fix TP

Signed-off-by: Lucas Wilkinson <lwilkinson@neuralmagic.com>

.buildkite/check-wheel-size.py

@@ -1,14 +1,12 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import os
 import sys
 import zipfile
 
-# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 400 MiB
+# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 300 MiB
 # Note that we have 400 MiB quota, please use it wisely.
 # See https://github.com/pypi/support/issues/3792 .
 # Please also sync the value with the one in Dockerfile.
-VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 400))
+VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 300))
 
 
 def print_top_10_largest_files(zip_file):

.buildkite/generate_index.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import os
 

.buildkite/lm-eval-harness/configs/SparseLlama3.1_2of4_fp8_compressed.yaml

@@ -1,11 +0,0 @@
-# bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
-model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.6353
-  - name: "exact_match,flexible-extract"
-    value: 0.637
-limit: null
-num_fewshot: null 

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 """
 LM eval harness on model to compare vs HF baseline computed offline.
 Configs are found in configs/$MODEL.yaml

.buildkite/nightly-benchmarks/README.md

@@ -1,13 +1,15 @@
 # vLLM benchmark suite
 
+
 ## Introduction
 
 This directory contains two sets of benchmark for vllm.
-
 - Performance benchmark: benchmark vllm's performance under various workload, for **developers** to gain clarity on whether their PR improves/degrades vllm's performance
 - Nightly benchmark: compare vllm's performance against alternatives (tgi, trt-llm and lmdeploy), for **the public** to know when to choose vllm.
 
-See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+
+See  [vLLM performance dashboard](https://perf.vllm.ai) for the latest performance benchmark results and [vLLM GitHub README](https://github.com/vllm-project/vllm/blob/main/README.md) for latest nightly benchmark results.
+
 
 ## Performance benchmark quick overview
 
@@ -17,14 +19,17 @@ See [vLLM performance dashboard](https://perf.vllm.ai) for the latest performanc
 
 **For benchmarking developers**: please try your best to constraint the duration of benchmarking to about 1 hr so that it won't take forever to run.
 
+
 ## Nightly benchmark quick overview
 
-**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B.
+**Benchmarking Coverage**: Fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!) on Llama-3 8B, 70B and Mixtral 8x7B. 
 
 **Benchmarking engines**: vllm, TGI, trt-llm and lmdeploy.
 
 **Benchmarking Duration**: about 3.5hrs.
 
+
+
 ## Trigger the benchmark
 
 Performance benchmark will be triggered when:
@@ -34,11 +39,16 @@ Performance benchmark will be triggered when:
 Nightly benchmark will be triggered when:
 - Every commit for those PRs with `perf-benchmarks` label and `nightly-benchmarks` label.
 
+
+
+
 ## Performance benchmark details
 
+
 See [performance-benchmarks-descriptions.md](performance-benchmarks-descriptions.md) for detailed descriptions, and use `tests/latency-tests.json`, `tests/throughput-tests.json`, `tests/serving-tests.json` to configure the test cases.
 
-### Latency test
+
+#### Latency test
 
 Here is an example of one test inside `latency-tests.json`:
 
@@ -58,25 +68,23 @@ Here is an example of one test inside `latency-tests.json`:
 ```
 
 In this example:
-
-- The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
-- The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
+-  The `test_name` attributes is a unique identifier for the test. In `latency-tests.json`, it must start with `latency_`.
+-  The `parameters` attribute control the command line arguments to be used for `benchmark_latency.py`. Note that please use underline `_` instead of the dash `-` when specifying the command line arguments, and `run-performance-benchmarks.sh` will convert the underline to dash when feeding the arguments to `benchmark_latency.py`. For example, the corresponding command line arguments for `benchmark_latency.py` will be `--model meta-llama/Meta-Llama-3-8B --tensor-parallel-size 1 --load-format dummy --num-iters-warmup 5 --num-iters 15`
 
 Note that the performance numbers are highly sensitive to the value of the parameters. Please make sure the parameters are set correctly.
 
 WARNING: The benchmarking script will save json results by itself, so please do not configure `--output-json` parameter in the json file.
 
-### Throughput test
 
+#### Throughput test
 The tests are specified in `throughput-tests.json`. The syntax is similar to `latency-tests.json`, except for that the parameters will be fed forward to `benchmark_throughput.py`.
 
 The number of this test is also stable -- a slight change on the value of this number might vary the performance numbers by a lot.
 
-### Serving test
-
+#### Serving test
 We test the throughput by using `benchmark_serving.py` with request rate = inf to cover the online serving overhead. The corresponding parameters are in `serving-tests.json`, and here is an example:
 
-```json
+```
 [
     {
         "test_name": "serving_llama8B_tp1_sharegpt",
@@ -101,7 +109,6 @@ We test the throughput by using `benchmark_serving.py` with request rate = inf t
 ```
 
 Inside this example:
-
 - The `test_name` attribute is also a unique identifier for the test. It must start with `serving_`.
 - The `server-parameters` includes the command line arguments for vLLM server.
 - The `client-parameters` includes the command line arguments for `benchmark_serving.py`.
@@ -111,33 +118,36 @@ The number of this test is less stable compared to the delay and latency benchma
 
 WARNING: The benchmarking script will save json results by itself, so please do not configure `--save-results` or other results-saving-related parameters in `serving-tests.json`.
 
-### Visualizing the results
-
+#### Visualizing the results
 The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](tests/descriptions.md) with real benchmarking results.
 You can find the result presented as a table inside the `buildkite/performance-benchmark` job page.
 If you do not see the table, please wait till the benchmark finish running.
 The json version of the table (together with the json version of the benchmark) will be also attached to the markdown file.
 The raw benchmarking results (in the format of json files) are in the `Artifacts` tab of the benchmarking.
 
+
+
 ## Nightly test details
 
 See [nightly-descriptions.md](nightly-descriptions.md) for the detailed description on test workload, models and docker containers of benchmarking other llm engines.
 
-### Workflow
 
-- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines.
+#### Workflow
+
+- The [nightly-pipeline.yaml](nightly-pipeline.yaml) specifies the docker containers for different LLM serving engines. 
 - Inside each container, we run [run-nightly-suite.sh](run-nightly-suite.sh), which will probe the serving engine of the current container.
 - The `run-nightly-suite.sh` will redirect the request to `tests/run-[llm serving engine name]-nightly.sh`, which parses the workload described in [nightly-tests.json](tests/nightly-tests.json) and performs the benchmark.
 - At last, we run [scripts/plot-nightly-results.py](scripts/plot-nightly-results.py) to collect and plot the final benchmarking results, and update the results to buildkite.
 
-### Nightly tests
+#### Nightly tests
 
 In [nightly-tests.json](tests/nightly-tests.json), we include the command line arguments for benchmarking commands, together with the benchmarking test cases. The format is highly similar to performance benchmark.
 
-### Docker containers
+#### Docker containers
 
 The docker containers for benchmarking are specified in `nightly-pipeline.yaml`.
 
 WARNING: the docker versions are HARD-CODED and SHOULD BE ALIGNED WITH `nightly-descriptions.md`. The docker versions need to be hard-coded as there are several version-specific bug fixes inside `tests/run-[llm serving engine name]-nightly.sh`.
 
 WARNING: populating `trt-llm` to latest version is not easy, as it requires updating several protobuf files in [tensorrt-demo](https://github.com/neuralmagic/tensorrt-demo.git).
+

.buildkite/nightly-benchmarks/benchmark-pipeline.yaml

@@ -10,18 +10,12 @@ steps:
           - image: badouralix/curl-jq
             command:
             - sh .buildkite/nightly-benchmarks/scripts/wait-for-image.sh
-  - label: "Cleanup H100"
-    agents:
-      queue: H100
-    depends_on: ~
-    command: docker system prune -a --volumes --force
-  
+
   - label: "A100"
     # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
     agents:
       queue: A100
     depends_on: wait-for-container-image
-    if: build.branch == "main"
     plugins:
     - kubernetes:
         podSpec:
@@ -56,7 +50,6 @@ steps:
     agents:
       queue: H200
     depends_on: wait-for-container-image
-    if: build.branch == "main"
     plugins:
     - docker#v5.12.0:
         image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
@@ -82,7 +75,6 @@ steps:
     agents:
       queue: H100
     depends_on: wait-for-container-image
-    if: build.branch == "main"
     plugins:
     - docker#v5.12.0:
         image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
@@ -98,87 +90,3 @@ steps:
         environment:
         - VLLM_USAGE_SOURCE
         - HF_TOKEN
-
-  # Premerge benchmark
-  - label: "A100"
-    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
-    agents:
-      queue: A100
-    depends_on: wait-for-container-image
-    if: build.branch != "main"
-    plugins:
-    - kubernetes:
-        podSpec:
-          priorityClassName: perf-benchmark
-          containers:
-          - image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-            command:
-            - bash .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
-            resources:
-              limits:
-                nvidia.com/gpu: 8
-            volumeMounts:
-            - name: devshm
-              mountPath: /dev/shm
-            env:
-            - name: VLLM_USAGE_SOURCE
-              value: ci-test
-            - name: HF_TOKEN
-              valueFrom:
-                secretKeyRef:
-                  name: hf-token-secret
-                  key: token
-          nodeSelector:
-            nvidia.com/gpu.product: NVIDIA-A100-SXM4-80GB
-          volumes:
-          - name: devshm
-            emptyDir:
-              medium: Memory
-
-  - label: "H200"
-    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
-    agents:
-      queue: H200
-    depends_on: wait-for-container-image
-    if: build.branch != "main"
-    plugins:
-    - docker#v5.12.0:
-        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-        command:
-        - bash
-        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
-        mount-buildkite-agent: true
-        propagate-environment: true
-        ipc: host
-        gpus: 4,5,6,7
-        volumes:
-          - /data/benchmark-hf-cache:/root/.cache/huggingface
-        environment:
-        - VLLM_USAGE_SOURCE
-        - HF_TOKEN
-
-  #- block: "Run H100 Benchmark"
-    #key: block-h100
-    #depends_on: ~
-
-  - label: "H100"
-    # skip: "use this flag to conditionally skip the benchmark step, useful for PR testing"
-    agents:
-      queue: H100
-    depends_on: wait-for-container-image
-    if: build.branch != "main"
-    plugins:
-    - docker#v5.12.0:
-        image: public.ecr.aws/q9t5s3a7/vllm-ci-test-repo:$BUILDKITE_COMMIT
-        command:
-        - bash
-        - .buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh
-        mount-buildkite-agent: true
-        propagate-environment: true
-        ipc: host
-        gpus: all # see CUDA_VISIBLE_DEVICES for actual GPUs used
-        volumes:
-          - /data/benchmark-hf-cache:/root/.cache/huggingface
-        environment:
-        - VLLM_USAGE_SOURCE
-        - HF_TOKEN

.buildkite/nightly-benchmarks/nightly-annotation.md

@@ -9,19 +9,20 @@ This file contains the downloading link for benchmarking results.
 
 Please download the visualization scripts in the post
 
+
 ## Results reproduction
 
 - Find the docker we use in `benchmarking pipeline`
 - Deploy the docker, and inside the docker:
-  - Download `nightly-benchmarks.zip`.
-  - In the same folder, run the following code:
-
-  ```console
-  export HF_TOKEN=<your HF token>
-  apt update
-  apt install -y git
-  unzip nightly-benchmarks.zip
-  VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
-  ```
+  - Download `nightly-benchmarks.zip`. 
+  - In the same folder, run the following code
+```
+export HF_TOKEN=<your HF token>
+apt update
+apt install -y git
+unzip nightly-benchmarks.zip
+VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh
+```
 
 And the results will be inside `./benchmarks/results`.
+

.buildkite/nightly-benchmarks/nightly-descriptions.md

@@ -2,7 +2,6 @@
 # Nightly benchmark
 
 This benchmark aims to:
-
 - Provide performance clarity: Provide clarity on which one (vllm, tensorrt-llm, lmdeploy and SGLang) leads in performance in what workload.
 - Be reproducible: one can run the exact same set of benchmarking commands inside the exact same docker by following reproducing instructions.
 
@@ -10,6 +9,7 @@ Latest results: [results link](https://blog.vllm.ai/2024/09/05/perf-update.html)
 
 Latest reproduction guilde: [github issue link](https://github.com/vllm-project/vllm/issues/8176)
 
+
 ## Setup
 
 - Docker images:
@@ -33,7 +33,7 @@ Latest reproduction guilde: [github issue link](https://github.com/vllm-project/
     - Queries are randomly sampled, and arrival patterns are determined via Poisson process, but all with fixed random seed.
   - Evaluation metrics: Throughput (higher the better), TTFT (time to the first token, lower the better), ITL (inter-token latency, lower the better).
 
-## Known issues
+# Known issues
 
 - TRT-LLM crashes with Llama 3.1 8B [issue](https://github.com/NVIDIA/TensorRT-LLM/issues/2105).
-- TGI does not support `ignore-eos` flag.
+- TGI does not support `ignore-eos` flag.

.buildkite/nightly-benchmarks/performance-benchmarks-descriptions.md

@@ -7,8 +7,10 @@
 - Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: end-to-end latency (mean, median, p99).
 
+
 {latency_tests_markdown_table}
 
+
 ## Throughput tests
 
 - Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
@@ -17,8 +19,10 @@
 - Models: llama-3.1 8B, llama-3 70B, mixtral 8x7B.
 - Evaluation metrics: throughput.
 
+
 {throughput_tests_markdown_table}
 
+
 ## Serving tests
 
 - Input length: randomly sample 200 prompts from ShareGPT dataset (with fixed random seed).
@@ -29,11 +33,13 @@
 - We also added a speculative decoding test for llama-3 70B, under QPS 2
 - Evaluation metrics: throughput, TTFT (time to the first token, with mean, median and p99), ITL (inter-token latency, with mean, median and p99).
 
+
 {serving_tests_markdown_table}
 
+
 ## json version of the benchmarking tables
 
-This section contains the data of the markdown tables above in JSON format.
+This section contains the data of the markdown tables above in JSON format. 
 You can load the benchmarking tables into pandas dataframes as follows:
 
 ```python
@@ -48,9 +54,9 @@ serving_results = pd.DataFrame.from_dict(benchmarking_results["serving"])
 ```
 
 The json string for all benchmarking tables:
-
 ```json
 {benchmarking_results_in_json_string}
 ```
 
 You can also check the raw experiment data in the Artifact tab of the Buildkite page.
+

.buildkite/nightly-benchmarks/scripts/convert-results-json-to-markdown.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import json
 import os
 from pathlib import Path

.buildkite/nightly-benchmarks/scripts/download-tokenizer.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 
 from transformers import AutoTokenizer

.buildkite/nightly-benchmarks/scripts/generate-nightly-markdown.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import json
 from pathlib import Path

.buildkite/nightly-benchmarks/scripts/get-lmdeploy-modelname.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 from lmdeploy.serve.openai.api_client import APIClient
 
 api_client = APIClient("http://localhost:8000")

.buildkite/nightly-benchmarks/scripts/run-performance-benchmarks.sh

@@ -345,11 +345,6 @@ main() {
   check_gpus
   check_hf_token
 
-  # Set to v1 to run v1 benchmark
-  if [[ "${ENGINE_VERSION:-v0}" == "v1" ]]; then
-    export VLLM_USE_V1=1
-  fi
-
   # dependencies
   (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
   (which jq) || (apt-get update && apt-get -y install jq)

.buildkite/nightly-benchmarks/scripts/summary-nightly-results.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import datetime
 import json
 import os

.buildkite/nightly-benchmarks/scripts/wait-for-image.sh

@@ -1,10 +1,6 @@
 #!/bin/sh
 TOKEN=$(curl -s -L "https://public.ecr.aws/token?service=public.ecr.aws&scope=repository:q9t5s3a7/vllm-ci-postmerge-repo:pull" | jq -r .token)
-if [[ "$BUILDKITE_BRANCH" == "main" ]]; then
-    URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
-else
-    URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-test-repo/manifests/$BUILDKITE_COMMIT"
-fi
+URL="https://public.ecr.aws/v2/q9t5s3a7/vllm-ci-postmerge-repo/manifests/$BUILDKITE_COMMIT"
 
 TIMEOUT_SECONDS=10
 

.buildkite/nightly-benchmarks/tests/latency-tests.json

@@ -29,4 +29,4 @@
             "num-iters": 15
         }
     }
-]
+]

.buildkite/nightly-benchmarks/tests/serving-tests.json

@@ -66,7 +66,8 @@
             "swap_space": 16, 
             "speculative_model": "turboderp/Qwama-0.5B-Instruct",
             "num_speculative_tokens": 4,
-            "speculative_draft_tensor_parallel_size": 1
+            "speculative_draft_tensor_parallel_size": 1,
+            "use_v2_block_manager": ""
         },
         "client_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",

.buildkite/release-pipeline.yaml

@@ -56,11 +56,6 @@ steps:
     env:
       DOCKER_BUILDKIT: "1"
 
-  - input: "Provide Release version here"
-    fields:
-      - text: "What is the release version?"
-        key: "release-version"
-
   - block: "Build CPU release image"
     key: block-cpu-release-image-build
     depends_on: ~
@@ -71,7 +66,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --progress plain -f Dockerfile.cpu ."
-      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION --progress plain -f Dockerfile.cpu ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$RELEASE_VERSION"
     env:
       DOCKER_BUILDKIT: "1"

.buildkite/run-amd-test.sh

@@ -121,8 +121,6 @@ if [[ $commands == *"--shard-id="* ]]; then
         --rm \
         -e HIP_VISIBLE_DEVICES="${GPU}" \
         -e HF_TOKEN \
-        -e AWS_ACCESS_KEY_ID \
-        -e AWS_SECRET_ACCESS_KEY \
         -v "${HF_CACHE}:${HF_MOUNT}" \
         -e "HF_HOME=${HF_MOUNT}" \
         --name "${container_name}_${GPU}" \
@@ -150,8 +148,6 @@ else
           --rm \
           -e HIP_VISIBLE_DEVICES=0 \
           -e HF_TOKEN \
-          -e AWS_ACCESS_KEY_ID \
-          -e AWS_SECRET_ACCESS_KEY \
           -v "${HF_CACHE}:${HF_MOUNT}" \
           -e "HF_HOME=${HF_MOUNT}" \
           --name "${container_name}" \

.buildkite/run-cpu-test.sh

@@ -30,7 +30,7 @@ function cpu_tests() {
   # offline inference
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
     set -e
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+    python3 examples/offline_inference/basic.py"
 
   # Run basic model test
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "

.buildkite/run-gh200-test.sh

@@ -23,6 +23,6 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and test offline inference
-docker run -e HF_TOKEN -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
-    python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
+docker run --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
+    python3 examples/offline_inference/basic.py
 '

.buildkite/run-hpu-test.sh

@@ -20,5 +20,5 @@ trap remove_docker_container_and_exit EXIT
 remove_docker_container
 
 # Run the image and launch offline inference
-docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+docker run --runtime=habana --name=hpu-test --network=host -e HABANA_VISIBLE_DEVICES=all -e VLLM_SKIP_WARMUP=true --entrypoint="" hpu-test-env python3 examples/offline_inference/basic.py
 EXITCODE=$?

.buildkite/run-neuron-test.sh

@@ -29,6 +29,9 @@ if [ -f /tmp/neuron-docker-build-timestamp ]; then
         docker image prune -f
         # Remove unused volumes / force the system prune for old images as well.
         docker volume prune -f && docker system prune -f
+        # Remove huggingface model artifacts and compiler cache
+        rm -rf "${HF_MOUNT:?}/*"
+        rm -rf "${NEURON_COMPILE_CACHE_MOUNT:?}/*"
         echo "$current_time" > /tmp/neuron-docker-build-timestamp
     fi
 else

.buildkite/run-openvino-test.sh

@@ -13,4 +13,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and launch offline inference
-docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference/basic/generate.py --model facebook/opt-125m
+docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference/basic.py

.buildkite/run-xpu-test.sh

@@ -14,6 +14,6 @@ remove_docker_container
 
 # Run the image and test offline inference/tensor parallel
 docker run --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path --entrypoint="" xpu-test sh -c '
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
-    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
+    python3 examples/offline_inference/basic.py
+    python3 examples/offline_inference/cli.py -tp 2
 '

.buildkite/test-pipeline.yaml

@@ -2,7 +2,7 @@
 # adding a new command to an existing step. See different options here for examples.
 
 # This script will be feed into Jinja template in `test-template-aws.j2` at
-# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2
+# https://github.com/vllm-project/buildkite-ci/blob/main/scripts/test-template-aws.j2 
 # to generate the final pipeline yaml file.
 
 # Documentation
@@ -15,7 +15,7 @@
 # mirror_hardwares(list): the list of hardwares to run the test on as well. currently only supports [amd]
 # gpu(str): override the GPU selection for the test. default is on L4 GPUs. currently only supports a100
 # num_gpus(int): override the number of GPUs for the test. default to 1 GPU. currently support 2,4.
-# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host,
+# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host, 
 #     in this case, commands must be specified. the first command runs on first host, the second
 #     command runs on the second host.
 # working_dir(str): specify the place where command should execute, default to /vllm-workspace/tests
@@ -24,8 +24,8 @@
 # When adding a test
 # - If the test belong to an existing group, add it there
 # - If the test is short, add to any existing step
-# - If the test takes more than 10min, then it is okay to create a new step.
-#   Note that all steps execute in parallel.
+# - If the test takes more than 10min, then it is okay to create a new step. 
+#   Note that all steps execute in parallel. 
 
 steps:
 ##### fast check tests  #####
@@ -50,9 +50,9 @@ steps:
   - tests/multimodal
   - tests/test_utils
   - tests/worker
-  - tests/standalone_tests/lazy_imports.py
+  - tests/standalone_tests/lazy_torch_compile.py
   commands:
-  - python3 standalone_tests/lazy_imports.py
+  - python3 standalone_tests/lazy_torch_compile.py
   - pytest -v -s mq_llm_engine # MQLLMEngine
   - pytest -v -s async_engine # AsyncLLMEngine
   - NUM_SCHEDULER_STEPS=4 pytest -v -s async_engine/test_async_llm_engine.py
@@ -107,17 +107,13 @@ steps:
   mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
-  - tests/entrypoints/llm
-  - tests/entrypoints/openai
-  - tests/entrypoints/test_chat_utils
-  - tests/entrypoints/offline_mode
   commands:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/correctness/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py
   - pytest -v -s entrypoints/test_chat_utils.py
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
@@ -128,12 +124,10 @@ steps:
   source_file_dependencies:
   - vllm/distributed/
   - vllm/core/
-  - tests/distributed/test_utils
-  - tests/distributed/test_pynccl
+  - tests/distributed
   - tests/spec_decode/e2e/test_integration_dist_tp4
-  - tests/compile/test_basic_correctness
+  - tests/compile
   - examples/offline_inference/rlhf.py
-  - examples/offline_inference/rlhf_colocate.py
   commands:
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
@@ -142,17 +136,16 @@ steps:
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - python3 ../examples/offline_inference/rlhf.py
-  - RAY_DEDUP_LOGS=0 python3 ../examples/offline_inference/rlhf_colocate.py
 
 - label: Metrics, Tracing Test # 10min
-  num_gpus: 2
+  num_gpus: 2 
   fast_check: true
   source_file_dependencies:
   - vllm/
   - tests/metrics
   - tests/tracing
   commands:
-  - pytest -v -s metrics
+  - pytest -v -s metrics 
   - "pip install \
       'opentelemetry-sdk>=1.26.0,<1.27.0' \
       'opentelemetry-api>=1.26.0,<1.27.0' \
@@ -179,9 +172,6 @@ steps:
   - vllm/
   - tests/engine
   - tests/tokenization
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
   commands:
   - pytest -v -s engine test_sequence.py test_config.py test_logger.py
   # OOM in the CI unless we run this separately
@@ -203,9 +193,6 @@ steps:
     # TODO: accuracy does not match, whether setting
     # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
     - VLLM_USE_V1=1 pytest -v -s v1/e2e
-    # Integration test for streaming correctness (requires special branch).
-    - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
-    - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
 - label: Examples Test # 25min
   working_dir: "/vllm-workspace/examples"
@@ -215,18 +202,18 @@ steps:
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
-    - python3 offline_inference/basic/generate.py --model facebook/opt-125m
-    - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/chat.py
+    - python3 offline_inference/basic.py
+    - python3 offline_inference/cpu_offload.py
+    - python3 offline_inference/chat.py
     - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
     - python3 offline_inference/vision_language.py
     - python3 offline_inference/vision_language_multi_image.py
     - python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder.py
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
+    - python3 offline_inference/classification.py
+    - python3 offline_inference/embedding.py
+    - python3 offline_inference/scoring.py
     - python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
 
 - label: Prefix Caching Test # 9min
@@ -254,7 +241,7 @@ steps:
   - vllm/model_executor/guided_decoding
   - tests/test_logits_processor
   - tests/model_executor/test_guided_processors
-  commands:
+  commands: 
     - pytest -v -s test_logits_processor.py
     - pytest -v -s model_executor/test_guided_processors.py
 
@@ -265,7 +252,7 @@ steps:
   - vllm/model_executor/models/eagle.py
   commands:
     - pytest -v -s spec_decode/e2e/test_multistep_correctness.py
-    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py --ignore=spec_decode/e2e/test_mtp_correctness.py
+    - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s spec_decode --ignore=spec_decode/e2e/test_multistep_correctness.py
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
@@ -339,14 +326,6 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - bash ./run-tests.sh -c configs/models-small.txt -t 1
 
-- label: OpenAI API correctness
-  source_file_dependencies:
-  - csrc/
-  - vllm/entrypoints/openai/
-  - vllm/model_executor/models/whisper.py
-  commands: # LMEval+Transcription WER check
-  - pytest -s entrypoints/openai/correctness/
-
 - label: Encoder Decoder tests # 5min
   source_file_dependencies:
   - vllm/
@@ -370,7 +349,6 @@ steps:
   - vllm/
   - tests/models
   commands:
-    - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
     - pytest -v -s models/test_initialization.py
 
@@ -507,7 +485,6 @@ steps:
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
   # Avoid importing model tests that cause CUDA reinitialization error
-  - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
@@ -531,7 +508,6 @@ steps:
   - pip uninstall vllm_add_dummy_platform -y
   # end platform plugin tests
   # other tests continue here:
-  - pytest -v -s plugins_tests/test_scheduler_plugins.py
   - pip install -e ./plugins/vllm_add_dummy_model
   - pytest -v -s distributed/test_distributed_oot.py
   - pytest -v -s entrypoints/openai/test_oot_registration.py # it needs a clean process
@@ -581,7 +557,7 @@ steps:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # This test runs llama 13B, so it is required to run on 4 GPUs.
     - pytest -v -s -x lora/test_long_context.py
-    # There is some Tensor Parallelism related processing logic in LoRA that
+    # There is some Tensor Parallelism related processing logic in LoRA that 
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
@@ -606,7 +582,7 @@ steps:
   - vllm/
   - tests/weight_loading
   commands:
-    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt
+    - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large.txt 
 
 
 ##### multi gpus test #####
@@ -618,7 +594,7 @@ steps:
   num_gpus: 4
   source_file_dependencies:
   - vllm/
-  commands:
+  commands: 
   # NOTE: don't test llama model here, it seems hf implementation is buggy
   # see https://github.com/vllm-project/vllm/pull/5689 for details
   - pytest -v -s distributed/test_custom_all_reduce.py

.github/ISSUE_TEMPLATE/400-bug-report.yml

@@ -30,6 +30,15 @@ body:
       </details>
   validations:
     required: true
+- type: textarea
+  attributes:
+    label: Model Input Dumps
+    description: |
+      If you are facing crashing due to illegal memory access or other issues with model execution, vLLM may dump the problematic input of the model. In this case, you will see the message `Error in model execution (input dumped to /tmp/err_xxx.pkl)`. If you see this message, please zip the file (because GitHub doesn't support .pkl file format) and upload it here. This will help us to reproduce the issue and facilitate the debugging process.
+    placeholder: |
+      Upload the dumped input file.
+  validations:
+    required: false
 - type: textarea
   attributes:
     label: 🐛 Describe the bug

.github/PULL_REQUEST_TEMPLATE.md

@@ -2,5 +2,4 @@ FILL IN THE PR DESCRIPTION HERE
 
 FIX #xxxx (*link existing issues this PR will resolve*)
 
-<!--- pyml disable-next-line no-emphasis-as-heading -->
-**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>**
+**BEFORE SUBMITTING, PLEASE READ https://docs.vllm.ai/en/latest/contributing/overview.html **

.github/mergify.yml

@@ -35,43 +35,6 @@ pull_request_rules:
       add:
         - frontend
 
-- name: label-structured-output
-  description: Automatically apply structured-output label
-  conditions:
-    - or:
-      - files~=^vllm/model_executor/guided_decoding/
-      - files=tests/model_executor/test_guided_processors.py
-      - files=tests/entrypoints/llm/test_guided_generate.py
-      - files=benchmarks/benchmark_serving_guided.py
-      - files=benchmarks/benchmark_guided.py
-  actions:
-    label:
-      add:
-        - structured-output
-
-- name: label-speculative-decoding
-  description: Automatically apply speculative-decoding label
-  conditions:
-    - or:
-      - files~=^vllm/spec_decode/
-      - files=vllm/model_executor/layers/spec_decode_base_sampler.py
-      - files~=^tests/spec_decode/
-  actions:
-    label:
-      add:
-        - speculative-decoding
-
-- name: label-v1
-  description: Automatically apply v1 label
-  conditions:
-    - or:
-      - files~=^vllm/v1/
-      - files~=^tests/v1/
-  actions:
-    label:
-      add:
-        - v1
-
 - name: ping author on conflicts and add 'needs-rebase' label
   conditions:
       - conflict

.github/workflows/cleanup_pr_body.yml

@@ -16,7 +16,7 @@ jobs:
         uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
 
       - name: Set up Python
-        uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
+        uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
         with:
           python-version: '3.12'
 

.github/workflows/lint-and-deploy.yaml

@@ -17,12 +17,12 @@ jobs:
           version: v3.14.4
 
        #Python is required because ct lint runs Yamale and yamllint which require Python.
-      - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
+      - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
         with:
           python-version: '3.13'
 
       - name: Set up chart-testing
-        uses: helm/chart-testing-action@0d28d3144d3a25ea2cc349d6e59901c4ff469b3b # v2.7.0
+        uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1
         with:
           version: v3.10.1
 
@@ -47,7 +47,7 @@ jobs:
           aws --endpoint-url http://127.0.0.1:9000/ s3 cp opt-125m/ s3://testbucket/opt-125m --recursive
 
       - name: Create kind cluster
-        uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
+        uses: helm/kind-action@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0
 
       - name: Build the Docker image vllm cpu
         run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .

.github/workflows/pre-commit.yml

@@ -10,11 +10,10 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-    - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
+    - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
       with:
         python-version: "3.12"
     - run: echo "::add-matcher::.github/workflows/matchers/actionlint.json"
-    - run: echo "::add-matcher::.github/workflows/matchers/mypy.json"
     - uses: pre-commit/action@2c7b3805fd2a0fd8c1884dcaebf91fc102a13ecd # v3.0.1
       with:
         extra_args: --all-files --hook-stage manual

.github/workflows/reminder_comment.yml

@@ -2,6 +2,7 @@ name: PR Reminder Comment Bot
 on:
   pull_request_target:
     types: [opened]
+
 jobs:
   pr_reminder:
     runs-on: ubuntu-latest
@@ -14,12 +15,7 @@ jobs:
               owner: context.repo.owner,
               repo: context.repo.repo,
               issue_number: context.issue.number,
-              body: '👋 Hi! Thank you for contributing to the vLLM project.\n\n' +
-                '💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.\n\n' +
-                'Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your `fastcheck` build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping `simon-mo` or `khluu` to add you in our Buildkite org.\n\n' +
-                'Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n' +
-                'To run CI, PR reviewers can either: Add `ready` label to the PR or enable auto-merge.\n\n' +
-                '🚀'
+              body: '👋 Hi! Thank you for contributing to the vLLM project.\n Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run `fastcheck` CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your `fastcheck` build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping `simon-mo` or `khluu` to add you in our Buildkite org. \n\nOnce the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.\n\n To run CI, PR reviewers can do one of these:\n- Add `ready` label to the PR\n- Enable auto-merge.\n\n🚀'
             })
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/stale.yml

@@ -13,7 +13,7 @@ jobs:
       actions: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0
+      - uses: actions/stale@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0
         with:
           # Increasing this value ensures that changes to this workflow
           # propagate to all issues and PRs in days rather than months

.pre-commit-config.yaml

@@ -8,42 +8,36 @@ repos:
   - id: yapf
     args: [--in-place, --verbose]
     additional_dependencies: [toml] # TODO: Remove when yapf is upgraded
-    exclude: 'vllm/third_party/.*'
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.9.3
   hooks:
   - id: ruff
-    args: [--output-format, github, --fix]
-    exclude: 'vllm/third_party/.*'
+    args: [--output-format, github]
 - repo: https://github.com/codespell-project/codespell
   rev: v2.4.0
   hooks:
   - id: codespell
-    additional_dependencies: ['tomli']
-    args: ['--toml', 'pyproject.toml']
+    exclude: 'benchmarks/sonnet.txt|(build|tests/(lora/data|models/fixtures|prompts))/.*'
 - repo: https://github.com/PyCQA/isort
   rev: 5.13.2
   hooks:
   - id: isort
-    exclude: 'vllm/third_party/.*'
 - repo: https://github.com/pre-commit/mirrors-clang-format
   rev: v19.1.7
   hooks:
   - id: clang-format
-    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
+    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
 - repo: https://github.com/jackdewinter/pymarkdown
   rev: v0.9.27
   hooks:
   - id: pymarkdown
-    args: [fix]
-    exclude: 'vllm/third_party/.*'
+    files: docs/.*
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
   hooks:
   - id: actionlint
-    exclude: 'vllm/third_party/.*'
 - repo: local
   hooks:
   - id: mypy-local
@@ -53,7 +47,6 @@ repos:
     types: [python]
     additional_dependencies: &mypy_deps [mypy==1.11.1, types-setuptools, types-PyYAML, types-requests]
     stages: [pre-commit] # Don't run in CI
-    exclude: 'vllm/third_party/.*'
   - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.9
     entry: tools/mypy.sh 1 "3.9"
@@ -61,7 +54,6 @@ repos:
     types: [python]
     additional_dependencies: *mypy_deps
     stages: [manual] # Only run in CI
-    exclude: 'vllm/third_party/.*'
   - id: mypy-3.10 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.10
     entry: tools/mypy.sh 1 "3.10"
@@ -69,7 +61,6 @@ repos:
     types: [python]
     additional_dependencies: *mypy_deps
     stages: [manual] # Only run in CI
-    exclude: 'vllm/third_party/.*'
   - id: mypy-3.11 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.11
     entry: tools/mypy.sh 1 "3.11"
@@ -77,7 +68,6 @@ repos:
     types: [python]
     additional_dependencies: *mypy_deps
     stages: [manual] # Only run in CI
-    exclude: 'vllm/third_party/.*'
   - id: mypy-3.12 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.12
     entry: tools/mypy.sh 1 "3.12"
@@ -85,54 +75,19 @@ repos:
     types: [python]
     additional_dependencies: *mypy_deps
     stages: [manual] # Only run in CI
-    exclude: 'vllm/third_party/.*'
   - id: shellcheck
     name: Lint shell scripts
     entry: tools/shellcheck.sh
     language: script
     types: [shell]
-    exclude: 'vllm/third_party/.*'
   - id: png-lint
     name: Lint PNG exports from excalidraw
     entry: tools/png-lint.sh
     language: script
     types: [png]
-    exclude: 'vllm/third_party/.*'
-  - id: signoff-commit
-    name: Sign-off Commit
-    entry: bash
-    args:
-      - -c
-      - |
-        if ! grep -q "^Signed-off-by: $(git config user.name) <$(git config user.email)>" .git/COMMIT_EDITMSG; then
-          printf "\nSigned-off-by: $(git config user.name) <$(git config user.email)>\n" >> .git/COMMIT_EDITMSG
-        fi
-    language: system
-    verbose: true
-    stages: [commit-msg]
-    exclude: 'vllm/third_party/.*'
-  - id: check-spdx-header
-    name: Check SPDX headers
-    entry: python tools/check_spdx_header.py
-    language: python
-    types: [python]
-    exclude: 'vllm/third_party/.*'
-  - id: check-filenames
-    name: Check for spaces in all filenames
-    entry: bash
-    args:
-      - -c
-      - 'git ls-files | grep " " && echo "Filenames should not contain spaces!" && exit 1 || exit 0'
-    language: system
-    always_run: true
-    pass_filenames: false
-    exclude: 'vllm/third_party/.*'
-  # Keep `suggestion` last
   - id: suggestion
     name: Suggestion
     entry: bash -c 'echo "To bypass pre-commit hooks, add --no-verify to git commit."'
     language: system
     verbose: true
     pass_filenames: false
-    exclude: 'vllm/third_party/.*'
-  # Insert new entries above the `suggestion` entry

CMakeLists.txt

@@ -34,7 +34,7 @@ set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101")
 
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -192,7 +192,7 @@ set_gencode_flags_for_srcs(
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   message(STATUS "Enabling cumem allocator extension.")
   # link against cuda driver library
-  list(APPEND CUMEM_LIBS CUDA::cuda_driver)
+  list(APPEND CUMEM_LIBS cuda)
   define_gpu_extension_target(
     cumem_allocator
     DESTINATION vllm
@@ -228,8 +228,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
   # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
-  # Please keep this in sync with FetchContent_Declare line below.
-  set(CUTLASS_REVISION "v3.7.0" CACHE STRING "CUTLASS revision to use")
+  set(CUTLASS_REVISION "v3.6.0" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -246,8 +245,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     FetchContent_Declare(
         cutlass
         GIT_REPOSITORY https://github.com/nvidia/cutlass.git
-        # Please keep this in sync with CUTLASS_REVISION line above.
-        GIT_TAG v3.7.0
+        GIT_TAG v3.6.0
         GIT_PROGRESS TRUE
 
         # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
@@ -266,8 +264,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/custom_all_reduce.cu"
     "csrc/permute_cols.cu"
     "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
-    "csrc/quantization/fp4/nvfp4_quant_entry.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
+    "csrc/sparse/cutlass/sparse_compressor_entry.cu"
     "csrc/cutlass_extensions/common.cpp")
 
   set_gencode_flags_for_srcs(
@@ -301,12 +299,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # CUDA 12.0 or later (and only work on Hopper, 9.0a for now).
   cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
-    set(SRCS 
-       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
-       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
-       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8.cu"
-       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_azp_sm90_int8.cu"
-       "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8.cu")
+    set(SRCS "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
@@ -360,7 +353,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
   # require CUDA 12.2 or later (and only work on Hopper, 9.0a for now).
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
-    set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
+    set(SRCS "csrc/sparse/cutlass/sparse_compressor_c3x.cu"
+             "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
@@ -378,23 +372,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     endif()
   endif()
 
-  # FP4 Archs and flags
-  cuda_archs_loose_intersection(FP4_ARCHS "10.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND FP4_ARCHS)
-    set(SRCS 
-      "csrc/quantization/fp4/nvfp4_quant_kernels.cu"
-    )
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${FP4_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_NVFP4=1")
-    message(STATUS "Building NVFP4 for archs: ${FP4_ARCHS}")
-  else()
-    message(STATUS "Not building NVFP4 as no compatible archs were found.")
-    # clear FP4_ARCHS
-    set(FP4_ARCHS)
-  endif()
 
   #
   # Machete kernels
@@ -476,7 +453,7 @@ define_gpu_extension_target(
   SOURCES ${VLLM_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   USE_SABI 3
   WITH_SOABI)
 
@@ -599,7 +576,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 720c94869cf2e0ff5a706e9c7f1dce0939686ade
+          GIT_TAG d4e09037abf588af1ec47d0e966b237ee376876c
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

CODE_OF_CONDUCT.md

@@ -61,7 +61,7 @@ representative at an online or offline/IRL event.
 
 Instances of abusive, harassing, or otherwise unacceptable behavior may be
 reported to the community leaders responsible for enforcement in the #code-of-conduct
-channel in the [vLLM Slack](https://slack.vllm.ai).
+channel in the [vLLM Discord](https://discord.com/invite/jz7wjKhh6g).
 All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the
@@ -125,3 +125,4 @@ Community Impact Guidelines were inspired by
 For answers to common questions about this code of conduct, see the
 [Contributor Covenant FAQ](https://www.contributor-covenant.org/faq). Translations are available at
 [Contributor Covenant translations](https://www.contributor-covenant.org/translations).
+

Dockerfile

@@ -27,9 +27,6 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
     && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
     && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
     && python3 --version && python3 -m pip --version
-# Install uv for faster pip installs
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install uv
 
 # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
 # as it was causing spam when compiling the CUTLASS kernels
@@ -55,13 +52,13 @@ WORKDIR /workspace
 # after this step
 RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121";  \
+        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121";  \
     fi
 
 COPY requirements-common.txt requirements-common.txt
 COPY requirements-cuda.txt requirements-cuda.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -r requirements-cuda.txt
+    python3 -m pip install -r requirements-cuda.txt
 
 # cuda arch list used by torch
 # can be useful for both `dev` and `test`
@@ -82,7 +79,7 @@ ARG TARGETPLATFORM
 COPY requirements-build.txt requirements-build.txt
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -r requirements-build.txt
+    python3 -m pip install -r requirements-build.txt
 
 COPY . .
 ARG GIT_REPO_CHECK=0
@@ -130,7 +127,7 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
 # Check the size of the wheel if RUN_WHEEL_CHECK is true
 COPY .buildkite/check-wheel-size.py check-wheel-size.py
 # sync the default value with .buildkite/check-wheel-size.py
-ARG VLLM_MAX_SIZE_MB=400
+ARG VLLM_MAX_SIZE_MB=300
 ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
 ARG RUN_WHEEL_CHECK=true
 RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
@@ -147,7 +144,7 @@ COPY requirements-lint.txt requirements-lint.txt
 COPY requirements-test.txt requirements-test.txt
 COPY requirements-dev.txt requirements-dev.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -r requirements-dev.txt
+    python3 -m pip install -r requirements-dev.txt
 #################### DEV IMAGE ####################
 
 #################### vLLM installation IMAGE ####################
@@ -177,9 +174,6 @@ RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
     && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
     && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
     && python3 --version && python3 -m pip --version
-# Install uv for faster pip installs
-RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install uv
 
 # Workaround for https://github.com/openai/triton/issues/2507 and
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
@@ -193,30 +187,27 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 # after this step
 RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
+        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
     fi
 
 # Install vllm wheel first, so that torch etc will be installed.
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
     --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system dist/*.whl --verbose
+    python3 -m pip install dist/*.whl --verbose
 
-# If we need to build FlashInfer wheel before its release:
+# How to build this FlashInfer wheel:
 # $ export FLASHINFER_ENABLE_AOT=1
 # $ # Note we remove 7.0 from the arch list compared to the list below, since FlashInfer only supports sm75+
 # $ export TORCH_CUDA_ARCH_LIST='7.5 8.0 8.6 8.9 9.0+PTX'
 # $ git clone https://github.com/flashinfer-ai/flashinfer.git --recursive
 # $ cd flashinfer
 # $ git checkout 524304395bd1d8cd7d07db083859523fcaa246a4
-# $ rm -rf build
 # $ python3 setup.py bdist_wheel --dist-dir=dist --verbose
-# $ ls dist
-# $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
 
 RUN --mount=type=cache,target=/root/.cache/pip \
 . /etc/environment && \
 if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
-    uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
+    python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
 fi
 COPY examples examples
 
@@ -226,7 +217,7 @@ COPY examples examples
 # TODO: Remove this once FlashInfer AOT wheel is fixed
 COPY requirements-build.txt requirements-build.txt
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -r requirements-build.txt
+    python3 -m pip install -r requirements-build.txt
 
 #################### vLLM installation IMAGE ####################
 
@@ -239,15 +230,15 @@ ADD . /vllm-workspace/
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -r requirements-dev.txt
+    python3 -m pip install -r requirements-dev.txt
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system -e tests/vllm_test_utils
+    python3 -m pip install -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    uv pip install --system hf_transfer
+    python3 -m pip install hf_transfer
 ENV HF_HUB_ENABLE_HF_TRANSFER 1
 
 # Copy in the v1 package for testing (it isn't distributed yet)
@@ -268,9 +259,9 @@ FROM vllm-base AS vllm-openai-base
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
+        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     else \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
+        pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image

Dockerfile.neuron

@@ -23,12 +23,10 @@ WORKDIR ${APP_MOUNT}/vllm
 RUN python3 -m pip install --upgrade pip
 RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
 RUN python3 -m pip install sentencepiece transformers==4.45.2 -U
+RUN python3 -m pip install transformers-neuronx --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 RUN python3 -m pip install neuronx-cc==2.16.345.0 --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 RUN python3 -m pip install pytest
 
-# uninstall transformers-neuronx package explicitly to avoid version conflict
-RUN python3 -m pip uninstall -y transformers-neuronx
-
 COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
@@ -45,10 +43,6 @@ RUN --mount=type=bind,source=.git,target=.git \
 # install development dependencies (for testing)
 RUN python3 -m pip install -e tests/vllm_test_utils
 
-# install transformers-neuronx package as an optional dependencies (for V0)
-# FIXME: `--no-deps` argument is temporarily added to resolve transformers package version conflict
-RUN python3 -m pip install transformers-neuronx==0.13.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U --no-deps
-
 # overwrite entrypoint to run bash script
 RUN echo "import subprocess; import sys; subprocess.check_call(sys.argv[1:])" > /usr/local/bin/dockerd-entrypoint.py
 

Dockerfile.ppc64le

@@ -4,12 +4,12 @@ USER root
 
 ENV PATH="/usr/local/cargo/bin:$PATH:/opt/conda/bin/"
 
-RUN apt-get update -y && apt-get install -y git wget kmod curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 libssl-dev 
+RUN apt-get update -y && apt-get install -y git wget curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 libssl-dev 
 
 # Some packages in requirements-cpu are installed here
 # IBM provides optimized packages for ppc64le processors in the open-ce project for mamba
 # Currently these may not be available for venv or pip directly
-RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 rust && micromamba clean --all --yes
+RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 torchvision-cpu=0.16.2 rust && micromamba clean --all --yes
 
 COPY ./ /workspace/vllm
 
@@ -21,6 +21,7 @@ RUN --mount=type=bind,source=.git,target=.git \
 RUN --mount=type=cache,target=/root/.cache/pip  \
     RUSTFLAGS='-L /opt/conda/lib' pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
         'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
+        torch==2.3.1 \
         -r requirements-cpu.txt \
         xformers uvloop==0.20.0
 

Dockerfile.rocm_base

@@ -6,7 +6,7 @@ ARG RCCL_BRANCH="648a58d"
 ARG RCCL_REPO="https://github.com/ROCm/rccl"
 ARG TRITON_BRANCH="e5be006"
 ARG TRITON_REPO="https://github.com/triton-lang/triton.git"
-ARG PYTORCH_BRANCH="3a585126"
+ARG PYTORCH_BRANCH="8d4926e"
 ARG PYTORCH_VISION_BRANCH="v0.19.1"
 ARG PYTORCH_REPO="https://github.com/pytorch/pytorch.git"
 ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"

README.md

@@ -10,19 +10,14 @@ Easy, fast, and cheap LLM serving for everyone
 </h3>
 
 <p align="center">
-| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
+| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://discord.gg/jz7wjKhh6g"><b>Discord</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
 ---
 
-We are excited to invite you to our Menlo Park meetup with Meta, evening of Thursday, February 27! Meta engineers will discuss the improvements on top of vLLM, and vLLM contributors will share updates from the v0.7.x series of releases. [Register Now](https://lu.ma/h7g3kuj9)
-
----
-
 *Latest News* 🔥
-
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
-- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
+- [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing).
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
 - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
 - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
@@ -38,12 +33,10 @@ We are excited to invite you to our Menlo Park meetup with Meta, evening of Thur
 - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
 ---
-
 ## About
-
 vLLM is a fast and easy-to-use library for LLM inference and serving.
 
-Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evolved into a community-driven project with contributions from both academia and industry.
+Originally developed in the [Sky Computing Lab](https://sky.cs.berkeley.edu) at UC Berkeley, vLLM has evloved into a community-driven project with contributions from both academia and industry.
 
 vLLM is fast with:
 
@@ -134,7 +127,6 @@ We also have an official fundraising venue through [OpenCollective](https://open
 ## Citation
 
 If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):
-
 ```bibtex
 @inproceedings{kwon2023efficient,
   title={Efficient Memory Management for Large Language Model Serving with PagedAttention},
@@ -146,11 +138,12 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-- For technical questions and feature requests, please use Github issues or discussions.
-- For discussing with fellow users and coordinating contributions and development, please use Slack.
-- For security disclosures, please use Github's security advisory feature.
-- For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
+* For technical questions and feature requests, please use Github issues or discussions.
+* For discussing with fellow users, please use Discord.
+* For coordinating contributions and development, please use Slack.
+* For security disclosures, please use Github's security advisory feature.
+* For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
 
 ## Media Kit
 
-- If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).
+* If you wish to use vLLM's logo, please refer to [our media kit repo](https://github.com/vllm-project/media-kit).

benchmarks/README.md

@@ -3,7 +3,6 @@
 ## Downloading the ShareGPT dataset
 
 You can download the dataset by running:
-
 ```bash
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
@@ -12,18 +11,9 @@ wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/r
 
 The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
 will ignore a datapoint if the referred image is missing.
-
 ```bash
 wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
 mkdir coco -p
 wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
 unzip coco/train2017.zip -d coco/
 ```
-
-# Downloading the BurstGPT dataset
-
-You can download the BurstGPT v1.1 dataset by running:
-
-```bash
-wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
-```

benchmarks/backend_request_func.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import json
 import os
 import sys

benchmarks/benchmark_guided.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 """Benchmark guided decoding throughput."""
 import argparse
 import dataclasses

benchmarks/benchmark_latency.py

@@ -1,17 +1,13 @@
-# SPDX-License-Identifier: Apache-2.0
 """Benchmark the latency of processing a single batch of requests."""
-
 import argparse
 import dataclasses
 import json
-import os
 import time
 from pathlib import Path
-from typing import Any, Dict, List, Optional
+from typing import List, Optional
 
 import numpy as np
 import torch
-from benchmark_utils import convert_to_pytorch_benchmark_format
 from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
@@ -21,19 +17,6 @@ from vllm.sampling_params import BeamSearchParams
 from vllm.utils import FlexibleArgumentParser
 
 
-def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                     results: Dict[str, Any]) -> None:
-    pt_records = convert_to_pytorch_benchmark_format(
-        args=args,
-        metrics={"latency": results["latencies"]},
-        extra_info={k: results[k]
-                    for k in ["avg_latency", "percentiles"]})
-    if pt_records:
-        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
-        with open(pt_file, "w") as f:
-            json.dump(pt_records, f)
-
-
 def main(args: argparse.Namespace):
     print(args)
 
@@ -70,8 +53,7 @@ def main(args: argparse.Namespace):
                     beam_width=args.n,
                     max_tokens=args.output_len,
                     ignore_eos=True,
-                ),
-            )
+                ))
 
     def run_to_completion(profile_dir: Optional[str] = None):
         if profile_dir:
@@ -81,8 +63,7 @@ def main(args: argparse.Namespace):
                         torch.profiler.ProfilerActivity.CUDA,
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                        str(profile_dir)),
-            ) as p:
+                        str(profile_dir))) as p:
                 llm_generate()
             print(p.key_averages().table(sort_by="self_cuda_time_total"))
         else:
@@ -99,8 +80,9 @@ def main(args: argparse.Namespace):
     if args.profile:
         profile_dir = args.profile_result_dir
         if not profile_dir:
-            profile_dir = (Path(".") / "vllm_benchmark_result" /
-                           f"latency_result_{time.time()}")
+            profile_dir = Path(
+                "."
+            ) / "vllm_benchmark_result" / f"latency_result_{time.time()}"
         print(f"Profiling (results will be saved to '{profile_dir}')...")
         run_to_completion(profile_dir=profile_dir)
         return
@@ -112,9 +94,9 @@ def main(args: argparse.Namespace):
     latencies = np.array(latencies)
     percentages = [10, 25, 50, 75, 90, 99]
     percentiles = np.percentile(latencies, percentages)
-    print(f"Avg latency: {np.mean(latencies)} seconds")
+    print(f'Avg latency: {np.mean(latencies)} seconds')
     for percentage, percentile in zip(percentages, percentiles):
-        print(f"{percentage}% percentile latency: {percentile} seconds")
+        print(f'{percentage}% percentile latency: {percentile} seconds')
 
     # Output JSON results if specified
     if args.output_json:
@@ -125,51 +107,43 @@ def main(args: argparse.Namespace):
         }
         with open(args.output_json, "w") as f:
             json.dump(results, f, indent=4)
-        save_to_pytorch_benchmark_format(args, results)
 
 
-if __name__ == "__main__":
+if __name__ == '__main__':
     parser = FlexibleArgumentParser(
-        description="Benchmark the latency of processing a single batch of "
-        "requests till completion.")
-    parser.add_argument("--input-len", type=int, default=32)
-    parser.add_argument("--output-len", type=int, default=128)
-    parser.add_argument("--batch-size", type=int, default=8)
-    parser.add_argument(
-        "--n",
-        type=int,
-        default=1,
-        help="Number of generated sequences per prompt.",
-    )
-    parser.add_argument("--use-beam-search", action="store_true")
-    parser.add_argument(
-        "--num-iters-warmup",
-        type=int,
-        default=10,
-        help="Number of iterations to run for warmup.",
-    )
-    parser.add_argument("--num-iters",
+        description='Benchmark the latency of processing a single batch of '
+        'requests till completion.')
+    parser.add_argument('--input-len', type=int, default=32)
+    parser.add_argument('--output-len', type=int, default=128)
+    parser.add_argument('--batch-size', type=int, default=8)
+    parser.add_argument('--n',
+                        type=int,
+                        default=1,
+                        help='Number of generated sequences per prompt.')
+    parser.add_argument('--use-beam-search', action='store_true')
+    parser.add_argument('--num-iters-warmup',
+                        type=int,
+                        default=10,
+                        help='Number of iterations to run for warmup.')
+    parser.add_argument('--num-iters',
                         type=int,
                         default=30,
-                        help="Number of iterations to run.")
+                        help='Number of iterations to run.')
     parser.add_argument(
-        "--profile",
-        action="store_true",
-        help="profile the generation process of a single batch",
-    )
+        '--profile',
+        action='store_true',
+        help='profile the generation process of a single batch')
     parser.add_argument(
-        "--profile-result-dir",
+        '--profile-result-dir',
         type=str,
         default=None,
-        help=("path to save the pytorch profiler output. Can be visualized "
-              "with ui.perfetto.dev or Tensorboard."),
-    )
+        help=('path to save the pytorch profiler output. Can be visualized '
+              'with ui.perfetto.dev or Tensorboard.'))
     parser.add_argument(
-        "--output-json",
+        '--output-json',
         type=str,
         default=None,
-        help="Path to save the latency results in JSON format.",
-    )
+        help='Path to save the latency results in JSON format.')
 
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()

benchmarks/benchmark_long_document_qa_throughput.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 """
 Offline benchmark to test the long document QA throughput.
 

benchmarks/benchmark_prefix_caching.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 """
 Benchmark the efficiency of prefix caching.
 

benchmarks/benchmark_prioritization.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 """Benchmark offline prioritization."""
 import argparse
 import dataclasses

benchmarks/benchmark_serving.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 r"""Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
@@ -38,7 +37,6 @@ from datetime import datetime
 from typing import Any, AsyncGenerator, Collection, Dict, List, Optional, Tuple
 
 import numpy as np
-import pandas as pd
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
                                   RequestFuncOutput)
 from datasets import load_dataset
@@ -56,8 +54,6 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_utils import convert_to_pytorch_benchmark_format
-
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
 
@@ -134,35 +130,6 @@ def sample_sharegpt_requests(
     return filtered_dataset
 
 
-def sample_burstgpt_requests(
-    dataset_path: str,
-    num_requests: int,
-    random_seed: int,
-    tokenizer: PreTrainedTokenizerBase,
-) -> List[Tuple[str, int, int, None]]:
-    df = pd.read_csv(dataset_path)
-    gpt4_df = df[df["Model"] == "GPT-4"]
-    # Remove the failed requests (i.e., response length is 0)
-    gpt4_df = gpt4_df[gpt4_df["Response tokens"] > 0]
-    # Randomly sample num_requests from the dataset
-    if num_requests <= len(gpt4_df):
-        gpt4_df = gpt4_df.sample(n=num_requests, random_state=random_seed)
-    else:
-        gpt4_df = gpt4_df.sample(n=num_requests,
-                                 random_state=random_seed,
-                                 replace=True)
-    # Convert the dataframe to a list of tuples
-    dataset = gpt4_df.values.tolist()
-    input_requests = []
-    for i in range(num_requests):
-        input_len = int(dataset[i][2])
-        output_len = int(dataset[i][3])
-        prompt = tokenizer.decode([(i + j) % tokenizer.vocab_size
-                                   for j in range(input_len)])
-        input_requests.append((prompt, input_len, output_len, None))
-    return input_requests
-
-
 def sample_sonnet_requests(
     dataset_path: str,
     num_requests: int,
@@ -404,21 +371,21 @@ async def get_request(
     burstiness: float = 1.0,
 ) -> AsyncGenerator[Tuple[str, int, int], None]:
     """
-    Asynchronously generates requests at a specified rate
+    Asynchronously generates requests at a specified rate 
     with OPTIONAL burstiness.
-
+    
     Args:
-        input_requests:
+        input_requests: 
             A list of input requests, each represented as a tuple.
-        request_rate:
+        request_rate: 
             The rate at which requests are generated (requests/s).
-        burstiness (optional):
-            The burstiness factor of the request generation.
+        burstiness (optional): 
+            The burstiness factor of the request generation. 
             Only takes effect when request_rate is not inf.
             Default value is 1, which follows a Poisson process.
             Otherwise, the request intervals follow a gamma distribution.
-            A lower burstiness value (0 < burstiness < 1) results
-            in more bursty requests, while a higher burstiness value
+            A lower burstiness value (0 < burstiness < 1) results 
+            in more bursty requests, while a higher burstiness value 
             (burstiness > 1) results in a more uniform arrival of requests.
     """
     input_requests = iter(input_requests)
@@ -569,7 +536,6 @@ async def benchmark(
     ignore_eos: bool,
     goodput_config_dict: Dict[str, float],
     max_concurrency: Optional[int],
-    lora_modules: Optional[List[str]],
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -595,7 +561,6 @@ async def benchmark(
         multi_modal_content=test_mm_content,
         ignore_eos=ignore_eos,
     )
-
     test_output = await request_func(request_func_input=test_input)
     if not test_output.success:
         raise ValueError(
@@ -604,11 +569,6 @@ async def benchmark(
     else:
         print("Initial test run completed. Starting main benchmark run...")
 
-    if lora_modules:
-        # For each input request, choose a LoRA module at random.
-        lora_modules = iter(
-            [random.choice(lora_modules) for _ in range(len(input_requests))])
-
     if profile:
         print("Starting profiler...")
         profile_input = RequestFuncInput(model=model_id,
@@ -655,13 +615,8 @@ async def benchmark(
     tasks: List[asyncio.Task] = []
     async for request in get_request(input_requests, request_rate, burstiness):
         prompt, prompt_len, output_len, mm_content = request
-        req_model_id, req_model_name = model_id, model_name
-        if lora_modules:
-            req_lora_module = next(lora_modules)
-            req_model_id, req_model_name = req_lora_module, req_lora_module
-
-        request_func_input = RequestFuncInput(model=req_model_id,
-                                              model_name=req_model_name,
+        request_func_input = RequestFuncInput(model=model_id,
+                                              model_name=model_name,
                                               prompt=prompt,
                                               api_url=api_url,
                                               prompt_len=prompt_len,
@@ -819,32 +774,6 @@ def parse_goodput(slo_pairs):
     return goodput_config_dict
 
 
-def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                     results: Dict[str, Any],
-                                     file_name: str) -> None:
-    metrics = [
-        "median_ttft_ms", "mean_ttft_ms", "std_ttft_ms", "p99_ttft_ms",
-        "mean_tpot_ms", "median_tpot_ms", "std_tpot_ms", "p99_tpot_ms",
-        "median_itl_ms", "mean_itl_ms", "std_itl_ms", "p99_itl_ms"
-    ]
-    # These raw data might be useful, but they are rather big. They can be added
-    # later if needed
-    ignored_metrics = ["ttfts", "itls", "generated_texts", "errors"]
-    pt_records = convert_to_pytorch_benchmark_format(
-        args=args,
-        metrics={k: [results[k]]
-                 for k in metrics},
-        extra_info={
-            k: results[k]
-            for k in results if k not in metrics and k not in ignored_metrics
-        })
-    if pt_records:
-        # Don't use json suffix here as we don't want CI to pick it up
-        pt_file = f"{os.path.splitext(file_name)[0]}.pytorch.json"
-        with open(pt_file, "w") as f:
-            json.dump(pt_records, f)
-
-
 def main(args: argparse.Namespace):
     print(args)
     random.seed(args.seed)
@@ -888,14 +817,6 @@ def main(args: argparse.Namespace):
             fixed_output_len=args.sharegpt_output_len,
         )
 
-    elif args.dataset_name == "burstgpt":
-        input_requests = sample_burstgpt_requests(
-            dataset_path=args.dataset_path,
-            num_requests=args.num_prompts,
-            random_seed=args.seed,
-            tokenizer=tokenizer,
-        )
-
     elif args.dataset_name == "sonnet":
         # Do not format the prompt, pass to message directly
         if args.backend == "openai-chat":
@@ -978,7 +899,6 @@ def main(args: argparse.Namespace):
             ignore_eos=args.ignore_eos,
             goodput_config_dict=goodput_config_dict,
             max_concurrency=args.max_concurrency,
-            lora_modules=args.lora_modules,
         ))
 
     # Save config and results to json
@@ -1025,7 +945,6 @@ def main(args: argparse.Namespace):
             file_name = os.path.join(args.result_dir, file_name)
         with open(file_name, "w", encoding='utf-8') as outfile:
             json.dump(result_json, outfile)
-        save_to_pytorch_benchmark_format(args, result_json, file_name)
 
 
 if __name__ == "__main__":
@@ -1043,8 +962,7 @@ if __name__ == "__main__":
         default=None,
         help="Server or API base url if not using http host and port.",
     )
-    # Use 127.0.0.1 here instead of localhost to force the use of ipv4
-    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8000)
     parser.add_argument(
         "--endpoint",
@@ -1063,7 +981,7 @@ if __name__ == "__main__":
         "--dataset-name",
         type=str,
         default="sharegpt",
-        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
+        choices=["sharegpt", "sonnet", "random", "hf"],
         help="Name of the dataset to benchmark on.",
     )
     parser.add_argument("--dataset-path",
@@ -1305,12 +1223,11 @@ if __name__ == "__main__":
         '--tokenizer-mode',
         type=str,
         default="auto",
-        choices=['auto', 'slow', 'mistral', 'custom'],
+        choices=['auto', 'slow', 'mistral'],
         help='The tokenizer mode.\n\n* "auto" will use the '
         'fast tokenizer if available.\n* "slow" will '
         'always use the slow tokenizer. \n* '
-        '"mistral" will always use the `mistral_common` tokenizer. \n*'
-        '"custom" will use --tokenizer to select the preregistered tokenizer.')
+        '"mistral" will always use the `mistral_common` tokenizer.')
 
     parser.add_argument("--served-model-name",
                         type=str,
@@ -1319,12 +1236,5 @@ if __name__ == "__main__":
                         "If not specified, the model name will be the "
                         "same as the ``--model`` argument. ")
 
-    parser.add_argument("--lora-modules",
-                        nargs='+',
-                        default=None,
-                        help="A subset of LoRA module names passed in when "
-                        "launching the server. For each request, the "
-                        "script chooses a LoRA module at random.")
-
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_serving_guided.py

@@ -1,4 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
 r"""Benchmark online serving throughput with guided decoding.
 
 On the server side, run one of the following commands:
@@ -731,8 +730,7 @@ if __name__ == "__main__":
         default=None,
         help="Server or API base url if not using http host and port.",
     )
-    # Use 127.0.0.1 here instead of localhost to force the use of ipv4
-    parser.add_argument("--host", type=str, default="127.0.0.1")
+    parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8000)
     parser.add_argument(
         "--endpoint",

benchmarks/benchmark_throughput.py

@@ -1,17 +1,14 @@
-# SPDX-License-Identifier: Apache-2.0
 """Benchmark offline inference throughput."""
 import argparse
 import dataclasses
 import json
-import os
 import random
 import time
 from functools import cache
-from typing import Any, Dict, List, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
 
 import torch
 import uvloop
-from benchmark_utils import convert_to_pytorch_benchmark_format
 from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
@@ -340,25 +337,6 @@ def run_mii(
     return end - start
 
 
-def save_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                     results: Dict[str, Any]) -> None:
-    pt_records = convert_to_pytorch_benchmark_format(
-        args=args,
-        metrics={
-            "requests_per_second": [results["requests_per_second"]],
-            "tokens_per_second": [results["tokens_per_second"]],
-        },
-        extra_info={
-            k: results[k]
-            for k in ["elapsed_time", "num_requests", "total_num_tokens"]
-        })
-    if pt_records:
-        # Don't use json suffix here as we don't want CI to pick it up
-        pt_file = f"{os.path.splitext(args.output_json)[0]}.pytorch.json"
-        with open(pt_file, "w") as f:
-            json.dump(pt_records, f)
-
-
 def main(args: argparse.Namespace):
     print(args)
     random.seed(args.seed)
@@ -456,7 +434,6 @@ def main(args: argparse.Namespace):
         }
         with open(args.output_json, "w") as f:
             json.dump(results, f, indent=4)
-        save_to_pytorch_benchmark_format(args, results)
 
 
 if __name__ == "__main__":

benchmarks/benchmark_utils.py

@@ -1,39 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import argparse
-import os
-from typing import Any, Dict, List
-
-
-def convert_to_pytorch_benchmark_format(args: argparse.Namespace,
-                                        metrics: Dict[str, List],
-                                        extra_info: Dict[str, Any]) -> List:
-    """
-    Save the benchmark results in the format used by PyTorch OSS benchmark with
-    on metric per record
-    https://github.com/pytorch/pytorch/wiki/How-to-integrate-with-PyTorch-OSS-benchmark-database
-    """
-    records = []
-    if not os.environ.get("SAVE_TO_PYTORCH_BENCHMARK_FORMAT", False):
-        return records
-
-    for name, benchmark_values in metrics.items():
-        record = {
-            "benchmark": {
-                "name": "vLLM benchmark",
-                "extra_info": {
-                    "args": vars(args),
-                },
-            },
-            "model": {
-                "name": args.model,
-            },
-            "metric": {
-                "name": name,
-                "benchmark_values": benchmark_values,
-                "extra_info": extra_info,
-            },
-        }
-        records.append(record)
-
-    return records

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import copy
 import itertools

benchmarks/cutlass_benchmarks/utils.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 # Cutlass bench utils
 from typing import Iterable, Tuple
 

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -1,11 +1,9 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import copy
 import itertools
 import pickle as pkl
 import time
-from typing import Callable, Iterable, List, Optional, Tuple
+from typing import Callable, Iterable, List, Tuple
 
 import torch
 import torch.utils.benchmark as TBenchmark
@@ -14,8 +12,6 @@ from utils import make_rand_tensors
 from weight_shapes import WEIGHT_SHAPES
 
 from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    w8a8_block_fp8_matmul)
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -42,15 +38,8 @@ def bench_fn(label: str, sub_label: str, description: str, fn: Callable, *args,
     ).blocked_autorange(min_run_time=min_run_time)
 
 
-def bench_int8(
-        dtype: torch.dtype,
-        m: int,
-        k: int,
-        n: int,
-        label: str,
-        sub_label: str,
-        bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
-    """Benchmark INT8-based kernels."""
+def bench_int8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+               sub_label: str) -> Iterable[TMeasurement]:
     assert dtype == torch.int8
     a, b = make_rand_tensors(torch.int8, m, n, k)
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
@@ -59,132 +48,155 @@ def bench_int8(
     azp = torch.zeros((m, ), device="cuda", dtype=torch.int32)
     azp_adj = torch.zeros((n, ), device="cuda", dtype=torch.int32)
 
-    bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        "cutlass_i8_i8_bf16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
-        "cutlass_i8_i8_bf16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
-                                      bias),
-        "cutlass_i8_i8_bf16_scaled_mm_azp":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_bias":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, None, bias),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, azp),
-        "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias":
-        lambda: ops.cutlass_scaled_mm_azp(a, b, scale_a, scale_b, torch.
-                                          bfloat16, azp_adj, azp, bias),
-    }
-
     timers = []
-    for name, fn in bench_fns.items():
-        # If bench_kernels is None, run all. Otherwise, run only exact matches.
-        if bench_kernels is None or name in bench_kernels:
-            print(f"Running {name}")
-            timers.append(bench_fn(label, sub_label, name, fn))
+    # pytorch impl - bfloat16
+    timers.append(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16),
+                 b.to(dtype=torch.bfloat16)))
+
+    # pytorch impl - float16
+    timers.append(
+        bench_fn(label, sub_label,
+                 "pytorch_fp16_fp16_fp16_matmul-no-scales", torch.mm,
+                 a.to(dtype=torch.float16), b.to(dtype=torch.float16)))
+
+    # cutlass impl
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
+
+    # cutlass with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
+                 bias))
+
+    # cutlass with azp per-tensor
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj))
+
+    # cutlass with azp per-tensor + bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_bias",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, None, bias))
+
+    # cutlass with azp per-token
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, azp))
+
+    # cutlass with azp per-token + bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_i8_i8_bf16_scaled_mm_azp_pt_bias",
+                 ops.cutlass_scaled_mm_azp, a, b, scale_a, scale_b,
+                 torch.bfloat16, azp_adj, azp, bias))
 
     return timers
 
 
-def bench_fp8(
-        dtype: torch.dtype,
-        m: int,
-        k: int,
-        n: int,
-        label: str,
-        sub_label: str,
-        bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
-    """Benchmark FP8-based kernels."""
+def bench_fp8(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+              sub_label: str) -> Iterable[TMeasurement]:
     assert dtype == torch.float8_e4m3fn
     a, b = make_rand_tensors(torch.float8_e4m3fn, m, n, k)
-    a_cont = a.contiguous()
     scale_a = torch.tensor(1.0, device="cuda", dtype=torch.float32)
     scale_b = torch.tensor(1.0, device="cuda", dtype=torch.float32)
-    block_scale_a = torch.rand((m, k // 128),
-                               device="cuda",
-                               dtype=torch.float32)
-    block_scale_b = torch.rand((k // 128, n // 128),
-                               device="cuda",
-                               dtype=torch.float32)
-    block_scale_a_M_major = block_scale_a.t().contiguous().t()
-    block_scale_b_K_major = block_scale_b.t().contiguous().t()
     bias = torch.zeros((n, ), device="cuda", dtype=torch.bfloat16)
 
-    print(m, k, n)
-
-    bench_fns = {
-        "pytorch_bf16_bf16_bf16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.bfloat16), b.to(dtype=torch.bfloat16)
-                         ),
-        "pytorch_fp16_fp16_fp16_matmul-no-scales":
-        lambda: torch.mm(a.to(dtype=torch.float16), b.to(dtype=torch.float16)),
-        "pytorch_fp8_fp8_fp16_scaled_mm":
-        lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.float16),
-        "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum":
-        lambda: torch._scaled_mm(a,
-                                 b,
-                                 scale_a,
-                                 scale_b,
-                                 out_dtype=torch.float16,
-                                 use_fast_accum=True),
-        "pytorch_fp8_fp8_bf16_scaled_mm":
-        lambda: torch._scaled_mm(
-            a, b, scale_a, scale_b, out_dtype=torch.bfloat16),
-        "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum":
-        lambda: torch._scaled_mm(a,
-                                 b,
-                                 scale_a,
-                                 scale_b,
-                                 out_dtype=torch.bfloat16,
-                                 use_fast_accum=True),
-        "cutlass_fp8_fp8_bf16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16),
-        "cutlass_fp8_fp8_fp16_scaled_mm":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16),
-        "cutlass_fp8_fp8_bf16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.bfloat16,
-                                      bias),
-        "cutlass_fp8_fp8_fp16_scaled_mm_bias":
-        lambda: ops.cutlass_scaled_mm(a, b, scale_a, scale_b, torch.float16,
-                                      bias.to(dtype=torch.float16)),
-        "triton_fp8_fp8_fp16_scaled_mm_blockwise":
-        lambda: w8a8_block_fp8_matmul(a_cont, b.t(), block_scale_a,
-                                      block_scale_b.t(), (128, 128)),
-        "cutlass_fp8_fp8_fp16_scaled_mm_blockwise":
-        lambda: ops.cutlass_scaled_mm(a, b, block_scale_a_M_major,
-                                      block_scale_b_K_major, torch.float16),
-    }
-
     timers = []
-    for name, fn in bench_fns.items():
-        # If bench_kernels is None, run all. Otherwise, run only exact matches.
-        if bench_kernels is None or name in bench_kernels:
-            print(f"Running {name}")
-            timers.append(bench_fn(label, sub_label, name, fn))
+
+    # pytorch impl w. bf16
+    timers.append(
+        bench_fn(label, sub_label, "pytorch_bf16_bf16_bf16_matmul-no-scales",
+                 torch.mm, a.to(dtype=torch.bfloat16, device="cuda"),
+                 b.to(dtype=torch.bfloat16, device="cuda")))
+
+    # pytorch impl: bf16 output, without fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16))
+
+    # pytorch impl: bf16 output, with fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_bf16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.bfloat16,
+                 use_fast_accum=True))
+
+    # pytorch impl: fp16 output, without fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16))
+
+    # pytorch impl: fp16 output, with fp8 fast accum
+    timers.append(
+        bench_fn(label,
+                 sub_label,
+                 "pytorch_fp8_fp8_fp16_scaled_mm_fast_accum",
+                 torch._scaled_mm,
+                 a,
+                 b,
+                 scale_a=scale_a,
+                 scale_b=scale_b,
+                 out_dtype=torch.float16,
+                 use_fast_accum=True))
+
+    # cutlass impl: bf16 output
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b,
+                 torch.bfloat16))
+    # cutlass impl: fp16 output
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16))
+
+    # cutlass impl: bf16 output, with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_bf16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.bfloat16,
+                 bias))
+
+    # cutlass impl: fp16 output, with bias
+    timers.append(
+        bench_fn(label, sub_label, "cutlass_fp8_fp8_fp16_scaled_mm_bias",
+                 ops.cutlass_scaled_mm, a, b, scale_a, scale_b, torch.float16,
+                 bias.to(dtype=torch.float16)))
 
     return timers
 
 
-def bench(dtype: torch.dtype,
-          m: int,
-          k: int,
-          n: int,
-          label: str,
-          sub_label: str,
-          bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
+def bench(dtype: torch.dtype, m: int, k: int, n: int, label: str,
+          sub_label: str) -> Iterable[TMeasurement]:
     if dtype == torch.int8:
-        return bench_int8(dtype, m, k, n, label, sub_label, bench_kernels)
+        return bench_int8(dtype, m, k, n, label, sub_label)
     if dtype == torch.float8_e4m3fn:
-        return bench_fp8(dtype, m, k, n, label, sub_label, bench_kernels)
+        return bench_fp8(dtype, m, k, n, label, sub_label)
     raise ValueError("unsupported type")
 
 
@@ -195,22 +207,18 @@ def print_timers(timers: Iterable[TMeasurement]):
 
 
 def run(dtype: torch.dtype,
-        MKNs: Iterable[Tuple[int, int, int]],
-        bench_kernels: Optional[List[str]] = None) -> Iterable[TMeasurement]:
+        MKNs: Iterable[Tuple[int, int, int]]) -> Iterable[TMeasurement]:
     results = []
     for m, k, n in MKNs:
-        timers = bench(dtype,
-                       m,
-                       k,
-                       n,
-                       f"scaled-{dtype}-gemm",
-                       f"MKN=({m}x{k}x{n})",
-                       bench_kernels=bench_kernels)
+        timers = bench(dtype, m, k, n, f"scaled-{dtype}-gemm",
+                       f"MKN=({m}x{k}x{n})")
         print_timers(timers)
         results.extend(timers)
+
     return results
 
 
+# output makers
 def make_output(data: Iterable[TMeasurement],
                 MKNs: Iterable[Tuple[int, int, int]],
                 base_description: str,
@@ -224,11 +232,15 @@ def make_output(data: Iterable[TMeasurement],
         pkl.dump(data, f)
 
 
+# argparse runners
+
+
 def run_square_bench(args):
     dim_sizes = list(
         range(args.dim_start, args.dim_end + 1, args.dim_increment))
     MKNs = list(zip(dim_sizes, dim_sizes, dim_sizes))
-    data = run(args.dtype, MKNs, bench_kernels=args.kernels)
+    data = run(args.dtype, MKNs)
+
     make_output(data, MKNs, f"square_bench-{args.dtype}")
 
 
@@ -239,7 +251,8 @@ def run_range_bench(args):
     Ks = [args.k_constant] * n if args.k_constant is not None else dim_sizes
     Ns = [args.n_constant] * n if args.n_constant is not None else dim_sizes
     MKNs = list(zip(Ms, Ks, Ns))
-    data = run(args.dtype, MKNs, bench_kernels=args.kernels)
+    data = run(args.dtype, MKNs)
+
     make_output(data, MKNs, f"range_bench-{args.dtype}")
 
 
@@ -265,7 +278,7 @@ def run_model_bench(args):
             for k, n in KNs:
                 MKNs.append((m, k, n))
 
-        data = run(args.dtype, MKNs, bench_kernels=args.kernels)
+        data = run(args.dtype, MKNs)
         model_bench_data.append(data)
 
     # Print all results
@@ -315,15 +328,6 @@ Benchmark Cutlass GEMM.
                         type=to_torch_dtype,
                         required=True,
                         help="Available options are ['int8', 'fp8']")
-    parser.add_argument(
-        "--kernels",
-        nargs="+",
-        type=str,
-        default=None,
-        help=
-        "Exact names of the kernels to benchmark. If not set, runs all kernels."
-    )
-
     subparsers = parser.add_subparsers(dest="cmd")
 
     square_parser = subparsers.add_parser("square_bench")
@@ -358,4 +362,4 @@ Benchmark Cutlass GEMM.
     model_parser.set_defaults(func=run_model_bench)
 
     args = parser.parse_args()
-    args.func(args)
+    args.func(args)

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 # Weight Shapes are in the format
 # ([K, N], TP_SPLIT_DIM)
 # Example:

benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import os
 
 import aiohttp

benchmarks/disagg_benchmarks/round_robin_proxy.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import asyncio
 import itertools
 

benchmarks/disagg_benchmarks/visualize_benchmark_results.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import json
 
 import matplotlib.pyplot as plt

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import pickle as pkl
 import time
 from dataclasses import dataclass

benchmarks/kernels/benchmark_aqlm.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import os
 import sys
 from typing import Optional

benchmarks/kernels/benchmark_layernorm.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import time
 
 import torch

benchmarks/kernels/benchmark_lora.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import copy
 import json

benchmarks/kernels/benchmark_machete.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import copy
 import itertools

benchmarks/kernels/benchmark_marlin.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 from typing import List
 
 import torch

benchmarks/kernels/benchmark_moe.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import argparse
 import time
 from datetime import datetime
@@ -345,13 +343,9 @@ class BenchmarkWorker:
         op_config = get_moe_configs(num_experts, shard_intermediate_size // 2,
                                     dtype_str)
         if op_config is None:
-            config = get_default_config(num_tokens,
-                                        num_experts,
-                                        shard_intermediate_size,
-                                        hidden_size,
-                                        topk,
-                                        dtype_str,
-                                        is_marlin=False)
+            config = get_default_config(num_tokens, num_experts,
+                                        shard_intermediate_size, hidden_size,
+                                        topk, dtype_str)
         else:
             config = op_config[min(op_config.keys(),
                                    key=lambda x: abs(x - num_tokens))]
@@ -542,11 +536,7 @@ if __name__ == "__main__":
     parser.add_argument("--model",
                         type=str,
                         default="mistralai/Mixtral-8x7B-Instruct-v0.1")
-    parser.add_argument("--tp-size",
-                        "-tp",
-                        "--tensor-parallel-size",
-                        type=int,
-                        default=2)
+    parser.add_argument("--tp-size", "-tp", type=int, default=2)
     parser.add_argument("--dtype",
                         type=str,
                         choices=["auto", "fp8_w8a8", "int8_w8a16"],

benchmarks/kernels/benchmark_paged_attention.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import random
 import time
 from typing import List, Optional

benchmarks/kernels/benchmark_quant.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import time
 
 import torch

benchmarks/kernels/benchmark_rmsnorm.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import itertools
 from typing import Optional, Tuple, Union
 

benchmarks/kernels/benchmark_rope.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 from itertools import accumulate
 from typing import List, Optional
 

benchmarks/kernels/benchmark_shapes.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 WEIGHT_SHAPES = {
     "ideal": [[4 * 256 * 32, 256 * 32]],
     "mistralai/Mistral-7B-v0.1/TP1": [

benchmarks/kernels/graph_machete_bench.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import math
 import pickle
 import re

benchmarks/kernels/utils.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import dataclasses
 from typing import Any, Callable, Iterable, Optional
 

benchmarks/kernels/weight_shapes.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 # Weight Shapes are in the format
 # ([K, N], TP_SPLIT_DIM)
 # Example:

benchmarks/overheads/benchmark_hashing.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import cProfile
 import pstats
 

cmake/hipify.py

@@ -1,5 +1,4 @@
 #!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
 
 #
 # A command line tool for running pytorch's hipify preprocessor on CUDA

cmake/utils.cmake

@@ -257,9 +257,9 @@ endmacro()
 #  where `<=` is the version comparison operator.
 # In other words, for each version in `TGT_CUDA_ARCHS` find the highest version
 #  in `SRC_CUDA_ARCHS` that is less or equal to the version in `TGT_CUDA_ARCHS`.
-# We have special handling for x.0a, if x.0a is in `SRC_CUDA_ARCHS` and x.0 is
-#  in `TGT_CUDA_ARCHS` then we should remove x.0a from `SRC_CUDA_ARCHS` and add
-#  x.0a to the result (and remove x.0 from TGT_CUDA_ARCHS). 
+# We have special handling for 9.0a, if 9.0a is in `SRC_CUDA_ARCHS` and 9.0 is
+#  in `TGT_CUDA_ARCHS` then we should remove 9.0a from `SRC_CUDA_ARCHS` and add
+#  9.0a to the result (and remove 9.0 from TGT_CUDA_ARCHS). 
 # The result is stored in `OUT_CUDA_ARCHS`.
 #
 # Example:
@@ -272,8 +272,8 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
   list(REMOVE_DUPLICATES SRC_CUDA_ARCHS)
   set(TGT_CUDA_ARCHS_ ${TGT_CUDA_ARCHS})
 
-  # if x.0a is in SRC_CUDA_ARCHS and x.0 is in CUDA_ARCHS then we should
-  # remove x.0a from SRC_CUDA_ARCHS and add x.0a to _CUDA_ARCHS
+  # if 9.0a is in SRC_CUDA_ARCHS and 9.0 is in CUDA_ARCHS then we should
+  # remove 9.0a from SRC_CUDA_ARCHS and add 9.0a to _CUDA_ARCHS
   set(_CUDA_ARCHS)
   if ("9.0a" IN_LIST SRC_CUDA_ARCHS)
     list(REMOVE_ITEM SRC_CUDA_ARCHS "9.0a")
@@ -283,14 +283,6 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
     endif()
   endif()
 
-  if ("10.0a" IN_LIST SRC_CUDA_ARCHS)
-    list(REMOVE_ITEM SRC_CUDA_ARCHS "10.0a")
-    if ("10.0" IN_LIST TGT_CUDA_ARCHS)
-      list(REMOVE_ITEM TGT_CUDA_ARCHS_ "10.0")
-      set(_CUDA_ARCHS "10.0a")
-    endif()
-  endif()
-
   list(SORT SRC_CUDA_ARCHS COMPARE NATURAL ORDER ASCENDING)
 
   # for each ARCH in TGT_CUDA_ARCHS find the highest arch in SRC_CUDA_ARCHS that

collect_env.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 # ruff: noqa
 # code borrowed from https://github.com/pytorch/pytorch/blob/main/torch/utils/collect_env.py
 

csrc/cache.h

@@ -15,9 +15,6 @@ void copy_blocks(std::vector<torch::Tensor> const& key_caches,
                  std::vector<torch::Tensor> const& value_caches,
                  const torch::Tensor& block_mapping);
 
-void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
-                     const torch::Tensor& block_mapping);
-
 void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
                        torch::Tensor& key_cache, torch::Tensor& value_cache,
                        torch::Tensor& slot_mapping,

csrc/cache_kernels.cu

@@ -46,10 +46,7 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
   char* src_ptr = static_cast<char*>(src.data_ptr());
   char* dst_ptr = static_cast<char*>(dst.data_ptr());
 
-  // We use the stride instead of numel in case the cache is padded for memory
-  // alignment reasons, we assume the blocks data (inclusive of any padding)
-  // is contiguous in memory
-  const int64_t block_size_in_bytes = src.element_size() * src.stride(0);
+  const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
   const at::cuda::OptionalCUDAGuard device_guard(
       src_device.is_cuda() ? src_device : dst_device);
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -96,24 +93,6 @@ __global__ void copy_blocks_kernel(int64_t* key_cache_ptrs,
   }
 }
 
-// Kernel for MLA, which works on a single joint kv_cache
-// Grid: (num_layers, num_pairs)
-template <typename scalar_t>
-__global__ void copy_blocks_mla_kernel(
-    int64_t* cache_ptrs, const int64_t* __restrict__ block_mapping,
-    const int mem_footprint_per_block) {
-  const int layer_idx = blockIdx.x;
-  const int pair_idx = blockIdx.y;
-  scalar_t* cache = reinterpret_cast<scalar_t*>(cache_ptrs[layer_idx]);
-  int64_t src_block = block_mapping[2 * pair_idx];
-  int64_t dst_block = block_mapping[2 * pair_idx + 1];
-  int64_t src_offset = src_block * mem_footprint_per_block;
-  int64_t dst_offset = dst_block * mem_footprint_per_block;
-  for (int i = threadIdx.x; i < mem_footprint_per_block; i += blockDim.x) {
-    cache[dst_offset + i] = cache[src_offset + i];
-  }
-}
-
 }  // namespace vllm
 
 // Note: the key_caches and value_caches vectors are constant but
@@ -168,42 +147,6 @@ void copy_blocks(std::vector<torch::Tensor> const& key_caches,
       }));
 }
 
-// copy blocks kernel for MLA (assumes a joint KV-cache)
-void copy_blocks_mla(std::vector<torch::Tensor> const& kv_caches,
-                     const torch::Tensor& block_mapping) {
-  int num_layers = kv_caches.size();
-  if (num_layers == 0) {
-    return;
-  }
-  torch::Device cache_device = kv_caches[0].device();
-  TORCH_CHECK(cache_device.is_cuda(), "kv_cache must be on CUDA");
-
-  std::vector<int64_t> cache_ptrs(num_layers);
-  for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
-    cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(kv_caches[layer_idx].data_ptr());
-  }
-  torch::Tensor cache_ptrs_tensor =
-      torch::from_blob(cache_ptrs.data(), {num_layers}, torch::kInt64)
-          .to(cache_device);
-
-  int num_pairs = block_mapping.size(0);
-  // We use the stride instead of numel in case the cache is padded for memory
-  // alignment reasons, we assume the blocks data (inclusive of any padding)
-  // is contiguous in memory
-  int mem_footprint_per_block = kv_caches[0].stride(0);
-  dim3 grid(num_layers, num_pairs);
-  dim3 block(std::min(1024, mem_footprint_per_block));
-  const at::cuda::OptionalCUDAGuard device_guard(cache_device);
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
-      kv_caches[0].scalar_type(), "copy_blocks_mla_kernel", ([&] {
-        vllm::copy_blocks_mla_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            cache_ptrs_tensor.data_ptr<int64_t>(),
-            block_mapping.data_ptr<int64_t>(), mem_footprint_per_block);
-      }));
-}
-
 namespace vllm {
 
 template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
@@ -311,7 +254,6 @@ __global__ void concat_and_cache_mla_kernel(
                                      // + pe_dim)]
     const int64_t* __restrict__ slot_mapping,  // [num_tokens]
     const int block_stride,                    //
-    const int entry_stride,                    //
     const int kv_c_stride,                     //
     const int k_pe_stride,                     //
     const int kv_lora_rank,                    //
@@ -332,8 +274,9 @@ __global__ void concat_and_cache_mla_kernel(
                   int src_stride, int dst_stride, int size, int offset) {
     for (int i = threadIdx.x; i < size; i += blockDim.x) {
       const int64_t src_idx = token_idx * src_stride + i;
-      const int64_t dst_idx =
-          block_idx * block_stride + block_offset * entry_stride + i + offset;
+      const int64_t dst_idx = block_idx * block_stride +
+                              block_offset * (kv_lora_rank + pe_dim) + i +
+                              offset;
       if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
         dst[dst_idx] = src[src_idx];
       } else {
@@ -448,14 +391,14 @@ void reshape_and_cache_flash(
 // KV_T is the stored data type of kv-cache.
 // CACHE_T is the data type of key and value tensors.
 // KV_DTYPE is the real data type of kv-cache.
-#define CALL_CONCAT_AND_CACHE_MLA(KV_T, CACHE_T, KV_DTYPE)              \
-  vllm::concat_and_cache_mla_kernel<KV_T, CACHE_T, KV_DTYPE>            \
-      <<<grid, block, 0, stream>>>(                                     \
-          reinterpret_cast<KV_T*>(kv_c.data_ptr()),                     \
-          reinterpret_cast<KV_T*>(k_pe.data_ptr()),                     \
-          reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),              \
-          slot_mapping.data_ptr<int64_t>(), block_stride, entry_stride, \
-          kv_c_stride, k_pe_stride, kv_lora_rank, pe_dim, block_size,   \
+#define CALL_CONCAT_AND_CACHE_MLA(KV_T, CACHE_T, KV_DTYPE)             \
+  vllm::concat_and_cache_mla_kernel<KV_T, CACHE_T, KV_DTYPE>           \
+      <<<grid, block, 0, stream>>>(                                    \
+          reinterpret_cast<KV_T*>(kv_c.data_ptr()),                    \
+          reinterpret_cast<KV_T*>(k_pe.data_ptr()),                    \
+          reinterpret_cast<CACHE_T*>(kv_cache.data_ptr()),             \
+          slot_mapping.data_ptr<int64_t>(), block_stride, kv_c_stride, \
+          k_pe_stride, kv_lora_rank, pe_dim, block_size,               \
           reinterpret_cast<const float*>(scale.data_ptr()));
 
 void concat_and_cache_mla(
@@ -485,7 +428,6 @@ void concat_and_cache_mla(
   int kv_c_stride = kv_c.stride(0);
   int k_pe_stride = k_pe.stride(0);
   int block_stride = kv_cache.stride(0);
-  int entry_stride = kv_cache.stride(1);
 
   dim3 grid(num_tokens);
   dim3 block(std::min(kv_lora_rank, 512));

csrc/core/math.hpp

@@ -1,14 +1,7 @@
-#pragma once
-
 #include <climits>
 #include <iostream>
 
-inline constexpr uint32_t next_pow_2(uint32_t const num) {
+inline uint32_t next_pow_2(uint32_t const num) {
   if (num <= 1) return num;
   return 1 << (CHAR_BIT * sizeof(num) - __builtin_clz(num - 1));
-}
-
-template <typename T>
-inline constexpr std::enable_if_t<std::is_integral_v<T>, T> ceil_div(T a, T b) {
-  return (a + b - 1) / b;
 }

csrc/cuda_utils.h

@@ -1,7 +1,5 @@
 #pragma once
 
-#include <stdio.h>
-
 #if defined(__CUDACC__) || defined(_NVHPC_CUDA)
   #define HOST_DEVICE_INLINE __forceinline__ __host__ __device__
   #define DEVICE_INLINE __forceinline__ __device__
@@ -12,16 +10,6 @@
   #define HOST_INLINE inline
 #endif
 
-#define CUDA_CHECK(cmd)                                             \
-  do {                                                              \
-    cudaError_t e = cmd;                                            \
-    if (e != cudaSuccess) {                                         \
-      printf("Failed: Cuda error %s:%d '%s'\n", __FILE__, __LINE__, \
-             cudaGetErrorString(e));                                \
-      exit(EXIT_FAILURE);                                           \
-    }                                                               \
-  } while (0)
-
 int64_t get_device_attribute(int64_t attribute, int64_t device_id);
 
 int64_t get_max_shared_memory_per_block_device_attribute(int64_t device_id);

csrc/cuda_utils_kernels.cu

@@ -1,22 +1,16 @@
-#include "cuda_utils.h"
 #ifdef USE_ROCM
   #include <hip/hip_runtime.h>
   #include <hip/hip_runtime_api.h>
 #endif
-
 int64_t get_device_attribute(int64_t attribute, int64_t device_id) {
-  // Return the cached value on subsequent calls
-  static int value = [=]() {
-    int device = static_cast<int>(device_id);
-    if (device < 0) {
-      CUDA_CHECK(cudaGetDevice(&device));
-    }
-    int value;
-    CUDA_CHECK(cudaDeviceGetAttribute(
-        &value, static_cast<cudaDeviceAttr>(attribute), device));
-    return static_cast<int>(value);
-  }();
-
+  int device, value;
+  if (device_id < 0) {
+    cudaGetDevice(&device);
+  } else {
+    device = device_id;
+  }
+  cudaDeviceGetAttribute(&value, static_cast<cudaDeviceAttr>(attribute),
+                         device);
   return value;
 }
 

csrc/cumem_allocator.cpp

@@ -12,21 +12,15 @@ extern "C" {
 #include <cuda_runtime_api.h>
 #include <cuda.h>
 
-char error_msg[10240];  // 10KB buffer to store error messages
-CUresult no_error = CUresult(0);
-CUresult error_code = no_error;  // store error code
-
-#define CUDA_CHECK(condition)                                           \
-  do {                                                                  \
-    CUresult error = condition;                                         \
-    if (error != 0) {                                                   \
-      error_code = error;                                               \
-      char* error_string;                                               \
-      cuGetErrorString(error, (const char**)&error_string);             \
-      snprintf(error_msg, sizeof(error_msg), "CUDA Error: %s at %s:%d", \
-               error_string, __FILE__, __LINE__);                       \
-      std::cerr << error_msg << std::endl;                              \
-    }                                                                   \
+#define CUDA_CHECK(condition)                                                  \
+  do {                                                                         \
+    CUresult error = condition;                                                \
+    if (error != 0) {                                                          \
+      char* error_string;                                                      \
+      cuGetErrorString(error, (const char**)&error_string);                    \
+      std::cerr << "CUDA Error: " << error_string << " at " << __FILE__ << ":" \
+                << __LINE__ << std::endl;                                      \
+    }                                                                          \
   } while (0)
 
 // Global references to Python callables
@@ -60,22 +54,14 @@ void create_and_map(unsigned long long device, ssize_t size, CUdeviceptr d_mem,
 
   // Allocate memory using cuMemCreate
   CUDA_CHECK(cuMemCreate(p_memHandle, size, &prop, 0));
-  if (error_code != 0) {
-    return;
-  }
   CUDA_CHECK(cuMemMap(d_mem, size, 0, *p_memHandle, 0));
-  if (error_code != 0) {
-    return;
-  }
+
   CUmemAccessDesc accessDesc = {};
   accessDesc.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
   accessDesc.location.id = device;
   accessDesc.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
 
   CUDA_CHECK(cuMemSetAccess(d_mem, size, &accessDesc, 1));
-  if (error_code != 0) {
-    return;
-  }
   // std::cout << "create_and_map: device=" << device << ", size=" << size << ",
   // d_mem=" << d_mem << ", p_memHandle=" << p_memHandle << std::endl;
 }
@@ -87,13 +73,7 @@ void unmap_and_release(unsigned long long device, ssize_t size,
   // ", d_mem=" << d_mem << ", p_memHandle=" << p_memHandle << std::endl;
   ensure_context(device);
   CUDA_CHECK(cuMemUnmap(d_mem, size));
-  if (error_code != 0) {
-    return;
-  }
   CUDA_CHECK(cuMemRelease(*p_memHandle));
-  if (error_code != 0) {
-    return;
-  }
 }
 
 PyObject* create_tuple_from_c_integers(unsigned long long a,
@@ -141,16 +121,12 @@ void* my_malloc(ssize_t size, int device, CUstream stream) {
   size_t granularity;
   CUDA_CHECK(cuMemGetAllocationGranularity(&granularity, &prop,
                                            CU_MEM_ALLOC_GRANULARITY_MINIMUM));
-  if (error_code != 0) {
-    return nullptr;
-  }
+
   size_t alignedSize = ((size + granularity - 1) / granularity) * granularity;
 
   CUdeviceptr d_mem;
   CUDA_CHECK(cuMemAddressReserve(&d_mem, alignedSize, 0, 0, 0));
-  if (error_code != 0) {
-    return nullptr;
-  }
+
   // allocate the CUmemGenericAllocationHandle
   CUmemGenericAllocationHandle* p_memHandle =
       (CUmemGenericAllocationHandle*)malloc(
@@ -232,9 +208,6 @@ void my_free(void* ptr, ssize_t size, int device, CUstream stream) {
 
   // free address and the handle
   CUDA_CHECK(cuMemAddressFree(d_mem, size));
-  if (error_code != 0) {
-    return;
-  }
   free(p_memHandle);
 }
 
@@ -285,12 +258,6 @@ static PyObject* python_unmap_and_release(PyObject* self, PyObject* args) {
 
   unmap_and_release(recv_device, recv_size, d_mem_ptr, p_memHandle);
 
-  if (error_code != 0) {
-    error_code = no_error;
-    PyErr_SetString(PyExc_RuntimeError, error_msg);
-    return nullptr;
-  }
-
   Py_RETURN_NONE;
 }
 
@@ -315,12 +282,6 @@ static PyObject* python_create_and_map(PyObject* self, PyObject* args) {
 
   create_and_map(recv_device, recv_size, d_mem_ptr, p_memHandle);
 
-  if (error_code != 0) {
-    error_code = no_error;
-    PyErr_SetString(PyExc_RuntimeError, error_msg);
-    return nullptr;
-  }
-
   Py_RETURN_NONE;
 }
 

csrc/cutlass_extensions/common.hpp

@@ -32,20 +32,3 @@ inline int get_cuda_max_shared_memory_per_block_opt_in(int const device) {
 }
 
 int32_t get_sm_version_num();
-
-/**
- * A wrapper for a kernel that is used to guard against compilation on
- * architectures that will never use the kernel. The purpose of this is to
- * reduce the size of the compiled binary.
- * __CUDA_ARCH__ is not defined in host code, so this lets us smuggle the ifdef
- * into code that will be executed on the device where it is defined.
- */
-template <typename Kernel>
-struct enable_sm90_or_later : Kernel {
-  template <typename... Args>
-  CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__ && __CUDA_ARCH__ >= 900
-    Kernel::operator()(std::forward<Args>(args)...);
-#endif
-  }
-};

csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp

@@ -16,30 +16,6 @@ namespace vllm::c3x {
 
 using namespace cute;
 
-template <typename T>
-struct identity {
-  CUTLASS_HOST_DEVICE
-  T operator()(T lhs) const { return lhs; }
-};
-
-template <typename ElementAcc, typename ElementD, typename EpilogueDescriptor>
-struct TrivialEpilogue {
- private:
-  using Accum = cutlass::epilogue::fusion::Sm90AccFetch;
-  using Compute = cutlass::epilogue::fusion::Sm90Compute<
-      cutlass::epilogue::thread::Identity, ElementD, ElementAcc,
-      cutlass::FloatRoundStyle::round_to_nearest>;
-
- public:
-  using EVTCompute = cutlass::epilogue::fusion::Sm90EVT<Compute, Accum>;
-  using ArgumentType = typename EVTCompute::Arguments;
-
-  template <typename... Args>
-  static ArgumentType prepare_args(Args... args) {
-    return {};
-  }
-};
-
 /*
  * This class provides the common load descriptors for the
  * ScaledEpilogue[...] classes
@@ -198,49 +174,6 @@ struct ScaledEpilogueBias
   }
 };
 
-/*
- * This epilogue performs the same operation as ScaledEpilogueBias, but the
- * bias is a column vector instead of a row vector. Useful e.g. if we are
- * computing a GEMM via C^T += B^T A^T. This happens in the 2:4 sparse kernels.
- */
-template <typename ElementAcc, typename ElementD, typename EpilogueDescriptor>
-struct ScaledEpilogueColumnBias
-    : private ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor> {
- private:
-  using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor>;
-  using Accum = typename SUPER::Accum;
-  using ScaleA = typename SUPER::template ColOrScalarLoad<float>;
-  using ScaleB = typename SUPER::template RowOrScalarLoad<float>;
-  using Bias = typename SUPER::template ColLoad<ElementD>;
-
-  using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
-      cutlass::multiplies, float, float,
-      cutlass::FloatRoundStyle::round_to_nearest>;
-
-  using EVTCompute0 =
-      cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
-
-  using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
-      cutlass::multiply_add, ElementD, float,
-      cutlass::FloatRoundStyle::round_to_nearest>;
-
- public:
-  using EVTCompute =
-      cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0, Bias>;
-
-  using ArgumentType = typename EVTCompute::Arguments;
-  static ArgumentType prepare_args(torch::Tensor const& a_scales,
-                                   torch::Tensor const& b_scales,
-                                   torch::Tensor const& bias) {
-    auto a_args = SUPER::template args_from_tensor<ScaleA, float>(a_scales);
-    auto b_args = SUPER::template args_from_tensor<ScaleB, float>(b_scales);
-    auto bias_args = SUPER::template args_from_tensor<Bias, ElementD>(bias);
-
-    typename EVTCompute0::Arguments evt0_args{b_args};
-    return ArgumentType{a_args, evt0_args, bias_args};
-  }
-};
-
 /*
  * This epilogue directly supports per-tensor azp in int32 form.
  * As opposed to the per-token epilogue below, this epilogue only has an azp_adj
@@ -381,4 +314,4 @@ struct ScaledEpilogueBiasAzpToken
   }
 };
 
-};  // namespace vllm::c3x
+};  // namespace vllm::c3x

csrc/cutlass_extensions/gemm/collective/collective_builder.hpp

@@ -1,123 +0,0 @@
-// Modified from: cutlass/gemm/collective/builders/sm90_gmma_builder.inl
-// clang-format off
-#pragma once
-
-#include "cutlass/gemm/collective/builders/sm90_gmma_builder.inl"
-
-#include "cutlass_extensions/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp"
-
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::gemm::collective {
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-// GMMA_TMA_WS_SS (BlockScaled Builders)
-template <
-  class ElementA,
-  class GmemLayoutATag,
-  int AlignmentA,
-  class ElementB,
-  class GmemLayoutBTag,
-  int AlignmentB,
-  class ElementAccumulator,
-  class TileShape_MNK,
-  class ClusterShape_MNK,
-  class StageCountType,
-  int ScaleGranularityM
->
-struct CollectiveBuilder<
-    arch::Sm90,
-    arch::OpClassTensorOp,
-    ElementA,
-    GmemLayoutATag,
-    AlignmentA,
-    ElementB,
-    GmemLayoutBTag,
-    AlignmentB,
-    ElementAccumulator,
-    TileShape_MNK,
-    ClusterShape_MNK,
-    StageCountType,
-    KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>,
-    cute::enable_if_t<
-      not detail::is_use_rmem_A<ElementA, GmemLayoutATag, ElementB, GmemLayoutBTag>()>
-> {
-  using KernelScheduleType = KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>;
-
-  static_assert(is_static<TileShape_MNK>::value);
-  static_assert(is_static<ClusterShape_MNK>::value);
-#ifndef CUTLASS_SM90_COLLECTIVE_BUILDER_SUPPORTED
-  static_assert(cutlass::detail::dependent_false<ElementA>, "Unsupported Toolkit for SM90 Collective Builder\n");
-#endif
-  static_assert(detail::is_aligned<ElementA, AlignmentA, ElementB, AlignmentB, detail::tma_alignment_bytes>(),
-                "Should meet TMA alignment requirement\n");
-
-  static constexpr bool IsArrayOfPointersGemm = (cute::is_any_of_v<KernelScheduleType,
-                                                                   KernelPtrArrayTmaWarpSpecializedCooperative,
-                                                                   KernelPtrArrayTmaWarpSpecializedPingpong>);
-  static constexpr bool IsFP8Input = detail::is_input_fp8<ElementA, ElementB>();
-  static_assert((!IsFP8Input || !IsArrayOfPointersGemm),
-                "KernelTmaWarpSpecializedCooperativeFP8BlockScaledAccum is only compatible with FP8 Blocked Scaled version right now.");
-
-  // For fp32 types, map to tf32 MMA value type
-  using ElementAMma = cute::conditional_t<cute::is_same_v<ElementA, float>, tfloat32_t, ElementA>;
-  using ElementBMma = cute::conditional_t<cute::is_same_v<ElementB, float>, tfloat32_t, ElementB>;
-
-  static constexpr cute::GMMA::Major GmmaMajorA = detail::gmma_ss_tag_to_major_A<ElementAMma, GmemLayoutATag>();
-  static constexpr cute::GMMA::Major GmmaMajorB = detail::gmma_ss_tag_to_major_B<ElementBMma, GmemLayoutBTag>();
-
-  static constexpr bool IsCooperative = cute::is_any_of_v<KernelScheduleType,
-                                                          KernelTmaWarpSpecializedCooperative,
-                                                          KernelPtrArrayTmaWarpSpecializedCooperative,
-                                                          KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<ScaleGranularityM>>;
-  using AtomLayoutMNK = cute::conditional_t<IsCooperative,
-      Layout<Shape<_2,_1,_1>>, Layout<Shape<_1,_1,_1>>>;
-
-  using TiledMma = decltype(cute::make_tiled_mma(cute::GMMA::ss_op_selector<
-      ElementAMma, ElementBMma, ElementAccumulator, TileShape_MNK, GmmaMajorA, GmmaMajorB>(), AtomLayoutMNK{}));
-
-  using GmemTiledCopyA = decltype(detail::sm90_cluster_shape_to_tma_atom(shape<1>(ClusterShape_MNK{})));
-  using GmemTiledCopyB = decltype(detail::sm90_cluster_shape_to_tma_atom(shape<0>(ClusterShape_MNK{})));
-
-  using SmemLayoutAtomA = decltype(detail::ss_smem_selector<
-      GmmaMajorA, ElementAMma, decltype(cute::get<0>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
-  using SmemLayoutAtomB = decltype(detail::ss_smem_selector<
-      GmmaMajorB, ElementBMma, decltype(cute::get<1>(TileShape_MNK{})), decltype(cute::get<2>(TileShape_MNK{}))>());
-
-  static constexpr size_t TensorMapStorage = IsArrayOfPointersGemm ? sizeof(cute::TmaDescriptor) * 2 /* for A and B */ : 0;
-  static constexpr int KernelSmemCarveout = static_cast<int>(TensorMapStorage);
-
-  static constexpr int PipelineStages = detail::compute_stage_count_or_override<detail::sm90_smem_capacity_bytes - KernelSmemCarveout,
-      ElementAMma, ElementBMma, TileShape_MNK>(StageCountType{});
-  using DispatchPolicy = MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<PipelineStages, ClusterShape_MNK, KernelScheduleType, ScaleGranularityM>;
-
-  using SmemCopyAtomA = void;
-  using SmemCopyAtomB = void;
-
-  using CollectiveOp = CollectiveMma<
-      DispatchPolicy,
-      TileShape_MNK,
-      ElementA,
-      TagToStrideA_t<GmemLayoutATag>,
-      ElementB,
-      TagToStrideB_t<GmemLayoutBTag>,
-      TiledMma,
-      GmemTiledCopyA,
-      SmemLayoutAtomA,
-      SmemCopyAtomA,
-      cute::identity,
-      GmemTiledCopyB,
-      SmemLayoutAtomB,
-      SmemCopyAtomB,
-      cute::identity
-    >;
-};
-
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass::gemm::collective
-
-/////////////////////////////////////////////////////////////////////////////////////////////////

csrc/cutlass_extensions/gemm/collective/fp8_accumulation.hpp

@@ -1,183 +0,0 @@
-// clang-format off
-// adapted from: https://github.com/soundOfDestiny/cutlass/blob/a4208aa6958864923505cade9c63eb2a6daf16e5/include/cutlass/gemm/collective/fp8_accumulation.hpp
-
-/***************************************************************************************************
- * Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice, this
- * list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
- * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-#pragma once
-
-#include "cute/algorithm/clear.hpp"
-#include "cute/tensor.hpp"
-
-//////////////////////////////////////////////////////////////////////////////
-///////////////////////////////////FP8 Accumulation///////////////////////////
-//////////////////////////////////////////////////////////////////////////////
-/// This class provides API to promote (add) or scale (multiply_add) the results
-/// from the tensor core accumulators to the main accumulators when the number 
-/// of MMAs reaches the max number of MMA interval specified by user, after that
-/// the tensor core accumulators are zeroed.
-//////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::gemm::collective {
-
-template <
-    class EngineAccum,
-    class LayoutAccum>
-struct GmmaFP8AccumulationWithScale {  
-  using TensorAccum = cute::Tensor<EngineAccum, LayoutAccum>;
-  using ElementAccumulator = typename EngineAccum::value_type;
-
-  static_assert(is_static<LayoutAccum>::value, "Accumulator Layout should be static");
-  static_assert(is_rmem<TensorAccum>::value , "Accumulator tensor must be rmem resident.");
-
-private:
-  TensorAccum& accum_;
-  TensorAccum accum_temp_;
-
-  uint32_t accum_promotion_interval_;         // defines the max num of executed MMAs after which accum should be promoted.
-  uint32_t mma_count_per_mainloop_iteration_; // num of MMAs per k_tile of mainloop
-  uint32_t mma_count_;                        // current executed MMAs
-  uint32_t reset_accum_flag_;                 // accum needs to be zeroed or not. 
-
-  // promote or `add` the partial accumulators to main accumulator (FADD).
-  CUTLASS_DEVICE
-  void promote_core() {
-    warpgroup_wait<0>();
-    CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < size(accum_); ++i) {
-      accum_(i) += accum_temp_(i);
-    }
-  }
-
-  // `multiply` scale the partial accumulators and `add` to main accumulator (FFMA).
-  template <
-    class EngineScale,
-    class LayoutScale>
-  CUTLASS_DEVICE
-  void scale_core(const cute::Tensor<EngineScale, LayoutScale> &scale) {
-    using TensorScale = cute::Tensor<EngineScale, LayoutScale>;
-
-    static_assert(is_static<LayoutScale>::value, "Scale Layout should be static");
-    static_assert(is_rmem<TensorScale>::value , "Scale tensor must be rmem resident.");
-
-    static_assert(LayoutAccum{}.shape() == LayoutScale{}.shape(), "Accumulator and scale must have same shape.");
-
-    warpgroup_wait<0>();
-    CUTLASS_PRAGMA_UNROLL
-    for (int i = 0; i < size(accum_); ++i) {
-      accum_(i) += accum_temp_(i) * scale(i);
-    }
-  }
-
-public:
-  CUTLASS_DEVICE
-  GmmaFP8AccumulationWithScale(
-      TensorAccum &accum,
-      uint32_t accum_promotion_interval,
-      uint32_t mma_count_per_mainloop_iteration)
-      : accum_(accum), 
-        accum_promotion_interval_(accum_promotion_interval),
-        mma_count_per_mainloop_iteration_(mma_count_per_mainloop_iteration),
-        mma_count_(0), 
-        reset_accum_flag_(0) 
-  {
-    accum_temp_ = cute::make_fragment_like(accum);
-  }
-
-  //
-  // Methods (Common)
-  //
-
-  CUTLASS_DEVICE 
-  TensorAccum& operator()() {
-    return accum_temp_;
-  }
-
-  /// prepare the MMA accumulators when initialization or zeroing is required.
-  CUTLASS_DEVICE
-  bool prepare_if_needed() { 
-    return reset_accum_flag_;
-  }
-
-  //
-  // Methods (for FADD version)
-  //
-
-  /// promote (add) the results from the MMA accumulators to main accumulator if needed.
-  CUTLASS_DEVICE
-  void promote_if_needed() {
-    mma_count_ += mma_count_per_mainloop_iteration_;
-    reset_accum_flag_ = __shfl_sync(0xffffffff, mma_count_ == accum_promotion_interval_, 0);
-    if (reset_accum_flag_) {
-      promote_core();
-      mma_count_ = 0;
-    }
-  }
-
-  /// promote (add) the residue results from the MMA accumulators to main accumulator if needed.
-  CUTLASS_DEVICE
-  void promote_residue_if_needed() {
-    if (__shfl_sync(0xffffffff, mma_count_ > 0, 0)) {
-      promote_core();
-    }
-  }
-
-  //
-  // Methods (for FFMA version)
-  //
-
-  /// scale (multiply_add) the results from the MMA accumulators to main accumulator if needed.
-  template <
-    class EngineScale,
-    class LayoutScale>
-  CUTLASS_DEVICE
-  void scale_if_needed(const cute::Tensor<EngineScale, LayoutScale> &scale) {
-    mma_count_ += mma_count_per_mainloop_iteration_;
-    reset_accum_flag_ = __shfl_sync(0xffffffff, mma_count_ == accum_promotion_interval_, 0);
-    if (reset_accum_flag_) {
-      scale_core(scale);
-      mma_count_ = 0;
-    }
-  }
-
-  /// scale (multiply_add) the residue results from the MMA accumulators to main accumulator if needed.
-  template <
-    class EngineScale,
-    class LayoutScale>
-  CUTLASS_DEVICE
-  void scale_residue_if_needed(const cute::Tensor<EngineScale, LayoutScale> &scale) {
-    if (__shfl_sync(0xffffffff, mma_count_ > 0, 0)) {
-      scale_core(scale);
-    }
-  }
-};
-
-} // namespace cutlass::gemm::collective

csrc/cutlass_extensions/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp

@@ -1,730 +0,0 @@
-// clang-format off
-// Adapted (Heavily) from: https://github.com/soundOfDestiny/cutlass/blob/9d997ce0dea4c5fa1a617db6b7ff29aa9235822c/include/cutlass/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp
-
-/***************************************************************************************************
- * Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
- * SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice, this
- * list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
- * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
- * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
- * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
- * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
- * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
- * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
- * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-#pragma once
-
-#include "cutlass/cutlass.h"
-#include "cutlass/gemm/dispatch_policy.hpp"
-#include "cutlass/trace.h"
-#include "cutlass/numeric_types.h"
-
-#include "cute/arch/cluster_sm90.hpp"
-#include "cute/arch/copy_sm80.hpp"
-#include "cute/arch/copy_sm90.hpp"
-#include "cute/algorithm/functional.hpp"
-#include "cute/atom/mma_atom.hpp"
-#include "cute/algorithm/gemm.hpp"
-#include "cute/tensor_predicate.hpp"
-#include "cute/numeric/arithmetic_tuple.hpp"
-
-#include "cutlass_extensions/gemm/dispatch_policy.hpp"
-#include "cutlass_extensions/gemm/collective/fp8_accumulation.hpp"
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-namespace cutlass::gemm::collective {
-using namespace cute;
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-// WarpSpecialized Mainloop
-template <
-  int Stages,
-  class ClusterShape,
-  class KernelSchedule,
-  int ScaleGranularityM_,
-  class TileShape_,
-  class ElementA_,
-  class StrideA_,
-  class ElementB_,
-  class StrideB_,
-  class TiledMma_,
-  class GmemTiledCopyA_,
-  class SmemLayoutAtomA_,
-  class SmemCopyAtomA_,
-  class TransformA_,
-  class GmemTiledCopyB_,
-  class SmemLayoutAtomB_,
-  class SmemCopyAtomB_,
-  class TransformB_>
-struct CollectiveMma<
-    MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<Stages, ClusterShape, KernelSchedule, ScaleGranularityM_>,
-    TileShape_,
-    ElementA_,
-    StrideA_,
-    ElementB_,
-    StrideB_,
-    TiledMma_,
-    GmemTiledCopyA_,
-    SmemLayoutAtomA_,
-    SmemCopyAtomA_,
-    TransformA_,
-    GmemTiledCopyB_,
-    SmemLayoutAtomB_,
-    SmemCopyAtomB_,
-    TransformB_>
-{
-  //
-  // Type Aliases
-  //
-  using DispatchPolicy = MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8<Stages, ClusterShape, KernelSchedule, ScaleGranularityM_>;
-  using TileShape = TileShape_;
-  using ElementA = ElementA_;
-  using StrideA = StrideA_;
-  using ElementB = ElementB_;
-  using StrideB = StrideB_;
-  using TiledMma = TiledMma_;
-  using ElementAccumulator = typename TiledMma::ValTypeC;
-  using ElementBlockScale = ElementAccumulator;
-  using GmemTiledCopyA = GmemTiledCopyA_;
-  using GmemTiledCopyB = GmemTiledCopyB_;
-  using SmemLayoutAtomA = SmemLayoutAtomA_;
-  using SmemLayoutAtomB = SmemLayoutAtomB_;
-  using SmemCopyAtomA = SmemCopyAtomA_;
-  using SmemCopyAtomB = SmemCopyAtomB_;
-  using TransformA = TransformA_;
-  using TransformB = TransformB_;
-  using ArchTag = typename DispatchPolicy::ArchTag;
-
-  using CtaShape_MNK = decltype(shape_div(TileShape{}, ClusterShape{}));
-  using MainloopPipeline = cutlass::PipelineTmaAsync<DispatchPolicy::Stages>;
-  using PipelineState = cutlass::PipelineState<DispatchPolicy::Stages>;
-  using PipelineParams = typename MainloopPipeline::Params;
-
-  // Two threads per CTA are producers (1 for operand tile and 32 for scales)
-  static constexpr int NumProducerThreadEvents = 33; 
-
-  static constexpr int ScaleGranularityM = ScaleGranularityM_ == 0 ? size<0>(TileShape{}) : ScaleGranularityM_;
-  static constexpr int ScaleMsPerTile = size<0>(TileShape{}) / ScaleGranularityM;
-
-  static_assert(cute::rank(SmemLayoutAtomA{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
-  static_assert((size<0>(TileShape{}) % size<0>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
-  static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomA{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
-
-  static_assert(cute::rank(SmemLayoutAtomB{}) == 2, "SmemLayoutAtom must be rank 2 (M/N, K)");
-  static_assert((size<1>(TileShape{}) % size<0>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
-  static_assert((size<2>(TileShape{}) % size<1>(SmemLayoutAtomB{})) == 0, "SmemLayoutAtom must evenly divide tile shape.");
-
-  static_assert((size<0>(TileShape{}) % ScaleGranularityM) == 0, "FP8 scaling granularity must evenly divide tile shape along M.");
-
-  // Tile along modes in a way that maximizes the TMA box size.
-  using SmemLayoutA = decltype(tile_to_shape(
-      SmemLayoutAtomA{},
-      make_shape(shape<0>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
-      cute::conditional_t< ::cutlass::gemm::detail::is_major<0,StrideA>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
-  using SmemLayoutB = decltype(tile_to_shape(
-      SmemLayoutAtomB{},
-      make_shape(shape<1>(TileShape{}), shape<2>(TileShape{}), Int<DispatchPolicy::Stages>{}),
-      cute::conditional_t< ::cutlass::gemm::detail::is_major<0,StrideB>(), Step<_2,_1,_3>, Step<_1,_2,_3>>{}));
-  
-  // Block scaling gmem-to-smem copy atom 
-  using SmemBlockScalingCopyAtomA = Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<ElementBlockScale>, ElementBlockScale>;
-  using SmemBlockScalingCopyAtomB = Copy_Atom<SM80_CP_ASYNC_CACHEALWAYS<ElementBlockScale>, ElementBlockScale>;
-  
-  // Block scaling smem layout
-  using SmemLayoutScaleA = Layout<Shape<Int<ScaleMsPerTile>, Int<DispatchPolicy::Stages>>>;
-  using SmemLayoutScaleB = Layout<Shape<Int<DispatchPolicy::Stages>>, Stride<_1>>; // `ScaleNsPerTile` is always 1.
-
-  static_assert(DispatchPolicy::Stages >= 2, "Specialization requires Stages set to value 1 or more.");
-  static_assert(cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeA>::value &&
-                cute::is_base_of<cute::GMMA::DescriptorIterator, typename TiledMma::FrgTypeB>::value,
-                "MMA atom must source both A and B operand from smem_desc for this mainloop.");
-  static_assert(cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>,
-      "GmemTiledCopy - invalid SM90 TMA copy atom specified.");
-  static_assert(cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD> || cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>,
-      "GmemTiledCopy - invalid SM90 TMA copy atom specified.");
-  static_assert(cute::is_same_v<ElementAccumulator, ElementBlockScale>,
-             "ElementAccumulator and ElementBlockScale should be same datatype");
-
-  struct SharedStorage
-  {
-    struct TensorStorage : cute::aligned_struct<128> {
-      cute::array_aligned<typename TiledMma::ValTypeA, cute::cosize_v<SmemLayoutA>> smem_A;  // mxk
-      cute::array_aligned<typename TiledMma::ValTypeB, cute::cosize_v<SmemLayoutB>> smem_B;  // nxk
-      cute::array_aligned<ElementBlockScale, cute::cosize_v<SmemLayoutScaleA>> smem_scale_A; // ScaleMsPerTile x k
-      cute::array_aligned<ElementBlockScale, cute::cosize_v<SmemLayoutScaleB>> smem_scale_B; // 1xk
-    } tensors;
-
-    using PipelineStorage = typename MainloopPipeline::SharedStorage;
-    PipelineStorage pipeline;
-  };
-  using TensorStorage = typename SharedStorage::TensorStorage;
-  using PipelineStorage = typename SharedStorage::PipelineStorage;
-
-  // Host side kernel arguments
-  struct Arguments {
-    ElementA const* ptr_A;
-    StrideA dA;
-    ElementB const* ptr_B;
-    StrideB dB;
-    ElementBlockScale const* ptr_scale_A; 
-    ElementBlockScale const* ptr_scale_B;
-  };
-
-  // Device side kernel params
-  struct Params {
-    // Assumption: StrideA is congruent with Problem_MK
-    using TMA_A = decltype(make_tma_copy_A_sm90(
-        GmemTiledCopyA{},
-        make_tensor(static_cast<ElementA const*>(nullptr), repeat_like(StrideA{}, int32_t(0)), StrideA{}),
-        SmemLayoutA{}(_,_,0),
-        TileShape{},
-        ClusterShape{}));
-    // Assumption: StrideB is congruent with Problem_NK
-    using TMA_B = decltype(make_tma_copy_B_sm90(
-        GmemTiledCopyB{},
-        make_tensor(static_cast<ElementB const*>(nullptr), repeat_like(StrideB{}, int32_t(0)), StrideB{}),
-        SmemLayoutB{}(_,_,0),
-        TileShape{},
-        ClusterShape{}));
-    TMA_A tma_load_a;
-    TMA_B tma_load_b;
-    uint32_t tma_transaction_bytes = TmaTransactionBytes;
-    uint32_t tma_transaction_bytes_mk = TmaTransactionBytesMK;
-    uint32_t tma_transaction_bytes_nk = TmaTransactionBytesNK;
-    // Block scaling factors for A and B
-    ElementBlockScale const* ptr_scale_A; 
-    ElementBlockScale const* ptr_scale_B;
-  };
-
-  //
-  // Methods
-  //
-
-  template <class ProblemShape>
-  static constexpr Params
-  to_underlying_arguments(ProblemShape const& problem_shape, Arguments const& args, void* workspace) {
-    (void) workspace;
-
-    // Optionally append 1s until problem shape is rank-4 (MNKL), in case it is only rank-3 (MNK)
-    auto problem_shape_MNKL = append<4>(problem_shape, 1);
-    auto [M,N,K,L] = problem_shape_MNKL;
-
-    auto ptr_A = reinterpret_cast<ElementA const*>(args.ptr_A);
-    auto ptr_B = reinterpret_cast<ElementB const*>(args.ptr_B);
-
-    Tensor tensor_a = make_tensor(ptr_A, make_layout(make_shape(M,K,L), args.dA));
-    Tensor tensor_b = make_tensor(ptr_B, make_layout(make_shape(N,K,L), args.dB));
-    typename Params::TMA_A tma_load_a = make_tma_copy_A_sm90(
-        GmemTiledCopyA{},
-        tensor_a,
-        SmemLayoutA{}(_,_,cute::Int<0>{}),
-        TileShape{},
-        ClusterShape{});
-    typename Params::TMA_B tma_load_b = make_tma_copy_B_sm90(
-        GmemTiledCopyB{},
-        tensor_b,
-        SmemLayoutB{}(_,_,cute::Int<0>{}),
-        TileShape{},
-        ClusterShape{});
-    uint32_t transaction_bytes_mk = TmaTransactionBytesMK;
-    uint32_t transaction_bytes_nk = TmaTransactionBytesNK;
-    uint32_t transaction_bytes = transaction_bytes_mk + transaction_bytes_nk;
-
-    return {
-      tma_load_a,
-      tma_load_b,
-      transaction_bytes,
-      transaction_bytes_mk,
-      transaction_bytes_nk,
-      args.ptr_scale_A,
-      args.ptr_scale_B
-    };
-  }
-
-  template<class ProblemShape>
-  static bool
-  can_implement(
-      ProblemShape const& problem_shape,
-      [[maybe_unused]] Arguments const& args) {
-    constexpr int tma_alignment_bits = 128;
-    auto problem_shape_MNKL = append<4>(problem_shape, 1);
-    auto [M,N,K,L] = problem_shape_MNKL;
-    
-    bool implementable = true;
-    constexpr int min_tma_aligned_elements_A = tma_alignment_bits / cutlass::sizeof_bits<ElementA>::value;
-    implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_A>(cute::make_shape(M,K,L), StrideA{});
-    constexpr int min_tma_aligned_elements_B = tma_alignment_bits / cutlass::sizeof_bits<ElementB>::value;
-    implementable = implementable && cutlass::detail::check_alignment<min_tma_aligned_elements_B>(cute::make_shape(N,K,L), StrideB{});
-
-    if (!implementable) {
-      CUTLASS_TRACE_HOST("  CAN IMPLEMENT: Problem Size doesn't meet the minimum alignment requirements for TMA.\n");
-    }
-    return implementable;
-  }
-
-  static constexpr int K_PIPE_MAX = DispatchPolicy::Stages;
-  static constexpr int K_PIPE_MMAS = 1;
-  static constexpr uint32_t TmaTransactionBytesMK =
-        cutlass::bits_to_bytes(size<0>(SmemLayoutA{}) * size<1>(SmemLayoutA{}) * static_cast<uint32_t>(sizeof_bits<ElementA>::value));
-  static constexpr uint32_t TmaTransactionBytesNK =
-        cutlass::bits_to_bytes(size<0>(SmemLayoutB{}) * size<1>(SmemLayoutB{}) * static_cast<uint32_t>(sizeof_bits<ElementB>::value));
-  static constexpr uint32_t TmaTransactionBytes = TmaTransactionBytesMK + TmaTransactionBytesNK;
-
-  /// Issue Tma Descriptor Prefetch -- ideally from a single thread for best performance
-  CUTLASS_DEVICE
-  static void prefetch_tma_descriptors(Params const& mainloop_params)
-  {
-    cute::prefetch_tma_descriptor(mainloop_params.tma_load_a.get_tma_descriptor());
-    cute::prefetch_tma_descriptor(mainloop_params.tma_load_b.get_tma_descriptor());
-  }
-
-  /// Set up the data needed by this collective for load and mma.
-  /// Returns a tuple of tensors. The collective and the kernel layer have the contract
-  /// Returned tuple must contain at least two elements, with the first two elements being:
-  /// gA_mkl - The tma tensor, A after a local tile so it has shape  (BLK_M,BLK_K,m,k,l)
-  /// gB_nkl - The tma tensor, B after a local tile so it has shape  (BLK_N,BLK_K,n,k,l)
-  template <class ProblemShape_MNKL>
-  CUTLASS_DEVICE auto
-  load_init(ProblemShape_MNKL const& problem_shape_MNKL, Params const& mainloop_params) const {
-    using X = Underscore;
-    // Separate out problem shape for convenience
-    auto [M,N,K,L] = problem_shape_MNKL;
-
-    // TMA requires special handling of strides to deal with coord codomain mapping
-    // Represent the full tensors -- get these from TMA
-    Tensor mA_mkl = mainloop_params.tma_load_a.get_tma_tensor(make_shape(M,K,L));                            // (m,k,l)
-    Tensor mB_nkl = mainloop_params.tma_load_b.get_tma_tensor(make_shape(N,K,L));                            // (n,k,l)
-
-    // Make tiled views, defer the slice
-    Tensor gA_mkl = local_tile(mA_mkl, TileShape{}, make_coord(_,_,_), Step<_1, X,_1>{});        // (BLK_M,BLK_K,m,k,l)
-    Tensor gB_nkl = local_tile(mB_nkl, TileShape{}, make_coord(_,_,_), Step< X,_1,_1>{});        // (BLK_N,BLK_K,n,k,l)
-
-    constexpr auto scales_m = Int<ScaleMsPerTile>{};
-    auto tM = get<2>(gA_mkl.shape());
-    auto tN = get<2>(gB_nkl.shape());
-    auto tK = get<3>(gA_mkl.shape());
-
-    // Make the tiled views of scale tensors
-    auto scaleA_shape = make_shape(M / ScaleGranularityM, tK, L); // (scale_m,k,l)
-    auto scaleA_layout = make_ordered_layout(scaleA_shape,  Step<_0, _1, _2>{});
-    auto scaleB_shape = make_shape(tN, tK, L); // (n,k,l)
-    auto scaleB_layout = make_ordered_layout(scaleB_shape, Step<_1, _0, _2>{});
-
-    // Note that mScaleA_mkl and mScaleB_nkl are already blocked tiled in the `m` host and 
-    // gScaleA_mkl and gScaleB_nkl in `g` global memory are same as mScaleA_mkl and mScaleB_nkl.
-    Tensor mScaleA_mkl = make_tensor(make_gmem_ptr(mainloop_params.ptr_scale_A), scaleA_layout); // (scale_m,k,l)
-    Tensor mScaleB_nkl = make_tensor(make_gmem_ptr(mainloop_params.ptr_scale_B), scaleB_layout); // (n,k,l)
-
-    return cute::make_tuple(gA_mkl, gB_nkl, mScaleA_mkl, mScaleB_nkl);
-  }
-
-  /// Perform a collective-scoped matrix multiply-accumulate
-  /// Producer Perspective
-  template <
-    class TensorA, class TensorB,
-    class TensorScaleA, class TensorScaleB,
-    class KTileIterator, class BlockCoord
-  >
-  CUTLASS_DEVICE void
-  load(
-      Params const& mainloop_params,
-      MainloopPipeline pipeline,
-      PipelineState smem_pipe_write,
-      cute::tuple<TensorA, TensorB, TensorScaleA, TensorScaleB> const& load_inputs,
-      BlockCoord const& blk_coord,
-      KTileIterator k_tile_iter, int k_tile_count,
-      int thread_idx,
-      uint32_t block_rank_in_cluster,
-      TensorStorage& shared_tensors) {
-    int lane_predicate = cute::elect_one_sync();
-
-    // Blockscaling: Tma loads for load_input and CpAsync for load_scale
-    Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{});        // (BLK_M,BLK_K,PIPE)
-    Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{});        // (BLK_N,BLK_K,PIPE)
-    Tensor sScaleA = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_A.data()), SmemLayoutScaleA{}); // (ScaleMsPerTile,k)
-    Tensor sScaleB = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_B.data()), SmemLayoutScaleB{}); // (k)
-
-    //
-    // Prepare the TMA loads for A and B
-    //
-
-    constexpr uint32_t cluster_shape_x = get<0>(ClusterShape());
-    uint2 cluster_local_block_id = {block_rank_in_cluster % cluster_shape_x, block_rank_in_cluster / cluster_shape_x};
-
-    Tensor gA_mkl = get<0>(load_inputs);
-    Tensor gB_nkl = get<1>(load_inputs);
-
-    auto block_tma_a = mainloop_params.tma_load_a.get_slice(cluster_local_block_id.y);
-    auto block_tma_b = mainloop_params.tma_load_b.get_slice(cluster_local_block_id.x);
-
-    // Partition the inputs based on the current block coordinates.
-    auto [m_coord, n_coord, k_coord, l_coord] = blk_coord;
-    Tensor gA = gA_mkl(_,_,m_coord,_,l_coord);                                                     // (BLK_M,BLK_K,k)
-    Tensor gB = gB_nkl(_,_,n_coord,_,l_coord);                                                     // (BLK_N,BLK_K,k)
-
-
-    // Block scaling: load_scale has scaling tensors in global memory which are not tiled
-    Tensor mScaleA_mkl = get<2>(load_inputs);
-    Tensor mScaleB_nkl = get<3>(load_inputs);
-    auto scales_m = get<0>(mScaleA_mkl.shape());
-
-    Tensor cScaleA_mkl = make_identity_tensor(mScaleA_mkl.shape());
-
-    Tensor gScaleA = local_tile( 
-      mScaleA_mkl, make_tile(Int<ScaleMsPerTile>{}), 
-      make_coord(m_coord,_,l_coord));                   // (ScaleMsPerTile,k,1)
-    Tensor cScaleA = local_tile( 
-      cScaleA_mkl, make_tile(Int<ScaleMsPerTile>{}), 
-      make_coord(m_coord,_,l_coord));
-    Tensor gScaleB = mScaleB_nkl(n_coord,_,l_coord);                                           // (1,k,1)
-
-    // TODO: test `scale_copy_a` with `ScaleMsPerTile` < 128
-    TiledCopy scale_copy_a = make_tiled_copy(SmemBlockScalingCopyAtomA{}, 
-      Layout<Shape<_32, _1>>{}, Layout<Shape<_4, _1>>{}); // (1,1,1)
-    TiledCopy scale_copy_b = make_tiled_copy(SmemBlockScalingCopyAtomB{}, 
-      Layout<Shape<_1>>{}, Layout<Shape<_1>>{}); // (1,1,1)
-    ThrCopy thr_scale_copy_a = scale_copy_a.get_slice(threadIdx.x);
-    ThrCopy thr_scale_copy_b = scale_copy_b.get_slice(threadIdx.x);
-    
-    Tensor tAgA_ScaleA = thr_scale_copy_a.partition_S(gScaleA);
-    Tensor tAcA_ScaleA = thr_scale_copy_a.partition_S(cScaleA);
-    Tensor tAsA_ScaleA = thr_scale_copy_a.partition_D(sScaleA);
-    
-    Tensor tBgB_ScaleB = thr_scale_copy_b.partition_S(gScaleB);
-    Tensor tBsB_ScaleB = thr_scale_copy_b.partition_D(sScaleB);
-
-    // Applies the mapping from block_tma_a
-    Tensor tAgA = block_tma_a.partition_S(gA);                                              // (TMA,TMA_M,TMA_K,k)
-    Tensor tAsA = block_tma_a.partition_D(sA);                                              // (TMA,TMA_M,TMA_K,PIPE)
-
-    Tensor tBgB = block_tma_b.partition_S(gB);                                              // (TMA,TMA_N,TMA_K,k)
-    Tensor tBsB = block_tma_b.partition_D(sB);                                              // (TMA,TMA_N,TMA_K,PIPE)
-
-    uint16_t mcast_mask_a = 0;
-    uint16_t mcast_mask_b = 0;
-
-    // Issue TmaLoads for GEMM operands A/B and CpAsync for scale tensors
-    // Maps the tile -> block, value
-    if constexpr (cute::is_same_v<GmemTiledCopyA, SM90_TMA_LOAD_MULTICAST>) {
-      auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{};                       // (m,n) -> block_id
-      for (int n = 0; n < size<1>(block_layout); ++n) {
-        mcast_mask_a |= (uint16_t(1) << block_layout(cluster_local_block_id.x,n,Int<0>{}));
-      }
-    }
-
-    if constexpr (cute::is_same_v<GmemTiledCopyB, SM90_TMA_LOAD_MULTICAST>) {
-      auto block_layout = Layout<typename DispatchPolicy::ClusterShape>{};                       // (m,n) -> block_id
-      for (int m = 0; m < size<0>(block_layout); ++m) {
-        mcast_mask_b |= (uint16_t(1) << block_layout(m,cluster_local_block_id.y,Int<0>{}));
-      }
-    }
-
-    // Allocate predicate tensors for a_scales (since we can't guarantee that 
-    // all scales are valid, since we could have a partial tiles along M)
-    Tensor tApA_ScaleA = make_tensor<bool>(shape(tAsA_ScaleA(_,_,0)));
-    #pragma unroll
-    for (int i = 0; i < size(tApA_ScaleA); ++i) {
-      tApA_ScaleA(i) = get<0>(tAcA_ScaleA(i)) < scales_m;
-    }
-
-    // Mainloop
-    CUTLASS_PRAGMA_NO_UNROLL
-    for ( ; k_tile_count > 0; --k_tile_count) {
-      // LOCK smem_pipe_write for _writing_
-      pipeline.producer_acquire(smem_pipe_write);
-
-      //
-      // Copy gmem to smem for *k_tile_iter
-      //
-      int write_stage = smem_pipe_write.index();
-      using BarrierType = typename MainloopPipeline::ProducerBarrierType;
-      BarrierType* tma_barrier = pipeline.producer_get_barrier(smem_pipe_write);
-
-      // Copy operands A and B from global memory to shared memory
-      if (lane_predicate) copy(mainloop_params.tma_load_a.with(*tma_barrier, mcast_mask_a), tAgA(_,_,_,*k_tile_iter), tAsA(_,_,_,write_stage));
-      if (lane_predicate) copy(mainloop_params.tma_load_b.with(*tma_barrier, mcast_mask_b), tBgB(_,_,_,*k_tile_iter), tBsB(_,_,_,write_stage));
-
-      // Copy scale tensors from global memory to shared memory
-      copy_if(scale_copy_a, tApA_ScaleA, tAgA_ScaleA(_,_,*k_tile_iter), tAsA_ScaleA(_,_,write_stage));
-      copy(scale_copy_b, tBgB_ScaleB(_,*k_tile_iter), tBsB_ScaleB(_,write_stage));
-      pipeline.producer_commit(smem_pipe_write, cutlass::arch::cpasync_barrier_arrive_noinc);
-
-      ++k_tile_iter;
-
-      // Advance smem_pipe_write
-      ++smem_pipe_write;
-    }
-  }
-
-  /// Perform a Producer Epilogue to prevent early exit of blocks in a Cluster
-  CUTLASS_DEVICE void
-  load_tail(
-      MainloopPipeline pipeline,
-      PipelineState smem_pipe_write) {
-    int lane_predicate = cute::elect_one_sync();
-
-    // Issue the epilogue waits
-    if (lane_predicate) {
-      /* This helps avoid early exit of blocks in Cluster
-       * Waits for all stages to either be released (all
-       * Consumer UNLOCKs), or if the stage was never used
-       * then would just be acquired since the phase was
-       * still inverted from make_producer_start_state
-       */
-      pipeline.producer_tail(smem_pipe_write);
-    }
-  }
-
-  /// Perform a collective-scoped matrix multiply-accumulate
-  /// Consumer Perspective
-  template <
-    class FrgTensorC
-  >
-  CUTLASS_DEVICE void
-  mma(MainloopPipeline pipeline,
-      PipelineState smem_pipe_read,
-      FrgTensorC& accum,
-      int k_tile_count,
-      int thread_idx,
-      TensorStorage& shared_tensors,
-      Params const& mainloop_params) {
-
-
-    static_assert(is_rmem<FrgTensorC>::value, "C tensor must be rmem resident.");
-    static_assert(cute::rank(SmemLayoutA{}) == 3, "Smem layout must be rank 3.");
-    static_assert(cute::rank(SmemLayoutB{}) == 3, "Smem layout must be rank 3.");
-    static_assert(cute::is_void_v<SmemCopyAtomA>,
-      "SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");
-    static_assert(cute::is_void_v<SmemCopyAtomB>,
-      "SM90 GMMA mainloops cannot have a non-void copy atom for smem sourced instructions.");
-
-    Tensor sA = make_tensor(make_smem_ptr(shared_tensors.smem_A.data()), SmemLayoutA{});          // (BLK_M,BLK_K,PIPE)
-    Tensor sB = make_tensor(make_smem_ptr(shared_tensors.smem_B.data()), SmemLayoutB{});          // (BLK_N,BLK_K,PIPE)
-    
-    // Block scaling
-    Tensor sScaleAViewAsC = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_A.data()),
-      Layout<
-        Shape<Shape<Int<ScaleGranularityM>, Int<ScaleMsPerTile>>, cute::tuple_element_t<1, TileShape>, Int<DispatchPolicy::Stages>>,
-        Stride<Stride<_0, _1>, _0, Int<ScaleMsPerTile>>
-      >{}); // ((ScaleGranularityM,ScaleMsPerTile),n,k)
-    Tensor sScaleB = make_tensor(cute::make_smem_ptr(shared_tensors.smem_scale_B.data()), SmemLayoutScaleB{}); // (k)
-
-    //
-    // Define C accumulators and A/B partitioning
-    //
-    
-    // Layout of warp group to thread mapping
-
-    static_assert(stride<0>(typename TiledMma::ALayout{}) == 0 and 
-                  stride<0>(typename TiledMma::BLayout{}) == 0 and
-                  size<0>(typename TiledMma::ALayout{}) == NumThreadsPerWarpGroup and
-                  size<0>(typename TiledMma::BLayout{}) == NumThreadsPerWarpGroup, 
-                  "Stride of the first mode must be 0 and the size of the mode must be NumThreadsPerWarpGroup");
-
-    constexpr int MmaWarpGroups = size(TiledMma{}) / NumThreadsPerWarpGroup;
-    Layout warp_group_thread_layout = make_layout(Int<MmaWarpGroups>{}, 
-                                                  Int<NumThreadsPerWarpGroup>{});
-
-    int warp_group_idx = __shfl_sync(0xFFFFFFFF, thread_idx / NumThreadsPerWarpGroup, 0);
-
-    TiledMma tiled_mma;
-    auto thread_mma = tiled_mma.get_slice(warp_group_thread_layout(warp_group_idx));
-
-    Tensor tCsScaleAViewAsC = tiled_mma.get_slice(thread_idx).partition_C(sScaleAViewAsC);    // (MMA,MMA_M,MMA_N,PIPE), `thread_mma` above is correct when partitioning A and B, but it is not correct when partitioning C.
-
-    Tensor tCsA = thread_mma.partition_A(sA);                                                 // (MMA,MMA_M,MMA_K,PIPE)
-    Tensor tCsB = thread_mma.partition_B(sB);                                                 // (MMA,MMA_N,MMA_K,PIPE)
-
-    // Allocate "fragments/descriptors"
-    Tensor tCrA = thread_mma.make_fragment_A(tCsA);                                           // (MMA,MMA_M,MMA_K,PIPE)
-    Tensor tCrB = thread_mma.make_fragment_B(tCsB);                                           // (MMA,MMA_N,MMA_K,PIPE)
-
-    CUTE_STATIC_ASSERT_V(size<1>(tCsA) == size<1>(accum));                                                         // M
-    CUTE_STATIC_ASSERT_V(size<1>(tCsB) == size<2>(accum));                                                         // N
-    CUTE_STATIC_ASSERT_V(size<2>(tCsA) == size<2>(tCsB));                                                          // K
-    CUTE_STATIC_ASSERT_V(size<3>(tCsA) == size<3>(tCsB));                                                       // PIPE
-    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sA));                                         // PIPE
-    CUTE_STATIC_ASSERT_V(Int<DispatchPolicy::Stages>{} == size<2>(sB));                                         // PIPE
-
-    //
-    // PIPELINED MAIN LOOP
-    //
-    static_assert((0 <= K_PIPE_MMAS) && (K_PIPE_MMAS <  K_PIPE_MAX),
-        "ERROR : Incorrect number of MMAs in flight");
-
-    // We release buffers to producer warps(dma load) with some mmas in flight
-    PipelineState smem_pipe_release = smem_pipe_read;
-    
-    // Per block scale values for operand A and B
-
-    using RegLayoutScaleAViewAsC = decltype(make_layout_like(tCsScaleAViewAsC(_, _, _, 0).layout())); // `make_layout_like` makes a compact layout.
-    using RegLayoutScaleAEssential = decltype(filter_zeros(RegLayoutScaleAViewAsC{}.stride(), RegLayoutScaleAViewAsC{}.shape())); // an interface to traverse the underlying storage for the compact layout mentioned above
-
-    Tensor tCrScaleAViewAsC = make_tensor<ElementBlockScale>(RegLayoutScaleAViewAsC{});              // (MMA,MMA_M,MMA_N)
-    ElementBlockScale scale_b;
-
-    // Prologue GMMAs
-    int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);
-
-    tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
-
-    GmmaFP8AccumulationWithScale accumulation(accum, size<2>(TileShape{}) / size<2>(typename TiledMma::AtomShape_MNK{}), size<2>(tCrA));
-    warpgroup_fence_operand(accumulation());
-    CUTLASS_PRAGMA_UNROLL
-    for (int k_tile_prologue = prologue_mma_count; k_tile_prologue > 0; --k_tile_prologue)
-    {
-      // WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
-      auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
-      pipeline.consumer_wait(smem_pipe_read, barrier_token);
-
-      if (accumulation.prepare_if_needed()) {
-        tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
-      }
-
-      int read_stage = smem_pipe_read.index();
-      
-      // Load per block scale values from shared memory to registers.
-      scale_b = sScaleB[read_stage];
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
-        tCrScaleAViewAsC.data()[i] = tCsScaleAViewAsC(_, _, _, read_stage)(idx2crd(i, RegLayoutScaleAEssential{}));
-      }
-      if constexpr (ScaleMsPerTile == 1) {
-        static_assert(size(RegLayoutScaleAEssential{}) == 1);
-        tCrScaleAViewAsC.data()[0] = __shfl_sync(0xffffffff, tCrScaleAViewAsC.data()[0] * scale_b, 0); // `tCrScaleAViewAsC.data()[0]` are all same in a warp group when `ScaleMsPerTile == 1`.
-      } else {
-        CUTLASS_PRAGMA_UNROLL
-        for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
-          tCrScaleAViewAsC.data()[i] = tCrScaleAViewAsC.data()[i] * scale_b;
-        }
-      }
-
-      warpgroup_arrive();
-      // Unroll the K mode manually to set scale D to 1
-      CUTLASS_PRAGMA_UNROLL
-      for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
-        // (V,M,K) x (V,N,K) => (V,M,N)
-        cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accumulation());
-        tiled_mma.accumulate_ = GMMA::ScaleOut::One;
-      }
-      warpgroup_commit_batch();
-
-      // Block scale the accumulators with reg tensor `tCrScaleAViewAsC`
-      accumulation.scale_if_needed(tCrScaleAViewAsC);
-
-      ++smem_pipe_read;
-    }
-
-    warpgroup_fence_operand(accumulation());
-    // Mainloop GMMAs
-    k_tile_count -= prologue_mma_count;
-
-    CUTLASS_PRAGMA_NO_UNROLL
-    for ( ; k_tile_count > 0; --k_tile_count)
-    {
-      // WAIT on smem_pipe_read until its data are available (phase bit flips from rdPhaseBit value)
-      auto barrier_token = pipeline.consumer_try_wait(smem_pipe_read);
-      pipeline.consumer_wait(smem_pipe_read, barrier_token);
-
-      //
-      // Compute on k_tile
-      //
-
-      int read_stage = smem_pipe_read.index();
-
-      // Load per block scale values from shared memory to registers (at most twice per block along M and exactly once per block along N) 
-      scale_b = sScaleB[read_stage];
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
-        tCrScaleAViewAsC.data()[i] = tCsScaleAViewAsC(_, _, _, read_stage)(idx2crd(i, RegLayoutScaleAEssential{}));
-      }
-      if constexpr (ScaleMsPerTile == 1) {
-        static_assert(size(RegLayoutScaleAEssential{}) == 1);
-        tCrScaleAViewAsC.data()[0] = __shfl_sync(0xffffffff, tCrScaleAViewAsC.data()[0] * scale_b, 0); // `tCrScaleAViewAsC.data()[0]` are all same in a warp group when `ScaleMsPerTile == 1`.
-      } else {
-        CUTLASS_PRAGMA_UNROLL
-        for (int i = 0; i < size(RegLayoutScaleAEssential{}); i++) {
-          tCrScaleAViewAsC.data()[i] = tCrScaleAViewAsC.data()[i] * scale_b;
-        }
-      }
-
-      if (accumulation.prepare_if_needed()) {
-        tiled_mma.accumulate_ = GMMA::ScaleOut::Zero;
-      }
-
-      warpgroup_fence_operand(accumulation());
-      warpgroup_arrive();
-      // Unroll the K mode manually to set scale D to 1
-      CUTLASS_PRAGMA_UNROLL
-      for (int k_block = 0; k_block < size<2>(tCrA); ++k_block) {
-        // (V,M,K) x (V,N,K) => (V,M,N)
-        cute::gemm(tiled_mma, tCrA(_,_,k_block,read_stage), tCrB(_,_,k_block,read_stage), accumulation());
-        tiled_mma.accumulate_ = GMMA::ScaleOut::One;
-      }
-      warpgroup_commit_batch();
-
-      /// Wait on the GMMA barrier for K_PIPE_MMAS (or fewer) outstanding to ensure smem_pipe_write is consumed
-      warpgroup_wait<K_PIPE_MMAS>();
-      warpgroup_fence_operand(accumulation());
-
-      // Block scale the accumulators with reg tensor `tCrScaleAViewAsC`
-      accumulation.scale_if_needed(tCrScaleAViewAsC);
-
-      pipeline.consumer_release(smem_pipe_release);                 // UNLOCK smem_pipe_release, done _computing_ on it
-
-      // Advance smem_pipe_read and smem_pipe_release
-      ++smem_pipe_read;
-      ++smem_pipe_release;
-    }
-    
-    accumulation.scale_residue_if_needed(tCrScaleAViewAsC);
-
-    warpgroup_fence_operand(accumulation());
-  }
-
-  /// Perform a Consumer Epilogue to release all buffers
-  CUTLASS_DEVICE void
-  mma_tail(MainloopPipeline pipeline, PipelineState smem_pipe_release, int k_tile_count) {
-    // Prologue GMMAs
-    int prologue_mma_count = min(K_PIPE_MMAS, k_tile_count);
-    k_tile_count -= prologue_mma_count;
-
-    smem_pipe_release.advance(k_tile_count);
-
-    // Wait on all GMMAs to complete
-    warpgroup_wait<0>();
-
-    for (int count = 0; count < prologue_mma_count; ++count) {
-      pipeline.consumer_release(smem_pipe_release);                 // UNLOCK smem_pipe_release, done _computing_ on it
-      ++smem_pipe_release;
-    }
-  }
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-} // namespace cutlass::gemm::collective
-
-/////////////////////////////////////////////////////////////////////////////////////////////////

csrc/cutlass_extensions/gemm/dispatch_policy.hpp

@@ -1,39 +0,0 @@
-#pragma once
-
-#include "cutlass/gemm/dispatch_policy.hpp"
-
-namespace cutlass::gemm {
-
-//////////////////////////////////////////////////////////////////////////////
-
-// FP8 related policies (including Blocked Scaled Accumulation)
-//  `ScaleGranularityM` specifies scaling granularity along M, while zero-value
-//  `ScaleGranularityM` indicates that scaling granularity is
-//  `size<0>(TileShape_MNK{})` along M.
-template <int ScaleGranularityM = 0>
-struct KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum
-    : KernelTmaWarpSpecializedCooperative {};
-
-// n-buffer in smem (Hopper TMA), pipelined with Hopper GMMA and TMA, Warp
-// specialized dynamic schedule For FP8 kernels with Block Scaling
-template <int Stages_, class ClusterShape_ = Shape<_1, _1, _1>,
-          class KernelSchedule = KernelTmaWarpSpecialized,
-          int ScaleGranularityM =
-              0  // `ScaleGranularityM` specifies scaling granularity along M,
-                 // while zero-value `ScaleGranularityM` indicates that scaling
-                 // granularity is `size<0>(TileShape_MNK{})` along M.
-          >
-struct MainloopSm90TmaGmmaWarpSpecializedBlockScalingSubGroupMFP8
-    : MainloopSm90TmaGmmaWarpSpecialized<Stages_, ClusterShape_,
-                                         KernelSchedule> {
-  static_assert(
-      cute::is_same_v<
-          KernelSchedule,
-          KernelTmaWarpSpecializedCooperativeFP8BlockScaledSubGroupMAccum<
-              ScaleGranularityM>>,
-      "KernelSchedule must be one of the warp specialized policies");
-};
-
-//////////////////////////////////////////////////////////////////////////////
-
-}  // namespace cutlass::gemm

csrc/cutlass_extensions/vllm_collective_builder.cuh

@@ -1,6 +1,6 @@
 #pragma once
 
-#include "cutlass_extensions/gemm/collective/collective_builder.hpp"
+#include "cutlass/gemm/collective/collective_builder.hpp"
 
 namespace cutlass::gemm::collective {
 using namespace cute;

csrc/cutlass_extensions/vllm_cutlass_library_extension.py

@@ -1,5 +1,3 @@
-# SPDX-License-Identifier: Apache-2.0
-
 import enum
 from typing import Dict, Union
 

csrc/moe/moe_align_sum_kernels.cu

@@ -3,7 +3,7 @@
 #include <c10/cuda/CUDAGuard.h>
 
 #include <ATen/ATen.h>
-#include <ATen/cuda/Atomic.cuh>
+#include <THC/THCAtomics.cuh>
 
 #include "../cuda_compat.h"
 #include "../dispatch_utils.h"
@@ -197,83 +197,6 @@ __global__ void moe_align_block_size_global_mem_kernel(
   }
 }
 
-// taken from
-// https://github.com/sgl-project/sglang/commit/cdae77b03dfc6fec3863630550b45bbfc789f957
-template <typename scalar_t>
-__global__ void sgl_moe_align_block_size_kernel(
-    scalar_t* __restrict__ topk_ids, int32_t* sorted_token_ids,
-    int32_t* expert_ids, int32_t* total_tokens_post_pad, int32_t num_experts,
-    int32_t block_size, size_t numel, int32_t* cumsum) {
-  __shared__ int32_t shared_counts[32][8];
-
-  const int warp_id = threadIdx.x / 32;
-  const int experts_per_warp = 8;
-  const int my_expert_start = warp_id * experts_per_warp;
-
-  // Initialize shared_counts for this warp's experts
-  for (int i = 0; i < experts_per_warp; ++i) {
-    if (my_expert_start + i < num_experts) {
-      shared_counts[warp_id][i] = 0;
-    }
-  }
-
-  __syncthreads();
-
-  const size_t tokens_per_thread = CEILDIV(numel, blockDim.x);
-  const size_t start_idx = threadIdx.x * tokens_per_thread;
-
-  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
-    int expert_id = topk_ids[i];
-    int warp_idx = expert_id / experts_per_warp;
-    int expert_offset = expert_id % experts_per_warp;
-    atomicAdd(&shared_counts[warp_idx][expert_offset], 1);
-  }
-
-  __syncthreads();
-
-  // Single thread computes cumulative sum and total tokens
-  if (threadIdx.x == 0) {
-    cumsum[0] = 0;
-    for (int i = 1; i <= num_experts; ++i) {
-      int expert_count = 0;
-      int warp_idx = (i - 1) / experts_per_warp;
-      int expert_offset = (i - 1) % experts_per_warp;
-      expert_count = shared_counts[warp_idx][expert_offset];
-
-      cumsum[i] =
-          cumsum[i - 1] + CEILDIV(expert_count, block_size) * block_size;
-    }
-    *total_tokens_post_pad = cumsum[num_experts];
-  }
-
-  __syncthreads();
-
-  // Assign expert IDs to blocks
-  if (threadIdx.x < num_experts) {
-    for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1];
-         i += block_size) {
-      expert_ids[i / block_size] = threadIdx.x;
-    }
-  }
-}
-
-// taken from
-// https://github.com/sgl-project/sglang/commit/cdae77b03dfc6fec3863630550b45bbfc789f957
-template <typename scalar_t>
-__global__ void sgl_moe_token_sort_kernel(scalar_t* __restrict__ topk_ids,
-                                          int32_t* sorted_token_ids,
-                                          int32_t* cumsum_buffer,
-                                          size_t numel) {
-  const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
-  const size_t stride = blockDim.x * gridDim.x;
-
-  for (size_t i = tid; i < numel; i += stride) {
-    int32_t expert_id = topk_ids[i];
-    int32_t rank_post_pad = atomicAdd(&cumsum_buffer[expert_id], 1);
-    sorted_token_ids[rank_post_pad] = i;
-  }
-}
-
 template <typename scalar_t, int TOPK>
 __global__ void moe_sum_kernel(
     scalar_t* __restrict__ out,          // [..., d]
@@ -382,43 +305,6 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
   }
 }
 
-void sgl_moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
-                              int64_t block_size,
-                              torch::Tensor sorted_token_ids,
-                              torch::Tensor experts_ids,
-                              torch::Tensor num_tokens_post_pad) {
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  TORCH_CHECK(num_experts == 256,
-              "sgl_moe_align_block_size kernel only supports deepseek v3.");
-
-  VLLM_DISPATCH_INTEGRAL_TYPES(
-      topk_ids.scalar_type(), "sgl_moe_align_block_size_kernel", [&] {
-        // calc needed amount of shared mem for `cumsum` tensors
-        auto options_int =
-            torch::TensorOptions().dtype(torch::kInt).device(topk_ids.device());
-        torch::Tensor cumsum_buffer =
-            torch::zeros({num_experts + 1}, options_int);
-
-        auto align_kernel =
-            vllm::moe::sgl_moe_align_block_size_kernel<scalar_t>;
-        align_kernel<<<1, 1024, 0, stream>>>(
-            topk_ids.data_ptr<scalar_t>(), sorted_token_ids.data_ptr<int32_t>(),
-            experts_ids.data_ptr<int32_t>(),
-            num_tokens_post_pad.data_ptr<int32_t>(), num_experts, block_size,
-            topk_ids.numel(), cumsum_buffer.data_ptr<int32_t>());
-
-        const int block_threads = 256;
-        const int num_blocks =
-            (topk_ids.numel() + block_threads - 1) / block_threads;
-        const int max_blocks = 65535;
-        const int actual_blocks = std::min(num_blocks, max_blocks);
-        auto sort_kernel = vllm::moe::sgl_moe_token_sort_kernel<scalar_t>;
-        sort_kernel<<<actual_blocks, block_threads, 0, stream>>>(
-            topk_ids.data_ptr<scalar_t>(), sorted_token_ids.data_ptr<int32_t>(),
-            cumsum_buffer.data_ptr<int32_t>(), topk_ids.numel());
-      });
-}
-
 void moe_sum(torch::Tensor& input,   // [num_tokens, topk, hidden_size]
              torch::Tensor& output)  // [num_tokens, hidden_size]
 {

csrc/moe/moe_ops.h

@@ -12,9 +12,3 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                           int64_t block_size, torch::Tensor sorted_token_ids,
                           torch::Tensor experts_ids,
                           torch::Tensor num_tokens_post_pad);
-
-void sgl_moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
-                              int64_t block_size,
-                              torch::Tensor sorted_token_ids,
-                              torch::Tensor experts_ids,
-                              torch::Tensor num_tokens_post_pad);

csrc/moe/torch_bindings.cpp

@@ -22,15 +22,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "                     Tensor! num_tokens_post_pad) -> ()");
   m.impl("moe_align_block_size", torch::kCUDA, &moe_align_block_size);
 
-  // temporarily adapted from
-  // https://github.com/sgl-project/sglang/commit/ded9fcd09a43d5e7d5bb31a2bc3e9fc21bf65d2a
-  m.def(
-      "sgl_moe_align_block_size(Tensor topk_ids, int num_experts,"
-      "                         int block_size, Tensor! sorted_token_ids,"
-      "                         Tensor! experts_ids,"
-      "                         Tensor! num_tokens_post_pad) -> ()");
-  m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size);
-
 #ifndef USE_ROCM
   m.def(
       "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, "

csrc/ops.h

@@ -153,7 +153,6 @@ torch::Tensor ggml_mul_mat_a8(torch::Tensor W, torch::Tensor X, int64_t type,
 
 #ifndef USE_ROCM
 bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
-bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
 
 void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
@@ -176,11 +175,8 @@ void cutlass_scaled_sparse_mm(torch::Tensor& out, torch::Tensor const& a,
                               torch::Tensor const& b_scales,
                               std::optional<torch::Tensor> const& bias);
 
-std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a);
-
-void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
-                      torch::Tensor& output_scale,
-                      torch::Tensor const& input_scale);
+bool cutlass_sparse_compress_entry(torch::Tensor& a_compressed,
+                                   torch::Tensor& e, torch::Tensor const& a);
 #endif
 
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,

csrc/pos_encoding_kernels.cu

@@ -124,54 +124,18 @@ __global__ void batched_rotary_embedding_kernel(
 void rotary_embedding(
     torch::Tensor& positions,  // [batch_size, seq_len] or [num_tokens]
     torch::Tensor& query,  // [batch_size, seq_len, num_heads * head_size] or
-                           // [num_tokens, num_heads * head_size] or
-                           // [batch_size, seq_len, num_heads, head_size] or
-                           // [num_tokens, num_heads, head_size]
+                           // [num_tokens, num_heads * head_size]
     torch::Tensor& key,    // [batch_size, seq_len, num_kv_heads * head_size] or
-                           // [num_tokens, num_kv_heads * head_size] or
-                           // [batch_size, seq_len, num_heads, head_size] or
-                           // [num_tokens, num_heads, head_size]
+                           // [num_tokens, num_kv_heads * head_size]
     int64_t head_size,
     torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
     bool is_neox) {
-  // num_tokens = batch_size * seq_len
-  int64_t num_tokens = positions.numel();
-  int positions_ndim = positions.dim();
-
-  // Make sure num_tokens dim is consistent across positions, query, and key.
-  TORCH_CHECK(
-      positions_ndim == 1 || positions_ndim == 2,
-      "positions must have shape [num_tokens] or [batch_size, seq_len]");
-  if (positions_ndim == 1) {
-    TORCH_CHECK(
-        query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
-        "query, key and positions must have the same number of tokens");
-  }
-  if (positions_ndim == 2) {
-    TORCH_CHECK(
-        query.size(0) == positions.size(0) &&
-            key.size(0) == positions.size(0) &&
-            query.size(1) == positions.size(1) &&
-            key.size(1) == positions.size(1),
-        "query, key and positions must have the same batch_size and seq_len");
-  }
-
-  // Make sure head_size is valid for query and key
-  // hidden_size = num_heads * head_size
-  int query_hidden_size = query.numel() / num_tokens;
-  int key_hidden_size = key.numel() / num_tokens;
-  TORCH_CHECK(query_hidden_size % head_size == 0);
-  TORCH_CHECK(key_hidden_size % head_size == 0);
-
-  // Make sure query and key have consistent number of heads
-  int num_heads = query_hidden_size / head_size;
-  int num_kv_heads = key_hidden_size / head_size;
-  TORCH_CHECK(num_heads % num_kv_heads == 0);
-
+  int64_t num_tokens = query.numel() / query.size(-1);
   int rot_dim = cos_sin_cache.size(1);
-  int seq_dim_idx = positions_ndim - 1;
-  int64_t query_stride = query.stride(seq_dim_idx);
-  int64_t key_stride = key.stride(seq_dim_idx);
+  int num_heads = query.size(-1) / head_size;
+  int num_kv_heads = key.size(-1) / head_size;
+  int64_t query_stride = query.stride(-2);
+  int64_t key_stride = key.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
@@ -201,58 +165,19 @@ and process in batched manner.
 void batched_rotary_embedding(
     torch::Tensor& positions,  // [batch_size, seq_len] or [num_tokens]
     torch::Tensor& query,  // [batch_size, seq_len, num_heads * head_size] or
-                           // [num_tokens, num_heads * head_size] or
-                           // [batch_size, seq_len, num_heads, head_size] or
-                           // [num_tokens, num_heads, head_size]
+                           // [num_tokens, num_heads * head_size]
     torch::Tensor& key,    // [batch_size, seq_len, num_kv_heads * head_size] or
-                           // [num_tokens, num_kv_heads * head_size] or
-                           // [batch_size, seq_len, num_heads, head_size] or
-                           // [num_tokens, num_heads, head_size]
+                           // [num_tokens, num_kv_heads * head_size]
     int64_t head_size,
     torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
     bool is_neox, int64_t rot_dim,
-    torch::Tensor& cos_sin_cache_offsets  // [num_tokens] or [batch_size]
+    torch::Tensor& cos_sin_cache_offsets  // [num_tokens]
 ) {
-  // num_tokens = batch_size * seq_len
   int64_t num_tokens = cos_sin_cache_offsets.size(0);
-  TORCH_CHECK(
-      positions.size(0) == num_tokens || positions.numel() == num_tokens,
-      "positions must have the same num_tokens or batch_size as "
-      "cos_sin_cache_offsets");
-
-  int positions_ndim = positions.dim();
-  // Make sure num_tokens dim is consistent across positions, query, and key.
-  TORCH_CHECK(
-      positions_ndim == 1 || positions_ndim == 2,
-      "positions must have shape [num_tokens] or [batch_size, seq_len]");
-  if (positions_ndim == 1) {
-    TORCH_CHECK(
-        query.size(0) == positions.size(0) && key.size(0) == positions.size(0),
-        "query, key and positions must have the same number of tokens");
-  }
-  if (positions_ndim == 2) {
-    TORCH_CHECK(
-        query.size(0) == positions.size(0) &&
-            key.size(0) == positions.size(0) &&
-            query.size(1) == positions.size(1) &&
-            key.size(1) == positions.size(1),
-        "query, key and positions must have the same batch_size and seq_len");
-  }
-
-  // Make sure head_size is valid for query and key
-  int query_hidden_size = query.numel() / num_tokens;
-  int key_hidden_size = key.numel() / num_tokens;
-  TORCH_CHECK(query_hidden_size % head_size == 0);
-  TORCH_CHECK(key_hidden_size % head_size == 0);
-
-  // Make sure query and key have concistent number of heads
-  int num_heads = query_hidden_size / head_size;
-  int num_kv_heads = key_hidden_size / head_size;
-  TORCH_CHECK(num_heads % num_kv_heads == 0);
-
-  int seq_dim_idx = positions_ndim - 1;
-  int64_t query_stride = query.stride(seq_dim_idx);
-  int64_t key_stride = key.stride(seq_dim_idx);
+  int num_heads = query.size(-1) / head_size;
+  int num_kv_heads = key.size(-1) / head_size;
+  int64_t query_stride = query.stride(-2);
+  int64_t key_stride = key.stride(-2);
 
   dim3 grid(num_tokens);
   dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));

csrc/quantization/awq/gemm_kernels.cu

@@ -334,7 +334,7 @@ __global__ void __launch_bounds__(64)
   }
 
   // TODO: Shang: Hoist loop invariance.
-  for (int ax1_0_1 = 0; ax1_0_1 < (N / 32); ++ax1_0_1) {
+  for (int ax1_0_1 = 0; ax1_0_1 < 4; ++ax1_0_1) {
     for (int local_id = 0; local_id < 8; ++local_id) {
       int row_offset = (((int)blockIdx_y) / j_factors1) * 16 +
                        ((int)threadIdx.x) / 4 + (local_id % 4) / 2 * 8;