM API 3.x](./media/docs/cpp/gemm_api_3x.md), involving the host-facing arguments and the underlying `Params` structs.
+* [FMHA Backward Pass](https://github.com/NVIDIA/cutlass/tree/main/examples/41_fused_multi_head_attention/fused_multi_head_attention_backward.cu) from Meta xFormers.
+* [Streamk GEMM with Broadcast](https://github.com/NVIDIA/cutlass/tree/main/examples/47_ampere_gemm_universal_streamk/ampere_gemm_universal_streamk_broadcast.cu) enables epilogue broadcast with StreamK GEMM.
+* [Batched B2B GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) now can run multiple Back-to-Back GEMM with the same problem size in parallel.
+* [Batched Strided GEMV](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemv.cu) support both row major and column major input matrix.
+* [Permute + GEMM fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/39_gemm_permute) can fuse Permute with following GEMM now.  Before, we only support fusing GEMM with Permute in the epilogue.
+* [Row Broadcast](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/threadblock/predicated_tile_iterator_row_broadcast.h) can be fused in the epilogue.
+* The GitHub branch is renamed from `master` to `main` in this release.
+* Optimal performance using [**CUDA 12.1**](https://developer.nvidia.com/cuda-downloads)
+* Updates and bugfixes from the community (thanks!)
+
+## [3.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v3.0.0) (2023-01-23)
+* [CuTe](./media/docs/cpp/cute/00_quickstart.md), a [new core library and backend](./include/cute) for CUTLASS 3.0 that defines a single Layout vocabulary type and an associated algebra of layouts for a much more expressive and composable abstraction for tensors, sets of parallel agents, and operations by said agents on tensors.
+* [A new conceptual operation hierarchy](./media/docs/cpp/cutlass_3x_design.md) that replaces the architecture-centric hierarchy of CUTLASS 2.x and [documentation for CUTLASS 3.0's GEMM API changes](./media/docs/cpp/gemm_api_3x.md).
+* Strict API backwards compatibility that exposes both 2.x and 3.x API kernels through the same [`device::GemmUniversalAdapter`](./include/cutlass/gemm/device/gemm_universal_adapter.h) and [`kernel::GemmUniversal`](./include/cutlass/gemm/kernel/gemm_universal.hpp) types, allowing users to include both APIs in the same translation units. More information can be found in the [3.x backwards compatibility section](./media/docs/cpp/cutlass_3x_backwards_compatibility.md).
+* Updates to [Functionality](./media/docs/cpp/functionality.md) which directs users on which kernels are supported via CUTLASS-2 and CUTLASS-3.
+* Updates to [Compatibility](./README.md#compatibility) Section regarding supported compilers, operating systems, CUDA Toolkits, Hardware Architectures and [Target Architecture](./README.md#target-architecture).
+* New warp-specialized GEMM [kernel schedules](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/kernel/sm90_gemm_tma_warpspecialized.hpp) and [mainloops](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized.hpp) targeting Hopper architecture that achieve great performance with TMA, WGMMA, and threadblock clusters.
+* Extensions to CUTLASS profiler to support threadblock cluster shapes in library and profiler tile configurations.
+* [CUTLASS library integration](https://github.com/NVIDIA/cutlass/tree/main/tools/library/src/gemm_operation_3x.hpp) for 3.x API kernels built through the new `CollectiveBuilder` API, enabling CUTLASS profiler.
+* Support for [Hopper GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm) through the new 3.0 API with CuTe-based exposure of the Hopper [Tensor Memory Accelerator](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions-cp-async-bulk-tensor) and [WGMMA Tensor Core](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions) features.
+* Set of examples that demonstrate the usage of the new 3.0 API to easily build GEMM kernels targeting Hopper: examples [48](https://github.com/NVIDIA/cutlass/tree/main/examples/48_hopper_warp_specialized_gemm), [49](https://github.com/NVIDIA/cutlass/tree/main/examples/49_hopper_gemm_schedules_with_collective_builder), and [50](https://github.com/NVIDIA/cutlass/tree/main/examples/50_hopper_gemm_with_epilogue_swizzle).
+
+# CUTLASS 2.x
+
+## [2.11.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.11.0) (2022-11-19)
+* [Stream-K](https://github.com/NVIDIA/cutlass/tree/main/examples/47_ampere_gemm_universal_streamk), which is a new general way to do split-K.  It can not only improve performance, but can also significantly reduce the number of tile sizes that need to be profiled to find the best one.
+* [Fused multi-head attention Kernel](https://github.com/NVIDIA/cutlass/tree/main/examples/41_fused_multi_head_attention).  It has two variants: one uses batched GEMM for the fixed sequence length, and the other one uses group GEMM for the variable sequence length.  Both versions just need one kernel.
+* [Dual GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/45_dual_gemm), which can fuse A x B and A x C into one kernel. Two GEMMs has no producer-consumer dependency.
+* Hopper improves [double precision matrix multiplication](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f64n_f64t_f64t_tensor_op_f64_sm90.cu) by 2x compared to Ampere at iso-clocks. It is supported since CUDA 11.8.
+* [BLAS3](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/hemm_cf64_cf64_cf64_tensor_op_f64_sm90.cu) functions with Hoppers new double precision matrix multiplication instructions.
+* [ELL Block Sparse GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/43_ell_block_sparse_gemm), which uses an [ELL matrix](https://developer.nvidia.com/blog/accelerating-matrix-multiplication-with-block-sparse-format-and-nvidia-tensor-cores/) to describe the sparsity of A matrix.  B and output matrices are still dense. The block size can be arbitary.
+* Optimized [Group Conv](https://github.com/NVIDIA/cutlass/tree/main/examples/42_ampere_tensorop_group_conv) for SingleGroup mode, which requires that the output channel per group is a multiple of Threadblock tile N.
+* [Optimized DepthWise Conv](https://github.com/NVIDIA/cutlass/tree/main/examples/46_depthwise_simt_conv2dfprop/depthwise_simt_conv2dfprop.cu).  Two new modes are added
+  * [kOptimized](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - use direct conv to compute instead of implicit GEMM.
+    *  The restrictions are: 1) input ,output channel and group number should be multiple of (128 / sizeof(input element)). 2) The input filter size should be the same as the template parameter configuration.
+  * [kFixedStrideDilation](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_direct_conv_fixed_stride_dilation_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) - which puts stride and dilation into templates to further improve the performance. In this mode, kernel persistents some inputs into register to squeeze more performance, so large filter/stride/dilation is not recommanded.
+    * The restrictions are: 1) input, output channel and group number should be multiple of (128 / sizeof(input element)). 2) input filter size, stride, dilation should same as the template parameter configuration.
+* [Scripts](https://github.com/NVIDIA/cutlass/tree/main/examples/44_multi_gemm_ir_and_codegen) to fuse multiple back-to-back GEMM.  Its implementation was discussed in a GTC'22 Spring [talk](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41606/).
+* [FP8 data type definition](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/float8.h) and [conversion routines](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/numeric_conversion.h#L1274-2115).
+* Updates and bugfixes from the community (thanks!).  Big shout out to Meta's [xFormers](https://github.com/facebookresearch/xformers).
+
+* **Deprecation announcement:** CUTLASS plans to deprecate the following:
+  * Maxwell and Pascal GPU architectures
+  * Ubuntu 16.04
+  * CUDA 10.2
+
+## [2.10.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.10.0) (2022-08-23)
+* [CUTLASS Python](https://github.com/NVIDIA/cutlass/tree/main/examples/40_cutlass_py) now supports GEMM, CONV, Group GEMM for different data types as well as different epilogue flavours.
+* Optimizations for CUTLASS's [Grouped GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped/gemm_grouped.cu) kernel.  Threadblock scheduling part is improved.  Some computation can be moved to the host side if applicable.  [Grouped Syr2k](https://github.com/NVIDIA/cutlass/tree/main/examples/38_syr2k_grouped/syr2k_grouped.cu) kernels are added, too.
+* Optimizations for [GEMM+Softmax](https://github.com/NVIDIA/cutlass/tree/main/examples/35_gemm_softmax).  All the reduction computation is fused into the previous GEMM.  More template arguments are provided to fine tune the performance.
+* [Grouped GEMM for Multihead Attention](https://github.com/NVIDIA/cutlass/tree/main/examples/41_multi_head_attention).  This general group gemm based MHA does not require the sequence length of all GEMMs to be the same which makes it most useful for natural language processing.
+* [GEMM + Layer norm fusion for Ampere](https://github.com/NVIDIA/cutlass/tree/main/examples/37_gemm_layernorm_gemm_fusion/) splits the layernorm into two parts and both of them can be fused into the GEMMs before and after separately.  In addition to use square sum to compute variance of layernorm, [Shift-K](https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Computing_shifted_data) is provided if square sum raise numerical issues.
+* [GEMM Epilogue Permutation Fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/39_gemm_permute) can apply user provided permutation layout mapping in the GEMM epilogue.
+* [Grouped convolution targeting implicit GEMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/group_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) introduces the first group convolution implementation to CUTLASS.  It is an Analytical implementation, not an Optimized.  The restrictions are: 1) input and output channel number should be multiple of group number. 2) split-K is not supported.  The implementation has 2 modes:
+  * kSingleGroup: output channel per group is multiple of Threadblock tile N.
+  * kMultipleGroup: Threadblock tile N is multiple of output channel per group.
+* [Depthwise separable convolution](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/depthwise_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_simt_f16_sm60.cu) introduces the first depthwise convolution which is also Analytical for now.  The restrictions are: 1) SIMT only 2) No split-K 3) input channel equals to output channel equals to group number.
+* Standalone [Layernorm](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util/device_layernorm.h) and [Pooling](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util/device_nhwc_pooling.h) kernels.
+* [Back-to-back GEMM/CONV](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) relaxes the requirement that the first GEMM K dimension needs to be the multiple of Threadblock Tile K dimension.
+* Optimal performance using [**CUDA 11.6u2**](https://developer.nvidia.com/cuda-downloads)
+* Updates and bugfixes from the community (thanks!)
+
+## [2.9.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.9.0) (2022-04-21)
+
+* [First layer Convolution kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_fixed_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu) specialized for small channel counts and reduced alignment
+  * [Few channels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_few_channels.h) specialization for reduced alignment capabilities
+  * [Fixed channels](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_fixed_channels.h) further specialized when channel count perfectly matches the access vector size
+  * [Unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_few_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu)
+  * [Python-based instance emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/generator.py) in the CUTLASS Library and support in the Profiler
+* [BLAS3](https://docs.nvidia.com/cuda/cublas/index.html#cublas-level-3-function-reference) operators accelerated by Tensor Cores
+  * Supported types: f32, cf32, f64, cf64, tf32x3, complex tf32x3
+  * [HERK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/her2k_cf32h_cf32n_tensor_op_fast_f32_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/rank_k_operation.py)
+  * [SYRK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/syrk_f32n_f32t_tensor_op_fast_f32_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/rank_k_operation.py)
+  * [SYMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/symm_f32n_f32n_tensor_op_fast_f32_ls_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/symm_operation.py)
+  * [TRMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/trmm_f32n_f32t_f32t_tensor_op_fast_f32_ls_sm80.cu) with [emitter](https://github.com/NVIDIA/cutlass/tree/main/python/cutlass_library/trmm_operation.py)
+  * [Unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/testbed_rank_k_universal.h)
+* [CUTLASS Python](https://github.com/NVIDIA/cutlass/tree/main/examples/40_cutlass_py) demonstrating JIT compilation of CUTLASS kernels and a Python-based runtime using [CUDA Python](https://developer.nvidia.com/cuda-python)
+  * [Python-based runtime](https://github.com/NVIDIA/cutlass/tree/main/tools/library/scripts/rt.py) interoperable with existing emitters
+* [GEMM + Softmax example](https://github.com/NVIDIA/cutlass/tree/main/examples/35_gemm_softmax)
+* [Gather and Scatter Fusion with GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/36_gather_scatter_fusion) can gather inputs and scatters outputs based on indices vectors in the same GEMM kernel.
+  * It can select random rows in a row major matrix.
+  * It can select random columns in a column major matrix.
+* [Back-to-back GEMM/CONV](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion) fully supports buffering the first GEMM/CONV results in the shared memory for the latter one to use.  It can eliminate register spill when the tile size is big.  Additionally, bias vector add is supported in the first GEMM/CONV.
+  * Supported kernels: GEMM and CONV.
+  * Supported types: fp16 and int8.
+  * Supported architectures: Turing and Ampere.
+* [Transposed Convolution](https://github.com/NVIDIA/cutlass/tree/main/examples/34_transposed_conv2d) (a.k.a Deconvolution) support which reuses Dgrad implementation.
+* [Utility functions](https://github.com/NVIDIA/cutlass/tree/main/tools/util/include/cutlass/util) that can pad NHWC and convert between NCHW and NHWC.
+* [Small alignment implicit gemm](https://github.com/NVIDIA/cutlass/issues/242) support for Fprop/Dgrad/Wgrad so that padding is no longer mandated to use tensor cores in these kernels.
+* Epilogue enhancement:
+  * Eliminate bank conflicts in int8 tensor core kernels.
+  * Half2 usage if epilogue compute type is fp16.
+  * More activation functions: Silu, Hardswish, Leaky Relu.
+  * New elementwise fusion pattern for [residual block](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_residual_block.h).
+* [Group GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped) thread block number calculation fix which helps to launch the intended number of threadblocks to fully occupy the GPUs.
+* [Parallel GEMM splitk](https://github.com/NVIDIA/cutlass/pull/277) support in the CUTLASS profiler.
+* Optimal performance using [**CUDA 11.6u2**](https://developer.nvidia.com/cuda-downloads)
+* Updates and bugfixes from the community (thanks!)
+
+
+## [2.8.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.8.0) (2021-11-19)
+
+* **TF32x3:** emulated single-precision using Tensor Cores
+  * 45+ TFLOPs on NVIDIA A100
+  * [GEMM SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm/27_ampere_3xtf32_fast_accurate_tensorop_gemm.cu) (real)
+  * [COMPLEX GEMM SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm/29_3xtf32_complex_gemm.cu) (complex)
+  * [Implicit GEMM Convolution SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/28_ampere_3xtf32_fast_accurate_tensorop_fprop/ampere_3xtf32_fast_accurate_tensorop_fprop.cu)
+* **Mainloop fusion for Convolution:** convolution with fused per-channel scale-bias-relu
+  * [Conv Fprop SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/25_ampere_fprop_mainloop_fusion/ampere_fprop_mainloop_fusion.cu)
+  * [Conv WGrad SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/26_ampere_wgrad_mainloop_fusion/ampere_wgrad_mainloop_fusion.cu)
+  * [cutlass::conv::device::ImplicitGemmConvolutionFusion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/device/implicit_gemm_convolution_fusion.h)
+* **Grouped GEMM:** similar to batched GEMM with distinct problem size per group
+  * [SDK example](https://github.com/NVIDIA/cutlass/tree/main/examples/24_gemm_grouped) with performance comparison with Batched Strided GEMM
+  * [cutlass::gemm::device::GemmGrouped](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/gemm/device/gemm_grouped.h)
+* [Implicit GEMM Convolution fusion](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/) supports staging 1st convolution's output accumulator in the shared memory on Turing. This allows more flexible warp tile sizes and less regsiter pressue.
+* Optimal performance using [**CUDA 11.5**](https://developer.nvidia.com/cuda-downloads)
+* Updates from the community (thanks!)
+
+* **Deprecation announcement:** CUTLASS plans to deprecate the following:
+  * Maxwell and Pascal GPU architectures
+  * Ubuntu 16.04
+  * CUDA 10.2
+
+## [2.7.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.7.0) (2021-09-24)
+  * Mainloop fusion for GEMM: [summation over A or B](https://github.com/NVIDIA/cutlass/tree/main/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu)
+  * [Strided DGRAD (optimized iterators)](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/kernel/default_conv2d_dgrad.h)
+  * [Half-precision GELU_taylor activation functions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/activation.h#L196)
+    * Use these when accumulation and epilogue compute types are all `cutlass::half_t`
+  * Tuning and bug fixes to [fused GEMM + GEMM example](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/)
+  * Support for smaller than 128b aligned Convolutions: [see examples](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.cu#L272)
+  * Caching of results to accelerate Convolution [unit tests](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/cache_testbed_output.h)
+    * Can be enabled or disabled by running `cmake .. -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=OFF`
+  * Corrections and bug fixes reported by the CUTLASS community
+    * Thank you for filing these issues!
+
+## [2.6.1](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.1) (2021-09-03)
+  * Arbitrary padding and striding for CUTLASS Strided DGRAD Convolution operator (Analytic Iterators)
+  * Tuning for GEMMs fused with partial reductions
+  * Corrections and bug fixes reported by the CUTLASS community
+    * Thank you for filing these issues!
+
+## [2.6.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.6.0) (2021-07-22)
+  * Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
+    * Adopt the new L2 prefetch feature in [cp.async](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/memory.h) and [global load](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/arch/memory_sm80.h)
+  * Fused operators with GEMM and Convolution
+    * [Fused broadcast in epilogue](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu)
+    * [Fused partial reduction in epilogue](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu)
+  * 64b tensor strides and leading dimensions support for GEMMs
+  * Affine rank=2 matrix layouts
+    * Row stride and column stride for matrices using [cutlass::layout::AffineRank2](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/layout/matrix.h)
+    * Support [FP64 tensor core](https://github.com/NVIDIA/cutlass/tree/main/examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) and SIMT GEMM.
+  * [Batched GEMV](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemv.cu) preview implementation
+  * [New strided Dgrad](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation
+    * Accelerates over previous implementation by cutting down redundant math by 4x
+    * Support using new `Dy` and `w` analytic iterators and existing `cutlass::conv::device::ImplicitGemmConvolution` interface
+  * Quaternion-valued GEMM and Convolution in single- and double-precision (targeting CUDA Cores)
+    * Updates to [quaternion.h](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/quaternion.h) and [functional.h](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/functional.h)
+    * SDK Example for [GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/21_quaternion_gemm/quaternion_gemm.cu) and [Convolution](https://github.com/NVIDIA/cutlass/tree/main/examples/22_quaternion_conv/quaternion_conv.cu)
+    * [Unit tests for GEMM](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/simt_qgemm_nn_sm50.cu) and [Convolution](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv2d_fprop_implicit_gemm_qf32nhwc_qf32nhwc_qf32nhwc_simt_f32_sm50.cu)
+  * Many improvements to the epilogue.
+    * Provide an [option](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/threadblock/epilogue.h) to not fully unroll the epilogue to reduce the code size and improve the performance when using complicated elementwise operations
+    * Performance improvement for FP16 tensor core kernels
+    * Bug fixes
+  * Enhanced Clang support and the combination of Clang 13 and CUDA 11.4 can build and run kernels from Pascal and Ampere.
+  * Updated minimum CUDA Toolkit requirement to 10.2
+    * [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit) recommended
+  * Corrections and bug fixes reported by the CUTLASS community
+    * Thank you for filing these issues!
+
+## [2.5.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.5.0) (2021-02-26)
+  * Tensor reductions
+    * _m_-to-_n_ reductions of tensors with affine layout
+    * [Specializations](https://github.com/NVIDIA/cutlass/tree/main/test/unit/reduction/device/tensor_reduce_contiguous.cu) for reductions including contiguous dimension
+    * [Specializations](https://github.com/NVIDIA/cutlass/tree/main/test/unit/reduction/device/tensor_reduce_strided.cu) for reductions excluding contiguous dimension
+    * Custom reduction functors such as `cutlass::logical_and`
+    * Large tensor support, up to 2^63 elements (however, each dimension is limited to an extent of 2^31)
+  * Optimizations for 3-D convolution
+    * [Optimized tile iterators](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h) using precomputed delta table for 3-D convolution
+    * Full coverage of [forward](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv3d_fprop_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) and [backwards](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device/conv3d_dgrad_implicit_gemm_f16ndhwc_f16ndhwc_f32ndhwc_tensor_op_f32_sm80.cu) passes for 3D convolution
+  * [Fused Convolution+Convolution example](https://github.com/NVIDIA/cutlass/tree/main/examples/13_two_tensor_op_fusion/README.md)
+  * Corrections and bug fixes reported by the CUTLASS community
+    * Thank you for filing these issues!
+
+
+## [2.4.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.4.0) (2020-11-19)
+  * Implicit GEMM convolution kernels supporting CUDA and Tensor Cores on NVIDIA GPUs
+    * Operators: forward (Fprop), backward data gradient (Dgrad), and backward weight gradient (Wgrad) convolution
+    * Data type: FP32, complex<FP32>, Tensor Float 32 (TF32), BFloat16 (BF16), Float16, Int4, Int8, Int32
+    * Spatial dimensions: 1-D, 2-D, and 3-D
+    * Layout: NHWC, NCxHWx
+  * Implicit GEMM convolution components:
+    * Global memory iterators supporting Fprop, Dgrad, and Wgrad
+    * `MmaMultistage` for implicit GEMM convolution for NVIDIA Ampere architecture
+    * `MmaPipeline` for implicit GEMM convolution for NVIDIA Volta and Turing architectures
+    * [Documentation](./media/docs/cpp/implicit_gemm_convolution.md) describing Implicit GEMM Convolution algorithm and implementation
+
+## [2.3.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.3.0) (2020-09-23)
+ * [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
+   * [Sparse Tensor Core GEMM kernels](https://github.com/NVIDIA/cutlass/tree/main/test/unit/gemm/device/gemm_f16n_f16n_f32t_tensor_op_f32_sparse_sm80.cu):
+     * Direct access to Sparse Tensor Cores and maximum performance via [`mma.sp.sync`](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma-and-friends)
+   * Fast SGEMM targeting GeForce RTX 30-series CUDA Cores
+ * Minor Features:
+   * [Activation functions](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/activation.h) such as [GeLU](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_gelu.h) and [Sigmoid](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/epilogue/thread/linear_combination_sigmoid.h)
+   * Small [matrix](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/matrix.h) and [quaternion](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/quaternion.h) template classes in device code
+   * [Floating-point constants](https://github.com/NVIDIA/cutlass/tree/main/include/cutlass/constants.h)
+ * NVIDIA Ampere GPU Architecture examples and documentation:
+   * [Tensor Float 32](https://github.com/NVIDIA/cutlass/tree/main/examples/14_ampere_tf32_tensorop_gemm/ampere_tf32_tensorop_gemm.cu) and
+   * [Sparse Tensor Cores](https://github.com/NVIDIA/cutlass/tree/main/examples/15_ampere_sparse_tensorop_gemm/ampere_sparse_tensorop_gemm.cu)
+   * Documentation added on CUTLASS [efficient row-major epilogue](./media/docs/cpp/gemm_api.md#efficient-epilogue)
+
+## [2.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.2.0) (2020-06-08)
+ * [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
+   * Fast Tensor Core operations:
+    * Maximum performance via [`mma.sync`](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma-and-friends)
+    * Tensor Float 32, BFloat16, and double-precision data types
+    * Mixed integer data types (int8, int4, bin1)
+   * Asynchronous copy for deep software pipelines via [`cp.async`](https://docs.nvidia.com/cuda/parallel-thread-execution)
+   * Described in [GTC 2020 Webinar (SR 21745)](https://developer.nvidia.com/gtc/2020/video/s21745) (free registration required)
+ * Features:
+   * SDK examples showing GEMM fused with bias+relu and fused GEMM+GEMM
+   * Complex-valued GEMMs targeting NVIDIA Ampere Tensor Cores in double-precision and Tensor Float 32
+   * Gaussian complex GEMMs using 3m complex multiply algorithm
+   * Universal GEMM kernel supporting two batch modes and two algorithms for parallel reductions
+ * Policy updates:
+   * [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit) needed to enable NVIDIA Ampere Architecture features
+   * Disabled F16C by default for compatibility - enable on cmake command line with `-DCUTLASS_ENABLE_F16C=ON`
+
+## [2.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.1.0) (2020-04-06)
+ * BLAS-style host-side API added to [CUTLASS Library](./media/docs/cpp/quickstart.md#cutlass-library)
+    * API to launch compiled kernel instances for GEMM and planar complex GEMM
+ * Planar Complex GEMM kernels targeting Volta and Turing Tensor Cores
+    * Computes complex matrix products on matrices stored as disjoint real and imaginary parts
+    * [SDK Examples of Planar Complex GEMMs](https://github.com/NVIDIA/cutlass/tree/main/examples/10_planar_complex/planar_complex.cu)
+ * Minor enhancements and bug fixes
+
+## [2.0.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.0.0) (2019-11-19)
+ * Substantially refactored for
+    * Better performance, particularly for native Turing Tensor Cores
+    * Robust and durable templates spanning the design space
+    * Encapsulated functionality embodying modern C++11 programming techniques
+    * Optimized containers and data types for efficient, generic, portable device code
+  * Updates to:
+    * [Quick start guide](./media/docs/cpp/quickstart.md)
+    * [Documentation](./README.md#documentation)
+    * [Utilities](./media/docs/cpp/utilities.md)
+    * [CUTLASS Profiler](./media/docs/cpp/profiler.md)
+ * Native Turing Tensor Cores
+    * Efficient GEMM kernels targeting Turing Tensor Cores
+    * Mixed-precision floating point, 8-bit integer, 4-bit integer, and binarized operands
+ * Coverage of existing CUTLASS functionality
+    * GEMM kernels targeting CUDA and Tensor Cores in NVIDIA GPUs
+    * Volta Tensor Cores through native mma.sync and through WMMA API
+    * Optimizations such as parallel reductions, threadblock rasterization, and intra-threadblock reductions
+    * Batched GEMM operations
+    * Complex-valued GEMMs
+ * **Note: a host compiler supporting C++11 or greater is required.**
+
+# CUTLASS 1.x
+
+## [1.3.2](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.2) (2019-07-09)
+ * Performance improvement for Volta Tensor Cores TN and TT layouts.
+
+## [1.3.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.1) (2019-04-09)
+ * Corrected NVRTC unit tests.
+
+## [1.3.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.3.0) (2019-03-20)
+ * Efficient GEMM kernel targeting Volta Tensor Cores via `mma.sync` instruction added in CUDA 10.1.
+
+## [1.2.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.2.0) (2018-10-26)
+ * Parallelized reductions across threadblocks ("Split-K")
+   * Improved IGEMM performance
+ * Batched strided WMMA GEMMs
+
+## [1.1.0](https://github.com/NVIDIA/cutlass/releases/tag/v1.1.0) (2018-09-19)
+  * Turing Features
+    * WMMA GEMM targeting TensorCores - INT8, INT4, 1-bit
+  * Batched Strided GEMM
+  * Threadblock rasterization strategies
+    * Improved performance for adverse problem sizes and data layouts
+  * Extended CUTLASS Core comonents
+    * Tensor views support arbitrary matrix and tensor layouts
+    * Zip iterators for structuring multiple data streams
+  * Enhanced CUTLASS utilities
+    * Reference code for tensor operations in host and device code
+    * Added HostMatrix<> for simplified matrix creation
+  * Examples
+    * Basic GEMM, tensor views, CUTLASS utilities, batched GEMM, WMMA GEMM
+
+## [1.0.1](https://github.com/NVIDIA/cutlass/releases/tag/v1.0.1) (2018-06-11)
+
+  * Intra-threadblock reduction added for small threadblock tile sizes
+    * sgemm_64x128x16, sgemm_128x128x16, sgemm_128x64x16, sgemm_128x32x16, sgemm_64x64x16, sgemm_64x32x16
+    * igemm_32x32x128
+  * GEMM _K_ residue handled during prologue prior to mainloop
+  * Replaced Google Test copy with submodule. Use `git submodule init --recursive --update`
+
+## [1.0.0](https://github.com/NVIDIA/cutlass/commit/2028ebe120aab22bfd0b2baf8902d4c9627eb33f) (2018-05-16)
+
+  * Substantial rewrite to accommodate new architecture
+  * Kernels: SGEMM, DGEMM, IGEMM, HGEMM, WMMA GEMM
+  * Unit and performance tests
+
+## [0.0.1](https://github.com/NVIDIA/cutlass/commit/d08ba8ac46e2fa3f745e070c390182edb56b2e91) (2017-12-04)
+
+  * Initial release
+
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

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+title: CUTLASS
+message: >-
+  If you use this software, please cite using the
+  following metadata.
+type: software
+authors:
+  - given-names: Vijay
+    family-names: Thakkar
+    email: vithakkar@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Pradeep
+    family-names: Ramani
+    email: prramani@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Cris
+    family-names: Cecka
+    email: ccecka@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Aniket
+    family-names: Shivam
+    email: ashivam@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Honghao
+    family-names: Lu
+    email: honghaol@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Ethan
+    family-names: Yan
+    email: etyan@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Jack
+    family-names: Kosaian
+    email: jkosaian@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Mark
+    family-names: Hoemmen
+    email: mhoemmen@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Haicheng
+    family-names: Wu
+    email: haichengw@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Andrew
+    family-names: Kerr
+    email: akerr@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Matt
+    family-names: Nicely
+    email: mnicely@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Duane
+    family-names: Merrill
+    email: dumerrill@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Dustyn
+    family-names: Blasig
+    email: dblasig@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Aditya
+    family-names: Atluri
+    email: aatluri@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Fengqi
+    family-names: Qiao
+    email: fqiao@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Piotr
+    family-names: Majcher
+    email: pmajcher@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Paul
+    family-names: Springer
+    email: pspringer@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Markus
+    family-names: Hohnerbach
+    affiliation: NVIDIA
+    email: mhohnerbach@nvidia.com
+  - given-names: Jin
+    family-names: Wang
+    email: jinw@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Manish
+    family-names: Gupta
+    affiliation: Google
+    email: manigupta@google.com
+
+
+repository-code: 'https://github.com/NVIDIA/cutlass'
+abstract: >-
+  CUTLASS is a collection of CUDA C++ template
+  abstractions for implementing high-performance
+  matrix-multiplication (GEMM) and related
+  computations at all levels and scales within CUDA.
+  It incorporates strategies for hierarchical
+  decomposition and data movement similar to those
+  used to implement cuBLAS and cuDNN. CUTLASS
+  decomposes these "moving parts" into reusable,
+  modular software components abstracted by C++
+  template classes. These thread-wide, warp-wide,
+  block-wide, and device-wide primitives can be
+  specialized and tuned via custom tiling sizes, data
+  types, and other algorithmic policy. The resulting
+  flexibility simplifies their use as building blocks
+  within custom kernels and applications.
+keywords:
+  - 'cutlass, tensor cores, cuda, cute, nvidia, gpu, linear algebra, matrix computations'
+license: BSD-3-Clause
+license-url: https://github.com/NVIDIA/cutlass/blob/v3.0.0/LICENSE.txt
+version: '3.0.0'
+date-released: '2023-01-23'
+identifiers:
+  - type: url
+    value: "https://github.com/NVIDIA/cutlass/tree/v3.0.0"
+    description: The GitHub release URL of tag 3.0.0

CMake/bin2hex.cmake

@@ -1,26 +0,0 @@
-# A small utility function which generates a C-header from an input file
-function(FILE_TO_C_STRING FILENAME VARIABLE_NAME OUTPUT_STRING ZERO_TERMINATED)
-  FILE(READ "${FILENAME}" HEX_INPUT HEX)
-  if (${ZERO_TERMINATED})
-    string(APPEND HEX_INPUT "00")
-  endif()
-
-  string(REGEX REPLACE "(....)" "\\1\n" HEX_OUTPUT ${HEX_INPUT})
-  string(REGEX REPLACE "([0-9a-f][0-9a-f])" "0x\\1," HEX_OUTPUT ${HEX_OUTPUT})
-
-  set(HEX_OUTPUT "static char const ${VARIABLE_NAME}[] = {\n  ${HEX_OUTPUT}\n};\n")
-
-  set(${OUTPUT_STRING} "${HEX_OUTPUT}" PARENT_SCOPE)
-endfunction()
-
-message("Create header file for ${FILE_IN}")
-message("Create header file for ${FILE_OUT}")
-file_to_c_string(${FILE_IN} ${VARIABLE_NAME} OUTPUT_STRING ZERO_TERMINATED)
-
-set(RESULT "#pragma once\n")
-string(APPEND RESULT "namespace cutlass {\n")
-string(APPEND RESULT "namespace nvrtc {\n")
-string(APPEND RESULT "${OUTPUT_STRING}")
-string(APPEND RESULT "} // namespace nvrtc\n")
-string(APPEND RESULT "} // namespace cutlass\n")
-file(WRITE "${FILE_OUT}" "${RESULT}")

CONTRIBUTORS.md

@@ -0,0 +1,203 @@
+![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png "CUTLASS")
+
+[README](./README.md#documentation) > **Contributors**
+
+# CUTLASS C++ Developers **
+
+Andrew Kerr<br />
+Paul Springer<br />
+Dustyn Blasig<br />
+Albert Xu<br />
+Junkai Wu<br />
+Xiuxia Zhang<br />
+Haicheng Wu<br />
+Jack Yang<br />
+Pradeep Ramani<br />
+Aditya Atluri<br />
+Han Li<br />
+Nick Zhao<br />
+Ivan Yin<br />
+Yu-Jung Chen<br />
+Markus Hoehnerbach<br />
+Honghao Lu<br />
+Mihir Awatramani<br />
+Hao Sheng<br />
+Zekun Fan<br />
+Aniket Shivam<br />
+Siyu Liu<br />
+Richard Cai<br />
+Vikas Gupta<br />
+Ethan Yan<br />
+Vijay Thakkar<br />
+Cris Cecka<br />
+Lawrence Ryan<br />
+Qun Song<br />
+Daniel Ricketts<br />
+dePaul Miller<br />
+Yuhan Li<br />
+Saman Ashkiani<br />
+Jack Chen<br />
+Shang Zhang<br />
+Petrick Liu<br />
+Questa Wang<br />
+Pramod Shenoy<br />
+Jack Kosaian<br />
+Yujia Zhai<br />
+Zhaodong Chen<br />
+Manas Sahni<br />
+Shunfan Shao<br />
+Fengqi Qiao<br />
+Serif Yesil<br />
+Aragorn Guan<br />
+Heidi He<br />
+Xiao Song<br />
+Sergey Klevtsov<br />
+Jiang Shao<br />
+Ruqing Xu<br />
+Mengyu Guo<br />
+Tao Xie<br />
+Linfeng Zheng<br />
+Harrison Barclay<br />
+Wenfei Tang<br />
+Diksha Gohlyan<br />
+Alexander Zhurkevich<br />
+Siyuan Fu<br />
+Hua Huang<br />
+Xiufan Liang<br />
+Ian Tramble<br />
+Ali Hassani<br />
+Shreya Gaur<br />
+
+** _The list is sorted in order of the author's first contribution to the CUTLASS project._
+
+# CUTLASS DSL Developers ***
+
+Albert Di<br />
+Albert Xu<br />
+Anakin Zheng<br />
+Arvin Jou<br />
+Brandon Sun<br />
+Chenyang Xu<br />
+Chunyu Wang<br />
+Cris Cecka<br />
+dePaul Miller<br />
+Edward Cao<br />
+Fung Xie<br />
+Guray Ozen<br />
+Hao Hu<br />
+Hong Wang<br />
+Jeremy Furtek<br />
+Jie Fang <br />
+JingZe Cui<br />
+Kihiro Bando<br />
+Linfeng Zheng<br />
+Longsheng Du<br />
+Mina Sun<br />
+Mindy Li<br />
+Pradeep Ramani<br />
+Questa Wang<br />
+Serif Yesil<br />
+Tao Xie<br />
+Tina Li<br />
+Vicki Wang<br />
+Vincent Zhang<br />
+Vijay Thakkar<br />
+Xiao Dong<br />
+Xiaolei Shi<br />
+Xinyu Wang<br />
+Yihan Chen<br />
+Yuhan Li<br />
+Zekun Fan<br />
+
+*** _Sorted in alphabetical order._
+
+
+# CuTe Developers
+
+Cris Cecka<br />
+Vijay Thakkar<br />
+
+
+# CUTLASS Product Manager
+
+Matthew Nicely<br />
+
+
+# Former CUTLASS Developers
+
+Manish Gupta<br />
+Duane Merrill<br />
+Piotr Majcher<br />
+Naila Farooqui<br />
+Mark Hoemmen<br />
+Rawn Henry<br />
+Jin Wang<br />
+Timmy Liu<br />
+Manikandan Ananth<br />
+David Tanner<br />
+
+
+# Acknowledgements
+
+Tri Dao<br />
+Jay Shah<br />
+Mehdi Amini<br />
+Larry Wu<br />
+Justin Holewinski<br />
+Timothy Costa<br />
+Julien Demouth<br />
+Brian Fahs<br />
+Michael Garland<br />
+Michael Goldfarb<br />
+Mostafa Hagog<br />
+Fei Hu<br />
+Alan Kaatz<br />
+Wei Liu<br />
+Tim Martin<br />
+Kevin Siu<br />
+Markus Tavenrath<br />
+John Tran<br />
+Yang Xu<br />
+Scott Yokim<br />
+Girish Bharambe<br />
+Luke Durant<br />
+Carter Edwards<br />
+Olivier Giroux<br />
+Stephen Jones<br />
+Rishkul Kulkarni<br />
+Bryce Lelbach<br />
+Joel McCormack<br />
+Kyrylo Perelygin<br />
+Sean Treichler<br />
+
+# Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

CUDA.cmake

@@ -0,0 +1,369 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+if (CUDA_COMPILER MATCHES "[Cc]lang")
+  message(WARNING "CUDA_COMPILER flag is deprecated, set CMAKE_CUDA_COMPILER to desired compiler executable.")
+  set(__CLANG_DEVICE_COMPILATION_REQUESTED ON)
+elseif(CUDA_COMPILER)
+  message(WARNING "Deprecated flag CUDA_COMPILER used with unknown argument ${CUDA_COMPILER}, ignoring.")
+endif()
+
+if (__CLANG_DEVICE_COMPILATION_REQUESTED AND NOT DEFINED CMAKE_CUDA_COMPILER)
+  set(CMAKE_CUDA_COMPILER clang++) # We will let the system find Clang or error out
+endif()
+
+enable_language(CUDA)
+find_package(CUDAToolkit REQUIRED)
+
+if(NOT CUDA_VERSION)
+  # For backward compatibility with older CMake code.
+  set(CUDA_VERSION ${CUDAToolkit_VERSION})
+  set(CUDA_VERSION_MAJOR ${CUDAToolkit_VERSION_MAJOR})
+  set(CUDA_VERSION_MINOR ${CUDAToolkit_VERSION_MINOR})
+endif()
+if(NOT CUDA_TOOLKIT_ROOT_DIR)
+  # In some scenarios, such as clang device compilation, the toolkit root may not be set, so we 
+  # force it here to the nvcc we found via the CUDAToolkit package.
+  get_filename_component(CUDA_TOOLKIT_ROOT_DIR "${CUDAToolkit_NVCC_EXECUTABLE}/../.." ABSOLUTE)
+endif()
+
+if (CMAKE_CUDA_COMPILER_ID MATCHES "(nvcc|[Nn][Vv][Ii][Dd][Ii][Aa])")
+  set(CUTLASS_NVCC_DEVICE_COMPILE ON CACHE BOOL "Using nvcc tools for device compilation")
+elseif (CMAKE_CUDA_COMPILER_ID MATCHES "[Cc]lang")
+  set(CUTLASS_CLANG_DEVICE_COMPILE ON CACHE BOOL "Using Clang tools for device compilation")
+else()
+  message(FATAL_ERROR "Unknown device-side compiler ${CMAKE_CUDA_COMPILER_ID} found. Set CMAKE_CUDA_COMPILER to either nvcc or clang++.")
+endif()
+
+if (CUTLASS_CLANG_DEVICE_COMPILE AND CMAKE_VERSION VERSION_LESS_EQUAL "3.30")
+  message(FATAL_ERROR "Clang device compilation for CUTLASS requires CMake 3.30 or higher.")
+endif()
+
+if (CUDA_VERSION VERSION_LESS 9.2)
+  message(FATAL_ERROR "CUDA 9.2+ required, found ${CUDA_VERSION}.")
+endif()
+
+find_library(
+  CUDART_LIBRARY cudart
+  PATHS
+  ${CUDA_TOOLKIT_ROOT_DIR}
+  PATH_SUFFIXES
+  lib/x86_64-linux-gnu
+  lib/x64
+  lib64
+  lib
+  NO_DEFAULT_PATH
+  # We aren't going to search any system paths. We want to find the runtime 
+  # in the CUDA toolkit we're building against.
+  )
+
+if(NOT TARGET cudart AND CUDART_LIBRARY)
+
+  message(STATUS "CUDART: ${CUDART_LIBRARY}")
+
+  if(WIN32)
+    add_library(cudart STATIC IMPORTED GLOBAL)
+    # Even though we're linking against a .dll, in Windows you statically link against
+    # the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
+    # from the PATH search.
+  else()
+    add_library(cudart SHARED IMPORTED GLOBAL)
+  endif()  
+
+  add_library(nvidia::cudart ALIAS cudart)
+  
+  set_property(
+    TARGET cudart
+    PROPERTY IMPORTED_LOCATION
+    ${CUDART_LIBRARY}
+    )
+
+elseif(TARGET cudart)
+
+  message(STATUS "CUDART: Already Found")
+
+else()
+
+  message(STATUS "CUDART: Not Found")
+
+endif()
+
+find_library(
+  CUDA_DRIVER_LIBRARY cuda
+  PATHS
+  ${CUDA_TOOLKIT_ROOT_DIR}
+  PATH_SUFFIXES
+  lib/x86_64-linux-gnu
+  lib/x64
+  lib64
+  lib
+  lib64/stubs
+  lib/stubs
+  NO_DEFAULT_PATH
+  # We aren't going to search any system paths. We want to find the runtime 
+  # in the CUDA toolkit we're building against.
+  )
+
+if(NOT TARGET cuda_driver AND CUDA_DRIVER_LIBRARY)
+
+  message(STATUS "CUDA Driver: ${CUDA_DRIVER_LIBRARY}")
+
+  if(WIN32)
+    add_library(cuda_driver STATIC IMPORTED GLOBAL)
+    # Even though we're linking against a .dll, in Windows you statically link against
+    # the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
+    # from the PATH search.
+  else()
+    add_library(cuda_driver SHARED IMPORTED GLOBAL)
+  endif()  
+
+  add_library(nvidia::cuda_driver ALIAS cuda_driver)
+  
+  set_property(
+    TARGET cuda_driver
+    PROPERTY IMPORTED_LOCATION
+    ${CUDA_DRIVER_LIBRARY}
+    )
+
+elseif(TARGET cuda_driver)
+
+  message(STATUS "CUDA Driver: Already Found")
+
+else()
+
+  message(STATUS "CUDA Driver: Not Found")
+
+endif()
+
+find_library(
+  NVRTC_LIBRARY nvrtc
+  PATHS
+  ${CUDA_TOOLKIT_ROOT_DIR}
+  PATH_SUFFIXES
+  lib/x64
+  lib64
+  lib
+  NO_DEFAULT_PATH
+  # We aren't going to search any system paths. We want to find the runtime 
+  # in the CUDA toolkit we're building against.
+  )
+
+if(NOT TARGET nvrtc AND NVRTC_LIBRARY)
+
+  message(STATUS "NVRTC: ${NVRTC_LIBRARY}")
+
+  if(WIN32)
+    add_library(nvrtc STATIC IMPORTED GLOBAL)
+    # Even though we're linking against a .dll, in Windows you statically link against
+    # the .lib file found under lib/x64. The .dll will be loaded at runtime automatically
+    # from the PATH search.
+  else()
+    add_library(nvrtc SHARED IMPORTED GLOBAL)
+  endif()  
+  
+  add_library(nvidia::nvrtc ALIAS nvrtc)
+  
+  set_property(
+    TARGET nvrtc
+    PROPERTY IMPORTED_LOCATION
+    ${NVRTC_LIBRARY}
+    )
+
+elseif(TARGET nvrtc)
+
+  message(STATUS "NVRTC: Already Found")
+
+else()
+
+  message(STATUS "NVRTC: Not Found")
+
+endif()
+
+include_directories(SYSTEM ${CUDA_INCLUDE_DIRS})
+# Some platforms (e.g. Visual Studio) don't add the CUDA include directories to the system include
+# paths by default, so we add it explicitly here.
+
+if (MSVC OR CUTLASS_LIBRARY_KERNELS MATCHES "all")
+  set(CUTLASS_UNITY_BUILD_ENABLED_INIT ON)
+else()
+  set(CUTLASS_UNITY_BUILD_ENABLED_INIT OFF)
+endif()
+
+set(CUTLASS_UNITY_BUILD_ENABLED ${CUTLASS_UNITY_BUILD_ENABLED_INIT} CACHE BOOL "Enable combined source compilation")
+
+if (MSVC)
+  set(CUTLASS_UNITY_BUILD_BATCH_SIZE_INIT 8)
+else()
+  set(CUTLASS_UNITY_BUILD_BATCH_SIZE_INIT 16)
+endif()
+
+set(CUTLASS_UNITY_BUILD_BATCH_SIZE ${CUTLASS_UNITY_BUILD_BATCH_SIZE_INIT} CACHE STRING "Batch size for unified source files")
+
+function(cutlass_unify_source_files TARGET_ARGS_VAR)
+
+  set(options)
+  set(oneValueArgs BATCH_SOURCES BATCH_SIZE)
+  set(multiValueArgs)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if (NOT DEFINED TARGET_ARGS_VAR)
+    message(FATAL_ERROR "TARGET_ARGS_VAR parameter is required")
+  endif()
+
+  if (NOT DEFINED __BATCH_SOURCES)
+    set(__BATCH_SOURCES ON)
+  endif()
+
+  if (__BATCH_SOURCES AND NOT DEFINED __BATCH_SIZE)
+    set(__BATCH_SIZE ${CUTLASS_UNITY_BUILD_BATCH_SIZE})
+  endif()
+
+  if (CUTLASS_UNITY_BUILD_ENABLED AND __BATCH_SOURCES AND __BATCH_SIZE GREATER 1)
+
+    set(CUDA_FILE_ARGS)
+    set(TARGET_SOURCE_ARGS)
+    
+    foreach(ARG ${__UNPARSED_ARGUMENTS})
+      if(${ARG} MATCHES ".*\.cu$")
+        list(APPEND CUDA_FILE_ARGS ${ARG})
+      else()
+        list(APPEND TARGET_SOURCE_ARGS ${ARG})
+      endif()
+    endforeach()
+    
+    list(LENGTH CUDA_FILE_ARGS NUM_CUDA_FILE_ARGS)
+    while(NUM_CUDA_FILE_ARGS GREATER 0)
+      list(SUBLIST CUDA_FILE_ARGS 0 ${__BATCH_SIZE} CUDA_FILE_BATCH)
+      string(SHA256 CUDA_FILE_BATCH_HASH "${CUDA_FILE_BATCH}")
+      string(SUBSTRING ${CUDA_FILE_BATCH_HASH} 0 12 CUDA_FILE_BATCH_HASH)
+      set(BATCH_FILE ${CMAKE_CURRENT_BINARY_DIR}/${NAME}.unity.${CUDA_FILE_BATCH_HASH}.cu)
+      message(STATUS "Generating ${BATCH_FILE}")
+      file(WRITE ${BATCH_FILE} "// Unity File - Auto Generated!\n")
+      foreach(CUDA_FILE ${CUDA_FILE_BATCH})
+        get_filename_component(CUDA_FILE_ABS_PATH ${CUDA_FILE} ABSOLUTE)
+        file(APPEND ${BATCH_FILE} "#include \"${CUDA_FILE_ABS_PATH}\"\n")
+      endforeach()
+      list(APPEND TARGET_SOURCE_ARGS ${BATCH_FILE})
+      if (NUM_CUDA_FILE_ARGS LESS_EQUAL __BATCH_SIZE)
+        break()
+      endif()
+      list(SUBLIST CUDA_FILE_ARGS ${__BATCH_SIZE} -1 CUDA_FILE_ARGS)
+      list(LENGTH CUDA_FILE_ARGS NUM_CUDA_FILE_ARGS)
+    endwhile()
+
+  else()
+
+    set(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
+
+  endif()
+
+  set(${TARGET_ARGS_VAR} ${TARGET_SOURCE_ARGS} PARENT_SCOPE)
+
+endfunction()
+function(cutlass_add_library NAME)
+
+  set(options SKIP_GENCODE_FLAGS)
+  set(oneValueArgs EXPORT_NAME)
+  set(multiValueArgs)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
+  
+  add_library(${NAME} ${TARGET_SOURCE_ARGS} "")
+
+  cutlass_apply_standard_compile_options(${NAME})
+
+  if (NOT __SKIP_GENCODE_FLAGS)
+    cutlass_apply_cuda_gencode_flags(${NAME})
+  endif()
+
+  target_compile_features(
+   ${NAME}
+   INTERFACE
+   cxx_std_11
+   )
+
+  get_target_property(TARGET_TYPE ${NAME} TYPE)
+
+  if (TARGET_TYPE MATCHES "SHARED")
+    set_target_properties(${NAME} PROPERTIES CUDA_RUNTIME_LIBRARY Shared)
+  elseif(TARGET_TYPE MATCHES "STATIC")
+    set_target_properties(${NAME} PROPERTIES CUDA_RUNTIME_LIBRARY Static)
+  endif()
+
+  if(__EXPORT_NAME)
+    add_library(nvidia::cutlass::${__EXPORT_NAME} ALIAS ${NAME})
+    set_target_properties(${NAME} PROPERTIES EXPORT_NAME ${__EXPORT_NAME})
+  endif()
+
+endfunction()
+
+function(cutlass_add_executable NAME)
+
+  set(options)
+  set(oneValueArgs CUDA_RUNTIME_LIBRARY)
+  set(multiValueArgs)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if (NOT DEFINED __CUDA_RUNTIME_LIBRARY)
+    set(__CUDA_RUNTIME_LIBRARY Shared)
+  endif()
+
+  set(__CUDA_RUNTIME_LIBRARY_ALLOWED None Shared Static)
+  if (NOT __CUDA_RUNTIME_LIBRARY IN_LIST __CUDA_RUNTIME_LIBRARY_ALLOWED)
+    message(FATAL_ERROR "CUDA_RUNTIME_LIBRARY value '${__CUDA_RUNTIME_LIBRARY}' is not in allowed list of '${__CUDA_RUNTIME_LIBRARY_ALLOWED}'")
+  endif()
+
+  cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
+
+  add_executable(${NAME} ${TARGET_SOURCE_ARGS})
+
+  cutlass_apply_standard_compile_options(${NAME})
+  cutlass_apply_cuda_gencode_flags(${NAME})
+
+  target_compile_features(
+   ${NAME}
+   INTERFACE
+   cxx_std_11
+   )
+
+  set_target_properties(${NAME} PROPERTIES CUDA_RUNTIME_LIBRARY ${__CUDA_RUNTIME_LIBRARY})
+
+endfunction()
+
+function(cutlass_target_sources NAME)
+
+  set(options)
+  set(oneValueArgs)
+  set(multiValueArgs)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  cutlass_unify_source_files(TARGET_SOURCE_ARGS ${__UNPARSED_ARGUMENTS})
+  target_sources(${NAME} ${TARGET_SOURCE_ARGS})
+
+endfunction()

Doxyfile

@@ -32,7 +32,7 @@ DOXYFILE_ENCODING      = UTF-8
 # title of most generated pages and in a few other places.
 # The default value is: My Project.
 
-PROJECT_NAME           = "Cutlass"
+PROJECT_NAME           = "CUTLASS"
 
 # The PROJECT_NUMBER tag can be used to enter a project or revision number. This
 # could be handy for archiving the generated documentation or if some version
@@ -51,14 +51,14 @@ PROJECT_BRIEF          = "CUDA Templates for Linear Algebra Subroutines and Solv
 # and the maximum width should not exceed 200 pixels. Doxygen will copy the logo
 # to the output directory.
 
-PROJECT_LOGO           =
+PROJECT_LOGO           = media/images/cutlass-logo-small.png
 
 # The OUTPUT_DIRECTORY tag is used to specify the (relative or absolute) path
 # into which the generated documentation will be written. If a relative path is
 # entered, it will be relative to the location where doxygen was started. If
 # left blank the current directory will be used.
 
-OUTPUT_DIRECTORY       = docs
+OUTPUT_DIRECTORY       = doxygen
 
 # If the CREATE_SUBDIRS tag is set to YES, then doxygen will create 4096 sub-
 # directories (in 2 levels) under the output directory of each output format and
@@ -206,7 +206,7 @@ SEPARATE_MEMBER_PAGES  = NO
 # uses this value to replace tabs by spaces in code fragments.
 # Minimum value: 1, maximum value: 16, default value: 4.
 
-TAB_SIZE               = 4
+TAB_SIZE               = 2
 
 # This tag can be used to specify a number of aliases that act as commands in
 # the documentation. An alias has the form:
@@ -297,7 +297,7 @@ AUTOLINK_SUPPORT       = YES
 # diagrams that involve STL classes more complete and accurate.
 # The default value is: NO.
 
-BUILTIN_STL_SUPPORT    = NO
+BUILTIN_STL_SUPPORT    = YES
 
 # If you use Microsoft's C++/CLI language, you should set this option to YES to
 # enable parsing support.
@@ -734,7 +734,9 @@ WARN_LOGFILE           =
 # spaces.
 # Note: If this tag is empty the current directory is searched.
 
-INPUT                  = cutlass
+INPUT                  = include/cutlass tools/util/include/cutlass/ tools/library/include/cutlass/
+
+INPUT += media/docs/doxygen_mainpage.md
 
 # This tag can be used to specify the character encoding of the source files
 # that doxygen parses. Internally doxygen uses the UTF-8 encoding. Doxygen uses
@@ -870,7 +872,7 @@ FILTER_SOURCE_PATTERNS =
 # (index.html). This can be useful if you have a project on for instance GitHub
 # and want to reuse the introduction page also for the doxygen output.
 
-USE_MDFILE_AS_MAINPAGE =
+USE_MDFILE_AS_MAINPAGE = media/docs/doxygen_mainpage.md
 
 #---------------------------------------------------------------------------
 # Configuration options related to source browsing
@@ -999,7 +1001,7 @@ GENERATE_HTML          = YES
 # The default directory is: html.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_OUTPUT            = generated-html
+HTML_OUTPUT            = 
 
 # The HTML_FILE_EXTENSION tag can be used to specify the file extension for each
 # generated HTML page (for example: .htm, .php, .asp).
@@ -1080,7 +1082,7 @@ HTML_EXTRA_FILES       =
 # Minimum value: 0, maximum value: 359, default value: 220.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_COLORSTYLE_HUE    = 82
+HTML_COLORSTYLE_HUE    = 100
 
 # The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors
 # in the HTML output. For a value of 0 the output will use grayscales only. A
@@ -1088,7 +1090,7 @@ HTML_COLORSTYLE_HUE    = 82
 # Minimum value: 0, maximum value: 255, default value: 100.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_COLORSTYLE_SAT    = 100
+HTML_COLORSTYLE_SAT    = 50
 
 # The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the
 # luminance component of the colors in the HTML output. Values below 100
@@ -1107,7 +1109,7 @@ HTML_COLORSTYLE_GAMMA  = 80
 # The default value is: YES.
 # This tag requires that the tag GENERATE_HTML is set to YES.
 
-HTML_TIMESTAMP         = YES
+HTML_TIMESTAMP         = NO
 
 # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML
 # documentation will contain sections that can be hidden and shown after the

EULA.txt

@@ -0,0 +1,188 @@
+NVIDIA Software License Agreement
+
+IMPORTANT NOTICE – PLEASE READ AND AGREE BEFORE USING THE SOFTWARE
+This software license agreement (“Agreement”) is a legal agreement between you, whether an individual or entity, (“you”) and NVIDIA Corporation (“NVIDIA”) and governs the use of the NVIDIA CUTLASS DSLs software and materials that NVIDIA delivers to you under this Agreement (“Software”).
+NVIDIA and you are each a “party” and collectively the “parties.”
+This Agreement can be accepted only by an adult of legal age of majority in the country in which the Software is used.
+If you don’t have the required age or authority to accept this Agreement, or if you don’t accept all the terms and conditions of this Agreement, do not use the Software.
+
+1. License Grants
+
+    1.1. License Grant to You. The Software made available by NVIDIA to you is licensed, not sold.
+    Subject to the terms of this Agreement, NVIDIA grants you a limited, non-exclusive, revocable, non-transferable, and non-sublicensable (except as expressly granted in this Agreement), license to:
+
+    a. install and use copies of the Software,
+    b. configure the Software using configuration files provided (if applicable),
+    c. modify and create derivative works of any sample or example source code NVIDIA delivers to you as part of the Software (“Derivatives”) (if applicable), and
+    d. distribute python files in the Software package in source format as incorporated into a software application subject to the following distribution requirements:
+
+       i. Your application must have material additional functionality, beyond the included portions of the Software.
+       ii. The distributable portions of the Software shall only be accessed by your application.
+       iii. The following notice shall be included in modifications and derivative works of sample source code distributed: “This software contains source code provided by NVIDIA Corporation.”
+       iv. Unless a developer tool is identified in this Agreement as distributable, it is delivered for your internal use only.
+       v. The terms under which you distribute your application must be consistent with the terms of this Agreement, including (without limitation) terms relating to the license grant and license restrictions and protection of NVIDIA’s intellectual property rights.
+       vi. Additionally, you agree that you will protect the privacy, security and legal rights of your application users.
+
+    The foregoing (a) through (d) are, collectively, the “Purpose”, and the developed applications are only for use in systems with NVIDIA GPUs.
+
+    1.2. License Grant to NVIDIA. Subject to the terms of this Agreement, you grant NVIDIA and its affiliates a non-exclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit at NVIDIA’s discretion any Derivatives created by or for you.
+    You may, but are not required to, deliver any Derivatives to NVIDIA.
+
+2. License Restrictions
+
+    Your license to use the Software and Derivatives is restricted as stated in this Section 2 (“License Restrictions”).
+    You will cooperate with NVIDIA and, upon NVIDIA’s written request, you will confirm in writing and provide reasonably requested information to verify your compliance with the terms of this Agreement.
+    You may not:
+
+    2.1. Use the Software or Derivatives for any purpose other than the Purpose;
+
+    2.2. Sell, rent, sublicense, transfer, distribute or otherwise make available to others (except authorized users as stated in Section 3 (“Authorized Users”)) any portion of the Software or Derivatives, except as expressly granted in Section 1.1 (“License Grant to You”);
+
+    2.3. Reverse engineer, decompile, or disassemble the Software components provided in binary form, nor attempt in any other manner to obtain source code of such Software;
+
+    2.4. Modify or create derivative works of the Software, except as expressly granted in Section 1.1 (“License Grant to You”);
+
+    2.5. Change or remove copyright or other proprietary notices in the Software;
+
+    2.6. Bypass, disable, or circumvent any technical limitation, encryption, security, digital rights management or authentication mechanism in the Software;
+
+    2.7. Use the Software or Derivatives in any manner that would cause them to become subject to an open source software license, subject to the terms in Section 6 (“Components Under Other Licenses”);
+
+    2.8. Use the Software or Derivatives in violation of any applicable law or regulation in relevant jurisdictions
+
+    2.9. Indicate that a product or service developed with the Software or Derivatives is sponsored or endorsed by NVIDIA;
+
+    2.10. Replace any NVIDIA software components in the Software that are governed by this Agreement with other software that implements NVIDIA APIs;
+
+    2.11. Reverse engineer, decompile or disassemble any portion of the output generated using Software elements for the purpose of translating such output artifacts to target a non-NVIDIA platform; or
+
+3. Authorized Users
+
+    You may allow employees and contractors of your entity or of your subsidiary(ies), and for educational institutions also enrolled students, to internally access and use the Software as authorized by this Agreement from your secure network to perform the work authorized by this Agreement on your behalf.
+    You are responsible for the compliance with the terms of this Agreement by your authorized users.
+    Any act or omission that if committed by you would constitute a breach of this Agreement will be deemed to constitute a breach of this Agreement if committed by your authorized users.
+
+4. Pre-Release
+
+    Software versions identified as alpha, beta, preview, early access or otherwise as pre-release (“Pre-Release”) may not be fully functional, may contain errors or design flaws, and may have reduced or different security, privacy, availability and reliability standards relative to NVIDIA commercial offerings.
+    You use Pre-Release Software at your own risk. NVIDIA did not design or test the Software for use in production or business-critical systems.
+    NVIDIA may choose not to make available a commercial version of Pre-Release Software.
+    NVIDIA may also choose to abandon development and terminate the availability of Pre-Release Software at any time without liability.
+
+5. Updates
+
+    NVIDIA may at any time and at its option, change, discontinue, or deprecate any part, or all, of the Software, or change or remove features or functionality, or make available patches, workarounds or other updates to the Software.
+    Unless the updates are provided with their separate governing terms, they are deemed part of the Software licensed to you under this Agreement, and your continued use of the Software is deemed acceptance of such changes.
+
+6. Components Under Other Licenses
+
+    The Software may include or be distributed with components provided with separate legal notices or terms that accompany the components, such as open source software licenses and other license terms (“Other Licenses”).
+    The components are subject to the applicable Other Licenses, including any proprietary notices, disclaimers, requirements and extended use rights;
+    except that this Agreement will prevail regarding the use of third-party open source software, unless a third-party open source software license requires its license terms to prevail.
+    Open source software license means any software, data or documentation subject to any license identified as an open source license by the Open Source Initiative (http://opensource.org), Free Software Foundation (http://www.fsf.org) or other similar open source organization or listed by the Software Package Data Exchange (SPDX) Workgroup under the Linux Foundation (http://www.spdx.org).
+
+7. Ownership
+
+    7.1. NVIDIA Ownership. The Software, including all intellectual property rights, is and will remain the sole and exclusive property of NVIDIA or its licensors.
+    Except as expressly granted in this Agreement, (a) NVIDIA reserves all rights, interests and remedies in connection with the Software, and (b) no other license or right is granted to you by implication, estoppel or otherwise.
+
+    7.2. Your Ownership. Subject to the rights of NVIDIA and its suppliers in the Software, which continue to be licensed as stated in this Agreement, even when incorporated in your products or services, and the extent permitted by applicable law, as between you and NVIDIA, you hold all rights, title and interest in and to your products, services and Derivatives you develop as permitted in this Agreement including their respective intellectual property rights.
+
+8. Feedback
+
+    You may, but you are not obligated to, provide suggestions, requests, fixes, modifications, enhancements, or other feedback regarding the Software (collectively, “Feedback”).
+    Feedback, even if designated as confidential by you, will not create any confidentiality obligation for NVIDIA or its affiliates.
+    If you provide Feedback, you grant NVIDIA, its affiliates and its designees a non-exclusive, perpetual, irrevocable, sublicensable, worldwide, royalty-free, fully paid-up and transferable license, under your intellectual property rights, to publicly perform, publicly display, reproduce, use, make, have made, sell, offer for sale, distribute (through multiple tiers of distribution), import, create derivative works of and otherwise commercialize and exploit the Feedback at NVIDIA’s discretion.
+
+9. Termination
+
+    9.1. Termination. This Agreement will automatically terminate without notice from NVIDIA if you fail to comply with any of the terms in this Agreement or if you commence or participate in any legal proceeding against NVIDIA with respect to the Software.
+    Additionally, either party may terminate this Agreement at any time with thirty (30) days’ advance written notice to the other party.
+
+    9.2. Effect of Termination. Upon any expiration or termination of this Agreement, you will promptly (a) stop using and return, delete or destroy NVIDIA confidential information and all Software received under this Agreement, and (b) delete or destroy Derivatives created under this Agreement, unless an authorized NVIDIA representative provides prior written approval that you may keep a copy of the Derivatives solely for archival purposes.
+    Upon written request, you will certify in writing that you have complied with your obligations under this Section 9.2 (“Effect of Termination”).
+
+    9.3. Survival. Section 1.2 (“License Grant to NVIDIA”), Section 5 (“Updates”), Section 6 (“Components Under Other Licenses”), Section 7 (“Ownership”), Section 8 (“Feedback), Section 9.2 (“Effect of Termination”), Section 9.3 (“Survival”), Section 10 (“Disclaimer of Warranties”), Section 11 (“Limitation of Liability”), Section 12 (“Use in Mission Critical Applications”), Section 13 (“Governing Law and Jurisdiction”), Section 14 (“Indemnity”) and Section 15 (“General”) will survive any expiration or termination of this Agreement.
+
+10. Disclaimer of Warranties
+
+    THE SOFTWARE IS PROVIDED BY NVIDIA AS-IS AND WITH ALL FAULTS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA DISCLAIMS ALL WARRANTIES AND REPRESENTATIONS OF ANY KIND, WHETHER
+    EXPRESS, IMPLIED OR STATUTORY, RELATING TO OR ARISING UNDER THIS AGREEMENT, INCLUDING, WITHOUT LIMITATION, THE WARRANTIES OF TITLE, NONINFRINGEMENT, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, USAGE OF TRADE AND COURSE OF DEALING. NVIDIA DOES NOT WARRANT OR ASSUME RESPONSIBILITY FOR THE ACCURACY OR COMPLETENESS OF ANY THIRD-PARTY INFORMATION, TEXT, GRAPHICS, LINKS CONTAINED IN THE SOFTWARE.
+    WITHOUT LIMITING THE FOREGOING, NVIDIA DOES NOT WARRANT THAT THE SOFTWARE WILL MEET YOUR REQUIREMENTS, ANY DEFECTS OR ERRORS WILL BE CORRECTED, ANY CERTAIN CONTENT WILL BE AVAILABLE; OR THAT THE SOFTWARE IS FREE OF VIRUSES OR OTHER HARMFUL COMPONENTS. NO INFORMATION OR ADVICE GIVEN BY NVIDIA WILL IN ANY WAY INCREASE THE SCOPE OF ANY WARRANTY EXPRESSLY PROVIDED IN THIS AGREEMENT.
+    NVIDIA does not warrant or assume responsibility for the accuracy or completeness of any third-party information, text, graphics or links contained in the Software.
+
+11. Limitations of Liability
+
+    11.1. EXCLUSIONS. TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, IN NO EVENT WILL NVIDIA BE LIABLE FOR ANY (I) INDIRECT, PUNITIVE, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES, OR (ii) DAMAGES FOR (a) THE COST OF PROCURING SUBSTITUTE GOODS, OR (b) LOSS OF PROFITS, REVENUES, USE, DATA OR GOODWILL ARISING OUT OF OR RELATED TO THIS AGREEMENT, WHETHER BASED ON BREACH OF CONTRACT, TORT (INCLUDING NEGLIGENCE), STRICT LIABILITY, OR OTHERWISE, AND EVEN IF NVIDIA HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES AND EVEN IF A PARTY’S REMEDIES FAIL THEIR ESSENTIAL PURPOSE.
+
+    11.2. DAMAGES CAP. ADDITIONALLY, TO THE MAXIMUM EXTENT PERMITTED BY APPLICABLE LAW, NVIDIA’S TOTAL CUMULATIVE AGGREGATE LIABILITY FOR ANY AND ALL LIABILITIES, OBLIGATIONS OR CLAIMS ARISING OUT OF OR RELATED TO THIS AGREEMENT WILL NOT EXCEED FIVE U.S. DOLLARS (US$5).
+
+12. Use in Mission Critical Applications
+
+    You acknowledge that the Software provided under this Agreement is not designed or tested by NVIDIA for use in any system or application where the use or failure of such system or application developed with NVIDIA’s Software could result in injury, death or catastrophic damage (each, a “Mission Critical Application”).
+    Examples of Mission Critical Applications include use in avionics, navigation, autonomous vehicle applications, AI solutions for automotive products, military, medical, life support or other mission-critical or life-critical applications.
+    NVIDIA will not be liable to you or any third party, in whole or in part, for any claims or damages arising from these uses.
+    You are solely responsible for ensuring that systems and applications developed with the Software include sufficient safety and redundancy features and comply with all applicable legal and regulatory standards and requirements.
+
+13. Governing Law and Jurisdiction
+
+    This Agreement will be governed in all respects by the laws of the United States and the laws of the State of Delaware, without regard to conflict of laws principles or the United Nations Convention on Contracts for the International Sale of Goods.
+    The state and federal courts residing in Santa Clara County, California will have exclusive jurisdiction over any dispute or claim arising out of or related to this Agreement, and the parties irrevocably consent to personal jurisdiction and venue in those courts;
+    except that either party may apply for injunctive remedies or an equivalent type of urgent legal relief in any jurisdiction.
+
+14. Indemnity
+
+    By using the Software you agree to defend, indemnify and hold harmless NVIDIA and its affiliates and their respective officers, directors, employees and agents from and against any claims, disputes, demands, liabilities, damages, losses, costs and expenses arising out of or in any way connected with (i) products or services that have been developed or deployed with or use the Software, or claims that they violate laws, or infringe, violate, or misappropriate any third party right;
+    or (ii) use of the Software in breach of the terms of this Agreement.
+
+15. General
+
+    15.1. Independent Contractors.
+       The parties are independent contractors, and this Agreement does not create a joint venture, partnership, agency, or other form of business association between the parties.
+       Neither party will have the power to bind the other party or incur any obligation on its behalf without the other party’s prior written consent.
+       Nothing in this Agreement prevents either party from participating in similar arrangements with third parties.
+
+    15.2. No Assignment.
+       NVIDIA may assign, delegate or transfer its rights or obligations under this Agreement by any means or operation of law.
+       You may not, without NVIDIA’s prior written consent, assign, delegate or transfer any of your rights or obligations under this Agreement by any means or operation of law, and any attempt to do so is null and void.
+
+    15.3. No Waiver.
+       No failure or delay by a party to enforce any term or obligation of this Agreement will operate as a waiver by that party, or prevent the enforcement of such term or obligation later.
+
+    15.4. Trade Compliance.
+       You agree to comply with all applicable export, import, trade and economic sanctions laws and regulations, as amended, including without limitation U.S. Export Administration Regulations and Office of Foreign Assets Control regulations.
+       You confirm (a) your understanding that export or reexport of certain NVIDIA products or technologies may require a license or other approval from appropriate authorities and (b) that you will not export or reexport any products or technology, directly or indirectly, without first obtaining any required license or other approval from appropriate authorities, (i) to any countries that are subject to any U.S. or local export restrictions (currently including, but not necessarily limited to, Belarus, Cuba, Iran, North Korea, Russia, Syria, the Region of Crimea, Donetsk People’s Republic Region and Luhansk People’s Republic Region);
+       (ii) to any end-user who you know or have reason to know will utilize them in the design, development or production of nuclear, chemical or biological weapons, missiles, rocket systems, unmanned air vehicles capable of a maximum range of at least 300 kilometers, regardless of payload, or intended for military end-use, or any weapons of mass destruction;
+       (iii) to any end-user who has been prohibited from participating in the U.S. or local export transactions by any governing authority;
+       or (iv) to any known military or military-intelligence end-user or for any known military or military-intelligence end-use in accordance with U.S. trade compliance laws and regulations.
+
+    15.5. Government Rights.
+       The Software, documentation and technology (“Protected Items”) are “Commercial products” as this term is defined at 48 C.F.R.
+       2.101, consisting of “commercial computer software” and “commercial computer software documentation” as such terms are used in, respectively, 48 C.F.R.
+       12.212 and 48 C.F.R. 227.7202 & 252.227-7014(a)(1). Before any Protected Items are supplied to the U.S. Government, you will (i) inform the U.S. Government in writing that the Protected Items are and must be treated as commercial computer software and commercial computer software documentation developed at private expense;
+       (ii) inform the U.S. Government that the Protected Items are provided subject to the terms of the Agreement;
+       and (iii) mark the Protected Items as commercial computer software and commercial computer software documentation developed at private expense.
+       In no event will you permit the U.S. Government to acquire rights in Protected Items beyond those specified in 48 C.F.R.
+       52.227-19(b)(1)-(2) or 252.227-7013(c) except as expressly approved by NVIDIA in writing.
+
+    15.6. Notices.
+       Please direct your legal notices or other correspondence to legalnotices@nvidia.com with a copy mailed to NVIDIA Corporation, 2788 San Tomas Expressway, Santa Clara, California 95051, United States of America, Attention: Legal Department.
+       If NVIDIA needs to contact you, you consent to receive the notices by email and agree that such notices will satisfy any legal communication requirements.
+
+    15.7. Severability.
+       If a court of competent jurisdiction rules that a provision of this Agreement is unenforceable, that provision will be deemed modified to the extent necessary to make it enforceable and the remainder of this Agreement will continue in full force and effect.
+
+    15.8. Amendment.
+       Any amendment to this Agreement must be in writing and signed by authorized representatives of both parties.
+
+    15.9. Construction.
+       The headings in the Agreement are included solely for convenience and are not intended to affect the meaning or interpretation of the Agreement.
+       As required by the context of the Agreement, the singular of a term includes the plural and vice versa.
+
+    15.10. Force Majeure.
+        Neither party will be liable during any period where an event or circumstance prevents or delays that party from performing its obligations under this Agreement and that event or circumstance: (i) is not within the reasonable control of that party and is not the result of that party’s negligence, and (ii) cannot be overcome or avoided by that party using reasonably diligent efforts.
+
+    15.11. Entire Agreement.
+        Regarding the subject matter of this Agreement, the parties agree that (a) this Agreement constitutes the entire and exclusive agreement between the parties and supersedes all prior and contemporaneous communications and (b) any additional or different terms or conditions, whether contained in purchase orders, order acknowledgments, invoices or otherwise, will not be binding and are null and void.
+
+(v. May 8, 2025)

LICENSE.TXT

@@ -1,23 +0,0 @@
-Copyright (c) 2017, NVIDIA CORPORATION.  All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-   *  Redistributions of source code must retain the above copyright
-      notice, this list of conditions and the following disclaimer.
-   *  Redistributions in binary form must reproduce the above copyright
-      notice, this list of conditions and the following disclaimer in the
-      documentation and/or other materials provided with the distribution.
-   *  Neither the name of the NVIDIA CORPORATION nor the
-      names of its contributors may be used to endorse or promote products
-      derived from this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
-ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
-WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
-DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
-(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
-LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
-ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
-(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
-SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

LICENSE.txt

@@ -0,0 +1,34 @@
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+this list of conditions and the following disclaimer in the documentation
+and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+contributors may be used to endorse or promote products derived from
+this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+Certain files within this repository are subject to separate licensing terms:
+
+- The files located in the `python/CuTeDSL` directory are licensed under the
+  NVIDIA End User License Agreement (EULA). Please refer to
+  https://docs.nvidia.com/cutlass/media/docs/pythonDSL/license.html
+  for the full terms.

PUBLICATIONS.md

@@ -0,0 +1,104 @@
+# Publications Using Cutlass
+
+## 2025
+
+- ["Comet: Fine-grained Computation-communication Overlapping for Mixture-of-Experts"](https://arxiv.org/abs/2502.19811). Shulai Zhang, Ningxin Zheng, Haibin Lin, Ziheng Jiang, Wenlei Bao, Chengquan Jiang, Qi Hou, Weihao Cui, Size Zheng, Li-Wen Chang, Quan Chen, Xin Liu. _arXiv_, February 2025.
+
+- ["ParetoQ: Scaling Laws in Extremely Low-bit LLM Quantization"](https://arxiv.org/abs/2502.02631). Zechun Liu, Changsheng Zhao, Hanxian Huang, Sijia Chen, Jing Zhang, Jiawei Zhao, Scott Roy, Lisa Jin, Yunyang Xiong, Yangyang Shi, Lin Xiao, Yuandong Tian, Bilge Soran, Raghuraman Krishnamoorthi, Tijmen Blankevoort, Vikas Chandra. _arXiv_, February 2025.
+
+- ["Generalized Neighborhood Attention: Multi-dimensional Sparse Attention at the Speed of Light"](https://arxiv.org/abs/2504.16922). Ali Hassani, Fengzhe Zhou, Aditya Kane, Jiannan Huang, Chieh-Yun Chen, Min Shi, Steven Walton, Markus Hoehnerbach, Vijay Thakkar, Michael Isaev, Qinsheng Zhang, Bing Xu, Haicheng Wu, Wen-mei Hwu, Ming-Yu Liu, Humphrey Shi. _arXiv_, April 2025.
+
+## 2024
+
+- ["DeepSeek-V3 Technical Report"](https://arxiv.org/abs/2412.19437). DeepSeek-AI. _arXiv_, December 2024.
+
+- ["ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference"](https://arxiv.org/abs/2410.21465). Hanshi Sun, Li-Wen Chang, Wenlei Bao, Size Zheng, Ningxin Zheng, Xin Liu, Harry Dong, Yuejie Chi, Beidi Chen. _arXiv_, October 2024.
+
+- ["FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion"](https://arxiv.org/abs/2406.06858). Li-Wen Chang, Wenlei Bao, Qi Hou, Chengquan Jiang, Ningxin Zheng, Yinmin Zhong, Xuanrun Zhang, Zuquan Song, Chengji Yao, Ziheng Jiang, Haibin Lin, Xin Jin, Xin Liu. _arXiv_, June 2024.
+
+- ["EVT: Accelerating Deep Learning Training with Epilogue Visitor Tree"](https://dl.acm.org/doi/10.1145/3620666.3651369). Zhaodong Chen, Andrew Kerr, Richard Cai, Jack Kosaian, Haicheng Wu, Yufei Ding, and Yuan Xie. _Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems_, April 2024.
+
+- ["Faster Neighborhood Attention: Reducing the O(n^2) Cost of Self Attention at the Threadblock Level"](https://arxiv.org/abs/2403.04690). Ali Hassani, Wen-Mei Hwu, Humphrey Shi. _arXiv_, March 2024.
+
+## 2023
+
+- ["A Case Study in CUDA Kernel Fusion: Implementing FlashAttention-2 on NVIDIA Hopper Architecture using the CUTLASS Library"](https://arxiv.org/abs/2312.11918). Ganesh Bikshandi, Jay Shah. _arXiv_, December 2023.
+
+- ["Benchmarking GPU Tensor Cores on General Matrix Multiplication Kernels through CUTLASS"](https://www.mdpi.com/2076-3417/13/24/13022). Xuanteng Huang, Xianwei Zhang, Panfei Yang, Nong Xiao. _Journal of Applied Sciences_, December 2023.
+
+- ["A Speed Odyssey for Deployable Quantization of LLMs"](https://arxiv.org/abs/2311.09550). Qingyuan Li, Ran Meng, Yiduo Li, Bo Zhang, Liang Li, Yifan Lu, Xiangxiang Chu, Yerui Sun, Yuchen Xie. _arXiv_, November 2023.
+
+- ["FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning"](https://arxiv.org/abs/2307.08691). Tri Dao. _Technical Report_, July 2023.
+
+- ["MegaBlocks: Efficient Sparse Training with Mixture-of-Experts"](https://arxiv.org/abs/2211.15841). Trevor Gale, Deepak Narayanan, Cliff Young, Matei Zaharia. _Proceedings of the Sixth Machine Learning and Systems_, May 2023.
+
+- ["ByteTransformer: A High-Performance Transformer Boosted for Variable-Length Inputs"](https://arxiv.org/abs/2210.03052). Yujia Zhai, Chengquan Jiang, Leyuan Wang, Xiaoying Jia, Shang Zhang, Zizhong Chen, Xin Liu, Yibo Zhu. _Proceedings of the 37th IEEE International Parallel & Distributed Processing Symposium (Best Paper)_, May 2023.
+
+- ["A Framework for Fine-Grained Synchronization of Dependent GPU Kernels"](https://arxiv.org/abs/2305.13450). Abhinav Jangda, Saeed Maleki, Maryam Mehri Dehnavi, Madan Musuvathi, Olli Saarikivi. _Computing Research Repository_, May 2023.
+
+- ["Graphene: An IR for Optimized Tensor Computations on GPUs"](https://dl.acm.org/doi/pdf/10.1145/3582016.3582018). Hagedorn, Bastian, Bin Fan, Hanfeng Chen, Cris Cecka, Michael Garland, Vinod Grover. _Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems_, March 2023.
+
+- ["Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search"](https://arxiv.org/abs/2302.01382). Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang. _arXiv_, February 2023.
+
+- ["Dynamic N:M Fine-Grained Structured Sparse Attention Mechanism"](https://dl.acm.org/doi/abs/10.1145/3572848.3577500). Zhaodong Chen, Zheng Qu, Yuying Quan, Liu Liu, Yufei Ding, Yuan Xie. _Proceedings of the 28th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming_, February 2023.
+
+- ["Stream-K: Work-centric Parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU"](https://arxiv.org/abs/2301.03598). Muhammad Osama, Duane Merrill, Cris Cecka, Michael Garland, John D. Owens. _arXiv_, January 2023.
+
+## 2022
+
+- ["GPU Load Balancing"](https://arxiv.org/abs/2212.08964). Muhammad Osama. _Doctoral dissertation, University of California, Davis_, December 2022.
+
+- ["Who Says Elephants Can't Run: Bringing Large Scale MoE Models into Cloud Scale Production"](https://arxiv.org/abs/2211.10017). Young Jin Kim, Rawn Henry, Raffy Fahim, Hany Hassan Awadalla. _Proceedings of the Third Workshop on Simple and Efficient Natural Language Processing_, December 2022.
+
+- ["Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance"](https://arxiv.org/abs/2110.15238). Jiarong Xing, Leyuan Wang, Shang Zhang, Jack Chen, Ang Chen, Yibo Zhu. _Proceedings of the 5th MLSys Conference_, August 2022.
+
+- ["Recovering single precision accuracy from Tensor Cores while surpassing the FP32 theoretical peak performance"](https://arxiv.org/abs/2203.03341). Hiroyuki Ootomo, Rio Yokota. _International Journal of High Performance Computing_, March 2022.
+
+- ["Breaking the Computation and Communication Abstraction Barrier in Distributed Machine Learning Workloads"](https://arxiv.org/abs/2105.05720). Abhinav Jangda, Jun Huang, Guodong Liu, Amir Hossein Nodehi Sabet, Saeed Maleki, Youshan Miao, Madanlal Musuvathi, Todd Mytkowicz, Olli Sarikivi. _Proceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems_, February 2022.
+
+## 2021
+
+- ["Arithmetic-intensity-guided fault tolerance for neural network inference on GPUs"](https://dl.acm.org/doi/abs/10.1145/3458817.3476184). Jack Kosaian, K. V. Rashmi. _Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis_, November 2021.
+
+- ["Real-time Neural Radiance Caching for Path Tracing"](https://dl.acm.org/doi/abs/10.1145/3450626.3459812). Thomas Muller, Fabrice Rousselle, Jan Novak, Alex Keller. _ACM Trans. Graph._, August 2021.
+
+## 2020
+
+- ["Scalable Knowledge Graph Analytics at 136 Petaflop/s"](https://www.computer.org/csdl/proceedings-article/sc/2020/999800a061/1oeORDgCM0g). Ramakrishnan Kannan, Piyush Sao, Hao Lu, Drahomira Herrmannova, Vijay Thakkar,  Robert Patton, Richard Vuduc, Thomas Potok. _Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis_, November 2020.
+
+- ["Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity
+"](https://arxiv.org/abs/2008.13006). Cong Guo, Bo Yang Hsueh, Jingwen Leng, Yuxian Qiu, Yue Guan, Zehuan Wang, Xiaoying Jia, Xipeng Li, Minyi Guo, Yuhao Zhu. _Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis_, November 2020.
+
+- ["Strassen's Algorithm Reloaded on GPUs"](https://dl.acm.org/doi/10.1145/3372419). Jianyu Huang, Chenhan D. Yu, Robert A. van de Geijn. _ACM Transactions on Mathematical Software_, March 2020.
+
+## Copyright
+
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
+
+```
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
+
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+```

README.md

@@ -1,219 +1,663 @@
-![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
+![ALT](./media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
+# Overview
 
-# CUTLASS 1.0
+# CUTLASS 4.3.0
 
-CUTLASS 1.0 is a collection of CUDA C++ template abstractions for implementing
-high-performance matrix-multiplication (GEMM) at all levels and scales within CUDA.
-It incorporates strategies for hierarchical decomposition and data movement similar
-to those used to implement cuBLAS.  CUTLASS decomposes these "moving parts" into
-reusable, modular software components abstracted by C++ template classes.  These
-thread-wide, warp-wide, block-wide, and device-wide primitives can be specialized
-and tuned via custom tiling sizes, data types, and other algorithmic policy. The
-resulting flexibility simplifies their use as building blocks within custom kernels
-and applications.
+_CUTLASS 4.3.0 - Oct 2025_
 
-To support a wide variety of applications, CUTLASS provides extensive support for
-mixed-precision computations, providing specialized data-movement and
-multiply-accumulate abstractions for 8-bit integer, half-precision floating
-point (FP16), single-precision floating point (FP32), and double-precision floating
-point (FP64) types.  Furthermore, CUTLASS demonstrates CUDA's WMMA API for targeting
-the programmable, high-throughput _Tensor Cores_ provided by NVIDIA's Volta architecture
-and beyond.
+CUTLASS is a collection of abstractions for implementing high-performance matrix-matrix multiplication (GEMM)
+and related computations at all levels and scales within CUDA. It incorporates strategies for
+hierarchical decomposition and data movement. CUTLASS decomposes these "moving parts" into reusable, modular
+software components and abstractions.
 
-CUTLASS 1.0 has changed substantially from our preview release described in
-the [CUTLASS Parallel For All](https://devblogs.nvidia.com/parallelforall/cutlass-linear-algebra-cuda)
-post. We have decomposed the structure of the GEMM computation into deeper, structured
-primitives for loading data, computing predicate masks, streaming data at each level of
-the GEMM hierarchy, and updating the output matrix.
+Primitives for different levels of a conceptual parallelization hierarchy can be specialized and tuned
+via custom tiling sizes, data types, and other algorithmic policy. The resulting flexibility simplifies
+their use as building blocks within custom kernels and applications.
 
-CUTLASS 1.0 is described in the [Doxygen documentation](https://github.com/NVIDIA/cutlass/docs)
-and our talk at the [GPU Technology Conference 2018](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf).
+CUTLASS has been providing CUDA C++ template abstractions for high-performance linear algebra since 2017 and
+these abstractions provide extensive support for a wide range of computations including
+mixed-precision computations, specialized data-movement (async copy) and
+multiply-accumulate abstractions for FP64, FP32, TF32, FP16, BF16,
+[FP32 emulation via tensor core instruction](https://github.com/NVIDIA/cutlass/tree/main/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm),
+ 8b floating point types (e5m2 and e4m3),
+ block scaled data types (NVIDIA NVFP4 and OCP standard MXFP4, MXFP6, MXFP8),
+ narrow integer types (4 and 8b signed and unsigned integers),
+ and binary 1b data types (where architectures allow for the
+native support of such data types) across NVIDIA's Volta, Turing, Ampere, Ada, Hopper, and Blackwell architectures.
+
+To this rich ecosystem of C++ based kernel programming abstractions, CUTLASS 4 adds CUTLASS DSLs. These are Python native interfaces for writing high-performance CUDA kernels based on core CUTLASS and CuTe concepts without any performance compromises. This allows for a much smoother learning curve, orders of magnitude faster compile times, native integration with DL frameworks without writing glue code, and much more intuitive metaprogramming that does not require deep C++ expertise.
+
+Overall we envision CUTLASS DSLs as a family of domain-specific languages (DSLs). With the release of 4.0, we are releasing the first of these in CuTe DSL. This is a low level programming model that is fully consistent with CuTe C++ abstractions — exposing core concepts such as layouts, tensors, hardware atoms, and full control over the hardware thread and data hierarchy.
+
+CuTe DSL demonstrates optimal matrix multiply and other linear algebra operations
+targeting the programmable, high-throughput _Tensor Cores_ implemented by
+NVIDIA's Ampere, Hopper, and Blackwell architectures.
+
+We believe it will become an indispensable tool for students, researchers, and performance
+engineers alike — flattening the learning curve of GPU programming, rapidly prototyping kernel
+designs, and bringing optimized solutions into production.
+
+CuTe DSL is currently in public beta and will graduate out of beta by end of summer 2025.
+
+To get started quickly - please refer :
+  - [CUTLASS C++ Quick Start Guide](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html).
+  - [CuTe DSL Quick Start Guide](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/quick_start.html).
+
+# What's New in CUTLASS 4.3
+
+## CuTe DSL
+* Debuggability improvements:
+    - Supported source location tracking for DSL APIs
+    - Supported dumping PTX and CUBIN code
+* More examples and notebooks to get started with CuTe DSL:
+    - [Kernel launch with Programmatic Dependent Launch](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/programmatic_dependent_launch.py)
+    - Improved performance of elementwise kernel (https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/elementwise_apply.py):
+        + Generalize code to handle list of input tensors
+        + Generalize TV layout computation to handle different data types
+    - Demonstrate the new Pipeline APIs in [Blackwell SM100 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_gemm_persistent.py):
+        + New Pipeline API `PipelineProducer` and `PipelineConsumer` to simplify code (no more explicit pipeline state management)
+    - Separate epilogue code for non-TMA and TMA implementation
+        + Note that the updates simplifies the codes but existing APIs still work and are supported
+    - [Basic Blackwell SM100 GEMM with decent performance](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/tutorial_gemm/fp16_gemm_0.py)
+        + Simple tutorial achieves 84% SOL performance with MNK 8K
+    - Reworked [elementwise add notebook](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks/elementwise_add.ipynb) with more details and detailed explanation about TV layout
+        + Updated implementation to handle general data type and multiple inputs
+        + Updated explanation for TV layout in simpler language
+        + Added visualization of TV Layout with 3rd party utils
+    - [Benchmark and autotune demonstration](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/notebooks/benchmark_autotune.ipynb)
+* More examples of authorizing peak-performance kernels:
+    - [Blackwell SM100 mixed-input GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mixed_input_gemm.py)
+    - [Blackwell SM100 persistent blockwise dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/blockwise_gemm.py)
+    - [Blackwell SM100 persistent blockwise contiguous grouped dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/contiguous_grouped_gemm.py)
+    - [Blackwell SM100 persistent blockwise masked grouped dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/blockwise_gemm/masked_grouped_gemm.py)
+    - [Blackwell SM100 fmha bwd](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/fmha_bwd.py)
+    - [Blackwell SM100 mla](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/mla.py)
+    - [Hopper SM90 persistent dense GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/hopper/dense_gemm_persistent.py)
+    - [Blackwell GeForce batched dense GEMM](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell_geforce/dense_gemm.py)
+    - [Ampere HSTU Attention](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/ampere/hstu_attention.py)
+* API updates:
+    - Please refer to [DSL API changelog](https://docs.nvidia.com/cutlass/latest/media/docs/pythonDSL/cute_dsl_api/changelog.html) for details
+* Bug fixings and improvements
+    - Add mma_tiler_n=64 and mma_tiler_n=192 support in [Blackwell SM100 persistent dense blockscaled GEMM with static scheduling](https://github.com/NVIDIA/cutlass/tree/main/examples/python/CuTeDSL/blackwell/dense_blockscaled_gemm_persistent.py).
+    - Fixed ``TensorSSA.reduce`` to support static value as initial value
+    - Updated docstring for following APIs to be more concise and easier to understand:
+        - ``make_layout_tv``
+        - ``is_static``
+        - ``PipelineAsync``
+        - ``SmemAllocator``
+    - Fixed documentation for ``pipeline``, ``utils`` and ``cute.math``
+
+## CUTLASS C++
+* Further enhance Blackwell SM100 Attention kernels in [example 77](https://github.com/NVIDIA/cutlass/tree/main/examples/77_blackwell_fmha/).
+    - Add softmax skip correction.
+    - Fix a shared memory allocation bug where it needs to opt in maximum dynamics shared memory explicitly once it exceeds 48KB.
+    - Fix a dead hang issue caused by early return warp.
+* Add Ragged Contiguous Grouped gemm kernel in [example 92](https://github.com/NVIDIA/cutlass/tree/main/examples/92_blackwell_moe_gemm/).
+    - This kernel uses a TMA 3D load to load the weights matrix and use the tensormap update method to load activations.
+* Optimize group gemm kernels by enabling async TMA desc update.
+* Support Blackwell SM100 convolution stream-K kernel.
+    - Unit tests: [fprop_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/fprop/sm100_conv3d_fprop_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu), [dgrad_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/dgrad/sm100_conv3d_dgrad_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu), [wgrad_streamK](https://github.com/NVIDIA/cutlass/tree/main/test/unit/conv/device_3x/wgrad/sm100_conv2d_wgrad_implicit_gemm_f16_f16_f16_tensorop_f16_streamk.cu).
+* Add profiler support for Blackwell SM100 and SM120 blockscaled sparse kernels.
+* Fix some kernel issues:
+    - Fix a race check issue of Blackwell SM103 kernels by adding missing elect one for prefetch barrier initialization.
+    - Allow user to directly specify the number of stages for Hopper sm90 mixed input gemm.
+    - Remove warnings caused by cuda vector type alignment setting in CUDA 13.
+    - Remove problematic `cutlass::int8_t` and replace it with `int8_t`.
+* Fix some profiler issues:
+    - Add some missing reference kernels.
+    - Add calculation of scale factor A and B in function `bytes_with_problem_shape` of block scaled profiler.
+
+Note: CUTLASS 4.x builds are known to be down on Windows platforms for all CUDA toolkits.
+CUTLASS team is working on a fix.
+
+**See the [CHANGELOG](https://docs.nvidia.com/cutlass/latest/CHANGELOG.html) for details of all past releases and updates.**
 
 # Performance
 
-<p align="center"><img src=/media/images/cutlass-performance-plot.png></p>
-
 CUTLASS primitives are very efficient.  When used to construct device-wide GEMM kernels,
-they exhibit performance comparable to cuBLAS for scalar GEMM
-computations. The above figure shows CUTLASS performance relative to cuBLAS
-for large matrix dimensions (M=10240, N=K=4096) running on an NVIDIA Titan V GPU
-when compiled with CUDA 9.2.
+they exhibit nearly optimal utilization of peak theoretical throughput. The figure below
+shows CUTLASS 3.8's performance as a % of theoretical peak utilization
+on various input and output data types when run on NVIDIA Blackwell SM100 architecture GPU.
+
+![ALT](media/images/cutlass-3.8-blackwell-gemm-peak-performance.svg "")
+
+The two figures below show the continual CUTLASS performance improvements
+on an [NVIDIA H100](https://www.nvidia.com/en-us/data-center/h100/) (NVIDIA Hopper architecture) since
+CUTLASS 3.1.
+CUTLASS 3.5.1 was compiled with the [CUDA 12.5u1 Toolkit](https://developer.nvidia.com/cuda-downloads).
+Tensor Core operations are implemented using CUDA's
+[mma](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma) and
+[wgmma](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#asynchronous-warpgroup-level-matrix-instructions) instructions.
+
+![ALT](media/images/cutlass-3.5.1-gemm-peak-performance.png "")
+![ALT](media/images/cutlass-3.5.1-gemm-peak-performance-fp8.png "")
+
+# CuTe
+
+CUTLASS 3.0 introduced a new core library, CuTe, to describe and manipulate tensors of threads and data.
+CuTe is a collection of C++ CUDA template abstractions for
+defining and operating on hierarchically multidimensional layouts of threads and data.
+CuTe provides `Layout` and `Tensor` objects that compactly package the type,
+shape, memory space, and layout of data, while performing the complicated indexing for the user.
+This lets programmers focus on the logical descriptions of their algorithms while
+CuTe does the mechanical bookkeeping for them. With these tools, we can quickly design,
+implement, and modify all dense linear algebra operations.
+
+The core abstractions of CuTe are hierarchically multidimensional layouts
+which can be composed with data arrays to represent tensors.
+The representation of layouts is powerful enough to represent nearly
+everything we need to implement efficient dense linear algebra.
+Layouts can also be combined and manipulated via functional composition, on which we build a large set of common operations such as tiling and partitioning.
+
+CUTLASS 3.0 and beyond adopts CuTe throughout the GEMM hierarchy in its templates.
+This greatly simplifies the design and improves code composability and readability.
+More documentation specific to CuTe can be found in its
+[dedicated documentation directory](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/cute/00_quickstart.html).
 
 # Compatibility
 
-CUTLASS requires CUDA 9 and performs best with [CUDA 9.2 Toolkit](ttps://developer.nvidia.com/cuda-toolkit) or later.
+Minimum requirements:
+
+- Architecture: Volta (compute capability 7.0)
+- Compiler: Must support at least C++17
+- CUDA Toolkit version: 11.4
+
+CUTLASS requires a C++17 host compiler and
+performs best when built with the [**CUDA 12.8 Toolkit**](https://developer.nvidia.com/cuda-downloads).
+It is also compatible with CUDA 11.4, CUDA 11.5, CUDA 11.6, CUDA 11.7, CUDA 11.8, and all other CUDA 12.x versions.
+
+## Operating Systems
+
+We have tested the following environments.
 
 |**Operating System** | **Compiler** |
 |-----------------|----------|
-| Windows 10      | Microsoft Visual Studio 2015|
-|                 | Microsoft Visual Studio 2017|
-| Ubuntu 14.04 | GCC 4.8.2 |
-| Ubuntu 16.04 | GCC 5.4.0 |
+| Ubuntu 18.04 | GCC 7.5.0  |
+| Ubuntu 20.04 | GCC 10.3.0 |
+| Ubuntu 22.04 | GCC 11.2.0 |
 
+Note: GCC 8.5.0 has known regressions regarding fold expressions and overloaded operators. Using GCC 7.5.0 or (preferred) GCC >= 9 is recommended.
 
-CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on
-any Maxwell-, Pascal-, or Volta-architecture NVIDIA GPU.
+Note: CUTLASS 3.x builds are known to be down on Windows platforms for all CUDA toolkits.
+CUTLASS team is working on a fix.
 
-|**GPU**|
-|---|
-|NVIDIA GeForce 1080|
-|NVIDIA TitanXP|
-|NVIDIA Tesla P100|
-|NVIDIA Tesla V100|
-|NVIDIA TitanV|
+## Hardware
 
+CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on Volta, Turing, Ampere, Ada, and Hopper architecture based NVIDIA GPUs.
+
+|**GPU**|**CUDA Compute Capability**|**Minimum CUDA Toolkit Required by CUTLASS-3**|
+|---|---|---|
+|NVIDIA V100 Tensor Core GPU            |7.0|11.4|
+|NVIDIA TitanV                          |7.0|11.4|
+|NVIDIA GeForce RTX 20x0 series         |7.5|11.4|
+|NVIDIA T4                              |7.5|11.4|
+|NVIDIA A100 Tensor Core GPU            |8.0|11.4|
+|NVIDIA A10                             |8.6|11.4|
+|NVIDIA GeForce RTX 30x0 series         |8.6|11.4|
+|NVIDIA GeForce RTX 40x0 series         |8.9|11.8|
+|NVIDIA L40                             |8.9|11.8|
+|NVIDIA H100 Tensor Core GPU            |9.0|11.8|
+|NVIDIA H200 Tensor Core GPU            |9.0|11.8|
+|NVIDIA B200 Tensor Core GPU            |10.0|12.8|
+|NVIDIA B300 Tensor Core GPU            |10.3|13.0|
+|NVIDIA DRIVE Thor                      |11.0|13.0|
+|NVIDIA GeForce RTX 50x0 series         |12.0|12.8|
+|NVIDIA DGX Spark                       |12.1|13.0|
+
+## Target Architecture
+
+In general, PTX code generated for one target architecture can be run on future architectures
+(i.e., it is forward compatible).
+However, CUDA 12.0 introduced the concept of "architecture-accelerated features" whose
+PTX does not have forward compatibility guarantees.
+Several Hopper and Blackwell PTX instructions fall under this category of
+architecture-accelerated features, and thus require a `sm_90a` or `sm100a` target architecture
+(note the "a" appended). For more details on this and other architecture-accelerated instructions,
+please refer to the [CUDA Documentation](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#feature-availability).
+
+The target architecture information is passed on to CUTLASS via the cmake flag
+`CUTLASS_NVCC_ARCHS`. In order to maximize performance on Hopper GH100,
+users are required to build CUTLASS with `90a` as the target architecture.
+If a user accidentally builds a kernel which uses SM90a features
+(e.g. Hopper Tensor Core Instructions), using the SM90 target
+(note the lack of "a"), with either CUDA Toolkit 12 or 11.8,
+the kernel is expected to fail with a runtime error.
+
+```
+cmake .. -DCUTLASS_NVCC_ARCHS="90a"
+```
+Or
+
+```
+cmake .. -DCUTLASS_NVCC_ARCHS="100a"
+```
+
+Note: The NVIDIA Blackwell SM100 architecture used in the datacenter
+products has a different compute capability than the one underpinning
+NVIDIA Blackwell GeForce RTX 50 series GPUs (SM120). As a result, kernels
+compiled for Blackwell SM100 architecture with arch conditional features
+(using `sm100a`) are not compatible with RTX 50 series GPUs.
+
+Please refer to the [functionality documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/functionality.html)
+for details on which kernels require which target architectures.
+
+# Documentation
+
+CUTLASS is described in the following documents and the accompanying
+[Doxygen documentation](https://nvidia.github.io/cutlass).
+
+- [Quick Start Guide](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html) - basics of building and running CUTLASS
+- [Functionality](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/functionality.html) - summarizes functionality available in CUTLASS
+- [Efficient GEMM in CUDA](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/efficient_gemm.html) - describes how GEMM kernels may be implemented efficiently in CUDA
+- [CUTLASS 3.x Design](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/cutlass_3x_design.html) - describes the CUTLASS 3.x design, its benefits, and how CuTe enables us to write much more composable components
+- [GEMM API 3.x](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/gemm_api_3x.html) - describes the CUTLASS 3.x GEMM model and C++ template concepts
+- [GEMM API 2.x](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/gemm_api.html) - describes the CUTLASS 2.x GEMM model and C++ template concepts
+- [Implicit GEMM Convolution](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/implicit_gemm_convolution.html) - describes 2-D and 3-D convolution in CUTLASS
+- [Code Organization](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/code_organization.html) - describes the organization and contents of the CUTLASS project
+- [Terminology](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/terminology.html) - describes terms used in the code
+- [Programming Guidelines](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/programming_guidelines.html) - guidelines for writing efficient modern CUDA C++
+- [Fundamental types](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/fundamental_types.html) - describes basic C++ classes used in CUTLASS to represent numeric quantities and arrays
+- [Layouts](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/layout.html) - describes layouts of matrices and tensors in memory
+- [Tile Iterators](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/tile_iterator_concept.html) - describes C++ concepts for iterating over tiles of matrices in memory
+- [CUTLASS Profiler](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html) - command-line driven profiling application
+- [CUTLASS Utilities](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/utilities.html) - additional templates used to facilitate rapid development
+- [Dependent kernel launch](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/dependent_kernel_launch.html) - describes a new feature in Hopper which allows overlapping dependent
+kernels in the same stream, and how it is used in CUTLASS.
+
+# Resources
+We have also described the structure of an efficient GEMM in our talk at the
+[GPU Technology Conference 2018](http://on-demand.gputechconf.com/gtc/2018/presentation/s8854-cutlass-software-primitives-for-dense-linear-algebra-at-all-levels-and-scales-within-cuda.pdf).
+
+- [CUTLASS: Software Primitives for Dense Linear Algebra at All Levels and Scales within CUDA](https://www.nvidia.com/en-us/on-demand/session/gtcsiliconvalley2018-s8854/)
+- [Developing CUDA Kernels to Push Tensor Cores to the Absolute Limit on NVIDIA A100](https://www.nvidia.com/en-us/on-demand/session/gtcsj20-s21745/)
+- [Accelerating Convolution with Tensor Cores in CUTLASS](https://www.nvidia.com/en-us/on-demand/session/gtcspring21-s31883/)
+- [Accelerating Backward Data Gradient by Increasing Tensor Core Utilization in CUTLASS](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-s41996/)
+- [CUTLASS: Python API, Enhancements, and NVIDIA Hopper](https://www.nvidia.com/en-us/on-demand/session/gtcfall22-a41131/)
 
 # Building CUTLASS
 
 CUTLASS is a header-only template library and does not need to be built to be used by other
-projects. However, we distribute extensive unit tests and utility programs to demonstrate
-CUTLASS. These instructions are for building those test programs.
+projects. Client applications should target CUTLASS's `include/` directory in their include
+paths.
 
-CUTLASS's unit tests depend on Google Test which exists as a git submodule. You can fetch
-submodules as follows.
+CUTLASS unit tests, examples, and utilities can be build with CMake.
+The minimum version of CMake is given in the [Quickstart guide](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html).
+Make sure the `CUDACXX` environment  variable points to NVCC in the CUDA Toolkit installed
+on your system.
 
-```
-$ git submodule update --init --recursive
+```bash
+$ export CUDACXX=${CUDA_INSTALL_PATH}/bin/nvcc
 ```
 
-CUTLASS can be build with CMake starting version 3.10. By default CUTLASS will build kernels
-for CUDA architecture versions 5.0, 6.0, 6.1 and 7.0. To reduce compile time you can specify
+Create a build directory within the CUTLASS project, then run CMake. By default CUTLASS will build kernels
+for CUDA architecture versions 5.0, 6.0, 6.1, 7.0, 7.5, 8.0, 8.6, 8.9, and 9.0.
+To reduce compile time you can specify
 the architectures to build CUTLASS for by changing the CMake configuration setting
 `CUTLASS_NVCC_ARCHS`.
 
-Create a build directory within the CUTLASS project, then run CMake once.
-
-```
+```bash
 $ mkdir build && cd build
-$ cmake ..
+
+$ cmake .. -DCUTLASS_NVCC_ARCHS=80               # compiles for NVIDIA's Ampere Architecture
 ```
 
-Compile the CUTLASS project by running Make. Include the -j argument to compile sources in
-parallel and speed up the build process.
+From the `build/` directory, compile and run the CUTLASS unit tests by building the target `test_unit` with make.
 
-```
-$ make -j12
-...
-$
-```
+The unit tests are organized as several binaries mirroring the top-level namespaces of CUTLASS,
+and they may be executed in parallel via make's `-j` command line argument.
 
-Verify CUTLASS has been built correctly by running the unit tests from the build/ directory.
-
-```
-$ ./tools/test/unit/cutlass_unit_test
+```bash
+$ make test_unit -j
 ...
 ...
 ...
 [----------] Global test environment tear-down
-[==========] 481 tests from 24 test cases ran. (5954 ms total)
-[  PASSED  ] 481 tests.
+[==========] 946 tests from 57 test cases ran. (10812 ms total)
+[  PASSED  ] 946 tests.
 ```
 
-All tests should pass, though the exact number of tests may vary over time.
+All tests should pass on supported platforms, though the exact number of tests may vary over time.
 
 
 # Project Structure
 
-CUTLASS is arranged as a header-only library with several example test programs
-that demonstrate instantiating a GEMM task within a CUDA kernel. The Doxygen documentation
-provides a complete list of files, classes, and template concepts defined in the CUTLASS
-project. A brief summary is described below.
+CUTLASS is arranged as a header-only library along with Utilities, Tools, Examples, and unit tests.
+[Doxygen documentation](https://nvidia.github.io/cutlass) provides a complete list of files, classes,
+and template concepts defined in the CUTLASS project.
 
-The CUTLASS library is defined in the cutlass/ directory and consists of CUDA C++ template
-classes and other definitions for implementing efficient GPU GEMM kernels. A set of core
-classes and templates define basic primitives that are then applied to compute GEMM via
-templates in the cutlass/gemm directory.
+A detailed explanation of the source code organization may be found in the
+[CUTLASS documentation](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/code_organization.html), but several main components are summarized below.
+
+## CUTLASS Template Library
 
 ```
-cutlass/
-    gemm/
-    util/
-    <core API components>
+include/                     # client applications should target this directory in their build's include paths
+
+  cutlass/                   # CUDA Templates for Linear Algebra Subroutines and Solvers - headers only
+
+    arch/                    # direct exposure of architecture features (including instruction-level GEMMs)
+
+    conv/                    # code specialized for convolution
+
+    epilogue/                # code specialized for the epilogue of gemm/convolution
+
+    gemm/                    # code specialized for general matrix product computations
+
+    layout/                  # layout definitions for matrices, tensors, and other mathematical objects in memory
+
+    platform/                # CUDA-capable Standard Library components
+
+    reduction/               # bandwidth-limited reduction kernels that do not fit the "gemm" model
+
+    thread/                  # simt code that can be performed within a CUDA thread
+
+    transform/               # code specialized for layout, type, and domain transformations
+
+    *                        # core vocabulary types, containers, and basic numeric operations
+
+  cute/                      # CuTe Layout, layout algebra, MMA/Copy atoms, tiled MMA/Copy
+
+    algorithm/               # Definitions of core operations such as copy, gemm, and operations on cute::tuples
+
+    arch/                    # Bare bones PTX wrapper structs for copy and math instructions
+
+    atom/                    # Meta-information either link to or built from arch/ operators
+
+      mma_atom.hpp           # cute::Mma_Atom and cute::TiledMma
+
+      copy_atom.hpp          # cute::Copy_Atom and cute::TiledCopy
+
+      *sm*.hpp               # Arch specific meta-information for copy and math operations
+
+    *                        # Core library types such as Shape, Stride, Layout, Tensor, and associated operations
+
 ```
 
-Several tools and test programs are also distributed with the CUTLASS library. They are
-contained in the following directories.
+### CUTLASS SDK Examples
+
+[CUTLASS SDK examples](https://github.com/NVIDIA/cutlass/tree/main/examples) apply CUTLASS templates to implement basic computations.
+
+### Tools
 
 ```
 tools/
-    test/
-        unit/
-            core/
-            gemm/
-        perf/
-    util/
-        <utilities>
+  library/                   # CUTLASS Instance Library - contains instantiations of all supported CUTLASS templates
+    include/
+      cutlass/
+        library/
+
+  profiler/                  # CUTLASS Profiler         - command-line utility for executing operations in the
+                             #                            CUTLASS Library
+
+  util/                      # CUTLASS Utilities        - contains numerous helper classes for
+    include/                 #                            managing tensors in device memory, reference
+      cutlass/               #                            implementations for GEMM, random initialization
+        util/                #                            of tensors, and I/O.
 ```
 
+### Test
+
 The `test/unit/` directory consist of unit tests implemented with Google Test that demonstrate
 basic usage of Core API components and complete tests of the CUTLASS GEMM computations.
 
+Instructions for building and running the Unit tests are described in the [Quickstart guide](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html).
+
 # Performance Profiling
 
-The `test/perf/` directory contains a command-line utility for launching each of the GEMM kernels.
-Its usage is shown below.
+The `tools/profiler/` directory contains a command-line utility for launching each of the GEMM kernels.
+It can be built as follows:
 
-Program usage:
+```bash
+$ make cutlass_profiler -j16
+```
+## Building all GEMM and Convolution kernels (_long_ build times)
+
+By default, only one tile size is instantiated for each data type, math instruction, and layout.
+To instantiate all, set the following environment variable when running CMake from an empty `build/` directory.
+Beware, this results in *tens of thousands* of kernels and long build times.
+This would also result in a large binary size and on some platforms linker to fail on building the library.
+Therefore, it's highly recommended to generate only a subset of kernels as demonstrated in the sub-section below.
+```bash
+$ cmake .. -DCUTLASS_NVCC_ARCHS=90a -DCUTLASS_LIBRARY_KERNELS=all
+...
+$ make cutlass_profiler -j16
+```
+
+## Building a subset of GEMM and Convolution kernels (_reduced_ build times)
+
+To compile strictly one kernel or a small set of kernels, a comma-delimited list of kernel names with
+wildcard characters may be used to reduce the set of kernels. The following examples show building exactly one
+or a subset of kernels for NVIDIA Ampere and Turing architecture:
+
+### Building a subset Tensor Core GEMM kernels
+
+To compile a subset of Tensor Core GEMM kernels with FP32 accumulation and FP16 input targeting NVIDIA Ampere and Turing architecture,
+use the below cmake command line:
+```bash
+$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*gemm_f16_*_nt_align8
+...
+$ make cutlass_profiler -j16
+```
+
+Example command line for profiling a subset of Tensor Core GEMM kernels is as follows:
+```bash
+./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*gemm_f16_*_nt_align8 --m=3456 --n=4096 --k=4096
+
+...
+=============================
+  Problem ID: 1
+
+        Provider: CUTLASS
+   OperationKind: gemm
+       Operation: cutlass_tensorop_s1688gemm_f16_256x128_32x2_nt_align8
+
+          Status: Success
+    Verification: ON
+     Disposition: Passed
+
+reference_device: Passed
+          cuBLAS: Passed
+
+       Arguments: --gemm_kind=universal --m=3456 --n=4096 --k=4096 --A=f16:column --B=f16:row --C=f32:column --alpha=1  \
+                  --beta=0 --split_k_slices=1 --batch_count=1 --op_class=tensorop --accum=f32 --cta_m=256 --cta_n=128  \
+                  --cta_k=32 --stages=2 --warps_m=4 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=8 --min_cc=75  \
+                  --max_cc=1024
+
+           Bytes: 118489088  bytes
+           FLOPs: 115992428544  flops
+
+         Runtime: 1.55948  ms
+          Memory: 70.7616 GiB/s
+
+            Math: 74378.8 GFLOP/s
+
+
+
+=============================
+...
+```
+
+### Building one CUDA Core GEMM kernel
+
+To compile one SGEMM kernel targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:
+```bash
+$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sgemm_128x128_8x2_nn_align1
+...
+$ make cutlass_profiler -j16
+```
+
+Example command line for profiling single SGEMM CUDA kernel is as follows:
+```bash
+$ ./tools/profiler/cutlass_profiler --kernels=sgemm --m=3456 --n=4096 --k=4096
+
+=============================
+  Problem ID: 1
+
+        Provider: CUTLASS
+   OperationKind: gemm
+       Operation: cutlass_simt_sgemm_128x128_8x2_nn_align1
+
+          Status: Success
+    Verification: ON
+     Disposition: Passed
+
+          cuBLAS: Passed
+
+       Arguments: --m=3456 --n=4096 --k=4096 --A=f32:column --B=f32:column --C=f32:column --alpha=1 --beta=0 --split_k_slices=1  \
+                  --batch_count=1 --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4  \
+                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
+
+           Bytes: 180355072  bytes
+           FLOPs: 115992428544  flops
+
+         Runtime: 6.73655  ms
+          Memory: 24.934 GiB/s
+
+            Math: 17218.4 GFLOP/s
+
+=============================
+```
+
+### Building a subset of Tensor Core Convolution kernels
+
+To compile a subset of Tensor core convolution kernels implementing forward propagation (fprop) with FP32 accumulation
+and FP16 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:
+```bash
+$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_tensorop_s*fprop_optimized_f16
+...
+$ make cutlass_profiler -j16
+```
+
+Example command line for profiling a subset of Tensor Core convolution kernels is as follows:
+
+```bash
+$ ./tools/profiler/cutlass_profiler --kernels=cutlass_tensorop_s*fprop_optimized_f16 --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
+
+...
+=============================
+  Problem ID: 1
+
+        Provider: CUTLASS
+   OperationKind: conv2d
+       Operation: cutlass_tensorop_s16816fprop_optimized_f16_128x128_32x5_nhwc
+
+          Status: Success
+    Verification: ON
+     Disposition: Passed
+
+reference_device: Passed
+
+       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
+                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f16:nhwc --Filter=f16:nhwc --Output=f32:nhwc  \
+                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
+                  --eq_gemm_provider=none --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=32 --stages=5  \
+                  --warps_m=2 --warps_n=2 --warps_k=1 --inst_m=16 --inst_n=8 --inst_k=16 --min_cc=80 --max_cc=1024
+
+           Bytes: 1130659840  bytes
+           FLOPs: 118482796544  flops
+
+         Runtime: 0.711496  ms
+          Memory: 1479.99 GiB/s
+
+            Math: 166526 GFLOP/s
+
+=============================
+...
+```
+
+
+### Building one Convolution CUDA kernel
+
+To compile and run one CUDA Core convolution kernel implementing forward propagation (fprop) with F32 accumulation
+and FP32 input targeting NVIDIA Ampere and Turing architecture, use the below cmake command line:
+```bash
+$ cmake .. -DCUTLASS_NVCC_ARCHS='75;80' -DCUTLASS_LIBRARY_KERNELS=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
+...
+$ make cutlass_profiler -j16
+```
+
+Example command line for profiling one CUDA Core convolution kernel:
+
+```bash
+$ ./tools/profiler/cutlass_profiler --kernels=cutlass_simt_sfprop_optimized_128x128_8x2_nhwc --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3
+
+
+=============================
+  Problem ID: 1
+
+        Provider: CUTLASS
+   OperationKind: conv2d
+       Operation: cutlass_simt_sfprop_optimized_128x128_8x2_nhwc
+
+          Status: Success
+    Verification: ON
+     Disposition: Passed
+
+reference_device: Passed
+
+       Arguments: --conv_kind=fprop --n=8 --h=224 --w=224 --c=128 --k=128 --r=3 --s=3 --p=224 --q=224 --pad_h=1 --pad_w=1  \
+                  --stride_h=1 --stride_w=1 --dilation_h=1 --dilation_w=1 --Activation=f32:nhwc --Filter=f32:nhwc --Output=f32:nhwc  \
+                  --conv_mode=cross --iterator_algorithm=optimized --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1  \
+                  --eq_gemm_provider=none --op_class=simt --accum=f32 --cta_m=128 --cta_n=128 --cta_k=8 --stages=2 --warps_m=4  \
+                  --warps_n=2 --warps_k=1 --inst_m=1 --inst_n=1 --inst_k=1 --min_cc=50 --max_cc=1024
+
+           Bytes: 2055798784  bytes
+           FLOPs: 118482796544  flops
+
+         Runtime: 7.34266  ms
+          Memory: 260.752 GiB/s
+
+            Math: 16136.2 GFLOP/s
+
+
+=============================
 
 ```
-  cutlass_perf_test [options]
 
-    --help
-    --append=<true|false*>                            If true, appends output to existing CSV file. If false, overwrites.
-    --alpha=<alpha>                                   Value for alpha to be used in GEMM experiments
-    --beta=<beta>                                     Value for beta to be used in GEMM experiments
-    --dist=<distribution>                             Describes the random distribution of each of the input matrix operands.
-    --execution_mode=<mode>                           Specifies execution mode: profile, verify, single
-    --output=<filename.csv>                           Writes summary of profiling to specified .csv file
-    --iterations=<timing iterations>                  maximum number of iterations to execute when profiling
-    --m=<height>[:max height[:step]]                  Height of GEMM problem (number of rows of C). May specify a range with optional step size.
-    --n=<width>[:max width[:step]]                    Width of GEMM problem (number of columns of C). May specify a range with optional step size.
-    --k=<depth>[:max depth[:step]]                    Size of inner dimension of A and B. May specify a range with optional step size.
-    --kernels=<{s|d|h|i|wmma}gemm_{nn,nt,tn,tt}>      Select GEMM datatype and layout to use for tests
-    --peak=<bool>                                     If true, only reports peak performance per kernel after profiling specified problem space.
-    --save_workspace={*never,incorrect,always}        Specifies when to save the GEMM inputs and results to the filesystem.
-    --seed=<seed>                                     Random seed used by the random number generator in initializing input matrices.
-    --tags=<column:tag,...>                           Inserts leading columns in output table and uniform values for each column.
+## More Details on Compiling CUTLASS Kernels and CUTLASS Profiler
+- Please follow the links for more CMake examples on selectively compiling CUTLASS kernels:
+  - [GEMM CMake Examples](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html#gemm-cmake-examples)
+  - [Implicit GEMM convolution CMake Examples](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/quickstart.html#convolution-cmake-examples)
+- [Further details about the CUTLASS Profiler are described here.](https://docs.nvidia.com/cutlass/latest/media/docs/cpp/profiler.html)
 
 
-  Example usage:
-
-  # Runs one problem size for all kernels
-  $ ./tools/test/perf/cutlass_perf_test --m=10240 --n=1024 --k=1024
-
-  # Varies GEMM K dimension for SGEMM and IGEMM with column-major multiplicands
-  $ ./tools/test/perf/cutlass_perf_test --m=10240 --n=4096 --k=1024:8192:128 --kernels=sgemm_nn,igemm_nn
-```
-
 # About
 
 CUTLASS is released by NVIDIA Corporation as Open Source software under the
-3-clause "New" BSD license.
+[3-clause "New" BSD license](LICENSE.txt).
 
+# Contributors
+
+The official list of CUTLASS developers and contributors is available here: [CONTRIBUTORS](CONTRIBUTORS.md).
 
 # Copyright
 
-Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+SPDX-License-Identifier: BSD-3-Clause
 
 ```
-  Redistribution and use in source and binary forms, with or without modification, are permitted
-  provided that the following conditions are met:
-      * Redistributions of source code must retain the above copyright notice, this list of
-        conditions and the following disclaimer.
-      * Redistributions in binary form must reproduce the above copyright notice, this list of
-        conditions and the following disclaimer in the documentation and/or other materials
-        provided with the distribution.
-      * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
-        to endorse or promote products derived from this software without specific prior written
-        permission.
+  Redistribution and use in source and binary forms, with or without
+  modification, are permitted provided that the following conditions are met:
 
-  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
-  IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
-  FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
-  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
-  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
-  OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
-  STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+  1. Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+  2. Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+  3. Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+  DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+  FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+  DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+  SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+  OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
   OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 ```
-

bin2hex.cmake

@@ -0,0 +1,54 @@
+# Copyright (c) 2019 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# A small utility function which generates a C-header from an input file
+function(FILE_TO_C_STRING FILENAME VARIABLE_NAME OUTPUT_STRING ZERO_TERMINATED)
+  FILE(READ "${FILENAME}" HEX_INPUT HEX)
+  if (${ZERO_TERMINATED})
+    string(APPEND HEX_INPUT "00")
+  endif()
+
+  string(REGEX REPLACE "(....)" "\\1\n" HEX_OUTPUT ${HEX_INPUT})
+  string(REGEX REPLACE "([0-9a-f][0-9a-f])" "char(0x\\1)," HEX_OUTPUT ${HEX_OUTPUT})
+
+  set(HEX_OUTPUT "static char const ${VARIABLE_NAME}[] = {\n  ${HEX_OUTPUT}\n};\n")
+
+  set(${OUTPUT_STRING} "${HEX_OUTPUT}" PARENT_SCOPE)
+endfunction()
+
+# message("Create header file for ${FILE_IN}")
+# message("Create header file for ${FILE_OUT}")
+file_to_c_string(${FILE_IN} ${VARIABLE_NAME} OUTPUT_STRING ZERO_TERMINATED)
+
+set(RESULT "#pragma once\n")
+string(APPEND RESULT "namespace cutlass {\n")
+string(APPEND RESULT "namespace nvrtc {\n")
+string(APPEND RESULT "${OUTPUT_STRING}")
+string(APPEND RESULT "} // namespace nvrtc\n")
+string(APPEND RESULT "} // namespace cutlass\n")
+file(WRITE "${FILE_OUT}" "${RESULT}")

clang-format.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-
-set -e
-
-function formatFiles {
-    for f in `find "$1" -type f -name "*.$2"` ; do
-        COMMAND="clang-format -i $f"
-        echo $COMMAND
-        $COMMAND
-    done
-}
-
-formatFiles "cutlass" "h"
-formatFiles "tools/test" "h"
-formatFiles "tools/test" "cpp"
-formatFiles "tools/util" "h"
-

cmake/CTestTestfile.configure.cmake

@@ -0,0 +1,52 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+# Generated file
+
+set(TEST_SETS_SUPPORTED @TEST_SETS_SUPPORTED@)
+
+if (NOT DEFINED ENV{CUTLASS_TEST_SETS})
+  set(ENV{CUTLASS_TEST_SETS} @CUTLASS_DEFAULT_ACTIVE_TEST_SETS@)
+endif()
+
+foreach(TEST_SET_REQUESTED IN ITEMS $ENV{CUTLASS_TEST_SETS})
+  if (NOT TEST_SET_REQUESTED IN_LIST TEST_SETS_SUPPORTED) 
+    message(STATUS "Skipping tests for @TEST_EXE_PATH@ as ${TEST_SET_REQUESTED} is not in the set of [${TEST_SETS_SUPPORTED}].")
+    return()
+  endif()
+endforeach()
+
+set(TEST_EXE_PATH @TEST_EXE_PATH@)
+set(TEST_EXE_WORKING_DIRECTORY @TEST_EXE_WORKING_DIRECTORY@)
+set(CUTLASS_USE_EXTENDED_ADD_TEST_FORMAT @TEST_USE_EXTENDED_FORMAT@)
+
+if (DEFINED ENV{CUTLASS_TEST_EXECUTION_ENVIRONMENT})
+  set(_CUTLASS_TEST_EXECUTION_ENVIRONMENT $ENV{CUTLASS_TEST_EXECUTION_ENVIRONMENT})
+else()
+  set(_CUTLASS_TEST_EXECUTION_ENVIRONMENT @CUTLASS_TEST_EXECUTION_ENVIRONMENT@)
+endif()

cmake/CTestTestfile.test.configure.cmake

@@ -0,0 +1,43 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+if (CUTLASS_USE_EXTENDED_ADD_TEST_FORMAT)
+  # The longform/extended format allows generator expressions to be
+  # expanded property and is useful in contexts where the files need
+  # to be immediately included into being-processed cmake code.
+  add_test(NAME @TESTCASE_NAME@ COMMAND ${_CUTLASS_TEST_EXECUTION_ENVIRONMENT} "${TEST_EXE_PATH}" @TEST_COMMAND_OPTIONS@)
+else()
+  add_test(@TESTCASE_NAME@ ${_CUTLASS_TEST_EXECUTION_ENVIRONMENT} "${TEST_EXE_PATH}" @TEST_COMMAND_OPTIONS@)
+endif()
+
+if (TEST_EXE_WORKING_DIRECTORY)
+  set_tests_properties(@TESTCASE_NAME@ PROPERTIES WORKING_DIRECTORY "${TEST_EXE_WORKING_DIRECTORY}")
+endif()
+
+set_tests_properties(@TESTCASE_NAME@ PROPERTIES DISABLED @__DISABLE_TESTS@)
+

cmake/NvidiaCutlassConfig.cmake.in

@@ -0,0 +1,9 @@
+get_filename_component(NvidiaCutlass_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH)
+
+include(CMakeFindDependencyMacro)
+
+if(TARGET nvidia::cutlass::CUTLASS)
+  return()
+endif()
+
+include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake")

cmake/NvidiaCutlassPackageConfig.cmake

@@ -0,0 +1,42 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+set(CPACK_PACKAGE_NAME NvidiaCutlass)
+set(CPACK_PACKAGE_VENDOR NVIDIA)
+set(CPACK_PACKAGE_CONTACT info@nvidia.com)
+set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "CUTLASS CUDA C++ Template Linear Algebra Library")
+set(CPACK_PACKAGE_INSTALL_DIRECTORY ${CPACK_PACKAGE_NAME})
+set(CPACK_PACKAGE_VERSION_MAJOR ${PROJECT_VERSION_MAJOR})
+set(CPACK_PACKAGE_VERSION_MINOR ${PROJECT_VERSION_MINOR})
+set(CPACK_PACKAGE_VERSION_PATCH ${PROJECT_VERSION_PATCH})
+set(CPACK_VERBATIM_VARIABLES YES)
+# set(CPACK_PACKAGE_DESCRIPTION_FILE ${CMAKE_CURRENT_LIST_DIR}/Description.txt)
+# set(CPACK_RESOURCE_FILE_WELCOME ${CMAKE_CURRENT_LIST_DIR}/Welcome.txt)
+# set(CPACK_RESOURCE_FILE_LICENSE ${CMAKE_CURRENT_LIST_DIR}/License.txt)
+# set(CPACK_RESOURCE_FILE_README ${CMAKE_CURRENT_LIST_DIR}/Readme.txt)
+include(CPack)

cmake/googletest.cmake

@@ -0,0 +1,52 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+include(FetchContent)
+
+set(GOOGLETEST_DIR "" CACHE STRING "Location of local GoogleTest repo to build against")
+
+if(GOOGLETEST_DIR)
+  set(FETCHCONTENT_SOURCE_DIR_GOOGLETEST ${GOOGLETEST_DIR} CACHE STRING "GoogleTest source directory override")
+endif()
+
+set(GTEST_REPOSITORY "https://github.com/google/googletest.git" CACHE STRING "GoogleTest repo to fetch")
+FetchContent_Declare(
+  googletest
+  GIT_REPOSITORY ${GTEST_REPOSITORY}
+  GIT_TAG        v1.14.0
+  )
+
+FetchContent_GetProperties(googletest)
+
+if(NOT googletest_POPULATED)
+  FetchContent_Populate(googletest)
+  if (MSVC)
+    set(gtest_force_shared_crt ON CACHE BOOL "" FORCE)
+  endif()
+  add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL)
+endif()

cmake/nop.cu

@@ -0,0 +1,49 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+/*! \file
+    \brief Basic CUDA file for testing compiler flags.
+*/
+
+__device__ int inner()
+{
+  return -1;
+}
+
+__global__ void test()
+{
+  inner();
+}
+
+int main()
+{
+  test<<<1,1>>>();
+  return 0;
+}

cmake/version_extended.h.in

@@ -0,0 +1,34 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+#pragma once
+
+#define CUTLASS_BUILD @CUTLASS_VERSION_BUILD@
+#define CUTLASS_REVISION "@CUTLASS_REVISION@"

cuBLAS.cmake

@@ -0,0 +1,152 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+message(STATUS "Configuring cublas ...")
+
+if((DEFINED CUTLASS_ENABLE_CUBLAS AND NOT CUTLASS_ENABLE_CUBLAS) OR
+   (DEFINED CUBLAS_ENABLED AND NOT CUBLAS_ENABLED))
+  
+  # Don't add cuBLAS if it's defined and false, assume it's not found.
+
+  set(CUBLAS_FOUND OFF)
+  message(STATUS "cuBLAS Disabled.")
+
+elseif(NOT TARGET cublas)
+ 
+  find_path(
+    _CUBLAS_INCLUDE_DIR
+    NAMES cublas_v2.h
+    HINTS
+      ${CUBLAS_INCLUDE_PATH}
+      ENV CUBLAS_INCLUDE_PATH
+      ${CUBLAS_PATH}
+      ENV CUBLAS_PATH
+      ${CUDA_TOOLKIT_ROOT_DIR}
+    PATH_SUFFIXES
+      include
+    )
+
+  find_library(
+    _CUBLAS_LIBRARY
+    NAMES cublas
+    HINTS
+      ${CUBLAS_LIBRARY_PATH}
+      ENV CUBLAS_LIBRARY_PATH
+      ${_CUBLAS_INCLUDE_DIR}/..
+      ${CUBLAS_PATH}
+      ENV CUBLAS_PATH
+      ${CUDA_TOOLKIT_ROOT_DIR}
+    PATH_SUFFIXES
+      lib64
+      lib/x64
+      lib
+    )
+
+  if(_CUBLAS_INCLUDE_DIR AND _CUBLAS_LIBRARY)
+
+    message(STATUS "cuBLAS: ${_CUBLAS_LIBRARY}")
+    message(STATUS "cuBLAS: ${_CUBLAS_INCLUDE_DIR}")
+    
+    set(CUBLAS_FOUND ON CACHE INTERNAL "cublas Library Found")
+    set(CUBLAS_LIBRARY ${_CUBLAS_LIBRARY})
+    set(CUBLAS_INCLUDE_DIR ${_CUBLAS_INCLUDE_DIR})
+
+  else()
+
+    message(STATUS "cublas not found.")
+    set(CUBLAS_FOUND OFF CACHE INTERNAL "cublas Library Found")
+
+  endif()
+
+endif()
+
+set(CUTLASS_ENABLE_CUBLAS ${CUBLAS_FOUND} CACHE BOOL "Enable CUTLASS to build with cuBLAS library.")
+
+if(CUTLASS_ENABLE_CUBLAS AND NOT CUBLAS_FOUND)
+  message(FATAL_ERROR "CUTLASS_ENABLE_CUBLAS enabled but cuBLAS library could not be found.")
+endif()
+
+if(CUTLASS_ENABLE_CUBLAS AND NOT TARGET cublas)
+
+  if(WIN32)
+    add_library(cublas STATIC IMPORTED GLOBAL)
+  else()
+    add_library(cublas SHARED IMPORTED GLOBAL)
+  endif()
+
+  add_library(nvidia::cublas ALIAS cublas)
+
+  set_property(
+    TARGET cublas
+    PROPERTY IMPORTED_LOCATION
+    ${CUBLAS_LIBRARY})
+    
+  target_include_directories(
+    cublas
+    INTERFACE
+    $<INSTALL_INTERFACE:include>
+    $<BUILD_INTERFACE:${CUBLAS_INCLUDE_DIR}>)
+
+  find_library(
+    _CUBLASLT_LIBRARY
+    NAMES cublasLt
+    HINTS
+      ${CUBLAS_LIBRARY_PATH}
+      ENV CUBLAS_LIBRARY_PATH
+      ${_CUBLAS_INCLUDE_DIR}/..
+      ${CUBLAS_PATH}
+      ENV CUBLAS_PATH
+      ${CUDA_TOOLKIT_ROOT_DIR}
+    PATH_SUFFIXES
+      lib64
+      lib/x64
+      lib
+    )
+
+  if(_CUBLASLT_LIBRARY AND NOT TARGET cublasLt)
+
+    if(WIN32)
+      add_library(cublasLt STATIC IMPORTED GLOBAL)
+    else()
+      add_library(cublasLt SHARED IMPORTED GLOBAL)
+    endif()
+    
+    set_property(
+      TARGET cublasLt
+      PROPERTY IMPORTED_LOCATION
+      ${_CUBLASLT_LIBRARY})
+  
+    add_library(nvidia::cublasLt ALIAS cublasLt)
+
+    target_link_libraries(cublas INTERFACE cublasLt)
+
+  endif()
+
+endif()
+
+message(STATUS "Configuring cuBLAS ... done.")

cuDNN.cmake

@@ -0,0 +1,112 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+if(DEFINED CUDNN_ENABLED)
+    set(CUTLASS_ENABLE_CUDNN ${CUDNN_ENABLED} CACHE BOOL "Enable CUTLASS to build with cuDNN library.")
+endif()
+
+if(DEFINED CUTLASS_ENABLE_CUDNN AND NOT CUTLASS_ENABLE_CUDNN)
+  return()
+endif()
+  
+message(STATUS "Configuring cuDNN ...")
+
+find_path(
+    _CUDNN_INCLUDE_DIR cudnn.h
+    PATHS
+    ${CUDA_TOOLKIT_ROOT_DIR}/include
+    $ENV{CUDNN_PATH}/include
+    $ENV{CUDA_PATH}/include
+    ${CUDNN_PATH}/include
+    /usr/include)
+
+find_library(
+    _CUDNN_LIBRARY cudnn
+    HINTS
+    ${CUDA_TOOLKIT_ROOT_DIR}/lib64
+    ${CUDA_TOOLKIT_ROOT_DIR}/lib/x64
+    ${CUDA_TOOLKIT_ROOT_DIR}/lib
+    $ENV{CUDNN_PATH}/lib64
+    $ENV{CUDNN_PATH}/lib/x64
+    $ENV{CUDNN_PATH}/lib
+    $ENV{CUDA_PATH}/lib64
+    $ENV{CUDA_PATH}/lib/x64
+    $ENV{CUDA_PATH}/lib
+    ${CUDNN_PATH}/lib64
+    ${CUDNN_PATH}/lib/x64
+    ${CUDNN_PATH}/lib
+    /usr/lib/x86_64-linux-gnu
+    /usr/lib)
+
+if(_CUDNN_INCLUDE_DIR AND _CUDNN_LIBRARY)
+
+    message(STATUS "cuDNN: ${_CUDNN_LIBRARY}")
+    message(STATUS "cuDNN: ${_CUDNN_INCLUDE_DIR}")
+    
+    set(CUDNN_FOUND ON CACHE INTERNAL "cuDNN Library Found")
+
+else()
+
+    message(STATUS "cuDNN not found.")
+    set(CUDNN_FOUND OFF CACHE INTERNAL "cuDNN Library Found")
+
+endif()
+
+set(CUTLASS_ENABLE_CUDNN ${CUDNN_FOUND} CACHE BOOL "Enable CUTLASS to build with cuDNN library.")
+
+if (CUTLASS_ENABLE_CUDNN AND NOT TARGET cudnn)
+
+  set(CUDNN_INCLUDE_DIR ${_CUDNN_INCLUDE_DIR})
+  set(CUDNN_LIBRARY ${_CUDNN_LIBRARY})
+
+  if(WIN32)
+    add_library(cudnn STATIC IMPORTED GLOBAL)
+  else()
+    add_library(cudnn SHARED IMPORTED GLOBAL)
+  endif()
+
+  add_library(nvidia::cudnn ALIAS cudnn)
+
+  set_property(
+    TARGET cudnn
+    PROPERTY IMPORTED_LOCATION
+    ${CUDNN_LIBRARY})
+    
+  target_include_directories(
+    cudnn
+    INTERFACE
+    $<INSTALL_INTERFACE:include>
+    $<BUILD_INTERFACE:${CUDNN_INCLUDE_DIR}>)
+
+endif()
+
+if(CUTLASS_ENABLE_CUDNN AND NOT CUDNN_FOUND)
+  message(FATAL_ERROR "CUTLASS_ENABLE_CUDNN enabled but cuDNN library could not be found.")
+endif()
+
+message(STATUS "Configuring cuDNN ... done.")

customConfigs.cmake

@@ -0,0 +1,98 @@
+# Copyright (c) 2017 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+
+
+
+# Profiler based functional testing
+set(CUTLASS_BUILD_FOR_PROFILER_REGRESSIONS OFF CACHE BOOL "Utilize profiler-based functional regressions")
+set(CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  ${CUTLASS_TEST_LEVEL} CACHE STRING "Profiler functional regression test level")
+
+find_package(Python3 3.5 COMPONENTS Interpreter REQUIRED)
+
+
+function(cutlass_generate_kernel_filter_and_testlist_files)
+
+  set(options)
+  set(oneValueArgs TEST_SET_NAME)
+  set(multiValueArgs)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  execute_process(
+    COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CUTLASS_LIBRARY_PACKAGE_DIR}
+      ${Python3_EXECUTABLE} ${CUTLASS_SOURCE_DIR}/python/cutlass_library/generator.py 
+      --generator-target=${__TEST_SET_NAME} 
+      --cuda-version=${CUDA_VERSION_MAJOR}.${CUDA_VERSION_MINOR}
+      --architectures=${CUTLASS_NVCC_ARCHS}
+      --kernels=\*
+      --disable-cutlass-package-imports
+    WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}
+    RESULT_VARIABLE cutlass_FILTER_GENERATION_RESULT
+    OUTPUT_VARIABLE cutlass_FILTER_GENERATION_OUTPUT
+    OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/library_filter_generation.log
+    ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/library_filter_generation.log
+  )
+
+  if(NOT cutlass_FILTER_GENERATION_RESULT EQUAL 0)
+    message(FATAL_ERROR "Error generating kernel filters and testlist files. See ${CMAKE_CURRENT_BINARY_DIR}/library_filter_generation.log")
+  endif()
+endfunction()
+
+if(CUTLASS_BUILD_FOR_PROFILER_REGRESSIONS)
+
+    set(PROFILER_ARCH_LIST 100a 100f 103a 120a 120f 121a)
+    if (CUDA_VERSION VERSION_LESS 13.0)
+      list(APPEND PROFILER_ARCH_LIST 101a 101f)
+    else()
+      list(APPEND PROFILER_ARCH_LIST 110a 110f)
+    endif()
+    foreach(ARCH IN LISTS CUTLASS_NVCC_ARCHS)
+      if(NOT (ARCH IN_LIST PROFILER_ARCH_LIST))
+        message(FATAL_ERROR "Only SM${PROFILER_ARCH_LIST} compute capabilities are supported with profiler-based unit tests")
+      endif()
+    endforeach()
+
+    if(CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  EQUAL 0)
+
+      message(STATUS "Building for L0 profiler-based functional regressions")
+      cutlass_generate_kernel_filter_and_testlist_files(TEST_SET_NAME kernel_testlist_l0)
+      set(KERNEL_FILTER_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L0_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm_kernel_filter.list CACHE STRING "Kernel set")
+      set(CUTLASS_PROFILER_REGRESSION_LIST_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L0_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm.csv CACHE STRING "Regression set")
+
+    elseif (CUTLASS_PROFILER_REGRESSION_TEST_LEVEL  EQUAL 1)
+      
+      message(STATUS "Building for L1 profiler-based functional regressions")
+      cutlass_generate_kernel_filter_and_testlist_files(TEST_SET_NAME kernel_testlist_l1)
+      set(KERNEL_FILTER_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L1_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm_kernel_filter.list CACHE STRING "Kernel set")
+      set(CUTLASS_PROFILER_REGRESSION_LIST_FILE ${CMAKE_CURRENT_BINARY_DIR}/FK_functional_L1_testlist_SM${CUTLASS_NVCC_ARCHS}_cutlass3x_gemm.csv CACHE STRING "Regression set")
+
+    endif()
+endif()
+
+

cutlass/convert.h

@@ -1,102 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*!
-  \file
-  \brief Defines conversion operations among Fragments of different base type.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename InputFragment_, typename OutputFragment_>
-struct Convert {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename InputScalar_, typename OutputScalar_, int kScalars_>
-struct Convert<Fragment<InputScalar_, kScalars_>, Fragment<OutputScalar_, kScalars_> > {
-  /// The input fragment.
-  typedef Fragment<InputScalar_, kScalars_> InputFragment;
-  /// The output fragment.
-  typedef Fragment<OutputScalar_, kScalars_> OutputFragment;
-
-  /// Ctor.
-  CUTLASS_DEVICE Convert() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
-    transform(src, 0, dst);
-  }
-
-  /// Transform a fragment.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
-    for (int i = 0; i < kScalars_; ++i) {
-      dst[i] = static_cast<OutputScalar_>(src[i + offset]);
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Fragment_>
-struct Copy {
-  /// The input fragment.
-  typedef Fragment_ InputFragment;
-  /// The output fragment.
-  typedef Fragment_ OutputFragment;
-
-  /// Ctor.
-  CUTLASS_DEVICE Copy() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(Fragment_ const& src, Fragment_& dst) { transform(src, 0, dst); }
-
-  /// Transform a fragment.
-  template <typename InputFragment_>
-  CUTLASS_DEVICE void transform(InputFragment_ const& src, int offset, Fragment_& dst) {
-    if (sizeof(typename Fragment_::Element) == 8) {
-      uint64_t const* src_ptr = reinterpret_cast<uint64_t const*>(&src[offset]);
-      uint64_t* dst_ptr = reinterpret_cast<uint64_t*>(&dst[0]);
-      for (int i = 0; i < sizeof(Fragment_) / 8; ++i) {
-        dst_ptr[i] = src_ptr[i];
-      }
-    } else {
-      uint32_t const* src_ptr = reinterpret_cast<uint32_t const*>(&src[offset]);
-      uint32_t* dst_ptr = reinterpret_cast<uint32_t*>(&dst[0]);
-      for (int i = 0; i < sizeof(Fragment_) / 4; ++i) {
-        dst_ptr[i] = src_ptr[i];
-      }
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/coord.h

@@ -1,287 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief A Coord is a coordinate of arbitrary rank into a tensor or matrix
-*/
-
-#pragma once
-
-#include <cutlass/cutlass.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Describes identity elements
-struct Identity {
-  /// Enumeration describing identity elements. Value assignments are significant.
-  /// Feel free to add or multiply by these, respectively.
-  enum Kind { Additive = 0, Multiplicative = 1 };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Statically-sized array specifying Coords within a tensor
-template <int N_>
-struct Coord {
-  //
-  // Type and constant definitions
-  //
-
-  static int const N = N_;
-
-  //
-  // Data members
-  //
-
-  /// Indices
-  int idx[N];
-
-  //
-  // Methods
-  //
-
-  /// Default ctor initializes uniformly
-  CUTLASS_HOST_DEVICE
-  Coord(int value = 0) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] = value;
-    }
-  }
-
-  /// Constructs from an array of integers
-  CUTLASS_HOST_DEVICE
-  Coord(int _idx[]) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] = _idx[i];
-    }
-  }
-
-  /// Element-wise addition
-  CUTLASS_HOST_DEVICE
-  Coord operator+(Coord const& b) const {
-    Coord c;
-    for (int i = 0; i < N; ++i) {
-      c.idx[i] = idx[i] + b.idx[i];
-    }
-    return c;
-  }
-
-  /// Element-wise subtraction
-  CUTLASS_HOST_DEVICE
-  Coord operator-(Coord const& b) const {
-    Coord c;
-    for (int i = 0; i < N; ++i) {
-      c.idx[i] = idx[i] - b.idx[i];
-    }
-    return c;
-  }
-
-  /// Element-wise multiplication
-  CUTLASS_HOST_DEVICE
-  Coord operator*(Coord const& b) const {
-    Coord c;
-    for (int i = 0; i < N; ++i) {
-      c.idx[i] = idx[i] * b.idx[i];
-    }
-    return c;
-  }
-
-  /// Element-wise division
-  CUTLASS_HOST_DEVICE
-  Coord operator/(Coord const& b) const {
-    Coord c;
-    for (int i = 0; i < N; ++i) {
-      c.idx[i] = idx[i] / b.idx[i];
-    }
-    return c;
-  }
-
-  /// In-place addition
-  CUTLASS_HOST_DEVICE
-  Coord& operator+=(Coord const& b) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] += b.idx[i];
-    }
-    return *this;
-  }
-
-  /// In-place subtraction
-  CUTLASS_HOST_DEVICE
-  Coord& operator-=(Coord const& b) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] -= b.idx[i];
-    }
-    return *this;
-  }
-
-  /// In-place multiplication
-  CUTLASS_HOST_DEVICE
-  Coord& operator*=(Coord const& b) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] *= b.idx[i];
-    }
-    return *this;
-  }
-
-  /// In-place division
-  CUTLASS_HOST_DEVICE
-  Coord& operator/=(Coord const& b) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] /= b.idx[i];
-    }
-    return *this;
-  }
-
-  /// Member access operator
-  CUTLASS_HOST_DEVICE int& operator[](int dim) { return idx[dim]; }
-
-  /// Member access operator
-  CUTLASS_HOST_DEVICE int const& operator[](int dim) const { return idx[dim]; }
-
-  /// Computes the dot product of two Coord instances
-  template <typename T>
-  CUTLASS_HOST_DEVICE T dot(Coord const& b, T sum) const {
-    for (int i = 0; i < N; ++i) {
-      sum += idx[i] * b.idx[i];
-    }
-    return sum;
-  }
-
-  /// Computes the dot product of two Coord instances
-  template <typename T>
-  CUTLASS_HOST_DEVICE T dot(Coord const& b) const {
-    T sum = T(0);
-    for (int i = 0; i < N; ++i) {
-      sum += idx[i] * b.idx[i];
-    }
-    return sum;
-  }
-
-  /// Gets the index of a given Coord element
-  template <int Dim>
-  CUTLASS_HOST_DEVICE int& at() {
-    return idx[Dim];
-  }
-
-  /// Access via index; may limit unrolling potential
-  CUTLASS_HOST_DEVICE
-  int& at(int dim) { return idx[dim]; }
-
-  /// Gets the index of a given Coord element
-  template <int Dim>
-  CUTLASS_HOST_DEVICE int const& at() const {
-    return idx[Dim];
-  }
-
-  /// Access via index; may limit unrolling potential
-  CUTLASS_HOST_DEVICE
-  int const& at(int dim) const { return idx[dim]; }
-
-  /// Determines if two Coord<> objects are equal
-  CUTLASS_HOST_DEVICE
-  bool operator==(Coord<N> const& b) const {
-    bool equal = true;
-    for (int i = 0; equal && i < N; ++i) {
-      equal = (idx[i] == b.idx[i]);
-    }
-    return equal;
-  }
-
-  /// Not equal
-  CUTLASS_HOST_DEVICE
-  bool operator!=(Coord<N> const& b) const { return !(*this == b); }
-
-  /// Clamps a coordinate to a range specified by maximum and minimum values
-  CUTLASS_HOST_DEVICE
-  Coord& clamp(Coord<N> const& max, Coord<N> const& min = Coord<N>()) {
-    for (int i = 0; i < N; ++i) {
-      idx[i] = __NV_STD_MAX(__NV_STD_MIN(idx[i], max.idx[i]), min.idx[i]);
-    }
-    return *this;
-  }
-
-  /// Returns the product of all elements
-  CUTLASS_HOST_DEVICE
-  int count() const {
-    int product = idx[0];
-    for (int i = 1; i < N; ++i) {
-      product *= idx[i];
-    }
-    return product;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Helper to make a 2-element coordinate
-CUTLASS_HOST_DEVICE
-Coord<1> make_Coord(int _0) {
-  int values[1] = {_0};
-  return Coord<1>(values);
-}
-
-/// Helper to make a 2-element coordinate
-CUTLASS_HOST_DEVICE
-Coord<2> make_Coord(int _0, int _1) {
-  int values[2] = {_0, _1};
-  return Coord<2>(values);
-}
-
-/// Helper to make a 3-element coordinate
-CUTLASS_HOST_DEVICE
-Coord<3> make_Coord(int _0, int _1, int _2) {
-  int values[3] = {_0, _1, _2};
-  return Coord<3>(values);
-}
-
-/// Helper to make a 4-element coordinate
-CUTLASS_HOST_DEVICE
-Coord<4> make_Coord(int _0, int _1, int _2, int _3) {
-  int values[4] = {_0, _1, _2, _3};
-  return Coord<4>(values);
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Getter
-CUTLASS_HOST_DEVICE
-Coord<2> get_Coord_hw(Coord<3> const& coord) { return make_Coord(coord[1], coord[2]); }
-
-/// Getter
-CUTLASS_HOST_DEVICE
-Coord<2> get_Coord_hw(Coord<4> const& coord) { return make_Coord(coord[1], coord[2]); }
-
-/// Getter
-CUTLASS_HOST_DEVICE
-Coord<3> get_Coord_hwc(Coord<4> const& coord) { return make_Coord(coord[1], coord[2], coord[3]); }
-
-/// Getter
-CUTLASS_HOST_DEVICE
-Coord<3> get_Coord_dhw(Coord<4> const& coord) { return make_Coord(coord[0], coord[1], coord[2]); }
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/core_io.h

@@ -1,44 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-#pragma once
-
-/*! \file
-    \brief Helpers for printing cutlass/core objects
-*/
-
-#pragma once
-
-#include <iosfwd>
-#include <typeinfo>
-
-#include <cutlass/coord.h>
-
-template <int Rank>
-std::ostream& operator<<(std::ostream& out, cutlass::Coord<Rank> const& coord) {
-  for (int i = 0; i < Rank; ++i) {
-    out << (i ? ", " : "") << coord.idx[i];
-  }
-  return out;
-}

cutlass/cutlass.h

@@ -1,73 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-/*! \file
-    \brief Basic include for CUTLASS macros
-*/
-
-#pragma once
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#define CUTLASS_MAJOR 1
-#define CUTLASS_MINOR 0
-#define CUTLASS_PATCH 0
-#define CUTLASS_VERSION ((CUTLASS_MAJOR)*100 + (CUTLASS_MINOR)*10 + CUTLASS_PATCH)
-
-#ifdef __NVCC__
-#define CUTLASS_HOST_DEVICE __forceinline__ __device__ __host__
-#define CUTLASS_DEVICE __forceinline__ __device__
-#elif defined(__CUDACC_RTC__)
-#define CUTLASS_HOST_DEVICE __forceinline__ __device__
-#define CUTLASS_DEVICE __forceinline__ __device__
-#else
-#define CUTLASS_HOST_DEVICE
-// CUTLASS_DEVICE is an error if not compiling device code
-#endif
-
-// CUTLASS_PRAGMA_UNROLL inserts a CUTLASS_PRAGMA_UNROLL if supported by the compiler
-#if defined(__CUDA_ARCH__)
-#if defined(_MSC_VER)
-#define CUTLASS_PRAGMA_UNROLL __pragma("unroll")
-#define CUTLASS_PRAGMA_NO_UNROLL __pragma("unroll 1")
-#else
-#define CUTLASS_PRAGMA_UNROLL _Pragma("unroll")
-#define CUTLASS_PRAGMA_NO_UNROLL _Pragma("unroll 1")
-#endif
-#else
-#define CUTLASS_PRAGMA_UNROLL
-#define CUTLASS_PRAGMA_NO_UNROLL
-#endif
-
-#define CUTLASS_ASSERT(x) assert(x)
-
-namespace cutlass {
-
-/// NVIDIA GPU Warp size
-static const int kWarpSize = 32;
-
-}  // namespace cutlass
-
-////////////////////////////////////////////////////////////////////////////////////////////////////

cutlass/fragment.h

@@ -1,278 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines Fragment, a statically-sized array for storing parts of matrices within a
-   thread's registers.
-*/
-#pragma once
-
-#include <assert.h>
-#include <cutlass/shape.h>
-#include <cutlass/util/cutlass_math.h>
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup fragment_concept Fragment Concept
-@{
-
-\ref fragment_concept is a statically sized array for storing parts of tiles held by individual CUDA
-threads.
-
-@par \ref fragment_concept
-   Types satisfying \ref fragment_concept define the following members
-  - <b>Element</b> - type of each access held within the fragment
-  - <b>kElements</b> - number of elements stored by the fragment
-  - <b>clear()</b> - overwrites the fragment storage with zeros
-  - <b>Element & operator[](int i)</b> - by-reference access of the ith element
-  - <b>Element const & operator[](int i) const</b> - const by-reference access of the ith element
-@}
-*/
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup fragment_iterator_concept Fragment Iterator Concept
-@{
-
-\ref fragment_iterator_concept provides structured access to the elements within a fragment with an
-optional bitcast to the desired access type
-
-@par \ref fragment_iterator_concept
-   Types satisfying \ref fragment_iterator_concept define the following members
-  - <b>AccessType& operator[](int i)</b> - provides access to the ith element of the fragment
-  - <b>AccessType& at(int d, int h, int w, int c)</b> - applies \ref layout_concept to fragment and
-provides access to element at (d, h, w, c)
-
-@}
-*/
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <int kAlignment_>
-struct StorageType {
-  typedef uint64_t Type;
-};
-template <>
-struct StorageType<4> {
-  typedef uint32_t Type;
-};
-template <>
-struct StorageType<2> {
-  typedef uint16_t Type;
-};
-template <>
-struct StorageType<1> {
-  typedef uint8_t Type;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief A template defining \ref fragment_concept
-* @concept{fragment_concept}
-*/
-template <typename Element_, int kElements_, size_t kAlignment_ = 16>
-struct Fragment : public AlignedStruct<kAlignment_> {
-  /// Make sure the alignment makes sense wrt the size of elements.
-  static_assert(kAlignment_ == 16 || kAlignment_ >= sizeof(Element_), "Alignment is too small");
-  /// Alignment must be a power of two
-  static_assert(is_pow2<kAlignment_>::value, "Alignment must be a power of two");
-
-  /// This class.
-  typedef Fragment<Element_, kElements_> This_;
-  /// The element.
-  typedef Element_ Element;
-  /// The number of elements.
-  static int const kElements = kElements_;
-
-  /// Clear a fragment.
-  CUTLASS_DEVICE void clear() {
-    // Avoid element-wise access for sub 32b element type
-    if (kAlignment_ >= 8 && (kElements * sizeof(Element)) % 8 == 0) {
-      uint64_t* ptr = reinterpret_cast<uint64_t*>(storage);
-      for (int i = 0; i < (kElements * sizeof(Element)) / 8; ++i) {
-        ptr[i] = uint64_t(0);
-      }
-    } else if (kAlignment_ >= 4 && (kElements * sizeof(Element)) % 4 == 0) {
-      uint32_t* ptr = reinterpret_cast<uint32_t*>(storage);
-      for (int i = 0; i < (kElements * sizeof(Element)) / 4; ++i) {
-        ptr[i] = uint32_t(0);
-      }
-    } else if (kAlignment_ >= 2 && (kElements * sizeof(Element)) % 2 == 0) {
-      uint16_t* ptr = reinterpret_cast<uint16_t*>(storage);
-      for (int i = 0; i < (kElements * sizeof(Element)) / 2; ++i) {
-        ptr[i] = uint16_t(0);
-      }
-    } else {
-      for (int i = 0; i < kElements; ++i) {
-        storage[i] = 0;
-      }
-    }
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE Element& operator[](int i) {
-    assert(i < kElements_);
-    return reinterpret_cast<Element*>(storage)[i];
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE Element const& operator[](int i) const {
-    assert(i < kElements_);
-    return reinterpret_cast<Element const*>(storage)[i];
-  }
-
- private:
-  /// Storage type to use for Elements
-  typedef typename StorageType<kAlignment_>::Type StorageType;
-
-  /// Number of elements in the storage
-  static int const kStorageCount =
-      (sizeof(Element_) * kElements_ + sizeof(StorageType) - 1) / sizeof(StorageType);
-  /// The storage.
-  StorageType storage[kStorageCount];
-
-  /// Ensure that there's enough storage for all elements
-  static_assert(sizeof(StorageType) <= kAlignment_, "StorageType is too big for given alignment");
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief A template defining \ref fragment_iterator_concept
-* @concept{fragment_iterator_concept}
-*/
-template <typename Fragment_, typename Iterations_, typename AccessType_>
-struct FragmentIterator {
-  /// This class.
-  typedef FragmentIterator<Fragment_, Iterations_, AccessType_> This_;
-  /// The fragment.
-  typedef Fragment_ Fragment;
-  /// The number of iterations.
-  typedef Iterations_ Iterations;
-  /// The access type.
-  typedef AccessType_ AccessType;
-
-  /// The element.
-  typedef typename Fragment::Element Element;
-  /// The number of elements per access.
-  static int const kElementsPerAccess = (int)(sizeof(AccessType) / sizeof(Element));
-  /// The shape of the the fragment.
-  typedef typename ShapeMul<Iterations, Shape<1, 1, 1, kElementsPerAccess> >::Shape FragmentShape;
-  /// The linear strides for iterations.
-  typedef typename ShapeStrides<FragmentShape>::Shape Strides;
-
-  /// Ctor.
-  template <typename OtherFragment_>
-  CUTLASS_DEVICE FragmentIterator(OtherFragment_& fragment, int offset = 0)
-      : pointer(reinterpret_cast<Element*>(&fragment[offset])) {
-    static_assert(OtherFragment_::kElements >= Fragment::kElements, "");
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType const& at(int d, int h, int w, int c = 0) const {
-    int const imm = ComputeOffsetFromStrides<Strides>::get(d, h, w, c);
-    return reinterpret_cast<AccessType const&>(pointer[imm]);
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType& at(int d, int h, int w, int c = 0) {
-    int const imm = ComputeOffsetFromStrides<Strides>::get(d, h, w, c);
-    return reinterpret_cast<AccessType&>(pointer[imm]);
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType const& operator[](int i) const {
-    return reinterpret_cast<AccessType const&>(pointer[i * kElementsPerAccess]);
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType& operator[](int i) {
-    return reinterpret_cast<AccessType&>(pointer[i * kElementsPerAccess]);
-  }
-
-  /// Is the iterator valid?
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
-
-  /// The pointer.
-  Element* pointer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Fragment_, typename Iterations_, typename AccessType_>
-struct FragmentConstIterator {
-  /// This class.
-  typedef FragmentIterator<Fragment_, Iterations_, AccessType_> This_;
-  /// The fragment.
-  typedef Fragment_ Fragment;
-  /// The number of iterations.
-  typedef Iterations_ Iterations;
-  /// The access type.
-  typedef AccessType_ AccessType;
-
-  /// The element.
-  typedef typename Fragment::Element Element;
-  /// The number of elements per access.
-  static int const kElementsPerAccess = (int)(sizeof(AccessType) / sizeof(Element));
-  /// The shape of the the fragment.
-  typedef typename ShapeMul<Iterations, Shape<1, 1, 1, kElementsPerAccess> >::Shape FragmentShape;
-  /// The linear strides for iterations.
-  typedef typename ShapeStrides<FragmentShape>::Shape IterationsStrides;
-
-  /// Ctor.
-  template <typename OtherFragment_>
-  CUTLASS_DEVICE FragmentConstIterator(OtherFragment_& fragment, int offset = 0)
-      : pointer(reinterpret_cast<Element const*>(&fragment[offset])) {
-    static_assert(OtherFragment_::kElements >= Fragment::kElements, "");
-  }
-  /// Create from non-constant FragmentIterator
-  CUTLASS_DEVICE FragmentConstIterator(
-      FragmentIterator<Fragment_, Iterations_, AccessType_> const& rhs_)
-      : pointer(reinterpret_cast<Element const*>(rhs_.offset)) {}
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType const& at(int d, int h, int w, int c = 0) const {
-    int const imm = ComputeOffsetFromStrides<IterationsStrides>::get(d, h, w, c);
-    return reinterpret_cast<AccessType const&>(pointer[imm]);
-  }
-
-  /// The accessor.
-  CUTLASS_DEVICE AccessType const& operator[](int i) const {
-    return reinterpret_cast<AccessType const&>(pointer[i * kElementsPerAccess]);
-  }
-
-  /// Is the iterator valid?
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
-
-  /// The pointer.
-  Element const* pointer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/fragment_load_store.h

@@ -1,135 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines accessors for loading and storing fragments to memory efficiently.
-*/
-#pragma once
-
-#include <cutlass/load_store.h>
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <IteratorFragment::Kind kIteratorFragment,
-          int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentLoad {};
-
-template <int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentLoad<IteratorFragment::kWmmaMatrix,
-                    kAccessSize,
-                    Scalar_,
-                    Memory_,
-                    FragmentElement_,
-                    kStride> {
-  /// The output type.
-  typedef FragmentElement_ AccessType;
-
-  /// The load function.
-  static CUTLASS_DEVICE void load(AccessType& value, Scalar_ const* pointer, int offset) {
-    value.load(&pointer[offset], kStride);
-  }
-};
-
-template <int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentLoad<IteratorFragment::kScalar,
-                    kAccessSize,
-                    Scalar_,
-                    Memory_,
-                    FragmentElement_,
-                    kStride> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, kAccessSize>::Type AccessType;
-
-  /// The load function.
-  static CUTLASS_DEVICE void load(AccessType& value, Scalar_ const* pointer, int offset) {
-    Load<Scalar_, kAccessSize, Memory_>::load(value, pointer, offset);
-  }
-};
-
-template <IteratorFragment::Kind kIteratorFragment,
-          int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentStore {};
-
-template <int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentStore<IteratorFragment::kWmmaMatrix,
-                     kAccessSize,
-                     Scalar_,
-                     Memory_,
-                     FragmentElement_,
-                     kStride> {
-  /// The input type.
-  typedef FragmentElement_ AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& value, Scalar_* pointer, int offset) {
-    value.store(&pointer[offset], kStride);
-  }
-};
-
-template <int kAccessSize,
-          typename Scalar_,
-          MemorySpace::Kind Memory_,
-          typename FragmentElement_,
-          int kStride>
-struct FragmentStore<IteratorFragment::kScalar,
-                     kAccessSize,
-                     Scalar_,
-                     Memory_,
-                     FragmentElement_,
-                     kStride> {
-  /// The input type.
-  typedef typename Vectorize<Scalar_, kAccessSize>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& value, Scalar_* pointer, int offset) {
-    Store<Scalar_, kAccessSize, Memory_>::store(value, pointer, offset);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  /// namespace cutlass

cutlass/fragment_multiply_add.h

@@ -1,131 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines multiply-add operations on fragments within a thread.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_>
-struct FragmentMultiplyAdd {
-  /// The shape of the instruction.
-  typedef Shape<1, 1, 1, 1> InstructionShape;
-  /// The type for A.
-  typedef Scalar_ ScalarA;
-  /// The type for B.
-  typedef Scalar_ ScalarB;
-  /// The type for C and D.
-  typedef Scalar_ ScalarC;
-
-  /// Ctor.
-  CUTLASS_DEVICE FragmentMultiplyAdd() {}
-
-  /// Multiply : d = a*b.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void multiply(Scalar_ a, Fragment_ const& b, Fragment_& d) {
-    for (int j = 0; j < Fragment_::kElements; ++j) {
-      d[j] = a * b[j];
-    }
-  }
-
-  /// Multiply : d = a*b + c.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void multiply_add(Scalar_ a,
-                                   Fragment_ const& b,
-                                   Fragment_ const& c,
-                                   Fragment_& d) {
-    for (int j = 0; j < Fragment_::kElements; ++j) {
-      d[j] = a * b[j] + c[j];
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
-template <>
-struct FragmentMultiplyAdd<half> {
-  /// The shape of the instruction.
-  typedef Shape<1, 1, 1, 1> InstructionShape;
-  /// The type for A.
-  typedef half ScalarA;
-  /// The type for B.
-  typedef half ScalarB;
-  /// The type for C and D.
-  typedef half ScalarC;
-
-  /// Ctor.
-  CUTLASS_DEVICE FragmentMultiplyAdd() {}
-
-  /// Multiply : d = a*b.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void multiply(half a, Fragment_ const& b, Fragment_& d) {
-#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
-    // The input.
-    __half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
-    // The output.
-    __half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
-
-    // Assemble a half2 from a.
-    __half2 const a_half2 = __half2half2(a);
-
-    for (int i = 0; i < Fragment_::kElements / 2; ++i) {
-      d_half2[i] = __hmul2(a_half2, b_half2[i]);
-    }
-#endif
-  }
-
-  /// Multiply : d = a*b + c.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void multiply_add(half a, Fragment_ const& b, Fragment_ const& c, Fragment_& d) {
-#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
-    // The inputs.
-    __half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
-    __half2 const* c_half2 = reinterpret_cast<__half2 const*>(&c[0]);
-    // The output.
-    __half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
-
-    // Assemble a half2 from a.
-    __half2 const a_half2 = __half2half2(a);
-
-    for (int i = 0; i < Fragment_::kElements / 2; ++i) {
-      d_half2[i] = __hfma2(a_half2, b_half2[i], c_half2[i]);
-    }
-#endif
-  }
-};
-
-#endif
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/clear_accumulators.h

@@ -1,55 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines abstractions for efficiently clearing accumulator tiles.
-*/
-#pragma once
-
-#include <cutlass/vector.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int kLanes_ = 1>
-struct ClearAccumulators {
-  /// The shared storage.
-  struct SharedStorage {};
-
-  /// Ctor.
-  CUTLASS_DEVICE ClearAccumulators(SharedStorage& shared_storage) {}
-
-  /// Clear the fragment.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void clear(Fragment_& fragment) {
-    fragment.clear();
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/dgemm_traits.h

@@ -1,127 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines structural traits of double-precision GEMM.
-*/
-#pragma once
-
-#include <cutlass/gemm/gemm.h>
-#include <cutlass/gemm/gemm_epilogue.h>
-#include <cutlass/gemm/gemm_epilogue_traits.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/gemm/gemm_shared_tile.h>
-#include <cutlass/gemm/gemm_traits.h>
-#include <cutlass/gemm/thread_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_ = 1,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_ = 1>
-struct DgemmConfig
-    : public GemmConfig<
-          /// The scalar type for A.
-          double,
-          /// The scalar type for B.
-          double,
-          /// The scalar type for C.
-          double,
-          /// The scalar type for D.
-          double,
-          /// The tile size for the GEMM KxNxM.
-          OutputTile_,
-          /// The functor to do the math in the main loop.
-          ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, double, double, double>,
-          /// The number of scalars per LDG for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per STS for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per LDS for A.
-          2,
-          /// The number of scalars per LDG for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per STS for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per LDS for B.
-          2,
-          /// The number of scalars per LDG for C and STG for D.
-          1,
-          /// The number of scalars per STS for D.
-          2,
-          /// The number of scalars per LDS for D.
-          1,
-          /// The number of stages in shared memory.
-          2> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_ = Shape<8, 64, 128>,
-    /// The functor to use in the epilogue.
-    typename EpilogueFunctor_ = LinearScaling<double>,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<8, 8, 8>,
-    /// The number of doubles loaded in one LDG for A.
-    int kScalarsPerLdgA_ = 1,
-    /// The number of doubles loaded in one LDG for B.
-    int kScalarsPerLdgB_ = 1,
-    /// The index.
-    typename Index_ = int,
-    /// The DGEMM config.
-    typename GemmConfig_ =
-        DgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>,
-    /// The traits class for the epilogue.
-    typename GemmEpilogueTraits_ =
-        SimplifiedGemmEpilogueTraits<GemmConfig_, EpilogueFunctor_, Index_> >
-struct DgemmTraits : public SimplifiedGemmTraits<
-                         // The layout for A.
-                         kLayoutA_,
-                         // The layout for B.
-                         kLayoutB_,
-                         // The config.
-                         GemmConfig_,
-                         // The epilogue.
-                         GemmEpilogue<GemmEpilogueTraits_>,
-                         // The index.
-                         Index_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm.h

@@ -1,319 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements a software-pipelined efficient GEMM.
-*/
-#pragma once
-
-#if !defined(__CUDACC_RTC__)
-#include <cuda.h>
-#endif
-
-#include <cutlass/coord.h>
-#include <cutlass/util/platform.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Gemm_>
-__global__ void gemm_kernel(typename Gemm_::Params params) {
-  // Declare shared memory.
-  __shared__ typename Gemm_::SharedStorage shared_storage;
-
-  // Construct the GEMM object.
-  Gemm_ gemm(params, shared_storage);
-  // Run GEMM.
-  gemm.multiply_add();
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Index_ = int>
-struct GemmDesc {
-  /// The dimensions of the GEMM.
-  Index_ m, n, k;
-  /// The alpha/beta scaling values.
-  Scalar_ alpha, beta;
-  /// The source matrix A.
-  void const* d_a;
-  /// The stride for A.
-  Index_ lda;
-  /// The source matrix B.
-  void const* d_b;
-  /// The stride for B.
-  Index_ ldb;
-  /// The source matrix C.
-  void const* d_c;
-  /// The stride for C.
-  Index_ ldc;
-  /// The destination matrix D.
-  void* d_d;
-  /// The stride for D.
-  Index_ ldd;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmTraits_>
-struct Gemm {
-  /// This class.
-  typedef Gemm<GemmTraits_> This_;
-  /// The traits.
-  typedef GemmTraits_ Traits;
-  /// The shared storage.
-  typedef typename Traits::SharedStorage SharedStorage;
-
-  /// The scalar for A.
-  typedef typename Traits::ScalarA ScalarA;
-  /// The scalar for B.
-  typedef typename Traits::ScalarB ScalarB;
-  /// The scalar in the epilogue.
-  typedef typename Traits::Epilogue::Scalar ScalarEpilogue;
-  /// The scalar for C.
-  typedef typename Traits::Epilogue::ScalarC ScalarC;
-  /// The scalar for D.
-  typedef typename Traits::Epilogue::ScalarD ScalarD;
-  /// The index.
-  typedef typename Traits::Index Index;
-
-  /// The number of threads.
-  static int const kThreads = Traits::GemmConfig::kThreads;
-
-  /// The params.
-  struct Params : public Traits::Params {
-    CUTLASS_HOST_DEVICE int initialize(Index m,
-                                       Index n,
-                                       Index k,
-                                       ScalarEpilogue alpha,
-                                       ScalarA const* d_a,
-                                       Index lda,
-                                       ScalarB const* d_b,
-                                       Index ldb,
-                                       ScalarEpilogue beta,
-                                       ScalarC const* d_c,
-                                       Index ldc,
-                                       ScalarD* d_d,
-                                       Index ldd) {
-      GemmDesc<ScalarEpilogue, Index> desc;
-      desc.m = m;
-      desc.n = n;
-      desc.k = k;
-      desc.alpha = alpha;
-      desc.beta = beta;
-      desc.d_a = reinterpret_cast<void const*>(d_a);
-      desc.lda = lda;
-      desc.d_b = reinterpret_cast<void const*>(d_b);
-      desc.ldb = ldb;
-      desc.d_c = reinterpret_cast<void const*>(d_c);
-      desc.ldc = ldc;
-      desc.d_d = reinterpret_cast<void*>(d_d);
-      desc.ldd = ldd;
-      return Traits::Params::initialize(desc);
-    }
-  };
-
-#if !defined(__CUDACC_RTC__)
-  /// Launch the kernel.
-  static __host__ cudaError_t launch(Params const& params,
-                                     cudaStream_t stream = cudaStreamDefault) {
-    // Setup the grid.
-    dim3 grid;
-    grid.x = (params.m + Traits::OutputTile::kW - 1) / Traits::OutputTile::kW;
-    grid.y = (params.n + Traits::OutputTile::kH - 1) / Traits::OutputTile::kH;
-
-    // The number of threads.
-    dim3 block;
-    block.x = kThreads;
-
-    // Launch the kernel.
-    void const* params_ = reinterpret_cast<void const*>(&params);
-
-    return cudaLaunchKernel(reinterpret_cast<void*>(&gemm_kernel<This_>),
-                            grid,
-                            block,
-                            const_cast<void**>(&params_),
-                            0,
-                            stream);
-  }
-
-  /// Launch the kernel.
-  static __host__ cudaError_t launch(CUfunction kernel,
-                                     Params const& params,
-                                     CUstream stream = CU_STREAM_LEGACY) {
-    // Setup the grid.
-    dim3 grid;
-    grid.x = (params.m + Traits::OutputTile::kW - 1) / Traits::OutputTile::kW;
-    grid.y = (params.n + Traits::OutputTile::kH - 1) / Traits::OutputTile::kH;
-
-    // The number of threads.
-    dim3 block;
-    block.x = kThreads;
-
-    // Launch the kernel.
-    void* params_[] = {const_cast<void*>(reinterpret_cast<void const*>(&params))};
-
-    // return cudaLaunchKernel(reinterpret_cast<void*>(&gemm_kernel<This_>), grid, block,
-    //  const_cast<void**>(&params_), 0, stream);
-    CUresult result = cuLaunchKernel(
-        kernel, grid.x, grid.y, grid.z, block.x, block.y, block.z, 0, stream, params_, 0);
-
-    if (result != CUDA_SUCCESS) {
-      return cudaErrorLaunchFailure;
-    }
-    return cudaSuccess;
-  }
-
-#endif
-
-  /// Ctor.
-  CUTLASS_DEVICE Gemm(Params const& params_, SharedStorage& shared_storage_)
-      : params(params_), shared_storage(shared_storage_) {}
-
-  /// Do the GEMM.
-  CUTLASS_DEVICE void multiply_add() {
-    // Swizzle the IDs of the block (to enable better cache behavior).
-    typename Traits::BlockSwizzle block_swizzle;
-    dim3 block = block_swizzle.swizzle();
-
-    // Scale the id.
-    block.x *= Traits::OutputTile::kW;
-    block.y *= Traits::OutputTile::kH;
-
-    // We may want to use shared memory to clear the registers.
-    typedef typename Traits::ClearAccumulators ClearAccumulators;
-
-    // The streams to read A/B from global memory to shared memory.
-    typename Traits::GlobalLoadStream global_stream(params, shared_storage, block);
-
-    // Create the accumulator clear.
-    ClearAccumulators clear(shared_storage.main_loop.clear);
-
-    /// Define the mainloop iteration size
-    typedef typename Traits::MultiplyAdd MultiplyAdd;
-
-    // By how much we unroll the main loop.
-    Index const kUnroll = static_cast<Index>(MultiplyAdd::AccumulatorsPerWarp::kD);
-
-    // If we do not have enough steps in the main loop, trigger the residue code.
-    if (params.k < kUnroll) {
-      global_stream.residue(params.k, true);
-    }
-
-    // Fetch the fragments for A and B from global memory.
-    global_stream.copy();
-
-    // Copy the elements to shared memory (after transformation if needed).
-    global_stream.commit();
-
-    // Make sure the data is in shared memory.
-    Traits::shared_store_fence(false);
-
-    // The unrolling steps for the main loop.
-    int const kUnrollingSteps =
-        MultiplyAdd::AccumulatorsPerWarp::kD / MultiplyAdd::InstructionShape::kD;
-
-    // Make sure we have at least 2 unrolling steps or our pipeling is not going to work.
-    static_assert(kUnrollingSteps >= 2, "The pipelining assumes at least two steps");
-
-    // The stream of data from shared memory to fragments.
-    typename Traits::SharedLoadStream shared_load_stream(params, shared_storage);
-
-    // Trigger the copy from shared memory for the 1st stream.
-    shared_load_stream.copy(0);
-
-    // Allocate the accumulators.
-    typename MultiplyAdd::Accumulators accumulators;
-    // Clear the accumulators.
-    clear.clear(accumulators);
-
-    // Enter the main loop and iterate.
-    typedef typename Traits::Index Index;
-    for (Index outer_k = params.k - kUnroll; outer_k > -kUnroll; outer_k -= kUnroll) {
-      // If that's the last "load iteration" update the predicates.
-      int const is_residue = outer_k <= kUnroll;
-      if (is_residue) {
-        global_stream.residue(outer_k);
-      }
-
-      // Load data for the next iteration of the main loop.
-      global_stream.copy();
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int step = 0; step < kUnrollingSteps - 1; ++step) {
-        // Trigger the copy from shared memory for the next A/B values.
-        shared_load_stream.copy(step + 1);
-        // Make sure the values are available for the current iteration to do the multiply-add.
-        shared_load_stream.commit(step);
-
-        // Do the math on the fragments of the current iteration.
-        MultiplyAdd multiply_add;
-        multiply_add.multiply_add(shared_load_stream.fragment_a(step),
-                                  shared_load_stream.fragment_b(step),
-                                  accumulators,
-                                  accumulators);
-      }
-
-      // Make sure the data from shared memory has been entirely consumed.
-      Traits::shared_load_fence(true);
-
-      // Commit the data in shared memory for A/B.
-      global_stream.commit();
-
-      // Make sure the data is in shared memory.
-      Traits::shared_store_fence(true);
-
-      // Move to the next stage for the load (if it makes sense).
-      shared_load_stream.inc_stage();
-      // Trigger the copy from shared memory for the next loop iteration.
-      shared_load_stream.copy(0);
-      // Make sure the values are available for the current iteration to do the multiply-add.
-      shared_load_stream.commit(kUnrollingSteps - 1);
-
-      // Do the math on the fragments of the current iteration.
-      MultiplyAdd multiply_add;
-      multiply_add.multiply_add(shared_load_stream.fragment_a(kUnrollingSteps - 1),
-                                shared_load_stream.fragment_b(kUnrollingSteps - 1),
-                                accumulators,
-                                accumulators);
-    }
-
-    // Epilogue.
-    typedef typename Traits::Epilogue Epilogue;
-    Epilogue epilogue(params.epilogue, shared_storage.epilogue, params.m, params.n);
-    epilogue.epilogue(cutlass::make_Coord(0, block.y, block.x), accumulators);
-  }
-
-  /// The params.
-  Params const& params;
-  /// The shared storage.
-  SharedStorage& shared_storage;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_epilogue.h

@@ -1,225 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements the epilogue phase of the GEMM kernel that efficiently updates global memory
-   with
-      the computed matrix product.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/coord.h>
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename T>
-CUTLASS_DEVICE bool is_zero(T x) {
-  return x == T(0);
-}
-
-#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
-CUTLASS_DEVICE bool is_zero(half x) { return reinterpret_cast<int16_t&>(x) == int16_t(0); }
-#endif
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmEpilogueTraits_>
-struct GemmEpilogue {
-  /// The traits class.
-  typedef GemmEpilogueTraits_ Traits;
-  /// The params.
-  typedef typename Traits::Params Params;
-  /// The shared storage.
-  typedef typename Traits::SharedStorage SharedStorage;
-
-  /// The output tile.
-  typedef typename Traits::OutputTile OutputTile;
-  /// The number of iterations.
-  typedef typename Traits::Iterations Iterations;
-  /// The accumulators.
-  typedef typename Traits::Accumulators Accumulators;
-  /// The scalar.
-  typedef typename Traits::Scalar Scalar;
-  /// The functor in charge of the math.
-  typedef typename Traits::Functor Functor;
-
-  /// We do not support 3D or 4D shapes.
-  static_assert(Iterations::kD == 1 && Iterations::kC == 1, "Unsupported 3D/4D shapes");
-
-  /// The iterator for C in global memory.
-  typedef typename Traits::GlobalLoadIteratorC GlobalLoadIteratorC;
-  /// The transformer for C.
-  typedef typename Traits::GlobalTransformerC GlobalTransformerC;
-  /// The transformer for D.
-  typedef typename Traits::GlobalTransformerD GlobalTransformerD;
-  /// The iterator for D in global memory.
-  typedef typename Traits::GlobalStoreIteratorD GlobalStoreIteratorD;
-  /// The iterator to store D in shared memory.
-  typedef typename Traits::SharedStoreIteratorD SharedStoreIteratorD;
-  /// The shared store transformer for D.
-  typedef typename Traits::SharedStoreTransformerD SharedStoreTransformerD;
-  /// The iterator to load D in shared memory.
-  typedef typename Traits::SharedLoadIteratorD SharedLoadIteratorD;
-  /// The shared load transformer for D.
-  typedef Copy<typename SharedLoadIteratorD::Fragment> SharedLoadTransformerD;
-
-  /// The index.
-  typedef typename Traits::Index Index;
-
-  /// The scalar for C.
-  typedef typename GlobalLoadIteratorC::Scalar ScalarC;
-  /// The scalar for D.
-  typedef typename GlobalStoreIteratorD::Scalar ScalarD;
-
-  /// Ctor.
-  CUTLASS_DEVICE GemmEpilogue(Params const& params_,
-                              SharedStorage& shared_storage_,
-                              Index m_,
-                              Index n_)
-      : params(params_), shared_storage(shared_storage_), m(m_), n(n_) {}
-
-  /// Execute the epilogue.
-  CUTLASS_DEVICE void epilogue(Coord<3> const& block, Accumulators& accumulators) {
-    if (is_zero(params.functor.beta)) {
-      epilogue_with_or_without_beta<true>(block, accumulators);
-    } else {
-      epilogue_with_or_without_beta<false>(block, accumulators);
-    }
-  }
-
-  template <bool kBetaIsZero_>
-  CUTLASS_DEVICE void epilogue_with_or_without_beta(Coord<3> const& block,
-                                                    Accumulators& accumulators) {
-
-    Coord<3> const bounds = cutlass::make_Coord(0, n, m);
-
-    // The functor.
-    Functor functor(params.functor);
-    // The C fragment.
-    typename GlobalLoadIteratorC::Fragment fragment_c;
-    // The transformed C fragment.
-    typename GlobalTransformerC::OutputFragment transformed_c;
-
-    CUTLASS_PRAGMA_UNROLL
-    for (int h = 0; h < Iterations::kH; ++h) {
-      // Compute pointer and predicate offsets for C and D global iterators.
-      int const pointer_offset =
-          ((params.iterator_d.inc_h * (GlobalStoreIteratorD::Iterations::kH - 1) +
-            params.iterator_d.inc_advance) *
-               Iterations::kW +
-           params.stride_h) *
-          h;
-      int const predicate_offset =
-          ((params.iterator_d.predicate_inc_h * (GlobalStoreIteratorD::Iterations::kH - 1) +
-            params.iterator_d.predicate_inc_advance) *
-               Iterations::kW +
-           Traits::Delta::kH) *
-          h;
-
-      // The iterator to load the elements of the C matrix.
-      GlobalLoadIteratorC global_load_iterator(
-          params.iterator_c, bounds, block, pointer_offset, predicate_offset);
-      // The transformer for C.
-      GlobalTransformerC transformer_c;
-      // The transformer for D.
-      GlobalTransformerD transformer_d;
-      // The iterator to store into the D matrix.
-      GlobalStoreIteratorD global_store_iterator(
-          params.iterator_d, bounds, block, pointer_offset, predicate_offset);
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int w = 0; w < Iterations::kW; ++w) {
-        // Load the C matrix into fragment.
-        if (!kBetaIsZero_) {
-          iterator_load(global_load_iterator, fragment_c);
-        }
-
-        // Make sure we can write to shared memory.
-        shared_load_fence();
-
-        // Copy the accumulators to shared memory.
-        int const offset = (h * Iterations::kW + w) * SharedStoreIteratorD::Fragment::kElements;
-
-        SharedStoreTransformerD shared_store_transformer;
-        typename SharedStoreTransformerD::OutputFragment shared_store_transformed_d;
-        shared_store_transformer.transform(accumulators, offset, shared_store_transformed_d);
-
-        SharedStoreIteratorD shared_store_iterator(params.shared_store_iterator_d,
-                                                   shared_storage.shared_stream.store);
-        shared_iterator_store(shared_store_iterator, shared_store_transformed_d);
-
-        // Make sure the data is in shared memory.
-        shared_store_fence();
-
-        // Copy the accumulators back to registers from shared memory.
-        SharedLoadIteratorD shared_load_iterator(params.shared_load_iterator_d,
-                                                 shared_storage.shared_stream.load);
-        typename SharedLoadIteratorD::Fragment fetched_d;
-        shared_iterator_load(shared_load_iterator, fetched_d);
-
-        // Do the math.
-        typename GlobalTransformerD::InputFragment fragment_d;
-
-        if (kBetaIsZero_) {
-          functor.evaluate(fetched_d, fragment_d);
-        } else {
-          // Transform C fragment.
-          transformer_c.transform(fragment_c, transformed_c);
-          // Do the math.
-          functor.evaluate(fetched_d, transformed_c, fragment_d);
-        }
-
-        // Transform D fragment.
-        typename GlobalTransformerD::OutputFragment transformed_d;
-        transformer_d.transform(fragment_d, transformed_d);
-
-        // Copy the results to global memory.
-        iterator_store(global_store_iterator, transformed_d);
-      }
-    }
-  }
-
-  /// The memory fence for shared loads.
-  CUTLASS_DEVICE void shared_load_fence() { __syncthreads(); }
-
-  /// The memory fence for shared stores.
-  CUTLASS_DEVICE void shared_store_fence() { __syncthreads(); }
-
-  /// The params.
-  Params const& params;
-  /// The shared storage.
-  SharedStorage& shared_storage;
-  /// The dimensions of the GEMM.
-  Index m, n;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_epilogue_traits.h

@@ -1,331 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines structural properties of the GEMM epilogue.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/coord.h>
-#include <cutlass/gemm/gemm_global_stream.h>
-#include <cutlass/gemm/gemm_shared_stream.h>
-#include <cutlass/gemm/linear_scaling.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The output tile.
-    typename OutputTile_,
-    /// The accumulators.
-    typename Accumulators_,
-    /// The iterator to load C from global memory.
-    typename GlobalLoadIteratorC_,
-    /// The transformer for C.
-    typename GlobalTransformerC_,
-    /// The transformer for D.
-    typename GlobalTransformerD_,
-    /// The iterator to store D to global memory.
-    typename GlobalStoreIteratorD_,
-    /// The iterator to store D to shared memory.
-    typename SharedStoreIteratorD_,
-    /// The shared store transformer for D.
-    typename SharedStoreTransformerD_,
-    /// The iterator to load D from shared memory.
-    typename SharedLoadIteratorD_,
-    /// The number of iterations in the epilogue.
-    typename Iterations_,
-    /// The iterations strides.
-    typename Delta_,
-    /// The functor to be used in the epilogue.
-    typename Functor_,
-    /// The index.
-    typename Index_ = int>
-struct GemmEpilogueTraits {
-  //
-  /// The output tile.
-  typedef OutputTile_ OutputTile;
-  /// The number of iterations.
-  /// The accumulators.
-  typedef Accumulators_ Accumulators;
-  /// The iterator for C in global memory.
-  typedef GlobalLoadIteratorC_ GlobalLoadIteratorC;
-  /// The transformer for C.
-  typedef GlobalTransformerC_ GlobalTransformerC;
-  /// The transformer for D.
-  typedef GlobalTransformerD_ GlobalTransformerD;
-  /// The iterator for D in global memory.
-  typedef GlobalStoreIteratorD_ GlobalStoreIteratorD;
-  /// The iterator to store D in shared memory.
-  typedef SharedStoreIteratorD_ SharedStoreIteratorD;
-  /// The shared store transformer for D.
-  typedef SharedStoreTransformerD_ SharedStoreTransformerD;
-  /// The iterator to store D in shared memory.
-  typedef SharedLoadIteratorD_ SharedLoadIteratorD;
-  /// typedef typename GemmConfig::EpilogueIterations Iterations;
-  typedef Iterations_ Iterations;
-  /// The iterations strides.
-  typedef Delta_ Delta;
-
-  /// The functor in charge of the math.
-  typedef Functor_ Functor;
-  /// The index.
-  typedef Index_ Index;
-
-  /// We do not support 3D or 4D shapes.
-  static_assert(Iterations::kD == 1 && Iterations::kC == 1, "Unsupported 3D/4D shapes");
-
-  /// The scalar.
-  typedef typename Functor::Scalar Scalar;
-  /// The scalar for C.
-  typedef typename GlobalLoadIteratorC::Scalar ScalarC;
-  /// The scalar for D.
-  typedef typename GlobalStoreIteratorD::Scalar ScalarD;
-
-  /// The params.
-  struct Params {
-    /// The strides for H and W in the different iterations of the epilogue.
-    Index stride_h, stride_w;
-    /// The params for the C iterator.
-    typename GlobalLoadIteratorC::Params iterator_c;
-    /// The params for the D global iterator.
-    typename GlobalStoreIteratorD::Params iterator_d;
-    /// The params for the D shared store iterator.
-    typename SharedStoreIteratorD::Params shared_store_iterator_d;
-    /// The params for the D shared load iterator.
-    typename SharedLoadIteratorD::Params shared_load_iterator_d;
-    /// The functor params.
-    typename Functor::Params functor;
-
-    /// Setup the params.
-    template <typename GemmDesc_>
-    CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
-      // The parameters for the functor.
-      int error_code = functor.initialize(desc);
-      if (error_code) {
-        return error_code;
-      }
-
-      // At the end of the H iteration, we jump over a number of columns.
-      this->stride_h = desc.ldd * Delta::kH;
-      // Nothing to do here.
-      this->stride_w = 0;
-
-      // Setup the params for the global memory iterator for C.
-      error_code = iterator_c.initialize(
-          reinterpret_cast<ScalarC const*>(desc.d_c), desc.ldc, desc.n, stride_w, Delta::kW);
-      if (error_code) {
-        return error_code;
-      }
-
-      // Setup the params for the global memory iterator for D.
-      return iterator_d.initialize(
-          reinterpret_cast<ScalarD*>(desc.d_d), desc.ldd, desc.n, stride_w, Delta::kW);
-    }
-  };
-
-  /// The shared memory storage to exchange data.
-  union StreamSharedStorage {
-    // The storage for the store iterator.
-    typename SharedStoreIteratorD::SharedStorage store;
-    // The storage for the store iterator.
-    typename SharedLoadIteratorD::SharedStorage load;
-  };
-
-  /// The shared memory to swizzle the data in the epilogue.
-  struct SharedStorage {
-    // The storage for the shared stream D.
-    StreamSharedStorage shared_stream;
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_, typename EpilogueFunctor_, typename Index_ = int>
-struct GemmEpilogueTraitsHelper {
-  /// The scalar.
-  typedef typename EpilogueFunctor_::Scalar Scalar;
-  /// The output tile.
-  typedef typename GemmConfig_::OutputTile OutputTile;
-
-  /// The number of iterations in the epilogue.
-  typedef Shape<1,
-                GemmConfig_::MultiplyAdd::AccumulatorsPerThread::kH /
-                    GemmConfig_::kAccumulatorsPerLdsB,
-                GemmConfig_::kAccumulatorsPerLdsB>
-      Iterations;
-  // The iteration strides in the H/W dimension.
-  typedef Shape<0,
-                GemmConfig_::kAccumulatorsPerLdsB*(
-                    GemmConfig_::Warps::kH* GemmConfig_::MultiplyAdd::ThreadsPerWarp::kH - 1),
-                0>
-      Delta;
-  /// The functor to do the math in the epilogue.
-  typedef EpilogueFunctor_ Functor;
-
-  /// The traits class to build the iterator to store to shared memory for D.
-  typedef GemmSharedStoreTileDTraits<
-      // The pointer is float.
-      typename Functor::Scalar,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The number of scalars per STS.
-      GemmConfig_::kScalarsPerStsD,
-      // The skew -- 128 / sizeof(ScalarD) / kScalarsPerStsD is the number of threads involved in
-      // a single STS. We divide by 2 as our objective is to add a skew to the odd threads to
-      // avoid bank conflicts between odd and even threads.
-      128 / sizeof(typename GemmConfig_::ScalarD) / GemmConfig_::kScalarsPerStsD / 2 *
-          GemmConfig_::kScalarsPerStsD>
-      SharedStoreTileTraits;
-
-  /// The iterator to store D to shared memory.
-  typedef TileStoreIterator<SharedStoreTileTraits,
-                            typename SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorD;
-
-  /// The shared store transformer for D.
-  typedef Copy<typename SharedStoreIteratorD::Fragment> SharedStoreTransformerD;
-
-  /// The traits class to build the iterator to load from shared memory for D.
-  typedef GemmSharedLoadTileDTraits<
-      // The pointer is float.
-      typename Functor::Scalar,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The number of columns of the output tile written by iteration.
-      GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsD,
-      // The skew.
-      SharedStoreTileTraits::kSkew>
-      SharedLoadTileTraits;
-
-  /// The iterator to load D from shared memory.
-  typedef TileLoadIterator<SharedLoadTileTraits,
-                           typename SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorD;
-
-  /// The traits class to build the iterator to load data from global memory for C^N.
-  typedef GemmGlobalTileCdTraits<
-      // The pointer is float const.
-      typename GemmConfig_::ScalarC const,
-      // The tile has size (N / Iterations)xM in GEMM's terminology.
-      Shape<1,
-            GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
-            GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // How many elements do we jump over at each iteration?
-      Iterations::kW,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgC>
-      GlobalLoadTileTraits;
-
-  /// The iterator to load C.
-  typedef GemmGlobalIteratorCd<GlobalLoadTileTraits, Index_> GlobalLoadIteratorC;
-  /// The transformer for C.
-  typedef Copy<typename GlobalLoadIteratorC::Fragment> GlobalTransformerC;
-
-  /// The traits class to build the iterator to store data to global memory for D^N.
-  typedef GemmGlobalTileCdTraits<
-      // The pointer is float.
-      typename GemmConfig_::ScalarD,
-      // The tile has size (N / Iterations)xM in GEMM's terminology.
-      Shape<1,
-            GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
-            GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // How many elements do we jump over at each iteration?
-      Iterations::kW,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerStgD>
-      GlobalStoreTileTraits;
-
-  /// The iterator to store D.
-  typedef GemmGlobalIteratorCd<GlobalStoreTileTraits, Index_> GlobalStoreIteratorD;
-  /// The transformer for D.
-  typedef Copy<typename GlobalStoreIteratorD::Fragment> GlobalTransformerD;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The GEMM config.
-    typename GemmConfig_,
-    /// The epilogue functor to do the math in the epilogue.
-    typename EpilogueFunctor_,
-    /// The index.
-    typename Index_ = int,
-    /// The helper to create the traits class.
-    typename Helper_ = GemmEpilogueTraitsHelper<GemmConfig_, EpilogueFunctor_, Index_> >
-struct SimplifiedGemmEpilogueTraits : public GemmEpilogueTraits<
-                                          // The output tile.
-                                          typename GemmConfig_::OutputTile,
-                                          // The accumulators.
-                                          typename GemmConfig_::Accumulators,
-                                          // The global iterator for C.
-                                          typename Helper_::GlobalLoadIteratorC,
-                                          // The transformer for C.
-                                          typename Helper_::GlobalTransformerC,
-                                          // The transformer for D.
-                                          typename Helper_::GlobalTransformerD,
-                                          // The global iterator for D.
-                                          typename Helper_::GlobalStoreIteratorD,
-                                          // The iterator to store D to shared memory.
-                                          typename Helper_::SharedStoreIteratorD,
-                                          // The shared store transformer for D.
-                                          typename Helper_::SharedStoreTransformerD,
-                                          // The iterator to load D from shared memory.
-                                          typename Helper_::SharedLoadIteratorD,
-                                          // The number of iterations.
-                                          typename Helper_::Iterations,
-                                          // The strides between iterations.
-                                          typename Helper_::Delta,
-                                          // The functor to be used in the epilogue.
-                                          EpilogueFunctor_,
-                                          // The index.
-                                          Index_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_global_stream.h

@@ -1,175 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements efficient loading of the thread block-level tile from global memory and
-   storing
-      to shared memory.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/iterator_access.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The load iterator.
-    typename LoadIterator_,
-    /// The store iterator to copy to shared memory.
-    typename StoreIterator_,
-    /// The transformer to be applied after the data has been copied from global memory.
-    typename Transformer_>
-
-struct GlobalLoadStreamBase {
-  /// The load iterator.
-  typedef LoadIterator_ LoadIterator;
-  /// The transformer.
-  typedef Transformer_ Transformer;
-  /// The store iterator to write to shared memory.
-  typedef StoreIterator_ StoreIterator;
-
-  /// The fragment that is copied from shared memory.
-  typedef typename LoadIterator::Fragment FetchedFragment;
-  /// The fragment that is obtained after the transformation by the transformer.
-  typedef typename Transformer::OutputFragment TransformedFragment;
-  /// Make sure the fragments match.
-  static_assert((platform::is_same<FetchedFragment, typename Transformer::InputFragment>::value),
-                "");
-  /// The output fragment.
-  typedef TransformedFragment Fragment;
-  /// Make sure the transformed fragment is the same as the store fragment.
-  static_assert((platform::is_same<TransformedFragment, typename StoreIterator::Fragment>::value),
-                "");
-
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = LoadIterator::kLayout;
-  /// The scalar type of the iterator.
-  typedef typename LoadIterator::Scalar Scalar;
-  /// The pointer.
-  typedef typename LoadIterator::Pointer Pointer;
-  /// The index.
-  typedef typename LoadIterator::Index Index;
-
-  /// The params.
-  struct Params {
-    // The load iterator.
-    typename LoadIterator::Params load_iterator;
-    // The store iterator.
-    typename StoreIterator::Params store_iterator;
-
-    /// Setup the params.
-    CUTLASS_HOST_DEVICE int initialize(Pointer pointer, Index ld) {
-      int error_code = load_iterator.initialize(pointer, ld);
-      if (error_code) {
-        return error_code;
-      }
-
-      return store_iterator.initialize();
-    }
-  };
-
-  /// The amount of storage in shared memory needed to store the tile.
-  typedef typename StoreIterator::SharedStorage SharedStoreStorage;
-
-  /// The storage in shared memory needed by that stream.
-  union SharedStorage {
-    // The load iterator.
-    typename LoadIterator::SharedStorage load_iterator;
-    // The store iterator.
-    SharedStoreStorage store_iterator;
-  };
-
-  /// Ctor.
-  CUTLASS_DEVICE GlobalLoadStreamBase(Params const& params,
-                                      SharedStorage& shared_storage,
-                                      Coord<3> const bounds,
-                                      Coord<3> const& block)
-      : load_iterator(params.load_iterator, bounds, block),
-        transformer(),
-        store_iterator(params.store_iterator, shared_storage.store_iterator)
-
-  {
-    fetched_fragment.clear();
-  }
-
-  /// Load the data from shared memory to the fetch fragment.
-  CUTLASS_DEVICE void copy() { iterator_load(load_iterator, fetched_fragment); }
-
-  /// Commit the data.
-  CUTLASS_DEVICE void commit() {
-    transformer.transform(fetched_fragment, transformed_fragment);
-    iterator_store(store_iterator, transformed_fragment);
-    store_iterator.inc_stage();
-  }
-
-  /// Execute the residue code.
-  CUTLASS_DEVICE void residue(Index k, bool skip_clear = false) {
-    load_iterator.residue(k);
-    if (!skip_clear) {
-      fetched_fragment.clear();
-    }
-  }
-
-  /// The iterator.
-  LoadIterator load_iterator;
-  /// The fragment to fetch from shared memory.
-  FetchedFragment fetched_fragment;
-  /// The transformer.
-  Transformer transformer;
-  /// The fragment to convert the data after it has been fetched from shared memory.
-  TransformedFragment transformed_fragment;
-  /// The store iterator.
-  StoreIterator store_iterator;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The load iterator.
-    typename LoadIterator_,
-    /// The store iterator to copy to shared memory.
-    typename StoreIterator_,
-    /// The transformer to be applied after the data has been copied from global memory.
-    typename Transformer_ = Copy<typename LoadIterator_::Fragment> >
-
-struct GlobalLoadStream : public GlobalLoadStreamBase<LoadIterator_, StoreIterator_, Transformer_> {
-  /// The base class.
-  typedef GlobalLoadStreamBase<LoadIterator_, StoreIterator_, Transformer_> Base;
-
-  /// Ctor.
-  CUTLASS_DEVICE GlobalLoadStream(typename Base::Params const& params,
-                                  typename Base::SharedStorage& shared_storage,
-                                  Coord<3> const& bounds,
-                                  Coord<3> const& block)
-      : Base(params, shared_storage, bounds, block) {}
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_global_tile.h

@@ -1,478 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines iterators for efficiently loading and storing to global memory.
-*/
-#pragma once
-
-#include <cutlass/coord.h>
-#include <cutlass/util/platform.h>
-
-#include <cutlass/gemm/gemm_operand.h>
-#include <cutlass/matrix_traits.h>
-#include <cutlass/predicate_vector.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-// The following functor reshapes a tile of threads to match a tile of data. The idea is that when
-// the user wants to build the iterator traits, he/she may want to specify the tile independently
-// from the number of scalars loaded/stored per instruction. For example, in the row-major version
-// with a tile of size 128x8 - the user may want to that the iterator works with 32x8 threads if
-// each thread loads 1 scalar per LDG. If the user changes to 4 scalars per LDG, then the tile of
-// threads has to change. The code below detects that and correct the code automatically - it is
-// a helper when the user does not specify the right configuration.
-
-template <typename Tile_, typename Threads_, bool = (Tile_::kW < Threads_::kW)>
-struct ReshapeThreads {
-  typedef Threads_ Threads;
-};
-
-template <typename Tile_, typename Threads_>
-struct ReshapeThreads<Tile_, Threads_, true> {
-  typedef Shape<Threads_::kD, Threads_::kH * Threads_::kW / Tile_::kW, Tile_::kW, 1> Threads;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <GemmOperand::Kind kOperand_,
-          MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename Tile_,
-          typename Threads_,
-          int kAccessSize_>
-struct GemmGlobalTileTraits {
-  /// Identity of the operand
-  static GemmOperand::Kind const kOperand = kOperand_;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = kLayout_;
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The number of scalars per LDG/STG.
-  static int const kAccessSize = kAccessSize_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kGlobal;
-
-  /// The tile shape
-  typedef typename ReshapeTile<Tile_, kAccessSize_>::Tile Tile;
-  /// The threads shape
-  typedef typename ReshapeThreads<Tile, Threads_>::Threads Threads;
-  /// The relative offset between two elements in the H/W dimension in adjacent threads.
-  typedef Shape<1, 1, Tile::kC> ThreadsDelta;
-
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, Threads::kH, Threads::kW * kAccessSize> Delta;
-  /// Strides for immediate offset computation
-  typedef Shape<0, 0, Threads::kW * ThreadsDelta::kW, kAccessSize> ImmediateOffsetStrides;
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1, Tile::kH / Threads::kH, Tile::kW / Threads::kW, Tile::kC / kAccessSize>
-      Iterations;
-
-  typedef GemmMultiplicandTraits<Tile, kOperand, kLayout> MultiplicandTraits;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
-      int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
-
-      return make_Coord(0, thread_offset_h, thread_offset_w, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Tile_, typename Threads_, int kStrideH_, int kAccessSize_>
-struct GemmGlobalTileCdTraits : public GemmGlobalTileTraits<GemmOperand::kC,
-                                                            MatrixLayout::kColumnMajor,
-                                                            Scalar_,
-                                                            Tile_,
-                                                            Threads_,
-                                                            kAccessSize_> {
-  /// The base class.
-  typedef GemmGlobalTileTraits<GemmOperand::kC,
-                               MatrixLayout::kColumnMajor,
-                               Scalar_,
-                               Tile_,
-                               Threads_,
-                               kAccessSize_>
-      Base;
-
-  /// The stride in the H dimension.
-  static int const kStrideH = kStrideH_;
-  /// Override the strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> Delta;
-
-  typedef typename Base::Iterations Iterations;
-
-  typedef typename Base::Threads Threads;
-
-  typedef typename Base::ThreadsDelta ThreadsDelta;
-
-  typedef typename Base::ImmediateOffsetStrides ImmediateOffsetStrides;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int thread_offset_h = threadIdx.x / Threads::kW * kStrideH * Iterations::kH;
-      int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
-
-      return make_Coord(0, thread_offset_h, thread_offset_w, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename TileTraits_, typename Index_ = int>
-struct GemmGlobalIteratorAb
-    : public TileLoadIterator<TileTraits_,
-                              typename TileTraits_::Scalar,
-                              TileTraits_::MultiplicandTraits::kKstrided ? IteratorAdvance::kH
-                                                                         : IteratorAdvance::kW,
-                              MemorySpace::kGlobal,
-                              Index_> {
-  /// This class.
-  typedef GemmGlobalIteratorAb<TileTraits_, Index_> This_;  /// The base class.
-
-  typedef TileLoadIterator<TileTraits_,
-                           typename TileTraits_::Scalar,
-                           TileTraits_::MultiplicandTraits::kKstrided ? IteratorAdvance::kH
-                                                                      : IteratorAdvance::kW,
-                           MemorySpace::kGlobal,
-                           Index_>
-      Base;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
-  /// Fragment type loaded by the iterator
-  typedef typename Base::Fragment Fragment;
-  /// The scalar.
-  typedef typename TileTraits_::Scalar Scalar;
-  /// The threads.
-  typedef typename TileTraits_::Threads Threads;
-  /// The index.
-  typedef Index_ Index;
-  /// The thread offset
-  typedef typename TileTraits_::ThreadOffset ThreadOffset;
-  /// Specifies in which dimension post-increment accesses advance.
-  static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
-
-  typedef cutlass::PredicateVector<ShapeCount<typename Base::Iterations>::kCount> PredicateVector;
-
-  /// Iterator parameters type
-  typedef typename Base::Params BaseParams;
-
-  struct Params : public BaseParams {
-    /// Initializes params to load a strip-mined tile, given pointer and stride_h.
-    CUTLASS_HOST_DEVICE int initialize(Scalar const* ptr, Index stride_h) {
-      Index inc_d = 0;
-      Index inc_advance = 0;
-      // Move by some columns for each iteration in the H dimension.
-      Index inc_h = Base::Delta::kH * stride_h;
-
-      // Move by some more columns in the number of iterations if the D dimension is > 1.
-      if (Base::Delta::kD > 0) {
-        inc_d = Base::Delta::kD * stride_h - (Base::Iterations::kH - 1) * inc_h;
-      }
-
-      // Move to the beginning of the next iteration.
-      if (kAdvance == IteratorAdvance::kH && Base::Delta::kD > 0) {
-        inc_advance = inc_d;
-      } else if (kAdvance == IteratorAdvance::kH) {
-        inc_advance = inc_h;
-      } else if (Base::Delta::kD > 0) {
-        inc_advance = (Base::Iterations::kW + 0) * ShapeCount<typename Base::Delta>::kWc -
-                      (Base::Iterations::kH - 1) * inc_h -
-                      (Base::Iterations::kD - 1) * Base::Delta::kD * stride_h;
-      } else {
-        inc_advance = (Base::Iterations::kW + 0) * ShapeCount<typename Base::Delta>::kWc -
-                      (Base::Iterations::kH - 1) * inc_h;
-      }
-
-      Base::Params::initialize(ptr, 0, stride_h, 0, inc_d, inc_h, 0, inc_advance);
-      return 0;
-    }
-  };
-
-  /// Offset of an individual lane from the start of the tile
-  Coord<4> thread_offset;
-  /// The parameters
-  Params params;
-
-  CUTLASS_DEVICE void initialize_predicates(const Coord<3>& bounds, const Coord<3>& block) {
-    // Setup the masks to control loads.
-    predicates.fill(0);
-
-    int bounds_h, bounds_w;
-    if (kAdvance == IteratorAdvance::kH) {
-      bounds_w = bounds[2] - block[2];
-      bounds_h = bounds[1];
-
-    } else {
-      bounds_w = bounds[1];
-      bounds_h = bounds[2] - block[1];
-    }
-
-    // Fill in the bits of the predicate vector.
-    for (int d = 0; d < Base::Iterations::kD; ++d) {
-      for (int h = 0; h < Base::Iterations::kH; ++h) {
-        for (int w = 0; w < Base::Iterations::kW; ++w) {
-          for (int c = 0; c < Base::Iterations::kC; ++c) {
-            bool flag = w * Base::Delta::kW < bounds_w;
-            if (kAdvance == IteratorAdvance::kH) {
-              flag = flag && (h * Base::Delta::kH + d * Base::Delta::kD) < bounds_h;
-            } else {
-              flag = flag && (h * Base::Delta::kH) < bounds_h;
-            }
-            int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
-            predicates.set(bit, flag);
-          }
-        }
-      }
-    }
-  }
-
-  /// Ctor.
-  CUTLASS_DEVICE GemmGlobalIteratorAb(Params const& _params,
-                                      const Coord<3>& bounds,
-                                      const Coord<3>& block,
-                                      ThreadOffset thread_offset_func = ThreadOffset())
-      : params(_params) {
-    thread_offset = thread_offset_func();
-    // The column.
-    Index block_h = thread_offset[1];
-    // The contiguous dimension.
-    Index block_w = thread_offset[2];
-
-    // Add the blocks indices.
-    if (kAdvance == IteratorAdvance::kH) {
-      block_h += block[1];
-      block_w += block[2];
-
-    } else {
-      block_h += block[2];
-      block_w += block[1];
-    }
-
-    // Setup the pointer.
-    params.pointer += (block_h * params.stride_h + block_w);
-
-    // Initialize predicates
-    initialize_predicates(bounds, make_Coord(0, block_h, block_w));
-  }
-
-  /// Increment the pointer in the H dimension.
-  CUTLASS_DEVICE void inc_h() { params.pointer += params.inc_h; }
-  /// Increment the pointer in the D dimension.
-  CUTLASS_DEVICE void inc_d() { params.pointer += params.inc_d; }
-  /// Increment the pointer to move to the next iteration.
-  CUTLASS_DEVICE void inc_advance() { params.pointer += params.inc_advance; }
-
-  /// Returns the current pointer
-  CUTLASS_HOST_DEVICE
-  Scalar const* data() const { return params.pointer; }
-
-  /// That's the residue! Update the predicates.
-  CUTLASS_DEVICE void residue(Index k) {
-    // The coordinates of the thread.
-    Index block_h = thread_offset[1];
-    // The contiguous dimension.
-    Index block_w = thread_offset[2];
-
-    // Update the predicate vector.
-    for (int d = 0; d < Base::Iterations::kD; ++d) {
-      for (int h = 0; h < Base::Iterations::kH; ++h) {
-        for (int w = 0; w < Base::Iterations::kW; ++w) {
-          for (int c = 0; c < Base::Iterations::kC; ++c) {
-            Index offset = 0;
-            if (kAdvance == IteratorAdvance::kH) {
-              offset += block_h + h * Base::Delta::kH + d * Base::Delta::kD;
-            } else {
-              offset += block_w + w * Base::Delta::kW;
-            }
-
-            int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
-            if (offset >= k) {
-              predicates.set(bit, false);
-            }
-          }
-        }
-      }
-    }
-  }
-
-  /// Is the iterator valid?
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
-    int const bit = ComputeOffsetFromShape<typename Base::Iterations>::get(d, h, w, c);
-    return predicates[bit];
-  }
-
-  /// The predicates.
-  PredicateVector predicates;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename TileTraits_, typename Index_ = int>
-struct GemmGlobalIteratorCd : public TileIteratorBase<TileTraits_,
-                                                      typename TileTraits_::Scalar,
-                                                      IteratorAdvance::kH,
-                                                      MemorySpace::kGlobal,
-                                                      Index_> {
-  /// This class.
-  typedef GemmGlobalIteratorCd<TileTraits_, Index_> This_;
-  /// The base class.
-  typedef TileIteratorBase<TileTraits_,
-                           typename TileTraits_::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kGlobal,
-                           Index_>
-      Base;
-
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
-
-  /// The scalar.
-  typedef typename TileTraits_::Scalar Scalar;
-  /// The pointer.
-  typedef typename TileTraits_::Pointer Pointer;
-  /// The threads.
-  typedef typename TileTraits_::Threads Threads;
-  /// The index.
-  typedef Index_ Index;
-  /// The thread offset
-  typedef typename TileTraits_::ThreadOffset ThreadOffset;
-
-  /// The params.
-  struct Params {
-    /// The pointer.
-    Pointer pointer;
-    /// The stride in the H dimension to setup the thread in the block.
-    Index stride_h;
-    /// The strides to increment the pointer.
-    Index inc_advance, inc_h;
-    /// The strides to increment the predicate offset
-    Index predicate_inc_advance, predicate_inc_h;
-    /// The column offset to compute the predicate for the columns.
-    Index predicate_offset;
-
-    /// Setup the params.
-    CUTLASS_HOST_DEVICE int initialize(
-        Pointer pointer, Index ld, Index bound, Index epilogue_stride_w, Index epilogue_delta_w) {
-      // The pointer.
-      this->pointer = pointer;
-      // Each column of the matrix.
-      stride_h = TileTraits_::ThreadsDelta::kH * ld;
-      // Each thread output 1 column per iteration. The stride between columns is given by the
-      // number of scalars that are loaded per LDS for B.
-      inc_h = ld * TileTraits_::kStrideH;
-      inc_advance =
-          (ld - ld * TileTraits_::kStrideH * (Base::Iterations::kH - 1)) + epilogue_stride_w;
-
-      predicate_offset = bound;
-      predicate_inc_h = TileTraits_::kStrideH;
-      predicate_inc_advance =
-          -((TileTraits_::kStrideH * (Base::Iterations::kH - 1) - 1) + epilogue_delta_w);
-
-      return 0;
-    }
-  };
-
-  Params params;
-  /// Offset of an individual lane from the start of the tile
-  Coord<4> thread_offset;
-
-  /// Ctor.
-  CUTLASS_DEVICE GemmGlobalIteratorCd() {}
-
-  /// Ctor.
-  CUTLASS_DEVICE GemmGlobalIteratorCd(Params const& params,
-                                      const Coord<3>& bounds,
-                                      const Coord<3>& block,
-                                      int offset = 0,
-                                      int pred_offset = 0,
-                                      ThreadOffset thread_offset_func = ThreadOffset())
-      : params(params) {
-    thread_offset = thread_offset_func();
-    // Each warp works on a different column of the tile.
-    int const h = thread_offset[1] + block[1];
-    // Each lane writes a different element.
-    int const w = thread_offset[2] + block[2];
-    // Setup the pointer.
-    this->params.pointer += ((h * params.stride_h + w) + offset);
-
-    // Prepare the vector of predicates.
-    for (int i = 0; i < Base::Iterations::kW; ++i) {
-      predicates.set(i, w + i * Base::Delta::kW < bounds[2]);
-    }
-    this->params.predicate_offset -= (h + pred_offset);
-  }
-
-  /// Increment the pointer in the C dimension.
-  CUTLASS_DEVICE void inc_c() {}
-  /// Increment the pointer in the W dimension.
-  CUTLASS_DEVICE void inc_w() {}
-  /// Increment the pointer in the H dimension.
-  CUTLASS_DEVICE void inc_h() {
-    params.pointer += params.inc_h;
-    params.predicate_offset -= params.predicate_inc_h;
-  }
-  /// Increment the pointer in the D dimension.
-  CUTLASS_DEVICE void inc_d() {}
-  /// Increment the pointer to move to the next iteration.
-  CUTLASS_DEVICE void inc_advance() {
-    params.pointer += params.inc_advance;
-    this->params.predicate_offset -= params.predicate_inc_advance;
-  }
-
-  /// Test the validity of the iterator.
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
-    return predicates.at(w) && params.predicate_offset > 0;
-  }
-
-  /// Returns the raw pointer
-  CUTLASS_HOST_DEVICE
-  Pointer data() { return params.pointer; }
-
-  CUTLASS_HOST_DEVICE
-  Pointer const data() const { return params.pointer; }
-
-  /// The predicates for the row.
-  cutlass::PredicateVector<Base::Iterations::kW> predicates;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_operand.h

@@ -1,141 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines constant expressions for mapping GEMM problem size and strides onto pitch-linear
-   memory.
-*/
-#pragma once
-
-#include <cutlass/matrix_traits.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/util/platform.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Helper to describe attributes of GEMM matrix operands
-template <GemmOperand::Kind kOperand_, MatrixLayout::Kind kLayout_>
-struct GemmOperandTraitsAb {
-  static const bool Congruous =
-      (kOperand_ == GemmOperand::kA ^ kLayout_ == MatrixLayout::kRowMajor);
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmOperand::Kind kOperand_, typename Tile_>
-struct GetExtent;
-
-template <typename Tile_>
-struct GetExtent<GemmOperand::kA, Tile_> {
-  static const int kExtent = Tile_::kW;
-};
-
-template <typename Tile_>
-struct GetExtent<GemmOperand::kB, Tile_> {
-  static const int kExtent = Tile_::kH;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Determines the shape of a multiplicand tile in terms of strided (H) and contiguous (W)
-/// dimensions
-template <typename ThreadBlockTile_, GemmOperand::Kind Usage, MatrixLayout::Kind Layout>
-struct GemmMultiplicandTraits {
-  // Only defined for A or B
-  static_assert(Usage == GemmOperand::kA || Usage == GemmOperand::kB,
-                "MultiplicandTileShape defined only for A or B operands.");
-
-  /// Shape of GEMM thread block tile (K, N, M)
-  typedef ThreadBlockTile_ ThreadBlockTile;
-
-  /// Identifies multiplicand
-  static GemmOperand::Kind const kUsage = Usage;
-
-  /// Layout of tile
-  static MatrixLayout::Kind const kLayout = Layout;
-
-  // True if K is the strided dimension
-  static bool const kKstrided = (kUsage == GemmOperand::kA ^ kLayout == MatrixLayout::kRowMajor);
-
-  /// Map the ThreadBlockShape onto (kH, kW) dimensions for A and B operand
-  typedef typename platform::conditional<
-      kKstrided,
-      Shape<1, ThreadBlockTile::kD, GetExtent<Usage, ThreadBlockTile>::kExtent>,
-      Shape<1, GetExtent<Usage, ThreadBlockTile>::kExtent, ThreadBlockTile::kD> >::type Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Project's a coordinate (K, N, M) onto inner and outer dimensions defined for each
-/// operand.
-template <GemmOperand::Kind operand, bool Kstrided = true>
-struct ProjectOperand;
-
-/// Project A operand - (0, K, M)
-template <bool Kstrided>
-struct ProjectOperand<GemmOperand::kA, Kstrided> {
-  CUTLASS_HOST_DEVICE
-  static Coord<3> project(Coord<3> const &coord) {
-    if (Kstrided) {
-      return make_Coord(0, coord[0], coord[2]);
-    } else {
-      return make_Coord(0, coord[2], coord[0]);
-    }
-  }
-};
-
-/// Project B operand - (0, K, N)
-template <bool Kstrided>
-struct ProjectOperand<GemmOperand::kB, Kstrided> {
-  CUTLASS_HOST_DEVICE
-  static Coord<3> project(Coord<3> const &coord) {
-    if (Kstrided) {
-      return make_Coord(0, coord[0], coord[1]);
-    } else {
-      return make_Coord(0, coord[1], coord[0]);
-    }
-  }
-};
-
-/// Project C operand - (0, N, M)
-template <>
-struct ProjectOperand<GemmOperand::kC, true> {
-  CUTLASS_HOST_DEVICE
-  static Coord<3> project(Coord<3> const &coord) { return make_Coord(0, coord[1], coord[2]); }
-};
-
-/// Project D operand - (0, N, M)
-template <>
-struct ProjectOperand<GemmOperand::kD, true> {
-  CUTLASS_HOST_DEVICE
-  static Coord<3> project(Coord<3> const &coord) { return make_Coord(0, coord[1], coord[2]); }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_shared_stream.h

@@ -1,113 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines abstractions for managing loading and storing fragments to shared memory in the
-      efficient GEMM pipeline.
-*/
-#pragma once
-
-#include <cutlass/gemm/gemm_shared_tile.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The load iterator.
-    typename Iterator_,
-    /// The transformer to be applied after the data has been copied from shared memory.
-    typename Transformer_ = Copy<typename Iterator_::Fragment> >
-
-struct SharedLoadStream {
-  /// The load iterator.
-  typedef Iterator_ Iterator;
-  /// The transformer.
-  typedef Transformer_ Transformer;
-
-  /// The fragment that is copied from shared memory.
-  typedef typename Iterator::Fragment FetchedFragment;
-  /// The fragment that is obtained after the transformation by the transformer.
-  typedef typename Transformer::OutputFragment TransformedFragment;
-  /// Make sure the fragments match.
-  static_assert((platform::is_same<FetchedFragment, typename Transformer::InputFragment>::value),
-                "");
-  /// The output fragment.
-  typedef TransformedFragment Fragment;
-
-  /// The params.
-  struct Params {
-    /// The iterator params.
-    typename Iterator::Params iterator;
-
-    /// Setup the params.
-    CUTLASS_HOST_DEVICE int initialize() { return iterator.initialize(); }
-  };
-
-  /// The storage in shared memory needed by that stream.
-  typedef typename Iterator::Storage SharedStorage;
-
-  /// Ctor.
-  CUTLASS_DEVICE SharedLoadStream() {}
-
-  /// Ctor.
-  CUTLASS_DEVICE SharedLoadStream(Params const &params, SharedStorage &shared_storage) {
-    this->initialize(params, shared_storage);
-  }
-
-  /// Initialize the stream.
-  CUTLASS_DEVICE void initialize(Params const &params, SharedStorage &shared_storage) {
-    // The iterator.
-    iterator = Iterator(params.iterator, shared_storage);
-    // The transformer.
-    transformer = Transformer();
-  }
-
-  /// Load the data from shared memory to the fetch fragment.
-  CUTLASS_DEVICE void copy(FetchedFragment &fetched) { shared_iterator_load(iterator, fetched); }
-
-  /// Load the data from shared memory to the fetch fragment.
-  CUTLASS_DEVICE void copy(int d, FetchedFragment &fetched) {
-    shared_iterator_load(iterator, fetched, d);
-  }
-
-  /// Commit the data.
-  CUTLASS_DEVICE void commit(FetchedFragment &fetched, TransformedFragment &transformed) {
-    transformer.transform(fetched, transformed);
-  }
-
-  /// Increment the stage.
-  CUTLASS_DEVICE void inc_stage() { iterator.inc_stage(); }
-
-  /// The iterator.
-  Iterator iterator;
-  /// The transformer.
-  Transformer transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_shared_tile.h

@@ -1,406 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines iterators for efficiently loading and storing tiles to and from shared memory.
-*/
-#pragma once
-
-#include <cutlass/gemm/gemm_operand.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerSts_>
-struct GemmSharedStoreTileAbTraits {
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The tile.
-  typedef typename ReshapeTile<Tile_, kScalarsPerSts_>::Tile Tile;
-  /// The threads.
-  typedef Threads_ Threads;
-  /// The strides to compute the base position of the thread.
-  typedef Shape<0, ShapeCount<Tile>::kWc, Tile::kC, kScalarsPerSts_> ThreadsStrides;
-  /// The skew.
-  static int const kSkew = 0;
-  /// The number of scalars per LDG/STG.
-  static int const kAccessSize = kScalarsPerSts_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1,
-                Tile::kH / Threads::kH,
-                Tile::kW / Threads::kW,
-                Tile::kC / Threads::kC / kAccessSize>
-      Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kAccessSize> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kAccessSize>
-      ImmediateOffsetStrides;
-
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerSts_, int kSkew_>
-struct GemmSharedStoreWithSkewTileAbTraits {
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The tile without skews.
-  typedef typename ReshapeTile<Tile_, kScalarsPerSts_>::Tile TileWithoutSkew;
-  /// The tile.
-  typedef typename ReshapeTile<Shape<Tile_::kD, Tile_::kH, Tile_::kW + kSkew_>,
-                               kScalarsPerSts_>::Tile Tile;
-  /// The threads.
-  typedef Threads_ Threads;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The number of scalars per STS.
-  static int const kAccessSize = kScalarsPerSts_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1, TileWithoutSkew::kH / Threads::kW, TileWithoutSkew::kW / Threads::kH> Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, ShapeCount<Tile>::kWc, Threads::kH * kAccessSize> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, ShapeCount<Tile>::kWc, Threads::kH * kAccessSize> ImmediateOffsetStrides;
-
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-
- protected:
-  /// The strides to compute the base position of the thread.
-  typedef Shape<0, kScalarsPerSts_, ShapeCount<Tile>::kHwc / Threads::kW> ThreadsStrides;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          typename OutputTile_,
-          typename Warps_,
-          typename ThreadsPerWarp_,
-          typename InstructionShape_,
-          int kStages_,
-          int kScalarsPerLds_,
-          int kSkew_ = 0>
-struct GemmSharedLoadTileATraits {
-  static GemmOperand::Kind const kOperand = GemmOperand::kA;
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The tile without skew.
-  typedef Shape<kStages_,
-                OutputTile_::kD / InstructionShape_::kD,
-                GetExtent<kOperand, OutputTile_>::kExtent * InstructionShape_::kD>
-      TileWithoutSkew_;
-  /// The tile with skew.
-  typedef Shape<kStages_, TileWithoutSkew_::kH, TileWithoutSkew_::kW + kSkew_> TileWithSkew;
-  /// The tile without skew after reshaping.
-  typedef typename ReshapeTile<TileWithoutSkew_, kScalarsPerLds_>::Tile TileWithoutSkew;
-  /// The tile.
-  typedef typename ReshapeTile<TileWithSkew, kScalarsPerLds_>::Tile Tile;
-  /// The number of warps.
-  typedef Warps_ Warps;
-  /// The threads in a warp.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of scalars per LDG/STG.
-  // static int const kScalarsPerLds = kScalarsPerLds_;
-  static int const kAccessSize = kScalarsPerLds_;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of warps.
-  static int const kWarps = GetExtent<kOperand, Warps>::kExtent;
-  /// The number of threads in one dimension of the warp.
-  static int const kThreadsPerWarp = GetExtent<kOperand, ThreadsPerWarp>::kExtent;
-
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1, 1, TileWithoutSkew::kW / kWarps / kThreadsPerWarp /* / kScalarsPerLds*/>
-      Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<TileWithSkew::kW, 0, kWarps * kThreadsPerWarp * kAccessSize, 0> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<TileWithSkew::kW, 0, kWarps * kThreadsPerWarp * kAccessSize, 0>
-      ImmediateOffsetStrides;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // Extract the warp.
-      int const warp = threadIdx.x / kWarpSize % Warps::kW;
-      // Compute the row offset for each thread
-      int const lane = (threadIdx.x & 0x0e) / 2;
-      // The offset.
-      int const offset = (warp * ThreadsPerWarp::kW + lane) * kAccessSize;
-
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          typename OutputTile_,
-          typename Warps_,
-          typename ThreadsPerWarp_,
-          typename InstructionShape_,
-          int kStages_,
-          int kScalarsPerLds_,
-          int kSkew_ = 0>
-struct GemmSharedLoadTileBTraits {
-  static GemmOperand::Kind const kOperand = GemmOperand::kB;
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The tile without skew.
-  typedef Shape<kStages_,
-                OutputTile_::kD / InstructionShape_::kD,
-                GetExtent<kOperand, OutputTile_>::kExtent * InstructionShape_::kD>
-      TileWithoutSkew_;
-  /// The tile with skew.
-  typedef Shape<kStages_, TileWithoutSkew_::kH, TileWithoutSkew_::kW + kSkew_> TileWithSkew;
-  /// The tile without skew after reshaping.
-  typedef typename ReshapeTile<TileWithoutSkew_, kScalarsPerLds_>::Tile TileWithoutSkew;
-  /// The tile.
-  typedef typename ReshapeTile<TileWithSkew, kScalarsPerLds_>::Tile Tile;
-  /// The number of warps.
-  typedef Warps_ Warps;
-  /// The threads in a warp.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of scalars per LDG/STG.
-  static int const kAccessSize = kScalarsPerLds_;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of warps.
-  static int const kWarps = GetExtent<kOperand, Warps>::kExtent;
-  /// The number of threads in one dimension of the warp.
-  static int const kThreadsPerWarp = GetExtent<kOperand, ThreadsPerWarp>::kExtent;
-
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1, 1, TileWithoutSkew::kW / kWarps / kThreadsPerWarp /* / kAccessSize*/> Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<TileWithSkew::kW, 0, kWarps * kThreadsPerWarp * kAccessSize, 0> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<TileWithSkew::kW, 0, kWarps * kThreadsPerWarp * kAccessSize, 0>
-      ImmediateOffsetStrides;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // The position of the warp.
-      int const warp = threadIdx.x / (Warps::kW * kWarpSize);
-
-      // Compute the column offset for each thread
-      int const lane = (threadIdx.x & 0x10) / 8 + (threadIdx.x & 0x01);
-      // The offset.
-      int const offset = (warp * ThreadsPerWarp::kH + lane) * kAccessSize;
-
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          typename OutputTile_,
-          typename Warps_,
-          typename ThreadsPerWarp_,
-          int kScalarsPerSts_,
-          int kSkew_ = 0>
-struct GemmSharedStoreTileDTraits {
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The dimension of the output tile.
-  typedef OutputTile_ OutputTile;
-  /// The warps in the tile.
-  typedef Warps_ Warps;
-  /// The threads in the warps.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of scalars per LDG/STG.
-  static int const kAccessSize = kScalarsPerSts_;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of scalars per thread.
-  static int const kScalarsPerThread = OutputTile_::kW / Warps::kW / ThreadsPerWarp::kW;
-  /// The number of threads.
-  static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
-  /// The number of scalars per row. We build a tile with 2 rows (to avoid bank conflicts).
-  static int const kScalarsPerRow = kThreads / 2 * kScalarsPerThread + kSkew;
-
-  /// The tile.
-  typedef Shape<1, 2, kScalarsPerRow / kAccessSize, kAccessSize> Tile;
-  /// The number of iterations needed to store the tile.
-  typedef Shape<1, 1, kScalarsPerThread / kAccessSize> Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Warps::kW * ThreadsPerWarp::kW * kAccessSize> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Warps::kW * ThreadsPerWarp::kW * kAccessSize> ImmediateOffsetStrides;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // We issue STS.128 in the epilogue to store the accumulators to shared memory. When we use
-      // STS.128, we have to guarantee that threads in groups of 8 do not have bank conflicts (i.e
-      // they write to different banks).
-
-      // Odd threads go to the second half of shared memory.
-      int const row = threadIdx.x & 0x01;
-
-      int const warp_id = (threadIdx.x >> 5);
-
-      int const warp_row = (warp_id % Warps::kW);
-      int const warp_col = (warp_id / Warps::kW);
-
-      int hi_halfwarp_offset = OutputTile::kW * ((threadIdx.x >> 4) & 1);
-      int lo_halfwarp_offset = (((threadIdx.x >> 1) & 0x7) + warp_row * ThreadsPerWarp::kW);
-
-      int col = kAccessSize * lo_halfwarp_offset +
-                warp_col * (ThreadsPerWarp::kH / 2) * OutputTile::kW + hi_halfwarp_offset;
-
-      int offset = row * kScalarsPerRow + col;
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          typename OutputTile_,
-          typename Warps_,
-          typename ThreadsPerWarp_,
-          int kTileH_,
-          int kScalarsPerLds_,
-          int kSkew_ = 0>
-struct GemmSharedLoadTileDTraits {
-  /// The scalar.
-  typedef typename platform::remove_const<Scalar_>::type Scalar;
-  /// The pointer.
-  typedef Scalar_* Pointer;
-  /// The dimension of the output tile.
-  typedef OutputTile_ OutputTile;
-  /// The warps in the tile.
-  typedef Warps_ Warps;
-  /// The threads in the warps.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of scalars per LDG/STG.
-  static int const kAccessSize = kScalarsPerLds_;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The number of scalars per thread.
-  static int const kScalarsPerThread = OutputTile_::kW / Warps::kW / ThreadsPerWarp::kW;
-  /// The number of threads.
-  static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
-  /// The number of scalars per row. We build a tile with 2 rows (to avoid bank conflicts).
-  static int const kScalarsPerRow = kThreads / 2 * kScalarsPerThread + kSkew;
-
-  /// The tile.
-  typedef Shape<1, 2, kScalarsPerRow / kAccessSize, kAccessSize> Tile;
-
-  // Compute the number of iterations per warp in the Tile::kH dimension.
-  static int const kIterationsInHPerWarp = kTileH_ / ShapeCount<Warps>::kCount;
-
-  // As shown above, the shared memory tile is composed of 2 rows and each rows is made of
-  // kScalarsPerRow. A warp is expected to read from the 1st row, then move to the 2nd row and go
-  // back to the 1st row. To model that scheme we define the Iterations shape as Shape<X, 2, ...>.
-  // However, in some cases, we have only 1 iteration per warp. In that case, we must define the
-  // shape as Shape<1, 1, ...>. The following code does that.
-  static int const kIterationsH = kIterationsInHPerWarp == 1 ? 1 : 2;
-  // As soon as we know kIterationsH, it is trivial to compute kIterationsD:
-  static int const kIterationsD = kIterationsInHPerWarp / kIterationsH;
-
-  /// The number of iterations needed to store the tile.
-  typedef Shape<kIterationsD, kIterationsH, OutputTile::kW / kWarpSize / kAccessSize> Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<OutputTile::kW, kScalarsPerRow, kWarpSize * kAccessSize> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<OutputTile::kW, kScalarsPerRow, kWarpSize * kAccessSize> ImmediateOffsetStrides;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // Each warp works on a different column.
-      int const h = threadIdx.x / kWarpSize;
-      // Compute the row.
-      int const w = (threadIdx.x & (kWarpSize - 1)) * kAccessSize;
-      int offset = 0;
-      if (Iterations::kH == 1) {
-        int const row = h & 0x1;
-        int const col = h / 2;
-        offset = row * ShapeCount<Tile>::kWc + col * OutputTile::kW * Iterations::kD + w;
-      } else {
-        offset = h * OutputTile::kW * Iterations::kD + w;
-      }
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/gemm_traits.h

@@ -1,747 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines structural properties of complete GEMM computation.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/gemm/clear_accumulators.h>
-#include <cutlass/gemm/gemm_global_stream.h>
-#include <cutlass/gemm/gemm_operand.h>
-#include <cutlass/gemm/gemm_shared_stream.h>
-#include <cutlass/gemm/identity_block_swizzle.h>
-#include <cutlass/matrix_traits.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The scalar type for A.
-    typename ScalarA_,
-    /// The scalar type for B.
-    typename ScalarB_,
-    /// The scalar type for C.
-    typename ScalarC_,
-    /// The scalar type for D.
-    typename ScalarD_,
-    /// The output tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The functor to do the math.
-    typename MultiplyAdd_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_,
-    /// The number of scalars per STS for A.
-    int kScalarsPerStsA_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdsA_,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_,
-    /// The number of scalars per STS for B.
-    int kScalarsPerStsB_,
-    /// The number of scalars per LDS for B.
-    int kScalarsPerLdsB_,
-    /// The number of scalars per LDG for C and STG for D.
-    int kScalarsPerLdgCAndStgD_,
-    /// The number of scalars per STS for D.
-    int kScalarsPerStsD_,
-    /// The number of scalars per LDS for D.
-    int kScalarsPerLdsD_,
-    /// The number of stages in shared memory to do single/double/triple-buffering.
-    int kStages_>
-
-struct GemmConfig {
-  //
-  /// The scalar for A.
-  typedef ScalarA_ ScalarA;
-  /// The scalar for B.
-  typedef ScalarB_ ScalarB;
-  /// The scalar for C.
-  typedef ScalarC_ ScalarC;
-  /// The scalar for D.
-  typedef ScalarD_ ScalarD;
-
-  /// The tile.
-  typedef OutputTile_ OutputTile;
-  /// The functor to do D = A*B + C.
-  typedef MultiplyAdd_ MultiplyAdd;
-  /// The shape of the instruction.
-  typedef typename MultiplyAdd::InstructionShape InstructionShape;
-  /// The number of accumulators per warp.
-  typedef typename MultiplyAdd::AccumulatorsPerWarp AccumulatorsPerWarp;
-  /// The accumulators.
-  typedef typename MultiplyAdd::Accumulators Accumulators;
-
-  /// The number of warps.
-  typedef typename ShapeDiv<OutputTile, AccumulatorsPerWarp>::Shape Warps;
-  /// The default warp size (32 threads per warp).
-  static int const kWarpSize = cutlass::kWarpSize;
-  /// The numnber of threads.
-  static int const kThreads = ShapeCount<Warps>::kCount * kWarpSize;
-
-  /// The number of scalars per LDG/STS/LDS for A.
-  static int const kScalarsPerLdgA = kScalarsPerLdgA_;
-  static int const kScalarsPerStsA = kScalarsPerStsA_;
-  static int const kScalarsPerLdsA = kScalarsPerLdsA_;
-
-  /// The number of scalars per LDG/STS/LDS for B.
-  static int const kScalarsPerLdgB = kScalarsPerLdgB_;
-  static int const kScalarsPerStsB = kScalarsPerStsB_;
-  static int const kScalarsPerLdsB = kScalarsPerLdsB_;
-
-  /// The number of scalars per LDG for C.
-  static int const kScalarsPerLdgC = kScalarsPerLdgCAndStgD_;
-
-  /// The number of scalars per STS/LDS/STG for D.
-  static int const kScalarsPerStgD = kScalarsPerLdgCAndStgD_;
-  static int const kScalarsPerStsD = kScalarsPerStsD_;
-  static int const kScalarsPerLdsD = kScalarsPerLdsD_;
-
-  /// The number of accumulators that are going to be fed from one LDS A/B.
-  static int const kAccumulatorsPerLdsA = kScalarsPerLdsA / InstructionShape::kD;
-  static int const kAccumulatorsPerLdsB = kScalarsPerLdsB / InstructionShape::kD;
-
-  /// The number of stages in shared memory to implement double, triple, more-buffering.
-  static int const kStages = kStages_;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind, typename GemmConfig_>
-struct GemmTileTraitsHelperA {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarA Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
-
-  /// The traits class to build the iterator to load data from global memory for A^N.
-  typedef GemmGlobalTileTraits<
-      // That's A.
-      GemmOperand::kA,
-      // A is column-major.
-      MatrixLayout::kColumnMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgA>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for A^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer is float.
-      MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsA>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for A^N.
-  typedef GemmSharedLoadTileATraits<
-      // The pointer is float const.
-      MultiplyAddScalar const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsA,
-      // The skew.
-      0>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarA Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
-
-  /// The traits class to build the iterator to load data from global memory for A^T.
-  typedef GemmGlobalTileTraits<
-      // That's A.
-      GemmOperand::kA,
-      // A is row-major.
-      MatrixLayout::kRowMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size MxK in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgA>
-      GlobalTileTraits;
-
-  /// The number of scalars in 4B.
-  static int const kScalarsIn4B = sizeof(MultiplyAddScalar) > 4 ? 1 : 4 / sizeof(MultiplyAddScalar);
-  /// The traits class to build the iterator to store data to shared memory for A^T.
-  typedef GemmSharedStoreWithSkewTileAbTraits<
-      // The pointer is float.
-      MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS.
-      GemmConfig_::kScalarsPerStsA,
-      // The skew to avoid bank conflicts added in the tile W dimension.
-      128 / sizeof(MultiplyAddScalar) / GemmConfig_::kScalarsPerStsA /
-          GlobalTileTraits::Threads::kW * kScalarsIn4B>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for A^T.
-  typedef GemmSharedLoadTileATraits<
-      // The pointer is float const.
-      MultiplyAddScalar const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsA,
-      // The skew.
-      SharedStoreTileTraits::kSkew>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind, typename GemmConfig_>
-struct GemmTileTraitsHelperB {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarB Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
-
-  /// The traits class to build the iterator to load data from global memory for B^N.
-  typedef GemmGlobalTileTraits<
-      // That's B.
-      GemmOperand::kB,
-      // B is column-major.
-      MatrixLayout::kColumnMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size MxK in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgB>
-      GlobalTileTraits;
-
-  /// The number of scalars in 4B.
-  static int const kScalarsIn4B = sizeof(MultiplyAddScalar) > 4 ? 1 : 4 / sizeof(MultiplyAddScalar);
-  /// The traits class to build the iterator to store data to shared memory for B^N.
-  typedef GemmSharedStoreWithSkewTileAbTraits<
-      // The pointer is float.
-      MultiplyAddScalar,
-      // The tile has size KxN in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS.
-      GemmConfig_::kScalarsPerStsB,
-      // The skew to avoid bank conflicts added in the tile W dimension.
-      128 / sizeof(MultiplyAddScalar) / GemmConfig_::kScalarsPerStsB /
-          GlobalTileTraits::Threads::kW * kScalarsIn4B>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for B^N.
-  typedef GemmSharedLoadTileBTraits<
-      // The pointer is float const.
-      MultiplyAddScalar const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsB,
-      // The skew.
-      SharedStoreTileTraits::kSkew>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarB Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
-
-  /// The traits class to build the iterator to load data from global memory for B^T.
-  typedef GemmGlobalTileTraits<
-      // That's B.
-      GemmOperand::kB,
-      // B is row-major.
-      MatrixLayout::kRowMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size KxN in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kH>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgB>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for B^T.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer is float.
-      MultiplyAddScalar,
-      // The tile has size KxN in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsB>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for B^T.
-  typedef GemmSharedLoadTileBTraits<
-      // The pointer is float const.
-      MultiplyAddScalar const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsB,
-      // The skew.
-      0>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The GEMM configuration.
-    typename GemmConfig_,
-    /// The stream to load A from global memory to shared memory.
-    typename GlobalLoadStreamA_,
-    /// The stream to load B from global memory to shared memory.
-    typename GlobalLoadStreamB_,
-    /// The stream to load A from shared memory.
-    typename SharedLoadStreamA_,
-    /// The stream to load B from shared memory.
-    typename SharedLoadStreamB_,
-    /// The epilogue.
-    typename Epilogue_,
-    /// The block swizzle to reorganize the grid.
-    typename BlockSwizzle_ = IdentityBlockSwizzle,
-    /// The index.
-    typename Index_ = int,
-    /// The tool used to clear accumulators.
-    typename ClearAccumulators_ = ClearAccumulators<typename GemmConfig_::Accumulators::Scalar> >
-
-struct GemmTraits {
-  /// The configuration.
-  typedef GemmConfig_ GemmConfig;
-  /// The output tile.
-  typedef typename GemmConfig::OutputTile OutputTile;
-
-  /// The stream to load A from global memory to shared memory.
-  typedef GlobalLoadStreamA_ GlobalLoadStreamA;
-  /// The layout of A.
-  static MatrixLayout::Kind const kLayoutA = GlobalLoadStreamA::kLayout;
-  /// The scalar for A.
-  typedef typename GlobalLoadStreamA_::Scalar ScalarA;
-
-  /// The stream to load B from global memory to shared memory.
-  typedef GlobalLoadStreamB_ GlobalLoadStreamB;
-  /// The layout of B.
-  static MatrixLayout::Kind const kLayoutB = GlobalLoadStreamB::kLayout;
-  /// The scalar for B.
-  typedef typename GlobalLoadStreamB_::Scalar ScalarB;
-
-  /// The iterator for A to load from shared memory.
-  typedef SharedLoadStreamA_ SharedLoadStreamA;
-  /// The iterator for B to load from shared memory.
-  typedef SharedLoadStreamB_ SharedLoadStreamB;
-
-  /// The shared storage for A.
-  typedef typename GlobalLoadStreamA::SharedStoreStorage SharedStoreStorageA;
-  // Btw, make sure we did not messed up with the size of the storage.
-  static_assert(sizeof(SharedStoreStorageA) == sizeof(typename SharedLoadStreamA::SharedStorage),
-                "");
-
-  /// The shared storage for B.
-  typedef typename GlobalLoadStreamB::SharedStoreStorage SharedStoreStorageB;
-  // Btw, make sure we did not messed up with the size of the storage.
-  static_assert(sizeof(SharedStoreStorageB) == sizeof(typename SharedLoadStreamB::SharedStorage),
-                "");
-
-  /// The multiply-add functor.
-  typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
-  /// The epilogue.
-  typedef Epilogue_ Epilogue;
-  /// The scalars in the epilogue.
-  typedef typename Epilogue::ScalarC ScalarC;
-  typedef typename Epilogue::ScalarD ScalarD;
-
-  /// The block swizzle to reorganize the grid.
-  typedef BlockSwizzle_ BlockSwizzle;
-  /// The index.
-  typedef Index_ Index;
-  /// Clear the accumulators.
-  typedef ClearAccumulators_ ClearAccumulators;
-
-  /// The params.
-  struct Params {
-    /// The dimensions of the GEMM.
-    Index m, n, k;
-    /// The params for the A stream.
-    typename GlobalLoadStreamA::Params global_stream_a;
-    /// The params for the B stream.
-    typename GlobalLoadStreamB::Params global_stream_b;
-    /// The params for the A stream from shared memory.
-    typename SharedLoadStreamA::Params shared_stream_a;
-    /// The params for the B stream from shared memory.
-    typename SharedLoadStreamB::Params shared_stream_b;
-    /// The params for the epilogue.
-    typename Epilogue::Params epilogue;
-
-    /// Initialize the parameters.
-    template <typename GemmDesc_>
-    CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
-      // Set the problem size.
-      this->m = desc.m;
-      this->n = desc.n;
-      this->k = desc.k;
-
-      // Initialize the iterator for A.
-      int error_code =
-          global_stream_a.initialize(reinterpret_cast<ScalarA const*>(desc.d_a), desc.lda);
-
-      if (error_code) {
-        return error_code;
-      }
-
-      // Initialize the iterator for B.
-      error_code = global_stream_b.initialize(reinterpret_cast<ScalarB const*>(desc.d_b), desc.ldb);
-
-      if (error_code) {
-        return error_code;
-      }
-
-      // The epilogue.
-      return epilogue.initialize(desc);
-    }
-  };
-
-  // The storage for A.
-  template <typename GlobalLoadStream_, typename SharedLoadStream_>
-  union StreamSharedStorage {
-    // The storage needed by the global stream.
-    typename GlobalLoadStream_::SharedStorage global;
-    // The storage needed by the shared stream.
-    typename SharedLoadStream_::SharedStorage shared;
-  };
-
-  // The storage for the main loop + prologue.
-  struct MainLoopSharedStorage {
-    // The storage to shuffle the A matrix in shared memory.
-    StreamSharedStorage<GlobalLoadStreamA, SharedLoadStreamA> stream_a;
-    // The storage to shuffle the B matrix in shared memory.
-    StreamSharedStorage<GlobalLoadStreamB, SharedLoadStreamB> stream_b;
-    // The storage to clear the accumulators if needed.
-    typename ClearAccumulators::SharedStorage clear;
-  };
-
-  /// The storage in shared memory.
-  union SharedStorage {
-    // The storage for the main loop.
-    MainLoopSharedStorage main_loop;
-    // The storage for the epilogue.
-    typename Epilogue::SharedStorage epilogue;
-  };
-
-  /// Assemble the global load streams for A/B.
-  struct GlobalLoadStream {
-    /// Ctor.
-    CUTLASS_DEVICE GlobalLoadStream(Params const& params,
-                                    SharedStorage& shared_storage,
-                                    dim3 const& block)
-        : stream_a(params.global_stream_a,
-                   shared_storage.main_loop.stream_a.global,
-                   cutlass::make_Coord(0, params.k, params.m),
-                   cutlass::make_Coord(0, 0, block.x)),
-          stream_b(params.global_stream_b,
-                   shared_storage.main_loop.stream_b.global,
-                   cutlass::make_Coord(0, params.k, params.n),
-                   make_Coord(0, 0, block.y)) {}
-
-    /// Trigger the copies from shared memory to registers.
-    CUTLASS_DEVICE void copy() {
-      stream_a.copy();
-      stream_b.copy();
-    }
-
-    /// Commit the data.
-    CUTLASS_DEVICE void commit() {
-      stream_a.commit();
-      stream_b.commit();
-    }
-
-    /// Execute the residue code.
-    CUTLASS_DEVICE void residue(Index k, bool skip_clear = false) {
-      stream_a.residue(k, skip_clear);
-      stream_b.residue(k, skip_clear);
-    }
-
-    /// The stream for A.
-    GlobalLoadStreamA stream_a;
-    /// The stream for B.
-    GlobalLoadStreamB stream_b;
-  };
-
-  /// Assemble the shared load stream for A/B.
-  struct SharedLoadStream {
-    /// Ctor.
-    CUTLASS_DEVICE SharedLoadStream(Params const& params, SharedStorage& shared_storage) {
-      stream_a.initialize(params.shared_stream_a, shared_storage.main_loop.stream_a.shared);
-      stream_b.initialize(params.shared_stream_b, shared_storage.main_loop.stream_b.shared);
-    }
-
-    /// Trigger the copies from shared memory to registers.
-    CUTLASS_DEVICE void copy(int step) {
-      stream_a.copy(step, fetched_a[step % 2]);
-      stream_b.copy(step, fetched_b[step % 2]);
-    }
-
-    /// Commit the data.
-    CUTLASS_DEVICE void commit(int step) {
-      stream_a.commit(fetched_a[step % 2], transformed_a[step % 2]);
-      stream_b.commit(fetched_b[step % 2], transformed_b[step % 2]);
-    }
-
-    /// The fragment A.
-    CUTLASS_DEVICE typename SharedLoadStreamA::Fragment const& fragment_a(int step) const {
-      return transformed_a[step % 2];
-    }
-
-    /// The fragment B.
-    CUTLASS_DEVICE typename SharedLoadStreamB::Fragment const& fragment_b(int step) const {
-      return transformed_b[step % 2];
-    }
-
-    /// Increment the stage.
-    CUTLASS_DEVICE void inc_stage() {
-      stream_a.inc_stage();
-      stream_b.inc_stage();
-    }
-
-    /// The stream for A.
-    SharedLoadStreamA stream_a;
-    /// The fragments to fetch A.
-    typename SharedLoadStreamA::FetchedFragment fetched_a[2];
-    /// The fragments to transform A.
-    typename SharedLoadStreamA::TransformedFragment transformed_a[2];
-    /// The stream for B.
-    SharedLoadStreamB stream_b;
-    /// The fragments to fetch B.
-    typename SharedLoadStreamB::FetchedFragment fetched_b[2];
-    /// The fragments to transform B.
-    typename SharedLoadStreamB::TransformedFragment transformed_b[2];
-  };
-
-  /// The memory fence for shared loads.
-  static CUTLASS_DEVICE void shared_load_fence(bool in_loop) {
-    if (SharedLoadStreamA::Iterator::kRequiresLoadFence ||
-        SharedLoadStreamB::Iterator::kRequiresLoadFence) {
-      __syncthreads();
-    }
-  }
-
-  /// The memory fence for shared stores.
-  static CUTLASS_DEVICE void shared_store_fence(bool in_loop) { __syncthreads(); }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmTileTraitsHelperA_, typename GemmTileTraitsHelperB_, typename Index_>
-struct SimplifiedGemmTraitsHelper {
-  /// The global iterator to load A from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA_::GlobalTileTraits, Index_>
-      GlobalLoadIteratorA;
-  /// The data converter for A before storing to shared memory.
-  typedef Copy<typename GlobalLoadIteratorA::Fragment> GlobalTransformerA;
-  /// The iterator to store A to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperA_::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperA_::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorA;
-  /// The stream to load A from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
-      GlobalLoadStreamA;
-
-  /// The global iterator to load B from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB_::GlobalTileTraits, Index_>
-      GlobalLoadIteratorB;
-  /// The data converter for B before storing to shared memory.
-  typedef Copy<typename GlobalLoadIteratorB::Fragment> GlobalTransformerB;
-  /// The iterator to store B to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperB_::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperB_::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorB;
-  /// The stream to load B from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
-      GlobalLoadStreamB;
-
-  /// The iterator to load A from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperA_::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperA_::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorA;
-  /// The stream to load A from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
-  /// The iterator to load B from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperB_::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperB_::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorB;
-  /// The stream to load B from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The config for the GEMM.
-    typename GemmConfig_,
-    /// The epilogue.
-    typename Epilogue_,
-    /// The index.
-    typename Index_ = int,
-    // The configuration for the A matrix.
-    typename GemmTileTraitsHelperA_ = GemmTileTraitsHelperA<kLayoutA_, GemmConfig_>,
-    // The configuration for the B matrix.
-    typename GemmTileTraitsHelperB_ = GemmTileTraitsHelperB<kLayoutB_, GemmConfig_>,
-    // The helper class to create the streams and iterators.
-    typename Helper_ =
-        SimplifiedGemmTraitsHelper<GemmTileTraitsHelperA_, GemmTileTraitsHelperB_, Index_> >
-struct SimplifiedGemmTraits : public GemmTraits<
-                                  // The config.
-                                  GemmConfig_,
-                                  // The stream to load A from global memory to shared memory.
-                                  typename Helper_::GlobalLoadStreamA,
-                                  // The stream to load B from global memory to shared memory.
-                                  typename Helper_::GlobalLoadStreamB,
-                                  // The stream to load A from shared memory.
-                                  typename Helper_::SharedLoadStreamA,
-                                  // The stream to load B from shared memory.
-                                  typename Helper_::SharedLoadStreamB,
-                                  // The epilogue.
-                                  Epilogue_,
-                                  // The block swizzle to reorganize the grid.
-                                  IdentityBlockSwizzle,
-                                  // The index.
-                                  Index_,
-                                  // The tool used to clear accumulators.
-                                  ClearAccumulators<typename GemmConfig_::Accumulators::Element> > {
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/hgemm_global_tile.h

@@ -1,90 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Tile traits used to construct global tile iterator for HGEMM. This is intended to
-      partition the thread block-level tile into 2D subtiles loaded by the threads and facilitate
-      memory accesses larger than 16 bits.
-*/
-#pragma once
-
-#include <cutlass/coord.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/matrix_traits.h>
-#include <cutlass/reshape_tile.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <GemmOperand::Kind kOperand_,
-          MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename Tile_,
-          typename Threads_,
-          int kAccessSize_>
-struct HgemmCrosswiseGlobalTileTraits : public GemmGlobalTileTraits<
-                                            // Which GEMM operand?
-                                            kOperand_,
-                                            // The layout.
-                                            kLayout_,
-                                            // The scalar.
-                                            Scalar_,
-                                            // The tile.
-                                            Tile_,
-                                            // The threads.
-                                            Threads_,
-                                            // The number of scalars per LDG/STG.
-                                            kAccessSize_> {
-  /// The base class.
-  typedef GemmGlobalTileTraits<kOperand_, kLayout_, Scalar_, Tile_, Threads_, kAccessSize_> Base;
-  /// The threads.
-  typedef typename Base::Threads Threads;
-  /// The threads strides.
-  typedef Shape<1, 2, Base::Tile::kC> ThreadsDelta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<Base::Threads::kH * 2, 1, Base::Threads::kW, Base::kAccessSize> Delta;
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<Base::Tile::kH / Base::Threads::kH / 2,
-                2,
-                Base::Tile::kW / Base::Threads::kW,
-                Base::Tile::kC / Base::kAccessSize>
-      Iterations;
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
-      int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
-
-      return make_Coord(0, thread_offset_h, thread_offset_w, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/hgemm_multiply_add.h

@@ -1,104 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Specialization implementing multiply-add operation on half-precision floating point
-   fragments.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-#include <cutlass/gemm/thread_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Template performing matrix multiply-add operation within a thread
-template <typename AccumulatorsPerThread_, typename ThreadsPerWarp_>
-struct ThreadMultiplyAdd<AccumulatorsPerThread_, ThreadsPerWarp_, half, half, half> {
-  /// The shape of the instruction.
-  typedef Shape<1, 1, 2, 1> InstructionShape;
-  /// The number of accumulators per thread.
-  typedef AccumulatorsPerThread_ AccumulatorsPerThread;
-  /// The number of threads per warp.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of accumulators per warp.
-  typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
-  /// The type for A.
-  typedef half ScalarA;
-  /// The fragment for A.
-  typedef Fragment<ScalarA, AccumulatorsPerThread::kW> FragmentA;
-  /// The type for B.
-  typedef half ScalarB;
-  /// The fragment for B.
-  typedef Fragment<ScalarB, AccumulatorsPerThread::kH> FragmentB;
-  /// The type for C and D.
-  typedef half ScalarC;
-  /// The accumulators.
-  typedef Fragment<half, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW> Accumulators;
-
-  /// Make sure there's an even number of elements in both dimensions.
-  static_assert(AccumulatorsPerThread::kH % 2 == 0, "Invalid size");
-  static_assert(AccumulatorsPerThread::kW % 2 == 0, "Invalid size");
-
-  /// Ctor.
-  CUTLASS_DEVICE ThreadMultiplyAdd() {}
-
-  /// Multiply : d = a*b + c.
-  CUTLASS_DEVICE void multiply_add(FragmentA const& a,
-                                   FragmentB const& b,
-                                   Accumulators const& c,
-                                   Accumulators& d) {
-#if defined(__CUDACC__) && __CUDA_ARCH__ >= 530
-    // The inputs.
-    __half2 const* a_half2 = reinterpret_cast<__half2 const*>(&a[0]);
-    __half2 const* b_half2 = reinterpret_cast<__half2 const*>(&b[0]);
-    __half2 const* c_half2 = reinterpret_cast<__half2 const*>(&c[0]);
-
-    // The output.
-    __half2* d_half2 = reinterpret_cast<__half2*>(&d[0]);
-
-    for (int j = 0; j < AccumulatorsPerThread::kH / 2; ++j) {
-      for (int i = 0; i < AccumulatorsPerThread::kW / 2; ++i) {
-        // The offsets in the output fragment.
-        int const k0 = (2 * j + 0) * (AccumulatorsPerThread::kW / 2) + i;
-        int const k1 = (2 * j + 1) * (AccumulatorsPerThread::kW / 2) + i;
-
-        // Compute the product a[i] * b[j].H0_H0.
-        d_half2[k0] = __hfma2(a_half2[i], __low2half2(b_half2[j]), c_half2[k0]);
-        // Compute the product a[i] * b[j].H1_H1.
-        d_half2[k1] = __hfma2(a_half2[i], __high2half2(b_half2[j]), c_half2[k1]);
-      }
-    }
-#endif
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/hgemm_swizzle.h

@@ -1,94 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Transposes a tile of 16b elements. Used by HGEMM to construct a K-strided layout in
-      shared memory for multiplicands.
-*/
-#pragma once
-
-#include <cuda_fp16.h>
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GlobalIterator_>
-struct HgemmSwizzle {
-  /// The global iterator.
-  typedef GlobalIterator_ GlobalIterator;
-  /// The source fragment.
-  typedef typename GlobalIterator::Fragment Fragment;
-  /// The shape of the source fragment.
-  typedef typename GlobalIterator::FragmentShape FragmentShape;
-
-  /// The input fragment.
-  typedef Fragment InputFragment;
-  /// The output fragment.
-  typedef Fragment OutputFragment;
-
-  /// The src/dst must be half fragments.
-  static_assert((platform::is_same<typename Fragment::Element, half>::value), "Works on half");
-
-  /// The number of elements must be a multiple of 2.
-  static_assert(FragmentShape::kH == 2 && ShapeCount<FragmentShape>::kWc == 2, "Not multiple of 2");
-
-  /// Ctor.
-  CUTLASS_DEVICE HgemmSwizzle() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(Fragment const& src, Fragment& dst) {
-    // Expose src/dst as int arrays.
-    int const* src_int = reinterpret_cast<int const*>(&src[0]);
-    int* dst_int = reinterpret_cast<int*>(&dst[0]);
-
-    // Transpose the data.
-    for (int d = 0; d < FragmentShape::kD; ++d) {
-      // The indices to read two consecutive "rows".
-      int const i0 = 2 * d + 0;
-      int const i1 = 2 * d + 1;
-
-      int a0 = src_int[i0];
-      int a1 = src_int[i1];
-
-      int b0, b1;
-      asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b0) : "r"(a0), "r"(a1));
-      asm volatile("prmt.b32 %0, %1, %2, 0x7632;" : "=r"(b1) : "r"(a0), "r"(a1));
-
-      // The indices to store with "strides".
-      int const j0 = 0 * (ShapeCount<FragmentShape>::kDhw / 2) + d;
-      int const j1 = 1 * (ShapeCount<FragmentShape>::kDhw / 2) + d;
-
-      dst_int[j0] = b0;
-      dst_int[j1] = b1;
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/hgemm_traits.h

@@ -1,391 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defies structural properties of half-precision GEMM computation.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/reshape_tile.h>
-
-#include <cutlass/gemm/gemm.h>
-#include <cutlass/gemm/gemm_epilogue.h>
-#include <cutlass/gemm/gemm_epilogue_traits.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/gemm/gemm_shared_tile.h>
-#include <cutlass/gemm/gemm_traits.h>
-#include <cutlass/gemm/hgemm_global_tile.h>
-#include <cutlass/gemm/hgemm_multiply_add.h>
-#include <cutlass/gemm/hgemm_swizzle.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_ = 2,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_ = 2>
-struct HgemmConfig
-    : public GemmConfig<
-          /// The scalar type for A.
-          half,
-          /// The scalar type for B.
-          half,
-          /// The scalar type for C.
-          half,
-          /// The scalar type for D.
-          half,
-          /// The tile size for the GEMM KxNxM.
-          OutputTile_,
-          /// The functor to do the math in the main loop.
-          ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, half, half, half>,
-          /// The number of scalars per LDG for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per STS for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per LDS for A.
-          8,
-          /// The number of scalars per LDG for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per STS for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per LDS for B.
-          8,
-          /// The number of scalars per LDG for C and STG for D.
-          2,
-          /// The number of scalars per STS for D.
-          8,
-          /// The number of scalars per LDS for D.
-          2,
-          /// The number of stages in shared memory.
-          2> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
-struct HgemmTransformerA {};
-
-template <typename Iterator_>
-struct HgemmTransformerA<MatrixLayout::kColumnMajor, Iterator_> {
-  typedef Convert<typename Iterator_::Fragment, typename Iterator_::Fragment> Transformer;
-};
-
-template <typename Iterator_>
-struct HgemmTransformerA<MatrixLayout::kRowMajor, Iterator_> {
-  typedef HgemmSwizzle<Iterator_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
-struct HgemmTransformerB {};
-
-template <typename Iterator_>
-struct HgemmTransformerB<MatrixLayout::kRowMajor, Iterator_> {
-  typedef Convert<typename Iterator_::Fragment, typename Iterator_::Fragment> Transformer;
-};
-
-template <typename Iterator_>
-struct HgemmTransformerB<MatrixLayout::kColumnMajor, Iterator_> {
-  typedef HgemmSwizzle<Iterator_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct HgemmTileTraitsHelperA : public GemmTileTraitsHelperA<kLayout_, GemmConfig_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct HgemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_>
-    : public GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> Base;
-
-  /// The traits class to build the iterator to load data from global memory for A^T.
-  typedef HgemmCrosswiseGlobalTileTraits<
-      GemmOperand::kA,
-      // The layout.
-      MatrixLayout::kRowMajor,
-      // The pointer.
-      half const,
-      // The tile has size MxK in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as (threads / K ) x K (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc)
-      GemmConfig_::kScalarsPerLdgA>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for A^T.
-  typedef GemmSharedStoreWithSkewTileAbTraits<
-      // The pointer.
-      half,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kW * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as warps x 32(the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      2,
-      // The skew to avoid bank conflicts added in the tile W dimension.
-      128 / sizeof(half) / GlobalTileTraits::Threads::kW / 2>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for A^T.
-  typedef GemmSharedLoadTileATraits<
-      // The pointer.
-      half const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      8,
-      // The skew.
-      SharedStoreTileTraits::kSkew>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct HgemmTileTraitsHelperB : public GemmTileTraitsHelperB<kLayout_, GemmConfig_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct HgemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_>
-    : public GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> Base;
-
-  /// The traits class to build the iterator to load data from global memory for B^N.
-  typedef HgemmCrosswiseGlobalTileTraits<
-      GemmOperand::kB,
-      // The layout.
-      MatrixLayout::kColumnMajor,
-      // The pointer.
-      half const,
-      // The tile has size KxN in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc)
-      GemmConfig_::kScalarsPerLdgB>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for B^N.
-  typedef GemmSharedStoreWithSkewTileAbTraits<
-      // The pointer.
-      half,
-      // The tile has size KxN in GEMM's terminology.
-      Shape<GemmConfig_::kStages,
-            GemmConfig_::OutputTile::kD / GemmConfig_::InstructionShape::kD,
-            GemmConfig_::OutputTile::kH * GemmConfig_::InstructionShape::kD>,
-      // The threads are distributed as (threads / K) x K (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      2,
-      // The skew to avoid bank conflicts added in the tile W dimension.
-      128 / sizeof(half) / GlobalTileTraits::Threads::kW / 2>
-      SharedStoreTileTraits;
-
-  /// The traits class to build the iterator to load from shared memory for B^N.
-  typedef GemmSharedLoadTileBTraits<
-      // The pointer.
-      half const,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The number of threads per warp.
-      typename GemmConfig_::MultiplyAdd::ThreadsPerWarp,
-      // The shape of the FMA instruction.
-      typename GemmConfig_::InstructionShape,
-      // The number of stages.
-      GemmConfig_::kStages,
-      // The number of scalars per LDS.
-      8,
-      // The skew.
-      SharedStoreTileTraits::kSkew>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<32, 8, 8>,
-    /// The number of halfs loaded in one LDG for A.
-    int kScalarsPerLdgA_ = 2,
-    /// The number of halfs loaded in one LDG for B.
-    int kScalarsPerLdgB_ = 2,
-    /// The index.
-    typename Index_ = int>
-struct HgemmTraitsHelper {
-  /// The HGEMM config.
-  typedef HgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>
-      GemmConfig;
-  /// The GEMM config for A.
-  typedef HgemmTileTraitsHelperA<kLayoutA_, GemmConfig> GemmTileTraitsHelperA;
-  /// The GEMM config for B.
-  typedef HgemmTileTraitsHelperB<kLayoutB_, GemmConfig> GemmTileTraitsHelperB;
-
-  /// The iterator to load A from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA::GlobalTileTraits, Index_>
-      GlobalLoadIteratorA;
-  /// The default transformer for A.
-  typedef typename HgemmTransformerA<GemmTileTraitsHelperA::kLayout,
-                                     GlobalLoadIteratorA>::Transformer GlobalTransformerA;
-  /// The iterator to store A to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorA;
-  /// The stream to load A from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
-      GlobalLoadStreamA;
-
-  /// The iterator to load B from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB::GlobalTileTraits, Index_>
-      GlobalLoadIteratorB;
-  // The default transformer for B.
-  typedef typename HgemmTransformerB<GemmTileTraitsHelperB::kLayout,
-                                     GlobalLoadIteratorB>::Transformer GlobalTransformerB;
-  /// The iterator to store B to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorB;
-  /// The stream to load B from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
-      GlobalLoadStreamB;
-
-  /// The iterator to load A from shared memory
-  typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorA;
-  /// The stream to load A from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
-  /// The iterator to load B from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorB;
-  /// The stream to load B from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
-
-  /// The functor to do the multiply-add in the main loop.
-  typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
-  /// The object to clear accumulators.
-  typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
-
-  /// The traits class for the epilogue.
-  typedef SimplifiedGemmEpilogueTraits<GemmConfig, EpilogueFunctor_, Index_> GemmEpilogueTraits;
-  /// The epilogue.
-  typedef GemmEpilogue<GemmEpilogueTraits> Epilogue;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_ = Shape<8, 128, 128>,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_ = LinearScaling<half>,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<8, 8, 16>,
-    /// The number of halfs loaded in one LDG for A.
-    int kScalarsPerLdgA_ = 2,
-    /// The number of halfs loaded in one LDG for B.
-    int kScalarsPerLdgB_ = 2,
-    /// The index.
-    typename Index_ = int,
-    /// The helper class.
-    typename Helper_ = HgemmTraitsHelper<kLayoutA_,
-                                         kLayoutB_,
-                                         OutputTile_,
-                                         EpilogueFunctor_,
-                                         AccumulatorsPerThread_,
-                                         kScalarsPerLdgA_,
-                                         kScalarsPerLdgB_,
-                                         Index_> >
-struct HgemmTraits : public GemmTraits<
-                         // The config.
-                         typename Helper_::GemmConfig,
-                         // The stream to load A from global memory to shared memory.
-                         typename Helper_::GlobalLoadStreamA,
-                         // The stream to load B from global memory to shared memory.
-                         typename Helper_::GlobalLoadStreamB,
-                         // The stream to load A from shared memory.
-                         typename Helper_::SharedLoadStreamA,
-                         // The stream to load B from shared memory.
-                         typename Helper_::SharedLoadStreamB,
-                         // The epilogue.
-                         typename Helper_::Epilogue,
-                         // The block swizzle to reorganize the grid.
-                         IdentityBlockSwizzle,
-                         // The index.
-                         Index_,
-                         // The tool used to clear accumulators.
-                         typename Helper_::ClearAccumulators> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/identity_block_swizzle.h

@@ -1,48 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defies functors for mapping blockIdx to partitions of the GEMM computation.
-
-    Currently, we only implement an identity mapping.
-*/
-#pragma once
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-struct IdentityBlockSwizzle {
-  /// Ctor.
-  CUTLASS_DEVICE IdentityBlockSwizzle() {}
-
-  /// Swizzle the block index.
-  CUTLASS_DEVICE dim3 swizzle() { return blockIdx; }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/igemm_epilogue.h

@@ -1,320 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines the epilogue phase of the GEMM computation for IGEMM, supporting integer and
-      floating-point output matrix formats.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/fragment.h>
-#include <cutlass/gemm/gemm_global_stream.h>
-#include <cutlass/gemm/gemm_shared_stream.h>
-#include <cutlass/gemm/igemm_global_tile.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <int kElements_>
-struct IgemmFloatToInt8Converter {
-  /// The input fragment.
-  typedef Fragment<float, kElements_> InputFragment;
-  /// The output fragment.
-  typedef Fragment<int8_t, kElements_> OutputFragment;
-
-  // We are packing 4 floats into int32 registers so we need kElements to be multiple of 4.
-  static_assert(kElements_ % 4 == 0, "kElements must be multiple of 4");
-
-  /// Ctor.
-  CUTLASS_DEVICE IgemmFloatToInt8Converter() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
-    transform(src, 0, dst);
-  }
-
-  /// Transform a fragment.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
-    // The inputs.
-    float4 const* src_f4 = reinterpret_cast<float4 const*>(&src[0]);
-    // The outputs.
-    int* dst_int = reinterpret_cast<int*>(&dst[0]);
-
-    // Iterate over the floats and pack them together to produce ints.
-    for (int i = 0; i < kElements_ / 4; ++i) {
-      // Read the float4.
-      float4 f4 = src_f4[i];
-
-      // Clamp the 4 elements of the floats to the [-128, +127] range.
-      float x = fmaxf(-128.f, fminf(127.f, f4.x));
-      float y = fmaxf(-128.f, fminf(127.f, f4.y));
-      float z = fmaxf(-128.f, fminf(127.f, f4.z));
-      float w = fmaxf(-128.f, fminf(127.f, f4.w));
-
-      // Convert to integers.
-      int ix = (int)x;
-      int iy = (int)y;
-      int iz = (int)z;
-      int iw = (int)w;
-
-      // Extract the lower bytes to build an int32 with 4 int8.
-      asm volatile("prmt.b32 %0, %0, %1, 0x1140;" : "+r"(ix) : "r"(iy));
-      asm volatile("prmt.b32 %0, %0, %1, 0x1140;" : "+r"(iz) : "r"(iw));
-      asm volatile("prmt.b32 %0, %0, %1, 0x5410;" : "+r"(ix) : "r"(iz));
-
-      // Store the int.
-      dst_int[i] = ix;
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename InputScalar_, typename OutputFragment_>
-struct IgemmGlobalStoreTransformer {
-  typedef Convert<Fragment<InputScalar_, OutputFragment_::kElements>, OutputFragment_> Transformer;
-};
-
-template <int kElements_>
-struct IgemmGlobalStoreTransformer<float, Fragment<int8_t, kElements_> > {
-  typedef IgemmFloatToInt8Converter<kElements_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <int kElements_>
-struct IgemmInt8ToFloatConverter {
-  /// The input fragment.
-  typedef Fragment<int8_t, kElements_> InputFragment;
-  /// The output fragment.
-  typedef Fragment<float, kElements_> OutputFragment;
-
-  // We are unpacking 4 int8s from int32.
-  static_assert(kElements_ % 4 == 0, "kElements must be multiple of 4");
-
-  /// Ctor.
-  CUTLASS_DEVICE IgemmInt8ToFloatConverter() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(InputFragment const& src, OutputFragment& dst) {
-    transform(src, 0, dst);
-  }
-
-  /// Transform a fragment.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void transform(Fragment_ const& src, int offset, OutputFragment& dst) {
-    // The inputs.
-    int const* src_int = reinterpret_cast<int const*>(&src[0]);
-    // The outputs.
-    float4* dst_f4 = reinterpret_cast<float4*>(&dst[0]);
-
-    // Iterate over the int8 and unpack them together to produce floats.
-    for (int i = 0; i < kElements_ / 4; ++i) {
-      // Read the int.
-      int ix, iy, iz, iw = src_int[i];
-
-      // Extract the 4 bytes.
-      asm volatile("prmt.b32 %0, 0x0, %1, 0x4440;" : "=r"(ix) : "r"(iw));
-      asm volatile("prmt.b32 %0, 0x0, %1, 0x4441;" : "=r"(iy) : "r"(iw));
-      asm volatile("prmt.b32 %0, 0x0, %1, 0x4442;" : "=r"(iz) : "r"(iw));
-      asm volatile("prmt.b32 %0, 0x0, %1, 0x4443;" : "=r"(iw) : "r"(iw));
-
-      // The floats.
-      float fx, fy, fz, fw;
-
-      // Convert to floats (make sure we generate I2F.F32.S8).
-      asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fx) : "r"(ix));
-      asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fy) : "r"(iy));
-      asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fz) : "r"(iz));
-      asm volatile("cvt.rn.f32.s8 %0, %1;" : "=f"(fw) : "r"(iw));
-
-      // Store the float4.
-      dst_f4[i] = make_float4(fx, fy, fz, fw);
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename InputFragment_, typename OutputScalar_>
-struct IgemmGlobalLoadTransformer {
-  typedef Convert<InputFragment_, Fragment<OutputScalar_, InputFragment_::kElements> > Transformer;
-};
-
-template <int kElements_>
-struct IgemmGlobalLoadTransformer<Fragment<int8_t, kElements_>, float> {
-  typedef IgemmInt8ToFloatConverter<kElements_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename InputScalar_, typename OutputFragment_>
-struct IgemmSharedStoreTransformer {
-  typedef Convert<Fragment<InputScalar_, OutputFragment_::kElements>, OutputFragment_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename IgemmConfig_, typename EpilogueFunctor_, typename Index_>
-struct IgemmEpilogueTraitsHelper
-    : public GemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> {
-  /// The base class.
-  typedef GemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> Base;
-  /// The config.
-  typedef IgemmConfig_ IgemmConfig;
-
-  /// The scalar type of the epilogue.
-  typedef typename Base::Scalar Scalar;
-  /// The iterations.
-  typedef typename Base::Iterations Iterations;
-  /// The iterations strides.
-  typedef typename Base::Delta Delta;
-
-  /// The traits class for the iterator.
-  typedef typename Base::GlobalLoadTileTraits GlobalLoadTileTraits;
-  /// The iterator to store to shared memory.
-  typedef GemmGlobalIteratorCd<GlobalLoadTileTraits> GlobalLoadIteratorC;
-  /// The fragment that needs to be produced by the load iterator.
-  typedef typename GlobalLoadIteratorC::Fragment GlobalFragmentC;
-  /// The transformer from loaded data to math fragment.
-  typedef
-      typename IgemmGlobalLoadTransformer<GlobalFragmentC, Scalar>::Transformer GlobalTransformerC;
-
-  /// The traits class for the iterator.
-  typedef typename Base::GlobalStoreTileTraits GlobalStoreTileTraits;
-  /// The iterator to store to shared memory.
-  typedef GemmGlobalIteratorCd<GlobalStoreTileTraits> GlobalStoreIteratorD;
-  /// The fragment that needs to be passed to that store iterator.
-  typedef typename GlobalStoreIteratorD::Fragment GlobalFragmentD;
-  /// The transformer from accumulators to shared memory fragments.
-  typedef
-      typename IgemmGlobalStoreTransformer<Scalar, GlobalFragmentD>::Transformer GlobalTransformerD;
-
-  /// The traits class for the shared iterator to store D to shared memory.
-  typedef typename Base::SharedStoreTileTraits SharedStoreTileTraits;
-  /// The shared iterator to store D to shared memory.
-  typedef TileStoreIterator<SharedStoreTileTraits,
-                            typename SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kGlobal>
-      SharedStoreIteratorD;
-  /// The fragment that needs to be passed to that store iterator.
-  typedef typename SharedStoreIteratorD::Fragment SharedStoreFragmentD;
-  /// The transformer from accumulators to shared memory fragments.
-  typedef typename IgemmSharedStoreTransformer<typename IgemmConfig::Accumulators::Element,
-                                               SharedStoreFragmentD>::Transformer
-      SharedStoreTransformerD;
-  /// The traits class for the shared iterator to load D from shared memory.
-  typedef typename Base::SharedLoadTileTraits SharedLoadTileTraits;
-  /// The shared iterator to load D from shared memory.
-  typedef TileLoadIterator<SharedLoadTileTraits,
-                           typename SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorD;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The config.
-    typename IgemmConfig_,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_,
-    /// The index.
-    typename Index_ = int,
-    /// The helper class to assemble the traits.
-    typename Helper_ = IgemmEpilogueTraitsHelper<IgemmConfig_, EpilogueFunctor_, Index_> >
-struct IgemmEpilogueTraits : public GemmEpilogueTraits<
-                                 // The output tile.
-                                 typename IgemmConfig_::OutputTile,
-                                 // The accumulators.
-                                 typename IgemmConfig_::Accumulators,
-                                 // The global iterator for C.
-                                 typename Helper_::GlobalLoadIteratorC,
-                                 // The transformer for C.
-                                 typename Helper_::GlobalTransformerC,
-                                 // The transformer for D.
-                                 typename Helper_::GlobalTransformerD,
-                                 // The global iterator for D.
-                                 typename Helper_::GlobalStoreIteratorD,
-                                 // The iterator to store D to shared memory.
-                                 typename Helper_::SharedStoreIteratorD,
-                                 // The shared store transformer for D.
-                                 typename Helper_::SharedStoreTransformerD,
-                                 // The iterator to load D from shared memory.
-                                 typename Helper_::SharedLoadIteratorD,
-                                 // The iterations.
-                                 typename Helper_::Iterations,
-                                 // The strides between iterations.
-                                 typename Helper_::Delta,
-                                 // The functor to be used in the epilogue.
-                                 EpilogueFunctor_,
-                                 // The index.
-                                 Index_> {
-  /// Do we output in int8?
-  static bool const kInt8Output =
-      platform::is_same<typename IgemmConfig_::ScalarC, int8_t>::value != 0;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmEpilogueTraits_, bool = GemmEpilogueTraits_::kInt8Output>
-struct IgemmEpilogue : public GemmEpilogue<GemmEpilogueTraits_> {
-  /// The base class.
-  typedef GemmEpilogue<GemmEpilogueTraits_> Base;
-
-  /// Ctor.
-  CUTLASS_DEVICE IgemmEpilogue(typename Base::Params const& params_,
-                               typename Base::SharedStorage& shared_storage_,
-                               typename Base::Index m_,
-                               typename Base::Index n_)
-      : Base(params_, shared_storage_, m_, n_) {}
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmEpilogueTraits_>
-struct IgemmEpilogue<GemmEpilogueTraits_, true> : public GemmEpilogue<GemmEpilogueTraits_> {
-  /// The base class.
-  typedef GemmEpilogue<GemmEpilogueTraits_> Base;
-
-  /// Ctor.
-  CUTLASS_DEVICE IgemmEpilogue(typename Base::Params const& params_,
-                               typename Base::SharedStorage& shared_storage_,
-                               typename Base::Index m_,
-                               typename Base::Index n_)
-      : Base(params_, shared_storage_, m_, n_) {}
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/igemm_global_tile.h

@@ -1,95 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements tile iterators to partition the thread block tile into 2D subtiles and
-      efficiently load each. Applies permute transformation to construct 'interleaved K-strided'
-      data layout in which 4-element dot products from the same K index are arranged in consecutive
-      locations within shared memory.
-
-      Supports efficient loads from shared memory to target the DP4A instruction.
-*/
-#pragma once
-
-#include <cutlass/coord.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/matrix_traits.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <GemmOperand::Kind kOperand_,
-          MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename Tile_,
-          typename Threads_,
-          int kAccessSize_>
-struct IgemmContiguousGlobalTileTraits : public GemmGlobalTileTraits<
-                                             // Which GEMM operand?
-                                             kOperand_,
-                                             // The layout.
-                                             kLayout_,
-                                             // The scalar.
-                                             Scalar_,
-                                             // The tile.
-                                             Tile_,
-                                             // The threads.
-                                             Threads_,
-                                             // The number of scalars per LDG/STG.
-                                             kAccessSize_> {
-  /// The base class.
-  typedef GemmGlobalTileTraits<kOperand_, kLayout_, Scalar_, Tile_, Threads_, kAccessSize_> Base;
-  /// The threads.
-  typedef typename Base::Threads Threads;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<Base::Threads::kH * 4, 1, Base::Threads::kW, Base::kAccessSize> Delta;
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<Base::Tile::kH / Base::Threads::kH / 4,
-                4,
-                Base::Tile::kW / Base::Threads::kW,
-                Base::Tile::kC / Base::kAccessSize>
-      Iterations;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int thread_offset_h = threadIdx.x / Threads::kW * ThreadsDelta::kH;
-      int thread_offset_w = threadIdx.x % Threads::kW * ThreadsDelta::kW;
-
-      return make_Coord(0, thread_offset_h, thread_offset_w, 0);
-    }
-  };
-
- public:
-  /// The threads strides.
-  typedef Shape<1, 4, Base::Tile::kC> ThreadsDelta;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/igemm_multiply_add.h

@@ -1,89 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements matrix multiply accumulate operation of 8-bit integer data using DP4A
-   instruction.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-#include <cutlass/gemm/thread_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Template performing matrix multiply-add operation within a thread
-template <typename AccumulatorsPerThread_, typename ThreadsPerWarp_>
-struct ThreadMultiplyAdd<AccumulatorsPerThread_, ThreadsPerWarp_, int8_t, int8_t, int> {
-  /// The shape of the instruction.
-  typedef Shape<4, 1, 1> InstructionShape;
-  /// The number of accumulators per thread.
-  typedef AccumulatorsPerThread_ AccumulatorsPerThread;
-  /// The number of threads per warp.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of accumulators per warp.
-  typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
-  /// The type for A.
-  typedef int8_t ScalarA;
-  /// The fragment for A.
-  typedef Fragment<ScalarA, AccumulatorsPerThread::kW * 4> FragmentA;
-  /// The type for B.
-  typedef int8_t ScalarB;
-  /// The fragment for B.
-  typedef Fragment<ScalarB, AccumulatorsPerThread::kH * 4> FragmentB;
-  /// The type for C and D.
-  typedef int ScalarC;
-  /// The accumulators.
-  typedef Fragment<ScalarC, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW> Accumulators;
-
-  /// Ctor.
-  CUTLASS_DEVICE ThreadMultiplyAdd() {}
-
-  /// Multiply : d = a*b + c.
-  CUTLASS_DEVICE void multiply_add(FragmentA const& a,
-                                   FragmentB const& b,
-                                   Accumulators const& c,
-                                   Accumulators& d) {
-    // The inputs.
-    int const* a_int = reinterpret_cast<int const*>(&a[0]);
-    int const* b_int = reinterpret_cast<int const*>(&b[0]);
-
-    for (int j = 0; j < AccumulatorsPerThread::kH; ++j) {
-      for (int i = 0; i < AccumulatorsPerThread::kW; ++i) {
-        asm volatile("dp4a.s32.s32 %0, %1, %2, %3;"
-                     : "=r"(d[j * AccumulatorsPerThread::kW + i])
-                     : "r"(a_int[i]), "r"(b_int[j]), "r"(c[j * AccumulatorsPerThread::kW + i]));
-      }
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/igemm_swizzle.h

@@ -1,115 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Transposes a fragment of data containing packed 8-bit integer elements.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GlobalIterator_>
-struct IgemmSwizzle {
-  /// The global iterator.
-  typedef GlobalIterator_ GlobalIterator;
-  /// The source fragment.
-  typedef typename GlobalIterator::Fragment Fragment;
-  /// The shape of the source fragment.
-  typedef typename GlobalIterator::FragmentShape FragmentShape;
-
-  /// The source fragment.
-  typedef Fragment InputFragment;
-  /// The destination fragment.
-  typedef Fragment OutputFragment;
-
-  /// The src/dst must be int8 fragments.
-  static_assert((platform::is_same<typename Fragment::Element, int8_t>::value), "Works on int8");
-
-  /// The number of elements must be a multiple of 4.
-  static_assert(FragmentShape::kH % 4 == 0 && ShapeCount<FragmentShape>::kWc % 4 == 0,
-                "Not multiple of 4");
-
-  /// Ctor.
-  CUTLASS_DEVICE IgemmSwizzle() {}
-
-  /// Transform a fragment.
-  CUTLASS_DEVICE void transform(Fragment const& src, Fragment& dst) {
-    // Expose src/dst as int arrays.
-    int const* src_int = reinterpret_cast<int const*>(&src[0]);
-    int* dst_int = reinterpret_cast<int*>(&dst[0]);
-
-    // Transpose the data.
-    for (int d = 0; d < FragmentShape::kD; ++d) {
-      for (int h = 0; h < FragmentShape::kH / 4; ++h) {
-        for (int w = 0; w < ShapeCount<FragmentShape>::kWc / 4; ++w) {
-          int const i0 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
-                         (4 * h + 0) * (ShapeCount<FragmentShape>::kWc / 4) + w;
-          int const i1 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
-                         (4 * h + 1) * (ShapeCount<FragmentShape>::kWc / 4) + w;
-          int const i2 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
-                         (4 * h + 2) * (ShapeCount<FragmentShape>::kWc / 4) + w;
-          int const i3 = d * (ShapeCount<FragmentShape>::kHwc / 4) +
-                         (4 * h + 3) * (ShapeCount<FragmentShape>::kWc / 4) + w;
-
-          int a0 = src_int[i0];
-          int a1 = src_int[i1];
-          int a2 = src_int[i2];
-          int a3 = src_int[i3];
-
-          int b0, b1, b2, b3, c0;
-          asm volatile("prmt.b32 %0, %1, %2, 0x0040;" : "=r"(b0) : "r"(a0), "r"(a1));
-          asm volatile("prmt.b32 %0, %1, %2, 0x0040;" : "=r"(c0) : "r"(a2), "r"(a3));
-          asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b0) : "r"(b0), "r"(c0));
-
-          asm volatile("prmt.b32 %0, %1, %2, 0x0051;" : "=r"(b1) : "r"(a0), "r"(a1));
-          asm volatile("prmt.b32 %0, %1, %2, 0x0051;" : "=r"(c0) : "r"(a2), "r"(a3));
-          asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b1) : "r"(b1), "r"(c0));
-
-          asm volatile("prmt.b32 %0, %1, %2, 0x0062;" : "=r"(b2) : "r"(a0), "r"(a1));
-          asm volatile("prmt.b32 %0, %1, %2, 0x0062;" : "=r"(c0) : "r"(a2), "r"(a3));
-          asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b2) : "r"(b2), "r"(c0));
-
-          asm volatile("prmt.b32 %0, %1, %2, 0x0073;" : "=r"(b3) : "r"(a0), "r"(a1));
-          asm volatile("prmt.b32 %0, %1, %2, 0x0073;" : "=r"(c0) : "r"(a2), "r"(a3));
-          asm volatile("prmt.b32 %0, %1, %2, 0x5410;" : "=r"(b3) : "r"(b3), "r"(c0));
-
-          dst_int[i0] = b0;
-          dst_int[i1] = b1;
-          dst_int[i2] = b2;
-          dst_int[i3] = b3;
-        }
-      }
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/igemm_traits.h

@@ -1,393 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defies structural properties of mixed-precision integer GEMM. Multiplicands are assumed
-      to be packed 8bit integers, accumulators are assumed to be 32b signed integers, and output
-      formats vary.
-*/
-#pragma once
-
-#include <cutlass/convert.h>
-#include <cutlass/gemm/gemm.h>
-#include <cutlass/gemm/gemm_epilogue.h>
-#include <cutlass/gemm/gemm_epilogue_traits.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/gemm/gemm_shared_tile.h>
-#include <cutlass/gemm/gemm_traits.h>
-#include <cutlass/gemm/igemm_epilogue.h>
-#include <cutlass/gemm/igemm_global_tile.h>
-#include <cutlass/gemm/igemm_multiply_add.h>
-#include <cutlass/gemm/igemm_swizzle.h>
-#include <cutlass/reshape_tile.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The output type.
-    typename ScalarD_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_>
-struct IgemmConfig
-    : public GemmConfig<
-          /// The scalar type for A.
-          int8_t,
-          /// The scalar type for B.
-          int8_t,
-          /// The scalar type for C.
-          ScalarD_,
-          /// The scalar type for D.
-          ScalarD_,
-          /// The tile size for the GEMM KxNxM.
-          OutputTile_,
-          /// The functor to do the math in the main loop.
-          ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, int8_t, int8_t, int>,
-          /// The number of scalars per LDG for A.
-          4,
-          /// The number of scalars per STS for A.
-          4,
-          /// The number of scalars per LDS for A.
-          16,
-          /// The number of scalars per LDG for B.
-          4,
-          /// The number of scalars per STS for B.
-          4,
-          /// The number of scalars per LDS for B.
-          16,
-          /// The number of scalars per LDG for C and STG for D.
-          1,
-          /// The number of scalars per STS for D.
-          4,
-          /// The number of scalars per LDS for D.
-          1,
-          /// The number of stages in shared memory.
-          2> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename OutputTile_, typename AccumulatorsPerThread_>
-struct IgemmConfig<OutputTile_, int8_t, AccumulatorsPerThread_>
-    : public GemmConfig<
-          /// The scalar type for A.
-          int8_t,
-          /// The scalar type for B.
-          int8_t,
-          /// The scalar type for C.
-          int8_t,
-          /// The scalar type for D.
-          int8_t,
-          /// The tile size for the GEMM KxNxM.
-          OutputTile_,
-          /// The functor to do the math in the main loop.
-          ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, int8_t, int8_t, int>,
-          /// The number of scalars per LDG for A.
-          4,
-          /// The number of scalars per STS for A.
-          4,
-          /// The number of scalars per LDS for A.
-          16,
-          /// The number of scalars per LDG for B.
-          4,
-          /// The number of scalars per STS for B.
-          4,
-          /// The number of scalars per LDS for B.
-          16,
-          /// The number of scalars per LDG for C and STG for D.
-          4,
-          /// The number of scalars per STS for D.
-          4,
-          /// The number of scalars per LDS for D.
-          4,
-          /// The number of stages in shared memory.
-          2> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct IgemmTileTraitsHelperA : public GemmTileTraitsHelperA<kLayout_, GemmConfig_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct IgemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_>
-    : public GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> Base;
-
-  /// The number of scalars per LDG/STS/LDS for A.
-  static int const kScalarsPerStsA = 16;
-
-  /// The traits class to build the iterator to load data from global memory for A^N.
-  typedef IgemmContiguousGlobalTileTraits<
-      GemmOperand::kA,
-      // The layout.
-      MatrixLayout::kColumnMajor,
-      // The pointer is float const.
-      int8_t const,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      4>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for A^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer is float.
-      int8_t,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kW * 4>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      kScalarsPerStsA>
-      SharedStoreTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct IgemmTileTraitsHelperB : public GemmTileTraitsHelperB<kLayout_, GemmConfig_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct IgemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_>
-    : public GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> Base;
-
-  /// The number of scalars per LDG/STS/LDS for B.
-  static int const kScalarsPerStsB = 16;
-
-  /// The traits class to build the iterator to load data from global memory for B^T.
-  typedef IgemmContiguousGlobalTileTraits<
-      GemmOperand::kB,
-      // The layout.
-      MatrixLayout::kRowMajor,
-      // The pointer is float const.
-      int8_t const,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kH>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      4>
-      GlobalTileTraits;
-
-  /// The traits class to build the iterator to store data to shared memory for B^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer is float.
-      int8_t,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<GemmConfig_::kStages, GemmConfig_::OutputTile::kD / 4, GemmConfig_::OutputTile::kH * 4>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      kScalarsPerStsB>
-      SharedStoreTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
-struct IgemmTransformerA {};
-
-template <typename Iterator_>
-struct IgemmTransformerA<MatrixLayout::kRowMajor, Iterator_> {
-  typedef Copy<typename Iterator_::Fragment> Transformer;
-};
-
-template <typename Iterator_>
-struct IgemmTransformerA<MatrixLayout::kColumnMajor, Iterator_> {
-  typedef IgemmSwizzle<Iterator_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename Iterator_>
-struct IgemmTransformerB {};
-
-template <typename Iterator_>
-struct IgemmTransformerB<MatrixLayout::kColumnMajor, Iterator_> {
-  typedef Copy<typename Iterator_::Fragment> Transformer;
-};
-
-template <typename Iterator_>
-struct IgemmTransformerB<MatrixLayout::kRowMajor, Iterator_> {
-  typedef IgemmSwizzle<Iterator_> Transformer;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_,
-    /// The output type.
-    typename ScalarD_,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<32, 8, 8>,
-    /// The index.
-    typename Index_ = int>
-struct IgemmTraitsHelper {
-  /// The IGEMM config.
-  typedef IgemmConfig<OutputTile_, ScalarD_, AccumulatorsPerThread_> GemmConfig;
-  /// The GEMM config for A.
-  typedef IgemmTileTraitsHelperA<kLayoutA_, GemmConfig> GemmTileTraitsHelperA;
-  /// The GEMM config for B.
-  typedef IgemmTileTraitsHelperB<kLayoutB_, GemmConfig> GemmTileTraitsHelperB;
-
-  /// The iterator to load A from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA::GlobalTileTraits, Index_>
-      GlobalLoadIteratorA;
-  /// The default transformer for A.
-  typedef typename IgemmTransformerA<GemmTileTraitsHelperA::kLayout,
-                                     GlobalLoadIteratorA>::Transformer GlobalTransformerA;
-  /// The iterator to store A to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorA;
-  /// The stream to load A from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
-      GlobalLoadStreamA;
-
-  /// The iterator to load B from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB::GlobalTileTraits, Index_>
-      GlobalLoadIteratorB;
-  // The default transformer for B.
-  typedef typename IgemmTransformerB<GemmTileTraitsHelperB::kLayout,
-                                     GlobalLoadIteratorB>::Transformer GlobalTransformerB;
-  /// The iterator to store B to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorB;
-  /// The stream to load B from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
-      GlobalLoadStreamB;
-
-  /// The iterator to load A from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorA;
-  /// The stream to load A from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorA, Copy<typename SharedLoadIteratorA::Fragment> >
-      SharedLoadStreamA;
-  /// The iterator to load B from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorB;
-  /// The stream to load B from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorB, Copy<typename SharedLoadIteratorB::Fragment> >
-      SharedLoadStreamB;
-
-  /// The multiply-add functor.
-  typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
-  /// The object to clear accumulators.
-  typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
-
-  /// The epilogue.
-  typedef IgemmEpilogue<IgemmEpilogueTraits<GemmConfig, EpilogueFunctor_> > Epilogue;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename ScalarD_>
-struct IgemmEpilogueScalar {
-  typedef float Scalar;
-};
-
-template <>
-struct IgemmEpilogueScalar<int> {
-  typedef int Scalar;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_ = Shape<32, 128, 128>,
-    /// The output type.
-    typename ScalarD_ = int,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_ = LinearScaling<typename IgemmEpilogueScalar<ScalarD_>::Scalar>,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<32, 8, 8>,
-    /// The index.
-    typename Index_ = int,
-    /// The helper class.
-    typename Helper_ = IgemmTraitsHelper<kLayoutA_,
-                                         kLayoutB_,
-                                         OutputTile_,
-                                         ScalarD_,
-                                         EpilogueFunctor_,
-                                         AccumulatorsPerThread_,
-                                         Index_> >
-struct IgemmTraits : public GemmTraits<
-                         // The config.
-                         typename Helper_::GemmConfig,
-                         // The stream to load A from global memory to shared memory.
-                         typename Helper_::GlobalLoadStreamA,
-                         // The stream to load B from global memory to shared memory.
-                         typename Helper_::GlobalLoadStreamB,
-                         // The stream to load A from shared memory.
-                         typename Helper_::SharedLoadStreamA,
-                         // The stream to load B from shared memory.
-                         typename Helper_::SharedLoadStreamB,
-                         // The epilogue.
-                         typename Helper_::Epilogue,
-                         // The block swizzle to reorganize the grid.
-                         IdentityBlockSwizzle,
-                         // The index.
-                         Index_,
-                         // The tool used to clear accumulators.
-                         typename Helper_::ClearAccumulators> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/linear_scaling.h

@@ -1,86 +0,0 @@
-
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements the BLAS linear scaling function alpha*AB + beta*C
-*/
-#pragma once
-
-#include <cutlass/fragment_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Functor to compute linear combination of fragments
-template <typename Scalar_, typename FragmentMultiplyAdd_ = FragmentMultiplyAdd<Scalar_> >
-struct LinearScaling {
-  // The scalar.
-  typedef Scalar_ Scalar;
-  // The adapater.
-  typedef FragmentMultiplyAdd_ FragmentMultiplyAdd;
-
-  /// The parameters.
-  struct Params {
-    /// The alpha/beta scaling params.
-    Scalar alpha, beta;
-
-    /// Initialize the parameters.
-    template <typename GemmDesc_>
-    CUTLASS_HOST_DEVICE int initialize(GemmDesc_ const& desc) {
-      alpha = desc.alpha;
-      beta = desc.beta;
-      return 0;
-    }
-  };
-
-  /// Ctor.
-  CUTLASS_DEVICE LinearScaling(Params const& params) : alpha(params.alpha), beta(params.beta) {}
-
-  /// Evaluate the functor.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void evaluate(Fragment_ const& accum, Fragment_& output) {
-    FragmentMultiplyAdd mad;
-    mad.multiply(alpha, accum, output);
-  }
-
-  /// Evaluate the functor.
-  template <typename Fragment_>
-  CUTLASS_DEVICE void evaluate(Fragment_ const& accum, Fragment_ const& old, Fragment_& output) {
-    FragmentMultiplyAdd mad;
-    Fragment_ tmp;
-    mad.multiply(beta, old, tmp);
-    mad.multiply_add(alpha, accum, tmp, output);
-  }
-
-  /// The alpha/beta scaling factors.
-  Scalar alpha, beta;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/sgemm_traits.h

@@ -1,127 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defies structural properties of single-precision GEMM.
-*/
-#pragma once
-
-#include <cutlass/gemm/gemm.h>
-#include <cutlass/gemm/gemm_epilogue.h>
-#include <cutlass/gemm/gemm_epilogue_traits.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/gemm/gemm_shared_tile.h>
-#include <cutlass/gemm/gemm_traits.h>
-#include <cutlass/gemm/thread_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_ = 1,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_ = 1>
-struct SgemmConfig
-    : public GemmConfig<
-          /// The scalar type for A.
-          float,
-          /// The scalar type for B.
-          float,
-          /// The scalar type for C.
-          float,
-          /// The scalar type for D.
-          float,
-          /// The tile size for the GEMM KxNxM.
-          OutputTile_,
-          /// The functor to do the math in the main loop.
-          ThreadMultiplyAdd<AccumulatorsPerThread_, Shape<1, 4, 8>, float, float, float>,
-          /// The number of scalars per LDG for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per STS for A.
-          kScalarsPerLdgA_,
-          /// The number of scalars per LDS for A.
-          4,
-          /// The number of scalars per LDG for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per STS for B.
-          kScalarsPerLdgB_,
-          /// The number of scalars per LDS for B.
-          4,
-          /// The number of scalars per LDG for C and STG for D.
-          1,
-          /// The number of scalars per STS for D.
-          4,
-          /// The number of scalars per LDS for D.
-          1,
-          /// The number of stages in shared memory.
-          2> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_ = Shape<8, 128, 128>,
-    /// The functor to use in the epilogue.
-    typename EpilogueFunctor_ = LinearScaling<float>,
-    /// The number of accumulators per thread.
-    typename AccumulatorsPerThread_ = Shape<8, 8, 8>,
-    /// The number of floats loaded in one LDG for A.
-    int kScalarsPerLdgA_ = 1,
-    /// The number of floats loaded in one LDG for B.
-    int kScalarsPerLdgB_ = 1,
-    /// The index.
-    typename Index_ = int,
-    /// The SGEMM config.
-    typename GemmConfig_ =
-        SgemmConfig<OutputTile_, AccumulatorsPerThread_, kScalarsPerLdgA_, kScalarsPerLdgB_>,
-    /// The traits class for the epilogue.
-    typename GemmEpilogueTraits_ =
-        SimplifiedGemmEpilogueTraits<GemmConfig_, EpilogueFunctor_, Index_> >
-struct SgemmTraits : public SimplifiedGemmTraits<
-                         // The layout for A.
-                         kLayoutA_,
-                         // The layout for B.
-                         kLayoutB_,
-                         // The config.
-                         GemmConfig_,
-                         // The epilogue.
-                         GemmEpilogue<GemmEpilogueTraits_>,
-                         // The index.
-                         Index_> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/thread_multiply_add.h

@@ -1,84 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Template implementing matrix multiply-add operations on fragments.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Template performing matrix multiply-add operation within a thread
-template <typename AccumulatorsPerThread_,
-          typename ThreadsPerWarp_,
-          typename ScalarA_,
-          typename ScalarB_,
-          typename ScalarC_>
-struct ThreadMultiplyAdd {
-  /// The shape of the instruction.
-  typedef Shape<1, 1, 1, 1> InstructionShape;
-  /// The number of accumulators per thread.
-  typedef AccumulatorsPerThread_ AccumulatorsPerThread;
-  /// The number of threads per warp.
-  typedef ThreadsPerWarp_ ThreadsPerWarp;
-  /// The number of accumulators per warp.
-  typedef typename ShapeMul<AccumulatorsPerThread, ThreadsPerWarp>::Shape AccumulatorsPerWarp;
-  /// The type for A.
-  typedef ScalarA_ ScalarA;
-  /// The fragment for A.
-  typedef Fragment<ScalarA, AccumulatorsPerThread::kW> FragmentA;
-  /// The type for B.
-  typedef ScalarB_ ScalarB;
-  /// The fragment for B.
-  typedef Fragment<ScalarB, AccumulatorsPerThread::kH> FragmentB;
-  /// The type for C and D.
-  typedef ScalarC_ ScalarC;
-  /// The accumulators.
-  typedef Fragment<ScalarC, AccumulatorsPerThread::kH * AccumulatorsPerThread::kW, 16> Accumulators;
-
-  /// Ctor.
-  CUTLASS_DEVICE ThreadMultiplyAdd() {}
-
-  /// Multiply : d = a*b + c.
-  CUTLASS_DEVICE void multiply_add(FragmentA const& a,
-                                   FragmentB const& b,
-                                   Accumulators const& c,
-                                   Accumulators& d) {
-    for (int j = 0; j < AccumulatorsPerThread::kH; ++j) {
-      for (int i = 0; i < AccumulatorsPerThread::kW; ++i) {
-        d[j * AccumulatorsPerThread::kW + i] = a[i] * b[j] + c[j * AccumulatorsPerThread::kW + i];
-      }
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/wmma_gemm_epilogue_traits.h

@@ -1,161 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines structural properties of WMMA GEMM's epilogue phase.
-*/
-#pragma once
-
-#include <cutlass/wmma_matrix.h>
-#ifdef CUTLASS_USE_WMMA_API
-
-#include <cutlass/convert.h>
-#include <cutlass/coord.h>
-#include <cutlass/gemm/gemm_global_stream.h>
-#include <cutlass/gemm/gemm_shared_stream.h>
-#include <cutlass/gemm/linear_scaling.h>
-#include <cutlass/gemm/wmma_gemm_global_tile.h>
-#include <cutlass/gemm/wmma_gemm_shared_tile.h>
-#include <cutlass/reshape_tile.h>
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_, typename EpilogueFunctor_, typename Index_ = int>
-struct WmmaGemmEpilogueTraitsHelper {
-  /// The scalar.
-  typedef typename EpilogueFunctor_::Scalar Scalar;
-  /// The output tile.
-  typedef typename GemmConfig_::OutputTile OutputTile;
-
-  /// The number of WMMAs in the H dimension.
-  static int const kWmmasPerH =
-      GemmConfig_::AccumulatorsPerWarp::kH / GemmConfig_::InstructionShape::kH;
-  /// The number of iterations in the epilogue. That's the number of "horizontal" WMMAs.
-  typedef Shape<1, 1, kWmmasPerH> Iterations;
-  // The iteration strides in the H/W dimension.
-  typedef Shape<0, 0, 0> Delta;
-  /// The functor to do the math in the epilogue.
-  typedef EpilogueFunctor_ Functor;
-
-  /// The traits class to build the iterator to store to shared memory for D.
-  typedef WmmaGemmSharedStoreTileDTraits<
-      // The output layout.
-      MatrixLayout::kColumnMajor,
-      // The pointer is float.
-      typename Functor::Scalar,
-      // The output tile size.
-      typename GemmConfig_::OutputTile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The shape of the instruction.
-      typename GemmConfig_::InstructionShape>
-      SharedStoreTileTraits;
-
-  typedef WmmaMatrix<GemmOperand::kC,
-                     MatrixLayout::kColumnMajor,
-                     Scalar,
-                     typename GemmConfig_::InstructionShape>
-      WmmaMatrix;
-
-  /// The iterator to store D to shared memory.
-  typedef TileStoreIterator<SharedStoreTileTraits,
-                            typename SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared,
-                            Index_,
-                            WmmaMatrix,
-                            IteratorFragment::kWmmaMatrix>
-      SharedStoreIteratorD;
-
-  /// The shared store transformer for D.
-  typedef Copy<typename SharedStoreIteratorD::Fragment> SharedStoreTransformerD;
-
-  /// The traits class to build the iterator to load from shared memory for D.
-  typedef WmmaGemmSharedLoadTileDTraits<
-      // The pointer.
-      typename Functor::Scalar,
-      // The tile size.
-      typename SharedStoreIteratorD::Tile,
-      // The number of threads.
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDS.
-      GemmConfig_::kScalarsPerLdsD>
-      SharedLoadTileTraits;
-
-  /// The iterator to load D from shared memory.
-  typedef TileLoadIterator<SharedLoadTileTraits,
-                           typename SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared>
-      SharedLoadIteratorD;
-
-  /// The traits class to build the iterator to load data from global memory for C^N.
-  typedef WmmaGemmGlobalIteratorCdTraits<
-      // The pointer is float const.
-      typename GemmConfig_::ScalarC const,
-      // The tile has size (N / Iterations)xM in GEMM's terminology.
-      Shape<1,
-            GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
-            GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgC>
-      GlobalLoadTileTraits;
-
-  /// The iterator to load C.
-  typedef WmmaGemmGlobalIteratorCd<GlobalLoadTileTraits, Index_> GlobalLoadIteratorC;
-  /// The transformer for C.
-  typedef Copy<typename GlobalLoadIteratorC::Fragment> GlobalTransformerC;
-
-  /// The traits class to build the iterator to store data to global memory for D^N.
-  typedef WmmaGemmGlobalIteratorCdTraits<
-      // The pointer is float.
-      typename GemmConfig_::ScalarD,
-      // The tile has size (N / Iterations)xM in GEMM's terminology.
-      Shape<1,
-            GemmConfig_::OutputTile::kH / ShapeCount<Iterations>::kCount,
-            GemmConfig_::OutputTile::kW>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, ShapeCount<typename GemmConfig_::Warps>::kCount, GemmConfig_::kWarpSize>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerStgD>
-      GlobalStoreTileTraits;
-
-  /// The iterator to store D.
-  typedef WmmaGemmGlobalIteratorCd<GlobalStoreTileTraits, Index_> GlobalStoreIteratorD;
-  /// The transformer for D.
-  typedef Copy<typename GlobalStoreIteratorD::Fragment> GlobalTransformerD;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass
-
-#endif  // defined CUTLASS_USE_WMMA_API

cutlass/gemm/wmma_gemm_global_tile.h

@@ -1,203 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines tile iterator traits for loading thread block-level tile from global memory.
-*/
-#pragma once
-
-#include <cutlass/gemm/gemm_global_tile.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Tile_, typename Threads_, int kAccessSize_>
-struct WmmaGemmGlobalIteratorCdTraits : public GemmGlobalTileTraits<GemmOperand::kC,
-                                                                    MatrixLayout::kColumnMajor,
-                                                                    Scalar_,
-                                                                    Tile_,
-                                                                    Threads_,
-                                                                    kAccessSize_> {
-  /// The base class.
-  typedef GemmGlobalTileTraits<GemmOperand::kC,
-                               MatrixLayout::kColumnMajor,
-                               Scalar_,
-                               Tile_,
-                               Threads_,
-                               kAccessSize_>
-      Base;
-
-  /// Override the strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> Delta;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int thread_offset_h = threadIdx.x / Base::Threads::kW;
-      int thread_offset_w = threadIdx.x % Base::Threads::kW * Base::ThreadsDelta::kW;
-
-      return make_Coord(0, thread_offset_h, thread_offset_w, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename TileTraits_, typename Index_ = int>
-struct WmmaGemmGlobalIteratorCd : public TileIteratorBase<TileTraits_,
-                                                          typename TileTraits_::Scalar,
-                                                          IteratorAdvance::kH,
-                                                          MemorySpace::kGlobal,
-                                                          Index_> {
-  /// This class.
-  typedef WmmaGemmGlobalIteratorCd<TileTraits_, Index_> This_;
-  /// The traits.
-  typedef TileTraits_ Traits;
-  /// The base class.
-  typedef TileIteratorBase<Traits,
-                           typename TileTraits_::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kGlobal,
-                           Index_>
-      Base;
-  /// Override the strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Base::Delta::kW, Base::Delta::kC> ImmediateOffsetStrides;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = TileTraits_::kLayout;
-
-  /// The scalar.
-  typedef typename TileTraits_::Scalar Scalar;
-  /// The pointer.
-  typedef typename TileTraits_::Pointer Pointer;
-  /// The threads.
-  typedef typename TileTraits_::Threads Threads;
-  /// The index.
-  typedef Index_ Index;
-  /// The thread offset functor.
-  typedef typename TileTraits_::ThreadOffset ThreadOffset;
-
-  /// The params.
-  struct Params {
-    /// The pointer.
-    Pointer pointer;
-    /// The stride in the H dimension to setup the thread in the block.
-    Index stride_h;
-    /// The strides to increment the pointer.
-    Index inc_h, inc_advance;
-    /// The column offset to compute the predicate for the columns.
-    Index predicate_offset;
-    /// The strides to increment the predicate offset.
-    Index predicate_inc_h, predicate_inc_advance;
-
-    /// Setup the params.
-    CUTLASS_HOST_DEVICE int initialize(
-        Pointer pointer, Index ld, Index n, Index epilogue_stride_w, Index epilogue_delta_w) {
-      // The pointer.
-      this->pointer = pointer;
-      // Setup the base stride. One "group of threads" per column.
-      stride_h = ld;
-      // Each thread output 1 column per iteration. .
-      inc_h = ld * TileTraits_::Threads::kH;
-      inc_advance = inc_h + epilogue_stride_w;
-
-      predicate_offset = n;
-      predicate_inc_h = TileTraits_::Threads::kH;
-      predicate_inc_advance = predicate_inc_h + epilogue_delta_w;
-
-      // It worked.
-      return 0;
-    }
-  };
-
-  Params params;
-
-  Coord<4> thread_offset;
-
-  /// Ctor.
-  CUTLASS_DEVICE WmmaGemmGlobalIteratorCd() {}
-
-  /// Ctor.
-  CUTLASS_DEVICE WmmaGemmGlobalIteratorCd(Params const& params,
-                                          const Coord<3>& bounds,
-                                          const Coord<3>& block,
-                                          int const pointer_offset = 0,
-                                          int const pred_offset = 0,
-                                          ThreadOffset thread_offset_func = ThreadOffset())
-
-      : params(params) {
-    thread_offset = thread_offset_func();
-    // Each warp works on a different column of the tile.
-    int const h = thread_offset[1] + block[1];
-    // Each lane writes a different element.
-    int const w = thread_offset[2] + block[2];
-    // Setup the pointer.
-    this->params.pointer += ((h * params.stride_h + w) + pointer_offset);
-
-    // Prepare the vector of predicates.
-    for (int i = 0; i < Base::Iterations::kW; ++i) {
-      predicates.set(i, w + i * Base::Delta::kW < bounds[2]);
-    }
-    this->params.predicate_offset -= (h + pred_offset);
-  }
-
-  /// Increment the pointer in the C dimension.
-  CUTLASS_DEVICE void inc_c() {}
-  /// Increment the pointer in the W dimension.
-  CUTLASS_DEVICE void inc_w() {}
-  /// Increment the pointer in the H dimension.
-  CUTLASS_DEVICE void inc_h() {
-    params.pointer += params.inc_h;
-    params.predicate_offset -= params.predicate_inc_h;
-  }
-  /// Increment the pointer in the D dimension.
-  CUTLASS_DEVICE void inc_d() {}
-  /// Increment the pointer to move to the next iteration.
-  CUTLASS_DEVICE void inc_advance() {
-    params.pointer += params.inc_advance;
-    params.predicate_offset -= params.predicate_inc_advance;
-  }
-
-  /// Test the predicate.
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const {
-    return predicates.at(w) && params.predicate_offset > 0;
-  }
-
-  /// Returns the raw pointer
-  CUTLASS_HOST_DEVICE
-  Pointer data() { return params.pointer; }
-
-  CUTLASS_HOST_DEVICE
-  Pointer const data() const { return params.pointer; }
-
-  /// The predicates for the row.
-  cutlass::PredicateVector<Base::Iterations::kW> predicates;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass

cutlass/gemm/wmma_gemm_multiply_add.h

@@ -1,108 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Implements warp-level matrix multiply-accumulate operation using CUDA WMMA API.
-*/
-#pragma once
-
-#include <cutlass/wmma_matrix.h>
-#ifdef CUTLASS_USE_WMMA_API
-#include <cutlass/fragment.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MatrixLayout::Kind kLayoutA_,
-          typename ScalarA_,
-          MatrixLayout::Kind kLayoutB_,
-          typename ScalarB_,
-          MatrixLayout::Kind kLayoutC_,
-          typename ScalarC_,
-          typename AccumulatorsPerWarp_,
-          typename InstructionShape_>
-struct WmmaGemmMultiplyAdd {
-  /// The shape of the instruction.
-  typedef InstructionShape_ InstructionShape;
-  /// The number of threads per warp. That's a dummy configuration.
-  typedef Shape<1, InstructionShape_::kH, InstructionShape_::kW> ThreadsPerWarp;
-  /// The dimensions.
-  typedef AccumulatorsPerWarp_ AccumulatorsPerWarp;
-  /// The type for A.
-  typedef ScalarA_ ScalarA;
-  /// The type for B.
-  typedef ScalarB_ ScalarB;
-  /// The type for C and D.
-  typedef ScalarC_ ScalarC;
-  /// The number of iterations.
-  typedef typename ShapeDiv<AccumulatorsPerWarp, InstructionShape>::Shape Iterations;
-
-  /// The element for A.
-  typedef WmmaMatrix<GemmOperand::kA, kLayoutA_, ScalarA, InstructionShape> ElementA;
-  /// The fragment for A.
-  typedef Fragment<ElementA, Iterations::kW> FragmentA;
-
-  /// The element for B.
-  typedef WmmaMatrix<GemmOperand::kB, kLayoutB_, ScalarB, InstructionShape> ElementB;
-  /// The fragment for B.
-  typedef Fragment<ElementB, Iterations::kH> FragmentB;
-
-  /// The element for C.
-  typedef WmmaMatrix<GemmOperand::kC, kLayoutC_, ScalarC, InstructionShape> ElementC;
-  /// The fragment for C.
-  typedef Fragment<ElementC, Iterations::kH * Iterations::kW> Accumulators;
-
-  /// Ctor.
-  CUTLASS_DEVICE WmmaGemmMultiplyAdd() {}
-
-  /// Multiply : d = a*b.
-  CUTLASS_DEVICE void multiply_add(FragmentA const& a,
-                                   FragmentB const& b,
-                                   Accumulators const& c,
-                                   Accumulators& d) {
-    for (int j = 0; j < Iterations::kH; ++j) {
-      for (int i = 0; i < Iterations::kW; ++i) {
-        // The input elements.
-        ElementA const& elt_a = a[i];
-        ElementB const& elt_b = b[j];
-        ElementC const& elt_c = c[j * Iterations::kW + i];
-
-        // The output element.
-        ElementC& elt_d = d[j * Iterations::kW + i];
-
-        // The wmma instruction.
-        nvcuda::wmma::mma_sync(elt_d, elt_a, elt_b, elt_c);
-      }
-    }
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass
-
-#endif  // defined CUTLASS_USE_WMMA_API

cutlass/gemm/wmma_gemm_shared_tile.h

@@ -1,240 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines iterator traits for efficiently loading and storing fragment to and from shared
-      memory, specialized for WMMA GEMM.
-*/
-#pragma once
-
-#include <cutlass/wmma_matrix.h>
-#ifdef CUTLASS_USE_WMMA_API
-
-#include <cutlass/gemm/gemm_operand.h>
-#include <cutlass/reshape_tile.h>
-
-namespace cutlass {
-namespace gemm {
-
-template <class>
-struct Debug {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename Tile_,
-          typename Warps_,
-          int kWarpStride_,
-          typename Iterations_,
-          typename Delta_,
-          typename WmmaShape_>
-struct WmmaGemmSharedLoadTileATraits {
-  /// The operand.
-  static GemmOperand::Kind const kOperand = GemmOperand::kA;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = kLayout_;
-  /// The scalar.
-  typedef Scalar_ Scalar;
-  /// The pointer.
-  typedef Scalar const* Pointer;
-  /// The access size
-  static int const kAccessSize = 1;
-  /// The tile with skew.
-  typedef Tile_ Tile;
-  /// The number of warps.
-  typedef Warps_ Warps;
-  /// The warps strides.
-  static int const kWarpStride = kWarpStride_;
-  /// The number of iterations.
-  typedef Iterations_ Iterations;
-  /// The strides between iterations.
-  typedef Delta_ Delta;
-  /// The strides between iterations.
-  typedef Delta_ ImmediateOffsetStrides;
-  /// The shape of the WMMA instruction.
-  typedef WmmaShape_ WmmaShape;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-  /// ThreadOffset
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // The warp id.
-      int const warp = threadIdx.x / kWarpSize;
-      // The offset.
-      int const offset = warp % Warps::kW * kWarpStride;
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename Tile_,
-          typename Warps_,
-          int kWarpStride_,
-          typename Iterations_,
-          typename Delta_,
-          typename WmmaShape_>
-struct WmmaGemmSharedLoadTileBTraits {
-  /// The operand.
-  static GemmOperand::Kind const kOperand = GemmOperand::kB;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = kLayout_;
-  /// The scalar.
-  typedef Scalar_ Scalar;
-  /// The pointer.
-  typedef Scalar const* Pointer;
-  /// The access size
-  static int const kAccessSize = 1;
-  /// The tile with skew.
-  typedef Tile_ Tile;
-  /// The number of warps.
-  typedef Warps_ Warps;
-  /// The warps strides.
-  static int const kWarpStride = kWarpStride_;
-  /// The number of iterations.
-  typedef Iterations_ Iterations;
-  /// The strides between iterations.
-  typedef Delta_ Delta;
-  /// The strides between iterations.
-  typedef Delta_ ImmediateOffsetStrides;
-  /// The shape of the WMMA instruction.
-  typedef WmmaShape_ WmmaShape;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-  /// ThreadOffset
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // The warp id.
-      int const warp = threadIdx.x / kWarpSize;
-      // The offset.
-      int const offset = warp / Warps::kW * kWarpStride;
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename OutputTile_,
-          typename Warps_,
-          typename WmmaShape_,
-          int kSkew_ = 0>
-struct WmmaGemmSharedStoreTileDTraits {
-  /// The operand.
-  static GemmOperand::Kind const kOperand = GemmOperand::kC;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = kLayout_;
-  /// The scalar.
-  typedef Scalar_ Scalar;
-  // The access size
-  static int const kAccessSize = 1;
-  /// The pointer.
-  typedef Scalar* Pointer;
-  /// The number of warps.
-  typedef Warps_ Warps;
-  /// The shape of the WMMA instruction.
-  typedef WmmaShape_ WmmaShape;
-  /// The skew.
-  static int const kSkew = kSkew_;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-  /// The tile with skew.
-  typedef Shape<1, Warps_::kH * WmmaShape_::kH, OutputTile_::kW + kSkew_> Tile;
-  /// The number of iterations needed to store the tile.
-  typedef Shape<1, 1, OutputTile_::kW / Warps::kW / WmmaShape_::kW> Iterations;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Warps::kW * WmmaShape_::kW, 0> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, 0, Warps::kW * WmmaShape_::kW, 0> ImmediateOffsetStrides;
-
-  /// ThreadOffset
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // The warp id.
-      int const warp = threadIdx.x / kWarpSize;
-      // The starting column.
-      int const h = warp / Warps::kW * WmmaShape::kH;
-      // The w.
-      int const w = warp % Warps::kW * WmmaShape::kW;
-      // The offset.
-      int const offset = h * Tile::kW + w;
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, typename Tile_, typename Threads_, int kScalarsPerLds_>
-struct WmmaGemmSharedLoadTileDTraits {
-  /// The scalar.
-  typedef Scalar_ Scalar;
-  /// The pointer.
-  typedef Scalar const* Pointer;
-  /// The access size
-  static int const kAccessSize = kScalarsPerLds_;
-  /// The tile.
-  typedef typename ReshapeTile<Tile_, kScalarsPerLds_>::Tile Tile;
-  /// The threads.
-  typedef typename ReshapeThreads<Tile, Threads_>::Threads Threads;
-  /// The threads strides.
-  typedef Shape<1, Tile::kW * Tile::kC, Tile::kC> ThreadsStrides;
-  /// The memory space.
-  static MemorySpace::Kind const kMemorySpace = MemorySpace::kShared;
-
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kScalarsPerLds_> Delta;
-  /// The strides in each dimension between different loads/stores.
-  typedef Shape<0, Threads::kH * ShapeCount<Tile>::kWc, Threads::kW * kScalarsPerLds_>
-      ImmediateOffsetStrides;
-  /// The number of iterations needed to load/store the tile.
-  typedef Shape<1, Tile::kH / Threads::kH, Tile::kW / Threads::kW, Tile::kC / kScalarsPerLds_>
-      Iterations;
-
-  /// ThreadOffset
-  struct ThreadOffset {
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      // The offset.
-      int const offset = ComputeThreadOffsetFromStrides<Threads, ThreadsStrides>::get();
-      return make_Coord(0, 0, offset, 0);
-    }
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass
-
-#endif  // defined CUTLASS_USE_WMMA_API

cutlass/gemm/wmma_gemm_traits.h

@@ -1,574 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defies structural properties of GEMM targeting WMMA API in CUDA.
-*/
-#pragma once
-
-#include <cutlass/wmma_matrix.h>
-#ifdef CUTLASS_USE_WMMA_API
-
-#include <cutlass/convert.h>
-#include <cutlass/gemm/gemm.h>
-#include <cutlass/gemm/gemm_epilogue.h>
-#include <cutlass/gemm/gemm_epilogue_traits.h>
-#include <cutlass/gemm/gemm_global_tile.h>
-#include <cutlass/gemm/gemm_shared_tile.h>
-#include <cutlass/gemm/gemm_traits.h>
-#include <cutlass/gemm/wmma_gemm_epilogue_traits.h>
-#include <cutlass/gemm/wmma_gemm_global_tile.h>
-#include <cutlass/gemm/wmma_gemm_multiply_add.h>
-
-namespace cutlass {
-namespace gemm {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_,
-    /// The output type.
-    typename ScalarC_,
-    /// The accumulator type.
-    typename Accumulator_,
-    /// The number of accumulators per warp.
-    typename AccumulatorsPerWarp_,
-    /// The shape of the WMMA instruction.
-    typename InstructionShape_,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_>
-struct WmmaGemmConfig : public GemmConfig<
-                            /// The scalar type for A.
-                            half,
-                            /// The scalar type for B.
-                            half,
-                            /// The scalar type for C.
-                            ScalarC_,
-                            /// The scalar type for D.
-                            ScalarC_,
-                            /// The tile size for the GEMM KxNxM.
-                            OutputTile_,
-                            /// The functor to do the math in the main loop.
-                            WmmaGemmMultiplyAdd<kLayoutA_,
-                                                half,
-                                                kLayoutB_,
-                                                half,
-                                                MatrixLayout::kColumnMajor,
-                                                Accumulator_,
-                                                AccumulatorsPerWarp_,
-                                                InstructionShape_>,
-                            /// The number of scalars per LDG for A.
-                            kScalarsPerLdgA_,
-                            /// The number of scalars per STS for A.
-                            kScalarsPerLdgA_,
-                            /// The number of scalars per LDS for A.
-                            8,
-                            /// The number of scalars per LDG for B.
-                            kScalarsPerLdgB_,
-                            /// The number of scalars per STS for B.
-                            kScalarsPerLdgB_,
-                            /// The number of scalars per LDS for B.
-                            8,
-                            /// The number of scalars per LDG for C and STG for D.
-                            16 / sizeof(ScalarC_),
-                            /// The number of scalars per STS for D.
-                            16 / sizeof(ScalarC_),
-                            /// The number of scalars per LDS for D.
-                            16 / sizeof(ScalarC_),
-                            /// The number of stages in shared memory.
-                            1> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperA {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_>
-    : public GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperA<MatrixLayout::kColumnMajor, GemmConfig_> Base;
-
-  /// The skew.
-  static int const kSkew = 16 / sizeof(typename Base::MultiplyAddScalar);
-  /// The shared tile size.
-  typedef Shape<GemmConfig_::kStages,
-                GemmConfig_::OutputTile::kD,
-                GemmConfig_::OutputTile::kW + kSkew>
-      Tile;
-
-  /// WMMA matrix
-  typedef WmmaMatrix<GemmOperand::kA,
-                     MatrixLayout::kColumnMajor,
-                     typename Base::MultiplyAddScalar,
-                     typename GemmConfig_::InstructionShape>
-      WmmaMatrix;
-
-  /// The traits class to build the iterator to store data to shared memory for A^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer.
-      typename Base::MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Tile,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename Base::GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsA>
-      SharedStoreTileTraits;
-
-  /// The number of elements loaded in one LDG.
-  static int const kScalarsPerW = GemmConfig_::InstructionShape::kW * GemmConfig_::Warps::kW;
-  /// The number of scalars loaded per iteration.
-  static int const kScalarsPerIteration = Tile::kW * GemmConfig_::InstructionShape::kD;
-  /// The traits class to build the iterator to load from shared memory for A.
-  typedef WmmaGemmSharedLoadTileATraits<
-      // The layout of the matrix.
-      MatrixLayout::kColumnMajor,
-      // The pointer.
-      typename Base::MultiplyAddScalar,
-      // The output tile size.
-      Tile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The strides between warps.
-      GemmConfig_::InstructionShape::kW,
-      // The number of iterations to load the data.
-      Shape<1, 1, GemmConfig_::OutputTile::kW / kScalarsPerW>,
-      // The stride between iterations.
-      Shape<kScalarsPerIteration, 0, kScalarsPerW, 0>,
-      // The shape of the instruction.
-      typename GemmConfig_::InstructionShape>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperA<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kRowMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarA Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarA MultiplyAddScalar;
-
-  /// WMMA matrix
-  typedef WmmaMatrix<GemmOperand::kA,
-                     MatrixLayout::kRowMajor,
-                     MultiplyAddScalar,
-                     typename GemmConfig_::InstructionShape>
-      WmmaMatrix;
-
-  /// The traits class to build the iterator to load data from global memory for A^T.
-  typedef GemmGlobalTileTraits<
-      // That's A.
-      GemmOperand::kA,
-      // A is row-major.
-      MatrixLayout::kRowMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kW, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgA>
-      GlobalTileTraits;
-
-  /// The skew.
-  static int const kSkew = 16 / sizeof(MultiplyAddScalar);
-  /// The tile.
-  typedef Shape<GemmConfig_::kStages,
-                GemmConfig_::OutputTile::kW,
-                GemmConfig_::OutputTile::kD + kSkew>
-      Tile;
-
-  /// The traits class to build the iterator to store data to shared memory for A^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer.
-      MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Tile,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsA>
-      SharedStoreTileTraits;
-
-  /// The number of elements loaded in one LDG.
-  static int const kScalarsPerW = GemmConfig_::InstructionShape::kW * GemmConfig_::Warps::kW;
-  /// The traits class to build the iterator to load from shared memory for A.
-  typedef WmmaGemmSharedLoadTileATraits<
-      // The layout of the matrix.
-      MatrixLayout::kRowMajor,
-      // The pointer.
-      MultiplyAddScalar,
-      // The tile in shared memory.
-      Tile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The strides between warps.
-      GemmConfig_::InstructionShape::kW * Tile::kW,
-      // The number of iterations to load the data.
-      Shape<1, 1, GemmConfig_::OutputTile::kW / kScalarsPerW>,
-      // The stride between iterations.
-      Shape<GemmConfig_::InstructionShape::kD, 0, kScalarsPerW * Tile::kW>,
-      // The shape of the instruction.
-      typename GemmConfig_::InstructionShape>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <enum MatrixLayout::Kind kLayout_, typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperB {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_>
-    : public GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> {
-  /// The base config.
-  typedef GemmTileTraitsHelperB<MatrixLayout::kRowMajor, GemmConfig_> Base;
-
-  /// The skew.
-  static int const kSkew = 16 / sizeof(typename Base::MultiplyAddScalar);
-  /// The shared tile size.
-  typedef Shape<GemmConfig_::kStages,
-                GemmConfig_::OutputTile::kD,
-                GemmConfig_::OutputTile::kH + kSkew>
-      Tile;
-
-  /// WMMA matrix
-  typedef WmmaMatrix<GemmOperand::kB,
-                     MatrixLayout::kRowMajor,
-                     typename Base::MultiplyAddScalar,
-                     typename GemmConfig_::InstructionShape>
-      WmmaMatrix;
-
-  /// The traits class to build the iterator to store data to shared memory for B^T.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer.
-      typename Base::MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Tile,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename Base::GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsB>
-      SharedStoreTileTraits;
-
-  /// The number of elements loaded in one LDG.
-  static int const kScalarsPerW = GemmConfig_::InstructionShape::kH * GemmConfig_::Warps::kH;
-  /// The number of scalars loaded per iteration.
-  static int const kScalarsPerIteration = Tile::kW * GemmConfig_::InstructionShape::kD;
-  /// The traits class to build the iterator to load from shared memory for B.
-  typedef WmmaGemmSharedLoadTileBTraits<
-      // The layout of the matrix.
-      MatrixLayout::kRowMajor,
-      // The pointer.
-      typename Base::MultiplyAddScalar,
-      // The output tile size.
-      Tile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The strides between warps.
-      GemmConfig_::InstructionShape::kH,
-      // The number of iterations to load the data.
-      Shape<1, 1, GemmConfig_::OutputTile::kH / kScalarsPerW>,
-      // The stride between iterations.
-      Shape<kScalarsPerIteration, 0, kScalarsPerW, 0>,
-      // The shape of the instruction.
-      typename GemmConfig_::InstructionShape>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename GemmConfig_>
-struct WmmaGemmTileTraitsHelperB<MatrixLayout::kColumnMajor, GemmConfig_> {
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = MatrixLayout::kColumnMajor;
-
-  /// The input scalar.
-  typedef typename GemmConfig_::ScalarB Scalar;
-  /// The scalar stored in shared memory.
-  typedef typename GemmConfig_::MultiplyAdd::ScalarB MultiplyAddScalar;
-
-  /// WMMA matrix
-  typedef WmmaMatrix<GemmOperand::kB,
-                     MatrixLayout::kColumnMajor,
-                     MultiplyAddScalar,
-                     typename GemmConfig_::InstructionShape>
-      WmmaMatrix;
-
-  /// The traits class to build the iterator to load data from global memory for B^N.
-  typedef GemmGlobalTileTraits<
-      // That's B.
-      GemmOperand::kB,
-      // A is row-major.
-      MatrixLayout::kColumnMajor,
-      // The pointer is float const.
-      Scalar const,
-      // The tile has size KxM in GEMM's terminology.
-      Shape<1, GemmConfig_::OutputTile::kH, GemmConfig_::OutputTile::kD>,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      Shape<1, GemmConfig_::kThreads / GemmConfig_::OutputTile::kD, GemmConfig_::OutputTile::kD>,
-      // The number of scalars per LDG (LDG.32 or LDG.128, etc).
-      GemmConfig_::kScalarsPerLdgB>
-      GlobalTileTraits;
-
-  /// The skew.
-  static int const kSkew = 16 / sizeof(MultiplyAddScalar);
-  /// The tile.
-  typedef Shape<GemmConfig_::kStages,
-                GemmConfig_::OutputTile::kH,
-                GemmConfig_::OutputTile::kD + kSkew>
-      Tile;
-
-  /// The traits class to build the iterator to store data to shared memory for B^N.
-  typedef GemmSharedStoreTileAbTraits<
-      // The pointer.
-      MultiplyAddScalar,
-      // The tile has size KxM in GEMM's terminology.
-      Tile,
-      // The threads are distributed as warps x 32 (the traits may reorganize).
-      typename GlobalTileTraits::Threads,
-      // The number of scalars per STS (STS.32 or STS.128, etc).
-      GemmConfig_::kScalarsPerStsB>
-      SharedStoreTileTraits;
-
-  /// The number of elements loaded in one LDG.
-  static int const kScalarsPerW = GemmConfig_::InstructionShape::kH * GemmConfig_::Warps::kH;
-  /// The traits class to build the iterator to load from shared memory for B.
-  typedef WmmaGemmSharedLoadTileBTraits<
-      // The layout of the matrix.
-      MatrixLayout::kColumnMajor,
-      // The pointer.
-      MultiplyAddScalar,
-      // The tile in shared memory.
-      Tile,
-      // The number of warps.
-      typename GemmConfig_::Warps,
-      // The strides between warps.
-      GemmConfig_::InstructionShape::kH * Tile::kW,
-      // The number of iterations to load the data.
-      Shape<1, 1, GemmConfig_::OutputTile::kH / kScalarsPerW>,
-      // The stride between iterations.
-      Shape<GemmConfig_::InstructionShape::kD, 0, kScalarsPerW * Tile::kW>,
-      // The shape of the instruction.
-      typename GemmConfig_::InstructionShape>
-      SharedLoadTileTraits;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The output tile.
-    typename OutputTile_,
-    /// The output type.
-    typename ScalarC_,
-    /// The accumulator type.
-    typename Accumulator_,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_,
-    /// The number of accumulators per warp.
-    typename AccumulatorsPerWarp_,
-    /// The shape of the WMMA instruction.
-    typename InstructionShape_,
-    /// The number of halfs loaded in one LDG for A.
-    int kScalarsPerLdgA_,
-    /// The number of halfs loaded in one LDG for B.
-    int kScalarsPerLdgB_,
-    /// The index.
-    typename Index_>
-struct WmmaGemmTraitsHelper {
-  /// The WMMA GEMM config.
-  typedef WmmaGemmConfig<kLayoutA_,
-                         kLayoutB_,
-                         OutputTile_,
-                         ScalarC_,
-                         Accumulator_,
-                         AccumulatorsPerWarp_,
-                         InstructionShape_,
-                         kScalarsPerLdgA_,
-                         kScalarsPerLdgB_>
-      GemmConfig;
-
-  /// The GEMM config for A.
-  typedef WmmaGemmTileTraitsHelperA<kLayoutA_, GemmConfig> GemmTileTraitsHelperA;
-  /// The GEMM config for B.
-  typedef WmmaGemmTileTraitsHelperB<kLayoutB_, GemmConfig> GemmTileTraitsHelperB;
-
-  /// The iterator to load A from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperA::GlobalTileTraits, Index_>
-      GlobalLoadIteratorA;
-  /// The default transformer for A.
-  typedef Copy<typename GlobalLoadIteratorA::Fragment> GlobalTransformerA;
-  /// The iterator to store A to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperA::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperA::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorA;
-  /// The stream to load A from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorA, SharedStoreIteratorA, GlobalTransformerA>
-      GlobalLoadStreamA;
-
-  /// The iterator to load B from global memory.
-  typedef GemmGlobalIteratorAb<typename GemmTileTraitsHelperB::GlobalTileTraits, Index_>
-      GlobalLoadIteratorB;
-  // The default transformer for B.
-  typedef Copy<typename GlobalLoadIteratorB::Fragment> GlobalTransformerB;
-  /// The iterator to store B to shared memory.
-  typedef TileStoreIterator<typename GemmTileTraitsHelperB::SharedStoreTileTraits,
-                            typename GemmTileTraitsHelperB::SharedStoreTileTraits::Scalar,
-                            IteratorAdvance::kH,
-                            MemorySpace::kShared>
-      SharedStoreIteratorB;
-  /// The stream to load B from global memory to shared memory.
-  typedef GlobalLoadStream<GlobalLoadIteratorB, SharedStoreIteratorB, GlobalTransformerB>
-      GlobalLoadStreamB;
-
-  /// The iterator to load A from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperA::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperA::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared,
-                           Index_,
-                           typename GemmTileTraitsHelperA::WmmaMatrix,
-                           IteratorFragment::kWmmaMatrix>
-      SharedLoadIteratorA;
-  /// The stream to load A from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorA> SharedLoadStreamA;
-  /// The iterator to load B from shared memory.
-  typedef TileLoadIterator<typename GemmTileTraitsHelperB::SharedLoadTileTraits,
-                           typename GemmTileTraitsHelperB::SharedLoadTileTraits::Scalar,
-                           IteratorAdvance::kH,
-                           MemorySpace::kShared,
-                           Index_,
-                           typename GemmTileTraitsHelperB::WmmaMatrix,
-                           IteratorFragment::kWmmaMatrix>
-      SharedLoadIteratorB;
-  /// The stream to load B from shared memory.
-  typedef SharedLoadStream<SharedLoadIteratorB> SharedLoadStreamB;
-
-  /// The functor to do the multiply-add in the main loop.
-  typedef typename GemmConfig::MultiplyAdd MultiplyAdd;
-  /// The object to clear accumulators.
-  typedef ClearAccumulators<typename MultiplyAdd::ScalarC> ClearAccumulators;
-
-  /// The helper to create the epilogue traits.
-  typedef WmmaGemmEpilogueTraitsHelper<GemmConfig, EpilogueFunctor_, Index_> EpilogueTraitsHelper;
-  /// The traits class for the epilogue.
-  typedef SimplifiedGemmEpilogueTraits<GemmConfig, EpilogueFunctor_, Index_, EpilogueTraitsHelper>
-      GemmEpilogueTraits;
-  /// The epilogue.
-  typedef GemmEpilogue<GemmEpilogueTraits> Epilogue;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename OutputTile_, typename DefaultShape_ = Shape<64, 32, 64> >
-struct WmmaGemmAccumulatorsPerWarp {
-  typedef typename ShapeMin<OutputTile_, DefaultShape_>::Shape Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <
-    /// The layout for A.
-    MatrixLayout::Kind kLayoutA_,
-    /// The layout for B.
-    MatrixLayout::Kind kLayoutB_,
-    /// The tile size for the GEMM KxNxM.
-    typename OutputTile_ = Shape<64, 128, 128>,
-    /// The output type.
-    typename ScalarC_ = float,
-    /// The functor to do the math in the epilogue.
-    typename EpilogueFunctor_ = LinearScaling<ScalarC_>,
-    /// The accumulator type.
-    typename Accumulator_ = ScalarC_,
-    /// The number of accumulators per warp.
-    typename AccumulatorsPerWarp_ = typename WmmaGemmAccumulatorsPerWarp<OutputTile_>::Shape,
-    /// The shape of the WMMA instruction.
-    typename InstructionShape_ = Shape<16, 16, 16>,
-    /// The number of scalars per LDG for A.
-    int kScalarsPerLdgA_ = 8,
-    /// The number of scalars per LDG for B.
-    int kScalarsPerLdgB_ = 8,
-    /// The index.
-    typename Index_ = int,
-    /// The helper class.
-    typename Helper_ = WmmaGemmTraitsHelper<kLayoutA_,
-                                            kLayoutB_,
-                                            OutputTile_,
-                                            ScalarC_,
-                                            Accumulator_,
-                                            EpilogueFunctor_,
-                                            AccumulatorsPerWarp_,
-                                            InstructionShape_,
-                                            kScalarsPerLdgA_,
-                                            kScalarsPerLdgB_,
-                                            Index_> >
-struct WmmaGemmTraits : public GemmTraits<
-                            // The config.
-                            typename Helper_::GemmConfig,
-                            // The stream to load A from global memory to shared memory.
-                            typename Helper_::GlobalLoadStreamA,
-                            // The stream to load B from global memory to shared memory.
-                            typename Helper_::GlobalLoadStreamB,
-                            // The stream to load A from shared memory.
-                            typename Helper_::SharedLoadStreamA,
-                            // The stream to load B from shared memory.
-                            typename Helper_::SharedLoadStreamB,
-                            // The epilogue.
-                            typename Helper_::Epilogue,
-                            // The block swizzle to reorganize the grid.
-                            IdentityBlockSwizzle,
-                            // The index.
-                            Index_,
-                            // The tool used to clear accumulators.
-                            typename Helper_::ClearAccumulators> {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace gemm
-}  // namespace cutlass
-
-#endif  // defined CUTLASS_USE_WMMA_API

cutlass/iterator_access.h

@@ -1,325 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Free functions for loading and storing to implementations of tile iteartor concepts.
-*/
-#pragma once
-
-#include <cutlass/fragment_load_store.h>
-#include <cutlass/load_store.h>
-#include <cutlass/predicate_vector.h>
-#include <cutlass/shape.h>
-
-namespace cutlass {
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Loads a fragment from an input iterator
-template <typename InputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_load(InputIterator &iterator, Fragment &fragment) {
-  typename InputIterator::FragmentIterator frag_iterator(fragment);
-  for (int d = 0; d < InputIterator::Iterations::kD; ++d) {
-    for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
-      for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
-        for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
-          if (iterator.valid(d, h, w, c)) {
-            int const offset =
-                ComputeOffsetFromStrides<typename InputIterator::ImmediateOffsetStrides>::get(
-                    0, 0, w, c);
-            Load<typename Fragment::Element, InputIterator::Tile::kC, InputIterator::kMemorySpace>::
-                load(reinterpret_cast<typename InputIterator::AccessType &>(
-                         frag_iterator.at(d, h, w, c)),
-                     iterator.data(),
-                     offset);
-          }
-        }
-        if (w < InputIterator::Iterations::kW - 1) {
-          iterator.inc_w();
-        }
-      }
-      if (h < InputIterator::Iterations::kH - 1) {
-        iterator.inc_h();
-      }
-    }
-    if (d < InputIterator::Iterations::kD - 1) {
-      iterator.inc_d();
-    }
-  }
-  iterator.inc_advance();
-}
-
-/// Loads a fragment from a shared memory input iterator
-template <typename InputIterator, typename Fragment>
-CUTLASS_DEVICE void shared_iterator_load(InputIterator &iterator, Fragment &fragment) {
-  typename InputIterator::FragmentIterator frag_iterator(fragment);
-  for (int d = 0; d < InputIterator::Iterations::kD; ++d) {
-    for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
-      for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
-        for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
-          int const offset =
-              ComputeOffsetFromStrides<typename InputIterator::ImmediateOffsetStrides>::get(
-                  d, h, w, c);
-
-          FragmentLoad<InputIterator::kIteratorFragment,
-                       InputIterator::Tile::kC,
-                       typename InputIterator::Scalar,
-                       InputIterator::kMemorySpace,
-                       typename InputIterator::FragmentElement,
-                       InputIterator::Tile::kW>::load(frag_iterator.at(d, h, w, c),
-                                                      iterator.data(),
-                                                      offset);
-        }
-      }
-    }
-  }
-}
-
-/// Loads a fragment from a shared memory input iterator
-template <typename InputIterator, typename Fragment>
-CUTLASS_DEVICE void shared_iterator_load(InputIterator &iterator, Fragment &fragment, int d) {
-  typename InputIterator::FragmentIterator frag_iterator(fragment);
-  for (int h = 0; h < InputIterator::Iterations::kH; ++h) {
-    for (int w = 0; w < InputIterator::Iterations::kW; ++w) {
-      for (int c = 0; c < InputIterator::Iterations::kC; ++c) {
-        int const offset =
-            ComputeOffsetFromStrides<typename InputIterator::ImmediateOffsetStrides>::get(
-                d, h, w, c);
-
-        FragmentLoad<InputIterator::kIteratorFragment,
-                     InputIterator::Tile::kC,
-                     typename InputIterator::Scalar,
-                     InputIterator::kMemorySpace,
-                     typename InputIterator::FragmentElement,
-                     InputIterator::Tile::kW>::load(frag_iterator.at(0, h, w, c),
-                                                    iterator.data(),
-                                                    offset);
-      }
-    }
-  }
-}
-
-/// Loads a fragment from an input iterator, masked by a predicate iterator
-template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
-                                                      Fragment &fragment,
-                                                      typename InputIterator::Index offset,
-                                                      ConstPredicateAdapter predicate_adapter) {
-  for (int d = 0; d < InputIterator::Iterations::kD; ++d, iterator.inc_d()) {
-    for (int h = 0; h < InputIterator::Iterations::kH; ++h, iterator.inc_h()) {
-      for (int w = 0; w < InputIterator::Iterations::kW; ++w, iterator.inc_w()) {
-        if (predicate_adapter.at(d, h, w, 0)) {
-          int idx = InputIterator::Tile::kC *
-                    (w + InputIterator::Iterations::kW * (h + InputIterator::Iterations::kH * d));
-
-          Load<typename Fragment::Element, InputIterator::Tile::kC, InputIterator::kMemorySpace>::
-              load(reinterpret_cast<typename InputIterator::AccessType &>(fragment[idx]),
-                   iterator.data(),
-                   offset);
-        }
-      }
-    }
-  }
-}
-
-/// Loads a fragment from an input iterator
-template <typename InputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
-                                                      Fragment &fragment,
-                                                      typename InputIterator::Index offset = 0) {
-  TrivialPredicateTileAdapter pred;
-  iterator_load_post_increment(iterator, fragment, offset, pred);
-}
-
-/// Loads a fragment from an input iterator
-template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_load_post_increment(InputIterator &iterator,
-                                                      Fragment &fragment,
-                                                      ConstPredicateAdapter pred_it) {
-  iterator_load_post_increment(iterator, fragment, 0, pred_it);
-}
-
-template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &_iterator,
-                                       Fragment &fragment,
-                                       typename InputIterator::Index offset,
-                                       ConstPredicateAdapter predicate_adapter) {
-  InputIterator iterator(_iterator);
-  iterator_load_post_increment(iterator, fragment, offset, predicate_adapter);
-}
-
-/// Loads a fragment from an input iterator
-template <typename InputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &iterator,
-                                       Fragment &fragment,
-                                       typename InputIterator::Index offset = 0) {
-  TrivialPredicateTileAdapter pred;
-  iterator_load(iterator, fragment, offset, pred);
-}
-
-/// Loads a fragment from an input iterator
-template <typename InputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_load(InputIterator const &iterator,
-                                       Fragment &fragment,
-                                       ConstPredicateAdapter pred_it) {
-  iterator_load(iterator, fragment, 0, pred_it);
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Stores a fragment to an output iterator
-template <typename OutputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_store(OutputIterator &iterator, Fragment &fragment) {
-  typename OutputIterator::FragmentIterator frag_iterator(fragment);
-  for (int d = 0; d < OutputIterator::Iterations::kD; ++d) {
-    for (int h = 0; h < OutputIterator::Iterations::kH; ++h) {
-      for (int w = 0; w < OutputIterator::Iterations::kW; ++w) {
-        if (iterator.valid(d, h, w, 0)) {
-          int const offset =
-              ComputeOffsetFromStrides<typename OutputIterator::ImmediateOffsetStrides>::get(
-                  d, h, w, 0);
-
-          Store<typename Fragment::Element,
-                OutputIterator::Tile::kC,
-                OutputIterator::kMemorySpace>::
-              store(reinterpret_cast<typename OutputIterator::AccessType &>(
-                        frag_iterator.at(d, h, w, 0)),
-                    iterator.data(),
-                    offset);
-        }
-        if (w < OutputIterator::Iterations::kW - 1) {
-          iterator.inc_w();
-        }
-      }
-      if (h < OutputIterator::Iterations::kH - 1) {
-        iterator.inc_h();
-      }
-    }
-    if (d < OutputIterator::Iterations::kD - 1) {
-      iterator.inc_d();
-    }
-  }
-  iterator.inc_advance();
-}
-
-/// Stores a fragment to a shared memory output iterator
-template <typename OutputIterator, typename Fragment>
-CUTLASS_DEVICE void shared_iterator_store(OutputIterator &iterator, Fragment const &fragment) {
-  typename OutputIterator::FragmentConstIterator frag_iterator(fragment);
-  for (int d = 0; d < OutputIterator::Iterations::kD; ++d) {
-    for (int h = 0; h < OutputIterator::Iterations::kH; ++h) {
-      for (int w = 0; w < OutputIterator::Iterations::kW; ++w) {
-        for (int c = 0; c < OutputIterator::Iterations::kC; ++c) {
-          int const offset =
-              ComputeOffsetFromStrides<typename OutputIterator::ImmediateOffsetStrides>::get(
-                  d, h, w, c);
-
-          FragmentStore<OutputIterator::kIteratorFragment,
-                        OutputIterator::Tile::kC,
-                        typename OutputIterator::Scalar,
-                        OutputIterator::kMemorySpace,
-                        typename OutputIterator::FragmentElement,
-                        OutputIterator::Tile::kW>::store(frag_iterator.at(d, h, w, c),
-                                                         iterator.data(),
-                                                         offset);
-        }
-      }
-    }
-  }
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Stores a fragment to an output iterator, masked by a predicate iterator
-template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
-                                                       Fragment const &fragment,
-                                                       typename OutputIterator::Index offset,
-                                                       ConstPredicateAdapter predicate_adapter) {
-  for (int d = 0; d < OutputIterator::Iterations::kD; ++d, iterator.inc_d()) {
-    for (int h = 0; h < OutputIterator::Iterations::kH; ++h, iterator.inc_h()) {
-      for (int w = 0; w < OutputIterator::Iterations::kW; ++w, iterator.inc_w()) {
-        if (predicate_adapter.at(d, h, w, 0)) {
-          int idx = OutputIterator::Tile::kC *
-                    (w + OutputIterator::Iterations::kW * (h + OutputIterator::Iterations::kH * d));
-
-          Store<typename Fragment::Element,
-                OutputIterator::Tile::kC,
-                OutputIterator::kMemorySpace>::
-              store(reinterpret_cast<typename OutputIterator::AccessType const &>(fragment[idx]),
-                    iterator.data(),
-                    offset);
-        }
-      }
-    }
-  }
-}
-
-/// Stores a fragment to an output iterator
-template <typename OutputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
-                                                       Fragment const &fragment,
-                                                       typename OutputIterator::Index offset = 0) {
-  TrivialPredicateTileAdapter pred;
-  iterator_store_post_increment(iterator, fragment, offset, pred);
-}
-
-/// Stores a fragment to an output iterator
-template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_store_post_increment(OutputIterator &iterator,
-                                                       Fragment const &fragment,
-                                                       ConstPredicateAdapter pred_it) {
-  iterator_store_post_increment(iterator, fragment, 0, pred_it);
-}
-
-/// Stores a fragment to an output iterator, masked by a predicate iterator
-template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &_iterator,
-                                        Fragment const &fragment,
-                                        typename OutputIterator::Index offset,
-                                        ConstPredicateAdapter predicate_adapter) {
-  OutputIterator iterator(_iterator);
-  iterator_store_post_increment(iterator, fragment, offset, predicate_adapter);
-}
-
-/// Stores a fragment to an output iterator
-template <typename OutputIterator, typename Fragment>
-CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &iterator,
-                                        Fragment const &fragment,
-                                        typename OutputIterator::Index offset = 0) {
-  TrivialPredicateTileAdapter pred;
-  iterator_store(iterator, fragment, offset, pred);
-}
-
-/// Stores a fragment to an output iterator
-template <typename OutputIterator, typename Fragment, typename ConstPredicateAdapter>
-CUTLASS_HOST_DEVICE void iterator_store(OutputIterator const &iterator,
-                                        Fragment const &fragment,
-                                        ConstPredicateAdapter pred_it) {
-  iterator_store(iterator, fragment, 0, pred_it);
-}
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/load_store.h

@@ -1,199 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines abstractions for efficiently loading and storing vectors to memory.
-*/
-#pragma once
-
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Enum to specify which memory space data resides in.
-*/
-struct MemorySpace {
-  enum Kind {
-    kGeneric,  // Data accessed through pointer dereferencing
-    kShared,   // Data resides in shared memory
-    kGlobal    // Data resides in global memory
-  };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          int Lanes_,
-          MemorySpace::Kind Memory_,
-          bool = (Lanes_ > 1),
-          size_t = (sizeof(Scalar_) * Lanes_)>
-struct Load {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The load function.
-  static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
-    dst = reinterpret_cast<AccessType const*>(&pointer[offset])[0];
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Load<Scalar_, Lanes_, Memory_, true, 4> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
-    dst.registers[0] = reinterpret_cast<uint32_t const*>(&pointer[offset])[0];
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Load<Scalar_, Lanes_, Memory_, true, 8> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
-    uint2 tmp = reinterpret_cast<uint2 const*>(&pointer[offset])[0];
-    dst.registers[0] = tmp.x;
-    dst.registers[1] = tmp.y;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MemorySpace::Kind Memory_>
-struct Load<double, 2, Memory_, true, 16> {
-  /// The output type.
-  typedef typename Vectorize<double, 2>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void load(AccessType& dst, double const* pointer, int offset) {
-    double2 tmp = reinterpret_cast<double2 const*>(&pointer[offset])[0];
-    dst[0] = tmp.x;
-    dst[1] = tmp.y;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Load<Scalar_, Lanes_, Memory_, true, 16> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void load(AccessType& dst, Scalar_ const* pointer, int offset) {
-    uint4 tmp = reinterpret_cast<uint4 const*>(&pointer[offset])[0];
-    dst.registers[0] = tmp.x;
-    dst.registers[1] = tmp.y;
-    dst.registers[2] = tmp.z;
-    dst.registers[3] = tmp.w;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_,
-          int Lanes_,
-          MemorySpace::Kind Memory_,
-          bool = (Lanes_ > 1),
-          size_t = (sizeof(Scalar_) * Lanes_)>
-struct Store {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
-    pointer[offset] = src;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Store<Scalar_, Lanes_, Memory_, true, 4> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
-    uint32_t* addr = reinterpret_cast<uint32_t*>(&pointer[offset]);
-    addr[0] = src.registers[0];
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Store<Scalar_, Lanes_, Memory_, true, 8> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
-    uint2* addr = reinterpret_cast<uint2*>(&pointer[offset]);
-    addr[0] = make_uint2(src.registers[0], src.registers[1]);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <MemorySpace::Kind Memory_>
-struct Store<double, 2, Memory_, true, 16> {
-  /// The output type.
-  typedef typename Vectorize<double, 2>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& src, double* pointer, int offset) {
-    double2* addr = reinterpret_cast<double2*>(&pointer[offset]);
-    addr[0] = make_double2(src[0], src[1]);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int Lanes_, MemorySpace::Kind Memory_>
-struct Store<Scalar_, Lanes_, Memory_, true, 16> {
-  /// The output type.
-  typedef typename Vectorize<Scalar_, Lanes_>::Type AccessType;
-
-  /// The store function.
-  static CUTLASS_DEVICE void store(AccessType const& src, Scalar_* pointer, int offset) {
-    uint4* addr = reinterpret_cast<uint4*>(&pointer[offset]);
-    addr[0] = make_uint4(src.registers[0], src.registers[1], src.registers[2], src.registers[3]);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/matrix_traits.h

@@ -1,48 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines properties of matrices used to denote layout and operands to GEMM kernels.
-*/
-#pragma once
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Describes layouts of matrices
-struct MatrixLayout {
-  enum Kind { kRowMajor, kColumnMajor };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Gemm operand - D = A * B + C
-struct GemmOperand {
-  enum Kind { kA, kB, kC, kD };
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/predicate_vector.h

@@ -1,493 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines container classes and iterators for managing a statically sized vector
-      of boolean predicates.
-*/
-#pragma once
-
-#include <stdint.h>
-
-#include <cutlass/cutlass.h>
-#include <cutlass/shape.h>
-
-#include <cutlass/util/platform.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup predicate_vector_concept Predicate Vector Concept
-@{
-
-Implementations of \ref predicate_vector_concept contain an ordered set of boolean predicates which
-may be used as conditionals in other device-side operations. Both random access and iterators
-offering sequential access are provided.
-
-@par Predicate Vector
-   A \ref predicate_vector_concept satisfies the following expressions
-  - <b>at(int idx)</b> - returns the value of the indexed predicate
-  - <b>set(int idx, bool value)</b> - sets the value of the indexed predicate
-  - <b>begin()</b> - returns a \ref predicate_iterator_concept pointing to the first predicate
-
-@}
-*/
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup predicate_iterator_concept Predicate Iterator Concept
-@{
-
-Implementations of \ref predicate_iterator_concept enables accessing and traversing elements of a
-bit vector.
-
-@par Const Predicate Iterator
-  A const \ref predicate_iterator_concept satisfies the following expressions
- - <b>++it</b> increments the iterator to the next predicate
- - <b>*it</b> returns the value of the currently pointed-to predicate
-
-@par Mutable Predicate Iterator
- A \ref predicate_iterator_concept that is non-const <b>also</b> satisfies the following expressions
- - <b>it.set(bool value)</b> sets the value of the currently pointed-to predicate
-
-@}
-*/
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup predicate_tile_adapter Predicate Tile Adapter Concept
-@{
-
-Implementations of \ref predicate_tile_adapter provide a mapping between a the elements of a \ref
-tile_traits_concept and a \ref predicate_vector_concept.
-
-@par Predicate Tile Adapter
-  A \ref predicate_tile_adapter satisfies the following expressions
- - <b>at(int d, int h, int w, int c)</b> - returns the value of a predicate corresponding to the
-   access (d, h, w, c) within the tile.
-
-@}
-*/
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Statically sized array of bits implementing @concept{predicate_vector_concept}.
-template <
-    /// Number of predicates conatined in predicate vector
-    int kPredicates_,
-    /// Number of predicates contained in each byte of internal storage
-    int kPredicatesPerByte_ = 4,
-    /// Location of first predicate within byte of internal storage
-    int kPredicateStart_ = 0>
-struct PredicateVector {
-  /// Number of bits stored by the PredicateVector
-  static int const kPredicates = kPredicates_;
-
-  /// Number of bits stored within each byte of the predicate bit vector
-  static int const kPredicatesPerByte = kPredicatesPerByte_;
-
-  /// First bit withing each byte containing predicates
-  static int const kPredicateStart = kPredicateStart_;
-
-  // Make sure no one tries to put more than 8 bits in a byte :)
-  static_assert(kPredicatesPerByte <= 8, "kPredicatesPerByte must fit within an actual byte");
-  // Make sure the "offsetted" bits fit in one byte.
-  static_assert(kPredicateStart + kPredicatesPerByte < 8,
-                "The offsetted predicates must fit within an actual byte.");
-
-  /// Storage type of individual elements
-  typedef uint32_t Storage;
-
-  /// Number of bytes needed
-  static int const kBytes = (kPredicates + kPredicatesPerByte - 1) / kPredicatesPerByte;
-
-  /// Number of storage elements needed
-  static int const kWordCount = (kBytes + sizeof(Storage) - 1) / sizeof(Storage);
-
- private:
-  //
-  // Data members
-  //
-
-  /// Words of bit vector
-  Storage storageData[kWordCount];
-
-  //
-  // Methods
-  //
-
-  /// Computes the word and bit corresponding to a logical predicate index
-  CUTLASS_HOST_DEVICE void computeStorageOffset(int &word, int &bit, int idx) const {
-    CUTLASS_ASSERT(idx < kPredicates);
-
-    int byte = (idx / kPredicatesPerByte);
-    int bit_offset = (idx % kPredicatesPerByte);
-
-    word = byte / sizeof(Storage);
-    int byte_offset = (byte % sizeof(Storage));
-
-    bit = byte_offset * 8 + bit_offset + kPredicateStart;
-  }
-
-  /// Accesses a given word with optional assertions
-  CUTLASS_HOST_DEVICE Storage &storage(int word) {
-    CUTLASS_ASSERT(word < kWordCount);
-    return storageData[word];
-  }
-
-  /// Accesses a given word with optional assertions
-  CUTLASS_HOST_DEVICE Storage const &storage(int word) const {
-    CUTLASS_ASSERT(word < kWordCount);
-    return storageData[word];
-  }
-
- public:
-  //
-  // Iterator
-  //
-
-  /**
-  * @brief A const iterator implementing \ref predicate_iterator_concept enabling sequential
-  * read-only access to prediactes.
-  * @concept{predicate_iterator_concept}
-  */
-  class ConstIterator {
-    /// Reference to PredicateVector instance
-    PredicateVector const &vec_;
-
-    /// Index into PredicateVector
-    int bit_;
-
-   public:
-    /// Copy constructor
-    CUTLASS_HOST_DEVICE
-    ConstIterator(ConstIterator const &it) : vec_(it.vec_), bit_(it.bit_) {}
-
-    ///
-    CUTLASS_HOST_DEVICE
-    ConstIterator(PredicateVector const &_vec, int _start = 0) : vec_(_vec), bit_(_start) {}
-
-    /// Pre-increment
-    CUTLASS_HOST_DEVICE
-    ConstIterator &operator++() {
-      ++bit_;
-      return *this;
-    }
-
-    /// Pre-decrement
-    CUTLASS_HOST_DEVICE
-    ConstIterator &operator--() {
-      --bit_;
-      return *this;
-    }
-
-    /// Post-increment
-    CUTLASS_HOST_DEVICE
-    ConstIterator operator++(int) {
-      ConstIterator ret(*this);
-      ret.bit_++;
-      return ret;
-    }
-
-    /// Post-decrement
-    CUTLASS_HOST_DEVICE
-    ConstIterator operator--(int) {
-      ConstIterator ret(*this);
-      ret.bit_--;
-      return ret;
-    }
-
-    /// Returns true if iterators point to the same bit
-    CUTLASS_HOST_DEVICE
-    bool operator==(ConstIterator const &it) const { return bit_ == it.bit_; }
-
-    /// Returns false if iterators point to the same bit
-    CUTLASS_HOST_DEVICE
-    bool operator!=(ConstIterator const &it) const { return bit_ != it.bit_; }
-
-    /// Dereferences iterator
-    CUTLASS_HOST_DEVICE
-    bool operator*() const { return vec_[bit_]; }
-  };
-
-  /**
-  * @brief An iterator implementing \ref predicate_iterator_concept enabling sequential
-  * read and write access to predicates.
-  * @concept{predicate_iterator_concept}
-  */
-  class Iterator {
-    /// Reference to PredicateVector instance
-    PredicateVector &vec_;
-
-    /// Index into PredicateVector
-    int bit_;
-
-   public:
-    /// Copy constructor
-    CUTLASS_HOST_DEVICE
-    Iterator(Iterator const &it) : vec_(it.vec_), bit_(it.bit_) {}
-
-    /// Constructs an iterator from a PredicateVector
-    CUTLASS_HOST_DEVICE
-    Iterator(PredicateVector &_vec, int _start = 0) : vec_(_vec), bit_(_start) {}
-
-    /// Pre-increment
-    CUTLASS_HOST_DEVICE
-    Iterator &operator++() {
-      ++bit_;
-      return *this;
-    }
-
-    /// Pre-decrement
-    CUTLASS_HOST_DEVICE
-    Iterator &operator--() {
-      --bit_;
-      return *this;
-    }
-
-    /// Post-increment
-    CUTLASS_HOST_DEVICE
-    Iterator operator++(int) {
-      Iterator ret(*this);
-      ret.bit_++;
-      return ret;
-    }
-
-    /// Post-decrement
-    CUTLASS_HOST_DEVICE
-    Iterator operator--(int) {
-      Iterator ret(*this);
-      ret.bit_--;
-      return ret;
-    }
-
-    /// Returns true if iterators point to the same bit
-    CUTLASS_HOST_DEVICE
-    bool operator==(Iterator const &it) const { return bit_ == it.bit_; }
-
-    /// Returns false if iterators point to the same bit
-    CUTLASS_HOST_DEVICE
-    bool operator!=(Iterator const &it) const { return bit_ != it.bit_; }
-
-    /// Gets the bit at the pointed to location
-    CUTLASS_HOST_DEVICE
-    bool get() { return vec_[bit_]; }
-
-    /// Dereferences iterator
-    CUTLASS_HOST_DEVICE
-    bool operator*() const { return vec_[bit_]; }
-
-    /// Sets the bit at the pointed to location
-    CUTLASS_HOST_DEVICE
-    void set(bool value = true) { vec_.set(bit_, value); }
-  };
-
-  /// Iterator that always returns true
-  struct TrivialIterator {
-    /// Constructor
-    CUTLASS_HOST_DEVICE
-    TrivialIterator() {}
-
-    /// Copy constructor
-    CUTLASS_HOST_DEVICE
-    TrivialIterator(Iterator const &it) {}
-
-    /// Constructs an iterator from a PredicateVector
-    CUTLASS_HOST_DEVICE
-    TrivialIterator(PredicateVector const &_vec) {}
-
-    /// Pre-increment
-    CUTLASS_HOST_DEVICE
-    TrivialIterator &operator++() { return *this; }
-
-    /// Post-increment
-    CUTLASS_HOST_DEVICE
-    TrivialIterator operator++(int) { return *this; }
-
-    /// Dereferences iterator
-    CUTLASS_HOST_DEVICE
-    bool operator*() const { return true; }
-  };
-
- public:
-  //
-  // Methods
-  //
-
-  /// Initialize the predicate vector
-  CUTLASS_HOST_DEVICE PredicateVector(bool value = true) { fill(value); }
-
-  /// Fills all predicates with a given value
-  CUTLASS_HOST_DEVICE void fill(bool value = true) {
-    Storage item = (value ? ~Storage(0) : Storage(0));
-
-    CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < kWordCount; ++i) {
-      storage(i) = item;
-    }
-  }
-
-  /// Accesses a bit within the predicate vector.
-  CUTLASS_HOST_DEVICE bool operator[](int idx) const { return at(idx); }
-
-  /// Accesses a bit within the predicate vector.
-  CUTLASS_HOST_DEVICE bool at(int idx) const {
-    int bit, word;
-    computeStorageOffset(word, bit, idx);
-
-    return ((storage(word) >> bit) & 1);
-  }
-
-  /// Set a bit within the predicate vector.
-  CUTLASS_HOST_DEVICE void set(int idx, bool value = true) {
-    int bit, word;
-    computeStorageOffset(word, bit, idx);
-
-    Storage disable_mask = (~(Storage(1) << bit));
-    Storage enable_mask = (Storage(value) << bit);
-
-    storage(word) = ((storage(word) & disable_mask) | enable_mask);
-  }
-
-  /// Computes the intersection of two identical predicate vectors.
-  CUTLASS_HOST_DEVICE PredicateVector &operator&=(PredicateVector const &predicates) {
-    CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < kWordCount; ++i) {
-      storage(i) = (storage(i) & predicates.storage(i));
-    }
-    return *this;
-  }
-
-  /// Computes the union of two identical predicate vectors.
-  CUTLASS_HOST_DEVICE PredicateVector &operator|=(PredicateVector const &predicates) {
-    CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < kWordCount; ++i) {
-      storage(i) = (storage(i) | predicates.storage(i));
-    }
-    return *this;
-  }
-
-  /// Returns true if entire predicate array is zero.
-  CUTLASS_HOST_DEVICE bool is_zero() const {
-    Storage mask(0);
-    for (int byte = 0; byte < sizeof(Storage); ++byte) {
-      Storage byte_mask = (((1 << kPredicatesPerByte) - 1) << kPredicateStart);
-      mask |= (byte_mask << (byte * 8));
-    }
-    uint32_t result = 0;
-    for (int word = 0; word < kWordCount; ++word) {
-      result |= storage(word);
-    }
-    return result == 0;
-  }
-
-  /// Returns an iterator to the start of the bit vector
-  CUTLASS_DEVICE
-  Iterator begin() { return Iterator(*this); }
-
-  /// Returns an iterator
-  CUTLASS_DEVICE
-  Iterator end() { return Iterator(*this, kPredicates); }
-
-  /// Returns a ConstIterator
-  CUTLASS_DEVICE
-  ConstIterator const_begin() const { return ConstIterator(*this); }
-
-  /// Returns a ConstIterator
-  CUTLASS_DEVICE
-  ConstIterator const_end() const { return ConstIterator(*this, kPredicates); }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Always returns true predicate.
-struct TrivialPredicateTileAdapter {
-  /// Ctor.
-  CUTLASS_HOST_DEVICE TrivialPredicateTileAdapter() {}
-
-  /// The value at location (d, h, w, c).
-  CUTLASS_HOST_DEVICE bool at(int, int, int, int) const { return true; }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to enable random access to predicates via logical coordinate within a tile.
-template <typename PredicateVector_, typename Iterations_>
-struct PredicateTileAdapter {
-  /// The vector of predicates.
-  typedef PredicateVector_ PredicateVector;
-  /// The iterations.
-  typedef Iterations_ Iterations;
-
- private:
-  /// The predicates.
-  PredicateVector &predicates;
-
- public:
-  /// Ctor.
-  CUTLASS_DEVICE PredicateTileAdapter(PredicateVector &predicates_) : predicates(predicates_) {}
-
-  /// Get the value at location (d, h, w, c).
-  CUTLASS_DEVICE bool at(int d, int h, int w, int c) const {
-    int const bit = ComputeOffsetFromShape<Iterations>::get(d, h, w, c);
-    return predicates.at(bit);
-  }
-
-  /// Set the value at location (d, h, w, c).
-  CUTLASS_DEVICE void set(int d, int h, int w, int c, bool value) {
-    int const bit = ComputeOffsetFromShape<Iterations>::get(d, h, w, c);
-    predicates.set(bit, value);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to enable random access to predicates via logical coordinate within a tile.
-template <typename PredicateVector_, typename Iterations_>
-struct ConstPredicateTileAdapter {
-  /// The vector of predicates.
-  typedef PredicateVector_ PredicateVector;
-  /// The iterations.
-  typedef Iterations_ Iterations;
-
- private:
-  /// The predicates.
-  PredicateVector const &predicates;
-
- public:
-  /// Ctor.
-  CUTLASS_DEVICE ConstPredicateTileAdapter(PredicateVector const &predicates_)
-      : predicates(predicates_) {}
-
-  /// Get the value at location (d, h, w, c).
-  CUTLASS_DEVICE bool at(int d, int h, int w, int c) const {
-    int const bit = ComputeOffsetFromShape<Iterations>::get(d, h, w, c);
-    return predicates.at(bit);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/reshape_tile.h

@@ -1,58 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines a type for restructuring a tile.
-*/
-#pragma once
-
-#include <cutlass/shape.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-// The following functor reshapes a tile of data. The goal is to have at least kAccessSize in
-// the inner-most dimension. If the user respects that constraint, there is nothing to be done. If
-// that's not the case, this functor will correct that and "extract" the right number of elements
-// from the next dimension.
-
-template <typename Tile_, int kAccessSize_, bool = (Tile_::kC < kAccessSize_)>
-struct ReshapeTile {
-  typedef Tile_ Tile;
-};
-
-template <typename Tile_, int kAccessSize_>
-struct ReshapeTile<Tile_, kAccessSize_, true> {
-  // Make sure the W dimension of the tile is large enough.
-  static_assert(Tile_::kW >= kAccessSize_, "The W dimension is too small");
-  // Make sure the dimension can be divided by the number of scalars.
-  static_assert(Tile_::kW % kAccessSize_ == 0, "Not supported");
-  // Collapse the W dimension.
-  typedef Shape<Tile_::kD, Tile_::kH, Tile_::kW / kAccessSize_, kAccessSize_> Tile;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/shape.h

@@ -1,301 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines Shape implementing the Layout concept for representing a 4D hypercube of objects.
-*/
-#pragma once
-
-#include <cutlass/cutlass.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup layout_concept Layout Concept
-* @{
-* @par Implementations of \ref layout_concept are used to describe a cube with DxHxW elements and C
-scalars per element.
- A HxW slice of a cube is called an image and a cube consists of D images.
-*
-* @par Notations
-*   Let Layout be an implementation of the \ref layout_concept.
-*
-* @par Valid Expressions
-* - <b>Layout::D</b> specifies the depth of a cube
-* - <b>Layout::H</b> specifies the height of a cube
-* - <b>Layout::W</b> specifies the height of a cube
-* - <b>Layout::C</b> specifies the number of channels of each element in a cube
-* - <b>Layout::W_c</b> specifies the number of scalars of each row in one image of a cube.
-* - <b>Layout::H_w</b> specifies the number of elements in an image slice.
-* - <b>Layout::H_w_c</b>_specifies the number of scalars in an image slice.
-* - <b>Layout::D_h_w</b> specifies the number of elements in a cube.
-* - <b>Layout::D_h_w_c</b> specifies the number of scalars in a cube.
-* - <b>Layout::Strides</b> is a \ref layout_concept specifying the strides.
-* @}
-*/
-
-/**
-* @brief A Shape implementing \ref layout_concept describing the dimensions of a cube.
-* @concept{layout_concept}
-*/
-template <int kD_ = 1, int kH_ = 1, int kW_ = 1, int kC_ = 1>
-struct Shape {
-  /// The depth of the cube.
-  static int const kD = kD_;
-  /// The height of the cube.
-  static int const kH = kH_;
-  /// The width of the cube.
-  static int const kW = kW_;
-  /// The number of scalars per element.
-  static int const kC = kC_;
-};
-
-/**
-* @brief Compute derived counted of a \ref layout_concept based class
-*/
-template <typename Shape>
-struct ShapeCount {
-  /// The number of elements per row.
-  static int const kWc = Shape::kW * Shape::kC;
-  /// The number of pixels per image.
-  static int const kHw = Shape::kH * Shape::kW;
-  /// The number of elements per image.
-  static int const kHwc = Shape::kH * kWc;
-  /// The number of pixels per cube.
-  static int const kDhw = Shape::kD * kHw;
-  /// The number of elements in the 4D space.
-  static int const kDhwc = Shape::kD * kHwc;
-  /// The number of elements in the 4D space.
-  static int const kCount = kDhwc;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, int kScale_>
-struct ShapeScale {
-  typedef Shape<A_::kD * kScale_, A_::kH * kScale_, A_::kW * kScale_, A_::kC * kScale_> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeAdd {
-  typedef Shape<A_::kD + B_::kD, A_::kH + B_::kH, A_::kW + B_::kW, A_::kC + B_::kC> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeSub {
-  typedef Shape<A_::kD - B_::kD, A_::kH - B_::kH, A_::kW - B_::kW, A_::kC - B_::kC> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeMul {
-  typedef Shape<A_::kD * B_::kD, A_::kH * B_::kH, A_::kW * B_::kW, A_::kC * B_::kC> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeDiv {
-  typedef Shape<A_::kD / B_::kD, A_::kH / B_::kH, A_::kW / B_::kW, A_::kC / B_::kC> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeMax {
-  typedef Shape<(A_::kD > B_::kD ? A_::kD : B_::kD),
-                (A_::kH > B_::kH ? A_::kH : B_::kH),
-                (A_::kW > B_::kW ? A_::kW : B_::kW),
-                (A_::kC > B_::kC ? A_::kC : B_::kC)>
-      Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename A_, typename B_>
-struct ShapeMin {
-  typedef Shape<(A_::kD < B_::kD ? A_::kD : B_::kD),
-                (A_::kH < B_::kH ? A_::kH : B_::kH),
-                (A_::kW < B_::kW ? A_::kW : B_::kW),
-                (A_::kC < B_::kC ? A_::kC : B_::kC)>
-      Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Shape_>
-struct ShapeStrides {
-  typedef Shape<Shape_::kH * Shape_::kW * Shape_::kC, Shape_::kW * Shape_::kC, Shape_::kC, 1> Shape;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube
-* @tparam A \ref layout_concept where each dimension of the cube specifies the corresponding stride.
-*/
-template <typename Shape_>
-struct ComputeOffsetFromShape {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
-    // clang-format off
-    return d * Shape_::kH * Shape_::kW * Shape_::kC +
-           h * Shape_::kW * Shape_::kC +
-           w * Shape_::kC +
-           c;
-    // clang-format on
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube with a depth of 1
-* @tparam kSh Elements in the H dimension
-* @tparam kSw Elements in the W dimension
-* @tparam kSc Separation between two elements in "elements"
-*/
-template <int kSh_, int kSw_, int kSc_>
-struct ComputeOffsetFromShape<Shape<1, kSh_, kSw_, kSc_> > {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
-    return h * kSw_ * kSc_ + w * kSc_ + c;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube with one channel and a depth of 1
-* @tparam kSh Elements in the H dimension
-* @tparam kSw Elements in the W dimension
-*/
-template <int kSh_, int kSw_>
-struct ComputeOffsetFromShape<Shape<1, kSh_, kSw_, 1> > {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) { return h * kSw_ + w; }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube
-* @tparam A \ref layout_concept where each dimension of the cube specifies the corresponding stride.
-*/
-template <typename Strides_>
-struct ComputeOffsetFromStrides {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
-    return d * Strides_::kD + h * Strides_::kH + w * Strides_::kW + c * Strides_::kC;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube with a depth of 1
-* @tparam S_h Stride in the H dimension in scalars
-* @tparam S_w Stride in the W dimension in scalars
-* @tparam S_c Stride between two scalars.
-*/
-template <int S_h_, int S_w_, int S_c_>
-struct ComputeOffsetFromStrides<Shape<1, S_h_, S_w_, S_c_> > {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) {
-    return h * S_h_ + w * S_w_ + c * S_c_;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Compute the offset for the given coordinates in a cube with one channel and a depth of 1
-* @tparam S_h Stride in the H dimension in scalars
-* @tparam S_w Stride in the W dimension in scalars
-*/
-template <int S_h_, int S_w_>
-struct ComputeOffsetFromStrides<Shape<1, S_h_, S_w_, 1> > {
-  static CUTLASS_DEVICE int get(int d, int h, int w, int c) { return h * S_h_ + w * S_w_; }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief Decompose threadId.x into coordinate of a cube whose dimensions are specified by Threads_.
-* Afterwards compute the offset of those coordinates using Strides_
-* @tparam Threads_ The dimension of the cube the threadIdx.x value is mapped on
-* @tparam Strides_ The strides to use when compute the offsets based on the coordinates of the cube.
-*/
-template <typename Threads_, typename Strides_>
-struct ComputeThreadOffsetFromStrides {
-  static CUTLASS_DEVICE int get() {
-    // Decompose the thread index.
-    int c = threadIdx.x % Threads_::kC;
-    int w = threadIdx.x / Threads_::kC % Threads_::kW;
-    int h = threadIdx.x / Threads_::kC / Threads_::kW % Threads_::kH;
-    int d = threadIdx.x / Threads_::kC / Threads_::kW / Threads_::kH;
-
-    // Compute the offset.
-    return d * Strides_::kD + h * Strides_::kH + w * Strides_::kW + c * Strides_::kC;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-/**
-*@brief Specialization for D=1
-*/
-template <int T_h_, int T_w_, int T_c_, int S_h_, int S_w_, int S_c_>
-struct ComputeThreadOffsetFromStrides<Shape<1, T_h_, T_w_, T_c_>, Shape<1, S_h_, S_w_, S_c_> > {
-  static CUTLASS_DEVICE int get() {
-    // Decompose the thread index.
-    int c = threadIdx.x % T_c_;
-    int w = threadIdx.x / T_c_ % T_w_;
-    int h = threadIdx.x / T_c_ / T_w_ % T_h_;
-
-    // Compute the offset.
-    return h * S_h_ + w * S_w_ + c * S_c_;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-*@brief Specialization for D=1 and C=1
-*/
-template <int T_h_, int T_w_, int S_h_, int S_w_>
-struct ComputeThreadOffsetFromStrides<Shape<1, T_h_, T_w_, 1>, Shape<1, S_h_, S_w_, 1> > {
-  static CUTLASS_DEVICE int get() {
-    // Decompose the thread index.
-    int w = threadIdx.x % T_w_;
-    int h = threadIdx.x / T_w_;
-
-    // Compute the offset.
-    return h * S_h_ + w * S_w_;
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/tensor_ref.h

@@ -1,151 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines a structure containing strides, bounds, and a pointer to tensor data.
-*/
-#pragma once
-
-#include <typeinfo>
-
-#include <cutlass/coord.h>
-#include <cutlass/cutlass.h>
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Structure modeling a pointer and stride into a tensor
-template <typename Storage_, int Rank_>
-class TensorRef {
- public:
-  /// Data type of individual access
-  typedef Storage_ Storage;
-
-  /// Rank of tensor
-  static int const Rank = Rank_;
-
- private:
-  //
-  // Data members
-  //
-
-  /// Pointer to storage element
-  Storage* ptr_;
-
-  /// Stride information
-  Coord<Rank> stride_;
-
- public:
-  //
-  // Methods
-  //
-
-  /// Default ctor
-  CUTLASS_HOST_DEVICE
-  TensorRef() : ptr_(nullptr) {}
-
-  /// Constructs from a pointer, size, and stride
-  CUTLASS_HOST_DEVICE
-  TensorRef(Storage* ptr, Coord<Rank> stride) : ptr_(ptr), stride_(stride) {}
-
-  /// Updates the pointer, stride, and location within a TensorRef
-  CUTLASS_HOST_DEVICE
-  void reset(Storage* ptr = nullptr, Coord<Rank> stride = Coord<Rank>(0)) {
-    ptr_ = ptr;
-    stride_ = stride;
-  }
-
-  /// Conversion function
-  template <typename T>
-  TensorRef<T, Rank> convert() {
-    Coord<Rank> converted_stride;
-    for (int i = 0; i < Rank - 1; ++i) {
-      converted_stride[i] = stride_[i] * Extent<Storage>::kValue / Extent<T>::kValue;
-    }
-    converted_stride[Rank - 1] = stride_[Rank - 1];
-
-    return TensorRef<T, Rank>(reinterpret_cast<T*>(ptr_), converted_stride);
-  }
-
-  /// Returns true if the TensorRef may be safely accessed
-  CUTLASS_HOST_DEVICE
-  bool good() const { return ptr_ != nullptr; }
-
-  /// Returns the pointer to referenced data
-  CUTLASS_HOST_DEVICE
-  Storage* data() const { return ptr_; }
-
-  /// Returns the stride of the tensor
-  CUTLASS_HOST_DEVICE
-  Coord<Rank> const& stride() const { return stride_; }
-
-  /// Returns the stride of the tensor in the given dimension
-  CUTLASS_HOST_DEVICE
-  int const& stride(int dim) const { return stride_.at(dim); }
-
-  /// Returns the maximum stride element as the 'leading dimension'
-  CUTLASS_HOST_DEVICE
-  int leading_dim() const { return __NV_STD_MAX(stride_[1], stride_[2]); }
-
-  /// Computes the offset of an index from the origin of the tensor
-  CUTLASS_HOST_DEVICE
-  long long offset(Coord<Rank> const& coord) const {
-    return stride_.template dot<long long>(coord);
-  }
-
-  /// Returns a reference to the element at a given Coord
-  CUTLASS_HOST_DEVICE
-  Storage& at(Coord<Rank> const& coord) const { return ptr_[offset(coord)]; }
-
-  /// Element-wise accessor
-  Storage& operator[](Coord<Rank> const& coord) const { return at(coord); }
-
-  /// Returns a reference to the element at a given Coord
-  CUTLASS_HOST_DEVICE
-  Storage& at(int idx) const { return ptr_[idx]; }
-
-  /// Element-wise accessor
-  Storage& operator[](int idx) const { return at(idx); }
-
-  /// Adds an offset to the pointer
-  CUTLASS_HOST_DEVICE
-  TensorRef& advance(Coord<Rank> const& b) {
-    ptr_ += offset(b);
-    return *this;
-  }
-
-  /// Returns a TensorRef offset by a given amount
-  CUTLASS_HOST_DEVICE
-  TensorRef operator+(Coord<Rank> const& b) const { return TensorRef(ptr_ + offset(b), stride_); }
-
-  /// Returns a TensorRef offset by a given amount
-  CUTLASS_HOST_DEVICE
-  TensorRef operator-(Coord<Rank> const& b) const { return TensorRef(ptr_ - offset(b), stride_); }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/tensor_view.h

@@ -1,172 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines a structure containing strides and a pointer to tensor data.
-*/
-
-#pragma once
-
-#include <cmath>
-
-#include <cutlass/cutlass.h>
-#include <cutlass/tensor_ref.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Host-side reference implementation of tensor operations
-template <typename T>
-class TensorView : public TensorRef<T, 4> {
- public:
-  /// Reference and stride
-  typedef TensorRef<T, 4> Base;
-
-  /// Reference and stride
-  typedef Base TensorRef_t;
-
-  /// Reference to constant type
-  typedef TensorRef<T const, 4> ConstTensorRef_t;
-
-  /// Rank of tensor
-  static int const Rank = TensorRef_t::Rank;
-
-  /// Type used to compute the offset of an element to the base of a tensor
-  typedef int Offset_t;
-
-  /// Coordinate into tensor
-  typedef Coord<Rank> Coord_t;
-
- private:
-  //
-  // Data members
-  //
-
-  /// Pointer to pitch-linear memory
-  TensorRef_t ref_;
-
-  /// Dimensions of coordinate (independent of stride)
-  Coord_t size_;
-
- public:
-  //
-  // Device and Host Methods
-  //
-
-  /// Default constructor
-  CUTLASS_HOST_DEVICE
-  TensorView() {}
-
-  /// Constructs a Tensor_view from a TensorRef and size
-  CUTLASS_HOST_DEVICE
-  TensorView(TensorRef_t const& _ref, Coord_t const& _size) : Base(_ref), size_(_size) {}
-
-  /// Returns true if the Tensor_view is bound to some memory
-  CUTLASS_HOST_DEVICE
-  bool good() const { return ref().good(); }
-
-  /// Returns a pointer to data
-  CUTLASS_HOST_DEVICE
-  T* data() const { return ref().data(); }
-
-  /// Updates the reference and size of a Tensor_view object
-  CUTLASS_HOST_DEVICE
-  void reset(TensorRef_t const& _ref = TensorRef_t(0), Coord_t const& _size = Coord_t()) {
-    Base::operator=(_ref);
-    size_ = _size;
-  }
-
-  /// Accesses the tensor reference pointing to data
-  CUTLASS_HOST_DEVICE
-  TensorRef_t& ref() { return *this; }
-
-  ///
-  CUTLASS_HOST_DEVICE
-  ConstTensorRef_t const_ref() { return ConstTensorRef_t(data(), stride()); }
-
-  /// Accesses the tensor reference pointing to data
-  CUTLASS_HOST_DEVICE
-  TensorRef_t const& ref() const { return *this; }
-
-  /// Accesses the size
-  CUTLASS_HOST_DEVICE
-  Coord_t const& size() const { return size_; }
-
-  /// Accesses the size
-  CUTLASS_HOST_DEVICE
-  int size(int dim) const { return size_.at(dim); }
-
-  /// Accesses the stride
-  CUTLASS_HOST_DEVICE
-  Coord_t const& stride() const { return ref().stride(); }
-
-  /// Accesses the stride
-  CUTLASS_HOST_DEVICE
-  int const& stride(int dim) const { return ref().stride(dim); }
-
-  /// Assigns the Tensor_view
-  CUTLASS_HOST_DEVICE
-  TensorView& operator=(TensorView const& _tensor) {
-    Base::operator=(_tensor._ref);
-    size_ = _tensor.size_;
-    return *this;
-  }
-
-  /// Returns the index of an element
-  CUTLASS_HOST_DEVICE
-  Offset_t offset(Coord_t const& coord) const { return ref().offset(coord); }
-
-  /// Determines whether a location is within a tensor
-  CUTLASS_HOST_DEVICE
-  bool contains(Coord_t const& coord) const {
-    for (int dim = 0; dim < Rank; ++dim) {
-      if (coord.at(dim) >= size_.at(dim)) {
-        return false;
-      }
-    }
-    return true;
-  }
-
-  /// Element-wise accessor
-  CUTLASS_HOST_DEVICE
-  T& at(Coord_t const& coord) const { return ref().at(coord); }
-
-  /// Element-wise accessor
-  T& operator[](Coord<Rank> const& coord) const { return at(coord); }
-
-  /// Element-wise accessor
-  CUTLASS_HOST_DEVICE
-  T& at(Offset_t idx) const { return ref().at(idx); }
-
-  /// Returns a Tensor_view given location and size quantities
-  CUTLASS_HOST_DEVICE
-  TensorView<T> subview(Coord_t const& location, Coord_t size) const {
-    return TensorView<T>(ref() + location, size.clamp(size_ - location));
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/tile_iterator.h

@@ -1,881 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines the Tile Traits concept and iterators for loading and storing to tiles
-   efficiently.
-*/
-#pragma once
-
-#include <cutlass/fragment.h>
-#include <cutlass/load_store.h>
-#include <cutlass/predicate_vector.h>
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup tile_traits_concept Tile Traits Concept
-@{
-
-\ref tile_traits_concept is a type definining the shape of a tile and the distribution of accesses
-by individual entities, either threads or other.
-
-@par Tile Traits Concept
-   Types satisfying \ref tile_traits_concept define the following members
-  - <b>Tile</b> - a type satisfying \ref layout_concept describing the dimensions of the tile
-  - <b>Delta</b> - a type satisfying \ref layout_concept describing the increments between accesses
-along each dimension
-  - <b>Iterations</b> - a type satisfying \ref layout_concept describing the number of accesses
-along each dimension
-  - <b>Offset</b> - the type of a <i>functor</i> computing the offset of each participating entity
-as a Coord<4>.
-@}
-*/
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Specifies dimension in which post-increment accesses advance
-struct IteratorAdvance {
-  enum Kind { kD, kH, kW };
-};
-
-/// Specifies whether iterator storage fragment consists of Scalar values or WMMA matrix
-struct IteratorFragment {
-  enum Kind { kScalar, kWmmaMatrix };
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief A template defining \ref tile_traits_concept
-* @concept{tile_traits_concept}
-*/
-template <typename Tile_, typename Delta_, typename Iterations_, typename ThreadOffset_>
-struct TileTraits {
-  /// Shape of the tile
-  typedef Tile_ Tile;
-
-  /// Number of steps between accesses along each dimension
-  typedef Delta_ Delta;
-
-  /// Number of accesses performed
-  typedef Iterations_ Iterations;
-
-  /// Functor that returns the logical coordinate of each entity's initial offset in the tile
-  typedef ThreadOffset_ ThreadOffset;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Iterator for accessing a stripmined tile in memory
-template <typename Traits_,
-          typename Scalar_,
-          IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
-          MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
-          typename Index_ = int,
-          typename FragmentElement_ = Scalar_,
-          IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
-          typename Skew_ = Shape<0, 0, 0, 0> >
-struct TileIteratorBase {
-  /// concept TileTraits
-  typedef Traits_ Traits;
-
-  /// Scalar element
-  typedef Scalar_ Scalar;
-
-  /// Fragment element
-  typedef FragmentElement_ FragmentElement;
-
-  /// Specifies dimension in which post-increment accesses advance.
-  static IteratorAdvance::Kind const kAdvance = Advance_;
-
-  /// Specifies iterator storage fragment type (Scalar or WmmaMatrix)
-  static IteratorFragment::Kind const kIteratorFragment = IteratorFragment_;
-
-  /// Source or destination memory space
-  static MemorySpace::Kind const kMemorySpace = MemorySpace;
-
-  /// Index type
-  typedef Index_ Index;
-
-  /// Skew quantity
-  typedef Skew_ Skew;
-
-  /// Tile shape
-  typedef typename Traits::Tile Tile;
-
-  /// Distance along each dimension
-  typedef typename Traits::Delta Delta;
-
-  /// The strides in each dimension between different loads/stores.
-  typedef typename Traits::ImmediateOffsetStrides ImmediateOffsetStrides;
-
-  /// Iterations
-  typedef typename Traits::Iterations Iterations;
-
-  /// Thread offset
-  typedef typename Traits::ThreadOffset ThreadOffset;
-
-  /// The number of scalars accessed per load/store.
-  static int const kAccessSize = Tile::kC;
-
-  /// The elements loaded/store by one instruction.
-  typedef typename Vectorize<FragmentElement, kAccessSize>::Type AccessType;
-
-  /// The size of storage needed per fragment
-  static int const kFragmentSize =
-      (kIteratorFragment == IteratorFragment::kWmmaMatrix ? 16 : sizeof(AccessType));
-  /// The storage.
-  typedef Fragment<Scalar, ShapeCount<Tile>::kCount, kFragmentSize> Storage;
-  /// The fragment.
-  typedef Fragment<FragmentElement, ShapeCount<Iterations>::kCount * kAccessSize> Fragment;
-  /// The fragment iterator.
-  typedef FragmentIterator<Fragment, Iterations, AccessType> FragmentIterator;
-  /// The fragment const iterator.
-  typedef FragmentConstIterator<Fragment, Iterations, AccessType> FragmentConstIterator;
-  /// The shape of the fragment.
-  typedef typename FragmentIterator::FragmentShape FragmentShape;
-
-  /// Default predicate mask type
-  typedef PredicateVector<ShapeCount<Iterations>::kCount> PredicateVector;
-
-  //
-  // Params struct
-  //
-
-  /// Parameters to the iterator
-  struct Params {
-    Index stride_d;
-    Index stride_h;
-    Index stride_w;
-
-    Index inc_d;
-    Index inc_h;
-    Index inc_w;
-
-    Index inc_advance;
-
-    /// Initializes params
-    CUTLASS_HOST_DEVICE
-    int initialize(Index _stride_d,
-                   Index _stride_h,
-                   Index _stride_w,
-                   Index _inc_d,
-                   Index _inc_h,
-                   Index _inc_w,
-                   Index _inc_advance) {
-      stride_d = _stride_d;
-      stride_h = _stride_h;
-      stride_w = _stride_w;
-
-      inc_d = _inc_d;
-      inc_h = _inc_h;
-      inc_w = _inc_w;
-      inc_advance = _inc_advance;
-
-      return 0;
-    }
-
-    CUTLASS_HOST_DEVICE
-    int initialize(Index _stride_d, Index _stride_h, Index _stride_w) {
-      stride_d = _stride_d;
-      stride_h = _stride_h;
-      stride_w = _stride_w;
-
-      inc_w = stride_w * Delta::kW;
-      inc_h = stride_h * Delta::kH - stride_w * Delta::kW * (Iterations::kW - 1);
-
-      if (kAdvance == IteratorAdvance::kH) {
-        // Advance in the H dimension.
-        inc_d = 0;
-      } else if (kAdvance == IteratorAdvance::kW) {
-        // Advance in the W dimension.
-        inc_d = stride_w * Tile::kW - stride_h * Tile::kH;
-      } else {
-        // Advance in the D dimension.
-        inc_d = stride_d;
-      }
-
-      inc_advance = 0;
-
-      return 0;
-    }
-
-    CUTLASS_HOST_DEVICE int initialize() {
-      stride_d = 0;
-      stride_h = 0;
-      stride_w = 1;
-
-      inc_d = inc_h = inc_w = inc_advance = 0;
-
-      return 0;
-    }
-  };
-
-  /// Is the iterator valid?
-  CUTLASS_DEVICE bool valid(int d, int h, int w, int c) const { return true; }
-
-  //
-  // Static function members
-  //
-
-  /// Initializes a predicate vector
-  template <typename PredicateIterator>
-  CUTLASS_DEVICE static void initialize_predicates(PredicateIterator predicate_it,
-                                                   Coord<3> const &bounds,
-                                                   Coord<3> const &offset = make_Coord(0, 0, 0)) {
-    for (int d = 0; d < Iterations::kD; ++d) {
-      bool enable_d = (d * Delta::kD + offset[0] < bounds[0]);
-      for (int h = 0; h < Iterations::kH; ++h) {
-        bool enable_h = (h * Delta::kH + offset[1] < bounds[1]);
-        for (int w = 0; w < Iterations::kW; ++w) {
-          bool enable_w = (w * Tile::kC * Delta::kW + offset[2] < bounds[2]);
-          predicate_it.set(d, h, w, 0, enable_d && enable_h && enable_w);
-        }
-      }
-    }
-  }
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup tile_load_iterator_concept Tile Load Iterator Concept
-@{
-
-\ref tile_load_iterator_concept enables loading a tile from addressable memory into a fragment
-
-@par Tile Load Iterator Concept
-  Types satisfying \ref tile_load_iterator_concept define the following members
-  - <b>PredicateVector</b> - a \ref predicate_vector_concept with sufficient predicate storage for
-each access implied by the tile traits
-  - <b>Fragment</b> - the destination fragment type satisfying \ref fragment_concept
-  - <b>initialize_predicates(pred_it, bounds, block_offset)</b> - function initializing a predicate
-vector according to externally specified bounds
-  - <b>load_post_increment(fragment, pred_it)</b> - a method that loads a fragment and increments
-the iterator to the next tile, guarded by a \ref predicate_iterator_concept
-  - <b>load_post_increment(fragment)</b> - a method that loads a fragment and increments the
-iterator to the next tile
-  - <b>load(fragment, pred_it)</b> - a const method that loads a fragment, guarded by a \ref
-predicate_iterator_concept
-  - <b>load(fragment)</b> - a method that loads a fragment
-
-@}
-*/
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief An iterator implementing \ref tile_load_iterator_concept for loading a tile from memory
-* @concept{tile_load_iterator_concept}
-*/
-template <typename Traits_,
-          typename Scalar_,
-          IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
-          MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
-          typename Index_ = int,
-          typename FragmentElement_ = Scalar_,
-          IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
-          typename Skew_ = Shape<0, 0, 0, 0> >
-struct TileLoadIterator : public TileIteratorBase<Traits_,
-                                                  Scalar_,
-                                                  Advance_,
-                                                  MemorySpace,
-                                                  Index_,
-                                                  FragmentElement_,
-                                                  IteratorFragment_,
-                                                  Skew_> {
-  /// Base class
-  typedef TileIteratorBase<Traits_,
-                           Scalar_,
-                           Advance_,
-                           MemorySpace,
-                           Index_,
-                           FragmentElement_,
-                           IteratorFragment_,
-                           Skew_>
-      Base;
-
-  /// concept TileTraits
-  typedef typename Base::Traits Traits;
-
-  /// Scalar element
-  typedef typename Base::Scalar Scalar;
-
-  /// Fragment element
-  typedef typename Base::FragmentElement FragmentElement;
-
-  /// Specifies in which dimension post-increment accesses advance.
-  static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
-
-  /// Specifies type of iterator fragment storage (Salar or WmmaMatrix)
-  static IteratorFragment::Kind const kIteratorFragment = Base::kIteratorFragment;
-
-  /// Source or destination memory space
-  static MemorySpace::Kind const kMemorySpace = Base::kMemorySpace;
-
-  /// Index type
-  typedef typename Base::Index Index;
-
-  /// Skew quantity
-  typedef typename Base::Skew Skew;
-
-  /// Tile shape
-  typedef typename Base::Tile Tile;
-
-  /// Delta
-  typedef typename Base::Delta Delta;
-
-  /// Iterations
-  typedef typename Base::Iterations Iterations;
-
-  /// ThreadOffset functor
-  typedef typename Base::ThreadOffset ThreadOffset;
-
-  /// Fragment type
-  typedef typename Base::FragmentShape FragmentShape;
-
-  /// Memory access type
-  typedef typename Base::AccessType AccessType;
-
-  /// Fragment definition
-  typedef typename Base::Fragment Fragment;
-
-  /// Fragment iterator definition
-  typedef typename Base::FragmentIterator FragmentIterator;
-
-  /// Fragment const iterator definition
-  typedef typename Base::FragmentConstIterator FragmentConstIterator;
-
-  /// Default predicate mask type
-  typedef typename Base::PredicateVector PredicateVector;
-
-  /// Storage object that may be loaded from
-  typedef typename Base::Storage SharedStorage;
-
-  /// IteratorBase parameters
-  typedef typename Base::Params BaseParams;
-
-  /// Do we require a fence?
-  enum { kRequiresLoadFence = Tile::kD == 1 };
-
-  /// The pointer type
-  typedef Scalar const *Pointer;
-
-  /// Parameters
-  struct Params : public BaseParams {
-    /// Pointer to memory
-    Scalar const *pointer;
-
-    /// Initialize params to access storage object
-    CUTLASS_HOST_DEVICE
-    int initialize(SharedStorage const &storage) {
-      pointer = &storage[0];
-      return 0;
-    }
-
-    /// Initializes params to access a raw pointer
-    CUTLASS_HOST_DEVICE
-    int initialize(Scalar const *ptr, Index stride_d, Index stride_h, Index stride_w) {
-      Base::Params::initialize(stride_d, stride_h, stride_w);
-      pointer = ptr;
-      return 0;
-    }
-
-    /// Initializes params
-    CUTLASS_HOST_DEVICE
-    int initialize(Scalar const *ptr,
-                   Index _stride_d,
-                   Index _stride_h,
-                   Index _stride_w,
-                   Index _inc_d,
-                   Index _inc_h,
-                   Index _inc_w,
-                   Index _inc_advance) {
-      pointer = ptr;
-      Base::Params::initialize(
-          _stride_d, _stride_h, _stride_w, _inc_d, _inc_h, _inc_w, _inc_advance);
-      return 0;
-    }
-
-    // Initializes params to default values
-    CUTLASS_HOST_DEVICE
-    int initialize() { return Base::Params::initialize(); }
-  };
-
-  //
-  // Data members
-  //
-
-  /// Parameters structure
-  Params params;
-
-  /// Offset of an individual lane from the start of the tile
-  Coord<4> thread_offset;
-
-  /// Stage argument enables wrapping after some number of tiles have been loaded.
-  int stage;
-
-  //
-  // Static member functions
-  //
-
-  /// Initializes a predicate vector
-  template <typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void initialize_predicates(PredicateIterator predicate_it,
-                                                 Coord<3> const &bounds,
-                                                 Coord<3> const &block_offset = make_Coord(0,
-                                                                                           0,
-                                                                                           0)) {
-    Base::initialize_predicates(
-        predicate_it,
-        bounds,
-        block_offset + make_Coord(0, thread_offset[1], thread_offset[2] * Tile::kC));
-  }
-
-  //
-  // Methods
-  //
-
-  /// Default constructor
-  CUTLASS_HOST_DEVICE
-  TileLoadIterator() {}
-
-  /// Constructs a tile load iterator
-  CUTLASS_HOST_DEVICE
-  TileLoadIterator(Params const &_params,
-                   Coord<3> const &block_offset = make_Coord(0, 0, 0),
-                   ThreadOffset thread_offset_func = ThreadOffset())
-      : params(_params), stage(0) {
-    thread_offset = thread_offset_func();
-
-    Index block_offset_h = 0;
-    Index block_offset_w = 0;
-    if (kAdvance == IteratorAdvance::kH) {
-      block_offset_h = block_offset[1];
-      block_offset_w = block_offset[2];
-    } else {
-      block_offset_h = block_offset[2];
-      block_offset_w = block_offset[1];
-    }
-
-    params.pointer += block_offset[0] * params.stride_d +
-                      (block_offset_h + thread_offset[1]) * params.stride_h +
-                      (block_offset_w + thread_offset[2] * Tile::kC) / Tile::kC * params.stride_w;
-  }
-
-  /// Constructs a tile load iterator
-  CUTLASS_HOST_DEVICE
-  TileLoadIterator(Params const &,
-                   SharedStorage &shared_storage,
-                   Coord<3> const &block_offset = make_Coord(0, 0, 0),
-                   ThreadOffset thread_offset_func = ThreadOffset())
-      : stage(0) {
-    int const offset = thread_offset_func()[2];
-    params.pointer = &shared_storage[offset];
-  }
-
-  /// Returns the current pointer
-  CUTLASS_HOST_DEVICE
-  Scalar const *data() const { return params.pointer; }
-
-  /// Increment in the D dimension
-  CUTLASS_HOST_DEVICE void inc_d() { params.pointer += params.inc_d; }
-
-  /// Increment in the H dimension
-  CUTLASS_HOST_DEVICE void inc_h() { params.pointer += params.inc_h; }
-
-  /// Increment in the W dimension
-  CUTLASS_HOST_DEVICE void inc_w() { params.pointer += params.inc_w; }
-
-  /// Increment in the next dimension
-  CUTLASS_HOST_DEVICE void inc_advance() { params.pointer += params.inc_advance; }
-
-  /// Increment the stage.
-  CUTLASS_DEVICE void inc_stage() {
-    if (Tile::kD > 1) {
-      int const kStageSize = Tile::kH * Tile::kW * Tile::kC;
-      if (stage == Tile::kD - 1) {
-        params.pointer -= (Tile::kD - 1) * kStageSize;
-        stage = 0;
-      } else {
-        params.pointer += kStageSize;
-        stage = stage + 1;
-      }
-    }
-  }
-
- public:
-  /// Loads a fragment and advances the iterator to the next tile.
-  template <typename Fragment, typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void load_post_increment(Fragment &fragment, PredicateIterator pred_it) {
-    FragmentIterator frag_iterator(fragment);
-
-    for (int d = 0; d < Iterations::kD; ++d) {
-      for (int h = 0; h < Iterations::kH; ++h) {
-        for (int w = 0; w < Iterations::kW; ++w, ++pred_it) {
-          if (*pred_it) {
-            Load<typename Fragment::Element, Tile::kC, kMemorySpace>::load(
-                reinterpret_cast<AccessType &>(frag_iterator.at(d, h, w, 0)), data(), 0);
-          }
-
-          if (w < Iterations::kW - 1) {
-            inc_w();
-          }
-        }
-        if (h < Iterations::kH - 1) {
-          inc_h();
-        }
-      }
-      if (d < Iterations::kD - 1) {
-        inc_d();
-      }
-    }
-    inc_advance();
-  }
-
-  /// Loads a fragment and advances the iterator to the next tile.
-  template <typename Fragment>
-  CUTLASS_HOST_DEVICE void load_post_increment(Fragment &fragment) {
-    typename PredicateVector::TrivialIterator pred_it;
-    load_post_increment(fragment, pred_it);
-  }
-
-  /// Loads a fragment without advancing the iterator..
-  template <typename Fragment, typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void load(Fragment &fragment, PredicateIterator pred_it) const {
-    TileLoadIterator _load_it(*this);
-    _load_it.load_post_increment(fragment, pred_it);
-  }
-
-  /// Loads a fragment without advancing the iterator..
-  template <typename Fragment>
-  CUTLASS_HOST_DEVICE void load(Fragment &fragment) const {
-    typename PredicateVector::TrivialIterator pred_it;
-    load(fragment, pred_it);
-  }
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/*!@defgroup tile_store_iterator_concept Tile Store Iterator Concept
-@{
-
-\ref tile_store_iterator_concept enables storing a tile to addressable memory
-
-@par Tile Store Iterator Concept
-  Types satisfying \ref tile_load_iterator_concept define the following members
-  - <b>PredicateVector</b> - a \ref predicate_vector_concept with sufficient predicate storage for
-each access implied by the tile traits
-  - <b>Fragment</b> - the destination fragment type satisfying \ref fragment_concept
-  - <b>initialize_predicates(pred_it, bounds, block_offset)</b> - function initializing a predicate
-vector according to externally specified bounds
-  - <b>store_post_increment(fragment, pred_it)</b> - a method that stores a fragment and increments
-the iterator to the next tile, guarded by a \ref predicate_iterator_concept
-  - <b>store_post_increment(fragment)</b> - a method that stores a fragment and increments the
-iterator to the next tile
-  - <b>store(fragment, pred_it)</b> - a const method that stores a fragment, guarded by a \ref
-predicate_iterator_concept
-  - <b>store(fragment)</b> - a method that loads a fragment
-
-@}
-*/
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/**
-* @brief An iterator implementing \ref tile_store_iterator_concept for storing a tile to memory
-* @concept{tile_store_iterator_concept}
-*/
-template <typename Traits_,
-          typename Scalar_,
-          IteratorAdvance::Kind Advance_ = IteratorAdvance::kH,
-          MemorySpace::Kind MemorySpace = MemorySpace::kGeneric,
-          typename Index_ = int,
-          typename FragmentElement_ = Scalar_,
-          IteratorFragment::Kind IteratorFragment_ = IteratorFragment::kScalar,
-          typename Skew_ = Shape<0, 0, 0, 0> >
-struct TileStoreIterator : public TileIteratorBase<Traits_,
-                                                   Scalar_,
-                                                   Advance_,
-                                                   MemorySpace,
-                                                   Index_,
-                                                   FragmentElement_,
-                                                   IteratorFragment_,
-                                                   Skew_> {
-  /// Base class
-  typedef TileIteratorBase<Traits_,
-                           Scalar_,
-                           Advance_,
-                           MemorySpace,
-                           Index_,
-                           FragmentElement_,
-                           IteratorFragment_,
-                           Skew_>
-      Base;
-
-  /// concept TileTraits
-  typedef typename Base::Traits Traits;
-
-  /// Scalar element
-  typedef typename Base::Scalar Scalar;
-
-  /// Fragment element
-  typedef typename Base::FragmentElement FragmentElement;
-
-  /// Specifies in which dimension post-increment accesses advance.
-  static IteratorAdvance::Kind const kAdvance = Base::kAdvance;
-
-  /// Specifies type of iterator fragment storage (Salar or WmmaMatrix)
-  static IteratorFragment::Kind const kIteratorFragment = Base::kIteratorFragment;
-
-  /// Source or destination memory space
-  static MemorySpace::Kind const kMemorySpace = Base::kMemorySpace;
-
-  /// Index type
-  typedef typename Base::Index Index;
-
-  /// Skew quantity
-  typedef typename Base::Skew Skew;
-
-  /// Tile shape
-  typedef typename Base::Tile Tile;
-
-  /// Delta
-  typedef typename Base::Delta Delta;
-
-  /// Iterations
-  typedef typename Base::Iterations Iterations;
-
-  /// ThreadOffset functor
-  typedef typename Base::ThreadOffset ThreadOffset;
-
-  /// Fragment type
-  typedef typename Base::FragmentShape FragmentShape;
-
-  /// Memory access type
-  typedef typename Base::AccessType AccessType;
-
-  /// Fragment definition
-  typedef typename Base::Fragment Fragment;
-
-  /// Fragment iterator definition
-  typedef typename Base::FragmentIterator FragmentIterator;
-
-  /// Fragment const iterator definition
-  typedef typename Base::FragmentConstIterator FragmentConstIterator;
-
-  /// Default predicate mask type
-  typedef typename Base::PredicateVector PredicateVector;
-
-  /// Storage object which may be stored to
-  typedef typename Base::Storage SharedStorage;
-
-  /// IteratorBase parameters
-  typedef typename Base::Params BaseParams;
-
-  /// Parameters
-  struct Params : public BaseParams {
-    /// Pointer to memory
-    Scalar *pointer;
-
-    /// Initialize params to access storage object
-    CUTLASS_HOST_DEVICE
-    int initialize(SharedStorage &storage) {
-      pointer = &storage[0];
-      return 0;
-    }
-
-    /// Initializes params to access a raw pointer
-    CUTLASS_HOST_DEVICE
-    int initialize(Scalar *ptr, Index stride_d, Index stride_h, Index stride_w) {
-      Base::Params::initialize(stride_d, stride_h, stride_w);
-      pointer = ptr;
-      return 0;
-    }
-
-    /// Initializes params
-    CUTLASS_HOST_DEVICE
-    int initialize(Scalar *ptr,
-                   Index _stride_d,
-                   Index _stride_h,
-                   Index _stride_w,
-                   Index _inc_d,
-                   Index _inc_h,
-                   Index _inc_w,
-                   Index _inc_advance) {
-      pointer = ptr;
-      Base::Params::initialize(
-          _stride_d, _stride_h, _stride_w, _inc_d, _inc_h, _inc_w, _inc_advance);
-      return 0;
-    }
-
-    /// Initializes params to default values
-    CUTLASS_HOST_DEVICE
-    int initialize() { return Base::Params::initialize(); }
-  };
-
-  //
-  // Data members
-  //
-
-  /// Parameters structure
-  Params params;
-
-  /// Offset of an individual lane from the start of the tile
-  Coord<4> thread_offset;
-
-  /// The stage.
-  int stage;
-
-  //
-  // Static member functions
-  //
-
-  /// Initializes a predicate vector
-  template <typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void initialize_predicates(PredicateIterator predicate_it,
-                                                 Coord<3> const &bounds,
-                                                 Coord<3> const &block_offset = make_Coord(0,
-                                                                                           0,
-                                                                                           0)) {
-    Base::initialize_predicates(
-        predicate_it,
-        bounds,
-        block_offset + make_Coord(0, thread_offset[1], thread_offset[2] * Tile::kC));
-  }
-
-  //
-  // Methods
-  //
-
-  /// Default constructor
-  CUTLASS_HOST_DEVICE
-  TileStoreIterator() {}
-
-  /// Constructs a tile store iterator
-  CUTLASS_HOST_DEVICE
-  TileStoreIterator(Params const &_params,
-                    Coord<3> const &block_offset = make_Coord(0, 0, 0),
-                    ThreadOffset thread_offset_func = ThreadOffset())
-      : params(_params), stage(0) {
-    thread_offset = thread_offset_func();
-
-    params.pointer += block_offset[0] * params.stride_d +
-                      (block_offset[1] + thread_offset[1]) * params.stride_h +
-                      (block_offset[2] + thread_offset[2] * Tile::kC) / Tile::kC * params.stride_w;
-  }
-
-  /// Constructs a tile store iterator
-  CUTLASS_HOST_DEVICE
-  TileStoreIterator(Params const &,
-                    SharedStorage &shared_storage,
-                    Coord<3> const &block_offset = make_Coord(0, 0, 0),
-                    ThreadOffset thread_offset_func = ThreadOffset())
-      : stage(0) {
-    int const offset = thread_offset_func()[2];
-    params.pointer = &shared_storage[offset];
-  }
-
-  /// Returns the current pointer
-  CUTLASS_HOST_DEVICE
-  Scalar *data() const { return params.pointer; }
-
-  /// Increment in the D dimension
-  CUTLASS_HOST_DEVICE void inc_d() { params.pointer += params.inc_d; }
-
-  /// Increment in the H dimension
-  CUTLASS_HOST_DEVICE void inc_h() { params.pointer += params.inc_h; }
-
-  /// Increment in the W dimension
-  CUTLASS_HOST_DEVICE void inc_w() { params.pointer += params.inc_w; }
-
-  /// Increment in the next dimension
-  CUTLASS_HOST_DEVICE void inc_advance() {}
-
-  /// Increment the stage.
-  CUTLASS_DEVICE void inc_stage() {
-    if (Tile::kD > 1) {
-      int const kStageSize = Tile::kH * Tile::kW * Tile::kC;
-      if (stage == Tile::kD - 1) {
-        params.pointer -= (Tile::kD - 1) * kStageSize;
-        stage = 0;
-      } else {
-        params.pointer += kStageSize;
-        stage = stage + 1;
-      }
-    }
-  }
-
- public:
-  /// Stores a fragment and advances to the next tile.
-  template <typename Fragment, typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void store_post_increment(Fragment &fragment, PredicateIterator pred_it) {
-    FragmentIterator frag_iterator(fragment);
-
-    for (int d = 0; d < Iterations::kD; ++d) {
-      for (int h = 0; h < Iterations::kH; ++h) {
-        for (int w = 0; w < Iterations::kW; ++w, ++pred_it) {
-          if (*pred_it) {
-            Store<typename Fragment::Element, Tile::kC, kMemorySpace>::store(
-                reinterpret_cast<AccessType &>(frag_iterator.at(d, h, w, 0)), data(), 0);
-          }
-          if (w < Iterations::kW - 1) {
-            inc_w();
-          }
-        }
-        if (h < Iterations::kH - 1) {
-          inc_h();
-        }
-      }
-      if (d < Iterations::kD - 1) {
-        inc_d();
-      }
-    }
-    inc_advance();
-  }
-
-  /// Stores a fragment and advances to the next tile.
-  template <typename Fragment>
-  CUTLASS_HOST_DEVICE void store_post_increment(Fragment &fragment) {
-    typename PredicateVector::TrivialIterator pred_it;
-    store_post_increment(fragment, pred_it);
-  }
-
-  /// Stores a fragment without advancing the iterator.
-  template <typename Fragment, typename PredicateIterator>
-  CUTLASS_HOST_DEVICE void store(Fragment &fragment, PredicateIterator pred_it) const {
-    TileStoreIterator _store_it(*this);
-    _store_it.store_post_increment(fragment, pred_it);
-  }
-
-  /// Stores a fragment without advancing the iterator.
-  template <typename Fragment>
-  CUTLASS_HOST_DEVICE void store(Fragment &fragment) const {
-    typename PredicateVector::TrivialIterator pred_it;
-    store(fragment, pred_it);
-  }
-};
-}

cutlass/tile_traits_standard.h

@@ -1,238 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines tile traits for several tile partitioning arrangements of threads expected to
-      achieve efficient streaming performance.
-*/
-#pragma once
-
-#include <cutlass/tile_iterator.h>
-
-namespace cutlass {
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Basic thread offset function computed from a thread shape
-template <typename ThreadShape>
-struct TiledThreadOffset {
-  /// Computes the logical coordinate from thread shape
-  CUTLASS_HOST_DEVICE
-  Coord<4> operator()() const {
-    Coord<4> thread_offset;
-
-    int index = threadIdx.x;
-
-    thread_offset[3] = (index % ThreadShape::kC);
-    index = (index / ThreadShape::kC);
-
-    thread_offset[2] = (index % ThreadShape::kW);
-    index = (index / ThreadShape::kW);
-
-    thread_offset[1] = (index % ThreadShape::kH);
-    index = (index / ThreadShape::kH);
-
-    thread_offset[0] = index;
-
-    return thread_offset;
-  }
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Tiling in which the number of threads is greater than the
-/// contiguous dimension of the tile.
-template <typename Tile_, int Threads>
-struct TileTraitsStrideMajor {
-  /// Shape of tile
-  typedef Tile_ Tile;
-
-  /// Number of participating threads
-  static int const kThreads = Threads;
-
-  // Static assertions
-  static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
-                "Tiling undefined if elements not divisible by threads.");
-
-  static_assert(Tile::kW <= kThreads,
-                "This specialization assumes there are more threads than the contiguous dimension "
-                "of the tile.");
-
-  /// Shape of threads
-  typedef Shape<1, kThreads / Tile::kW, Tile::kW, 1> ThreadShape;
-
-  /// Delta along each dimension
-  typedef Shape<1, ThreadShape::kH, 1, 1> Delta;
-
-  /// Number of iterations
-  typedef Shape<1, Tile::kH / ThreadShape::kH, 1, 1> Iterations;
-
-  /// Computes the initial offset
-  typedef TiledThreadOffset<ThreadShape> ThreadOffset;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Tiling in which the number of threads is fewer than the tile size
-/// in the contiguous dimension.
-template <typename Tile_, int Threads>
-struct TileTraitsContiguousMajor {
-  /// Shape of tile
-  typedef Tile_ Tile;
-
-  /// Number of participating threads
-  static int const kThreads = Threads;
-
-  // Static assertions
-  static_assert(Tile::kW >= kThreads,
-                "This specialization assumes there are more threads than the contiguous dimension "
-                "of the tile.");
-
-  static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
-                "Tiling undefined if elements not divisible by threads.");
-
-  static_assert(!(Tile::kW % kThreads),
-                "The contiguous size of the tile must be divisible by the number of threads.");
-
-  /// Thread shape
-  typedef Shape<1, 1, kThreads> ThreadShape;
-
-  /// Delta between each thread's access
-  typedef Shape<1, 1, kThreads> Delta;
-
-  /// Number of iterations
-  typedef Shape<1, Tile::kH, Tile::kW / kThreads> Iterations;
-
-  /// Computes the initial offset
-  typedef TiledThreadOffset<ThreadShape> ThreadOffset;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Tiling in which warps rake across the contiguous dimension
-template <typename Tile_, int Threads>
-struct TileTraitsWarpRake {
-  /// Shape of tile
-  typedef Tile_ Tile;
-
-  /// Number of participating threads
-  static int const kThreads = Threads;
-
-  /// Hard-coded warp size
-  static int const kWarpSize = 32;
-
-  /// Number of participating warps
-  static int const kWarpCount = kThreads / kWarpSize;
-
-  // Static assertions
-  static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
-                "Tiling undefined if elements not divisible by threads.");
-
-  static_assert(!(kThreads % kWarpSize), "Number of threads must be divisible by the warp size.");
-
-  static_assert(!(Tile::kW % kWarpSize), "Contiguous dimension must be divisible by the warp size");
-
-  /// Warps strip-mined across strided dimension
-  static int const kWarpsStrided = __NV_STD_MIN(kWarpCount, Tile::kH);
-
-  /// Warps stripmined contiguous dimension
-  static int const kWarpsContiguous = kWarpCount / kWarpsStrided;
-
-  /// Arrangement of threads
-  typedef Shape<1, kWarpsStrided, kWarpsContiguous * kWarpSize> ThreadShape;
-
-  /// The same warp rakes along the contiguous dimension
-  typedef Shape<1, kWarpsStrided, kWarpSize> Delta;
-
-  /// Number of iterations
-  typedef Shape<1, Tile::kH / Delta::kH, Tile::kW / ThreadShape::kW> Iterations;
-
-  /// Computes the thread offset in (H, W) based on thread ID
-  struct ThreadOffset {
-    /// Basic thread offset function computed from a thread shape
-    CUTLASS_HOST_DEVICE
-    Coord<4> operator()() const {
-      int tid = threadIdx.x;
-      int warp = (tid / kWarpSize);
-      int lane = (tid % kWarpSize);
-
-      static int const kWarpSpanContiguous = kWarpSize * Iterations::kW;
-
-      int warp_w = (warp % kWarpsContiguous);
-      int warp_h = (warp / kWarpsContiguous);
-
-      return make_Coord(0, warp_h, lane + kWarpSpanContiguous * warp_w, 0);
-    }
-  };
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Chooses 'best' shape to enable warp raking along contiguous dimension if possible.
-template <typename Tile_, int Threads>
-struct TileTraitsStandard {
-  /// Shape of tile
-  typedef Tile_ Tile;
-
-  /// Number of participating threads
-  static int const kThreads = Threads;
-
-  /// Hard-coded warp size
-  static int const kWarpSize = 32;
-
-  /// Number of participating warps
-  static int const kWarpCount = kThreads / kWarpSize;
-
-  // Static assertions
-  static_assert(!(ShapeCount<Tile>::kDhw % kThreads),
-                "Tiling undefined if elements not divisible by threads.");
-
-  /// Choose the stride-major contiguous tiling if the contiguous dimension is
-  /// smaller than the warp size. Otherwise, if it is divisible by the warp size,
-  /// choose the warp rake arrangement.
-  typedef typename platform::conditional <
-      Tile::kW<kWarpSize,
-               TileTraitsStrideMajor<Tile, Threads>,
-               typename platform::conditional<!(Tile::kW % kWarpSize),
-                                              TileTraitsWarpRake<Tile, Threads>,
-                                              TileTraitsContiguousMajor<Tile, Threads> >::type>::
-          type Traits;
-
-  /// Delta between accesses
-  typedef typename Traits::Delta Delta;
-
-  /// Delta between each thread's access
-  /// TODO MTA this is wrong for sure, but Delta is used for stride computation at the moment
-  typedef Delta ImmediateOffsetStrides;
-
-  /// Number of accesses
-  typedef typename Traits::Iterations Iterations;
-
-  /// Thread offset functor
-  typedef typename Traits::ThreadOffset ThreadOffset;
-};
-
-///////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/util/cutlass_math.h

@@ -1,131 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-#pragma once
-
-/**
- * \file
- * \brief Math utilities
- */
-
-#include <cutlass/util/platform.h>
-
-namespace cutlass {
-
-/******************************************************************************
- * Static math utilities
- ******************************************************************************/
-
-/**
- * Statically determine if N is a power-of-two
- */
-template <int N>
-struct is_pow2 : platform::integral_constant<bool, (N & (N - 1)) == 0> {};
-
-/**
- * Statically determine log2(N), rounded down
- */
-template <int N, int CurrentVal = N, int Count = 0>
-struct log2_down {
-  /// Static logarithm value
-  enum { value = log2_down<N, (CurrentVal >> 1), Count + 1>::value };
-};
-
-// Base case
-template <int N, int Count>
-struct log2_down<N, 1, Count> {
-  enum { value = Count };
-};
-
-/**
- * Statically determine log2(N), rounded up
- */
-template <int N, int CurrentVal = N, int Count = 0>
-struct log2_up {
-  /// Static logarithm value
-  enum { value = log2_up<N, (CurrentVal >> 1), Count + 1>::value };
-};
-
-// Base case
-template <int N, int Count>
-struct log2_up<N, 1, Count> {
-  enum { value = ((1 << Count) < N) ? Count + 1 : Count };
-};
-
-/**
- * Statically estimate sqrt(N) to the nearest power-of-two
- */
-template <int N>
-struct sqrt_est {
-  enum { value = 1 << (log2_up<N>::value / 2) };
-};
-
-/**
- * For performing a constant-division with a compile-time assertion that the
- * Divisor evenly-divides the Dividend.
- */
-template <int Dividend, int Divisor>
-struct divide_assert {
-  enum { value = Dividend / Divisor };
-
-  static_assert((Dividend % Divisor == 0), "Not an even multiple");
-};
-
-/******************************************************************************
- * Rounding
- ******************************************************************************/
-
-/**
- * Round dividend up to the nearest multiple of divisor
- */
-template <typename dividend_t, typename divisor_t>
-CUTLASS_HOST_DEVICE dividend_t round_nearest(dividend_t dividend, divisor_t divisor) {
-  return ((dividend + divisor - 1) / divisor) * divisor;
-}
-
-/**
- * Greatest common divisor
- */
-template <typename value_t>
-CUTLASS_HOST_DEVICE value_t gcd(value_t a, value_t b) {
-  for (;;) {
-    if (a == 0) return b;
-    b %= a;
-    if (b == 0) return a;
-    a %= b;
-  }
-}
-
-/**
- * Least common multiple
- */
-template <typename value_t>
-CUTLASS_HOST_DEVICE value_t lcm(value_t a, value_t b) {
-  value_t temp = gcd(a, b);
-
-  return temp ? (a / temp * b) : 0;
-}
-
-}  // namespace cutlass

cutlass/util/debug.h

@@ -1,122 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-#pragma once
-
-/**
- * \file
- * \brief Debugging and logging functionality
- */
-
-#include <stdio.h>
-
-namespace cutlass {
-
-/******************************************************************************
- * Debug and logging macros
- ******************************************************************************/
-
-/**
- * Formats and prints the given message to stdout
- */
-#if !defined(CUDA_LOG)
-#if !defined(__CUDA_ARCH__)
-#define CUDA_LOG(format, ...) printf(format, __VA_ARGS__)
-#else
-#define CUDA_LOG(format, ...)                              \
-  printf("[block (%d,%d,%d), thread (%d,%d,%d)]: " format, \
-         blockIdx.x,                                       \
-         blockIdx.y,                                       \
-         blockIdx.z,                                       \
-         threadIdx.x,                                      \
-         threadIdx.y,                                      \
-         threadIdx.z,                                      \
-         __VA_ARGS__);
-#endif
-#endif
-
-/**
- * Formats and prints the given message to stdout only if DEBUG is defined
- */
-#if !defined(CUDA_LOG_DEBUG)
-#ifdef DEBUG
-#define CUDA_LOG_DEBUG(format, ...) CUDA_LOG(format, __VA_ARGS__)
-#else
-#define CUDA_LOG_DEBUG(format, ...)
-#endif
-#endif
-
-/**
- * \brief The corresponding error message is printed to \p stderr (or \p stdout in device code)
- * along with the supplied source context.
- *
- * \return The CUDA error.
- */
-__host__ CUTLASS_DEVICE cudaError_t cuda_perror_impl(cudaError_t error,
-                                                     const char* filename,
-                                                     int line) {
-  (void)filename;
-  (void)line;
-  if (error) {
-#if !defined(__CUDA_ARCH__)
-    fprintf(
-        stderr, "CUDA error %d [%s, %d]: %s\n", error, filename, line, cudaGetErrorString(error));
-    fflush(stderr);
-#else
-    printf("CUDA error %d [%s, %d]\n", error, filename, line);
-#endif
-  }
-  return error;
-}
-
-/**
- * \brief Perror macro
- */
-#ifndef CUDA_PERROR
-#define CUDA_PERROR(e) cuda_perror_impl((cudaError_t)(e), __FILE__, __LINE__)
-#endif
-
-/**
- * \brief Perror macro with exit
- */
-#ifndef CUDA_PERROR_EXIT
-#define CUDA_PERROR_EXIT(e)                                     \
-  if (cuda_perror_impl((cudaError_t)(e), __FILE__, __LINE__)) { \
-    exit(1);                                                    \
-  }
-#endif
-
-/**
- * \brief Perror macro only if DEBUG is defined
- */
-#ifndef CUDA_PERROR_DEBUG
-#ifdef DEBUG
-#define CUDA_PERROR_DEBUG(e) CUDA_PERROR(e)
-#else
-#define CUDA_PERROR_DEBUG(e) (e)
-#endif
-#endif
-
-}  // namespace cutlass

cutlass/util/platform.h

@@ -1,801 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-#pragma once
-
-/**
- * \file
- * \brief C++ features that may be otherwise unimplemented for CUDA device functions.
- *
- * This file has three components:
- *
- *   (1) Macros:
- *       - Empty macro defines for C++ keywords not supported by the current
- *         version of C++. These simply allow compilation to proceed (but do
- *         not provide the added semantics).
- *           - \p noexcept
- *           - \p constexpr
- *           - \p nullptr
- *           - \p static_assert
- *
- *       - Macro functions that we need in constant expressions because the
- *         C++ equivalents require constexpr compiler support.  These are
- *         prefixed with \p __NV_STD_*
- *           - \p __NV_STD_MAX
- *           - \p __NV_STD_MIN
- *
- *   (2) Re-implementations of STL functions and types:
- *       - C++ features that need the \p __device__ annotation.  These are
- *         placed into the \p platform namespace.
- *           - \p plus
- *           - \p less
- *           - \p greater
- *           - \p min
- *           - \p max
- *           - \p methods on std::pair (==, !=, <, <=, >, >=, and make_pair())
- *
- *   (3) Stop-gap implementations of unsupported STL functions and types:
- *       - STL functions and types defined by C++ 11/14/17/etc. that are not
- *         provided by the current version of C++. These are placed into the
- *         \p platform namespace
- *           - \p integral_constant
- *           - \p nullptr_t
- *           - \p true_type
- *           - \p false_type
- *           - \p bool_constant
- *           - \p enable_if
- *           - \p conditional
- *           - \p is_same
- *           - \p is_base_of
- *           - \p remove_const
- *           - \p remove_volatile
- *           - \p remove_cv
- *           - \p is_volatile
- *           - \p is_pointer
- *           - \p is_void
- *           - \p is_integral
- *           - \p is_floating_point
- *           - \p is_arithmetic
- *           - \p is_fundamental
- *           - \p is_trivially_copyable
- *           - \p alignment_of
- *           - \p aligned_storage
- *
- *   (4) Functions and types that are STL-like (but aren't in the STL):
- *           - \p TODO: min and max functors?
- *
- * The idea is that, as we drop support for older compilers, we can simply #define
- * the \p __NV_STD_XYZ macros and \p platform namespace to alias their C++
- * counterparts (or trivially find-and-replace their occurrences in code text).
- */
-
-//-----------------------------------------------------------------------------
-// Dependencies
-//-----------------------------------------------------------------------------
-
-#include <stdint.h>
-
-#if !defined(__CUDACC_RTC__)
-//-----------------------------------------------------------------------------
-// Include STL files that platform provides functionality for
-//-----------------------------------------------------------------------------
-
-#include <algorithm>   // Minimum/maximum operations
-#include <cstddef>     // nullptr_t
-#include <functional>  // Arithmetic operations
-#include <utility>     // For methods on std::pair
-#if (!defined(_MSC_VER) && (__cplusplus >= 201103L)) || (defined(_MSC_VER) && (_MS_VER >= 1500))
-#include <type_traits>  // For integral constants, conditional metaprogramming, and type traits
-#endif
-
-#include <cutlass/cutlass.h>
-
-#endif
-/******************************************************************************
- * Macros
- ******************************************************************************/
-//-----------------------------------------------------------------------------
-// Keywords
-//-----------------------------------------------------------------------------
-
-/// noexcept, constexpr
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1900))
-#ifndef noexcept
-#define noexcept
-#endif
-#ifndef constexpr
-#define constexpr
-#endif
-#endif
-
-/// nullptr
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1310))
-#ifndef nullptr
-#define nullptr 0
-#endif
-#endif
-
-/// static_assert
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1600))
-#ifndef static_assert
-#define __platform_cat_(a, b) a##b
-#define __platform_cat(a, b) __platform_cat_(a, b)
-#define static_assert(__e, __m) typedef int __platform_cat(AsSeRt, __LINE__)[(__e) ? 1 : -1]
-#endif
-#endif
-
-//-----------------------------------------------------------------------------
-// Functions
-//-----------------------------------------------------------------------------
-
-/// Select maximum(a, b)
-#ifndef __NV_STD_MAX
-#define __NV_STD_MAX(a, b) (((b) > (a)) ? (b) : (a))
-#endif
-
-/// Select minimum(a, b)
-#ifndef __NV_STD_MIN
-#define __NV_STD_MIN(a, b) (((b) < (a)) ? (b) : (a))
-#endif
-
-/******************************************************************************
- * Re-implementations
- ******************************************************************************/
-namespace cutlass {
-namespace platform {
-
-//-----------------------------------------------------------------------------
-// Arithmetic operations, comparisons <functional>
-//-----------------------------------------------------------------------------
-
-/// platform::plus
-template <typename T>
-struct plus {
-  CUTLASS_HOST_DEVICE constexpr T operator()(const T& lhs, const T& rhs) const { return lhs + rhs; }
-};
-
-/// std::less
-template <typename T>
-struct less {
-  CUTLASS_HOST_DEVICE constexpr bool operator()(const T& lhs, const T& rhs) const {
-    return lhs < rhs;
-  }
-};
-
-/// std::greater
-template <typename T>
-struct greater {
-  CUTLASS_HOST_DEVICE constexpr bool operator()(const T& lhs, const T& rhs) const {
-    return lhs > rhs;
-  }
-};
-
-//-----------------------------------------------------------------------------
-// Minimum/maximum operations <algorithm>
-//-----------------------------------------------------------------------------
-
-/// std::min
-template <typename T>
-CUTLASS_HOST_DEVICE constexpr const T& min(const T& a, const T& b) {
-  return (b < a) ? b : a;
-}
-
-/// std::max
-template <typename T>
-CUTLASS_HOST_DEVICE constexpr const T& max(const T& a, const T& b) {
-  return (a < b) ? b : a;
-}
-
-#if !defined(__CUDACC_RTC__)
-//-----------------------------------------------------------------------------
-// Methods on std::pair
-//-----------------------------------------------------------------------------
-
-using std::pair;
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator==(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return (lhs.first == rhs.first) && (lhs.second == rhs.second);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator!=(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return (lhs.first != rhs.first) && (lhs.second != rhs.second);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator<(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return (lhs.first < rhs.first) ? true : (rhs.first < lhs.first) ? false
-                                                                  : (lhs.second < rhs.second);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator<=(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return !(rhs < lhs);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator>(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return (rhs < lhs);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE constexpr bool operator>=(const pair<T1, T2>& lhs, const pair<T1, T2>& rhs) {
-  return !(lhs < rhs);
-}
-
-template <class T1, class T2>
-CUTLASS_HOST_DEVICE std::pair<T1, T2> make_pair(T1 t, T2 u) {
-  std::pair<T1, T2> retval;
-  retval.first = t;
-  retval.second = u;
-  return retval;
-}
-#endif
-
-}  // namespace platform
-
-/******************************************************************************
- * Implementations of C++ 11/14/17/... STL features
- ******************************************************************************/
-
-namespace platform {
-
-//-----------------------------------------------------------------------------
-// Integral constant helper types <type_traits>
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1500))
-
-/// std::integral_constant
-template <typename value_t, value_t V>
-struct integral_constant;
-
-/// std::integral_constant
-template <typename value_t, value_t V>
-struct integral_constant {
-  static const value_t value = V;
-
-  typedef value_t value_type;
-  typedef integral_constant<value_t, V> type;
-
-  CUTLASS_HOST_DEVICE operator value_type() const { return value; }
-
-  CUTLASS_HOST_DEVICE const value_type operator()() const { return value; }
-};
-
-#else
-
-using std::integral_constant;
-using std::pair;
-
-#endif
-
-/// The type used as a compile-time boolean with true value.
-typedef integral_constant<bool, true> true_type;
-
-/// The type used as a compile-time boolean with false value.
-typedef integral_constant<bool, false> false_type;
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201402L)) || (defined(_MSC_VER) && (_MSC_VER < 1900))
-
-/// std::bool_constant
-template <bool V>
-struct bool_constant : platform::integral_constant<bool, V> {};
-
-#else
-
-using std::bool_constant;
-
-#endif
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1700))
-
-/// std::nullptr_t
-struct nullptr_t {};
-
-#else
-
-using std::nullptr_t;
-
-#endif
-
-//-----------------------------------------------------------------------------
-// Conditional metaprogramming <type_traits>
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1600))
-
-/// std::enable_if (true specialization)
-template <bool C, typename T = void>
-struct enable_if {
-  typedef T type;
-};
-
-/// std::enable_if (false specialization)
-template <typename T>
-struct enable_if<false, T> {};
-
-/// std::conditional (true specialization)
-template <bool B, class T, class F>
-struct conditional {
-  typedef T type;
-};
-
-/// std::conditional (false specialization)
-template <class T, class F>
-struct conditional<false, T, F> {
-  typedef F type;
-};
-
-#else
-
-using std::enable_if;
-using std::conditional;
-
-#endif
-
-//-----------------------------------------------------------------------------
-// Const/volatility specifiers <type_traits>
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1500))
-
-/// std::remove_const (non-const specialization)
-template <typename T>
-struct remove_const {
-  typedef T type;
-};
-
-/// std::remove_const (const specialization)
-template <typename T>
-struct remove_const<const T> {
-  typedef T type;
-};
-
-/// std::remove_volatile (non-volatile specialization)
-template <typename T>
-struct remove_volatile {
-  typedef T type;
-};
-
-/// std::remove_volatile (volatile specialization)
-template <typename T>
-struct remove_volatile<volatile T> {
-  typedef T type;
-};
-
-/// std::remove_cv
-template <typename T>
-struct remove_cv {
-  typedef typename remove_volatile<typename remove_const<T>::type>::type type;
-};
-
-#else
-
-using std::remove_const;
-using std::remove_volatile;
-using std::remove_cv;
-
-#endif
-
-//-----------------------------------------------------------------------------
-// Type relationships <type_traits>
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1500))
-
-/// std::is_same (false specialization)
-template <typename A, typename B>
-struct is_same : false_type {};
-
-/// std::is_same (true specialization)
-template <typename A>
-struct is_same<A, A> : true_type {};
-
-/// Helper for std::is_base_of
-template <typename BaseT, typename DerivedT>
-struct is_base_of_helper {
-  typedef char (&yes)[1];
-  typedef char (&no)[2];
-
-  template <typename B, typename D>
-  struct dummy {
-    CUTLASS_HOST_DEVICE operator B*() const;
-    CUTLASS_HOST_DEVICE operator D*();
-  };
-
-  template <typename T>
-  CUTLASS_HOST_DEVICE static yes check(DerivedT*, T);
-
-  CUTLASS_HOST_DEVICE static no check(BaseT*, int);
-
-  static const bool value = sizeof(check(dummy<BaseT, DerivedT>(), int())) == sizeof(yes);
-};
-
-/// std::is_base_of
-template <typename BaseT, typename DerivedT>
-struct is_base_of
-    : integral_constant<bool,
-                        (is_base_of_helper<typename remove_cv<BaseT>::type,
-                                           typename remove_cv<DerivedT>::type>::value) ||
-                            (is_same<typename remove_cv<BaseT>::type,
-                                     typename remove_cv<DerivedT>::type>::value)> {};
-
-#else
-
-using std::is_same;
-using std::is_base_of;
-
-#endif
-
-//-----------------------------------------------------------------------------
-// Type properties <type_traits>
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1500))
-
-/// std::is_volatile
-template <typename T>
-struct is_volatile : false_type {};
-template <typename T>
-struct is_volatile<volatile T> : true_type {};
-
-/// Helper for std::is_pointer (false specialization)
-template <typename T>
-struct is_pointer_helper : false_type {};
-
-/// Helper for std::is_pointer (true specialization)
-template <typename T>
-struct is_pointer_helper<T*> : true_type {};
-
-/// std::is_pointer
-template <typename T>
-struct is_pointer : is_pointer_helper<typename remove_cv<T>::type> {};
-
-/// std::is_void
-template <typename T>
-struct is_void : is_same<void, typename remove_cv<T>::type> {};
-
-/// std::is_integral
-template <typename T>
-struct is_integral : false_type {};
-template <>
-struct is_integral<char> : true_type {};
-template <>
-struct is_integral<signed char> : true_type {};
-template <>
-struct is_integral<unsigned char> : true_type {};
-template <>
-struct is_integral<short> : true_type {};
-template <>
-struct is_integral<unsigned short> : true_type {};
-template <>
-struct is_integral<int> : true_type {};
-template <>
-struct is_integral<unsigned int> : true_type {};
-template <>
-struct is_integral<long> : true_type {};
-template <>
-struct is_integral<unsigned long> : true_type {};
-template <>
-struct is_integral<long long> : true_type {};
-template <>
-struct is_integral<unsigned long long> : true_type {};
-template <typename T>
-struct is_integral<volatile T> : is_integral<T> {};
-template <typename T>
-struct is_integral<const T> : is_integral<T> {};
-template <typename T>
-struct is_integral<const volatile T> : is_integral<T> {};
-
-/// std::is_floating_point
-template <typename T>
-struct is_floating_point
-    : integral_constant<bool,
-                        (is_same<float, typename remove_cv<T>::type>::value ||
-                         is_same<double, typename remove_cv<T>::type>::value)> {};
-
-/// std::is_arithmetic
-template <typename T>
-struct is_arithmetic
-    : integral_constant<bool, (is_integral<T>::value || is_floating_point<T>::value)> {};
-
-/// std::is_fundamental
-template <typename T>
-struct is_fundamental
-    : integral_constant<bool,
-                        (is_arithmetic<T>::value || is_void<T>::value ||
-                         is_same<nullptr_t, typename remove_cv<T>::type>::value)> {};
-
-#else
-
-using std::is_volatile;
-using std::is_pointer;
-using std::is_void;
-using std::is_integral;
-using std::is_floating_point;
-using std::is_arithmetic;
-using std::is_fundamental;
-
-#endif
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1800)) || \
-    (defined(__GNUG__) && (__GNUC__ < 5))
-
-/**
-     * std::is_trivially_copyable
-     *
-     * This implementation only evaluates true if T is fundamental or pointer
-     *
-     * Without help from partial template specializations provided by the user for
-     * a specific class or struct, this trait will never report that the specified
-     * class or struct  is trivially-copyable ; this is always safe,
-     * if possibly sub-optimal.
-     */
-template <typename T>
-struct is_trivially_copyable
-    : integral_constant<bool, (is_fundamental<T>::value || is_pointer<T>::value)> {};
-
-#else
-
-using std::is_trivially_copyable;
-
-#endif
-
-//-----------------------------------------------------------------------------
-// Alignment and layout utilities
-//-----------------------------------------------------------------------------
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1500))
-
-/// std::alignment_of
-template <typename value_t>
-struct alignment_of {
-  struct pad {
-    value_t val;
-    char byte;
-  };
-
-  enum { value = sizeof(pad) - sizeof(value_t) };
-};
-
-#else
-
-template <typename value_t>
-struct alignment_of : std::alignment_of<value_t> {};
-
-#endif
-
-/* 16B specializations where 32-bit Win32 host compiler disagrees with device compiler */
-template <>
-struct alignment_of<int4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<uint4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<float4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<long4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<ulong4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<longlong2> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<ulonglong2> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<double2> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<longlong4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<ulonglong4> {
-  enum { value = 16 };
-};
-template <>
-struct alignment_of<double4> {
-  enum { value = 16 };
-};
-
-// Specializations for volatile/const qualified types
-template <typename value_t>
-struct alignment_of<volatile value_t> : alignment_of<value_t> {};
-template <typename value_t>
-struct alignment_of<const value_t> : alignment_of<value_t> {};
-template <typename value_t>
-struct alignment_of<const volatile value_t> : alignment_of<value_t> {};
-
-#if (!defined(_MSC_VER) && (__cplusplus < 201103L)) || (defined(_MSC_VER) && (_MSC_VER < 1800))
-
-template <size_t Align>
-struct aligned_chunk;
-template <>
-struct __align__(1) aligned_chunk<1> {
-  uint8_t buff;
-};
-template <>
-struct __align__(2) aligned_chunk<2> {
-  uint16_t buff;
-};
-template <>
-struct __align__(4) aligned_chunk<4> {
-  uint32_t buff;
-};
-template <>
-struct __align__(8) aligned_chunk<8> {
-  uint32_t buff[2];
-};
-template <>
-struct __align__(16) aligned_chunk<16> {
-  uint32_t buff[4];
-};
-template <>
-struct __align__(32) aligned_chunk<32> {
-  uint32_t buff[8];
-};
-template <>
-struct __align__(64) aligned_chunk<64> {
-  uint32_t buff[16];
-};
-template <>
-struct __align__(128) aligned_chunk<128> {
-  uint32_t buff[32];
-};
-template <>
-struct __align__(256) aligned_chunk<256> {
-  uint32_t buff[64];
-};
-template <>
-struct __align__(512) aligned_chunk<512> {
-  uint32_t buff[128];
-};
-template <>
-struct __align__(1024) aligned_chunk<1024> {
-  uint32_t buff[256];
-};
-template <>
-struct __align__(2048) aligned_chunk<2048> {
-  uint32_t buff[512];
-};
-template <>
-struct __align__(4096) aligned_chunk<4096> {
-  uint32_t buff[1024];
-};
-
-/// std::aligned_storage
-template <size_t Len, size_t Align>
-struct aligned_storage {
-  typedef aligned_chunk<Align> type[Len / sizeof(aligned_chunk<Align>)];
-};
-
-#else
-
-using std::aligned_storage;
-
-#endif
-
-#if !defined(__CUDACC_RTC__)
-/// Default deleter
-template <typename T>
-struct default_delete {
-  void operator()(T* ptr) const { delete ptr; }
-};
-
-/// Partial specialization for deleting array types
-template <typename T>
-struct default_delete<T[]> {
-  void operator()(T* ptr) const { delete[] ptr; }
-};
-
-/// std::unique_ptr
-template <class T, class Deleter = default_delete<T> >
-class unique_ptr {
- public:
-  typedef T* pointer;
-  typedef T element_type;
-  typedef Deleter deleter_type;
-
- private:
-  /// Pointer to memory
-  pointer _ptr;
-
-  /// Deleter
-  deleter_type _deleter;
-
- public:
-  unique_ptr() : _ptr(nullptr) {}
-  unique_ptr(pointer p) : _ptr(p) {}
-
-  ~unique_ptr() {
-    if (_ptr) {
-      _deleter(_ptr);
-    }
-  }
-  /// Returns a pointer to the managed object or nullptr if no object is owned.
-  pointer get() const noexcept { return _ptr; }
-
-  /// Releases ownership of the managed object, if any
-  pointer release() noexcept {
-    pointer p(_ptr);
-    _ptr = nullptr;
-    return p;
-  }
-
-  /// Replaces the managed object, deleting the old object.
-  void reset(pointer p = pointer()) noexcept {
-    pointer old_ptr = _ptr;
-    _ptr = p;
-    if (old_ptr != nullptr) {
-      get_deleter()(old_ptr);
-    }
-  }
-
-  /// Swaps the managed objects with *this and another unique_ptr
-  void swap(unique_ptr& other) noexcept { std::swap(_ptr, other._ptr); }
-
-  /// Returns the deleter object
-  Deleter& get_deleter() noexcept { return _deleter; }
-
-  /// Returns the deleter object
-  Deleter const& get_deleter() const noexcept { return _deleter; }
-
-  /// Checks whether an object is owned
-  operator bool() const noexcept { return _ptr != nullptr; }
-
-  /// Dereferences the unique_ptr
-  T& operator*() const { return *_ptr; }
-
-  /// Returns a pointer to the managed object
-  pointer operator->() const noexcept { return _ptr; }
-
-  /// Array access to managed object
-  T& operator[](size_t i) const { return _ptr[i]; }
-};
-
-/// Specializes the swap algorithm
-template <typename T, typename Deleter>
-void swap(unique_ptr<T, Deleter>& lhs, unique_ptr<T, Deleter>& rhs) noexcept {
-  lhs.swap(rhs);
-}
-#endif
-
-};  // namespace platform
-};  // namespace cutlass

cutlass/vector.h

@@ -1,229 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Defines a 1D vector of elements held in the registers of each thread.
-*/
-#pragma once
-
-#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
-#include <cuda_fp16.h>
-#endif
-
-#include <cutlass/util/platform.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <size_t kAlignment_>
-struct AlignedStruct {};
-
-template <>
-struct __align__(1) AlignedStruct<1>{};
-template <>
-struct __align__(2) AlignedStruct<2>{};
-template <>
-struct __align__(4) AlignedStruct<4>{};
-template <>
-struct __align__(8) AlignedStruct<8>{};
-template <>
-struct __align__(16) AlignedStruct<16>{};
-template <>
-struct __align__(32) AlignedStruct<32>{};
-template <>
-struct __align__(64) AlignedStruct<64>{};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int kLanes_>
-union Vector {
-  /// The scalar type.
-  typedef Scalar_ Scalar;
-
-  /// The number of elements in the vector.
-  enum { kLanes = kLanes_ };
-  /// The size of the vector.
-  enum { kVectorSize = kLanes * (int)sizeof(Scalar) };
-  /// The number of registers needed to store the vector.
-  enum { kRegisters = kVectorSize < 4 ? 1 : kVectorSize / 4 };
-
-  // Make sure that the vector type makes sense.
-  static_assert(kVectorSize <= 16, "Vector type is too large");
-
-  /// The aligned storage to make sure we have good alignment.
-  AlignedStruct<kVectorSize> aligned_;
-  /// The associated array of scalars.
-  Scalar scalars[kLanes];
-  /// The data in registers.
-  uint32_t registers[kRegisters];
-
-  /// Accessor to the ith lane.
-  CUTLASS_DEVICE Scalar const& operator[](uint32_t i) const { return scalars[i]; }
-  /// Accessor to the ith lane.
-  CUTLASS_DEVICE Scalar& operator[](uint32_t i) { return scalars[i]; }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-#if !defined(__CUDACC_RTC__) || defined(CUTLASS_NVRTC_HAS_FP16)
-
-template <int kLanes_>
-union Vector<half, kLanes_> {
-  /// The scalar type.
-  typedef half Scalar;
-
-  /// The number of elements in the vector.
-  enum { kLanes = kLanes_ };
-  /// The size of the vector.
-  enum { kVectorSize = kLanes * (int)sizeof(Scalar) };
-  /// The number of registers needed to store the vector.
-  enum { kRegisters = kVectorSize < 4 ? 1 : kVectorSize / 4 };
-
-  // Make sure that the vector type makes sense.
-  static_assert(kVectorSize <= size_t(16), "Vector type is too large");
-
-  /// The aligned storage to make sure we have good alignment.
-  AlignedStruct<kVectorSize> aligned_;
-  /// The associated array of scalars.
-  uint16_t scalars[kLanes];
-  /// The data in registers.
-  uint32_t registers[kRegisters];
-
-  /// Accessor to the ith lane.
-  CUTLASS_DEVICE Scalar const& operator[](uint32_t i) const {
-    return reinterpret_cast<Scalar const&>(scalars[i]);
-  }
-  /// Accessor to the ith lane.
-  CUTLASS_DEVICE Scalar& operator[](uint32_t i) { return reinterpret_cast<Scalar&>(scalars[i]); }
-};
-
-#endif
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_>
-CUTLASS_DEVICE void make_zero(Scalar_& x) {
-  x = Scalar_(0);
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Element_, int kLanes_ = 1>
-struct Vectorize {
-  typedef Vector<Element_, kLanes_> Type;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Element_>
-struct Vectorize<Element_, 1> {
-  typedef Element_ Type;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-template <typename Scalar_, int kLanes_>
-CUTLASS_DEVICE void make_zero(Vector<Scalar_, kLanes_>& vec) {
-  for (int i = 0; i < Vector<Scalar_, kLanes_>::kRegisters; ++i) {
-    vec.registers[i] = 0;
-  }
-}
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-//
-// cutlass::Extent similar to std::extent but applicable to CUTLASS types
-//
-
-/// Returns the extent of a scalar or vector
-template <typename T>
-struct Extent {
-  static size_t const kValue = 1;
-};
-
-/// Returns the number of lanes of a vector if need be
-template <typename T, int Lanes>
-struct Extent<Vector<T, Lanes> > {
-  static size_t const kValue = Lanes;
-};
-
-/// Returns the number of lanes of a vector if need be
-template <typename T, int Lanes>
-struct Extent<Vector<T, Lanes> const> {
-  static size_t const kValue = Lanes;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Traits describing properties of vectors and scalar-as-vectors
-template <typename T>
-struct VectorTraits {
-  /// Scalar type
-  typedef T Scalar;
-
-  /// Number of lanes of vector
-  static int const kLanes = 1;
-
-  /// True if the type is actually a cutlass::Vector, otherwise false
-  static bool const IsVector = false;
-
-  /// Type that is always a vector
-  typedef Vector<T, 1> Vector;
-};
-
-/// Partial specialization for actual cutlass::Vector
-template <typename T, int Lanes>
-struct VectorTraits<Vector<T, Lanes> > {
-  /// Scalar type
-  typedef T Scalar;
-
-  /// Number of lanes of vector
-  static int const kLanes = Lanes;
-
-  /// Type is actually a cutlass::Vector
-  static bool const IsVector = true;
-
-  /// Type that is always a Vector
-  typedef Vector<T, Lanes> Vector;
-};
-
-/// Partial specialization for actual cutlass::Vector
-template <typename T, int Lanes>
-struct VectorTraits<Vector<T, Lanes> const> {
-  /// Scalar type
-  typedef T Scalar;
-
-  /// Number of lanes of vector
-  static int const kLanes = Lanes;
-
-  /// Type is actually a cutlass::Vector
-  static bool const IsVector = true;
-
-  /// Type that is always a Vector
-  typedef Vector<T, Lanes> Vector;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass

cutlass/wmma_matrix.h

@@ -1,193 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2018, NVIDIA CORPORATION.  All rights reserved.
- *
- * Redistribution and use in source and binary forms, with or without modification, are permitted
- * provided that the following conditions are met:
- *     * Redistributions of source code must retain the above copyright notice, this list of
- *       conditions and the following disclaimer.
- *     * Redistributions in binary form must reproduce the above copyright notice, this list of
- *       conditions and the following disclaimer in the documentation and/or other materials
- *       provided with the distribution.
- *     * Neither the name of the NVIDIA CORPORATION nor the names of its contributors may be used
- *       to endorse or promote products derived from this software without specific prior written
- *       permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR
- * IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
- * FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
- * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
- * OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT,
- * STRICT LIABILITY, OR TOR (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Abstractions for loading and storing matrices using the CUDA WMMA API.
-*/
-#pragma once
-
-#if defined(__CUDACC__) && (!defined(__CUDA_ARCH__) || __CUDA_ARCH__ >= 700)
-
-// Dependent header files should use the following macro to guard all code using
-// nvcuda::wmma:: to enable compilation for CUDA Compute Capabilities < sm_70.
-// Earlier shader models not support Tensor Cores.
-#define CUTLASS_USE_WMMA_API
-
-#include "stdio.h"
-
-#include <crt/mma.h>
-#include <cutlass/fragment.h>
-#include <cutlass/load_store.h>
-#include <cutlass/matrix_traits.h>
-#include <cutlass/shape.h>
-#include <cutlass/vector.h>
-
-namespace cutlass {
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Statically maps cutlass::MatrixLayout => nvcuda::wmma layout tags
-template <MatrixLayout::Kind kLayout_>
-struct WmmaLayout {
-  typedef nvcuda::wmma::col_major Layout;
-};
-
-/// Statically maps cutlass::MatrixLayout => nvcuda::wmma layout tags
-template <>
-struct WmmaLayout<MatrixLayout::kRowMajor> {
-  typedef nvcuda::wmma::row_major Layout;
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to nvcuda::wmma fragment load and store operations
-template <GemmOperand::Kind kOperand_,
-          MatrixLayout::Kind kLayout_,
-          typename Scalar_,
-          typename WmmaShape_>
-struct WmmaMatrix {};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to nvcuda::wmma fragment accessors for A operand
-template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
-struct WmmaMatrix<GemmOperand::kA, kLayout_, Scalar_, WmmaShape_>
-    : public nvcuda::wmma::fragment<
-          /// The nvcuda::wmma operand name.
-          nvcuda::wmma::matrix_a,
-          /// The dimensions.
-          WmmaShape_::kW,
-          WmmaShape_::kH,
-          WmmaShape_::kD,
-          /// The scalar.
-          Scalar_,
-          /// The layout.
-          typename WmmaLayout<kLayout_>::Layout> {
-  /// This type.
-  typedef WmmaMatrix<GemmOperand::kA, kLayout_, Scalar_, WmmaShape_> This_;
-
-  /// Fill-in the element.
-  CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
-    nvcuda::wmma::fill_fragment(*this, x);
-    return *this;
-  }
-
-  /// Load from memory.
-  CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
-    nvcuda::wmma::load_matrix_sync(*this, pointer, stride);
-  }
-
-  /// Store to memory.
-  CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
-    nvcuda::wmma::store_matrix_sync(pointer, *this, stride);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to nvcuda::wmma fragment accessors for B operand
-template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
-struct WmmaMatrix<GemmOperand::kB, kLayout_, Scalar_, WmmaShape_>
-    : public nvcuda::wmma::fragment<
-          /// The nvcuda::wmma operand name.
-          nvcuda::wmma::matrix_b,
-          /// The dimensions.
-          WmmaShape_::kW,
-          WmmaShape_::kH,
-          WmmaShape_::kD,
-          /// The scalar.
-          Scalar_,
-          /// The layout.
-          typename WmmaLayout<kLayout_>::Layout> {
-  /// This type.
-  typedef WmmaMatrix<GemmOperand::kB, kLayout_, Scalar_, WmmaShape_> This_;
-
-  /// Fill-in the element.
-  CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
-    nvcuda::wmma::fill_fragment(*this, x);
-    return *this;
-  }
-
-  /// Load from memory.
-  CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
-    nvcuda::wmma::load_matrix_sync(*this, pointer, stride);
-  }
-
-  /// Store to memory.
-  CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
-    nvcuda::wmma::store_matrix_sync(pointer, *this, stride);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-/// Adapter to nvcuda::wmma fragment accessors for C operand
-template <MatrixLayout::Kind kLayout_, typename Scalar_, typename WmmaShape_>
-struct WmmaMatrix<GemmOperand::kC, kLayout_, Scalar_, WmmaShape_>
-    : public nvcuda::wmma::fragment<
-          /// The nvcuda::wmma operand name.
-          nvcuda::wmma::accumulator,
-          /// The dimensions.
-          WmmaShape_::kW,
-          WmmaShape_::kH,
-          WmmaShape_::kD,
-          /// The scalar.
-          Scalar_> {
-  /// This type.
-  typedef WmmaMatrix<GemmOperand::kC, kLayout_, Scalar_, WmmaShape_> This_;
-  /// The layout.
-  static MatrixLayout::Kind const kLayout = kLayout_;
-
-  /// Fill-in the element.
-  CUTLASS_DEVICE This_& operator=(Scalar_ const& x) {
-    nvcuda::wmma::fill_fragment(*this, x);
-    return *this;
-  }
-
-  /// Load from memory.
-  CUTLASS_DEVICE void load(Scalar_ const* pointer, int const stride) {
-    bool const kIsRowMajor = kLayout == MatrixLayout::kRowMajor;
-    nvcuda::wmma::load_matrix_sync(
-        *this,
-        pointer,
-        stride,
-        kIsRowMajor ? nvcuda::wmma::mem_row_major : nvcuda::wmma::mem_col_major);
-  }
-
-  /// Store to memory.
-  CUTLASS_DEVICE void store(Scalar_* pointer, int const stride) const {
-    bool const kIsRowMajor = kLayout == MatrixLayout::kRowMajor;
-    nvcuda::wmma::store_matrix_sync(
-        pointer,
-        *this,
-        stride,
-        kIsRowMajor ? nvcuda::wmma::mem_row_major : nvcuda::wmma::mem_col_major);
-  }
-};
-
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass
-
-#endif  // defined CUTLASS_USE_WMMA_API

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+</div><!--header-->
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+
+<p>AlignedBuffer is a container for trivially copyable elements suitable for use in unions and shared memory.  
+<a href="#details">More...</a></p>
+<div class="textblock"><code>#include &quot;<a class="el" href="cutlass_8h_source.html">cutlass/cutlass.h</a>&quot;</code><br />
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+Classes</h2></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1AlignedBuffer.html">cutlass::AlignedBuffer&lt; T, N, Align &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Modifies semantics of cutlass::Array&lt;&gt; to provide guaranteed alignment.  <a href="structcutlass_1_1AlignedBuffer.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
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+<div class="textblock">Here are the classes, structs, unions and interfaces with brief descriptions:</div><div class="directory">
+<div class="levels">[detail level <span onclick="javascript:toggleLevel(1);">1</span><span onclick="javascript:toggleLevel(2);">2</span><span onclick="javascript:toggleLevel(3);">3</span><span onclick="javascript:toggleLevel(4);">4</span><span onclick="javascript:toggleLevel(5);">5</span><span onclick="javascript:toggleLevel(6);">6</span>]</div><table class="directory">
+<tr id="row_0_" class="even"><td class="entry"><span style="width:0px;display:inline-block;">&#160;</span><span id="arr_0_" class="arrow" onclick="toggleFolder('0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass.html" target="_self">cutlass</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_0_" class="arrow" onclick="toggleFolder('0_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1arch.html" target="_self">arch</a></td><td class="desc"></td></tr>
+<tr id="row_0_0_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma.html" target="_self">Mma</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_30fa42e1ad201df010637cd22fc070a1.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; double &gt;, LayoutA, complex&lt; double &gt;, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_48b3a43bc03fff93a111ac01abe7e40d.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; double &gt;, LayoutA, double, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_76f9d24016e1b4167b16f4d7628c9546.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; float &gt;, LayoutA, complex&lt; float &gt;, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_f1c9d2ee842455cd0c5b71d56108d468.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; float &gt;, LayoutA, float, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_070b94670e040ed5855e5b42d5ca8a443.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, double, LayoutA, complex&lt; double &gt;, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_0aa57e6a2e6b5da37d10688bf99419a23.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, double, LayoutA, double, LayoutB, double, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01ElementAb6e65b2cf5ede7f41cb070a767158dee.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator &gt;</a></td><td class="desc">Matrix multiply-add operation - specialized for 1x1x1x1 matrix multiply operation </td></tr>
+<tr id="row_0_0_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_00e3e12e263df6506b8cf06c3f4d478b8e.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, float, LayoutA, complex&lt; float &gt;, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_004bb3fd76ca2af7b3210676fa9644d95b.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, float, LayoutA, float, LayoutB, float, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01half__t_4f30ee91f7bb3844ff7579c68d078818.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, half_t, LayoutA, half_t, LayoutB, float, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01int_00_00b2dff9ce8caad9aff5bc6a355539161.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, int, LayoutA, int, LayoutB, int, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_012_01_4_00_011_00_01int16__t8c4bac365710598317a69c489f7239db.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 2 &gt;, 1, int16_t, layout::RowMajor, int16_t, layout::ColumnMajor, int, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_014_01_4_00_011_00_01int8__t_a1ef6624fc8c10126f17f4ee88283d72.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 1, 4 &gt;, 1, int8_t, LayoutA, int8_t, LayoutB, int, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_012_00_011_01_4_00_011_00_01half__t_f3dc2e59f857ada163d1e0781ea8f391.html" target="_self">Mma&lt; gemm::GemmShape&lt; 1, 2, 1 &gt;, 1, half_t, LayoutA, half_t, LayoutB, half_t, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_0116_00_0116_00_014_01_4_00_0132_00_01half_0bcc4d05f9811035f08cc1b7f0154a4d.html" target="_self">Mma&lt; gemm::GemmShape&lt; 16, 16, 4 &gt;, 32, half_t, LayoutA, half_t, LayoutB, ElementC, LayoutC, Operator &gt;</a></td><td class="desc">Matrix multiply-add operation specialized for the entire warp </td></tr>
+<tr id="row_0_0_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_0116_00_018_00_018_01_4_00_0132_00_01half__02a3f19a78995f97d793a668e0e4d4f0.html" target="_self">Mma&lt; gemm::GemmShape&lt; 16, 8, 8 &gt;, 32, half_t, layout::RowMajor, half_t, layout::ColumnMajor, float, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: F32 = F16 * F16 + F32 </td></tr>
+<tr id="row_0_0_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_0116_00_018_00_018_01_4_00_0132_00_01half__96363097c47b056f0ca1911afd7f8b7a.html" target="_self">Mma&lt; gemm::GemmShape&lt; 16, 8, 8 &gt;, 32, half_t, layout::RowMajor, half_t, layout::ColumnMajor, half_t, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation - F16 = F16 * F16 + F16 </td></tr>
+<tr id="row_0_0_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_012_00_011_00_011_01_4_00_011_00_01half__t_8cf78649807b93684f3d431bfa34ee28.html" target="_self">Mma&lt; gemm::GemmShape&lt; 2, 1, 1 &gt;, 1, half_t, LayoutA, half_t, LayoutB, half_t, LayoutC, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_012_00_012_00_011_01_4_00_011_00_01half__t_ccde11d1bbbdab3702772ce44eb9729a.html" target="_self">Mma&lt; gemm::GemmShape&lt; 2, 2, 1 &gt;, 1, half_t, layout::ColumnMajor, half_t, layout::RowMajor, half_t, layout::ColumnMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_012_00_012_00_011_01_4_00_011_00_01half__t_c07cc6439298fa5486a719e577be2538.html" target="_self">Mma&lt; gemm::GemmShape&lt; 2, 2, 1 &gt;, 1, half_t, layout::ColumnMajor, half_t, layout::RowMajor, half_t, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_01128_01_4_00_0132_00_01uint15918972b95027764b3a849b03075ed2b.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 128 &gt;, 32, uint1b_t, layout::RowMajor, uint1b_t, layout::ColumnMajor, int, layout::RowMajor, OpXorPopc &gt;</a></td><td class="desc">Matrix multiply-add operation </td></tr>
+<tr id="row_0_0_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01int8__927179f46017ea5f58f859f1196c4829.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, int8_t, layout::RowMajor, int8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * S8 + S32 </td></tr>
+<tr id="row_0_0_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01int8__8ebae0cbdf333fddfe5c24d35ebe8e02.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, int8_t, layout::RowMajor, int8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * S8 + S32 </td></tr>
+<tr id="row_0_0_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01int8__5299c9c90c8f2f521be0c8cec1c3eb08.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, int8_t, layout::RowMajor, uint8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * U8 + S32 </td></tr>
+<tr id="row_0_0_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01int8__f083347e265b1e9eea5572d86ddb6bf9.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, int8_t, layout::RowMajor, uint8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * U8 + S32 </td></tr>
+<tr id="row_0_0_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01uint8_a62aa63a212985df306fb27e8a50aeae.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, uint8_t, layout::RowMajor, int8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = U8 * S8 + S32 </td></tr>
+<tr id="row_0_0_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01uint8_ab741d81fdc991345cb9e43c29fca573.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, uint8_t, layout::RowMajor, int8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = U8 * S8 + S32 </td></tr>
+<tr id="row_0_0_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01uint8_5221708cec5828d35db1d1c47cb4964e.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, uint8_t, layout::RowMajor, uint8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * U8 + S32 </td></tr>
+<tr id="row_0_0_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0116_01_4_00_0132_00_01uint8_bef0c048bc0f8ba2d875cb7ab26d363b.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 16 &gt;, 32, uint8_t, layout::RowMajor, uint8_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S8 * U8 + S32 </td></tr>
+<tr id="row_0_0_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01int4b_6e513ccbc44ae7909a60d93b9b5435b3.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, int4b_t, layout::RowMajor, int4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S4 * S4 + S32 </td></tr>
+<tr id="row_0_0_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01int4b_0ee08a4520882d24ba9026879265e892.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, int4b_t, layout::RowMajor, int4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S4 * S4 + S32 </td></tr>
+<tr id="row_0_0_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01int4b_4746fc55e614df0016c518d3fda2677e.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, int4b_t, layout::RowMajor, uint4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S4 * U4 + S32 </td></tr>
+<tr id="row_0_0_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01int4b_546e9ec6de6a5970b326da6f6280f1d4.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, int4b_t, layout::RowMajor, uint4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = S4 * U4 + S32 </td></tr>
+<tr id="row_0_0_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01uint4b03e3b50dbcb30d0d1ac062f3a9d5abef.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, uint4b_t, layout::RowMajor, int4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = U4 * S4 + S32 </td></tr>
+<tr id="row_0_0_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01uint4b6d968039dde5c9f062ab15f90a8049fe.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, uint4b_t, layout::RowMajor, int4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAddSaturate &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = U4 * S4 + S32 </td></tr>
+<tr id="row_0_0_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_0132_01_4_00_0132_00_01uint4bc4b6ba004e25c44bfd9266c61f937dfb.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 32 &gt;, 32, uint4b_t, layout::RowMajor, uint4b_t, layout::ColumnMajor, int, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: S32 = U4 * U4 + S32 </td></tr>
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+<tr id="row_0_0_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_018_00_018_00_014_01_4_00_018_00_01half__t_b0242d7a01097510effbc4718040d3e5.html" target="_self">Mma&lt; gemm::GemmShape&lt; 8, 8, 4 &gt;, 8, half_t, layout::ColumnMajor, half_t, layout::ColumnMajor, float, layout::RowMajor, OpMultiplyAdd &gt;</a></td><td class="desc">Matrix multiply-add operation: F32 = F16 * F16 + F32 </td></tr>
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+<tr id="row_0_2_0_5_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1thread_1_1ReductionOpPlus_1_1Params.html" target="_self">Params</a></td><td class="desc">Host-constructable parameters structure </td></tr>
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+<tr id="row_0_2_1_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_0_" class="arrow" onclick="toggleFolder('0_2_1_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1threadblock_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_0_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1detail_1_1RowArrangement.html" target="_self">RowArrangement</a></td><td class="desc"><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1detail_1_1RowArrangement.html" title="RowArrangement determines how one or more warps cover a region of consecutive rows. ">RowArrangement</a> determines how one or more warps cover a region of consecutive rows </td></tr>
+<tr id="row_0_2_1_0_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1detail_1_1RowArrangement_3_01Shape_00_01WarpsRemaini91159e6f7e123d881e3ec45101fa4f81.html" target="_self">RowArrangement&lt; Shape, WarpsRemaining, ElementsPerAccess, ElementSize, false &gt;</a></td><td class="desc"><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1detail_1_1RowArrangement.html" title="RowArrangement determines how one or more warps cover a region of consecutive rows. ">RowArrangement</a> in which each warp's access is a 1D tiled arrangement </td></tr>
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+<tr id="row_0_2_1_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultEpilogueComplexTensorOp.html" target="_self">DefaultEpilogueComplexTensorOp</a></td><td class="desc">Defines sensible defaults for epilogues for TensorOps </td></tr>
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+<tr id="row_0_2_1_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultEpilogueVoltaTensorOp.html" target="_self">DefaultEpilogueVoltaTensorOp</a></td><td class="desc">Defines sensible defaults for epilogues for TensorOps </td></tr>
+<tr id="row_0_2_1_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultEpilogueWmmaTensorOp.html" target="_self">DefaultEpilogueWmmaTensorOp</a></td><td class="desc">Defines sensible defaults for epilogues for WMMA TensorOps </td></tr>
+<tr id="row_0_2_1_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultInterleavedEpilogueTensorOp.html" target="_self">DefaultInterleavedEpilogueTensorOp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_7_" class="arrow" onclick="toggleFolder('0_2_1_7_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultInterleavedThreadMapTensorOp.html" target="_self">DefaultInterleavedThreadMapTensorOp</a></td><td class="desc">Defines the optimal thread map for TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_7_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultInterleavedThreadMapTensorOp_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_8_" class="arrow" onclick="toggleFolder('0_2_1_8_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapSimt.html" target="_self">DefaultThreadMapSimt</a></td><td class="desc">Defines the optimal thread map for SIMT accumulator layouts </td></tr>
+<tr id="row_0_2_1_8_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapSimt_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_9_" class="arrow" onclick="toggleFolder('0_2_1_9_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapTensorOp.html" target="_self">DefaultThreadMapTensorOp</a></td><td class="desc">Defines the optimal thread map for TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_9_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapTensorOp_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_10_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp.html" target="_self">DefaultThreadMapVoltaTensorOp</a></td><td class="desc">Defines the optimal thread map for TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_11_" class="arrow" onclick="toggleFolder('0_2_1_11_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__95db04b7b72e34283958bd7fbf851d16.html" target="_self">DefaultThreadMapVoltaTensorOp&lt; ThreadblockShape_, WarpShape_, PartitionsK, ElementOutput_, ElementsPerAccess, float &gt;</a></td><td class="desc">Defines the optimal thread map for TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_11_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__52116c60c62f0fd520071558e42b814f.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_12_" class="arrow" onclick="toggleFolder('0_2_1_12_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__d58c94abc36b7c5c109b55202c6992e7.html" target="_self">DefaultThreadMapVoltaTensorOp&lt; ThreadblockShape_, WarpShape_, PartitionsK, ElementOutput_, ElementsPerAccess, half_t &gt;</a></td><td class="desc">Defines the optimal thread map for TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_12_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapVoltaTensorOp_3_01ThreadblockShape__4433cc988100e98097a748d2670fb0fc.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_13_" class="arrow" onclick="toggleFolder('0_2_1_13_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapWmmaTensorOp.html" target="_self">DefaultThreadMapWmmaTensorOp</a></td><td class="desc">Defines the optimal thread map for Wmma TensorOp accumulator layouts </td></tr>
+<tr id="row_0_2_1_13_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DefaultThreadMapWmmaTensorOp_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_14_" class="arrow" onclick="toggleFolder('0_2_1_14_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp.html" target="_self">DirectEpilogueTensorOp</a></td><td class="desc"><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1Epilogue.html" title="Epilogue operator without splitk. ">Epilogue</a> operator </td></tr>
+<tr id="row_0_2_1_14_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure for host-constructible state </td></tr>
+<tr id="row_0_2_1_14_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1DirectEpilogueTensorOp_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared storage allocation needed by the epilogue </td></tr>
+<tr id="row_0_2_1_15_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1Epilogue.html" target="_self">Epilogue</a></td><td class="desc"><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1Epilogue.html" title="Epilogue operator without splitk. ">Epilogue</a> operator without splitk </td></tr>
+<tr id="row_0_2_1_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_16_" class="arrow" onclick="toggleFolder('0_2_1_16_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1EpilogueBase.html" target="_self">EpilogueBase</a></td><td class="desc">Base class for epilogues defining warp-level </td></tr>
+<tr id="row_0_2_1_16_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1EpilogueBase_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared storage allocation needed by the epilogue </td></tr>
+<tr id="row_0_2_1_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_17_" class="arrow" onclick="toggleFolder('0_2_1_17_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1InterleavedEpilogue.html" target="_self">InterleavedEpilogue</a></td><td class="desc"><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1Epilogue.html" title="Epilogue operator without splitk. ">Epilogue</a> operator without splitk </td></tr>
+<tr id="row_0_2_1_17_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedEpilogue_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared storage allocation needed by the epilogue </td></tr>
+<tr id="row_0_2_1_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_18_" class="arrow" onclick="toggleFolder('0_2_1_18_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedOutputTileThreadMap.html" target="_self">InterleavedOutputTileThreadMap</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_18_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedOutputTileThreadMap_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_19_" class="arrow" onclick="toggleFolder('0_2_1_19_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator.html" target="_self">InterleavedPredicatedTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_19_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator_1_1Mask.html" target="_self">Mask</a></td><td class="desc"><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator_1_1Mask.html" title="Mask object. ">Mask</a> object </td></tr>
+<tr id="row_0_2_1_19_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1InterleavedPredicatedTileIterator_1_1Params.html" target="_self">Params</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_20_" class="arrow" onclick="toggleFolder('0_2_1_20_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileOptimalThreadMap.html" target="_self">OutputTileOptimalThreadMap</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_20_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileOptimalThreadMap_1_1CompactedThreadMap.html" target="_self">CompactedThreadMap</a></td><td class="desc">Compacted thread map in which the 4D region is contiguous </td></tr>
+<tr id="row_0_2_1_20_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileOptimalThreadMap_1_1Detail.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_21_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileShape.html" target="_self">OutputTileShape</a></td><td class="desc">Tuple defining point in output tile </td></tr>
+<tr id="row_0_2_1_22_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1OutputTileThreadMap.html" target="_self">OutputTileThreadMap</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_1_23_" class="arrow" onclick="toggleFolder('0_2_1_23_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator.html" target="_self">PredicatedTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_23_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator_1_1Mask.html" target="_self">Mask</a></td><td class="desc"><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator_1_1Mask.html" title="Mask object. ">Mask</a> object </td></tr>
+<tr id="row_0_2_1_23_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1threadblock_1_1PredicatedTileIterator_1_1Params.html" target="_self">Params</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_1_24_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1threadblock_1_1SharedLoadIterator.html" target="_self">SharedLoadIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_2_2_" class="arrow" onclick="toggleFolder('0_2_2_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1epilogue_1_1warp.html" target="_self">warp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorComplexTensorOp.html" target="_self">FragmentIteratorComplexTensorOp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorComplexTensorOp_3_01WarpShape___00_01Operato8cf03c624cf3210c71b7cbd580b080f8.html" target="_self">FragmentIteratorComplexTensorOp&lt; WarpShape_, OperatorShape_, OperatorElementC_, OperatorFragmentC_, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorSimt.html" target="_self">FragmentIteratorSimt</a></td><td class="desc">Fragment iterator for SIMT accumulator arrangements </td></tr>
+<tr id="row_0_2_2_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorSimt_3_01WarpShape___00_01Operator___00_01la3f2abc523201c1b0228df99119ab88e1.html" target="_self">FragmentIteratorSimt&lt; WarpShape_, Operator_, layout::RowMajor, MmaSimtPolicy_ &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorTensorOp.html" target="_self">FragmentIteratorTensorOp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorTensorOp_3_01WarpShape___00_01OperatorShape_e459aab140a2ce78336e584f95886726.html" target="_self">FragmentIteratorTensorOp&lt; WarpShape_, OperatorShape_, OperatorElementC_, OperatorFragmentC_, layout::ColumnMajorInterleaved&lt; InterleavedK &gt; &gt;</a></td><td class="desc">Dedicated to interleaved layout </td></tr>
+<tr id="row_0_2_2_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorTensorOp_3_01WarpShape___00_01OperatorShape_5e78dabe303f20d76b00c600aab61eda.html" target="_self">FragmentIteratorTensorOp&lt; WarpShape_, OperatorShape_, OperatorElementC_, OperatorFragmentC_, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorVoltaTensorOp.html" target="_self">FragmentIteratorVoltaTensorOp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1Gdb805a2dc5571ac3b66e0fe6ffdcede2.html" target="_self">FragmentIteratorVoltaTensorOp&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, float, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_9_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1G16e08718cffa0989cce3fe8dbc4b075b.html" target="_self">FragmentIteratorVoltaTensorOp&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, half_t, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_10_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorWmmaTensorOp.html" target="_self">FragmentIteratorWmmaTensorOp</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_11_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1FragmentIteratorWmmaTensorOp_3_01WarpShape___00_01OperatorShfdb1f120c6797383663f9fd11d0fc599.html" target="_self">FragmentIteratorWmmaTensorOp&lt; WarpShape_, OperatorShape_, OperatorElementC_, OperatorFragmentC_, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major shared memory </td></tr>
+<tr id="row_0_2_2_12_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1SimtPolicy.html" target="_self">SimtPolicy</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_13_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1SimtPolicy_3_01WarpShape___00_01Operator___00_01layout_1_1Rcef1c60e23e997017ae176c92931151d.html" target="_self">SimtPolicy&lt; WarpShape_, Operator_, layout::RowMajor, MmaSimtPolicy_ &gt;</a></td><td class="desc">Partial specialization for row-major </td></tr>
+<tr id="row_0_2_2_14_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TensorOpPolicy.html" target="_self">TensorOpPolicy</a></td><td class="desc">Policy details related to the epilogue </td></tr>
+<tr id="row_0_2_2_15_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TensorOpPolicy_3_01WarpShape_00_01OperatorShape_00_01layout69549d10c3610d943987eb90e827bc05.html" target="_self">TensorOpPolicy&lt; WarpShape, OperatorShape, layout::ColumnMajorInterleaved&lt; InterleavedK &gt; &gt;</a></td><td class="desc">Partial specialization for column-major-interleaved </td></tr>
+<tr id="row_0_2_2_16_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TensorOpPolicy_3_01WarpShape_00_01OperatorShape_00_01layout_1_1RowMajor_01_4.html" target="_self">TensorOpPolicy&lt; WarpShape, OperatorShape, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major </td></tr>
+<tr id="row_0_2_2_17_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorSimt.html" target="_self">TileIteratorSimt</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_18_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorSimt_3_01WarpShape___00_01Operator___00_01Elemenf2bd262ed3e202b25d5802d83965bf3b.html" target="_self">TileIteratorSimt&lt; WarpShape_, Operator_, Element_, layout::RowMajor, MmaSimtPolicy_ &gt;</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_19_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorTensorOp.html" target="_self">TileIteratorTensorOp</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_2_20_" class="arrow" onclick="toggleFolder('0_2_2_20_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorTensorOp_3_01WarpShape___00_01OperatorShape___003cbb32beb84b4984cb7853662096d289.html" target="_self">TileIteratorTensorOp&lt; WarpShape_, OperatorShape_, Element_, layout::RowMajor &gt;</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_20_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TileIteratorTensorOp_3_01WarpShape___00_01OperatorShape___05f11e023c9e6ee5f7a888fa4c5bbf6d1.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_21_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TileIteratorVoltaTensorOp.html" target="_self">TileIteratorVoltaTensorOp</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_2_22_" class="arrow" onclick="toggleFolder('0_2_2_22_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1GemmS2fe0c60b727c738c622c18fc3dd76644.html" target="_self">TileIteratorVoltaTensorOp&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, float, layout::RowMajor &gt;</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_22_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TileIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1Gemm770cbca45441d295d5d7433e8222a700.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_2_2_23_" class="arrow" onclick="toggleFolder('0_2_2_23_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1GemmSa0ceeeddc22575876eb977da7f5416a8.html" target="_self">TileIteratorVoltaTensorOp&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, half_t, layout::RowMajor &gt;</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_23_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1TileIteratorVoltaTensorOp_3_01WarpShape___00_01gemm_1_1Gemmffcab2297c8de8d0013602a39c525b78.html" target="_self">Detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_2_24_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorWmmaTensorOp.html" target="_self">TileIteratorWmmaTensorOp</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_25_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1warp_1_1TileIteratorWmmaTensorOp_3_01WarpShape___00_01OperatorShape_fd6a91cd8bbd07ecd1344326b830e3a4.html" target="_self">TileIteratorWmmaTensorOp&lt; WarpShape_, OperatorShape_, OperatorFragment_, layout::RowMajor &gt;</a></td><td class="desc">Template for reading and writing tiles of accumulators to shared memory </td></tr>
+<tr id="row_0_2_2_26_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1VoltaTensorOpPolicy.html" target="_self">VoltaTensorOpPolicy</a></td><td class="desc">Policy details related to the epilogue </td></tr>
+<tr id="row_0_2_2_27_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1VoltaTensorOpPolicy_3_01WarpShape___00_01gemm_1_1GemmShape_136ce744d4c1c6e8707f5a9785196194.html" target="_self">VoltaTensorOpPolicy&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, float, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major </td></tr>
+<tr id="row_0_2_2_28_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1warp_1_1VoltaTensorOpPolicy_3_01WarpShape___00_01gemm_1_1GemmShape_1d48185f49e4d066f8e9327bf0856b7f.html" target="_self">VoltaTensorOpPolicy&lt; WarpShape_, gemm::GemmShape&lt; 32, 32, 4 &gt;, half_t, layout::RowMajor &gt;</a></td><td class="desc">Partial specialization for row-major </td></tr>
+<tr id="row_0_2_3_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_2_3_" class="arrow" onclick="toggleFolder('0_2_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1epilogue_1_1EpilogueWorkspace.html" target="_self">EpilogueWorkspace</a></td><td class="desc"></td></tr>
+<tr id="row_0_2_3_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1EpilogueWorkspace_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_2_3_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1epilogue_1_1EpilogueWorkspace_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared storage allocation needed by the epilogue </td></tr>
+<tr id="row_0_3_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_3_" class="arrow" onclick="toggleFolder('0_3_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm.html" target="_self">gemm</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_0_" class="arrow" onclick="toggleFolder('0_3_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1device.html" target="_self">device</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration.html" target="_self">DefaultGemmConfiguration</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassSimt_00_01ArchTag286687c5e6abe22d241f789fe344a465.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassSimt, ArchTag, ElementA, ElementB, ElementC, ElementAccumulator &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassSimt_00_01ArchTag3026e48abb8c905d1cc6d13d669700e4.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassSimt, ArchTag, int8_t, int8_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arc567cad318a31d04b70ea615d6321decd.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm70, ElementA, ElementB, ElementC, ElementAccumulator &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arcde61af9be1337dac1fdb210e7e7a6e01.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, ElementA, ElementB, ElementC, ElementAccumulator &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arc485a4f0b5a7d2d4ab2c1a24da6328048.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, int4b_t, int4b_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arc8e2604a56dff3a7595da9ee0604ae55e.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, int4b_t, uint4b_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arc4fada4957d463c80a2831e47f28157c4.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, int8_t, int8_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arc8ab5fd2693c6a6ec43e447acb07f784c.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, int8_t, uint8_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_0_9_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1DefaultGemmConfiguration_3_01arch_1_1OpClassTensorOp_00_01arcffcf31256aed23d4d8d0eab627bc0cad.html" target="_self">DefaultGemmConfiguration&lt; arch::OpClassTensorOp, arch::Sm75, uint4b_t, int4b_t, ElementC, int32_t &gt;</a></td><td class="desc"></td></tr>
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+<tr id="row_0_3_0_19_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1GemmComplex_3_01ElementA___00_01LayoutA___00_01ElementB___00_a3923967cafb5cb9774c320dc24baa77.html" target="_self">Arguments</a></td><td class="desc">Argument structure </td></tr>
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+<tr id="row_0_3_0_21_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1device_1_1GemmSplitKParallel_3_01ElementA___00_01LayoutA___00_01Elementafcb1aeaf2035a7ac769d7acc233423b.html" target="_self">Arguments</a></td><td class="desc">Argument structure </td></tr>
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+<tr id="row_0_3_1_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemm.html" target="_self">DefaultGemm</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemm_3_01ElementA_00_01layout_1_1ColumnMajorInterleave661fe54d13cc2c9153dcdf31e4beaa30.html" target="_self">DefaultGemm&lt; ElementA, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, kAlignmentA, ElementB, layout::RowMajorInterleaved&lt; InterleavedK &gt;, kAlignmentB, ElementC, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, int32_t, arch::OpClassTensorOp, arch::Sm75, ThreadblockShape, WarpShape, InstructionShape, EpilogueOutputOp, ThreadblockSwizzle, 2, SplitKSerial, Operator, IsBetaZero &gt;</a></td><td class="desc">Partial specialization for Turing Integer Matrix Multiply Interleaved layout </td></tr>
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+<tr id="row_0_3_1_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemm_3_01ElementA_00_01LayoutA_00_01kAlignmentA_00_01E044b039b2fe402f29b04a9f5feee5342.html" target="_self">DefaultGemm&lt; ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementC, layout::RowMajor, ElementAccumulator, arch::OpClassTensorOp, arch::Sm70, ThreadblockShape, WarpShape, GemmShape&lt; 8, 8, 4 &gt;, EpilogueOutputOp, ThreadblockSwizzle, 2, SplitKSerial, Operator &gt;</a></td><td class="desc">Partial specialization for Volta architecture </td></tr>
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+<tr id="row_0_3_1_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemm_3_01int8__t_00_01LayoutA_00_01kAlignmentA_00_01inf48440732c1c5f42ddbfaba179861815.html" target="_self">DefaultGemm&lt; int8_t, LayoutA, kAlignmentA, int8_t, LayoutB, kAlignmentB, ElementC, LayoutC, ElementAccumulator, arch::OpClassSimt, ArchTag, ThreadblockShape, WarpShape, GemmShape&lt; 1, 1, 4 &gt;, EpilogueOutputOp, ThreadblockSwizzle, 2, SplitKSerial, Operator, false &gt;</a></td><td class="desc">Partial specialization for SIMT DP4A </td></tr>
+<tr id="row_0_3_1_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemmSplitKParallel.html" target="_self">DefaultGemmSplitKParallel</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1DefaultGemv.html" target="_self">DefaultGemv</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_1_9_" class="arrow" onclick="toggleFolder('0_3_1_9_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1Gemm.html" target="_self">Gemm</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_9_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1Gemm_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_3_1_9_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1Gemm_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared memory storage structure </td></tr>
+<tr id="row_0_3_1_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_1_10_" class="arrow" onclick="toggleFolder('0_3_1_10_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1GemmBatched.html" target="_self">GemmBatched</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_10_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1GemmBatched_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_3_1_10_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1GemmBatched_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared memory storage structure </td></tr>
+<tr id="row_0_3_1_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_1_11_" class="arrow" onclick="toggleFolder('0_3_1_11_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1GemmSplitKParallel.html" target="_self">GemmSplitKParallel</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_1_11_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1kernel_1_1GemmSplitKParallel_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_3_1_11_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="unioncutlass_1_1gemm_1_1kernel_1_1GemmSplitKParallel_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc">Shared memory storage structure </td></tr>
+<tr id="row_0_3_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_2_" class="arrow" onclick="toggleFolder('0_3_2_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_2_0_" class="arrow" onclick="toggleFolder('0_3_2_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1thread_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1EnableMma__Crow__SM60.html" target="_self">EnableMma_Crow_SM60</a></td><td class="desc">Determines whether to enable thread::Gemm&lt;&gt; specializations compatible with SM50 </td></tr>
+<tr id="row_0_3_2_0_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2.html" target="_self">Mma_HFMA2</a></td><td class="desc">Structure to compute the matrix product for HFMA </td></tr>
+<tr id="row_0_3_2_0_2_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1ColumnMajor_00_72621f7ab9ae4a4ba4fe9725cf8e89c1.html" target="_self">Mma_HFMA2&lt; Shape, layout::ColumnMajor, layout::ColumnMajor, layout::ColumnMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_3_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1ColumnMajor_00_94c813e3bbfb6f9857c155166f772687.html" target="_self">Mma_HFMA2&lt; Shape, layout::ColumnMajor, layout::ColumnMajor, layout::RowMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_4_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1ColumnMajor_00_17070298bc4cced0a1b98aee2bb6b455.html" target="_self">Mma_HFMA2&lt; Shape, layout::ColumnMajor, layout::RowMajor, layout::ColumnMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_5_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1ColumnMajor_00_bf6d29bb09a025e7b96942809743e28a.html" target="_self">Mma_HFMA2&lt; Shape, layout::ColumnMajor, layout::RowMajor, layout::RowMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_6_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1RowMajor_00_01l26a133b13650c1d058273e3649f60f04.html" target="_self">Mma_HFMA2&lt; Shape, layout::RowMajor, layout::ColumnMajor, layout::ColumnMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_7_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1RowMajor_00_01lbba3a796be96a0276693ef6b259ecc4a.html" target="_self">Mma_HFMA2&lt; Shape, layout::RowMajor, layout::ColumnMajor, layout::RowMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_8_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1RowMajor_00_01l2aa4d2fd2e940e0d0cf7c47bc8f6017c.html" target="_self">Mma_HFMA2&lt; Shape, layout::RowMajor, layout::RowMajor, layout::ColumnMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_9_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01layout_1_1RowMajor_00_01l086c058a15d6c79558e4f3d9ff1dc148.html" target="_self">Mma_HFMA2&lt; Shape, layout::RowMajor, layout::RowMajor, layout::RowMajor, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_10_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01LayoutA_00_01LayoutB_00_0e1104c65871c539155bd3a0c7631928b.html" target="_self">Mma_HFMA2&lt; Shape, LayoutA, LayoutB, layout::ColumnMajor, false &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_0_11_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1detail_1_1Mma__HFMA2_3_01Shape_00_01LayoutA_00_01LayoutB_00_07ac147cb320ee0d28ff8e78eb4cd330e.html" target="_self">Mma_HFMA2&lt; Shape, LayoutA, LayoutB, layout::RowMajor, false &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_2_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma.html" target="_self">Mma</a></td><td class="desc">Structure to compute the matrix product </td></tr>
+<tr id="row_0_3_2_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma_3_01Shape___00_01ElementA___00_01LayoutA___00_01ElementB_e41c1cd6078b6d1347fac239b0639d56.html" target="_self">Mma&lt; Shape_, ElementA_, LayoutA_, ElementB_, LayoutB_, ElementC_, LayoutC_, arch::OpMultiplyAdd, bool &gt;</a></td><td class="desc">Gemplate that handles conventional layouts for FFMA and DFMA GEMM </td></tr>
+<tr id="row_0_3_2_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma_3_01Shape___00_01half__t_00_01LayoutA_00_01half__t_00_01L066c9d2371712cdf0cac099ca9bcc578.html" target="_self">Mma&lt; Shape_, half_t, LayoutA, half_t, LayoutB, half_t, LayoutC, arch::OpMultiplyAdd &gt;</a></td><td class="desc">Structure to compute the matrix product </td></tr>
+<tr id="row_0_3_2_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma_3_01Shape___00_01half__t_00_01LayoutA___00_01half__t_00_088f0e99e501b6012297eb30b4e89bcea.html" target="_self">Mma&lt; Shape_, half_t, LayoutA_, half_t, LayoutB_, half_t, layout::RowMajor, arch::OpMultiplyAdd, typename platform::enable_if&lt; detail::EnableMma_Crow_SM60&lt; LayoutA_, LayoutB_ &gt;::value &gt;::type &gt;</a></td><td class="desc">Computes matrix product when C is row-major </td></tr>
+<tr id="row_0_3_2_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma_3_01Shape___00_01int8__t_00_01layout_1_1ColumnMajor_00_013f3785e722edc6e9aab6f866309b8623.html" target="_self">Mma&lt; Shape_, int8_t, layout::ColumnMajor, int8_t, layout::RowMajor, int32_t, LayoutC_, arch::OpMultiplyAdd, int8_t &gt;</a></td><td class="desc">Gemplate that handles conventional layouts for IDP4A </td></tr>
+<tr id="row_0_3_2_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1Mma_3_01Shape___00_01int8__t_00_01layout_1_1RowMajor_00_01int89c659e7faf47264972bdba6cd80f42b.html" target="_self">Mma&lt; Shape_, int8_t, layout::RowMajor, int8_t, layout::ColumnMajor, int32_t, LayoutC_, arch::OpMultiplyAdd, bool &gt;</a></td><td class="desc">Gemplate that handles conventional layouts for IDP4A </td></tr>
+<tr id="row_0_3_2_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1thread_1_1MmaGeneric.html" target="_self">MmaGeneric</a></td><td class="desc">Gemplate that handles all packed matrix layouts </td></tr>
+<tr id="row_0_3_3_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_3_3_" class="arrow" onclick="toggleFolder('0_3_3_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1gemm_1_1threadblock.html" target="_self">threadblock</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultGemvCore.html" target="_self">DefaultGemvCore</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMma.html" target="_self">DefaultMma</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMma_3_01ElementA_00_01LayoutA_00_01kAlignmentA_0010764e1fd5a3251a57eddafbd83eab8e.html" target="_self">DefaultMma&lt; ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementAccumulator, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, OperatorClass, ArchTag, ThreadblockShape, WarpShape, InstructionShape, 2, Operator, true &gt;</a></td><td class="desc">Specialization for column-major-interleaved output </td></tr>
+<tr id="row_0_3_3_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMma_3_01ElementA_00_01LayoutA_00_01kAlignmentA_00c67c16f9881e4f2fda76d8ed83ebabd6.html" target="_self">DefaultMma&lt; ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementAccumulator, layout::RowMajor, arch::OpClassSimt, ArchTag, ThreadblockShape, WarpShape, InstructionShape, 2, Operator, false &gt;</a></td><td class="desc">Specialization for row-major output (OperatorClass Simt) </td></tr>
+<tr id="row_0_3_3_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMma_3_01ElementA_00_01LayoutA_00_01kAlignmentA_00ce36642cae579bce6605ff8edde3c6ab.html" target="_self">DefaultMma&lt; ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, ArchTag, ThreadblockShape, WarpShape, InstructionShape, 2, Operator, false &gt;</a></td><td class="desc">Specialization for row-major output (OperatorClass Simt) </td></tr>
+<tr id="row_0_3_3_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMma_3_01int8__t_00_01LayoutA_00_01kAlignmentA_00_07e7230d4011ada5e22cfcb29103b696.html" target="_self">DefaultMma&lt; int8_t, LayoutA, kAlignmentA, int8_t, LayoutB, kAlignmentB, ElementAccumulator, layout::RowMajor, arch::OpClassSimt, ArchTag, ThreadblockShape, WarpShape, GemmShape&lt; 1, 1, 4 &gt;, 2, Operator, false &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore.html" target="_self">DefaultMmaCore</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmShab94a11a77dd0565102710907089acee0.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 1 &gt;, ElementA_, layout::ColumnMajor, ElementB_, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmShafafd5c61db86cbfe90863578ddd11092.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 1 &gt;, ElementA_, layout::ColumnMajor, ElementB_, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_9_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha46446d1e3871e31d2e728f710d78c8c1.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 1 &gt;, ElementA_, layout::ColumnMajor, ElementB_, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_, &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_10_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha8da7a0cfbbe859b701fdd9f2b8566aa7.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 1 &gt;, ElementA_, layout::RowMajor, ElementB_, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_11_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha84e9f8afb6a4ca9f5dcd219b182d16e7.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 1 &gt;, ElementA_, layout::RowMajor, ElementB_, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_12_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha2c0d0b7cdb5c4bcb11e83c058eb65345.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 4 &gt;, int8_t, layout::ColumnMajor, int8_t, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc">Partial specialization: </td></tr>
+<tr id="row_0_3_3_13_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha34a52cc7b2942e8c290f0032b6779b52.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 4 &gt;, int8_t, layout::ColumnMajor, int8_t, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_14_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmShaaf312aafe9da92ea9d417bcc12a8e7dc.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 4 &gt;, int8_t, layout::RowMajor, int8_t, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc">Partial specialization: </td></tr>
+<tr id="row_0_3_3_15_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha863d4139ccaa713bc4bde32c425f4067.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 1, 1, 4 &gt;, int8_t, layout::RowMajor, int8_t, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassSimt, 2, Operator_ &gt;</a></td><td class="desc">Partial specialization: </td></tr>
+<tr id="row_0_3_3_16_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmShaf03a122202ad10acdc96f280106d678b.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 8, 8, 4 &gt;, ElementA_, layout::ColumnMajor, ElementB_, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassTensorOp, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_17_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha69bef08ea63dd930f99d9788105873dd.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 8, 8, 4 &gt;, ElementA_, layout::ColumnMajor, ElementB_, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassTensorOp, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_18_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmSha3adf608332a8c9ee7014fced0da8a9ca.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 8, 8, 4 &gt;, ElementA_, layout::RowMajor, ElementB_, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassTensorOp, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_19_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01GemmShab7edfba3cdf43a07e3c4d719d87565a4.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, GemmShape&lt; 8, 8, 4 &gt;, ElementA_, layout::RowMajor, ElementB_, layout::RowMajor, ElementC_, LayoutC_, arch::OpClassTensorOp, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_3_20_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1threadblock_1_1DefaultMmaCore_3_01Shape___00_01WarpShape___00_01Instruc803d38bc1e4618c07c47f54c87ae2678.html" target="_self">DefaultMmaCore&lt; Shape_, WarpShape_, InstructionShape_, ElementA_, layout::ColumnMajor, ElementB_, layout::ColumnMajor, ElementC_, LayoutC_, arch::OpClassTensorOp, 2, Operator_ &gt;</a></td><td class="desc"></td></tr>
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+<tr id="row_0_3_4_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_22_" class="arrow" onclick="toggleFolder('0_3_4_22_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operand___0ed7daaeba1c095e77f68533d4d2c475c.html" target="_self">MmaTensorOpMultiplicandTileIterator&lt; Shape_, Operand_, Element_, cutlass::layout::TensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, 64 &gt;, InstructionShape_, OpDelta_, 32, PartitionsK_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_22_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operand___07638f8b7761f6e2e2e6918e2c05e739.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_23_" class="arrow" onclick="toggleFolder('0_3_4_23_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operand___0c7d419c589d601ce4eb603be566fea21.html" target="_self">MmaTensorOpMultiplicandTileIterator&lt; Shape_, Operand_, Element_, cutlass::layout::TensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, InstructionShape_, OpDelta_, 32, PartitionsK_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_23_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operand___0784c74bd670999ec23ad8ef9dc55777.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_24_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaTensorOpPolicy.html" target="_self">MmaTensorOpPolicy</a></td><td class="desc">Policy </td></tr>
+<tr id="row_0_3_4_25_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOp.html" target="_self">MmaVoltaTensorOp</a></td><td class="desc">Structure to compute the matrix product targeting CUDA cores and SIMT math instructions </td></tr>
+<tr id="row_0_3_4_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_26_" class="arrow" onclick="toggleFolder('0_3_4_26_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpAccumulatorTileIterator.html" target="_self">MmaVoltaTensorOpAccumulatorTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_26_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpAccumulatorTileIterator_1_1Policy.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_27_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_28_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operan0d3248553e52cd61ed8a2b3b12a20343.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand::kA, Element_, cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_29_" class="arrow" onclick="toggleFolder('0_3_4_29_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operan34be8e21a40af3ebd2dc3dff460dca72.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand::kA, Element_, cutlass::layout::VoltaTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_29_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Opera33cdf53848564e894d4407637dc86caf.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_30_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operand734577b7e54a074d143aba59828c2f2.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand::kB, Element_, cutlass::layout::RowMajorVoltaTensorOpMultiplicandBCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_31_" class="arrow" onclick="toggleFolder('0_3_4_31_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operan16c56cdc2dda5eeb996af8ec0242d501.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand::kB, Element_, cutlass::layout::VoltaTensorOpMultiplicandBCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_31_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Opera6fa6d2d3725bb3ec613d5c527ea3ffe7.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_32_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operan5a221944f4a0e16ccab77ba684856942.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand_, Element_, cutlass::layout::ColumnMajorVoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, KBlock &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_33_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operandcc9821c435540895138bc9af495f321.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand_, Element_, cutlass::layout::RowMajorVoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, KBlock &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_3_4_34_" class="arrow" onclick="toggleFolder('0_3_4_34_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operana2f40b28f0d2286b84d86f7238d67b52.html" target="_self">MmaVoltaTensorOpMultiplicandTileIterator&lt; Shape_, Operand_, Element_, cutlass::layout::VoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, KBlock &gt;, InstructionShape_, OpDelta_, 32 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_4_34_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1MmaVoltaTensorOpMultiplicandTileIterator_3_01Shape___00_01Operafa294175b280756dd8388f9ffe7b72c4.html" target="_self">Policy</a></td><td class="desc">Internal structure of iterator - made public to enable introspection </td></tr>
+<tr id="row_0_3_4_35_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1warp_1_1WarpSize.html" target="_self">WarpSize</a></td><td class="desc">Query the number of threads per warp </td></tr>
+<tr id="row_0_3_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1BatchedGemmCoord.html" target="_self">BatchedGemmCoord</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1GemmCoord.html" target="_self">GemmCoord</a></td><td class="desc"></td></tr>
+<tr id="row_0_3_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1gemm_1_1GemmShape.html" target="_self">GemmShape</a></td><td class="desc">Shape of a matrix multiply-add operation </td></tr>
+<tr id="row_0_4_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_4_" class="arrow" onclick="toggleFolder('0_4_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1layout.html" target="_self">layout</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1ColumnMajor.html" target="_self">ColumnMajor</a></td><td class="desc">Mapping function for column-major matrices </td></tr>
+<tr id="row_0_4_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorBlockLinear.html" target="_self">ColumnMajorBlockLinear</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorInterleaved.html" target="_self">ColumnMajorInterleaved</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCongruous.html" target="_self">ColumnMajorTensorOpMultiplicandCongruous</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorTensorOpMultiplicandCrosswise.html" target="_self">ColumnMajorTensorOpMultiplicandCrosswise</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandBCongruous.html" target="_self">ColumnMajorVoltaTensorOpMultiplicandBCongruous</a></td><td class="desc">Template mapping a column-major view of pitch-linear memory to <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html" title="Template based on element size (in bits) - defined in terms of pitch-linear memory. ">VoltaTensorOpMultiplicandCongruous</a> </td></tr>
+<tr id="row_0_4_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCongruous.html" target="_self">ColumnMajorVoltaTensorOpMultiplicandCongruous</a></td><td class="desc">Template mapping a column-major view of pitch-linear memory to <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html" title="Template based on element size (in bits) - defined in terms of pitch-linear memory. ">VoltaTensorOpMultiplicandCongruous</a> </td></tr>
+<tr id="row_0_4_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ColumnMajorVoltaTensorOpMultiplicandCrosswise.html" target="_self">ColumnMajorVoltaTensorOpMultiplicandCrosswise</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1ContiguousMatrix.html" target="_self">ContiguousMatrix</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1GeneralMatrix.html" target="_self">GeneralMatrix</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1LayoutTranspose.html" target="_self">LayoutTranspose</a></td><td class="desc">Defines transposes of matrix layouts </td></tr>
+<tr id="row_0_4_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1LayoutTranspose_3_01layout_1_1ColumnMajor_01_4.html" target="_self">LayoutTranspose&lt; layout::ColumnMajor &gt;</a></td><td class="desc">Transpose of column-major is row-major </td></tr>
+<tr id="row_0_4_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1LayoutTranspose_3_01layout_1_1RowMajor_01_4.html" target="_self">LayoutTranspose&lt; layout::RowMajor &gt;</a></td><td class="desc">Transpose of row-major is column-major </td></tr>
+<tr id="row_0_4_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1PackedVectorLayout.html" target="_self">PackedVectorLayout</a></td><td class="desc">Tensor layout for densely packed vectors </td></tr>
+<tr id="row_0_4_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1PitchLinear.html" target="_self">PitchLinear</a></td><td class="desc">Mapping function for pitch-linear memory </td></tr>
+<tr id="row_0_4_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1PitchLinearCoord.html" target="_self">PitchLinearCoord</a></td><td class="desc">Coordinate in pitch-linear space </td></tr>
+<tr id="row_0_4_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1PitchLinearShape.html" target="_self">PitchLinearShape</a></td><td class="desc">Template defining a shape used by pitch-linear operators </td></tr>
+<tr id="row_0_4_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1RowMajor.html" target="_self">RowMajor</a></td><td class="desc">Mapping function for row-major matrices </td></tr>
+<tr id="row_0_4_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorBlockLinear.html" target="_self">RowMajorBlockLinear</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorInterleaved.html" target="_self">RowMajorInterleaved</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorTensorOpMultiplicandCongruous.html" target="_self">RowMajorTensorOpMultiplicandCongruous</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorTensorOpMultiplicandCrosswise.html" target="_self">RowMajorTensorOpMultiplicandCrosswise</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandBCongruous.html" target="_self">RowMajorVoltaTensorOpMultiplicandBCongruous</a></td><td class="desc">Template mapping a row-major view of pitch-linear memory to <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html" title="Template based on element size (in bits) - defined in terms of pitch-linear memory. ">VoltaTensorOpMultiplicandCongruous</a> </td></tr>
+<tr id="row_0_4_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandCongruous.html" target="_self">RowMajorVoltaTensorOpMultiplicandCongruous</a></td><td class="desc">Template mapping a row-major view of pitch-linear memory to <a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html" title="Template based on element size (in bits) - defined in terms of pitch-linear memory. ">VoltaTensorOpMultiplicandCongruous</a> </td></tr>
+<tr id="row_0_4_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1RowMajorVoltaTensorOpMultiplicandCrosswise.html" target="_self">RowMajorVoltaTensorOpMultiplicandCrosswise</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1TensorCxRSKx.html" target="_self">TensorCxRSKx</a></td><td class="desc">Mapping function for 4-D CxRSKx tensors </td></tr>
+<tr id="row_0_4_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1TensorNCHW.html" target="_self">TensorNCHW</a></td><td class="desc">Mapping function for 4-D NCHW tensors </td></tr>
+<tr id="row_0_4_27_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1TensorNCxHWx.html" target="_self">TensorNCxHWx</a></td><td class="desc">Mapping function for 4-D NC/xHWx tensors </td></tr>
+<tr id="row_0_4_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1layout_1_1TensorNHWC.html" target="_self">TensorNHWC</a></td><td class="desc">Mapping function for 4-D NHWC tensors </td></tr>
+<tr id="row_0_4_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicand.html" target="_self">TensorOpMultiplicand</a></td><td class="desc"></td></tr>
+<tr id="row_0_4_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1TensorOpMultiplicandColumnMajorInterleaved.html" target="_self">TensorOpMultiplicandColumnMajorInterleaved</a></td><td class="desc">Template based on element size (in bits) - defined in terms of pitch-linear memory </td></tr>
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+<tr id="row_0_4_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandBCongruous.html" target="_self">VoltaTensorOpMultiplicandBCongruous</a></td><td class="desc">Template based on element size (in bits) - defined in terms of pitch-linear memory </td></tr>
+<tr id="row_0_4_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1layout_1_1VoltaTensorOpMultiplicandCongruous.html" target="_self">VoltaTensorOpMultiplicandCongruous</a></td><td class="desc">Template based on element size (in bits) - defined in terms of pitch-linear memory </td></tr>
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+<tr id="row_0_5_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_5_" class="arrow" onclick="toggleFolder('0_5_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1library.html" target="_self">library</a></td><td class="desc"></td></tr>
+<tr id="row_0_5_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmArguments.html" target="_self">GemmArguments</a></td><td class="desc">Arguments for GEMM </td></tr>
+<tr id="row_0_5_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmArrayArguments.html" target="_self">GemmArrayArguments</a></td><td class="desc">Arguments for GEMM - used by all the GEMM operations </td></tr>
+<tr id="row_0_5_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmArrayConfiguration.html" target="_self">GemmArrayConfiguration</a></td><td class="desc">Configuration for batched GEMM in which multiple matrix products are computed </td></tr>
+<tr id="row_0_5_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmBatchedConfiguration.html" target="_self">GemmBatchedConfiguration</a></td><td class="desc">Configuration for batched GEMM in which multiple matrix products are computed </td></tr>
+<tr id="row_0_5_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmConfiguration.html" target="_self">GemmConfiguration</a></td><td class="desc">Configuration for basic GEMM operations </td></tr>
+<tr id="row_0_5_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmDescription.html" target="_self">GemmDescription</a></td><td class="desc">Description of all GEMM computations </td></tr>
+<tr id="row_0_5_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmPlanarComplexBatchedConfiguration.html" target="_self">GemmPlanarComplexBatchedConfiguration</a></td><td class="desc">Batched complex valued GEMM in which real and imaginary parts are separated by a stride </td></tr>
+<tr id="row_0_5_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1GemmPlanarComplexConfiguration.html" target="_self">GemmPlanarComplexConfiguration</a></td><td class="desc">Complex valued GEMM in which real and imaginary parts are separated by a stride </td></tr>
+<tr id="row_0_5_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1library_1_1Manifest.html" target="_self">Manifest</a></td><td class="desc"><a class="el" href="classcutlass_1_1library_1_1Manifest.html" title="Manifest of CUTLASS Library. ">Manifest</a> of CUTLASS Library </td></tr>
+<tr id="row_0_5_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1MathInstructionDescription.html" target="_self">MathInstructionDescription</a></td><td class="desc"></td></tr>
+<tr id="row_0_5_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1library_1_1Operation.html" target="_self">Operation</a></td><td class="desc">Base class for all device-wide operations </td></tr>
+<tr id="row_0_5_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1OperationDescription.html" target="_self">OperationDescription</a></td><td class="desc">High-level description of an operation </td></tr>
+<tr id="row_0_5_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1TensorDescription.html" target="_self">TensorDescription</a></td><td class="desc">Structure describing the properties of a tensor </td></tr>
+<tr id="row_0_5_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1library_1_1TileDescription.html" target="_self">TileDescription</a></td><td class="desc">Structure describing the tiled structure of a GEMM-like computation </td></tr>
+<tr id="row_0_6_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_6_" class="arrow" onclick="toggleFolder('0_6_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1platform.html" target="_self">platform</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1aligned__chunk.html" target="_self">aligned_chunk</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1aligned__storage.html" target="_self">aligned_storage</a></td><td class="desc">Std::aligned_storage </td></tr>
+<tr id="row_0_6_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_6_2_" class="arrow" onclick="toggleFolder('0_6_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of.html" target="_self">alignment_of</a></td><td class="desc">Std::alignment_of </td></tr>
+<tr id="row_0_6_2_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_1_1pad.html" target="_self">pad</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01const_01value__t_01_4.html" target="_self">alignment_of&lt; const value_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01const_01volatile_01value__t_01_4.html" target="_self">alignment_of&lt; const volatile value_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01double2_01_4.html" target="_self">alignment_of&lt; double2 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01double4_01_4.html" target="_self">alignment_of&lt; double4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01float4_01_4.html" target="_self">alignment_of&lt; float4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01int4_01_4.html" target="_self">alignment_of&lt; int4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01long4_01_4.html" target="_self">alignment_of&lt; long4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_10_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01longlong2_01_4.html" target="_self">alignment_of&lt; longlong2 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_11_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01longlong4_01_4.html" target="_self">alignment_of&lt; longlong4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01uint4_01_4.html" target="_self">alignment_of&lt; uint4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01ulong4_01_4.html" target="_self">alignment_of&lt; ulong4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01ulonglong2_01_4.html" target="_self">alignment_of&lt; ulonglong2 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01ulonglong4_01_4.html" target="_self">alignment_of&lt; ulonglong4 &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1alignment__of_3_01volatile_01value__t_01_4.html" target="_self">alignment_of&lt; volatile value_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1bool__constant.html" target="_self">bool_constant</a></td><td class="desc">Std::bool_constant </td></tr>
+<tr id="row_0_6_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1conditional.html" target="_self">conditional</a></td><td class="desc">Std::conditional (true specialization) </td></tr>
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+<tr id="row_0_6_20_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1default__delete.html" target="_self">default_delete</a></td><td class="desc">Default deleter </td></tr>
+<tr id="row_0_6_21_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1default__delete_3_01T[]_4.html" target="_self">default_delete&lt; T[]&gt;</a></td><td class="desc">Partial specialization for deleting array types </td></tr>
+<tr id="row_0_6_22_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1enable__if.html" target="_self">enable_if</a></td><td class="desc">Std::enable_if (true specialization) </td></tr>
+<tr id="row_0_6_23_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1enable__if_3_01false_00_01T_01_4.html" target="_self">enable_if&lt; false, T &gt;</a></td><td class="desc">Std::enable_if (false specialization) </td></tr>
+<tr id="row_0_6_24_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1integral__constant.html" target="_self">integral_constant</a></td><td class="desc">Std::integral_constant </td></tr>
+<tr id="row_0_6_25_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__arithmetic.html" target="_self">is_arithmetic</a></td><td class="desc">Std::is_arithmetic </td></tr>
+<tr id="row_0_6_26_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__base__of.html" target="_self">is_base_of</a></td><td class="desc">Std::is_base_of </td></tr>
+<tr id="row_0_6_27_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_6_27_" class="arrow" onclick="toggleFolder('0_6_27_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__base__of__helper.html" target="_self">is_base_of_helper</a></td><td class="desc">Helper for std::is_base_of </td></tr>
+<tr id="row_0_6_27_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__base__of__helper_1_1dummy.html" target="_self">dummy</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__floating__point.html" target="_self">is_floating_point</a></td><td class="desc">Std::is_floating_point </td></tr>
+<tr id="row_0_6_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__fundamental.html" target="_self">is_fundamental</a></td><td class="desc">Std::is_fundamental </td></tr>
+<tr id="row_0_6_30_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral.html" target="_self">is_integral</a></td><td class="desc">Std::is_integral </td></tr>
+<tr id="row_0_6_31_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01char_01_4.html" target="_self">is_integral&lt; char &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_32_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01const_01T_01_4.html" target="_self">is_integral&lt; const T &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_33_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01const_01volatile_01T_01_4.html" target="_self">is_integral&lt; const volatile T &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_34_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01int_01_4.html" target="_self">is_integral&lt; int &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_35_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01long_01_4.html" target="_self">is_integral&lt; long &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_36_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01long_01long_01_4.html" target="_self">is_integral&lt; long long &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_37_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01short_01_4.html" target="_self">is_integral&lt; short &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_38_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01signed_01char_01_4.html" target="_self">is_integral&lt; signed char &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_39_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01unsigned_01char_01_4.html" target="_self">is_integral&lt; unsigned char &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_40_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01unsigned_01int_01_4.html" target="_self">is_integral&lt; unsigned int &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_41_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01unsigned_01long_01_4.html" target="_self">is_integral&lt; unsigned long &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_42_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01unsigned_01long_01long_01_4.html" target="_self">is_integral&lt; unsigned long long &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_43_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01unsigned_01short_01_4.html" target="_self">is_integral&lt; unsigned short &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_44_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__integral_3_01volatile_01T_01_4.html" target="_self">is_integral&lt; volatile T &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_45_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__pointer.html" target="_self">is_pointer</a></td><td class="desc">Std::is_pointer </td></tr>
+<tr id="row_0_6_46_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__pointer__helper.html" target="_self">is_pointer_helper</a></td><td class="desc">Helper for std::is_pointer (false specialization) </td></tr>
+<tr id="row_0_6_47_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__pointer__helper_3_01T_01_5_01_4.html" target="_self">is_pointer_helper&lt; T * &gt;</a></td><td class="desc">Helper for std::is_pointer (true specialization) </td></tr>
+<tr id="row_0_6_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__same.html" target="_self">is_same</a></td><td class="desc">Std::is_same (false specialization) </td></tr>
+<tr id="row_0_6_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__same_3_01A_00_01A_01_4.html" target="_self">is_same&lt; A, A &gt;</a></td><td class="desc">Std::is_same (true specialization) </td></tr>
+<tr id="row_0_6_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__trivially__copyable.html" target="_self">is_trivially_copyable</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__void.html" target="_self">is_void</a></td><td class="desc">Std::is_void </td></tr>
+<tr id="row_0_6_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__volatile.html" target="_self">is_volatile</a></td><td class="desc">Std::is_volatile </td></tr>
+<tr id="row_0_6_53_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1is__volatile_3_01volatile_01T_01_4.html" target="_self">is_volatile&lt; volatile T &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_6_54_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1nullptr__t.html" target="_self">nullptr_t</a></td><td class="desc">Std::nullptr_t </td></tr>
+<tr id="row_0_6_55_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1remove__const.html" target="_self">remove_const</a></td><td class="desc">Std::remove_const (non-const specialization) </td></tr>
+<tr id="row_0_6_56_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1remove__const_3_01const_01T_01_4.html" target="_self">remove_const&lt; const T &gt;</a></td><td class="desc">Std::remove_const (const specialization) </td></tr>
+<tr id="row_0_6_57_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1remove__cv.html" target="_self">remove_cv</a></td><td class="desc">Std::remove_cv </td></tr>
+<tr id="row_0_6_58_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1remove__volatile.html" target="_self">remove_volatile</a></td><td class="desc">Std::remove_volatile (non-volatile specialization) </td></tr>
+<tr id="row_0_6_59_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1platform_1_1remove__volatile_3_01volatile_01T_01_4.html" target="_self">remove_volatile&lt; volatile T &gt;</a></td><td class="desc">Std::remove_volatile (volatile specialization) </td></tr>
+<tr id="row_0_6_60_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1platform_1_1unique__ptr.html" target="_self">unique_ptr</a></td><td class="desc">Std::unique_ptr </td></tr>
+<tr id="row_0_7_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_7_" class="arrow" onclick="toggleFolder('0_7_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction.html" target="_self">reduction</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_7_0_" class="arrow" onclick="toggleFolder('0_7_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction_1_1kernel.html" target="_self">kernel</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_0_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_7_0_0_" class="arrow" onclick="toggleFolder('0_7_0_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1reduction_1_1kernel_1_1ReduceSplitK.html" target="_self">ReduceSplitK</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_0_0_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1kernel_1_1ReduceSplitK_1_1Params.html" target="_self">Params</a></td><td class="desc"><a class="el" href="structcutlass_1_1reduction_1_1kernel_1_1ReduceSplitK_1_1Params.html" title="Params structure. ">Params</a> structure </td></tr>
+<tr id="row_0_7_0_0_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1kernel_1_1ReduceSplitK_1_1SharedStorage.html" target="_self">SharedStorage</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_1_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_7_1_" class="arrow" onclick="toggleFolder('0_7_1_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reduction_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_1_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html" target="_self">Reduce</a></td><td class="desc">Structure to compute the thread level reduction </td></tr>
+<tr id="row_0_7_1_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01AlignedArray_3_01half__t_00_01N_01_4_01_4.html" target="_self">Reduce&lt; plus&lt; half_t &gt;, AlignedArray&lt; half_t, N &gt; &gt;</a></td><td class="desc">Partial specializations of <a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html" title="Structure to compute the thread level reduction. ">Reduce</a> for AlignedArray&lt;half_t, N&gt; </td></tr>
+<tr id="row_0_7_1_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01half__t_01_4_00_01Array_3_01half__t_00_01N_01_4_01_4.html" target="_self">Reduce&lt; plus&lt; half_t &gt;, Array&lt; half_t, N &gt; &gt;</a></td><td class="desc">Partial specializations of <a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html" title="Structure to compute the thread level reduction. ">Reduce</a> for Array&lt;half_t, N&gt; </td></tr>
+<tr id="row_0_7_1_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">Reduce&lt; plus&lt; T &gt;, Array&lt; T, N &gt; &gt;</a></td><td class="desc">Partial specialization of <a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html" title="Structure to compute the thread level reduction. ">Reduce</a> for Array&lt;T, N&gt; </td></tr>
+<tr id="row_0_7_1_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce_3_01plus_3_01T_01_4_00_01T_01_4.html" target="_self">Reduce&lt; plus&lt; T &gt;, T &gt;</a></td><td class="desc">Partial Specialization of <a class="el" href="structcutlass_1_1reduction_1_1thread_1_1Reduce.html" title="Structure to compute the thread level reduction. ">Reduce</a> for "plus" (a functional operator) </td></tr>
+<tr id="row_0_7_1_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_7_1_5_" class="arrow" onclick="toggleFolder('0_7_1_5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1ReduceAdd.html" target="_self">ReduceAdd</a></td><td class="desc">Mixed-precision reduction </td></tr>
+<tr id="row_0_7_1_5_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1thread_1_1ReduceAdd_1_1Params.html" target="_self">Params</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1BatchedReduction.html" target="_self">BatchedReduction</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_3_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_7_3_" class="arrow" onclick="toggleFolder('0_7_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits.html" target="_self">BatchedReductionTraits</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_3_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1BatchedReductionTraits_1_1Params.html" target="_self">Params</a></td><td class="desc"></td></tr>
+<tr id="row_0_7_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reduction_1_1DefaultBlockSwizzle.html" target="_self">DefaultBlockSwizzle</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_8_" class="arrow" onclick="toggleFolder('0_8_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference.html" target="_self">reference</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_8_0_" class="arrow" onclick="toggleFolder('0_8_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1detail_1_1Cast.html" target="_self">Cast</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_0_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1detail_1_1Cast_3_01float_00_01int8__t_01_4.html" target="_self">Cast&lt; float, int8_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_0_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1detail_1_1Cast_3_01float_00_01uint8__t_01_4.html" target="_self">Cast&lt; float, uint8_t &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_8_1_" class="arrow" onclick="toggleFolder('0_8_1_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device.html" target="_self">device</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_" class="arrow" onclick="toggleFolder('0_8_1_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1detail.html" target="_self">detail</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_0_" class="arrow" onclick="toggleFolder('0_8_1_0_0_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1RandomGaussianFunc.html" target="_self">RandomGaussianFunc</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_0_0_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1RandomGaussianFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
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+<tr id="row_0_8_1_0_1_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1RandomUniformFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_2_" class="arrow" onclick="toggleFolder('0_8_1_0_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorCopyDiagonalInFunc.html" target="_self">TensorCopyDiagonalInFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_2_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorCopyDiagonalInFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_3_" class="arrow" onclick="toggleFolder('0_8_1_0_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorCopyDiagonalOutFunc.html" target="_self">TensorCopyDiagonalOutFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_3_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorCopyDiagonalOutFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_4_" class="arrow" onclick="toggleFolder('0_8_1_0_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillDiagonalFunc.html" target="_self">TensorFillDiagonalFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_4_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillDiagonalFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_5_" class="arrow" onclick="toggleFolder('0_8_1_0_5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillLinearFunc.html" target="_self">TensorFillLinearFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_5_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillLinearFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_6_" class="arrow" onclick="toggleFolder('0_8_1_0_6_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillRandomGaussianFunc.html" target="_self">TensorFillRandomGaussianFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_6_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillRandomGaussianFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_7_" class="arrow" onclick="toggleFolder('0_8_1_0_7_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillRandomUniformFunc.html" target="_self">TensorFillRandomUniformFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_7_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorFillRandomUniformFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_8_" class="arrow" onclick="toggleFolder('0_8_1_0_8_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorUpdateDiagonalFunc.html" target="_self">TensorUpdateDiagonalFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_8_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorUpdateDiagonalFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_0_9_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_0_9_" class="arrow" onclick="toggleFolder('0_8_1_0_9_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorUpdateOffDiagonalFunc.html" target="_self">TensorUpdateOffDiagonalFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_1_0_9_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1detail_1_1TensorUpdateOffDiagonalFunc_1_1Params.html" target="_self">Params</a></td><td class="desc">Parameters structure </td></tr>
+<tr id="row_0_8_1_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_8_1_1_" class="arrow" onclick="toggleFolder('0_8_1_1_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1kernel.html" target="_self">kernel</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_1_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span id="arr_0_8_1_1_0_" class="arrow" onclick="toggleFolder('0_8_1_1_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1kernel_1_1detail.html" target="_self">detail</a></td><td class="desc">Defines several helpers </td></tr>
+<tr id="row_0_8_1_1_0_0_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1kernel_1_1detail_1_1TensorForEachHelper.html" target="_self">TensorForEachHelper</a></td><td class="desc">Helper to perform for-each operation </td></tr>
+<tr id="row_0_8_1_1_0_1_" style="display:none;"><td class="entry"><span style="width:96px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1kernel_1_1detail_1_1TensorForEachHelper_3_01Func_00_01Rank_00_010_01_4.html" target="_self">TensorForEachHelper&lt; Func, Rank, 0 &gt;</a></td><td class="desc">Helper to perform for-each operation </td></tr>
+<tr id="row_0_8_1_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_8_1_2_" class="arrow" onclick="toggleFolder('0_8_1_2_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1device_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_2_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1thread_1_1Gemm.html" target="_self">Gemm</a></td><td class="desc">Thread-level blocked general matrix product </td></tr>
+<tr id="row_0_8_1_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1BlockForEach.html" target="_self">BlockForEach</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1Gemm.html" target="_self">Gemm</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_1_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01Layout4e016ab7cfc644acd7cb4ae770339773.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, AccumulatorType, arch::OpMultiplyAdd &gt;</a></td><td class="desc">Partial specialization for multiply-add </td></tr>
+<tr id="row_0_8_1_6_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01Layout30b72addd464a2ca4a26785cbfd77a8e.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, AccumulatorType, arch::OpMultiplyAddSaturate &gt;</a></td><td class="desc">Partial specialization for multiply-add-saturate </td></tr>
+<tr id="row_0_8_1_7_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01Layout660562b232f408218828ca5915b7e73a.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, AccumulatorType, arch::OpXorPopc &gt;</a></td><td class="desc">Partial specialization for XOR-popc </td></tr>
+<tr id="row_0_8_1_8_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1TensorDiagonalForEach.html" target="_self">TensorDiagonalForEach</a></td><td class="desc">Launches a kernel calling a functor for each element along a tensor's diagonal </td></tr>
+<tr id="row_0_8_1_9_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1device_1_1TensorForEach.html" target="_self">TensorForEach</a></td><td class="desc">Launches a kernel calling a functor for each element in a tensor's index space </td></tr>
+<tr id="row_0_8_2_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_8_2_" class="arrow" onclick="toggleFolder('0_8_2_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1host.html" target="_self">host</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_2_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_8_2_0_" class="arrow" onclick="toggleFolder('0_8_2_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1reference_1_1host_1_1detail.html" target="_self">detail</a></td><td class="desc">Defines several helpers </td></tr>
+<tr id="row_0_8_2_0_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomGaussianFunc.html" target="_self">RandomGaussianFunc</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_2_0_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomGaussianFunc_3_01complex_3_01Element_01_4_01_4.html" target="_self">RandomGaussianFunc&lt; complex&lt; Element &gt; &gt;</a></td><td class="desc">Partial specialization for initializing a complex value </td></tr>
+<tr id="row_0_8_2_0_2_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomUniformFunc.html" target="_self">RandomUniformFunc</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_2_0_3_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1RandomUniformFunc_3_01complex_3_01Element_01_4_01_4.html" target="_self">RandomUniformFunc&lt; complex&lt; Element &gt; &gt;</a></td><td class="desc">Partial specialization for initializing a complex value </td></tr>
+<tr id="row_0_8_2_0_4_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorContainsFunc.html" target="_self">TensorContainsFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_5_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorCopyIf.html" target="_self">TensorCopyIf</a></td><td class="desc">Helper to conditionally copy between tensor views </td></tr>
+<tr id="row_0_8_2_0_6_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorEqualsFunc.html" target="_self">TensorEqualsFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_7_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillDiagonalFunc.html" target="_self">TensorFillDiagonalFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_8_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillFunc.html" target="_self">TensorFillFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_9_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillGaussianFunc.html" target="_self">TensorFillGaussianFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_2_0_10_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillLinearFunc.html" target="_self">TensorFillLinearFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_11_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFillRandomUniformFunc.html" target="_self">TensorFillRandomUniformFunc</a></td><td class="desc">Computes a random Gaussian distribution </td></tr>
+<tr id="row_0_8_2_0_12_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorForEachHelper.html" target="_self">TensorForEachHelper</a></td><td class="desc">Helper to perform for-each operation </td></tr>
+<tr id="row_0_8_2_0_13_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorForEachHelper_3_01Func_00_01Rank_00_010_01_4.html" target="_self">TensorForEachHelper&lt; Func, Rank, 0 &gt;</a></td><td class="desc">Helper to perform for-each operation </td></tr>
+<tr id="row_0_8_2_0_14_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorFuncBinaryOp.html" target="_self">TensorFuncBinaryOp</a></td><td class="desc">Helper to apply a binary operator in place </td></tr>
+<tr id="row_0_8_2_0_15_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TensorUpdateOffDiagonalFunc.html" target="_self">TensorUpdateOffDiagonalFunc</a></td><td class="desc">&lt; Layout function </td></tr>
+<tr id="row_0_8_2_0_16_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1detail_1_1TrivialConvert.html" target="_self">TrivialConvert</a></td><td class="desc">Helper to convert between types </td></tr>
+<tr id="row_0_8_2_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1BlockForEach.html" target="_self">BlockForEach</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_2_2_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1Gemm.html" target="_self">Gemm</a></td><td class="desc"></td></tr>
+<tr id="row_0_8_2_3_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_193dd3a37f00deff1e5dcd7c310afb1f.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAdd &gt;</a></td><td class="desc">Partial specialization for multiply-add </td></tr>
+<tr id="row_0_8_2_4_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_55729eac7dbd6bf311ea36f680e83e93.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpMultiplyAddSaturate &gt;</a></td><td class="desc">Partial specialization for multiply-add-saturate </td></tr>
+<tr id="row_0_8_2_5_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1reference_1_1host_1_1Gemm_3_01ElementA_00_01LayoutA_00_01ElementB_00_01LayoutB_4f3f32c4b336238abfd741e87bfced46.html" target="_self">Gemm&lt; ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, ScalarType, ComputeType, arch::OpXorPopc &gt;</a></td><td class="desc">Partial specialization for XOR-popc </td></tr>
+<tr id="row_0_9_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_9_" class="arrow" onclick="toggleFolder('0_9_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
+<tr id="row_0_9_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1thread_1_1Matrix.html" target="_self">Matrix</a></td><td class="desc">Per-thread matrix object storing a packed matrix </td></tr>
+<tr id="row_0_10_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_10_" class="arrow" onclick="toggleFolder('0_10_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1transform.html" target="_self">transform</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_0_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_10_0_" class="arrow" onclick="toggleFolder('0_10_0_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1transform_1_1thread.html" target="_self">thread</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_0_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1thread_1_1Transpose.html" target="_self">Transpose</a></td><td class="desc">Transforms a fragment by doing a transpose </td></tr>
+<tr id="row_0_10_0_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1thread_1_1Transpose_3_01ElementCount___00_01layout_1_1PitchLinearS99f8e05faf0bb5ed48a0154afe740d81.html" target="_self">Transpose&lt; ElementCount_, layout::PitchLinearShape&lt; 4, 4 &gt;, int8_t &gt;</a></td><td class="desc">Specialization for int8_t 4x4 transpose </td></tr>
+<tr id="row_0_10_1_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_10_1_" class="arrow" onclick="toggleFolder('0_10_1_')">&#9658;</span><span class="icona"><span class="icon">N</span></span><a class="el" href="namespacecutlass_1_1transform_1_1threadblock.html" target="_self">threadblock</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_0_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator.html" target="_self">PredicatedTileAccessIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_1_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile.html" target="_self">PredicatedTileAccessIterator2dThreadTile</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_2_" class="arrow" onclick="toggleFolder('0_10_1_2_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__da632779aba661c0f4cfaaa78126b771.html" target="_self">PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_2_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__18e9cf25bb3b8edfaad595241a6dc2d7.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_3_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_3_" class="arrow" onclick="toggleFolder('0_10_1_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__1790abaa54a01f277d75766d5882fec8.html" target="_self">PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_3_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__8ccc62d47a092afc8bee32ffe9d1e4ba.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_4_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_4_" class="arrow" onclick="toggleFolder('0_10_1_4_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__7327fa15996bcb8502cdfcc192350fe1.html" target="_self">PredicatedTileAccessIterator2dThreadTile&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_4_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator2dThreadTile_3_01Shape__a56cbccec33ee916292ad9d068474609.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_5_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_5_" class="arrow" onclick="toggleFolder('0_10_1_5_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen89c687c583745a73cb485041911a4c4e.html" target="_self">PredicatedTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_5_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemenc07b5ec72f83e782121ac629288d61fe.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_6_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_6_" class="arrow" onclick="toggleFolder('0_10_1_6_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemenab63a1e105bf37f6371516cb9e2c5a7a.html" target="_self">PredicatedTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_6_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemena9b06926a275b569ee9f7f142604b997.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_7_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_7_" class="arrow" onclick="toggleFolder('0_10_1_7_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen784a0e9da3f55064c47e5613791f51f7.html" target="_self">PredicatedTileAccessIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_7_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen41e459f664d17473570cf22fb616845f.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_8_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_8_" class="arrow" onclick="toggleFolder('0_10_1_8_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen9838736ad62fae54213fbaf722a989ab.html" target="_self">PredicatedTileAccessIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_8_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen44ce348364e78f5a56fa0c2cef6af930.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_9_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_9_" class="arrow" onclick="toggleFolder('0_10_1_9_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen809793e785fb4211888c6b4e5dcfcb39.html" target="_self">PredicatedTileAccessIterator&lt; Shape_, Element_, layout::RowMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessType_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_9_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileAccessIterator_3_01Shape___00_01Elemen058417e2cdd86f3cd6ad5458581571c8.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_10_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator.html" target="_self">PredicatedTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_11_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile.html" target="_self">PredicatedTileIterator2dThreadTile</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_12_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_12_" class="arrow" onclick="toggleFolder('0_10_1_12_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_0165b39a630d10785a3558406f9adb99b9.html" target="_self">PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, Transpose_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_12_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_01e11ed7192af5d7ad1bce5641fa13112e.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_13_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_13_" class="arrow" onclick="toggleFolder('0_10_1_13_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_017a517f3c73efd795ab05059cc9b111e1.html" target="_self">PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, Transpose_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_13_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_0b878062cc0cd214bf7e17d74ff17e246.html" target="_self">AccessType</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_13_1_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_0145ef045e8f7d57dc718098adcb00cf3d.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_14_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_14_" class="arrow" onclick="toggleFolder('0_10_1_14_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_013671177d6219bfeb0e1b4dc4c1b5bf11.html" target="_self">PredicatedTileIterator2dThreadTile&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, Transpose_ &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_14_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator2dThreadTile_3_01Shape___00_0102e766863c6ac9ec2063a02c4803eecb.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_15_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_15_" class="arrow" onclick="toggleFolder('0_10_1_15_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___0068b3e874b5d93d11f0fa902c7f1d11d9.html" target="_self">PredicatedTileIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, AccessSize &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_15_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00a6b756b1bcfbb35fe4a3e68ff074e380.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_16_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_16_" class="arrow" onclick="toggleFolder('0_10_1_16_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00f6b3a9dfab5e7c72d5233f7e5e6e3b9b.html" target="_self">PredicatedTileIterator&lt; Shape_, Element_, layout::ColumnMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessSize &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_16_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00ebd1a63351e1085d0b718582ec7b06c8.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_17_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_17_" class="arrow" onclick="toggleFolder('0_10_1_17_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00e7c2c404e7aedfe60ad56bb5571306a1.html" target="_self">PredicatedTileIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, AccessSize &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_17_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___006a5f2f7a8271031e6cdc5daa5441f2af.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_18_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_18_" class="arrow" onclick="toggleFolder('0_10_1_18_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___0041ea81994f8af0d4d071fdb9e66b5ff0.html" target="_self">PredicatedTileIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, AccessSize &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_18_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___004d0f9b5e19c29acc17bcdc360dafebbd.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_19_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_19_" class="arrow" onclick="toggleFolder('0_10_1_19_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___00d670f969180a8d182dffb356ebcc957e.html" target="_self">PredicatedTileIterator&lt; Shape_, Element_, layout::RowMajorInterleaved&lt; InterleavedK &gt;, AdvanceRank, ThreadMap_, AccessSize &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_19_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1PredicatedTileIterator_3_01Shape___00_01Element___009fd89f6dad84238fd7d63df0a0c0364f.html" target="_self">Params</a></td><td class="desc">Parameters object is precomputed state and is host-constructible </td></tr>
+<tr id="row_0_10_1_20_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator.html" target="_self">RegularTileAccessIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_21_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__eb7d20f8b9d69e0ae5e7ef51dc480867.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_22_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__2c1476eaf582bfe972793e17babfe985.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajorTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_23_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__a3c11cf1f00ef7a1efb8389ac6e4c6e0.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::ColumnMajorTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_24_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__0855e9d9ab619202d2397180c1e4c4a5.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_25_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__f04332958a49a47d6fb2b25201764630.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_26_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__6baada077236f1a368c61c5e11b45b72.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::RowMajorTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_27_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__0184b7188941788a96624510a4b2f876.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::RowMajorTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_28_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_28_" class="arrow" onclick="toggleFolder('0_10_1_28_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__ebf4714349612673e8b6609b763eeb6f.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::TensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_28_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element_0a9491607d11be8e1780e79ad711aa42.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_29_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_29_" class="arrow" onclick="toggleFolder('0_10_1_29_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element__e9a9e0f4286f652f55eb9b863b21effe.html" target="_self">RegularTileAccessIterator&lt; Shape_, Element_, layout::TensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_29_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileAccessIterator_3_01Shape___00_01Element_3be8b96d170d886f39b6b30acab65e7a.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_30_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator.html" target="_self">RegularTileIterator</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_31_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator2dThreadTile.html" target="_self">RegularTileIterator2dThreadTile</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_32_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator2dThreadTile_3_01Shape___00_01Eleb60d066756d1c18f05fceee6a27bdb8a.html" target="_self">RegularTileIterator2dThreadTile&lt; Shape_, Element_, layout::ColumnMajorInterleaved&lt; 4 &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for interleaved layout + 2d thread-tiled threadmapping </td></tr>
+<tr id="row_0_10_1_33_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator2dThreadTile_3_01Shape___00_01Ele76ed82829532ae1c17f4c78158f036c7.html" target="_self">RegularTileIterator2dThreadTile&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for pitch-linear + 2d thread-tiled threadmapping </td></tr>
+<tr id="row_0_10_1_34_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator2dThreadTile_3_01Shape___00_01Ele654c8f6161ae5340f040397a4e2e045c.html" target="_self">RegularTileIterator2dThreadTile&lt; Shape_, Element_, layout::RowMajorInterleaved&lt; 4 &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for interleaved layout + 2d thread-tiled threadmapping </td></tr>
+<tr id="row_0_10_1_35_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_011d3637dbd8bc58bcb020b51bf57fbfc0.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajor, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for pitch-linear </td></tr>
+<tr id="row_0_10_1_36_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_017982f81d4ef592e19c8427de2ea933a3.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajorTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_37_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_010889a732373c350de9b9a9f6c13cd761.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajorTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_38_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01187f8574e1fe9d7d5e8fbf09bd834bf0.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajorVoltaTensorOpMultiplicandBCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_39_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01793f74bfd8f116a827948ab01a37349a.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajorVoltaTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_40_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01bd31b3810c1fedf2e7e5959ff92b5d3d.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::ColumnMajorVoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Shape_::kRow &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_41_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_0184a89653916f5d51ab59d1b386989a17.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::PitchLinear, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for pitch-linear </td></tr>
+<tr id="row_0_10_1_42_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_0149454d361ea5885cf5166a920b5145df.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajor, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc">Regular tile iterator specialized for pitch-linear </td></tr>
+<tr id="row_0_10_1_43_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01c20d35180520077a5a09b1e33543c1a5.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajorTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_44_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01a31b454d9c930525c1e9ca406a514f40.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajorTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_45_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_0104ad31bd559a88cc418ae1cab7492ed5.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajorVoltaTensorOpMultiplicandBCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_46_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01f6f6511b5033cad31083644ac69c54d8.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajorVoltaTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_47_" style="display:none;"><td class="entry"><span style="width:64px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01b3fa5720e807697de61b9f937b269cd0.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::RowMajorVoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Shape_::kColumn &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_48_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_48_" class="arrow" onclick="toggleFolder('0_10_1_48_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01efd5013a2503d6567e2bf6b40c97360c.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::TensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value, int(128/sizeof(Element_))&gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_48_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_052caec9d5bceeb59b9a13cb3338ce64d.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_49_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_49_" class="arrow" onclick="toggleFolder('0_10_1_49_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_0197fef2242a3454a7d1cebe61aee28b43.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::TensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Crosswise &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_49_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_039093927f4b1ee61538c569bf1ae4efd.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_50_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_50_" class="arrow" onclick="toggleFolder('0_10_1_50_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01a75d2cd74e722d6ad6a3b41aabfd432d.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::VoltaTensorOpMultiplicandBCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_50_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_02d305cfb0b55c6fb236a52cf2240651e.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_51_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_51_" class="arrow" onclick="toggleFolder('0_10_1_51_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01f96bbeb63e6d4ce4a2551279de3a9f0e.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::VoltaTensorOpMultiplicandCongruous&lt; sizeof_bits&lt; Element_ &gt;::value &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_51_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_032f88d1be8b209e44a4815c707ba35bb.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_1_52_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span id="arr_0_10_1_52_" class="arrow" onclick="toggleFolder('0_10_1_52_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_01dbd6b8468d5bd787308d2f615a24d123.html" target="_self">RegularTileIterator&lt; Shape_, Element_, layout::VoltaTensorOpMultiplicandCrosswise&lt; sizeof_bits&lt; Element_ &gt;::value, Shape_::kContiguous &gt;, AdvanceRank, ThreadMap_, Alignment &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_1_52_0_" style="display:none;"><td class="entry"><span style="width:80px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1threadblock_1_1RegularTileIterator_3_01Shape___00_01Element___00_0390833403016f5d817416e20828845df.html" target="_self">Detail</a></td><td class="desc">Internal details made public to facilitate introspection </td></tr>
+<tr id="row_0_10_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1PitchLinear2DThreadTileStripminedThreadMap.html" target="_self">PitchLinear2DThreadTileStripminedThreadMap</a></td><td class="desc"></td></tr>
+<tr id="row_0_10_3_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span id="arr_0_10_3_" class="arrow" onclick="toggleFolder('0_10_3_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1transform_1_1PitchLinear2DThreadTileStripminedThreadMap_3_01Shape___00_01Thread0082c3467229b12cc9dd996283ee7160.html" target="_self">PitchLinear2DThreadTileStripminedThreadMap&lt; Shape_, Threads, cutlass::layout::PitchLinearShape&lt; 4, 4 &gt; &gt;</a></td><td class="desc"></td></tr>
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+<tr id="row_0_52_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1MatrixCoord.html" target="_self">MatrixCoord</a></td><td class="desc"></td></tr>
+<tr id="row_0_53_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1MatrixShape.html" target="_self">MatrixShape</a></td><td class="desc">Describes the size of a matrix tile </td></tr>
+<tr id="row_0_54_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1Max.html" target="_self">Max</a></td><td class="desc"></td></tr>
+<tr id="row_0_55_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1maximum.html" target="_self">maximum</a></td><td class="desc"></td></tr>
+<tr id="row_0_56_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1maximum_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">maximum&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_57_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1maximum_3_01float_01_4.html" target="_self">maximum&lt; float &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_58_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1Min.html" target="_self">Min</a></td><td class="desc"></td></tr>
+<tr id="row_0_59_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minimum.html" target="_self">minimum</a></td><td class="desc"></td></tr>
+<tr id="row_0_60_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minimum_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">minimum&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_61_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minimum_3_01float_01_4.html" target="_self">minimum&lt; float &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_62_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minus.html" target="_self">minus</a></td><td class="desc"></td></tr>
+<tr id="row_0_63_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minus_3_01Array_3_01half__t_00_01N_01_4_01_4.html" target="_self">minus&lt; Array&lt; half_t, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_64_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1minus_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">minus&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_65_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiplies.html" target="_self">multiplies</a></td><td class="desc"></td></tr>
+<tr id="row_0_66_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiplies_3_01Array_3_01half__t_00_01N_01_4_01_4.html" target="_self">multiplies&lt; Array&lt; half_t, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_67_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiplies_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">multiplies&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_68_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add.html" target="_self">multiply_add</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_69_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add_3_01Array_3_01half__t_00_01N_01_4_00_01Array_3_01half__t_00_01N_01adaeadb27c0e4439444709c0eb30963.html" target="_self">multiply_add&lt; Array&lt; half_t, N &gt;, Array&lt; half_t, N &gt;, Array&lt; half_t, N &gt; &gt;</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_70_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add_3_01Array_3_01T_00_01N_01_4_00_01Array_3_01T_00_01N_01_4_00_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">multiply_add&lt; Array&lt; T, N &gt;, Array&lt; T, N &gt;, Array&lt; T, N &gt; &gt;</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_71_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add_3_01complex_3_01T_01_4_00_01complex_3_01T_01_4_00_01complex_3_01T_01_4_01_4.html" target="_self">multiply_add&lt; complex&lt; T &gt;, complex&lt; T &gt;, complex&lt; T &gt; &gt;</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_72_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add_3_01complex_3_01T_01_4_00_01T_00_01complex_3_01T_01_4_01_4.html" target="_self">multiply_add&lt; complex&lt; T &gt;, T, complex&lt; T &gt; &gt;</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_73_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1multiply__add_3_01T_00_01complex_3_01T_01_4_00_01complex_3_01T_01_4_01_4.html" target="_self">multiply_add&lt; T, complex&lt; T &gt;, complex&lt; T &gt; &gt;</a></td><td class="desc">Fused multiply-add </td></tr>
+<tr id="row_0_74_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1negate.html" target="_self">negate</a></td><td class="desc"></td></tr>
+<tr id="row_0_75_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1negate_3_01Array_3_01half__t_00_01N_01_4_01_4.html" target="_self">negate&lt; Array&lt; half_t, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_76_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1negate_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">negate&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_77_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericArrayConverter.html" target="_self">NumericArrayConverter</a></td><td class="desc">Conversion operator for Array </td></tr>
+<tr id="row_0_78_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericArrayConverter_3_01float_00_01half__t_00_012_00_01Round_01_4.html" target="_self">NumericArrayConverter&lt; float, half_t, 2, Round &gt;</a></td><td class="desc">Partial specialization for Array&lt;float, 2&gt; &lt;= Array&lt;half_t, 2&gt;, round to nearest </td></tr>
+<tr id="row_0_79_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericArrayConverter_3_01float_00_01half__t_00_01N_00_01Round_01_4.html" target="_self">NumericArrayConverter&lt; float, half_t, N, Round &gt;</a></td><td class="desc">Partial specialization for Array&lt;half&gt; &lt;= Array&lt;float&gt; </td></tr>
+<tr id="row_0_80_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericArrayConverter_3_01half__t_00_01float_00_012_00_01FloatRoundStyle_1_1round__to__nearest_01_4.html" target="_self">NumericArrayConverter&lt; half_t, float, 2, FloatRoundStyle::round_to_nearest &gt;</a></td><td class="desc">Partial specialization for Array&lt;half, 2&gt; &lt;= Array&lt;float, 2&gt;, round to nearest </td></tr>
+<tr id="row_0_81_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericArrayConverter_3_01half__t_00_01float_00_01N_00_01Round_01_4.html" target="_self">NumericArrayConverter&lt; half_t, float, N, Round &gt;</a></td><td class="desc">Partial specialization for Array&lt;half&gt; &lt;= Array&lt;float&gt; </td></tr>
+<tr id="row_0_82_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter.html" target="_self">NumericConverter</a></td><td class="desc"></td></tr>
+<tr id="row_0_83_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter_3_01float_00_01half__t_00_01Round_01_4.html" target="_self">NumericConverter&lt; float, half_t, Round &gt;</a></td><td class="desc">Partial specialization for float &lt;= <a class="el" href="structcutlass_1_1half__t.html" title="IEEE half-precision floating-point type. ">half_t</a> </td></tr>
+<tr id="row_0_84_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter_3_01half__t_00_01float_00_01FloatRoundStyle_1_1round__to__nearest_01_4.html" target="_self">NumericConverter&lt; half_t, float, FloatRoundStyle::round_to_nearest &gt;</a></td><td class="desc">Specialization for round-to-nearest </td></tr>
+<tr id="row_0_85_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter_3_01half__t_00_01float_00_01FloatRoundStyle_1_1round__toward__zero_01_4.html" target="_self">NumericConverter&lt; half_t, float, FloatRoundStyle::round_toward_zero &gt;</a></td><td class="desc">Specialization for round-toward-zero </td></tr>
+<tr id="row_0_86_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter_3_01int8__t_00_01float_00_01Round_01_4.html" target="_self">NumericConverter&lt; int8_t, float, Round &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_87_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverter_3_01T_00_01T_00_01Round_01_4.html" target="_self">NumericConverter&lt; T, T, Round &gt;</a></td><td class="desc">Partial specialization for float &lt;= <a class="el" href="structcutlass_1_1half__t.html" title="IEEE half-precision floating-point type. ">half_t</a> </td></tr>
+<tr id="row_0_88_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1NumericConverterClamp.html" target="_self">NumericConverterClamp</a></td><td class="desc"></td></tr>
+<tr id="row_0_89_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1plus.html" target="_self">plus</a></td><td class="desc"></td></tr>
+<tr id="row_0_90_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1plus_3_01Array_3_01half__t_00_01N_01_4_01_4.html" target="_self">plus&lt; Array&lt; half_t, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_91_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1plus_3_01Array_3_01T_00_01N_01_4_01_4.html" target="_self">plus&lt; Array&lt; T, N &gt; &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_92_" style="display:none;"><td class="entry"><span style="width:16px;display:inline-block;">&#160;</span><span id="arr_0_92_" class="arrow" onclick="toggleFolder('0_92_')">&#9658;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1PredicateVector.html" target="_self">PredicateVector</a></td><td class="desc">Statically sized array of bits implementing </td></tr>
+<tr id="row_0_92_0_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1PredicateVector_1_1ConstIterator.html" target="_self">ConstIterator</a></td><td class="desc">An iterator implementing <a class="el" href="group__predicate__iterator__concept.html">Predicate Iterator Concept</a> enabling sequential read and write access to predicates </td></tr>
+<tr id="row_0_92_1_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1PredicateVector_1_1Iterator.html" target="_self">Iterator</a></td><td class="desc">An iterator implementing <a class="el" href="group__predicate__iterator__concept.html">Predicate Iterator Concept</a> enabling sequential read and write access to predicates </td></tr>
+<tr id="row_0_92_2_" style="display:none;"><td class="entry"><span style="width:48px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1PredicateVector_1_1TrivialIterator.html" target="_self">TrivialIterator</a></td><td class="desc"><a class="el" href="classcutlass_1_1PredicateVector_1_1Iterator.html" title="An iterator implementing Predicate Iterator Concept enabling sequential read and write access to pred...">Iterator</a> that always returns true </td></tr>
+<tr id="row_0_93_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1RealType.html" target="_self">RealType</a></td><td class="desc">Used to determine the real-valued underlying type of a numeric type T </td></tr>
+<tr id="row_0_94_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1RealType_3_01complex_3_01T_01_4_01_4.html" target="_self">RealType&lt; complex&lt; T &gt; &gt;</a></td><td class="desc">Partial specialization for complex-valued type </td></tr>
+<tr id="row_0_95_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1ReferenceFactory.html" target="_self">ReferenceFactory</a></td><td class="desc"></td></tr>
+<tr id="row_0_96_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1ReferenceFactory_3_01Element_00_01false_01_4.html" target="_self">ReferenceFactory&lt; Element, false &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_97_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1ReferenceFactory_3_01Element_00_01true_01_4.html" target="_self">ReferenceFactory&lt; Element, true &gt;</a></td><td class="desc"></td></tr>
+<tr id="row_0_98_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1ScalarIO.html" target="_self">ScalarIO</a></td><td class="desc">Helper to enable formatted printing of CUTLASS scalar types to an ostream </td></tr>
+<tr id="row_0_99_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="classcutlass_1_1Semaphore.html" target="_self">Semaphore</a></td><td class="desc">CTA-wide semaphore for inter-CTA synchronization </td></tr>
+<tr id="row_0_100_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits.html" target="_self">sizeof_bits</a></td><td class="desc">Defines the size of an element in bits </td></tr>
+<tr id="row_0_101_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits_3_01Array_3_01T_00_01N_00_01RegisterSized_01_4_01_4.html" target="_self">sizeof_bits&lt; Array&lt; T, N, RegisterSized &gt; &gt;</a></td><td class="desc">Statically sized array for any data type </td></tr>
+<tr id="row_0_102_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits_3_01bin1__t_01_4.html" target="_self">sizeof_bits&lt; bin1_t &gt;</a></td><td class="desc">Defines the size of an element in bits - specialized for bin1_t </td></tr>
+<tr id="row_0_103_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits_3_01int4b__t_01_4.html" target="_self">sizeof_bits&lt; int4b_t &gt;</a></td><td class="desc">Defines the size of an element in bits - specialized for int4b_t </td></tr>
+<tr id="row_0_104_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits_3_01uint1b__t_01_4.html" target="_self">sizeof_bits&lt; uint1b_t &gt;</a></td><td class="desc">Defines the size of an element in bits - specialized for uint1b_t </td></tr>
+<tr id="row_0_105_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sizeof__bits_3_01uint4b__t_01_4.html" target="_self">sizeof_bits&lt; uint4b_t &gt;</a></td><td class="desc">Defines the size of an element in bits - specialized for uint4b_t </td></tr>
+<tr id="row_0_106_" style="display:none;"><td class="entry"><span style="width:32px;display:inline-block;">&#160;</span><span class="icona"><span class="icon">C</span></span><a class="el" href="structcutlass_1_1sqrt__est.html" target="_self">sqrt_est</a></td><td class="desc"></td></tr>
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+<p>Templates exposing architecture support for multiply-add operations.  
+<a href="#details">More...</a></p>
+<div class="textblock"><code>#include &quot;<a class="el" href="array_8h_source.html">cutlass/array.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="numeric__types_8h_source.html">cutlass/numeric_types.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="include_2cutlass_2gemm_2gemm_8h_source.html">cutlass/gemm/gemm.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="arch_2mma__sm50_8h_source.html">cutlass/arch/mma_sm50.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="arch_2mma__sm60_8h_source.html">cutlass/arch/mma_sm60.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="arch_2mma__sm61_8h_source.html">cutlass/arch/mma_sm61.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="mma__sm70_8h_source.html">cutlass/arch/mma_sm70.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="mma__sm75_8h_source.html">cutlass/arch/mma_sm75.h</a>&quot;</code><br />
+</div><div class="textblock"><div class="dynheader">
+Include dependency graph for arch/mma.h:</div>
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+<div class="center"><img src="arch_2mma_8h__incl.png" border="0" usemap="#mma_8h" alt=""/></div>
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+</map>
+</div>
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+<div class="center"><img src="arch_2mma_8h__dep__incl.png" border="0" usemap="#mma_8hdep" alt=""/></div>
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+<p><a href="arch_2mma_8h_source.html">Go to the source code of this file.</a></p>
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+Classes</h2></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma.html">cutlass::arch::Mma&lt; Shape_, kThreads_, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01ElementAb6e65b2cf5ede7f41cb070a767158dee.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, ElementA, LayoutA, ElementB, LayoutB, ElementC, LayoutC, Operator &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation - specialized for 1x1x1x1 matrix multiply operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01ElementAb6e65b2cf5ede7f41cb070a767158dee.html#details">More...</a><br /></td></tr>
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+<p>Matrix multiply.  
+<a href="#details">More...</a></p>
+<div class="textblock"><code>#include &quot;<a class="el" href="arch_2mma_8h_source.html">cutlass/arch/mma.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="complex_8h_source.html">cutlass/complex.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="layout_2matrix_8h_source.html">cutlass/layout/matrix.h</a>&quot;</code><br />
+<code>#include &quot;<a class="el" href="include_2cutlass_2gemm_2gemm_8h_source.html">cutlass/gemm/gemm.h</a>&quot;</code><br />
+</div><div class="textblock"><div class="dynheader">
+Include dependency graph for arch/mma_sm50.h:</div>
+<div class="dyncontent">
+<div class="center"><img src="arch_2mma__sm50_8h__incl.png" border="0" usemap="#mma__sm50_8h" alt=""/></div>
+<map name="mma__sm50_8h" id="mma__sm50_8h">
+</map>
+</div>
+</div><div class="textblock"><div class="dynheader">
+This graph shows which files directly or indirectly include this file:</div>
+<div class="dyncontent">
+<div class="center"><img src="arch_2mma__sm50_8h__dep__incl.png" border="0" usemap="#mma__sm50_8hdep" alt=""/></div>
+<map name="mma__sm50_8hdep" id="mma__sm50_8hdep">
+</map>
+</div>
+</div>
+<p><a href="arch_2mma__sm50_8h_source.html">Go to the source code of this file.</a></p>
+<table class="memberdecls">
+<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="nested-classes"></a>
+Classes</h2></td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_004bb3fd76ca2af7b3210676fa9644d95b.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, float, LayoutA, float, LayoutB, float, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_004bb3fd76ca2af7b3210676fa9644d95b.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_0aa57e6a2e6b5da37d10688bf99419a23.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, double, LayoutA, double, LayoutB, double, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_0aa57e6a2e6b5da37d10688bf99419a23.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01int_00_00b2dff9ce8caad9aff5bc6a355539161.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, int, LayoutA, int, LayoutB, int, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01int_00_00b2dff9ce8caad9aff5bc6a355539161.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_76f9d24016e1b4167b16f4d7628c9546.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; float &gt;, LayoutA, complex&lt; float &gt;, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_76f9d24016e1b4167b16f4d7628c9546.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_f1c9d2ee842455cd0c5b71d56108d468.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; float &gt;, LayoutA, float, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_f1c9d2ee842455cd0c5b71d56108d468.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_00e3e12e263df6506b8cf06c3f4d478b8e.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, float, LayoutA, complex&lt; float &gt;, LayoutB, complex&lt; float &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01float_00e3e12e263df6506b8cf06c3f4d478b8e.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_30fa42e1ad201df010637cd22fc070a1.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; double &gt;, LayoutA, complex&lt; double &gt;, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_30fa42e1ad201df010637cd22fc070a1.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_48b3a43bc03fff93a111ac01abe7e40d.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, complex&lt; double &gt;, LayoutA, double, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01complex_48b3a43bc03fff93a111ac01abe7e40d.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_070b94670e040ed5855e5b42d5ca8a443.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, double, LayoutA, complex&lt; double &gt;, LayoutB, complex&lt; double &gt;, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01double_070b94670e040ed5855e5b42d5ca8a443.html#details">More...</a><br /></td></tr>
+<tr class="separator:"><td class="memSeparator" colspan="2">&#160;</td></tr>
+<tr class="memitem:"><td class="memItemLeft" align="right" valign="top">struct &#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01half__t_4f30ee91f7bb3844ff7579c68d078818.html">cutlass::arch::Mma&lt; gemm::GemmShape&lt; 1, 1, 1 &gt;, 1, half_t, LayoutA, half_t, LayoutB, float, LayoutC, OpMultiplyAdd &gt;</a></td></tr>
+<tr class="memdesc:"><td class="mdescLeft">&#160;</td><td class="mdescRight">Matrix multiply-add operation.  <a href="structcutlass_1_1arch_1_1Mma_3_01gemm_1_1GemmShape_3_011_00_011_00_011_01_4_00_011_00_01half__t_4f30ee91f7bb3844ff7579c68d078818.html#details">More...</a><br /></td></tr>
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docs/arch_2mma__sm50_8h__dep__incl.md5

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