New updates for 2.11 (#775)

* New updates.

* Minor profiler updates

Co-authored-by: Aniket Shivam <ashivam@nvidia.com>

.github/ISSUE_TEMPLATE/bug_report.md

@@ -0,0 +1,23 @@
+---
+name: Bug report
+about: Create a bug report to help us improve CUTLASS
+title: "[BUG]"
+labels: "? - Needs Triage, bug"
+assignees: ''
+
+---
+
+**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.

.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.md

@@ -0,0 +1,20 @@
+---
+name: Feature request
+about: Suggest an idea for CUTLASS
+title: "[FEA]"
+labels: "? - Needs Triage, feature request"
+assignees: ''
+
+---
+
+**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.

.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/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

CHANGELOG.md

@@ -1,6 +1,121 @@
 # NVIDIA CUTLASS Changelog
 
-# CUTLASS 2.x
+## [2.11.0](https://github.com/NVIDIA/cutlass/releases/tag/v2.11.0) (2022-11-19)
+* [Stream-K](/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](/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](/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](/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](/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](/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](/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](/examples/46_depthwise_simt_conv2dfprop/depthwise_simt_conv2dfprop.cu).  Two new modes are added
+  * [kOptimized](/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](/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](/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](/include/cutlass/float8.h) and [conversion routines](/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](/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](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](examples/38_syr2k_grouped/syr2k_grouped.cu) kernels are added, too.
+* Optimizations for [GEMM+Softmax](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](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](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](examples/39_gemm_permute) can apply user provided permutation layout mapping in the GEMM epilogue.
+* [Grouped convolution targeting implicit GEMM](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](test/unit/conv/device/depthwise_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](/tools/util/include/cutlass/util/device_layernorm.h) and [Pooling](/tools/util/include/cutlass/util/device_nhwc_pooling.h) kernels.
+* [Back-to-back GEMM/CONV](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](/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](/include/cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_few_channels.h) specialization for reduced alignment capabilities
+  * [Fixed channels](/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](/test/unit/conv/device/conv2d_fprop_few_channels_f16nhwc_f16nhwc_f16nhwc_tensor_op_f32_sm80.cu)
+  * [Python-based instance emitter](/tools/library/scripts/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](/test/unit/gemm/device/her2k_cf32h_cf32n_tensor_op_fast_f32_sm80.cu) with [emitter](/tools/library/scripts/rank_k_operation.py)
+  * [SYRK](/test/unit/gemm/device/syrk_f32n_f32t_tensor_op_fast_f32_sm80.cu) with [emitter](/tools/library/scripts/rank_k_operation.py)
+  * [SYMM](/test/unit/gemm/device/symm_f32n_f32n_tensor_op_fast_f32_ls_sm80.cu) with [emitter](/tools/library/scripts/symm_operation.py)
+  * [TRMM](/test/unit/gemm/device/trmm_f32n_f32t_f32t_tensor_op_fast_f32_ls_sm80.cu) with [emitter](/tools/library/scripts/trmm_operation.py)
+  * [Unit tests](/test/unit/gemm/device/testbed_rank_k_universal.h)
+* [CUTLASS Python](/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](/tools/library/scripts/rt.py) interoperable with existing emitters
+* [GEMM + Softmax example](/examples/35_gemm_softmax)
+* [Gather and Scatter Fusion with GEMM](/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](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](/examples/34_transposed_conv2d) (a.k.a Deconvolution) support which reuses Dgrad implementation.
+* [Utility functions](/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](/include/cutlass/epilogue/thread/linear_combination_residual_block.h).
+* [Group GEMM](/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](/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm/27_ampere_3xtf32_fast_accurate_tensorop_gemm.cu) (real)
+  * [COMPLEX GEMM SDK example](/examples/29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm/29_ampere_3xtf32_fast_accurate_tensorop_complex_gemm.cu) (complex)
+  * [Implicit GEMM Convolution SDK example](/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](/examples/25_ampere_fprop_mainloop_fusion/ampere_fprop_mainloop_fusion.cu)
+  * [Conv WGrad SDK example](/examples/26_ampere_wgrad_mainloop_fusion/ampere_wgrad_mainloop_fusion.cu) 
+  * [cutlass::conv::device::ImplicitGemmConvolutionFusion](/include/cutlass/conv/device/implicit_gemm_convolution_fusion.h)
+* **Grouped GEMM:** similar to batched GEMM with distinct problem size per group
+  * [SDK example](/examples/24_gemm_grouped) with performance comparison with Batched Strided GEMM
+  * [cutlass::gemm::device::GemmGrouped](/include/cutlass/gemm/device/gemm_grouped.h)
+* [Implicit GEMM Convolution fusion](/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](/examples/23_ampere_gemm_operand_reduction_fusion/ampere_gemm_operand_reduction_fusion.cu)
+  * [Strided DGRAD (optimized iterators)](/include/cutlass/conv/kernel/default_conv2d_dgrad.h)
+  * [Half-precision GELU_taylor activation functions](/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](/examples/13_two_tensor_op_fusion/)
+  * Support for smaller than 128b aligned Convolutions: [see examples](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](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)
@@ -23,7 +138,8 @@
   * Many improvements to the epilogue.
     * Provide an [option](/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    
+    * 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
@@ -169,27 +285,33 @@
 
 ## Copyright
 
-Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2017 - 2023 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
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-  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
+  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"
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+  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.
 ```
 

CITATION.cff

@@ -0,0 +1,82 @@
+cff-version: 1.2.0
+title: CUTLASS
+message: >-
+  If you use this software, please cite using the
+  following metadata.
+type: software
+authors:
+  - given-names: Andrew
+    email: akerr@nvidia.com
+    family-names: Kerr
+    affiliation: NVIDIA
+  - given-names: Haicheng
+    family-names: Wu
+    affiliation: NVIDIA
+    email: haichengw@nvidia.com
+  - given-names: Manish
+    family-names: Gupta
+    affiliation: Google
+    email: manigupta@google.com
+  - given-names: Dustyn
+    family-names: Blasig
+    email: dblasig@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Pradeep
+    family-names: Ramini
+    email: prramani@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Duane
+    family-names: Merrill
+    email: dumerrill@nvidia.com
+    affiliation: NVIDIA
+  - given-names: Aniket
+    family-names: Shivam
+    email: ashivam@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: Matt
+    family-names: Nicely
+    email: mnicely@nvidia.com
+    affiliation: NVIDIA
+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'
+license: BSD-3-Clause
+license-url: https://github.com/NVIDIA/cutlass/blob/v2.11.0/LICENSE.txt
+version: '2.11.0'
+date-released: '2022-11-19'
+identifiers:
+  - type: url
+    value: "https://github.com/NVIDIA/cutlass/tree/v2.11.0"
+    description: The GitHub release URL of tag 2.11.0

CMakeLists.txt

@@ -1,23 +1,29 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 cmake_minimum_required(VERSION 3.12.4 FATAL_ERROR)
@@ -31,8 +37,9 @@ else()
 endif()
 
 message(STATUS "CMake Version: ${CMAKE_VERSION}")
+set(IMPLICIT_CMAKE_CXX_STANDARD OFF CACHE BOOL "Do not explicitly specify -std=c++11 if set")
 
-project(CUTLASS VERSION 2.6.0 LANGUAGES CXX)
+project(CUTLASS VERSION 2.11.0 LANGUAGES CXX)
 include(${CMAKE_CURRENT_SOURCE_DIR}/CUDA.cmake)
 
 if (CUDA_VERSION VERSION_LESS 10.2)
@@ -46,15 +53,19 @@ find_package(Doxygen QUIET)
 #
 # CUTLASS 2.x requires C++11
 #
-set(CMAKE_CXX_STANDARD 11)
-set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_EXTENSIONS OFF)
+if (NOT IMPLICIT_CMAKE_CXX_STANDARD)
+  set(CMAKE_CXX_STANDARD 11)
+  set(CMAKE_CXX_STANDARD_REQUIRED ON)
+  set(CMAKE_CXX_EXTENSIONS OFF)
+endif()
 
 if(CUTLASS_NATIVE_CUDA)
   set(CMAKE_CUDA_STANDARD 11)
   set(CMAKE_CUDA_STANDARD_REQUIRED ON)
 else()
-  list(APPEND CUTLASS_CUDA_NVCC_FLAGS --std=c++11)
+  if (NOT IMPLICIT_CMAKE_CXX_STANDARD)
+    list(APPEND CUTLASS_CUDA_NVCC_FLAGS --std=c++11)
+  endif()
 endif()
 
 if(CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
@@ -67,21 +78,24 @@ set(CUTLASS_ENABLE_HEADERS_ONLY OFF CACHE BOOL "Enable only the header library")
 
 if(CUTLASS_ENABLE_HEADERS_ONLY)
   set(CUTLASS_ENABLE_EXAMPLES_INIT OFF)
-  set(CUTLASS_ENABLE_TOOLS_INIT OFF)
+  set(CUTLASS_ENABLE_TOOLS_INIT ON)
+  set(CUTLASS_ENABLE_LIBRARY_INIT OFF)
 else()
   set(CUTLASS_ENABLE_EXAMPLES_INIT ON)
   set(CUTLASS_ENABLE_TOOLS_INIT ON)
+  set(CUTLASS_ENABLE_LIBRARY_INIT ON)
 endif()
 
 set(CUTLASS_TEST_UNIT_ENABLE_WARNINGS OFF CACHE BOOL "Enable warnings on waived unit tests.")
 
 set(CUTLASS_ENABLE_EXAMPLES ${CUTLASS_ENABLE_EXAMPLES_INIT} CACHE BOOL "Enable CUTLASS Examples")
 set(CUTLASS_ENABLE_TOOLS ${CUTLASS_ENABLE_TOOLS_INIT} CACHE BOOL "Enable CUTLASS Tools")
-set(CUTLASS_ENABLE_LIBRARY ${CUTLASS_ENABLE_TOOLS} CACHE BOOL "Enable CUTLASS Library")
-set(CUTLASS_ENABLE_PROFILER ${CUTLASS_ENABLE_TOOLS} CACHE BOOL "Enable CUTLASS Profiler")
+set(CUTLASS_ENABLE_LIBRARY ${CUTLASS_ENABLE_LIBRARY_INIT} CACHE BOOL "Enable CUTLASS Library")
+set(CUTLASS_ENABLE_PROFILER ${CUTLASS_ENABLE_LIBRARY} CACHE BOOL "Enable CUTLASS Profiler")
+set(CUTLASS_ENABLE_PERFORMANCE ${CUTLASS_ENABLE_PROFILER} CACHE BOOL "Enable CUTLASS Proformance")
 
 if(${CMAKE_PROJECT_NAME} STREQUAL ${PROJECT_NAME})
-  set(CUTLASS_ENABLE_TESTS_INIT ${CUTLASS_ENABLE_TOOLS_INIT})
+  set(CUTLASS_ENABLE_TESTS_INIT ${CUTLASS_ENABLE_LIBRARY}})
 else()
   set(CUTLASS_ENABLE_TESTS_INIT OFF)
 endif()
@@ -114,6 +128,9 @@ endif()
 if (NOT CUDA_VERSION VERSION_LESS 11.1 AND NOT CUDA_COMPILER MATCHES "[Cc]lang")
   list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 86)
 endif()
+if (NOT CUDA_VERSION VERSION_LESS 11.8 AND NOT CUDA_COMPILER MATCHES "[Cc]lang")
+  list(APPEND CUTLASS_NVCC_ARCHS_SUPPORTED 90)
+endif()
 set(CUTLASS_NVCC_ARCHS ${CUTLASS_NVCC_ARCHS_SUPPORTED} CACHE STRING "The SM architectures requested.")
 set(CUTLASS_NVCC_ARCHS_ENABLED ${CUTLASS_NVCC_ARCHS} CACHE STRING "The SM architectures to build code for.")
 
@@ -168,6 +185,11 @@ if (${CUTLASS_NVCC_VERBOSE})
   list(APPEND CUTLASS_CUDA_NVCC_FLAGS -v)
 endif()
 
+#
+# CUTLASS NAMESPACE 
+#
+set(CUTLASS_NAMESPACE "cutlass" CACHE STRING "Top level namespace of CUTLASS")
+
 set(CUTLASS_NVCC_EMBED_CUBIN ON CACHE BOOL "Embed compiled CUDA kernel binaries into executables.")
 set(CUTLASS_NVCC_EMBED_PTX ON CACHE BOOL "Embed compiled PTX into executables.")
 set(CUTLASS_NVCC_KEEP OFF CACHE BOOL "Keep intermediate files generated by NVCC.")
@@ -180,13 +202,27 @@ set(CUTLASS_LIBRARY_OPERATIONS "all" CACHE STRING "Comma delimited list of opera
 set(CUTLASS_LIBRARY_KERNELS "" CACHE STRING "Comma delimited list of kernel name filters. If unspecified, only the largest tile size is enabled. If 'all' is specified, all kernels are enabled.")
 set(CUTLASS_LIBRARY_IGNORE_KERNELS "" CACHE STRING "Comma delimited list of kernel names to exclude from build.")
 
-
 # Test Levels L0, L1, L2
 set(CUTLASS_TEST_LEVEL "0" CACHE STRING "Level of tests to compile.")
+
+set(CUTLASS_TEST_ENABLE_CACHED_RESULTS ON CACHE BOOL "Enable caching and reuse of test results in unit tests")
+
 set_property(CACHE CUTLASS_TEST_LEVEL PROPERTY STRINGS 0 1 2)
 list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_TEST_LEVEL=${CUTLASS_TEST_LEVEL})
 list(APPEND CUTLASS_CUDA_CLANG_FLAGS -DCUTLASS_TEST_LEVEL=${CUTLASS_TEST_LEVEL})
 
+if (CUTLASS_TEST_ENABLE_CACHED_RESULTS)
+  message(STATUS "Enable caching of reference results in conv unit tests")
+  list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_TEST_ENABLE_CACHED_RESULTS=1)
+endif()
+
+set(CUTLASS_CONV_UNIT_TEST_RIGOROUS_SIZE_ENABLED ON CACHE BOOL "Enable/Disable rigorous conv problem sizes in conv unit tests")
+
+if (CUTLASS_CONV_UNIT_TEST_RIGOROUS_SIZE_ENABLED)
+  message(STATUS "Enable rigorous conv problem sizes in conv unit tests")
+  list(APPEND CUTLASS_CUDA_NVCC_FLAGS -DCUTLASS_CONV_UNIT_TEST_RIGOROUS_SIZE_ENABLED=1)
+endif()
+
 #
 # CUDA 10.1 introduces "mma" in PTX performing collective matrix multiply operations.
 #
@@ -239,7 +275,7 @@ if (NOT MSVC AND CUTLASS_NVCC_KEEP)
   # MSVC flow handles caching already, but for other generators we handle it here.
   set(CUTLASS_NVCC_KEEP_DIR ${CMAKE_CURRENT_BINARY_DIR}/tmp CACHE PATH "Location to store NVCC scratch files")
   file(MAKE_DIRECTORY ${CUTLASS_NVCC_KEEP_DIR})
-  list(APPEND CUTLASS_CUDA_NVCC_FLAGS --keep) # --keep-dir may not work with nvcc for some directories.
+  list(APPEND CUTLASS_CUDA_NVCC_FLAGS --keep -v) # --keep-dir may not work with nvcc for some directories.
   list(APPEND CUTLASS_CUDA_CLANG_FLAGS -save-temps=${CUTLASS_NVCC_KEEP_DIR})
 endif()
 
@@ -307,6 +343,11 @@ if(CUDA_COMPILER MATCHES "[Cc]lang")
   link_libraries(nvidia::cudart)
 endif()
 
+# Support for 128-bit integers if using NVIDIA C++ compiler 
+if (${CMAKE_CXX_COMPILER_ID} MATCHES "PGI" OR ${CMAKE_CXX_COMPILER_ID} MATCHES "NVHPC")
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Mint128 ")
+endif()
+
 if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
   # CMake 3.18 added support for CUDA_ARCHITECTURES target property. We will use this
   # property for CMake 3.18+, so we request the NEW behavior for correct compatibility.
@@ -315,11 +356,21 @@ if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.18)
 endif()
 
 function(cutlass_apply_cuda_gencode_flags TARGET)
+  set(options)
+  set(oneValueArgs)
+  set(multiValueArgs SM_ARCHS)
+  cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
+
+  if (__SM_ARCHS)
+    set(ARCHS_ENABLED ${__SM_ARCHS})
+  else()
+    set(ARCHS_ENABLED ${CUTLASS_NVCC_ARCHS_ENABLED})
+  endif()
 
   set(NVCC_FLAGS)
   set(CLANG_FLAGS)
   set(__CMAKE_CUDA_ARCHS)
-  foreach(ARCH ${CUTLASS_NVCC_ARCHS_ENABLED})
+  foreach(ARCH ${ARCHS_ENABLED})
     list(APPEND CLANG_FLAGS --cuda-gpu-arch=sm_${ARCH})
     set(CODES)
     if(CUTLASS_NVCC_EMBED_CUBIN)
@@ -333,21 +384,37 @@ function(cutlass_apply_cuda_gencode_flags TARGET)
     list(JOIN CODES "," CODES_STR)
     list(APPEND NVCC_FLAGS -gencode=arch=compute_${ARCH},code=[${CODES_STR}])
   endforeach()
-
-  if (CUDA_COMPILER MATCHES "[Cc]lang")
-    target_compile_options(
-      ${TARGET}
-      PRIVATE
-      $<$<COMPILE_LANGUAGE:CXX>:${CLANG_FLAGS}>
-      )
-  elseif(CMAKE_VERSION GREATER_EQUAL 3.18)
-    set_property(TARGET ${TARGET} PROPERTY CUDA_ARCHITECTURES ${__CMAKE_CUDA_ARCHS})
+ 
+  if (NOT __SM_ARCHS)
+    if (CUDA_COMPILER MATCHES "[Cc]lang")
+      target_compile_options(
+        ${TARGET}
+        PRIVATE
+        $<$<COMPILE_LANGUAGE:CXX>:${CLANG_FLAGS}>
+        )
+    elseif(CMAKE_VERSION GREATER_EQUAL 3.18)
+      set_property(TARGET ${TARGET} PROPERTY CUDA_ARCHITECTURES ${__CMAKE_CUDA_ARCHS})
+    else()
+      target_compile_options(
+        ${TARGET}
+        PRIVATE
+        $<$<COMPILE_LANGUAGE:CUDA>:${NVCC_FLAGS}>
+        )
+    endif()
   else()
-    target_compile_options(
-      ${TARGET}
-      PRIVATE
-      $<$<COMPILE_LANGUAGE:CUDA>:${NVCC_FLAGS}>
-      )
+    list(JOIN CLANG_FLAGS " " CLANG_FLAGS_STR)
+    list(JOIN NVCC_FLAGS " " STR_NVCC_FLAGS)
+    if (CUDA_COMPILER MATCHES "[Cc]lang")
+      if(${TARGET} MATCHES ".*\.cpp")
+        set_source_files_properties(${TARGET} PROPERTIES COMPILE_FLAGS ${CLANG_FLAGS_STR})
+      endif()
+    elseif(CMAKE_VERSION GREATER_EQUAL 3.18)
+      set_source_files_properties(${TARGET} PROPERTIES CUDA_ARCHITECTURES ${STR_NVCC_FLAGS})
+    else()
+      if(${TARGET} MATCHES ".*\.cu")
+        set_source_files_properties(${TARGET} PROPERTIES COMPILE_FLAGS ${STR_NVCC_FLAGS})
+      endif()
+    endif()
   endif()
 
 endfunction()
@@ -383,6 +450,8 @@ function(cutlass_apply_standard_compile_options TARGET)
     set(_FLAGS_DEBUG ${__CUTLASS_CUDA_FLAGS_DEBUG} ${__CUTLASS_CUDA_NVCC_FLAGS_DEBUG})
   endif()
 
+  target_link_libraries(${TARGET} PRIVATE CUTLASS)
+
   target_compile_options(
     ${TARGET}
     PRIVATE
@@ -426,6 +495,10 @@ include_directories(${CUTLASS_INCLUDE_DIR})
 
 target_compile_features(CUTLASS INTERFACE cxx_std_11)
 
+if (NOT CUTLASS_NAMESPACE STREQUAL "cutlass")
+  target_compile_definitions(CUTLASS INTERFACE CUTLASS_NAMESPACE=${CUTLASS_NAMESPACE})
+endif()
+
 if (NOT DEFINED CUTLASS_REVISION)
 
   find_package(Git QUIET)
@@ -534,6 +607,9 @@ install(DIRECTORY DESTINATION ${CUTLASS_TEST_INSTALL_PREFIX}/ctest)
 
 ################################################################################
 
+set(CUTLASS_ENABLE_CUBLAS OFF CACHE BOOL "cuBLAS usage for tests")
+set(CUTLASS_ENABLE_CUDNN OFF CACHE BOOL "cuDNN usage for tests")
+
 include(${CMAKE_CURRENT_SOURCE_DIR}/cuBLAS.cmake)
 
 if (CUTLASS_ENABLE_CUBLAS)
@@ -564,10 +640,12 @@ function(cutlass_add_executable_tests NAME TARGET)
 # TEST_COMMAND_OPTIONS: A list of variables (i.e. by reference params) which contain command line arguments
 #   to pass to the test executable. A unique test with suffix _0, _1, ... is generated for each set of 
 #   options given. If this option is not used, a single test with no arguments is generated.
+# RESULT_CACHE_FILE: A file to be installed alongside the test executable with pre-computed
+#   test results to speed up test runtime.
 # 
 
   set(options DISABLE_EXECUTABLE_INSTALL_RULE)
-  set(oneValueArgs DISABLE_TESTS)
+  set(oneValueArgs DISABLE_TESTS RESULT_CACHE_FILE)
   set(multiValueArgs DEPENDS DEPENDEES TEST_COMMAND_OPTIONS)
   cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
 
@@ -575,6 +653,17 @@ function(cutlass_add_executable_tests NAME TARGET)
     set(__DISABLE_TESTS OFF)
   endif()
 
+  if (__RESULT_CACHE_FILE)
+
+    add_custom_command(
+      TARGET ${TARGET}
+      POST_BUILD
+      COMMAND ${CMAKE_COMMAND}
+      ARGS -E copy ${__RESULT_CACHE_FILE} "$<TARGET_FILE_DIR:${TARGET}>"
+      )
+
+  endif()
+
   if (NOT __DISABLE_EXECUTABLE_INSTALL_RULE AND CUTLASS_INSTALL_TESTS)
   
     # file(RELATIVE_PATH CMAKE_CURRENT_BINARY_RELATIVE_DIR ${CMAKE_BINARY_DIR} ${CMAKE_CURRENT_BINARY_DIR})
@@ -583,6 +672,15 @@ function(cutlass_add_executable_tests NAME TARGET)
       TARGETS ${TARGET}
       RUNTIME DESTINATION ${CUTLASS_TEST_INSTALL_BINDIR}
       )
+
+    if (__RESULT_CACHE_FILE)
+
+     install(
+       FILES ${__RESULT_CACHE_FILE}
+       DESTINATION ${CUTLASS_TEST_INSTALL_BINDIR}/
+       )
+
+    endif()
   
   endif()
 
@@ -675,7 +773,7 @@ if (CUTLASS_ENABLE_TOOLS)
   add_subdirectory(tools)
   if (CUTLASS_ENABLE_PROFILER)
     add_dependencies(test_all test_profiler)
-  endif()  
+  endif()
 endif()
 if (CUTLASS_ENABLE_EXAMPLES)
   add_subdirectory(examples)
@@ -698,7 +796,7 @@ if (CUTLASS_INSTALL_TESTS)
 
   install(
     FILES "${CMAKE_BINARY_DIR}/cmake/CTestTestfile.cmake"
-    DESTINATION ${CUTLASS_TEST_INSTALL_PREFIX}/
+    DESTINATION "${CUTLASS_TEST_INSTALL_PREFIX}/"
     )
 
 endif()
@@ -718,15 +816,23 @@ endif()
 
 ################################################################################
 
+include(CMakePackageConfigHelpers)
+
+write_basic_package_version_file(
+  ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfigVersion.cmake 
+  COMPATIBILITY AnyNewerVersion)
+
 install(
-  FILES ${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassConfig.cmake
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/
+  FILES 
+    ${CMAKE_CURRENT_SOURCE_DIR}/cmake/NvidiaCutlassConfig.cmake
+    ${CMAKE_CURRENT_BINARY_DIR}/NvidiaCutlassConfigVersion.cmake 
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/NvidiaCutlass/
   )
 
 install(
   EXPORT NvidiaCutlass
   NAMESPACE nvidia::cutlass::
-  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/
+  DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/NvidiaCutlass/
   FILE NvidiaCutlassTargets.cmake
   )
 

CONTRIBUTORS.md

@@ -7,57 +7,63 @@
 This is the official list of CUTLASS developers and contributors.
 
 ## DEVELOPERS
-Andrew Kerr  
-Haicheng Wu  
-Manish Gupta  
-Dustyn Blasig  
-Pradeep Ramani  
-Naila Farooqui  
-Piotr Majcher  
-Paul Springer  
-Jin Wang   
-Aniket Shivam  
-Chinmay Talegaonkar  
-Shang Zhang   
-Scott Yokim      
-Markus Hohnerbach  
-Aditya Atluri  
-David Tanner  
-Manikandan Ananth  
+Andrew Kerr 
+Haicheng Wu 
+Manish Gupta
+Dustyn Blasig
+Pradeep Ramani
+Cris Cecka
+Vijay Thakkar
+Aniket Shivam
+Honghao Lu
+Ethan Yan
+Zhaodong Chen
+Jack Kosaian
+Yujia Zhai
+Naila Farooqui
+Piotr Majcher
+Paul Springer
+Jin Wang
+Chinmay Talegaonkar
+Shang Zhang
+Scott Yokim
+Markus Hohnerbach
+Aditya Atluri
+David Tanner
+Manikandan Ananth
+
+## CUTLASS Product Manager
+Matthew Nicely
   
 ## CONTRIBUTORS
-Timothy Costa  
-Julien Demouth  
-Brian Fahs  
-Michael Goldfarb  
-Mostafa Hagog  
-Fei Hu  
-Alan Kaatz  
-Tina Li  
-Timmy Liu  
-Duane Merrill  
-Kevin Siu  
-Markus Tavenrath  
-John Tran  
-Vicki Wang  
-Junkai Wu  
-Fung Xie  
-Albert Xu  
-Jack Yang  
-Xiuxia Zhang  
-Nick Zhao  
+Timothy Costa
+Julien Demouth
+Brian Fahs
+Michael Goldfarb
+Mostafa Hagog
+Fei Hu
+Alan Kaatz
+Tina Li
+Timmy Liu
+Duane Merrill
+Kevin Siu
+Markus Tavenrath
+John Tran
+Vicki Wang
+Junkai Wu
+Fung Xie
+Albert Xu
+Jack Yang
+Xiuxia Zhang
+Nick Zhao
 
 ## ACKNOWLEDGEMENTS
 
-Girish Bharambe  
-Cris Cecka  
-Luke Durant  
-Olivier Giroux  
-Stephen Jones  
-Rishkul Kulkarni  
-Bryce Lelbach  
-Matthew Nicely  
-Joel McCormack  
-Kyrylo Perelygin  
-
-
+Girish Bharambe
+Luke Durant
+Olivier Giroux
+Stephen Jones
+Rishkul Kulkarni
+Bryce Lelbach
+Joel McCormack
+Kyrylo Perelygin

CUDA.cmake

@@ -1,23 +1,29 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 if(CUDA_COMPILER MATCHES "[Cc]lang")
@@ -213,8 +219,7 @@ function(cutlass_correct_source_file_language_property)
   endif()
 endfunction()
 
-# If building with all kernels, set UNITY build on by default.
-if (CUTLASS_LIBRARY_KERNELS MATCHES "all")
+if (MSVC OR CUTLASS_LIBRARY_KERNELS MATCHES "all")
   set(CUTLASS_UNITY_BUILD_ENABLED_INIT ON)
 else()
   set(CUTLASS_UNITY_BUILD_ENABLED_INIT OFF)
@@ -280,10 +285,9 @@ function(cutlass_unify_source_files TARGET_ARGS_VAR)
   set(${TARGET_ARGS_VAR} ${TARGET_SOURCE_ARGS} PARENT_SCOPE)
 
 endfunction()
-
 function(cutlass_add_library NAME)
 
-  set(options)
+  set(options SKIP_GENCODE_FLAGS)
   set(oneValueArgs EXPORT_NAME)
   set(multiValueArgs)
   cmake_parse_arguments(_ "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
@@ -299,7 +303,9 @@ function(cutlass_add_library NAME)
   endif()
 
   cutlass_apply_standard_compile_options(${NAME})
+  if (NOT __SKIP_GENCODE_FLAGS)
   cutlass_apply_cuda_gencode_flags(${NAME})
+  endif()
 
   target_compile_features(
    ${NAME}

LICENSE.txt

@@ -1,23 +1,27 @@
-Copyright (c) 2017 - 2020, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2017 - 2023 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.
 
-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.
+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.

PUBLICATIONS.md

@@ -1,8 +1,30 @@
 # Publications Using Cutlass
 
+## 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.
+
+- ["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://d1qx31qr3h6wln.cloudfront.net/publications/paper_4.pdf). 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.

README.md

@@ -1,15 +1,15 @@
 ![ALT](/media/images/gemm-hierarchy-with-epilogue-no-labels.png "Complete CUDA GEMM decomposition")
 
-# CUTLASS 2.6
+# CUTLASS 2.11
 
-_CUTLASS 2.6 - July 2021_
+_CUTLASS 2.11 - November 2022_
 
 CUTLASS 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
+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.
@@ -18,128 +18,104 @@ To support a wide variety of applications, CUTLASS provides extensive support fo
 mixed-precision computations, providing specialized data-movement and
 multiply-accumulate abstractions for half-precision floating
 point (FP16), BFloat16 (BF16), Tensor Float 32 (TF32),
-single-precision floating point (FP32), double-precision floating
+single-precision floating point (FP32),
+[FP32 emulation via tensor core instruction](/examples/27_ampere_3xtf32_fast_accurate_tensorop_gemm),
+double-precision floating
 point (FP64) types, integer data types (4b and 8b), and binary data types (1b). 
-
-Furthermore, CUTLASS demonstrates warp-synchronous matrix multiply operations 
+CUTLASS demonstrates warp-synchronous matrix multiply operations 
 targeting the  programmable, high-throughput _Tensor Cores_ implemented by 
 NVIDIA's Volta, Turing, and Ampere architectures.
 
-Additionaly, CUTLASS implements high-performance convolution (implicit GEMM). 
-Implicit GEMM is the formulation of a convolution operation as a GEMM. This allows CUTLASS 
-to build convolutions by reusing highly optimized warp-wide GEMM components and below. 
+CUTLASS implements high-performance Convolution via the implicit GEMM algorithm.
+Implicit GEMM is the formulation of a convolution operation as a GEMM thereby taking advantage of
+CUTLASS's modular GEMM pipeline. 
+This allows CUTLASS to build convolutions by reusing highly optimized warp-wide GEMM components and below. 
 
 See the [Quick Start Guide](/media/docs/quickstart.md) to get started quickly.
 
 See the [functionality listing](/media/docs/functionality.md) for the list of operations
 supported at each level of the execution model hierarchy.
 
-# What's New in CUTLASS 2.6
-CUTLASS 2.6 is a minor update to CUTLASS adding:
-- Fused [broadcast](test/unit/gemm/device/gemm_with_broadcast_f16n_f16n_f16n_tensorop_f32_sm75.cu) and [reductions](/test/unit/gemm/device/gemm_with_reduction_f16n_f16n_f16n_tensorop_f32_sm75.cu) in the epilogues of GEMM and Convolution
-- [Quaternion-valued GEMM](/examples/21_quaternion_gemm/quaternion_gemm.cu) and [Convolution](/examples/22_quaternion_conv/quaternion_conv.cu) in single-precision
-- [New strided Dgrad](test/unit/conv/device/conv2d_strided_dgrad_implicit_gemm_f16nhwc_f16nhwc_f32nhwc_tensor_op_f32_sm80.cu) implementation offers up to 4x performance improvements over previous strided Dgrad
-- 64-bit strides for large tensor allocations
-- [General affine layouts](/examples/18_ampere_fp64_tensorop_affine2_gemm/ampere_fp64_tensorop_affine2_gemm.cu) fp64 tensor core and simt GEMM
-- Enhanced functionality, boosted performance, and bug fixes in the epilogue.
-- Optimal performance when compiled with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
-- Adopt new L2 prefetch feature in [ptx instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#ptx-isa-version-7-4).
-- Numerous updates from the community (thanks!)
-- See the [CHANGELOG](CHANGELOG.md) for more details
+# What's New in CUTLASS 2.11
 
-# What's New in CUTLASS 2.5
-CUTLASS 2.5 is a minor update to CUTLASS adding:
-- [Tensor reductions](/test/unit/reduction/device/tensor_reduce_contiguous.cu)
-- [Optimizations for 3-D convolution](include/cutlass/conv/threadblock/conv3d_fprop_activation_tile_access_iterator_optimized.h)
-- [Fused Convolution+Convolution example](/examples/13_two_tensor_op_fusion/README.md)
+CUTLASS 2.11 is an update to CUTLASS adding:
+- [Stream-K](/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](/examples/41_fused_multi_head_attention).  It has two variants: one for fixed sequence lengths, and another for variable sequence lengths.
+- [Dual GEMM](/examples/45_dual_gemm).  It can run two GEMMs that share the same left input matrix in one kernel.
+- Hopper improves [double precision matrix multiplication](/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](/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](/examples/43_ell_block_sparse_gemm).
+- [Optimized Group Conv](/examples/42_ampere_tensorop_group_conv).
+- [Optimized DepthWise Conv](/examples/46_depthwise_simt_conv2dfprop).
+- [Scripts](/examples/44_multi_gemm_ir_and_codegen) to fuse multiple back-to-back GEMM.
+- [FP8 data type definition](/include/cutlass/float8.h) and [conversion routines](/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 in the next major release:
+  - Maxwell and Pascal GPU architectures
+  - Ubuntu 16.04
+  - CUDA 10.2
+  - C++ 11
+- **Future requirement announcement:** CUTLASS plans to add the following requirements in the next major release:
+  - Minimum C++ standard - C++17
 
-# What's New in CUTLASS 2.4
-CUTLASS 2.4 is a significant update to CUTLASS adding:
-- 1-D, 2-D, and 3-D convolution targeting Tensor and CUDA cores for NVIDIA Ampere, Turing, and Volta GPU architectures
-- CUTLASS profiler support for convolution
-- [Documentation](/media/docs/implicit_gemm_convolution.md) describing Implicit GEMM Convolution algorithm and implementation
-
-# What's New in CUTLASS 2.3
-
-CUTLASS 2.3 is a minor update to CUTLASS adding:
-- GEMMs targeting structured [Sparse Tensor Cores](test/unit/gemm/device/gemm_f16n_f16n_f32t_tensor_op_f32_sparse_sm80.cu) in NVIDIA Ampere Architecture GPUs
-- Fast SGEMM kernels targeting GeForce RTX 30-series CUDA Cores
-- Intended to be compiled with [CUDA 11.1 Toolkit](https://developer.nvidia.com/cuda-toolkit) or later
-
-# What's New in CUTLASS 2.2
-
-CUTLASS 2.2 is a significant update to CUTLASS adding:
-
-- Coverage of [NVIDIA Ampere Architecture features](https://devblogs.nvidia.com/nvidia-ampere-architecture-in-depth/)
-- Tensor Core-accelerated GEMMs targeting Tensor Float 32, BFloat16, and double-precision data types
-- Deep software pipelines using asynchronous copy
-- Described in [GTC 2020 Webinar (SR 21745)](https://developer.nvidia.com/gtc/2020/video/s21745)
-- Intended to be compiled with [CUDA 11 Toolkit](https://developer.nvidia.com/cuda-toolkit) or later
-
-# What's New in CUTLASS 2.1
-
-CUTLASS 2.1 is a minor update to CUTLASS adding:
-
-- [Planar complex GEMM kernels](/examples/10_planar_complex/planar_complex.cu) targeting Volta and Turing Tensor Cores
-- BLAS-style API to launch kernels compiled into the [CUTLASS Library](/media/docs/quickstart.md#cutlass-library)
-
-# What's New in CUTLASS 2.0
-
-CUTLASS 2.0 is a substantial refactoring from the previous version, intended to offer:
-
-- Better performance over 1.x, particularly for kernels targeting Turing Tensor Cores
-- Robust and durable templates that reliably span the design space
-- Encapsulated functionality that may be reusable in other contexts
-
-**See the [CHANGELOG](CHANGELOG.md) for more details.**
+**See the [CHANGELOG](CHANGELOG.md) for a detailed listing of releases and updates.**
 
 # Performance
 
-<p align="center"><img src=/media/images/cutlass-performance-plot.png></p>
+<p align="center"><img src=/media/images/cutlass-2.8-gemm-performance.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 on an NVIDIA GeForce 2080 Ti, an NVIDIA A100, and an NVIDIA TitanV
-using CUDA 11.0 Toolkit. Tensor Core operations are implemented using CUDA's 
+for large matrix dimensions on an [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/), 
+an [NVIDIA A2](https://www.nvidia.com/en-us/data-center/products/a2/), 
+an [NVIDIA TitanV](https://www.nvidia.com/en-us/titan/titan-v/), 
+and an [NVIDIA GeForce 2080 Ti](https://www.nvidia.com/en-us/geforce/graphics-cards/rtx-2080-ti/)
+compiled with the [CUDA 11.5 Toolkit](https://developer.nvidia.com/cuda-downloads). Tensor Core operations are implemented using CUDA's 
+[mma instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma).
+
+<p align="center"><img src=/media/images/cutlass-2.9-implicit-gemm-performance.png></p>
+
+When using CUTLASS building blocks to construct device-wide implicit gemm (Fprop, Dgrad, and Wgrad)
+kernels, CUTLASS performance is also comparable to cuDNN when running Resnet-50 layers on an [NVIDIA A100](https://www.nvidia.com/en-us/data-center/a100/)
+as shown in the above figure.  Tensor Core operations are still implemented using CUDA's
 [mma instruction](https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#warp-level-matrix-instructions-mma).
 
 # Compatibility
 
-CUTLASS requires a C++11 host compiler and 
-performs best when built with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit).
-It is also compatible with CUDA 10.2, CUDA 11.0, CUDA 11.1, CUDA 11.2, and CUDA 11.3.
+CUTLASS requires a C++11 host compiler and performs best when built with the [**CUDA 11.8 Toolkit**](https://developer.nvidia.com/cuda-toolkit).
 
+It is also compatible with CUDA 11.x.
+
+## Operating Systems
 We have tested the following environments.
 
 |**Operating System** | **Compiler** |
 |-----------------|----------|
 | Windows 10      | Microsoft Visual Studio 2015|
 |                 | Microsoft Visual Studio 2017|
-| Ubuntu 16.04 | GCC 5.4.0 |
+|                 | Microsoft Visual Studio 2019|
 | Ubuntu 18.04 | GCC 7.5.0 |
-| Ubuntu 20.04 | GCC 10.2.0 |
+| Ubuntu 20.04 | GCC 10.3.0 |
+| Ubuntu 22.04 | GCC 11.2.0 |
 
 Additionally, CUTLASS may be built with clang. 
 See [these instructions](media/docs/quickstart.md#clang) for more details.
 
+## Hardware
 CUTLASS runs successfully on the following NVIDIA GPUs, and it is expected to be efficient on
-any Maxwell-, Pascal-, Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU. 
+any Volta-, Turing-, or NVIDIA Ampere- architecture NVIDIA GPU. 
 
-For all GPUs, we recommend compiling with the [CUDA 11.4 Toolkit](https://developer.nvidia.com/cuda-toolkit)
-for best performance. 
-
-|**GPU**|**CUDA Compute Capability**|**Minimum CUDA Toolkit**|**CUDA Toolkit Enabling Native Tensor Cores**|
+|**GPU**|**CUDA Compute Capability**|**Minimum CUDA Toolkit**|**Minimum CUDA Toolkit Enabling Native Tensor Cores**|
 |---|---|---|---|
-|NVIDIA Tesla P100|6.0|9.2|  |
-|NVIDIA GeForce 1080|6.1|9.2|  |
-|NVIDIA TitanXP|6.1|9.2|  |
 |NVIDIA Tesla V100|7.0|9.2|10.1|
 |NVIDIA TitanV|7.0|9.2|10.1|
 |NVIDIA GeForce RTX 2080 TI, 2080, 2070|7.5|10.0|10.2|
 |NVIDIA Tesla T4|7.5|10.0|10.2|
 |NVIDIA A100|8.0|11.0|11.0|
+|NVIDIA A10 |8.6|11.1|11.1|
 |NVIDIA GeForce 3090|8.6|11.1|11.1|
+|NVIDIA H100 PCIe|9.0|11.8|Double-precision: 11.8; Mixed precision: 12.0|
 
 # Documentation
 
@@ -160,9 +136,16 @@ CUTLASS is described in the following documents and the accompanying
 - [CUTLASS Profiler](media/docs/profiler.md) - command-line driven profiling application
 - [CUTLASS Utilities](media/docs/utilities.md) - additional templates used to facilate rapid development
 
+# 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
@@ -226,6 +209,8 @@ include/                     # client applications should target this directory
 
     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
@@ -233,6 +218,8 @@ include/                     # client applications should target this directory
     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
 
@@ -243,39 +230,6 @@ include/                     # client applications should target this directory
 
 [CUTLASS SDK examples](/examples) apply CUTLASS templates to implement basic computations.
 
-```
-examples/
-  00_basic_gemm/                   # launches a basic GEMM with single precision inputs and outputs
-
-  01_cutlass_utilities/            # demonstrates CUTLASS Utilities for allocating and initializing tensors
-  
-  02_dump_reg_smem/                # debugging utilities for printing register and shared memory contents
-  
-  03_visualize_layout/             # utility for visualizing all layout functions in CUTLASS
-
-  04_tile_iterator/                # example demonstrating an iterator over tiles in memory
-
-  05_batched_gemm/                 # example demonstrating CUTLASS's batched strided GEMM operation
-
-  06_splitK_gemm/                  # exmaple demonstrating CUTLASS's Split-K parallel reduction kernel
-
-  07_volta_tensorop_gemm/          # example demonstrating mixed precision GEMM using Volta Tensor Cores
-
-  08_turing_tensorop_gemm/         # example demonstrating integer GEMM using Turing Tensor Cores
-
-  09_turing_tensorop_conv2dfprop/  # example demonstrating integer implicit GEMM convolution (forward propagation) using Turing Tensor Cores
-
-  10_planar_complex/               # example demonstrating planar complex GEMM kernels
-
-  11_planar_complex_array/         # example demonstrating planar complex kernels with batch-specific problem sizes
-
-  12_gemm_bias_relu/               # example demonstrating GEMM fused with bias and relu
-
-  13_fused_two_gemms/              # example demonstrating two GEMms fused in one kernel
-
-  22_ampere_tensorop_conv2dfprop/  # example demonstrating integer implicit GEMM convolution (forward propagation) using Ampere Tensor Cores
-```
-
 ### Tools
 
 ```
@@ -529,27 +483,33 @@ The official list of CUTLASS developers and contributors is available here: [CON
 
 # Copyright
 
-Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2017 - 2023 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 TORT (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.
 ```
 

cmake/nop.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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.
  *
  **************************************************************************************************/

cuBLAS.cmake

@@ -1,23 +1,29 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 message(STATUS "Configuring cublas ...")

cuDNN.cmake

@@ -1,24 +1,29 @@
-
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 if(DEFINED CUDNN_ENABLED)

docs/_config.yml

@@ -0,0 +1 @@
+theme: jekyll-theme-minimal

examples/00_basic_gemm/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   00_basic_gemm
   basic_gemm.cu

examples/00_basic_gemm/basic_gemm.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/01_cutlass_utilities/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   01_cutlass_utilities
   cutlass_utilities.cu

examples/01_cutlass_utilities/cutlass_utilities.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/02_dump_reg_shmem/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   02_dump_reg_shmem 
   dump_reg_shmem.cu

examples/02_dump_reg_shmem/dump_reg_shmem.cu

@@ -1,27 +1,31 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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.
+ * 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 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.
+ * 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.
  *
  **************************************************************************************************/
 

examples/03_visualize_layout/CMakeLists.txt

@@ -1,27 +1,34 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 set(TEST_COMMAND_00 RowMajor --extent=16,16)
-set(TEST_COMMAND_01 "ColumnMajorInterleaved<4>" --extent=32,8 --output-shape=16 --vectorize=4)
 
 cutlass_example_add_executable(
   03_visualize_layout
@@ -29,6 +36,5 @@ cutlass_example_add_executable(
   register_layout.cu
   TEST_COMMAND_OPTIONS
   TEST_COMMAND_00
-  TEST_COMMAND_01
   )
 

examples/03_visualize_layout/options.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/03_visualize_layout/register_layout.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -55,27 +61,49 @@ void RegisterLayouts(std::map<std::string, std::unique_ptr<VisualizeLayoutBase>
        new VisualizeLayout<cutlass::layout::ColumnMajorInterleaved<4>>},
       {"RowMajorInterleaved<4>",
        new VisualizeLayout<cutlass::layout::RowMajorInterleaved<4>>},
+      // All Ampere/Turing H/Integer matrix multiply tensor core kernels uses the same swizzling
+      // layout implementation with different templates.
+      //
+      // mma.sync.aligned.m8n8k128.s32.b1.b1.s32 Interleaved-256
+      // mma.sync.aligned.m16n8k256.s32.b1.b1.s32 Interleaved-256
+      {"TensorOpMultiplicand<1,256>",
+       new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<1, 256>>},
+      // mma.sync.aligned.m8n8k128.s32.b1.b1.s32 TN kblock512
+      // mma.sync.aligned.m16n8k256.s32.b1.b1.s32 TN kblock512
+      {"TensorOpMultiplicand<1,512>",
+       new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<1, 512>>},
+      // mma.sync.aligned.m16n8k256.s32.b1.b1.s32 TN kblock1024
+      {"TensorOpMultiplicand<1,1024>",
+       new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<1, 1024>>},
       // Integer matrix multiply.int4 8832  Interleaved-64
+      // Integer matrix multiply.int4 16864 Interleaved-64
       {"TensorOpMultiplicand<4,64>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<4, 64>>},
       // Integer matrix multiply.int4 8832  TN kblock128
+      // Integer matrix multiply.int4 16864 TN kblock128
       {"TensorOpMultiplicand<4,128>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<4, 128>>},
       // Integer matrix multiply.int4 16864 TN kblock256
       {"TensorOpMultiplicand<4,256>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<4, 256>>},
       // Integer matrix multiply 8816  Interleaved-32
+      // Integer matrix multiply 16832 Interleaved-32
       {"TensorOpMultiplicand<8,32>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<8, 32>>},
       // Integer matrix multiply 8816  TN kblock64
+      // Integer matrix multiply 16832 TN kblock64
       {"TensorOpMultiplicand<8,64>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<8, 64>>},
+      // Integer matrix multiply 16832 TN kblock128
       {"TensorOpMultiplicand<8,128>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<8, 128>>},
       // Matrix Multiply 1688  TN kblock32
+      // Matrix multiply 16816 TN kblock32
       {"TensorOpMultiplicand<16,32>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<16, 32>>},
       // Matrix multiply 1688  NT
+      // Matrix multiply 16816 NT
+      // Matrix multiply 16816 TN kblock64
       {"TensorOpMultiplicand<16,64>",
        new VisualizeLayout<cutlass::layout::TensorOpMultiplicand<16, 64>>},
       // Matrix multiply 1688.TF32 TN kblock16

examples/03_visualize_layout/register_layout.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/03_visualize_layout/visualize_layout.cpp

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -89,7 +95,7 @@ void print_usage(std::ostream &out) {
          "--extent=16,16 --vectorize=2 --output-shape=16,4\n"
       << "$ 03_visualize_layout \"VoltaTensorOpMultiplicandCrosswise<16,32>\" "
          "--extent=32,64 --vectorize=4 --output-shape=64,4\n"
-      << "$ 03_visualize_layout \"VotlaTensorOpMultiplicandCongruous<16>\" "
+      << "$ 03_visualize_layout \"VoltaTensorOpMultiplicandCongruous<16>\" "
          "--extent=64,32 --vectorize=8 --output-shape=64,4\n";
 
   out << std::endl;

examples/03_visualize_layout/visualize_layout.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/04_tile_iterator/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   04_tile_iterator
   tile_iterator.cu

examples/04_tile_iterator/tile_iterator.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -44,7 +50,6 @@
 #include <iostream>
 #include <sstream>
 #include <vector>
-#include <fstream>
 
 // CUTLASS includes
 #include "cutlass/transform/threadblock/predicated_tile_iterator.h"

examples/05_batched_gemm/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   05_batched_gemm
   batched_gemm.cu

examples/05_batched_gemm/batched_gemm.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -28,12 +34,16 @@
 
 #include "cutlass/cutlass.h"
 #include "cutlass/layout/matrix.h"
+#include "cutlass/gemm/device/gemm_array.h"
 #include "cutlass/gemm/device/gemm_batched.h"
 
 #pragma warning( disable : 4503)
 
 /*
-This example demonstrates how to use cutlass to compute a batched strided gemm.
+This example demonstrates how to use cutlass to compute a batched strided gemm in two different ways:
+  1. By specifying pointers to the first matrices of the batch and the stride between the consecutive
+     matrices of the batch (this is called a strided batched gemm).
+  2. By copying pointers to all matrices of the batch to the device memory (this is called an array gemm).
 In this example, both A and B matrix are non-transpose and column major matrix
 batched_C = batched_A x batched_B
 As an example, matrix C can be seen as
@@ -71,7 +81,7 @@ matrix A can be seen as
 ---------------------------------------
      batch 0      |      batch 1
 , where batch size is 2, M is 6 and K is 2
-The stride (batch_stride_B) between the first element of two batches is lda * k
+The stride (batch_stride_A) between the first element of two batches is lda * k
 
 matrix B can be seen as
 -----------------------------
@@ -84,11 +94,50 @@ matrix B can be seen as
 (1,1,0) | (1,1,1) | (1,1,2) |
 -----------------------------
 , where the batch size is 2, N is 3 and K is 2
-The stride (batch_stride_C) between the first element of two batches is k
+The stride (batch_stride_B) between the first element of two batches is k
 
 
 */
 
+cudaError_t cutlass_array_sgemm(
+  int m,
+  int n,
+  int k,
+  float alpha,
+  float const * const *A,
+  int lda,
+  float const * const *B,
+  int ldb,
+  float * const *C,
+  int ldc,
+  float beta,
+  int batch_count) {
+
+  using Gemm = cutlass::gemm::device::GemmArray<
+    float, cutlass::layout::ColumnMajor,
+    float, cutlass::layout::ColumnMajor,
+    float, cutlass::layout::ColumnMajor
+  >;
+
+  Gemm gemm_op;
+
+  cutlass::Status status = gemm_op({
+    {m, n, k},
+    A, lda,
+    B, ldb,
+    C, ldc,
+    C, ldc,
+    {alpha, beta},
+    batch_count
+  });
+
+  if (status != cutlass::Status::kSuccess) {
+    return cudaErrorUnknown;
+  }
+
+  return cudaSuccess;
+}
+
 cudaError_t cutlass_strided_batched_sgemm(
   int m, 
   int n,
@@ -188,7 +237,11 @@ cudaError_t strided_batched_gemm_nn_reference(
   return result;
 }
 
-int main() {
+
+cudaError_t run_batched_gemm(bool use_array) {
+
+  const char* gemm_desc = use_array ? "array" : "strided batched";
+  std::cout << "Running " << gemm_desc << " gemm" << std::endl;
 
   // Arbitrary problem size
   int const m = 520;
@@ -293,11 +346,69 @@ int main() {
   }
 
   // run cutlass
-  result = cutlass_strided_batched_sgemm(
-    m, n, k, alpha, A, lda, batch_stride_A, B, ldb, batch_stride_B, C, ldc, batch_stride_C,
-    beta, batch_count);
-  if (result != cudaSuccess)
-    return result;
+  if (use_array) {
+    // allocate the host memory for the pointers to the matrices of the batch
+    std::vector<float*> host_ptr_A(batch_count);
+    std::vector<float*> host_ptr_B(batch_count);
+    std::vector<float*> host_ptr_C(batch_count);
+
+    // permute the batch elements to emphasize that GemmArray does not depend on matrices being separated by a fixed stride
+    std::vector<size_t> permutation = {14, 11, 3, 10, 1, 13, 9, 4, 6, 16, 8, 15, 7, 12, 0, 2, 5};
+    for (size_t b_idx = 0; b_idx < batch_count; b_idx++) {
+      host_ptr_A[b_idx] = A + permutation[b_idx] * batch_stride_A;
+      host_ptr_B[b_idx] = B + permutation[b_idx] * batch_stride_B;
+      host_ptr_C[b_idx] = C + permutation[b_idx] * batch_stride_C;
+    }
+
+    // allocate the corresponding device memory
+    float const **ptr_A;
+    float const **ptr_B;
+    float **ptr_C;
+
+    result = cudaMalloc(&ptr_A, batch_count * sizeof(float*));
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMalloc result = " << result << std::endl;
+      return result;
+    }
+    result = cudaMalloc(&ptr_B, batch_count * sizeof(float*));
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMalloc result = " << result << std::endl;
+      return result;
+    }
+    result = cudaMalloc(&ptr_C, batch_count * sizeof(float*));
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMalloc result = " << result << std::endl;
+      return result;
+    }
+
+    // copy the matrix pointers to the device
+    result = cudaMemcpy(ptr_A, host_ptr_A.data(), batch_count * sizeof(float*), cudaMemcpyHostToDevice);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMemcpy result = " << result << std::endl;
+      return result;
+    }
+    result = cudaMemcpy(ptr_B, host_ptr_B.data(), batch_count * sizeof(float*), cudaMemcpyHostToDevice);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMemcpy result = " << result << std::endl;
+      return result;
+    }
+    result = cudaMemcpy(ptr_C, host_ptr_C.data(), batch_count * sizeof(float*), cudaMemcpyHostToDevice);
+    if (result != cudaSuccess) {
+      std::cerr << "cudaMemcpy result = " << result << std::endl;
+      return result;
+    }
+
+    result = cutlass_array_sgemm(m, n, k, alpha, ptr_A, lda, ptr_B, ldb, ptr_C, ldc, beta, batch_count);
+
+    if (result != cudaSuccess)
+      return result;
+  } else {
+    result = cutlass_strided_batched_sgemm(
+      m, n, k, alpha, A, lda, batch_stride_A, B, ldb, batch_stride_B, C, ldc, batch_stride_C,
+      beta, batch_count);
+    if (result != cudaSuccess)
+      return result;
+  }
 
   // copy device memory to host
   result = cudaMemcpy(result_C.data(), C, count_C * sizeof(float), cudaMemcpyDeviceToHost);
@@ -314,7 +425,7 @@ int main() {
 
   // Expect bit-level accuracy for this simple example
   if (ref_C != result_C) {
-    std::cout << "CUTLASS strided batched gemm does not run correctly" << std::endl;
+    std::cout << "CUTLASS " << gemm_desc << " gemm does not run correctly" << std::endl;
     return cudaErrorUnknown;
   }
 
@@ -335,9 +446,19 @@ int main() {
     return result;
   }
 
+  return result;
+}
 
-  if (result == cudaSuccess) {
-    std::cout << "Passed." << std::endl;
+int main() {
+
+  cudaError_t result = cudaSuccess;
+  for (bool use_array : {false, true}) {
+    result = run_batched_gemm(use_array);
+    if (result == cudaSuccess) {
+      std::cout << "Passed." << std::endl;
+    } else {
+      break;
+    }
   }
 
   // Exit.

examples/06_splitK_gemm/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   06_splitK_gemm
   splitk_gemm.cu

examples/06_splitK_gemm/splitk_gemm.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -49,7 +55,7 @@ composed from lower level ones. Multiple thread-tiles (tile size each thread com
 to form warp-tiles (tile size each warp computes) and multiple warp tiles can be used to compute
 threadblock-tile (tile size computed by a threadblock).
 
-In thie example, we split variable initialization into
+In this example, we split variable initialization into
 1. Setting up data properties : describes how matrices are laid out in the memory and how the kernel
 can view them (logical to physical mapping)
 2. Setting up computation properties : describes how the above set matrices will be used to compute
@@ -68,10 +74,10 @@ ElementAccumulator (float), ElementComputeEpilogue (float), ElementInputA (cutla
 ElementInputB (cutlass::half_t), ElementOutput (float). Communicating just the data type is not
 enough. As the data is laid out linearly in memory, we have to convey the layout of matrices. We do
 that by initializing template variable LayoutInputA to column major cutlass variable, LayoutInputB
-to row major and LayoutOutput to row major. Next, we setup rules to comptue alpha * X + beta * C
+to row major and LayoutOutput to row major. Next, we setup rules to compute alpha * X + beta * C
 which is called epilogue of the kernel. We initialize template variable EpilogueOp, which takes the
-data type of output ElementOutput (int32_t), the number of elements per vector memory access (16),
-data type of accumulator (int32_t) and data type of computation of linear combination (alpha * X +
+data type of output ElementOutput (float), the number of elements per vector memory access (16),
+data type of accumulator (float) and data type of computation of linear combination (alpha * X +
 beta * C).
 
 Now that we setup the properties of data, we have to setup properties of computation.
@@ -79,7 +85,7 @@ Now that we setup the properties of data, we have to setup properties of computa
 Second, we create template variables of tile sizes for thread-block, warp and mma-op to 128x128x32,
 64x64x4, 8x8x4 (MxNxK) respectively. When passed to instantiate CUTLASS GEMM kernel, it internally
 deduce the amount of threads needed per thread-block, amount of shared memory, storing data in
-bank-conflict free manner, and ton of other variables required to compose, intialize and launch a
+bank-conflict free manner, and ton of other variables required to compose, initialize and launch a
 high performance GEMM kernel. This is the beauty of CUTLASS, it relieves developer from
 understanding and coding complicated hardware optimizations which can easily go wrong.
 
@@ -89,7 +95,7 @@ is done which threadblock launched on an SM, CUDA SM architecture of GPU you wan
 These are all put together to create a template variable which describes CUTLASS GEMM kernel using
 cutlass::gemm::device::GemmSplitKParallel template.
 
-The next step is to intialize physical data, instantiate and initialize CUTLASS kernel and run it.
+The next step is to initialize physical data, instantiate and initialize CUTLASS kernel and run it.
 We use CUTLASS utilities to initialize, fill, compare matrices as they are simple and doesn't come
 in the way of learning CUTLASS.
 
@@ -97,7 +103,7 @@ Once all the matrices are initialized and filled with data, create arguments tup
 kernel which takes problem size (M = 5120, N = 4096 and K = 4096), matrices, alpha, beta and the
 important one, split k-dimension factor. Along with that, we query CUTLASS if any scratch-space
 memory required by the kernel we instantiated. If yes, we create it and pass it along with other
-arguments created to intialize CUTLASS kernel then, the kernel is launched.
+arguments created to initialize CUTLASS kernel then, the kernel is launched.
 
 In this example, we later on launch a reference gemm kernel (from CUTLASS utilities) to compare if
 the output from CUTLASS kernel is same as reference GEMM kernel.
@@ -143,9 +149,6 @@ using ShapeMMAWarp = cutlass::gemm::GemmShape<64, 64, 32>;  // <- warp tile M =
 // This code section describes the size of MMA op
 using ShapeMMAOp = cutlass::gemm::GemmShape<8, 8, 4>;  // <- MMA Op tile M = 8, N = 8, K = 4
 
-// This code section describes how threadblocks are scheduled on GPU
-using SwizzleThreadBlock = cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>;  // <- ??
-
 // This code section describes ?
 using EpilogueOp = cutlass::epilogue::thread::LinearCombination<
     ElementOutput,                                     // <- data type of output matrix

examples/07_volta_tensorop_gemm/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   07_volta_tensorop_gemm
   volta_tensorop_gemm.cu

examples/07_volta_tensorop_gemm/volta_tensorop_gemm.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/08_turing_tensorop_gemm/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   08_turing_tensorop_gemm
   turing_tensorop_gemm.cu

examples/08_turing_tensorop_gemm/turing_tensorop_gemm.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/

examples/09_turing_tensorop_conv2dfprop/CMakeLists.txt

@@ -1,26 +1,34 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 
+
 cutlass_example_add_executable(
   09_turing_tensorop_conv2dfprop
   turing_tensorop_conv2dfprop.cu

examples/09_turing_tensorop_conv2dfprop/turing_tensorop_conv2dfprop.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -118,6 +124,7 @@ compare if the output from CUTLASS kernel is same as the reference implicit GEMM
 */
 
 #include <iostream>
+#include <fstream>
 #include <sstream>
 
 #include "cutlass/cutlass.h"
@@ -333,21 +340,21 @@ struct Options {
       << "  forward convolution on tensors of layout NHWC.\n\n"
       << "Options:\n\n"
       << "  --help               If specified, displays this usage statement.\n\n"
-      << "  --n <int>            Input tensor extent N\n"
-      << "  --h <int>            Input tensor extent H\n"
-      << "  --w <int>            Input tensor extent W\n"
-      << "  --c <int>            Input tensor extent C\n"
-      << "  --k <int>            Filter extent K\n"
-      << "  --r <int>            Filter extent R\n"
-      << "  --s <int>            Filter extent S\n\n"
-      << "  --alpha <float>      Epilogue scalar alpha\n"
-      << "  --beta <float>       Epilogue scalar beta\n\n"
+      << "  --n=<int>            Input tensor extent N\n"
+      << "  --h=<int>            Input tensor extent H\n"
+      << "  --w=<int>            Input tensor extent W\n"
+      << "  --c=<int>            Input tensor extent C\n"
+      << "  --k=<int>            Filter extent K\n"
+      << "  --r=<int>            Filter extent R\n"
+      << "  --s=<int>            Filter extent S\n\n"
+      << "  --alpha=<float>      Epilogue scalar alpha\n"
+      << "  --beta=<float>       Epilogue scalar beta\n\n"
       << "  --ref-check          If set (true), reference check on the host is computed\n"
       << "  --perf-check         If set (true), performance is measured.\n"
       << "  --benchmark          If set (true), performance benchmarking on several layers and batch-size.\n"
-      << "  --iterations <int>   Number of profiling iterations to perform.\n"
+      << "  --iterations=<int>   Number of profiling iterations to perform.\n"
       << "  --save-workspace     If set, workspace is written to a text file.\n"
-      << "  --tag <string>       String to replicate across the first column in the results table\n";
+      << "  --tag=<string>       String to replicate across the first column in the results table\n";
 
     out << "\n\nExamples:\n\n"
       << "$ ./examples/09_turing_tensorop_conv2dfprop/09_turing_tensorop_conv2dfprop  --n=32 --h=224 --w=224 --c=128 --k=256 --r=1 --s=1\n\n"

examples/10_planar_complex/CMakeLists.txt

@@ -1,26 +1,34 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 
+
 # Planar Complex GEMM example
 cutlass_example_add_executable(
   10_planar_complex

examples/10_planar_complex/planar_complex.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -68,7 +74,6 @@
 */
 
 #include <iostream>
-#include <fstream>
 #include <sstream>
 
 #include "cutlass/cutlass.h"
@@ -167,15 +172,15 @@ struct Options {
       << "  This example uses the CUTLASS Library to execute Planar Complex GEMM computations.\n\n"
       << "Options:\n\n"
       << "  --help                      If specified, displays this usage statement.\n\n"
-      << "  --m <int>                   GEMM M dimension\n"
-      << "  --n <int>                   GEMM N dimension\n"
-      << "  --k <int>                   GEMM K dimension\n"
-      << "  --batch <int>               Number of GEMM operations executed in one batch\n"
-      << "  --alpha <f32>               Epilogue scalar alpha (real part)\n"
-      << "  --alpha_i <f32>             Epilogue scalar alpha (imaginary part)\n"
-      << "  --beta <f32>                Epilogue scalar beta (real part)\n\n"
-      << "  --beta_i <f32>              Epilogue scalar beta (imaginary part)\n\n"
-      << "  --iterations <int>          Number of profiling iterations to perform.\n\n";
+      << "  --m=<int>                   GEMM M dimension\n"
+      << "  --n=<int>                   GEMM N dimension\n"
+      << "  --k=<int>                   GEMM K dimension\n"
+      << "  --batch=<int>               Number of GEMM operations executed in one batch\n"
+      << "  --alpha=<f32>               Epilogue scalar alpha (real part)\n"
+      << "  --alpha_i=<f32>             Epilogue scalar alpha (imaginary part)\n"
+      << "  --beta=<f32>                Epilogue scalar beta (real part)\n\n"
+      << "  --beta_i=<f32>              Epilogue scalar beta (imaginary part)\n\n"
+      << "  --iterations=<int>          Number of profiling iterations to perform.\n\n";
 
     out << "\n\nExamples:\n\n"
       << "$ ./examples/10_planar_complex/10_planar_complex  --batch=7 --m=1024 --n=512 --k=1024 \\\n"

examples/11_planar_complex_array/CMakeLists.txt

@@ -1,26 +1,34 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
 
+
 # Planar Complex Array GEMM example
 cutlass_example_add_executable(
   11_planar_complex_array

examples/11_planar_complex_array/planar_complex_array.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -66,7 +72,6 @@
 */
 
 #include <iostream>
-#include <fstream>
 #include <sstream>
 
 #include "cutlass/cutlass.h"
@@ -165,15 +170,15 @@ struct Options {
       << "  This example uses the CUTLASS Library to execute Planar Complex Array GEMM computations.\n\n"
       << "Options:\n\n"
       << "  --help                      If specified, displays this usage statement.\n\n"
-      << "  --m <int>                   GEMM M dimension\n"
-      << "  --n <int>                   GEMM N dimension\n"
-      << "  --k <int>                   GEMM K dimension\n"
-      << "  --batch <int>               Number of GEMM operations executed in one batch\n"
-      << "  --alpha <f32>               Epilogue scalar alpha (real part)\n"
-      << "  --alpha_i <f32>             Epilogue scalar alpha (imaginary part)\n"
-      << "  --beta <f32>                Epilogue scalar beta (real part)\n\n"
-      << "  --beta_i <f32>              Epilogue scalar beta (imaginary part)\n\n"
-      << "  --iterations <int>          Number of profiling iterations to perform.\n";
+      << "  --m=<int>                   GEMM M dimension\n"
+      << "  --n=<int>                   GEMM N dimension\n"
+      << "  --k=<int>                   GEMM K dimension\n"
+      << "  --batch=<int>               Number of GEMM operations executed in one batch\n"
+      << "  --alpha=<f32>               Epilogue scalar alpha (real part)\n"
+      << "  --alpha_i=<f32>             Epilogue scalar alpha (imaginary part)\n"
+      << "  --beta=<f32>                Epilogue scalar beta (real part)\n\n"
+      << "  --beta_i=<f32>              Epilogue scalar beta (imaginary part)\n\n"
+      << "  --iterations=<int>          Number of profiling iterations to perform.\n";
 
     out << "\n\nExamples:\n\n"
       << "$ ./examples/11_planar_complex_array/11_planar_complex_array\n\n";

examples/12_gemm_bias_relu/CMakeLists.txt

@@ -1,25 +1,33 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
+
 cutlass_example_add_executable(
   12_gemm_bias_relu
   gemm_bias_relu.cu

examples/12_gemm_bias_relu/gemm_bias_relu.cu

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -48,8 +54,9 @@ using ElementInputA = cutlass::half_t;              // <- data type of elements
 using ElementInputB = cutlass::half_t;              // <- data type of elements in input matrix B
 using ElementOutput = float;                        // <- data type of elements in output matrix D
 
-// The code section below describes matrix layout of input and output matrices.
-// Column Major for Matrix A, B and C.
+// Note that if the output is column major, the bias has to be per row. i.e. every row has different bias.
+// If the output is row major, the bias has to be per column, i.e. every column has different bias.
+// Below list some other notices:
 //
 // Note this example only works for ColumnMajor output because
 //   1) we only have row major epilogue.
@@ -294,4 +301,3 @@ int main() {
 
   return run();
 }
-  

examples/13_two_tensor_op_fusion/CMakeLists.txt

@@ -1,45 +1,82 @@
-# Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+
+# Copyright (c) 2017 - 2023 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 TORT (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.
 
-cutlass_example_add_executable(
-  13_fused_two_gemms
-  fused_gemm.cu
-  )
 
-cutlass_example_add_executable(
-  13_fused_two_convs
-  fused_conv2d.cu
-  )
-
-
-target_include_directories(
-  13_fused_two_gemms
-  PRIVATE
+include_directories(
   .
+)
+
+add_custom_target(13_fused_two_gemms)
+
+add_custom_target(13_fused_two_convs)
+
+add_custom_target(13_two_tensor_op_fusion
+DEPENDS 13_fused_two_gemms
+        13_fused_two_convs
+)
+
+foreach(FUSION_CONV_EXAMPLE
+  fused_two_convs_f16_sm75_rf
+  fused_two_convs_f16_sm75_shmem
+  fused_two_convs_f16_sm80_rf
+  fused_two_convs_f16_sm80_shmem
+  fused_two_convs_s8_sm75_rf
+  fused_two_convs_s8_sm75_shmem
+  fused_two_convs_s8_sm80_rf
+  fused_two_convs_s8_sm80_shmem
+)
+
+  cutlass_example_add_executable(
+    13_${FUSION_CONV_EXAMPLE}
+    ${FUSION_CONV_EXAMPLE}.cu
   )
 
-target_include_directories(
-  13_fused_two_convs
-  PRIVATE
-  .
+  add_dependencies(13_fused_two_convs 13_${FUSION_CONV_EXAMPLE})
+
+endforeach()
+
+foreach(FUSION_GEMM_EXAMPLE
+  fused_two_gemms_f16_sm75_rf
+  fused_two_gemms_f16_sm75_shmem
+  fused_two_gemms_f16_sm80_rf
+  fused_two_gemms_f16_sm80_shmem
+  fused_two_gemms_s8_sm75_rf
+  fused_two_gemms_s8_sm75_shmem
+  fused_two_gemms_s8_sm80_rf
+  fused_two_gemms_s8_sm80_shmem
+)
+  cutlass_example_add_executable(
+    13_${FUSION_GEMM_EXAMPLE}
+    ${FUSION_GEMM_EXAMPLE}.cu
   )
 
+  add_dependencies(13_fused_two_gemms 13_${FUSION_GEMM_EXAMPLE})
+
+endforeach()
+

examples/13_two_tensor_op_fusion/README.md

@@ -48,33 +48,71 @@ addition to its own input activation tile. Therefore the input activation warp t
 2nd GEMM/Conv only depends on the output warp accumulator of the 1st GEMM/Conv in the
 register file, and the operation can be fully register-file-resident.
 
+On the other hand, this constraint can be relaxed if the output accumulator of the 1st GEMM/CONV
+is staged in the shared memory and then used as input for the 2nd GEMM/CONV. In this case, the
+input of each warp tile can be loaded from the shared memory so they do not need to be RF-resident,
+therefore each warp does not need to store the entire input matrix of 2nd GEMM in its RF. This is
+illustrated in the diagram below.
+
+<p align="center"><img src=/media/images/13_example_shmem_resident_fusion.png></p>
+
+
 When applying the above constraint to convolutions, it is required that the 2nd Convolution
 kernel doesn't have halos such that data used by each threadblock doesn't depend on any other
 threadblock. Typically this requires the 2nd Convolution uses 1x1 filter without any paddings.
 
+# Build and run
+
+- Run cmake at top-level CUTLASS
+- `make 13_two_tensor_op_fusion`
+- Run individual benchmarks
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_f16_sm75_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_f16_sm75_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_f16_sm80_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_f16_sm80_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_s8_sm75_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_s8_sm75_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_s8_sm80_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_convs_s8_sm80_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_f16_sm75_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_f16_sm75_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_f16_sm80_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_f16_sm80_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_s8_sm75_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_s8_sm75_shmem`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_s8_sm80_rf`
+  - `./examples/13_two_tensor_op_fusion/13_fused_two_gemms_s8_sm80_shmem`
+ 
+
 # Copyright
 
-Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+Copyright (c) 2017 - 2023 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 TORT (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.
 ```
 

examples/13_two_tensor_op_fusion/b2b_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm75.h

@@ -1,371 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Tests for device-wide GEMM interface
-*/
-
-#include <iostream>
-
-#include "cutlass/cutlass.h"
-
-#include "cutlass/conv/kernel/default_conv2d_fprop.h"
-#include "cutlass/conv/device/implicit_gemm_convolution.h"
-
-#include "device/b2b_implicit_gemm_convolution.h"
-#include "b2b_conv2d_run.h"
-
-////////////////////////////////////////////////////////////////////////////////
-
-cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_0 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 3, 3, 64},   // filter size (KRSC)
-    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_1 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 1, 1, 64},   // filter size (KRSC)
-    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-
-bool run_nonfused_conv2d_fprop_f16_sm75() {
-
-  using ElementA           = cutlass::half_t;
-  using ElementB           = cutlass::half_t;
-  using ElementC           = cutlass::half_t;
-  using ElementAccumulator = cutlass::half_t;
-  using ElementCompute     = cutlass::half_t;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back FP16 Analytic Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_f16_sm75() {
-
-  using ElementA           = cutlass::half_t; 
-  using ElementB           = cutlass::half_t; 
-  using ElementC           = cutlass::half_t; 
-  using ElementAccumulator = cutlass::half_t; 
-  using ElementCompute     = cutlass::half_t; 
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back FP16 Analytic Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_nonfused_conv2d_fprop_optimized_f16_sm75() {
-
-  using ElementA           = cutlass::half_t;
-  using ElementB           = cutlass::half_t;
-  using ElementC           = cutlass::half_t;
-  using ElementAccumulator = cutlass::half_t;
-  using ElementCompute     = cutlass::half_t;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_optimized_f16_sm75() {
-
-  using ElementA           = cutlass::half_t; 
-  using ElementB           = cutlass::half_t; 
-  using ElementC           = cutlass::half_t; 
-  using ElementAccumulator = cutlass::half_t; 
-  using ElementCompute     = cutlass::half_t; 
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-
-////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/b2b_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.h

@@ -1,367 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Tests for device-wide GEMM interface
-*/
-
-#include <iostream>
-
-#include "cutlass/cutlass.h"
-
-#include "cutlass/conv/kernel/default_conv2d_fprop.h"
-#include "cutlass/conv/device/implicit_gemm_convolution.h"
-
-#include "device/b2b_implicit_gemm_convolution.h"
-#include "b2b_conv2d_run.h"
-
-////////////////////////////////////////////////////////////////////////////////
-
-cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_0 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 3, 3, 64},   // filter size (KRSC)
-    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_1 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 1, 1, 64},   // filter size (KRSC)
-    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-
-bool run_nonfused_conv2d_fprop_f16_sm80() {
-
-  using ElementA           = cutlass::half_t;
-  using ElementB           = cutlass::half_t;
-  using ElementC           = cutlass::half_t;
-  using ElementAccumulator = cutlass::half_t;
-  using ElementCompute     = cutlass::half_t;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back FP16 Analytic Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_f16_sm80() {
-
-  using ElementA           = cutlass::half_t; 
-  using ElementB           = cutlass::half_t; 
-  using ElementC           = cutlass::half_t; 
-  using ElementAccumulator = cutlass::half_t; 
-  using ElementCompute     = cutlass::half_t; 
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back FP16 Analytic Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_nonfused_conv2d_fprop_optimized_f16_sm80() {
-
-  using ElementA           = cutlass::half_t;
-  using ElementB           = cutlass::half_t;
-  using ElementC           = cutlass::half_t;
-  using ElementAccumulator = cutlass::half_t;
-  using ElementCompute     = cutlass::half_t;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_optimized_f16_sm80() {
-
-  using ElementA           = cutlass::half_t; 
-  using ElementB           = cutlass::half_t; 
-  using ElementC           = cutlass::half_t; 
-  using ElementAccumulator = cutlass::half_t; 
-  using ElementCompute     = cutlass::half_t; 
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      128 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNHWC,
-    ElementB, cutlass::layout::TensorNHWC,
-    ElementC, cutlass::layout::TensorNHWC,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAdd,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-////////////////////////////////////////////////////////////////////////////////
-

examples/13_two_tensor_op_fusion/b2b_conv2d_fprop_implicit_gemm_s8ncxhwx_s8cxrskx_s8ncxhwx_tensor_op_s32_sm75.h

@@ -1,371 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Tests for device-wide GEMM interface
-*/
-
-#include <iostream>
-
-#include "cutlass/cutlass.h"
-
-#include "cutlass/conv/kernel/default_conv2d_fprop.h"
-#include "cutlass/conv/device/implicit_gemm_convolution.h"
-
-#include "device/b2b_implicit_gemm_convolution.h"
-#include "b2b_interleaved_conv2d_run.h"
-
-////////////////////////////////////////////////////////////////////////////////
-
-cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_0 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 3, 3, 64},   // filter size (KRSC)
-    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_1 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 1, 1, 64},   // filter size (KRSC)
-    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-
-bool run_nonfused_conv2d_fprop_s8_sm75() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back INT8 interleaved Analytic Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_s8_sm75() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back INT8 interleaved Analytic Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_nonfused_conv2d_fprop_optimized_s8_sm75() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_optimized_s8_sm75() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      InstructionShape::kM * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm75,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-////////////////////////////////////////////////////////////////////////////////
-

examples/13_two_tensor_op_fusion/b2b_conv2d_fprop_implicit_gemm_s8ncxhwx_s8cxrskx_s8ncxhwx_tensor_op_s32_sm80.h

@@ -1,372 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-/*! \file
-    \brief Tests for device-wide GEMM interface
-*/
-
-#include <iostream>
-
-#include "cutlass/cutlass.h"
-
-#include "cutlass/conv/kernel/default_conv2d_fprop.h"
-#include "cutlass/conv/device/implicit_gemm_convolution.h"
-
-#include "device/b2b_implicit_gemm_convolution.h"
-#include "b2b_interleaved_conv2d_run.h"
-
-////////////////////////////////////////////////////////////////////////////////
-
-cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_0 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 3, 3, 64},   // filter size (KRSC)
-    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_1 (
-    {128, 56, 56, 64},    // input size (NHWC)
-    {64, 1, 1, 64},   // filter size (KRSC)
-    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
-    {1, 1},           // stride (stride_h, stride_w)
-    {1, 1},           // dilation (dilation_h, dilation_w)
-    {128, 56, 56, 64}     // output size (NPQK)
-  );
-
-bool run_nonfused_conv2d_fprop_s8_sm80() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back INT8 interleaved Analytic Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_s8_sm80() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      8 * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kAnalytic
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back INT8 interleaved Analytic Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_nonfused_conv2d_fprop_optimized_s8_sm80() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
-
-  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    WarpShape0,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
-
-  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape1,
-    WarpShape1,
-    InstructionShape,
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-
-  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
-
-  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
-
-  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
-  bool pass = nonFusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-bool run_fused_conv2d_fprop_optimized_s8_sm80() {
-
-  using ElementA           = int8_t;
-  using ElementB           = int8_t;
-  using ElementC           = int8_t;
-  using ElementAccumulator = int32_t;
-  using ElementCompute = float;
-
-  ElementCompute alpha0 = ElementCompute(1);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(1);
-  ElementCompute beta1 = ElementCompute(0);
-
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
-
-  using EpilogueOutputOp0 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      8 * InstructionShape::kN / 32,
-      ElementAccumulator,
-      ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
-    >;
-
-  using EpilogueOutputOp1 = 
-    cutlass::epilogue::thread::LinearCombinationRelu<
-      ElementC,
-      64 / cutlass::sizeof_bits<ElementC>::value,
-      ElementAccumulator,
-      ElementCompute
-    >;
-
-
-
-  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
-    ElementA, cutlass::layout::TensorNCxHWx<32>,
-    ElementB, cutlass::layout::TensorCxRSKx<32>,
-    ElementC, cutlass::layout::TensorNCxHWx<32>,
-    ElementAccumulator,
-    cutlass::arch::OpClassTensorOp,
-    cutlass::arch::Sm80,
-    ThreadblockShape0,
-    ThreadblockShape1,
-    WarpShape0,
-    WarpShape1,
-    InstructionShape,
-    EpilogueOutputOp0,
-    EpilogueOutputOp1,
-    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    3,
-    cutlass::arch::OpMultiplyAddSaturate,
-    cutlass::conv::IteratorAlgorithm::kOptimized
-  >::Kernel;
-  
-  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
-
-  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
-
-  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
-  bool pass = fusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
-      alpha0, beta0, alpha1, beta1);
-
-  if(pass)
-    std::cout << "Pass\n";
-  else
-    std::cout << "Fail\n";
-
-  return pass;
-}
-
-
-////////////////////////////////////////////////////////////////////////////////
-

examples/13_two_tensor_op_fusion/b2b_conv2d_run.h

@@ -1,30 +1,34 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-/*! \file
-    \brief Implicit GEMM testbed
-*/
+
 #pragma once
 
 #include <iostream>
@@ -50,6 +54,7 @@
 #include "cutlass/core_io.h"
 #include "cutlass/util/tensor_view_io.h"
 
+#include "reference/device/tensor_scale_bias.h"
 #include "helper.h"
 
 #define CHECK_GT(val1, val2) \
@@ -79,16 +84,19 @@ public:
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   cutlass::HostTensor<typename Conv2d0::ElementA, typename Conv2d0::LayoutA> tensor_A0;
   cutlass::HostTensor<typename Conv2d0::ElementB, typename Conv2d0::LayoutB> tensor_B0;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_C0;
+  cutlass::HostTensor<typename Conv2d0::ElementCompute, typename Conv2d0::LayoutC> tensor_Bias0;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_D0_computed;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_D0_reference;
 
   cutlass::HostTensor<typename Conv2d1::ElementB, typename Conv2d1::LayoutB> tensor_B1;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_C1;
+  cutlass::HostTensor<typename Conv2d1::ElementCompute, typename Conv2d0::LayoutC> tensor_Bias1;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_D1_computed;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_D1_reference;
 
@@ -99,9 +107,10 @@ public:
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) {
+    init_A(init_A_), init_B(init_B_), init_C(init_C_), init_Bias(init_Bias_), seed(seed_) {
 
   }
 
@@ -138,37 +147,51 @@ public:
 
       cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
     } 
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
+      std::cerr << "Not implemented\n";
     }
   }
 
   void initialize(
     cutlass::conv::Conv2dProblemSize const &problem_size_0,
-    cutlass::conv::Conv2dProblemSize const &problem_size_1, uint64_t seed = 2019) {
+    cutlass::conv::Conv2dProblemSize const &problem_size_1,
+    uint64_t seed = 2019) {
         
     tensor_A0.resize(implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size_0));
     tensor_B0.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_C0.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
+    tensor_Bias0.resize({1, 1, 1, problem_size_0.K});
     tensor_D0_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_D0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_B1.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_C1.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
+    tensor_Bias1.resize({1, 1, 1, problem_size_1.K});
     tensor_D1_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
     tensor_D1_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
 
     initialize_tensor(tensor_A0.host_view(), init_A, seed); 
     initialize_tensor(tensor_B0.host_view(), init_B, seed * 17); 
     initialize_tensor(tensor_C0.host_view(), init_C, seed * 39);
+    initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed * 83);
     initialize_tensor(tensor_B1.host_view(), init_B, seed * 18); 
     initialize_tensor(tensor_C1.host_view(), init_C, seed * 40);
+    initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed * 84);
 
     tensor_A0.sync_device();
     tensor_B0.sync_device();
     tensor_C0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0_computed.sync_device();
     tensor_D0_reference.sync_device();
     tensor_B1.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1_computed.sync_device();
     tensor_D1_reference.sync_device();
   }
@@ -196,7 +219,7 @@ public:
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
-      tensor_C0.device_ref(),
+      {tensor_Bias0.device_data(), typename Conv2d0::LayoutC::Stride(0)},
       tensor_D0_computed.device_ref(),
       {alpha0, beta0},
       split_k_mode
@@ -205,7 +228,7 @@ public:
       problem_size_1,
       tensor_D0_computed.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename Conv2d1::LayoutC::Stride(0)},
       tensor_D1_computed.device_ref(),
       {alpha1, beta1},
       split_k_mode
@@ -258,7 +281,7 @@ public:
     cudaEventElapsedTime(&totalTime, start, stop2);
     std::cout << "conv2d 0 time " << conv2d0Time / (float)runs << " ms\n";
     std::cout << "conv2d 1 time " << conv2d1Time / (float)runs << " ms\n";
-    std::cout << "total time " << totalTime / (float)runs << " ms\n";
+    std::cout << "Non-fusion time " << totalTime / (float)runs << " ms\n";
 
     tensor_D0_computed.sync_host();
     tensor_D1_computed.sync_host();
@@ -279,7 +302,7 @@ public:
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
-      tensor_C0.device_ref(),
+      {tensor_Bias0.device_data(), typename Conv2d0::LayoutC::Stride(0)},
       tensor_D0_reference.device_ref(),
       alpha0, 
       beta0);
@@ -302,7 +325,7 @@ public:
       problem_size_1,
       tensor_D0_reference.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename Conv2d1::LayoutC::Stride(0)},
       tensor_D1_reference.device_ref(),
       alpha1, 
       beta1);
@@ -344,10 +367,12 @@ public:
         << "\nA0:\n" << tensor_A0.host_view() << "\n"
         << "\nB0:\n" << tensor_B0.host_view() << "\n"
         << "\nC0:\n" << tensor_C0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nD0 reference:\n" << tensor_D0_reference.host_view() << "\n"
         << "\nD0 computed:\n" << tensor_D0_computed.host_view() << "\n"
         << "\nB1:\n" << tensor_B1.host_view() << "\n"
         << "\nC1:\n" << tensor_C1.host_view() << "\n"
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\nD1 reference:\n" << tensor_D1_reference.host_view() << "\n"
         << "\nD1 computed:\n" << tensor_D1_computed.host_view();
 
@@ -375,15 +400,21 @@ public:
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Scale;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   cutlass::HostTensor<typename B2bConv2d::ElementA, typename B2bConv2d::LayoutA> tensor_A0;
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B0;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_C0;
+  cutlass::HostTensor<typename B2bConv2d::ElementScaleBias, typename B2bConv2d::LayoutScaleBias> tensor_Scale0;
+  cutlass::HostTensor<typename B2bConv2d::ElementScaleBias, typename B2bConv2d::LayoutScaleBias> tensor_Bias0;
+  cutlass::HostTensor<ElementAccumulator, typename B2bConv2d::LayoutC> tensor_Z0_reference;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D0_reference;
 
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B1;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_C1;
+  cutlass::HostTensor<typename B2bConv2d::ElementCompute, typename B2bConv2d::LayoutC> tensor_Bias1;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D1_computed;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D1_reference;
 
@@ -394,9 +425,12 @@ public:
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Scale_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) {
+    init_A(init_A_), init_B(init_B_), init_C(init_C_),
+    init_Scale(init_Scale_), init_Bias(init_Bias_), seed(seed_) {
 
   }
 
@@ -433,35 +467,57 @@ public:
 
       cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
     } 
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
     }
   }
 
   void initialize(
     cutlass::conv::Conv2dProblemSize const &problem_size_0,
-    cutlass::conv::Conv2dProblemSize const &problem_size_1, uint64_t seed = 2019) {
+    cutlass::conv::Conv2dProblemSize const &problem_size_1,
+    ElementCompute alpha0,
+    ElementCompute alpha1,
+    uint64_t seed = 2019) {
         
     tensor_A0.resize(implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size_0));
     tensor_B0.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_C0.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.resize({1, problem_size_0.K});
+    tensor_Bias0.resize({1, problem_size_0.K});
+    tensor_Z0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_D0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_B1.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_C1.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
+    tensor_Bias1.resize({1, 1, 1, problem_size_1.K});
     tensor_D1_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
     tensor_D1_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
 
     initialize_tensor(tensor_A0.host_view(), init_A, seed); 
     initialize_tensor(tensor_B0.host_view(), init_B, seed * 17); 
     initialize_tensor(tensor_C0.host_view(), init_C, seed * 39);
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        initialize_tensor(tensor_Scale0.host_view(), init_Scale, seed * 61);
+    initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed * 83);
     initialize_tensor(tensor_B1.host_view(), init_B, seed * 18); 
     initialize_tensor(tensor_C1.host_view(), init_C, seed * 40);
+    initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed * 84);
 
     tensor_A0.sync_device();
     tensor_B0.sync_device();
     tensor_C0.sync_device();
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0_reference.sync_device();
     tensor_B1.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1_computed.sync_device();
     tensor_D1_reference.sync_device();
   }
@@ -479,7 +535,7 @@ public:
     int warm_ups = 1,
     int runs = 100) {
 
-    initialize(problem_size_0, problem_size_1);
+    initialize(problem_size_0, problem_size_1, alpha0, alpha1);
 
     // configure the operator
     B2bConv2d b2b_conv2d_op;
@@ -490,15 +546,31 @@ public:
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
       tensor_C0.device_ref(),
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename B2bConv2d::LayoutC::Stride(0)},
       tensor_D1_computed.device_ref(),
       {alpha0, beta0},
       {alpha1, beta1},
       split_k_mode
     );
 
-    cutlass::Status status = b2b_conv2d_op.initialize(b2b_conv2d_args);
+    cutlass::Status status = b2b_conv2d_op.can_implement(b2b_conv2d_args);
+    
+    if(status != cutlass::Status::kSuccess) {
+        std::cout << "Problem sizes not supported.\n"
+                << "Requirments:\n"
+                << "    problem_size_0.N*P*Q = problem_size_1.N*P*Q\n"
+                << "    problem_size_0.K = problem_size_1.C\n"
+                << "    problem_size_1.R = problem_size_1.S = 1\n"
+                << "    ThreadblockShape0::kN = problem_size_0.K\n"
+                << "    ThreadblockShape1::kN = problem_size_1.K" << std::endl;
+    }
+
+    CUTLASS_CHECK(status);
+
+    status = b2b_conv2d_op.initialize(b2b_conv2d_args);
 
     CUTLASS_CHECK(status);
 
@@ -528,7 +600,7 @@ public:
     cudaDeviceSynchronize();
     float conv2dTime;
     cudaEventElapsedTime(&conv2dTime, start, stop);
-    std::cout << "time " << conv2dTime / (float)runs << " ms\n";
+    std::cout << "Fusion time " << conv2dTime / (float)runs << " ms\n";
 
     tensor_D1_computed.sync_host();
     
@@ -539,19 +611,35 @@ public:
       typename B2bConv2d::LayoutA,
       typename B2bConv2d::ElementB,
       typename B2bConv2d::LayoutB,
-      typename B2bConv2d::ElementC,
+      ElementAccumulator,
       typename B2bConv2d::LayoutC,
-      ElementCompute,
+      ElementAccumulator,
       ElementAccumulator
     >(
       kConvolutionalOperator,
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
-      tensor_C0.device_ref(),
+      tensor_Z0_reference.device_ref(),
+      tensor_Z0_reference.device_ref(),
+      ElementAccumulator(1), // intermediate alpha = 1
+      ElementAccumulator(0)  // beta = 0
+    );
+
+    cutlass::reference::device::TensorScaleBiasConv2d<
+      ElementAccumulator,
+      typename B2bConv2d::ElementC,
+      typename B2bConv2d::LayoutC,
+      ElementCompute,
+      typename B2bConv2d::LayoutScaleBias
+    >(
+      problem_size_0,
+      tensor_Z0_reference.device_ref(),
       tensor_D0_reference.device_ref(),
-      alpha0, 
-      beta0);
+      alpha0,
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref()
+    );
 
     if(relu) {
        cutlass::reference::device::TensorReLu(tensor_D0_reference.device_view()); 
@@ -571,7 +659,7 @@ public:
       problem_size_1,
       tensor_D0_reference.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename B2bConv2d::LayoutC::Stride(0)},
       tensor_D1_reference.device_ref(),
       alpha1, 
       beta1);
@@ -612,8 +700,11 @@ public:
         << "\nA0:\n" << tensor_A0.host_view() << "\n"
         << "\nB0:\n" << tensor_B0.host_view() << "\n"
         << "\nC0:\n" << tensor_C0.host_view() << "\n"
+        << "\nScale0:\n" << tensor_Scale0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nB1:\n" << tensor_B1.host_view() << "\n"
         << "\nC1:\n" << tensor_C1.host_view() << "\n"
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\nD1 reference:\n" << tensor_D1_reference.host_view() << "\n"
         << "\nD1 computed:\n" << tensor_D1_computed.host_view();
 

examples/13_two_tensor_op_fusion/b2b_gemm_run.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -38,6 +44,7 @@
 #include "cutlass/util/reference/device/gemm.h"
 #include "cutlass/util/reference/device/tensor_relu.h"
 
+#include "reference/device/tensor_scale_bias.h"
 #include "helper.h"
 
 #define CHECK_GT(val1, val2) \
@@ -62,6 +69,7 @@ struct B2bNonFusedGemmRun
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   //
@@ -72,9 +80,10 @@ struct B2bNonFusedGemmRun
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform, 
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
+    init_A(init_A_), init_B(init_B_), init_C(init_C_), init_Bias(init_Bias_), seed(seed_) { }
 
   /// Helper to initialize a tensor view
   template <typename Element, typename Layout>
@@ -91,7 +100,7 @@ struct B2bNonFusedGemmRun
     else if (dist_kind == cutlass::Distribution::Identity) {
 
       cutlass::reference::host::TensorFillIdentity(view);
-    } 
+    }
     else if (dist_kind == cutlass::Distribution::Gaussian) {
 
       cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
@@ -100,9 +109,14 @@ struct B2bNonFusedGemmRun
 
       cutlass::reference::host::BlockFillSequential(
         view.data(), view.capacity());
-    } 
+    }
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
-      // TODO: Implement the rest
       std::cerr << "Not implemented\n";
       return false;
     }
@@ -141,6 +155,10 @@ struct B2bNonFusedGemmRun
       typename Gemm0::ElementC, 
       typename Gemm0::LayoutC> tensor_C0(problem_size_0.mn());
 
+    cutlass::HostTensor<
+      ElementCompute, 
+      typename Gemm0::LayoutC> tensor_Bias0({1, problem_size_0.n()});
+
     cutlass::HostTensor<
       typename Gemm0::ElementC, 
       typename Gemm0::LayoutC> tensor_D0(problem_size_0.mn());
@@ -157,6 +175,10 @@ struct B2bNonFusedGemmRun
       typename Gemm1::ElementC, 
       typename Gemm1::LayoutC> tensor_C1(problem_size_1.mn());
 
+    cutlass::HostTensor<
+      ElementCompute, 
+      typename Gemm1::LayoutC> tensor_Bias1({1, problem_size_1.n()});
+
     cutlass::HostTensor<
       typename Gemm1::ElementC, 
       typename Gemm1::LayoutC> tensor_D1(problem_size_1.mn());
@@ -169,8 +191,10 @@ struct B2bNonFusedGemmRun
     CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
     CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2018));
     CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
+    CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2014));
     CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2016));
     CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
+    CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2013));
 
     cutlass::reference::host::TensorFill(
       tensor_D0.host_view());
@@ -184,9 +208,11 @@ struct B2bNonFusedGemmRun
     tensor_A0.sync_device();
     tensor_B0.sync_device();
     tensor_C0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0.sync_device();
     tensor_B1.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1.sync_device();
     reference_D0.sync_device();
     reference_D1.sync_device();
@@ -199,7 +225,7 @@ struct B2bNonFusedGemmRun
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
-      tensor_C0.device_ref(),
+      {tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
       tensor_D0.device_ref(),
       {alpha0, beta0}
     };
@@ -208,7 +234,7 @@ struct B2bNonFusedGemmRun
       problem_size_1,
       tensor_D0.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
       tensor_D1.device_ref(),
       {alpha1, beta1}
     };
@@ -235,7 +261,6 @@ struct B2bNonFusedGemmRun
     //
     // Run the GEMM
     //
-
     cudaEvent_t start, stop1, stop2;
     cudaEventCreate(&start);
     cudaEventCreate(&stop1);
@@ -250,7 +275,6 @@ struct B2bNonFusedGemmRun
     }
     cudaEventRecord(stop1);
     for(int i = 0; i < runs; i++) {
-    
         status = gemm_op_1();
     
         CUTLASS_CHECK(status);
@@ -264,7 +288,7 @@ struct B2bNonFusedGemmRun
     cudaEventElapsedTime(&totalTime, start, stop2);
     std::cout << "gemm 0 time " << gemm0Time / (float)runs << " ms\n";
     std::cout << "gemm 1 time " << gemm1Time / (float)runs << " ms\n";
-    std::cout << "total time " << totalTime / (float)runs << " ms\n";
+    std::cout << "Non-fusion time " << totalTime / (float)runs << " ms\n";
 
     tensor_D0.sync_host();
     tensor_D1.sync_host();
@@ -292,7 +316,7 @@ struct B2bNonFusedGemmRun
       tensor_A0.device_ref(), 
       tensor_B0.device_ref(), 
       beta0, 
-      tensor_C0.device_ref(),
+      {tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
       reference_D0.device_ref()
     );
 
@@ -306,7 +330,7 @@ struct B2bNonFusedGemmRun
       reference_D0.device_ref(), 
       tensor_B1.device_ref(), 
       beta1,
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
       reference_D1.device_ref()
     );
     
@@ -319,7 +343,6 @@ struct B2bNonFusedGemmRun
     reference_D0.sync_host();
     reference_D1.sync_host();
 
-
     CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D0.host_view()), 0);
     CHECK_GT(cutlass::reference::host::TensorNorm(reference_D0.host_view()), 0);
     CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D1.host_view()), 0);
@@ -343,13 +366,14 @@ struct B2bNonFusedGemmRun
         << "A0 =\n" << tensor_A0.host_view()
         << "\nB0 =\n" << tensor_B0.host_view()
         << "\nC0 =\n" << tensor_C0.host_view()
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nD0 =\n" << tensor_D0.host_view()
         << "\nB1 =\n" << tensor_B1.host_view()
         << "\nC1 =\n" << tensor_C1.host_view()
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\n\nReference =\n" << reference_D1.host_view()
         << "\nComputed =\n" << tensor_D1.host_view();
     }
-
     return passed;
   }
 };
@@ -366,6 +390,8 @@ struct B2bFusedGemmRun
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Scale;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   //
@@ -376,9 +402,12 @@ struct B2bFusedGemmRun
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform, 
+    cutlass::Distribution::Kind init_Scale_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
+    init_A(init_A_), init_B(init_B_), init_C(init_C_),
+    init_Scale(init_Scale_), init_Bias(init_Bias_), seed(seed_) { }
 
   /// Helper to initialize a tensor view
   template <typename Element, typename Layout>
@@ -404,9 +433,14 @@ struct B2bFusedGemmRun
 
       cutlass::reference::host::BlockFillSequential(
         view.data(), view.capacity());
-    } 
+    }
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
-      // TODO: Implement the rest
       std::cerr << "Not implemented\n";
       return false;
     }
@@ -445,6 +479,21 @@ struct B2bFusedGemmRun
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_C0(problem_size_0.mn());
 
+    cutlass::HostTensor<
+      typename B2bGemm::ElementScaleBias, 
+      typename B2bGemm::LayoutScaleBias> tensor_Scale0;
+
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.resize({1, problem_size_0.n()});
+
+    cutlass::HostTensor<
+      typename B2bGemm::ElementScaleBias, 
+      typename B2bGemm::LayoutScaleBias> tensor_Bias0({1, problem_size_0.n()});
+
+    cutlass::HostTensor<
+      ElementAccumulator, 
+      typename B2bGemm::LayoutC> reference_Z0(problem_size_0.mn());
+
     cutlass::HostTensor<
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> reference_D0(problem_size_0.mn());
@@ -457,6 +506,10 @@ struct B2bFusedGemmRun
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_C1(problem_size_1.mn());
 
+    cutlass::HostTensor<
+      ElementCompute, 
+      typename B2bGemm::LayoutScaleBias> tensor_Bias1({1, problem_size_1.n()});
+
     cutlass::HostTensor<
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_D1(problem_size_1.mn());
@@ -469,21 +522,29 @@ struct B2bFusedGemmRun
     CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
     CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2018));
     CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+      CHECK_TRUE(initialize_tensor(tensor_Scale0.host_view(), init_Scale, seed + 2014));
+    CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2013));
     CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2016));
     CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
+    CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2012));
 
     cutlass::reference::host::TensorFill(
       tensor_D1.host_view());
     cutlass::reference::host::TensorFill(
-      reference_D0.host_view()); 
+      reference_D0.host_view());
     cutlass::reference::host::TensorFill(
       reference_D1.host_view());
 
     tensor_A0.sync_device();
     tensor_B0.sync_device();
     tensor_C0.sync_device();
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_B1.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1.sync_device();
     reference_D0.sync_device();
     reference_D1.sync_device();
@@ -498,8 +559,10 @@ struct B2bFusedGemmRun
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
       tensor_C0.device_ref(),
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref(),
       tensor_B1.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename B2bGemm::LayoutC::Stride(0)},
       tensor_D1.device_ref(),
       {alpha0, beta0},
       {alpha1, beta1},
@@ -507,7 +570,18 @@ struct B2bFusedGemmRun
 
     B2bGemm b2b_gemm_op;
 
-    cutlass::Status status = b2b_gemm_op.initialize(arguments);
+    cutlass::Status status = b2b_gemm_op.can_implement(arguments);
+
+    if(status != cutlass::Status::kSuccess) {
+        std::cout << "Problem sizes not supported.\n"
+                << "Requirments:\n"
+                << "    problem_size_0.M = problem_size_1.M\n"
+                << "    problem_size_0.N = problem_size_1.K\n"
+                << "    ThreadblockShape0::kN = problem_size_0.N\n"
+                << "    ThreadblockShape1::kN = problem_size_1.N" << std::endl;
+    }
+
+    status = b2b_gemm_op.initialize(arguments);
 
     CUTLASS_CHECK(status);
 
@@ -536,28 +610,49 @@ struct B2bFusedGemmRun
     cudaDeviceSynchronize();
     float gemmTime;
     cudaEventElapsedTime(&gemmTime, start, stop);
-    std::cout << "time " << gemmTime / (float)runs << " ms\n";
+    std::cout << "Fusion time " << gemmTime / (float)runs << " ms\n";
 
     tensor_D1.sync_host();
 
     //
     // Verify
     //
+
+    cutlass::reference::device::Gemm<
+        typename B2bGemm::ElementA, typename B2bGemm::LayoutA,
+        typename B2bGemm::ElementB, typename B2bGemm::LayoutB,
+        ElementAccumulator, typename B2bGemm::LayoutC, 
+        ElementAccumulator, ElementAccumulator>
+        reference_gemm_0;
+
     cutlass::reference::device::Gemm<
         typename B2bGemm::ElementA, typename B2bGemm::LayoutA,
         typename B2bGemm::ElementB, typename B2bGemm::LayoutB,
         typename B2bGemm::ElementC, typename B2bGemm::LayoutC, ElementCompute,
         ElementAccumulator, typename B2bGemm::Operator>
-        reference_gemm_0, reference_gemm_1;
+        reference_gemm_1;
 
     reference_gemm_0(
       problem_size_0,
-      alpha0, 
+      ElementAccumulator(1), //intermediate alpha=1
       tensor_A0.device_ref(), 
       tensor_B0.device_ref(), 
-      beta0,
-      tensor_C0.device_ref(),
-      reference_D0.device_ref()
+      ElementAccumulator(0), //beta = 0
+      reference_Z0.device_ref(),
+      reference_Z0.device_ref(),
+      ElementAccumulator(0)
+    );
+
+    cutlass::reference::device::TensorScaleBiasGemm<
+      ElementAccumulator, typename B2bGemm::ElementC, typename B2bGemm::LayoutC,
+      ElementCompute, typename B2bGemm::LayoutScaleBias
+    > (
+      problem_size_0,
+      reference_Z0.device_ref(),
+      reference_D0.device_ref(),
+      alpha0,
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref()
     );
 
     if(relu) {
@@ -570,18 +665,15 @@ struct B2bFusedGemmRun
       reference_D0.device_ref(), 
       tensor_B1.device_ref(), 
       beta1, 
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename B2bGemm::LayoutC::Stride(0)},
       reference_D1.device_ref()
     );
-  
     if(relu) {
        cutlass::reference::device::TensorReLu(reference_D1.device_view()); 
     }
-
     cudaDeviceSynchronize();
     reference_D0.sync_host();
     reference_D1.sync_host();
- 
 
     CHECK_GT(cutlass::reference::host::TensorNorm(reference_D0.host_view()), 0);
     CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D1.host_view()), 0);
@@ -592,7 +684,8 @@ struct B2bFusedGemmRun
       tensor_D1.host_view());
 
     CHECK_TRUE(passed);
-    if (!passed) {
+    if (!passed)
+    {
 
       std::stringstream fname;
 
@@ -605,12 +698,14 @@ struct B2bFusedGemmRun
         << "A0 =\n" << tensor_A0.host_view()
         << "\nB0 =\n" << tensor_B0.host_view()
         << "\nC0 =\n" << tensor_C0.host_view()
+        << "\nScale0:\n" << tensor_Scale0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nB1 =\n" << tensor_B1.host_view()
         << "\nC1 =\n" << tensor_C1.host_view()
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\n\nReference =\n" << reference_D1.host_view()
         << "\nComputed =\n" << tensor_D1.host_view();
     }
-
     return passed;
   }
 

examples/13_two_tensor_op_fusion/b2b_interleaved_conv2d_run.h

@@ -1,30 +1,34 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-/*! \file
-    \brief Implicit GEMM testbed
-*/
+
 #pragma once
 
 #include <iostream>
@@ -51,6 +55,7 @@
 #include "cutlass/core_io.h"
 #include "cutlass/util/tensor_view_io.h"
 
+#include "reference/device/tensor_scale_bias.h"
 #include "helper.h"
 
 #define CHECK_GT(val1, val2) \
@@ -80,18 +85,21 @@ public:
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   cutlass::HostTensor<typename Conv2d0::ElementA, typename Conv2d0::LayoutA> tensor_A0;
   cutlass::HostTensor<typename Conv2d0::ElementB, typename Conv2d0::LayoutB> tensor_B0;
   cutlass::HostTensor<typename Conv2d0::ElementB, typename Conv2d0::LayoutB> tensor_B0_reordered;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_C0;
+  cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_Bias0;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_D0_computed;
   cutlass::HostTensor<typename Conv2d0::ElementC, typename Conv2d0::LayoutC> tensor_D0_reference;
 
   cutlass::HostTensor<typename Conv2d1::ElementB, typename Conv2d1::LayoutB> tensor_B1;
   cutlass::HostTensor<typename Conv2d1::ElementB, typename Conv2d1::LayoutB> tensor_B1_reordered;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_C1;
+  cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d0::LayoutC> tensor_Bias1;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_D1_computed;
   cutlass::HostTensor<typename Conv2d1::ElementC, typename Conv2d1::LayoutC> tensor_D1_reference;
 
@@ -102,9 +110,10 @@ public:
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) {
+    init_A(init_A_), init_B(init_B_), init_C(init_C_), init_Bias(init_Bias_), seed(seed_) {
 
   }
 
@@ -141,6 +150,12 @@ public:
 
       cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
     } 
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
     }
   }
@@ -153,17 +168,20 @@ public:
     tensor_B0.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_B0_reordered.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_C0.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
+    tensor_Bias0.resize({1, 1, 1, problem_size_0.K});
     tensor_D0_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_D0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_B1.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_B1_reordered.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_C1.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
+    tensor_Bias1.resize({1, 1, 1, problem_size_1.K});
     tensor_D1_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
     tensor_D1_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
 
     initialize_tensor(tensor_A0.host_view(), init_A, seed); 
     initialize_tensor(tensor_B0.host_view(), init_B, seed * 17); 
     initialize_tensor(tensor_C0.host_view(), init_C, seed * 39);
+    initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed * 83);
     initialize_tensor(tensor_B1.host_view(), init_B, seed * 18); 
     initialize_tensor(tensor_C1.host_view(), init_C, seed * 40);
 
@@ -177,11 +195,13 @@ public:
     tensor_B0.sync_device();
     tensor_B0_reordered.sync_device();
     tensor_C0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0_computed.sync_device();
     tensor_D0_reference.sync_device();
     tensor_B1.sync_device();
     tensor_B1_reordered.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1_computed.sync_device();
     tensor_D1_reference.sync_device();
   }
@@ -271,7 +291,7 @@ public:
     cudaEventElapsedTime(&totalTime, start, stop2);
     std::cout << "conv2d 0 time " << conv2d0Time / (float)runs << " ms\n";
     std::cout << "conv2d 1 time " << conv2d1Time / (float)runs << " ms\n";
-    std::cout << "total time " << totalTime / (float)runs << " ms\n";
+    std::cout << "Non-fusion time " << totalTime / (float)runs << " ms\n";
 
     tensor_D0_computed.sync_host();
     tensor_D1_computed.sync_host();
@@ -360,11 +380,13 @@ public:
         << "\nB0:\n" << tensor_B0.host_view() << "\n"
         << "\nB0_reordered:\n" << tensor_B0_reordered.host_view() << "\n"
         << "\nC0:\n" << tensor_C0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nD0 reference:\n" << tensor_D0_reference.host_view() << "\n"
         << "\nD0 computed:\n" << tensor_D0_computed.host_view() << "\n"
         << "\nB1:\n" << tensor_B1.host_view() << "\n"
         << "\nB1_reordered:\n" << tensor_B1_reordered.host_view() << "\n"
         << "\nC1:\n" << tensor_C1.host_view() << "\n"
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\nD1 reference:\n" << tensor_D1_reference.host_view() << "\n"
         << "\nD1 computed:\n" << tensor_D1_computed.host_view();
 
@@ -392,17 +414,23 @@ public:
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Scale;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   cutlass::HostTensor<typename B2bConv2d::ElementA, typename B2bConv2d::LayoutA> tensor_A0;
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B0;
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B0_reordered;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_C0;
+  cutlass::HostTensor<typename B2bConv2d::ElementScaleBias, typename B2bConv2d::LayoutScaleBias> tensor_Scale0;
+  cutlass::HostTensor<typename B2bConv2d::ElementScaleBias, typename B2bConv2d::LayoutScaleBias> tensor_Bias0;
+  cutlass::HostTensor<ElementAccumulator, typename B2bConv2d::LayoutC> tensor_Z0_reference;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D0_reference;
 
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B1;
   cutlass::HostTensor<typename B2bConv2d::ElementB, typename B2bConv2d::LayoutB> tensor_B1_reordered;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_C1;
+  cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_Bias1;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D1_computed;
   cutlass::HostTensor<typename B2bConv2d::ElementC, typename B2bConv2d::LayoutC> tensor_D1_reference;
 
@@ -413,9 +441,12 @@ public:
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform,
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Scale_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) {
+    init_A(init_A_), init_B(init_B_), init_C(init_C_),
+    init_Scale(init_Scale_), init_Bias(init_Bias_), seed(seed_) {
 
   }
 
@@ -452,30 +483,48 @@ public:
 
       cutlass::reference::host::BlockFillSequential(view.data(), view.capacity());
     } 
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
     }
   }
 
   void initialize(
     cutlass::conv::Conv2dProblemSize const &problem_size_0,
-    cutlass::conv::Conv2dProblemSize const &problem_size_1, uint64_t seed = 2019) {
+    cutlass::conv::Conv2dProblemSize const &problem_size_1,
+    ElementCompute alpha0,
+    ElementCompute alpha1,
+    uint64_t seed = 2019) {
         
     tensor_A0.resize(implicit_gemm_tensor_a_extent(kConvolutionalOperator, problem_size_0));
     tensor_B0.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_B0_reordered.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_0));
     tensor_C0.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.resize({1, problem_size_0.K});
+    tensor_Bias0.resize({1, problem_size_0.K});
+    tensor_Z0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_D0_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_0));
     tensor_B1.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_B1_reordered.resize(implicit_gemm_tensor_b_extent(kConvolutionalOperator, problem_size_1));
     tensor_C1.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
+    tensor_Bias1.resize({1, 1, 1, problem_size_1.K});
     tensor_D1_computed.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
     tensor_D1_reference.resize(implicit_gemm_tensor_c_extent(kConvolutionalOperator, problem_size_1));
 
     initialize_tensor(tensor_A0.host_view(), init_A, seed); 
     initialize_tensor(tensor_B0.host_view(), init_B, seed * 17); 
     initialize_tensor(tensor_C0.host_view(), init_C, seed * 39);
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        initialize_tensor(tensor_Scale0.host_view(), init_Scale, seed * 61);
+    initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed * 83);
     initialize_tensor(tensor_B1.host_view(), init_B, seed * 18); 
     initialize_tensor(tensor_C1.host_view(), init_C, seed * 40);
+    initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed * 84);
 
     //Reorder B0 and B1
     cutlass::reorder_convK<16, InterleavedK>(
@@ -487,10 +536,14 @@ public:
     tensor_B0.sync_device();
     tensor_B0_reordered.sync_device();
     tensor_C0.sync_device();
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0_reference.sync_device();
     tensor_B1.sync_device();
     tensor_B1_reordered.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1_computed.sync_device();
     tensor_D1_reference.sync_device();
   }
@@ -508,7 +561,7 @@ public:
     int warm_ups = 1,
     int runs = 100) {
 
-    initialize(problem_size_0, problem_size_1);
+    initialize(problem_size_0, problem_size_1, alpha0, alpha1);
 
     // configure the operator
     B2bConv2d b2b_conv2d_op;
@@ -519,6 +572,8 @@ public:
       tensor_A0.device_ref(),
       tensor_B0_reordered.device_ref(),
       tensor_C0.device_ref(),
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref(),
       tensor_B1_reordered.device_ref(),
       tensor_C1.device_ref(),
       tensor_D1_computed.device_ref(),
@@ -527,7 +582,21 @@ public:
       split_k_mode
     );
 
-    cutlass::Status status = b2b_conv2d_op.initialize(b2b_conv2d_args);
+    cutlass::Status status = b2b_conv2d_op.can_implement(b2b_conv2d_args);
+    
+    if(status != cutlass::Status::kSuccess) {
+        std::cout << "Problem sizes not supported.\n"
+                << "Requirments:\n"
+                << "    problem_size_0.N*P*Q = problem_size_1.N*P*Q\n"
+                << "    problem_size_0.K = problem_size_1.C\n"
+                << "    problem_size_1.R = problem_size_1.S = 1\n"
+                << "    ThreadblockShape0::kN = problem_size_0.K\n"
+                << "    ThreadblockShape1::kN = problem_size_1.K" << std::endl;
+    }
+
+    CUTLASS_CHECK(status);
+
+    status = b2b_conv2d_op.initialize(b2b_conv2d_args);
 
     CUTLASS_CHECK(status);
 
@@ -557,7 +626,7 @@ public:
     cudaDeviceSynchronize();
     float conv2dTime;
     cudaEventElapsedTime(&conv2dTime, start, stop);
-    std::cout << "time " << conv2dTime / (float)runs << " ms\n";
+    std::cout << "Fusion time " << conv2dTime / (float)runs << " ms\n";
 
     tensor_D1_computed.sync_host();
     
@@ -568,20 +637,36 @@ public:
       typename B2bConv2d::LayoutA,
       typename B2bConv2d::ElementB,
       typename B2bConv2d::LayoutB,
-      typename B2bConv2d::ElementC,
-      typename B2bConv2d::LayoutC,
-      ElementCompute,
       ElementAccumulator,
-      cutlass::NumericConverterClamp<typename B2bConv2d::ElementC, ElementCompute>
+      typename B2bConv2d::LayoutC,
+      ElementAccumulator,
+      ElementAccumulator
     >(
       kConvolutionalOperator,
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0.device_ref(),
-      tensor_C0.device_ref(),
+      tensor_Z0_reference.device_ref(),
+      tensor_Z0_reference.device_ref(),
+      ElementAccumulator(1), // intermediate alpha = 1
+      ElementAccumulator(0)  // beta = 0
+    );
+
+    cutlass::reference::device::TensorScaleBiasConv2d<
+      ElementAccumulator,
+      typename B2bConv2d::ElementC,
+      typename B2bConv2d::LayoutC,
+      ElementCompute,
+      typename B2bConv2d::LayoutScaleBias,
+      cutlass::NumericConverterClamp<typename B2bConv2d::ElementC, ElementCompute>
+    >(
+      problem_size_0,
+      tensor_Z0_reference.device_ref(),
       tensor_D0_reference.device_ref(),
-      alpha0, 
-      beta0);
+      alpha0,
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref()
+    );
 
     if(relu) {
        cutlass::reference::device::TensorReLu(tensor_D0_reference.device_view()); 
@@ -644,9 +729,12 @@ public:
         << "\nB0:\n" << tensor_B0.host_view() << "\n"
         << "\nB0_reordered:\n" << tensor_B0_reordered.host_view() << "\n"
         << "\nC0:\n" << tensor_C0.host_view() << "\n"
+        << "\nScale0:\n" << tensor_Scale0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nB1:\n" << tensor_B1.host_view() << "\n"
         << "\nB1_reordered:\n" << tensor_B1_reordered.host_view() << "\n"
         << "\nC1:\n" << tensor_C1.host_view() << "\n"
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\nD1 reference:\n" << tensor_D1_reference.host_view() << "\n"
         << "\nD1 computed:\n" << tensor_D1_computed.host_view();
 

examples/13_two_tensor_op_fusion/b2b_interleaved_gemm_run.h

@@ -1,28 +1,33 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-
 #pragma once
 
 #include <iostream>
@@ -40,6 +45,7 @@
 #include "cutlass/util/reference/device/gemm.h"
 #include "cutlass/util/reference/device/tensor_relu.h"
 
+#include "reference/device/tensor_scale_bias.h"
 #include "helper.h"
 
 #define CHECK_GT(val1, val2) \
@@ -62,6 +68,7 @@ struct B2bInterleavedNonFusedGemmRun
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   //
@@ -72,9 +79,10 @@ struct B2bInterleavedNonFusedGemmRun
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform, 
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
+    init_A(init_A_), init_B(init_B_), init_C(init_C_), init_Bias(init_Bias_), seed(seed_) { }
 
   /// Helper to initialize a tensor view
   template <typename Element, typename Layout>
@@ -91,14 +99,23 @@ struct B2bInterleavedNonFusedGemmRun
     else if (dist_kind == cutlass::Distribution::Identity) {
 
       cutlass::reference::host::TensorFillIdentity(view);
-    } 
+    }
+    else if (dist_kind == cutlass::Distribution::Gaussian) {
+
+      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+    }
     else if (dist_kind == cutlass::Distribution::Sequential) {
 
       cutlass::reference::host::BlockFillSequential(
         view.data(), view.capacity());
-    } 
+    }
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
-      // TODO: Implement the rest
       std::cerr << "Not implemented\n";
       return false;
     }
@@ -141,6 +158,10 @@ struct B2bInterleavedNonFusedGemmRun
       typename Gemm0::ElementC, 
       typename Gemm0::LayoutC> tensor_C0(problem_size_0.mn());
 
+    cutlass::HostTensor<
+      typename Gemm0::ElementC, 
+      typename Gemm0::LayoutC> tensor_Bias0({1, problem_size_0.n()});
+
     cutlass::HostTensor<
       typename Gemm0::ElementC, 
       typename Gemm0::LayoutC> tensor_D0(problem_size_0.mn());
@@ -161,6 +182,10 @@ struct B2bInterleavedNonFusedGemmRun
       typename Gemm1::ElementC, 
       typename Gemm1::LayoutC> tensor_C1(problem_size_1.mn());
 
+    cutlass::HostTensor<
+      typename Gemm0::ElementC, 
+      typename Gemm1::LayoutC> tensor_Bias1({1, problem_size_1.n()});
+
     cutlass::HostTensor<
       typename Gemm1::ElementC, 
       typename Gemm1::LayoutC> tensor_D1(problem_size_1.mn());
@@ -173,8 +198,10 @@ struct B2bInterleavedNonFusedGemmRun
     CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
     CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2018));
     CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
+    CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2014));
     CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2016));
     CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
+    CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2013));
 
     //Reorder B0 and B1
     cutlass::reorder_column<InterleavedK_>(
@@ -195,10 +222,12 @@ struct B2bInterleavedNonFusedGemmRun
     tensor_B0.sync_device();
     tensor_B0_reordered.sync_device();
     tensor_C0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_D0.sync_device();
     tensor_B1.sync_device();
     tensor_B1_reordered.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1.sync_device();
     reference_D0.sync_device();
     reference_D1.sync_device();
@@ -211,7 +240,7 @@ struct B2bInterleavedNonFusedGemmRun
       problem_size_0,
       tensor_A0.device_ref(),
       tensor_B0_reordered.device_ref(),
-      tensor_C0.device_ref(),
+      {tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
       tensor_D0.device_ref(),
       {alpha0, beta0}
     };
@@ -220,7 +249,7 @@ struct B2bInterleavedNonFusedGemmRun
       problem_size_1,
       tensor_D0.device_ref(),
       tensor_B1_reordered.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
       tensor_D1.device_ref(),
       {alpha1, beta1}
     };
@@ -259,8 +288,7 @@ struct B2bInterleavedNonFusedGemmRun
     
         CUTLASS_CHECK(status);
     }
-    cudaEventRecord(stop1);    
-
+    cudaEventRecord(stop1);
     for(int i = 0; i < runs; i++) {
         status = gemm_op_1();
     
@@ -275,7 +303,7 @@ struct B2bInterleavedNonFusedGemmRun
     cudaEventElapsedTime(&totalTime, start, stop2);
     std::cout << "gemm 0 time " << gemm0Time / (float)runs << " ms\n";
     std::cout << "gemm 1 time " << gemm1Time / (float)runs << " ms\n";
-    std::cout << "total time " << totalTime / (float)runs << " ms\n";
+    std::cout << "Non-fusion time " << totalTime / (float)runs << " ms\n";
 
     tensor_D0.sync_host();
     tensor_D1.sync_host();
@@ -303,7 +331,7 @@ struct B2bInterleavedNonFusedGemmRun
       tensor_A0.device_ref(), 
       tensor_B0.device_ref(), 
       beta0, 
-      tensor_C0.device_ref(), 
+      {tensor_Bias0.device_data(), typename Gemm0::LayoutC::Stride(0)},
       reference_D0.device_ref()
     );
 
@@ -316,8 +344,8 @@ struct B2bInterleavedNonFusedGemmRun
       alpha1, 
       reference_D0.device_ref(), 
       tensor_B1.device_ref(), 
-      beta1, 
-      tensor_C1.device_ref(), 
+      beta1,
+      {tensor_Bias1.device_data(), typename Gemm1::LayoutC::Stride(0)},
       reference_D1.device_ref()
     );
   
@@ -325,6 +353,7 @@ struct B2bInterleavedNonFusedGemmRun
        cutlass::reference::device::TensorReLu(reference_D1.device_view()); 
     }
 
+    // Wait for kernels to finish
     cudaDeviceSynchronize();
     reference_D0.sync_host(); 
     reference_D1.sync_host(); 
@@ -353,14 +382,15 @@ struct B2bInterleavedNonFusedGemmRun
         << "\nB0 =\n" << tensor_B0.host_view()
         << "\nB0_reordered =\n" << tensor_B0_reordered.host_view()
         << "\nC0 =\n" << tensor_C0.host_view()
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nD0 =\n" << tensor_D0.host_view()
         << "\nB1 =\n" << tensor_B1.host_view()
         << "\nB1_reordered =\n" << tensor_B1_reordered.host_view()
         << "\nC1 =\n" << tensor_C1.host_view()
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\n\nReference =\n" << reference_D1.host_view()
         << "\nComputed =\n" << tensor_D1.host_view();
     }
-
     return passed;
   }
 };
@@ -377,6 +407,8 @@ struct B2bInterleavedFusedGemmRun
   cutlass::Distribution::Kind init_A;
   cutlass::Distribution::Kind init_B;
   cutlass::Distribution::Kind init_C;
+  cutlass::Distribution::Kind init_Scale;
+  cutlass::Distribution::Kind init_Bias;
   uint64_t seed;
 
   //
@@ -387,9 +419,12 @@ struct B2bInterleavedFusedGemmRun
     cutlass::Distribution::Kind init_A_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_B_ = cutlass::Distribution::Uniform, 
     cutlass::Distribution::Kind init_C_ = cutlass::Distribution::Uniform, 
+    cutlass::Distribution::Kind init_Scale_ = cutlass::Distribution::Uniform,
+    cutlass::Distribution::Kind init_Bias_ = cutlass::Distribution::Uniform,
     uint64_t seed_ = 2080
   ):
-    init_A(init_A_), init_B(init_B_), init_C(init_C_), seed(seed_) { }
+    init_A(init_A_), init_B(init_B_), init_C(init_C_),
+    init_Scale(init_Scale_), init_Bias(init_Bias_), seed(seed_) { }
 
   /// Helper to initialize a tensor view
   template <typename Element, typename Layout>
@@ -407,13 +442,22 @@ struct B2bInterleavedFusedGemmRun
 
       cutlass::reference::host::TensorFillIdentity(view);
     } 
+    else if (dist_kind == cutlass::Distribution::Gaussian) {
+
+      cutlass::reference::host::TensorFillRandomGaussian(view, seed, 0, 0.5);
+    }
     else if (dist_kind == cutlass::Distribution::Sequential) {
 
       cutlass::reference::host::BlockFillSequential(
         view.data(), view.capacity());
-    } 
+    }
+    else if (dist_kind == cutlass::Distribution::AllZeros) {
+      cutlass::reference::host::TensorFill(view, Element(0));
+    }
+    else if (dist_kind == cutlass::Distribution::AllOnes) {
+      cutlass::reference::host::TensorFill(view, Element(1));
+    }
     else {
-      // TODO: Implement the rest
       std::cerr << "Not implemented\n";
       return false;
     }
@@ -431,7 +475,7 @@ struct B2bInterleavedFusedGemmRun
     ElementCompute alpha0 = ElementCompute(1), 
     ElementCompute beta0 = ElementCompute(0),
     ElementCompute alpha1 = ElementCompute(1), 
-    ElementCompute beta1 = ElementCompute(0), 
+    ElementCompute beta1 = ElementCompute(0),
     bool relu = true,
     int warm_ups = 1,
     int runs = 100) {
@@ -456,6 +500,21 @@ struct B2bInterleavedFusedGemmRun
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_C0(problem_size_0.mn());
 
+    cutlass::HostTensor<
+      typename B2bGemm::ElementScaleBias, 
+      typename B2bGemm::LayoutScaleBias> tensor_Scale0;
+
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.resize({1, problem_size_0.n()});
+
+    cutlass::HostTensor<
+      typename B2bGemm::ElementScaleBias, 
+      typename B2bGemm::LayoutScaleBias> tensor_Bias0({1, problem_size_0.n()});
+
+    cutlass::HostTensor<
+      ElementAccumulator, 
+      typename B2bGemm::LayoutC> reference_Z0(problem_size_0.mn());
+
     cutlass::HostTensor<
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> reference_D0(problem_size_0.mn());
@@ -472,6 +531,10 @@ struct B2bInterleavedFusedGemmRun
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_C1(problem_size_1.mn());
 
+    cutlass::HostTensor<
+      typename B2bGemm::ElementC, 
+      typename B2bGemm::LayoutScaleBias> tensor_Bias1({1, problem_size_1.n()});
+
     cutlass::HostTensor<
       typename B2bGemm::ElementC, 
       typename B2bGemm::LayoutC> tensor_D1(problem_size_1.mn());
@@ -484,8 +547,12 @@ struct B2bInterleavedFusedGemmRun
     CHECK_TRUE(initialize_tensor(tensor_A0.host_view(), init_A, seed + 2019));
     CHECK_TRUE(initialize_tensor(tensor_B0.host_view(), init_B, seed + 2018));
     CHECK_TRUE(initialize_tensor(tensor_C0.host_view(), init_C, seed + 2017));
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+      CHECK_TRUE(initialize_tensor(tensor_Scale0.host_view(), init_Scale, seed + 2014));
+    CHECK_TRUE(initialize_tensor(tensor_Bias0.host_view(), init_Bias, seed + 2013));
     CHECK_TRUE(initialize_tensor(tensor_B1.host_view(), init_B, seed + 2016));
     CHECK_TRUE(initialize_tensor(tensor_C1.host_view(), init_C, seed + 2015));
+    CHECK_TRUE(initialize_tensor(tensor_Bias1.host_view(), init_Bias, seed + 2012));
 
     //Reorder B0
     cutlass::reorder_column<16>(
@@ -504,9 +571,13 @@ struct B2bInterleavedFusedGemmRun
     tensor_B0.sync_device();
     tensor_B0_reordered.sync_device();
     tensor_C0.sync_device();
+    if(alpha0 == ElementCompute(0)) //per-channel scale
+        tensor_Scale0.sync_device();
+    tensor_Bias0.sync_device();
     tensor_B1.sync_device();
     tensor_B1_reordered.sync_device();
     tensor_C1.sync_device();
+    tensor_Bias1.sync_device();
     tensor_D1.sync_device();
     reference_D0.sync_device();
     reference_D1.sync_device();
@@ -521,17 +592,29 @@ struct B2bInterleavedFusedGemmRun
       tensor_A0.device_ref(),
       tensor_B0_reordered.device_ref(),
       tensor_C0.device_ref(),
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref(),
       tensor_B1_reordered.device_ref(),
-      tensor_C1.device_ref(),
+      {tensor_Bias1.device_data(), typename B2bGemm::LayoutC::Stride(0)},
       tensor_D1.device_ref(),
       {alpha0, beta0},
       {alpha1, beta1},
-      1, /*threadblock_swizzle_k_tile*/
     };
 
     B2bGemm b2b_gemm_op;
 
-    cutlass::Status status = b2b_gemm_op.initialize(arguments);
+    cutlass::Status status = b2b_gemm_op.can_implement(arguments);
+
+    if(status != cutlass::Status::kSuccess) {
+        std::cout << "Problem sizes not supported.\n"
+                << "Requirments:\n"
+                << "    problem_size_0.M = problem_size_1.M\n"
+                << "    problem_size_0.N = problem_size_1.K\n"
+                << "    ThreadblockShape0::kN = problem_size_0.N\n"
+                << "    ThreadblockShape1::kN = problem_size_1.N" << std::endl;
+    }
+
+    status = b2b_gemm_op.initialize(arguments);
 
     CUTLASS_CHECK(status);
 
@@ -560,28 +643,49 @@ struct B2bInterleavedFusedGemmRun
     cudaDeviceSynchronize();
     float gemmTime;
     cudaEventElapsedTime(&gemmTime, start, stop);
-    std::cout << "time " << gemmTime / (float)runs << " ms\n";
+    std::cout << "Fusion time " << gemmTime / (float)runs << " ms\n";
 
     tensor_D1.sync_host();
 
     //
     // Verify
     //
+
+    cutlass::reference::device::Gemm<
+        typename B2bGemm::ElementA, typename B2bGemm::LayoutA,
+        typename B2bGemm::ElementB, typename B2bGemm::LayoutB,
+        ElementAccumulator, typename B2bGemm::LayoutC, 
+        ElementAccumulator, ElementAccumulator>
+        reference_gemm_0;
+
     cutlass::reference::device::Gemm<
         typename B2bGemm::ElementA, typename B2bGemm::LayoutA,
         typename B2bGemm::ElementB, typename B2bGemm::LayoutB,
         typename B2bGemm::ElementC, typename B2bGemm::LayoutC, ElementCompute,
         ElementAccumulator, typename B2bGemm::Operator>
-        reference_gemm_0, reference_gemm_1;
+        reference_gemm_1;
 
     reference_gemm_0(
       problem_size_0,
-      alpha0, 
+      ElementAccumulator(1), //intermediate alpha=1
       tensor_A0.device_ref(), 
       tensor_B0.device_ref(), 
-      beta0, 
-      tensor_C0.device_ref(), 
-      reference_D0.device_ref()
+      ElementAccumulator(0), //beta = 0
+      reference_Z0.device_ref(),
+      reference_Z0.device_ref(),
+      ElementAccumulator(0)
+    );
+
+    cutlass::reference::device::TensorScaleBiasGemm<
+      ElementAccumulator, typename B2bGemm::ElementC, typename B2bGemm::LayoutC,
+      ElementCompute, typename B2bGemm::LayoutScaleBias
+    > (
+      problem_size_0,
+      reference_Z0.device_ref(),
+      reference_D0.device_ref(),
+      alpha0,
+      tensor_Scale0.device_ref(),
+      tensor_Bias0.device_ref()
     );
 
     if(relu) {
@@ -594,18 +698,15 @@ struct B2bInterleavedFusedGemmRun
       reference_D0.device_ref(), 
       tensor_B1.device_ref(), 
       beta1, 
-      tensor_C1.device_ref(), 
+      {tensor_Bias1.device_data(), typename B2bGemm::LayoutC::Stride(0)},
       reference_D1.device_ref()
     );
-
-
     if(relu) {
        cutlass::reference::device::TensorReLu(reference_D1.device_view()); 
     }
-  
     cudaDeviceSynchronize();
-    reference_D0.sync_host(); 
-    reference_D1.sync_host(); 
+    reference_D0.sync_host();
+    reference_D1.sync_host();
 
     CHECK_GT(cutlass::reference::host::TensorNorm(reference_D0.host_view()), 0);
     CHECK_GT(cutlass::reference::host::TensorNorm(tensor_D1.host_view()), 0);
@@ -616,7 +717,8 @@ struct B2bInterleavedFusedGemmRun
       tensor_D1.host_view());
 
     CHECK_TRUE(passed);
-    if (!passed) {
+    if (!passed)
+    {
 
       std::stringstream fname;
 
@@ -630,13 +732,15 @@ struct B2bInterleavedFusedGemmRun
         << "\nB0 =\n" << tensor_B0.host_view()
         << "\nB0_reordered =\n" << tensor_B0_reordered.host_view()
         << "\nC0 =\n" << tensor_C0.host_view()
+        << "\nScale0:\n" << tensor_Scale0.host_view() << "\n"
+        << "\nBias0:\n" << tensor_Bias0.host_view() << "\n"
         << "\nB1 =\n" << tensor_B1.host_view()
         << "\nB1_reordered =\n" << tensor_B1_reordered.host_view()
         << "\nC1 =\n" << tensor_C1.host_view()
+        << "\nBias1:\n" << tensor_Bias1.host_view() << "\n"
         << "\n\nReference =\n" << reference_D1.host_view()
         << "\nComputed =\n" << tensor_D1.host_view();
     }
-
     return passed;
   }
 

examples/13_two_tensor_op_fusion/device/b2b_gemm.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -40,6 +46,7 @@
 
 #include "kernel/b2b_gemm.h"
 #include "kernel/default_b2b_gemm.h"
+#include "kernel/default_b2b_gemm_smem_accumulator.h"
 
 ////////////////////////////////////////////////////////////////////////////////
 
@@ -102,6 +109,8 @@ template <
     int Stages =
         DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
                                  ElementC_, ElementAccumulator_>::kStages,
+    /// Stage accumulator in shared memory
+    bool SmemAccumulator = false,
     /// Access granularity of A matrix in units of elements
     int AlignmentA =
         DefaultGemmConfiguration<OperatorClass_, ArchTag_, ElementA_, ElementB_,
@@ -149,6 +158,10 @@ class B2bGemm {
   static ComplexTransform const kTransformA = ComplexTransform::kNone;
   static ComplexTransform const kTransformB = ComplexTransform::kNone;
 
+  /// Derived types
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor;
+
   /// Define the kernel
   using B2bGemmKernel = typename kernel::DefaultB2bGemm<
     ElementA,
@@ -172,7 +185,8 @@ class B2bGemm {
     ThreadblockSwizzle,
     kStages,
     kSplitKSerial,
-    Operator
+    Operator,
+    SmemAccumulator
   >::B2bGemmKernel;
 
   /// Argument structure
@@ -187,6 +201,8 @@ class B2bGemm {
     TensorRef<ElementA const, LayoutA> ref_A0;
     TensorRef<ElementB const, LayoutB> ref_B0;
     TensorRef<ElementC const, LayoutC> ref_C0;
+    TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Scale0;
+    TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Bias0;
     TensorRef<ElementB const, LayoutB> ref_B1;
     TensorRef<ElementC const, LayoutC> ref_C1;
     TensorRef<ElementC, LayoutC> ref_D1;
@@ -212,6 +228,8 @@ class B2bGemm {
       TensorRef<ElementA const, LayoutA> ref_A0_,
       TensorRef<ElementB const, LayoutB> ref_B0_,
       TensorRef<ElementC const, LayoutC> ref_C0_,
+      TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Scale0_,
+      TensorRef<ElementScaleBias const, LayoutScaleBias> ref_Bias0_,
       TensorRef<ElementB const, LayoutB> ref_B1_,
       TensorRef<ElementC const, LayoutC> ref_C1_,
       TensorRef<ElementC, LayoutC> ref_D1_,
@@ -226,6 +244,8 @@ class B2bGemm {
       ref_A0(ref_A0_),
       ref_B0(ref_B0_),
       ref_C0(ref_C0_),
+      ref_Scale0(ref_Scale0_),
+      ref_Bias0(ref_Bias0_),
       ref_B1(ref_B1_),
       ref_C1(ref_C1_),
       ref_D1(ref_D1_),
@@ -338,6 +358,8 @@ public:
       args.ref_A0.non_const_ref(),
       args.ref_B0.non_const_ref(),
       args.ref_C0.non_const_ref(),
+      args.ref_Scale0.non_const_ref(),
+      args.ref_Bias0.non_const_ref(),
       args.ref_B1.non_const_ref(),
       args.ref_C1.non_const_ref(),
       args.ref_D1,
@@ -358,12 +380,14 @@ public:
       }
     }
 
-    params_.ref_A0.reset(args.ref_A.non_const_ref().data());
-    params_.ref_B0.reset(args.ref_B.non_const_ref().data());
-    params_.ref_C0.reset(args.ref_C.non_const_ref().data());
-    params_.ref_B1.reset(args.ref_B.non_const_ref().data());
-    params_.ref_C1.reset(args.ref_C.non_const_ref().data());
-    params_.ref_D1.reset(args.ref_D.data());
+    params_.ref_A0.reset(args.ref_A0.non_const_ref().data());
+    params_.ref_B0.reset(args.ref_B0.non_const_ref().data());
+    params_.ref_C0.reset(args.ref_C0.non_const_ref().data());
+    params_.ref_Scale0.reset(args.ref_Scale0.non_const_ref().data());
+    params_.ref_Bias0.reset(args.ref_Bias0.non_const_ref().data());
+    params_.ref_B1.reset(args.ref_B1.non_const_ref().data());
+    params_.ref_C1.reset(args.ref_C1.non_const_ref().data());
+    params_.ref_D1.reset(args.ref_D1.data());
     params_.output_op_0 = args.epilogue0;
     params_.output_op_1 = args.epilogue1;
     params_.semaphore = static_cast<int *>(workspace);
@@ -390,14 +414,6 @@ public:
       if (result != cudaSuccess) {
         return Status::kErrorInternal;
       }
-
-      result = cudaFuncSetAttribute(
-          Kernel<B2bGemmKernel>,
-          cudaFuncAttributePreferredSharedMemoryCarveout, 100);
-
-      if (result != cudaSuccess) {
-        return Status::kErrorInternal;
-      }
     }
 
     cutlass::Kernel<B2bGemmKernel><<<grid, block, smem_size, stream>>>(params_);

examples/13_two_tensor_op_fusion/device/b2b_implicit_gemm_convolution.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -36,6 +42,10 @@
 
 #include "kernel/b2b_implicit_gemm_convolution.h"
 #include "kernel/default_b2b_conv2d_fprop.h"
+#include "kernel/default_b2b_conv2d_fprop_sm75.h"
+#include "kernel/default_b2b_conv2d_fprop_sm80.h"
+#include "kernel/default_b2b_conv2d_fprop_smem_accumulator_sm75.h"
+#include "kernel/default_b2b_conv2d_fprop_smem_accumulator_sm80.h"
 
 namespace cutlass {
 namespace conv {
@@ -55,6 +65,8 @@ public:
   using LayoutC = typename B2bImplicitGemmKernel::LayoutC;
   using ElementAccumulator = typename B2bImplicitGemmKernel::ElementAccumulator;
   using ElementCompute = typename B2bImplicitGemmKernel::ElementCompute;
+  using ElementScaleBias = typename B2bImplicitGemmKernel::ElementScaleBias;
+  using LayoutScaleBias = typename B2bImplicitGemmKernel::LayoutScaleBias;
   using OperatorClass = typename B2bImplicitGemmKernel::OperatorClass;
   using ArchTag = typename B2bImplicitGemmKernel::ArchTag;
   using ThreadblockShape0 = typename B2bImplicitGemmKernel::ThreadblockShape0;
@@ -126,6 +138,26 @@ public:
       return Status::kErrorInvalidProblem;
     }
 
+    // Determine if fusion sizes are valid
+
+    cutlass::gemm::GemmCoord problem_size_0 = implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size_0);
+    cutlass::gemm::GemmCoord problem_size_1 = implicit_gemm_problem_size(kConvolutionalOperator, args.problem_size_1);
+
+    if(problem_size_0.m() != problem_size_1.m())
+      return Status::kErrorInvalidProblem;
+
+    if(problem_size_0.n() != problem_size_1.k())
+      return Status::kErrorInvalidProblem;
+
+    if(args.problem_size_1.R != 1 || args.problem_size_1.S != 1)
+      return Status::kErrorInvalidProblem;
+
+    if(problem_size_0.n() > ThreadblockShape0::kN)
+      return Status::kErrorInvalidProblem;
+    
+    if(problem_size_1.n() > ThreadblockShape1::kN)
+      return Status::kErrorInvalidProblem;
+
     return Status::kSuccess;
   }
 
@@ -197,14 +229,6 @@ public:
       if (result != cudaSuccess) {
         return Status::kErrorInternal;
       }
-
-      result = cudaFuncSetAttribute(
-          cutlass::Kernel<B2bImplicitGemmKernel>,
-          cudaFuncAttributePreferredSharedMemoryCarveout, 100);
-
-      if (result != cudaSuccess) {
-        return Status::kErrorInternal;
-      }
     }
     
     return Status::kSuccess;
@@ -217,6 +241,8 @@ public:
     params_.ptr_A0 = args.ref_A0.data();
     params_.ptr_B0 = args.ref_B0.data();
     params_.ptr_C0 = args.ref_C0.data();
+    params_.ptr_Scale0 = args.ref_Scale0.data();
+    params_.ptr_Bias0 = args.ref_Bias0.data();
     params_.ptr_B1 = args.ref_B1.data();
     params_.ptr_C1 = args.ref_C1.data();
     params_.ptr_D1 = args.ref_D1.data();

examples/13_two_tensor_op_fusion/fused_conv2d.cu

@@ -1,136 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-
-#include "b2b_conv2d_fprop_implicit_gemm_s8ncxhwx_s8cxrskx_s8ncxhwx_tensor_op_s32_sm75.h"
-#include "b2b_conv2d_fprop_implicit_gemm_s8ncxhwx_s8cxrskx_s8ncxhwx_tensor_op_s32_sm80.h"
-#include "b2b_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm75.h"
-#include "b2b_conv2d_fprop_implicit_gemm_f16nhwc_f16nhwc_f16nhwc_tensor_op_f16_sm80.h"
-
-int run_sm75() {
-  bool notSupported = false;
-
-  // Turing Tensor Core operations exposed with mma.sync are first available in CUDA 10.2.
-  //
-  // CUTLASS must be compiled with CUDA 10.2 Toolkit to run these examples.
-  if (!(__CUDACC_VER_MAJOR__ > 10 || (__CUDACC_VER_MAJOR__ == 10 && __CUDACC_VER_MINOR__ >= 2))) {
-    notSupported = true;
-  }
-
-  cudaDeviceProp props;
-
-  cudaError_t error = cudaGetDeviceProperties(&props, 0);
-  if (error != cudaSuccess) {
-    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
-    return -1;
-  }
-
-  if (!(props.major == 7 && props.minor >= 5)) {
-    notSupported = true;
-  }
-
-  if (notSupported) {
-    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
-    return 0;
-  }
-
-  bool pass = 1;
- 
-  std::cout << "Running on SM75" << std::endl;
-  pass &= run_nonfused_conv2d_fprop_optimized_f16_sm75();
-  pass &= run_fused_conv2d_fprop_optimized_f16_sm75();
-  pass &= run_nonfused_conv2d_fprop_optimized_s8_sm75();
-  pass &= run_fused_conv2d_fprop_optimized_s8_sm75();
-
-  if(pass)
-    return 1;
-  else
-    return -1;
-
-}
-
-int run_sm80() {
-  bool notSupported = false;
-
-  // Ampere Tensor Core operations exposed with mma.sync are first available in CUDA 11.0.
-  //
-  // CUTLASS must be compiled with CUDA 11 Toolkit to run Conv2dFprop examples.
-  if (!(__CUDACC_VER_MAJOR__ > 11 || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))) {
-    notSupported = true;
-  }
-
-  cudaDeviceProp props;
-
-  cudaError_t error = cudaGetDeviceProperties(&props, 0);
-  if (error != cudaSuccess) {
-    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
-    return -1;
-  }
-
-  if (!(props.major == 8 && props.minor >= 0)) {
-    notSupported = true;
-  }
-
-  if (notSupported) {
-    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
-    return 0;
-  }
-
-  bool pass = 1;
- 
-  std::cout << "Running on SM80" << std::endl;
-  pass &= run_nonfused_conv2d_fprop_optimized_f16_sm80();
-  pass &= run_fused_conv2d_fprop_optimized_f16_sm80();
-  pass &= run_nonfused_conv2d_fprop_optimized_s8_sm80();
-  pass &= run_fused_conv2d_fprop_optimized_s8_sm80();
-
-  if(pass)
-    return 1;
-  else
-    return -1;
-
-}
-
-
-int main() {
-
-  int result = 0;
-
-  result = run_sm80();
-
-  if(!result) { // not supported
-    result = run_sm75();
-
-    if(!result) {
-      std::cout << "This example isn't supported on current architecture" << std::endl;
-    }
-
-  }
-
-  if(result >= 0)
-    return 0;
-  else
-    return -1;
-}
-

examples/13_two_tensor_op_fusion/fused_gemm.cu

@@ -1,141 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2017-2021, 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.
- *
- **************************************************************************************************/
-
-#include "b2b_gemm_f16t_f16n_f16t_tensor_op_f16_sm75.h"
-#include "b2b_gemm_f16t_f16n_f16t_tensor_op_f16_sm80.h"
-#include "b2b_gemm_s8n_s8t_s8n_tensor_op_s32_sm75.h"
-#include "b2b_gemm_s8n_s8t_s8n_tensor_op_s32_sm80.h"
-
-int run_sm75() {
-  bool notSupported = false;
-
-  // Turing Tensor Core operations exposed with mma.sync are first available in CUDA 10.2.
-  //
-  // CUTLASS must be compiled with CUDA 10.2 Toolkit to run these examples.
-  if (!(__CUDACC_VER_MAJOR__ > 10 || (__CUDACC_VER_MAJOR__ == 10 && __CUDACC_VER_MINOR__ >= 2))) {
-    notSupported = true;
-
-  }
-
-  cudaDeviceProp props;
-
-  cudaError_t error = cudaGetDeviceProperties(&props, 0);
-  if (error != cudaSuccess) {
-    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
-    return -1;
-  }
-
-  if (!(props.major == 7 && props.minor >= 5)) {
-    notSupported = true;
-  }
-
-  if (notSupported) {
-    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
-    return 0;
-  }
-
-  bool pass = true;
- 
-  std::cout << "Running on SM75" << std::endl;
-  pass &= run_nonfused_gemm_f16();
-  pass &= run_fused_gemm_f16();
-  pass &= run_nonfused_gemm_s8();
-  pass &= run_fused_gemm_s8();
-
-  if(pass)
-    return 1;
-  else
-    return -1;
-    
-
-}
-
-int run_sm80() {
-  bool notSupported = false;
-
-  // Ampere Tensor Core operations exposed with mma.sync are first available in CUDA 11.0.
-  //
-  // CUTLASS must be compiled with CUDA 11 Toolkit to run Conv2dFprop examples.
-  if (!(__CUDACC_VER_MAJOR__ > 11 || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0))) {
-    notSupported = true;
-
-  }
-
-  cudaDeviceProp props;
-
-  cudaError_t error = cudaGetDeviceProperties(&props, 0);
-  if (error != cudaSuccess) {
-    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
-    return -1;
-  }
-
-  if (!(props.major == 8 && props.minor >= 0)) {
-    notSupported = true;
-  }
-
-  if (notSupported) {
-    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
-    return 0;
-  }
-
-  bool pass = true;
- 
-  std::cout << "Running on SM80" << std::endl;
-  pass &= run_nonfused_gemm_f16_sm80();
-  pass &= run_fused_gemm_f16_sm80();
-  pass &= run_nonfused_gemm_s8_sm80();
-  pass &= run_fused_gemm_s8_sm80();
-
-  if(pass)
-    return 1;
-  else
-    return -1;
-
-}
-
-
-int main() {
-
-  int result = 0;
-
-  result = run_sm80();
-
-  if(!result) { // not supported
-    result = run_sm75();
-
-    if(!result) {
-      std::cout << "This example isn't supported on current architecture" << std::endl;
-    }
-
-  }
-
-  if(result >= 0)
-    return 0;
-  else
-    return -1;
-}
-
-
-

examples/13_two_tensor_op_fusion/fused_two_convs_f16_sm75_rf.cu

@@ -0,0 +1,234 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {128, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 128}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_f16_sm75() {
+
+  using ElementA           = cutlass::half_t;
+  using ElementB           = cutlass::half_t;
+  using ElementC           = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute     = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_f16_sm75_rf_res() {
+
+  using ElementA           = cutlass::half_t; 
+  using ElementB           = cutlass::half_t; 
+  using ElementC           = cutlass::half_t; 
+  using ElementAccumulator = cutlass::half_t; 
+  using ElementCompute     = cutlass::half_t; 
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0); 
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //use beta for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<16, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<16, 128, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+  const bool SmemAccumulator = false;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops with RF Residency...\n";
+  bool pass = fusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_f16_sm75,
+    &run_fused_conv2d_fprop_optimized_f16_sm75_rf_res
+  };
+
+  return testRun(75, funcs, "conv f16 RF residency");
+
+}
+
+////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_convs_f16_sm75_shmem.cu

@@ -0,0 +1,234 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm75_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {256, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 256}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_f16_sm75() {
+
+  using ElementA           = cutlass::half_t;
+  using ElementB           = cutlass::half_t;
+  using ElementC           = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute     = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_f16_sm75_shmem() {
+
+  using ElementA           = cutlass::half_t; 
+  using ElementB           = cutlass::half_t; 
+  using ElementC           = cutlass::half_t; 
+  using ElementAccumulator = cutlass::half_t; 
+  using ElementCompute     = cutlass::half_t; 
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0); 
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+  const bool SmemAccumulator = true;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops with shared memory staging...\n";
+  bool pass = fusedConv2d.run(conv2d_f16_sm75_problem_size_0, conv2d_f16_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+   &run_nonfused_conv2d_fprop_optimized_f16_sm75,
+   &run_fused_conv2d_fprop_optimized_f16_sm75_shmem
+  };
+
+  return testRun(75, funcs, "conv f16 shmem staging");
+
+}
+
+////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_convs_f16_sm80_rf.cu

@@ -0,0 +1,233 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {128, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 128}     // output size (NPQK)
+  );
+
+
+bool run_nonfused_conv2d_fprop_optimized_f16_sm80() {
+
+  using ElementA           = cutlass::half_t;
+  using ElementB           = cutlass::half_t;
+  using ElementC           = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute     = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_f16_sm80_rf_res() {
+  using ElementA           = cutlass::half_t; 
+  using ElementB           = cutlass::half_t; 
+  using ElementC           = cutlass::half_t; 
+  using ElementAccumulator = cutlass::half_t; 
+  using ElementCompute     = cutlass::half_t; 
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops with RF Residency...\n";
+  bool pass = fusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+    return true;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_f16_sm80,
+    &run_fused_conv2d_fprop_optimized_f16_sm80_rf_res
+  };
+
+  return testRun(80, funcs, "conv f16 RF residency");
+
+}
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_convs_f16_sm80_shmem.cu

@@ -0,0 +1,236 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_f16_sm80_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {256, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 256}     // output size (NPQK)
+  );
+
+
+bool run_nonfused_conv2d_fprop_optimized_f16_sm80() {
+
+  using ElementA           = cutlass::half_t;
+  using ElementB           = cutlass::half_t;
+  using ElementC           = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute     = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back FP16 Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_f16_sm80_shmem() {
+
+  using ElementA           = cutlass::half_t; 
+  using ElementB           = cutlass::half_t; 
+  using ElementC           = cutlass::half_t; 
+  using ElementAccumulator = cutlass::half_t; 
+  using ElementCompute     = cutlass::half_t; 
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      128 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+  const bool SmemAccumulator = true;
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNHWC,
+    ElementB, cutlass::layout::TensorNHWC,
+    ElementC, cutlass::layout::TensorNHWC,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAdd,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bFusedConv2dRun<B2bConv2dFprop> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back FP16 Optimized Convolution Fprops with shared memory staging...\n";
+  bool pass = fusedConv2d.run(conv2d_f16_sm80_problem_size_0, conv2d_f16_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_f16_sm80,
+    &run_fused_conv2d_fprop_optimized_f16_sm80_shmem
+  };
+
+  return testRun(80, funcs, "conv f16 shmem staging");
+
+}
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_convs_s8_sm75_rf.cu

@@ -0,0 +1,238 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_interleaved_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {128, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 128}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_s8_sm75() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+
+bool run_fused_conv2d_fprop_optimized_s8_sm75_rf_res() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<16, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<16, 128, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+  const bool SmemAccumulator = false;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops with RF residency...\n";
+  bool pass = fusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_s8_sm75,
+    &run_fused_conv2d_fprop_optimized_s8_sm75_rf_res
+  };
+
+  return testRun(75, funcs, "conv int8 RF residency");
+
+}
+
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_convs_s8_sm75_shmem.cu

@@ -0,0 +1,238 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_interleaved_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm75_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {256, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 256}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_s8_sm75() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_s8_sm75_shmem() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+  const bool SmemAccumulator = true;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops with shared memory staging...\n";
+  bool pass = fusedConv2d.run(conv2d_s8_sm75_problem_size_0, conv2d_s8_sm75_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_s8_sm75,
+    &run_fused_conv2d_fprop_optimized_s8_sm75_shmem
+  };
+
+  return testRun(75, funcs, "conv int8 shmem staging");
+
+}
+
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_convs_s8_sm80_rf.cu

@@ -0,0 +1,236 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_interleaved_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {128, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 128}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_s8_sm80() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+
+bool run_fused_conv2d_fprop_optimized_s8_sm80_rf_res() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<16, 64, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<16, 128, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      8 * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops with RF residency...\n";
+  bool pass = fusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_s8_sm80,
+    &run_fused_conv2d_fprop_optimized_s8_sm80_rf_res
+  };
+
+  return testRun(80, funcs, "conv int8 RF residency");
+
+
+}
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_convs_s8_sm80_shmem.cu

@@ -0,0 +1,237 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/conv/kernel/default_conv2d_fprop.h"
+#include "cutlass/conv/device/implicit_gemm_convolution.h"
+
+#include "device/b2b_implicit_gemm_convolution.h"
+#include "b2b_interleaved_conv2d_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_0 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {64, 3, 3, 64},   // filter size (KRSC)
+    {1, 1, 1, 1},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 64}     // output size (NPQK)
+  );
+cutlass::conv::Conv2dProblemSize conv2d_s8_sm80_problem_size_1 (
+    {32, 56, 56, 64},    // input size (NHWC)
+    {256, 1, 1, 64},   // filter size (KRSC)
+    {0, 0, 0, 0},     // padding (pad_h, _, pad_w, _)
+    {1, 1},           // stride (stride_h, stride_w)
+    {1, 1},           // dilation (dilation_h, dilation_w)
+    {32, 56, 56, 256}     // output size (NPQK)
+  );
+
+bool run_nonfused_conv2d_fprop_optimized_s8_sm80() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using Conv2dFpropKernel0 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop0 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel0>;
+
+  using Conv2dFpropKernel1 = typename cutlass::conv::kernel::DefaultConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized
+  >::Kernel;
+
+  using Conv2dFprop1 = cutlass::conv::device::ImplicitGemmConvolution<Conv2dFpropKernel1>;
+
+  B2bInterleavedNonFusedConv2dRun<Conv2dFprop0, Conv2dFprop1, 32> nonFusedConv2d;
+
+  std::cout << "Running Non-fused back-to-back INT8 interleaved Optimized Convolution Fprops...\n";
+  bool pass = nonFusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_conv2d_fprop_optimized_s8_sm80_shmem() {
+
+  using ElementA           = int8_t;
+  using ElementB           = int8_t;
+  using ElementC           = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      8 * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementC,
+      64 / cutlass::sizeof_bits<ElementC>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+  const bool SmemAccumulator = true;
+
+  using B2bConv2dFpropKernel = typename cutlass::conv::kernel::DefaultB2bConv2dFprop<
+    ElementA, cutlass::layout::TensorNCxHWx<32>,
+    ElementB, cutlass::layout::TensorCxRSKx<32>,
+    ElementC, cutlass::layout::TensorNCxHWx<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    cutlass::arch::OpMultiplyAddSaturate,
+    cutlass::conv::IteratorAlgorithm::kOptimized,
+    SmemAccumulator
+  >::Kernel;
+  
+  using B2bConv2dFprop = cutlass::conv::device::B2bImplicitGemmConvolution<B2bConv2dFpropKernel>;
+
+  B2bInterleavedFusedConv2dRun<B2bConv2dFprop, 32> fusedConv2d;
+
+  std::cout << "Running Fused back-to-back INT8 interleaved Optimized Convolution Fprops with shared memory staging...\n";
+  bool pass = fusedConv2d.run(conv2d_s8_sm80_problem_size_0, conv2d_s8_sm80_problem_size_1, cutlass::conv::SplitKMode::kSerial,
+      alpha0, beta0, alpha1, beta1);
+
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_conv2d_fprop_optimized_s8_sm80,
+    &run_fused_conv2d_fprop_optimized_s8_sm80_shmem
+  };
+
+  return testRun(80, funcs, "conv int8 shmem staging");
+
+
+}
+
+
+
+////////////////////////////////////////////////////////////////////////////////
+

examples/13_two_tensor_op_fusion/fused_two_gemms_f16_sm75_rf.cu

@@ -0,0 +1,210 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/device/gemm.h"
+
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/gemm.h"
+
+#include "device/b2b_gemm.h"
+#include "b2b_gemm_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_1(128*640, 128, 64);
+
+bool run_nonfused_gemm_f16() {
+
+  using ElementOutput = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta = 1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta = 1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using Gemm0 = cutlass::gemm::device::Gemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2
+  >;
+  using Gemm1 = cutlass::gemm::device::Gemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2
+  >;
+
+  B2bNonFusedGemmRun<Gemm0, Gemm1> nonFusedGemm;
+
+  std::cout << "Running Non-fused back-to-back FP16 TN GEMMs...\n";
+  bool pass = nonFusedGemm.run(gemm_f16_sm75_problem_size_0, gemm_f16_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+
+bool run_fused_gemm_f16_rf_res() {
+
+  using ElementOutput = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+  using B2bGemm = cutlass::gemm::device::B2bGemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2
+  >;
+
+  B2bFusedGemmRun<B2bGemm> fusedGemm;
+
+  std::cout << "Running Fused back-to-back FP16 TN GEMMs with RF Residency...\n";
+  bool passed = fusedGemm.run(gemm_f16_sm75_problem_size_0, gemm_f16_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(passed)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return passed;
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_f16,
+    &run_fused_gemm_f16_rf_res
+  };
+
+  return testRun(75, funcs, "gemm f16 RF residency");
+
+
+}
+
+///////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_f16_sm75_shmem.cu

@@ -1,29 +1,33 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-#pragma once
-
 #include <iostream>
 
 #include "cutlass/cutlass.h"
@@ -38,11 +42,12 @@
 
 #include "device/b2b_gemm.h"
 #include "b2b_gemm_run.h"
+#include "test_run.h"
 
 ////////////////////////////////////////////////////////////////////////////////
 
-cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_0(128*1600, 64, 576);
-cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_1(128*1600, 128, 64);
+cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_f16_sm75_problem_size_1(128*640, 256, 64);
 
 bool run_nonfused_gemm_f16() {
 
@@ -50,13 +55,13 @@ bool run_nonfused_gemm_f16() {
   using ElementAccumulator = cutlass::half_t;
   using ElementCompute = cutlass::half_t;
 
-  ElementCompute alpha0 = ElementCompute(2);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta = 1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta = 1 for bias
 
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
   using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
   using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
   using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
@@ -79,7 +84,7 @@ bool run_nonfused_gemm_f16() {
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     2
@@ -101,7 +106,8 @@ bool run_nonfused_gemm_f16() {
       ElementOutput,
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     2
@@ -119,21 +125,22 @@ bool run_nonfused_gemm_f16() {
   return pass;
 }
 
-bool run_fused_gemm_f16() {
+bool run_fused_gemm_f16_shmem() {
 
   using ElementOutput = cutlass::half_t;
   using ElementAccumulator = cutlass::half_t;
   using ElementCompute = cutlass::half_t;
 
-  ElementCompute alpha0 = ElementCompute(2);
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
   ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 32>;
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
   using InstructionShape = cutlass::gemm::GemmShape<16, 8, 8>;
 
   using EpilogueOutputOp0 = 
@@ -150,10 +157,12 @@ bool run_fused_gemm_f16() {
       ElementOutput,
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >;
 
 
+  const bool SmemAccumulator = true;
 
   using B2bGemm = cutlass::gemm::device::B2bGemm<
     cutlass::half_t,
@@ -173,12 +182,13 @@ bool run_fused_gemm_f16() {
     EpilogueOutputOp0,
     EpilogueOutputOp1,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
-    2
+    2,
+    SmemAccumulator
   >;
 
   B2bFusedGemmRun<B2bGemm> fusedGemm;
 
-  std::cout << "Running Fused back-to-back FP16 TN GEMMs...\n";
+  std::cout << "Running Fused back-to-back FP16 TN GEMMs with shared memory staging...\n";
   bool passed = fusedGemm.run(gemm_f16_sm75_problem_size_0, gemm_f16_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
   if(passed)
     std::cout << "Pass\n";
@@ -187,4 +197,18 @@ bool run_fused_gemm_f16() {
 
   return passed;
 }
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_f16,
+    &run_fused_gemm_f16_shmem
+  };
+
+  return testRun(75, funcs, "gemm f16 shmem staging");
+
+
+}
+
+
 ////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_f16_sm80_rf.cu

@@ -1,29 +1,33 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-#pragma once
-
 #include <iostream>
 
 #include "cutlass/cutlass.h"
@@ -38,11 +42,12 @@
 
 #include "device/b2b_gemm.h"
 #include "b2b_gemm_run.h"
+#include "test_run.h"
 
 ////////////////////////////////////////////////////////////////////////////////
 
-cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_0(128*1600, 64, 576);
-cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_1(128*1600, 128, 64);
+cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_1(128*640, 128, 64);
 
 bool run_nonfused_gemm_f16_sm80() {
 
@@ -50,15 +55,15 @@ bool run_nonfused_gemm_f16_sm80() {
   using ElementAccumulator = cutlass::half_t;
   using ElementCompute = cutlass::half_t;
 
-  ElementCompute alpha0 = ElementCompute(2);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 32>;
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
   using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
 
   using Gemm0 = cutlass::gemm::device::Gemm<
@@ -79,7 +84,7 @@ bool run_nonfused_gemm_f16_sm80() {
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     3
@@ -101,7 +106,8 @@ bool run_nonfused_gemm_f16_sm80() {
       ElementOutput,
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     3
@@ -119,21 +125,22 @@ bool run_nonfused_gemm_f16_sm80() {
   return pass;
 }
 
-bool run_fused_gemm_f16_sm80() {
+bool run_fused_gemm_f16_sm80_rf_res() {
 
   using ElementOutput = cutlass::half_t;
   using ElementAccumulator = cutlass::half_t;
   using ElementCompute = cutlass::half_t;
 
-  ElementCompute alpha0 = ElementCompute(2);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0); 
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<16, 64, 32>;
   using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<16, 128, 32>;
   using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
 
   using EpilogueOutputOp0 = 
@@ -150,11 +157,10 @@ bool run_fused_gemm_f16_sm80() {
       ElementOutput,
       128 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >;
 
-
-
   using B2bGemm = cutlass::gemm::device::B2bGemm<
     cutlass::half_t,
     cutlass::layout::RowMajor,
@@ -178,7 +184,7 @@ bool run_fused_gemm_f16_sm80() {
 
   B2bFusedGemmRun<B2bGemm> fusedGemm;
 
-  std::cout << "Running Fused back-to-back FP16 TN GEMMs...\n";
+  std::cout << "Running Fused back-to-back FP16 TN GEMMs with RF residency...\n";
   bool passed = fusedGemm.run(gemm_f16_sm80_problem_size_0, gemm_f16_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
   if(passed)
     std::cout << "Pass\n";
@@ -188,4 +194,20 @@ bool run_fused_gemm_f16_sm80() {
   return passed;
 
 }
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_f16_sm80,
+    &run_fused_gemm_f16_sm80_rf_res
+  };
+
+  return testRun(80, funcs, "gemm f16 RF residency");
+
+
+}
+
+
+
+
 ////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_f16_sm80_shmem.cu

@@ -0,0 +1,217 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/device/gemm.h"
+
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/gemm.h"
+
+#include "device/b2b_gemm.h"
+#include "b2b_gemm_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_f16_sm80_problem_size_1(128*640, 256, 64);
+
+bool run_nonfused_gemm_f16_sm80() {
+
+  using ElementOutput = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using Gemm0 = cutlass::gemm::device::Gemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3
+  >;
+  using Gemm1 = cutlass::gemm::device::Gemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3
+  >;
+
+  B2bNonFusedGemmRun<Gemm0, Gemm1> nonFusedGemm;
+
+  std::cout << "Running Non-fused back-to-back FP16 TN GEMMs...\n";
+  bool pass = nonFusedGemm.run(gemm_f16_sm80_problem_size_0, gemm_f16_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_gemm_f16_sm80_shmem() {
+
+  using ElementOutput = cutlass::half_t;
+  using ElementAccumulator = cutlass::half_t;
+  using ElementCompute = cutlass::half_t;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0); 
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      128 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+
+
+  const bool SmemAccumulator = true;
+
+  using B2bGemm = cutlass::gemm::device::B2bGemm<
+    cutlass::half_t,
+    cutlass::layout::RowMajor,
+    cutlass::half_t,
+    cutlass::layout::ColumnMajor,
+    ElementOutput,
+    cutlass::layout::RowMajor,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    3,
+    SmemAccumulator
+  >;
+
+  B2bFusedGemmRun<B2bGemm> fusedGemm;
+
+  std::cout << "Running Fused back-to-back FP16 TN GEMMs with shared memory staging...\n";
+  bool passed = fusedGemm.run(gemm_f16_sm80_problem_size_0, gemm_f16_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(passed)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return passed;
+
+}
+
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_f16_sm80,
+    &run_fused_gemm_f16_sm80_shmem
+  };
+
+  return testRun(80, funcs, "gemm f16 shmem staging");
+
+
+}
+
+
+
+////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_s8_sm75_rf.cu

@@ -1,29 +1,33 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-#pragma once
-
 #include <iostream>
 
 #include "cutlass/cutlass.h"
@@ -38,11 +42,12 @@
 
 #include "device/b2b_gemm.h"
 #include "b2b_interleaved_gemm_run.h"
+#include "test_run.h"
 
 ////////////////////////////////////////////////////////////////////////////////
 
-cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_0(128*1600, 64, 576);
-cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_1(128*1600, 128, 64);
+cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_1(128*640, 128, 64);
 
 bool run_nonfused_gemm_s8() {
 
@@ -50,15 +55,15 @@ bool run_nonfused_gemm_s8() {
   using ElementAccumulator = int32_t;
   using ElementCompute = float;
 
-  ElementCompute alpha0 = ElementCompute(2);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta = 1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta = 1 for bias
 
   using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
   using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 32, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<32, 64, 64>;
   using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
 
   using Gemm0 = cutlass::gemm::device::Gemm<
@@ -79,7 +84,7 @@ bool run_nonfused_gemm_s8() {
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     2
@@ -101,7 +106,8 @@ bool run_nonfused_gemm_s8() {
       ElementOutput,
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
     2
@@ -119,21 +125,23 @@ bool run_nonfused_gemm_s8() {
   return pass;
 }
 
-bool run_fused_gemm_s8() {
+
+bool run_fused_gemm_s8_rf_res() {
 
   using ElementOutput = int8_t;
   using ElementAccumulator = int32_t;
   using ElementCompute = float;
 
-  ElementCompute alpha0 = ElementCompute(2);
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
   ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(1);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
-  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
-  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 64>;
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 32>;
   using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
 
   using EpilogueOutputOp0 = 
@@ -150,7 +158,8 @@ bool run_fused_gemm_s8() {
       ElementOutput,
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
-      ElementCompute
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >;
 
   using B2bGemm = cutlass::gemm::device::B2bGemm<
@@ -176,7 +185,7 @@ bool run_fused_gemm_s8() {
 
   B2bInterleavedFusedGemmRun<B2bGemm, 32> fusedGemm;
 
-  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs...\n";
+  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs with RF Residency...\n";
   bool passed = fusedGemm.run(gemm_s8_sm75_problem_size_0, gemm_s8_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
   if(passed)
     std::cout << "Pass\n";
@@ -186,4 +195,18 @@ bool run_fused_gemm_s8() {
   return passed;
 
 }
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_s8,
+    &run_fused_gemm_s8_rf_res
+  };
+
+  return testRun(75, funcs, "gemm int8 RF residency");
+
+
+}
+
+
 ////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_s8_sm75_shmem.cu

@@ -0,0 +1,214 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/device/gemm.h"
+
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/gemm.h"
+
+#include "device/b2b_gemm.h"
+#include "b2b_interleaved_gemm_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_s8_sm75_problem_size_1(128*640, 256, 64);
+
+bool run_nonfused_gemm_s8() {
+
+  using ElementOutput = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta = 1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta = 1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using Gemm0 = cutlass::gemm::device::Gemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2
+  >;
+  using Gemm1 = cutlass::gemm::device::Gemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2
+  >;
+
+  B2bInterleavedNonFusedGemmRun<Gemm0, Gemm1, 32> nonFusedGemm;
+
+  std::cout << "Running Non-fused back-to-back INT8 NT interleaved GEMMs...\n";
+  bool pass = nonFusedGemm.run(gemm_s8_sm75_problem_size_0, gemm_s8_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_gemm_s8_shmem() {
+
+  using ElementOutput = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 32>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 32>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 32>;
+  using InstructionShape = cutlass::gemm::GemmShape<8, 8, 16>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      InstructionShape::kM * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+  
+  const bool SmemAccumulator = true;
+
+  using B2bGemm = cutlass::gemm::device::B2bGemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<1>,
+    2,
+    SmemAccumulator
+  >;
+
+  B2bInterleavedFusedGemmRun<B2bGemm, 32> fusedGemm;
+
+  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs with shared memory staging...\n";
+  bool passed = fusedGemm.run(gemm_s8_sm75_problem_size_0, gemm_s8_sm75_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(passed)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return passed;
+
+}
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_s8,
+    &run_fused_gemm_s8_shmem
+  };
+
+  return testRun(75, funcs, "gemm int8 shmem staing");
+
+
+}
+
+
+////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_s8_sm80_rf.cu

@@ -1,29 +1,33 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
-#pragma once
-
 #include <iostream>
 
 #include "cutlass/cutlass.h"
@@ -38,11 +42,12 @@
 
 #include "device/b2b_gemm.h"
 #include "b2b_interleaved_gemm_run.h"
+#include "test_run.h"
 
 ////////////////////////////////////////////////////////////////////////////////
 
-cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_0(128*1600, 64, 576);
-cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_1(128*1600, 128, 64);
+cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_1(128*640, 128, 64);
 
 bool run_nonfused_gemm_s8_sm80() {
 
@@ -50,10 +55,10 @@ bool run_nonfused_gemm_s8_sm80() {
   using ElementAccumulator = int32_t;
   using ElementCompute = float;
 
-  ElementCompute alpha0 = ElementCompute(2);
-  ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(0);
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
   using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
   using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
@@ -79,7 +84,7 @@ bool run_nonfused_gemm_s8_sm80() {
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
     3,
@@ -106,7 +111,7 @@ bool run_nonfused_gemm_s8_sm80() {
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
     3,
@@ -128,21 +133,23 @@ bool run_nonfused_gemm_s8_sm80() {
   return pass;
 }
 
-bool run_fused_gemm_s8_sm80() {
+
+bool run_fused_gemm_s8_sm80_rf_res() {
 
   using ElementOutput = int8_t;
   using ElementAccumulator = int32_t;
   using ElementCompute = float;
 
-  ElementCompute alpha0 = ElementCompute(2);
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
   ElementCompute beta0 = ElementCompute(0);
-  ElementCompute alpha1 = ElementCompute(2);
-  ElementCompute beta1 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
 
   using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
-  using WarpShape0 = cutlass::gemm::GemmShape<32, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<16, 64, 64>;
   using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 128, 64>;
-  using WarpShape1 = cutlass::gemm::GemmShape<32, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<16, 128, 64>;
   using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
 
   using EpilogueOutputOp0 = 
@@ -160,9 +167,11 @@ bool run_fused_gemm_s8_sm80() {
       64 / cutlass::sizeof_bits<ElementOutput>::value,
       ElementAccumulator,
       ElementCompute,
-      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
     >;
 
+  const bool SmemAccumulator = false;
+
   using B2bGemm = cutlass::gemm::device::B2bGemm<
     int8_t,
     cutlass::layout::ColumnMajorInterleaved<32>,
@@ -182,6 +191,7 @@ bool run_fused_gemm_s8_sm80() {
     EpilogueOutputOp1,
     cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
     3,
+    SmemAccumulator,
     16,
     16,
     false,
@@ -190,7 +200,7 @@ bool run_fused_gemm_s8_sm80() {
 
   B2bInterleavedFusedGemmRun<B2bGemm, 32> fusedGemm;
 
-  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs...\n";
+  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs with RF residency...\n";
   bool passed = fusedGemm.run(gemm_s8_sm80_problem_size_0, gemm_s8_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
   if(passed)
     std::cout << "Pass\n";
@@ -199,4 +209,19 @@ bool run_fused_gemm_s8_sm80() {
 
   return passed;
 }
+
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_s8_sm80,
+    &run_fused_gemm_s8_sm80_rf_res
+  };
+
+  return testRun(80, funcs, "gemm int8 RF residency");
+
+
+}
+
+
 ////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/fused_two_gemms_s8_sm80_shmem.cu

@@ -0,0 +1,226 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/device/gemm.h"
+
+#include "cutlass/util/host_tensor.h"
+#include "cutlass/util/tensor_view_io.h"
+#include "cutlass/util/reference/host/tensor_fill.h"
+#include "cutlass/util/reference/host/tensor_copy.h"
+#include "cutlass/util/reference/host/tensor_compare.h"
+#include "cutlass/util/reference/host/gemm.h"
+
+#include "device/b2b_gemm.h"
+#include "b2b_interleaved_gemm_run.h"
+#include "test_run.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_0(128*640, 64, 576);
+cutlass::gemm::GemmCoord gemm_s8_sm80_problem_size_1(128*640, 256, 64);
+
+bool run_nonfused_gemm_s8_sm80() {
+
+  using ElementOutput = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  ElementCompute beta0 = ElementCompute(1); //beta=1 for bias
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<128, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<128, 128, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using Gemm0 = cutlass::gemm::device::Gemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    WarpShape0,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
+    3,
+    16,
+    16,
+    false,
+    cutlass::arch::OpMultiplyAddSaturate
+  >;
+  using Gemm1 = cutlass::gemm::device::Gemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape1,
+    WarpShape1,
+    InstructionShape,
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
+    3,
+    16,
+    16,
+    false,
+    cutlass::arch::OpMultiplyAddSaturate
+  >;
+
+  B2bInterleavedNonFusedGemmRun<Gemm0, Gemm1, 32> nonFusedGemm;
+
+  std::cout << "Running Non-fused back-to-back INT8 NT interleaved GEMMs...\n";
+  bool pass = nonFusedGemm.run(gemm_s8_sm80_problem_size_0, gemm_s8_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(pass)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return pass;
+}
+
+bool run_fused_gemm_s8_sm80_shmem() {
+
+  using ElementOutput = int8_t;
+  using ElementAccumulator = int32_t;
+  using ElementCompute = float;
+
+  ElementCompute alpha0 = ElementCompute(1);
+  //Fused kernel has built-in bias, setting beta=0
+  ElementCompute beta0 = ElementCompute(0);
+  ElementCompute alpha1 = ElementCompute(1);
+  ElementCompute beta1 = ElementCompute(1); //beta=1 for bias
+
+  using ThreadblockShape0 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using WarpShape0 = cutlass::gemm::GemmShape<32, 32, 64>;
+  using ThreadblockShape1 = cutlass::gemm::GemmShape<64, 256, 64>;
+  using WarpShape1 = cutlass::gemm::GemmShape<64, 64, 64>;
+  using InstructionShape = cutlass::gemm::GemmShape<16, 8, 32>;
+
+  using EpilogueOutputOp0 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      8 * InstructionShape::kN / 32,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::OnlyAlphaScaling
+    >;
+
+  using EpilogueOutputOp1 = 
+    cutlass::epilogue::thread::LinearCombinationRelu<
+      ElementOutput,
+      64 / cutlass::sizeof_bits<ElementOutput>::value,
+      ElementAccumulator,
+      ElementCompute,
+      cutlass::epilogue::thread::ScaleType::NoBetaScaling
+    >;
+  
+  const bool SmemAccumulator = true;
+
+  using B2bGemm = cutlass::gemm::device::B2bGemm<
+    int8_t,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    int8_t,
+    cutlass::layout::RowMajorInterleaved<32>,
+    ElementOutput,
+    cutlass::layout::ColumnMajorInterleaved<32>,
+    ElementAccumulator,
+    cutlass::arch::OpClassTensorOp,
+    cutlass::arch::Sm80,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    EpilogueOutputOp0,
+    EpilogueOutputOp1,
+    cutlass::gemm::threadblock::GemmIdentityThreadblockSwizzle<>,
+    3,
+    SmemAccumulator,
+    16,
+    16,
+    false,
+    cutlass::arch::OpMultiplyAddSaturate
+  >;
+
+  B2bInterleavedFusedGemmRun<B2bGemm, 32> fusedGemm;
+
+  std::cout << "Running Fused back-to-back INT8 NT interleaved GEMMs with shared memory staging...\n";
+  bool passed = fusedGemm.run(gemm_s8_sm80_problem_size_0, gemm_s8_sm80_problem_size_1, alpha0, beta0, alpha1, beta1);
+  if(passed)
+    std::cout << "Pass\n";
+  else
+    std::cout << "Fail\n";
+
+  return passed;
+}
+
+
+int main() {
+
+  std::vector<bool (*)()>funcs = {
+    &run_nonfused_gemm_s8_sm80,
+    &run_fused_gemm_s8_sm80_shmem
+  };
+
+  return testRun(80, funcs, "gemm int8 shmem staging");
+
+
+}
+
+
+////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/kernel/b2b_gemm.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -73,6 +79,8 @@ struct B2bGemm {
     typename B2bMma::IteratorB0::TensorRef ref_B0;
     typename Epilogue::OutputTileIterator::Params params_C0;
     typename Epilogue::OutputTileIterator::TensorRef ref_C0;
+    typename B2bMma::IteratorAccumulatorScaleBias::TensorRef ref_Scale0;
+    typename B2bMma::IteratorAccumulatorScaleBias::TensorRef ref_Bias0;
     typename B2bMma::IteratorB1::Params params_B1;
     typename B2bMma::IteratorB1::TensorRef ref_B1;
     typename Epilogue::OutputTileIterator::Params params_C1;
@@ -103,6 +111,8 @@ struct B2bGemm {
       typename B2bMma::IteratorA0::TensorRef ref_A0,
       typename B2bMma::IteratorB0::TensorRef ref_B0,
       typename Epilogue::OutputTileIterator::TensorRef ref_C0,
+      typename B2bMma::IteratorAccumulatorScaleBias::TensorRef ref_Scale0,
+      typename B2bMma::IteratorAccumulatorScaleBias::TensorRef ref_Bias0,
       typename B2bMma::IteratorB1::TensorRef ref_B1,
       typename Epilogue::OutputTileIterator::TensorRef ref_C1,
       typename Epilogue::OutputTileIterator::TensorRef ref_D1,
@@ -120,6 +130,8 @@ struct B2bGemm {
       ref_B0(ref_B0),
       params_C0(ref_C0.layout()),
       ref_C0(ref_C0),
+      ref_Scale0(ref_Scale0),
+      ref_Bias0(ref_Bias0),
       params_B1(ref_B1.layout()),
       ref_B1(ref_B1),
       params_C1(ref_C1.layout()),
@@ -202,6 +214,19 @@ struct B2bGemm {
       return Status::kErrorMisalignedOperand;
     }
 
+    // Determine if fusion sizes are valid
+    if(problem_size_0.m() != problem_size_1.m())
+      return Status::kErrorInvalidProblem;
+
+    if(problem_size_0.n() != problem_size_1.k())
+      return Status::kErrorInvalidProblem;
+
+    if(problem_size_0.n() > B2bMma::Shape0::kN)
+      return Status::kErrorInvalidProblem;
+    
+    if(problem_size_1.n() > B2bMma::Shape1::kN)
+      return Status::kErrorInvalidProblem;
+
     return Status::kSuccess;
   }
 
@@ -286,6 +311,29 @@ struct B2bGemm {
     int warp_idx = __shfl_sync(0x1f, threadIdx.x / 32, 0);
     int lane_idx = threadIdx.x % 32;
 
+    // Construct iterators to accumulator scale/bias vector
+    typename B2bMma::IteratorAccumulatorScaleBias iterator_Scale0(
+      params.ref_Scale0.data(),
+      {1, params.problem_size_0.n()},
+      thread_idx,
+      warp_idx,
+      MatrixCoord(
+        0, threadblock_tile_offset.n() * B2bMma::Shape0::kN
+      )
+    );
+
+    typename B2bMma::IteratorAccumulatorScaleBias iterator_Bias0(
+      params.ref_Bias0.data(),
+      {1, params.problem_size_0.n()},
+      thread_idx,
+      warp_idx,
+      MatrixCoord(
+        0, threadblock_tile_offset.n() * B2bMma::Shape0::kN
+      )
+    );
+
+
+
     //
     // Main loop
     //
@@ -293,7 +341,7 @@ struct B2bGemm {
     OutputOp0 output_op_0(params.output_op_0);
 
     // Construct thread-scoped matrix multiply
-    B2bMma b2bMma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx);
+    B2bMma b2bMma(shared_storage.main_loop, thread_idx, warp_idx, lane_idx, params.problem_size_0.n());
 
     typename B2bMma::FragmentC0 src_accum;
     typename B2bMma::FragmentC1 accumulators;
@@ -303,7 +351,8 @@ struct B2bGemm {
 
     if (!kSplitKSerial || gemm_k_iterations_0 > 0) {
       // Compute threadblock-scoped matrix multiply-add
-      b2bMma(gemm_k_iterations_0, accumulators, iterator_A0, iterator_B0, iterator_B1, src_accum, output_op_0);
+      b2bMma(gemm_k_iterations_0, accumulators, iterator_A0, iterator_B0,
+        iterator_Scale0, iterator_Bias0, iterator_B1, src_accum, output_op_0);
     }
 
     //

examples/13_two_tensor_op_fusion/kernel/b2b_implicit_gemm_convolution.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -79,6 +85,10 @@ struct B2bImplicitGemmConvolution {
   using ElementAccumulator = typename EpilogueOutputOp0::ElementAccumulator;
   using ElementCompute = typename EpilogueOutputOp0::ElementCompute;
 
+  /// Scale and Bias
+  using ElementScaleBias = typename B2bMma::IteratorAccumulatorScaleBias::Element;
+  using LayoutScaleBias = typename B2bMma::IteratorAccumulatorScaleBias::Layout;
+
   using WarpMmaOperator0 = typename B2bMma::Policy0::Operator;
   using WarpMmaOperator1 = typename B2bMma::Policy1::Operator;
 
@@ -103,13 +113,14 @@ struct B2bImplicitGemmConvolution {
 
   using TensorRefA0 = typename B2bMma::IteratorA0::TensorRef;
   using TensorRefB0 = typename B2bMma::IteratorB0::TensorRef;
+  using TensorRefScaleBias0 = typename B2bMma::IteratorAccumulatorScaleBias::TensorRef;
   using TensorRefB1 = typename B2bMma::IteratorB1::TensorRef;
   using TensorRefC = cutlass::TensorRef<ElementC, LayoutC>;
 
   /// Check iterator A and B convolution dimension are the same and 
   // set device::B2bImplicitGemmConvolution::kConvDim
   static_assert(B2bMma::IteratorA0::kConvDim == B2bMma::IteratorB0::kConvDim, 
-    "Convolution on different different dimensions is not supported");
+    "Convolution on different dimensions is not supported");
   static int const kConvDim = B2bMma::IteratorA0::kConvDim;
 
   /// Conv dimension and problem size structure (Conv2d or Conv3d)
@@ -148,6 +159,8 @@ struct B2bImplicitGemmConvolution {
     TensorRefA0 ref_A0;
     TensorRefB0 ref_B0;
     TensorRefC ref_C0;
+    TensorRefScaleBias0 ref_Scale0;
+    TensorRefScaleBias0 ref_Bias0;
     TensorRefB1 ref_B1;
     TensorRefC ref_C1;
     TensorRefC ref_D1;
@@ -178,6 +191,8 @@ struct B2bImplicitGemmConvolution {
       TensorRefA0 const & ref_A0,
       TensorRefB0 const & ref_B0,
       TensorRefC const & ref_C0,
+      TensorRefScaleBias0 const & ref_Scale0,
+      TensorRefScaleBias0 const & ref_Bias0,
       TensorRefB1 const & ref_B1,
       TensorRefC const & ref_C1,
       TensorRefC const & ref_D1,
@@ -190,6 +205,8 @@ struct B2bImplicitGemmConvolution {
       ref_A0(ref_A0),
       ref_B0(ref_B0),
       ref_C0(ref_C0),
+      ref_Scale0(ref_Scale0),
+      ref_Bias0(ref_Bias0),
       ref_B1(ref_B1),
       ref_C1(ref_C1),
       ref_D1(ref_D1),
@@ -218,6 +235,8 @@ struct B2bImplicitGemmConvolution {
     typename B2bMma::IteratorB0::Element const *ptr_B0;
     typename Epilogue::OutputTileIterator::Params iterator_C0;
     typename Epilogue::OutputTileIterator::Element *ptr_C0;
+    typename B2bMma::IteratorAccumulatorScaleBias::Element *ptr_Scale0;
+    typename B2bMma::IteratorAccumulatorScaleBias::Element *ptr_Bias0;
     typename B2bMma::IteratorB1::Params iterator_B1;
     typename B2bMma::IteratorB1::Element const *ptr_B1;
     typename Epilogue::OutputTileIterator::Params iterator_C1;
@@ -252,6 +271,8 @@ struct B2bImplicitGemmConvolution {
       ptr_B0(args.ref_B0.data()),
       iterator_C0(ConvOutputIteratorParameter::layout(args.ref_C0)),
       ptr_C0(args.ref_C0.data()),
+      ptr_Scale0(args.ref_Scale0.data()),
+      ptr_Bias0(args.ref_Bias0.data()),
       iterator_B1(args.problem_size_1, args.ref_B1.layout()),
       ptr_B1(args.ref_B1.data()),
       iterator_C1(ConvOutputIteratorParameter::layout(args.ref_C1)),
@@ -350,6 +371,28 @@ struct B2bImplicitGemmConvolution {
     int warp_idx = __shfl_sync(0xffffffff, threadIdx.x / 32, 0);
     int lane_idx = threadIdx.x % 32;
 
+    // Construct iterators to accumulator scale/bias vector
+    typename B2bMma::IteratorAccumulatorScaleBias iterator_Scale0(
+      params.ptr_Scale0,
+      {1, params.problem_size_0.K},
+      thread_idx,
+      warp_idx,
+      MatrixCoord(
+        0, threadblock_tile_idx.n() * B2bMma::Shape0::kN
+      )
+    );
+
+    typename B2bMma::IteratorAccumulatorScaleBias iterator_Bias0(
+      params.ptr_Bias0,
+      {1, params.problem_size_0.K},
+      thread_idx,
+      warp_idx,
+      MatrixCoord(
+        0, threadblock_tile_idx.n() * B2bMma::Shape0::kN
+      )
+    );
+
+
     //
     // Main loop
     //
@@ -366,7 +409,8 @@ struct B2bImplicitGemmConvolution {
     accumulators.clear();
 
     // Compute threadblock-scoped matrix multiply-add
-    b2bMma(params.gemm_k_iterations_0, accumulators, iterator_A0, iterator_B0, iterator_B1, src_accum, output_op_0);
+    b2bMma(params.gemm_k_iterations_0, accumulators, iterator_A0, iterator_B0, 
+        iterator_Scale0, iterator_Bias0, iterator_B1, src_accum, output_op_0);
 
     //
     // Epilogue

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_sm75.h

@@ -0,0 +1,749 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 
+    Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped 
+      matrix multiply-add with the appropriate threadblock-scoped epilogue.  
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/conv/kernel/default_conv2d.h"
+
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
+
+#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
+#include "cutlass/transform/threadblock/vector_iterator.h"
+#include "cutlass/transform/warp/vector_fragment_iterator.h"
+
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "kernel/default_b2b_conv2d_fprop.h"
+#include "kernel/b2b_implicit_gemm_convolution.h"
+#include "threadblock/b2b_implicit_gemm_pipelined.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+//                         OpClassTensorOp convolutions 
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm
+/// and 2 stage pipeline.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, LayoutA,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, LayoutB,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::ColumnMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, LayoutB,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelined<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename detail::DefaultConvEpilogue<
+    ArchTag,
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and 2 stage 
+/// pipeline with interleaved layout.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic,
+  false
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, layout::TensorNCxHWx<InterleavedK>,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelined<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm
+/// and 2 stage pipeline.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, LayoutA,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, LayoutB,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::ColumnMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, LayoutB,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelined<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename detail::DefaultConvEpilogue<
+    ArchTag,
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 2 stage 
+/// pipeline with interleaved layout.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, layout::TensorNCxHWx<InterleavedK>,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelined<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace kernel
+} // namespace conv
+} // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_sm80.h

@@ -0,0 +1,740 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 
+    Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped 
+      matrix multiply-add with the appropriate threadblock-scoped epilogue.  
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/conv/kernel/default_conv2d.h"
+
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
+
+#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
+#include "cutlass/transform/threadblock/vector_iterator.h"
+#include "cutlass/transform/warp/vector_fragment_iterator.h"
+
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "kernel/default_b2b_conv2d_fprop.h"
+#include "kernel/b2b_implicit_gemm_convolution.h"
+#include "threadblock/b2b_implicit_gemm_multistage.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+//                         OpClassTensorOp convolutions 
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and multistage 
+/// pipeline.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, LayoutA,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, LayoutB,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::ColumnMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, LayoutB,
+      ThreadMapB1
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistage<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and multistage 
+/// pipeline with interleaved layout.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, layout::TensorNCxHWx<InterleavedK>,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB1
+    >;
+ 
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistage<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 
+/// multistage pipeline.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, LayoutA,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, LayoutB,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::ColumnMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, LayoutB,
+      ThreadMapB1
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistage<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimzed IteratorAlgorithm and 
+// multistage pipeline with interleaved layout.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, layout::TensorNCxHWx<InterleavedK>,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  // Use fragment iterator for A operand
+  using AccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorA1 = 
+      cutlass::gemm::warp::MmaTensorOpFragmentIterator<
+          cutlass::MatrixShape<MmaCore1::WarpShape::kM, MmaCore1::InstructionShape::kK>, //warp shape
+          cutlass::MatrixShape<MmaCore0::WarpShape::kM, MmaCore0::WarpShape::kN>, //accumulator shape
+          MmaCore1::Shape::kK, //kBlocksColumn
+          ElementAccumulator, ElementA, AccumulatorLayout, InstructionShape, EpilogueOutputOp0>;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape1::kM, WarpShape1::kK>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Warp-level iterators to load scale and bias vectors
+  using FragmentIteratorA1ScaleBias = cutlass::transform::warp::VectorFragmentIterator<
+      MatrixShape<1, IteratorAccumulatorScaleBias::Fragment::kElements>, ElementScaleBias,
+      LayoutScaleBias, InstructionShape, kElementsPerAccess>;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB1
+    >;
+ 
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistage<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    ThreadblockShape1,
+    FragmentIteratorA1,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorA1ScaleBias,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace kernel
+} // namespace conv
+} // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_smem_accumulator_sm75.h

@@ -0,0 +1,817 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 
+    Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped 
+      matrix multiply-add with the appropriate threadblock-scoped epilogue.  
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/conv/kernel/default_conv2d.h"
+
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
+
+#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
+#include "cutlass/transform/threadblock/vector_iterator.h"
+#include "cutlass/transform/warp/vector_fragment_iterator.h"
+
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "kernel/default_b2b_conv2d_fprop.h"
+#include "kernel/b2b_implicit_gemm_convolution.h"
+#include "threadblock/b2b_implicit_gemm_pipelined_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm
+/// and 2 stage pipeline.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, LayoutA,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, LayoutB,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, LayoutB,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename detail::DefaultConvEpilogue<
+    ArchTag,
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and 2 stage 
+/// pipeline with interleaved layout.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, layout::TensorNCxHWx<InterleavedK>,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4; //For interleaved layout
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+ // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm
+/// and 2 stage pipeline.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      2, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, LayoutA,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, LayoutB,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, LayoutB,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename detail::DefaultConvEpilogue<
+    ArchTag,
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 2 stage 
+/// pipeline with interleaved layout.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      2, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+        ElementA, layout::TensorNCxHWx<InterleavedK>,
+        ThreadMapA0
+      >
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB0
+      >
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4; //For interleaved layout
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::TileIterator<
+      cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+        cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+        ElementB, layout::TensorCxRSKx<InterleavedK>,
+        ThreadMapB1
+      >
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmPipelinedSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    IteratorB0,
+    SmemIteratorB0,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    ElementC,
+    LayoutC,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace kernel
+} // namespace conv
+} // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/kernel/default_b2b_conv2d_fprop_smem_accumulator_sm80.h

@@ -0,0 +1,804 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 
+    Default kernel-level implicit GEMM convolution definitions combine threadblock-scoped 
+      matrix multiply-add with the appropriate threadblock-scoped epilogue.  
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/conv/kernel/default_conv2d.h"
+
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_analytic.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_activation_tile_access_iterator_optimized.h"
+#include "cutlass/conv/threadblock/conv2d_fprop_filter_tile_access_iterator_optimized.h"
+
+#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
+#include "cutlass/transform/threadblock/vector_iterator.h"
+#include "cutlass/transform/warp/vector_fragment_iterator.h"
+
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "kernel/default_b2b_conv2d_fprop.h"
+#include "kernel/b2b_implicit_gemm_convolution.h"
+#include "threadblock/b2b_implicit_gemm_multistage_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace kernel {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and multistage 
+/// pipeline.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, LayoutA,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, LayoutB,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, LayoutB,
+      ThreadMapB1
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistageSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Analytic IteratorAlgorithm and multistage 
+/// pipeline with interleaved layout.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kAnalytic,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, layout::TensorNCxHWx<InterleavedK>,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorAnalytic<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB1
+    >;
+ 
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistageSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimized IteratorAlgorithm and 
+/// multistage pipeline.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename LayoutA,
+  typename ElementB,
+  typename LayoutB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  LayoutA,
+  ElementB,
+  LayoutB,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::RowMajor,
+      ElementB, layout::ColumnMajor, ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp,
+      Stages, MathOperatorTag>;
+
+  // Define iterators over tiles from the A operand
+  using ThreadMapA0 = typename MmaCore0::IteratorThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, LayoutA,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB0 = typename MmaCore0::IteratorThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, LayoutB,
+      ThreadMapB0
+    >;
+  
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 2;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>,
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>,
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  // Define iterators over tiles from the B operand
+  using ThreadMapB1 = typename MmaCore1::IteratorThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, LayoutB,
+      ThreadMapB1
+    >;
+  
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::RowMajor;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIterator<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistageSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultEpilogueTensorOp<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Defines a kernel for Conv2dFprop specialization for Optimzed IteratorAlgorithm and 
+// multistage pipeline with interleaved layout.
+/// Accumulator will be staged in shared memory.
+template <
+  typename ElementA,
+  typename ElementB,
+  typename ElementC,
+  typename LayoutC,
+  typename ElementAccumulator,
+  typename ArchTag,
+  typename ThreadblockShape0,
+  typename ThreadblockShape1,
+  typename WarpShape0,
+  typename WarpShape1,
+  typename InstructionShape,
+  typename EpilogueOutputOp0,
+  typename EpilogueOutputOp1,
+  typename ThreadblockSwizzle,
+  int Stages,
+  typename MathOperatorTag,
+  int InterleavedK
+>
+struct DefaultB2bConv2dFprop <
+  ElementA,
+  layout::TensorNCxHWx<InterleavedK>,
+  ElementB,
+  layout::TensorCxRSKx<InterleavedK>,
+  ElementC,
+  LayoutC,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  ArchTag,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  Stages,
+  MathOperatorTag,
+  IteratorAlgorithm::kOptimized,
+  true
+> {
+
+  // Define the core components from GEMM
+  using MmaCore0 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape0, WarpShape0, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+  using MmaCore1 = typename cutlass::gemm::threadblock::DefaultMmaCore<
+      ThreadblockShape1, WarpShape1, InstructionShape, ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+      ElementB, layout::RowMajorInterleaved<InterleavedK>,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp,
+      Stages, MathOperatorTag, true>;
+
+  // Define iterators over tiles from the A operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapA0 = typename MmaCore0::SmemThreadMapA;
+  using IteratorA0 =
+    cutlass::conv::threadblock::Conv2dFpropActivationTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kK>,
+      ElementA, layout::TensorNCxHWx<InterleavedK>,
+      ThreadMapA0
+    >;
+
+  using SmemIteratorA0 = typename MmaCore0::SmemIteratorA;
+
+  // Define iterators over tiles from the B operand
+  // Note GEMM shared memory threadmap is used here because conv global memory
+  // layout needs to be mapped to fprop which is similar to the crosswise
+  // layout which is used by the interleaved GEMM shared memory threadmap.
+  // The Interleaved GEMM global memory layout is similar to the congruous
+  // layout.
+  using ThreadMapB0 = typename MmaCore0::SmemThreadMapB;
+  using IteratorB0 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape0::kK, ThreadblockShape0::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB0
+    >;
+
+  using SmemIteratorB0 = typename MmaCore0::SmemIteratorB;
+
+  /// Define iterators over tiles from scale/bias vectors
+  using ElementScaleBias = typename EpilogueOutputOp0::ElementCompute;
+  using LayoutScaleBias = layout::RowMajor; //vector layout doesn't really matter
+  static int const kElementsPerAccess = 4;
+  using IteratorAccumulatorScaleBias =
+    cutlass::transform::threadblock::VectorIterator<
+      cutlass::transform::threadblock::PredicatedVectorAccessIterator<
+          cutlass::MatrixShape<ThreadblockShape0::kM, ThreadblockShape0::kN>, 
+          cutlass::MatrixShape<WarpShape0::kM, WarpShape0::kN>, 
+          ElementScaleBias, LayoutScaleBias, kElementsPerAccess>
+    >;
+
+  using ThreadMapB1 = typename MmaCore1::SmemThreadMapB;
+  using IteratorB1 =
+    cutlass::conv::threadblock::Conv2dFpropFilterTileAccessIteratorOptimized<
+      cutlass::MatrixShape<ThreadblockShape1::kK, ThreadblockShape1::kN>,
+      ElementB, layout::TensorCxRSKx<InterleavedK>,
+      ThreadMapB1
+    >;
+ 
+  using SmemIteratorB1 = typename MmaCore1::SmemIteratorB;
+
+
+  // Warp-level GEMM components
+  using WarpMmaTensorOp0 = typename MmaCore0::MmaTensorOp;
+  using WarpMmaTensorOp1 = typename MmaCore1::MmaTensorOp;
+  using MmaPolicy0 = typename MmaCore0::MmaPolicy;
+  using MmaPolicy1 = typename MmaCore1::MmaPolicy;
+
+  // Use fragment iterator for the accumulator
+  using SmemAccumulatorLayout = cutlass::layout::ColumnMajorInterleaved<16>;
+  using FragmentIteratorAccumulator = cutlass::epilogue::warp::FragmentIteratorTensorOp<
+          WarpShape0, InstructionShape,
+          ElementAccumulator,
+          typename WarpMmaTensorOp0::Policy::Operator::FragmentC,
+          SmemAccumulatorLayout
+        >;
+
+
+  // Store Accumulator tiles to Shared Memory
+  using SmemIteratorD0 = 
+      cutlass::epilogue::warp::TileIteratorTensorOp<
+          WarpShape0,
+          InstructionShape,
+          ElementC,
+          SmemAccumulatorLayout
+        >;
+
+  static int const kThreadCount = 32;
+  // load warp tile from Shared Memory accumulator
+  using WarpIteratorA1 = cutlass::gemm::warp::MmaTensorOpMultiplicandTileIteratorCanonical<
+    MatrixShape<WarpShape1::kM, InstructionShape::kK>, cutlass::gemm::Operand::kA, 
+    ElementA, SmemAccumulatorLayout,
+    MatrixShape<InstructionShape::kM, InstructionShape::kK>,
+    WarpMmaTensorOp1::Policy::OpDelta::kRow, kThreadCount>;
+ 
+  // Define the Mma
+  using B2bMma = threadblock::B2bImplicitGemmMultistageSmemAccumulator<
+    ThreadblockShape0,
+    IteratorA0,
+    SmemIteratorA0,
+    arch::CacheOperation::Always,
+    IteratorB0,
+    SmemIteratorB0,
+    arch::CacheOperation::Global,
+    IteratorAccumulatorScaleBias,
+    FragmentIteratorAccumulator,
+    SmemIteratorD0,
+    ThreadblockShape1,
+    WarpIteratorA1,
+    IteratorB1,
+    SmemIteratorB1,
+    arch::CacheOperation::Global,
+    EpilogueOutputOp0,
+    MmaPolicy0,
+    MmaPolicy1,
+    Stages 
+  >;
+
+  // Define the epilogue
+  using Epilogue = typename epilogue::threadblock::DefaultInterleavedConvEpilogue<
+    ThreadblockShape1,
+    WarpMmaTensorOp1,
+    1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount,
+    InterleavedK
+  >::Epilogue;
+
+  // Define the kernel
+  using Kernel = cutlass::conv::kernel::B2bImplicitGemmConvolution<
+    B2bMma,
+    Epilogue,
+    ThreadblockSwizzle,
+    conv::Operator::kFprop
+  >;
+};
+
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace kernel
+} // namespace conv
+} // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/kernel/default_b2b_gemm.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -111,7 +117,9 @@ template <
   /// If true, kernel is configured to support serial reduction in the epilogue
   bool SplitKSerial,
   /// Operation performed by GEMM
-  typename Operator
+  typename Operator,
+  /// Stage accumulator in shared memory
+  bool SmemAccumulator = false
 >
 struct DefaultB2bGemm;
 

examples/13_two_tensor_op_fusion/kernel/default_b2b_gemm_smem_accumulator.h

@@ -0,0 +1,397 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 
+      Default kernel-level GEMM definitions combine threadblock-scoped matrix multiply-add with
+      the appropriate threadblock-scoped epilogue.
+  
+      Note, CUTLASS epilogues universally target row-major outputs. Column-major outputs are
+      accommodated by exchanging A and B operands and assuming transposed layouts. Partial
+      specializations here choose 'device::GemmTransposed' to implement this functionality.
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+
+#include "cutlass/layout/matrix.h"
+#include "cutlass/numeric_types.h"
+
+#include "cutlass/epilogue/threadblock/epilogue.h"
+#include "cutlass/epilogue/thread/linear_combination.h"
+
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/gemm/kernel/gemm_pipelined.h"
+#include "cutlass/gemm/threadblock/default_mma_core_sm75.h"
+#include "cutlass/gemm/threadblock/default_mma_core_sm70.h"
+#include "cutlass/gemm/threadblock/default_mma_core_sm80.h"
+#include "cutlass/gemm/threadblock/default_mma_core_simt.h"
+#include "cutlass/gemm/threadblock/threadblock_swizzle.h"
+#include "cutlass/epilogue/threadblock/default_epilogue_tensor_op.h"
+#include "cutlass/epilogue/threadblock/default_epilogue_volta_tensor_op.h"
+#include "cutlass/epilogue/threadblock/default_epilogue_simt.h"
+
+#include "cutlass/transform/threadblock/predicated_tile_iterator.h"
+#include "cutlass/transform/threadblock/vector_iterator.h"
+#include "cutlass/transform/threadblock/predicated_vector_access_iterator.h"
+
+#include "kernel/b2b_gemm.h"
+#include "threadblock/default_b2b_mma.h"
+#include "threadblock/default_b2b_mma_smem_accumulator.h"
+
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace kernel {
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Partial specialization for Ampere Architecture
+template <
+    /// Element type for A matrix operand
+    typename ElementA,
+    /// Layout type for A matrix operand
+    typename LayoutA,
+    /// Access granularity of A matrix in units of elements
+    int kAlignmentA,
+    /// Element type for B matrix operand
+    typename ElementB,
+    /// Layout type for B matrix operand
+    typename LayoutB,
+    /// Access granularity of A matrix in units of elements
+    int kAlignmentB,
+    /// Element type for C and D matrix operands
+    typename ElementC,
+    /// Element type for internal accumulation
+    typename ElementAccumulator,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape0,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape0,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename InstructionShape,
+    /// Epilogue output operator
+    typename EpilogueOutputOp0,
+    /// Epilogue output operator
+    typename EpilogueOutputOp1,
+    /// Threadblock-level swizzling operator
+    typename ThreadblockSwizzle,
+    /// Number of stages used in the pipelined mainloop
+    int Stages,
+    /// If true, kernel is configured to support serial reduction in the
+    /// epilogue
+    bool SplitKSerial,
+    /// Operation performed by GEMM
+    typename Operator>
+struct DefaultB2bGemm<ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, ElementC,
+                   layout::RowMajor, ElementAccumulator, arch::OpClassTensorOp,
+                   arch::Sm80, ThreadblockShape0, ThreadblockShape1,
+                   WarpShape0, WarpShape1, InstructionShape,
+                   EpilogueOutputOp0, EpilogueOutputOp1, ThreadblockSwizzle, Stages, SplitKSerial,
+                   Operator, true> {
+  /// Define the threadblock-scoped matrix multiply-accumulate
+  using B2bMma = typename cutlass::gemm::threadblock::DefaultB2bMma<
+      ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
+      ElementAccumulator, layout::RowMajor, arch::OpClassTensorOp, arch::Sm80,
+      ThreadblockShape0, ThreadblockShape1, WarpShape0, WarpShape1, 
+      InstructionShape, Stages, Operator, EpilogueOutputOp0, false, true>::ThreadblockB2bMma;
+
+  static const int kPartitionsK1 = ThreadblockShape1::kK / WarpShape1::kK;
+
+  /// Define the epilogue
+  using Epilogue =
+      typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
+          ThreadblockShape1, typename B2bMma::Operator1, kPartitionsK1, EpilogueOutputOp1,
+          EpilogueOutputOp1::kCount>::Epilogue;
+
+  /// Define the kernel-level GEMM operator.
+  using B2bGemmKernel = kernel::B2bGemm<B2bMma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
+};
+
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Partial specialization for Turing Architecture
+template <
+  /// Element type for A matrix operand
+  typename ElementA,
+  /// Layout type for A matrix operand
+  typename LayoutA,
+  /// Access granularity of A matrix in units of elements
+  int kAlignmentA,
+  /// Element type for B matrix operand
+  typename ElementB,
+  /// Layout type for B matrix operand
+  typename LayoutB,
+  /// Access granularity of B matrix in units of elements
+  int kAlignmentB,
+  /// Element type for C and D matrix operands
+  typename ElementC,
+  /// Element type for internal accumulation
+  typename ElementAccumulator,
+  /// Threadblock-level tile size (concept: GemmShape)
+  typename ThreadblockShape0,
+  /// Threadblock-level tile size (concept: GemmShape)
+  typename ThreadblockShape1,
+  /// Warp-level tile size (concept: GemmShape)
+  typename WarpShape0,
+  /// Warp-level tile size (concept: GemmShape)
+  typename WarpShape1,
+  /// Warp-level tile size (concept: GemmShape)
+  typename InstructionShape,
+  /// Epilogue output operator
+  typename EpilogueOutputOp0,
+  /// Epilogue output operator
+  typename EpilogueOutputOp1,
+  /// Threadblock-level swizzling operator
+  typename ThreadblockSwizzle,
+  /// If true, kernel is configured to support serial reduction in the epilogue
+  bool SplitKSerial,
+  /// Operation performed by GEMM
+  typename Operator
+>
+struct DefaultB2bGemm<
+  ElementA, LayoutA, kAlignmentA,
+  ElementB, LayoutB, kAlignmentB,
+  ElementC, layout::RowMajor,
+  ElementAccumulator,
+  arch::OpClassTensorOp,
+  arch::Sm75,
+  ThreadblockShape0,
+  ThreadblockShape1,
+  WarpShape0,
+  WarpShape1,
+  InstructionShape,
+  EpilogueOutputOp0,
+  EpilogueOutputOp1,
+  ThreadblockSwizzle,
+  2,
+  SplitKSerial,
+  Operator,
+  true
+> {
+
+  /// Define the threadblock-scoped matrix multiply-accumulate
+  using B2bMma = typename cutlass::gemm::threadblock::DefaultB2bMma<
+    ElementA,
+    LayoutA,
+    kAlignmentA,
+    ElementB,
+    LayoutB,
+    kAlignmentB,
+    ElementAccumulator,
+    layout::RowMajor,
+    arch::OpClassTensorOp,
+    arch::Sm75,
+    ThreadblockShape0,
+    ThreadblockShape1,
+    WarpShape0,
+    WarpShape1,
+    InstructionShape,
+    2,
+    Operator,
+    EpilogueOutputOp0,
+    false,
+    true
+  >::ThreadblockB2bMma;
+  
+  static const int kPartitionsK1 = ThreadblockShape1::kK / WarpShape1::kK;
+
+  /// Define the epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::DefaultEpilogueTensorOp<
+    ThreadblockShape1,
+    typename B2bMma::Operator1,
+    kPartitionsK1,
+    EpilogueOutputOp1,
+    EpilogueOutputOp1::kCount
+  >::Epilogue;
+
+  /// Define the kernel-level GEMM operator.
+  using B2bGemmKernel = kernel::B2bGemm<B2bMma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
+};
+
+
+/// Partial specialization for Ampere Integer Matrix Multiply Interleaved layout
+template <
+    /// Element type for A matrix operand
+    typename ElementA,
+    /// Access granularity of A matrix in units of elements
+    int kAlignmentA,
+    /// Element type for B matrix operand
+    typename ElementB,
+    /// Access granularity of B matrix in units of elements
+    int kAlignmentB,
+    /// Element type for C and D matrix operands
+    typename ElementC,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape0,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape0,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename InstructionShape,
+    /// Epilogue output operator
+    typename EpilogueOutputOp0,
+    /// Epilogue output operator
+    typename EpilogueOutputOp1,
+    /// Threadblock-level swizzling operator
+    typename ThreadblockSwizzle,
+    /// Number of stages used in the pipelined mainloop
+    int Stages,
+    /// Number of Interleaved k
+    int InterleavedK,
+    /// If true, kernel is configured to support serial reduction in the
+    /// epilogue
+    bool SplitKSerial,
+    /// Operation performed by GEMM
+    typename Operator>
+struct DefaultB2bGemm<
+    ElementA, layout::ColumnMajorInterleaved<InterleavedK>, kAlignmentA,
+    ElementB, layout::RowMajorInterleaved<InterleavedK>, kAlignmentB, 
+    ElementC, layout::ColumnMajorInterleaved<InterleavedK>, int32_t,
+    arch::OpClassTensorOp, arch::Sm80,
+    ThreadblockShape0, ThreadblockShape1, WarpShape0, WarpShape1,
+    InstructionShape, EpilogueOutputOp0, EpilogueOutputOp1,
+    ThreadblockSwizzle, Stages,
+    SplitKSerial, Operator, true> {
+  using LayoutA = layout::ColumnMajorInterleaved<InterleavedK>;
+  using LayoutB = layout::RowMajorInterleaved<InterleavedK>;
+  using LayoutC = layout::ColumnMajorInterleaved<InterleavedK>;
+
+  using ElementAccumulator = int32_t;
+
+  /// Define the threadblock-scoped matrix multiply-accumulate
+  using B2bMma = typename cutlass::gemm::threadblock::DefaultB2bMma<
+      ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB,
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp, arch::Sm80,
+      ThreadblockShape0, ThreadblockShape1, WarpShape0, WarpShape1,
+      InstructionShape, Stages, Operator, EpilogueOutputOp0,
+      true, true>::ThreadblockB2bMma;
+
+  static const int kPartitionsK1 = ThreadblockShape1::kK / WarpShape1::kK;
+
+  /// Define the epilogue
+  using Epilogue = typename cutlass::epilogue::threadblock::
+      DefaultInterleavedEpilogueTensorOp<
+          ThreadblockShape1, typename B2bMma::Operator1, kPartitionsK1, EpilogueOutputOp1,
+          64 / sizeof_bits<ElementC>::value, InterleavedK>::Epilogue;
+
+  /// Define the kernel-level GEMM operator.
+  using B2bGemmKernel = kernel::B2bGemm<B2bMma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
+};
+
+////////////////////////////////////////////////////////////////////////////////
+
+
+/// Partial specialization for Turing Integer Tensor Core Interleaved layout
+template <
+    /// Element type for A matrix operand
+    typename ElementA,
+    /// Access granularity of A matrix in units of elements
+    int kAlignmentA,
+    /// Element type for B matrix operand
+    typename ElementB,
+    /// Access granularity of B matrix in units of elements
+    int kAlignmentB,
+    /// Element type for C and D matrix operands
+    typename ElementC,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape0,
+    /// Threadblock-level tile size (concept: GemmShape)
+    typename ThreadblockShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape0,
+    /// Warp-level tile size (concept: GemmShape)
+    typename WarpShape1,
+    /// Warp-level tile size (concept: GemmShape)
+    typename InstructionShape,
+    /// Epilogue output operator
+    typename EpilogueOutputOp0,
+    /// Epilogue output operator
+    typename EpilogueOutputOp1,
+    /// Threadblock-level swizzling operator
+    typename ThreadblockSwizzle,
+    /// Number of Interleaved k
+    int InterleavedK,
+    /// If true, kernel is configured to support serial reduction in the
+    /// epilogue
+    bool SplitKSerial,
+    /// Operation performed by GEMM
+    typename Operator>
+struct DefaultB2bGemm<ElementA, layout::ColumnMajorInterleaved<InterleavedK>,
+                   kAlignmentA, ElementB,
+                   layout::RowMajorInterleaved<InterleavedK>, kAlignmentB,
+                   ElementC, layout::ColumnMajorInterleaved<InterleavedK>,
+                   int32_t, arch::OpClassTensorOp, arch::Sm75, 
+                   ThreadblockShape0, ThreadblockShape1, WarpShape0, WarpShape1,
+                   InstructionShape, EpilogueOutputOp0, EpilogueOutputOp1,
+                   ThreadblockSwizzle, 2, SplitKSerial, Operator, true> {
+  using LayoutA = layout::ColumnMajorInterleaved<InterleavedK>;
+  using LayoutB = layout::RowMajorInterleaved<InterleavedK>;
+  using LayoutC = layout::ColumnMajorInterleaved<InterleavedK>;
+
+  using ElementAccumulator = int32_t;
+
+  /// Define the threadblock-scoped matrix multiply-accumulate
+  using B2bMma = typename cutlass::gemm::threadblock::DefaultB2bMma<
+      ElementA, LayoutA, kAlignmentA, ElementB, LayoutB, kAlignmentB, 
+      ElementAccumulator, LayoutC, arch::OpClassTensorOp, arch::Sm75, 
+      ThreadblockShape0, ThreadblockShape1, WarpShape0, WarpShape1, 
+      InstructionShape, 2, Operator, EpilogueOutputOp0, true, true>::ThreadblockB2bMma;
+
+  static const int kPartitionsK1 = ThreadblockShape1::kK / WarpShape1::kK;
+
+  /// Define the epilogue for the 2nd Gemm
+  using Epilogue = typename cutlass::epilogue::threadblock::
+      DefaultInterleavedEpilogueTensorOp<
+          ThreadblockShape1, typename B2bMma::Operator1, kPartitionsK1, EpilogueOutputOp1,
+          64 / sizeof_bits<ElementC>::value, InterleavedK>::Epilogue;
+
+  /// Define the kernel-level GEMM operator.
+  using B2bGemmKernel = kernel::B2bGemm<B2bMma, Epilogue, ThreadblockSwizzle, SplitKSerial>;
+};
+
+////////////////////////////////////////////////////////////////////////////////
+
+////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace kernel
+}  // namespace gemm
+}  // namespace cutlass

examples/13_two_tensor_op_fusion/reference/device/tensor_scale_bias.h

@@ -0,0 +1,275 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Defines device-side elementwise operations on TensorView. Note, the operations defined
+    in this header are not specialized for any particular data layout and are therefore not
+    intended to offer the best possible performance. Rather, they are intended to be generic
+    reference implementations to support the CUTLASS unit tests.
+*/
+
+#pragma once
+
+// Cutlass includes
+#include "cutlass/cutlass.h"
+#include "cutlass/tensor_view.h"
+
+#include "cutlass/gemm/gemm.h"
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace reference {
+namespace device {
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace kernel {
+
+template <
+  typename TensorRefIn,                   ///< Input TensorRef Type
+  typename TensorRefOut,                  ///< Output TensorRef Type
+  typename ScalarType,                    ///< alpha Type
+  typename TensorRefScalar,               ///< Scale/Bias TensorRef Type
+  typename OutputTile,
+  typename ConvertOp = NumericConverter<typename TensorRefOut::Element, ScalarType>
+>
+__global__ void TensorScaleBiasGemm(
+  gemm::GemmCoord problem_size,
+  TensorRefIn tensor_in,                  ///< input tensor
+  TensorRefOut tensor_out,                ///< output tensor
+  ScalarType alpha,                       ///< alpha
+  TensorRefScalar tensor_scale,           ///< scale tensor
+  TensorRefScalar tensor_bias             ///< bias tensor
+) {
+    
+  ConvertOp convert_op;
+
+  MatrixCoord output_coord(
+    MatrixCoord::Index((threadIdx.x + blockIdx.x * blockDim.x) * OutputTile::kRow),
+    MatrixCoord::Index((threadIdx.y + blockIdx.y * blockDim.y) * OutputTile::kColumn)
+  );
+
+  // Update the output tensor
+  for (int j = 0; j < OutputTile::kRow; ++j) {
+    for (int i = 0; i < OutputTile::kColumn; ++i) {
+      MatrixCoord coord = output_coord + MatrixCoord(i, j);
+      if (coord.row() < problem_size.m() && coord.column() < problem_size.n()) {
+
+        ScalarType scale = alpha;
+        if(tensor_scale.good())
+          scale = tensor_scale.at({0, coord.column()});
+
+        ScalarType bias = ScalarType(0);
+
+        if(tensor_bias.good()) 
+          bias = tensor_bias.at({0, coord.column()});
+
+        tensor_out.at(coord) = convert_op(
+          scale * ScalarType(tensor_in.at(coord)) + bias);
+      }
+    }
+  }
+}
+
+template <
+  typename TensorRefIn,                   ///< Input TensorRef Type
+  typename TensorRefOut,                  ///< Output TensorRef Type
+  typename ScalarType,                    ///< alpha Type
+  typename TensorRefScalar,               ///< Scale/Bias TensorRef Type
+  typename ConvertOp = NumericConverter<typename TensorRefOut::Element, ScalarType>,
+  int kThreadM = 4,       // shape of a thread's tile in the GEMM M dimension
+  int kThreadN = 4,       // shape of a thread's tile in the GEMM N dimension
+  int kCtaShapeM = 16,    // shape of a threadblock in units of threads
+  int kCtaShapeN = 8      // shape of a threadblock in units of threads
+>
+__global__ void TensorScaleBiasConv2d(
+  conv::Conv2dProblemSize problem_size,
+  TensorRefIn tensor_in,                  ///< input tensor
+  TensorRefOut tensor_out,                ///< output tensor
+  ScalarType alpha,                       ///< alpha
+  TensorRefScalar tensor_scale,           ///< scale tensor
+  TensorRefScalar tensor_bias             ///< bias tensor
+) {
+    
+  ConvertOp convert_op;
+
+  int64_t npq_start = int64_t(blockIdx.x) * kCtaShapeM * kThreadM + threadIdx.x * kThreadM;
+  int k_start = blockIdx.y * kCtaShapeN * kThreadN + threadIdx.y * kThreadN;
+
+  int thread_n[kThreadM];
+  int thread_p[kThreadM];
+  int thread_q[kThreadM];
+
+  // Compute N, P, Q coordinates for each row of a thread's tile
+  int64_t PQ = int64_t(problem_size.P) * problem_size.Q;
+
+  CUTLASS_PRAGMA_UNROLL
+  for (int m = 0; m < kThreadM; ++m) {
+
+    int64_t npq = npq_start + m;
+
+    thread_n[m] = int(npq / PQ);
+    
+    int64_t residual = npq % PQ;
+    thread_p[m] = int(residual / problem_size.Q);
+    thread_q[m] = int(residual % problem_size.Q);
+  }
+
+  // Write out the results
+  CUTLASS_PRAGMA_UNROLL
+  for (int m = 0; m < kThreadM; ++m) {
+    if (thread_n[m] < problem_size.N && thread_p[m] < problem_size.P && thread_q[m] < problem_size.Q) {
+      CUTLASS_PRAGMA_UNROLL
+      for (int n = 0; n < kThreadN; ++n) {
+        int thread_k = k_start + n;
+        if (thread_k < problem_size.K) {
+
+          ScalarType scale = alpha;
+          if(tensor_scale.good())
+            scale = tensor_scale.at({0, thread_k});
+    
+          ScalarType bias = ScalarType(0);
+          if(tensor_bias.good()) 
+            bias = tensor_bias.at({0, thread_k});
+    
+          tensor_out.at({thread_n[m], thread_p[m], thread_q[m], thread_k}) = convert_op(
+            scale * ScalarType(
+              tensor_in.at({thread_n[m], thread_p[m], thread_q[m], thread_k})
+            ) + bias);
+        }
+      } 
+    }
+  }
+
+}
+
+}
+
+/// Apply scale and bias on a tensor
+template <
+  typename ElementIn,                   ///< Input Type
+  typename ElementOut,                  ///< Output Type
+  typename Layout,                      ///< Layout of input/output tensor
+  typename ScalarType,                  ///< alpha Type
+  typename LayoutScaleBias,             ///< Layout of scale and bias
+  typename ConvertOp = NumericConverter<ElementOut, ScalarType>
+>
+void TensorScaleBiasGemm(
+  gemm::GemmCoord problem_size,
+  TensorRef<ElementIn, Layout> tensor_in,              ///< input tensor
+  TensorRef<ElementOut, Layout> tensor_out,            ///< output tensor
+  ScalarType alpha,                                    ///< alpha
+  TensorRef<ScalarType, LayoutScaleBias> tensor_scale, ///< scale tensor
+  TensorRef<ScalarType, LayoutScaleBias> tensor_bias    ///< bias tensor
+) {
+
+  using OutputTile = MatrixShape<4, 4>;
+
+  dim3 block(16, 8);
+
+  dim3 grid(
+    (problem_size.m() + block.x * OutputTile::kRow - 1) / (block.x * OutputTile::kRow),
+    (problem_size.n() + block.y * OutputTile::kColumn - 1) / (block.y * OutputTile::kColumn)
+  );
+
+  kernel::TensorScaleBiasGemm<
+    TensorRef<ElementIn, Layout>,
+    TensorRef<ElementOut, Layout>,
+    ScalarType,
+    TensorRef<ScalarType, LayoutScaleBias>,
+    OutputTile,
+    ConvertOp
+  ><<< grid, block >>> (
+    problem_size,
+    tensor_in,
+    tensor_out,
+    alpha,
+    tensor_scale,
+    tensor_bias
+  );
+}
+
+/// Apply scale and bias on a tensor
+template <
+  typename ElementIn,                   ///< Input Type
+  typename ElementOut,                  ///< Output Type
+  typename Layout,                      ///< Layout of input/output tensor
+  typename ScalarType,                  ///< alpha Type
+  typename LayoutScaleBias,             ///< Layout of scale and bias
+  typename ConvertOp = NumericConverter<ElementOut, ScalarType>
+>
+void TensorScaleBiasConv2d(
+  conv::Conv2dProblemSize problem_size,
+  TensorRef<ElementIn, Layout> tensor_in,              ///< input tensor
+  TensorRef<ElementOut, Layout> tensor_out,            ///< output tensor
+  ScalarType alpha,                                    ///< alpha
+  TensorRef<ScalarType, LayoutScaleBias> tensor_scale, ///< scale tensor
+  TensorRef<ScalarType, LayoutScaleBias> tensor_bias    ///< bias tensor
+) {
+
+  int const kThreadM = 4;       // shape of a thread's tile in the GEMM M dimension
+  int const kThreadN = 4;       // shape of a thread's tile in the GEMM N dimension
+  int const kCtaShapeM = 16;    // shape of a threadblock in units of threads
+  int const kCtaShapeN = 8;     // shape of a threadblock in units of threads
+
+  int64_t npq = int64_t(problem_size.N) * problem_size.P * problem_size.Q;
+  int64_t blocks_m = (npq + (kCtaShapeM * kThreadM) - 1) / (kCtaShapeM * kThreadM);
+
+  dim3 block(kCtaShapeM, kCtaShapeN);
+  dim3 grid(uint32_t(blocks_m), (problem_size.K + (kCtaShapeN * kThreadN) - 1) / (kCtaShapeN * kThreadN));
+
+
+  kernel::TensorScaleBiasConv2d<
+    TensorRef<ElementIn, Layout>,
+    TensorRef<ElementOut, Layout>,
+    ScalarType,
+    TensorRef<ScalarType, LayoutScaleBias>,
+    ConvertOp,
+    kThreadM,
+    kThreadN,
+    kCtaShapeM,
+    kCtaShapeN
+  ><<< grid, block >>> (
+    problem_size,
+    tensor_in,
+    tensor_out,
+    alpha,
+    tensor_scale,
+    tensor_bias
+  );
+}
+
+///////////////////////////////////////////////////////////////////////////////////////////////////
+///////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace device
+} // namespace reference
+} // namespace cutlass

examples/13_two_tensor_op_fusion/test_run.h

@@ -0,0 +1,95 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 <iostream>
+
+// Run tests on GPUs 
+
+int testRun(int arch, std::vector<bool (*)()> & test_funcs, const std::string & test_name) {
+
+  bool supported = false;
+
+  int arch_major = arch / 10;
+  int arch_minor = arch - arch / 10 * 10;  
+
+  if(arch_major >= 8) {
+    // Ampere Tensor Core operations exposed with mma.sync are first available in CUDA 11.0.
+    //
+    // CUTLASS must be compiled with CUDA 11 Toolkit to run Conv2dFprop examples.
+    if (__CUDACC_VER_MAJOR__ > 11 || (__CUDACC_VER_MAJOR__ == 11 && __CUDACC_VER_MINOR__ >= 0)) {
+      supported = true;
+    }
+  }
+  else if(arch_major >= 7) {
+    // Turing Tensor Core operations exposed with mma.sync are first available in CUDA 10.2.
+    //
+    // CUTLASS must be compiled with CUDA 10.2 Toolkit to run these examples.
+    if (__CUDACC_VER_MAJOR__ > 10 || (__CUDACC_VER_MAJOR__ == 10 && __CUDACC_VER_MINOR__ >= 2)) {
+      supported = true;
+    }
+  }
+
+  cudaDeviceProp props;
+
+  cudaError_t error = cudaGetDeviceProperties(&props, 0);
+  if (error != cudaSuccess) {
+    std::cerr << "cudaGetDeviceProperties() returned an error: " << cudaGetErrorString(error) << std::endl;
+    return -1;
+  }
+
+  if (!(props.major == arch_major && props.minor == arch_minor)) {
+    supported = false;
+  }
+
+  if (!supported) {
+    // Returning zero so this test passes on older Toolkits. Its actions are no-op.
+    std::cout << "This example isn't supported on current architecture" << std::endl;
+    return 0;
+  }
+
+  bool pass = true;
+ 
+  std::cout << "Device: " << props.name << std::endl;
+  std::cout << "Arch: SM" << arch << std::endl;
+  std::cout << "Test: " << test_name << std::endl;
+  for(auto func : test_funcs) {
+    pass &= func();
+  }
+
+
+  if(pass)
+    return 0;
+  else
+    return -1;
+
+}
+

examples/13_two_tensor_op_fusion/threadblock/b2b_implicit_gemm_multistage.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -77,6 +83,11 @@ template <
     /// Iterates over the intermediate accumulator tile
     //  (concept::MmaTensorOpFragmentIterator) 
     typename FragmentIteratorA1_,
+    /// Iterates over vectors of scale and bias vector in global memory
+    //  (concept: VectorIterator)
+    typename IteratorAccumulatorScaleBias_,
+    /// WarpIterator to load Scale or Bias vector from threadblock fragment
+    typename FragmentIteratorA1ScaleBias_,
     /// Iterates over tiles of B operand in global memory
     //  (concept: ReadableTileIterator | ForwardTileIterator |
     //  MaskedTileIterator)
@@ -117,6 +128,10 @@ public:
   using Shape1 = Shape1_;
   ///< Iterates over tiles of A operand in global memory
   using FragmentIteratorA1 = FragmentIteratorA1_;
+  ///< Iterates over tiles of the scale and bias vectors in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;
+  ///< WarpIterator to load Scale or Bias vector from threadblock fragment
+  using FragmentIteratorA1ScaleBias = FragmentIteratorA1ScaleBias_;
   ///< Iterates over tiles of B operand in global memory
   using IteratorB1 = IteratorB1_;
   ///< Policy describing tuning details
@@ -126,6 +141,9 @@ public:
 
   ///< Epilogue after 1st Gemm
   using OutputOp = OutputOp_;
+  
+  static const bool PerChannelScale = (OutputOp::kScale ==
+      epilogue::thread::ScaleType::OnlyAlphaPerChannelScaling);
  
   static cutlass::arch::CacheOperation::Kind const kCacheOpA0 = CacheOpA0;
   static cutlass::arch::CacheOperation::Kind const kCacheOpB0 = CacheOpB0;
@@ -143,6 +161,9 @@ public:
   /// Warp-level Mma
   using Operator0 = typename Policy0::Operator;
   
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
+
   /// Fragment of accumulator tile
   using FragmentC1 = typename Policy1::Operator::FragmentC;
 
@@ -193,6 +214,8 @@ public:
   using WarpLoadedFragmentB0 = typename Operator0::FragmentB;
   /// Warp Fragment of operand A1 loaded from accmulator tile
   using WarpLoadedFragmentA1 = typename FragmentIteratorA1::Fragment;
+  using WarpLoadedFragmentA1ScaleBias =
+      typename FragmentIteratorA1ScaleBias::Fragment;
   using WarpLoadedFragmentB1 = typename Operator1::FragmentB;
   using WarpTransformedFragmentA0 = typename Operator0::TransformedFragmentA;
   using WarpTransformedFragmentB0 = typename Operator0::TransformedFragmentB;
@@ -229,9 +252,9 @@ public:
       int lane_idx
     ):
       Base(shared_storage, thread_idx, warp_idx, lane_idx),
-      smem_iterator_A0_(shared_storage.sharedStorage0.operand_A_ref(), thread_idx),
-      smem_iterator_B0_(shared_storage.sharedStorage0.operand_B_ref(), thread_idx),
-      smem_iterator_B1_(shared_storage.sharedStorage1.operand_B_ref(), thread_idx)
+      smem_iterator_A0_(shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+      smem_iterator_B0_(shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+      smem_iterator_B1_(shared_storage.shared_storage1.operand_B_ref(), thread_idx)
   {
     // Compute warp location within threadblock tile by mapping the warp_id to
     // three coordinates:
@@ -343,11 +366,15 @@ public:
       int gemm_k_iterations_0,
       ///< destination accumulator tile
       FragmentC1 &accum,
-      ///< iterator over A operand in global memory
+      ///< iterator over A0 operand in global memory
       IteratorA0 iterator_A0,
-      ///< iterator over B operand in global memory
+      ///< iterator over B0 operand in global memory
       IteratorB0 iterator_B0,
-      ///< iterator over B operand in global memory
+      ///< iterator over A1 operand scale vector in global memory
+      IteratorAccumulatorScaleBias iterator_A1_scale,
+      ///< iterator over A1 operand bias vector in global memory
+      IteratorAccumulatorScaleBias iterator_A1_bias,
+      ///< iterator over B1 operand in global memory
       IteratorB1 iterator_B1,
       ///< initial value of accumulator
       FragmentC0 const &src_accum,
@@ -571,6 +598,20 @@ public:
 
     /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
     FragmentIteratorA1 warp_tile_iterator_A1_(accum0);
+    FragmentA1ScaleBias tb_frag_A1_scale;
+    FragmentA1ScaleBias tb_frag_A1_bias;
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_scale_(tb_frag_A1_scale);
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_bias_(tb_frag_A1_bias);
+
+    if(PerChannelScale) {
+        tb_frag_A1_scale.clear();
+        iterator_A1_scale.load(tb_frag_A1_scale);
+        ++iterator_A1_scale;
+    }
+    tb_frag_A1_bias.clear();
+    iterator_A1_bias.load(tb_frag_A1_bias);
+    ++iterator_A1_bias;
+
 
     //
     // Prologue
@@ -619,18 +660,29 @@ public:
     // Pair of fragments used to overlap shared memory loads and math
     // instructions
     WarpLoadedFragmentA1 warp_loaded_frag_A1[2];
+    WarpLoadedFragmentA1ScaleBias warp_loaded_frag_A1_scale[2];
+    WarpLoadedFragmentA1ScaleBias warp_loaded_frag_A1_bias[2];
     WarpLoadedFragmentB1 warp_loaded_frag_B1[2];
     WarpTransformedFragmentA1 warp_transformed_frag_A1[2];
     WarpTransformedFragmentB1 warp_transformed_frag_B1[2];
 
     Operator1 warp_mma1;
 
-    this->warp_tile_iterator_B1_.set_kgroup_index(0);
-
-    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0], output_op_0);
-    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
+    if(PerChannelScale) {
+        warp_tile_iterator_A1_scale_.load(warp_loaded_frag_A1_scale[0]);
+        ++warp_tile_iterator_A1_scale_;
+    }
+    warp_tile_iterator_A1_bias_.load(warp_loaded_frag_A1_bias[0]);
+    ++warp_tile_iterator_A1_bias_;
 
+    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0], 
+        warp_loaded_frag_A1_scale[0],
+        warp_loaded_frag_A1_bias[0], 
+        output_op_0);
     ++warp_tile_iterator_A1_;
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
     ++this->warp_tile_iterator_B1_;
 
     // Start issuing the first group of the next stage outside of the mainloop
@@ -647,7 +699,7 @@ public:
     // Mainloop
     //
 
-    CUTLASS_GEMM_LOOP
+    CUTLASS_PRAGMA_UNROLL
     for (gemm_k_iterations_1 = FragmentIteratorA1::Policy::kIterations / Base::kWarpGemmIterations1 - (Base::kStages - 1); 
             gemm_k_iterations_1 > (-Base::kStages + 1); gemm_k_iterations_1--) {
       //
@@ -660,15 +712,37 @@ public:
       for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1;
            ++warp_mma_k) {
 
+        // Load threadblock-level scale/bias vector from global memory
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations1) {
+          if(PerChannelScale) {
+              tb_frag_A1_scale.clear();
+              iterator_A1_scale.load(tb_frag_A1_scale);
+              ++iterator_A1_scale;
+          }
+          tb_frag_A1_bias.clear();
+          iterator_A1_bias.load(tb_frag_A1_bias);
+          ++iterator_A1_bias;
+        }
+
+        // Load warp-level scale bias fragment from threadblock scale/bias vector
+        if(PerChannelScale) {
+          warp_tile_iterator_A1_scale_.load(warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2]);
+          ++warp_tile_iterator_A1_scale_;
+        }
+        warp_tile_iterator_A1_bias_.load(warp_loaded_frag_A1_bias[(warp_mma_k + 1) % 2]);
+        ++warp_tile_iterator_A1_bias_;
+
+        // Load warp-level tile from accumulator fragment
+        warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2],
+            warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2], 
+            warp_loaded_frag_A1_bias[(warp_mma_k + 1) % 2], 
+            output_op_0);
+        ++warp_tile_iterator_A1_;
+
         // Load warp-level tiles from shared memory, wrapping to k offset if
         // this is the last group as the case may be.
-
         this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
-        
-        warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2], output_op_0);
         this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
-
-        ++warp_tile_iterator_A1_;
         ++this->warp_tile_iterator_B1_;
 
         if (warp_mma_k > 0)

examples/13_two_tensor_op_fusion/threadblock/b2b_implicit_gemm_multistage_smem_accumulator.h

@@ -0,0 +1,816 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Template for a multistage threadblock-scoped Implicit GEMM Convolution kernel.
+*/
+
+#pragma once
+
+#include "cutlass/aligned_buffer.h"
+#include "cutlass/arch/memory.h"
+#include "cutlass/array.h"
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/matrix_shape.h"
+#include "cutlass/numeric_types.h"
+#include "cutlass/arch/cache_operation.h"
+#include "cutlass/gemm/threadblock/mma_base.h"
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "threadblock/b2b_mma_base_smem_accumulator.h"
+#include "cutlass/epilogue/threadblock/epilogue_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace threadblock {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Structure to compute the matrix product targeting CUDA cores and SIMT math
+/// instructions.
+template <
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape0_,
+    /// Iterates over tiles of A operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorA0_,
+    /// Iterates over tiles of A operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorA0_,
+    /// Cache operation for operand A
+    cutlass::arch::CacheOperation::Kind CacheOpA0,
+    /// Iterates over tiles of B operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorB0_,
+    /// Iterates over tiles of B operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorB0_,
+    /// Cache operation for operand B
+    cutlass::arch::CacheOperation::Kind CacheOpB0,
+    /// Iterates over vectors of scale and bias vector in global memory
+    //  (concept: VectorIterator)
+    typename IteratorAccumulatorScaleBias_,
+    /// Iterates over accumulator tile
+    typename FragmentIteratorAccumulator_,
+    /// Iterates over accumulator tile in shared memory
+    typename SmemIteratorD0_,
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape1_,
+    /// Iterates over the intermediate accumulator tile
+    //  (concept::MmaTensorOpFragmentIterator) 
+    typename WarpIteratorA1_,
+    /// Iterates over tiles of B operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorB1_,
+    /// Iterates over tiles of B operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorB1_,
+    /// Cache operation for operand B
+    cutlass::arch::CacheOperation::Kind CacheOpB1,
+    /// Output operator for 1st Gemm(concept: epilogue::thread::LinearCombinationClamp, etc...) 
+    typename OutputOp_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy0_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy1_,
+    /// Number of stages,
+    int Stages,
+    /// Used for partial specialization
+    typename Enable = bool>
+class B2bImplicitGemmMultistageSmemAccumulator : 
+  public gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, Stages> {
+public:
+  ///< Base class
+  using Base = gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, Stages>;
+  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using Shape0 = Shape0_;
+  ///< Iterates over tiles of A operand in global memory
+  using IteratorA0 = IteratorA0_;
+  ///< Iterates over tiles of B operand in global memory
+  using IteratorB0 = IteratorB0_;
+  ///< Iterates over tiles of the scale and bias vectors in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;
+  ///< Policy describing tuning details
+  using Policy0 = Policy0_;
+
+  using SmemIteratorA0 = SmemIteratorA0_;
+  using SmemIteratorB0 = SmemIteratorB0_;
+  using SmemIteratorD0 = SmemIteratorD0_; ///< Iterates over accumulator tile in shared memory
+
+  using FragmentIteratorAccumulator = FragmentIteratorAccumulator_;  ///< Iterates over accumulator tile
+
+  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using Shape1 = Shape1_;
+  ///< Iterates over tiles of B operand in global memory
+  using IteratorB1 = IteratorB1_;
+  ///< Policy describing tuning details
+  using Policy1 = Policy1_;
+
+  using SmemIteratorB1 = SmemIteratorB1_;
+  using WarpIteratorA1 = WarpIteratorA1_;   ///< Iterates over the intermediate accumulator tile in shared memory
+
+  ///< Epilogue after 1st Gemm
+  using OutputOp = OutputOp_;
+  
+  static const bool PerChannelScale = (OutputOp::kScale ==
+      epilogue::thread::ScaleType::OnlyAlphaPerChannelScaling);
+ 
+  static cutlass::arch::CacheOperation::Kind const kCacheOpA0 = CacheOpA0;
+  static cutlass::arch::CacheOperation::Kind const kCacheOpB0 = CacheOpB0;
+  static cutlass::arch::CacheOperation::Kind const kCacheOpB1 = CacheOpB1;
+
+  //
+  // Dependent types
+  //
+
+  using ElementC = typename Policy0::Operator::ElementC;
+
+  /// Fragment of accumulator tile
+  using FragmentC0 = typename Policy0::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC1 = typename Policy1::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator1 = typename Policy1::Operator;
+
+  /// Epilog in shared memory
+  using Epilogue0 = epilogue::threadblock::EpilogueSmemAccumulator<
+    SmemIteratorD0,                 ///< SmemTileIterator
+    FragmentIteratorAccumulator,    ///< AccumulatorFragmentIterator
+    IteratorAccumulatorScaleBias,   ///< ScaleBiasIterator
+    OutputOp>;                      ///< Output operator
+  
+  /// Internal structure exposed for introspection.
+  struct Detail {
+
+    static_assert(Base::kWarpGemmIterations0 > 1,
+                  "The pipelined structure requires at least two warp-level "
+                  "GEMM operations.");
+    static_assert(Base::kWarpGemmIterations1 > 1,
+                  "The pipelined structure requires at least two warp-level "
+                  "GEMM operations.");
+
+    /// Number of cp.async instructions to load one stage of operand A
+    static int const AsyncCopyIterationsPerStageA0 =
+        IteratorA0::ThreadMap::Iterations::kCount;
+
+    /// Number of cp.async instructions to load one stage of operand B
+    static int const AsyncCopyIterationsPerStageB0 =
+        IteratorB0::ThreadMap::Iterations::kCount;
+
+    /// Number of cp.async instructions to load one stage of operand B
+    static int const AsyncCopyIterationsPerStageB1 =
+        IteratorB1::ThreadMap::Iterations::kCount;
+
+    /// Number of stages
+    static int const kStages = Stages;
+
+    /// Number of cp.async instructions to load on group of operand A
+    static int const kAccessesPerGroupA0 =
+        (AsyncCopyIterationsPerStageA0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+
+    /// Number of cp.async instructions to load on group of operand B
+    static int const kAccessesPerGroupB0 =
+        (AsyncCopyIterationsPerStageB0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+
+    /// Number of cp.async instructions to load on group of operand B
+    static int const kAccessesPerGroupB1 =
+        (AsyncCopyIterationsPerStageB1 + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1;
+  };
+
+ private:
+
+  using WarpLoadedFragmentA0 = typename Operator0::FragmentA;
+  using WarpLoadedFragmentB0 = typename Operator0::FragmentB;
+  using WarpLoadedFragmentA1 = typename Operator1::FragmentA;
+  using WarpLoadedFragmentB1 = typename Operator1::FragmentB;
+  using WarpTransformedFragmentA0 = typename Operator0::TransformedFragmentA;
+  using WarpTransformedFragmentB0 = typename Operator0::TransformedFragmentB;
+  using WarpTransformedFragmentA1 = typename Operator1::TransformedFragmentA;
+  using WarpTransformedFragmentB1 = typename Operator1::TransformedFragmentB;
+
+ private:
+
+  //
+  // Data members
+  //
+
+  /// Iterator to write threadblock-scoped tile of A operand to shared memory
+  SmemIteratorA0 smem_iterator_A0_;
+
+  /// Iterator to write threadblock-scoped tile of B operand to shared memory
+  SmemIteratorB0 smem_iterator_B0_;
+
+  /// Shared Memory Iterator to store accumulator tile
+  SmemIteratorD0 smem_iterator_D0_;
+    
+  /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
+  WarpIteratorA1 warp_tile_iterator_A1_;
+
+  /// Iterator to write threadblock-scoped tile of B operand to shared memory
+  SmemIteratorB1 smem_iterator_B1_;
+
+public:
+
+  /// Construct from tensor references
+  CUTLASS_DEVICE
+  B2bImplicitGemmMultistageSmemAccumulator(
+      ///< Shared storage needed for internal use by threadblock-scoped GEMM
+      typename Base::B2bMmaSharedStorage &shared_storage,
+      ///< ID within the threadblock
+      int thread_idx,
+      ///< ID of warp
+      int warp_idx,
+      ///< ID of each thread within a warp
+      int lane_idx
+    ):
+      Base(shared_storage, thread_idx, warp_idx, lane_idx),
+      smem_iterator_A0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+      smem_iterator_B0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+      smem_iterator_D0_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+      warp_tile_iterator_A1_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+      smem_iterator_B1_(shared_storage.b2b_mma_shared_storage.shared_storage1.operand_B_ref(), thread_idx)
+  {
+    // Compute warp location within threadblock tile by mapping the warp_id to
+    // three coordinates:
+    //   _m: the warp's position within the threadblock along the M dimension
+    //   _n: the warp's position within the threadblock along the N dimension
+    //   _k: the warp's position within the threadblock along the K dimension
+
+    int warp_idx_mn_0 = warp_idx % (Base::WarpCount0::kM * Base::WarpCount0::kN);
+    int warp_idx_k_0 = warp_idx / (Base::WarpCount0::kM * Base::WarpCount0::kN);
+
+    int warp_idx_m_0 = warp_idx_mn_0 % Base::WarpCount0::kM;
+    int warp_idx_n_0 = warp_idx_mn_0 / Base::WarpCount0::kM;
+
+    int warp_idx_mn_1 = warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN);
+    int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN);
+
+    int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM;
+    int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM;
+
+    // Add per-warp offsets in units of warp-level tiles
+    this->warp_tile_iterator_A0_.add_tile_offset(
+        {warp_idx_m_0, Base::kWarpGemmIterations0 * warp_idx_k_0});
+    this->warp_tile_iterator_B0_.add_tile_offset(
+        {Base::kWarpGemmIterations0 * warp_idx_k_0, warp_idx_n_0});
+    warp_tile_iterator_A1_.add_tile_offset(
+        {warp_idx_m_1, Base::kWarpGemmIterations1 * warp_idx_k_1});
+    this->warp_tile_iterator_B1_.add_tile_offset(
+        {Base::kWarpGemmIterations1 * warp_idx_k_1, warp_idx_n_1});
+
+    // Add smem accumulator iterator warp offset
+    smem_iterator_D0_.add_tile_offset({ warp_idx_m_0 * SmemIteratorD0::TileIterations::kRow, 
+                                        warp_idx_n_0 * SmemIteratorD0::TileIterations::kColumn});
+  }
+
+  CUTLASS_DEVICE
+  void copy_tiles_and_advance_0(
+    IteratorA0 &iterator_A0, IteratorB0 &iterator_B0,
+    int group_start_A0 = 0, int group_start_B0 = 0) {
+
+    iterator_A0.set_iteration_index(group_start_A0);
+    this->smem_iterator_A0_.set_iteration_index(group_start_A0);
+      
+    // Async Copy for operand A
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupA0; ++j) {
+
+      if (group_start_A0 + j < Detail::AsyncCopyIterationsPerStageA0) {
+        typename IteratorA0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorA0::AccessType *>(
+                this->smem_iterator_A0_.get());
+
+        int const kSrcBytes = sizeof_bits<typename IteratorA0::Element>::value *
+                              IteratorA0::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA0>(
+                dst_ptr, iterator_A0.get(), iterator_A0.valid());
+
+        ++iterator_A0;
+
+        ++this->smem_iterator_A0_;
+      }
+    }
+
+    iterator_B0.set_iteration_index(group_start_B0);
+
+    this->smem_iterator_B0_.set_iteration_index(group_start_B0);
+    
+    // Async Copy for operand B
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupB0; ++j) {
+      if (group_start_B0 + j < Detail::AsyncCopyIterationsPerStageB0) {
+        typename IteratorB0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB0::AccessType *>(
+                this->smem_iterator_B0_.get());
+        
+        int const kSrcBytes = sizeof_bits<typename IteratorB0::Element>::value *
+                              IteratorB0::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB0>(
+                dst_ptr, iterator_B0.get(), iterator_B0.valid());
+
+        ++iterator_B0;
+        ++this->smem_iterator_B0_;
+      }
+    }
+  }
+
+  CUTLASS_DEVICE
+  void copy_tiles_and_advance_1(
+    IteratorB1 &iterator_B1,
+    int group_start_B1 = 0) {
+
+    iterator_B1.set_iteration_index(group_start_B1);
+
+    this->smem_iterator_B1_.set_iteration_index(group_start_B1);
+    
+    // Async Copy for operand B
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupB1; ++j) {
+      if (group_start_B1 + j < Detail::AsyncCopyIterationsPerStageB1) {
+        typename IteratorB1::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB1::AccessType *>(
+                this->smem_iterator_B1_.get());
+        
+        int const kSrcBytes = sizeof_bits<typename IteratorB1::Element>::value *
+                              IteratorB1::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB1>(
+                dst_ptr, iterator_B1.get(), iterator_B1.valid());
+
+        ++iterator_B1;
+        ++this->smem_iterator_B1_;
+      }
+    }
+  }
+
+  /// Perform a threadblock-scoped matrix multiply-accumulate
+  CUTLASS_DEVICE
+  void operator()(
+      ///< problem size of GEMM
+      int gemm_k_iterations_0,
+      ///< destination accumulator tile
+      FragmentC1 &accum,
+      ///< iterator over A0 operand in global memory
+      IteratorA0 iterator_A0,
+      ///< iterator over B0 operand in global memory
+      IteratorB0 iterator_B0,
+      ///< iterator over A1 operand scale vector in global memory
+      IteratorAccumulatorScaleBias iterator_accum0_scale,
+      ///< iterator over A1 operand bias vector in global memory
+      IteratorAccumulatorScaleBias iterator_accum0_bias,
+      ///< iterator over B1 operand in global memory
+      IteratorB1 iterator_B1,
+      ///< initial value of accumulator
+      FragmentC0 const &src_accum,
+      ///< epilogue operation after 1st Gemm
+      OutputOp output_op_0,
+      ///< Imaginary strides used for planar-complex only - ignored here
+      int64_t imag_stride_A = 0,
+      int64_t imag_stride_B = 0) {
+
+    //
+    // Prologue
+    //
+
+    // Issue several complete stages
+    CUTLASS_PRAGMA_UNROLL
+    for (int stage = 0; stage < Base::kStages - 1;
+         ++stage, --gemm_k_iterations_0) {
+
+      iterator_A0.set_iteration_index(0);
+      this->smem_iterator_A0_.set_iteration_index(0);
+
+      // Async Copy for operand A
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::AsyncCopyIterationsPerStageA0; ++j) {
+        typename IteratorA0::AccessType *dst_ptr =
+          reinterpret_cast<typename IteratorA0::AccessType *>(
+            this->smem_iterator_A0_.get());
+
+        int const kSrcBytes =
+            sizeof_bits<typename IteratorA0::Element>::value *
+            IteratorA0::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA0>(
+          dst_ptr, iterator_A0.get(), iterator_A0.valid());
+
+        ++iterator_A0;
+        ++this->smem_iterator_A0_;
+      }
+
+      iterator_B0.set_iteration_index(0);
+      this->smem_iterator_B0_.set_iteration_index(0);
+
+      // Async Copy for operand B
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::AsyncCopyIterationsPerStageB0; ++j) {
+        typename IteratorB0::AccessType *dst_ptr =
+          reinterpret_cast<typename IteratorB0::AccessType *>(
+              this->smem_iterator_B0_.get());
+
+        int const kSrcBytes =
+            sizeof_bits<typename IteratorB0::Element>::value *
+            IteratorB0::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB0>(
+            dst_ptr, iterator_B0.get(), iterator_B0.valid());
+
+        ++iterator_B0;
+        ++this->smem_iterator_B0_;
+      }
+
+      // Move to the next stage
+      iterator_A0.advance();
+      iterator_B0.advance();
+
+      this->smem_iterator_A0_.add_tile_offset({0, 1});
+      this->smem_iterator_B0_.add_tile_offset({1, 0});
+
+      // Inserts a fence to group cp.async instructions into stages.
+      cutlass::arch::cp_async_fence();
+    }
+
+    // Perform accumulation in the 'd' output operand
+    FragmentC0 accum0 = src_accum;
+
+    // Waits until kStages-2 stages have committed. 
+    cutlass::arch::cp_async_wait<Base::kStages - 2>();
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math
+    // instructions
+    WarpLoadedFragmentA0 warp_loaded_frag_A0[2];
+    WarpLoadedFragmentB0 warp_loaded_frag_B0[2];
+    WarpTransformedFragmentA0 warp_transformed_frag_A0[2];
+    WarpTransformedFragmentB0 warp_transformed_frag_B0[2];
+
+    Operator0 warp_mma0;
+
+    this->warp_tile_iterator_A0_.set_kgroup_index(0);
+    this->warp_tile_iterator_B0_.set_kgroup_index(0);
+
+    this->warp_tile_iterator_A0_.load(warp_loaded_frag_A0[0]);
+    this->warp_tile_iterator_B0_.load(warp_loaded_frag_B0[0]);
+
+    ++this->warp_tile_iterator_A0_;
+    ++this->warp_tile_iterator_B0_;
+
+    // Start issuing the first group of the next stage outside of the mainloop
+    copy_tiles_and_advance_0(iterator_A0, iterator_B0);
+
+    int smem_write_stage_idx = Base::kStages - 1;
+    int smem_read_stage_idx = 0;
+
+    warp_mma0.transform(warp_transformed_frag_A0[0], warp_transformed_frag_B0[0],
+                       warp_loaded_frag_A0[0], warp_loaded_frag_B0[0]);
+
+    //
+    // Mainloop
+    //
+
+    CUTLASS_GEMM_LOOP
+    for (; gemm_k_iterations_0 > (-Base::kStages + 1);) {
+    
+      //
+      // Loop over GEMM K dimension
+      //
+
+      // Computes a warp-level GEMM on data held in shared memory
+      // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations0;
+           ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if
+        // this is the last group as the case may be.
+
+        this->warp_tile_iterator_A0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        this->warp_tile_iterator_B0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        
+        this->warp_tile_iterator_A0_.load(warp_loaded_frag_A0[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B0_.load(warp_loaded_frag_B0[(warp_mma_k + 1) % 2]);
+
+        ++this->warp_tile_iterator_A0_;
+        ++this->warp_tile_iterator_B0_;
+
+        if (warp_mma_k > 0)
+          warp_mma0.transform(warp_transformed_frag_A0[warp_mma_k % 2],
+                             warp_transformed_frag_B0[warp_mma_k % 2],
+                             warp_loaded_frag_A0[warp_mma_k % 2],
+                             warp_loaded_frag_B0[warp_mma_k % 2]);
+
+        // Issue global->shared copies for the next stage
+        int group_start_iteration_A0, group_start_iteration_B0;
+
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations0) {
+          group_start_iteration_A0 = 0;
+          group_start_iteration_B0 = 0;
+        } else {
+          group_start_iteration_A0 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupA0;
+          group_start_iteration_B0 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupB0;
+        }
+
+        copy_tiles_and_advance_0(iterator_A0, iterator_B0, group_start_iteration_A0,
+                               group_start_iteration_B0);
+
+        warp_mma0(
+                accum0, 
+                warp_transformed_frag_A0[warp_mma_k % 2],
+                warp_transformed_frag_B0[warp_mma_k % 2], 
+                accum0
+                );
+
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations0)
+          warp_mma0.transform(warp_transformed_frag_A0[(warp_mma_k + 1) % 2],
+                             warp_transformed_frag_B0[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_A0[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_B0[(warp_mma_k + 1) % 2]);
+
+        if (warp_mma_k + 2 == Base::kWarpGemmIterations0) {
+          // Inserts a fence to group cp.async instructions into stages.
+          cutlass::arch::cp_async_fence();
+
+          // Waits until kStages-2 stages of cp.async have committed
+          arch::cp_async_wait<Base::kStages - 2>();
+          __syncthreads();
+
+          // Move to the next stage
+          iterator_A0.advance();
+          iterator_B0.advance();
+
+          this->smem_iterator_A0_.add_tile_offset({0, 1});
+          this->smem_iterator_B0_.add_tile_offset({1, 0});
+
+          // Add negative offsets to return iterators to the 'start' of the
+          // circular buffer in shared memory
+          if (smem_write_stage_idx == (Base::kStages - 1)) {
+            this->smem_iterator_A0_.add_tile_offset({0, -Base::kStages});
+            this->smem_iterator_B0_.add_tile_offset({-Base::kStages, 0});
+            smem_write_stage_idx = 0;
+          } else {
+            ++smem_write_stage_idx;
+          }
+
+          if (smem_read_stage_idx == (Base::kStages - 1)) {
+            this->warp_tile_iterator_A0_.add_tile_offset(
+                {0, -Base::kStages * Policy0::kPartitionsK *
+                        Base::kWarpGemmIterations0});
+            this->warp_tile_iterator_B0_.add_tile_offset(
+                {-Base::kStages * Policy0::kPartitionsK *
+                     Base::kWarpGemmIterations0,
+                 0});
+            smem_read_stage_idx = 0;
+          } else {
+            ++smem_read_stage_idx;
+          }
+
+          --gemm_k_iterations_0;
+        }
+      }
+
+    }
+
+    // Insert fence and wait for all outstanding cp.async operations to commit.
+    cutlass::arch::cp_async_fence();
+    cutlass::arch::cp_async_wait<0>();
+    __syncthreads();
+
+    /// Epilogue for the first Implicit Gemm
+    Epilogue0 epilogue0;
+
+    epilogue0(output_op_0, smem_iterator_D0_, accum0, iterator_accum0_scale, iterator_accum0_bias);
+
+    __syncthreads();
+
+    // 2nd Implicit Gemm
+
+    //
+    // Prologue
+    //
+    int gemm_k_iterations_1 = Shape0::kN / Shape1::kK;
+
+    // Issue several complete stages
+    CUTLASS_PRAGMA_UNROLL
+    for (int stage = 0; stage < Base::kStages - 1;
+         ++stage, --gemm_k_iterations_1) {
+
+      iterator_B1.set_iteration_index(0);
+      this->smem_iterator_B1_.set_iteration_index(0);
+
+      // Async Copy for operand B
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::AsyncCopyIterationsPerStageB1; ++j) {
+        typename IteratorB1::AccessType *dst_ptr =
+          reinterpret_cast<typename IteratorB1::AccessType *>(
+              this->smem_iterator_B1_.get());
+
+        int const kSrcBytes =
+            sizeof_bits<typename IteratorB1::Element>::value *
+            IteratorB1::ThreadMap::kElementsPerAccess / 8;
+
+        cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB1>(
+            dst_ptr, iterator_B1.get(), iterator_B1.valid());
+
+        ++iterator_B1;
+        ++this->smem_iterator_B1_;
+      }
+
+      // Move to the next stage
+      iterator_B1.advance();
+
+      this->smem_iterator_B1_.add_tile_offset({1, 0});
+
+      // Inserts a fence to group cp.async instructions into stages.
+      cutlass::arch::cp_async_fence();
+    }
+
+    // Waits until kStages-2 stages have committed. 
+    cutlass::arch::cp_async_wait<Base::kStages - 2>();
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math
+    // instructions
+    WarpLoadedFragmentA1 warp_loaded_frag_A1[2];
+    WarpLoadedFragmentB1 warp_loaded_frag_B1[2];
+    WarpTransformedFragmentA1 warp_transformed_frag_A1[2];
+    WarpTransformedFragmentB1 warp_transformed_frag_B1[2];
+
+    Operator1 warp_mma1;
+
+    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0]);
+    ++warp_tile_iterator_A1_;
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
+    ++this->warp_tile_iterator_B1_;
+
+    // Start issuing the first group of the next stage outside of the mainloop
+    copy_tiles_and_advance_1(iterator_B1);
+
+    smem_write_stage_idx = Base::kStages - 1;
+    smem_read_stage_idx = 0;
+
+    warp_mma1.transform(warp_transformed_frag_A1[0], warp_transformed_frag_B1[0],
+                       warp_loaded_frag_A1[0], warp_loaded_frag_B1[0]);
+
+
+    //
+    // Mainloop
+    //
+
+    CUTLASS_PRAGMA_UNROLL
+    for ( gemm_k_iterations_1 = Shape0::kN / Shape1::kK - (Base::kStages - 1); 
+            gemm_k_iterations_1 > (-Base::kStages + 1); gemm_k_iterations_1--) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      // Computes a warp-level GEMM on data held in shared memory
+      // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1;
+           ++warp_mma_k) {
+
+        // Load warp-level tile from accumulator fragment
+        // skip warp tile loading for the last kgroup
+        if(gemm_k_iterations_1 > (-Base::kStages + 2) || warp_mma_k < Base::kWarpGemmIterations1 - 1) {
+            warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2]);
+        }
+        ++warp_tile_iterator_A1_;
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if
+        // this is the last group as the case may be.
+        this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
+        this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
+        ++this->warp_tile_iterator_B1_;
+
+
+        if (warp_mma_k > 0)
+          warp_mma1.transform(warp_transformed_frag_A1[warp_mma_k % 2],
+                             warp_transformed_frag_B1[warp_mma_k % 2],
+                             warp_loaded_frag_A1[warp_mma_k % 2],
+                             warp_loaded_frag_B1[warp_mma_k % 2]);
+
+        // Issue global->shared copies for the next stage
+        int group_start_iteration_B1;
+
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations1) {
+          group_start_iteration_B1 = 0;
+        } else {
+          group_start_iteration_B1 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupB1;
+        }
+
+        copy_tiles_and_advance_1(iterator_B1,
+                               group_start_iteration_B1);
+
+        warp_mma1(
+                accum, 
+                warp_transformed_frag_A1[warp_mma_k % 2],
+                warp_transformed_frag_B1[warp_mma_k % 2], 
+                accum
+                );
+
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations1)
+          warp_mma1.transform(warp_transformed_frag_A1[(warp_mma_k + 1) % 2],
+                             warp_transformed_frag_B1[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_A1[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
+
+        if (warp_mma_k + 2 == Base::kWarpGemmIterations1) {
+          // Inserts a fence to group cp.async instructions into stages.
+          cutlass::arch::cp_async_fence();
+
+          // Waits until kStages-2 stages of cp.async have committed
+          arch::cp_async_wait<Base::kStages - 2>();
+          __syncthreads();
+
+          // Move to the next stage
+          iterator_B1.advance();
+
+          this->smem_iterator_B1_.add_tile_offset({1, 0});
+
+          // Add negative offsets to return iterators to the 'start' of the
+          // circular buffer in shared memory
+          if (smem_write_stage_idx == (Base::kStages - 1)) {
+            this->smem_iterator_B1_.add_tile_offset({-Base::kStages, 0});
+            smem_write_stage_idx = 0;
+          } else {
+            ++smem_write_stage_idx;
+          }
+
+          if (smem_read_stage_idx == (Base::kStages - 1)) {
+            this->warp_tile_iterator_B1_.add_tile_offset(
+                {-Base::kStages * Policy1::kPartitionsK *
+                     Base::kWarpGemmIterations1,
+                 0});
+            smem_read_stage_idx = 0;
+          } else {
+            ++smem_read_stage_idx;
+          }
+
+        }
+      }
+
+    }
+
+    // Insert fence and wait for all outstanding cp.async operations to commit.
+    cutlass::arch::cp_async_fence();
+    cutlass::arch::cp_async_wait<0>();
+    __syncthreads();
+
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace gemm
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/threadblock/b2b_implicit_gemm_pipelined.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -70,6 +76,12 @@ template <
   /// Iterates over the intermediate accumulator tile
   //  (concept::MmaTensorOpFragmentIterator) 
   typename FragmentIteratorA1_,
+  /// Iterates over vectors of scale and bias vector in global memory
+  //  (concept: VectorIterator)
+  typename IteratorAccumulatorScaleBias_,
+  /// FragmentIterator to load Scale or Bias vector from threadblock fragment
+  typename FragmentIteratorA1ScaleBias_,
+  //  (concept: VectorFragmentIterator) 
   /// Iterates over tiles of B operand in global memory
   //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
   typename IteratorB1_,
@@ -92,13 +104,13 @@ template <
     typename IteratorA0_::Element, 
     IteratorA0_::Fragment::kElements>,
   ///
-  /// Transformation applied to A operand
+  /// Transformation applied to B operand
   typename TransformB0_ = NumericArrayConverter<
     typename SmemIteratorB0_::Element, 
     typename IteratorB0_::Element, 
     IteratorB0_::Fragment::kElements>,
   ///
-  /// Transformation applied to A operand
+  /// Transformation applied to B operand
   typename TransformB1_ = NumericArrayConverter<
     typename SmemIteratorB1_::Element, 
     typename IteratorB1_::Element, 
@@ -106,7 +118,8 @@ template <
   /// Used for partial specialization
   typename Enable = bool
 >
-class B2bImplicitGemmPipelined : public gemm::threadblock::B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, 2> {
+class B2bImplicitGemmPipelined : 
+  public gemm::threadblock::B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, 2> {
 public:
 
   ///< Base class
@@ -121,17 +134,24 @@ public:
   using SmemIteratorB0 = SmemIteratorB0_;
 
   using Shape1 = Shape1_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
-  using FragmentIteratorA1 = FragmentIteratorA1_;     ///< Iterates over tiles of A operand in global memory
+  using FragmentIteratorA1 = FragmentIteratorA1_;     ///< Iterates over tiles of A1 operand from accumulator tile
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;   ///< Iterates over tiles of the scale and bias vectors in global memory
+  using FragmentIteratorA1ScaleBias = 
+    FragmentIteratorA1ScaleBias_;     ///< WarpIterator to load Scale or Bias vector from the threadblock fragment
   using IteratorB1 = IteratorB1_;     ///< Iterates over tiles of B operand in global memory
   using Policy1 = Policy1_;           ///< Policy1 describing tuning details
 
   using SmemIteratorB1 = SmemIteratorB1_;
 
+
   using ElementC = ElementC_;       ///< Data type of accumulator matrix
   using LayoutC = LayoutC_;         ///< Layout of accumulator matrix
   
   using OutputOp = OutputOp_;       ///< Epilogue after 1st Gemm
 
+  static const bool PerChannelScale = (OutputOp::kScale ==
+      epilogue::thread::ScaleType::OnlyAlphaPerChannelScaling);
+
   using TransformA0 = TransformA0_;
   using TransformB0 = TransformB0_;
   using TransformB1 = TransformB1_;
@@ -152,6 +172,9 @@ public:
   /// Warp-level Mma
   using Operator0 = typename Policy0::Operator;
 
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
+
   /// Fragment of operand B loaded from global memory
   using FragmentB1 = typename IteratorB1::Fragment;
 
@@ -182,6 +205,9 @@ private:
   using WarpFragmentB0 = typename Operator0::FragmentB;
   /// Warp Fragment of operand A1 loaded from accmulator tile
   using WarpFragmentA1 = typename FragmentIteratorA1::Fragment;
+  /// Warp Fragment of operand A1 scale and bias loaded from threadblock fragment
+  using WarpFragmentA1ScaleBias =
+      typename FragmentIteratorA1ScaleBias::Fragment;
   using WarpFragmentB1 = typename Operator1::FragmentB;
 
 protected:
@@ -206,9 +232,9 @@ public:
     int lane_idx                                        ///< ID of each thread within a warp
   ):
     Base(shared_storage, thread_idx, warp_idx, lane_idx),
-    smem_iterator_A_(shared_storage.sharedStorage0.operand_A_ref(), thread_idx),
-    smem_iterator_B0_(shared_storage.sharedStorage0.operand_B_ref(), thread_idx),
-    smem_iterator_B1_(shared_storage.sharedStorage1.operand_B_ref(), thread_idx) {
+    smem_iterator_A_(shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+    smem_iterator_B0_(shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+    smem_iterator_B1_(shared_storage.shared_storage1.operand_B_ref(), thread_idx) {
 
     // Compute warp location within threadblock tile by mapping the warp_id to
     // three coordinates:
@@ -240,9 +266,11 @@ public:
     FragmentC1 &accum,                                 ///< destination accumulator tile
     IteratorA0 iterator_A,                             ///< iterator over A operand in global memory
     IteratorB0 iterator_B0,                            ///< iterator over B0 operand in global memory
+    IteratorAccumulatorScaleBias iterator_A1_scale,    ///< iterator over A1 operand scale vectors in global memory
+    IteratorAccumulatorScaleBias iterator_A1_bias,     ///< iterator over A1 operand bias vectors in global memory
     IteratorB1 iterator_B1,                            ///< iterator over B1 operand in global memory
     FragmentC0 const &src_accum,                       ///< source accumulator tile
-    OutputOp output_op_0,                               ///< epilogue operation after 1st Gemm
+    OutputOp output_op_0,                              ///< epilogue operation after 1st Gemm
     TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
     TransformB0 transform_B0 = TransformB0(),           ///< transformation applied to B0 fragment
     TransformB1 transform_B1 = TransformB1()) {         ///< transformation applied to B1 fragment
@@ -370,18 +398,33 @@ public:
     /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
     FragmentIteratorA1 warp_tile_iterator_A1_(accum0);
 
+
+
     //
     // Prologue
     //
 
+    FragmentA1ScaleBias tb_frag_A1_scale;
+    FragmentA1ScaleBias tb_frag_A1_bias;
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_scale_(tb_frag_A1_scale);
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_bias_(tb_frag_A1_bias);
     FragmentB1 tb_frag_B1;
 
+    if(PerChannelScale)
+        tb_frag_A1_scale.clear();
+    tb_frag_A1_bias.clear();
     tb_frag_B1.clear();
 
     // The last kblock is loaded in the prolog
+    if(PerChannelScale)
+        iterator_A1_scale.load(tb_frag_A1_scale);
+    iterator_A1_bias.load(tb_frag_A1_bias);
     iterator_B1.load(tb_frag_B1);
 
 
+    if(PerChannelScale)
+        ++iterator_A1_scale;
+    ++iterator_A1_bias;
     ++iterator_B1;
 
     this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
@@ -391,15 +434,24 @@ public:
     __syncthreads();
 
     // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA1ScaleBias warp_frag_A1_scale[2];
+    WarpFragmentA1ScaleBias warp_frag_A1_bias[2];
     WarpFragmentA1 warp_frag_A1[2];
     WarpFragmentB1 warp_frag_B1[2];
 
     this->warp_tile_iterator_B1_.set_kgroup_index(0);
 
-    warp_tile_iterator_A1_.load(warp_frag_A1[0], output_op_0);
+    if(PerChannelScale)
+        warp_tile_iterator_A1_scale_.load(warp_frag_A1_scale[0]);
+    warp_tile_iterator_A1_bias_.load(warp_frag_A1_bias[0]);
+    warp_tile_iterator_A1_.load(warp_frag_A1[0], warp_frag_A1_scale[0], 
+        warp_frag_A1_bias[0], output_op_0);
     this->warp_tile_iterator_B1_.load(warp_frag_B1[0]);
 
     ++warp_tile_iterator_A1_;
+    if(PerChannelScale)
+        ++warp_tile_iterator_A1_scale_;
+    ++warp_tile_iterator_A1_bias_;
     ++this->warp_tile_iterator_B1_;
 
     Operator1 warp_mma1;
@@ -447,13 +499,31 @@ public:
           }
 
           smem_write_stage_idx ^= 1;
+
+          if(PerChannelScale) {
+            tb_frag_A1_scale.clear();
+            iterator_A1_scale.load(tb_frag_A1_scale);
+            ++iterator_A1_scale;
+          }
+          tb_frag_A1_bias.clear();
+          iterator_A1_bias.load(tb_frag_A1_bias);
+          ++iterator_A1_bias;
         }
 
         this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
         
-        warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2], output_op_0);
+        if(PerChannelScale)
+          warp_tile_iterator_A1_scale_.load(warp_frag_A1_scale[(warp_mma_k + 1) % 2]);
+        warp_tile_iterator_A1_bias_.load(warp_frag_A1_bias[(warp_mma_k + 1) % 2]);
+        warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2], 
+            warp_frag_A1_scale[(warp_mma_k + 1) % 2], 
+            warp_frag_A1_bias[(warp_mma_k + 1) % 2], 
+            output_op_0);
         this->warp_tile_iterator_B1_.load(warp_frag_B1[(warp_mma_k + 1) % 2]);
 
+        if(PerChannelScale)
+          ++warp_tile_iterator_A1_scale_;
+        ++warp_tile_iterator_A1_bias_;
         ++warp_tile_iterator_A1_;
         ++this->warp_tile_iterator_B1_;
 

examples/13_two_tensor_op_fusion/threadblock/b2b_implicit_gemm_pipelined_smem_accumulator.h

@@ -0,0 +1,535 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Template for a double-buffered threadblock-scoped GEMM kernel.
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/array.h"
+#include "cutlass/aligned_buffer.h"
+#include "cutlass/numeric_conversion.h"
+
+#include "cutlass/numeric_types.h"
+#include "cutlass/matrix_shape.h"
+
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "threadblock/b2b_mma_base_smem_accumulator.h"
+#include "cutlass/epilogue/threadblock/epilogue_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace conv {
+namespace threadblock {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Structure to compute the matrix product targeting CUDA cores and SIMT math instructions.
+template <
+  /// Size of the Gemm problem - concept: gemm::GemmShape<>
+  typename Shape0_,
+  /// Iterates over tiles of A operand in global memory 
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorA0_,
+  /// Iterates over tiles of A operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorA0_,
+  /// Iterates over tiles of B operand in global memory
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorB0_,
+  /// Iterates over tiles of B operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorB0_,
+  /// Iterates over vectors of scale and bias vector in global memory
+  //  (concept: VectorIterator)
+  typename IteratorAccumulatorScaleBias_,
+  /// Iterates over accumulator tile
+  typename FragmentIteratorAccumulator_,
+  /// Iterates over accumulator tile in shared memory
+  typename SmemIteratorD0_,
+  /// Size of the Gemm problem - concept: gemm::GemmShape<>
+  typename Shape1_,
+  /// Iterates over the intermediate accumulator tile in shared memory
+  typename WarpIteratorA1_,
+  /// Iterates over tiles of B operand in global memory
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorB1_,
+  /// Iterates over tiles of B operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorB1_,
+  /// Data type of accumulator matrix
+  typename ElementC_,
+  /// Data type of accumulator matrix
+  typename LayoutC_,
+  /// Output operator for 1st Gemm(concept: epilogue::thread::LinearCombinationClamp, etc...) 
+  typename OutputOp_,
+  /// Policy describing tuning details (concept: MmaPolicy)
+  typename Policy0_,
+  /// Policy describing tuning details (concept: MmaPolicy)
+  typename Policy1_,
+  /// Transformation applied to A0 operand
+  typename TransformA0_ = NumericArrayConverter<
+    typename SmemIteratorA0_::Element, 
+    typename IteratorA0_::Element, 
+    IteratorA0_::Fragment::kElements>,
+  ///
+  /// Transformation applied to B0 operand
+  typename TransformB0_ = NumericArrayConverter<
+    typename SmemIteratorB0_::Element, 
+    typename IteratorB0_::Element, 
+    IteratorB0_::Fragment::kElements>,
+  ///
+  /// Transformation applied to B1 operand
+  typename TransformB1_ = NumericArrayConverter<
+    typename SmemIteratorB1_::Element, 
+    typename IteratorB1_::Element, 
+    IteratorB1_::Fragment::kElements>,
+  /// Used for partial specialization
+  typename Enable = bool
+>
+class B2bImplicitGemmPipelinedSmemAccumulator : 
+  public gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, 2> {
+public:
+
+  ///< Base class
+  using Base = gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, 2>;
+
+  using Shape0 = Shape0_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using IteratorA0 = IteratorA0_;     ///< Iterates over tiles of A operand in global memory
+  using IteratorB0 = IteratorB0_;     ///< Iterates over tiles of B operand in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;   ///< Iterates over tiles of the scale and bias vectors in global memory
+  using Policy0 = Policy0_;           ///< Policy0 describing tuning details
+
+  using SmemIteratorA0 = SmemIteratorA0_;
+  using SmemIteratorB0 = SmemIteratorB0_;
+  using SmemIteratorD0 = SmemIteratorD0_; ///< Iterates over accumulator tile in shared memory
+
+  using FragmentIteratorAccumulator = FragmentIteratorAccumulator_;  ///< Iterates over accumulator tile
+
+  using Shape1 = Shape1_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using IteratorB1 = IteratorB1_;     ///< Iterates over tiles of B operand in global memory
+  using Policy1 = Policy1_;           ///< Policy1 describing tuning details
+
+  using SmemIteratorB1 = SmemIteratorB1_;
+  using WarpIteratorA1 = WarpIteratorA1_;   ///< Iterates over the intermediate accumulator tile in shared memory
+
+
+  using ElementC = ElementC_;       ///< Data type of accumulator matrix
+  using LayoutC = LayoutC_;         ///< Layout of accumulator matrix
+  
+  using OutputOp = OutputOp_;       ///< Epilogue after 1st Gemm
+
+  using TransformA0 = TransformA0_;
+  using TransformB0 = TransformB0_;
+  using TransformB1 = TransformB1_;
+
+  //
+  // Dependent types
+  //
+
+  /// Fragment of operand A loaded from global memory
+  using FragmentA0 = typename IteratorA0::Fragment;
+
+  /// Fragment of operand B loaded from global memory
+  using FragmentB0 = typename IteratorB0::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC0 = typename Policy0::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of operand B loaded from global memory
+  using FragmentB1 = typename IteratorB1::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC1 = typename Policy1::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator1 = typename Policy1::Operator;
+ 
+  /// Obtain the arch tag from the warp-level operator
+  using ArchTag = typename Policy0::Operator::ArchTag;
+
+  /// Complex transform on A0 operand
+  static ComplexTransform const kTransformA0 = Operator0::kTransformA;
+
+  /// Complex transform on B0 operand
+  static ComplexTransform const kTransformB0 = Operator0::kTransformB;
+
+  /// Complex transform on B1 operand
+  static ComplexTransform const kTransformB1 = Operator1::kTransformB;
+
+  /// staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline)
+  static_assert((Base::kStages==2), "MmaPipelined requires kStages set to value 2");
+
+  /// Epilog in shared memory
+  using Epilogue0 = epilogue::threadblock::EpilogueSmemAccumulator<
+    SmemIteratorD0,                 ///< SmemTileIterator
+    FragmentIteratorAccumulator,    ///< AccumulatorFragmentIterator
+    IteratorAccumulatorScaleBias,   ///< ScaleBiasIterator
+    OutputOp>;                      ///< Output operator
+
+
+
+private:
+
+  using WarpFragmentA0 = typename Operator0::FragmentA;
+  using WarpFragmentB0 = typename Operator0::FragmentB;
+  using WarpFragmentA1 = typename Operator1::FragmentA;
+  using WarpFragmentB1 = typename Operator1::FragmentB;
+
+protected:
+
+  /// Iterator to write threadblock-scoped tile of A operand to shared memory
+  SmemIteratorA0 smem_iterator_A_;
+
+  /// Iterator to write threadblock-scoped tile of B0 operand to shared memory
+  SmemIteratorB0 smem_iterator_B0_;
+    
+  /// Shared Memory Iterator to store accumulator tile
+  SmemIteratorD0 smem_iterator_D0_;
+    
+  /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
+  WarpIteratorA1 warp_tile_iterator_A1_;
+
+  /// Iterator to write threadblock-scoped tile of B1 operand to shared memory
+  SmemIteratorB1 smem_iterator_B1_;
+
+public:
+
+  /// Construct from tensor references
+  CUTLASS_DEVICE
+  B2bImplicitGemmPipelinedSmemAccumulator(
+    typename Base::B2bMmaSharedStorage &shared_storage, ///< Shared storage needed for internal use by threadblock-scoped GEMM
+    int thread_idx,                                     ///< ID within the threadblock
+    int warp_idx,                                       ///< ID of warp
+    int lane_idx                                        ///< ID of each thread within a warp
+  ):
+    Base(shared_storage, thread_idx, warp_idx, lane_idx),
+    smem_iterator_A_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+    smem_iterator_B0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+    smem_iterator_D0_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+    warp_tile_iterator_A1_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+    smem_iterator_B1_(shared_storage.b2b_mma_shared_storage.shared_storage1.operand_B_ref(), thread_idx) {
+
+    // Compute warp location within threadblock tile by mapping the warp_id to
+    // three coordinates:
+    //   _m: the warp's position within the threadblock along the M dimension
+    //   _n: the warp's position within the threadblock along the N dimension
+    //   _k: the warp's position within the threadblock along the K dimension
+
+    int warp_idx_mn_0 = warp_idx % (Base::WarpCount0::kM * Base::WarpCount0::kN);
+    int warp_idx_k_0 = warp_idx / (Base::WarpCount0::kM * Base::WarpCount0::kN);
+
+    int warp_idx_m_0 = warp_idx_mn_0 % Base::WarpCount0::kM;
+    int warp_idx_n_0 = warp_idx_mn_0 / Base::WarpCount0::kM;
+
+    int tile_offset_k_0 = Base::kWarpGemmIterations0 * warp_idx_k_0;
+
+    int warp_idx_mn_1 = warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN);
+    int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN);
+
+    int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM;
+    int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM;
+
+    int tile_offset_k_1 = Base::kWarpGemmIterations1 * warp_idx_k_1;
+
+    // Add per-warp offsets in units of warp-level tiles
+    this->warp_tile_iterator_A0_.add_tile_offset({warp_idx_m_0, tile_offset_k_0});
+    this->warp_tile_iterator_B0_.add_tile_offset({tile_offset_k_0, warp_idx_n_0});
+    warp_tile_iterator_A1_.add_tile_offset({warp_idx_m_1, tile_offset_k_1});
+    this->warp_tile_iterator_B1_.add_tile_offset({tile_offset_k_1, warp_idx_n_1});
+
+    // Add smem accumulator iterator warp offset
+    smem_iterator_D0_.add_tile_offset({ warp_idx_m_0 * SmemIteratorD0::TileIterations::kRow, 
+                                        warp_idx_n_0 * SmemIteratorD0::TileIterations::kColumn});
+
+  }
+
+  /// Perform a threadblock-scoped matrix multiply-accumulate
+  CUTLASS_DEVICE
+  void operator()(
+    int gemm_k_iterations_0,                             ///< number of iterations of the mainloop
+    FragmentC1 &accum,                                   ///< destination accumulator tile
+    IteratorA0 iterator_A,                               ///< iterator over A operand in global memory
+    IteratorB0 iterator_B0,                              ///< iterator over B0 operand in global memory
+    IteratorAccumulatorScaleBias iterator_accum0_scale,  ///< iterator over D0 scale vector in global memory
+    IteratorAccumulatorScaleBias iterator_accum0_bias,   ///< iterator over D0 bias vector in global memory
+    IteratorB1 iterator_B1,                              ///< iterator over B1 operand in global memory
+    FragmentC0 const &src_accum,                         ///< source accumulator tile
+    OutputOp output_op_0,                                ///< epilogue operation after 1st Gemm
+    TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
+    TransformB0 transform_B0 = TransformB0(),            ///< transformation applied to B0 fragment
+    TransformB1 transform_B1 = TransformB1()) {          ///< transformation applied to B1 fragment
+
+    //
+    // Prologue
+    //
+
+    // Perform accumulation in the 'd' output operand
+    FragmentC0 accum0 = src_accum;
+
+    FragmentA0 tb_frag_A;
+    FragmentB0 tb_frag_B0;
+
+    tb_frag_A.clear();
+    tb_frag_B0.clear();
+
+    // The last kblock is loaded in the prolog
+    iterator_A.load(tb_frag_A);
+    iterator_B0.load(tb_frag_B0);
+
+    ++iterator_A;
+    ++iterator_B0;
+
+    this->smem_iterator_A_.store(transform_A0(tb_frag_A));
+    this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
+
+    ++this->smem_iterator_A_;
+    ++this->smem_iterator_B0_;
+
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA0 warp_frag_A0[2];
+    WarpFragmentB0 warp_frag_B0[2];
+
+    this->warp_tile_iterator_A0_.set_kgroup_index(0);
+    this->warp_tile_iterator_B0_.set_kgroup_index(0);
+
+    this->warp_tile_iterator_A0_.load(warp_frag_A0[0]);
+    this->warp_tile_iterator_B0_.load(warp_frag_B0[0]);
+
+    ++this->warp_tile_iterator_A0_;
+    ++this->warp_tile_iterator_B0_;
+
+    Operator0 warp_mma0;
+
+    int smem_write_stage_idx = 1;
+
+    // Issue loads during the first warp-level matrix multiply-add *AFTER* issuing 
+    // shared memory loads (which have the tighest latency requirement).
+
+    //
+    // Mainloop
+    //
+
+    // Note: The main loop does not support Base::kWarpGemmIterations == 2.
+    CUTLASS_GEMM_LOOP
+    for (; gemm_k_iterations_0 > 0; --gemm_k_iterations_0) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations0; ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
+        // as the case may be.
+
+        if (warp_mma_k == Base::kWarpGemmIterations0 - 1) {
+
+          // Write fragments to shared memory
+          this->smem_iterator_A_.store(transform_A0(tb_frag_A));
+
+          this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
+
+          __syncthreads();
+          
+          ++this->smem_iterator_A_;
+          ++this->smem_iterator_B0_;
+
+          // Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
+          if (smem_write_stage_idx == 1) {
+            this->smem_iterator_A_.add_tile_offset({0, -Base::kStages});
+            this->smem_iterator_B0_.add_tile_offset({-Base::kStages, 0});
+          }
+          else {
+            this->warp_tile_iterator_A0_.add_tile_offset(
+                {0, -Base::kStages * Policy0::kPartitionsK * Base::kWarpGemmIterations0});
+            this->warp_tile_iterator_B0_.add_tile_offset(
+                {-Base::kStages * Policy0::kPartitionsK * Base::kWarpGemmIterations0,
+                 0});
+          }
+
+          smem_write_stage_idx ^= 1;
+        }
+
+        this->warp_tile_iterator_A0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        this->warp_tile_iterator_B0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        
+        this->warp_tile_iterator_A0_.load(warp_frag_A0[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B0_.load(warp_frag_B0[(warp_mma_k + 1) % 2]);
+
+        ++this->warp_tile_iterator_A0_;
+        ++this->warp_tile_iterator_B0_;
+
+        if (warp_mma_k == 0) {
+
+          iterator_A.load(tb_frag_A);
+          iterator_B0.load(tb_frag_B0);
+          ++iterator_A;
+          ++iterator_B0;
+        }
+
+        warp_mma0(accum0, warp_frag_A0[warp_mma_k % 2],
+                 warp_frag_B0[warp_mma_k % 2], accum0);
+
+      }
+    }
+
+    /// Epilogue for the first Implicit Gemm
+    Epilogue0 epilogue0;
+
+    epilogue0(output_op_0, smem_iterator_D0_, accum0, iterator_accum0_scale, iterator_accum0_bias);
+
+    __syncthreads();
+    
+    /// 2nd Implicit Gemm
+    
+
+    //
+    // Prologue
+    //
+
+    FragmentB1 tb_frag_B1;
+
+    tb_frag_B1.clear();
+
+    // The last kblock is loaded in the prolog
+    iterator_B1.load(tb_frag_B1);
+
+    ++iterator_B1;
+
+    this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
+
+    ++this->smem_iterator_B1_;
+
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA1 warp_frag_A1[2];
+    WarpFragmentB1 warp_frag_B1[2];
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+
+    warp_tile_iterator_A1_.load(warp_frag_A1[0]);
+    this->warp_tile_iterator_B1_.load(warp_frag_B1[0]);
+
+    ++warp_tile_iterator_A1_;
+    ++this->warp_tile_iterator_B1_;
+
+    Operator1 warp_mma1;
+
+    smem_write_stage_idx = 1;
+    
+    int gemm_k_iterations_1 = Shape0::kN / Shape1::kK;
+
+
+    //
+    // Mainloop
+    //
+
+    // Note: The main loop does not support Base::kWarpGemmIterations == 2.
+    CUTLASS_PRAGMA_UNROLL
+    for (; gemm_k_iterations_1 > 0; --gemm_k_iterations_1) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1; ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
+        // as the case may be.
+
+        if (warp_mma_k == Base::kWarpGemmIterations1 - 1) {
+
+          // Write fragments to shared memory
+          this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
+
+          __syncthreads();
+          
+          ++this->smem_iterator_B1_;
+
+          // Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
+          if (smem_write_stage_idx == 1) {
+            this->smem_iterator_B1_.add_tile_offset({-Base::kStages, 0});
+          }
+          else {
+            this->warp_tile_iterator_B1_.add_tile_offset(
+                {-Base::kStages * Policy1::kPartitionsK *
+                     Base::kWarpGemmIterations1,
+                 0});
+          }
+
+          smem_write_stage_idx ^= 1;
+
+        }
+
+        this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
+        
+        // skip warp tile loading for the last kgroup
+        if(gemm_k_iterations_1 > 1 || warp_mma_k < Base::kWarpGemmIterations1 - 1)
+          warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B1_.load(warp_frag_B1[(warp_mma_k + 1) % 2]);
+
+        ++warp_tile_iterator_A1_;
+        ++this->warp_tile_iterator_B1_;
+
+        if (warp_mma_k == 0) {
+
+          iterator_B1.load(tb_frag_B1);
+    
+          ++iterator_B1;
+        }
+
+        warp_mma1(accum, warp_frag_A1[warp_mma_k % 2],
+                 warp_frag_B1[warp_mma_k % 2], accum);
+
+      }
+    }
+
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace threadblock
+} // namespace gemm
+} // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_base.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -180,8 +186,8 @@ class B2bMmaBase {
   using SharedStorage0 = SharedStorage<Shape0, Policy0>;
   using SharedStorage1 = SharedStorage<Shape1, Policy1>;
   union B2bMmaSharedStorage {
-    SharedStorage0 sharedStorage0;
-    SharedStorage1 sharedStorage1;
+    SharedStorage0 shared_storage0;
+    SharedStorage1 shared_storage1;
   };
 
 
@@ -197,7 +203,7 @@ class B2bMmaBase {
   /// Iterator to load a warp-scoped tile of B0 operand from shared memory
   typename Operator0::IteratorB warp_tile_iterator_B0_;
 
-  /// Iterator to load a warp-scoped tile of B0 operand from shared memory
+  /// Iterator to load a warp-scoped tile of B1 operand from shared memory
   typename Operator1::IteratorB warp_tile_iterator_B1_;
 
 public:
@@ -214,9 +220,9 @@ public:
       ///< ID of each thread within a warp
       int lane_idx
     ):
-      warp_tile_iterator_A0_(shared_storage.sharedStorage0.operand_A_ref(), lane_idx),
-      warp_tile_iterator_B0_(shared_storage.sharedStorage0.operand_B_ref(), lane_idx),
-      warp_tile_iterator_B1_(shared_storage.sharedStorage1.operand_B_ref(), lane_idx) {
+      warp_tile_iterator_A0_(shared_storage.shared_storage0.operand_A_ref(), lane_idx),
+      warp_tile_iterator_B0_(shared_storage.shared_storage0.operand_B_ref(), lane_idx),
+      warp_tile_iterator_B1_(shared_storage.shared_storage1.operand_B_ref(), lane_idx) {
 
   }
 };

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_base_smem_accumulator.h

@@ -0,0 +1,179 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Template for a double-buffered threadblock-scoped GEMM kernel.
+*/
+
+#pragma once
+
+#include "cutlass/aligned_buffer.h"
+#include "cutlass/arch/memory.h"
+#include "cutlass/array.h"
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/matrix_shape.h"
+#include "cutlass/numeric_types.h"
+#include "threadblock/b2b_mma_base.h"
+////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////
+
+////////////////////////////////////////////////////////////////////////////////
+
+/// Structure to compute the matrix product targeting CUDA cores and SIMT math
+/// instructions.
+template <
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape0_,
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape1_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy0_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy1_,
+    /// Shared Memory Accumulator Iterator
+    typename SmemAccumulatorIterator0_,
+    /// Number of stages,
+    int Stages,
+    /// Used for partial specialization
+    typename Enable = bool>
+class B2bMmaBaseSmemAccumulator :
+  public B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, Stages> {
+
+ public:
+  ///< Base class
+  using Base = B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, Stages>;
+
+  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using Shape0 = Shape0_;
+  using Shape1 = Shape1_;
+
+  ///< Policy describing tuning details
+  using Policy0 = Policy0_;
+  using Policy1 = Policy1_;
+
+
+  using SmemAccumulatorIterator0 = SmemAccumulatorIterator0_;
+
+  //
+  // Nested structs
+  //
+  /// Shared storage object needed by accumulator
+  template<
+    typename Shape_,
+    typename Element_,
+    typename Layout_,
+    typename Padding_
+  >
+  class AccumulatorSharedStorage {
+   public:
+    //
+    // Type definitions
+    //
+    using Shape = Shape_;
+    using Element = Element_;
+    using Layout = Layout_;
+    using Padding = Padding_;
+
+    /// Tensor reference to the accumulator
+    using TensorRefAccum = TensorRef<Element, Layout>;
+
+    /// Shape of the accumulator matrix in shared memory
+    using ShapeAccum = MatrixShape<Shape::kM + Padding::kRow, 
+                                    Shape::kN + Padding::kColumn>;
+
+   public:
+    //
+    // Data members
+    //
+
+    /// Buffer for accumulator
+    AlignedBuffer<Element, ShapeAccum::kCount> accum;
+
+   public:
+
+    //
+    // Methods
+    //
+
+    /// Returns a layout object for the Accum matrix
+    CUTLASS_DEVICE
+    static Layout LayoutAccum() {
+      return Layout::packed({ShapeAccum::kRow, ShapeAccum::kColumn});
+    }
+
+    /// Returns a TensorRef to the Accumulator
+    CUTLASS_HOST_DEVICE
+    TensorRefAccum accum_ref() {
+      return TensorRefAccum{accum.data(), LayoutAccum()};
+    }
+
+  };
+
+  using AccumulatorSharedStorage0 = AccumulatorSharedStorage<
+                                    Shape0, typename SmemAccumulatorIterator0::Element, 
+                                    typename SmemAccumulatorIterator0::TensorLayout,
+                                    typename SmemAccumulatorIterator0::Padding>;
+
+  struct B2bMmaSharedStorage {
+    typename Base::B2bMmaSharedStorage b2b_mma_shared_storage;
+    AccumulatorSharedStorage0 accumulator_shared_storage0;
+  }; 
+
+public:
+
+  /// Construct from tensor references
+  CUTLASS_DEVICE
+  B2bMmaBaseSmemAccumulator(
+      ///< Shared storage needed for internal use by threadblock-scoped GEMM
+      B2bMmaSharedStorage &shared_storage,
+      ///< ID within the threadblock
+      int thread_idx,
+      ///< ID of warp
+      int warp_idx,
+      ///< ID of each thread within a warp
+      int lane_idx
+    ):
+      Base(shared_storage.b2b_mma_shared_storage, thread_idx, warp_idx, lane_idx) {
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace gemm
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_multistage.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -76,6 +82,11 @@ template <
     /// Iterates over the intermediate accumulator tile
     //  (concept::MmaTensorOpFragmentIterator) 
     typename FragmentIteratorA1_,
+    /// Iterates over vectors of scale and bias vector in global memory
+    //  (concept: VectorIterator)
+    typename IteratorAccumulatorScaleBias_,
+    /// WarpIterator to load Scale or Bias vector from threadblock fragment
+    typename FragmentIteratorA1ScaleBias_,
     /// Iterates over tiles of B operand in global memory
     //  (concept: ReadableTileIterator | ForwardTileIterator |
     //  MaskedTileIterator)
@@ -120,6 +131,10 @@ public:
   using Shape1 = Shape1_;
   ///< Iterates over intermediate accumulator tile
   using FragmentIteratorA1 = FragmentIteratorA1_;
+  ///< Iterates over tiles of the scale and bias vectors in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;
+  ///< WarpIterator to load Scale or Bias vector from threadblock fragment
+  using FragmentIteratorA1ScaleBias = FragmentIteratorA1ScaleBias_;
   ///< Iterates over tiles of B operand in global memory
   using IteratorB1 = IteratorB1_;
   ///< Policy describing tuning details
@@ -134,6 +149,9 @@ public:
 
   ///< Epilogue after 1st Gemm
   using OutputOp = OutputOp_;
+  
+  static const bool PerChannelScale = (OutputOp::kScale ==
+      epilogue::thread::ScaleType::OnlyAlphaPerChannelScaling);
  
   static cutlass::arch::CacheOperation::Kind const kCacheOpA0 = CacheOpA0;
   static cutlass::arch::CacheOperation::Kind const kCacheOpB0 = CacheOpB0;
@@ -148,6 +166,9 @@ public:
 
   /// Warp-level Mma
   using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
 
   /// Fragment of accumulator tile
   using FragmentC1 = typename Policy1::Operator::FragmentC;
@@ -178,15 +199,15 @@ public:
                   "GEMM operations.");
 
     /// Number of cp.async instructions to load one stage of operand A
-    static int const TBLDGSTSIterationsA0 =
+    static int const TBLoadIterationsA0 =
         IteratorA0::ThreadMap::Iterations::kCount;
 
     /// Number of cp.async instructions to load one stage of operand B
-    static int const TBLDGSTSIterationsB0 =
+    static int const TBLoadIterationsB0 =
         IteratorB0::ThreadMap::Iterations::kCount;
 
     /// Number of cp.async instructions to load one stage of operand B
-    static int const TBLDGSTSIterationsB1 =
+    static int const TBLoadIterationsB1 =
         IteratorB1::ThreadMap::Iterations::kCount;
 
     /// Number of stages
@@ -194,15 +215,15 @@ public:
 
     /// Number of cp.async instructions to load on group of operand A
     static int const kAccessesPerGroupA0 =
-        (TBLDGSTSIterationsA0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+        (TBLoadIterationsA0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
 
     /// Number of cp.async instructions to load on group of operand B
     static int const kAccessesPerGroupB0 =
-        (TBLDGSTSIterationsB0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+        (TBLoadIterationsB0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
 
     /// Number of cp.async instructions to load on group of operand B
     static int const kAccessesPerGroupB1 =
-        (TBLDGSTSIterationsB1 + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1;
+        (TBLoadIterationsB1 + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1;
   };
 
  private:
@@ -211,6 +232,8 @@ public:
   using WarpLoadedFragmentB0 = typename Operator0::FragmentB;
   /// Warp Fragment of operand A1 loaded from accmulator tile
   using WarpLoadedFragmentA1 = typename FragmentIteratorA1::Fragment;
+  using WarpLoadedFragmentA1ScaleBias =
+      typename FragmentIteratorA1ScaleBias::Fragment;
   using WarpLoadedFragmentB1 = typename Operator1::FragmentB;
   using WarpTransformedFragmentA0 = typename Operator0::TransformedFragmentA;
   using WarpTransformedFragmentB0 = typename Operator0::TransformedFragmentB;
@@ -244,12 +267,14 @@ public:
       ///< ID of warp
       int warp_idx,
       ///< ID of each thread within a warp
-      int lane_idx
+      int lane_idx,
+      ///< GEMM0 N is used for accumulator extent
+      int problem_size_0_n
     ):
       Base(shared_storage, thread_idx, warp_idx, lane_idx),
-      smem_iterator_A0_(shared_storage.sharedStorage0.operand_A_ref(), thread_idx),
-      smem_iterator_B0_(shared_storage.sharedStorage0.operand_B_ref(), thread_idx),
-      smem_iterator_B1_(shared_storage.sharedStorage1.operand_B_ref(), thread_idx)
+      smem_iterator_A0_(shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+      smem_iterator_B0_(shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+      smem_iterator_B1_(shared_storage.shared_storage1.operand_B_ref(), thread_idx)
   {
     // Compute warp location within threadblock tile by mapping the warp_id to
     // three coordinates:
@@ -279,10 +304,10 @@ public:
                                    IteratorA0::kAccessesPerVector);
     this->smem_iterator_A0_.set_iteration_index(group_start_A0);
 
-    // LDGSTS for operand A
+    // Load for operand A
     CUTLASS_PRAGMA_UNROLL
     for (int j = 0; j < Detail::kAccessesPerGroupA0; ++j) {
-      if (group_start_A0 + j < Detail::TBLDGSTSIterationsA0) {
+      if (group_start_A0 + j < Detail::TBLoadIterationsA0) {
         typename IteratorA0::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorA0::AccessType *>(
                 this->smem_iterator_A0_.get());
@@ -309,10 +334,10 @@ public:
                                    IteratorB0::kAccessesPerVector);
     this->smem_iterator_B0_.set_iteration_index(group_start_B0);
 
-    // LDGSTS for operand B
+    // Load for operand B
     CUTLASS_PRAGMA_UNROLL
     for (int j = 0; j < Detail::kAccessesPerGroupB0; ++j) {
-      if (group_start_B0 + j < Detail::TBLDGSTSIterationsB0) {
+      if (group_start_B0 + j < Detail::TBLoadIterationsB0) {
         typename IteratorB0::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorB0::AccessType *>(
                 this->smem_iterator_B0_.get());
@@ -342,10 +367,10 @@ public:
                                    IteratorB1::kAccessesPerVector);
     this->smem_iterator_B1_.set_iteration_index(group_start_B1);
 
-    // LDGSTS for operand B
+    // Load for operand B
     CUTLASS_PRAGMA_UNROLL
     for (int j = 0; j < Detail::kAccessesPerGroupB1; ++j) {
-      if (group_start_B1 + j < Detail::TBLDGSTSIterationsB1) {
+      if (group_start_B1 + j < Detail::TBLoadIterationsB1) {
         typename IteratorB1::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorB1::AccessType *>(
                 this->smem_iterator_B1_.get());
@@ -375,11 +400,15 @@ public:
       int gemm_k_iterations_0,
       ///< destination accumulator tile
       FragmentC1 &accum,
-      ///< iterator over A operand in global memory
+      ///< iterator over A0 operand in global memory
       IteratorA0 iterator_A0,
-      ///< iterator over B operand in global memory
+      ///< iterator over B0 operand in global memory
       IteratorB0 iterator_B0,
-      ///< iterator over B operand in global memory
+      ///< iterator over A1 operand scale vector in global memory
+      IteratorAccumulatorScaleBias iterator_A1_scale,
+      ///< iterator over A1 operand bias vector in global memory
+      IteratorAccumulatorScaleBias iterator_A1_bias,
+      ///< iterator over B1 operand in global memory
       IteratorB1 iterator_B1,
       ///< initial value of accumulator
       FragmentC0 const &src_accum,
@@ -395,17 +424,15 @@ public:
     for (int stage = 0; stage < Base::kStages - 1;
          ++stage, --gemm_k_iterations_0) {
 
-      if (gemm_k_iterations_0 == 0) {
-        iterator_A0.clear_mask();
-        iterator_B0.clear_mask();
-      }
+      iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+      iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
 
       iterator_A0.set_iteration_index(0);
       this->smem_iterator_A0_.set_iteration_index(0);
 
-      // LDGSTS for operand A
+      // Load for operand A
       CUTLASS_PRAGMA_UNROLL
-      for (int j = 0; j < Detail::TBLDGSTSIterationsA0; ++j) {
+      for (int j = 0; j < Detail::TBLoadIterationsA0; ++j) {
         typename IteratorA0::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorA0::AccessType *>(
                 this->smem_iterator_A0_.get());
@@ -431,9 +458,9 @@ public:
       iterator_B0.set_iteration_index(0);
       this->smem_iterator_B0_.set_iteration_index(0);
 
-      // LDGSTS for operand B
+      // Load for operand B
       CUTLASS_PRAGMA_UNROLL
-      for (int j = 0; j < Detail::TBLDGSTSIterationsB0; ++j) {
+      for (int j = 0; j < Detail::TBLoadIterationsB0; ++j) {
         typename IteratorB0::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorB0::AccessType *>(
                 this->smem_iterator_B0_.get());
@@ -490,10 +517,8 @@ public:
     ++this->warp_tile_iterator_A0_;
     ++this->warp_tile_iterator_B0_;
 
-    if (gemm_k_iterations_0 == 0) {
-      iterator_A0.clear_mask();
-      iterator_B0.clear_mask();
-    }
+    iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+    iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
 
     int smem_write_stage_idx = Base::kStages - 1;
     int smem_read_stage_idx = 0;
@@ -601,10 +626,8 @@ public:
           }
 
           --gemm_k_iterations_0;
-          if (gemm_k_iterations_0 == 0) {
-            iterator_A0.clear_mask();
-            iterator_B0.clear_mask();
-          }
+          iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+          iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
         }
 
         // Do any conversions feeding the first stage at the end of the loop so
@@ -618,32 +641,42 @@ public:
 
     }
 
-
     // 2nd Gemm
 
     /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
     FragmentIteratorA1 warp_tile_iterator_A1_(accum0);
+    FragmentA1ScaleBias tb_frag_A1_scale;
+    FragmentA1ScaleBias tb_frag_A1_bias;
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_scale_(tb_frag_A1_scale);
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_bias_(tb_frag_A1_bias);
 
+    if(PerChannelScale) {
+        tb_frag_A1_scale.clear();
+        iterator_A1_scale.load(tb_frag_A1_scale);
+        ++iterator_A1_scale;
+    }
+    tb_frag_A1_bias.clear();
+    iterator_A1_bias.load(tb_frag_A1_bias);
+    ++iterator_A1_bias;
+ 
     //
     // Prologue
     //
-    int gemm_k_iterations_1 = FragmentIteratorA1::Policy::kIterations / Base::kWarpGemmIterations1;
+    int gemm_k_iterations_1 = (FragmentIteratorA1::Policy::kIterations + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1;
 
     // Issue several complete stages
     CUTLASS_PRAGMA_UNROLL
     for (int stage = 0; stage < Base::kStages - 1;
          ++stage, --gemm_k_iterations_1) {
 
-      if (gemm_k_iterations_1 == 0) {
-        iterator_B1.clear_mask();
-      }
+      iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
 
       iterator_B1.set_iteration_index(0);
       this->smem_iterator_B1_.set_iteration_index(0);
 
-      // LDGSTS for operand B
+      // Load for operand B
       CUTLASS_PRAGMA_UNROLL
-      for (int j = 0; j < Detail::TBLDGSTSIterationsB1; ++j) {
+      for (int j = 0; j < Detail::TBLoadIterationsB1; ++j) {
         typename IteratorB1::AccessType *dst_ptr =
             reinterpret_cast<typename IteratorB1::AccessType *>(
                 this->smem_iterator_B1_.get());
@@ -680,23 +713,32 @@ public:
     // Pair of fragments used to overlap shared memory loads and math
     // instructions
     WarpLoadedFragmentA1 warp_loaded_frag_A1[2];
+    WarpLoadedFragmentA1ScaleBias warp_loaded_frag_A1_scale[2];
+    WarpLoadedFragmentA1ScaleBias warp_loaded_frag_A1_bias[2];
     WarpLoadedFragmentB1 warp_loaded_frag_B1[2];
     WarpTransformedFragmentA1 warp_transformed_frag_A1[2];
     WarpTransformedFragmentB1 warp_transformed_frag_B1[2];
 
     Operator1 warp_mma1;
 
-    this->warp_tile_iterator_B1_.set_kgroup_index(0);
-
-    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0], output_op_0);
-    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
+    if(PerChannelScale) {
+        warp_tile_iterator_A1_scale_.load(warp_loaded_frag_A1_scale[0]);
+        ++warp_tile_iterator_A1_scale_;
+    }
+    warp_tile_iterator_A1_bias_.load(warp_loaded_frag_A1_bias[0]);
+    ++warp_tile_iterator_A1_bias_;
 
+    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0], 
+        warp_loaded_frag_A1_scale[0],
+        warp_loaded_frag_A1_bias[0], 
+        output_op_0);
     ++warp_tile_iterator_A1_;
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
     ++this->warp_tile_iterator_B1_;
 
-    if (gemm_k_iterations_1 == 0) {
-      iterator_B1.clear_mask();
-    }
+    iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
 
     smem_write_stage_idx = Base::kStages - 1;
     smem_read_stage_idx = 0;
@@ -708,9 +750,9 @@ public:
     // Mainloop
     //
 
+    gemm_k_iterations_1 = (FragmentIteratorA1::Policy::kIterations + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1 - (Base::kStages - 1);
     CUTLASS_PRAGMA_UNROLL
-    for (gemm_k_iterations_1 = FragmentIteratorA1::Policy::kIterations / Base::kWarpGemmIterations1 - (Base::kStages - 1); 
-            gemm_k_iterations_1 > (-Base::kStages + 1); gemm_k_iterations_1--) {
+    for (; gemm_k_iterations_1 > (-Base::kStages + 1); gemm_k_iterations_1--) {
       //
       // Loop over GEMM K dimension
       //
@@ -721,15 +763,37 @@ public:
       for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1;
            ++warp_mma_k) {
 
+        // Load threadblock-level scale/bias vector from global memory
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations1) {
+          if(PerChannelScale) {
+              tb_frag_A1_scale.clear();
+              iterator_A1_scale.load(tb_frag_A1_scale);
+              ++iterator_A1_scale;
+          }
+          tb_frag_A1_bias.clear();
+          iterator_A1_bias.load(tb_frag_A1_bias);
+          ++iterator_A1_bias;
+        }
+
+        // Load warp-level scale bias fragment from threadblock scale/bias vector
+        if(PerChannelScale) {
+          warp_tile_iterator_A1_scale_.load(warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2]);
+          ++warp_tile_iterator_A1_scale_;
+        }
+        warp_tile_iterator_A1_bias_.load(warp_loaded_frag_A1_bias[(warp_mma_k + 1) % 2]);
+        ++warp_tile_iterator_A1_bias_;
+
+        // Load warp-level tile from accumulator fragment
+        warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2],
+            warp_loaded_frag_A1_scale[(warp_mma_k + 1) % 2], 
+            warp_loaded_frag_A1_bias[(warp_mma_k + 1) % 2], 
+            output_op_0);
+        ++warp_tile_iterator_A1_;
+
         // Load warp-level tiles from shared memory, wrapping to k offset if
         // this is the last group as the case may be.
-
         this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
-        
-        warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2], output_op_0);
         this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
-
-        ++warp_tile_iterator_A1_;
         ++this->warp_tile_iterator_B1_;
 
         if (warp_mma_k > 0)
@@ -793,9 +857,7 @@ public:
             ++smem_read_stage_idx;
           }
 
-          if (gemm_k_iterations_1 == 1) {
-            iterator_B1.clear_mask();
-          }
+          iterator_B1.clear_mask(gemm_k_iterations_1 == 1);
         }
 
         // Do any conversions feeding the first stage at the end of the loop so

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_multistage_smem_accumulator.h

@@ -0,0 +1,869 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Template for a double-buffered threadblock-scoped GEMM kernel.
+*/
+
+#pragma once
+
+#include "cutlass/aligned_buffer.h"
+#include "cutlass/arch/memory.h"
+#include "cutlass/array.h"
+#include "cutlass/cutlass.h"
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/matrix_shape.h"
+#include "cutlass/numeric_types.h"
+
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "threadblock/b2b_mma_base_smem_accumulator.h"
+#include "cutlass/epilogue/threadblock/epilogue_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace threadblock {
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Structure to compute the matrix product targeting CUDA cores and SIMT math
+/// instructions.
+template <
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape0_,
+    /// Iterates over tiles of A operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorA0_,
+    /// Iterates over tiles of A operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorA0_,
+    /// Cache operation for operand A
+    cutlass::arch::CacheOperation::Kind CacheOpA0,
+    /// Iterates over tiles of B operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorB0_,
+    /// Iterates over tiles of B operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorB0_,
+    /// Cache operation for operand B
+    cutlass::arch::CacheOperation::Kind CacheOpB0,
+    /// Iterates over vectors of scale and bias vector in global memory
+    //  (concept: VectorIterator)
+    typename IteratorAccumulatorScaleBias_,
+    /// Iterates over accumulator tile
+    typename FragmentIteratorAccumulator_,
+    /// Iterates over accumulator tile in shared memory
+    typename SmemIteratorD0_,
+    /// Size of the Gemm problem - concept: gemm::GemmShape<>
+    typename Shape1_,
+    /// Iterates over the intermediate accumulator tile in shared memory
+    typename WarpIteratorA1_,
+    /// Iterates over tiles of B operand in global memory
+    //  (concept: ReadableTileIterator | ForwardTileIterator |
+    //  MaskedTileIterator)
+    typename IteratorB1_,
+    /// Iterates over tiles of B operand in shared memory
+    /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+    typename SmemIteratorB1_,
+    /// Cache operation for operand B
+    cutlass::arch::CacheOperation::Kind CacheOpB1,
+    /// Data type of accumulator matrix
+    typename ElementC_,
+    /// Data type of accumulator matrix
+    typename LayoutC_,
+    /// Output operator for 1st Gemm(concept: epilogue::thread::LinearCombinationClamp, etc...) 
+    typename OutputOp_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy0_,
+    /// Policy describing tuning details (concept: MmaPolicy)
+    typename Policy1_,
+    /// Number of stages,
+    int Stages,
+    /// Used for partial specialization
+    typename Enable = bool>
+class B2bMmaMultistageSmemAccumulator : 
+  public gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, Stages> {
+public:
+  ///< Base class
+  using Base = gemm::threadblock::B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, Stages>;
+  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using Shape0 = Shape0_;
+  ///< Iterates over tiles of A operand in global memory
+  using IteratorA0 = IteratorA0_;
+  ///< Iterates over tiles of B operand in global memory
+  using IteratorB0 = IteratorB0_;
+  ///< Iterates over tiles of the scale and bias vectors in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;
+  ///< Policy describing tuning details
+  using Policy0 = Policy0_;
+
+  using SmemIteratorA0 = SmemIteratorA0_;
+  using SmemIteratorB0 = SmemIteratorB0_;
+  using SmemIteratorD0 = SmemIteratorD0_; ///< Iterates over accumulator tile in shared memory
+
+  using FragmentIteratorAccumulator = FragmentIteratorAccumulator_;  ///< Iterates over accumulator tile
+
+  ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using Shape1 = Shape1_;
+  ///< Iterates over tiles of B operand in global memory
+  using IteratorB1 = IteratorB1_;
+  ///< Policy describing tuning details
+  using Policy1 = Policy1_;
+
+  using SmemIteratorB1 = SmemIteratorB1_;
+  using WarpIteratorA1 = WarpIteratorA1_;   ///< Iterates over the intermediate accumulator tile in shared memory
+
+  ///< Data type of accumulator matrix
+  using ElementC = ElementC_;
+  ///< Layout of accumulator matrix
+  using LayoutC = LayoutC_;
+
+  ///< Epilogue after 1st Gemm
+  using OutputOp = OutputOp_;
+ 
+  static cutlass::arch::CacheOperation::Kind const kCacheOpA0 = CacheOpA0;
+  static cutlass::arch::CacheOperation::Kind const kCacheOpB0 = CacheOpB0;
+  static cutlass::arch::CacheOperation::Kind const kCacheOpB1 = CacheOpB1;
+
+  //
+  // Dependent types
+  //
+
+  /// Fragment of accumulator tile
+  using FragmentC0 = typename Policy0::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC1 = typename Policy1::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator1 = typename Policy1::Operator;
+
+  /// Epilog in shared memory
+  using Epilogue0 = epilogue::threadblock::EpilogueSmemAccumulator<
+    SmemIteratorD0,                 ///< SmemTileIterator
+    FragmentIteratorAccumulator,    ///< AccumulatorFragmentIterator
+    IteratorAccumulatorScaleBias,   ///< ScaleBiasIterator
+    OutputOp>;                      ///< Output operator
+ 
+  /// Minimum architecture is Sm80 to support cp.async
+  using ArchTag = arch::Sm80;
+  
+  /// Complex transform on A operand
+  static ComplexTransform const kTransformA0 = Operator0::kTransformA;
+
+  /// Complex transform on B operand
+  static ComplexTransform const kTransformB0 = Operator0::kTransformB;
+  
+  /// Complex transform on B operand
+  static ComplexTransform const kTransformB1 = Operator1::kTransformB;
+
+  /// Internal structure exposed for introspection.
+  struct Detail {
+
+    static_assert(Base::kWarpGemmIterations0 > 1,
+                  "The pipelined structure requires at least two warp-level "
+                  "GEMM operations.");
+    static_assert(Base::kWarpGemmIterations1 > 1,
+                  "The pipelined structure requires at least two warp-level "
+                  "GEMM operations.");
+
+    /// Number of cp.async instructions to load one stage of operand A
+    static int const TBLoadIterationsA0 =
+        IteratorA0::ThreadMap::Iterations::kCount;
+
+    /// Number of cp.async instructions to load one stage of operand B
+    static int const TBLoadIterationsB0 =
+        IteratorB0::ThreadMap::Iterations::kCount;
+
+    /// Number of cp.async instructions to load one stage of operand B
+    static int const TBLoadIterationsB1 =
+        IteratorB1::ThreadMap::Iterations::kCount;
+
+    /// Number of stages
+    static int const kStages = Stages;
+
+    /// Number of cp.async instructions to load on group of operand A
+    static int const kAccessesPerGroupA0 =
+        (TBLoadIterationsA0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+
+    /// Number of cp.async instructions to load on group of operand B
+    static int const kAccessesPerGroupB0 =
+        (TBLoadIterationsB0 + Base::kWarpGemmIterations0 - 1) / Base::kWarpGemmIterations0;
+
+    /// Number of cp.async instructions to load on group of operand B
+    static int const kAccessesPerGroupB1 =
+        (TBLoadIterationsB1 + Base::kWarpGemmIterations1 - 1) / Base::kWarpGemmIterations1;
+  };
+
+ private:
+
+  using WarpLoadedFragmentA0 = typename Operator0::FragmentA;
+  using WarpLoadedFragmentB0 = typename Operator0::FragmentB;
+  using WarpLoadedFragmentA1 = typename Operator1::FragmentA;
+  using WarpLoadedFragmentB1 = typename Operator1::FragmentB;
+  using WarpTransformedFragmentA0 = typename Operator0::TransformedFragmentA;
+  using WarpTransformedFragmentB0 = typename Operator0::TransformedFragmentB;
+  using WarpTransformedFragmentA1 = typename Operator1::TransformedFragmentA;
+  using WarpTransformedFragmentB1 = typename Operator1::TransformedFragmentB;
+
+ private:
+
+  //
+  // Data members
+  //
+
+  /// Iterator to write threadblock-scoped tile of A operand to shared memory
+  SmemIteratorA0 smem_iterator_A0_;
+
+  /// Iterator to write threadblock-scoped tile of B operand to shared memory
+  SmemIteratorB0 smem_iterator_B0_;
+
+  /// Shared Memory Iterator to store accumulator tile
+  SmemIteratorD0 smem_iterator_D0_;
+    
+  /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
+  WarpIteratorA1 warp_tile_iterator_A1_;
+
+  /// Iterator to write threadblock-scoped tile of B operand to shared memory
+  SmemIteratorB1 smem_iterator_B1_;
+
+public:
+
+  /// Construct from tensor references
+  CUTLASS_DEVICE
+  B2bMmaMultistageSmemAccumulator(
+      ///< Shared storage needed for internal use by threadblock-scoped GEMM
+      typename Base::B2bMmaSharedStorage &shared_storage,
+      ///< ID within the threadblock
+      int thread_idx,
+      ///< ID of warp
+      int warp_idx,
+      ///< ID of each thread within a warp
+      int lane_idx,
+      ///< GEMM0 N is used for accumulator extent
+      int problem_size_0_n
+    ):
+      Base(shared_storage, thread_idx, warp_idx, lane_idx),
+      smem_iterator_A0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+      smem_iterator_B0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+      smem_iterator_D0_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+      warp_tile_iterator_A1_(shared_storage.accumulator_shared_storage0.accum_ref(), {Base::WarpGemm1::kM, problem_size_0_n}, lane_idx ),
+      smem_iterator_B1_(shared_storage.b2b_mma_shared_storage.shared_storage1.operand_B_ref(), thread_idx)
+  {
+    // Compute warp location within threadblock tile by mapping the warp_id to
+    // three coordinates:
+    //   _m: the warp's position within the threadblock along the M dimension
+    //   _n: the warp's position within the threadblock along the N dimension
+    //   _k: the warp's position within the threadblock along the K dimension
+
+    int warp_idx_mn_0 = warp_idx % (Base::WarpCount0::kM * Base::WarpCount0::kN);
+    int warp_idx_k_0 = warp_idx / (Base::WarpCount0::kM * Base::WarpCount0::kN);
+
+    int warp_idx_m_0 = warp_idx_mn_0 % Base::WarpCount0::kM;
+    int warp_idx_n_0 = warp_idx_mn_0 / Base::WarpCount0::kM;
+
+    int warp_idx_mn_1 = warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN);
+    int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN);
+
+    int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM;
+    int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM;
+
+    // Add per-warp offsets in units of warp-level tiles
+    this->warp_tile_iterator_A0_.add_tile_offset(
+        {warp_idx_m_0, Base::kWarpGemmIterations0 * warp_idx_k_0});
+    this->warp_tile_iterator_B0_.add_tile_offset(
+        {Base::kWarpGemmIterations0 * warp_idx_k_0, warp_idx_n_0});
+    warp_tile_iterator_A1_.add_tile_offset(
+        {warp_idx_m_1, Base::kWarpGemmIterations1 * warp_idx_k_1});
+    this->warp_tile_iterator_B1_.add_tile_offset(
+        {Base::kWarpGemmIterations1 * warp_idx_k_1, warp_idx_n_1});
+
+    // Add smem accumulator iterator warp offset
+    smem_iterator_D0_.add_tile_offset({ warp_idx_m_0 * SmemIteratorD0::TileIterations::kRow, 
+                                        warp_idx_n_0 * SmemIteratorD0::TileIterations::kColumn});
+  }
+
+  CUTLASS_DEVICE
+  void copy_tiles_and_advance_0(IteratorA0 &iterator_A0, IteratorB0 &iterator_B0,
+                              int group_start_A0 = 0, int group_start_B0 = 0) {
+    iterator_A0.set_iteration_index(group_start_A0 *
+                                   IteratorA0::kAccessesPerVector);
+    this->smem_iterator_A0_.set_iteration_index(group_start_A0);
+
+    // cp.async for operand A
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupA0; ++j) {
+      if (group_start_A0 + j < Detail::TBLoadIterationsA0) {
+        typename IteratorA0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorA0::AccessType *>(
+                this->smem_iterator_A0_.get());
+
+        int const kSrcBytes = sizeof_bits<typename IteratorA0::Element>::value *
+                              IteratorA0::ThreadMap::kElementsPerAccess /
+                              IteratorA0::kAccessesPerVector / 8;
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorA0::kAccessesPerVector; ++v) {
+          auto gmem_ptr = iterator_A0.get();
+
+          cutlass::arch::cp_async<kSrcBytes, kCacheOpA0>(
+              dst_ptr + v, gmem_ptr, iterator_A0.valid());
+
+          ++iterator_A0;
+        }
+
+        ++this->smem_iterator_A0_;
+      }
+    }
+
+    iterator_B0.set_iteration_index(group_start_B0 *
+                                   IteratorB0::kAccessesPerVector);
+    this->smem_iterator_B0_.set_iteration_index(group_start_B0);
+
+    // cp.async for operand B
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupB0; ++j) {
+      if (group_start_B0 + j < Detail::TBLoadIterationsB0) {
+        typename IteratorB0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB0::AccessType *>(
+                this->smem_iterator_B0_.get());
+
+        int const kSrcBytes = sizeof_bits<typename IteratorB0::Element>::value *
+                              IteratorB0::ThreadMap::kElementsPerAccess /
+                              IteratorB0::kAccessesPerVector / 8;
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorB0::kAccessesPerVector; ++v) {
+          auto gmem_ptr = iterator_B0.get();
+
+          cutlass::arch::cp_async<kSrcBytes, kCacheOpB0>(
+              dst_ptr + v, gmem_ptr, iterator_B0.valid());
+
+          ++iterator_B0;
+        }
+        ++this->smem_iterator_B0_;
+      }
+    }
+  }
+
+  CUTLASS_DEVICE
+  void copy_tiles_and_advance_1(IteratorB1 &iterator_B1,
+                              int group_start_B1 = 0) {
+    iterator_B1.set_iteration_index(group_start_B1 *
+                                   IteratorB1::kAccessesPerVector);
+    this->smem_iterator_B1_.set_iteration_index(group_start_B1);
+
+    // cp.async for operand B
+    CUTLASS_PRAGMA_UNROLL
+    for (int j = 0; j < Detail::kAccessesPerGroupB1; ++j) {
+      if (group_start_B1 + j < Detail::TBLoadIterationsB1) {
+        typename IteratorB1::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB1::AccessType *>(
+                this->smem_iterator_B1_.get());
+
+        int const kSrcBytes = sizeof_bits<typename IteratorB1::Element>::value *
+                              IteratorB1::ThreadMap::kElementsPerAccess /
+                              IteratorB1::kAccessesPerVector / 8;
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorB1::kAccessesPerVector; ++v) {
+          auto gmem_ptr = iterator_B1.get();
+
+          cutlass::arch::cp_async<kSrcBytes, kCacheOpB1>(
+              dst_ptr + v, gmem_ptr, iterator_B1.valid());
+
+          ++iterator_B1;
+        }
+        ++this->smem_iterator_B1_;
+      }
+    }
+  }
+
+  /// Perform a threadblock-scoped matrix multiply-accumulate
+  CUTLASS_DEVICE
+  void operator()(
+      ///< problem size of GEMM
+      int gemm_k_iterations_0,
+      ///< destination accumulator tile
+      FragmentC1 &accum,
+      ///< iterator over A0 operand in global memory
+      IteratorA0 iterator_A0,
+      ///< iterator over B0 operand in global memory
+      IteratorB0 iterator_B0,
+      ///< iterator over A1 operand scale vector in global memory
+      IteratorAccumulatorScaleBias iterator_accum0_scale,
+      ///< iterator over A1 operand bias vector in global memory
+      IteratorAccumulatorScaleBias iterator_accum0_bias,
+      ///< iterator over B1 operand in global memory
+      IteratorB1 iterator_B1,
+      ///< initial value of accumulator
+      FragmentC0 const &src_accum,
+      ///< epilogue operation after 1st Gemm
+      OutputOp output_op_0)
+    {
+    //
+    // Prologue
+    //
+
+    // Issue several complete stages
+    CUTLASS_PRAGMA_UNROLL
+    for (int stage = 0; stage < Base::kStages - 1;
+         ++stage, --gemm_k_iterations_0) {
+
+      iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+      iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
+
+      iterator_A0.set_iteration_index(0);
+      this->smem_iterator_A0_.set_iteration_index(0);
+
+      // cp.async for operand A
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::TBLoadIterationsA0; ++j) {
+        typename IteratorA0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorA0::AccessType *>(
+                this->smem_iterator_A0_.get());
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorA0::kAccessesPerVector; ++v) {
+          int const kSrcBytes =
+              sizeof_bits<typename IteratorA0::Element>::value *
+              IteratorA0::ThreadMap::kElementsPerAccess /
+              IteratorA0::kAccessesPerVector / 8;
+
+          int src_bytes = (iterator_A0.valid() ? kSrcBytes : 0);
+
+          cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpA0>(
+              dst_ptr + v, iterator_A0.get(), iterator_A0.valid());
+
+          ++iterator_A0;
+        }
+
+        ++this->smem_iterator_A0_;
+      }
+
+      iterator_B0.set_iteration_index(0);
+      this->smem_iterator_B0_.set_iteration_index(0);
+
+      // cp.async for operand B
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::TBLoadIterationsB0; ++j) {
+        typename IteratorB0::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB0::AccessType *>(
+                this->smem_iterator_B0_.get());
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorB0::kAccessesPerVector; ++v) {
+          int const kSrcBytes =
+              sizeof_bits<typename IteratorB0::Element>::value *
+              IteratorB0::ThreadMap::kElementsPerAccess /
+              IteratorB0::kAccessesPerVector / 8;
+
+          cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB0>(
+              dst_ptr + v, iterator_B0.get(), iterator_B0.valid());
+
+          ++iterator_B0;
+        }
+
+        ++this->smem_iterator_B0_;
+      }
+
+      // Move to the next stage
+      iterator_A0.add_tile_offset({0, 1});
+      iterator_B0.add_tile_offset({1, 0});
+
+      this->smem_iterator_A0_.add_tile_offset({0, 1});
+      this->smem_iterator_B0_.add_tile_offset({1, 0});
+
+      // Defines the boundary of a stage of cp.async.
+      cutlass::arch::cp_async_fence();
+    }
+
+    // Perform accumulation in the 'd' output operand
+    FragmentC0 accum0 = src_accum;
+
+    // DEPBAR+SYNC
+    cutlass::arch::cp_async_wait<Base::kStages - 2>();
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math
+    // instructions
+    WarpLoadedFragmentA0 warp_loaded_frag_A0[2];
+    WarpLoadedFragmentB0 warp_loaded_frag_B0[2];
+    WarpTransformedFragmentA0 warp_transformed_frag_A0[2];
+    WarpTransformedFragmentB0 warp_transformed_frag_B0[2];
+
+    Operator0 warp_mma0;
+
+    this->warp_tile_iterator_A0_.set_kgroup_index(0);
+    this->warp_tile_iterator_B0_.set_kgroup_index(0);
+
+    this->warp_tile_iterator_A0_.load(warp_loaded_frag_A0[0]);
+    this->warp_tile_iterator_B0_.load(warp_loaded_frag_B0[0]);
+
+    ++this->warp_tile_iterator_A0_;
+    ++this->warp_tile_iterator_B0_;
+
+    iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+    iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
+
+    int smem_write_stage_idx = Base::kStages - 1;
+    int smem_read_stage_idx = 0;
+
+    warp_mma0.transform(warp_transformed_frag_A0[0], warp_transformed_frag_B0[0],
+                       warp_loaded_frag_A0[0], warp_loaded_frag_B0[0]);
+
+    //
+    // Mainloop
+    //
+
+    CUTLASS_GEMM_LOOP
+    for (; gemm_k_iterations_0 > (-Base::kStages + 1);) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      // Computes a warp-level GEMM on data held in shared memory
+      // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations0;
+           ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if
+        // this is the last group as the case may be.
+
+        this->warp_tile_iterator_A0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        this->warp_tile_iterator_B0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        
+        this->warp_tile_iterator_A0_.load(warp_loaded_frag_A0[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B0_.load(warp_loaded_frag_B0[(warp_mma_k + 1) % 2]);
+
+        ++this->warp_tile_iterator_A0_;
+        ++this->warp_tile_iterator_B0_;
+
+        if (warp_mma_k > 0)
+          warp_mma0.transform(warp_transformed_frag_A0[warp_mma_k % 2],
+                             warp_transformed_frag_B0[warp_mma_k % 2],
+                             warp_loaded_frag_A0[warp_mma_k % 2],
+                             warp_loaded_frag_B0[warp_mma_k % 2]);
+
+        warp_mma0(
+          accum0, 
+          warp_transformed_frag_A0[warp_mma_k % 2],
+          warp_transformed_frag_B0[warp_mma_k % 2], 
+          accum0
+        );
+
+        // Issue global->shared copies for the this stage
+        if (warp_mma_k < Base::kWarpGemmIterations0 - 1) {
+          int group_start_iteration_A0, group_start_iteration_B0;
+
+          group_start_iteration_A0 = warp_mma_k * Detail::kAccessesPerGroupA0;
+          group_start_iteration_B0 = warp_mma_k * Detail::kAccessesPerGroupB0;
+
+          copy_tiles_and_advance_0(iterator_A0, iterator_B0, group_start_iteration_A0, 
+                               group_start_iteration_B0);
+        }
+
+        if (warp_mma_k + 2 == Base::kWarpGemmIterations0) {
+          int group_start_iteration_A0, group_start_iteration_B0;
+          group_start_iteration_A0 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupA0;
+          group_start_iteration_B0 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupB0;
+
+          copy_tiles_and_advance_0(iterator_A0, iterator_B0, group_start_iteration_A0, 
+                               group_start_iteration_B0);
+
+          // Inserts a memory fence between stages of cp.async instructions.
+          cutlass::arch::cp_async_fence();
+
+          // Waits until kStages-2 stages have committed.
+          arch::cp_async_wait<Base::kStages - 2>();
+          __syncthreads();
+
+          // Move to the next stage
+          iterator_A0.add_tile_offset({0, 1});
+          iterator_B0.add_tile_offset({1, 0});
+
+          this->smem_iterator_A0_.add_tile_offset({0, 1});
+          this->smem_iterator_B0_.add_tile_offset({1, 0});
+
+          // Add negative offsets to return iterators to the 'start' of the
+          // circular buffer in shared memory
+          if (smem_write_stage_idx == (Base::kStages - 1)) {
+            this->smem_iterator_A0_.add_tile_offset({0, -Base::kStages});
+            this->smem_iterator_B0_.add_tile_offset({-Base::kStages, 0});
+            smem_write_stage_idx = 0;
+          } else {
+            ++smem_write_stage_idx;
+          }
+
+          if (smem_read_stage_idx == (Base::kStages - 1)) {
+            this->warp_tile_iterator_A0_.add_tile_offset(
+                {0, -Base::kStages * Policy0::kPartitionsK *
+                        Base::kWarpGemmIterations0});
+            this->warp_tile_iterator_B0_.add_tile_offset(
+                {-Base::kStages * Policy0::kPartitionsK *
+                     Base::kWarpGemmIterations0,
+                 0});
+            smem_read_stage_idx = 0;
+          } else {
+            ++smem_read_stage_idx;
+          }
+
+          --gemm_k_iterations_0;
+          iterator_A0.clear_mask(gemm_k_iterations_0 == 0);
+          iterator_B0.clear_mask(gemm_k_iterations_0 == 0);
+        }
+
+        // Do any conversions feeding the first stage at the end of the loop so
+        // we can start right away on mma instructions
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations0)
+          warp_mma0.transform(warp_transformed_frag_A0[(warp_mma_k + 1) % 2],
+                             warp_transformed_frag_B0[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_A0[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_B0[(warp_mma_k + 1) % 2]);
+      }
+
+    }
+
+    /// Epilogue for the first Implicit Gemm
+    Epilogue0 epilogue0;
+
+    epilogue0(output_op_0, smem_iterator_D0_, accum0, iterator_accum0_scale, iterator_accum0_bias);
+
+    __syncthreads();
+
+
+    // 2nd Gemm
+
+    //
+    // Prologue
+    //
+    int gemm_k_iterations_1 = Shape0::kN / Shape1::kK;
+
+    // Issue several complete stages
+    CUTLASS_PRAGMA_UNROLL
+    for (int stage = 0; stage < Base::kStages - 1;
+         ++stage, --gemm_k_iterations_1) {
+
+      iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
+
+      iterator_B1.set_iteration_index(0);
+      this->smem_iterator_B1_.set_iteration_index(0);
+
+      // cp.async for operand B
+      CUTLASS_PRAGMA_UNROLL
+      for (int j = 0; j < Detail::TBLoadIterationsB1; ++j) {
+        typename IteratorB1::AccessType *dst_ptr =
+            reinterpret_cast<typename IteratorB1::AccessType *>(
+                this->smem_iterator_B1_.get());
+
+        CUTLASS_PRAGMA_UNROLL
+        for (int v = 0; v < IteratorB1::kAccessesPerVector; ++v) {
+          int const kSrcBytes =
+              sizeof_bits<typename IteratorB1::Element>::value *
+              IteratorB1::ThreadMap::kElementsPerAccess /
+              IteratorB1::kAccessesPerVector / 8;
+
+          cutlass::arch::cp_async_zfill<kSrcBytes, kCacheOpB1>(
+              dst_ptr + v, iterator_B1.get(), iterator_B1.valid());
+
+          ++iterator_B1;
+        }
+
+        ++this->smem_iterator_B1_;
+      }
+
+      // Move to the next stage
+      iterator_B1.add_tile_offset({1, 0});
+
+      this->smem_iterator_B1_.add_tile_offset({1, 0});
+
+      // Defines the boundary of a stage of cp.async.
+      cutlass::arch::cp_async_fence();
+    }
+
+    // DEPBAR+SYNC
+    cutlass::arch::cp_async_wait<Base::kStages - 2>();
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math
+    // instructions
+    WarpLoadedFragmentA1 warp_loaded_frag_A1[2];
+    WarpLoadedFragmentB1 warp_loaded_frag_B1[2];
+    WarpTransformedFragmentA1 warp_transformed_frag_A1[2];
+    WarpTransformedFragmentB1 warp_transformed_frag_B1[2];
+
+    Operator1 warp_mma1;
+
+    warp_tile_iterator_A1_.load(warp_loaded_frag_A1[0]);
+    ++warp_tile_iterator_A1_;
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+    this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[0]);
+    ++this->warp_tile_iterator_B1_;
+
+    iterator_B1.clear_mask(gemm_k_iterations_1 == 0);
+
+    smem_write_stage_idx = Base::kStages - 1;
+    smem_read_stage_idx = 0;
+
+    warp_mma1.transform(warp_transformed_frag_A1[0], warp_transformed_frag_B1[0],
+                       warp_loaded_frag_A1[0], warp_loaded_frag_B1[0]);
+
+    //
+    // Mainloop
+    //
+
+    CUTLASS_PRAGMA_UNROLL
+    for ( gemm_k_iterations_1 = Shape0::kN / Shape1::kK - (Base::kStages - 1); 
+            gemm_k_iterations_1 > (-Base::kStages + 1); gemm_k_iterations_1--) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      // Computes a warp-level GEMM on data held in shared memory
+      // Each "warp_mma_k" refers to a warp-level matrix multiply-accumulate
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1;
+           ++warp_mma_k) {
+
+        // Load warp-level tile from accumulator fragment
+        // skip warp tile loading for the last kgroup
+        if(gemm_k_iterations_1 > (-Base::kStages + 2) || warp_mma_k < Base::kWarpGemmIterations1 - 1) {
+            warp_tile_iterator_A1_.load(warp_loaded_frag_A1[(warp_mma_k + 1) % 2]);
+        }
+        ++warp_tile_iterator_A1_;
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if
+        // this is the last group as the case may be.
+        this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
+        this->warp_tile_iterator_B1_.load(warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
+        ++this->warp_tile_iterator_B1_;
+
+
+        if (warp_mma_k > 0)
+          warp_mma1.transform(warp_transformed_frag_A1[warp_mma_k % 2],
+                             warp_transformed_frag_B1[warp_mma_k % 2],
+                             warp_loaded_frag_A1[warp_mma_k % 2],
+                             warp_loaded_frag_B1[warp_mma_k % 2]);
+
+
+        warp_mma1(
+          accum, 
+          warp_transformed_frag_A1[warp_mma_k % 2],
+          warp_transformed_frag_B1[warp_mma_k % 2], 
+          accum
+        );
+
+        // Issue global->shared copies for the this stage
+        if (warp_mma_k < Base::kWarpGemmIterations1 - 1) {
+          int group_start_iteration_B1;
+
+          group_start_iteration_B1 = warp_mma_k * Detail::kAccessesPerGroupB1;
+
+          copy_tiles_and_advance_1(iterator_B1, group_start_iteration_B1);
+        }
+
+        if (warp_mma_k + 2 == Base::kWarpGemmIterations1) {
+          int group_start_iteration_B1;
+          group_start_iteration_B1 =
+              (warp_mma_k + 1) * Detail::kAccessesPerGroupB1;
+
+          copy_tiles_and_advance_1(iterator_B1, group_start_iteration_B1);
+
+          // Inserts a memory fence between stages of cp.async instructions.
+          cutlass::arch::cp_async_fence();
+
+          // Waits until kStages-2 stages have committed.
+          arch::cp_async_wait<Base::kStages - 2>();
+          __syncthreads();
+
+          // Move to the next stage
+          iterator_B1.add_tile_offset({1, 0});
+
+          this->smem_iterator_B1_.add_tile_offset({1, 0});
+
+          // Add negative offsets to return iterators to the 'start' of the
+          // circular buffer in shared memory
+          if (smem_write_stage_idx == (Base::kStages - 1)) {
+            this->smem_iterator_B1_.add_tile_offset({-Base::kStages, 0});
+            smem_write_stage_idx = 0;
+          } else {
+            ++smem_write_stage_idx;
+          }
+
+          if (smem_read_stage_idx == (Base::kStages - 1)) {
+            this->warp_tile_iterator_B1_.add_tile_offset(
+                {-Base::kStages * Policy1::kPartitionsK *
+                     Base::kWarpGemmIterations1,
+                 0});
+            smem_read_stage_idx = 0;
+          } else {
+            ++smem_read_stage_idx;
+          }
+
+          iterator_B1.clear_mask(gemm_k_iterations_1 == 1);
+        }
+
+        // Do any conversions feeding the first stage at the end of the loop so
+        // we can start right away on mma instructions
+        if (warp_mma_k + 1 == Base::kWarpGemmIterations1)
+          warp_mma1.transform(warp_transformed_frag_A1[(warp_mma_k + 1) % 2],
+                             warp_transformed_frag_B1[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_A1[(warp_mma_k + 1) % 2],
+                             warp_loaded_frag_B1[(warp_mma_k + 1) % 2]);
+      }
+
+    }
+
+
+
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+}  // namespace threadblock
+}  // namespace gemm
+}  // namespace cutlass
+
+/////////////////////////////////////////////////////////////////////////////////////////////////

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_pipelined.h

@@ -1,24 +1,30 @@
 /***************************************************************************************************
- * Copyright (c) 2017-2021, NVIDIA CORPORATION.  All rights reserved.
+ * Copyright (c) 2017 - 2023 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 TORT (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.
  *
  **************************************************************************************************/
@@ -70,6 +76,11 @@ template <
   /// Iterates over the intermediate accumulator tile
   //  (concept::MmaTensorOpFragmentIterator) 
   typename FragmentIteratorA1_,
+  /// Iterates over vectors of scale and bias vector in global memory
+  //  (concept: VectorIterator)
+  typename IteratorAccumulatorScaleBias_,
+  /// FragmentIterator to load Scale or Bias vector from threadblock fragment
+  typename FragmentIteratorA1ScaleBias_,
   /// Iterates over tiles of B operand in global memory
   //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
   typename IteratorB1_,
@@ -106,7 +117,8 @@ template <
   /// Used for partial specialization
   typename Enable = bool
 >
-class B2bMmaPipelined : public B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, 2> {
+class B2bMmaPipelined : 
+  public B2bMmaBase<Shape0_, Shape1_, Policy0_, Policy1_, 2> {
 public:
 
   ///< Base class
@@ -122,6 +134,9 @@ public:
 
   using Shape1 = Shape1_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
   using FragmentIteratorA1 = FragmentIteratorA1_; ///< Iterates over intermediate accumulator tile
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;   ///< Iterates over tiles of the scale and bias vectors in global memory
+  using FragmentIteratorA1ScaleBias = 
+    FragmentIteratorA1ScaleBias_;     ///< WarpIterator to load Scale or Bias vector from the threadblock fragment
   using IteratorB1 = IteratorB1_;     ///< Iterates over tiles of B operand in global memory
   using Policy1 = Policy1_;           ///< Policy describing tuning details
 
@@ -130,9 +145,12 @@ public:
 
   using ElementC = ElementC_;       ///< Data type of accumulator matrix
   using LayoutC = LayoutC_;         ///< Layout of accumulator matrix
-
+  
   using OutputOp = OutputOp_;       ///< Epilogue after 1st Gemm
 
+  static const bool PerChannelScale = (OutputOp::kScale ==
+      epilogue::thread::ScaleType::OnlyAlphaPerChannelScaling);
+
   using TransformA0 = TransformA0_;
   using TransformB0 = TransformB0_;
   using TransformB1 = TransformB1_;
@@ -152,6 +170,9 @@ public:
 
   /// Warp-level Mma
   using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of Scale and Bias loaded from global memory
+  using FragmentA1ScaleBias = typename IteratorAccumulatorScaleBias::Fragment;
 
   /// Fragment of operand B loaded from global memory
   using FragmentB1 = typename IteratorB1::Fragment;
@@ -161,7 +182,7 @@ public:
 
   /// Warp-level Mma
   using Operator1 = typename Policy1::Operator;
-  
+ 
   /// Obtain the arch tag from the warp-level operator
   using ArchTag = typename Policy0::Operator::ArchTag;
 
@@ -174,7 +195,7 @@ public:
   /// Complex transform on B1 operand
   static ComplexTransform const kTransformB1 = Operator1::kTransformB;
 
-  // staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline)
+  /// staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline)
   static_assert((Base::kStages==2), "MmaPipelined requires kStages set to value 2");
 
 private:
@@ -183,6 +204,9 @@ private:
   using WarpFragmentB0 = typename Operator0::FragmentB;
   /// Warp Fragment of operand A1 loaded from accmulator tile
   using WarpFragmentA1 = typename FragmentIteratorA1::Fragment;
+  /// Warp Fragment of operand A1 scale and bias loaded from threadblock fragment
+  using WarpFragmentA1ScaleBias =
+      typename FragmentIteratorA1ScaleBias::Fragment;
   using WarpFragmentB1 = typename Operator1::FragmentB;
 
 protected:
@@ -204,12 +228,13 @@ public:
     typename Base::B2bMmaSharedStorage &shared_storage, ///< Shared storage needed for internal use by threadblock-scoped GEMM
     int thread_idx,                                     ///< ID within the threadblock
     int warp_idx,                                       ///< ID of warp
-    int lane_idx                                        ///< ID of each thread within a warp
+    int lane_idx,                                       ///< ID of each thread within a warp
+    int problem_size_0_n                                ///< GEMM0 N is used for accumulator extent
   ):
     Base(shared_storage, thread_idx, warp_idx, lane_idx),
-    smem_iterator_A_(shared_storage.sharedStorage0.operand_A_ref(), thread_idx), 
-    smem_iterator_B0_(shared_storage.sharedStorage0.operand_B_ref(), thread_idx),
-    smem_iterator_B1_(shared_storage.sharedStorage1.operand_B_ref(), thread_idx) {
+    smem_iterator_A_(shared_storage.shared_storage0.operand_A_ref(), thread_idx), 
+    smem_iterator_B0_(shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+    smem_iterator_B1_(shared_storage.shared_storage1.operand_B_ref(), thread_idx) {
 
 
     // Compute warp location within threadblock tile by mapping the warp_id to three coordinates:
@@ -237,16 +262,18 @@ public:
   /// Perform a threadblock-scoped matrix multiply-accumulate
   CUTLASS_DEVICE
   void operator()(
-    int gemm_k_iterations_0,                            ///< number of iterations of the mainloop
-    FragmentC1 &accum,                                  ///< destination accumulator tile
-    IteratorA0 iterator_A,                              ///< iterator over A operand in global memory
-    IteratorB0 iterator_B0,                             ///< iterator over B0 operand in global memory
-    IteratorB1 iterator_B1,                             ///< iterator over B1 operand in global memory  
-    FragmentC0 const &src_accum,                        ///< source accumualtor tile
-    OutputOp output_op_0,                               ///< epilogue operation after 1st Gemm
+    int gemm_k_iterations_0,                             ///< number of iterations of the mainloop
+    FragmentC1 &accum,                                   ///< destination accumulator tile
+    IteratorA0 iterator_A,                               ///< iterator over A operand in global memory
+    IteratorB0 iterator_B0,                              ///< iterator over B0 operand in global memory
+    IteratorAccumulatorScaleBias iterator_A1_scale,    ///< iterator over A1 operand scale vectors in global memory
+    IteratorAccumulatorScaleBias iterator_A1_bias,     ///< iterator over A1 operand bias vectors in global memory
+    IteratorB1 iterator_B1,                              ///< iterator over B1 operand in global memory  
+    FragmentC0 const &src_accum,                         ///< source accumualtor tile
+    OutputOp output_op_0,                                ///< epilogue operation after 1st Gemm
     TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
-    TransformB0 transform_B0 = TransformB0(),           ///< transformation applied to B0 fragment
-    TransformB1 transform_B1 = TransformB1()) {         ///< transformation applied to B1 fragment
+    TransformB0 transform_B0 = TransformB0(),            ///< transformation applied to B0 fragment
+    TransformB1 transform_B1 = TransformB1()) {          ///< transformation applied to B1 fragment
 
     //
     // Prologue
@@ -268,8 +295,8 @@ public:
     ++iterator_A;
     ++iterator_B0;
 
-    this->smem_iterator_A_.store(tb_frag_A);
-    this->smem_iterator_B0_.store(tb_frag_B0);
+    this->smem_iterator_A_.store(transform_A0(tb_frag_A));
+    this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
 
     ++this->smem_iterator_A_;
     ++this->smem_iterator_B0_;
@@ -294,23 +321,19 @@ public:
     int smem_write_stage_idx = 1;
 
     // Avoid reading out of bounds
-    if (gemm_k_iterations_0 <= 1) {
-      iterator_A.clear_mask();
-      iterator_B0.clear_mask();
-    }
+    iterator_A.clear_mask(gemm_k_iterations_0 <= 1);
+    iterator_B0.clear_mask(gemm_k_iterations_0 <= 1);
 
     // Issue loads during the first warp-level matrix multiply-add *AFTER* issuing 
     // shared memory loads (which have the tighest latency requirement).
-    iterator_A.load(tb_frag_A);
 
     //
     // Mainloop
     //
 
-    // Note: The main loop does not support Base::WarpGemmIterations == 2.
+    // Note: The main loop does not support Base::kWarpGemmIterations == 2.
     CUTLASS_GEMM_LOOP
     for (; gemm_k_iterations_0 > 0; --gemm_k_iterations_0) {
-
       //
       // Loop over GEMM K dimension
       //
@@ -324,19 +347,14 @@ public:
         if (warp_mma_k == Base::kWarpGemmIterations0 - 1) {
 
           // Write fragments to shared memory
-          this->smem_iterator_A_.store(tb_frag_A);
+          this->smem_iterator_A_.store(transform_A0(tb_frag_A));
 
-          this->smem_iterator_B0_.store(tb_frag_B0);
+          this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
 
           __syncthreads();
-
-          // Issue loads during the first warp-level matrix multiply-add *AFTER* issuing 
-          // shared memory loads (which have the tighest latency requirement).
-          iterator_A.load(tb_frag_A);
           
-          ++this->smem_iterator_B0_;
           ++this->smem_iterator_A_;
-        
+          ++this->smem_iterator_B0_;
 
           // Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
           if (smem_write_stage_idx == 1) {
@@ -365,19 +383,18 @@ public:
 
         if (warp_mma_k == 0) {
 
+          iterator_A.load(tb_frag_A);
           iterator_B0.load(tb_frag_B0);
-
           ++iterator_A;
           ++iterator_B0;
 
           // Avoid reading out of bounds if this was the last loop iteration
-          if (gemm_k_iterations_0 <= 2) {
-            iterator_A.clear_mask();
-            iterator_B0.clear_mask();
-          }
+          iterator_A.clear_mask(gemm_k_iterations_0 <= 2);
+          iterator_B0.clear_mask(gemm_k_iterations_0 <= 2);
         }
 
-        warp_mma0(accum0, warp_frag_A0[warp_mma_k % 2], warp_frag_B0[warp_mma_k % 2], accum0);
+        warp_mma0(accum0, warp_frag_A0[warp_mma_k % 2],
+                  warp_frag_B0[warp_mma_k % 2], accum0);
       }
     }
 
@@ -390,32 +407,53 @@ public:
     // Prologue
     //
 
+    FragmentA1ScaleBias tb_frag_A1_scale;
+    FragmentA1ScaleBias tb_frag_A1_bias;
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_scale_(tb_frag_A1_scale);
+    FragmentIteratorA1ScaleBias warp_tile_iterator_A1_bias_(tb_frag_A1_bias);
     FragmentB1 tb_frag_B1;
 
+    if(PerChannelScale)
+        tb_frag_A1_scale.clear();
+    tb_frag_A1_bias.clear();
     tb_frag_B1.clear();
 
     // The last kblock is loaded in the prolog
+    if(PerChannelScale)
+        iterator_A1_scale.load(tb_frag_A1_scale);
+    iterator_A1_bias.load(tb_frag_A1_bias);
     iterator_B1.load(tb_frag_B1);
 
+    if(PerChannelScale)
+        ++iterator_A1_scale;
+    ++iterator_A1_bias;
     ++iterator_B1;
 
-    this->smem_iterator_B1_.store(tb_frag_B1);
+    this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
 
     ++this->smem_iterator_B1_;
 
     __syncthreads();
 
     // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA1ScaleBias warp_frag_A1_scale[2];
+    WarpFragmentA1ScaleBias warp_frag_A1_bias[2];
     WarpFragmentA1 warp_frag_A1[2];
     WarpFragmentB1 warp_frag_B1[2];
 
-    //warp_tile_iterator_A1_.set_kgroup_index(0);
     this->warp_tile_iterator_B1_.set_kgroup_index(0);
 
-    warp_tile_iterator_A1_.load(warp_frag_A1[0], output_op_0);
+    if(PerChannelScale)
+        warp_tile_iterator_A1_scale_.load(warp_frag_A1_scale[0]);
+    warp_tile_iterator_A1_bias_.load(warp_frag_A1_bias[0]);
+    warp_tile_iterator_A1_.load(warp_frag_A1[0], warp_frag_A1_scale[0],
+        warp_frag_A1_bias[0], output_op_0);
     this->warp_tile_iterator_B1_.load(warp_frag_B1[0]);
 
     ++warp_tile_iterator_A1_;
+    if(PerChannelScale)
+        ++warp_tile_iterator_A1_scale_;
+    ++warp_tile_iterator_A1_bias_;
     ++this->warp_tile_iterator_B1_;
 
     Operator1 warp_mma1;
@@ -425,9 +463,7 @@ public:
     int gemm_k_iterations_1 = FragmentIteratorA1::Policy::kIterations / Base::kWarpGemmIterations1;
 
     // Avoid reading out of bounds
-    if (gemm_k_iterations_1 <= 1) {
-      iterator_B1.clear_mask();
-    }
+    iterator_B1.clear_mask(gemm_k_iterations_1 <= 1);
 
     //
     // Mainloop
@@ -450,8 +486,7 @@ public:
         if (warp_mma_k == Base::kWarpGemmIterations1 - 1) {
 
           // Write fragments to shared memory
-
-          this->smem_iterator_B1_.store(tb_frag_B1);
+          this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
 
           __syncthreads();
           ++this->smem_iterator_B1_;
@@ -468,14 +503,31 @@ public:
           }
 
           smem_write_stage_idx ^= 1;
+
+          if(PerChannelScale) {
+              tb_frag_A1_scale.clear();
+              iterator_A1_scale.load(tb_frag_A1_scale);
+              ++iterator_A1_scale;
+            }
+            tb_frag_A1_bias.clear();
+            iterator_A1_bias.load(tb_frag_A1_bias);
+            ++iterator_A1_bias;
         }
 
         this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
-        
-        warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2], output_op_0);
+
+        if(PerChannelScale)
+          warp_tile_iterator_A1_scale_.load(warp_frag_A1_scale[(warp_mma_k + 1) % 2]);
+        warp_tile_iterator_A1_bias_.load(warp_frag_A1_bias[(warp_mma_k + 1) % 2]);
+        warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2], 
+            warp_frag_A1_scale[(warp_mma_k + 1) % 2], 
+            warp_frag_A1_bias[(warp_mma_k + 1) % 2], 
+            output_op_0);
         this->warp_tile_iterator_B1_.load(warp_frag_B1[(warp_mma_k + 1) % 2]);
 
-
+        if(PerChannelScale)
+          ++warp_tile_iterator_A1_scale_;
+        ++warp_tile_iterator_A1_bias_;
         ++warp_tile_iterator_A1_;
         ++this->warp_tile_iterator_B1_;
 
@@ -484,17 +536,14 @@ public:
           iterator_B1.load(tb_frag_B1);
           ++iterator_B1;
 
-
           // Avoid reading out of bounds if this was the last loop iteration
-          if (gemm_k_iterations_1 <= 2) {
-            iterator_B1.clear_mask();
-          }
+          iterator_B1.clear_mask(gemm_k_iterations_1 <= 2);
         }
 
-        warp_mma1(accum, warp_frag_A1[warp_mma_k % 2], warp_frag_B1[warp_mma_k % 2], accum);
+        warp_mma1(accum, warp_frag_A1[warp_mma_k % 2], 
+                  warp_frag_B1[warp_mma_k % 2], accum);
       }
     }
-
   }
 };
 

examples/13_two_tensor_op_fusion/threadblock/b2b_mma_pipelined_smem_accumulator.h

@@ -0,0 +1,544 @@
+/***************************************************************************************************
+ * Copyright (c) 2017 - 2023 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 Template for a double-buffered threadblock-scoped Back-to-back fused GEMM kernel.
+*/
+
+#pragma once
+
+#include "cutlass/cutlass.h"
+#include "cutlass/array.h"
+#include "cutlass/aligned_buffer.h"
+#include "cutlass/numeric_conversion.h"
+
+#include "cutlass/numeric_types.h"
+#include "cutlass/matrix_shape.h"
+
+#include "cutlass/gemm/gemm.h"
+#include "cutlass/gemm/warp/mma_tensor_op_fragment_iterator.h"
+
+#include "threadblock/b2b_mma_base_smem_accumulator.h"
+#include "cutlass/epilogue/threadblock/epilogue_smem_accumulator.h"
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+namespace cutlass {
+namespace gemm {
+namespace threadblock {
+
+////////////////////////////////////////////////////////////////////////////////////////////////
+
+/// Structure to compute the matrix product targeting CUDA cores and SIMT math instructions.
+template <
+  /// Size of the Gemm problem - concept: gemm::GemmShape<>
+  typename Shape0_,
+  /// Iterates over tiles of A operand in global memory 
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorA0_,
+  /// Iterates over tiles of A operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorA0_,
+  /// Iterates over tiles of B operand in global memory
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorB0_,
+  /// Iterates over tiles of B operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorB0_,
+  /// Iterates over vectors of scale and bias vector in global memory
+  //  (concept: VectorIterator)
+  typename IteratorAccumulatorScaleBias_,
+  /// Iterates over accumulator tile
+  typename FragmentIteratorAccumulator_,
+  /// Iterates over accumulator tile in shared memory
+  typename SmemIteratorD0_,
+  /// Size of the Gemm problem - concept: gemm::GemmShape<>
+  typename Shape1_,
+  /// Iterates over the intermediate accumulator tile in shared memory
+  typename WarpIteratorA1_,
+  /// Iterates over tiles of B operand in global memory
+  //  (concept: ReadableTileIterator | ForwardTileIterator | MaskedTileIterator)
+  typename IteratorB1_,
+  /// Iterates over tiles of B operand in shared memory
+  /// (concept: WriteableTileIterator | RandomAccessTileIterator)
+  typename SmemIteratorB1_,
+  /// Data type of accumulator matrix
+  typename ElementC_,
+  /// Data type of accumulator matrix
+  typename LayoutC_,
+  /// Output operator for 1st Gemm(concept: epilogue::thread::LinearCombinationClamp, etc...) 
+  typename OutputOp_,
+  /// Policy describing tuning details (concept: MmaPipelinedPolicy)
+  typename Policy0_,
+  /// Policy describing tuning details (concept: MmaPipelinedPolicy)
+  typename Policy1_,
+  /// Transformation applied to A0 operand
+  typename TransformA0_ = NumericArrayConverter<
+    typename SmemIteratorA0_::Element, 
+    typename IteratorA0_::Element, 
+    IteratorA0_::Fragment::kElements>,
+  ///
+  /// Transformation applied to B0 operand
+  typename TransformB0_ = NumericArrayConverter<
+    typename SmemIteratorB0_::Element, 
+    typename IteratorB0_::Element, 
+    IteratorB0_::Fragment::kElements>,
+  ///
+  /// Transformation applied to B1 operand
+  typename TransformB1_ = NumericArrayConverter<
+    typename SmemIteratorB1_::Element, 
+    typename IteratorB1_::Element, 
+    IteratorB1_::Fragment::kElements>,
+  /// Used for partial specialization
+  typename Enable = bool
+>
+class B2bMmaPipelinedSmemAccumulator : 
+  public B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, 2> {
+public:
+
+  ///< Base class
+  using Base = B2bMmaBaseSmemAccumulator<Shape0_, Shape1_, Policy0_, Policy1_, SmemIteratorD0_, 2>;
+
+  using Shape0 = Shape0_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using IteratorA0 = IteratorA0_;     ///< Iterates over tiles of A operand in global memory
+  using IteratorB0 = IteratorB0_;     ///< Iterates over tiles of B operand in global memory
+  using IteratorAccumulatorScaleBias = IteratorAccumulatorScaleBias_;   ///< Iterates over tiles of the scale and bias vectors in global memory
+  using Policy0 = Policy0_;           ///< Policy0 describing tuning details
+
+  using SmemIteratorA0 = SmemIteratorA0_;
+  using SmemIteratorB0 = SmemIteratorB0_;
+  using SmemIteratorD0 = SmemIteratorD0_; ///< Iterates over accumulator tile in shared memory
+
+  using FragmentIteratorAccumulator = FragmentIteratorAccumulator_;  ///< Iterates over accumulator tile
+
+  using Shape1 = Shape1_;             ///< Size of the Gemm problem - concept: gemm::GemmShape<>
+  using IteratorB1 = IteratorB1_;     ///< Iterates over tiles of B operand in global memory
+  using Policy1 = Policy1_;           ///< Policy1 describing tuning details
+
+  using SmemIteratorB1 = SmemIteratorB1_;
+  using WarpIteratorA1 = WarpIteratorA1_;   ///< Iterates over the intermediate accumulator tile in shared memory
+
+
+  using ElementC = ElementC_;       ///< Data type of accumulator matrix
+  using LayoutC = LayoutC_;         ///< Layout of accumulator matrix
+  
+  using OutputOp = OutputOp_;       ///< Epilogue after 1st Gemm
+
+  using TransformA0 = TransformA0_;
+  using TransformB0 = TransformB0_;
+  using TransformB1 = TransformB1_;
+
+  //
+  // Dependent types
+  //
+
+  /// Fragment of operand A loaded from global memory
+  using FragmentA0 = typename IteratorA0::Fragment;
+
+  /// Fragment of operand B loaded from global memory
+  using FragmentB0 = typename IteratorB0::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC0 = typename Policy0::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator0 = typename Policy0::Operator;
+  
+  /// Fragment of operand B loaded from global memory
+  using FragmentB1 = typename IteratorB1::Fragment;
+
+  /// Fragment of accumulator tile
+  using FragmentC1 = typename Policy1::Operator::FragmentC;
+
+  /// Warp-level Mma
+  using Operator1 = typename Policy1::Operator;
+ 
+  /// Obtain the arch tag from the warp-level operator
+  using ArchTag = typename Policy0::Operator::ArchTag;
+
+  /// Complex transform on A0 operand
+  static ComplexTransform const kTransformA0 = Operator0::kTransformA;
+
+  /// Complex transform on B0 operand
+  static ComplexTransform const kTransformB0 = Operator0::kTransformB;
+
+  /// Complex transform on B1 operand
+  static ComplexTransform const kTransformB1 = Operator1::kTransformB;
+
+  /// staticaly assert kStages for MmaPipelined is two (Double-buffered pipeline)
+  static_assert((Base::kStages==2), "MmaPipelined requires kStages set to value 2");
+
+  /// Epilog in shared memory
+  using Epilogue0 = epilogue::threadblock::EpilogueSmemAccumulator<
+    SmemIteratorD0,                 ///< SmemTileIterator
+    FragmentIteratorAccumulator,    ///< AccumulatorFragmentIterator
+    IteratorAccumulatorScaleBias,   ///< ScaleBiasIterator
+    OutputOp>;                      ///< Output operator
+
+
+
+private:
+
+  using WarpFragmentA0 = typename Operator0::FragmentA;
+  using WarpFragmentB0 = typename Operator0::FragmentB;
+  using WarpFragmentA1 = typename Operator1::FragmentA;
+  using WarpFragmentB1 = typename Operator1::FragmentB;
+
+protected:
+
+  /// Iterator to write threadblock-scoped tile of A operand to shared memory
+  SmemIteratorA0 smem_iterator_A_;
+
+  /// Iterator to write threadblock-scoped tile of B0 operand to shared memory
+  SmemIteratorB0 smem_iterator_B0_;
+    
+  /// Shared Memory Iterator to store accumulator tile
+  SmemIteratorD0 smem_iterator_D0_;
+    
+  /// Iterator to load a warp-scoped tile of A1 operand from intermediate accumulator tile
+  WarpIteratorA1 warp_tile_iterator_A1_;
+
+  /// Iterator to write threadblock-scoped tile of B1 operand to shared memory
+  SmemIteratorB1 smem_iterator_B1_;
+
+public:
+
+  /// Construct from tensor references
+  CUTLASS_DEVICE
+  B2bMmaPipelinedSmemAccumulator(
+    typename Base::B2bMmaSharedStorage &shared_storage, ///< Shared storage needed for internal use by threadblock-scoped GEMM
+    int thread_idx,                                     ///< ID within the threadblock
+    int warp_idx,                                       ///< ID of warp
+    int lane_idx,                                        ///< ID of each thread within a warp
+    int problem_size_0_n                                ///< GEMM0 N is used for accumulator extent
+  ):
+    Base(shared_storage, thread_idx, warp_idx, lane_idx),
+    smem_iterator_A_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_A_ref(), thread_idx),
+    smem_iterator_B0_(shared_storage.b2b_mma_shared_storage.shared_storage0.operand_B_ref(), thread_idx),
+    smem_iterator_D0_(shared_storage.accumulator_shared_storage0.accum_ref(), lane_idx),
+    warp_tile_iterator_A1_(shared_storage.accumulator_shared_storage0.accum_ref(), {Base::WarpGemm1::kM, problem_size_0_n}, lane_idx),
+    smem_iterator_B1_(shared_storage.b2b_mma_shared_storage.shared_storage1.operand_B_ref(), thread_idx) {
+
+    // Compute warp location within threadblock tile by mapping the warp_id to
+    // three coordinates:
+    //   _m: the warp's position within the threadblock along the M dimension
+    //   _n: the warp's position within the threadblock along the N dimension
+    //   _k: the warp's position within the threadblock along the K dimension
+
+    int warp_idx_mn_0 = warp_idx % (Base::WarpCount0::kM * Base::WarpCount0::kN);
+    int warp_idx_k_0 = warp_idx / (Base::WarpCount0::kM * Base::WarpCount0::kN);
+
+    int warp_idx_m_0 = warp_idx_mn_0 % Base::WarpCount0::kM;
+    int warp_idx_n_0 = warp_idx_mn_0 / Base::WarpCount0::kM;
+
+    int tile_offset_k_0 = Base::kWarpGemmIterations0 * warp_idx_k_0;
+
+    int warp_idx_mn_1 = warp_idx % (Base::WarpCount1::kM * Base::WarpCount1::kN);
+    int warp_idx_k_1 = warp_idx / (Base::WarpCount1::kM * Base::WarpCount1::kN);
+
+    int warp_idx_m_1 = warp_idx_mn_1 % Base::WarpCount1::kM;
+    int warp_idx_n_1 = warp_idx_mn_1 / Base::WarpCount1::kM;
+
+    int tile_offset_k_1 = Base::kWarpGemmIterations1 * warp_idx_k_1;
+
+    // Add per-warp offsets in units of warp-level tiles
+    this->warp_tile_iterator_A0_.add_tile_offset({warp_idx_m_0, tile_offset_k_0});
+    this->warp_tile_iterator_B0_.add_tile_offset({tile_offset_k_0, warp_idx_n_0});
+    warp_tile_iterator_A1_.add_tile_offset({warp_idx_m_1, tile_offset_k_1});
+    this->warp_tile_iterator_B1_.add_tile_offset({tile_offset_k_1, warp_idx_n_1});
+
+    // Add smem accumulator iterator warp offset
+    smem_iterator_D0_.add_tile_offset({ warp_idx_m_0 * SmemIteratorD0::TileIterations::kRow, 
+                                        warp_idx_n_0 * SmemIteratorD0::TileIterations::kColumn});
+
+  }
+
+  /// Perform a threadblock-scoped matrix multiply-accumulate
+  CUTLASS_DEVICE
+  void operator()(
+    int gemm_k_iterations_0,                             ///< number of iterations of the mainloop
+    FragmentC1 &accum,                                   ///< destination accumulator tile
+    IteratorA0 iterator_A,                               ///< iterator over A operand in global memory
+    IteratorB0 iterator_B0,                              ///< iterator over B0 operand in global memory
+    IteratorAccumulatorScaleBias iterator_accum0_scale,  ///< iterator over D0 scale vector in global memory
+    IteratorAccumulatorScaleBias iterator_accum0_bias,   ///< iterator over D0 bias vector in global memory
+    IteratorB1 iterator_B1,                              ///< iterator over B1 operand in global memory  
+    FragmentC0 const &src_accum,                         ///< source accumualtor tile
+    OutputOp output_op_0,                                ///< epilogue operation after 1st Gemm
+    TransformA0 transform_A0 = TransformA0(),            ///< transformation applied to A0 fragment
+    TransformB0 transform_B0 = TransformB0(),            ///< transformation applied to B0 fragment
+    TransformB1 transform_B1 = TransformB1()) {          ///< transformation applied to B1 fragment
+
+    //
+    // Prologue
+    //
+
+    // Perform accumulation in the 'd' output operand
+    FragmentC0 accum0 = src_accum;
+
+    FragmentA0 tb_frag_A;
+    FragmentB0 tb_frag_B0;
+
+    tb_frag_A.clear();
+    tb_frag_B0.clear();
+
+    // The last kblock is loaded in the prolog
+    iterator_A.load(tb_frag_A);
+    iterator_B0.load(tb_frag_B0);
+
+    ++iterator_A;
+    ++iterator_B0;
+
+    this->smem_iterator_A_.store(transform_A0(tb_frag_A));
+    this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
+
+    ++this->smem_iterator_A_;
+    ++this->smem_iterator_B0_;
+
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA0 warp_frag_A0[2];
+    WarpFragmentB0 warp_frag_B0[2];
+
+    this->warp_tile_iterator_A0_.set_kgroup_index(0);
+    this->warp_tile_iterator_B0_.set_kgroup_index(0);
+
+    this->warp_tile_iterator_A0_.load(warp_frag_A0[0]);
+    this->warp_tile_iterator_B0_.load(warp_frag_B0[0]);
+
+    ++this->warp_tile_iterator_A0_;
+    ++this->warp_tile_iterator_B0_;
+
+    Operator0 warp_mma0;
+
+    int smem_write_stage_idx = 1;
+
+    // Avoid reading out of bounds
+    iterator_A.clear_mask(gemm_k_iterations_0 <= 1);
+    iterator_B0.clear_mask(gemm_k_iterations_0 <= 1);
+
+    // Issue loads during the first warp-level matrix multiply-add *AFTER* issuing 
+    // shared memory loads (which have the tighest latency requirement).
+
+    //
+    // Mainloop
+    //
+
+    // Note: The main loop does not support Base::kWarpGemmIterations == 2.
+    CUTLASS_GEMM_LOOP
+    for (; gemm_k_iterations_0 > 0; --gemm_k_iterations_0) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations0; ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
+        // as the case may be.
+
+        if (warp_mma_k == Base::kWarpGemmIterations0 - 1) {
+
+          // Write fragments to shared memory
+          this->smem_iterator_A_.store(transform_A0(tb_frag_A));
+
+          this->smem_iterator_B0_.store(transform_B0(tb_frag_B0));
+
+          __syncthreads();
+          
+          ++this->smem_iterator_A_;
+          ++this->smem_iterator_B0_;
+
+          // Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
+          if (smem_write_stage_idx == 1) {
+            this->smem_iterator_A_.add_tile_offset({0, -Base::kStages});
+            this->smem_iterator_B0_.add_tile_offset({-Base::kStages, 0});
+          }
+          else {
+            this->warp_tile_iterator_A0_.add_tile_offset(
+                {0, -Base::kStages * Policy0::kPartitionsK * Base::kWarpGemmIterations0});
+            this->warp_tile_iterator_B0_.add_tile_offset(
+                {-Base::kStages * Policy0::kPartitionsK * Base::kWarpGemmIterations0,
+                 0});
+          }
+
+          smem_write_stage_idx ^= 1;
+        }
+
+        this->warp_tile_iterator_A0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        this->warp_tile_iterator_B0_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations0);
+        
+        this->warp_tile_iterator_A0_.load(warp_frag_A0[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B0_.load(warp_frag_B0[(warp_mma_k + 1) % 2]);
+
+        ++this->warp_tile_iterator_A0_;
+        ++this->warp_tile_iterator_B0_;
+
+        if (warp_mma_k == 0) {
+
+          iterator_A.load(tb_frag_A);
+          iterator_B0.load(tb_frag_B0);
+          ++iterator_A;
+          ++iterator_B0;
+
+          // Avoid reading out of bounds if this was the last loop iteration
+          iterator_A.clear_mask(gemm_k_iterations_0 <= 2);
+          iterator_B0.clear_mask(gemm_k_iterations_0 <= 2);
+        }
+
+        warp_mma0(accum0, warp_frag_A0[warp_mma_k % 2],
+                  warp_frag_B0[warp_mma_k % 2], accum0);
+      }
+    }
+
+    /// Epilogue for the first Implicit Gemm
+    Epilogue0 epilogue0;
+
+    epilogue0(output_op_0, smem_iterator_D0_, accum0, iterator_accum0_scale, iterator_accum0_bias);
+
+    __syncthreads();
+ 
+    //2nd Gemm
+
+    //
+    // Prologue
+    //
+
+    FragmentB1 tb_frag_B1;
+
+    tb_frag_B1.clear();
+
+    // The last kblock is loaded in the prolog
+    iterator_B1.load(tb_frag_B1);
+
+    ++iterator_B1;
+
+    this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
+
+    ++this->smem_iterator_B1_;
+
+    __syncthreads();
+
+    // Pair of fragments used to overlap shared memory loads and math instructions
+    WarpFragmentA1 warp_frag_A1[2];
+    WarpFragmentB1 warp_frag_B1[2];
+
+    this->warp_tile_iterator_B1_.set_kgroup_index(0);
+
+    warp_tile_iterator_A1_.load(warp_frag_A1[0]);
+    this->warp_tile_iterator_B1_.load(warp_frag_B1[0]);
+
+    ++warp_tile_iterator_A1_;
+    ++this->warp_tile_iterator_B1_;
+
+    Operator1 warp_mma1;
+
+    smem_write_stage_idx = 1;
+    
+    int gemm_k_iterations_1 = Shape0::kN / Shape1::kK;
+
+    // Avoid reading out of bounds
+    iterator_B1.clear_mask(gemm_k_iterations_1 <= 1);
+
+    //
+    // Mainloop
+    //
+
+    // Note: The main loop does not support Base::kWarpGemmIterations == 2.
+    CUTLASS_PRAGMA_UNROLL
+    for (; gemm_k_iterations_1 > 0; --gemm_k_iterations_1) {
+      //
+      // Loop over GEMM K dimension
+      //
+
+      CUTLASS_PRAGMA_UNROLL
+      for (int warp_mma_k = 0; warp_mma_k < Base::kWarpGemmIterations1; ++warp_mma_k) {
+
+        // Load warp-level tiles from shared memory, wrapping to k offset if this is the last group
+        // as the case may be.
+
+        if (warp_mma_k == Base::kWarpGemmIterations1 - 1) {
+
+          // Write fragments to shared memory
+          this->smem_iterator_B1_.store(transform_B1(tb_frag_B1));
+
+          __syncthreads();
+          
+          ++this->smem_iterator_B1_;
+
+          // Add negative offsets to return iterators to the 'start' of the circular buffer in shared memory
+          if (smem_write_stage_idx == 1) {
+            this->smem_iterator_B1_.add_tile_offset({-Base::kStages, 0});
+          }
+          else {
+            this->warp_tile_iterator_B1_.add_tile_offset(
+                {-Base::kStages * Policy1::kPartitionsK *
+                     Base::kWarpGemmIterations1,
+                 0});
+          }
+
+          smem_write_stage_idx ^= 1;
+
+        }
+
+        this->warp_tile_iterator_B1_.set_kgroup_index((warp_mma_k + 1) % Base::kWarpGemmIterations1);
+        
+        // skip warp tile loading for the last kgroup
+        if(gemm_k_iterations_1 > 1 || warp_mma_k < Base::kWarpGemmIterations1 - 1)
+          warp_tile_iterator_A1_.load(warp_frag_A1[(warp_mma_k + 1) % 2]);
+        this->warp_tile_iterator_B1_.load(warp_frag_B1[(warp_mma_k + 1) % 2]);
+
+        ++warp_tile_iterator_A1_;
+        ++this->warp_tile_iterator_B1_;
+
+        if (warp_mma_k == 0) {
+
+          iterator_B1.load(tb_frag_B1);
+    
+          ++iterator_B1;
+
+          // Avoid reading out of bounds if this was the last loop iteration
+          iterator_B1.clear_mask(gemm_k_iterations_1 <= 2);
+        }
+
+        warp_mma1(accum, warp_frag_A1[warp_mma_k % 2],
+                 warp_frag_B1[warp_mma_k % 2], accum);
+
+      }
+    }
+  }
+};
+
+/////////////////////////////////////////////////////////////////////////////////////////////////
+
+} // namespace threadblock
+} // namespace gemm
+} // namespace cutlass