[BugFix] FA2 MLA Accuracy Issue (#18807)

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

.buildkite/check-wheel-size.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
 import sys

.buildkite/generate_index.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import os

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from pathlib import Path
 
 import pytest

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 LM eval harness on model to compare vs HF baseline computed offline.
 Configs are found in configs/$MODEL.yaml

.buildkite/nightly-benchmarks/README.md

@@ -113,7 +113,7 @@ WARNING: The benchmarking script will save json results by itself, so please do
 
 ### Visualizing the results
 
-The `convert-results-json-to-markdown.py` helps you put the benchmarking results inside a markdown table, by formatting [descriptions.md](performance-benchmarks-descriptions.md) with real benchmarking 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.

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import json
 import os

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import json

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from lmdeploy.serve.openai.api_client import APIClient
 

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

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import datetime
 import json

.buildkite/release-pipeline.yaml

@@ -1,6 +1,5 @@
 steps:
   - label: "Build wheel - CUDA 12.8"
-    id: build-wheel-cuda-12-8
     agents:
       queue: cpu_queue_postmerge
     commands:
@@ -12,7 +11,6 @@ steps:
       DOCKER_BUILDKIT: "1"
 
   - label: "Build wheel - CUDA 12.6"
-    id: build-wheel-cuda-12-6
     agents:
       queue: cpu_queue_postmerge
     commands:
@@ -30,7 +28,6 @@ steps:
 
   - label: "Build wheel - CUDA 11.8"
     # depends_on: block-build-cu118-wheel
-    id: build-wheel-cuda-11-8
     agents:
       queue: cpu_queue_postmerge
     commands:
@@ -47,7 +44,6 @@ steps:
 
   - label: "Build release image"
     depends_on: block-release-image-build
-    id: build-release-image
     agents:
       queue: cpu_queue_postmerge
     commands:
@@ -55,18 +51,6 @@ steps:
       - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.8.1 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
-  - label: "Annotate release workflow"
-    depends_on:
-      - build-release-image
-      - build-wheel-cuda-12-8
-      - build-wheel-cuda-12-6
-      - build-wheel-cuda-11-8
-    id: annotate-release-workflow
-    agents:
-      queue: cpu_queue_postmerge
-    commands:
-      - "bash .buildkite/scripts/annotate-release.sh"
-
   - label: "Build and publish TPU release image"
     depends_on: ~
     if: build.env("NIGHTLY") == "1"
@@ -86,10 +70,9 @@ steps:
       DOCKER_BUILDKIT: "1"
 
   - input: "Provide Release version here"
-    id: input-release-version
     fields:
       - text: "What is the release version?"
-        key: release-version
+        key: "release-version"
 
   - block: "Build CPU release image"
     key: block-cpu-release-image-build

.buildkite/scripts/annotate-release.sh

@@ -1,31 +0,0 @@
-#!/bin/bash
-
-set -ex
-
-# Get release version and strip leading 'v' if present
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version | sed 's/^v//')
-
-if [ -z "$RELEASE_VERSION" ]; then
-  echo "Error: RELEASE_VERSION is empty. 'release-version' metadata might not be set or is invalid."
-  exit 1
-fi
-
-buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
-To download the wheel:
-\`\`\`
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}/vllm-${RELEASE_VERSION}-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu126/vllm-${RELEASE_VERSION}+cu126-cp38-abi3-manylinux1_x86_64.whl .
-aws s3 cp s3://vllm-wheels/${RELEASE_VERSION}+cu118/vllm-${RELEASE_VERSION}+cu118-cp38-abi3-manylinux1_x86_64.whl . 
-\`\`\`
-
-To download and upload the image:
-
-\`\`\`
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT} vllm/vllm-openai
-docker tag vllm/vllm-openai vllm/vllm-openai:latest
-docker tag vllm/vllm-openai vllm/vllm-openai:v${RELEASE_VERSION}
-docker push vllm/vllm-openai:latest
-docker push vllm/vllm-openai:v${RELEASE_VERSION}
-\`\`\`
-EOF 

.buildkite/scripts/ci-clean-log.sh

@@ -1,17 +0,0 @@
-#!/bin/bash
-# Usage: ./ci_clean_log.sh ci.log
-# This script strips timestamps and color codes from CI log files.
-
-# Check if argument is given
-if [ $# -lt 1 ]; then
-    echo "Usage: $0 ci.log"
-    exit 1
-fi
-
-INPUT_FILE="$1"
-
-# Strip timestamps
-sed -i 's/^\[[0-9]\{4\}-[0-9]\{2\}-[0-9]\{2\}T[0-9]\{2\}:[0-9]\{2\}:[0-9]\{2\}Z\] //' "$INPUT_FILE"
-
-# Strip colorization
-sed -i -r 's/\x1B\[[0-9;]*[mK]//g' "$INPUT_FILE"

.buildkite/scripts/hardware_ci/run-amd-test.sh

@@ -94,10 +94,6 @@ if [[ $commands == *"pytest -v -s compile/test_basic_correctness.py"* ]]; then
   commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s compile/test_basic_correctness.py"}
 fi
 
-if [[ $commands == *"pytest -v -s lora"* ]]; then
-  commands=${commands//"pytest -v -s lora"/"VLLM_ROCM_CUSTOM_PAGED_ATTN=0 pytest -v -s lora"}
-fi
-
 #ignore certain kernels tests
 if [[ $commands == *" kernels/core"* ]]; then
   commands="${commands} \

.buildkite/scripts/hardware_ci/run-cpu-test-ppc64le.sh

@@ -7,7 +7,6 @@ set -ex
 # Setup cleanup
 remove_docker_container() {
   if [[ -n "$container_id" ]]; then
-      podman stop --all -t0
       podman rm -f "$container_id" || true
   fi
   podman system prune -f
@@ -38,7 +37,7 @@ function cpu_tests() {
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-facebook/opt-125m]
     pytest -v -s tests/models/language/generation/test_common.py::test_models[False-5-32-google/gemma-1.1-2b-it]
     pytest -v -s tests/models/language/pooling/test_classification.py::test_models[float-jason9693/Qwen2.5-1.5B-apeach]
-    pytest -v -s tests/models/language/pooling/test_embedding.py -m cpu_model"
+    pytest -v -s tests/models/language/pooling/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]"
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

.buildkite/scripts/hardware_ci/run-cpu-test.sh

@@ -6,67 +6,72 @@ set -ex
 
 # allow to bind to different cores
 CORE_RANGE=${CORE_RANGE:-48-95}
-OMP_CORE_RANGE=${OMP_CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
-export CMAKE_BUILD_PARALLEL_LEVEL=32
-
 # Setup cleanup
 remove_docker_container() { 
     set -e; 
-    docker rm -f cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"-avx2 || true; 
+    docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || true; 
+    docker image rm cpu-test-"$BUILDKITE_BUILD_NUMBER" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 || true; 
 }
 trap remove_docker_container EXIT
 remove_docker_container
 
 # Try building the docker image
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$NUMA_NODE" --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --tag cpu-test-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$NUMA_NODE"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
 
 # Run the image, setting --shm-size=4g for tensor parallel.
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE" cpu-test-"$NUMA_NODE"
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE"  \
-docker run -itd --cpuset-cpus="$CORE_RANGE" --cpuset-mems="$NUMA_NODE" --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --env VLLM_CPU_OMP_THREADS_BIND="$OMP_CORE_RANGE" --shm-size=4g --name cpu-test-"$NUMA_NODE"-avx2 cpu-test-"$NUMA_NODE"-avx2
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
+docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2
 
 function cpu_tests() {
   set -e
   export NUMA_NODE=$2
+  export BUILDKITE_BUILD_NUMBER=$3
 
   # offline inference
-  docker exec cpu-test-"$NUMA_NODE"-avx2 bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
     set -e
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
 
   # Run basic model test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
-    pytest -v -s tests/kernels/attention/test_cache.py -m cpu_model
+    pytest -v -s tests/kernels/test_cache.py -m cpu_model
-    pytest -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
+    pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model
-    pytest -v -s tests/models/language/generation -m cpu_model
+    pytest -v -s tests/models/decoder_only/language -m cpu_model
-    pytest -v -s tests/models/language/pooling -m cpu_model
+    pytest -v -s tests/models/embedding/language -m cpu_model
-    pytest -v -s tests/models/multimodal/generation --ignore=tests/models/multimodal/generation/test_mllama.py -m cpu_model"
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model
+    pytest -v -s tests/models/decoder_only/audio_language -m cpu_model
+    pytest -v -s tests/models/decoder_only/vision_language -m cpu_model"
 
   # Run compressed-tensor test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
     tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_static_setup \
     tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_dynamic_per_token"
 
   # Run AWQ test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
-    VLLM_USE_V1=0 pytest -s -v \
+    pytest -s -v \
     tests/quantization/test_ipex_quant.py"
 
   # Run chunked-prefill and prefix-cache test
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v -k cpu_model \
     tests/basic_correctness/test_chunked_prefill.py"  
 
   # online serving
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
+    export VLLM_CPU_KVCACHE_SPACE=10 
+    export VLLM_CPU_OMP_THREADS_BIND=$1
     python3 -m vllm.entrypoints.openai.api_server --model facebook/opt-125m --dtype half & 
     timeout 600 bash -c 'until curl localhost:8000/v1/models; do sleep 1; done' || exit 1
     python3 benchmarks/benchmark_serving.py \
@@ -78,7 +83,7 @@ function cpu_tests() {
       --tokenizer facebook/opt-125m"
 
   # Run multi-lora tests
-  docker exec cpu-test-"$NUMA_NODE" bash -c "
+  docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
     pytest -s -v \
     tests/lora/test_qwen2vl.py"
@@ -86,4 +91,4 @@ function cpu_tests() {
 
 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
-timeout 1h bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE $BUILDKITE_BUILD_NUMBER"

.buildkite/scripts/hardware_ci/run-tpu-v1-test.sh

@@ -2,184 +2,102 @@
 
 set -xu
 
-
-remove_docker_container() { 
-    docker rm -f tpu-test || true; 
-    docker rm -f vllm-tpu || true;
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
 # Build the docker image.
 docker build -f docker/Dockerfile.tpu -t vllm-tpu .
 
 # Set up cleanup.
-cleanup_docker() {
+remove_docker_container() { docker rm -f tpu-test || true; }
-  # Get Docker's root directory
+trap remove_docker_container EXIT
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
+# Remove the container that might not be cleaned up in the previous run.
-  if [ -z "$docker_root" ]; then
+remove_docker_container
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes / force the system prune for old images as well.
-    docker volume prune -f && docker system prune --force --filter "until=72h" --all
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
-cleanup_docker
 
 # For HF_TOKEN.
 source /etc/environment
-
+# Run a simple end-to-end example.
 docker run --privileged --net host --shm-size=16G -it \
     -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c '
+    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
-set -e # Exit immediately if a command exits with a non-zero status.
+    && python3 -m pip install pytest pytest-asyncio tpu-info \
-set -u # Treat unset variables as an error.
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && export VLLM_XLA_CACHE_PATH= \
+    && export VLLM_USE_V1=1 \
+    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
+    && echo HARDWARE \
+    && tpu-info \
+    && { \
+        echo TEST_0: Running test_perf.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_perf.py; \
+        echo TEST_0_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_1: Running test_compilation.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py; \
+        echo TEST_1_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_2: Running test_basic.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py; \
+        echo TEST_2_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_3: Running test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine; \
+        echo TEST_3_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_4: Running test_quantization_accuracy.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py; \
+        echo TEST_4_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_5: Running examples/offline_inference/tpu.py; \
+        python3 /workspace/vllm/examples/offline_inference/tpu.py; \
+        echo TEST_5_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_6: Running test_tpu_model_runner.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/tpu/worker/test_tpu_model_runner.py; \
+        echo TEST_6_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_7: Running test_sampler.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py; \
+        echo TEST_7_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_8: Running test_topk_topp_sampler.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py; \
+        echo TEST_8_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_9: Running test_multimodal.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py; \
+        echo TEST_9_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_10: Running test_pallas.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py; \
+        echo TEST_10_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_11: Running test_struct_output_generate.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py; \
+        echo TEST_11_EXIT_CODE: \$?; \
+    } & \
+    { \
+        echo TEST_12: Running test_moe_pallas.py; \
+        python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py; \
+        echo TEST_12_EXIT_CODE: \$?; \
+    } & \
+    # Disable the TPU LoRA tests until the feature is activated
+    # & { \
+    #     echo TEST_13: Running test_moe_pallas.py; \
+    #     python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/; \
+    #     echo TEST_13_EXIT_CODE: \$?; \
+    # } & \
+    wait \
+    && echo 'All tests have attempted to run. Check logs for individual test statuses and exit codes.' \
+"
 
-echo "--- Starting script inside Docker container ---"
-
-# Create results directory
-RESULTS_DIR=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $RESULTS_DIR"
-
-# Install dependencies
-echo "--- Installing Python dependencies ---"
-python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
-    && python3 -m pip install --progress-bar off lm_eval[api]==0.4.4
-echo "--- Python dependencies installed ---"
-export VLLM_USE_V1=1
-export VLLM_XLA_CHECK_RECOMPILATION=1
-export VLLM_XLA_CACHE_PATH=
-echo "Using VLLM V1"
-
-echo "--- Hardware Information ---"
-tpu-info
-echo "--- Starting Tests ---"
-set +e
-overall_script_exit_code=0
-
-# --- Test Definitions ---
-# If a test fails, this function will print logs and will not cause the main script to exit.
-run_test() {
-    local test_num=$1
-    local test_name=$2
-    local test_command=$3
-    local log_file="$RESULTS_DIR/test_${test_num}.log"
-    local actual_exit_code
-
-    echo "--- TEST_$test_num: Running $test_name ---"
-    
-    # Execute the test command.
-    eval "$test_command" > >(tee -a "$log_file") 2> >(tee -a "$log_file" >&2)
-    actual_exit_code=$?
-
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" # This goes to main log
-    echo "TEST_${test_num}_COMMAND_EXIT_CODE: $actual_exit_code" >> "$log_file" # Also to per-test log
-
-    if [ "$actual_exit_code" -ne 0 ]; then
-        echo "TEST_$test_num ($test_name) FAILED with exit code $actual_exit_code." >&2
-        echo "--- Log for failed TEST_$test_num ($test_name) ---" >&2
-        if [ -f "$log_file" ]; then
-            cat "$log_file" >&2
-        else
-            echo "Log file $log_file not found for TEST_$test_num ($test_name)." >&2
-        fi
-        echo "--- End of log for TEST_$test_num ($test_name) ---" >&2
-        return "$actual_exit_code" # Return the failure code
-    else
-        echo "TEST_$test_num ($test_name) PASSED."
-        return 0 # Return success
-    fi
-}
-
-# Helper function to call run_test and update the overall script exit code
-run_and_track_test() {
-    local test_num_arg="$1"
-    local test_name_arg="$2"
-    local test_command_arg="$3"
-
-    # Run the test
-    run_test "$test_num_arg" "$test_name_arg" "$test_command_arg"
-    local test_specific_exit_code=$?
-
-    # If the test failed, set the overall script exit code to 1
-    if [ "$test_specific_exit_code" -ne 0 ]; then
-        # No need for extra echo here, run_test already logged the failure.
-        overall_script_exit_code=1
-    fi
-}
-
-# --- Actual Test Execution ---
-run_and_track_test 0 "test_perf.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_perf.py"
-run_and_track_test 1 "test_compilation.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_compilation.py"
-run_and_track_test 2 "test_basic.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_basic.py"
-run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
-run_and_track_test 4 "test_quantization_accuracy.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
-run_and_track_test 5 "examples/offline_inference/tpu.py" \
-    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
-run_and_track_test 6 "test_tpu_model_runner.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
-run_and_track_test 7 "test_sampler.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py"
-run_and_track_test 8 "test_topk_topp_sampler.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py"
-run_and_track_test 9 "test_multimodal.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_multimodal.py"
-run_and_track_test 10 "test_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py"
-run_and_track_test 11 "test_struct_output_generate.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.py -k \"not test_structured_output_with_reasoning_matrices\""
-run_and_track_test 12 "test_moe_pallas.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_moe_pallas.py"
-run_and_track_test 13 "test_lora.py" \
-    "VLLM_XLA_CHECK_RECOMPILATION=0 python3 -m pytest -s -v /workspace/vllm/tests/tpu/lora/test_lora.py"
-run_and_track_test 14 "test_tpu_qkv_linear.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_tpu_qkv_linear.py"
-run_and_track_test 15 "test_spmd_model_weight_loading.py" \
-    "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/test_spmd_model_weight_loading.py"
-
-# After all tests have been attempted, exit with the overall status.
-if [ "$overall_script_exit_code" -ne 0 ]; then
-    echo "--- One or more tests FAILED. Overall script exiting with failure code 1. ---"
-else
-    echo "--- All tests have completed and PASSED. Overall script exiting with success code 0. ---"
-fi
-exit "$overall_script_exit_code"
-' # IMPORTANT: This is the closing single quote for the bash -c "..." command. Ensure it is present and correct.
-
-# Capture the exit code of the docker run command
-DOCKER_RUN_EXIT_CODE=$?
-
-# The trap will run for cleanup.
-# Exit the main script with the Docker run command's exit code.
-if [ "$DOCKER_RUN_EXIT_CODE" -ne 0 ]; then
-    echo "Docker run command failed with exit code $DOCKER_RUN_EXIT_CODE."
-    exit "$DOCKER_RUN_EXIT_CODE"
-else
-    echo "Docker run command completed successfully."
-    exit 0
-fi
 # TODO: This test fails because it uses RANDOM_SEED sampling
-# pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+# && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \

.buildkite/scripts/rerun-test.sh

@@ -1,18 +0,0 @@
-#!/bin/bash
-
-# Usage: ./rerun_test.sh path/to/test.py::test_name
-
-# Check if argument is given
-if [ $# -lt 1 ]; then
-    echo "Usage: $0 path/to/test.py::test_name"
-    echo "Example: $0 tests/v1/engine/test_engine_core_client.py::test_kv_cache_events[True-tcp]"
-    exit 1
-fi
-
-TEST=$1
-COUNT=1
-
-while pytest -sv "$TEST"; do
-    COUNT=$((COUNT + 1))
-    echo "RUN NUMBER ${COUNT}"
-done

.buildkite/scripts/tpu/cleanup_docker.sh

@@ -1,24 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-docker_root=$(docker info -f '{{.DockerRootDir}}')
-if [ -z "$docker_root" ]; then
-  echo "Failed to determine Docker root directory."
-  exit 1
-fi
-echo "Docker root directory: $docker_root"
-# Check disk usage of the filesystem where Docker's root directory is located
-disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-# Define the threshold
-threshold=70
-if [ "$disk_usage" -gt "$threshold" ]; then
-  echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-  # Remove dangling images (those that are not tagged and not used by any container)
-  docker image prune -f
-  # Remove unused volumes / force the system prune for old images as well.
-  docker volume prune -f && docker system prune --force --filter "until=72h" --all
-  echo "Docker images and volumes cleanup completed."
-else
-  echo "Disk usage is below $threshold%. No cleanup needed."
-fi

.buildkite/scripts/tpu/config_v6e_1.env

@@ -1,14 +0,0 @@
-# Environment config
-TEST_NAME=llama8b
-CONTAINER_NAME=vllm-tpu
-
-# vllm config
-MODEL=meta-llama/Llama-3.1-8B-Instruct
-MAX_NUM_SEQS=512
-MAX_NUM_BATCHED_TOKENS=512
-TENSOR_PARALLEL_SIZE=1
-MAX_MODEL_LEN=2048
-DOWNLOAD_DIR=/mnt/disks/persist
-EXPECTED_THROUGHPUT=8.0
-INPUT_LEN=1800
-OUTPUT_LEN=128

.buildkite/scripts/tpu/docker_run_bm.sh

@@ -1,102 +0,0 @@
-#!/bin/bash
-
-if [ ! -f "$1" ]; then
-  echo "Error: The env file '$1' does not exist."
-  exit 1  # Exit the script with a non-zero status to indicate an error
-fi
-
-ENV_FILE=$1
-
-# For testing on local vm, use `set -a` to export all variables
-source /etc/environment
-source $ENV_FILE
-
-remove_docker_container() { 
-    docker rm -f tpu-test || true; 
-    docker rm -f vllm-tpu || true;
-    docker rm -f $CONTAINER_NAME || true;
-}
-
-trap remove_docker_container EXIT
-
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
-# Build docker image.
-# TODO: build the image outside the script and share the image with other
-# tpu test if building time is too long.
-DOCKER_BUILDKIT=1 docker build \
-  --build-arg max_jobs=16 \
-  --build-arg USE_SCCACHE=1 \
-  --build-arg GIT_REPO_CHECK=0 \
-  --tag vllm/vllm-tpu-bm \
-  --progress plain -f docker/Dockerfile.tpu .
-
-LOG_ROOT=$(mktemp -d)
-# If mktemp fails, set -e will cause the script to exit.
-echo "Results will be stored in: $LOG_ROOT"
-
-if [ -z "$HF_TOKEN" ]; then
-  echo "Error: HF_TOKEN is not set or is empty."  
-  exit 1
-fi
-
-# Make sure mounted disk or dir exists
-if [ ! -d "$DOWNLOAD_DIR" ]; then
-    echo "Error: Folder $DOWNLOAD_DIR does not exist. This is useually a mounted drive. If no mounted drive, just create a folder."
-    exit 1
-fi
-
-echo "Run model $MODEL"
-echo
-
-echo "starting docker...$CONTAINER_NAME"
-echo    
-docker run \
- -v $DOWNLOAD_DIR:$DOWNLOAD_DIR \
- --env-file $ENV_FILE \
- -e HF_TOKEN="$HF_TOKEN" \
- -e TARGET_COMMIT=$BUILDKITE_COMMIT \
- -e MODEL=$MODEL \
- -e WORKSPACE=/workspace \
- --name $CONTAINER_NAME \
- -d \
- --privileged \
- --network host \
- -v /dev/shm:/dev/shm \
- vllm/vllm-tpu-bm tail -f /dev/null
-
-echo "run script..."
-echo
-docker exec "$CONTAINER_NAME" /bin/bash -c ".buildkite/scripts/hardware_ci/run_bm.sh"
-
-echo "copy result back..."
-VLLM_LOG="$LOG_ROOT/$TEST_NAME"_vllm_log.txt
-BM_LOG="$LOG_ROOT/$TEST_NAME"_bm_log.txt
-docker cp "$CONTAINER_NAME:/workspace/vllm_log.txt" "$VLLM_LOG" 
-docker cp "$CONTAINER_NAME:/workspace/bm_log.txt" "$BM_LOG"
-
-throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
-echo "throughput for $TEST_NAME at $BUILDKITE_COMMIT: $throughput"
-
-if [ "$BUILDKITE" = "true" ]; then
-  echo "Running inside Buildkite"
-  buildkite-agent artifact upload "$VLLM_LOG" 
-  buildkite-agent artifact upload "$BM_LOG"
-else
-  echo "Not running inside Buildkite"
-fi
-
-#
-# compare the throughput with EXPECTED_THROUGHPUT 
-# and assert meeting the expectation
-# 
-if [[ -z "$throughput" || ! "$throughput" =~ ^[0-9]+([.][0-9]+)?$ ]]; then
-  echo "Failed to get the throughput"
-  exit 1
-fi
-
-if (( $(echo "$throughput < $EXPECTED_THROUGHPUT" | bc -l) )); then
-  echo "Error: throughput($throughput) is less than expected($EXPECTED_THROUGHPUT)"
-  exit 1
-fi

.buildkite/scripts/tpu/run_bm.sh

@@ -1,94 +0,0 @@
-#!/bin/bash
-
-set -euo pipefail
-
-VLLM_LOG="$WORKSPACE/vllm_log.txt"
-BM_LOG="$WORKSPACE/bm_log.txt"
-
-if [ -n "$TARGET_COMMIT" ]; then
-  head_hash=$(git rev-parse HEAD)
-  if [ "$TARGET_COMMIT" != "$head_hash" ]; then
-    echo "Error: target commit $TARGET_COMMIT does not match HEAD: $head_hash"
-    exit 1
-  fi
-fi
-
-echo "model: $MODEL"
-echo
-
-#
-# create a log folder
-#
-mkdir "$WORKSPACE/log"
-
-# TODO: Move to image building.
-pip install pandas
-pip install datasets
-
-#
-# create sonnet_4x
-#
-echo "Create sonnet_4x.txt"
-echo "" > benchmarks/sonnet_4x.txt
-for _ in {1..4}
- do
-  cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
-done
-
-#
-# start vllm service in backend
-#
-echo "lanching vllm..."
-echo "logging to $VLLM_LOG"
-echo
-
-VLLM_USE_V1=1 vllm serve $MODEL \
- --seed 42 \
- --disable-log-requests \
- --max-num-seqs $MAX_NUM_SEQS \
- --max-num-batched-tokens $MAX_NUM_BATCHED_TOKENS \
- --tensor-parallel-size $TENSOR_PARALLEL_SIZE \
- --no-enable-prefix-caching \
- --download_dir $DOWNLOAD_DIR \
- --max-model-len $MAX_MODEL_LEN > "$VLLM_LOG" 2>&1 &
-
-
-echo "wait for 20 minutes.."
-echo
-# sleep 1200
-# wait for 10 minutes...
-for i in {1..120}; do
-    # TODO: detect other type of errors.
-    if grep -Fq "raise RuntimeError" "$VLLM_LOG"; then
-        echo "Detected RuntimeError, exiting."
-        exit 1
-    elif grep -Fq "Application startup complete" "$VLLM_LOG"; then
-        echo "Application started"
-        break
-    else
-        echo "wait for 10 seconds..."
-        sleep 10
-    fi
-done
-
-#
-# run test
-#
-echo "run benchmark test..."
-echo "logging to $BM_LOG"
-echo
-python benchmarks/benchmark_serving.py \
-    --backend vllm \
-    --model $MODEL  \
-    --dataset-name sonnet \
-    --dataset-path benchmarks/sonnet_4x.txt \
-    --sonnet-input-len $INPUT_LEN \
-    --sonnet-output-len $OUTPUT_LEN \
-    --ignore-eos > "$BM_LOG"
-
-echo "completed..."
-echo
-
-throughput=$(grep "Request throughput (req/s):" "$BM_LOG" | sed 's/[^0-9.]//g')
-echo "throughput: $throughput"
-echo

.buildkite/test-pipeline.yaml

@@ -145,7 +145,6 @@ steps:
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/engine/test_engine_core_client.py
   commands:
   # test with tp=2 and external_dp=2
   - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
@@ -155,7 +154,6 @@ steps:
   # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - pytest -v -s v1/engine/test_engine_core_client.py::test_kv_cache_events_dp
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
@@ -201,9 +199,8 @@ steps:
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
-  - tests/test_vllm_port
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py
   # OOM in the CI unless we run this separately
   - pytest -v -s tokenization
 
@@ -277,6 +274,17 @@ steps:
     - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
 
+- label: LogitsProcessor Test # 5min
+  mirror_hardwares: [amdexperimental, amdproduction]
+  source_file_dependencies:
+  - vllm/model_executor/layers
+  - vllm/model_executor/guided_decoding
+  - tests/test_logits_processor
+  - tests/model_executor/test_guided_processors
+  commands:
+    - pytest -v -s test_logits_processor.py
+    - pytest -v -s model_executor/test_guided_processors.py
+
 - label: Speculative decoding tests # 40min
   mirror_hardwares: [amdexperimental]
   source_file_dependencies:
@@ -289,7 +297,7 @@ steps:
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
-  mirror_hardwares: [amdexperimental, amdproduction]
+  mirror_hardwares: [amdexperimental]
   source_file_dependencies:
   - vllm/lora
   - tests/lora
@@ -320,7 +328,6 @@ steps:
   # these tests need to be separated, cannot combine
   - pytest -v -s compile/piecewise/test_simple.py
   - pytest -v -s compile/piecewise/test_toy_llama.py
-  - pytest -v -s compile/piecewise/test_full_cudagraph.py
 
 - label: PyTorch Fullgraph Test # 18min
   mirror_hardwares: [amdexperimental, amdproduction]
@@ -390,17 +397,6 @@ steps:
     - pytest -v -s tensorizer_loader
     - pytest -v -s entrypoints/openai/test_tensorizer_entrypoint.py
 
-- label: Model Executor Test
-  mirror_hardwares: [amdexperimental, amdproduction]
-  soft_fail: true
-  source_file_dependencies:
-  - vllm/model_executor
-  - tests/model_executor
-  commands:
-    - apt-get update && apt-get install -y curl libsodium23
-    - export VLLM_WORKER_MULTIPROC_METHOD=spawn
-    - pytest -v -s model_executor
-
 - label: Benchmarks # 9min
   mirror_hardwares: [amdexperimental, amdproduction]
   working_dir: "/vllm-workspace/.buildkite"
@@ -424,9 +420,6 @@ steps:
   - vllm/model_executor/layers/quantization
   - tests/quantization
   commands:
-  # temporary install here since we need nightly, will move to requirements/test.in
-  # after torchao 0.12 release
-  - pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization
 
 - label: LM Eval Small Models # 53min
@@ -624,11 +617,9 @@ steps:
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
   - tests/v1/test_async_llm_dp.py
-  - tests/v1/entrypoints/openai/test_multi_api_servers.py
   - vllm/v1/engine/
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - DP_SIZE=2 pytest -v -s v1/entrypoints/openai/test_multi_api_servers.py
   - pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py

.github/CODEOWNERS

@@ -10,17 +10,15 @@
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
-/vllm/model_executor/guided_decoding @mgoin @russellb @aarnphm
+/vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
 /vllm/vllm_flash_attn @LucasWilkinson
 /vllm/lora @jeejeelee
-/vllm/reasoning @aarnphm
-/vllm/entrypoints @aarnphm
 CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
-/vllm/v1/structured_output @mgoin @russellb @aarnphm
+/vllm/v1/structured_output @mgoin @russellb
 
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin @simon-mo
@@ -29,8 +27,8 @@ CMakeLists.txt @tlrmchlsmth
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
-/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo @aarnphm
+/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo
-/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb @aarnphm
+/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb
 /tests/kernels @tlrmchlsmth @WoosukKwon
 /tests/model_executor/test_guided_processors.py @mgoin @russellb
 /tests/models @DarkLight1337 @ywang96
@@ -40,8 +38,8 @@ CMakeLists.txt @tlrmchlsmth
 /tests/quantization @mgoin @robertgshaw2-redhat
 /tests/spec_decode @njhill @LiuXiaoxuanPKU
 /tests/test_inputs.py @DarkLight1337 @ywang96
-/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
+/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb
-/tests/v1/structured_output @mgoin @russellb @aarnphm
+/tests/v1/structured_output @mgoin @russellb
 /tests/weight_loading @mgoin @youkaichao
 /tests/lora @jeejeelee
 

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,18 +1,6 @@
-## Essential Elements of an Effective PR Description Checklist
+FILL IN THE PR DESCRIPTION HERE
-- [ ] The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
-- [ ] The test plan, such as providing test command.
-- [ ] The test results, such as pasting the results comparison before and after, or e2e results
-- [ ] (Optional) The necessary documentation update, such as updating `supported_models.md` and `examples` for a new model.
 
-PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS ABOVE HAVE BEEN CONSIDERED.
+FIX #xxxx (*link existing issues this PR will resolve*)
-
-## Purpose
-
-## Test Plan
-
-## Test Result
-
-## (Optional) Documentation Update
 
 <!--- pyml disable-next-line no-emphasis-as-heading -->
 **BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -36,20 +36,6 @@ pull_request_rules:
       add:
         - frontend
 
-- name: label-llama
-  description: Automatically apply llama label
-  conditions:
-    - or:
-      - files~=^examples/.*llama.*\.py
-      - files~=^tests/.*llama.*\.py
-      - files~=^vllm/entrypoints/openai/tool_parsers/llama.*\.py
-      - files~=^vllm/model_executor/models/.*llama.*\.py
-      - files~=^vllm/transformers_utils/configs/.*llama.*\.py
-  actions:
-    label:
-      add:
-        - llama
-
 - name: label-multi-modality
   description: Automatically apply multi-modality label
   conditions:

.pre-commit-config.yaml

@@ -11,8 +11,6 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
-    # Keep the same list from yapfignore here to avoid yapf failing without any inputs
-    exclude: '(.buildkite|benchmarks|build|examples)/.*'
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.11.7
   hooks:
@@ -60,7 +58,7 @@ repos:
     entry: tools/mypy.sh 0 "local"
     language: python
     types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
+    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
     stages: [pre-commit] # Don't run in CI
   - 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

CMakeLists.txt

@@ -23,9 +23,6 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")
 
-# Prevent installation of dependencies (cutlass) by default.
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
-
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
@@ -182,6 +179,9 @@ include(FetchContent)
 file(MAKE_DIRECTORY ${FETCHCONTENT_BASE_DIR}) # Ensure the directory exists
 message(STATUS "FetchContent base directory: ${FETCHCONTENT_BASE_DIR}")
 
+#
+# Set rocm version dev int.
+#
 if(VLLM_GPU_LANG STREQUAL "HIP")
   #
   # Overriding the default -O set up by cmake, adding ggdb3 for the most verbose devug info
@@ -189,6 +189,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
   set(CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG "${CMAKE_${VLLM_GPU_LANG}_FLAGS_DEBUG} -O0 -ggdb3")
   set(CMAKE_CXX_FLAGS_DEBUG "${CMAKE_CXX_FLAGS_DEBUG} -O0 -ggdb3")
 
+
   #
   # Certain HIP functions are marked as [[nodiscard]], yet vllm ignores the result which generates
   # a lot of warnings that always mask real issues. Suppressing until this is properly addressed.
@@ -242,7 +243,6 @@ set(VLLM_EXT_SRC
   "csrc/activation_kernels.cu"
   "csrc/layernorm_kernels.cu"
   "csrc/layernorm_quant_kernels.cu"
-  "csrc/sampler.cu"
   "csrc/cuda_view.cu"
   "csrc/quantization/gptq/q_gemm.cu"
   "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
@@ -308,7 +308,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # Keep building Marlin for 9.0 as there are some group sizes and shapes that
   # are not supported by Machete yet.
   # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(MARLIN_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
   if (MARLIN_ARCHS)
 
     #
@@ -454,7 +454,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # kernels for the remaining archs that are not already built for 3x.
   # (Build 8.9 for FP8)
   cuda_archs_loose_intersection(SCALED_MM_2X_ARCHS
-    "7.5;8.0;8.7;8.9+PTX" "${CUDA_ARCHS}")
+    "7.5;8.0;8.9+PTX" "${CUDA_ARCHS}")
   # subtract out the archs that are already built for 3x
   list(REMOVE_ITEM SCALED_MM_2X_ARCHS ${SCALED_MM_3X_ARCHS})
   if (SCALED_MM_2X_ARCHS)
@@ -543,8 +543,8 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # CUTLASS MoE kernels
 
   # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
-  # on Hopper). get_cutlass_(pplx_)moe_mm_data should only be compiled
+  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
-  # if it's possible to compile MoE kernels that use its output.
+  # to compile MoE kernels that use its output.
   cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;10.0a" "${CUDA_ARCHS}")
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
@@ -684,7 +684,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
   # 9.0 for latest bf16 atomicAdd PTX
-  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.7;9.0+PTX" "${CUDA_ARCHS}")
+  cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;9.0+PTX" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
 
     #
@@ -785,7 +785,5 @@ endif()
 # For CUDA we also build and ship some external projects.
 if (VLLM_GPU_LANG STREQUAL "CUDA")
     include(cmake/external_projects/flashmla.cmake)
-
-    # vllm-flash-attn should be last as it overwrites some CMake functions
     include(cmake/external_projects/vllm_flash_attn.cmake)
 endif ()

README.md

@@ -58,8 +58,8 @@ vLLM is fast with:
 - Efficient management of attention key and value memory with [**PagedAttention**](https://blog.vllm.ai/2023/06/20/vllm.html)
 - Continuous batching of incoming requests
 - Fast model execution with CUDA/HIP graph
-- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [AutoRound](https://arxiv.org/abs/2309.05516), INT4, INT8, and FP8
+- Quantizations: [GPTQ](https://arxiv.org/abs/2210.17323), [AWQ](https://arxiv.org/abs/2306.00978), [AutoRound](https://arxiv.org/abs/2309.05516),INT4, INT8, and FP8.
-- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer
+- Optimized CUDA kernels, including integration with FlashAttention and FlashInfer.
 - Speculative decoding
 - Chunked prefill
 
@@ -72,14 +72,14 @@ vLLM is flexible and easy to use with:
 - Tensor parallelism and pipeline parallelism support for distributed inference
 - Streaming outputs
 - OpenAI-compatible API server
-- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron
+- Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
 - Prefix caching support
 - Multi-LoRA support
 
 vLLM seamlessly supports most popular open-source models on HuggingFace, including:
 - Transformer-like LLMs (e.g., Llama)
 - Mixture-of-Expert LLMs (e.g., Mixtral, Deepseek-V2 and V3)
-- Embedding Models (e.g., E5-Mistral)
+- Embedding Models (e.g. E5-Mistral)
 - Multi-modal LLMs (e.g., LLaVA)
 
 Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
@@ -162,4 +162,4 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## 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).

SECURITY.md

@@ -8,6 +8,4 @@ Please report security issues privately using [the vulnerability submission form
 
 ---
 
-Please see the [Security Guide in the vLLM documentation](https://docs.vllm.ai/en/latest/usage/security.html) for more information on vLLM's security assumptions and recommendations.
-
 Please see [PyTorch's Security Policy](https://github.com/pytorch/pytorch/blob/main/SECURITY.md) for more information and recommendations on how to securely interact with models.

benchmarks/README.md

@@ -64,12 +64,6 @@ become available.
       <td style="text-align: center;">✅</td>
       <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
     </tr>
-    <tr>
-      <td><strong>Custom</strong></td>
-      <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">✅</td>
-      <td>Local file: <code>data.jsonl</code></td>
-    </tr>
   </tbody>
 </table>
 
@@ -130,38 +124,6 @@ P99 ITL (ms):                            8.39
 ==================================================
 ```
 
-### Custom Dataset
-If the dataset you want to benchmark is not supported yet in vLLM, even then you can benchmark on it using `CustomDataset`. Your data needs to be in `.jsonl` format and needs to have "prompt" field per entry, e.g., data.jsonl
-
-```
-{"prompt": "What is the capital of India?"}
-{"prompt": "What is the capital of Iran?"}
-{"prompt": "What is the capital of China?"}
-``` 
-
-```bash
-# start server
-VLLM_USE_V1=1 vllm serve meta-llama/Llama-3.1-8B-Instruct --disable-log-requests
-```
-
-```bash
-# run benchmarking script
-python3 benchmarks/benchmark_serving.py --port 9001 --save-result --save-detailed \
-  --backend vllm \
-  --model meta-llama/Llama-3.1-8B-Instruct \
-  --endpoint /v1/completions \
-  --dataset-name custom \
-  --dataset-path <path-to-your-data-jsonl> \
-  --custom-skip-chat-template \
-  --num-prompts 80 \
-  --max-concurrency 1 \
-  --temperature=0.3 \
-  --top-p=0.75 \
-  --result-dir "./log/"
-```
-
-You can skip applying chat template if your data already has it by using `--custom-skip-chat-template`.
-
 ### VisionArena Benchmark for Vision Language Models
 
 ```bash
@@ -184,9 +146,9 @@ python3 vllm/benchmarks/benchmark_serving.py \
 
 ``` bash
 VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
-    --speculative-config $'{"method": "ngram",
+    --ngram_prompt_lookup_min 2 \
-    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
+    --ngram-prompt-lookup-max 5 \
-    "prompt_lookup_min": 2}'
+    --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5}
 ```
 
 ``` bash
@@ -241,16 +203,6 @@ python3 vllm/benchmarks/benchmark_serving.py \
     --seed 42
 ```
 
-**`philschmid/mt-bench`**
-
-``` bash
-python3 vllm/benchmarks/benchmark_serving.py \
-    --model Qwen/QwQ-32B \
-    --dataset-name hf \
-    --dataset-path philschmid/mt-bench \
-    --num-prompts 80
-```
-
 ### Running With Sampling Parameters
 
 When using OpenAI-compatible backends such as `vllm`, optional sampling
@@ -321,9 +273,9 @@ python3 vllm/benchmarks/benchmark_throughput.py \
     --output-len=100 \
     --num-prompts=2048 \
     --async-engine \
-    --speculative-config $'{"method": "ngram",
+    --ngram_prompt_lookup_min=2 \
-    "num_speculative_tokens": 5, "prompt_lookup_max": 5,
+    --ngram-prompt-lookup-max=5 \
-    "prompt_lookup_min": 2}'
+    --speculative_config '{"model": "[ngram]", "num_speculative_tokens": 5}
 ```
 
 ```

benchmarks/auto_tune.sh

@@ -10,15 +10,11 @@
 # 3. Set variables (ALL REQUIRED)
 #   BASE: your directory for vllm repo
 #   MODEL: the model served by vllm
-#   TP: ways of tensor parallelism
 #   DOWNLOAD_DIR: directory to download and load model weights.
 #   INPUT_LEN: request input len
 #   OUTPUT_LEN: request output len
 #   MIN_CACHE_HIT_PCT: prefix cache rate
 #   MAX_LATENCY_ALLOWED_MS: (e2e) latency requirement. If there's no latency requirement, set it to a large number like 1000000000
-#   NUM_SEQS_LIST: a list of `max-num-seqs` you want to loop with.
-#   NUM_BATCHED_TOKENS_LIST: a list of `max-num-batched-tokens` you want to loop with.
-#   Note that the default NUM_SEQS_LIST and NUM_BATCHED_TOKENS_LIST are set for medium size input/output len, for extra short context (such as 20:20), you might need to include larger numbers in NUM_SEQS_LIST.
 # 4. Run the script, it might take a long time, you can use tmux to avoid the script stop if disconnection happens.
 # 5. The final result will be saved in RESULT file. 
 
@@ -34,27 +30,31 @@
 TAG=$(date +"%Y_%m_%d_%H_%M")
 BASE=""
 MODEL="meta-llama/Llama-3.1-8B-Instruct"
-TP=1
 DOWNLOAD_DIR=""
 INPUT_LEN=4000
 OUTPUT_LEN=16
-MIN_CACHE_HIT_PCT=0
+MIN_CACHE_HIT_PCT_PCT=0
 MAX_LATENCY_ALLOWED_MS=100000000000
-NUM_SEQS_LIST="128 256"
-NUM_BATCHED_TOKENS_LIST="512 1024 2048 4096"
 
 LOG_FOLDER="$BASE/auto-benchmark/$TAG"
 RESULT="$LOG_FOLDER/result.txt"
 
-echo "result file: $RESULT"
+echo "result file$ $RESULT"
 echo "model: $MODEL"
+echo
 
 rm -rf $LOG_FOLDER
 mkdir -p $LOG_FOLDER
 
 cd "$BASE/vllm"
+# create sonnet-4x.txt so that we can sample 2048 tokens for input
+echo "" > benchmarks/sonnet_4x.txt
+for _ in {1..4}
+do
+cat benchmarks/sonnet.txt >> benchmarks/sonnet_4x.txt
+done
 
-pip install -q datasets
+pip install datasets
 
 current_hash=$(git rev-parse HEAD)
 echo "hash:$current_hash" >> "$RESULT"
@@ -64,69 +64,53 @@ best_throughput=0
 best_max_num_seqs=0
 best_num_batched_tokens=0
 best_goodput=0
-
-start_server() {
-    local gpu_memory_utilization=$1
-    local max_num_seqs=$2
-    local max_num_batched_tokens=$3
-    local vllm_log=$4
-    
-    pkill -f vllm
-
-    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
-        --disable-log-requests \
-        --port 8004 \
-        --gpu-memory-utilization $gpu_memory_utilization \
-        --max-num-seqs $max_num_seqs \
-        --max-num-batched-tokens $max_num_batched_tokens \
-        --tensor-parallel-size $TP \
-        --enable-prefix-caching \
-        --load-format dummy \
-        --download-dir "$DOWNLOAD_DIR" \
-        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
-
-    # wait for 10 minutes...
-    server_started=0
-    for i in {1..60}; do  
-        RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
-        STATUS_CODE=$(echo "$RESPONSE" | tail -n 1) 
-        if [[ "$STATUS_CODE" -eq 200 ]]; then
-            server_started=1
-            break
-        else
-            sleep 10
-        fi
-    done
-    if (( ! server_started )); then
-        echo "server did not start within 10 minutes. Please check server log at $vllm_log".
-        return 1
-    else
-        return 0
-    fi
-}
-
 run_benchmark() {
     local max_num_seqs=$1
     local max_num_batched_tokens=$2
-    local gpu_memory_utilization=$3
     echo "max_num_seq: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
     local vllm_log="$LOG_FOLDER/vllm_log_${max_num_seqs}_${max_num_batched_tokens}.txt"
     echo "vllm_log: $vllm_log"
     echo
     rm -f $vllm_log
-    pkill -f vllm
 
-    echo "starting server..."
+    # start the server
-    start_server $gpu_memory_utilization $max_num_seqs $max_num_batched_tokens $vllm_log
+    VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 vllm serve $MODEL \
-    result=$?
+        --disable-log-requests \
-    if [[ "$result" -eq 1 ]]; then
+        --port 8004 \
-        echo "server failed to start. gpu_memory_utilization:$gpu_memory_utilization, max_num_seqs:$max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens"
+        --gpu-memory-utilization 0.98 \
-    else
+        --max-num-seqs $max_num_seqs \
-        echo "server started."
+        --max-num-batched-tokens $max_num_batched_tokens \
-    fi
+        --tensor-parallel-size 1 \
+        --enable-prefix-caching \
+        --load-format dummy \
+        --download-dir $DOWNLOAD_DIR \
+        --max-model-len $(( INPUT_LEN+OUTPUT_LEN )) > "$vllm_log" 2>&1 &
+    echo "wait for 10 minutes.."
     echo
+    # wait for 10 minutes...
+    server_started=0
+    for i in {1..60}; do        
+        if grep -Fq "Application startup complete" "$vllm_log"; then
+            echo "Application started"
+            server_started=1
+            break
+        else
+            # echo "wait for 10 seconds..."
+            sleep 10
+        fi
+    done
+ 
+    if (( ! server_started )); then
+        echo "server did not start within 10 minutes, terminate the benchmarking. Please check server log at $vllm_log"
+        echo "pkill -f vllm"
+        echo
+        pkill vllm
+        sleep 10
+        return 1
+    fi
     
     echo "run benchmark test..."
+    echo
     meet_latency_requirement=0
     # get a basic qps by using request-rate inf
     bm_log="$LOG_FOLDER/bm_log_${max_num_seqs}_${max_num_batched_tokens}_requestrate_inf.txt"
@@ -134,29 +118,29 @@ run_benchmark() {
     python benchmarks/benchmark_serving.py \
         --backend vllm \
         --model $MODEL  \
-        --dataset-name random \
+        --dataset-name sonnet \
-        --random-input-len $INPUT_LEN \
+        --dataset-path benchmarks/sonnet_4x.txt \
-        --random-output-len $OUTPUT_LEN \
+        --sonnet-input-len $INPUT_LEN \
+        --sonnet-output-len $OUTPUT_LEN \
         --ignore-eos \
         --disable-tqdm \
         --request-rate inf \
         --percentile-metrics ttft,tpot,itl,e2el \
         --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
-        --num-prompts 1000 \
+        --num-prompts 100 \
-        --random-prefix-len $prefix_len \
+        --sonnet-prefix-len $prefix_len \
-        --port 8004 &> "$bm_log"
+        --port 8004 > "$bm_log"
-    throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+    through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
     e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
     goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
 
     if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
         meet_latency_requirement=1
-        request_rate=inf
     fi
 
     if (( ! meet_latency_requirement )); then
-    # start from request-rate as int(throughput) + 1
+    # start from request-rate as int(through_put) + 1
-        request_rate=$((${throughput%.*} + 1))
+        request_rate=$((${through_put%.*} + 1))
         while ((request_rate > 0)); do
             # clear prefix cache
             curl -X POST http://0.0.0.0:8004/reset_prefix_cache
@@ -165,18 +149,19 @@ run_benchmark() {
             python benchmarks/benchmark_serving.py \
                 --backend vllm \
                 --model $MODEL  \
-                --dataset-name random \
+                --dataset-name sonnet \
-                --random-input-len $INPUT_LEN \
+                --dataset-path benchmarks/sonnet_4x.txt \
-                --random-output-len $OUTPUT_LEN \
+                --sonnet-input-len $INPUT_LEN \
-                --ignore-eos \
+                --sonnet-output-len $OUTPUT_LEN \
+                --ignore_eos \
                 --disable-tqdm \
                 --request-rate $request_rate \
                 --percentile-metrics ttft,tpot,itl,e2el \
                 --goodput e2el:$MAX_LATENCY_ALLOWED_MS \
                 --num-prompts 100 \
-                --random-prefix-len $prefix_len \
+                --sonnet-prefix-len $prefix_len \
-                --port 8004 &> "$bm_log"
+                --port 8004 > "$bm_log"
-            throughput=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
+            through_put=$(grep "Request throughput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
             e2el=$(grep "P99 E2EL (ms):" "$bm_log" | awk '{print $NF}')
             goodput=$(grep "Request goodput (req/s):" "$bm_log" | sed 's/[^0-9.]//g')
             if (( $(echo "$e2el <= $MAX_LATENCY_ALLOWED_MS" | bc -l) )); then
@@ -188,10 +173,10 @@ run_benchmark() {
     fi
     # write the results and update the best result.
     if ((meet_latency_requirement)); then
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, throughput: $throughput, goodput: $goodput"
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput"
-        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, throughput: $throughput, goodput: $goodput" >> "$RESULT"
+        echo "max_num_seqs: $max_num_seqs, max_num_batched_tokens: $max_num_batched_tokens, request_rate: $request_rate, e2el: $e2el, through put: $through_put, goodput: $goodput" >> "$RESULT"
-        if (( $(echo "$throughput > $best_throughput" | bc -l) )); then
+        if (( $(echo "$through_put > $best_throughput" | bc -l) )); then
-            best_throughput=$throughput
+            best_throughput=$through_put
             best_max_num_seqs=$max_num_seqs
             best_num_batched_tokens=$max_num_batched_tokens
             best_goodput=$goodput
@@ -203,39 +188,22 @@ run_benchmark() {
 
     echo "best_max_num_seqs: $best_max_num_seqs, best_num_batched_tokens: $best_num_batched_tokens, best_throughput: $best_throughput"
 
+    echo "pkill -f vllm"
+    echo
     pkill vllm
     sleep 10
+    rm -f $vllm_log
     printf '=%.0s' $(seq 1 20)
     return 0
 }
 
-read -r -a num_seqs_list <<< "$NUM_SEQS_LIST"
-read -r -a num_batched_tokens_list <<< "$NUM_BATCHED_TOKENS_LIST"
 
-# first find out the max gpu-memory-utilization without HBM OOM.
+num_seqs_list="128 256"
-gpu_memory_utilization=0.98
+num_batched_tokens_list="512 1024 2048 4096"
-find_gpu_memory_utilization=0
+for num_seqs in $num_seqs_list; do
-while (( $(echo "$gpu_memory_utilization >= 0.9" | bc -l) )); do
+    for num_batched_tokens in $num_batched_tokens_list; do
-    start_server $gpu_memory_utilization "${num_seqs_list[-1]}" "${num_batched_tokens_list[-1]}" "$LOG_FOLDER/vllm_log_gpu_memory_utilization_$gpu_memory_utilization.log"
+        run_benchmark $num_seqs $num_batched_tokens
-    result=$?
+        exit 0
-    if [[ "$result" -eq 0 ]]; then
-        find_gpu_memory_utilization=1
-        break
-    else
-        gpu_memory_utilization=$(echo "$gpu_memory_utilization - 0.01" | bc)
-    fi
-done
-
-if [[ "$find_gpu_memory_utilization" -eq 1 ]]; then
-    echo "Using gpu_memory_utilization=$gpu_memory_utilization to serve model."
-else
-    echo "Cannot find a proper gpu_memory_utilization over 0.9 to serve the model, please check logs in $LOG_FOLDER."
-    exit 1
-fi
-
-for num_seqs in "${num_seqs_list[@]}"; do
-    for num_batched_tokens in "${num_batched_tokens_list[@]}"; do
-        run_benchmark $num_seqs $num_batched_tokens $gpu_memory_utilization
     done
 done
 echo "finish permutations"

benchmarks/backend_request_func.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import io
 import json
@@ -325,7 +324,7 @@ async def async_request_openai_completions(
 
                                 most_recent_timestamp = timestamp
                                 generated_text += text or ""
-                            if usage := data.get("usage"):
+                            elif usage := data.get("usage"):
                                 output.output_tokens = usage.get("completion_tokens")
                     if first_chunk_received:
                         output.success = True
@@ -612,7 +611,6 @@ ASYNC_REQUEST_FUNCS = {
     "tensorrt-llm": async_request_trt_llm,
     "scalellm": async_request_openai_completions,
     "sglang": async_request_openai_completions,
-    "llama.cpp": async_request_openai_completions,
 }
 
 OPENAI_COMPATIBLE_BACKENDS = [

benchmarks/benchmark_dataset.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 This module defines a framework for sampling benchmark requests from various
 datasets. Each dataset subclass of BenchmarkDataset must implement sample
@@ -10,6 +9,9 @@ generation. Supported dataset types include:
   - BurstGPT
   - HuggingFace
   - VisionArena
+
+TODO: Implement CustomDataset to parse a JSON file and convert its contents into
+SampleRequest instances, similar to the approach used in ShareGPT.
 """
 
 import base64
@@ -440,97 +442,6 @@ class ShareGPTDataset(BenchmarkDataset):
         return samples
 
 
-# -----------------------------------------------------------------------------
-# Custom Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class CustomDataset(BenchmarkDataset):
-    """
-    Implements the Custom dataset.  Loads data from a JSONL file and generates
-    sample requests based on conversation turns. E.g.,
-    ```
-    {"prompt": "What is the capital of India?"}
-    {"prompt": "What is the capital of Iran?"}
-    {"prompt": "What is the capital of China?"}
-    ```
-    """
-
-    def __init__(self, **kwargs) -> None:
-        super().__init__(**kwargs)
-        self.load_data()
-
-    def load_data(self) -> None:
-        if self.dataset_path is None:
-            raise ValueError("dataset_path must be provided for loading data.")
-
-        # self.data will be a list of dictionaries
-        # e.g., [{"prompt": "What is the capital of India?"}, ...]
-        # This will be the standardized format which load_data()
-        # has to convert into depending on the filetype of dataset_path.
-        # sample() will assume this standardized format of self.data
-        self.data = []
-
-        # Load the JSONL file
-        if self.dataset_path.endswith(".jsonl"):
-            jsonl_data = pd.read_json(path_or_buf=self.dataset_path, lines=True)
-
-            # check if the JSONL file has a 'prompt' column
-            if "prompt" not in jsonl_data.columns:
-                raise ValueError("JSONL file must contain a 'prompt' column.")
-
-            # Convert each row to a dictionary and append to self.data
-            # This will convert the DataFrame to a list of dictionaries
-            # where each dictionary corresponds to a row in the DataFrame.
-            # This is the standardized format we want for self.data
-            for _, row in jsonl_data.iterrows():
-                self.data.append(row.to_dict())
-        else:
-            raise NotImplementedError(
-                "Only JSONL format is supported for CustomDataset."
-            )
-
-        random.seed(self.random_seed)
-        random.shuffle(self.data)
-
-    def sample(
-        self,
-        tokenizer: PreTrainedTokenizerBase,
-        num_requests: int,
-        lora_path: Optional[str] = None,
-        max_loras: Optional[int] = None,
-        output_len: Optional[int] = None,
-        enable_multimodal_chat: bool = False,
-        skip_chat_template: bool = False,
-        **kwargs,
-    ) -> list:
-        sampled_requests = []
-        for item in self.data:
-            if len(sampled_requests) >= num_requests:
-                break
-            prompt = item["prompt"]
-
-            # apply template
-            if not skip_chat_template:
-                prompt = tokenizer.apply_chat_template(
-                    [{"role": "user", "content": prompt}],
-                    add_generation_prompt=True,
-                    tokenize=False,
-                )
-
-            prompt_len = len(tokenizer(prompt).input_ids)
-            sampled_requests.append(
-                SampleRequest(
-                    prompt=prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                )
-            )
-        self.maybe_oversample_requests(sampled_requests, num_requests)
-
-        return sampled_requests
-
-
 # -----------------------------------------------------------------------------
 # Sonnet Dataset Implementation
 # -----------------------------------------------------------------------------
@@ -865,15 +776,7 @@ class InstructCoderDataset(HuggingFaceDataset):
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt = f"{item['input']}\n\n{item['instruction']} Just output \
+            prompt = f"{item['instruction']}:\n{item['input']}"
-            the code, do not include any explanation."
-
-            # apply template
-            prompt = tokenizer.apply_chat_template(
-                [{"role": "user", "content": prompt}],
-                add_generation_prompt=True,
-                tokenize=False,
-            )
             prompt_len = len(tokenizer(prompt).input_ids)
             sampled_requests.append(
                 SampleRequest(

benchmarks/benchmark_latency.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark the latency of processing a single batch of requests."""
 
 import argparse
@@ -7,12 +6,13 @@ import dataclasses
 import json
 import os
 import time
+from pathlib import Path
 from typing import Any, Optional
 
 import numpy as np
+import torch
 from tqdm import tqdm
 
-import vllm.envs as envs
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import EngineArgs
@@ -80,9 +80,17 @@ def main(args: argparse.Namespace):
 
     def run_to_completion(profile_dir: Optional[str] = None):
         if profile_dir:
-            llm.start_profile()
+            with torch.profiler.profile(
-            llm_generate()
+                activities=[
-            llm.stop_profile()
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ],
+                on_trace_ready=torch.profiler.tensorboard_trace_handler(
+                    str(profile_dir)
+                ),
+            ) as p:
+                llm_generate()
+            print(p.key_averages().table(sort_by="self_cuda_time_total"))
         else:
             start_time = time.perf_counter()
             llm_generate()
@@ -95,7 +103,11 @@ def main(args: argparse.Namespace):
         run_to_completion(profile_dir=None)
 
     if args.profile:
-        profile_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profile_dir = args.profile_result_dir
+        if not profile_dir:
+            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
@@ -152,6 +164,15 @@ if __name__ == "__main__":
         action="store_true",
         help="profile the generation process of a single batch",
     )
+    parser.add_argument(
+        "--profile-result-dir",
+        type=str,
+        default=None,
+        help=(
+            "path to save the pytorch profiler output. Can be visualized "
+            "with ui.perfetto.dev or Tensorboard."
+        ),
+    )
     parser.add_argument(
         "--output-json",
         type=str,
@@ -172,9 +193,4 @@ if __name__ == "__main__":
     # numbers. We need to disable prefix caching by default.
     parser.set_defaults(enable_prefix_caching=False)
     args = parser.parse_args()
-    if args.profile and not envs.VLLM_TORCH_PROFILER_DIR:
-        raise OSError(
-            "The environment variable 'VLLM_TORCH_PROFILER_DIR' is not set. "
-            "Please set it to a valid path to use torch profiler."
-        )
     main(args)

benchmarks/benchmark_long_document_qa_throughput.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Offline benchmark to test the long document QA throughput.
 

benchmarks/benchmark_prefix_caching.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Benchmark the efficiency of prefix caching.
 

benchmarks/benchmark_prioritization.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark offline prioritization."""
 
 import argparse

benchmarks/benchmark_serving.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 r"""Benchmark online serving throughput.
 
 On the server side, run one of the following commands:
@@ -61,7 +60,6 @@ from benchmark_dataset import (
     ASRDataset,
     BurstGPTDataset,
     ConversationDataset,
-    CustomDataset,
     HuggingFaceDataset,
     InstructCoderDataset,
     MTBenchDataset,
@@ -629,16 +627,7 @@ def main(args: argparse.Namespace):
             "'--dataset-path' if required."
         )
 
-    if args.dataset_name == "custom":
+    if args.dataset_name == "sonnet":
-        dataset = CustomDataset(dataset_path=args.dataset_path)
-        input_requests = dataset.sample(
-            num_requests=args.num_prompts,
-            tokenizer=tokenizer,
-            output_len=args.custom_output_len,
-            skip_chat_template=args.custom_skip_chat_template,
-        )
-
-    elif args.dataset_name == "sonnet":
         dataset = SonnetDataset(dataset_path=args.dataset_path)
         # For the "sonnet" dataset, formatting depends on the backend.
         if args.backend == "openai-chat":
@@ -773,10 +762,6 @@ def main(args: argparse.Namespace):
     if "temperature" not in sampling_params:
         sampling_params["temperature"] = 0.0  # Default to greedy decoding.
 
-    if args.backend == "llama.cpp":
-        # Disable prompt caching in llama.cpp backend
-        sampling_params["cache_prompt"] = False
-
     # Avoid GC processing "static" data - reduce pause times.
     gc.collect()
     gc.freeze()
@@ -849,8 +834,6 @@ def main(args: argparse.Namespace):
             ]:
                 if field in result_json:
                     del result_json[field]
-                if field in benchmark_result:
-                    del benchmark_result[field]
 
         # Save to file
         base_model_id = model_id.split("/")[-1]
@@ -863,7 +846,6 @@ def main(args: argparse.Namespace):
         if args.result_filename:
             file_name = args.result_filename
         if args.result_dir:
-            os.makedirs(args.result_dir, exist_ok=True)
             file_name = os.path.join(args.result_dir, file_name)
         with open(
             file_name, mode="a+" if args.append_result else "w", encoding="utf-8"
@@ -904,7 +886,7 @@ if __name__ == "__main__":
         "--dataset-name",
         type=str,
         default="sharegpt",
-        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf", "custom"],
+        choices=["sharegpt", "burstgpt", "sonnet", "random", "hf"],
         help="Name of the dataset to benchmark on.",
     )
     parser.add_argument(
@@ -1074,19 +1056,6 @@ if __name__ == "__main__":
     )
 
     # group for dataset specific arguments
-    custom_group = parser.add_argument_group("custom dataset options")
-    custom_group.add_argument(
-        "--custom-output-len",
-        type=int,
-        default=256,
-        help="Number of output tokens per request, used only for custom dataset.",
-    )
-    custom_group.add_argument(
-        "--custom-skip-chat-template",
-        action="store_true",
-        help="Skip applying chat template to prompt, used only for custom dataset.",
-    )
-
     sonnet_group = parser.add_argument_group("sonnet dataset options")
     sonnet_group.add_argument(
         "--sonnet-input-len",

benchmarks/benchmark_serving_structured_output.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 r"""Benchmark online serving throughput with structured outputs.
 
 On the server side, run one of the following commands:
@@ -12,6 +11,7 @@ On the client side, run:
         --model <your_model> \
         --dataset json \
         --structured-output-ratio 1.0 \
+        --structured-output-backend auto \
         --request-rate 10 \
         --num-prompts 1000
 

benchmarks/benchmark_throughput.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """Benchmark offline inference throughput."""
 
 import argparse

benchmarks/benchmark_utils.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import json
@@ -66,9 +65,4 @@ class InfEncoder(json.JSONEncoder):
 
 def write_to_json(filename: str, records: list) -> None:
     with open(filename, "w") as f:
-        json.dump(
+        json.dump(records, f, cls=InfEncoder)
-            records,
-            f,
-            cls=InfEncoder,
-            default=lambda o: f"<{type(o).__name__} object is not JSON serializable>",
-        )

benchmarks/cutlass_benchmarks/sparse_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy

benchmarks/cutlass_benchmarks/utils.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Cutlass bench utils
 from collections.abc import Iterable

benchmarks/cutlass_benchmarks/w8a8_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy

benchmarks/cutlass_benchmarks/weight_shapes.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Weight Shapes are in the format
 # ([K, N], TP_SPLIT_DIM)

benchmarks/disagg_benchmarks/disagg_prefill_proxy_server.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
 

benchmarks/disagg_benchmarks/round_robin_proxy.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import asyncio
 import itertools

benchmarks/disagg_benchmarks/visualize_benchmark_results.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import json
 

benchmarks/fused_kernels/layernorm_rms_benchmarks.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import pickle as pkl
 import time

benchmarks/kernels/bench_fp8_gemm.py

@@ -1,223 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import argparse
-import copy
-import itertools
-
-import torch
-from weight_shapes import WEIGHT_SHAPES
-
-from vllm._custom_ops import cutlass_scaled_mm as vllm_scaled_mm
-from vllm._custom_ops import scaled_fp8_quant as vllm_scaled_fp8_quant
-from vllm.triton_utils import triton
-
-
-@triton.testing.perf_report(
-    triton.testing.Benchmark(
-        x_names=["batch_size"],
-        x_vals=[1, 16, 64, 128, 256, 512, 1024, 2048, 4096, 8192, 16384],
-        x_log=False,
-        line_arg="provider",
-        line_vals=[
-            "torch-bf16",
-            # "fp8-tensor-w-token-a",
-            "fp8-tensor-w-tensor-a",
-            "fp8-channel-w-token-a",
-            # "fp8-channel-w-tensor-a",
-            # "fp8-tensor-w-token-a-noquant",
-            "fp8-tensor-w-tensor-a-noquant",
-            "fp8-channel-w-token-a-noquant",
-            # "fp8-channel-w-tensor-a-noquant",
-        ],
-        line_names=[
-            "torch-bf16",
-            # "fp8-tensor-w-token-a",
-            "fp8-tensor-w-tensor-a",
-            "fp8-channel-w-token-a",
-            # "fp8-channel-w-tensor-a",
-            # "fp8-tensor-w-token-a-noquant",
-            "fp8-tensor-w-tensor-a-noquant",
-            "fp8-channel-w-token-a-noquant",
-            # "fp8-channel-w-tensor-a-noquant",
-        ],
-        ylabel="TFLOP/s (larger is better)",
-        plot_name="BF16 vs FP8 GEMMs",
-        args={},
-    )
-)
-def benchmark(batch_size, provider, N, K):
-    M = batch_size
-    device = "cuda"
-    dtype = torch.bfloat16
-
-    # Create input tensors
-    a = torch.randn((M, K), device=device, dtype=dtype)
-    b = torch.randn((N, K), device=device, dtype=dtype)
-
-    quantiles = [0.5, 0.2, 0.8]
-
-    if "torch-bf16" in provider:
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: torch.nn.functional.linear(a, b), quantiles=quantiles
-        )
-
-    elif "fp8" in provider:
-        # Weights are always quantized ahead of time
-        if "noquant" in provider:
-            # For no quantization, we just measure the GEMM
-            if "tensor-w-token-a" in provider:
-                # Dynamic per-token quant for A, per-tensor quant for B
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b)
-                assert scale_b_fp8.numel() == 1
-                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
-                    a, use_per_token_if_dynamic=True
-                )
-
-                def run_quant():
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "tensor-w-tensor-a" in provider:
-                # Static per-tensor quantization with fixed scales
-                # for both A and B
-                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
-                scale_b = torch.tensor([1.0], device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                assert scale_b_fp8.numel() == 1
-                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
-
-                def run_quant():
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "channel-w-token-a" in provider:
-                # Static per-channel quantization for weights, per-token
-                # quant for A
-                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
-                assert scale_b_fp8.numel() == N
-                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
-                    a, use_per_token_if_dynamic=True
-                )
-
-                def run_quant():
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "channel-w-tensor-a" in provider:
-                # Static per-channel quantization for weights, per-tensor
-                # quant for A
-                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
-                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
-                assert scale_b_fp8.numel() == N
-                a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
-
-                def run_quant():
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-        else:
-            # In these cases, we quantize the activations during the GEMM call
-            if "tensor-w-token-a" in provider:
-                # Dynamic per-token quant for A, per-tensor quant for B
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b)
-                assert scale_b_fp8.numel() == 1
-
-                def run_quant():
-                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
-                        a, use_per_token_if_dynamic=True
-                    )
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "tensor-w-tensor-a" in provider:
-                # Static per-tensor quantization with fixed scales
-                # for both A and B
-                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
-                scale_b = torch.tensor([1.0], device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                assert scale_b_fp8.numel() == 1
-
-                def run_quant():
-                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "channel-w-token-a" in provider:
-                # Static per-channel quantization for weights, per-token
-                # quant for A
-                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
-                assert scale_b_fp8.numel() == N
-
-                def run_quant():
-                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(
-                        a, use_per_token_if_dynamic=True
-                    )
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-            elif "channel-w-tensor-a" in provider:
-                # Static per-channel quantization for weights, per-tensor
-                # quant for A
-                scale_a = torch.tensor([1.0], device=device, dtype=torch.float32)
-                scale_b = torch.tensor((N,), device=device, dtype=torch.float32)
-                b_fp8, scale_b_fp8 = vllm_scaled_fp8_quant(b, scale_b)
-                scale_b_fp8 = scale_b_fp8.expand(N).contiguous()
-                assert scale_b_fp8.numel() == N
-
-                def run_quant():
-                    a_fp8, scale_a_fp8 = vllm_scaled_fp8_quant(a, scale_a)
-                    return vllm_scaled_mm(a_fp8, b_fp8, scale_a_fp8, scale_b_fp8, dtype)
-
-        b_fp8 = b_fp8.t()
-
-        ms, min_ms, max_ms = triton.testing.do_bench_cudagraph(
-            lambda: run_quant(), quantiles=quantiles
-        )
-
-    # Calculate TFLOP/s, two flops per multiply-add
-    tflops = lambda ms: (2 * M * N * K) * 1e-12 / (ms * 1e-3)
-    return tflops(ms), tflops(max_ms), tflops(min_ms)
-
-
-def prepare_shapes(args):
-    KN_model_names = []
-    models_tps = list(itertools.product(args.models, args.tp_sizes))
-    for model, tp_size in models_tps:
-        assert model in WEIGHT_SHAPES
-        for KN, tp_split_dim in copy.deepcopy(WEIGHT_SHAPES[model]):
-            KN[tp_split_dim] = KN[tp_split_dim] // tp_size
-            KN.append(model)
-            KN_model_names.append(KN)
-    return KN_model_names
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=["meta-llama/Llama-3.1-8B-Instruct"],
-        choices=[*WEIGHT_SHAPES.keys()],
-        help="List of models to benchmark",
-    )
-    parser.add_argument(
-        "--tp-sizes",
-        nargs="+",
-        type=int,
-        default=[1],
-        help="List of tensor parallel sizes",
-    )
-    args = parser.parse_args()
-
-    KN_model_names = prepare_shapes(args)
-    for K, N, model_name in KN_model_names:
-        print(f"{model_name}, N={N} K={K}, BF16 vs FP8 GEMMs TFLOP/s:")
-        benchmark.run(
-            print_data=True,
-            show_plots=True,
-            save_path=f"bench_fp8_res_n{N}_k{K}",
-            N=N,
-            K=K,
-        )
-
-    print("Benchmark finished!")

benchmarks/kernels/benchmark_aqlm.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import os
 import sys

benchmarks/kernels/benchmark_bitblas.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Copyright (c) Microsoft Corporation.
 # Licensed under the MIT License.
 

benchmarks/kernels/benchmark_cutlass_fp4_moe.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 """
 Benchmark the performance of the cutlass_moe_fp4 kernel vs the triton_moe
 kernel. The cutlass_moe_fp4 kernel takes in fp4 quantized weights and 16-bit
@@ -91,7 +90,7 @@ def bench_run(
 
     score = torch.randn((m, num_experts), device=device, dtype=dtype)
 
-    topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)
+    topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
 
     quant_blocksize = 16
     w1_blockscale = torch.empty(

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import torch
 import torch.utils.benchmark as benchmark
@@ -7,8 +6,8 @@ from benchmark_shapes import WEIGHT_SHAPES_MOE
 
 from vllm import _custom_ops as ops
 from vllm.config import ParallelConfig, VllmConfig, set_current_vllm_config
-from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
 from vllm.model_executor.layers.fused_moe.fused_moe import (
+    cutlass_moe_fp8,
     fused_experts,
     fused_topk,
 )
@@ -70,9 +69,18 @@ def bench_run(
     w1_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
     w2_scale = torch.empty((num_experts, 1, 1), device="cuda", dtype=torch.float32)
 
+    ab_strides1 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+    c_strides1 = torch.full((num_experts,), 2 * n, device="cuda", dtype=torch.int64)
+    ab_strides2 = torch.full((num_experts,), n, device="cuda", dtype=torch.int64)
+    c_strides2 = torch.full((num_experts,), k, device="cuda", dtype=torch.int64)
+
     for expert in range(num_experts):
         w1_q[expert], w1_scale[expert] = ops.scaled_fp8_quant(w1[expert])
         w2_q[expert], w2_scale[expert] = ops.scaled_fp8_quant(w2[expert])
+    w1_q_notransp = w1_q.clone()
+    w2_q_notransp = w2_q.clone()
+    w1_q = w1_q.transpose(1, 2)
+    w2_q = w2_q.transpose(1, 2)
 
     score = torch.randn((m, num_experts), device="cuda", dtype=dtype)
 
@@ -113,6 +121,10 @@ def bench_run(
         w2_scale: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        c_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides2: torch.Tensor,
         num_repeats: int,
     ):
         for _ in range(num_repeats):
@@ -120,10 +132,14 @@ def bench_run(
                 a,
                 w1,
                 w2,
-                topk_weights,
-                topk_ids,
                 w1_scale,
                 w2_scale,
+                topk_weights,
+                topk_ids,
+                ab_strides1,
+                c_strides1,
+                ab_strides2,
+                c_strides2,
                 a1_scale=a_scale,
             )
 
@@ -136,6 +152,10 @@ def bench_run(
         w2_scale: torch.Tensor,
         topk_weights: torch.Tensor,
         topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        c_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides2: torch.Tensor,
     ):
         with set_current_vllm_config(
             VllmConfig(parallel_config=ParallelConfig(pipeline_parallel_size=1))
@@ -144,10 +164,14 @@ def bench_run(
                 a,
                 w1_q,
                 w2_q,
-                topk_weights,
-                topk_ids,
                 w1_scale,
                 w2_scale,
+                topk_weights,
+                topk_ids,
+                ab_strides1,
+                c_strides1,
+                ab_strides2,
+                c_strides2,
                 a1_scale=a_scale,
             )
 
@@ -193,6 +217,10 @@ def bench_run(
             w2_scale,
             topk_weights,
             topk_ids,
+            ab_strides1,
+            c_strides1,
+            ab_strides2,
+            c_strides2,
         )
     torch.cuda.synchronize()
 
@@ -201,8 +229,8 @@ def bench_run(
     with torch.cuda.graph(triton_graph, stream=triton_stream):
         run_triton_from_graph(
             a,
-            w1_q,
+            w1_q_notransp,
-            w2_q,
+            w2_q_notransp,
             topk_weights,
             topk_ids,
             w1_scale,
@@ -221,12 +249,18 @@ def bench_run(
         "w2": w2,
         "score": score,
         "topk": topk,
+        "w1_q_notransp": w1_q_notransp,
+        "w2_q_notransp": w2_q_notransp,
         # Cutlass params
         "a_scale": a_scale,
         "w1_q": w1_q,
         "w2_q": w2_q,
         "w1_scale": w1_scale,
         "w2_scale": w2_scale,
+        "ab_strides1": ab_strides1,
+        "c_strides1": c_strides1,
+        "ab_strides2": ab_strides2,
+        "c_strides2": c_strides2,
         # cuda graph params
         "cutlass_graph": cutlass_graph,
         "triton_graph": triton_graph,
@@ -244,8 +278,8 @@ def bench_run(
     # Warmup
     run_triton_moe(
         a,
-        w1_q,
+        w1_q_notransp,
-        w2_q,
+        w2_q_notransp,
         topk_weights,
         topk_ids,
         w1_scale,
@@ -256,7 +290,7 @@ def bench_run(
 
     results.append(
         benchmark.Timer(
-            stmt="run_triton_moe(a, w1_q, w2_q, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)",  # noqa: E501
+            stmt="run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids, w1_scale, w2_scale, a_scale, num_runs)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,
@@ -287,12 +321,16 @@ def bench_run(
         w2_scale,
         topk_weights,
         topk_ids,
+        ab_strides1,
+        c_strides1,
+        ab_strides2,
+        c_strides2,
         num_warmup,
     )
 
     results.append(
         benchmark.Timer(
-            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, num_runs)",  # noqa: E501
+            stmt="run_cutlass_moe(a, a_scale, w1_q, w2_q, w1_scale, w2_scale, topk_weights, topk_ids, ab_strides1, c_strides1, ab_strides2, c_strides2, num_runs)",  # noqa: E501
             globals=globals,
             label=label,
             sub_label=sub_label,

benchmarks/kernels/benchmark_layernorm.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import time
 

benchmarks/kernels/benchmark_lora.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy

benchmarks/kernels/benchmark_machete.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import copy

benchmarks/kernels/benchmark_marlin.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import torch
 import torch.utils.benchmark as benchmark

benchmarks/kernels/benchmark_moe.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 import json

benchmarks/kernels/benchmark_moe_permute_unpermute.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import argparse
 from typing import Any, TypedDict

benchmarks/kernels/benchmark_paged_attention.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import random
 import time

benchmarks/kernels/benchmark_quant.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import time
 

benchmarks/kernels/benchmark_rmsnorm.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import itertools
 from typing import Optional, Union

benchmarks/kernels/benchmark_rope.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 from itertools import accumulate
 from typing import Optional
@@ -23,7 +22,7 @@ def benchmark_rope_kernels_multi_lora(
     seed: int,
     device: str,
     max_position: int = 8192,
-    base: float = 10000,
+    base: int = 10000,
 ) -> None:
     current_platform.seed_everything(seed)
     torch.set_default_device(device)

benchmarks/kernels/benchmark_shapes.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 WEIGHT_SHAPES = {
     "ideal": [[4 * 256 * 32, 256 * 32]],

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Adapted from sglang quantization/tuning_block_wise_kernel.py
 
 import argparse

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # fmt: off
 # ruff: noqa: E501
 import time

benchmarks/kernels/graph_machete_bench.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import math
 import pickle

benchmarks/kernels/utils.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import dataclasses
 from collections.abc import Iterable

benchmarks/kernels/weight_shapes.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 # Weight Shapes are in the format
 # ([K, N], TP_SPLIT_DIM)
@@ -49,50 +48,4 @@ WEIGHT_SHAPES = {
         ([16384, 106496], 1),
         ([53248, 16384], 0),
     ],
-    "meta-llama/Llama-3.1-8B-Instruct": [
-        ([4096, 6144], 1),
-        ([4096, 4096], 0),
-        ([4096, 28672], 1),
-        ([14336, 4096], 0),
-    ],
-    "meta-llama/Llama-3.3-70B-Instruct": [
-        ([8192, 10240], 1),
-        ([8192, 8192], 0),
-        ([8192, 57344], 1),
-        ([28672, 8192], 0),
-    ],
-    "mistralai/Mistral-Large-Instruct-2407": [
-        ([12288, 14336], 1),
-        ([12288, 12288], 0),
-        ([12288, 57344], 1),
-        ([28672, 12288], 0),
-    ],
-    "Qwen/Qwen2.5-7B-Instruct": [
-        ([3584, 4608], 1),
-        ([3584, 3584], 0),
-        ([3584, 37888], 1),
-        ([18944, 3584], 0),
-    ],
-    "Qwen/Qwen2.5-32B-Instruct": [
-        ([5120, 7168], 1),
-        ([5120, 5120], 0),
-        ([5120, 55296], 1),
-        ([27648, 5120], 0),
-    ],
-    "Qwen/Qwen2.5-72B-Instruct": [
-        ([8192, 10240], 1),
-        ([8192, 8192], 0),
-        ([8192, 59136], 1),
-        ([29568, 8192], 0),
-    ],
-    "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct": [
-        ([2048, 3072], 1),
-        ([2048, 4096], 1),
-        ([2048, 2048], 0),
-        ([2048, 576], 0),
-        ([2048, 21888], 1),
-        ([10944, 2048], 0),
-        ([2048, 2816], 1),
-        ([1408, 2048], 0),
-    ],
 }

benchmarks/overheads/benchmark_hashing.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import cProfile
 import pstats

cmake/cpu_extension.cmake

@@ -75,7 +75,6 @@ if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
 else()
     find_isa(${CPUINFO} "avx2" AVX2_FOUND)
     find_isa(${CPUINFO} "avx512f" AVX512_FOUND)
-    find_isa(${CPUINFO} "Power11" POWER11_FOUND)
     find_isa(${CPUINFO} "POWER10" POWER10_FOUND)
     find_isa(${CPUINFO} "POWER9" POWER9_FOUND)
     find_isa(${CPUINFO} "asimd" ASIMD_FOUND) # Check for ARM NEON support
@@ -107,19 +106,13 @@ elseif (AVX2_FOUND)
     list(APPEND CXX_COMPILE_FLAGS "-mavx2")
     message(WARNING "vLLM CPU backend using AVX2 ISA")
     
-elseif (POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)
+elseif (POWER9_FOUND OR POWER10_FOUND)
     message(STATUS "PowerPC detected")
-    if (POWER9_FOUND)
+    # Check for PowerPC VSX support
-        list(APPEND CXX_COMPILE_FLAGS
+    list(APPEND CXX_COMPILE_FLAGS
-            "-mvsx"
+        "-mvsx"
-            "-mcpu=power9"
+        "-mcpu=native"
-            "-mtune=power9")
+        "-mtune=native")
-    elseif (POWER10_FOUND OR POWER11_FOUND)
-        list(APPEND CXX_COMPILE_FLAGS
-            "-mvsx"
-            "-mcpu=power10"
-            "-mtune=power10")
-    endif()
 
 elseif (ASIMD_FOUND)
     message(STATUS "ARMv8 or later architecture detected")

cmake/external_projects/vllm_flash_attn.cmake

@@ -46,38 +46,22 @@ else()
 endif()
 
 
-# Ensure the vllm/vllm_flash_attn directory exists before installation
-install(CODE "file(MAKE_DIRECTORY \"\${CMAKE_INSTALL_PREFIX}/vllm/vllm_flash_attn\")" ALL_COMPONENTS)
-
-# Make sure vllm-flash-attn install rules are nested under vllm/
-# This is here to support installing all components under the same prefix with cmake --install.
-# setup.py installs every component separately but uses the same prefix for all.
-# ALL_COMPONENTS is used to avoid duplication for FA2 and FA3,
-# and these statements don't hurt when installing neither component.
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" ALL_COMPONENTS)
-install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
-install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" ALL_COMPONENTS)
-
 # Fetch the vllm-flash-attn library
 FetchContent_MakeAvailable(vllm-flash-attn)
 message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")
 
-# Restore the install prefix
-install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" ALL_COMPONENTS)
-install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
-
 # Copy over the vllm-flash-attn python files (duplicated for fa2 and fa3, in
 # case only one is built, in the case both are built redundant work is done)
 install(
   DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-  DESTINATION vllm/vllm_flash_attn
+  DESTINATION vllm_flash_attn
   COMPONENT _vllm_fa2_C
   FILES_MATCHING PATTERN "*.py"
 )
 
 install(
   DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
-  DESTINATION vllm/vllm_flash_attn
+  DESTINATION vllm_flash_attn
   COMPONENT _vllm_fa3_C
   FILES_MATCHING PATTERN "*.py"
 )

cmake/hipify.py

@@ -1,6 +1,5 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 #
 # A command line tool for running pytorch's hipify preprocessor on CUDA

cmake/utils.cmake

@@ -76,7 +76,7 @@ function (hipify_sources_target OUT_SRCS NAME ORIG_SRCS)
   set(CSRC_BUILD_DIR ${CMAKE_CURRENT_BINARY_DIR}/csrc)
   add_custom_target(
     hipify${NAME}
-    COMMAND ${Python_EXECUTABLE} ${CMAKE_SOURCE_DIR}/cmake/hipify.py -p ${CMAKE_SOURCE_DIR}/csrc -o ${CSRC_BUILD_DIR} ${SRCS}
+    COMMAND ${CMAKE_SOURCE_DIR}/cmake/hipify.py -p ${CMAKE_SOURCE_DIR}/csrc -o ${CSRC_BUILD_DIR} ${SRCS}
     DEPENDS ${CMAKE_SOURCE_DIR}/cmake/hipify.py ${SRCS}
     BYPRODUCTS ${HIP_SRCS}
     COMMENT "Running hipify on ${NAME} extension source files.")

csrc/attention/mla/cutlass_mla_kernels.cu

@@ -119,7 +119,7 @@ typename T::Fmha::Arguments args_from_options(
       {static_cast<ElementOut*>(out.data_ptr()), stride_O,
        static_cast<ElementAcc*>(nullptr), stride_LSE},
       hw_info,
-      1,        // split_kv
+      -1,       // split_kv
       nullptr,  // is_var_split_kv
   };
   // TODO(kaixih@nvidia): When split_kv=-1 and is_var_split_kv=false, we compute

csrc/cutlass_extensions/vllm_cutlass_library_extension.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import enum
 from typing import Union

csrc/moe/marlin_moe_wna16/generate_kernels.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import glob
 import itertools
 import os

csrc/moe/moe_ops.h

@@ -31,7 +31,3 @@ torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
 #endif
 
 bool moe_permute_unpermute_supported();
-
-void shuffle_rows(const torch::Tensor& input_tensor,
-                  const torch::Tensor& dst2src_map,
-                  torch::Tensor& output_tensor);

csrc/moe/moe_permute_unpermute_op.cu

@@ -130,62 +130,6 @@ void moe_unpermute(
   });
 }
 
-template <typename T>
-__global__ void shuffleInputRowsKernel(const T* input,
-                                       const int32_t* dst2src_map, T* output,
-                                       int64_t num_src_rows,
-                                       int64_t num_dst_rows, int64_t num_cols) {
-  int64_t dest_row_idx = blockIdx.x;
-  int64_t const source_row_idx = dst2src_map[dest_row_idx];
-
-  if (blockIdx.x < num_dst_rows) {
-    // Load 128-bits per thread
-    constexpr int64_t ELEM_PER_THREAD = 128 / sizeof(T) / 8;
-    using DataElem = cutlass::Array<T, ELEM_PER_THREAD>;
-
-    // Duplicate and permute rows
-    auto const* source_row_ptr =
-        reinterpret_cast<DataElem const*>(input + source_row_idx * num_cols);
-    auto* dest_row_ptr =
-        reinterpret_cast<DataElem*>(output + dest_row_idx * num_cols);
-
-    int64_t const start_offset = threadIdx.x;
-    int64_t const stride = blockDim.x;
-    int64_t const num_elems_in_col = num_cols / ELEM_PER_THREAD;
-
-    for (int elem_index = start_offset; elem_index < num_elems_in_col;
-         elem_index += stride) {
-      dest_row_ptr[elem_index] = source_row_ptr[elem_index];
-    }
-  }
-}
-
-void shuffle_rows(const torch::Tensor& input_tensor,
-                  const torch::Tensor& dst2src_map,
-                  torch::Tensor& output_tensor) {
-  TORCH_CHECK(input_tensor.scalar_type() == output_tensor.scalar_type(),
-              "Input and output tensors must have the same data type");
-
-  auto stream = at::cuda::getCurrentCUDAStream().stream();
-  int64_t const blocks = output_tensor.size(0);
-  int64_t const threads = 256;
-  int64_t const num_dest_rows = output_tensor.size(0);
-  int64_t const num_src_rows = input_tensor.size(0);
-  int64_t const num_cols = input_tensor.size(1);
-
-  TORCH_CHECK(!(num_cols % (128 / sizeof(input_tensor.scalar_type()) / 8)),
-              "num_cols must be divisible by 128 / "
-              "sizeof(input_tensor.scalar_type()) / 8");
-
-  MOE_DISPATCH(input_tensor.scalar_type(), [&] {
-    shuffleInputRowsKernel<scalar_t><<<blocks, threads, 0, stream>>>(
-        reinterpret_cast<scalar_t*>(input_tensor.data_ptr()),
-        dst2src_map.data_ptr<int32_t>(),
-        reinterpret_cast<scalar_t*>(output_tensor.data_ptr()), num_src_rows,
-        num_dest_rows, num_cols);
-  });
-}
-
 #else
 
 void moe_permute(const torch::Tensor& input, const torch::Tensor& topk_weights,

csrc/moe/permute_unpermute_kernels/dispatch.h

@@ -14,13 +14,12 @@
     __VA_ARGS__();                                         \
     break;                                                 \
   }
-#define MOE_DISPATCH_FLOAT_CASE(...)                            \
+#define MOE_DISPATCH_FLOAT_CASE(...)                          \
-  MOE_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)         \
+  MOE_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)       \
-  MOE_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)          \
+  MOE_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)        \
-  MOE_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)      \
+  MOE_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)    \
-  MOE_DISPATCH_CASE(at::ScalarType::Float8_e5m2, __VA_ARGS__)   \
+  MOE_DISPATCH_CASE(at::ScalarType::Float8_e5m2, __VA_ARGS__) \
-  MOE_DISPATCH_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__) \
+  MOE_DISPATCH_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__)
-  MOE_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)
 
 #define MOE_DISPATCH(TYPE, ...) \
   MOE_SWITCH(TYPE, MOE_DISPATCH_FLOAT_CASE(__VA_ARGS__))
@@ -40,11 +39,6 @@ template <>
 struct ScalarType2CudaType<at::ScalarType::BFloat16> {
   using type = __nv_bfloat16;
 };
-// uint8 for packed fp4
-template <>
-struct ScalarType2CudaType<at::ScalarType::Byte> {
-  using type = uint8_t;
-};
 
 // #if __CUDA_ARCH__ >= 890
 // fp8

csrc/moe/topk_softmax_kernels.cu

@@ -516,8 +516,9 @@ void topk_softmax(
             topk,
             stream);
     }
-    else if (topk_indices.scalar_type() == at::ScalarType::UInt32)
+    else
     {
+        assert(topk_indices.scalar_type() == at::ScalarType::UInt32);
         vllm::moe::topkGatingSoftmaxKernelLauncher(
             gating_output.data_ptr<float>(),
             topk_weights.data_ptr<float>(),
@@ -529,17 +530,4 @@ void topk_softmax(
             topk,
             stream);
     }
-    else {
-        assert(topk_indices.scalar_type() == at::ScalarType::Int64);
-        vllm::moe::topkGatingSoftmaxKernelLauncher(
-            gating_output.data_ptr<float>(),
-            topk_weights.data_ptr<float>(),
-            topk_indices.data_ptr<int64_t>(),
-            token_expert_indices.data_ptr<int>(),
-            softmax_workspace.data_ptr<float>(),
-            num_tokens,
-            num_experts,
-            topk,
-            stream);
-    }
 }

csrc/moe/torch_bindings.cpp

@@ -81,12 +81,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
   m.def("moe_permute_unpermute_supported() -> bool");
   m.impl("moe_permute_unpermute_supported", &moe_permute_unpermute_supported);
 
-  // Row shuffle for MoE
-  m.def(
-      "shuffle_rows(Tensor input_tensor, Tensor dst2src_map, Tensor! "
-      "output_tensor) -> ()");
-  m.impl("shuffle_rows", torch::kCUDA, &shuffle_rows);
-
 #endif
 }
 

csrc/ops.h

@@ -92,11 +92,6 @@ void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
 void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
                         torch::Tensor& weight, double epsilon);
 
-void apply_repetition_penalties_(torch::Tensor& logits,
-                                 const torch::Tensor& prompt_mask,
-                                 const torch::Tensor& output_mask,
-                                 const torch::Tensor& repetition_penalties);
-
 void rms_norm_static_fp8_quant(torch::Tensor& out, torch::Tensor& input,
                                torch::Tensor& weight, torch::Tensor& scale,
                                double epsilon);
@@ -236,8 +231,7 @@ void cutlass_moe_mm(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides);
-    bool per_act_token, bool per_out_ch);
 
 void cutlass_fp4_group_mm(
     torch::Tensor& output, const torch::Tensor& a, const torch::Tensor& b,
@@ -249,16 +243,7 @@ void get_cutlass_moe_mm_data(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     torch::Tensor& input_permutation, torch::Tensor& output_permutation,
-    const int64_t num_experts, const int64_t n, const int64_t k,
+    const int64_t num_experts, const int64_t n, const int64_t k);
-    const std::optional<torch::Tensor>& blockscale_offsets);
-
-void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
-                                  torch::Tensor& problem_sizes1,
-                                  torch::Tensor& problem_sizes2,
-                                  const torch::Tensor& expert_num_tokens,
-                                  const int64_t num_local_experts,
-                                  const int64_t padded_m, const int64_t n,
-                                  const int64_t k);
 
 void cutlass_scaled_mm_azp(torch::Tensor& out, torch::Tensor const& a,
                            torch::Tensor const& b,

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8.cu

@@ -9,6 +9,10 @@ void cutlass_scaled_mm_blockwise_sm100_fp8(torch::Tensor& out,
                                            torch::Tensor const& b,
                                            torch::Tensor const& a_scales,
                                            torch::Tensor const& b_scales) {
+  TORCH_CHECK(
+      a.size(0) % 4 == 0,
+      "Input tensor must have a number of rows that is a multiple of 4. ",
+      "but got: ", a.size(0), " rows.");
   if (out.dtype() == torch::kBFloat16) {
     cutlass_gemm_blockwise_sm100_fp8_dispatch<cutlass::bfloat16_t>(
         out, a, b, a_scales, b_scales);

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm100_fp8_dispatch.cuh

@@ -1,6 +1,5 @@
 #pragma once
 
-#include "cuda_utils.h"
 #include "cutlass/cutlass.h"
 #include "cutlass/numeric_types.h"
 
@@ -23,49 +22,49 @@ namespace vllm {
 
 using namespace cute;
 
-// clang-format off
+template <typename OutType, typename MmaTileShape, typename ScalesPerTile,
-template <class OutType, int ScaleGranularityM,
+          class ClusterShape, typename EpilogueScheduler,
-          int ScaleGranularityN, int ScaleGranularityK,
+          typename MainloopScheduler>
-          class MmaTileShape, class ClusterShape,
-          class EpilogueScheduler, class MainloopScheduler,
-          bool swap_ab_ = false>
 struct cutlass_3x_gemm_fp8_blockwise {
-  static constexpr bool swap_ab = swap_ab_;
   using ElementAB = cutlass::float_e4m3_t;
 
   using ElementA = ElementAB;
   using LayoutA = cutlass::layout::RowMajor;
-  using LayoutA_Transpose = typename cutlass::layout::LayoutTranspose<LayoutA>::type;
   static constexpr int AlignmentA = 128 / cutlass::sizeof_bits<ElementA>::value;
 
   using ElementB = ElementAB;
   using LayoutB = cutlass::layout::ColumnMajor;
-  using LayoutB_Transpose = typename cutlass::layout::LayoutTranspose<LayoutB>::type;
   static constexpr int AlignmentB = 128 / cutlass::sizeof_bits<ElementB>::value;
 
+  using ElementC = void;
   using ElementD = OutType;
   using LayoutD = cutlass::layout::RowMajor;
-  using LayoutD_Transpose = typename cutlass::layout::LayoutTranspose<LayoutD>::type;
   static constexpr int AlignmentD = 128 / cutlass::sizeof_bits<ElementD>::value;
 
-  using ElementC = void; // TODO: support bias
   using LayoutC = LayoutD;
-  using LayoutC_Transpose = LayoutD_Transpose;
   static constexpr int AlignmentC = AlignmentD;
 
   using ElementAccumulator = float;
   using ElementCompute = float;
   using ElementBlockScale = float;
 
-  using ScaleConfig = conditional_t<swap_ab,
+  // MMA and Cluster Tile Shapes
-      cutlass::detail::Sm100BlockwiseScaleConfig<
+  // Shape of the tile computed by tcgen05 MMA, could be across 2 SMs if Cluster
-        ScaleGranularityM, ScaleGranularityN, ScaleGranularityK,
+  // Shape %2 == 0 using MmaTileShape_MNK = Shape<_128,_128,_128>;
-        cute::UMMA::Major::K, cute::UMMA::Major::MN>,
+  static constexpr int ScaleMsPerTile = size<0>(ScalesPerTile{});
-      cutlass::detail::Sm100BlockwiseScaleConfig<
+  static constexpr int ScaleGranularityM =
-        ScaleGranularityM, ScaleGranularityN, ScaleGranularityK,
+      size<0>(MmaTileShape{}) / ScaleMsPerTile;
-        cute::UMMA::Major::MN, cute::UMMA::Major::K>>;
+  static constexpr int ScaleGranularityN =
+      size<1>(MmaTileShape{}) / size<1>(ScalesPerTile{});
+  static constexpr int ScaleGranularityK =
+      size<2>(MmaTileShape{}) / size<2>(ScalesPerTile{});
 
-  // layout_SFA and layout_SFB cannot be swapped since they are deduced.
+  // Shape of the threadblocks in a cluster
+  using ClusterShape_MNK = ClusterShape;
+
+  using ScaleConfig = cutlass::detail::Sm100BlockwiseScaleConfig<
+      ScaleGranularityM, ScaleGranularityN, ScaleGranularityK,
+      cute::UMMA::Major::MN, cute::UMMA::Major::K>;
   using LayoutSFA = decltype(ScaleConfig::deduce_layoutSFA());
   using LayoutSFB = decltype(ScaleConfig::deduce_layoutSFB());
 
@@ -74,6 +73,7 @@ struct cutlass_3x_gemm_fp8_blockwise {
 
   static constexpr auto RoundStyle = cutlass::FloatRoundStyle::round_to_nearest;
   using ElementScalar = float;
+  // clang-format off
   using DefaultOperation = cutlass::epilogue::fusion::LinearCombination<ElementD, ElementCompute, ElementC, ElementScalar, RoundStyle>;
   using CollectiveEpilogue = typename cutlass::epilogue::collective::CollectiveBuilder<
       ArchTag,
@@ -84,47 +84,33 @@ struct cutlass_3x_gemm_fp8_blockwise {
       ElementAccumulator,
       ElementCompute,
       ElementC,
-      conditional_t<swap_ab, LayoutC_Transpose, LayoutC>,
+      LayoutC,
       AlignmentC,
       ElementD,
-      conditional_t<swap_ab, LayoutD_Transpose, LayoutD>,
+      LayoutD,
       AlignmentD,
       EpilogueScheduler,
       DefaultOperation
   >::CollectiveOp;
  
   using StageCountType = cutlass::gemm::collective::StageCountAuto; 
-  using CollectiveMainloop = conditional_t<swap_ab,
+  using CollectiveMainloop = typename cutlass::gemm::collective::CollectiveBuilder<
-      typename cutlass::gemm::collective::CollectiveBuilder<
+      ArchTag,
-          ArchTag,
+      OperatorClass,
-          OperatorClass,
+      ElementA,
-          ElementB,
+      cute::tuple<LayoutA, LayoutSFA>,
-          cute::tuple<LayoutB_Transpose, LayoutSFA>,
+      AlignmentA,
-          AlignmentB,
+      ElementB,
-          ElementA,
+      cute::tuple<LayoutB, LayoutSFB>,
-          cute::tuple<LayoutA_Transpose, LayoutSFB>,
+      AlignmentB,
-          AlignmentA,
+      ElementAccumulator,
-          ElementAccumulator,
+      MmaTileShape,
-          MmaTileShape,
+      ClusterShape,
-          ClusterShape,
+
           cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          MainloopScheduler
+      MainloopScheduler
-      >::CollectiveOp,
+  >::CollectiveOp;
-      typename cutlass::gemm::collective::CollectiveBuilder<
+  // clang-format on
-          ArchTag,
-          OperatorClass,
-          ElementA,
-          cute::tuple<LayoutA, LayoutSFA>,
-          AlignmentA,
-          ElementB,
-          cute::tuple<LayoutB, LayoutSFB>,
-          AlignmentB,
-          ElementAccumulator,
-          MmaTileShape,
-          ClusterShape,
-          cutlass::gemm::collective::StageCountAutoCarveout<static_cast<int>(sizeof(typename CollectiveEpilogue::SharedStorage))>,
-          MainloopScheduler
-      >::CollectiveOp>;
 
   using KernelType = enable_sm100_only<cutlass::gemm::kernel::GemmUniversal<
       Shape<int, int, int, int>, CollectiveMainloop, CollectiveEpilogue>>;
@@ -137,7 +123,6 @@ void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
                                    torch::Tensor const& b,
                                    torch::Tensor const& a_scales,
                                    torch::Tensor const& b_scales) {
-  static constexpr bool swap_ab = Gemm::swap_ab;
   using GemmKernel = typename Gemm::GemmKernel;
   using StrideA = typename Gemm::GemmKernel::StrideA;
   using StrideB = typename Gemm::GemmKernel::StrideB;
@@ -151,6 +136,7 @@ void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
   using ElementD = typename Gemm::ElementD;
 
   int32_t m = a.size(0), n = b.size(1), k = a.size(1);
+  auto prob_shape = cute::make_shape(m, n, k, 1);
 
   StrideA a_stride;
   StrideB b_stride;
@@ -160,13 +146,11 @@ void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
   b_stride =
       cutlass::make_cute_packed_stride(StrideB{}, cute::make_shape(n, k, 1));
   c_stride =
-      cutlass::make_cute_packed_stride(StrideC{}, swap_ab ? cute::make_shape(n, m, 1) : cute::make_shape(m, n, 1));
+      cutlass::make_cute_packed_stride(StrideC{}, cute::make_shape(m, n, 1));
 
-  LayoutSFA layout_SFA = swap_ab ? 
+  LayoutSFA layout_SFA =
-      ScaleConfig::tile_atom_to_shape_SFA(make_shape(n, m, k, 1)) :
       ScaleConfig::tile_atom_to_shape_SFA(make_shape(m, n, k, 1));
-  LayoutSFB layout_SFB = swap_ab ?
+  LayoutSFB layout_SFB =
-      ScaleConfig::tile_atom_to_shape_SFB(make_shape(n, m, k, 1)) :
       ScaleConfig::tile_atom_to_shape_SFB(make_shape(m, n, k, 1));
 
   auto a_ptr = static_cast<ElementAB*>(a.data_ptr());
@@ -174,22 +158,9 @@ void cutlass_gemm_caller_blockwise(torch::Tensor& out, torch::Tensor const& a,
   auto a_scales_ptr = static_cast<float*>(a_scales.data_ptr());
   auto b_scales_ptr = static_cast<float*>(b_scales.data_ptr());
 
-  auto mainloop_args = [&](){
+  typename GemmKernel::MainloopArguments mainloop_args{
-    // layout_SFA and layout_SFB cannot be swapped since they are deduced.
+      a_ptr,        a_stride,   b_ptr,        b_stride,
-    if (swap_ab) {
+      a_scales_ptr, layout_SFA, b_scales_ptr, layout_SFB};
-      return typename GemmKernel::MainloopArguments{
-          b_ptr,        b_stride,   a_ptr,        a_stride,
-          b_scales_ptr, layout_SFA, a_scales_ptr, layout_SFB
-      };
-    }
-    else {
-      return typename GemmKernel::MainloopArguments{
-          a_ptr,        a_stride,   b_ptr,        b_stride,
-          a_scales_ptr, layout_SFA, b_scales_ptr, layout_SFB
-      };
-    }
-  }();
-  auto prob_shape = swap_ab ? cute::make_shape(n, m, k, 1) : cute::make_shape(m, n, k, 1);
 
   auto c_ptr = static_cast<ElementD*>(out.data_ptr());
   typename GemmKernel::EpilogueArguments epilogue_args{
@@ -204,74 +175,29 @@ void cutlass_gemm_blockwise_sm100_fp8_dispatch(torch::Tensor& out,
                                                torch::Tensor const& b,
                                                torch::Tensor const& a_scales,
                                                torch::Tensor const& b_scales) {
-  int32_t m = a.size(0), n = b.size(1), k = a.size(1), sms;
+  auto m = a.size(0);
+  auto k = a.size(1);
+  auto n = b.size(1);
+  int sms;
   cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, a.get_device());
 
-  constexpr int TILE_K = 128;
+  auto should_use_2sm = [&sms](int m, int n, int tile1SM = 128) {
-  // TODO: better heuristics
+    return std::ceil(static_cast<float>(m) / tile1SM) *
-  bool swap_ab = (m < 16) || (m % 4 != 0);
+               std::ceil(static_cast<float>(n) / tile1SM) >=
-  bool use_tma_epilogue = (m * n) % 4 == 0;
+           sms;
-  if (!swap_ab) {
+  };
-    constexpr int TILE_N = 128;
+  bool use_2sm = should_use_2sm(m, n);
-    int tile_m = 256;
+  if (use_2sm) {
-    if (cuda_utils::ceil_div(n, TILE_N) * cuda_utils::ceil_div(m, 64) <= sms) {
-      tile_m = 64;
-    }
-    else if (cuda_utils::ceil_div(n, TILE_N) * cuda_utils::ceil_div(m, 128) <= sms) {
-      tile_m = 128;
-    }
-    if (tile_m == 64) {
-      if (use_tma_epilogue) {
-        cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-            OutType, 1, TILE_N, TILE_K, Shape<_64, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_1, _1, _1>, cutlass::epilogue::TmaWarpSpecialized1Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      } else {
-        cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-            OutType, 1, TILE_N, TILE_K, Shape<_64, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      }
-    } else if (tile_m == 128) {
-      if (use_tma_epilogue) {
-        cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-            OutType, 1, TILE_N, TILE_K, Shape<_128, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_1, _1, _1>, cutlass::epilogue::TmaWarpSpecialized1Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      } else {
-        cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-            OutType, 1, TILE_N, TILE_K, Shape<_128, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      }
-    } else { // tile_m == 256
-      if (use_tma_epilogue) {
-          cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-              OutType, 1, TILE_N, TILE_K, Shape<_256, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_2, _1, _1>, cutlass::epilogue::TmaWarpSpecialized2Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise2SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      } else {
-          cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-              OutType, 1, TILE_N, TILE_K, Shape<_256, Int<TILE_N>, Int<TILE_K>>,
-            Shape<_2, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized2Sm,
-            cutlass::gemm::KernelTmaWarpSpecializedBlockwise2SmSm100>>(
-            out, a, b, a_scales, b_scales);
-      }
-    }
-  } else {
-    // TODO: Test more tile N configs
-    constexpr int TILE_M = 128;
-    constexpr int TILE_N = 16;
-    // TMA epilogue isn't compatible with Swap A/B
     cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
-        OutType, TILE_M, 1, TILE_K, Shape<Int<TILE_M>, Int<TILE_N>, Int<TILE_K>>,
+        OutType, Shape<_256, _128, _128>, Shape<_256, _1, _1>,
-        Shape<_1, _1, _1>, cutlass::epilogue::NoSmemWarpSpecialized1Sm,
+        Shape<_2, _2, _1>, cutlass::epilogue::TmaWarpSpecialized2Sm,
-        cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100, true>>(
+        cutlass::gemm::KernelTmaWarpSpecializedBlockwise2SmSm100>>(
+        out, a, b, a_scales, b_scales);
+  } else {
+    cutlass_gemm_caller_blockwise<cutlass_3x_gemm_fp8_blockwise<
+        OutType, Shape<_128, _128, _128>, Shape<_128, _1, _1>,
+        Shape<_1, _1, _1>, cutlass::epilogue::TmaWarpSpecialized1Sm,
+        cutlass::gemm::KernelTmaWarpSpecializedBlockwise1SmSm100>>(
         out, a, b, a_scales, b_scales);
   }
 }

csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8_dispatch.cuh

@@ -15,7 +15,6 @@ using c3x::cutlass_gemm_caller;
 template <typename InType, typename OutType,
           template <typename, typename, typename> typename Epilogue>
 struct sm100_fp8_config_default {
-  // M in (128, inf)
   static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
   using KernelSchedule = cutlass::gemm::collective::KernelScheduleAuto;
   using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
@@ -26,34 +25,6 @@ struct sm100_fp8_config_default {
                             KernelSchedule, EpilogueSchedule>;
 };
 
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm100_fp8_config_M128 {
-  // M in (64, 128]
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule = cutlass::gemm::collective::KernelScheduleAuto;
-  using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
-  using TileShape = Shape<_128, _128, _64>;
-  using ClusterShape = Shape<_2, _2, _1>;
-  using Cutlass3xGemm =
-      cutlass_3x_gemm_sm100<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm100_fp8_config_M64 {
-  // M in [1, 64]
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule = cutlass::gemm::collective::KernelScheduleAuto;
-  using EpilogueSchedule = cutlass::epilogue::collective::EpilogueScheduleAuto;
-  using TileShape = Shape<_64, _64, _256>;
-  using ClusterShape = Shape<_1, _8, _1>;
-  using Cutlass3xGemm =
-      cutlass_3x_gemm_sm100<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
 template <typename InType, typename OutType,
           template <typename, typename, typename> typename Epilogue,
           typename... EpilogueArgs>
@@ -68,28 +39,8 @@ inline void cutlass_gemm_sm100_fp8_dispatch(torch::Tensor& out,
   using Cutlass3xGemmDefault =
       typename sm100_fp8_config_default<InType, OutType,
                                         Epilogue>::Cutlass3xGemm;
-  using Cutlass3xGemmM64 =
+  return cutlass_gemm_caller<Cutlass3xGemmDefault>(
-      typename sm100_fp8_config_M64<InType, OutType, Epilogue>::Cutlass3xGemm;
+      out, a, b, std::forward<EpilogueArgs>(args)...);
-  using Cutlass3xGemmM128 =
-      typename sm100_fp8_config_M128<InType, OutType, Epilogue>::Cutlass3xGemm;
-
-  uint32_t const m = a.size(0);
-  uint32_t const mp2 =
-      std::max(static_cast<uint32_t>(64), next_pow_2(m));  // next power of 2
-
-  if (mp2 <= 64) {
-    // m in [1, 64]
-    return cutlass_gemm_caller<Cutlass3xGemmM64>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  } else if (mp2 <= 128) {
-    // m in (64, 128]
-    return cutlass_gemm_caller<Cutlass3xGemmM128>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  } else {
-    // m in (128, inf)
-    return cutlass_gemm_caller<Cutlass3xGemmDefault>(
-        out, a, b, std::forward<EpilogueArgs>(args)...);
-  }
 }
 
 template <template <typename, typename, typename> typename Epilogue,

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu

@@ -84,8 +84,7 @@ void run_cutlass_moe_mm_sm90(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
-    bool per_act_token, bool per_out_ch) {
   TORCH_CHECK(a_tensors.size(0) > 0, "No input A tensors provided.");
   TORCH_CHECK(b_tensors.size(0) > 0, "No input B tensors provided.");
   TORCH_CHECK(out_tensors.size(0) > 0, "No output tensors provided.");
@@ -114,23 +113,19 @@ void run_cutlass_moe_mm_sm90(
   if (n >= 8192) {
     cutlass_group_gemm_caller<Cutlass3xGemmN8192>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   } else if (k >= 8192) {
     cutlass_group_gemm_caller<Cutlass3xGemmK8192>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   } else if (m <= 16) {
     cutlass_group_gemm_caller<Cutlass3xGemmM16>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   } else {
     cutlass_group_gemm_caller<Cutlass3xGemmDefault>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   }
 }
 
@@ -139,18 +134,15 @@ void dispatch_moe_mm_sm90(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
-    bool per_act_token, bool per_out_ch) {
   if (out_tensors.dtype() == torch::kBFloat16) {
     run_cutlass_moe_mm_sm90<cutlass::float_e4m3_t, cutlass::bfloat16_t>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   } else {
     run_cutlass_moe_mm_sm90<cutlass::float_e4m3_t, cutlass::half_t>(
         out_tensors, a_tensors, b_tensors, a_scales, b_scales, expert_offsets,
-        problem_sizes, a_strides, b_strides, c_strides, per_act_token,
+        problem_sizes, a_strides, b_strides, c_strides);
-        per_out_ch);
   }
 }
 
@@ -161,9 +153,8 @@ void cutlass_moe_mm_sm90(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
-    bool per_act_token, bool per_out_ch) {
   dispatch_moe_mm_sm90(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
                        expert_offsets, problem_sizes, a_strides, b_strides,
-                       c_strides, per_act_token, per_out_ch);
+                       c_strides);
 }

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cuh

@@ -76,8 +76,7 @@ void cutlass_group_gemm_caller(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
-    bool per_act_token, bool per_out_ch) {
   using ElementAB = typename Gemm::ElementAB;
   using ElementD = typename Gemm::ElementD;
 
@@ -85,6 +84,9 @@ void cutlass_group_gemm_caller(
   int k_size = a_tensors.size(1);
   int n_size = out_tensors.size(1);
 
+  bool per_act_token = a_scales.numel() != 1;
+  bool per_out_ch = b_scales.numel() != num_experts;
+
   auto stream = at::cuda::getCurrentCUDAStream(a_tensors.device().index());
 
   auto options_int =

csrc/quantization/cutlass_w8a8/moe/moe_data.cu

@@ -7,7 +7,7 @@
 
 constexpr uint64_t THREADS_PER_EXPERT = 512;
 
-__global__ void compute_problem_sizes(const uint32_t* __restrict__ topk_ids,
+__global__ void compute_problem_sizes(const int* __restrict__ topk_ids,
                                       int32_t* problem_sizes1,
                                       int32_t* problem_sizes2,
                                       int32_t* atomic_buffer,
@@ -45,24 +45,7 @@ __global__ void compute_expert_offsets(
   }
 }
 
-__global__ void compute_expert_blockscale_offsets(
+__global__ void compute_arg_sorts(const int* __restrict__ topk_ids,
-    const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
-    int32_t* blockscale_offsets, int32_t* atomic_buffer,
-    const int num_experts) {
-  int32_t tot_offset = 0;
-  int32_t tot_offset_round = 0;
-  expert_offsets[0] = 0;
-  blockscale_offsets[0] = 0;
-  for (int i = 0; i < num_experts; ++i) {
-    atomic_buffer[i] = tot_offset;
-    tot_offset += problem_sizes1[i * 3];
-    expert_offsets[i + 1] = tot_offset;
-    tot_offset_round += (problem_sizes1[i * 3] + (128 - 1)) / 128 * 128;
-    blockscale_offsets[i + 1] = tot_offset_round;
-  }
-}
-
-__global__ void compute_arg_sorts(const uint32_t* __restrict__ topk_ids,
                                   const int32_t* __restrict__ expert_offsets,
                                   int32_t* input_permutation,
                                   int32_t* output_permutation,
@@ -94,8 +77,7 @@ void get_cutlass_moe_mm_data_caller(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     torch::Tensor& input_permutation, torch::Tensor& output_permutation,
-    const int64_t num_experts, const int64_t n, const int64_t k,
+    const int64_t num_experts, const int64_t n, const int64_t k) {
-    const std::optional<torch::Tensor>& blockscale_offsets) {
   auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
   auto options_int32 =
       torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
@@ -103,61 +85,19 @@ void get_cutlass_moe_mm_data_caller(
 
   int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
   compute_problem_sizes<<<num_experts, num_threads, 0, stream>>>(
-      static_cast<const uint32_t*>(topk_ids.data_ptr()),
+      static_cast<const int32_t*>(topk_ids.data_ptr()),
       static_cast<int32_t*>(problem_sizes1.data_ptr()),
       static_cast<int32_t*>(problem_sizes2.data_ptr()),
       static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(), n, k);
-  if (blockscale_offsets.has_value()) {
+  compute_expert_offsets<<<1, 1, 0, stream>>>(
-    compute_expert_blockscale_offsets<<<1, 1, 0, stream>>>(
+      static_cast<const int32_t*>(problem_sizes1.data_ptr()),
-        static_cast<const int32_t*>(problem_sizes1.data_ptr()),
+      static_cast<int32_t*>(expert_offsets.data_ptr()),
-        static_cast<int32_t*>(expert_offsets.data_ptr()),
+      static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
-        static_cast<int32_t*>(blockscale_offsets.value().data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
-  } else {
-    compute_expert_offsets<<<1, 1, 0, stream>>>(
-        static_cast<const int32_t*>(problem_sizes1.data_ptr()),
-        static_cast<int32_t*>(expert_offsets.data_ptr()),
-        static_cast<int32_t*>(atomic_buffer.data_ptr()), num_experts);
-  }
   compute_arg_sorts<<<num_experts, num_threads, 0, stream>>>(
-      static_cast<const uint32_t*>(topk_ids.data_ptr()),
+      static_cast<const int32_t*>(topk_ids.data_ptr()),
       static_cast<const int32_t*>(expert_offsets.data_ptr()),
       static_cast<int32_t*>(input_permutation.data_ptr()),
       static_cast<int32_t*>(output_permutation.data_ptr()),
       static_cast<int32_t*>(atomic_buffer.data_ptr()), topk_ids.numel(),
       topk_ids.size(1));
 }
-
-__global__ void compute_pplx_data(int32_t* expert_offsets,
-                                  int32_t* problem_sizes1,
-                                  int32_t* problem_sizes2,
-                                  const int32_t* __restrict__ expert_num_tokens,
-                                  const int padded_m, const int n,
-                                  const int k) {
-  int expert_idx = threadIdx.x;
-
-  expert_offsets[expert_idx] = expert_idx * padded_m;
-  problem_sizes1[expert_idx * 3] = expert_num_tokens[expert_idx];
-  problem_sizes1[expert_idx * 3 + 1] = 2 * n;
-  problem_sizes1[expert_idx * 3 + 2] = k;
-  problem_sizes2[expert_idx * 3] = expert_num_tokens[expert_idx];
-  problem_sizes2[expert_idx * 3 + 1] = k;
-  problem_sizes2[expert_idx * 3 + 2] = n;
-}
-
-void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
-                                         torch::Tensor& problem_sizes1,
-                                         torch::Tensor& problem_sizes2,
-                                         const torch::Tensor& expert_num_tokens,
-                                         const int64_t num_local_experts,
-                                         const int64_t padded_m,
-                                         const int64_t n, const int64_t k) {
-  auto stream = at::cuda::getCurrentCUDAStream(expert_offsets.device().index());
-
-  compute_pplx_data<<<1, num_local_experts, 0, stream>>>(
-      static_cast<int32_t*>(expert_offsets.data_ptr()),
-      static_cast<int32_t*>(problem_sizes1.data_ptr()),
-      static_cast<int32_t*>(problem_sizes2.data_ptr()),
-      static_cast<const int32_t*>(expert_num_tokens.data_ptr()), padded_m, n,
-      k);
-}

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -36,8 +36,7 @@ void cutlass_moe_mm_sm90(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides);
-    bool per_act_token, bool per_out_ch);
 
 #endif
 
@@ -55,16 +54,7 @@ void get_cutlass_moe_mm_data_caller(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     torch::Tensor& input_permutation, torch::Tensor& output_permutation,
-    const int64_t num_experts, const int64_t n, const int64_t k,
+    const int64_t num_experts, const int64_t n, const int64_t k);
-    const std::optional<torch::Tensor>& blockscale_offsets);
-
-void get_cutlass_pplx_moe_mm_data_caller(torch::Tensor& expert_offsets,
-                                         torch::Tensor& problem_sizes1,
-                                         torch::Tensor& problem_sizes2,
-                                         const torch::Tensor& expert_num_tokens,
-                                         const int64_t num_local_experts,
-                                         const int64_t padded_m,
-                                         const int64_t n, const int64_t k);
 #endif
 
 void cutlass_scaled_mm_azp_sm75(torch::Tensor& c, torch::Tensor const& a,
@@ -216,13 +206,12 @@ void cutlass_moe_mm(
     torch::Tensor const& b_tensors, torch::Tensor const& a_scales,
     torch::Tensor const& b_scales, torch::Tensor const& expert_offsets,
     torch::Tensor const& problem_sizes, torch::Tensor const& a_strides,
-    torch::Tensor const& b_strides, torch::Tensor const& c_strides,
+    torch::Tensor const& b_strides, torch::Tensor const& c_strides) {
-    bool per_act_token, bool per_out_ch) {
   int32_t version_num = get_sm_version_num();
 #if defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90
   cutlass_moe_mm_sm90(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
                       expert_offsets, problem_sizes, a_strides, b_strides,
-                      c_strides, per_act_token, per_out_ch);
+                      c_strides);
   return;
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(
@@ -235,8 +224,7 @@ void get_cutlass_moe_mm_data(
     const torch::Tensor& topk_ids, torch::Tensor& expert_offsets,
     torch::Tensor& problem_sizes1, torch::Tensor& problem_sizes2,
     torch::Tensor& input_permutation, torch::Tensor& output_permutation,
-    const int64_t num_experts, const int64_t n, const int64_t k,
+    const int64_t num_experts, const int64_t n, const int64_t k) {
-    const std::optional<torch::Tensor>& blockscale_offsets) {
   // This function currently gets compiled only if we have a valid cutlass moe
   // mm to run it for.
   int32_t version_num = get_sm_version_num();
@@ -244,8 +232,7 @@ void get_cutlass_moe_mm_data(
     (defined ENABLE_SCALED_MM_SM100 && ENABLE_SCALED_MM_SM90)
   get_cutlass_moe_mm_data_caller(topk_ids, expert_offsets, problem_sizes1,
                                  problem_sizes2, input_permutation,
-                                 output_permutation, num_experts, n, k,
+                                 output_permutation, num_experts, n, k);
-                                 blockscale_offsets);
   return;
 #endif
   TORCH_CHECK_NOT_IMPLEMENTED(
@@ -255,29 +242,6 @@ void get_cutlass_moe_mm_data(
       version_num, ". Required capability: 90");
 }
 
-void get_cutlass_pplx_moe_mm_data(torch::Tensor& expert_offsets,
-                                  torch::Tensor& problem_sizes1,
-                                  torch::Tensor& problem_sizes2,
-                                  const torch::Tensor& expert_num_tokens,
-                                  const int64_t num_local_experts,
-                                  const int64_t padded_m, const int64_t n,
-                                  const int64_t k) {
-  // This function currently gets compiled only if we have a valid cutlass moe
-  // mm to run it for.
-  int32_t version_num = get_sm_version_num();
-#if defined ENABLE_CUTLASS_MOE_SM90 && ENABLE_CUTLASS_MOE_SM90
-  get_cutlass_pplx_moe_mm_data_caller(expert_offsets, problem_sizes1,
-                                      problem_sizes2, expert_num_tokens,
-                                      num_local_experts, padded_m, n, k);
-  return;
-#endif
-  TORCH_CHECK_NOT_IMPLEMENTED(
-      false,
-      "No compiled get_cutlass_pplx_moe_mm_data: no cutlass_scaled_mm kernel "
-      "for CUDA device capability: ",
-      version_num, ". Required capability: 90");
-}
-
 void cutlass_scaled_mm_azp(torch::Tensor& c, torch::Tensor const& a,
                            torch::Tensor const& b,
                            torch::Tensor const& a_scales,

csrc/quantization/fp8/common.cu

@@ -39,8 +39,8 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
   fp8_type* __restrict__ token_output = &out[offset];
 
   // For vectorization, token_input and token_output pointers need to be
-  // aligned at 32-byte and 16-byte addresses respectively.
+  // aligned at 8-byte and 4-byte addresses respectively.
-  bool const can_vectorize = hidden_size % 16 == 0;
+  bool const can_vectorize = hidden_size % 4 == 0;
 
   float absmax_val = 0.0f;
   if (can_vectorize) {
@@ -48,24 +48,24 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
   } else {
     for (int i = tid; i < hidden_size; i += blockDim.x) {
       float const x = static_cast<float>(token_input[i]);
-      absmax_val = fmaxf(absmax_val, fabsf(x));
+      absmax_val = max(absmax_val, fabs(x));
     }
   }
 
-  using BlockReduce = cub::BlockReduce<float, 256>;
+  using BlockReduce = cub::BlockReduce<float, 1024>;
   __shared__ typename BlockReduce::TempStorage reduceStorage;
   float const block_absmax_val_maybe =
       BlockReduce(reduceStorage).Reduce(absmax_val, cub::Max{}, blockDim.x);
   __shared__ float token_scale;
   if (tid == 0) {
     if (scale_ub) {
-      token_scale = fminf(block_absmax_val_maybe, *scale_ub);
+      token_scale = min(block_absmax_val_maybe, *scale_ub);
     } else {
       token_scale = block_absmax_val_maybe;
     }
     // token scale computation
-    token_scale = fmaxf(token_scale / quant_type_max_v<fp8_type>,
+    token_scale = max(token_scale / quant_type_max_v<fp8_type>,
-                        min_scaling_factor<fp8_type>::val());
+                      min_scaling_factor<fp8_type>::val());
     scale[token_idx] = token_scale;
   }
   __syncthreads();
@@ -88,11 +88,10 @@ void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                              torch::Tensor const& input,  // [..., d]
                              torch::Tensor const& scale)  // [1]
 {
-  int const block_size = 256;
+  int64_t num_tokens = input.numel() / input.size(-1);
-  int const num_tokens = input.numel() / input.size(-1);
+  int64_t num_elems = input.numel();
-  int const num_elems = input.numel();
+  dim3 grid(num_tokens);
-  dim3 const grid(num_tokens);
+  dim3 block(1024);
-  dim3 const block(block_size);
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(
@@ -111,11 +110,10 @@ void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
                               torch::Tensor const& input,  // [..., d]
                               torch::Tensor& scale)        // [1]
 {
-  int const block_size = 256;
+  int64_t num_tokens = input.numel() / input.size(-1);
-  int const num_tokens = input.numel() / input.size(-1);
+  int64_t num_elems = input.numel();
-  int const num_elems = input.numel();
+  dim3 grid(num_tokens);
-  dim3 const grid(num_tokens);
+  dim3 block(1024);
-  dim3 const block(block_size);
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(
@@ -143,9 +141,8 @@ void dynamic_per_token_scaled_fp8_quant(
 
   int const hidden_size = input.size(-1);
   int const num_tokens = input.numel() / hidden_size;
-  int const block_size = 256;
   dim3 const grid(num_tokens);
-  dim3 const block(std::min(hidden_size, block_size));
+  dim3 const block(std::min(hidden_size, 1024));
 
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();

csrc/quantization/fp8/common.cuh

@@ -46,7 +46,7 @@ __device__ __forceinline__ fp8_type scaled_fp8_conversion(float const val,
   }
 
   float r =
-      fmaxf(-quant_type_max_v<fp8_type>, fminf(x, quant_type_max_v<fp8_type>));
+      fmax(-quant_type_max_v<fp8_type>, fmin(x, quant_type_max_v<fp8_type>));
 #ifndef USE_ROCM
   return static_cast<fp8_type>(r);
 #else
@@ -65,7 +65,7 @@ template <typename scalar_t, typename fp8_type>
 __global__ void segmented_max_reduction(float* __restrict__ scale,
                                         const scalar_t* __restrict__ input,
                                         int64_t num_elems) {
-  __shared__ float cache[256];
+  __shared__ float cache[1024];
   int64_t i = blockDim.x * blockIdx.x + threadIdx.x;
 
   // First store maximum for all values processes by
@@ -73,7 +73,7 @@ __global__ void segmented_max_reduction(float* __restrict__ scale,
   scalar_t tmp = 0.0;
   while (i < num_elems) {
     float x = static_cast<float>(input[i]);
-    tmp = fmaxf(tmp, fabsf(x));
+    tmp = max(tmp, fabs(x));
     i += blockDim.x * gridDim.x;
   }
   cache[threadIdx.x] = tmp;
@@ -100,27 +100,25 @@ template <typename scalar_t>
 __device__ float thread_max_vec(scalar_t const* __restrict__ input,
                                 int64_t const num_elems, int const tid,
                                 int const step) {
-  constexpr size_t VEC_SIZE = 16;
-  using scalarxN_t = vec_n_t<scalar_t, VEC_SIZE>;
   // Vectorized input/output to better utilize memory bandwidth.
-  auto const* vectorized_in = reinterpret_cast<scalarxN_t const*>(input);
+  vec4_t<scalar_t> const* vectorized_in =
+      reinterpret_cast<vec4_t<scalar_t> const*>(input);
 
-  // num_elems / VEC_SIZE (which is 16)
+  int64_t const num_vec_elems = num_elems >> 2;
-  int64_t const num_vec_elems = num_elems >> 4;
   float absmax_val = 0.0f;
 
-#pragma unroll
+#pragma unroll 4
   for (int64_t i = tid; i < num_vec_elems; i += step) {
-    scalarxN_t in_vec = vectorized_in[i];
+    vec4_t<scalar_t> in_vec = vectorized_in[i];
-#pragma unroll
+    absmax_val = max(absmax_val, fabs(in_vec.x));
-    for (int j = 0; j < VEC_SIZE; ++j) {
+    absmax_val = max(absmax_val, fabs(in_vec.y));
-      absmax_val = fmaxf(absmax_val, fabsf(in_vec.val[j]));
+    absmax_val = max(absmax_val, fabs(in_vec.z));
-    }
+    absmax_val = max(absmax_val, fabs(in_vec.w));
   }
 
-  // Handle the remaining elements if num_elems is not divisible by VEC_SIZE
+  // Handle the remaining elements if num_elems is not divisible by 4
-  for (int64_t i = num_vec_elems * VEC_SIZE + tid; i < num_elems; i += step) {
+  for (int64_t i = num_vec_elems * 4 + tid; i < num_elems; i += step) {
-    absmax_val = fmaxf(absmax_val, fabsf(input[i]));
+    absmax_val = max(absmax_val, fabs(input[i]));
   }
 
   return absmax_val;
@@ -132,31 +130,31 @@ __device__ void scaled_fp8_conversion_vec(fp8_type* __restrict__ out,
                                           float const scale,
                                           int64_t const num_elems,
                                           int const tid, int const step) {
-  constexpr size_t VEC_SIZE = 16;
+  using float8x4_t = q8x4_t<fp8_type>;
-  using scalarxN_t = vec_n_t<scalar_t, VEC_SIZE>;
-  using float8xN_t = q8_n_t<fp8_type, VEC_SIZE>;
   // Vectorized input/output to better utilize memory bandwidth.
-  auto const* vectorized_in = reinterpret_cast<scalarxN_t const*>(input);
+  auto const* vectorized_in = reinterpret_cast<vec4_t<scalar_t> const*>(input);
-  auto* vectorized_out = reinterpret_cast<float8xN_t*>(out);
+  auto* vectorized_out = reinterpret_cast<float8x4_t*>(out);
 
-  // num_elems / VEC_SIZE (which is 16)
+  int64_t const num_vec_elems = num_elems >> 2;
-  int64_t const num_vec_elems = num_elems >> 4;
 
-#pragma unroll
+#pragma unroll 4
   for (int64_t i = tid; i < num_vec_elems; i += step) {
-    scalarxN_t in_vec = vectorized_in[i];
+    vec4_t<scalar_t> in_vec = vectorized_in[i];
-    float8xN_t out_vec;
+    float8x4_t out_vec;
 
-#pragma unroll
+    out_vec.x = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
-    for (int j = 0; j < VEC_SIZE; ++j) {
+        static_cast<float>(in_vec.x), scale);
-      out_vec.val[j] = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
+    out_vec.y = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
-          static_cast<float>(in_vec.val[j]), scale);
+        static_cast<float>(in_vec.y), scale);
-    }
+    out_vec.z = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
+        static_cast<float>(in_vec.z), scale);
+    out_vec.w = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
+        static_cast<float>(in_vec.w), scale);
     vectorized_out[i] = out_vec;
   }
 
-  // Handle the remaining elements if num_elems is not divisible by VEC_SIZE
+  // Handle the remaining elements if num_elems is not divisible by 4
-  for (int64_t i = num_vec_elems * VEC_SIZE + tid; i < num_elems; i += step) {
+  for (int64_t i = num_vec_elems * 4 + tid; i < num_elems; i += step) {
     out[i] = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(input[i]), scale);
   }

csrc/quantization/fused_kernels/layernorm_utils.cuh

@@ -140,7 +140,6 @@ __device__ void compute_rms(float* rms, scalar_t const* __restrict__ input,
   // sum of squares
   float ss = 0.0f;
 
-  const int VEC_SIZE = 4;
   int32_t const num_vec_elems = hidden_size >> 2;
 
 #pragma unroll 4
@@ -148,23 +147,22 @@ __device__ void compute_rms(float* rms, scalar_t const* __restrict__ input,
     vec4_t<scalar_t> in = vec_input[i];
 
     vec4_t<float> x;
-#pragma unroll
+    x.x = static_cast<float>(in.x);
-    for (int j = 0; j < VEC_SIZE; ++j) {
+    x.y = static_cast<float>(in.y);
-      x.val[j] = static_cast<float>(in.val[j]);
+    x.z = static_cast<float>(in.z);
-    }
+    x.w = static_cast<float>(in.w);
-
     if constexpr (has_residual) {
       vec4_t<scalar_t> r = vec_residual[i];
-#pragma unroll
+      x.x += static_cast<float>(r.x);
-      for (int j = 0; j < VEC_SIZE; ++j) {
+      x.y += static_cast<float>(r.y);
-        x.val[j] += static_cast<float>(r.val[j]);
+      x.z += static_cast<float>(r.z);
-      }
+      x.w += static_cast<float>(r.w);
     }
 
-#pragma unroll
+    ss += x.x * x.x;
-    for (int j = 0; j < VEC_SIZE; ++j) {
+    ss += x.y * x.y;
-      ss += x.val[j] * x.val[j];
+    ss += x.z * x.z;
-    }
+    ss += x.w * x.w;
   }
 
   using BlockReduce = cub::BlockReduce<float, 1024>;
@@ -205,7 +203,6 @@ __device__ void compute_dynamic_per_token_scales(
 
   constexpr scalar_out_t qmax{quant_type_max_v<scalar_out_t>};
 
-  const int VEC_SIZE = 4;
   int32_t const num_vec_elems = hidden_size >> 2;
   float block_absmax_val_maybe = 0.0f;
 
@@ -215,25 +212,26 @@ __device__ void compute_dynamic_per_token_scales(
     vec4_t<scalar_t> const w = vec_weight[i];
 
     vec4_t<float> x;
-#pragma unroll
+    x.x = static_cast<float>(in.x);
-    for (int j = 0; j < VEC_SIZE; ++j) {
+    x.y = static_cast<float>(in.y);
-      x.val[j] = static_cast<float>(in.val[j]);
+    x.z = static_cast<float>(in.z);
-    }
+    x.w = static_cast<float>(in.w);
-
     if constexpr (has_residual) {
       vec4_t<scalar_t> r = vec_residual[i];
-#pragma unroll
+      x.x += static_cast<float>(r.x);
-      for (int j = 0; j < VEC_SIZE; ++j) {
+      x.y += static_cast<float>(r.y);
-        x.val[j] += static_cast<float>(r.val[j]);
+      x.z += static_cast<float>(r.z);
-      }
+      x.w += static_cast<float>(r.w);
     }
 
-#pragma unroll
+    block_absmax_val_maybe = fmaxf(
-    for (int j = 0; j < VEC_SIZE; ++j) {
+        block_absmax_val_maybe, fabs(static_cast<scalar_t>(x.x * rms) * w.x));
-      block_absmax_val_maybe =
+    block_absmax_val_maybe = fmaxf(
-          fmaxf(block_absmax_val_maybe,
+        block_absmax_val_maybe, fabs(static_cast<scalar_t>(x.y * rms) * w.y));
-                fabs(static_cast<scalar_t>(x.val[j] * rms) * w.val[j]));
+    block_absmax_val_maybe = fmaxf(
-    }
+        block_absmax_val_maybe, fabs(static_cast<scalar_t>(x.z * rms) * w.z));
+    block_absmax_val_maybe = fmaxf(
+        block_absmax_val_maybe, fabs(static_cast<scalar_t>(x.w * rms) * w.w));
   }
 
   using BlockReduce = cub::BlockReduce<float, 1024>;
@@ -284,7 +282,6 @@ __device__ void norm_and_quant(scalar_out_t* __restrict__ output,
     vec_residual = reinterpret_cast<vec4_t<scalar_t>*>(&residual[token_offset]);
   }
 
-  const int VEC_SIZE = 4;
   int32_t const num_vec_elems = hidden_size >> 2;
 
 // TODO(luka/varun) extract into type-agnostic vectorized quant function to
@@ -295,31 +292,33 @@ __device__ void norm_and_quant(scalar_out_t* __restrict__ output,
     vec4_t<scalar_t> const w = vec_weight[i];
 
     vec4_t<float> x;
-#pragma unroll
+    x.x = static_cast<float>(in.x);
-    for (int j = 0; j < VEC_SIZE; ++j) {
+    x.y = static_cast<float>(in.y);
-      x.val[j] = static_cast<float>(in.val[j]);
+    x.z = static_cast<float>(in.z);
-    }
+    x.w = static_cast<float>(in.w);
-
     if constexpr (has_residual) {
       vec4_t<scalar_t> r = vec_residual[i];
-#pragma unroll
+      x.x += static_cast<float>(r.x);
-      for (int j = 0; j < VEC_SIZE; ++j) {
+      x.y += static_cast<float>(r.y);
-        x.val[j] += static_cast<float>(r.val[j]);
+      x.z += static_cast<float>(r.z);
-      }
+      x.w += static_cast<float>(r.w);
-// Update residual
+      // Update residual
-#pragma unroll
+      r.x = static_cast<scalar_t>(x.x);
-      for (int j = 0; j < VEC_SIZE; ++j) {
+      r.y = static_cast<scalar_t>(x.y);
-        r.val[j] = static_cast<scalar_t>(x.val[j]);
+      r.z = static_cast<scalar_t>(x.z);
-      }
+      r.w = static_cast<scalar_t>(x.w);
       vec_residual[i] = r;
     }
 
     q8x4_t<scalar_out_t> out;
-#pragma unroll
+    out.x = ScaledQuant<scalar_out_t, is_scale_inverted>::quant_fn(
-    for (int j = 0; j < VEC_SIZE; ++j) {
+        static_cast<scalar_t>(x.x * rms) * w.x, scale);
-      out.val[j] = ScaledQuant<scalar_out_t, is_scale_inverted>::quant_fn(
+    out.y = ScaledQuant<scalar_out_t, is_scale_inverted>::quant_fn(
-          static_cast<scalar_t>(x.val[j] * rms) * w.val[j], scale);
+        static_cast<scalar_t>(x.y * rms) * w.y, scale);
-    }
+    out.z = ScaledQuant<scalar_out_t, is_scale_inverted>::quant_fn(
+        static_cast<scalar_t>(x.z * rms) * w.z, scale);
+    out.w = ScaledQuant<scalar_out_t, is_scale_inverted>::quant_fn(
+        static_cast<scalar_t>(x.w * rms) * w.w, scale);
     vec_output[i] = out;
   }
 }

csrc/quantization/gptq_marlin/generate_kernels.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import glob
 import itertools
 import os

csrc/quantization/machete/generate.py

@@ -1,5 +1,4 @@
 # SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import itertools
 import math

csrc/quantization/vectorization.cuh

@@ -10,22 +10,23 @@
 namespace vllm {
 
 // Vectorization containers
-template <typename scalar_t, size_t vec_size>
+template <typename scalar_t>
-struct __align__(vec_size * sizeof(scalar_t)) vec_n_t {
+struct __align__(8) vec4_t {
-  scalar_t val[vec_size];
+  scalar_t x;
+  scalar_t y;
+  scalar_t z;
+  scalar_t w;
 };
 
-template <typename quant_type_t, size_t vec_size>
+template <typename quant_type_t>
-struct __align__(vec_size * sizeof(quant_type_t)) q8_n_t {
+struct __align__(4) q8x4_t {
   static_assert(std::is_same_v<quant_type_t, int8_t> ||
                 std::is_same_v<quant_type_t, c10::Float8_e4m3fn> ||
                 std::is_same_v<quant_type_t, c10::Float8_e4m3fnuz>);
-  quant_type_t val[vec_size];
+  quant_type_t x;
+  quant_type_t y;
+  quant_type_t z;
+  quant_type_t w;
 };
 
-template <typename scalar_t>
-using vec4_t = vec_n_t<scalar_t, 4>;
-template <typename quant_type_t>
-using q8x4_t = q8_n_t<quant_type_t, 4>;
-
 }  // namespace vllm

csrc/rocm/skinny_gemms.cu

@@ -13,34 +13,14 @@
 #include "dispatch_utils.h"
 #include "quantization/fp8/common.cuh"
 
-#if defined(__HIPCC__) && \
+#if defined(__HIPCC__) && (defined(__gfx90a__) || defined(__gfx942__))
-    (defined(__gfx90a__) || defined(__gfx942__) || defined(__gfx950__))
+  #define __HIP__MI300_MI250__
-  #define __HIP__GFX9__
 #endif
 
-#if defined(__HIPCC__) && (defined(__gfx942__) || defined(__gfx950__))
+#if defined(__HIPCC__) && defined(__gfx942__)
-  #define __HIP__MI3XX__
+  #define __HIP__MI300__
 #endif
 
-#if defined(__gfx950__)
-  #define LDS_SIZE 160 * 1024
-#else
-  #define LDS_SIZE 64 * 1024
-#endif
-
-int get_lds_size() {
-  static bool is_cached = false;
-  static int result;
-  if (is_cached == false) {
-    auto dprops = at::cuda::getCurrentDeviceProperties();
-    std::string device_arch = dprops->gcnArchName;
-    size_t substring = device_arch.find("gfx95");
-    result = (substring == std::string::npos ? 64 * 1024 : 160 * 1024);
-    is_cached = true;
-  }
-  return result;
-}
-
 #if defined(NDEBUG)
   #undef NDEBUG
   #include <assert.h>
@@ -287,7 +267,7 @@ torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
     V0 += (s.x + s.y);                                                        \
   }
 
-#if defined(__HIP__GFX9__)  // TODO: Add NAVI support
+#if defined(__HIP__MI300_MI250__)  // TODO: Add NAVI support
 // This version targets cases where A[] fits LDS capacity
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
@@ -295,8 +275,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
                      const scalar_t* __restrict__ A, scalar_t* C,
                      const int _WvPrGrp, const int CuCount) {
-  constexpr int max_lds_len = LDS_SIZE / 2;
+  #if defined(__HIP__MI300__)
-  #if defined(__HIP__MI3XX__)
   constexpr bool use_mfma = (std::is_same_v<scalar_t, __hip_bfloat16>);
   #else
   constexpr bool use_mfma = false;
@@ -316,13 +295,13 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   };
 
   //----------------------------------------------------
-  // Reserving 64/160 KB of LDS to have 1 WG / CU
+  // Reserving 64 KB of LDS to have 1 WG / CU
   // Goal is to bring the activation matrix A to the LDS
   // and use it across the lifetime of the work group
   // TODO: When activation matrix is larger than 64 KB
   //	     then this is not goint to work!
   //----------------------------------------------------
-  __shared__ scalar_t s[max_lds_len];
+  __shared__ scalar_t s[1024 * 32];
 
   //----------------------------------------------------
   // Fetch the activation matrix to LDS
@@ -333,11 +312,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, 32 * 1024);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, 32 * 1024)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -538,7 +517,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     m += CuCount * _WvPrGrp * YTILE;
   }
 }
-#else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
+#else   // !defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
@@ -546,9 +525,9 @@ __global__ void wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
                                  const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__GFX9__) TODO: Add NAVI support
+#endif  // defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 
-#if defined(__HIP__GFX9__)  // TODO: Add NAVI support
+#if defined(__HIP__MI300_MI250__)  // TODO: Add NAVI support
 // This version targets cases where A[] marginally exceeds LDS capacity
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
@@ -556,8 +535,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     wvSplitK_hf_(const int K, const int M, const scalar_t* B,
                  const scalar_t* __restrict__ A, scalar_t* C,
                  const int _WvPrGrp, const int CuCount) {
-  constexpr int max_lds_len = LDS_SIZE / 2;
+  #if defined(__HIP__MI300__)
-  #if defined(__HIP__MI3XX__)
   constexpr bool use_mfma = (std::is_same_v<scalar_t, __hip_bfloat16>);
   #else
   constexpr bool use_mfma = false;
@@ -583,7 +561,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // TODO: When activation matrix is larger than 64 KB
   //	     then this is not goint to work!
   //----------------------------------------------------
-  __shared__ scalar_t s[max_lds_len];
+  __shared__ scalar_t s[1024 * 32];
 
   //----------------------------------------------------
   // Computation of columns that need to be committed to memory!
@@ -620,11 +598,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   // - Then the WG will move to another 8 K elements
   // TODO: Logic below will only work when K is multiple of 8
   //----------------------------------------------------
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, 32 * 1024);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, 32 * 1024)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -708,7 +686,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
         // Fetch A activation matrix in interleaved fashion from LDS or memory
 
         for (int n = 0; n < N; n++) {
-          if (k_ + K * n < max_lds_len)
+          if (k_ + K * n < 32 * 1024)
             bigA[n][k2] = *((const bigType*)(&(s[k_ + K * n])));
           else
             bigA[n][k2] = *((const bigType*)(&(A[k_ + K * n])));
@@ -839,7 +817,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   }
 }
 
-#else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
+#else   // !defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void wvSplitK_hf_(const int K, const int M, const scalar_t* B,
@@ -847,9 +825,9 @@ __global__ void wvSplitK_hf_(const int K, const int M, const scalar_t* B,
                              const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__GFX9__) TODO: Add NAVI support
+#endif  // defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 
-#if defined(__HIP__GFX9__)  // TODO: Add NAVI support
+#if defined(__HIP__MI300_MI250__)  // TODO: Add NAVI support
 // This version targets big A[] cases, where it is much larger than LDS capacity
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
@@ -857,8 +835,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     wvSplitK_hf_big_(const int K, const int M, const scalar_t* B,
                      const scalar_t* __restrict__ A, scalar_t* C,
                      const int _WvPrGrp, const int CuCount) {
-  constexpr int max_lds_len = LDS_SIZE / 2;
+  #if defined(__HIP__MI300__)
-  #if defined(__HIP__MI3XX__)
   constexpr bool use_mfma = (std::is_same_v<scalar_t, __hip_bfloat16>);
   #else
   constexpr bool use_mfma = false;
@@ -878,13 +855,13 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   };
 
   //----------------------------------------------------
-  // Reserving 64/160 KB of LDS to have 1 WG / CU
+  // Reserving 64 KB of LDS to have 1 WG / CU
   // Goal is to bring the activation matrix A to the LDS
   // and use it across the lifetime of the work group
   // TODO: When activation matrix is larger than 64 KB
   //	     then this is not goint to work!
   //----------------------------------------------------
-  __shared__ scalar_t s[max_lds_len];
+  __shared__ scalar_t s[1024 * 32];
 
   //----------------------------------------------------
   // Computation of columns that need to be committed to memory!
@@ -925,11 +902,11 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   //----------------------------------------------------
   #define PCML
   #ifndef PCML
-  for (uint32_t k = 0; k < min(K * N, max_lds_len);
+  for (uint32_t k = 0; k < min(K * N, 32 * 1024);
        k += THRDS * WvPrGrp * A_CHUNK) {
     uint32_t k_in = k + ((threadIdx.y * THRDS + threadIdx.x) * A_CHUNK);
 
-    if (k_in >= min(K * N, max_lds_len)) break;
+    if (k_in >= min(K * N, 32 * 1024)) break;
 
     *((bigType*)(&s[k_in])) = *((bigType*)(&A[k_in]));
   }
@@ -939,7 +916,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
   #define TUC (THRDS * UNRL * A_CHUNK)
   uint32_t kBase = 0;
   // find biggest k size that fits in LDS
-  uint32_t kFit = (max_lds_len) / N;
+  uint32_t kFit = (32 * 1024) / N;
   // kFit = (kFit%TWC==0) ? kFit : (kFit-kFit%TWC+TWC); //round up to multiple
   // of TUC
   kFit = (kFit % TUC == 0)
@@ -1187,7 +1164,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     }
   }
 }
-#else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
+#else   // !defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void wvSplitK_hf_big_(const int K, const int M, const scalar_t* B,
@@ -1195,7 +1172,7 @@ __global__ void wvSplitK_hf_big_(const int K, const int M, const scalar_t* B,
                                  const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__GFX9__) TODO: Add NAVI support
+#endif  // defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 
 int mindiv(int N, int div1, int div2) {
   int nPrRnd = div1 * div2;
@@ -1245,18 +1222,17 @@ torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
 
   const at::cuda::OptionalCUDAGuard device_guard(device_of(in_a));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  const int max_lds_len = get_lds_size() / 2;
 
 #define WVSPLITK(_WvPrGrp, _YTILEs, _YTILEm, _YTILEb, _UNRLs, _UNRLm, _UNRLb, \
                  _N)                                                          \
   {                                                                           \
     dim3 block(64, _WvPrGrp);                                                 \
-    if ((K_in * N_in <= max_lds_len) && (M_in % _YTILEs == 0)) {              \
+    if ((K_in * N_in <= 32 * 1024) && (M_in % _YTILEs == 0)) {                \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEs, _WvPrGrp);              \
       wvSplitK_hf_sml_<fptype, 64, _YTILEs, _WvPrGrp, 8, _UNRLs, _N>          \
           <<<grid, block, 0, stream>>>(K_in, M_in, af4, bf4, c, __wvPrGrp,    \
                                        CuCount);                              \
-    } else if (K_in * N_in <= max_lds_len * 1.2) {                            \
+    } else if (K_in * N_in <= 32 * 1024 * 1.2) {                              \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEm, _WvPrGrp);              \
       wvSplitK_hf_<fptype, 64, _YTILEm, _WvPrGrp, 8, _UNRLm, _N>              \
           <<<grid, block, 0, stream>>>(K_in, M_in, af4, bf4, c, __wvPrGrp,    \
@@ -1296,7 +1272,7 @@ torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
   return out_c;
 }
 
-#if defined(__HIP__MI3XX__)  // TODO: Add NAVI support
+#if defined(__HIP__MI300__)  // TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
@@ -1305,7 +1281,6 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
                       const float* __restrict__ s_A,
                       const float* __restrict__ s_B, const int _WvPrGrp,
                       const int CuCount) {
-  constexpr int max_lds_len = LDS_SIZE;
   using scalar8 =
       __attribute__((__vector_size__((A_CHUNK / 4) * sizeof(float)))) float;
   using intx2 = __attribute__((__vector_size__(2 * sizeof(int)))) int;
@@ -1321,10 +1296,10 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     scalar8 h8;
   };
 
-  __shared__ fp8_t s[max_lds_len];
+  __shared__ fp8_t s[1024 * 64];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min(K * N, 64 * 1024); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();
@@ -1461,7 +1436,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     m += CuCount * _WvPrGrp * YTILE;
   }
 }
-#else   // !defined(__HIP__MI3XX__) TODO: Add NAVI support
+#else   // !defined(__HIP__MI300__) TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_sml_(const int K, const int Kp, const int M,
@@ -1471,9 +1446,9 @@ __global__ void wvSplitKQ_hf_sml_(const int K, const int Kp, const int M,
                                   const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__MI3XX__) TODO: Add NAVI support
+#endif  // defined(__HIP__MI300__) TODO: Add NAVI support
 
-#if defined(__HIP__MI3XX__)  // TODO: Add NAVI support
+#if defined(__HIP__MI300__)  // TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
@@ -1481,7 +1456,6 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
                   const fp8_t* __restrict__ A, scalar_t* C,
                   const float* __restrict__ s_A, const float* __restrict__ s_B,
                   const int _WvPrGrp, const int CuCount) {
-  constexpr int max_lds_len = LDS_SIZE;
   using scalar8 =
       __attribute__((__vector_size__((A_CHUNK / 4) * sizeof(float)))) float;
   using intx2 = __attribute__((__vector_size__(2 * sizeof(int)))) int;
@@ -1497,10 +1471,10 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     scalar8 h8;
   };
 
-  __shared__ fp8_t s[max_lds_len];
+  __shared__ fp8_t s[1024 * 64];
 
   for (uint32_t k = (threadIdx.y * THRDS + threadIdx.x) * A_CHUNK;
-       k < min(K * N, max_lds_len); k += THRDS * WvPrGrp * A_CHUNK) {
+       k < min(K * N, 64 * 1024); k += THRDS * WvPrGrp * A_CHUNK) {
     *((bigType*)(&s[k])) = *((bigType*)(&A[k]));
   }
   __syncthreads();
@@ -1543,7 +1517,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
         uint32_t k_ = k + threadIdx.x * A_CHUNK;
         if (k_ >= K) break;
         for (int n = 0; n < N; n++) {
-          if (k_ + K * n < max_lds_len)
+          if (k_ + K * n < 64 * 1024)
             bigA[n][k2] = *((const bigType*)(&(s[k_ + K * n])));
           else
             bigA[n][k2] = *((const bigType*)(&(A[k_ + K * n])));
@@ -1634,7 +1608,7 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     m += CuCount * _WvPrGrp * YTILE;
   }
 }
-#else   // !defined(__HIP__MI3XX__) TODO: Add NAVI support
+#else   // !defined(__HIP__MI300__) TODO: Add NAVI support
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_(const int K, const int Kp, const int M,
@@ -1644,7 +1618,7 @@ __global__ void wvSplitKQ_hf_(const int K, const int Kp, const int M,
                               const int CuCount) {
   UNREACHABLE_CODE
 }
-#endif  // defined(__HIP__MI3XX__) TODO: Add NAVI support
+#endif  // defined(__HIP__MI300__) TODO: Add NAVI support
 
 void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
                at::Tensor& scale_a, at::Tensor& scale_b,
@@ -1664,13 +1638,12 @@ void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
   dim3 grid(CuCount);
   const at::cuda::OptionalCUDAGuard device_guard(device_of(in_a));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  const int max_lds_len = get_lds_size();
 
 #define WVSPLITKQ(_WvPrGrp, _YTILEs, _YTILEm, _YTILEb, _UNRLs, _UNRLm, _UNRLb, \
                   _N)                                                          \
   {                                                                            \
     dim3 block(64, _WvPrGrp);                                                  \
-    if ((K_in * N_in <= max_lds_len) && (M_in % _YTILEs == 0)) {               \
+    if ((K_in * N_in <= 64 * 1024) && (M_in % _YTILEs == 0)) {                 \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEs, _WvPrGrp);               \
       wvSplitKQ_hf_sml_<fptype, fp8_t, 64, _YTILEs, _WvPrGrp, 16, _UNRLs, _N>  \
           <<<grid, block, 0, stream>>>(K_in, Kp_in, M_in, a_ptr, b_ptr, c_ptr, \

csrc/sampler.cu

@@ -1,86 +0,0 @@
-#include "dispatch_utils.h"
-
-#include <torch/cuda.h>
-#include <c10/cuda/CUDAGuard.h>
-
-#ifndef USE_ROCM
-  #include <cub/cub.cuh>
-#else
-  #include <hipcub/hipcub.hpp>
-#endif
-
-namespace vllm {
-
-template <typename scalar_t>
-__global__ void apply_repetition_penalties_kernel(
-    scalar_t* __restrict__ logits,         // [num_seqs, vocab_size]
-    const bool* __restrict__ prompt_mask,  // [num_seqs, vocab_size]
-    const bool* __restrict__ output_mask,  // [num_seqs, vocab_size]
-    const scalar_t* __restrict__ repetition_penalties,  // [num_seqs]
-    const int num_seqs, const int vocab_size, const int tile_size) {
-  // Each block handles one sequence and a tile of vocab
-  const int seq_idx = blockIdx.x;
-  if (seq_idx >= num_seqs) return;
-
-  const int tile_start = blockIdx.y * tile_size;
-  const int tile_end = min(tile_start + tile_size, vocab_size);
-
-  // Load repetition penalty for this sequence
-  const scalar_t penalty = repetition_penalties[seq_idx];
-
-  // Each thread processes multiple vocab items within the tile
-  for (int vocab_idx = tile_start + threadIdx.x; vocab_idx < tile_end;
-       vocab_idx += blockDim.x) {
-    const int64_t idx = static_cast<int64_t>(seq_idx) * vocab_size + vocab_idx;
-    const bool is_repeated = prompt_mask[idx] || output_mask[idx];
-    if (is_repeated) {
-      scalar_t logit = logits[idx];
-      if (logit > 0) {
-        logits[idx] = logit / penalty;
-      } else {
-        logits[idx] = logit * penalty;
-      }
-    }
-  }
-}
-
-}  // namespace vllm
-
-void apply_repetition_penalties_(
-    torch::Tensor& logits,             // [num_seqs, vocab_size], in-place
-    const torch::Tensor& prompt_mask,  // [num_seqs, vocab_size]
-    const torch::Tensor& output_mask,  // [num_seqs, vocab_size]
-    const torch::Tensor& repetition_penalties) {  // [num_seqs]
-  TORCH_CHECK(logits.is_contiguous());
-  TORCH_CHECK(prompt_mask.is_contiguous());
-  TORCH_CHECK(output_mask.is_contiguous());
-  TORCH_CHECK(repetition_penalties.is_contiguous());
-
-  int vocab_size = logits.size(-1);
-  int num_seqs = logits.size(0);
-
-  // Get number of SMs on the current device
-  int sms = 0;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount,
-                         logits.get_device());
-
-  // Compute tile_num and tile_size
-  int tile_num =
-      std::min(vocab_size, std::max(1, (sms + num_seqs - 1) / num_seqs));
-  int tile_size = (vocab_size + tile_num - 1) / tile_num;
-
-  // Each block handles one sequence and a tile of vocab
-  dim3 grid(num_seqs, tile_num);
-  dim3 block(std::min(tile_size, 1024));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(logits));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      logits.scalar_type(), "apply_repetition_penalties_kernel", [&] {
-        vllm::apply_repetition_penalties_kernel<scalar_t>
-            <<<grid, block, 0, stream>>>(
-                logits.data_ptr<scalar_t>(), prompt_mask.data_ptr<bool>(),
-                output_mask.data_ptr<bool>(),
-                repetition_penalties.data_ptr<scalar_t>(), num_seqs, vocab_size,
-                tile_size);
-      });
-}