[Bugfix] Fix deepseekv3 grouped topk error (#13474)

Signed-off-by: Chen-XiaoBing <chenxb002@whu.edu.cn>

.buildkite/check-wheel-size.py

@@ -1,12 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+
 import os
 import sys
 import zipfile
 
-# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 300 MiB
+# Read the VLLM_MAX_SIZE_MB environment variable, defaulting to 400 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', 300))
+VLLM_MAX_SIZE_MB = int(os.environ.get('VLLM_MAX_SIZE_MB', 400))
 
 
 def print_top_10_largest_files(zip_file):

.buildkite/generate_index.py

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

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

@@ -0,0 +1,11 @@
+# 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

@@ -1,3 +1,4 @@
+# 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

@@ -1,16 +1,14 @@
 # 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.
 
-
 ## Performance benchmark quick overview
 
 **Benchmarking Coverage**: latency, throughput and fix-qps serving on A100 (the support for FP8 benchmark on H100 is coming!), with different models.
@@ -19,7 +17,6 @@ See  [vLLM performance dashboard](https://perf.vllm.ai) for the latest performan
 
 **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.
@@ -28,8 +25,6 @@ See  [vLLM performance dashboard](https://perf.vllm.ai) for the latest performan
 
 **Benchmarking Duration**: about 3.5hrs.
 
-
-
 ## Trigger the benchmark
 
 Performance benchmark will be triggered when:
@@ -39,16 +34,11 @@ 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`:
 
@@ -68,6 +58,7 @@ 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`
 
@@ -75,16 +66,17 @@ Note that the performance numbers are highly sensitive to the value of the param
 
 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",
@@ -109,6 +101,7 @@ 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`.
@@ -118,36 +111,33 @@ 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
+### 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

@@ -10,12 +10,18 @@ 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:
@@ -50,6 +56,7 @@ 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:$BUILDKITE_COMMIT
@@ -75,6 +82,7 @@ steps:
     agents:
       queue: H100
     depends_on: wait-for-container-image
+    if: build.branch == "main"
     plugins:
     - docker#v5.12.0:
         image: public.ecr.aws/q9t5s3a7/vllm-ci-postmerge-repo:$BUILDKITE_COMMIT
@@ -90,3 +98,87 @@ 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

@@ -9,14 +9,14 @@ 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
-```
+  - In the same folder, run the following code:
+
+  ```console
   export HF_TOKEN=<your HF token>
   apt update
   apt install -y git
@@ -25,4 +25,3 @@ VLLM_SOURCE_CODE_LOC=./ bash .buildkite/nightly-benchmarks/scripts/run-nightly-b
   ```
 
 And the results will be inside `./benchmarks/results`.
-

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

@@ -2,6 +2,7 @@
 # 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.
 
@@ -9,7 +10,6 @@ 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.

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

@@ -7,10 +7,8 @@
 - 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).
@@ -19,10 +17,8 @@
 - 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).
@@ -33,10 +29,8 @@
 - 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.
@@ -54,9 +48,9 @@ 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

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

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

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

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

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

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

@@ -1,3 +1,5 @@
+# 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

@@ -345,6 +345,11 @@ 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

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

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

@@ -1,6 +1,10 @@
 #!/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
 
 TIMEOUT_SECONDS=10
 

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

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

.buildkite/run-amd-test.sh

@@ -121,6 +121,8 @@ 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}_${GPU}" \
@@ -148,6 +150,8 @@ else
           --rm \
           -e HIP_VISIBLE_DEVICES=0 \
           -e HF_TOKEN \
+          -e AWS_ACCESS_KEY_ID \
+          -e AWS_SECRET_ACCESS_KEY \
           -v "${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.py"
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
 
   # 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 --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
-    python3 examples/offline_inference/basic.py
+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
 '

.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.py
+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
 EXITCODE=$?

.buildkite/run-neuron-test.sh

@@ -29,9 +29,6 @@ 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.py
+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

.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.py
-    python3 examples/offline_inference/cli.py -tp 2
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/test-pipeline.yaml

@@ -50,9 +50,9 @@ steps:
   - tests/multimodal
   - tests/test_utils
   - tests/worker
-  - tests/standalone_tests/lazy_torch_compile.py
+  - tests/standalone_tests/lazy_imports.py
   commands:
-  - python3 standalone_tests/lazy_torch_compile.py
+  - python3 standalone_tests/lazy_imports.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
@@ -107,13 +107,17 @@ 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
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/correctness/
   - pytest -v -s entrypoints/test_chat_utils.py
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
@@ -124,10 +128,12 @@ steps:
   source_file_dependencies:
   - vllm/distributed/
   - vllm/core/
-  - tests/distributed
+  - tests/distributed/test_utils
+  - tests/distributed/test_pynccl
   - tests/spec_decode/e2e/test_integration_dist_tp4
-  - tests/compile
+  - tests/compile/test_basic_correctness
   - 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
@@ -136,6 +142,7 @@ 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
@@ -172,6 +179,9 @@ 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
@@ -193,6 +203,9 @@ 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"
@@ -202,18 +215,18 @@ steps:
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
-    - python3 offline_inference/basic.py
-    - python3 offline_inference/cpu_offload.py
-    - python3 offline_inference/chat.py
+    - 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/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/classification.py
-    - python3 offline_inference/embedding.py
-    - python3 offline_inference/scoring.py
+    - python3 offline_inference/basic/classify.py
+    - python3 offline_inference/basic/embed.py
+    - python3 offline_inference/basic/score.py
     - python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
 
 - label: Prefix Caching Test # 9min
@@ -252,7 +265,7 @@ 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
+    - 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
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
@@ -326,6 +339,14 @@ 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/
@@ -349,6 +370,7 @@ 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
 
@@ -485,6 +507,7 @@ 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)'
@@ -508,6 +531,7 @@ 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

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

@@ -30,15 +30,6 @@ 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

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

.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@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+        uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
         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@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+      - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
         with:
           python-version: '3.13'
 
       - name: Set up chart-testing
-        uses: helm/chart-testing-action@e6669bcd63d7cb57cb4380c33043eebe5d111992 # v2.6.1
+        uses: helm/chart-testing-action@0d28d3144d3a25ea2cc349d6e59901c4ff469b3b # v2.7.0
         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@0025e74a8c7512023d06dc019c617aa3cf561fde # v1.10.0
+        uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
 
       - name: Build the Docker image vllm cpu
         run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .

.github/workflows/pre-commit.yml

@@ -10,10 +10,11 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
-    - uses: actions/setup-python@0b93645e9fea7318ecaed2b359559ac225c90a2b # v5.3.0
+    - uses: actions/setup-python@42375524e23c412d93fb67b49958b491fce71c38 # v5.4.0
       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

@@ -2,7 +2,6 @@ name: PR Reminder Comment Bot
 on:
   pull_request_target:
     types: [opened]
-
 jobs:
   pr_reminder:
     runs-on: ubuntu-latest
@@ -15,7 +14,12 @@ 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 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🚀'
+              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' +
+                '🚀'
             })
         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@28ca1036281a5e5922ead5184a1bbf96e5fc984e # v9.0.0
+      - uses: actions/stale@5bef64f19d7facfb25b37b414482c7164d639639 # v9.1.0
         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

@@ -8,36 +8,42 @@ 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]
+    args: [--output-format, github, --fix]
+    exclude: 'vllm/third_party/.*'
 - repo: https://github.com/codespell-project/codespell
   rev: v2.4.0
   hooks:
   - id: codespell
-    exclude: 'benchmarks/sonnet.txt|(build|tests/(lora/data|models/fixtures|prompts))/.*'
+    additional_dependencies: ['tomli']
+    args: ['--toml', 'pyproject.toml']
 - 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))'
+    exclude: 'csrc/(moe/topk_softmax_kernels.cu|quantization/gguf/(ggml-common.h|dequantize.cuh|vecdotq.cuh|mmq.cuh|mmvq.cuh))|vllm/third_party/.*'
     types_or: [c++, cuda]
     args: [--style=file, --verbose]
 - repo: https://github.com/jackdewinter/pymarkdown
   rev: v0.9.27
   hooks:
   - id: pymarkdown
-    files: docs/.*
+    args: [fix]
+    exclude: 'vllm/third_party/.*'
 - repo: https://github.com/rhysd/actionlint
   rev: v1.7.7
   hooks:
   - id: actionlint
+    exclude: 'vllm/third_party/.*'
 - repo: local
   hooks:
   - id: mypy-local
@@ -47,6 +53,7 @@ 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"
@@ -54,6 +61,7 @@ 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"
@@ -61,6 +69,7 @@ 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"
@@ -68,6 +77,7 @@ 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"
@@ -75,16 +85,19 @@ 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
@@ -97,10 +110,29 @@ repos:
     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;gfx940;gfx941;gfx942;gfx1030;gfx1100;gfx1101")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;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)
+  list(APPEND CUMEM_LIBS CUDA::cuda_driver)
   define_gpu_extension_target(
     cumem_allocator
     DESTINATION vllm
@@ -228,7 +228,8 @@ 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.
-  set(CUTLASS_REVISION "v3.6.0" CACHE STRING "CUTLASS revision to use")
+  # Please keep this in sync with FetchContent_Declare line below.
+  set(CUTLASS_REVISION "v3.7.0" 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})
@@ -245,6 +246,7 @@ 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_PROGRESS TRUE
 
@@ -264,8 +266,8 @@ 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(
@@ -358,8 +360,7 @@ 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(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
-    set(SRCS "csrc/sparse/cutlass/sparse_compressor_c3x.cu"
-             "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
+    set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
       CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
@@ -377,6 +378,23 @@ 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
@@ -458,7 +476,7 @@ define_gpu_extension_target(
   SOURCES ${VLLM_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR}
   USE_SABI 3
   WITH_SOABI)
 
@@ -581,7 +599,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG d4e09037abf588af1ec47d0e966b237ee376876c
+          GIT_TAG 720c94869cf2e0ff5a706e9c7f1dce0939686ade
           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 Discord](https://discord.com/invite/jz7wjKhh6g).
+channel in the [vLLM Slack](https://slack.vllm.ai).
 All complaints will be reviewed and investigated promptly and fairly.
 
 All community leaders are obligated to respect the privacy and security of the
@@ -125,4 +125,3 @@ 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

@@ -27,6 +27,9 @@ 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
@@ -52,13 +55,13 @@ WORKDIR /workspace
 # after this step
 RUN --mount=type=cache,target=/root/.cache/pip \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121";  \
+        uv pip install --system --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 \
-    python3 -m pip install -r requirements-cuda.txt
+    uv pip install --system -r requirements-cuda.txt
 
 # cuda arch list used by torch
 # can be useful for both `dev` and `test`
@@ -79,7 +82,7 @@ ARG TARGETPLATFORM
 COPY requirements-build.txt requirements-build.txt
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install -r requirements-build.txt
+    uv pip install --system -r requirements-build.txt
 
 COPY . .
 ARG GIT_REPO_CHECK=0
@@ -127,7 +130,7 @@ 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=300
+ARG VLLM_MAX_SIZE_MB=400
 ENV VLLM_MAX_SIZE_MB=$VLLM_MAX_SIZE_MB
 ARG RUN_WHEEL_CHECK=true
 RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
@@ -144,7 +147,7 @@ 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 \
-    python3 -m pip install -r requirements-dev.txt
+    uv pip install --system -r requirements-dev.txt
 #################### DEV IMAGE ####################
 
 #################### vLLM installation IMAGE ####################
@@ -174,6 +177,9 @@ 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
@@ -187,27 +193,30 @@ 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 \
-        python3 -m pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
+        uv pip install --system --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 \
-    python3 -m pip install dist/*.whl --verbose
+    uv pip install --system dist/*.whl --verbose
 
-# How to build this FlashInfer wheel:
+# If we need to build FlashInfer wheel before its release:
 # $ 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 [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
-    python3 -m pip install https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.0.post1-cp${PYTHON_VERSION_STR}-cp${PYTHON_VERSION_STR}-linux_x86_64.whl; \
+    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 ; \
 fi
 COPY examples examples
 
@@ -217,7 +226,7 @@ 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 \
-    python3 -m pip install -r requirements-build.txt
+    uv pip install --system -r requirements-build.txt
 
 #################### vLLM installation IMAGE ####################
 
@@ -230,15 +239,15 @@ ADD . /vllm-workspace/
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install -r requirements-dev.txt
+    uv pip install --system -r requirements-dev.txt
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install -e tests/vllm_test_utils
+    uv pip install --system -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
 RUN --mount=type=cache,target=/root/.cache/pip \
-    python3 -m pip install hf_transfer
+    uv pip install --system hf_transfer
 ENV HF_HUB_ENABLE_HF_TRANSFER 1
 
 # Copy in the v1 package for testing (it isn't distributed yet)
@@ -259,9 +268,9 @@ 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 \
-        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]; \
+        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]; \
     else \
-        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]; \
+        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]; \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image

Dockerfile.neuron

@@ -23,10 +23,12 @@ 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 \
@@ -43,6 +45,10 @@ 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.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="8d4926e"
+ARG PYTORCH_BRANCH="3a585126"
 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

@@ -10,14 +10,19 @@ 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://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> |
+| <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> |
 </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).
+- [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).
 - [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!
@@ -33,10 +38,12 @@ Easy, fast, and cheap LLM serving for everyone
 - [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 evloved 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 evolved into a community-driven project with contributions from both academia and industry.
 
 vLLM is fast with:
 
@@ -127,6 +134,7 @@ 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},
@@ -138,12 +146,11 @@ 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, 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.
+- 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.
 
 ## 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

@@ -3,6 +3,7 @@
 ## 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
 ```
@@ -11,9 +12,18 @@ 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

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

benchmarks/benchmark_guided.py

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

benchmarks/benchmark_latency.py

@@ -1,13 +1,17 @@
+# 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 List, Optional
+from typing import Any, Dict, 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
@@ -17,6 +21,19 @@ 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)
 
@@ -53,7 +70,8 @@ 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:
@@ -63,7 +81,8 @@ 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:
@@ -80,9 +99,8 @@ 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
@@ -94,9 +112,9 @@ 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:
@@ -107,43 +125,51 @@ 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',
+        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',
+        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',
+        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

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

benchmarks/benchmark_prefix_caching.py

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

benchmarks/benchmark_prioritization.py

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

benchmarks/benchmark_serving.py

@@ -1,3 +1,4 @@
+# SPDX-License-Identifier: Apache-2.0
 r"""Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
@@ -37,6 +38,7 @@ 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
@@ -54,6 +56,8 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
+from benchmark_utils import convert_to_pytorch_benchmark_format
+
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
 
@@ -130,6 +134,35 @@ 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,
@@ -536,6 +569,7 @@ 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]
@@ -561,6 +595,7 @@ 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(
@@ -569,6 +604,11 @@ 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,
@@ -615,8 +655,13 @@ 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
-        request_func_input = RequestFuncInput(model=model_id,
-                                              model_name=model_name,
+        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,
                                               prompt=prompt,
                                               api_url=api_url,
                                               prompt_len=prompt_len,
@@ -774,6 +819,32 @@ 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)
@@ -817,6 +888,14 @@ 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":
@@ -899,6 +978,7 @@ 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
@@ -945,6 +1025,7 @@ 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__":
@@ -962,7 +1043,8 @@ if __name__ == "__main__":
         default=None,
         help="Server or API base url if not using http host and port.",
     )
-    parser.add_argument("--host", type=str, default="localhost")
+    # 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("--port", type=int, default=8000)
     parser.add_argument(
         "--endpoint",
@@ -981,7 +1063,7 @@ if __name__ == "__main__":
         "--dataset-name",
         type=str,
         default="sharegpt",
-        choices=["sharegpt", "sonnet", "random", "hf"],
+        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
         help="Name of the dataset to benchmark on.",
     )
     parser.add_argument("--dataset-path",
@@ -1223,11 +1305,12 @@ if __name__ == "__main__":
         '--tokenizer-mode',
         type=str,
         default="auto",
-        choices=['auto', 'slow', 'mistral'],
+        choices=['auto', 'slow', 'mistral', 'custom'],
         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.')
+        '"mistral" will always use the `mistral_common` tokenizer. \n*'
+        '"custom" will use --tokenizer to select the preregistered tokenizer.')
 
     parser.add_argument("--served-model-name",
                         type=str,
@@ -1236,5 +1319,12 @@ 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

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

benchmarks/benchmark_throughput.py

@@ -1,14 +1,17 @@
+# 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 Dict, List, Optional, Tuple
+from typing import Any, 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,
@@ -337,6 +340,25 @@ 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)
@@ -434,6 +456,7 @@ 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

@@ -0,0 +1,39 @@
+# 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

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

benchmarks/cutlass_benchmarks/utils.py

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

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

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

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -1,3 +1,5 @@
+# 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

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

benchmarks/disagg_benchmarks/round_robin_proxy.py

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

benchmarks/disagg_benchmarks/visualize_benchmark_results.py

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

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

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

benchmarks/kernels/benchmark_aqlm.py

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

benchmarks/kernels/benchmark_layernorm.py

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

benchmarks/kernels/benchmark_lora.py

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

benchmarks/kernels/benchmark_machete.py

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

benchmarks/kernels/benchmark_marlin.py

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

benchmarks/kernels/benchmark_moe.py

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

benchmarks/kernels/benchmark_paged_attention.py

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

benchmarks/kernels/benchmark_quant.py

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

benchmarks/kernels/benchmark_rmsnorm.py

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

benchmarks/kernels/benchmark_rope.py

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

benchmarks/kernels/benchmark_shapes.py

@@ -1,3 +1,5 @@
+# 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

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

benchmarks/kernels/utils.py

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

benchmarks/kernels/weight_shapes.py

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

benchmarks/overheads/benchmark_hashing.py

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

cmake/hipify.py

@@ -1,4 +1,5 @@
 #!/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 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). 
+# 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). 
 # 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 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
+  # 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
   set(_CUDA_ARCHS)
   if ("9.0a" IN_LIST SRC_CUDA_ARCHS)
     list(REMOVE_ITEM SRC_CUDA_ARCHS "9.0a")
@@ -283,6 +283,14 @@ 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

@@ -1,3 +1,5 @@
+# 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

@@ -15,6 +15,9 @@ 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

@@ -46,7 +46,10 @@ 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());
 
-  const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
+  // 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 at::cuda::OptionalCUDAGuard device_guard(
       src_device.is_cuda() ? src_device : dst_device);
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
@@ -93,6 +96,24 @@ __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
@@ -147,6 +168,42 @@ 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>
@@ -254,6 +311,7 @@ __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,                    //
@@ -274,9 +332,8 @@ __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 * (kv_lora_rank + pe_dim) + i +
-                              offset;
+      const int64_t dst_idx =
+          block_idx * block_stride + block_offset * entry_stride + i + offset;
       if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
         dst[dst_idx] = src[src_idx];
       } else {
@@ -397,8 +454,8 @@ void reshape_and_cache_flash(
           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,               \
+          slot_mapping.data_ptr<int64_t>(), block_stride, entry_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(
@@ -428,6 +485,7 @@ 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/cuda_utils.h

@@ -1,5 +1,7 @@
 #pragma once
 
+#include <stdio.h>
+
 #if defined(__CUDACC__) || defined(_NVHPC_CUDA)
   #define HOST_DEVICE_INLINE __forceinline__ __host__ __device__
   #define DEVICE_INLINE __forceinline__ __device__
@@ -10,6 +12,16 @@
   #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

@@ -1,16 +1,22 @@
+#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) {
-  int device, value;
-  if (device_id < 0) {
-    cudaGetDevice(&device);
-  } else {
-    device = 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));
     }
-  cudaDeviceGetAttribute(&value, static_cast<cudaDeviceAttr>(attribute),
-                         device);
+    int value;
+    CUDA_CHECK(cudaDeviceGetAttribute(
+        &value, static_cast<cudaDeviceAttr>(attribute), device));
+    return static_cast<int>(value);
+  }();
+
   return value;
 }
 

csrc/cumem_allocator.cpp

@@ -12,14 +12,20 @@ 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);             \
-      std::cerr << "CUDA Error: " << error_string << " at " << __FILE__ << ":" \
-                << __LINE__ << std::endl;                                      \
+      snprintf(error_msg, sizeof(error_msg), "CUDA Error: %s at %s:%d", \
+               error_string, __FILE__, __LINE__);                       \
+      std::cerr << error_msg << std::endl;                              \
     }                                                                   \
   } while (0)
 
@@ -54,14 +60,22 @@ 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;
 }
@@ -73,7 +87,13 @@ 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,
@@ -121,12 +141,16 @@ 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(
@@ -208,6 +232,9 @@ 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);
 }
 
@@ -258,6 +285,12 @@ 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;
 }
 
@@ -282,6 +315,12 @@ 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/epilogue/scaled_mm_epilogues_c3x.hpp

@@ -16,6 +16,30 @@ 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
@@ -174,6 +198,49 @@ 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

csrc/cutlass_extensions/vllm_cutlass_library_extension.py

@@ -1,3 +1,5 @@
+# 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 <THC/THCAtomics.cuh>
+#include <ATen/cuda/Atomic.cuh>
 
 #include "../cuda_compat.h"
 #include "../dispatch_utils.h"
@@ -197,6 +197,83 @@ __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]
@@ -305,6 +382,43 @@ 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

@@ -12,3 +12,9 @@ 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

@@ -22,6 +22,15 @@ 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

@@ -176,8 +176,11 @@ void cutlass_scaled_sparse_mm(torch::Tensor& out, torch::Tensor const& a,
                               torch::Tensor const& b_scales,
                               std::optional<torch::Tensor> const& bias);
 
-bool cutlass_sparse_compress_entry(torch::Tensor& a_compressed,
-                                   torch::Tensor& e, torch::Tensor const& a);
+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);
 #endif
 
 void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor const& input,

csrc/pos_encoding_kernels.cu

@@ -124,18 +124,54 @@ __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]
+                           // [num_tokens, num_heads * head_size] or
+                           // [batch_size, seq_len, num_heads, head_size] or
+                           // [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]
+                           // [num_tokens, num_kv_heads * head_size] or
+                           // [batch_size, seq_len, num_heads, head_size] or
+                           // [num_tokens, num_heads, head_size]
     int64_t head_size,
     torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
     bool is_neox) {
-  int64_t num_tokens = query.numel() / query.size(-1);
+  // 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);
+
   int rot_dim = cos_sin_cache.size(1);
-  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);
+  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);
 
   dim3 grid(num_tokens);
   dim3 block(std::min<int64_t>(num_heads * rot_dim / 2, 512));
@@ -165,19 +201,58 @@ 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]
+                           // [num_tokens, num_heads * head_size] or
+                           // [batch_size, seq_len, num_heads, head_size] or
+                           // [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]
+                           // [num_tokens, num_kv_heads * head_size] or
+                           // [batch_size, seq_len, num_heads, head_size] or
+                           // [num_tokens, num_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]
+    torch::Tensor& cos_sin_cache_offsets  // [num_tokens] or [batch_size]
 ) {
+  // num_tokens = batch_size * seq_len
   int64_t num_tokens = cos_sin_cache_offsets.size(0);
-  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);
+  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);
 
   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 < 4; ++ax1_0_1) {
+  for (int ax1_0_1 = 0; ax1_0_1 < (N / 32); ++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;

csrc/quantization/cutlass_w8a8/Epilogues.md

@@ -1,6 +1,7 @@
 # CUTLASS Epilogues
 
 ## Introduction
+
 This document describes the various CUTLASS epilogues implemented for fusing de-quantization operations onto GEMMs.
 
 Currently, we only support symmetric quantization for weights,
@@ -8,10 +9,11 @@ and symmetric and asymmetric quantization for activations.
 Both can be quantized per-tensor or per-channel (weights) / per-token (activations).
 
 There are 4 epilogues:
-1. ScaledEpilogue: symmetric quantization for activations, no bias.
-1. ScaledEpilogueBias: symmetric quantization for activations, supports bias.
-1. ScaledEpilogueAzp: asymmetric per-tensor quantization for activations, supports bias.
-1. ScaledEpilogueAzpPerToken: asymmetric per-token quantization for activations, supports bias.
+
+1. `ScaledEpilogue`: symmetric quantization for activations, no bias.
+1. `ScaledEpilogueBias`: symmetric quantization for activations, supports bias.
+1. `ScaledEpilogueAzp`: asymmetric per-tensor quantization for activations, supports bias.
+1. `ScaledEpilogueAzpPerToken`: asymmetric per-token quantization for activations, supports bias.
 
 We do not have epilogues for asymmetric quantization of activations without bias in order to reduce final binary size.
 Instead, if no bias is passed, the epilogue will use 0 as the bias.
@@ -26,12 +28,15 @@ If $` \widehat X `$ is the quantized $` X `$, our matrices become the following
 ```math
 A = s_a (\widehat A - J_a z_a)
 ```
+
 ```math
 B = s_b \widehat B
 ```
+
 ```math
 D = A B + C
 ```
+
 ```math
 D = s_a s_b \widehat D + C
 ```
@@ -48,9 +53,11 @@ Expanding further, we can calculate $` \widehat D `$ as follows:
 ```math
 A B = s_a ( \widehat A - J_a z_a ) s_b \widehat B
 ```
+
 ```math
 A B = s_a s_b \left( \widehat A \widehat B - J_a z_a \widehat B \right)
 ```
+
 ```math
 \widehat D = \widehat A \widehat B - z_a J_a \widehat B
 ```
@@ -61,16 +68,19 @@ Each row of it is equal to $` \mathbf 1 \widehat B `$, which is a row-vector of
 
 ## Epilogues
 
-### ScaledEpilogue
+### `ScaledEpilogue`
+
 This epilogue computes the symmetric quantization for activations without bias, meaning $` C = 0 `$ and $` z_a = 0 `$.
 The output of the GEMM is:
 
 ```math
 \widehat D = \widehat A \widehat B
 ```
+
 ```math
 D = s_a s_b \widehat D
 ```
+
 ```math
 D = s_a s_b \widehat A \widehat B
 ```
@@ -79,36 +89,42 @@ Epilogue parameters:
 - `scale_a` is the scale for activations, can be per-tensor (scalar) or per-token (column-vector).
 - `scale_b` is the scale for weights, can be per-tensor (scalar) or per-channel (row-vector).
 
-### ScaledEpilogueBias
+### `ScaledEpilogueBias`
+
 This epilogue computes the symmetric quantization for activations with bias, meaning $` z_a = 0 `$.
 The output of the GEMM is:
 
 ```math
 \widehat D = \widehat A \widehat B
 ```
+
 ```math
 D = s_a s_b \widehat D + C 
 ```
+
 ```math
 D = s_a s_b \widehat A \widehat B + C
 ```
 
-
 Epilogue parameters:
+
 - `scale_a` is the scale for activations, can be per-tensor (scalar) or per-token (column-vector).
 - `scale_b` is the scale for weights, can be per-tensor (scalar) or per-channel (row-vector).
 - `bias` is the bias, is always per-channel (row-vector).
 
-### ScaledEpilogueAzp
+### `ScaledEpilogueAzp`
+
 This epilogue computes the asymmetric per-tensor quantization for activations with bias.
 The output of the GEMM is:
 
 ```math
 \widehat D = \widehat A \widehat B - z_a J_a \widehat B
 ```
+
 ```math
 D = s_a s_b \widehat D + C 
 ```
+
 ```math
 D = s_a s_b \left( \widehat A \widehat B - z_a J_a \widehat B \right) + C
 ```
@@ -117,6 +133,7 @@ Because $` z_a `$ is a scalar, the zero-point term $` z_a J_a \widehat B `$ has
 That is precomputed and stored in `azp_with_adj` as a row-vector.
 
 Epilogue parameters:
+
 - `scale_a` is the scale for activations, can be per-tensor (scalar) or per-token (column-vector).
   - Generally this will be per-tensor as the zero-points are per-tensor.
 - `scale_b` is the scale for weights, can be per-tensor (scalar) or per-channel (row-vector).
@@ -125,13 +142,15 @@ Epilogue parameters:
 
 To use these kernels efficiently, users must precompute the `azp_with_adj` term offline and pass it to the kernel.
 
-### ScaledEpilogueAzpPerToken
+### `ScaledEpilogueAzpPerToken`
+
 This epilogue computes the asymmetric per-token quantization for activations with bias.
 
 The output of the GEMM is the same as above, but the $` z_a `$ is a column-vector.
 That means the zero-point term $` z_a J_a \widehat B `$ becomes an outer product of $` z_a `$ and $` \mathbf 1 \widehat B `$.
 
 Epilogue parameters:
+
 - `scale_a` is the scale for activations, can be per-tensor (scalar) or per-token (column-vector).
   - Generally this will be per-token as the zero-points are per-token.
 - `scale_b` is the scale for weights, can be per-tensor (scalar) or per-channel (row-vector).
@@ -142,6 +161,7 @@ Epilogue parameters:
 To use these kernels efficiently, users must precompute the `azp_adj` term offline and pass it to the kernel.
 
 The epilogue performs the following computation (where `Dq` is the raw quantized output of the GEMM):
-```
+
+```math
 out = scale_a * scale_b * (Dq - azp_adj * azp) + bias
 ```

csrc/quantization/cutlass_w8a8/c3x/scaled_mm.cuh

@@ -53,12 +53,17 @@ struct cutlass_3x_gemm {
 
   using EVTCompute = typename Epilogue::EVTCompute;
 
+  // These are the minimum alignments needed for the kernels to compile
+  static constexpr int AlignmentAB =
+      128 / cutlass::sizeof_bits<ElementAB>::value;
+  static constexpr int AlignmentCD = 4;
+
   using CollectiveEpilogue =
       typename cutlass::epilogue::collective::CollectiveBuilder<
           cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
           ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
-          ElementAcc, float, ElementC, StrideC, 4, ElementD, StrideD, 4,
-          EpilogueSchedule, EVTCompute>::CollectiveOp;
+          ElementAcc, float, ElementC, StrideC, AlignmentCD, ElementD, StrideD,
+          AlignmentCD, EpilogueSchedule, EVTCompute>::CollectiveOp;
 
   static constexpr size_t CEStorageSize =
       sizeof(typename CollectiveEpilogue::SharedStorage);
@@ -69,8 +74,8 @@ struct cutlass_3x_gemm {
   using CollectiveMainloop =
       typename cutlass::gemm::collective::CollectiveBuilder<
           cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, 
-          ElementAB, cutlass::layout::RowMajor, 16, 
-          ElementAB, cutlass::layout::ColumnMajor, 16, 
+          ElementAB, cutlass::layout::RowMajor, AlignmentAB, 
+          ElementAB, cutlass::layout::ColumnMajor, AlignmentAB, 
           ElementAcc, TileShape, ClusterShape,
           Stages,
           KernelSchedule>::CollectiveOp;

csrc/quantization/cutlass_w8a8/scaled_mm_c2x.cuh

@@ -103,14 +103,19 @@ struct cutlass_2x_gemm {
 
   using EVTD = cutlass::epilogue::threadblock::Sm80EVT<D, EVTCompute>;
 
+  // These are the minimum alignments needed for the kernels to compile
+  static constexpr int AlignmentAB =
+      128 / cutlass::sizeof_bits<ElementAB>::value;
+  static constexpr int AlignmentCD = 4;
+
   // clang-format off
   using RowMajor = typename cutlass::layout::RowMajor;
   using ColumnMajor = typename cutlass::layout::ColumnMajor;
   using KernelType =
     ArchGuard<typename cutlass::gemm::kernel::DefaultGemmWithVisitor<
-      ElementAB, RowMajor, cutlass::ComplexTransform::kNone, 16,
-      ElementAB, ColumnMajor, cutlass::ComplexTransform::kNone, 16,
-      float, cutlass::layout::RowMajor, 4,
+      ElementAB, RowMajor, cutlass::ComplexTransform::kNone, AlignmentAB,
+      ElementAB, ColumnMajor, cutlass::ComplexTransform::kNone, AlignmentAB,
+      float, cutlass::layout::RowMajor, AlignmentCD,
       ElementAcc, float, cutlass::arch::OpClassTensorOp,
       Arch,
       TileShape, WarpShape, InstructionShape,

csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu

@@ -16,29 +16,11 @@ void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
   TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
   TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
 
-  using GroupShape = std::array<int64_t, 2>;
-
   int M = a.size(0), N = b.size(1), K = a.size(1);
 
-  GroupShape a_scale_group_shape = [&, &s = a_scales]() -> GroupShape {
-    if (s.numel() == 1) return {M, K};  // tensor-wise
-    if (s.dim() == 2)
-      return {ceil_div(a.size(0), s.size(0)), ceil_div(a.size(1), s.size(1))};
-    TORCH_CHECK(false, "Unsupported scale shape for scale_a");
-  }();
-
-  GroupShape b_scale_group_shape = [&, &s = b_scales]() -> GroupShape {
-    if (s.numel() == 1) return {K, N};  // tensor-wise
-    if (s.dim() == 2)
-      return {ceil_div(b.size(0), s.size(0)), ceil_div(b.size(1), s.size(1))};
-    TORCH_CHECK(false, "Unsupported scale shape for scale_b");
-  }();
-
-  if ((a_scale_group_shape == GroupShape{M, K} ||
-       a_scale_group_shape == GroupShape{1, K}) &&
-      (b_scale_group_shape == GroupShape{K, N} ||
-       b_scale_group_shape == GroupShape{K, 1})) {
-    // "standard per-tensor/per-token/per-channel" scaling
+  if ((a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
+      (b_scales.numel() == 1 || b_scales.numel() == b.size(1))) {
+    // Standard per-tensor/per-token/per-channel scaling
     TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
     if (a.dtype() == torch::kFloat8_e4m3fn) {
       vllm::cutlass_scaled_mm_sm90_fp8(c, a, b, a_scales, b_scales, bias);
@@ -46,25 +28,32 @@ void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
       TORCH_CHECK(a.dtype() == torch::kInt8);
       vllm::cutlass_scaled_mm_sm90_int8(c, a, b, a_scales, b_scales, bias);
     }
-  } else if (a_scale_group_shape == GroupShape{1, 128} &&
-             b_scale_group_shape == GroupShape{128, 128}) {
+  } else {
+    using GroupShape = std::array<int64_t, 2>;
+    auto make_group_shape = [](torch::Tensor const& x,
+                               torch::Tensor const& s) -> GroupShape {
+      TORCH_CHECK(s.dim() == 2, "cutlass_scaled_mm group scales must be 2D");
+      return {ceil_div(x.size(0), s.size(0)), ceil_div(x.size(1), s.size(1))};
+    };
+
+    GroupShape a_scale_group_shape = make_group_shape(a, a_scales);
+    GroupShape b_scale_group_shape = make_group_shape(b, b_scales);
+
     // 1x128 per-token group scales for activations
     // 128x128 blockwise scales for weights
-    TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn &&
-                    b.dtype() == torch::kFloat8_e4m3fn,
-                "Currently only FP8 is supported for A group shape 1x128 and "
-                "B group shape 128x128");
+    TORCH_CHECK((a_scale_group_shape == GroupShape{1, 128} &&
+                 b_scale_group_shape == GroupShape{128, 128} &&
+                 a.dtype() == torch::kFloat8_e4m3fn &&
+                 b.dtype() == torch::kFloat8_e4m3fn),
+                "cutlass_scaled_mm only supports datatype float8_e4m3fn.\n"
+                "a_scale_group_shape must be [1, 128]. Got: [",
+                a_scale_group_shape[0], ", ", a_scale_group_shape[1],
+                "]\n"
+                "b_scale_group_shape must be [128, 128]. Got: [",
+                b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
     TORCH_CHECK(!bias, "Bias not yet supported blockwise scaled_mm");
 
     vllm::cutlass_scaled_mm_blockwise_sm90_fp8(c, a, b, a_scales, b_scales);
-  } else {
-    TORCH_CHECK(false,
-                "Unsupported scale group shapes for CUTLASS 3.x GEMM.\n "
-                "a_scale_group_shape must be [1, 128], got: [",
-                a_scale_group_shape[0], ", ", a_scale_group_shape[1],
-                "]\n"
-                "b_scale_group_shape must be [128, 128], got: [",
-                b_scale_group_shape[0], ", ", b_scale_group_shape[1], "]");
   }
 }
 

csrc/quantization/fp4/nvfp4_quant_entry.cu

@@ -0,0 +1,32 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#if defined ENABLE_NVFP4 && ENABLE_NVFP4
+void scaled_fp4_quant_sm100a(torch::Tensor const& output,
+                             torch::Tensor const& input,
+                             torch::Tensor const& output_sf,
+                             torch::Tensor const& input_sf);
+#endif
+
+void scaled_fp4_quant(torch::Tensor& output, torch::Tensor const& input,
+                      torch::Tensor& output_sf, torch::Tensor const& input_sf) {
+#if defined ENABLE_NVFP4 && ENABLE_NVFP4
+  return scaled_fp4_quant_sm100a(output, input, output_sf, input_sf);
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(false, "No compiled nvfp4 quantization");
+}

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -0,0 +1,379 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#include <cuda_runtime_api.h>
+#include <cuda_runtime.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include <cuda_fp8.h>
+
+#include "cuda_utils.h"
+
+// Get type2 from type or vice versa (applied to half and bfloat16)
+template <typename T>
+struct TypeConverter {
+  using Type = half2;
+};  // keep for generality
+
+template <>
+struct TypeConverter<half2> {
+  using Type = half;
+};
+
+template <>
+struct TypeConverter<half> {
+  using Type = half2;
+};
+
+template <>
+struct TypeConverter<__nv_bfloat162> {
+  using Type = __nv_bfloat16;
+};
+
+template <>
+struct TypeConverter<__nv_bfloat16> {
+  using Type = __nv_bfloat162;
+};
+
+#define ELTS_PER_THREAD 8
+
+constexpr int CVT_FP4_ELTS_PER_THREAD = 8;
+constexpr int CVT_FP4_SF_VEC_SIZE = 16;
+
+// Convert 8 float32 values into 8 e2m1 values (represented as one uint32_t).
+inline __device__ uint32_t fp32_vec_to_e2m1(float (&array)[8]) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+  uint32_t val;
+  asm volatile(
+      "{\n"
+      ".reg .b8 byte0;\n"
+      ".reg .b8 byte1;\n"
+      ".reg .b8 byte2;\n"
+      ".reg .b8 byte3;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte0, %2, %1;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte1, %4, %3;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
+      "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
+      "}"
+      : "=r"(val)
+      : "f"(array[0]), "f"(array[1]), "f"(array[2]), "f"(array[3]),
+        "f"(array[4]), "f"(array[5]), "f"(array[6]), "f"(array[7]));
+  return val;
+#else
+  return 0;
+#endif
+}
+
+// Convert 4 float2 values into 8 e2m1 values (represented as one uint32_t).
+inline __device__ uint32_t fp32_vec_to_e2m1(float2 (&array)[4]) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+  uint32_t val;
+  asm volatile(
+      "{\n"
+      ".reg .b8 byte0;\n"
+      ".reg .b8 byte1;\n"
+      ".reg .b8 byte2;\n"
+      ".reg .b8 byte3;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte0, %2, %1;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte1, %4, %3;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte2, %6, %5;\n"
+      "cvt.rn.satfinite.e2m1x2.f32   byte3, %8, %7;\n"
+      "mov.b32 %0, {byte0, byte1, byte2, byte3};\n"
+      "}"
+      : "=r"(val)
+      : "f"(array[0].x), "f"(array[0].y), "f"(array[1].x), "f"(array[1].y),
+        "f"(array[2].x), "f"(array[2].y), "f"(array[3].x), "f"(array[3].y));
+  return val;
+#else
+  return 0;
+#endif
+}
+
+// Fast reciprocal.
+inline __device__ float reciprocal_approximate_ftz(float a) {
+  float b;
+  asm volatile("rcp.approx.ftz.f32 %0, %1;\n" : "=f"(b) : "f"(a));
+  return b;
+}
+
+template <class SFType, int CVT_FP4_NUM_THREADS_PER_SF>
+__device__ uint8_t* cvt_quant_to_fp4_get_sf_out_offset(int rowIdx, int colIdx,
+                                                       int numCols,
+                                                       SFType* SFout) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+  static_assert(CVT_FP4_NUM_THREADS_PER_SF == 1 ||
+                CVT_FP4_NUM_THREADS_PER_SF == 2);
+
+  // One pair of threads write one SF to global memory.
+  // TODO: stage through smem for packed STG.32
+  // is it better than STG.8 from 4 threads ?
+  if (threadIdx.x % CVT_FP4_NUM_THREADS_PER_SF == 0) {
+    // SF vector index (16 elements share one SF in the K dimension).
+    int32_t kIdx = colIdx / CVT_FP4_NUM_THREADS_PER_SF;
+    int32_t mIdx = rowIdx;
+
+    // SF layout [numMTiles, numKTiles, 32 (mTile), 4 (mTile), 4(kTile)]
+    // --> index [mTileIdx, kTileIdx, outerMIdx, innerMIdx, innerKIdx]
+
+    int32_t mTileIdx = mIdx / (32 * 4);
+    // SF vector size 16.
+    int factor = CVT_FP4_SF_VEC_SIZE * 4;
+    int32_t numKTiles = (numCols + factor - 1) / factor;
+    int64_t mTileStride = numKTiles * 32 * 4 * 4;
+
+    int32_t kTileIdx = (kIdx / 4);
+    int64_t kTileStride = 32 * 4 * 4;
+
+    // M tile layout [32, 4] is column-major.
+    int32_t outerMIdx = (mIdx % 32);
+    int64_t outerMStride = 4 * 4;
+
+    int32_t innerMIdx = (mIdx % (32 * 4)) / 32;
+    int64_t innerMStride = 4;
+
+    int32_t innerKIdx = (kIdx % 4);
+    int64_t innerKStride = 1;
+
+    // Compute the global offset.
+    int64_t SFOffset = mTileIdx * mTileStride + kTileIdx * kTileStride +
+                       outerMIdx * outerMStride + innerMIdx * innerMStride +
+                       innerKIdx * innerKStride;
+
+    return reinterpret_cast<uint8_t*>(SFout) + SFOffset;
+  }
+#endif
+  return nullptr;
+}
+
+// Define a 16 bytes packed data type.
+template <class Type>
+struct PackedVec {
+  typename TypeConverter<Type>::Type elts[4];
+};
+
+template <>
+struct PackedVec<__nv_fp8_e4m3> {
+  __nv_fp8x2_e4m3 elts[8];
+};
+
+// Quantizes the provided PackedVec into the uint32_t output
+template <class Type, bool UE8M0_SF = false>
+__device__ uint32_t cvt_warp_fp16_to_fp4(PackedVec<Type>& vec, float SFScaleVal,
+                                         uint8_t* SFout) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+  // Get absolute maximum values among the local 8 values.
+  auto localMax = __habs2(vec.elts[0]);
+
+  // Local maximum value.
+  #pragma unroll
+  for (int i = 1; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
+    localMax = __hmax2(localMax, __habs2(vec.elts[i]));
+  }
+
+  // Get the absolute maximum among all 16 values (two threads).
+  localMax = __hmax2(__shfl_xor_sync(uint32_t(-1), localMax, 1), localMax);
+  // Get the final absolute maximum values.
+  float vecMax = float(__hmax(localMax.x, localMax.y));
+
+  // Get the SF (max value of the vector / max value of e2m1).
+  // maximum value of e2m1 = 6.0.
+  // TODO: use half as compute data type.
+  float SFValue = SFScaleVal * (vecMax * reciprocal_approximate_ftz(6.0f));
+  // 8 bits representation of the SF.
+  uint8_t fp8SFVal;
+  // Write the SF to global memory (STG.8).
+  if constexpr (UE8M0_SF) {
+    // Extract the 8 exponent bits from float32.
+    // float 32bits = 1 sign bit + 8 exponent bits + 23 mantissa bits.
+    uint32_t tmp = reinterpret_cast<uint32_t&>(SFValue) >> 23;
+    fp8SFVal = tmp & 0xff;
+    // Convert back to fp32.
+    reinterpret_cast<uint32_t&>(SFValue) = tmp << 23;
+  } else {
+    // Here SFValue is always positive, so E4M3 is the same as UE4M3.
+    __nv_fp8_e4m3 tmp = __nv_fp8_e4m3(SFValue);
+    reinterpret_cast<__nv_fp8_e4m3&>(fp8SFVal) = tmp;
+    // Convert back to fp32.
+    SFValue = float(tmp);
+  }
+  // Get the output scale.
+  // Recipe: final_scale = reciprocal(fp32(fp8(SFValue * SFScaleVal))) *
+  //                       reciprocal(SFScaleVal))
+  float outputScale =
+      SFValue != 0 ? reciprocal_approximate_ftz(
+                         SFValue * reciprocal_approximate_ftz(SFScaleVal))
+                   : 0.0f;
+
+  if (SFout) {
+    // Write the SF to global memory (STG.8).
+    *SFout = fp8SFVal;
+  }
+
+  // Convert the input to float.
+  float2 fp2Vals[CVT_FP4_ELTS_PER_THREAD / 2];
+
+  #pragma unroll
+  for (int i = 0; i < CVT_FP4_ELTS_PER_THREAD / 2; i++) {
+    if constexpr (std::is_same_v<Type, half>) {
+      fp2Vals[i] = __half22float2(vec.elts[i]);
+    } else {
+      fp2Vals[i] = __bfloat1622float2(vec.elts[i]);
+    }
+    fp2Vals[i].x *= outputScale;
+    fp2Vals[i].y *= outputScale;
+  }
+
+  // Convert to e2m1 values.
+  uint32_t e2m1Vec = fp32_vec_to_e2m1(fp2Vals);
+
+  // Write the e2m1 values to global memory.
+  return e2m1Vec;
+#else
+  return 0;
+#endif
+}
+
+// Use UE4M3 by default.
+template <class Type, bool UE8M0_SF = false>
+__global__ void
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+__launch_bounds__(512, 4) cvt_fp16_to_fp4(
+#else
+cvt_fp16_to_fp4(
+#endif
+    int32_t numRows, int32_t numCols, Type const* in, float const* SFScale,
+    uint32_t* out, uint32_t* SFout) {
+#if defined(__CUDA_ARCH__) && (__CUDA_ARCH__ >= 1000)
+  using PackedVec = PackedVec<Type>;
+  static constexpr int CVT_FP4_NUM_THREADS_PER_SF =
+      (CVT_FP4_SF_VEC_SIZE / CVT_FP4_ELTS_PER_THREAD);
+  static_assert(sizeof(PackedVec) == sizeof(Type) * CVT_FP4_ELTS_PER_THREAD,
+                "Vec size is not matched.");
+
+  // Get the global scaling factor, which will be applied to the SF.
+  // Note SFScale is the same as next GEMM's alpha, which is
+  // (448.f / (Alpha_A / 6.f)).
+  float const SFScaleVal = SFScale == nullptr ? 1.0f : SFScale[0];
+
+  // Input tensor row/col loops.
+  for (int rowIdx = blockIdx.x; rowIdx < numRows; rowIdx += gridDim.x) {
+    for (int colIdx = threadIdx.x; colIdx < numCols / CVT_FP4_ELTS_PER_THREAD;
+         colIdx += blockDim.x) {
+      int64_t inOffset = rowIdx * (numCols / CVT_FP4_ELTS_PER_THREAD) + colIdx;
+      PackedVec in_vec = reinterpret_cast<PackedVec const*>(in)[inOffset];
+      // Get the output tensor offset.
+      // Same as inOffset because 8 elements are packed into one uint32_t.
+      int64_t outOffset = inOffset;
+      auto& out_pos = out[outOffset];
+
+      auto sf_out =
+          cvt_quant_to_fp4_get_sf_out_offset<uint32_t,
+                                             CVT_FP4_NUM_THREADS_PER_SF>(
+              rowIdx, colIdx, numCols, SFout);
+
+      out_pos =
+          cvt_warp_fp16_to_fp4<Type, UE8M0_SF>(in_vec, SFScaleVal, sf_out);
+    }
+  }
+#endif
+}
+
+template <typename T>
+void invokeFP4Quantization(int m, int n, T const* input, float const* SFScale,
+                           int64_t* output, int32_t* SFOuput, bool useUE8M0,
+                           int multiProcessorCount, cudaStream_t stream) {
+  // Grid, Block size.
+  // Each thread converts 8 values.
+  dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
+  // Get number of blocks per SM (assume we can fully utilize the SM).
+  int const numBlocksPerSM = 2048 / block.x;
+  dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
+
+  // Launch the cvt kernel.
+  if (useUE8M0) {
+    cvt_fp16_to_fp4<T, true><<<grid, block, 0, stream>>>(
+        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output),
+        reinterpret_cast<uint32_t*>(SFOuput));
+  } else {
+    cvt_fp16_to_fp4<T, false><<<grid, block, 0, stream>>>(
+        m, n, input, SFScale, reinterpret_cast<uint32_t*>(output),
+        reinterpret_cast<uint32_t*>(SFOuput));
+  }
+}
+
+// Instantiate the function.
+template void invokeFP4Quantization(int m, int n, half const* input,
+                                    float const* SFScale, int64_t* output,
+                                    int32_t* SFOuput, bool useUE8M0,
+                                    int multiProcessorCount,
+                                    cudaStream_t stream);
+
+template void invokeFP4Quantization(int m, int n, __nv_bfloat16 const* input,
+                                    float const* SFScale, int64_t* output,
+                                    int32_t* SFOuput, bool useUE8M0,
+                                    int multiProcessorCount,
+                                    cudaStream_t stream);
+
+void scaled_fp4_quant_sm100a(torch::Tensor const& output,
+                             torch::Tensor const& input,
+                             torch::Tensor const& output_sf,
+                             torch::Tensor const& input_sf) {
+  int32_t m = input.size(0);
+  int32_t n = input.size(1);
+
+  TORCH_CHECK(n % 16 == 0, "The N dimension must be multiple of 16.");
+
+  int multiProcessorCount =
+      get_device_attribute(cudaDevAttrMultiProcessorCount, -1);
+
+  auto input_sf_ptr = static_cast<float const*>(input_sf.data_ptr());
+  auto sf_out = static_cast<int32_t*>(output_sf.data_ptr());
+  auto output_ptr = static_cast<int64_t*>(output.data_ptr());
+  at::cuda::CUDAGuard device_guard{(char)input.get_device()};
+  auto stream = at::cuda::getStreamFromPool(false, input.get_device());
+  if (stream == nullptr) {
+    std::cerr << "Warning: Null CUDA stream" << std::endl;
+  }
+
+  // We don't support e8m0 scales at this moment.
+  bool useUE8M0 = false;
+
+  switch (input.scalar_type()) {
+    case torch::kHalf: {
+      auto input_ptr = reinterpret_cast<half const*>(input.data_ptr());
+      invokeFP4Quantization(m, n, input_ptr, input_sf_ptr, output_ptr, sf_out,
+                            useUE8M0, multiProcessorCount, stream);
+      break;
+    }
+    case torch::kBFloat16: {
+      auto input_ptr = reinterpret_cast<__nv_bfloat16 const*>(input.data_ptr());
+      invokeFP4Quantization(m, n, input_ptr, input_sf_ptr, output_ptr, sf_out,
+                            useUE8M0, multiProcessorCount, stream);
+      break;
+    }
+    default: {
+      std::cerr << "Observing: " << input.scalar_type()
+                << " for the input datatype which is invalid";
+      throw std::runtime_error(
+          "Unsupported input data type for quantize_to_fp4.");
+    }
+  }
+}

csrc/quantization/fp8/amd/hip_float8_impl.h

@@ -1,7 +1,6 @@
 #pragma once
 
-#if defined(__HIPCC__) && \
-    (defined(__gfx940__) || defined(__gfx941__) || defined(__gfx942__))
+#if defined(__HIPCC__) && defined(__gfx942__)
   #define __HIP__MI300__
 #endif
 

csrc/quantization/machete/Readme.md

@@ -6,7 +6,7 @@ Machete is a spiritual successor to the Marlin kernel but optimized for Hopper a
 
 Machete effectively performs
 
-```
+```python
 scale_type = w_s.dtype
 compute_type = a.dtype
 out = (w_q.to(scale_type) * w_s - w_z.to(scale_type)) @ a
@@ -24,7 +24,7 @@ applied.
 
 The main optimization within Machete is prepacking the weight matrix to more closely match the tensor core layouts, allowing for wider shared memory loads when loading the weight matrix. This means that the weight matrix must be prepacked before calling `machete_gemm`. The flow looks something like:
 
-```
+```python
 from vllm import _custom_ops as ops
 
 ...

csrc/quantization/machete/generate.py

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

csrc/rocm/attention.cu

@@ -24,8 +24,7 @@
 #include "../attention/dtype_fp8.cuh"
 #include "../quantization/fp8/amd/quant_utils.cuh"
 
-#if defined(__HIPCC__) && (defined(__gfx90a__) || defined(__gfx940__) || \
-                           defined(__gfx941__) || defined(__gfx942__))
+#if defined(__HIPCC__) && (defined(__gfx90a__) || defined(__gfx942__))
   #define __HIP__MI300_MI250__
 #endif
 

csrc/sparse/cutlass/sparse_compressor_c3x.cu

@@ -1,165 +0,0 @@
-// clang-format will break include orders
-// clang-format off
-#include <cudaTypedefs.h>
-
-#if defined CUDA_VERSION && CUDA_VERSION >= 12020
-#include "sparse_scaled_mm_c3x.cuh"
-
-#include "cutlass/numeric_conversion.h"
-#include "cutlass/transform/device/transform_universal_adapter.hpp"
-#include "cutlass/transform/kernel/sparse_gemm_compressor.hpp"
-#include "cutlass/epilogue/collective/default_epilogue.hpp"
-
-#include "cutlass/util/host_tensor.h"
-#include "cutlass/util/packed_stride.hpp"
-// clang-format on
-
-using namespace cute;
-using namespace vllm;
-
-/// Make A structured sparse by replacing elements with 0 and compress it
-template <typename ElementA_, typename ElementAcc_>
-bool cutlass_sparse_compress(torch::Tensor& a_nzs, torch::Tensor& a_meta,
-                             torch::Tensor const& a) {
-  // Checks for conformality
-  TORCH_CHECK(a.dtype() == torch::kInt8 || a.dtype() == torch::kFloat8_e4m3fn ||
-              a.dtype() == torch::kFloat16 || a.dtype() == torch::kBFloat16);
-  TORCH_CHECK(a.dim() == 2)
-  // Check for strides and alignment
-  TORCH_CHECK(a.stride(0) % 4 == 0)  // Required for semi-structured sparsity
-  TORCH_CHECK(a.stride(1) == 1)
-
-  int m = a.size(0);
-  int k = a.size(1);
-
-  // Sparse kernel setup; this kernel is not used for matmul,
-  // but just for setting up the compressor utility
-  // A matrix configuration
-  using ElementA = ElementA_;
-  using LayoutTagA = cutlass::layout::RowMajor;
-  constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
-  // B matrix configuration
-  using ElementB = ElementA;
-  using LayoutTagB = cutlass::layout::ColumnMajor;
-  constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
-  // C/D matrix configuration
-  using ElementC = float;
-  using LayoutTagC = cutlass::layout::ColumnMajor;
-  constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementC>::value;
-  // Core kernel configurations
-  using ElementAccumulator = ElementAcc_;
-  using TileShape = Shape<_128, _128, _128>;
-  using TileShapeRef = Shape<_128, _128, _64>;
-  using ClusterShape = Shape<_1, _2, _1>;
-  using KernelSchedule = typename std::conditional<
-      std::is_same_v<ElementA, cutlass::float_e4m3_t>,
-      cutlass::gemm::KernelTmaWarpSpecializedFP8FastAccum,
-      cutlass::gemm::KernelTmaWarpSpecialized>::type;
-
-  using EpilogueSchedule = cutlass::epilogue::TmaWarpSpecialized;
-  using ProblemShape = Shape<int, int, int, int>;
-
-  using CollectiveEpilogue =
-      typename cutlass::epilogue::collective::CollectiveBuilder<
-          cutlass::arch::Sm90, cutlass::arch::OpClassTensorOp, TileShape,
-          ClusterShape, cutlass::epilogue::collective::EpilogueTileAuto,
-          ElementAccumulator, ElementAccumulator, ElementC, LayoutTagC,
-          AlignmentC, ElementC, LayoutTagC, AlignmentC,
-          EpilogueSchedule>::CollectiveOp;
-
-  using CollectiveMainloop =
-      typename cutlass::gemm::collective::CollectiveBuilder<
-          cutlass::arch::Sm90, cutlass::arch::OpClassSparseTensorOp, ElementA,
-          LayoutTagA, AlignmentA, ElementB, LayoutTagB, AlignmentB,
-          ElementAccumulator, TileShape, ClusterShape,
-          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(
-              sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          KernelSchedule>::CollectiveOp;
-
-  using GemmKernel =
-      cutlass::gemm::kernel::GemmUniversal<ProblemShape, CollectiveMainloop,
-                                           CollectiveEpilogue>;
-
-  using Gemm = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
-
-  using StrideA = cutlass::gemm::TagToStrideA_t<LayoutTagA>;
-  using StrideE = StrideA;
-
-  using StrideA = Stride<int64_t, Int<1>, int64_t>;
-
-  // The n (=1) dimension does not matter for the compressor
-  typename GemmKernel::ProblemShape prob_shape{m, 1, k, 1};
-
-  using LayoutA = typename GemmKernel::CollectiveMainloop::LayoutA;
-  using LayoutE = typename GemmKernel::CollectiveMainloop::LayoutE;
-
-  using ElementE = typename GemmKernel::CollectiveMainloop::ElementE;
-  using SparseConfig = typename GemmKernel::CollectiveMainloop::SparseConfig;
-
-  // Offline compressor kernel
-  using CompressorUtility =
-      cutlass::transform::kernel::StructuredSparseCompressorUtility<
-          ProblemShape, ElementA, LayoutTagA, SparseConfig>;
-
-  using CompressorKernel =
-      cutlass::transform::kernel::StructuredSparseCompressor<
-          ProblemShape, ElementA, LayoutTagA, SparseConfig,
-          cutlass::arch::Sm90>;
-
-  using Compressor =
-      cutlass::transform::device::TransformUniversalAdapter<CompressorKernel>;
-
-  auto [M, N, K, L] = prob_shape;
-
-  StrideA stride_A;
-  stride_A =
-      cutlass::make_cute_packed_stride(StrideA{}, cute::make_shape(M, K, L));
-
-  CompressorUtility compressor_utility(prob_shape, stride_A);
-
-  int ME = compressor_utility.get_metadata_m_physical();
-  int KE = compressor_utility.get_metadata_k_physical();
-  int KC = compressor_utility.get_tensorA_k_physical();
-
-  auto a_ptr = static_cast<ElementA*>(a.data_ptr());
-
-  auto a_nzs_ptr = static_cast<ElementA*>(a_nzs.data_ptr());
-  auto a_meta_ptr = static_cast<typename Gemm::CollectiveMainloop::ElementE*>(
-      a_meta.data_ptr());
-
-  cutlass::KernelHardwareInfo hw_info;
-  hw_info.device_id = 0;
-  hw_info.sm_count =
-      cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
-          hw_info.device_id);
-  typename Compressor::Arguments arguments{
-      prob_shape, {a_ptr, stride_A, a_nzs_ptr, a_meta_ptr}, {hw_info}};
-
-  Compressor compressor_op;
-  size_t workspace_size = Compressor::get_workspace_size(arguments);
-  cutlass::device_memory::allocation<uint8_t> workspace(workspace_size);
-
-  CUTLASS_CHECK(compressor_op.can_implement(arguments));
-  CUTLASS_CHECK(compressor_op.initialize(arguments, workspace.get()));
-  CUTLASS_CHECK(compressor_op.run());
-  CUDA_CHECK(cudaDeviceSynchronize());
-
-  return true;
-}
-
-bool cutlass_sparse_compress_sm90(torch::Tensor& a_nzs, torch::Tensor& a_meta,
-                                  torch::Tensor const& a) {
-  if (a.dtype() == torch::kBFloat16) {
-    return cutlass_sparse_compress<cutlass::bfloat16_t, float>(a_nzs, a_meta,
-                                                               a);
-  } else if (a.dtype() == torch::kFloat16) {
-    return cutlass_sparse_compress<cutlass::half_t, float>(a_nzs, a_meta, a);
-  } else if (a.dtype() == torch::kFloat8_e4m3fn) {
-    return cutlass_sparse_compress<cutlass::float_e4m3_t, float>(a_nzs, a_meta,
-                                                                 a);
-  } else if (a.dtype() == torch::kInt8) {
-    return cutlass_sparse_compress<int8_t, int32_t>(a_nzs, a_meta, a);
-  }
-  return false;
-}
-#endif