merge

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

.buildkite/lm-eval-harness/configs/Qwen1.5-MoE-W4A16-compressed-tensors.yaml

@@ -1,11 +0,0 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16 -b auto -l 1319 -f 5 -t 1
-model_name: "nm-testing/Qwen1.5-MoE-A2.7B-Chat-quantized.w4a16"
-tasks:
-- name: "gsm8k"
-  metrics:
-  - name: "exact_match,strict-match"
-    value: 0.31
-  - name: "exact_match,flexible-extract"
-    value: 0.47
-limit: 1319
-num_fewshot: 5

.buildkite/lm-eval-harness/configs/models-small.txt

@@ -4,7 +4,7 @@ Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml
 Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml
 Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml
-Qwen1.5-MoE-W4A16-compressed-tensors.yaml
+Minitron-4B-Base-FP8.yaml
 Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
 Qwen2-1.5B-Instruct-FP8W8.yaml
 Meta-Llama-3-8B-QQQ.yaml

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

@@ -10,24 +10,15 @@ set -x
 set -o pipefail
 
 check_gpus() {
-  if command -v nvidia-smi; then
-    # check the number of GPUs and GPU type.
-    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
-  elif command -v amd-smi; then
-    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
-  fi
-
+  # check the number of GPUs and GPU type.
+  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
   if [[ $gpu_count -gt 0 ]]; then
     echo "GPU found."
   else
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  if command -v nvidia-smi; then
-    declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
-  elif command -v amd-smi; then
-    declare -g gpu_type=$(amd-smi static -g 0 -a | grep 'MARKET_NAME' | awk '{print $2}')
-  fi
+  declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
   echo "GPU type is $gpu_type"
 }
 
@@ -99,15 +90,9 @@ kill_gpu_processes() {
 
 
   # wait until GPU memory usage smaller than 1GB
-  if command -v nvidia-smi; then
-    while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
-      sleep 1
-    done
-  elif command -v amd-smi; then
-    while [ "$(amd-smi metric -g 0 | grep 'USED_VRAM' | awk '{print $2}')" -ge 1000 ]; do
-      sleep 1
-    done
-  fi
+  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
+    sleep 1
+  done
 
   # remove vllm config file
   rm -rf ~/.config/vllm

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

@@ -63,12 +63,10 @@
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "disable_log_requests": "", 
             "tensor_parallel_size": 4,
-            "swap_space": 16,
-            "speculative_config": {
-                "model": "turboderp/Qwama-0.5B-Instruct",
-                "num_speculative_tokens": 4,
-                "draft_tensor_parallel_size": 1
-            }
+            "swap_space": 16, 
+            "speculative_model": "turboderp/Qwama-0.5B-Instruct",
+            "num_speculative_tokens": 4,
+            "speculative_draft_tensor_parallel_size": 1
         },
         "client_parameters": {
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",

.buildkite/release-pipeline.yaml

@@ -3,10 +3,10 @@ steps:
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "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=12.4.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "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=12.4.0 --tag vllm-ci:build-image --target build --progress plain ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
+      - "bash .buildkite/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
@@ -14,10 +14,10 @@ steps:
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "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=12.1.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "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=12.1.0 --tag vllm-ci:build-image --target build --progress plain ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
+      - "bash .buildkite/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
@@ -31,10 +31,10 @@ steps:
     agents:
       queue: cpu_queue_postmerge
     commands:
-      - "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=11.8.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+      - "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=11.8.0 --tag vllm-ci:build-image --target build --progress plain ."
       - "mkdir artifacts"
       - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-      - "bash .buildkite/scripts/upload-wheels.sh"
+      - "bash .buildkite/upload-wheels.sh"
     env:
       DOCKER_BUILDKIT: "1"
 
@@ -48,7 +48,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
 
   - label: "Build and publish TPU release image"
@@ -57,7 +57,7 @@ steps:
     agents:
       queue: tpu_queue_postmerge
     commands:
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
       - "docker push vllm/vllm-tpu:nightly"
       - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
     plugins:
@@ -82,7 +82,7 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain -f Dockerfile.cpu ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"

.buildkite/run-amd-test.sh

@@ -105,33 +105,19 @@ fi
 if [[ $commands == *" entrypoints/openai "* ]]; then
   commands=${commands//" entrypoints/openai "/" entrypoints/openai \
   --ignore=entrypoints/openai/test_audio.py \
+  --ignore=entrypoints/openai/test_chat.py \
   --ignore=entrypoints/openai/test_shutdown.py \
   --ignore=entrypoints/openai/test_completion.py \
   --ignore=entrypoints/openai/test_sleep.py \
   --ignore=entrypoints/openai/test_models.py \
-  --ignore=entrypoints/openai/test_lora_adapters.py \
-  --ignore=entrypoints/openai/test_return_tokens_as_ids.py \
-  --ignore=entrypoints/openai/test_root_path.py \
-  --ignore=entrypoints/openai/test_tokenization.py \
   --ignore=entrypoints/openai/test_prompt_validation.py "}
 fi
 
 #ignore certain Entrypoints/llm tests
-if [[ $commands == *" entrypoints/llm "* ]]; then
-  commands=${commands//" entrypoints/llm "/" entrypoints/llm \
-  --ignore=entrypoints/llm/test_chat.py \
-  --ignore=entrypoints/llm/test_accuracy.py \
-  --ignore=entrypoints/llm/test_init.py \
-  --ignore=entrypoints/llm/test_generate_multiple_loras.py \
-  --ignore=entrypoints/llm/test_prompt_validation.py "}
+if [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
+  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
 fi
 
-#Obsolete currently
-##ignore certain Entrypoints/llm tests
-#if [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
-#  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
-#fi
-
 # --ignore=entrypoints/openai/test_encoder_decoder.py \
 # --ignore=entrypoints/openai/test_embedding.py \
 # --ignore=entrypoints/openai/test_oot_registration.py
@@ -148,10 +134,9 @@ if [[ $commands == *"--shard-id="* ]]; then
     # assign shard-id for each shard
     commands_gpu=${commands//"--shard-id= "/"--shard-id=${GPU} "}
     echo "Shard ${GPU} commands:$commands_gpu"
-    echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
     docker run \
-        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-        --network=host \
+        --device /dev/kfd --device /dev/dri \
+        --network host \
         --shm-size=16gb \
         --rm \
         -e HIP_VISIBLE_DEVICES="${GPU}" \
@@ -178,10 +163,9 @@ if [[ $commands == *"--shard-id="* ]]; then
     fi
   done
 else
-  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
   docker run \
-          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
-          --network=host \
+          --device /dev/kfd --device /dev/dri \
+          --network host \
           --shm-size=16gb \
           --rm \
           -e HIP_VISIBLE_DEVICES=0 \

.buildkite/run-benchmarks.sh

@@ -5,8 +5,8 @@
 set -ex
 set -o pipefail
 
-# cd 2 levels into the working directory
-cd "$(dirname "${BASH_SOURCE[0]}")/../.."
+# cd into parent directory of this file
+cd "$(dirname "${BASH_SOURCE[0]}")/.."
 
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 

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

@@ -10,5 +10,5 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Try building the docker image
-docker build -t cpu-test -f docker/Dockerfile.ppc64le .
+docker build -t cpu-test -f Dockerfile.ppc64le .
 

.buildkite/run-cpu-test.sh

@@ -8,19 +8,15 @@ set -ex
 CORE_RANGE=${CORE_RANGE:-48-95}
 NUMA_NODE=${NUMA_NODE:-1}
 
+# Try building the docker image
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build -t cpu-test-"$BUILDKITE_BUILD_NUMBER" -f Dockerfile.cpu .
+numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" -t cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 -f Dockerfile.cpu .
+
 # Setup cleanup
-remove_docker_container() { 
-    set -e; 
-    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; 
-}
+remove_docker_container() { set -e; docker rm -f cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" || 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-"$BUILDKITE_BUILD_NUMBER" --target vllm-test -f docker/Dockerfile.cpu .
-numactl -C "$CORE_RANGE" -N "$NUMA_NODE" docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" --tag cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2 --target vllm-test -f docker/Dockerfile.cpu .
-
 # Run the image, setting --shm-size=4g for tensor parallel.
 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-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
@@ -40,6 +36,8 @@ function cpu_tests() {
   # Run basic model test
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
+    pip install -r vllm/requirements/test.txt
+    pip install -r vllm/requirements/cpu.txt
     pytest -v -s tests/kernels/test_cache.py -m cpu_model
     pytest -v -s tests/kernels/test_mla_decode_cpu.py -m cpu_model
     pytest -v -s tests/models/decoder_only/language -m cpu_model

.buildkite/run-gh200-test.sh

@@ -9,7 +9,6 @@ python3 use_existing_torch.py
 
 # Try building the docker image
 DOCKER_BUILDKIT=1 docker build . \
-  --file docker/Dockerfile \
   --target vllm-openai \
   --platform "linux/arm64" \
   -t gh200-test \

.buildkite/run-hpu-test.sh

@@ -5,7 +5,7 @@
 set -ex
 
 # Try building the docker image
-docker build -t hpu-test-env -f docker/Dockerfile.hpu .
+docker build -t hpu-test-env -f Dockerfile.hpu .
 
 # Setup cleanup
 # certain versions of HPU software stack have a bug that can

.buildkite/run-multi-node-test.sh

@@ -3,7 +3,7 @@
 set -euox pipefail
 
 if [[ $# -lt 4 ]]; then
-    echo "Usage: .buildkite/scripts/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
+    echo "Usage: .buildkite/run-multi-node-test.sh WORKING_DIR NUM_NODES NUM_GPUS DOCKER_IMAGE COMMAND1 COMMAND2 ... COMMANDN"
     exit 1
 fi
 

.buildkite/run-neuron-test.sh

@@ -35,7 +35,7 @@ else
     date "+%s" > /tmp/neuron-docker-build-timestamp
 fi
 
-docker build -t "${image_name}" -f docker/Dockerfile.neuron .
+docker build -t "${image_name}" -f Dockerfile.neuron .
 
 # Setup cleanup
 remove_docker_container() {

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

@@ -1,9 +1,9 @@
 #!/bin/bash
 
-set -xue
+set -e
 
 # Build the docker image.
-docker build -f docker/Dockerfile.tpu -t vllm-tpu .
+docker build -f Dockerfile.tpu -t vllm-tpu .
 
 # Set up cleanup.
 remove_docker_container() { docker rm -f tpu-test || true; }
@@ -21,10 +21,8 @@ docker run --privileged --net host --shm-size=16G -it \
     && python3 -m pip install lm_eval[api]==0.4.4 \
     && export VLLM_USE_V1=1 \
     && export VLLM_XLA_CHECK_RECOMPILATION=1 \
-    && echo TEST_0 \
-    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_perf.py \
     && echo TEST_1 \
-    && pytest -v -s /workspace/vllm/tests/tpu/test_compilation.py \
+    && pytest /workspace/vllm/tests/tpu/test_compilation.py \
     && echo TEST_2 \
     && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
     && echo TEST_3 \
@@ -34,14 +32,9 @@ docker run --privileged --net host --shm-size=16G -it \
     && echo TEST_5 \
     && python3 /workspace/vllm/examples/offline_inference/tpu.py \
     && echo TEST_6 \
-    && pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py \
-    && echo TEST_7 \
-    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_sampler.py \
-    && echo TEST_8 \
-    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_topk_topp_sampler.py \
-    && echo TEST_9 \
-    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py" \
+    && pytest -s -v /workspace/vllm/tests/tpu/worker/test_tpu_model_runner.py" \
 
 
 # TODO: This test fails because it uses RANDOM_SEED sampling
 # && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+

.buildkite/run-xpu-test.sh

@@ -8,7 +8,7 @@ image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
 container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
 
 # Try building the docker image
-docker build -t ${image_name} -f docker/Dockerfile.xpu .
+docker build -t ${image_name} -f Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() { 

.buildkite/test-pipeline.yaml

@@ -104,7 +104,7 @@ steps:
 - label: Entrypoints Test # 40min
   working_dir: "/vllm-workspace/tests"
   fast_check: true
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/llm
@@ -135,14 +135,12 @@ steps:
   - examples/offline_inference/rlhf.py
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
-  - tests/v1/test_async_llm_dp.py
   commands:
   # test with tp=2 and external_dp=2
   - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   # 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 distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
@@ -150,12 +148,11 @@ steps:
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference
-  - python3 rlhf.py
-  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  - VLLM_ENABLE_V1_MULTIPROCESSING=0 python3 rlhf.py
+  - VLLM_ENABLE_V1_MULTIPROCESSING=0 RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
   - popd
 
 - label: Metrics, Tracing Test # 10min
-  mirror_hardwares: [amd]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -163,13 +160,18 @@ steps:
   - tests/tracing
   commands:
   - pytest -v -s metrics
+  - "pip install \
+      'opentelemetry-sdk>=1.26.0,<1.27.0' \
+      'opentelemetry-api>=1.26.0,<1.27.0' \
+      'opentelemetry-exporter-otlp>=1.26.0,<1.27.0' \
+      'opentelemetry-semantic-conventions-ai>=0.4.1,<0.5.0'"
   - pytest -v -s tracing
 
 ##### fast check tests  #####
 #####  1 GPU test  #####
 
 - label: Regression Test # 5min
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/test_regression
@@ -200,6 +202,7 @@ steps:
   commands:
     # split the test to avoid interference
     - pytest -v -s v1/core
+    - pytest -v -s v1/entrypoints
     - pytest -v -s v1/engine
     - pytest -v -s v1/entrypoints
     - pytest -v -s v1/sample
@@ -280,21 +283,13 @@ steps:
     - pytest -v -s spec_decode/e2e/test_eagle_correctness.py
 
 - label: LoRA Test %N # 15min each
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/lora
   - tests/lora
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py
+  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py --ignore=lora/test_chatglm3_tp.py --ignore=lora/test_llama_tp.py --ignore=lora/test_minicpmv_tp.py  --ignore=lora/test_transfomers_model.py
   parallelism: 4
 
-- label: PyTorch Compilation Unit Tests
-  source_file_dependencies:
-    - vllm/
-    - tests/compile
-  commands:
-    - pytest -v -s compile/test_pass_manager.py
-    - pytest -v -s compile/test_fusion.py
-
 - label: PyTorch Fullgraph Smoke Test # 9min
   source_file_dependencies:
   - vllm/
@@ -304,6 +299,7 @@ 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/test_pass_manager.py
 
 - label: PyTorch Fullgraph Test # 18min
   source_file_dependencies:
@@ -313,7 +309,7 @@ steps:
   - pytest -v -s compile/test_full_graph.py
 
 - label: Kernels Test %N # 1h each
-  # mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   source_file_dependencies:
   - csrc/
   - vllm/attention
@@ -323,7 +319,7 @@ steps:
   parallelism: 4
 
 - label: Tensorizer Test # 11min
-  # mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
@@ -339,7 +335,7 @@ steps:
   source_file_dependencies:
   - benchmarks/
   commands:
-  - bash scripts/run-benchmarks.sh
+  - bash run-benchmarks.sh
 
 - label: Quantization Test # 33min
   source_file_dependencies:
@@ -374,7 +370,7 @@ steps:
 
 - label: OpenAI-Compatible Tool Use # 20 min
   fast_check: false
-  #mirror_hardwares: [ amd ]
+  mirror_hardwares: [ amd ]
   source_file_dependencies:
     - vllm/
     - tests/tool_use
@@ -391,8 +387,7 @@ steps:
     - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
     # V1 Test: https://github.com/vllm-project/vllm/issues/14531
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4'
-    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py
 
 - label: Language Models Test (Standard) # 32min
   #mirror_hardwares: [amd]
@@ -429,12 +424,11 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/multimodal
     - pytest -v -s models/decoder_only/audio_language -m 'core_model or quant_model'
-    - pytest -v -s models/decoder_only/vision_language -m 'core_model or quant_model'
+    - pytest -v -s --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
     - pytest -v -s models/embedding/vision_language -m core_model
     - pytest -v -s models/encoder_decoder/audio_language -m core_model
     - pytest -v -s models/encoder_decoder/language -m core_model
     - pytest -v -s models/encoder_decoder/vision_language -m core_model
-    - pytest -v -s models/decoder_only/vision_language/test_interleaved.py
 
 - label: Multi-Modal Models Test (Extended) 1 # 48m
   optional: true
@@ -448,7 +442,10 @@ steps:
     - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
     - pytest -v -s models/decoder_only/audio_language -m 'not core_model and not quant_model'
     - pytest -v -s models/decoder_only/vision_language/test_models.py -m 'split(group=0) and not core_model and not quant_model'
-    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
+    # HACK - run phi3v tests separately to sidestep this transformers bug
+    # https://github.com/huggingface/transformers/issues/34307
+    - pytest -v -s models/decoder_only/vision_language/test_phi3v.py
+    - pytest -v -s --ignore models/decoder_only/vision_language/test_models.py --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'not core_model and not quant_model'
     - pytest -v -s models/embedding/vision_language -m 'not core_model'
     - pytest -v -s models/encoder_decoder/language -m 'not core_model'
     - pytest -v -s models/encoder_decoder/vision_language -m 'not core_model'
@@ -464,7 +461,6 @@ steps:
 
 # This test is used only in PR development phase to test individual models and should never run on main
 - label: Custom Models Test
-  mirror_hardwares: [amd]
   optional: true
   commands:
     - echo 'Testing custom models...'
@@ -476,7 +472,6 @@ steps:
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
-  mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -519,11 +514,8 @@ steps:
   - vllm/worker/worker.py
   - vllm/worker/model_runner.py
   - entrypoints/llm/test_collective_rpc.py
-  - tests/v1/test_async_llm_dp.py
-  - vllm/v1/engine/
   commands:
-  - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - VLLM_ENABLE_V1_MULTIPROCESSING=0 pytest -v -s entrypoints/llm/test_collective_rpc.py
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
@@ -600,10 +592,14 @@ steps:
     # FIXIT: find out which code initialize cuda before running the test
     # before the fix, we need to use spawn to test it
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+    # This test runs llama 13B, so it is required to run on 4 GPUs.
+    - pytest -v -s -x lora/test_long_context.py
     # There is some Tensor Parallelism related processing logic in LoRA that
     # requires multi-GPU testing for validation.
     - pytest -v -s -x lora/test_chatglm3_tp.py
     - pytest -v -s -x lora/test_llama_tp.py
+    - pytest -v -s -x lora/test_minicpmv_tp.py
+    - pytest -v -s -x lora/test_transfomers_model.py
 
 
 - label: Weight Loading Multiple GPU Test  # 33min

.github/ISSUE_TEMPLATE/600-new-model.yml

@@ -9,7 +9,7 @@ body:
     value: >
       #### Before submitting an issue, please make sure the issue hasn't been already addressed by searching through [the existing and past issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue+sort%3Acreated-desc+).
 
-      #### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/index.html first to understand how to add a new model.
+      #### We also highly recommend you read https://docs.vllm.ai/en/latest/contributing/model/adding_model.html first to understand how to add a new model.
 - type: textarea
   attributes:
     label: The model to consider.

.github/PULL_REQUEST_TEMPLATE.md

@@ -3,4 +3,4 @@ FILL IN THE PR DESCRIPTION HERE
 FIX #xxxx (*link existing issues this PR will resolve*)
 
 <!--- pyml disable-next-line no-emphasis-as-heading -->
-**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>**

.github/mergify.yml

@@ -19,7 +19,7 @@ pull_request_rules:
       - files~=\.buildkite/
       - files~=^cmake/
       - files=CMakeLists.txt
-      - files~=^docker/Dockerfile
+      - files~=^Dockerfile
       - files~=^requirements.*\.txt
       - files=setup.py
   actions:
@@ -88,36 +88,6 @@ pull_request_rules:
       add:
         - v1
 
-- name: label-tpu
-  description: Automatically apply tpu label
-  # Keep this list in sync with `label-tpu-remove` conditions
-  conditions:
-    - or:
-      - files~=tpu.py
-      - files~=_tpu
-      - files~=tpu_
-      - files~=/tpu/
-      - files~=pallas
-  actions:
-    label:
-      add:
-        - tpu
-
-- name: label-tpu-remove
-  description: Automatically remove tpu label
-  # Keep this list in sync with `label-tpu` conditions
-  conditions:
-    - and:
-      - -files~=tpu.py
-      - -files~=_tpu
-      - -files~=tpu_
-      - -files~=/tpu/
-      - -files~=pallas
-  actions:
-    label:
-      remove:
-        - tpu
-
 - name: ping author on conflicts and add 'needs-rebase' label
   conditions:
       - conflict

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

@@ -50,7 +50,7 @@ jobs:
         uses: helm/kind-action@a1b0e391336a6ee6713a0583f8c6240d70863de3 # v1.12.0
 
       - name: Build the Docker image vllm cpu
-        run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
+        run: docker buildx build -f Dockerfile.cpu -t vllm-cpu-env .
 
       - name: Configuration of docker images, network and namespace for the kind cluster
         run: |

.pre-commit-config.yaml

@@ -1,6 +1,3 @@
-default_install_hook_types:
-  - pre-commit
-  - commit-msg
 default_stages:
   - pre-commit # Run locally
   - manual # Run in CI
@@ -122,12 +119,6 @@ repos:
     language: system
     always_run: true
     pass_filenames: false
-  - id: update-dockerfile-graph
-    name: Update Dockerfile dependency graph
-    entry: tools/update-dockerfile-graph.sh
-    language: script
-    files: ^docker/Dockerfile$
-    pass_filenames: false
   # Keep `suggestion` last
   - id: suggestion
     name: Suggestion

CMakeLists.txt

@@ -34,7 +34,7 @@ set(PYTHON_SUPPORTED_VERSIONS "3.9" "3.10" "3.11" "3.12")
 set(CUDA_SUPPORTED_ARCHS "7.0;7.2;7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0")
 
 # Supported AMD GPU architectures.
-set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
 
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -44,7 +44,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1
 #
 # Note: the CUDA torch version is derived from pyproject.toml and various
 # requirements.txt files and should be kept consistent.  The ROCm torch
-# versions are derived from docker/Dockerfile.rocm
+# versions are derived from Dockerfile.rocm
 #
 set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
@@ -228,14 +228,13 @@ endif()
 
 set(VLLM_EXT_SRC
   "csrc/cache_kernels.cu"
+  "csrc/block_table.cu"
   "csrc/attention/paged_attention_v1.cu"
   "csrc/attention/paged_attention_v2.cu"
-  "csrc/attention/merge_attn_states.cu"
   "csrc/pos_encoding_kernels.cu"
   "csrc/activation_kernels.cu"
   "csrc/layernorm_kernels.cu"
   "csrc/layernorm_quant_kernels.cu"
-  "csrc/cuda_view.cu"
   "csrc/quantization/gptq/q_gemm.cu"
   "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
   "csrc/quantization/fp8/common.cu"
@@ -243,7 +242,6 @@ set(VLLM_EXT_SRC
   "csrc/quantization/gguf/gguf_kernel.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/prepare_inputs/advance_step.cu"
-  "csrc/custom_all_reduce.cu"
   "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
@@ -285,6 +283,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     "csrc/mamba/causal_conv1d/causal_conv1d.cu"
     "csrc/quantization/aqlm/gemm_kernels.cu"
     "csrc/quantization/awq/gemm_kernels.cu"
+    "csrc/custom_all_reduce.cu"
     "csrc/permute_cols.cu"
     "csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu"
     "csrc/quantization/fp4/nvfp4_quant_entry.cu"
@@ -463,33 +462,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(FP4_ARCHS)
   endif()
 
-  #
-  # CUTLASS MoE kernels
-
-  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
-  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
-  # to compile MoE kernels that use its output.
-  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-    set(SRCS "csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu"
-             "csrc/quantization/cutlass_w8a8/moe/moe_data.cu")
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MOE_SM90=1")
-    message(STATUS "Building grouped_mm_c3x for archs: ${SCALED_MM_ARCHS}")
-  else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.3 AND SCALED_MM_ARCHS)
-      message(STATUS "Not building grouped_mm_c3x kernels as CUDA Compiler version is "
-                     "not >= 12.3, we recommend upgrading to CUDA 12.3 or later "
-                     "if you intend on running FP8 quantized MoE models on Hopper.")
-    else()
-      message(STATUS "Not building grouped_mm_c3x as no compatible archs found "
-                     "in CUDA target architectures")
-    endif()
-  endif()
-
   #
   # Machete kernels
 

Dockerfile.cpu

@@ -0,0 +1,69 @@
+# This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform.
+
+FROM ubuntu:22.04 AS cpu-test-1
+
+ENV CCACHE_DIR=/root/.cache/ccache
+
+ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
+
+RUN --mount=type=cache,target=/var/cache/apt \
+    apt-get update -y \
+    && apt-get install -y curl ccache git wget vim numactl gcc-12 g++-12 python3 python3-pip libtcmalloc-minimal4 libnuma-dev \
+    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
+    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
+
+# https://intel.github.io/intel-extension-for-pytorch/cpu/latest/tutorials/performance_tuning/tuning_guide.html
+# intel-openmp provides additional performance improvement vs. openmp
+# tcmalloc provides better memory allocation efficiency, e.g, holding memory in caches to speed up access of commonly-used objects.
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install intel-openmp==2025.0.1
+
+ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/lib/libiomp5.so"
+
+RUN echo 'ulimit -c 0' >> ~/.bashrc
+
+RUN pip install intel_extension_for_pytorch==2.6.0
+
+WORKDIR /workspace
+
+ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
+ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
+    pip install --upgrade pip && \
+    pip install -r requirements/build.txt
+
+FROM cpu-test-1 AS build
+
+WORKDIR /workspace/vllm
+
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
+    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
+    pip install -v -r requirements/cpu.txt
+
+COPY . .
+ARG GIT_REPO_CHECK=0
+RUN --mount=type=bind,source=.git,target=.git \
+    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
+
+# Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
+ARG VLLM_CPU_DISABLE_AVX512
+ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
+
+RUN --mount=type=cache,target=/root/.cache/pip \
+    --mount=type=cache,target=/root/.cache/ccache \
+    --mount=type=bind,source=.git,target=.git \
+    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel && \
+    pip install dist/*.whl && \
+    rm -rf dist
+
+WORKDIR /workspace/
+
+RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
+
+# install development dependencies (for testing)
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install -e tests/vllm_test_utils
+
+ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

Dockerfile.hpu

@@ -1,4 +1,4 @@
-FROM vault.habana.ai/gaudi-docker/1.20.1/ubuntu22.04/habanalabs/pytorch-installer-2.6.0:latest
+FROM vault.habana.ai/gaudi-docker/1.19.1/ubuntu22.04/habanalabs/pytorch-installer-2.5.1:latest
 
 COPY ./ /workspace/vllm
 

Dockerfile.neuron

@@ -1,6 +1,6 @@
 # default base image
 # https://gallery.ecr.aws/neuron/pytorch-inference-neuronx
-ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.5.1-neuronx-py310-sdk2.22.0-ubuntu22.04"
+ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.5.1-neuronx-py310-sdk2.21.0-ubuntu22.04"
 
 FROM $BASE_IMAGE
 
@@ -21,9 +21,9 @@ VOLUME [ ${APP_MOUNT} ]
 WORKDIR ${APP_MOUNT}/vllm
 
 RUN python3 -m pip install --upgrade pip
-RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas tenacity
-RUN python3 -m pip install sentencepiece transformers==4.48.0 -U
-RUN python3 -m pip install neuronx-cc==2.17.194.0 --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
+RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
+RUN python3 -m pip install sentencepiece transformers==4.45.2 -U
+RUN python3 -m pip install neuronx-cc==2.16.345.0 --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 RUN python3 -m pip install pytest
 
 # uninstall transformers-neuronx package explicitly to avoid version conflict

Dockerfile.ppc64le

@@ -38,7 +38,7 @@ RUN microdnf install -y openssl-devel dnf \
     && ln -sf /usr/lib64/libatomic.so.1 /usr/lib64/libatomic.so \
     && python${PYTHON_VERSION} -m venv ${VIRTUAL_ENV} \
     && python -m pip install -U pip uv \
-    && uv pip install wheel build "setuptools<70" setuptools_scm setuptools_rust meson-python 'cmake<4' ninja cython scikit_build_core scikit_build \
+    && uv pip install wheel build "setuptools<70" setuptools_scm setuptools_rust meson-python cmake ninja cython scikit_build_core scikit_build \
     && curl -sL https://ftp2.osuosl.org/pub/ppc64el/openblas/latest/Openblas_${OPENBLAS_VERSION}_ppc64le.tar.gz | tar xvf - -C /usr/local \
     && curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y \
     && cd /tmp && touch control
@@ -238,7 +238,7 @@ RUN --mount=type=cache,target=/root/.cache/uv \
     && python -m pip install -U pip uv --no-cache \
     && curl -sL https://ftp2.osuosl.org/pub/ppc64el/openblas/latest/Openblas_${OPENBLAS_VERSION}_ppc64le.tar.gz | tar xvf - -C /usr/local \
     && make -C /numactl install \
-    && uv pip install 'cmake<4' \
+    && uv pip install cmake \
     && cmake --install /lapack/build \
     && uv pip uninstall cmake
 

Dockerfile.rocm

@@ -12,8 +12,7 @@ ENV PYTORCH_ROCM_ARCH=${ARG_PYTORCH_ROCM_ARCH:-${PYTORCH_ROCM_ARCH}}
 
 # Install some basic utilities
 RUN apt-get update -q -y && apt-get install -q -y \
-    sqlite3 libsqlite3-dev libfmt-dev libmsgpack-dev libsuitesparse-dev \
-    apt-transport-https ca-certificates wget curl
+    sqlite3 libsqlite3-dev libfmt-dev libmsgpack-dev libsuitesparse-dev
 # Remove sccache    
 RUN python3 -m pip install --upgrade pip && pip install setuptools_scm
 RUN apt-get purge -y sccache; python3 -m pip uninstall -y sccache; rm -f "$(which sccache)"

Dockerfile.rocm_base

@@ -1,18 +1,18 @@
 ARG BASE_IMAGE=rocm/dev-ubuntu-22.04:6.3.1-complete
-ARG HIPBLASLT_BRANCH="db8e93b4"
+ARG HIPBLASLT_BRANCH="4d40e36"
 ARG HIPBLAS_COMMON_BRANCH="7c1566b"
 ARG LEGACY_HIPBLASLT_OPTION=
 ARG RCCL_BRANCH="648a58d"
 ARG RCCL_REPO="https://github.com/ROCm/rccl"
 ARG TRITON_BRANCH="e5be006"
 ARG TRITON_REPO="https://github.com/triton-lang/triton.git"
-ARG PYTORCH_BRANCH="295f2ed4"
-ARG PYTORCH_VISION_BRANCH="v0.21.0"
+ARG PYTORCH_BRANCH="3a585126"
+ARG PYTORCH_VISION_BRANCH="v0.19.1"
 ARG PYTORCH_REPO="https://github.com/pytorch/pytorch.git"
 ARG PYTORCH_VISION_REPO="https://github.com/pytorch/vision.git"
-ARG FA_BRANCH="1a7f4dfa"
-ARG FA_REPO="https://github.com/Dao-AILab/flash-attention.git"
-ARG AITER_BRANCH="8970b25b"
+ARG FA_BRANCH="b7d29fb"
+ARG FA_REPO="https://github.com/ROCm/flash-attention.git"
+ARG AITER_BRANCH="21d47a9"
 ARG AITER_REPO="https://github.com/ROCm/aiter.git"
 
 FROM ${BASE_IMAGE} AS base
@@ -20,7 +20,7 @@ FROM ${BASE_IMAGE} AS base
 ENV PATH=/opt/rocm/llvm/bin:$PATH
 ENV ROCM_PATH=/opt/rocm
 ENV LD_LIBRARY_PATH=/opt/rocm/lib:/usr/local/lib:
-ARG PYTORCH_ROCM_ARCH=gfx90a;gfx942;gfx1100;gfx1101;gfx1200;gfx1201
+ARG PYTORCH_ROCM_ARCH=gfx90a;gfx942
 ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}
 
 ARG PYTHON_VERSION=3.12
@@ -31,7 +31,7 @@ ENV DEBIAN_FRONTEND=noninteractive
 
 # Install Python and other dependencies
 RUN apt-get update -y \
-    && apt-get install -y software-properties-common git curl sudo vim less libgfortran5 \
+    && apt-get install -y software-properties-common git curl sudo vim less \
     && add-apt-repository ppa:deadsnakes/ppa \
     && apt-get update -y \
     && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
@@ -42,7 +42,7 @@ RUN apt-get update -y \
     && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
     && python3 --version && python3 -m pip --version
 
-RUN pip install -U packaging 'cmake<4' ninja wheel setuptools pybind11 Cython
+RUN pip install -U packaging cmake ninja wheel setuptools pybind11 Cython
 
 FROM base AS build_hipblaslt
 ARG HIPBLASLT_BRANCH
@@ -60,8 +60,7 @@ RUN cd hipBLAS-common \
 RUN git clone https://github.com/ROCm/hipBLASLt
 RUN cd hipBLASLt \
     && git checkout ${HIPBLASLT_BRANCH} \
-    && apt-get install -y llvm-dev \
-    && ./install.sh -dc --architecture ${PYTORCH_ROCM_ARCH} ${LEGACY_HIPBLASLT_OPTION} \
+    && ./install.sh -d --architecture ${PYTORCH_ROCM_ARCH} ${LEGACY_HIPBLASLT_OPTION} \
     && cd build/release \
     && make package
 RUN mkdir -p /app/install && cp /app/hipBLASLt/build/release/*.deb /app/hipBLAS-common/build/*.deb /app/install
@@ -111,24 +110,11 @@ RUN git clone ${FA_REPO}
 RUN cd flash-attention \
     && git checkout ${FA_BRANCH} \
     && git submodule update --init \
-    && GPU_ARCHS=$(echo ${PYTORCH_ROCM_ARCH} | sed -e 's/;gfx1[0-9]\{3\}//g') python3 setup.py bdist_wheel --dist-dir=dist
+    && MAX_JOBS=64 GPU_ARCHS=${PYTORCH_ROCM_ARCH} python3 setup.py bdist_wheel --dist-dir=dist
 RUN mkdir -p /app/install && cp /app/pytorch/dist/*.whl /app/install \
     && cp /app/vision/dist/*.whl /app/install \
     && cp /app/flash-attention/dist/*.whl /app/install
 
-FROM base AS build_aiter
-ARG AITER_BRANCH
-ARG AITER_REPO
-RUN --mount=type=bind,from=build_pytorch,src=/app/install/,target=/install \
-    pip install /install/*.whl
-RUN git clone --recursive ${AITER_REPO}
-RUN cd aiter \
-    && git checkout ${AITER_BRANCH} \
-    && git submodule update --init --recursive \
-    && pip install -r requirements.txt
-RUN pip install pyyaml && cd aiter && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py bdist_wheel --dist-dir=dist && ls /app/aiter/dist/*.whl
-RUN mkdir -p /app/install && cp /app/aiter/dist/*.whl /app/install
-
 FROM base AS final
 RUN --mount=type=bind,from=build_hipblaslt,src=/app/install/,target=/install \
     dpkg -i /install/*deb \
@@ -144,12 +130,19 @@ RUN --mount=type=bind,from=build_amdsmi,src=/app/install/,target=/install \
     pip install /install/*.whl
 RUN --mount=type=bind,from=build_pytorch,src=/app/install/,target=/install \
     pip install /install/*.whl
-RUN --mount=type=bind,from=build_aiter,src=/app/install/,target=/install \
-    pip install /install/*.whl
+
+ARG AITER_REPO
+ARG AITER_BRANCH
+RUN git clone --recursive ${AITER_REPO}
+RUN cd aiter \
+    && git checkout ${AITER_BRANCH} \
+    && git submodule update --init --recursive \
+    && pip install -r requirements.txt \
+    && PREBUILD_KERNELS=1 GPU_ARCHS=gfx942 python3 setup.py develop && pip show aiter 
 
 ARG BASE_IMAGE
-ARG HIPBLAS_COMMON_BRANCH
 ARG HIPBLASLT_BRANCH
+ARG HIPBLAS_COMMON_BRANCH
 ARG LEGACY_HIPBLASLT_OPTION
 ARG RCCL_BRANCH
 ARG RCCL_REPO
@@ -161,8 +154,6 @@ ARG PYTORCH_REPO
 ARG PYTORCH_VISION_REPO
 ARG FA_BRANCH
 ARG FA_REPO
-ARG AITER_BRANCH
-ARG AITER_REPO
 RUN echo "BASE_IMAGE: ${BASE_IMAGE}" > /app/versions.txt \
     && echo "HIPBLAS_COMMON_BRANCH: ${HIPBLAS_COMMON_BRANCH}" >> /app/versions.txt \
     && echo "HIPBLASLT_BRANCH: ${HIPBLASLT_BRANCH}" >> /app/versions.txt \
@@ -176,5 +167,6 @@ RUN echo "BASE_IMAGE: ${BASE_IMAGE}" > /app/versions.txt \
     && echo "PYTORCH_REPO: ${PYTORCH_REPO}" >> /app/versions.txt \
     && echo "PYTORCH_VISION_REPO: ${PYTORCH_VISION_REPO}" >> /app/versions.txt \
     && echo "FA_BRANCH: ${FA_BRANCH}" >> /app/versions.txt \
+    && echo "FA_REPO: ${FA_REPO}" >> /app/versions.txt \
     && echo "AITER_BRANCH: ${AITER_BRANCH}" >> /app/versions.txt \
     && echo "AITER_REPO: ${AITER_REPO}" >> /app/versions.txt

README.md

@@ -15,9 +15,14 @@ Easy, fast, and cheap LLM serving for everyone
 
 ---
 
+[2025/03] We are collaborating with Ollama to host an [Inference Night](https://lu.ma/vllm-ollama) at Y Combinator in San Francisco on Thursday, March 27, at 6 PM. Discuss all things inference local or data center!
+
+[2025/04] We're hosting our first-ever *vLLM Asia Developer Day* in Singapore on *April 3rd*! This is a full-day event (9 AM - 9 PM SGT) in partnership with SGInnovate, AMD, and Embedded LLM. Meet the vLLM team and learn about LLM inference for RL, MI300X, and more! [Register Now](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)
+
+---
+
 *Latest News* 🔥
-- [2025/04] We hosted [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
-- [2025/03] We hosted [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama)! Please find the meetup slides from the vLLM team [here](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
+
 - [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
 - [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
@@ -98,7 +103,7 @@ Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
 ## Contributing
 
 We welcome and value any contributions and collaborations.
-Please check out [Contributing to vLLM](https://docs.vllm.ai/en/stable/contributing/overview.html) for how to get involved.
+Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
 
 ## Sponsors
 
@@ -121,7 +126,6 @@ Compute Resources:
 - Databricks
 - DeepInfra
 - Google Cloud
-- Intel
 - Lambda Lab
 - Nebius
 - Novita AI

benchmarks/README.md

@@ -41,39 +41,29 @@ become available.
       <td><code>synthetic</code></td>
     </tr>
     <tr>
-      <td><strong>HuggingFace-VisionArena</strong></td>
-      <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">✅</td>
-      <td><code>lmarena-ai/VisionArena-Chat</code></td>
+      <td><strong>HuggingFace</strong></td>
+      <td style="text-align: center;">🟡</td>
+      <td style="text-align: center;">🟡</td>
+      <td>Specify your dataset path on HuggingFace</td>
     </tr>
     <tr>
-      <td><strong>HuggingFace-InstructCoder</strong></td>
+      <td><strong>VisionArena</strong></td>
       <td style="text-align: center;">✅</td>
       <td style="text-align: center;">✅</td>
-      <td><code>likaixin/InstructCoder</code></td>
-    </tr>
-      <tr>
-      <td><strong>HuggingFace-AIMO</strong></td>
-      <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">✅</td>
-      <td><code>AI-MO/aimo-validation-aime</code> , <code>AI-MO/NuminaMath-1.5</code>, <code>AI-MO/NuminaMath-CoT</code></td>
-    </tr>
-    <tr>
-      <td><strong>HuggingFace-Other</strong></td>
-      <td style="text-align: center;">✅</td>
-      <td style="text-align: center;">✅</td>
-      <td><code>lmms-lab/LLaVA-OneVision-Data</code>, <code>Aeala/ShareGPT_Vicuna_unfiltered</code></td>
+      <td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</td>
     </tr>
   </tbody>
 </table>
 
 ✅: supported
 
-🟡: Partial support
-
 🚧: to be supported
 
-**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
+🟡: Partial support. Currently, HuggingFaceDataset only supports dataset formats
+similar to `lmms-lab/LLaVA-OneVision-Data` and `Aeala/ShareGPT_Vicuna_unfiltered`.
+If you need support for other dataset formats, please consider contributing.
+
+**Note**: VisionArena’s `dataset-name` should be set to `hf`
 
 ---
 ## Example - Online Benchmark
@@ -81,7 +71,8 @@ become available.
 First start serving your model
 
 ```bash
-vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+vllm serve ${MODEL_NAME} --disable-log-requests
 ```
 
 Then run the benchmarking script
@@ -89,13 +80,12 @@ Then run the benchmarking script
 ```bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-python3 vllm/benchmarks/benchmark_serving.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --endpoint /v1/completions \
-  --dataset-name sharegpt \
-  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --num-prompts 10
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+NUM_PROMPTS=10
+BACKEND="vllm"
+DATASET_NAME="sharegpt"
+DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
+python3 vllm/benchmarks/benchmark_serving.py --backend ${BACKEND} --model ${MODEL_NAME} --endpoint /v1/completions --dataset-name ${DATASET_NAME} --dataset-path ${DATASET_PATH} --num-prompts ${NUM_PROMPTS}
 ```
 
 If successful, you will see the following output
@@ -132,105 +122,88 @@ vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 ```bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+BACKEND="openai-chat"
+DATASET_NAME="hf"
+DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
+DATASET_SPLIT='train'
+
 python3 vllm/benchmarks/benchmark_serving.py \
-  --backend openai-chat \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --endpoint /v1/chat/completions \
-  --dataset-name hf \
-  --dataset-path lmarena-ai/VisionArena-Chat \
-  --hf-split train \
-  --num-prompts 1000
+  --backend "${BACKEND}" \
+  --model "${MODEL_NAME}" \
+  --endpoint "/v1/chat/completions" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --hf-split "${DATASET_SPLIT}" \
+  --num-prompts "${NUM_PROMPTS}"
 ```
 
-### InstructCoder Benchmark with Speculative Decoding
+### HuggingFaceDataset Examples
 
-``` bash
-VLLM_USE_V1=1 vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
-    --speculative-model "[ngram]" \
-    --ngram_prompt_lookup_min 2 \
-    --ngram-prompt-lookup-max 5 \
-    --num_speculative_tokens 5
-```
-
-``` bash
-python3 benchmarks/benchmark_serving.py \
-    --model meta-llama/Meta-Llama-3-8B-Instruct \
-    --dataset-name hf \
-    --dataset-path likaixin/InstructCoder \
-    --num-prompts 2048
-```
-
-### Other HuggingFaceDataset Examples
+Currently, HuggingFaceDataset only supports dataset formats
+similar to `lmms-lab/LLaVA-OneVision-Data` and `Aeala/ShareGPT_Vicuna_unfiltered`. If you need support for other dataset
+formats, please consider contributing.
 
 ```bash
+# need a model with vision capability here
 vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
 ```
 
 **`lmms-lab/LLaVA-OneVision-Data`**
 
 ```bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+BACKEND="openai-chat"
+DATASET_NAME="hf"
+DATASET_PATH="lmms-lab/LLaVA-OneVision-Data"
+DATASET_SPLIT='train'
+DATASET_SUBSET='chart2text(cauldron)'
 python3 vllm/benchmarks/benchmark_serving.py \
-  --backend openai-chat \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --endpoint /v1/chat/completions \
-  --dataset-name hf \
-  --dataset-path lmms-lab/LLaVA-OneVision-Data \
-  --hf-split train \
-  --hf-subset "chart2text(cauldron)" \
-  --num-prompts 10
+  --backend "${BACKEND}" \
+  --model "${MODEL_NAME}" \
+  --endpoint "/v1/chat/completions" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --hf-split "${DATASET_SPLIT}" \
+  --num-prompts "${NUM_PROMPTS}" \
+  --hf-subset "${DATASET_SUBSET}"
 ```
 
 **`Aeala/ShareGPT_Vicuna_unfiltered`**
 
 ```bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+BACKEND="openai-chat"
+DATASET_NAME="hf"
+DATASET_PATH="Aeala/ShareGPT_Vicuna_unfiltered"
+DATASET_SPLIT='train'
 python3 vllm/benchmarks/benchmark_serving.py \
-  --backend openai-chat \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --endpoint /v1/chat/completions \
-  --dataset-name hf \
-  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
-  --hf-split train \
-  --num-prompts 10
-```
-
-**`AI-MO/aimo-validation-aime`**
-
-``` bash
-python3 vllm/benchmarks/benchmark_serving.py \
-    --model Qwen/QwQ-32B \
-    --dataset-name hf \
-    --dataset-path AI-MO/aimo-validation-aime \
-    --num-prompts 10 \
-    --seed 42
-```
-
-### Running With Sampling Parameters
-
-When using OpenAI-compatible backends such as `vllm`, optional sampling
-parameters can be specified. Example client command:
-
-```bash
-python3 vllm/benchmarks/benchmark_serving.py \
-  --backend vllm \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --endpoint /v1/completions \
-  --dataset-name sharegpt \
-  --dataset-path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --top-k 10 \
-  --top-p 0.9 \
-  --temperature 0.5 \
-  --num-prompts 10
+  --backend "${BACKEND}" \
+  --model "${MODEL_NAME}" \
+  --endpoint "/v1/chat/completions" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --hf-split "${DATASET_SPLIT}" \
+  --num-prompts "${NUM_PROMPTS}" \
 ```
 
 ---
 ## Example - Offline Throughput Benchmark
 
 ```bash
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+NUM_PROMPTS=10
+DATASET_NAME="sonnet"
+DATASET_PATH="vllm/benchmarks/sonnet.txt"
+
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model NousResearch/Hermes-3-Llama-3.1-8B \
-  --dataset-name sonnet \
-  --dataset-path vllm/benchmarks/sonnet.txt \
-  --num-prompts 10
+  --model "${MODEL_NAME}" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --num-prompts "${NUM_PROMPTS}"
 ```
 
 If successful, you will see the following output
@@ -244,13 +217,19 @@ Total num output tokens:  1500
 ### VisionArena Benchmark for Vision Language Models
 
 ``` bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+DATASET_NAME="hf"
+DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
+DATASET_SPLIT="train"
+
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --backend vllm-chat \
-  --dataset-name hf \
-  --dataset-path lmarena-ai/VisionArena-Chat \
-  --num-prompts 1000 \
-  --hf-split train
+  --model "${MODEL_NAME}" \
+  --backend "vllm-chat" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --num-prompts "${NUM_PROMPTS}" \
+  --hf-split "${DATASET_SPLIT}"
 ```
 
 The `num prompt tokens` now includes image token counts
@@ -261,83 +240,29 @@ Total num prompt tokens:  14527
 Total num output tokens:  1280
 ```
 
-### InstructCoder Benchmark with Speculative Decoding
-
-``` bash
-VLLM_WORKER_MULTIPROC_METHOD=spawn \
-VLLM_USE_V1=1 \
-python3 vllm/benchmarks/benchmark_throughput.py \
-    --dataset-name=hf \
-    --dataset-path=likaixin/InstructCoder \
-    --model=meta-llama/Meta-Llama-3-8B-Instruct \
-    --input-len=1000 \
-    --output-len=100 \
-    --num-prompts=2048 \
-    --async-engine \
-    --speculative-model="[ngram]" \
-    --ngram_prompt_lookup_min=2 \
-    --ngram-prompt-lookup-max=5 \
-    --num_speculative_tokens=5
-```
-
-```
-Throughput: 104.77 requests/s, 23836.22 total tokens/s, 10477.10 output tokens/s
-Total num prompt tokens:  261136
-Total num output tokens:  204800
-```
-
-### Other HuggingFaceDataset Examples
-
-**`lmms-lab/LLaVA-OneVision-Data`**
-
-```bash
-python3 vllm/benchmarks/benchmark_throughput.py \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --backend vllm-chat \
-  --dataset-name hf \
-  --dataset-path lmms-lab/LLaVA-OneVision-Data \
-  --hf-split train \
-  --hf-subset "chart2text(cauldron)" \
-  --num-prompts 10
-```
-
-**`Aeala/ShareGPT_Vicuna_unfiltered`**
-
-```bash
-python3 vllm/benchmarks/benchmark_throughput.py \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --backend vllm-chat \
-  --dataset-name hf \
-  --dataset-path Aeala/ShareGPT_Vicuna_unfiltered \
-  --hf-split train \
-  --num-prompts 10
-```
-
-**`AI-MO/aimo-validation-aime`**
-
-```bash
-python3 benchmarks/benchmark_throughput.py \
-  --model Qwen/QwQ-32B \
-  --backend vllm \
-  --dataset-name hf \
-  --dataset-path AI-MO/aimo-validation-aime \
-  --hf-split train \
-  --num-prompts 10
-```
-
 ### Benchmark with LoRA Adapters
 
 ``` bash
 # download dataset
 # wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+MODEL_NAME="meta-llama/Llama-2-7b-hf"
+BACKEND="vllm"
+DATASET_NAME="sharegpt"
+DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
+NUM_PROMPTS=10
+MAX_LORAS=2
+MAX_LORA_RANK=8
+ENABLE_LORA="--enable-lora"
+LORA_PATH="yard1/llama-2-7b-sql-lora-test"
+
 python3 vllm/benchmarks/benchmark_throughput.py \
-  --model meta-llama/Llama-2-7b-hf \
-  --backend vllm \
-  --dataset_path <your data path>/ShareGPT_V3_unfiltered_cleaned_split.json \
-  --dataset_name sharegpt \
-  --num-prompts 10 \
-  --max-loras 2 \
-  --max-lora-rank 8 \
-  --enable-lora \
-  --lora-path yard1/llama-2-7b-sql-lora-test
+  --model "${MODEL_NAME}" \
+  --backend "${BACKEND}" \
+  --dataset_path "${DATASET_PATH}" \
+  --dataset_name "${DATASET_NAME}" \
+  --num-prompts "${NUM_PROMPTS}" \
+  --max-loras "${MAX_LORAS}" \
+  --max-lora-rank "${MAX_LORA_RANK}" \
+  ${ENABLE_LORA} \
+  --lora-path "${LORA_PATH}"
   ```

benchmarks/backend_request_func.py

@@ -219,15 +219,7 @@ async def async_request_deepspeed_mii(
                 if response.status == 200:
                     parsed_resp = await response.json()
                     output.latency = time.perf_counter() - st
-                    if "choices" in parsed_resp:
-                        output.generated_text = parsed_resp["choices"][0][
-                            "text"]
-                    elif "text" in parsed_resp:
-                        output.generated_text = parsed_resp["text"][0]
-                    else:
-                        output.error = ("Unexpected response format: "
-                                        "neither 'choices' nor 'text' found")
-                        output.success = False
+                    output.generated_text = parsed_resp["text"][0]
                     output.success = True
                 else:
                     output.error = response.reason or ""
@@ -497,9 +489,3 @@ ASYNC_REQUEST_FUNCS = {
     "scalellm": async_request_openai_completions,
     "sglang": async_request_openai_completions,
 }
-
-OPENAI_COMPATIBLE_BACKENDS = [
-    k for k, v in ASYNC_REQUEST_FUNCS.items()
-    if v in (async_request_openai_completions,
-             async_request_openai_chat_completions)
-]

benchmarks/benchmark_dataset.py

@@ -23,8 +23,7 @@ from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from dataclasses import dataclass
 from functools import cache
-from io import BytesIO
-from typing import Any, Callable, Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -240,24 +239,21 @@ def process_image(image: Any) -> Mapping[str, Any]:
     """
     Process a single image input and return a multimedia content dictionary.
 
-    Supports three input types:
+    For a PIL.Image.Image input:
+      - Converts the image to RGB.
+      - Saves the image as a JPEG in-memory.
+      - Encodes the JPEG data as a base64 string.
+      - Returns a dictionary with the image as a base64 data URL.
 
-    1. Dictionary with raw image bytes: - Expects a dict with a 'bytes' key
-       containing raw image data.  - Loads the bytes as a PIL.Image.Image.
-
-    2. PIL.Image.Image input: - Converts the image to RGB.  - Saves the image as
-       a JPEG in memory.  - Encodes the JPEG data as a base64 string.  - Returns
-       a dictionary with the image as a base64 data URL.
-
-    3. String input: - Treats the string as a URL or local file path.  -
-       Prepends "file://" if the string doesn't start with "http://" or
-       "file://".  - Returns a dictionary with the image URL.
+    For a string input:
+      - Treats the string as a URL or file path.
+      - Prepends "file://" if the string doesn't start with "http://" or
+        "file://".
+      - Returns a dictionary with the image URL.
 
     Raises:
-        ValueError: If the input is not a supported type.
+      ValueError: If the input is neither a PIL.Image.Image nor a string.
     """
-    if isinstance(image, dict) and 'bytes' in image:
-        image = Image.open(BytesIO(image['bytes']))
     if isinstance(image, Image.Image):
         image = image.convert("RGB")
         with io.BytesIO() as image_data:
@@ -276,8 +272,8 @@ def process_image(image: Any) -> Mapping[str, Any]:
             ("http://", "file://")) else f"file://{image}")
         return {"type": "image_url", "image_url": {"url": image_url}}
 
-    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
-                     " or str or dictionary with raw image bytes.")
+    raise ValueError(
+        f"Invalid image input {image}. Must be a PIL.Image.Image or str.")
 
 
 # -----------------------------------------------------------------------------
@@ -288,7 +284,7 @@ def process_image(image: Any) -> Mapping[str, Any]:
 class RandomDataset(BenchmarkDataset):
     # Default values copied from benchmark_serving.py for the random dataset.
     DEFAULT_PREFIX_LEN = 0
-    DEFAULT_RANGE_RATIO = 0.0
+    DEFAULT_RANGE_RATIO = 1.0
     DEFAULT_INPUT_LEN = 1024
     DEFAULT_OUTPUT_LEN = 128
 
@@ -308,32 +304,19 @@ class RandomDataset(BenchmarkDataset):
         output_len: int = DEFAULT_OUTPUT_LEN,
         **kwargs,
     ) -> list[SampleRequest]:
-        # Enforce range_ratio < 1
-        assert range_ratio < 1.0, (
-            "random_range_ratio must be < 1.0 to ensure a valid sampling range"
-        )
-
         vocab_size = tokenizer.vocab_size
 
         prefix_token_ids = (np.random.randint(
             0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
 
-        # New sampling logic: [X * (1 - b), X * (1 + b)]
-        input_low = int(input_len * (1 - range_ratio))
-        input_high = int(input_len * (1 + range_ratio))
-        output_low = int(output_len * (1 - range_ratio))
-        output_high = int(output_len * (1 + range_ratio))
-
-        # Add logging for debugging
-        logger.info("Sampling input_len from [%s, %s]", input_low, input_high)
-        logger.info("Sampling output_len from [%s, %s]", output_low,
-                    output_high)
+        input_low = int(input_len * range_ratio)
+        output_low = int(output_len * range_ratio)
 
         input_lens = np.random.randint(input_low,
-                                       input_high + 1,
+                                       input_len + 1,
                                        size=num_requests)
         output_lens = np.random.randint(output_low,
-                                        output_high + 1,
+                                        output_len + 1,
                                         size=num_requests)
         offsets = np.random.randint(0, vocab_size, size=num_requests)
 
@@ -485,11 +468,11 @@ class SonnetDataset(BenchmarkDataset):
 
         # Determine how many poem lines to use.
         num_input_lines = round((input_len - base_offset) / avg_len)
-        num_prefix_lines = max(round((prefix_len - base_offset) / avg_len), 0)
+        num_prefix_lines = round((prefix_len - base_offset) / avg_len)
         prefix_lines = self.data[:num_prefix_lines]
 
         samples = []
-        while len(samples) < num_requests:
+        for _ in range(num_requests):
             extra_lines = random.choices(self.data,
                                          k=num_input_lines - num_prefix_lines)
             prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
@@ -497,14 +480,13 @@ class SonnetDataset(BenchmarkDataset):
             prompt_formatted = tokenizer.apply_chat_template(
                 msg, add_generation_prompt=True, tokenize=False)
             prompt_len = len(tokenizer(prompt_formatted).input_ids)
-            if prompt_len <= input_len:
-                samples.append(
-                    SampleRequest(
-                        prompt=prompt_formatted
-                        if return_prompt_formatted else prompt,
-                        prompt_len=prompt_len,
-                        expected_output_len=output_len,
-                    ))
+            samples.append(
+                SampleRequest(
+                    prompt=prompt_formatted
+                    if return_prompt_formatted else prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
         return samples
 
 
@@ -580,47 +562,48 @@ class BurstGPTDataset(BenchmarkDataset):
 
 
 # -----------------------------------------------------------------------------
-# HuggingFace Dataset Base Implementation
+# HuggingFace Dataset Implementation
 # -----------------------------------------------------------------------------
-class HuggingFaceDataset(BenchmarkDataset):
-    """Base class for datasets hosted on HuggingFace."""
 
-    SUPPORTED_DATASET_PATHS: Union[set[str], dict[str, Callable]] = set()
+
+class HuggingFaceDataset(BenchmarkDataset):
+    """
+    Dataset class for processing a HuggingFace dataset with conversation data
+    and optional images.
+    """
 
     def __init__(
         self,
-        dataset_path: str,
         dataset_split: str,
         dataset_subset: Optional[str] = None,
         **kwargs,
     ) -> None:
-        super().__init__(dataset_path=dataset_path, **kwargs)
-
+        super().__init__(**kwargs)
         self.dataset_split = dataset_split
         self.dataset_subset = dataset_subset
+
         self.load_data()
 
     def load_data(self) -> None:
-        """Load data from HuggingFace datasets."""
+        if not self.dataset_path:
+            raise ValueError("dataset_path must be provided for loading data.")
+
         self.data = load_dataset(
             self.dataset_path,
             name=self.dataset_subset,
             split=self.dataset_split,
             streaming=True,
         )
-        self.data = self.data.shuffle(seed=self.random_seed)
-
-
-# -----------------------------------------------------------------------------
-# Conversation Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class ConversationDataset(HuggingFaceDataset):
-    """Dataset for conversation data with multimodal support."""
-    SUPPORTED_DATASET_PATHS = {
-        'lmms-lab/LLaVA-OneVision-Data', 'Aeala/ShareGPT_Vicuna_unfiltered'
-    }
+        if self.data.features is None or "conversations" \
+            not in self.data.features:
+            raise ValueError(
+                "HuggingFaceDataset currently only supports datasets with "
+                "a 'conversations' column like lmms-lab/LLaVA-OneVision-Data. "
+                "Please consider contributing if you would like to add "
+                "support for additional dataset formats.")
+        # Shuffle and filter examples with at least 2 conversations.
+        self.data = self.data.shuffle(seed=self.random_seed).filter(
+            lambda x: len(x["conversations"]) >= 2)
 
     def sample(self,
                tokenizer: PreTrainedTokenizerBase,
@@ -628,13 +611,10 @@ class ConversationDataset(HuggingFaceDataset):
                output_len: Optional[int] = None,
                enable_multimodal_chat: bool = False,
                **kwargs) -> list:
-        # Filter examples with at least 2 conversations
-        filtered_data = self.data.filter(
-            lambda x: len(x["conversations"]) >= 2)
         sampled_requests = []
         dynamic_output = output_len is None
 
-        for item in filtered_data:
+        for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
             conv = item["conversations"]
@@ -679,12 +659,29 @@ class VisionArenaDataset(HuggingFaceDataset):
     """
 
     DEFAULT_OUTPUT_LEN = 128
-    SUPPORTED_DATASET_PATHS = {
-        "lmarena-ai/VisionArena-Chat":
-        lambda x: x["conversation"][0][0]["content"],
-        "lmarena-ai/vision-arena-bench-v0.1":
-        lambda x: x["turns"][0][0]["content"]
-    }
+    VISION_ARENA_DATASET_PATH = "lmarena-ai/vision-arena-bench-v0.1"
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        if self.dataset_path != self.VISION_ARENA_DATASET_PATH:
+            raise ValueError(f"Only support Vision Arena dataset.\
+                    This data path {self.dataset_path} is not valid.")
+        if self.dataset_subset is None and self.dataset_split != "train":
+            raise ValueError("Dataset split must be 'train'.")
+
+        self.load_data()
+
+    def load_data(self) -> None:
+        dataset = load_dataset(
+            self.dataset_path,
+            name=self.dataset_subset,
+            split=self.dataset_split,
+            streaming=True,
+        )
+        self.data = dataset.shuffle(seed=self.random_seed)
 
     def sample(
         self,
@@ -700,11 +697,7 @@ class VisionArenaDataset(HuggingFaceDataset):
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            parser_fn = self.SUPPORTED_DATASET_PATHS.get(self.dataset_path)
-            if parser_fn is None:
-                raise ValueError(
-                    f"Unsupported dataset path: {self.dataset_path}")
-            prompt = parser_fn(item)
+            prompt = item["turns"][0][0]["content"]
             mm_content = process_image(item["images"][0])
             prompt_len = len(tokenizer(prompt).input_ids)
             if enable_multimodal_chat:
@@ -722,96 +715,3 @@ class VisionArenaDataset(HuggingFaceDataset):
                 ))
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
-
-
-# -----------------------------------------------------------------------------
-# Instruct Coder Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class InstructCoderDataset(HuggingFaceDataset):
-    """
-    InstructCoder Dataset.
-    https://huggingface.co/datasets/likaixin/InstructCoder
-
-    InstructCoder is the dataset designed for general code editing.  It consists
-    of 114,239 instruction-input-output triplets, and covers multiple distinct
-    code editing scenario.
-    """
-
-    DEFAULT_OUTPUT_LEN = 200  # this is the average default output length
-    SUPPORTED_DATASET_PATHS = {
-        "likaixin/InstructCoder",
-    }
-
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               enable_multimodal_chat: bool = False,
-               **kwargs) -> list:
-        output_len = (output_len
-                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
-        sampled_requests = []
-        for item in self.data:
-            if len(sampled_requests) >= num_requests:
-                break
-            prompt = f"{item['instruction']}:\n{item['input']}"
-            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
-
-
-# -----------------------------------------------------------------------------
-# AIMO Dataset Implementation
-# -----------------------------------------------------------------------------
-
-
-class AIMODataset(HuggingFaceDataset):
-    """
-    Dataset class for processing a AIMO dataset with reasoning questions.
-    """
-    SUPPORTED_DATASET_PATHS = {
-        "AI-MO/aimo-validation-aime", "AI-MO/NuminaMath-1.5",
-        "AI-MO/NuminaMath-CoT"
-    }
-
-    def sample(self,
-               tokenizer: PreTrainedTokenizerBase,
-               num_requests: int,
-               output_len: Optional[int] = None,
-               **kwargs) -> list:
-        sampled_requests = []
-        dynamic_output = output_len is None
-
-        for item in self.data:
-            if len(sampled_requests) >= num_requests:
-                break
-            prompt, completion = item['problem'], item["solution"]
-
-            prompt_ids = tokenizer(prompt).input_ids
-            completion_ids = tokenizer(completion).input_ids
-            prompt_len = len(prompt_ids)
-            completion_len = len(completion_ids)
-            output_len = completion_len if dynamic_output else output_len
-            assert isinstance(output_len, int) and output_len > 0
-            if dynamic_output and not is_valid_sequence(prompt_len,
-                                                        completion_len,
-                                                        max_prompt_len=2048,
-                                                        max_total_len=32000):
-                continue
-            sampled_requests.append(
-                SampleRequest(
-                    prompt=prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                    multi_modal_data=None,
-                ))
-        self.maybe_oversample_requests(sampled_requests, num_requests)
-        return sampled_requests

benchmarks/benchmark_serving.py

@@ -7,6 +7,9 @@ On the server side, run one of the following commands:
         --swap-space 16 \
         --disable-log-requests
 
+    (TGI backend)
+    ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
+
 On the client side, run:
     python benchmarks/benchmark_serving.py \
         --backend <backend> \
@@ -34,8 +37,7 @@ from datetime import datetime
 from typing import Any, Optional
 
 import numpy as np
-from backend_request_func import (ASYNC_REQUEST_FUNCS,
-                                  OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput,
+from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
                                   RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
@@ -50,11 +52,9 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_dataset import (AIMODataset, BurstGPTDataset,
-                               ConversationDataset, HuggingFaceDataset,
-                               InstructCoderDataset, RandomDataset,
-                               SampleRequest, ShareGPTDataset, SonnetDataset,
-                               VisionArenaDataset)
+from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
+                               RandomDataset, SampleRequest, ShareGPTDataset,
+                               SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -156,7 +156,7 @@ def calculate_metrics(
         if outputs[i].success:
             output_len = outputs[i].output_tokens
 
-            if not output_len:
+            if output_len is None:
                 # We use the tokenizer to count the number of output tokens
                 # for some serving backends instead of looking at
                 # len(outputs[i].itl) since multiple output tokens may be
@@ -261,7 +261,6 @@ async def benchmark(
     goodput_config_dict: dict[str, float],
     max_concurrency: Optional[int],
     lora_modules: Optional[Iterable[str]],
-    extra_body: Optional[dict],
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -289,7 +288,6 @@ async def benchmark(
         logprobs=logprobs,
         multi_modal_content=test_mm_content,
         ignore_eos=ignore_eos,
-        extra_body=extra_body,
     )
 
     test_output = await request_func(request_func_input=test_input)
@@ -316,8 +314,7 @@ async def benchmark(
                                          output_len=test_output_len,
                                          logprobs=logprobs,
                                          multi_modal_content=test_mm_content,
-                                         ignore_eos=ignore_eos,
-                                         extra_body=extra_body)
+                                         ignore_eos=ignore_eos)
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
             print("Profiler started")
@@ -367,8 +364,7 @@ async def benchmark(
                                               output_len=output_len,
                                               logprobs=logprobs,
                                               multi_modal_content=mm_content,
-                                              ignore_eos=ignore_eos,
-                                              extra_body=extra_body)
+                                              ignore_eos=ignore_eos)
         tasks.append(
             asyncio.create_task(
                 limited_request_func(request_func_input=request_func_input,
@@ -590,39 +586,19 @@ def main(args: argparse.Namespace):
                                             return_prompt_formatted=True)
 
     elif args.dataset_name == "hf":
-        # all following datasets are implemented from the
-        # HuggingFaceDataset base class
-        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
-            dataset_class = VisionArenaDataset
-            args.hf_split = "train"
-            args.hf_subset = None
-        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
-            dataset_class = InstructCoderDataset
-            args.hf_split = "train"
-        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
-            dataset_class = ConversationDataset
-        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
-            dataset_class = AIMODataset
-            args.hf_split = "train"
-        else:
-            supported_datasets = set([
-                dataset_name for cls in HuggingFaceDataset.__subclasses__()
-                for dataset_name in cls.SUPPORTED_DATASET_PATHS
-            ])
-            raise ValueError(
-                f"Unsupported dataset path: {args.dataset_path}. "
-                "Huggingface dataset only supports dataset_path"
-                f" from one of following: {supported_datasets}. "
-                "Please consider contributing if you would "
-                "like to add support for additional dataset formats.")
+        # Choose between VisionArenaDataset
+        # and HuggingFaceDataset based on provided parameters.
+        dataset_class = (VisionArenaDataset if args.dataset_path
+                         == VisionArenaDataset.VISION_ARENA_DATASET_PATH
+                         and args.hf_subset is None else HuggingFaceDataset)
         input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,
             dataset_split=args.hf_split,
-            random_seed=args.seed,
         ).sample(
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
+            random_seed=args.seed,
             output_len=args.hf_output_len,
         )
 
@@ -657,26 +633,6 @@ def main(args: argparse.Namespace):
             raise ValueError(f"Unknown dataset: {args.dataset_name}") from err
     goodput_config_dict = check_goodput_args(args)
 
-    # Collect the sampling parameters.
-    sampling_params = {
-        k: v
-        for k, v in {
-            "top_p": args.top_p,
-            "top_k": args.top_k,
-            "min_p": args.min_p,
-            "temperature": args.temperature
-        }.items() if v is not None
-    }
-
-    # Sampling parameters are only supported by openai-compatible backend.
-    if sampling_params and args.backend not in OPENAI_COMPATIBLE_BACKENDS:
-        raise ValueError(
-            "Sampling parameters are only supported by openai-compatible "
-            "backends.")
-
-    if "temperature" not in sampling_params:
-        sampling_params["temperature"] = 0.0  # Default to greedy decoding.
-
     # Avoid GC processing "static" data - reduce pause times.
     gc.collect()
     gc.freeze()
@@ -703,7 +659,6 @@ def main(args: argparse.Namespace):
             goodput_config_dict=goodput_config_dict,
             max_concurrency=args.max_concurrency,
             lora_modules=args.lora_modules,
-            extra_body=sampling_params,
         ))
 
     # Save config and results to json
@@ -921,7 +876,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-separated list of selected metrics to report percentils. "
+        help="Comma-seperated list of selected metrics to report percentils. "
         "This argument specifies the metrics to report percentiles. "
         "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
         "Default value is \"ttft,tpot,itl\".")
@@ -929,7 +884,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-separated list of percentiles for selected metrics. "
+        help="Comma-seperated list of percentiles for selected metrics. "
         "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
         "Default value is \"99\". "
         "Use \"--percentile-metrics\" to select metrics.",
@@ -996,23 +951,18 @@ if __name__ == "__main__":
     random_group.add_argument(
         "--random-range-ratio",
         type=float,
-        default=0.0,
-        help="Range ratio for sampling input/output length, "
-        "used only for random sampling. Must be in the range [0, 1) to define "
-        "a symmetric sampling range"
-        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
+        default=1.0,
+        help="Range of sampled ratio of input/output length, "
+        "used only for random sampling.",
     )
     random_group.add_argument(
         "--random-prefix-len",
         type=int,
         default=0,
-        help=("Number of fixed prefix tokens before the random context "
-              "in a request. "
-              "The total input length is the sum of `random-prefix-len` and "
-              "a random "
-              "context length sampled from [input_len * (1 - range_ratio), "
-              "input_len * (1 + range_ratio)]."),
-    )
+        help="Number of fixed prefix tokens before random "
+        " context. The length range of context in a random "
+        " request is [random-prefix-len, "
+        " random-prefix-len + random-prefix-len * random-range-ratio).")
 
     hf_group = parser.add_argument_group("hf dataset options")
     hf_group.add_argument("--hf-subset",
@@ -1031,33 +981,6 @@ if __name__ == "__main__":
         "from the sampled HF dataset.",
     )
 
-    sampling_group = parser.add_argument_group("sampling parameters")
-    sampling_group.add_argument(
-        "--top-p",
-        type=float,
-        default=None,
-        help="Top-p sampling parameter. Only has effect on openai-compatible "
-        "backends.")
-    sampling_group.add_argument(
-        "--top-k",
-        type=int,
-        default=None,
-        help="Top-k sampling parameter. Only has effect on openai-compatible "
-        "backends.")
-    sampling_group.add_argument(
-        "--min-p",
-        type=float,
-        default=None,
-        help="Min-p sampling parameter. Only has effect on openai-compatible "
-        "backends.")
-    sampling_group.add_argument(
-        "--temperature",
-        type=float,
-        default=None,
-        help="Temperature sampling parameter. Only has effect on "
-        "openai-compatible backends. If not specified, default to greedy "
-        "decoding (i.e. temperature==0.0).")
-
     parser.add_argument(
         '--tokenizer-mode',
         type=str,

benchmarks/benchmark_serving_structured_output.py

@@ -5,13 +5,16 @@ On the server side, run one of the following commands:
     (vLLM OpenAI API server)
     vllm serve <your_model> --disable-log-requests
 
+    (TGI backend)
+    ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
+
 On the client side, run:
     python benchmarks/benchmark_serving_structured_output.py \
         --backend <backend> \
         --model <your_model> \
         --dataset json \
         --structured-output-ratio 1.0 \
-        --structured-output-backend auto \
+        --structured-output-backend xgrammar \
         --request-rate 10 \
         --num-prompts 1000
 
@@ -130,11 +133,10 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
                         "description":
                         "An unique optional field to avoid cached schemas"
                     }
-        else:
-            json_schemas = [schema] * args.num_prompts
 
         def gen_prompt(index: int):
-            return f"Generate an example of a user profile given the following schema: {json.dumps(get_schema(index))}"  # noqa: E501
+            schema = json_schemas[index % len(json_schemas)]
+            return f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
 
         def get_schema(index: int):
             return json_schemas[index % len(json_schemas)]
@@ -964,7 +966,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-separated list of selected metrics to report percentils. "
+        help="Comma-seperated list of selected metrics to report percentils. "
         "This argument specifies the metrics to report percentiles. "
         "Allowed metric names are \"ttft\", \"tpot\", \"itl\", \"e2el\". "
         "Default value is \"ttft,tpot,itl\".")
@@ -972,7 +974,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-separated list of percentiles for selected metrics. "
+        help="Comma-seperated list of percentiles for selected metrics. "
         "To report 25-th, 50-th, and 75-th percentiles, use \"25,50,75\". "
         "Default value is \"99\". "
         "Use \"--percentile-metrics\" to select metrics.",
@@ -997,14 +999,12 @@ if __name__ == "__main__":
                         type=float,
                         default=1.0,
                         help="Ratio of Structured Outputs requests")
-    parser.add_argument("--structured-output-backend",
-                        type=str,
-                        choices=[
-                            "outlines", "lm-format-enforcer", "xgrammar",
-                            "guidance", "auto"
-                        ],
-                        default="auto",
-                        help="Backend to use for structured outputs")
+    parser.add_argument(
+        "--structured-output-backend",
+        type=str,
+        choices=["outlines", "lm-format-enforcer", "xgrammar", "guidance"],
+        default="xgrammar",
+        help="Backend to use for structured outputs")
 
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -11,8 +11,7 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from benchmark_dataset import (AIMODataset, BurstGPTDataset,
-                               ConversationDataset, InstructCoderDataset,
+from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
                                RandomDataset, SampleRequest, ShareGPTDataset,
                                SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -213,17 +212,14 @@ def run_hf(
     max_prompt_len = 0
     max_output_len = 0
     for i in range(len(requests)):
-        prompt = requests[i].prompt
-        prompt_len = requests[i].prompt_len
-        output_len = requests[i].expected_output_len
+        prompt, prompt_len, output_len = requests[i]
         # Add the prompt to the batch.
         batch.append(prompt)
         max_prompt_len = max(max_prompt_len, prompt_len)
         max_output_len = max(max_output_len, output_len)
         if len(batch) < max_batch_size and i != len(requests) - 1:
             # Check if we can add more requests to the batch.
-            next_prompt_len = requests[i + 1].prompt_len
-            next_output_len = requests[i + 1].expected_output_len
+            _, next_prompt_len, next_output_len = requests[i + 1]
             if (max(max_prompt_len, next_prompt_len) +
                     max(max_output_len, next_output_len)) <= 2048:
                 # We can add more requests to the batch.
@@ -304,7 +300,6 @@ def get_requests(args, tokenizer):
         "input_len": args.input_len,
         "output_len": args.output_len,
     }
-
     if args.dataset_path is None or args.dataset_name == "random":
         sample_kwargs["range_ratio"] = args.random_range_ratio
         sample_kwargs["prefix_len"] = args.prefix_len
@@ -322,23 +317,18 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        if args.dataset_path in VisionArenaDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = VisionArenaDataset
-            common_kwargs['dataset_subset'] = None
-            common_kwargs['dataset_split'] = "train"
-            sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in InstructCoderDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = InstructCoderDataset
-            common_kwargs['dataset_split'] = "train"
-        elif args.dataset_path in ConversationDataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = ConversationDataset
-            common_kwargs['dataset_subset'] = args.hf_subset
-            common_kwargs['dataset_split'] = args.hf_split
-            sample_kwargs["enable_multimodal_chat"] = True
-        elif args.dataset_path in AIMODataset.SUPPORTED_DATASET_PATHS:
-            dataset_cls = AIMODataset
-            common_kwargs['dataset_subset'] = None
-            common_kwargs['dataset_split'] = "train"
+        if args.backend != "vllm-chat":
+            raise ValueError(
+                "hf datasets only are supported by vllm-chat backend")
+        # Choose between VisionArenaDataset and HuggingFaceDataset based on
+        # provided parameters.
+        dataset_cls = (VisionArenaDataset if args.dataset_path
+                       == VisionArenaDataset.VISION_ARENA_DATASET_PATH
+                       and args.hf_subset is None else HuggingFaceDataset)
+        common_kwargs['dataset_subset'] = args.hf_subset
+        common_kwargs['dataset_split'] = args.hf_split
+        sample_kwargs["enable_multimodal_chat"] = True
+
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
     # Remove None values
@@ -472,17 +462,9 @@ def validate_args(args):
         warnings.warn("--hf-subset and --hf-split will be ignored \
                 since --dataset-name is not 'hf'.",
                       stacklevel=2)
-    elif args.dataset_name == "hf":
-        if args.dataset_path in (
-                VisionArenaDataset.SUPPORTED_DATASET_PATHS.keys()
-                | ConversationDataset.SUPPORTED_DATASET_PATHS):
-            assert args.backend == "vllm-chat", f"{args.dataset_path} needs to use vllm-chat as the backend."  #noqa: E501
-        elif args.dataset_path in (InstructCoderDataset.SUPPORTED_DATASET_PATHS
-                                   | AIMODataset.SUPPORTED_DATASET_PATHS):
-            assert args.backend == "vllm", f"{args.dataset_path} needs to use vllm as the backend."  #noqa: E501
-        else:
-            raise ValueError(
-                f"{args.dataset_path} is not supported by hf dataset.")
+    elif args.dataset_name == "hf" and args.backend != "vllm-chat":
+        raise ValueError(
+            "When --dataset-name is 'hf', backend must be 'vllm-chat'")
 
     # --random-range-ratio: only used when dataset_name is 'random'
     if args.dataset_name != 'random' and args.random_range_ratio is not None:
@@ -594,22 +576,18 @@ if __name__ == "__main__":
         default=None,
         help="Path to the lora adapters to use. This can be an absolute path, "
         "a relative path, or a Hugging Face model identifier.")
-    parser.add_argument(
-        "--prefix-len",
-        type=int,
-        default=0,
-        help="Number of fixed prefix tokens before the random "
-        "context in a request (default: 0).",
-    )
+    parser.add_argument("--prefix-len",
+                        type=int,
+                        default=None,
+                        help="Number of prefix tokens per request."
+                        "This is for the RandomDataset and SonnetDataset")
     # random dataset
     parser.add_argument(
         "--random-range-ratio",
         type=float,
-        default=0.0,
-        help="Range ratio for sampling input/output length, "
-        "used only for RandomDataset. Must be in the range [0, 1) to define "
-        "a symmetric sampling range "
-        "[length * (1 - range_ratio), length * (1 + range_ratio)].",
+        default=None,
+        help="Range of sampled ratio of input/output length, "
+        "used only for RandomDataSet.",
     )
 
     # hf dtaset

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -1,340 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-
-import torch
-import torch.utils.benchmark as benchmark
-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.fused_moe import (cutlass_moe_fp8,
-                                                            fused_experts,
-                                                            fused_topk)
-from vllm.utils import FlexibleArgumentParser
-
-DEFAULT_MODELS = [
-    "nm-testing/Mixtral-8x7B-Instruct-v0.1", "nm-testing/deepseekv2-lite",
-    "ibm-granite/granite-3.0-1b-a400m", "ibm-granite/granite-3.0-3b-a800m"
-]
-DEFAULT_BATCH_SIZES = [1, 4, 8, 16, 32, 64, 128, 256, 512]
-DEFAULT_TP_SIZES = [1]
-
-PER_ACT_TOKEN_OPTS = [False]
-PER_OUT_CH_OPTS = [False]
-
-
-def to_fp8(tensor: torch.Tensor):
-    finfo = torch.finfo(torch.float8_e4m3fn)
-    return torch.round(tensor.clamp(
-        min=finfo.min, max=finfo.max)).to(dtype=torch.float8_e4m3fn)
-
-
-def bench_run(results: list[benchmark.Measurement], model: str,
-              num_experts: int, topk: int, per_act_token: bool,
-              per_out_ch: bool, mkn: tuple[int, int, int]):
-    label = "Quant Matmul"
-
-    sub_label = (
-        "{}, num_experts={}, topk={}, per_act_token={} per_out_ch={}, "
-        "MKN=({})".format(model, num_experts, topk, per_act_token, per_out_ch,
-                          mkn))
-
-    print(f"Testing: {sub_label}")
-
-    (m, k, n) = mkn
-
-    dtype = torch.half
-
-    a = torch.randn((m, k), device="cuda", dtype=dtype) / 10
-    w1 = torch.randn((num_experts, 2 * n, k), device="cuda", dtype=dtype) / 10
-    w2 = torch.randn((num_experts, k, n), device="cuda", dtype=dtype) / 10
-
-    _, a_scale = ops.scaled_fp8_quant(a)
-
-    w1_q = torch.empty((num_experts, 2 * n, k),
-                       device="cuda",
-                       dtype=torch.float8_e4m3fn)
-    w2_q = torch.empty((num_experts, k, n),
-                       device="cuda",
-                       dtype=torch.float8_e4m3fn)
-    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)
-
-    topk_weights, topk_ids = fused_topk(a, score, topk, renormalize=False)
-
-    def run_triton_moe(a: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor,
-                       topk_weights: torch.Tensor, topk_ids: torch.Tensor,
-                       w1_scale: torch.Tensor, w2_scale: torch.Tensor,
-                       a_scale: torch.Tensor, num_repeats: int):
-        for _ in range(num_repeats):
-            fused_experts(a,
-                          w1,
-                          w2,
-                          topk_weights,
-                          topk_ids,
-                          use_fp8_w8a8=True,
-                          w1_scale=w1_scale,
-                          w2_scale=w2_scale,
-                          a1_scale=a_scale)
-
-    def run_cutlass_moe(a: torch.Tensor, a_scale: torch.Tensor,
-                        w1: torch.Tensor, w2: torch.Tensor,
-                        w1_scale: torch.Tensor, 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):
-            cutlass_moe_fp8(a,
-                            w1,
-                            w2,
-                            w1_scale,
-                            w2_scale,
-                            topk_weights,
-                            topk_ids,
-                            ab_strides1,
-                            c_strides1,
-                            ab_strides2,
-                            c_strides2,
-                            a1_scale=a_scale)
-
-    def run_cutlass_from_graph(
-            a: torch.Tensor, a_scale: torch.Tensor, w1_q: torch.Tensor,
-            w2_q: torch.Tensor, w1_scale: torch.Tensor, 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))):
-            return cutlass_moe_fp8(a,
-                                   w1_q,
-                                   w2_q,
-                                   w1_scale,
-                                   w2_scale,
-                                   topk_weights,
-                                   topk_ids,
-                                   ab_strides1,
-                                   c_strides1,
-                                   ab_strides2,
-                                   c_strides2,
-                                   a1_scale=a_scale)
-
-    def run_triton_from_graph(a: torch.Tensor, w1: torch.Tensor,
-                              w2: torch.Tensor, topk_weights: torch.Tensor,
-                              topk_ids: torch.Tensor, w1_scale: torch.Tensor,
-                              w2_scale: torch.Tensor, a_scale: torch.Tensor):
-        with set_current_vllm_config(
-                VllmConfig(parallel_config=ParallelConfig(
-                    pipeline_parallel_size=1))):
-            return fused_experts(a,
-                                 w1,
-                                 w2,
-                                 topk_weights,
-                                 topk_ids,
-                                 use_fp8_w8a8=True,
-                                 w1_scale=w1_scale,
-                                 w2_scale=w2_scale,
-                                 a1_scale=a_scale)
-
-    def replay_graph(graph, num_repeats):
-        for _ in range(num_repeats):
-            graph.replay()
-        torch.cuda.synchronize()
-
-    cutlass_stream = torch.cuda.Stream()
-    cutlass_graph = torch.cuda.CUDAGraph()
-    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
-        run_cutlass_from_graph(a, a_scale, w1_q, w2_q, w1_scale, w2_scale,
-                               topk_weights, topk_ids, ab_strides1, c_strides1,
-                               ab_strides2, c_strides2)
-    torch.cuda.synchronize()
-
-    triton_stream = torch.cuda.Stream()
-    triton_graph = torch.cuda.CUDAGraph()
-    with torch.cuda.graph(triton_graph, stream=triton_stream):
-        run_triton_from_graph(a, w1_q_notransp, w2_q_notransp, topk_weights,
-                              topk_ids, w1_scale, w2_scale, a_scale)
-    torch.cuda.synchronize()
-
-    min_run_time = 5
-    num_warmup = 5
-    num_runs = 25
-
-    globals = {
-        # Baseline params
-        "w1": w1,
-        "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,
-        # Gen params
-        "a": a,
-        "topk_weights": topk_weights,
-        "topk_ids": topk_ids,
-        "num_runs": num_runs,
-        # Kernels
-        "run_triton_moe": run_triton_moe,
-        "run_cutlass_moe": run_cutlass_moe,
-        "replay_graph": replay_graph,
-    }
-
-    # Warmup
-    run_triton_moe(a, w1_q_notransp, w2_q_notransp, topk_weights, topk_ids,
-                   w1_scale, w2_scale, a_scale, num_warmup)
-
-    results.append(
-        benchmark.Timer(
-            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,
-            description="triton_moe",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-    # Warmup
-    replay_graph(triton_graph, num_warmup)
-
-    results.append(
-        benchmark.Timer(
-            stmt="replay_graph(triton_graph, num_runs)",
-            globals=globals,
-            label=label,
-            sub_label=sub_label,
-            description="triton_moe_cuda_graphs",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-    # Warmup
-    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_warmup)
-
-    results.append(
-        benchmark.Timer(
-            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,
-            description="grouped_gemm_moe",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-    # Warmup
-    replay_graph(cutlass_graph, num_warmup)
-
-    results.append(
-        benchmark.Timer(
-            stmt="replay_graph(cutlass_graph, num_runs)",
-            globals=globals,
-            label=label,
-            sub_label=sub_label,
-            description="grouped_gemm_moe_cuda_graphs",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-
-def main(args):
-    print("Benchmarking models:")
-    for i, model in enumerate(args.models):
-        print(f"[{i}]  {model}")
-
-    results: list[benchmark.Measurement] = []
-
-    for model in args.models:
-        for tp in args.tp_sizes:
-            for layer in WEIGHT_SHAPES_MOE[model]:
-                num_experts = layer[0]
-                topk = layer[1]
-                size_k = layer[2]
-                size_n = layer[3] // tp
-
-                if len(args.limit_k) > 0 and size_k not in args.limit_k:
-                    continue
-
-                if len(args.limit_n) > 0 and size_n not in args.limit_n:
-                    continue
-
-                for per_act_token in PER_ACT_TOKEN_OPTS:
-                    for per_out_ch in PER_OUT_CH_OPTS:
-                        for size_m in DEFAULT_BATCH_SIZES:
-                            mkn = (size_m, size_k, size_n)
-                            bench_run(results, model, num_experts, topk,
-                                      per_act_token, per_out_ch, mkn)
-
-    compare = benchmark.Compare(results)
-    compare.print()
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(
-        description="Benchmark Marlin across specified models/shapes/batches")
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=DEFAULT_MODELS,
-        choices=WEIGHT_SHAPES_MOE.keys(),
-    )
-    parser.add_argument("--tp-sizes",
-                        nargs="+",
-                        type=int,
-                        default=DEFAULT_TP_SIZES)
-    parser.add_argument("--batch-sizes",
-                        nargs="+",
-                        type=int,
-                        default=DEFAULT_BATCH_SIZES)
-    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-num-groups", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-per-act-token",
-                        nargs="+",
-                        type=int,
-                        default=[])
-    parser.add_argument("--limit-per-out-ch", nargs="+", type=int, default=[])
-
-    args = parser.parse_args()
-    main(args)

benchmarks/kernels/benchmark_moe.py

@@ -30,18 +30,19 @@ class BenchmarkConfig(TypedDict):
     num_stages: int
 
 
-def benchmark_config(config: BenchmarkConfig,
-                     num_tokens: int,
-                     num_experts: int,
-                     shard_intermediate_size: int,
-                     hidden_size: int,
-                     topk: int,
-                     dtype: torch.dtype,
-                     use_fp8_w8a8: bool,
-                     use_int8_w8a16: bool,
-                     num_iters: int = 100,
-                     block_quant_shape: List[int] = None,
-                     use_deep_gemm: bool = False) -> float:
+def benchmark_config(
+    config: BenchmarkConfig,
+    num_tokens: int,
+    num_experts: int,
+    shard_intermediate_size: int,
+    hidden_size: int,
+    topk: int,
+    dtype: torch.dtype,
+    use_fp8_w8a8: bool,
+    use_int8_w8a16: bool,
+    num_iters: int = 100,
+    block_quant_shape: List[int] = None,
+) -> float:
     init_dtype = torch.float16 if use_fp8_w8a8 else dtype
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)
     if use_int8_w8a16:
@@ -114,41 +115,22 @@ def benchmark_config(config: BenchmarkConfig,
     def run():
         from vllm.model_executor.layers.fused_moe import override_config
         with override_config(config):
-            if use_deep_gemm:
-                topk_weights, topk_ids = fused_topk(x, input_gating, topk,
-                                                    False)
-                return fused_experts(
-                    x,
-                    w1,
-                    w2,
-                    topk_weights,
-                    topk_ids,
-                    inplace=True,
-                    use_fp8_w8a8=use_fp8_w8a8,
-                    w1_scale=w1_scale,
-                    w2_scale=w2_scale,
-                    a1_scale=a1_scale,
-                    a2_scale=a2_scale,
-                    block_shape=block_quant_shape,
-                    allow_deep_gemm=True,
-                )
-            else:
-                fused_moe(
-                    x,
-                    w1,
-                    w2,
-                    input_gating,
-                    topk,
-                    renormalize=True,
-                    inplace=True,
-                    use_fp8_w8a8=use_fp8_w8a8,
-                    use_int8_w8a16=use_int8_w8a16,
-                    w1_scale=w1_scale,
-                    w2_scale=w2_scale,
-                    a1_scale=a1_scale,
-                    a2_scale=a2_scale,
-                    block_shape=block_quant_shape,
-                )
+            fused_moe(
+                x,
+                w1,
+                w2,
+                input_gating,
+                topk,
+                renormalize=True,
+                inplace=True,
+                use_fp8_w8a8=use_fp8_w8a8,
+                use_int8_w8a16=use_int8_w8a16,
+                w1_scale=w1_scale,
+                w2_scale=w2_scale,
+                a1_scale=a1_scale,
+                a2_scale=a2_scale,
+                block_shape=block_quant_shape,
+            )
 
     # JIT compilation & warmup
     run()
@@ -384,7 +366,6 @@ class BenchmarkWorker:
         use_fp8_w8a8: bool,
         use_int8_w8a16: bool,
         block_quant_shape: List[int] = None,
-        use_deep_gemm: bool = False,
     ) -> tuple[dict[str, int], float]:
         current_platform.seed_everything(self.seed)
         dtype_str = get_config_dtype_str(dtype,
@@ -415,8 +396,7 @@ class BenchmarkWorker:
                                        use_fp8_w8a8,
                                        use_int8_w8a16,
                                        num_iters=100,
-                                       block_quant_shape=block_quant_shape,
-                                       use_deep_gemm=use_deep_gemm)
+                                       block_quant_shape=block_quant_shape)
         return config, kernel_time
 
     def tune(
@@ -431,7 +411,6 @@ class BenchmarkWorker:
         use_int8_w8a16: bool,
         search_space: list[dict[str, int]],
         block_quant_shape: list[int],
-        use_deep_gemm: bool,
     ) -> dict[str, int]:
         best_config = None
         best_time = float("inf")
@@ -457,8 +436,7 @@ class BenchmarkWorker:
                         use_fp8_w8a8,
                         use_int8_w8a16,
                         num_iters=20,
-                        block_quant_shape=block_quant_shape,
-                        use_deep_gemm=use_deep_gemm)
+                        block_quant_shape=block_quant_shape)
                 except triton.runtime.autotuner.OutOfResources:
                     # Some configurations may be invalid and fail to compile.
                     continue
@@ -553,9 +531,6 @@ def main(args: argparse.Namespace):
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
-        if not hasattr(config, "hidden_size"):
-            # Support for llama4
-            config = config.text_config
         # Default: Mixtral.
         E = config.num_local_experts
         topk = config.num_experts_per_tok
@@ -575,8 +550,6 @@ def main(args: argparse.Namespace):
     else:
         batch_sizes = [args.batch_size]
 
-    use_deep_gemm = bool(args.use_deep_gemm)
-
     ray.init()
     num_gpus = int(ray.available_resources()["GPU"])
     workers = [BenchmarkWorker.remote(args.seed) for _ in range(num_gpus)]
@@ -599,10 +572,10 @@ def main(args: argparse.Namespace):
 
         start = time.time()
         configs = _distribute(
-            "tune", [(batch_size, E, shard_intermediate_size, hidden_size,
-                      topk, dtype, use_fp8_w8a8, use_int8_w8a16, search_space,
-                      block_quant_shape, use_deep_gemm)
-                     for batch_size in batch_sizes])
+            "tune",
+            [(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
+              use_fp8_w8a8, use_int8_w8a16, search_space, block_quant_shape)
+             for batch_size in batch_sizes])
         best_configs = {
             M: sort_config(config)
             for M, config in zip(batch_sizes, configs)
@@ -616,7 +589,7 @@ def main(args: argparse.Namespace):
         outputs = _distribute(
             "benchmark",
             [(batch_size, E, shard_intermediate_size, hidden_size, topk, dtype,
-              use_fp8_w8a8, use_int8_w8a16, block_quant_shape, use_deep_gemm)
+              use_fp8_w8a8, use_int8_w8a16, block_quant_shape)
              for batch_size in batch_sizes])
 
         for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
@@ -638,7 +611,6 @@ if __name__ == "__main__":
                         type=str,
                         choices=["auto", "fp8_w8a8", "int8_w8a16"],
                         default="auto")
-    parser.add_argument("--use-deep-gemm", action="store_true")
     parser.add_argument("--seed", type=int, default=0)
     parser.add_argument("--batch-size", type=int, required=False)
     parser.add_argument("--tune", action="store_true")

benchmarks/kernels/benchmark_paged_attention.py

@@ -7,13 +7,10 @@ from typing import Optional
 import torch
 
 from vllm import _custom_ops as ops
-from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, FlexibleArgumentParser,
                         create_kv_caches_with_random)
 
-logger = init_logger(__name__)
-
 NUM_BLOCKS = 128 * 1024
 PARTITION_SIZE = 512
 PARTITION_SIZE_ROCM = 256
@@ -196,9 +193,6 @@ def main(
 
 
 if __name__ == '__main__':
-    logger.warning("This script benchmarks the paged attention kernel. "
-                   "By default this is no longer used in vLLM inference.")
-
     parser = FlexibleArgumentParser(
         description="Benchmark the paged attention kernel.")
     parser.add_argument("--version",

benchmarks/kernels/benchmark_shapes.py

@@ -75,19 +75,3 @@ WEIGHT_SHAPES = {
         [7168, 8192],
     ],
 }
-
-WEIGHT_SHAPES_MOE = {
-    "nm-testing/Mixtral-8x7B-Instruct-v0.1": [
-        [8, 2, 4096, 28672],
-        [8, 2, 14336, 4096],
-    ],
-    "nm-testing/deepseekv2-lite": [
-        [64, 6, 2048, 1408],
-    ],
-    "ibm-granite/granite-3.0-1b-a400m": [
-        [32, 8, 1024, 1024],
-    ],
-    "ibm-granite/granite-3.0-3b-a800m": [
-        [40, 8, 1024, 1536],
-    ],
-}

benchmarks/launch_tgi_server.sh

@@ -0,0 +1,16 @@
+#!/bin/bash
+
+PORT=8000
+MODEL=$1
+TOKENS=$2
+
+docker run -e "HF_TOKEN=$HF_TOKEN" --gpus all --shm-size 1g -p $PORT:80 \
+           -v "$PWD/data:/data" \
+           ghcr.io/huggingface/text-generation-inference:2.2.0 \
+           --model-id "$MODEL" \
+           --sharded false  \
+           --max-input-length 1024 \
+           --max-total-tokens 2048 \
+           --max-best-of 5 \
+           --max-concurrent-requests 5000 \
+           --max-batch-total-tokens "$TOKENS"

cmake/cpu_extension.cmake

@@ -33,6 +33,8 @@ endif()
 
 if(MACOSX_FOUND)
     list(APPEND CXX_COMPILE_FLAGS
+        "-Xpreprocessor"
+        "-fopenmp"
         "-DVLLM_CPU_EXTENSION")
 else()
     list(APPEND CXX_COMPILE_FLAGS
@@ -195,7 +197,6 @@ set(VLLM_EXT_SRC
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     set(VLLM_EXT_SRC
         "csrc/cpu/quant.cpp"
-        "csrc/cpu/shm.cpp"
         ${VLLM_EXT_SRC})
 endif()
 

collect_env.py

@@ -105,14 +105,8 @@ def run(command):
     else:
         enc = locale.getpreferredencoding()
     output = raw_output.decode(enc)
-    if command == 'nvidia-smi topo -m':
-        # don't remove the leading whitespace of `nvidia-smi topo -m`
-        #   because they are meaningful
-        output = output.rstrip()
-    else:
-        output = output.strip()
     err = raw_err.decode(enc)
-    return rc, output, err.strip()
+    return rc, output.strip(), err.strip()
 
 
 def run_and_read_all(run_lambda, command):
@@ -488,28 +482,16 @@ def get_pip_packages(run_lambda, patterns=None):
     if patterns is None:
         patterns = DEFAULT_PIP_PATTERNS
 
-    def run_with_pip():
-        try:
-            import importlib.util
-            pip_spec = importlib.util.find_spec('pip')
-            pip_available = pip_spec is not None
-        except ImportError:
-            pip_available = False
-
-        if pip_available:
-            cmd = [sys.executable, '-mpip', 'list', '--format=freeze']
-        elif os.environ.get("UV") is not None:
-            print("uv is set")
-            cmd = ["uv", "pip", "list", "--format=freeze"]
-        else:
-            raise RuntimeError("Could not collect pip list output (pip or uv module not available)")
-
-        out = run_and_read_all(run_lambda, cmd)
+    # People generally have `pip` as `pip` or `pip3`
+    # But here it is invoked as `python -mpip`
+    def run_with_pip(pip):
+        out = run_and_read_all(run_lambda, pip + ["list", "--format=freeze"])
         return "\n".join(line for line in out.splitlines()
                          if any(name in line for name in patterns))
 
     pip_version = 'pip3' if sys.version[0] == '3' else 'pip'
-    out = run_with_pip()
+    out = run_with_pip([sys.executable, '-mpip'])
+
     return pip_version, out
 
 

csrc/attention/merge_attn_states.cu

@@ -1,173 +0,0 @@
-#include <optional>
-#include <torch/all.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include <algorithm>
-
-#include "attention_dtypes.h"
-#include "attention_utils.cuh"
-
-namespace vllm {
-
-// Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005
-// can be used to combine partial attention results (in the split-KV case)
-template <typename scalar_t, const uint NUM_THREADS>
-__global__ void merge_attn_states_kernel(
-    scalar_t* output, float* output_lse, const scalar_t* prefix_output,
-    const float* prefix_lse, const scalar_t* suffix_output,
-    const float* suffix_lse, const uint num_tokens, const uint num_heads,
-    const uint head_size) {
-  using pack_128b_t = uint4;
-  const uint pack_size = 16 / sizeof(scalar_t);
-  const uint threads_per_head = head_size / pack_size;
-
-  const uint global_idx = blockIdx.x * NUM_THREADS + threadIdx.x;
-  const uint token_head_threads = num_tokens * num_heads * threads_per_head;
-
-  if (global_idx >= token_head_threads) return;
-
-  // global_idx -> token_idx + head_idx + pack_idx
-  const uint token_head_idx = global_idx / threads_per_head;
-  const uint pack_idx = global_idx % threads_per_head;
-
-  const uint token_idx = token_head_idx / num_heads;
-  const uint head_idx = token_head_idx % num_heads;
-
-  const uint pack_offset = pack_idx * pack_size;  // (0~15)*8, etc.
-  const uint head_offset =
-      token_idx * num_heads * head_size + head_idx * head_size;
-  const scalar_t* prefix_head_ptr = prefix_output + head_offset;
-  const scalar_t* suffix_head_ptr = suffix_output + head_offset;
-  scalar_t* output_head_ptr = output + head_offset;
-
-  float p_lse = prefix_lse[head_idx * num_tokens + token_idx];
-  float s_lse = suffix_lse[head_idx * num_tokens + token_idx];
-  p_lse = std::isinf(p_lse) ? -std::numeric_limits<float>::infinity() : p_lse;
-  s_lse = std::isinf(s_lse) ? -std::numeric_limits<float>::infinity() : s_lse;
-
-  const float max_lse = fmaxf(p_lse, s_lse);
-  p_lse = p_lse - max_lse;
-  s_lse = s_lse - max_lse;
-  const float p_se = expf(p_lse);
-  const float s_se = expf(s_lse);
-  const float out_se = p_se + s_se;
-  const float p_scale = p_se / out_se;
-  const float s_scale = s_se / out_se;
-
-  if (pack_offset < head_size) {
-    // Pack 128b load
-    pack_128b_t p_out_pack = reinterpret_cast<const pack_128b_t*>(
-        prefix_head_ptr)[pack_offset / pack_size];
-    pack_128b_t s_out_pack = reinterpret_cast<const pack_128b_t*>(
-        suffix_head_ptr)[pack_offset / pack_size];
-    pack_128b_t o_out_pack;
-
-#pragma unroll
-    for (uint i = 0; i < pack_size; ++i) {
-      // Always use float for FMA to keep high precision.
-      // half(uint16_t), bfloat16, float -> float.
-      const float p_out_f =
-          vllm::to_float(reinterpret_cast<const scalar_t*>(&p_out_pack)[i]);
-      const float s_out_f =
-          vllm::to_float(reinterpret_cast<const scalar_t*>(&s_out_pack)[i]);
-      // fma: a * b + c = p_out_f * p_scale + (s_out_f * s_scale)
-      const float o_out_f = p_out_f * p_scale + (s_out_f * s_scale);
-      // float -> half(uint16_t), bfloat16, float.
-      vllm::from_float(reinterpret_cast<scalar_t*>(&o_out_pack)[i], o_out_f);
-    }
-
-    // Pack 128b storage
-    reinterpret_cast<pack_128b_t*>(output_head_ptr)[pack_offset / pack_size] =
-        o_out_pack;
-  }
-  // We only need to write to output_lse once per head.
-  if (output_lse != nullptr && pack_idx == 0) {
-    float out_lse = logf(out_se) + max_lse;
-    output_lse[head_idx * num_tokens + token_idx] = out_lse;
-  }
-}
-
-}  // namespace vllm
-
-// The following macro is used to dispatch the conversion function based on
-// the output data type. The FN is a macro that calls a function with
-// template<typename scalar_t>.
-#define DISPATCH_BY_SCALAR_DTYPE(scalar_dtype, fn)                      \
-  {                                                                     \
-    if (scalar_dtype == at::ScalarType::Float) {                        \
-      fn(float);                                                        \
-    } else if (scalar_dtype == at::ScalarType::Half) {                  \
-      fn(uint16_t);                                                     \
-    } else if (scalar_dtype == at::ScalarType::BFloat16) {              \
-      fn(__nv_bfloat16);                                                \
-    } else {                                                            \
-      TORCH_CHECK(false, "Unsupported data type of O: ", scalar_dtype); \
-    }                                                                   \
-  }
-
-#define LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS)                     \
-  {                                                                         \
-    vllm::merge_attn_states_kernel<scalar_t, NUM_THREADS><<<grid, block>>>( \
-        reinterpret_cast<scalar_t*>(output.data_ptr()), output_lse_ptr,     \
-        reinterpret_cast<scalar_t*>(prefix_output.data_ptr()),              \
-        reinterpret_cast<float*>(prefix_lse.data_ptr()),                    \
-        reinterpret_cast<scalar_t*>(suffix_output.data_ptr()),              \
-        reinterpret_cast<float*>(suffix_lse.data_ptr()), num_tokens,        \
-        num_heads, head_size);                                              \
-  }
-
-/*@brief Merges the attention states from prefix and suffix
- * into the output tensor. NUM_TOKENS: n, NUM_HEADS: h, HEAD_SIZE: d
- *
- * @param output [n,h,d] The output tensor to store the merged attention states.
- * @param output_lse [h,d] Optional tensor to store the log-sum-exp values.
- * @param prefix_output [n,h,d] The prefix attention states.
- * @param prefix_lse [h,d] The log-sum-exp values for the prefix attention
- * states.
- * @param suffix_output [n,h,d] The suffix attention states.
- * @param suffix_lse [h,d] The log-sum-exp values for the suffix attention
- * states.
- */
-template <typename scalar_t>
-void merge_attn_states_launcher(torch::Tensor& output,
-                                std::optional<torch::Tensor> output_lse,
-                                const torch::Tensor& prefix_output,
-                                const torch::Tensor& prefix_lse,
-                                const torch::Tensor& suffix_output,
-                                const torch::Tensor& suffix_lse) {
-  constexpr uint NUM_THREADS = 128;
-  const uint num_tokens = output.size(0);
-  const uint num_heads = output.size(1);
-  const uint head_size = output.size(2);
-  const uint pack_size = 16 / sizeof(scalar_t);
-  TORCH_CHECK(head_size % pack_size == 0,
-              "headsize must be multiple of pack_size:", pack_size);
-  float* output_lse_ptr = nullptr;
-  if (output_lse.has_value()) {
-    output_lse_ptr = output_lse.value().data_ptr<float>();
-  }
-  // process one pack elements per thread. float -> 4, half/bf16 -> 8
-  const uint threads_per_head = head_size / pack_size;
-  const uint total_threads = num_tokens * num_heads * threads_per_head;
-
-  dim3 block(NUM_THREADS);
-  dim3 grid((total_threads + NUM_THREADS - 1) / NUM_THREADS);
-
-  LAUNCH_MERGE_ATTN_STATES(scalar_t, NUM_THREADS);
-}
-
-#define CALL_MERGE_ATTN_STATES_LAUNCHER(scalar_t)                           \
-  {                                                                         \
-    merge_attn_states_launcher<scalar_t>(output, output_lse, prefix_output, \
-                                         prefix_lse, suffix_output,         \
-                                         suffix_lse);                       \
-  }
-
-void merge_attn_states(torch::Tensor& output,
-                       std::optional<torch::Tensor> output_lse,
-                       const torch::Tensor& prefix_output,
-                       const torch::Tensor& prefix_lse,
-                       const torch::Tensor& suffix_output,
-                       const torch::Tensor& suffix_lse) {
-  DISPATCH_BY_SCALAR_DTYPE(output.dtype(), CALL_MERGE_ATTN_STATES_LAUNCHER);
-}

csrc/block_table.cu

@@ -0,0 +1,92 @@
+#include <torch/all.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+namespace vllm {
+__global__ void append_kernel(const int* __restrict__ row_indices,
+                              const int* __restrict__ cu_num_appends,
+                              const int* __restrict__ block_ids,
+                              int* __restrict__ block_table,
+                              int max_num_blocks_per_row) {
+  int bid = blockIdx.x;
+  int tgt_row = row_indices[2 * bid];
+  int tgt_offset = row_indices[2 * bid + 1];
+
+  int start = cu_num_appends[bid];
+  int end = cu_num_appends[bid + 1];
+  int length = end - start;
+  int tid = threadIdx.x;
+  int64_t offset = tgt_row * max_num_blocks_per_row + tgt_offset;
+  for (int i = tid; i < length; i += blockDim.x) {
+    block_table[offset + i] = block_ids[start + i];
+  }
+}
+
+__global__ void move_kernel(const int* __restrict__ src_dst_n,
+                            int* __restrict__ block_table,
+                            int max_num_blocks_per_row) {
+  int bid = blockIdx.x;
+  int src_row = src_dst_n[3 * bid];
+  int tgt_row = src_dst_n[3 * bid + 1];
+  int num_blocks = src_dst_n[3 * bid + 2];
+
+  int tid = threadIdx.x;
+  for (int i = tid; i < num_blocks; i += blockDim.x) {
+    block_table[tgt_row * max_num_blocks_per_row + i] =
+        block_table[src_row * max_num_blocks_per_row + i];
+  }
+}
+}  // namespace vllm
+
+void block_table_appends(
+    torch::Tensor& append_row_indices,
+    torch::Tensor& append_row_indices_cpu,
+    torch::Tensor& append_cumsums,
+    torch::Tensor& append_cumsums_cpu,
+    torch::Tensor& append_block_ids,
+    torch::Tensor& append_block_ids_cpu,
+    torch::Tensor& block_table,
+    int64_t num_appends,
+    int64_t total_num_append_blocks) {
+  int* append_row_indices_ptr = append_row_indices.data_ptr<int>();
+  const int* append_row_indices_cpu_ptr = append_row_indices_cpu.data_ptr<int>();
+  int* append_cumsums_ptr = append_cumsums.data_ptr<int>();
+  const int* append_cumsums_cpu_ptr = append_cumsums_cpu.data_ptr<int>();
+  int* append_block_ids_ptr = append_block_ids.data_ptr<int>();
+  const int* append_block_ids_cpu_ptr = append_block_ids_cpu.data_ptr<int>();
+  int* block_table_ptr = block_table.data_ptr<int>();
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(block_table));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  cudaMemcpyAsync(append_row_indices_ptr, append_row_indices_cpu_ptr,
+                  num_appends * 2 * sizeof(int), cudaMemcpyHostToDevice, stream);
+  cudaMemcpyAsync(append_cumsums_ptr, append_cumsums_cpu_ptr,
+                  (num_appends + 1) * sizeof(int), cudaMemcpyHostToDevice, stream);
+  cudaMemcpyAsync(append_block_ids_ptr, append_block_ids_cpu_ptr,
+                  total_num_append_blocks * sizeof(int), cudaMemcpyHostToDevice, stream);
+
+  int64_t max_num_blocks_per_row = block_table.size(1);
+  vllm::append_kernel<<<num_appends, 1024, 0, stream>>>(
+      append_row_indices_ptr, append_cumsums_ptr, append_block_ids_ptr,
+      block_table_ptr, max_num_blocks_per_row);
+}
+
+void block_table_moves(
+    torch::Tensor& src_dst_n,
+    torch::Tensor& src_dst_n_cpu,
+    torch::Tensor& block_table,
+    int64_t num_moves) {
+  int* src_dst_n_ptr = src_dst_n.data_ptr<int>();
+  const int* src_dst_n_cpu_ptr = src_dst_n_cpu.data_ptr<int>();
+  int* block_table_ptr = block_table.data_ptr<int>();
+
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(block_table));
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  cudaMemcpyAsync(src_dst_n_ptr, src_dst_n_cpu_ptr,
+                  num_moves * 3 * sizeof(int), cudaMemcpyHostToDevice, stream);
+
+  int64_t max_num_blocks_per_row = block_table.size(1);
+  vllm::move_kernel<<<num_moves, 1024, 0, stream>>>(
+      src_dst_n_ptr, block_table_ptr, max_num_blocks_per_row);
+}

csrc/cpu/cpu_types_x86.hpp

@@ -78,14 +78,9 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
 
   __m256i reg;
 
-  // normal load
   explicit FP16Vec16(const void* ptr)
       : reg((__m256i)_mm256_loadu_si256((__m256i*)ptr)) {}
 
-  // non-temproal load
-  explicit FP16Vec16(bool, void* ptr)
-      : reg(_mm256_stream_load_si256((__m256i*)ptr)) {}
-
   explicit FP16Vec16(const FP32Vec16&);
 
   void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
@@ -115,14 +110,9 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
 
   __m256i reg;
 
-  // normal load
   explicit BF16Vec16(const void* ptr)
       : reg((__m256i)_mm256_loadu_si256((__m256i*)ptr)) {}
 
-  // non-temproal load
-  explicit BF16Vec16(bool, void* ptr)
-      : reg(_mm256_stream_load_si256((__m256i*)ptr)) {}
-
   explicit BF16Vec16(const FP32Vec16&);
 
   void save(void* ptr) const { *reinterpret_cast<__m256i*>(ptr) = reg; }
@@ -323,13 +313,8 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16() : reg(_mm512_set1_ps(0.0)) {}
 
-  // normal load
   explicit FP32Vec16(const float* ptr) : reg(_mm512_loadu_ps(ptr)) {}
 
-  // non-temproal load
-  explicit FP32Vec16(bool, void* ptr)
-      : reg((__m512)_mm512_stream_load_si512(ptr)) {}
-
   explicit FP32Vec16(__m512 data) : reg(data) {}
 
   explicit FP32Vec16(const FP32Vec4& data)
@@ -562,33 +547,6 @@ struct INT8Vec16 : public Vec<INT8Vec16> {
     _mm_mask_storeu_epi8(ptr, mask, reg);
   }
 };
-
-struct INT8Vec64 : public Vec<INT8Vec64> {
-  constexpr static int VEC_ELEM_NUM = 64;
-  union AliasReg {
-    __m512i reg;
-    int8_t values[VEC_ELEM_NUM];
-  };
-
-  __m512i reg;
-
-  // normal load
-  explicit INT8Vec64(void* ptr) : reg(_mm512_loadu_epi8(ptr)) {}
-
-  // non-temproal load
-  explicit INT8Vec64(bool, void* ptr) : reg(_mm512_stream_load_si512(ptr)) {}
-
-  void save(void* ptr) const { _mm512_storeu_epi8(ptr, reg); }
-
-  void save(int8_t* ptr, const int elem_num) const {
-    constexpr uint64_t M = 0xFFFFFFFFFFFFFFFF;
-    __mmask64 mask = _cvtu64_mask64(M >> (64 - elem_num));
-    _mm512_mask_storeu_epi8(ptr, mask, reg);
-  }
-
-  // non-temproal save
-  void nt_save(int8_t* ptr) { _mm512_stream_si512((__m512i*)ptr, reg); }
-};
 #endif
 
 template <typename T>
@@ -699,22 +657,6 @@ inline BF16Vec16::BF16Vec16(const FP32Vec16& v) {
 
 inline void prefetch(const void* addr) { _mm_prefetch(addr, _MM_HINT_T1); }
 
-#ifdef __AVX512F__
-inline void non_temporal_save(FP16Vec16& vec, void* ptr) {
-  _mm256_stream_si256((__m256i*)ptr, vec.reg);
-}
-inline void non_temporal_save(BF16Vec32& vec, void* ptr) {
-  _mm512_stream_si512((__m512i*)ptr, vec.reg);
-}
-inline void non_temporal_save(BF16Vec16& vec, void* ptr) {
-  _mm256_stream_si256((__m256i*)ptr, vec.reg);
-}
-inline void non_temporal_save(FP32Vec16& vec, void* ptr) {
-  _mm512_stream_ps((float*)ptr, vec.reg);
-}
-#endif
-
-inline void mem_barrier() { _mm_mfence(); }
 };  // namespace vec_op
 
 #endif

csrc/cpu/shm.cpp

@@ -1,781 +0,0 @@
-#include "cpu/cpu_types.hpp"
-
-#include <fcntl.h>
-#include <sys/mman.h>
-#include <sys/stat.h>
-#include <unistd.h>
-
-namespace {
-#define MAX_SHM_RANK_NUM 8
-#define MAX_THREAD_NUM 12
-#define PER_THREAD_SHM_BUFFER_BYTES (4 * 1024 * 1024)
-#define MIN_THREAD_PROCESS_SIZE (8 * 1024)
-#define MAX_P2P_SEND_TENSOR_NUM 8
-
-template <typename scalar_t>
-struct KernelVecType {
-  using scalar_vec_t = void;
-};
-
-template <>
-struct KernelVecType<float> {
-  using scalar_vec_t = vec_op::FP32Vec16;
-};
-
-template <>
-struct KernelVecType<c10::BFloat16> {
-  using scalar_vec_t = vec_op::BF16Vec16;
-};
-
-template <>
-struct KernelVecType<c10::Half> {
-  using scalar_vec_t = vec_op::FP16Vec16;
-};
-
-enum class ThreadSHMStat : char { THREAD_READY = 0, SHM_DATA_READY, DONE };
-
-struct ThreadSHMContext {
-  volatile ThreadSHMStat thread_stats[MAX_SHM_RANK_NUM];
-  int thread_id;
-  int thread_num;
-  int rank;
-  int group_size;
-  size_t _spinning_count;
-  int swizzled_ranks[MAX_SHM_RANK_NUM];
-  void* thread_shm_ptrs[MAX_SHM_RANK_NUM];
-  ThreadSHMContext* shm_contexts[MAX_SHM_RANK_NUM];
-
-  ThreadSHMContext(const int thread_id, const int thread_num, const int rank,
-                   const int group_size, void* thread_shm_ptr)
-      : thread_id(thread_id),
-        thread_num(thread_num),
-        rank(rank),
-        group_size(group_size),
-        _spinning_count(0) {
-    static_assert(sizeof(ThreadSHMContext) % 64 == 0);
-    TORCH_CHECK(group_size <= MAX_SHM_RANK_NUM);
-    TORCH_CHECK((size_t)this % 64 == 0);
-    TORCH_CHECK((size_t)thread_shm_ptr % 64 == 0);
-    for (int i = 0; i < MAX_SHM_RANK_NUM; ++i) {
-      shm_contexts[i] = nullptr;
-      thread_shm_ptrs[i] = nullptr;
-      swizzled_ranks[i] = (i + rank) % group_size;
-      thread_stats[i] = ThreadSHMStat::DONE;
-    }
-    set_context(rank, this, thread_shm_ptr);
-  }
-
-  void set_context(int rank, ThreadSHMContext* ptr, void* thread_shm_ptr) {
-    TORCH_CHECK(rank < MAX_SHM_RANK_NUM);
-    TORCH_CHECK(ptr);
-    TORCH_CHECK(thread_shm_ptr);
-    TORCH_CHECK_EQ(ptr->thread_num, thread_num);
-    TORCH_CHECK_EQ(ptr->thread_id, thread_id);
-    shm_contexts[rank] = ptr;
-    thread_shm_ptrs[rank] = thread_shm_ptr;
-  }
-
-  template <typename T>
-  T* get_thread_shm_ptr(int rank) {
-    return reinterpret_cast<T*>(thread_shm_ptrs[rank]);
-  }
-
-  int get_swizzled_rank(int idx) { return swizzled_ranks[idx]; }
-
-  void wait_for_all(ThreadSHMStat prev_stat) {
-    for (int idx = 0; idx < group_size; ++idx) {
-      int rank = get_swizzled_rank(idx);
-      while (thread_stats[rank] == prev_stat) {
-        ++_spinning_count;
-        _mm_pause();
-      }
-    }
-    vec_op::mem_barrier();
-  }
-
-  void wait_for_one(int rank, ThreadSHMStat prev_stat) {
-    while (thread_stats[rank] == prev_stat) {
-      ++_spinning_count;
-      _mm_pause();
-    }
-    vec_op::mem_barrier();
-  }
-
-  void set_thread_stat(ThreadSHMStat stat) {
-    for (int idx = 0; idx < group_size; ++idx) {
-      int rank = get_swizzled_rank(idx);
-      shm_contexts[rank]->thread_stats[this->rank] = stat;
-    }
-  }
-
-  void set_thread_stat(int target_rank, ThreadSHMStat stat) {
-    for (int idx = 0; idx < group_size; ++idx) {
-      int rank = get_swizzled_rank(idx);
-      shm_contexts[rank]->thread_stats[target_rank] = stat;
-    }
-  }
-
-  // barrier for all ranks in the group, used for all2all ops
-  // DONE -> THREAD_READY -> SHM_DATA_READY -> DONE -> ...
-  void barrier(ThreadSHMStat next_stat) {
-    if (next_stat == ThreadSHMStat::THREAD_READY) {
-      set_thread_stat(ThreadSHMStat::THREAD_READY);
-      wait_for_all(ThreadSHMStat::DONE);
-    } else if (next_stat == ThreadSHMStat::SHM_DATA_READY) {
-      set_thread_stat(ThreadSHMStat::SHM_DATA_READY);
-      wait_for_all(ThreadSHMStat::THREAD_READY);
-    } else if (next_stat == ThreadSHMStat::DONE) {
-      set_thread_stat(ThreadSHMStat::DONE);
-      wait_for_all(ThreadSHMStat::SHM_DATA_READY);
-    } else {
-      TORCH_CHECK(false, "Invalid next_stat to barrier.");
-    }
-  }
-
-  std::string to_string() const {
-    std::stringstream ss;
-    ss << "SHMContext:";
-    ss << "\nrank: " << rank;
-    ss << "\ngroup_size: " << group_size;
-    ss << "\nthread_num: " << thread_num;
-    ss << "\nthread_id: " << thread_id;
-
-    ss << "\nshm_ctx_stat_loop_seq: [";
-    for (int i = 0; i < group_size; ++i) {
-      ss << swizzled_ranks[i] << ", ";
-    }
-    ss << "]";
-
-    ss << "\nshm_contexts: [";
-    for (int i = 0; i < group_size; ++i) {
-      if (shm_contexts[i]) {
-        ss << shm_contexts[i]->rank << ", ";
-      }
-    }
-    ss << "]";
-
-    return ss.str();
-  }
-};
-
-class SHMManager {
- public:
-  explicit SHMManager(const std::string& name, const int rank,
-                      const int group_size)
-      : _rank(rank),
-        _group_size(group_size),
-        _thread_num(std::min(torch::get_num_threads(), MAX_THREAD_NUM)),
-        _shm_names({""}),
-        _shared_mem_ptrs({nullptr}),
-        _shm_ctx(nullptr) {
-    _shm_names[rank] = get_shm_name(name, rank);
-    _shared_mem_ptrs[rank] = init_shm(rank);
-    _shm_ctx = reinterpret_cast<ThreadSHMContext*>(_shared_mem_ptrs[rank]);
-
-    for (int i = 0; i < _thread_num; ++i) {
-      ThreadSHMContext* ctx = new (_shm_ctx + i)
-          ThreadSHMContext(i, _thread_num, _rank, _group_size,
-                           compute_thread_shm_ptr(_shm_ctx, i));
-    }
-  }
-
-  void join(const std::string& name) {
-    for (int rank_idx = 0; rank_idx < _group_size; ++rank_idx) {
-      if (rank_idx != _rank) {
-        TORCH_CHECK(_shm_names[rank_idx].empty());
-        TORCH_CHECK(_shared_mem_ptrs[rank_idx] == nullptr);
-        _shm_names[rank_idx] = get_shm_name(name, rank_idx);
-        _shared_mem_ptrs[rank_idx] = init_shm(rank_idx);
-        ThreadSHMContext* target_ctx =
-            reinterpret_cast<ThreadSHMContext*>(_shared_mem_ptrs[rank_idx]);
-        for (int thread_idx = 0; thread_idx < _thread_num; ++thread_idx) {
-          _shm_ctx[thread_idx].set_context(
-              rank_idx, target_ctx + thread_idx,
-              compute_thread_shm_ptr(target_ctx, thread_idx));
-        }
-      }
-    }
-  }
-
-  ~SHMManager() { destroy_shm(); }
-
-  ThreadSHMContext* get_shm_ctx() const { return _shm_ctx; }
-
-  static std::string get_shm_name(const std::string& name, int rank) {
-    return name + "_" + std::to_string(rank);
-  }
-
-  static int64_t create_singleton_instance(const std::string& name,
-                                           const int group_size,
-                                           const int rank) {
-    std::lock_guard<std::mutex> guard(SingletonInstancesLock);
-    SingletonInstances.emplace_back(
-        std::make_unique<SHMManager>(name, rank, group_size));
-    return static_cast<int64_t>(SingletonInstances.size() - 1);
-  }
-
-  static SHMManager* get_singleton_instance(int64_t handle) {
-    return SingletonInstances[handle].get();
-  }
-
- protected:
-  static std::vector<std::unique_ptr<SHMManager>> SingletonInstances;
-  static std::mutex SingletonInstancesLock;
-
- private:
-  static size_t round_to_alignment(size_t num) {
-    return ((num + 63) / 64) * 64;
-  }
-
-  int8_t* compute_thread_shm_ptr(ThreadSHMContext* ctx, int thread_id) {
-    int8_t* thread_shm_ptr =
-        reinterpret_cast<int8_t*>(ctx) +
-        round_to_alignment(_thread_num * sizeof(ThreadSHMContext));
-    return thread_shm_ptr +
-           thread_id * round_to_alignment(PER_THREAD_SHM_BUFFER_BYTES);
-  }
-
-  size_t compute_shm_size() {
-    const size_t rounded_rank_buffer_size =
-        round_to_alignment(PER_THREAD_SHM_BUFFER_BYTES) * _thread_num;
-    const size_t rounded_thread_shm_ctx_size =
-        round_to_alignment(_thread_num * sizeof(ThreadSHMContext));
-    const size_t shm_size =
-        rounded_thread_shm_ctx_size + rounded_rank_buffer_size;
-    return shm_size;
-  }
-
-  void* init_shm(int target_rank) {
-    const std::string& shm_name = _shm_names[target_rank];
-    const int local_rank = _rank;
-    const size_t shm_size = compute_shm_size();
-
-    int fd = -1;
-    if (local_rank == target_rank) {
-      fd = shm_open(shm_name.c_str(), O_CREAT | O_EXCL | O_RDWR,
-                    S_IRUSR | S_IWUSR);
-
-      if (fd == -1)
-        TORCH_CHECK(false, "create shm in SHMManager failed. errno: " +
-                               std::to_string(errno));
-
-      if (ftruncate(fd, shm_size) == -1)
-        TORCH_CHECK(false, "ftruncate in SHMManager failed. errno: " +
-                               std::to_string(errno));
-    } else {
-      fd = shm_open(shm_name.c_str(), O_RDWR, S_IRUSR | S_IWUSR);
-
-      if (fd == -1)
-        TORCH_CHECK(false, "open shm in SHMManager failed. errno: " +
-                               std::to_string(errno));
-    }
-
-    void* shm_ptr = mmap(nullptr, shm_size, PROT_READ | PROT_WRITE,
-                         MAP_SHARED | MAP_POPULATE, fd, 0);
-
-    if (shm_ptr == MAP_FAILED) {
-      TORCH_CHECK(false,
-                  "mmap in SHMManager failed. errno: " + std::to_string(errno));
-    }
-
-    if (close(fd) != 0) {
-      TORCH_CHECK(
-          false, "close in SHMManager failed. errno: " + std::to_string(errno));
-    }
-
-    TORCH_CHECK((size_t)shm_ptr % 64 == 0);
-
-    return shm_ptr;
-  }
-
-  void destroy_shm() {
-    std::stringstream ss;
-    ss << "local rank " << _rank << ": [";
-    for (int thread_id = 0; thread_id < _thread_num; ++thread_id) {
-      ss << _shm_ctx[thread_id]._spinning_count << ", ";
-    }
-    ss << "]\n";
-
-    for (int i = 0; i < MAX_SHM_RANK_NUM; ++i) {
-      if (_shared_mem_ptrs[i] != nullptr) {
-        munmap(_shared_mem_ptrs[i], compute_shm_size());
-      }
-
-      if (!_shm_names[i].empty()) {
-        shm_unlink(_shm_names[i].c_str());
-      }
-    }
-  }
-
-  int _rank;
-  int _group_size;
-  int _thread_num;
-  std::array<std::string, MAX_SHM_RANK_NUM> _shm_names;
-  std::array<void*, MAX_SHM_RANK_NUM> _shared_mem_ptrs;
-  ThreadSHMContext* _shm_ctx;
-};
-
-namespace shm_cc_ops {
-template <typename scalar_t, typename F>
-void shm_cc_loop(ThreadSHMContext* ctx, int64_t elem_num, F&& inner_func) {
-  int thread_num = ctx->thread_num;
-  int64_t total_bytes = elem_num * sizeof(scalar_t);
-  int64_t total_units_num =
-      (total_bytes + MIN_THREAD_PROCESS_SIZE - 1) / MIN_THREAD_PROCESS_SIZE;
-  int64_t per_thread_units_num =
-      (total_units_num + thread_num - 1) / thread_num;
-  int64_t per_unit_elem_num = MIN_THREAD_PROCESS_SIZE / sizeof(scalar_t);
-  int64_t max_per_thread_iteration_elem_num =
-      PER_THREAD_SHM_BUFFER_BYTES / sizeof(scalar_t);
-  int64_t per_thread_elem_num = per_unit_elem_num * per_thread_units_num;
-
-#pragma omp parallel for schedule(static, 1)
-  for (int i = 0; i < thread_num; ++i) {
-    int64_t offset = i * per_thread_elem_num;
-    int64_t end = std::min(elem_num, offset + per_thread_elem_num);
-    int64_t curr_elem_num =
-        std::min(max_per_thread_iteration_elem_num, end - offset);
-    ThreadSHMContext* thread_ctx = ctx + i;
-
-    while (curr_elem_num > 0) {
-      inner_func(thread_ctx, offset, curr_elem_num);
-
-      offset += max_per_thread_iteration_elem_num;
-      curr_elem_num = std::min(max_per_thread_iteration_elem_num, end - offset);
-    }
-  }
-}
-};  // namespace shm_cc_ops
-
-namespace shm_cc_ops {
-
-void memcpy_from_shm(void* dst, void* src, const int64_t bytes) {
-  const int64_t aligned_bytes = ((bytes >> 6) << 6);  // 64 bytes aligned
-  int64_t i = 0;
-#pragma GCC unroll 4
-  for (; i < aligned_bytes; i += 64) {
-    vec_op::INT8Vec64 data(
-        true, (int8_t*)src + i);  // stream loading shm to avoid caching
-    data.save((int8_t*)dst + i);
-  }
-  if (aligned_bytes < bytes) {
-    vec_op::INT8Vec64 data(true, (int8_t*)src + aligned_bytes);
-    data.save((int8_t*)dst + aligned_bytes, bytes - aligned_bytes);
-  }
-}
-
-void memcpy_to_shm(void* dst, void* src, const int64_t bytes) {
-#pragma GCC unroll 4
-  for (int64_t i = 0; i < bytes; i += 64) {
-    vec_op::INT8Vec64 data((int8_t*)src + i);
-    data.nt_save((int8_t*)dst + i);
-  }
-}
-
-void memcpy(void* dst, void* src, const int64_t bytes) {
-  const int64_t aligned_bytes = ((bytes >> 6) << 6);  // 64 bytes aligned
-  int64_t i = 0;
-#pragma GCC unroll 4
-  for (; i < aligned_bytes; i += 64) {
-    vec_op::INT8Vec64 data((int8_t*)src + i);
-    data.save((int8_t*)dst + i);
-  }
-  if (aligned_bytes < bytes) {
-    vec_op::INT8Vec64 data((int8_t*)src + aligned_bytes);
-    data.save((int8_t*)dst + aligned_bytes, bytes - aligned_bytes);
-  }
-}
-
-template <typename scalar_t, int RANKS>
-void all_reduce_sum_impl(ThreadSHMContext* ctx, scalar_t* data,
-                         size_t elem_num) {
-  CPU_KERNEL_GUARD_IN(all_reduce_sum_impl)
-  using vec_t = typename KernelVecType<scalar_t>::scalar_vec_t;
-  constexpr int64_t vec_elem_num = vec_t::get_elem_num();
-  const int worldsize = ctx->group_size;
-
-  shm_cc_ops::shm_cc_loop<scalar_t>(
-      ctx, elem_num,
-      [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
-          int64_t data_elem_num) {
-        int rank = thread_ctx->rank;
-        scalar_t* thread_shm_ptr =
-            thread_ctx->get_thread_shm_ptr<scalar_t>(rank);
-        scalar_t* thread_data_ptr = data + data_offset;
-        int64_t thread_data_elem_num = data_elem_num * sizeof(scalar_t);
-
-        scalar_t* remote_data_ptrs[RANKS - 1];
-        vec_op::unroll_loop<int, RANKS - 1>([&](int idx) {
-          remote_data_ptrs[idx] = thread_ctx->get_thread_shm_ptr<scalar_t>(
-              thread_ctx->get_swizzled_rank(idx + 1));
-        });
-
-        thread_ctx->barrier(ThreadSHMStat::THREAD_READY);
-
-        shm_cc_ops::memcpy_to_shm(thread_shm_ptr, thread_data_ptr,
-                                  thread_data_elem_num);
-
-        thread_ctx->barrier(ThreadSHMStat::SHM_DATA_READY);
-
-        int64_t aligned_data_elem_num =
-            (data_elem_num / vec_elem_num) * vec_elem_num;
-        int64_t i = 0;
-#pragma GCC unroll 4
-        for (; i < aligned_data_elem_num; i += vec_elem_num) {
-          vec_t local_data(thread_data_ptr + i);  // load from cache
-          vec_op::FP32Vec16 local_data_fp32(local_data);
-          vec_op::unroll_loop<int, RANKS - 1>([&](int idx) {
-            vec_t remote_data(
-                true, remote_data_ptrs[idx] + i);  // stream load from shm
-            vec_op::FP32Vec16 remote_data_fp32(remote_data);
-            local_data_fp32 = local_data_fp32 + remote_data_fp32;  // sum reduce
-          });
-          vec_t reduced_data(local_data_fp32);
-          reduced_data.save(thread_data_ptr + i);
-        }
-
-        if (i < data_elem_num) {
-          vec_t local_data(thread_data_ptr + i);  // load from cache
-          vec_op::FP32Vec16 local_data_fp32(local_data);
-          vec_op::unroll_loop<int, RANKS - 1>([&](int idx) {
-            vec_t remote_data(
-                true, remote_data_ptrs[idx] + i);  // stream load from shm
-            vec_op::FP32Vec16 remote_data_fp32(remote_data);
-            local_data_fp32 = local_data_fp32 + remote_data_fp32;  // sum reduce
-          });
-          vec_t reduced_data(local_data_fp32);
-          reduced_data.save(thread_data_ptr + i,
-                            data_elem_num - aligned_data_elem_num);
-        }
-
-        thread_ctx->barrier(ThreadSHMStat::DONE);
-      });
-
-  return;
-}
-};  // namespace shm_cc_ops
-
-std::vector<std::unique_ptr<SHMManager>> SHMManager::SingletonInstances = {};
-std::mutex SHMManager::SingletonInstancesLock = {};
-
-template <typename scalar_t>
-void shm_allreduce_sum(ThreadSHMContext* ctx, scalar_t* data, size_t elem_num) {
-  switch (ctx->group_size) {
-    case 2:
-      shm_cc_ops::all_reduce_sum_impl<scalar_t, 2>(ctx, data, elem_num);
-      break;
-    case 3:
-      shm_cc_ops::all_reduce_sum_impl<scalar_t, 3>(ctx, data, elem_num);
-      break;
-    case 4:
-      shm_cc_ops::all_reduce_sum_impl<scalar_t, 4>(ctx, data, elem_num);
-      break;
-    case 8:
-      shm_cc_ops::all_reduce_sum_impl<scalar_t, 8>(ctx, data, elem_num);
-      break;
-    default:
-      TORCH_CHECK(false,
-                  "Invalid world size: " + std::to_string(ctx->group_size));
-  }
-}
-
-template <typename scalar_t>
-void shm_gather_impl(ThreadSHMContext* ctx, scalar_t* data, size_t elem_num,
-                     scalar_t** outputs, const int dst) {
-  CPU_KERNEL_GUARD_IN(shm_gather_impl)
-  const int worldsize = ctx->group_size;
-  TORCH_CHECK_LT(dst, worldsize);
-  shm_cc_ops::shm_cc_loop<scalar_t>(
-      ctx, elem_num,
-      [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
-          int64_t data_elem_num) {
-        int rank = thread_ctx->rank;
-        scalar_t* thread_shm_ptr =
-            thread_ctx->get_thread_shm_ptr<scalar_t>(rank);
-
-        thread_ctx->barrier(ThreadSHMStat::THREAD_READY);
-
-        shm_cc_ops::memcpy_to_shm(thread_shm_ptr, data + data_offset,
-                                  data_elem_num * sizeof(scalar_t));
-
-        thread_ctx->barrier(ThreadSHMStat::SHM_DATA_READY);
-
-        if (rank == dst) {
-          shm_cc_ops::memcpy(outputs[rank] + data_offset, data + data_offset,
-                             data_elem_num * sizeof(scalar_t));
-          for (int i = 1; i < worldsize; ++i) {
-            int src_rank = thread_ctx->get_swizzled_rank(i);
-            scalar_t* src_ptr =
-                thread_ctx->get_thread_shm_ptr<scalar_t>(src_rank);  // shm
-            scalar_t* dst_ptr = outputs[src_rank] + data_offset;
-            shm_cc_ops::memcpy_from_shm(dst_ptr, src_ptr,
-                                        data_elem_num * sizeof(scalar_t));
-          }
-        }
-
-        thread_ctx->barrier(ThreadSHMStat::DONE);
-      });
-
-  return;
-}
-
-struct MemPiece {
-  void* ptr;
-  int64_t size;
-
-  template <typename T>
-  T* data_ptr() {
-    return reinterpret_cast<T*>(ptr);
-  }
-};
-
-struct TensorListMeta {
-  int64_t tensor_bytes[MAX_P2P_SEND_TENSOR_NUM];
-  torch::ScalarType tensor_types[MAX_P2P_SEND_TENSOR_NUM];
-  int64_t tensor_num;
-  int64_t total_bytes;
-
-  TensorListMeta() : tensor_num(0), total_bytes(0) {
-    static_assert(sizeof(TensorListMeta) % 64 == 0);
-    static_assert(sizeof(TensorListMeta) <
-                  MIN_THREAD_PROCESS_SIZE);  // To ensure the metadata always
-                                             // hold by the thread 0
-    for (int i = 0; i < MAX_P2P_SEND_TENSOR_NUM; ++i) {
-      tensor_bytes[i] = 0;
-      tensor_ptrs[i] = nullptr;
-      tensor_types[i] = torch::ScalarType::Undefined;
-    }
-  }
-
-  // For send and recv
-  void bind_tensor_list(std::vector<torch::Tensor>& tensor_list) {
-    TORCH_CHECK(tensor_types[0] == torch::ScalarType::Undefined,
-                "Re-bind TensorListMeta is not allowed.")
-    TORCH_CHECK_LE(tensor_list.size(), MAX_P2P_SEND_TENSOR_NUM);
-    tensor_num = tensor_list.size();
-    int64_t bytes_sum = 0;
-    for (int i = 0; i < tensor_list.size(); ++i) {
-      torch::Tensor& t = tensor_list[i];
-      TORCH_CHECK(t.is_contiguous());
-      tensor_bytes[i] = t.nbytes();
-      tensor_types[i] = t.scalar_type();
-      tensor_ptrs[i] = t.data_ptr();
-      bytes_sum += t.nbytes();
-    }
-    total_bytes = bytes_sum;
-  }
-
-  // For recv
-  std::vector<torch::Tensor> generate_tensor_list() {
-    std::vector<torch::Tensor> tensor_list;
-    tensor_list.reserve(tensor_num);
-
-    for (int i = 0; i < tensor_num; ++i) {
-      int64_t bytes = tensor_bytes[i];
-      auto type = tensor_types[i];
-      int64_t elem_bytes = torch::elementSize(type);
-
-      TORCH_CHECK_EQ(bytes % elem_bytes, 0);
-      int64_t elem_num = bytes / elem_bytes;
-      auto options = torch::TensorOptions().dtype(type).device(torch::kCPU);
-      tensor_list.emplace_back(torch::empty({elem_num}, options));
-    }
-    return tensor_list;
-  }
-
-  MemPiece get_data(int64_t offset) {
-    for (int i = 0; i < tensor_num; ++i) {
-      if (offset < tensor_bytes[i]) {
-        return {reinterpret_cast<int8_t*>(tensor_ptrs[i]) + offset,
-                tensor_bytes[i] - offset};
-      }
-      offset -= tensor_bytes[i];
-    }
-    return {nullptr, 0};
-  }
-
- private:
-  void* tensor_ptrs[MAX_P2P_SEND_TENSOR_NUM];
-  int8_t _padding[40];
-};
-
-void shm_send_tensor_list_impl(ThreadSHMContext* ctx,
-                               const std::vector<torch::Tensor>& tensor_list) {
-  CPU_KERNEL_GUARD_IN(shm_send_tensor_list_impl)
-  std::vector<torch::Tensor> tensor_list_with_metadata;
-  tensor_list_with_metadata.reserve(1 + tensor_list.size());
-
-  auto options = torch::TensorOptions().dtype(torch::kInt8).device(torch::kCPU);
-  tensor_list_with_metadata.emplace_back(
-      torch::empty({sizeof(TensorListMeta)}, options));
-  tensor_list_with_metadata.insert(tensor_list_with_metadata.end(),
-                                   tensor_list.begin(), tensor_list.end());
-
-  torch::Tensor& metadata_tensor = tensor_list_with_metadata[0];
-  TORCH_CHECK_EQ(metadata_tensor.nbytes(), sizeof(TensorListMeta));
-
-  TensorListMeta* metadata = new (metadata_tensor.data_ptr()) TensorListMeta();
-  metadata->bind_tensor_list(tensor_list_with_metadata);
-
-  shm_cc_ops::shm_cc_loop<int8_t>(
-      ctx, metadata->total_bytes,
-      [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
-          int64_t data_elem_num) {
-        int rank = thread_ctx->rank;
-        // Wait until the receiver set the stat to DONE
-        thread_ctx->wait_for_one(rank, ThreadSHMStat::SHM_DATA_READY);
-
-        int64_t curr_shm_offset = 0;
-        while (curr_shm_offset < data_elem_num) {
-          MemPiece frag = metadata->get_data(data_offset + curr_shm_offset);
-          frag.size = std::min(frag.size, data_elem_num - curr_shm_offset);
-          shm_cc_ops::memcpy(
-              thread_ctx->get_thread_shm_ptr<int8_t>(rank) + curr_shm_offset,
-              frag.ptr, frag.size);
-          curr_shm_offset += frag.size;
-        }
-
-        thread_ctx->set_thread_stat(rank, ThreadSHMStat::SHM_DATA_READY);
-      });
-}
-
-std::vector<torch::Tensor> shm_recv_tensor_list_impl(ThreadSHMContext* ctx,
-                                                     int64_t src) {
-  CPU_KERNEL_GUARD_IN(shm_recv_tensor_list_impl)
-  auto options = torch::TensorOptions().dtype(torch::kInt8).device(torch::kCPU);
-  torch::Tensor metadata_tensor =
-      torch::empty({sizeof(TensorListMeta)}, options);
-
-  // Wait until the sender set the stat of the thread 0 to SHM_DATA_READY
-  ctx->wait_for_one(src, ThreadSHMStat::DONE);
-  shm_cc_ops::memcpy(metadata_tensor.data_ptr(),
-                     ctx->get_thread_shm_ptr<void>(src),
-                     sizeof(TensorListMeta));
-  TensorListMeta* src_metadata =
-      reinterpret_cast<TensorListMeta*>(metadata_tensor.data_ptr());
-  std::vector<torch::Tensor> tensor_list_with_metadata =
-      src_metadata->generate_tensor_list();
-
-  TensorListMeta metadata;
-  metadata.bind_tensor_list(tensor_list_with_metadata);
-  TORCH_CHECK_EQ(metadata.tensor_num, src_metadata->tensor_num);
-  TORCH_CHECK_EQ(metadata.total_bytes, src_metadata->total_bytes);
-
-  shm_cc_ops::shm_cc_loop<int8_t>(
-      ctx, metadata.total_bytes,
-      [&](ThreadSHMContext* thread_ctx, int64_t data_offset,
-          int64_t data_elem_num) {
-        // Wait until the sender set the stat to SHM_DATA_READY
-        thread_ctx->wait_for_one(src, ThreadSHMStat::DONE);
-        int64_t curr_shm_offset = 0;
-        while (curr_shm_offset < data_elem_num) {
-          MemPiece frag = metadata.get_data(data_offset + curr_shm_offset);
-          frag.size = std::min(frag.size, data_elem_num - curr_shm_offset);
-          shm_cc_ops::memcpy(
-              frag.ptr,
-              thread_ctx->get_thread_shm_ptr<int8_t>(src) + curr_shm_offset,
-              frag.size);
-          curr_shm_offset += frag.size;
-        }
-
-        thread_ctx->set_thread_stat(src, ThreadSHMStat::DONE);
-      });
-
-  std::vector<torch::Tensor> tensor_list;
-  tensor_list.reserve(metadata.tensor_num - 1);
-  tensor_list.insert(tensor_list.begin(), tensor_list_with_metadata.begin() + 1,
-                     tensor_list_with_metadata.end());
-
-  return tensor_list;
-}
-}  // namespace
-
-void shm_gather(int64_t handle, torch::Tensor& data,
-                const std::optional<std::vector<torch::Tensor>>& outputs,
-                int64_t dst) {
-  TORCH_CHECK(data.is_contiguous())
-  VLLM_DISPATCH_FLOATING_TYPES(data.scalar_type(), "shm_gather_impl", [&] {
-    CPU_KERNEL_GUARD_IN(shm_gather_impl)
-
-    if (outputs.has_value()) {
-      TORCH_CHECK_LE(outputs->size(), MAX_SHM_RANK_NUM);
-      scalar_t* output_ptrs[MAX_SHM_RANK_NUM] = {nullptr};
-      for (int i = 0; i < outputs->size(); ++i) {
-        output_ptrs[i] = outputs->at(i).data_ptr<scalar_t>();
-      }
-      shm_gather_impl(SHMManager::get_singleton_instance(handle)->get_shm_ctx(),
-                      data.data_ptr<scalar_t>(), data.numel(), output_ptrs,
-                      dst);
-    } else {
-      shm_gather_impl(SHMManager::get_singleton_instance(handle)->get_shm_ctx(),
-                      data.data_ptr<scalar_t>(), data.numel(), (scalar_t**)(0),
-                      dst);
-    }
-
-    CPU_KERNEL_GUARD_OUT(shm_gather_impl)
-  });
-}
-
-void shm_all_gather(int64_t handle, const torch::Tensor& data,
-                    torch::Tensor& output) {
-  TORCH_CHECK(data.is_contiguous())
-  TORCH_CHECK(output.is_contiguous())
-
-  const int64_t input_elem_num = data.numel();
-  const int64_t output_elem_num = output.numel();
-  TORCH_CHECK_EQ(output_elem_num % input_elem_num, 0);
-  const int world_size = output_elem_num / input_elem_num;
-
-  VLLM_DISPATCH_FLOATING_TYPES(data.scalar_type(), "shm_all_gather_impl", [&] {
-    CPU_KERNEL_GUARD_IN(shm_all_gather_impl)
-    auto ctx = SHMManager::get_singleton_instance(handle)->get_shm_ctx();
-    TORCH_CHECK_EQ(ctx->group_size, world_size);
-
-    scalar_t* output_ptrs[MAX_SHM_RANK_NUM] = {nullptr};
-    for (int i = 0; i < world_size; ++i) {
-      output_ptrs[i] = output.data_ptr<scalar_t>() + i * input_elem_num;
-    }
-    shm_gather_impl(ctx, data.data_ptr<scalar_t>(), data.numel(), output_ptrs,
-                    ctx->rank);
-    CPU_KERNEL_GUARD_OUT(shm_all_gather_impl)
-  });
-}
-
-void shm_allreduce(int64_t handle, torch::Tensor& data) {
-  TORCH_CHECK(data.is_contiguous())
-  VLLM_DISPATCH_FLOATING_TYPES(data.scalar_type(), "shm_allreduce_sum", [&] {
-    CPU_KERNEL_GUARD_IN(shm_allreduce_sum)
-    shm_allreduce_sum(SHMManager::get_singleton_instance(handle)->get_shm_ctx(),
-                      data.data_ptr<scalar_t>(), data.numel());
-    CPU_KERNEL_GUARD_OUT(shm_allreduce_sum)
-  });
-}
-
-void shm_send_tensor_list(int64_t handle,
-                          const std::vector<torch::Tensor>& tensor_list,
-                          int64_t dst) {
-  CPU_KERNEL_GUARD_IN(shm_send_tensor_list)
-  shm_send_tensor_list_impl(
-      SHMManager::get_singleton_instance(handle)->get_shm_ctx(), tensor_list);
-  CPU_KERNEL_GUARD_OUT(shm_send_tensor_list)
-}
-
-std::vector<torch::Tensor> shm_recv_tensor_list(int64_t handle, int64_t src) {
-  CPU_KERNEL_GUARD_IN(shm_recv_tensor_list)
-  auto tensor_list = shm_recv_tensor_list_impl(
-      SHMManager::get_singleton_instance(handle)->get_shm_ctx(), src);
-  CPU_KERNEL_GUARD_OUT(shm_recv_tensor_list)
-  return tensor_list;
-}
-
-int64_t init_shm_manager(const std::string& name, const int64_t group_size,
-                         const int64_t rank) {
-  return SHMManager::create_singleton_instance(name, group_size, rank);
-}
-
-std::string join_shm_manager(int64_t handle, const std::string& name) {
-  auto shm_manager = SHMManager::get_singleton_instance(handle);
-  TORCH_CHECK(shm_manager);
-  shm_manager->join(name);
-  return shm_manager->get_shm_ctx()->to_string();
-}

csrc/cpu/torch_bindings.cpp

@@ -22,26 +22,6 @@ void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
                         torch::Tensor& kv_cache, double scale,
                         torch::Tensor& block_tables, torch::Tensor& seq_lens);
 
-int64_t init_shm_manager(const std::string& name, const int64_t group_size,
-                         const int64_t rank);
-
-std::string join_shm_manager(int64_t handle, const std::string& name);
-
-void shm_allreduce(int64_t handle, torch::Tensor& data);
-
-void shm_gather(int64_t handle, torch::Tensor& data,
-                const std::optional<std::vector<torch::Tensor>>& outputs,
-                int64_t dst);
-
-void shm_all_gather(int64_t handle, const torch::Tensor& data,
-                    torch::Tensor& output);
-
-void shm_send_tensor_list(int64_t handle,
-                          const std::vector<torch::Tensor>& tensor_list,
-                          int64_t dst);
-
-std::vector<torch::Tensor> shm_recv_tensor_list(int64_t handle, int64_t src);
-
 TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
 
@@ -151,29 +131,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                  Tensor? azp, Tensor? bias) -> ()");
   ops.impl("cutlass_scaled_mm_azp", torch::kCPU, &int8_scaled_mm_azp);
 #endif
-
-// SHM CCL
-#ifdef __AVX512F__
-  ops.def("init_shm_manager(str name, int group_size, int rank) -> int",
-          &init_shm_manager);
-  ops.def("join_shm_manager(int handle, str name) -> str", &join_shm_manager);
-  ops.def("shm_allreduce(int handle, Tensor! data) -> ()");
-  ops.impl("shm_allreduce", torch::kCPU, &shm_allreduce);
-  ops.def(
-      "shm_gather(int handle, Tensor data, Tensor[](a!)? outputs, int dst) -> "
-      "()");
-  ops.impl("shm_gather", torch::kCPU, &shm_gather);
-  ops.def(
-      "shm_all_gather(int handle, Tensor data, Tensor! output) -> "
-      "()");
-  ops.impl("shm_all_gather", torch::kCPU, &shm_all_gather);
-  ops.def(
-      "shm_send_tensor_list(int handle, Tensor[](a) tensor_list, int dst) -> "
-      "()");
-  ops.impl("shm_send_tensor_list", torch::kCPU, &shm_send_tensor_list);
-  ops.def("shm_recv_tensor_list(int handle, int src) -> Tensor[](a)",
-          &shm_recv_tensor_list);
-#endif
 }
 
 TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {

csrc/cpu/utils.cpp

@@ -4,11 +4,6 @@
   #include <string>
   #include <sched.h>
 #endif
-#if __GLIBC__ == 2 && __GLIBC_MINOR__ < 30
-  #include <unistd.h>
-  #include <sys/syscall.h>
-  #define gettid() syscall(SYS_gettid)
-#endif
 
 #include "cpu_types.hpp"
 
@@ -23,7 +18,7 @@ std::string init_cpu_threads_env(const std::string& cpu_ids) {
 
 #ifndef VLLM_NUMA_DISABLED
 std::string init_cpu_threads_env(const std::string& cpu_ids) {
-  bitmask* omp_cpu_mask = numa_parse_cpustring_all(cpu_ids.c_str());
+  bitmask* omp_cpu_mask = numa_parse_cpustring(cpu_ids.c_str());
   TORCH_CHECK(omp_cpu_mask->size > 0);
   std::vector<int> omp_cpu_ids;
   omp_cpu_ids.reserve(omp_cpu_mask->size);

csrc/cuda_view.cu

@@ -1,39 +0,0 @@
-#include <torch/all.h>
-#include <torch/cuda.h>
-#include <cuda_runtime.h>
-
-// This function assumes that `cpu_tensor` is a CPU tensor allocated with pinned
-// memory, and that UVA (Unified Virtual Addressing) is enabled.
-torch::Tensor get_cuda_view_from_cpu_tensor(torch::Tensor& cpu_tensor) {
-  TORCH_CHECK(cpu_tensor.device().is_cpu(), "Input tensor must be on CPU");
-
-  // Get raw host pointer from CPU tensor
-  void* host_ptr = cpu_tensor.data_ptr();
-
-  // Get a device pointer corresponding to the pinned host memory
-  void* device_ptr = nullptr;
-  cudaError_t err = cudaHostGetDevicePointer(&device_ptr, host_ptr, 0);
-  TORCH_CHECK(err == cudaSuccess,
-              "cudaHostGetDevicePointer failed: ", cudaGetErrorString(err));
-
-  // We'll use the same sizes, strides, and dtype as the CPU tensor.
-  // TODO: check if layout is respected.
-  auto sizes = cpu_tensor.sizes();
-  auto strides = cpu_tensor.strides();
-  auto options = cpu_tensor.options().device(torch::kCUDA);
-
-  // from_blob signature: from_blob(void *data, IntArrayRef sizes, ..., Deleter,
-  // const TensorOptions &) Provide a no-op deleter. The CPU tensor holds the
-  // memory, so we don't free it here.
-  auto deleter = [](void*) {
-    // no-op, since the memory is owned by the original CPU tensor
-  };
-
-  torch::Tensor cuda_tensor =
-      torch::from_blob(device_ptr, sizes, strides, deleter, options);
-
-  TORCH_CHECK(cuda_tensor.device().is_cuda(),
-              "Resulting tensor is not on CUDA device");
-
-  return cuda_tensor;
-}

csrc/custom_all_reduce.cu

@@ -12,7 +12,7 @@ static_assert(sizeof(void*) == sizeof(fptr_t));
 
 fptr_t init_custom_ar(const std::vector<fptr_t>& fake_ipc_ptrs,
                       torch::Tensor& rank_data, int64_t rank,
-                      bool fully_connected) {
+                      bool full_nvlink) {
   int world_size = fake_ipc_ptrs.size();
   if (world_size > 8)
     throw std::invalid_argument("world size > 8 is not supported");
@@ -27,7 +27,7 @@ fptr_t init_custom_ar(const std::vector<fptr_t>& fake_ipc_ptrs,
   }
   return (fptr_t) new vllm::CustomAllreduce(ipc_ptrs, rank_data.data_ptr(),
                                             rank_data.numel(), rank, world_size,
-                                            fully_connected);
+                                            full_nvlink);
 }
 
 /**
@@ -142,48 +142,3 @@ void register_graph_buffers(fptr_t _fa,
   bytes.reserve(handles.size());
   fa->register_graph_buffers(bytes, offsets);
 }
-
-std::tuple<fptr_t, torch::Tensor> allocate_shared_buffer_and_handle(
-    int64_t size) {
-  auto device_index = c10::cuda::current_device();
-  at::DeviceGuard device_guard(at::Device(at::DeviceType::CUDA, device_index));
-  void* buffer;
-  cudaStreamCaptureMode mode = cudaStreamCaptureModeRelaxed;
-  auto stream = c10::cuda::getCurrentCUDAStream().stream();
-  AT_CUDA_CHECK(cudaThreadExchangeStreamCaptureMode(&mode));
-
-  // Allocate buffer
-#if defined(USE_ROCM)
-  // data buffers need to be "uncached" for signal on MI200
-  AT_CUDA_CHECK(
-      hipExtMallocWithFlags((void**)&buffer, size, hipDeviceMallocUncached));
-#else
-  AT_CUDA_CHECK(cudaMalloc((void**)&buffer, size));
-#endif
-  AT_CUDA_CHECK(cudaMemsetAsync(buffer, 0, size, stream));
-  AT_CUDA_CHECK(cudaStreamSynchronize(stream));
-  AT_CUDA_CHECK(cudaThreadExchangeStreamCaptureMode(&mode));
-
-  // Create IPC memhandle for the allocated buffer.
-  // Will use it in open_mem_handle.
-  auto options =
-      torch::TensorOptions().dtype(torch::kUInt8).device(torch::kCPU);
-  auto handle =
-      torch::empty({static_cast<int64_t>(sizeof(cudaIpcMemHandle_t))}, options);
-  AT_CUDA_CHECK(
-      cudaIpcGetMemHandle((cudaIpcMemHandle_t*)handle.data_ptr(), buffer));
-
-  return std::make_tuple(reinterpret_cast<fptr_t>(buffer), handle);
-}
-
-fptr_t open_mem_handle(torch::Tensor& mem_handle) {
-  void* ipc_ptr;
-  AT_CUDA_CHECK(cudaIpcOpenMemHandle(
-      (void**)&ipc_ptr, *((const cudaIpcMemHandle_t*)mem_handle.data_ptr()),
-      cudaIpcMemLazyEnablePeerAccess));
-  return reinterpret_cast<fptr_t>(ipc_ptr);
-}
-
-void free_shared_buffer(fptr_t buffer) {
-  AT_CUDA_CHECK(cudaFree(reinterpret_cast<void*>(buffer)));
-}

csrc/custom_all_reduce.cuh

@@ -5,10 +5,6 @@
 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
 
-#if defined(USE_ROCM)
-typedef __hip_bfloat16 nv_bfloat16;
-#endif
-
 #include <iostream>
 #include <array>
 #include <limits>
@@ -16,7 +12,6 @@ typedef __hip_bfloat16 nv_bfloat16;
 #include <unordered_map>
 #include <vector>
 
-namespace vllm {
 #define CUDACHECK(cmd)                                              \
   do {                                                              \
     cudaError_t e = cmd;                                            \
@@ -27,37 +22,24 @@ namespace vllm {
     }                                                               \
   } while (0)
 
-// Maximal number of blocks in allreduce kernel.
+namespace vllm {
+
 constexpr int kMaxBlocks = 36;
-
-// Default number of blocks in allreduce kernel.
-#ifndef USE_ROCM
-const int defaultBlockLimit = 36;
-CUpointer_attribute rangeStartAddrAttr = CU_POINTER_ATTRIBUTE_RANGE_START_ADDR;
-#else
-const int defaultBlockLimit = 16;
-hipPointer_attribute rangeStartAddrAttr =
-    HIP_POINTER_ATTRIBUTE_RANGE_START_ADDR;
-#endif
-
 // Counter may overflow, but it's fine since unsigned int overflow is
 // well-defined behavior.
 using FlagType = uint32_t;
-
-// Two sets of peer counters are needed for two syncs: starting and ending an
-// operation. The reason is that it's possible for peer GPU block to arrive at
-// the second sync point while the current GPU block haven't passed the first
-// sync point. Thus, peer GPU may write counter+1 while current GPU is busy
-// waiting for counter. We use alternating counter array to avoid this
-// possibility.
 struct Signal {
-  alignas(128) FlagType start[kMaxBlocks][8];
-  alignas(128) FlagType end[kMaxBlocks][8];
-  alignas(128) FlagType _flag[kMaxBlocks];  // incremental flags for each rank
+  alignas(128) FlagType self_counter[kMaxBlocks][8];
+  // Two sets of peer counters are needed for two syncs. The reason is that
+  // it's possible for peer GPU block to arrive at the second sync point while
+  // the current GPU block haven't passed the first sync point. Thus, peer GPU
+  // may write counter+1 while current GPU is busy waiting for counter. We use
+  // alternating counter array to avoid this possibility.
+  alignas(128) FlagType peer_counter[2][kMaxBlocks][8];
 };
 
 struct __align__(16) RankData {
-  const void* ptrs[8];
+  const void* __restrict__ ptrs[8];
 };
 
 struct __align__(16) RankSignals {
@@ -152,29 +134,27 @@ DINLINE O downcast(array_t<float, O::size> val) {
   }
 }
 
-#if !defined(USE_ROCM)
-
 static DINLINE void st_flag_release(FlagType* flag_addr, FlagType flag) {
-  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
   asm volatile("st.release.sys.global.u32 [%1], %0;" ::"r"(flag),
                "l"(flag_addr));
-  #else
+#else
   asm volatile("membar.sys; st.volatile.global.u32 [%1], %0;" ::"r"(flag),
                "l"(flag_addr));
-  #endif
+#endif
 }
 
 static DINLINE FlagType ld_flag_acquire(FlagType* flag_addr) {
   FlagType flag;
-  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
   asm volatile("ld.acquire.sys.global.u32 %0, [%1];"
                : "=r"(flag)
                : "l"(flag_addr));
-  #else
+#else
   asm volatile("ld.volatile.global.u32 %0, [%1]; membar.gl;"
                : "=r"(flag)
                : "l"(flag_addr));
-  #endif
+#endif
   return flag;
 }
 
@@ -190,99 +170,37 @@ static DINLINE FlagType ld_flag_volatile(FlagType* flag_addr) {
   return flag;
 }
 
-// This function is meant to be used as the first synchronization in the all
-// reduce kernel. Thus, it doesn't need to make any visibility guarantees for
-// prior memory accesses. Note: volatile writes will not be reordered against
-// other volatile writes.
-template <int ngpus>
-DINLINE void barrier_at_start(const RankSignals& sg, Signal* self_sg,
-                              int rank) {
-  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
+// is_start: whether this is the very first synchronization barrier.
+// need_fence: whether a memory fence is needed. If true, a release-acquire
+// semantic is used to enforce memory access order before and after this
+// barrier.
+template <int ngpus, bool is_start, bool need_fence = false>
+DINLINE void multi_gpu_barrier(const RankSignals& sg, Signal* self_sg,
+                               int rank) {
+  if constexpr (!is_start) __syncthreads();
+  static_assert(
+      !(is_start && need_fence));  // Start barrier shouldn't need fence.
   if (threadIdx.x < ngpus) {
-    auto peer_counter_ptr = &sg.signals[threadIdx.x]->start[blockIdx.x][rank];
-    auto self_counter_ptr = &self_sg->start[blockIdx.x][threadIdx.x];
-    // Write the expected counter value to peer and wait for correct value
-    // from peer.
-    st_flag_volatile(peer_counter_ptr, flag);
-    while (ld_flag_volatile(self_counter_ptr) != flag);
-  }
-  __syncthreads();
-  // use one thread to update flag
-  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
-}
-
-// This function is meant to be used as the second or the final
-// synchronization barrier in the all reduce kernel. If it's the final
-// synchronization barrier, we don't need to make any visibility guarantees
-// for prior memory accesses.
-template <int ngpus, bool final_sync = false>
-DINLINE void barrier_at_end(const RankSignals& sg, Signal* self_sg, int rank) {
-  __syncthreads();
-  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
-  if (threadIdx.x < ngpus) {
-    auto peer_counter_ptr = &sg.signals[threadIdx.x]->end[blockIdx.x][rank];
-    auto self_counter_ptr = &self_sg->end[blockIdx.x][threadIdx.x];
+    // Increment the counter. Technically we only need one counter, but we use
+    // multiple per block to eliminate the need to share the counter via smem.
+    auto val = self_sg->self_counter[blockIdx.x][threadIdx.x] += 1;
     // Write the expected counter value to peer and wait for correct value from
     // peer.
-    if constexpr (!final_sync) {
-      st_flag_release(peer_counter_ptr, flag);
-      while (ld_flag_acquire(self_counter_ptr) != flag);
+    auto peer_counter_ptr =
+        &sg.signals[threadIdx.x]->peer_counter[val % 2][blockIdx.x][rank];
+    auto self_counter_ptr =
+        &self_sg->peer_counter[val % 2][blockIdx.x][threadIdx.x];
+    if constexpr (need_fence) {
+      st_flag_release(peer_counter_ptr, val);
+      while (ld_flag_acquire(self_counter_ptr) != val);
     } else {
-      st_flag_volatile(peer_counter_ptr, flag);
-      while (ld_flag_volatile(self_counter_ptr) != flag);
+      st_flag_volatile(peer_counter_ptr, val);
+      while (ld_flag_volatile(self_counter_ptr) != val);
     }
   }
-  if constexpr (!final_sync) __syncthreads();
-
-  // use one thread to update flag
-  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
+  if constexpr (is_start || need_fence) __syncthreads();
 }
 
-#else
-
-template <int ngpus>
-DINLINE void barrier_at_start(const RankSignals& sg, Signal* self_sg,
-                              int rank) {
-  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
-  if (threadIdx.x < ngpus) {
-    // simultaneously write to the corresponding flag of all ranks.
-    // Latency = 1 p2p write
-    __scoped_atomic_store_n(&sg.signals[threadIdx.x]->start[blockIdx.x][rank],
-                            flag, __ATOMIC_RELAXED, __MEMORY_SCOPE_SYSTEM);
-    // wait until we got true from all ranks
-    while (__scoped_atomic_load_n(&self_sg->start[blockIdx.x][threadIdx.x],
-                                  __ATOMIC_RELAXED,
-                                  __MEMORY_SCOPE_DEVICE) < flag);
-  }
-  __syncthreads();
-  // use one thread to update flag
-  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
-}
-
-template <int ngpus, bool final_sync = false>
-DINLINE void barrier_at_end(const RankSignals& sg, Signal* self_sg, int rank) {
-  __syncthreads();
-  uint32_t flag = self_sg->_flag[blockIdx.x] + 1;
-  if (threadIdx.x < ngpus) {
-    // simultaneously write to the corresponding flag of all ranks.
-    // Latency = 1 p2p write
-    __scoped_atomic_store_n(&sg.signals[threadIdx.x]->end[blockIdx.x][rank],
-                            flag,
-                            final_sync ? __ATOMIC_RELAXED : __ATOMIC_RELEASE,
-                            __MEMORY_SCOPE_SYSTEM);
-    // wait until we got true from all ranks
-    while (
-        __scoped_atomic_load_n(&self_sg->end[blockIdx.x][threadIdx.x],
-                               final_sync ? __ATOMIC_RELAXED : __ATOMIC_ACQUIRE,
-                               __MEMORY_SCOPE_DEVICE) < flag);
-  }
-  if constexpr (!final_sync) __syncthreads();
-  // use one thread to update flag
-  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
-}
-
-#endif
-
 template <typename P, int ngpus, typename A>
 DINLINE P packed_reduce(const P* ptrs[], int idx) {
   A tmp = upcast(ptrs[0][idx]);
@@ -302,13 +220,13 @@ __global__ void __launch_bounds__(512, 1)
   // note: we don't reorder the address so the accumulation order is the same
   // for all ranks, ensuring bitwise identical results
   auto dp = *_dp;
-  barrier_at_start<ngpus>(sg, self_sg, rank);
+  multi_gpu_barrier<ngpus, true>(sg, self_sg, rank);
   // do the actual reduction
   for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
        idx += gridDim.x * blockDim.x) {
     ((P*)result)[idx] = packed_reduce<P, ngpus, A>((const P**)&dp.ptrs[0], idx);
   }
-  barrier_at_end<ngpus, true>(sg, self_sg, rank);
+  multi_gpu_barrier<ngpus, false>(sg, self_sg, rank);
 }
 
 template <typename P>
@@ -337,20 +255,18 @@ __global__ void __launch_bounds__(512, 1)
     tmps[i] = get_tmp_buf<P>(sg.signals[target]);
   }
   auto tmp_out = tmps[0];
-  barrier_at_start<ngpus>(sg, self_sg, rank);
-
+  multi_gpu_barrier<ngpus, true>(sg, self_sg, rank);
   // stage 1: reduce scatter
   for (int idx = start + tid; idx < end; idx += stride) {
     tmp_out[idx - start] = packed_reduce<P, ngpus, A>(ptrs, idx);
   }
-  barrier_at_end<ngpus>(sg, self_sg, rank);
+  multi_gpu_barrier<ngpus, false, true>(sg, self_sg, rank);
 
   // stage 2: allgather. Note: it's important to match the tid between
   // the two stages, because visibility across devices is only guaranteed
   // between threads that have the same tid. If thread i computes the sum of
-  // start + i in the first stage, then thread i also gathers start + i from
-  // all ranks.
-
+  // start + i in the first stage, then thread i also gathers start + i from all
+  // ranks.
   for (int idx = tid; idx < largest_part; idx += stride) {
 #pragma unroll
     for (int i = 0; i < ngpus; i++) {
@@ -371,22 +287,21 @@ class CustomAllreduce {
  public:
   int rank_;
   int world_size_;
-  // Full NVLink or xGMI connection between GPUs.
-  bool fully_connected_;
+  bool full_nvlink_;
 
   RankSignals sg_;
-  // Stores an map from a pointer to its peer pointers from all ranks.
+  // Stores an map from a pointer to its peer pointters from all ranks.
   std::unordered_map<void*, RankData*> buffers_;
   Signal* self_sg_;
 
   // Stores rank data from all ranks. This is mainly for cuda graph purposes.
   // For cuda graph to work, all kernel arguments must be fixed during graph
-  // capture time. However, the peer pointers are not known during graph
-  // capture time. Therefore, during capture, we increment the rank data
-  // pointer and use that as the argument to the kernel. The kernel arguments
-  // are stored in graph_unreg_buffers_. The actual peer pointers will be
-  // filled in at the memory pointed to by the pointers in
-  // graph_unreg_buffers_ when the IPC handles are exchanged between ranks.
+  // capture time. However, the peer pointers are not known during graph capture
+  // time. Therefore, during capture, we increment the rank data pointer and use
+  // that as the argument to the kernel. The kernel arguments are stored in
+  // graph_unreg_buffers_. The actual peer pointers will be filled in at the
+  // memory pointed to by the pointers in graph_unreg_buffers_ when
+  // the IPC handles are exchanged between ranks.
   //
   // The overall process looks like this:
   // 1. Graph capture.
@@ -404,18 +319,17 @@ class CustomAllreduce {
    * Signals are an array of ipc-enabled buffers from all ranks.
    * For each of the buffer, the layout is as follows:
    * | -- sizeof(Signal) -- | ------ a few MB ----- |
-   * The first section is for allreduce synchronization, and the second
-   * section is for storing the intermediate results required by some
-   * allreduce algos.
+   * The first section is for allreduce synchronization, and the second section
+   * is for storing the intermediate results required by some allreduce algos.
    *
    * Note: this class does not own any device memory. Any required buffers
    * are passed in from the constructor.
    */
   CustomAllreduce(Signal** signals, void* rank_data, size_t rank_data_sz,
-                  int rank, int world_size, bool fully_connected = true)
+                  int rank, int world_size, bool full_nvlink = true)
       : rank_(rank),
         world_size_(world_size),
-        fully_connected_(fully_connected),
+        full_nvlink_(full_nvlink),
         self_sg_(signals[rank]),
         d_rank_data_base_(reinterpret_cast<RankData*>(rank_data)),
         d_rank_data_end_(d_rank_data_base_ + rank_data_sz / sizeof(RankData)) {
@@ -447,7 +361,8 @@ class CustomAllreduce {
       void* base_ptr;
       // note: must share the base address of each allocation, or we get wrong
       // address
-      if (cuPointerGetAttribute(&base_ptr, rangeStartAddrAttr,
+      if (cuPointerGetAttribute(&base_ptr,
+                                CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,
                                 (CUdeviceptr)ptr) != CUDA_SUCCESS)
         throw std::runtime_error("failed to get pointer attr");
       CUDACHECK(cudaIpcGetMemHandle(
@@ -481,11 +396,11 @@ class CustomAllreduce {
 
   // Note: when registering graph buffers, we intentionally choose to not
   // deduplicate the addresses. That means if the allocator reuses some
-  // addresses, they will be registered again. This is to account for the
-  // remote possibility of different allocation patterns between ranks. For
-  // example, rank 1 may get the same input address for the second allreduce,
-  // but rank 2 got a different address. IPC handles have internal reference
-  // counting mechanism so overhead should be small.
+  // addresses, they will be registered again. This is to account for the remote
+  // possibility of different allocation patterns between ranks. For example,
+  // rank 1 may get the same input address for the second allreduce, but rank 2
+  // got a different address. IPC handles have internal reference counting
+  // mechanism so overhead should be small.
   void register_graph_buffers(
       const std::vector<std::string>& handles,
       const std::vector<std::vector<int64_t>>& offsets) {
@@ -516,15 +431,15 @@ class CustomAllreduce {
   /**
    * Performs allreduce, assuming input has already been registered.
    *
-   * Block and grid default configs are results after careful grid search.
-   * Using 36 blocks give the best or close to the best runtime on the devices
-   * I tried: A100, A10, A30, T4, V100. You'll notice that NCCL kernels also
-   * only take a small amount of SMs. Not quite sure the underlying reason,
-   * but my guess is that too many SMs will cause contention on NVLink bus.
+   * Block and grid default configs are results after careful grid search. Using
+   * 36 blocks give the best or close to the best runtime on the devices I
+   * tried: A100, A10, A30, T4, V100. You'll notice that NCCL kernels also only
+   * take a small amount of SMs. Not quite sure the underlying reason, but my
+   * guess is that too many SMs will cause contention on NVLink bus.
    */
   template <typename T>
   void allreduce(cudaStream_t stream, T* input, T* output, int size,
-                 int threads = 512, int block_limit = defaultBlockLimit) {
+                 int threads = 512, int block_limit = 36) {
     auto d = packed_t<T>::P::size;
     if (size % d != 0)
       throw std::runtime_error(
@@ -558,11 +473,13 @@ class CustomAllreduce {
 #define KL(ngpus, name)                                                       \
   name<T, ngpus><<<blocks, threads, 0, stream>>>(ptrs, sg_, self_sg_, output, \
                                                  rank_, size);
+    // TODO(hanzhi713): Threshold is different for A100 and H100.
+    // Add per device threshold.
 #define REDUCE_CASE(ngpus)                            \
   case ngpus: {                                       \
     if (world_size_ == 2) {                           \
       KL(ngpus, cross_device_reduce_1stage);          \
-    } else if (fully_connected_) {                    \
+    } else if (full_nvlink_) {                        \
       if ((world_size_ <= 4 && bytes < 512 * 1024) || \
           (world_size_ <= 8 && bytes < 256 * 1024)) { \
         KL(ngpus, cross_device_reduce_1stage);        \
@@ -580,8 +497,7 @@ class CustomAllreduce {
       REDUCE_CASE(8)
       default:
         throw std::runtime_error(
-            "custom allreduce only supports num gpus in (2,4,6,8). Actual "
-            "num "
+            "custom allreduce only supports num gpus in (2,4,6,8). Actual num "
             "gpus = " +
             std::to_string(world_size_));
     }
@@ -595,11 +511,10 @@ class CustomAllreduce {
     }
   }
 };
-
 /**
- * To inspect PTX/SASS, copy paste this header file to compiler explorer and
- add a template instantiation:
+ * To inspect PTX/SASS, copy paste this header file to compiler explorer and add
+ a template instantiation:
  * template void vllm::CustomAllreduce::allreduce<half>(cudaStream_t, half *,
  half *, int, int, int);
 */
-}  // namespace vllm
+}  // namespace vllm

csrc/custom_all_reduce_test.cu

@@ -1,9 +1,9 @@
 /**
  * This is a standalone test for custom allreduce.
  * To compile, make sure you have MPI and NCCL installed in your system.
- * export MPI_HOME=XXX
+ * export MPI_HOME=xxx
  * nvcc -O2 -arch=native -std=c++17 custom_all_reduce_test.cu -o
- * custom_all_reduce_test -lnccl -I${MPI_HOME}/include -lmpi
+ * custom_all_reduce_test -lnccl -I${MPI_HOME} -lmpi
  *
  * Warning: this C++ test is not designed to be very readable and was used
  * during the rapid prototyping process.
@@ -22,15 +22,7 @@
 #include "cuda_profiler_api.h"
 #include "custom_all_reduce.cuh"
 #include "mpi.h"
-#ifdef USE_ROCM
-  #include <hip/hip_bf16.h>
-typedef __hip_bfloat16 nv_bfloat16;
-  #include "rccl/rccl.h"
-  #include "custom_all_reduce_hip.cuh"
-#else
-  #include "nccl.h"
-  #include "custom_all_reduce.cuh"
-#endif
+#include "nccl.h"
 
 #define MPICHECK(cmd)                                                  \
   do {                                                                 \
@@ -51,29 +43,16 @@ typedef __hip_bfloat16 nv_bfloat16;
     }                                                               \
   } while (0)
 
-#ifdef USE_ROCM
 __global__ void dummy_kernel() {
-  for (int i = 0; i < 100; i++) {
-    uint64_t start = wall_clock64();
-    uint64_t cycles_elapsed;
-    do {
-      cycles_elapsed = wall_clock64() - start;
-    } while (cycles_elapsed < 100);
-  }
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
   for (int i = 0; i < 100; i++) __nanosleep(1000000);  // 100ms
-}
 #else
-__global__ void dummy_kernel() {
-  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
-  for (int i = 0; i < 100; i++) __nanosleep(1000000);  // 100ms
-  #else
   for (int i = 0; i < 100; i++) {
     long long int start = clock64();
     while (clock64() - start < 150000000);  // approximately 98.4ms on P40
   }
-  #endif
-}
 #endif
+}
 
 template <typename T>
 __global__ void set_data(T* data, int size, int myRank) {
@@ -142,14 +121,8 @@ void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
    * registration, they are allocated and registered together in the test for
    * convenience.
    */
-#ifdef USE_ROCM
-  CUDACHECK(hipExtMallocWithFlags(
-      (void**)&buffer, 2 * data_size * sizeof(T) + sizeof(vllm::Signal),
-      hipDeviceMallocUncached));
-#else
   CUDACHECK(
       cudaMalloc(&buffer, 2 * data_size * sizeof(T) + sizeof(vllm::Signal)));
-#endif
   CUDACHECK(
       cudaMemset(buffer, 0, 2 * data_size * sizeof(T) + sizeof(vllm::Signal)));
   CUDACHECK(cudaMalloc(&self_data_copy, data_size * sizeof(T)));
@@ -338,18 +311,13 @@ int main(int argc, char** argv) {
 
   bool performance_test = true;
   cudaProfilerStart();
-// Uncomment to scan through different block size configs.
-// for (int threads : {256, 512, 1024}) {
-//   for (int block_limit = 16; block_limit < 112; block_limit += 4) {
-//     run<half>(myRank, nRanks, comm, threads, block_limit, 1024 * 1024,
-//     performance_test);
-//   }
-// }
-#ifdef USE_ROCM
-  const int block_limit = 16;
-#else
-  const int block_limit = 36;
-#endif
+  // Uncomment to scan through different block size configs.
+  // for (int threads : {256, 512, 1024}) {
+  //   for (int block_limit = 16; block_limit < 112; block_limit += 4) {
+  //     run<half>(myRank, nRanks, comm, threads, block_limit, 1024 * 1024,
+  //     performance_test);
+  //   }
+  // }
   // Scan through different sizes to test performance.
   for (int sz = 512; sz <= (8 << 20); sz *= 2) {
     run<half>(myRank, nRanks, comm, 512, 36, sz + 8 * 47, performance_test);
@@ -358,4 +326,4 @@ int main(int argc, char** argv) {
   cudaProfilerStop();
   MPICHECK(MPI_Finalize());
   return EXIT_SUCCESS;
-}
+}

csrc/cutlass_extensions/common.hpp

@@ -48,14 +48,4 @@ struct enable_sm90_or_later : Kernel {
     Kernel::operator()(std::forward<Args>(args)...);
 #endif
   }
-};
-
-template <typename Kernel>
-struct enable_sm90_only : Kernel {
-  template <typename... Args>
-  CUTLASS_DEVICE void operator()(Args&&... args) {
-#if defined __CUDA_ARCH__ && __CUDA_ARCH__ == 900
-    Kernel::operator()(std::forward<Args>(args)...);
-#endif
-  }
-};
+};

csrc/cutlass_extensions/epilogue/broadcast_load_epilogue_array_c3x.hpp

@@ -1,457 +0,0 @@
-/***************************************************************************************************
- * Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights
- *reserved. SPDX-License-Identifier: BSD-3-Clause
- *
- * Redistribution and use in source and binary forms, with or without
- * modification, are permitted provided that the following conditions are met:
- *
- * 1. Redistributions of source code must retain the above copyright notice,
- *this list of conditions and the following disclaimer.
- *
- * 2. Redistributions in binary form must reproduce the above copyright notice,
- * this list of conditions and the following disclaimer in the documentation
- * and/or other materials provided with the distribution.
- *
- * 3. Neither the name of the copyright holder nor the names of its
- * contributors may be used to endorse or promote products derived from
- * this software without specific prior written permission.
- *
- * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
- * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
- * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
- *ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
- *LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
- *CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
- *SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
- *INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
- *CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
- *ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
- *POSSIBILITY OF SUCH DAMAGE.
- *
- **************************************************************************************************/
-
-//
-// This file is a modified excerpt of
-// include/cutlass/epilogue/fusion/sm90_visitor_load_tma_warpspecialized.hpp
-// from https://github.com/NVIDIA/cutlass v3.5.0
-// It has been modified to support either row/column or scalar broadcasting
-// where the tensor being loaded from is always passed in via a device pointer.
-// This lets one compiled kernel handle all cases of per-tensor or
-// per-channel/per-token quantization.
-//
-// This interface also allows the scales to be passed in as tensors that
-// consistently reside on the device, which avoids an issue with a previous
-// implementation where scalars needed to be on the CPU since they
-// were passed in via float values. This created a potential performance hazard
-// if scales were initially on the device, and caused torch.compile graphs
-// breaks when moving scales to the CPU.
-//
-#pragma once
-
-// Turn off clang-format for the entire file to keep it close to upstream
-// clang-format off
-
-#include "cutlass/cutlass.h"
-#include "cutlass/arch/barrier.h"
-
-#include "cute/tensor.hpp"
-#include "cutlass/epilogue/fusion/sm90_visitor_tma_warpspecialized.hpp"
-
-namespace cutlass::epilogue::fusion {
-
-using namespace cute;
-using namespace detail;
-
-// Row vector broadcast
-template<
-  int Stages,
-  class CtaTileShapeMNK,
-  class Element,
-  class StrideMNL = Stride<_0,_1,_0>,
-  int Alignment = 128 / sizeof_bits_v<Element>
->
-struct Sm90RowOrScalarBroadcastArray {
-  static_assert(Stages == 0, "Row broadcast doesn't support smem usage");
-  static_assert(is_static_v<decltype(take<0,2>(StrideMNL{}))>); // batch stride can be dynamic or static
-  static_assert(take<0,2>(StrideMNL{}) == Stride<_0,_1>{});
-
-  struct SharedStorage { 
-    array_aligned<Element, size<1>(CtaTileShapeMNK{})> smem;
-  };
-
-  // This struct has been modified to have a bool indicating that ptr_row is a 
-  // scalar that must be broadcast, instead of containing a scalar that is 
-  // valid if ptr_row is null.
-  struct Arguments {
-    const Element* const* ptr_row_array = nullptr;
-    bool row_broadcast = true;
-    StrideMNL dRow = {};
-  };
-
-  using Params = Arguments;
-
-  template <class ProblemShape>
-  static constexpr Params
-  to_underlying_arguments(ProblemShape const& problem_shape, Arguments const& args, void* workspace) {
-    return args;
-  }
-
-  template <class ProblemShape>
-  static bool
-  can_implement(ProblemShape const& problem_shape, Arguments const& args) {
-    return true;
-  }
-
-  template <class ProblemShape>
-  static size_t
-  get_workspace_size(ProblemShape const& problem_shape, Arguments const& args) {
-    return 0;
-  }
-
-  template <class ProblemShape>
-  static cutlass::Status
-  initialize_workspace(ProblemShape const& problem_shape, Arguments const& args, void* workspace, cudaStream_t stream,
-    CudaHostAdapter* cuda_adapter = nullptr) {
-    return cutlass::Status::kSuccess;
-  }
-
-  CUTLASS_HOST_DEVICE
-  Sm90RowOrScalarBroadcastArray() { }
-
-  CUTLASS_HOST_DEVICE
-  Sm90RowOrScalarBroadcastArray(Params const& params, SharedStorage const& shared_storage)
-      : params(params)
-      , smem(const_cast<Element*>(shared_storage.smem.data())) { }
-
-  Params params;
-  Element *smem = nullptr;
-
-  CUTLASS_DEVICE bool
-  is_producer_load_needed() const {
-    return false;
-  }
-
-  CUTLASS_DEVICE bool
-  is_C_load_needed() const {
-    return false;
-  }
-
-  CUTLASS_DEVICE bool
-  is_zero() const {
-    return (!params.row_broadcast && *(params.ptr_row_array[group]) == Element(0));
-  }
-
-  template <class... Args>
-  CUTLASS_DEVICE auto
-  get_producer_load_callbacks(ProducerLoadArgs<Args...> const& args) {
-    return EmptyProducerLoadCallbacks{};
-  }
-
-  template <class GS_GTensor, class GS_STensor, class GS_CTensor, class Tiled_G2S, class SR_STensor, class SR_RTensor, class CTensor, class ThrResidue, class ThrNum>
-  struct ConsumerStoreCallbacks : EmptyConsumerStoreCallbacks {
-    CUTLASS_DEVICE
-    ConsumerStoreCallbacks(
-        GS_GTensor tGS_gRow_, GS_STensor tGS_sRow_, 
-        GS_CTensor tGS_cRow_, Tiled_G2S tiled_g2s_, 
-        SR_STensor tSR_sRow_, SR_RTensor tSR_rRow_,
-        CTensor tCcRow_, ThrResidue residue_tCcRow_, ThrNum thr_num_,
-        int group, Params const& params_)
-      : tGS_gRow(tGS_gRow_)
-      , tGS_sRow(tGS_sRow_)
-      , tGS_cRow(tGS_cRow_)
-      , tiled_G2S(tiled_g2s_)
-      , tSR_sRow(tSR_sRow_)
-      , tSR_rRow(tSR_rRow_)
-      , tCcRow(tCcRow_)
-      , residue_tCcRow(residue_tCcRow_)
-      , group(group)
-      , params(params_) {}
-
-    GS_GTensor tGS_gRow;                                                         // (CPY,CPY_M,CPY_N)
-    GS_STensor tGS_sRow;                                                         // (CPY,CPY_M,CPY_N)
-    GS_CTensor tGS_cRow;                                                         // (CPY,CPY_M,CPY_N)
-    Tiled_G2S tiled_G2S;
-
-    SR_STensor tSR_sRow;                                                         // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-    SR_RTensor tSR_rRow;                                                         // (CPY,CPY_M,CPY_N,EPI_M,EPI_N) 
-  
-    CTensor tCcRow;                                                              // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-    ThrResidue residue_tCcRow;                                                   // (m, n)
-    ThrNum thr_num;
-    int group;
-    Params const& params;
-
-    CUTLASS_DEVICE void
-    begin() {
-      if (!params.row_broadcast) {
-        fill(tSR_rRow, *(params.ptr_row_array[group]));
-        return;
-      }
-
-      auto synchronize = [&] () { cutlass::arch::NamedBarrier::sync(thr_num, cutlass::arch::ReservedNamedBarriers::EpilogueBarrier); };
-      Tensor tGS_gRow_flt = filter_zeros(tGS_gRow);
-      Tensor tGS_sRow_flt = filter_zeros(tGS_sRow);
-      Tensor tGS_cRow_flt = make_tensor(tGS_cRow.data(), make_layout(tGS_gRow_flt.shape(), tGS_cRow.stride()));
-
-      for (int i = 0; i < size(tGS_gRow_flt); ++i) {
-        if (get<1>(tGS_cRow_flt(i)) >= size<1>(CtaTileShapeMNK{})) {
-          continue; // OOB of SMEM, 
-        }
-        if (elem_less(tGS_cRow_flt(i), make_coord(get<0>(residue_tCcRow), get<1>(residue_tCcRow)))) {
-          tGS_sRow_flt(i) = tGS_gRow_flt(i);
-        }
-        else {
-          tGS_sRow_flt(i) = Element(0); // Set to Zero when OOB so LDS could be issue without any preds.
-        }
-      }
-      synchronize();
-    }
-
-    CUTLASS_DEVICE void
-    begin_loop(int epi_m, int epi_n) {
-      if (epi_m == 0) { // Assumes M-major subtile loop
-        if (!params.row_broadcast) return; // Do not issue LDS when row is scalar 
-        Tensor tSR_sRow_flt = filter_zeros(tSR_sRow(_,_,_,epi_m,epi_n));
-        Tensor tSR_rRow_flt = filter_zeros(tSR_rRow);
-        copy(tSR_sRow_flt, tSR_rRow_flt);
-      }
-    }
-
-    template <typename ElementAccumulator, int FragmentSize>
-    CUTLASS_DEVICE Array<Element, FragmentSize>
-    visit(Array<ElementAccumulator, FragmentSize> const& frg_acc, int epi_v, int epi_m, int epi_n) {
-      Array<Element, FragmentSize> frg_row;
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < FragmentSize; ++i) {
-        frg_row[i] = tSR_rRow(epi_v * FragmentSize + i);
-      }
-
-      return frg_row;
-    }
-  };
-
-  template <
-    bool ReferenceSrc, // do register tensors reference the src or dst layout of the tiled copy
-    class... Args
-  >
-  CUTLASS_DEVICE auto
-  get_consumer_store_callbacks(ConsumerStoreArgs<Args...> const& args) {
-    auto [M, N, K, L] = args.problem_shape_mnkl;
-    auto [m, n, k, l] = args.tile_coord_mnkl;
-    using ThreadCount = decltype(size(args.tiled_copy));
-
-    Tensor mRow = make_tensor(make_gmem_ptr(params.ptr_row_array[l]), make_shape(M,N,1), params.dRow);
-    Tensor gRow = local_tile(mRow(_,_,l), take<0,2>(args.tile_shape_mnk), make_coord(m, n));          // (CTA_M, CTA_N)
-    Tensor sRow = make_tensor(make_smem_ptr(smem), 
-        make_shape(size<0>(CtaTileShapeMNK{}), size<1>(CtaTileShapeMNK{})), make_shape(_0{}, _1{}));  // (CTA_M, CTA_N)
-    //// G2S: Gmem to Smem
-    auto tiled_g2s = make_tiled_copy(Copy_Atom<DefaultCopy, Element>{},
-                                     Layout< Shape<_1, ThreadCount>, 
-                                            Stride<_0,          _1>>{}, 
-                                     Layout<_1>{});   
-    auto thr_g2s = tiled_g2s.get_slice(args.thread_idx);
-    Tensor tGS_gRow = thr_g2s.partition_S(gRow);
-    Tensor tGS_sRow = thr_g2s.partition_D(sRow);
-
-    //// G2S: Coord 
-    auto cRow = make_identity_tensor(make_shape(size<0>(CtaTileShapeMNK{}), size<1>(CtaTileShapeMNK{})));
-    Tensor tGS_cRow = thr_g2s.partition_S(cRow);
-
-    //// S2R: Smem to Reg
-    Tensor tSR_sRow = sm90_partition_for_epilogue<ReferenceSrc>(sRow, args.epi_tile, args.tiled_copy, args.thread_idx);
-    Tensor tSR_rRow = make_tensor_like(take<0,3>(tSR_sRow));                                           // (CPY,CPY_M,CPY_N)
-
-    return ConsumerStoreCallbacks<decltype(tGS_gRow), decltype(tGS_sRow), decltype(tGS_cRow), decltype(tiled_g2s), decltype(tSR_sRow), decltype(tSR_rRow), decltype(args.tCcD), decltype(args.residue_cD), ThreadCount>(
-      tGS_gRow, 
-      tGS_sRow, 
-      tGS_cRow, tiled_g2s, 
-      tSR_sRow, 
-      tSR_rRow, 
-      args.tCcD, 
-      args.residue_cD,
-      ThreadCount{}, 
-      l,
-      params);
-  }
-};
-
-/////////////////////////////////////////////////////////////////////////////////////////////////
-
-// Column vector broadcast
-template<
-  int Stages,
-  class CtaTileShapeMNK,
-  class Element,
-  class StrideMNL = Stride<_1,_0,_0>,
-  int Alignment = 128 / sizeof_bits_v<Element>
->
-struct Sm90ColOrScalarBroadcastArray {
-  static_assert(Stages == 0, "Column broadcast doesn't support smem usage yet");
-  static_assert(Alignment * sizeof_bits_v<Element> % 128 == 0, "sub-16B alignment not supported yet");
-  static_assert(
-    (cute::is_same_v<StrideMNL, Stride<_1,_0, _0>>) || // col vector broadcast, e.g. per-row alpha/bias
-    (cute::is_same_v<StrideMNL, Stride<_1,_0,int>>));  // batched col vector broadcast, e.g. batched per-row bias
-
-  // Accumulator distributes col elements evenly amongst threads so we can just directly load from gmem
-  struct SharedStorage { };
-
-  // This struct has been modified to have a bool indicating that ptr_col is a 
-  // scalar that must be broadcast, instead of containing a scalar that is 
-  // valid if ptr_col is null.
-  struct Arguments {
-    const Element* const* ptr_col_array = nullptr;
-    bool col_broadcast = true;
-    StrideMNL dCol = {};
-  };
-
-  using Params = Arguments;
-
-  template <class ProblemShape>
-  static constexpr Params
-  to_underlying_arguments(ProblemShape const& problem_shape, Arguments const& args, void* workspace) {
-    return args;
-  }
-
-  template <class ProblemShape>
-  static bool
-  can_implement(ProblemShape const& problem_shape, Arguments const& args) {
-    return true;
-  }
-
-  template <class ProblemShape>
-  static size_t
-  get_workspace_size(ProblemShape const& problem_shape, Arguments const& args) {
-    return 0;
-  }
-
-  template <class ProblemShape>
-  static cutlass::Status
-  initialize_workspace(ProblemShape const& problem_shape, Arguments const& args, void* workspace, cudaStream_t stream,
-    CudaHostAdapter* cuda_adapter = nullptr) {
-    return cutlass::Status::kSuccess;
-  }
-
-  CUTLASS_DEVICE bool
-  is_producer_load_needed() const {
-    return false;
-  }
-
-  CUTLASS_DEVICE bool
-  is_C_load_needed() const {
-    return false;
-  }
-
-  CUTLASS_DEVICE bool
-  is_zero() const {
-    return (!params.col_broadcast && *(params.ptr_col_array[group]) == Element(0));
-  }
-
-  CUTLASS_HOST_DEVICE
-  Sm90ColOrScalarBroadcastArray() { }
-
-  CUTLASS_HOST_DEVICE
-  Sm90ColOrScalarBroadcastArray(Params const& params, SharedStorage const& shared_storage)
-      : params(params) { }
-
-  Params params;
-
-  template <class... Args>
-  CUTLASS_DEVICE auto
-  get_producer_load_callbacks(ProducerLoadArgs<Args...> const& args) {
-    return EmptyProducerLoadCallbacks{};
-  }
-
-  template<class GTensor, class RTensor, class CTensor, class ProblemShape>
-  struct ConsumerStoreCallbacks : EmptyConsumerStoreCallbacks {
-    CUTLASS_DEVICE
-    ConsumerStoreCallbacks(
-      GTensor&& tCgCol,
-      RTensor&& tCrCol,
-      CTensor&& tCcCol,
-      ProblemShape problem_shape,
-      int group,
-      Params const& params
-    ): 
-      tCgCol(cute::forward<GTensor>(tCgCol)),
-      tCrCol(cute::forward<RTensor>(tCrCol)),
-      tCcCol(cute::forward<CTensor>(tCcCol)),
-      m(get<0>(problem_shape)),
-      group(group),
-      params(params) {}
-
-    GTensor tCgCol;                                                                    // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-    RTensor tCrCol;
-    CTensor tCcCol;                                                                    // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-    Params const& params;
-    int m;
-    int group;
-
-    CUTLASS_DEVICE void
-    begin() {
-      Tensor pred = make_tensor<bool>(shape(tCgCol));
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < size(pred); ++i) {
-        pred(i) = get<0>(tCcCol(i)) < m;
-      }
-
-      if (!params.col_broadcast) {
-        fill(tCrCol, *(params.ptr_col_array[group]));
-        return;
-      }
-
-      // Filter so we don't issue redundant copies over stride-0 modes
-      // (only works if 0-strides are in same location, which is by construction)
-      copy_if(pred, filter(tCgCol), filter(tCrCol));
-    }
-
-    template <typename ElementAccumulator, int FragmentSize>
-    CUTLASS_DEVICE Array<Element, FragmentSize>
-    visit(Array<ElementAccumulator, FragmentSize> const& frg_acc, int epi_v, int epi_m, int epi_n) {
-      Array<Element, FragmentSize> frg_col;
-      Tensor tCrCol_mn = tCrCol(_,_,_,epi_m,epi_n);
-
-      CUTLASS_PRAGMA_UNROLL
-      for (int i = 0; i < FragmentSize; ++i) {
-        frg_col[i] = tCrCol_mn(epi_v * FragmentSize + i);
-      }
-
-      return frg_col;
-    }
-
-  };
-
-  template <
-    bool ReferenceSrc, // do register tensors reference the src or dst layout of the tiled copy
-    class... Args
-  >
-  CUTLASS_DEVICE auto
-  get_consumer_store_callbacks(ConsumerStoreArgs<Args...> const& args) {
-
-    auto [M, N, K, L] = args.problem_shape_mnkl;
-    auto [m, n, k, l] = args.tile_coord_mnkl;
-
-    Tensor mCol = make_tensor(make_gmem_ptr(params.ptr_col_array[l]), make_shape(M,N,1), params.dCol);
-    Tensor tCgCol = sm90_partition_for_epilogue<ReferenceSrc>(                         // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-      mCol, args.tile_shape_mnk, args.tile_coord_mnkl, args.epi_tile, args.tiled_copy, args.thread_idx);
-    Tensor tCrCol = make_tensor_like(tCgCol);                                          // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-
-    // Generate an identity tensor matching the shape of the global tensor and 
-    //  partition the same way, this will be used to generate the predicate
-    //  tensor for loading
-    Tensor cCol = make_identity_tensor(mCol.shape());
-    Tensor tCcCol = sm90_partition_for_epilogue<ReferenceSrc>(                         // (CPY,CPY_M,CPY_N,EPI_M,EPI_N)
-      cCol, args.tile_shape_mnk, args.tile_coord_mnkl, args.epi_tile, args.tiled_copy, args.thread_idx);
-
-    return ConsumerStoreCallbacks(
-      cute::move(tCgCol), 
-      cute::move(tCrCol), 
-      cute::move(tCcCol), 
-      args.problem_shape_mnkl,
-      l,
-      params
-    );
-  }
-};
-
-}

csrc/cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp

@@ -1,7 +1,6 @@
 #pragma once
 
 #include "cutlass_extensions/epilogue/broadcast_load_epilogue_c3x.hpp"
-#include "cutlass_extensions/epilogue/broadcast_load_epilogue_array_c3x.hpp"
 
 /*
    This file defines custom epilogues for fusing channel scales, token scales,
@@ -70,16 +69,6 @@ struct ScaledEpilogueBase {
       0 /*Stages*/, TileShape, T, T, Stride<Int<0>, Int<1>, Int<0>>,
       128 / sizeof_bits_v<T>, EnableNullPtr>;
 
-  template <typename T>
-  using ColOrScalarLoadArray =
-      cutlass::epilogue::fusion::Sm90ColOrScalarBroadcastArray<
-          0 /*Stages*/, TileShape, T, Stride<Int<1>, Int<0>, Int<0>>>;
-
-  template <typename T>
-  using RowOrScalarLoadArray =
-      cutlass::epilogue::fusion::Sm90RowOrScalarBroadcastArray<
-          0 /*Stages*/, TileShape, T, Stride<Int<0>, Int<1>, Int<0>>>;
-
   // This utility function constructs the arguments for the load descriptors
   // from a tensor. It can handle both row and column, as well as row/column or
   // scalar cases.
@@ -107,14 +96,6 @@ struct ScaledEpilogueBase {
                   std::is_same_v<Descriptor, RowLoad<T, true>>);
     return Arguments{data_ptr};
   }
-
-  template <typename Descriptor, typename T>
-  static auto args_from_tensor(const T* const* data_ptr, bool do_broadcast) {
-    using Arguments = typename Descriptor::Arguments;
-    static_assert(std::is_same_v<Descriptor, ColOrScalarLoadArray<T>> ||
-                  std::is_same_v<Descriptor, RowOrScalarLoadArray<T>>);
-    return Arguments{data_ptr, do_broadcast};
-  }
 };
 
 /*
@@ -400,51 +381,4 @@ struct ScaledEpilogueBiasAzpToken
   }
 };
 
-/*
-    This epilogue works like ScaledEpilogue, but ScaleA and ScaleB are pointers
-    to arrays containing different scales used in group gemm. The number of
-   pointers in ScaleA and the number of pointers in ScaleB are equal to the
-   group size.
-*/
-template <typename ElementAcc, typename ElementD, typename EpilogueDescriptor>
-struct ScaledEpilogueArray
-    : private ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor> {
- private:
-  using SUPER = ScaledEpilogueBase<ElementAcc, ElementD, EpilogueDescriptor>;
-  using Accum = typename SUPER::Accum;
-  using ScaleA = typename SUPER::template ColOrScalarLoadArray<float>;
-  using ScaleB = typename SUPER::template RowOrScalarLoadArray<float>;
-
-  using Compute0 = cutlass::epilogue::fusion::Sm90Compute<
-      cutlass::multiplies, float, float,
-      cutlass::FloatRoundStyle::round_to_nearest>;
-
-  using EVTCompute0 =
-      cutlass::epilogue::fusion::Sm90EVT<Compute0, ScaleB, Accum>;
-
-  using Compute1 = cutlass::epilogue::fusion::Sm90Compute<
-      cutlass::multiplies, ElementD, float,
-      cutlass::FloatRoundStyle::round_to_nearest>;
-
- public:
-  using EVTCompute =
-      cutlass::epilogue::fusion::Sm90EVT<Compute1, ScaleA, EVTCompute0>;
-  using ArgumentType = typename EVTCompute::Arguments;
-
-  using ScaleAArray = typename SUPER::template ColOrScalarLoadArray<float>;
-  using ScaleBArray = typename SUPER::template RowOrScalarLoadArray<float>;
-
-  static ArgumentType prepare_args(float const* const* a_scales_ptr,
-                                   float const* const* b_scales_ptr,
-                                   bool a_col_broadcast, bool b_row_broadcast) {
-    auto a_args = SUPER::template args_from_tensor<ScaleAArray, float>(
-        a_scales_ptr, a_col_broadcast);
-    auto b_args = SUPER::template args_from_tensor<ScaleBArray, float>(
-        b_scales_ptr, b_row_broadcast);
-
-    typename EVTCompute0::Arguments evt0_args{b_args, {}, {}};
-    return ArgumentType{a_args, evt0_args, {}};
-  }
-};
-
 };  // namespace vllm::c3x

csrc/mamba/causal_conv1d/causal_conv1d.cu

@@ -422,7 +422,7 @@ void causal_conv1d_fwd_kernel(ConvParamsBase params) {
         int final_state_position =  ((seqlen - (kWidth - 1)) - (n_chunks - 1) * kChunkSize);
         // in case the final state is separated between the last "smem_exchange" and 
         // and the one before it (chunk = n_chunks - 1 and chunk = n_chunks - 2), 
-        // (which occurs when `final_state_position` is a non-positive index)
+        // (which occurs when `final_state_position` is a non-positivie index)
         // we load the correct data from smem_exchange from both chunks, the last chunk iteration and the one before it
         if (conv_states != nullptr && final_state_position < 0 && seqlen > kWidth){
             input_t vals_load[kNElts] = {0};

csrc/ops.h

@@ -52,15 +52,6 @@ void paged_attention_v2(
     const int64_t blocksparse_vert_stride, const int64_t blocksparse_block_size,
     const int64_t blocksparse_head_sliding_step);
 
-#ifndef USE_ROCM
-void merge_attn_states(torch::Tensor& output,
-                       std::optional<torch::Tensor> output_lse,
-                       const torch::Tensor& prefix_output,
-                       const torch::Tensor& prefix_lse,
-                       const torch::Tensor& suffix_output,
-                       const torch::Tensor& suffix_lse);
-#endif
-
 void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
               double epsilon);
 
@@ -128,7 +119,17 @@ void advance_step_flashinfer(
     torch::Tensor& paged_kv_indices, torch::Tensor& paged_kv_indptr,
     torch::Tensor& paged_kv_last_page_len, torch::Tensor& block_table_bounds);
 
-torch::Tensor get_cuda_view_from_cpu_tensor(torch::Tensor& cpu_tensor);
+void block_table_appends(torch::Tensor& append_row_indices,
+                         torch::Tensor& append_row_indices_cpu,
+                         torch::Tensor& append_cumsums,
+                         torch::Tensor& append_cumsums_cpu,
+                         torch::Tensor& append_block_ids,
+                         torch::Tensor& append_block_ids_cpu,
+                         torch::Tensor& block_table, int64_t num_appends,
+                         int64_t total_num_append_blocks);
+
+void block_table_moves(torch::Tensor& src_dst_n, torch::Tensor& src_dst_n_cpu,
+                       torch::Tensor& block_table, int64_t num_moves);
 
 #ifndef USE_ROCM
 torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
@@ -154,8 +155,7 @@ torch::Tensor permute_cols(torch::Tensor const& A, torch::Tensor const& perm);
 #endif
 
 torch::Tensor ggml_dequantize(torch::Tensor W, int64_t type, int64_t m,
-                              int64_t n,
-                              std::optional<at::ScalarType> const& dtype);
+                              int64_t n);
 
 torch::Tensor ggml_mul_mat_vec_a8(torch::Tensor W, torch::Tensor X,
                                   int64_t type, int64_t row);
@@ -176,7 +176,6 @@ int64_t ggml_moe_get_block_size(int64_t type);
 bool cutlass_scaled_mm_supports_fp4(int64_t cuda_device_capability);
 bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
 bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
-bool cutlass_group_gemm_supported(int64_t cuda_device_capability);
 
 void cutlass_scaled_fp4_mm(torch::Tensor& D, torch::Tensor const& A,
                            torch::Tensor const& B, torch::Tensor const& A_sf,
@@ -188,19 +187,6 @@ void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b_scales,
                        std::optional<torch::Tensor> const& bias);
 
-void cutlass_moe_mm(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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);
-
-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);
-
 void cutlass_scaled_mm_azp(torch::Tensor& out, torch::Tensor const& a,
                            torch::Tensor const& b,
                            torch::Tensor const& a_scales,
@@ -277,10 +263,10 @@ void causal_conv1d_fwd(const at::Tensor& x, const at::Tensor& weight,
                        const std::optional<at::Tensor>& has_initial_state,
                        bool silu_activation, int64_t pad_slot_id);
 
+#ifndef USE_ROCM
 using fptr_t = int64_t;
 fptr_t init_custom_ar(const std::vector<int64_t>& fake_ipc_ptrs,
-                      torch::Tensor& rank_data, int64_t rank,
-                      bool fully_connected);
+                      torch::Tensor& rank_data, int64_t rank, bool full_nvlink);
 void all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
                 fptr_t reg_buffer, int64_t reg_buffer_sz_bytes);
 void dispose(fptr_t _fa);
@@ -291,7 +277,4 @@ get_graph_buffer_ipc_meta(fptr_t _fa);
 void register_graph_buffers(fptr_t _fa,
                             const std::vector<std::vector<int64_t>>& handles,
                             const std::vector<std::vector<int64_t>>& offsets);
-std::tuple<int64_t, torch::Tensor> allocate_shared_buffer_and_handle(
-    int64_t size);
-int64_t open_mem_handle(torch::Tensor& mem_handle);
-void free_shared_buffer(int64_t buffer);
+#endif

csrc/quantization/cutlass_w8a8/moe/get_group_starts.cuh

@@ -1,80 +0,0 @@
-#pragma once
-
-#include <cuda.h>
-#include <torch/all.h>
-#include <c10/cuda/CUDAStream.h>
-
-#include "core/scalar_type.hpp"
-#include "cutlass/bfloat16.h"
-#include "cutlass/float8.h"
-
-template <typename ElementAB, typename ElementC, typename ElementAccumulator>
-__global__ void get_group_gemm_starts(
-    int32_t* expert_offsets, ElementAB** a_offsets, ElementAB** b_offsets,
-    ElementC** out_offsets, ElementAccumulator** a_scales_offsets,
-    ElementAccumulator** b_scales_offsets, ElementAB* a_base_as_int,
-    ElementAB* b_base_as_int, ElementC* out_base_as_int,
-    ElementAccumulator* a_scales_base_as_int,
-    ElementAccumulator* b_scales_base_as_int, int64_t n, int64_t k,
-    bool per_act_token, bool per_out_ch) {
-  int expert_id = threadIdx.x;
-
-  int64_t expert_offset = expert_offsets[expert_id];
-
-  a_offsets[expert_id] = a_base_as_int + expert_offset * k;
-  b_offsets[expert_id] = b_base_as_int + expert_id * k * n;
-  out_offsets[expert_id] = out_base_as_int + expert_offset * n;
-  a_scales_offsets[expert_id] =
-      a_scales_base_as_int + (per_act_token ? expert_offset : 0);
-  b_scales_offsets[expert_id] =
-      b_scales_base_as_int + (per_out_ch ? n * expert_id : expert_id);
-}
-
-#define __CALL_GET_STARTS_KERNEL(TENSOR_C_TYPE, C_TYPE)                    \
-  else if (out_tensors.dtype() == TENSOR_C_TYPE) {                         \
-    get_group_gemm_starts<cutlass::float_e4m3_t, C_TYPE, float>            \
-        <<<1, num_experts, 0, stream>>>(                                   \
-            static_cast<int32_t*>(expert_offsets.data_ptr()),              \
-            static_cast<cutlass::float_e4m3_t**>(a_ptrs.data_ptr()),       \
-            static_cast<cutlass::float_e4m3_t**>(b_ptrs.data_ptr()),       \
-            static_cast<C_TYPE**>(out_ptrs.data_ptr()),                    \
-            static_cast<float**>(a_scales_ptrs.data_ptr()),                \
-            static_cast<float**>(b_scales_ptrs.data_ptr()),                \
-            static_cast<cutlass::float_e4m3_t*>(a_tensors.data_ptr()),     \
-            static_cast<cutlass::float_e4m3_t*>(b_tensors.data_ptr()),     \
-            static_cast<C_TYPE*>(out_tensors.data_ptr()),                  \
-            static_cast<float*>(a_scales.data_ptr()),                      \
-            static_cast<float*>(b_scales.data_ptr()), out_tensors.size(1), \
-            a_tensors.size(1), per_act_token, per_out_ch);                 \
-  }
-
-namespace {
-
-void run_get_group_gemm_starts(
-    torch::Tensor const& expert_offsets, torch::Tensor& a_ptrs,
-    torch::Tensor& b_ptrs, torch::Tensor& out_ptrs,
-    torch::Tensor& a_scales_ptrs, torch::Tensor& b_scales_ptrs,
-    torch::Tensor const& a_tensors, torch::Tensor const& b_tensors,
-    torch::Tensor& out_tensors, torch::Tensor const& a_scales,
-    torch::Tensor const& b_scales) {
-  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
-
-  int num_experts = static_cast<int>(expert_offsets.size(0));
-  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());
-
-  if (false) {
-  }
-  __CALL_GET_STARTS_KERNEL(torch::kBFloat16, cutlass::bfloat16_t)
-  __CALL_GET_STARTS_KERNEL(torch::kFloat16, half)
-  else {
-    TORCH_CHECK(false, "Invalid output type (must be float16 or bfloat16)");
-  }
-}
-
-}  // namespace

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cu

@@ -1,160 +0,0 @@
-#include <cudaTypedefs.h>
-
-#include <c10/cuda/CUDAGuard.h>
-#include <torch/all.h>
-
-#include "cutlass/cutlass.h"
-#include "grouped_mm_c3x.cuh"
-
-using namespace cute;
-
-namespace {
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_default {
-  // M in (16, inf)
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule =
-      cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
-  using EpilogueSchedule =
-      cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
-  using TileShape = cute::Shape<cute::_64, cute::_256, cute::_128>;
-  using ClusterShape = cute::Shape<cute::_1, cute::_2, cute::_1>;
-
-  using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_M16 {
-  // M in [1, 16]
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule =
-      cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
-  using EpilogueSchedule =
-      cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
-  using TileShape = cute::Shape<cute::_64, cute::_64, cute::_128>;
-  using ClusterShape = cute::Shape<cute::_1, cute::_4, cute::_1>;
-
-  using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_K8192 {
-  // K in [8192, inf)
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule =
-      cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
-  using EpilogueSchedule =
-      cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
-  using TileShape = cute::Shape<cute::_128, cute::_128, cute::_128>;
-  using ClusterShape = cute::Shape<cute::_1, cute::_8, cute::_1>;
-
-  using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType,
-          template <typename, typename, typename> typename Epilogue>
-struct sm90_fp8_config_N8192 {
-  // N in [8192, inf)
-  static_assert(std::is_same<InType, cutlass::float_e4m3_t>());
-  using KernelSchedule =
-      cutlass::gemm::KernelPtrArrayTmaWarpSpecializedPingpongFP8FastAccum;
-  using EpilogueSchedule =
-      cutlass::epilogue::PtrArrayTmaWarpSpecializedPingpong;
-  using TileShape = cute::Shape<cute::_64, cute::_128, cute::_256>;
-  using ClusterShape = cute::Shape<cute::_1, cute::_8, cute::_1>;
-
-  using Cutlass3xGemm =
-      cutlass_3x_group_gemm<InType, OutType, Epilogue, TileShape, ClusterShape,
-                            KernelSchedule, EpilogueSchedule>;
-};
-
-template <typename InType, typename OutType>
-void run_cutlass_moe_mm_sm90(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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_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.");
-
-  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn,
-              "A tensors must be of type float8_e4m3fn.");
-  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn,
-              "B tensors must be of type float8_e4m3fn.");
-
-  TORCH_CHECK(a_tensors.dtype() == torch::kFloat8_e4m3fn);
-  TORCH_CHECK(b_tensors.dtype() == torch::kFloat8_e4m3fn);
-
-  using Cutlass3xGemmN8192 = typename sm90_fp8_config_N8192<
-      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
-  using Cutlass3xGemmK8192 = typename sm90_fp8_config_K8192<
-      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
-  using Cutlass3xGemmM16 = typename sm90_fp8_config_M16<
-      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
-  using Cutlass3xGemmDefault = typename sm90_fp8_config_default<
-      InType, OutType, vllm::c3x::ScaledEpilogueArray>::Cutlass3xGemm;
-
-  uint32_t const m = a_tensors.size(0);
-  uint32_t const n = out_tensors.size(1);
-  uint32_t const k = a_tensors.size(1);
-
-  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);
-  } 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);
-  } 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);
-  } 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);
-  }
-}
-
-void dispatch_moe_mm_sm90(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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) {
-  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);
-  } 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);
-  }
-}
-
-}  // namespace
-
-void cutlass_moe_mm_sm90(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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) {
-  dispatch_moe_mm_sm90(out_tensors, a_tensors, b_tensors, a_scales, b_scales,
-                       expert_offsets, problem_sizes, a_strides, b_strides,
-                       c_strides);
-}

csrc/quantization/cutlass_w8a8/moe/grouped_mm_c3x.cuh

@@ -1,149 +0,0 @@
-#pragma once
-
-#include "cutlass/cutlass.h"
-
-#include "cutlass/gemm/collective/collective_builder.hpp"
-#include "cutlass/epilogue/collective/collective_builder.hpp"
-#include "cutlass/gemm/device/gemm_universal_adapter.h"
-
-#include "cutlass_extensions/epilogue/scaled_mm_epilogues_c3x.hpp"
-#include "cutlass_extensions/common.hpp"
-#include "get_group_starts.cuh"
-
-using namespace cute;
-
-namespace {
-
-using ProblemShape =
-    cutlass::gemm::GroupProblemShape<cute::Shape<int, int, int>>;
-
-using ElementAccumulator = float;
-using ArchTag = cutlass::arch::Sm90;
-using OperatorClass = cutlass::arch::OpClassTensorOp;
-
-using LayoutA = cutlass::layout::RowMajor;
-using LayoutB = cutlass::layout::ColumnMajor;
-using LayoutC = cutlass::layout::RowMajor;
-
-template <typename ElementAB_, typename ElementC_,
-          template <typename, typename, typename> typename Epilogue_,
-          typename TileShape, typename ClusterShape, typename KernelSchedule,
-          typename EpilogueSchedule>
-struct cutlass_3x_group_gemm {
-  using ElementAB = ElementAB_;
-  using ElementC = void;
-  using ElementD = ElementC_;
-  using ElementAccumulator = float;
-
-  using Epilogue = Epilogue_<ElementAccumulator, ElementD, TileShape>;
-
-  using StrideC =
-      cute::remove_pointer_t<cute::Stride<int64_t, cute::Int<1>, cute::Int<0>>>;
-
-  static constexpr int AlignmentAB =
-      128 / cutlass::sizeof_bits<ElementAB>::value;
-  static constexpr int AlignmentC = 128 / cutlass::sizeof_bits<ElementD>::value;
-
-  using EVTCompute = typename Epilogue::EVTCompute;
-
-  using CollectiveEpilogue =
-      typename cutlass::epilogue::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, TileShape, ClusterShape,
-          cutlass::epilogue::collective::EpilogueTileAuto, ElementAccumulator,
-          ElementAccumulator, ElementC, LayoutC*, AlignmentC, ElementD,
-          LayoutC*, AlignmentC, EpilogueSchedule, EVTCompute>::CollectiveOp;
-
-  static constexpr size_t CEStorageSize =
-      sizeof(typename CollectiveEpilogue::SharedStorage);
-  using Stages = typename cutlass::gemm::collective::StageCountAutoCarveout<
-      static_cast<int>(CEStorageSize)>;
-
-  using CollectiveMainloop =
-      typename cutlass::gemm::collective::CollectiveBuilder<
-          ArchTag, OperatorClass, ElementAB, LayoutA*, AlignmentAB, ElementAB,
-          LayoutB*, AlignmentAB, ElementAccumulator, TileShape, ClusterShape,
-          Stages, KernelSchedule>::CollectiveOp;
-
-  using KernelType = enable_sm90_only<cutlass::gemm::kernel::GemmUniversal<
-      ProblemShape, CollectiveMainloop, CollectiveEpilogue>>;
-
-  struct GemmKernel : public KernelType {};
-};
-
-template <typename Gemm>
-void cutlass_group_gemm_caller(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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) {
-  using ElementAB = typename Gemm::ElementAB;
-  using ElementD = typename Gemm::ElementD;
-
-  int num_experts = static_cast<int>(expert_offsets.size(0));
-  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 =
-      torch::TensorOptions().dtype(torch::kInt64).device(a_tensors.device());
-
-  torch::Tensor a_ptrs = torch::empty(num_experts, options_int);
-  torch::Tensor b_ptrs = torch::empty(num_experts, options_int);
-  torch::Tensor out_ptrs = torch::empty(num_experts, options_int);
-  torch::Tensor a_scales_ptrs = torch::empty(num_experts, options_int);
-  torch::Tensor b_scales_ptrs = torch::empty(num_experts, options_int);
-
-  run_get_group_gemm_starts(expert_offsets, a_ptrs, b_ptrs, out_ptrs,
-                            a_scales_ptrs, b_scales_ptrs, a_tensors, b_tensors,
-                            out_tensors, a_scales, b_scales);
-
-  using GemmKernel = typename Gemm::GemmKernel;
-  using StrideA = Stride<int64_t, Int<1>, Int<0>>;
-  using StrideB = Stride<int64_t, Int<1>, Int<0>>;
-  using StrideC = typename GemmKernel::InternalStrideC;
-
-  ProblemShape::UnderlyingProblemShape* problem_sizes_as_shapes =
-      static_cast<ProblemShape::UnderlyingProblemShape*>(
-          problem_sizes.data_ptr());
-  ProblemShape prob_shape{num_experts, problem_sizes_as_shapes, nullptr};
-
-  typename GemmKernel::MainloopArguments mainloop_args{
-      static_cast<const ElementAB**>(a_ptrs.data_ptr()),
-      static_cast<StrideA*>(a_strides.data_ptr()),
-      static_cast<const ElementAB**>(b_ptrs.data_ptr()),
-      static_cast<StrideB*>(b_strides.data_ptr())};
-
-  // Currently, we are only able to do broadcast on either all or none a_scales
-  // and on either all or none b_scales
-  typename GemmKernel::EpilogueArguments epilogue_args{
-      Gemm::Epilogue::prepare_args(
-          static_cast<const ElementAccumulator**>(a_scales_ptrs.data_ptr()),
-          static_cast<const ElementAccumulator**>(b_scales_ptrs.data_ptr()),
-          per_act_token, per_out_ch),
-      nullptr, static_cast<StrideC*>(c_strides.data_ptr()),
-      static_cast<ElementD**>(out_ptrs.data_ptr()),
-      static_cast<StrideC*>(c_strides.data_ptr())};
-
-  typename GemmKernel::Arguments args{
-      cutlass::gemm::GemmUniversalMode::kGrouped, prob_shape, mainloop_args,
-      epilogue_args};
-
-  using GemmOp = cutlass::gemm::device::GemmUniversalAdapter<GemmKernel>;
-  GemmOp gemm_op;
-  CUTLASS_CHECK(gemm_op.can_implement(args));
-
-  size_t workspace_size = gemm_op.get_workspace_size(args);
-  auto const workspace_options =
-      torch::TensorOptions().dtype(torch::kUInt8).device(a_tensors.device());
-  auto workspace = torch::empty(workspace_size, workspace_options);
-
-  cutlass::Status status = gemm_op.run(args, workspace.data_ptr(), stream);
-  CUTLASS_CHECK(status);
-}
-
-}  // namespace

csrc/quantization/cutlass_w8a8/moe/moe_data.cu

@@ -1,90 +0,0 @@
-#include <cudaTypedefs.h>
-
-#include <c10/cuda/CUDAGuard.h>
-#include <torch/all.h>
-
-#include <iostream>
-
-constexpr uint64_t THREADS_PER_EXPERT = 512;
-
-__global__ void compute_problem_sizes(const int* __restrict__ topk_ids,
-                                      int32_t* problem_sizes1,
-                                      int32_t* problem_sizes2,
-                                      int32_t* atomic_buffer,
-                                      const int topk_length, const int n,
-                                      const int k) {
-  int expert_id = blockIdx.x;
-
-  int occurrences = 0;
-  for (int i = threadIdx.x; i < topk_length; i += THREADS_PER_EXPERT) {
-    occurrences += (topk_ids[i] == expert_id);
-  }
-  atomicAdd(&atomic_buffer[expert_id], occurrences);
-  __syncthreads();
-
-  if (threadIdx.x == 0) {
-    int final_occurrences = atomic_buffer[expert_id];
-    problem_sizes1[expert_id * 3] = final_occurrences;
-    problem_sizes1[expert_id * 3 + 1] = 2 * n;
-    problem_sizes1[expert_id * 3 + 2] = k;
-    problem_sizes2[expert_id * 3] = final_occurrences;
-    problem_sizes2[expert_id * 3 + 1] = k;
-    problem_sizes2[expert_id * 3 + 2] = n;
-  }
-}
-
-__global__ void compute_expert_offsets(
-    const int32_t* __restrict__ problem_sizes1, int32_t* expert_offsets,
-    int32_t* atomic_buffer, const int num_experts) {
-  int32_t tot_offset = 0;
-  expert_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;
-  }
-}
-
-__global__ void compute_arg_sorts(const int* __restrict__ topk_ids,
-                                  int32_t* input_permutation,
-                                  int32_t* output_permutation,
-                                  int32_t* atomic_buffer, const int topk_length,
-                                  const int topk) {
-  int expert_id = blockIdx.x;
-
-  for (int i = threadIdx.x; i < topk_length; i += THREADS_PER_EXPERT) {
-    if (topk_ids[i] == expert_id) {
-      int start = atomicAdd(&atomic_buffer[expert_id], 1);
-      input_permutation[start] = i / topk;
-      output_permutation[i] = start;
-    }
-  }
-}
-
-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) {
-  auto stream = at::cuda::getCurrentCUDAStream(topk_ids.device().index());
-  auto options_int32 =
-      torch::TensorOptions().dtype(torch::kInt32).device(topk_ids.device());
-  torch::Tensor atomic_buffer = torch::zeros(num_experts, options_int32);
-
-  int num_threads = min(THREADS_PER_EXPERT, topk_ids.numel());
-  compute_problem_sizes<<<num_experts, num_threads, 0, stream>>>(
-      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);
-  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 int32_t*>(topk_ids.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));
-}

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -29,20 +29,6 @@ void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales,
                             std::optional<torch::Tensor> const& bias);
-
-void cutlass_moe_mm_sm90(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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);
-
-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);
-
 #endif
 
 #if defined ENABLE_SCALED_MM_SM100 && ENABLE_SCALED_MM_SM100
@@ -116,19 +102,6 @@ bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability) {
   return false;
 }
 
-bool cutlass_group_gemm_supported(int64_t cuda_device_capability) {
-  // CUTLASS groped FP8 kernels need at least CUDA 12.3
-  // and SM90 (Hopper)
-
-#if defined CUDA_VERSION
-  if (cuda_device_capability == 90) {
-    return CUDA_VERSION >= 12030;
-  }
-#endif
-
-  return false;
-}
-
 void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
                        torch::Tensor const& b_scales,
@@ -195,46 +168,6 @@ void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
       version_num);
 }
 
-void cutlass_moe_mm(
-    torch::Tensor& out_tensors, torch::Tensor const& a_tensors,
-    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) {
-  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);
-  return;
-#endif
-  TORCH_CHECK_NOT_IMPLEMENTED(
-      false,
-      "No compiled cutlass_scaled_mm for CUDA device capability: ", version_num,
-      ". Required capability: 90");
-}
-
-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) {
-  // 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_moe_mm_data_caller(topk_ids, expert_offsets, problem_sizes1,
-                                 problem_sizes2, input_permutation,
-                                 output_permutation, num_experts, n, k);
-  return;
-#endif
-  TORCH_CHECK_NOT_IMPLEMENTED(
-      false,
-      "No compiled get_cutlass_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

@@ -30,6 +30,9 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
     fp8_type* __restrict__ out, float* __restrict__ scale,
     scalar_t const* __restrict__ input, float const* __restrict__ scale_ub,
     const int hidden_size) {
+  float const min_scaling_factor =
+      1.0f / (fp8_e4m3_adjusted_max_v<fp8_type> * 512.f);
+
   int const tid = threadIdx.x;
   int const token_idx = blockIdx.x;
 
@@ -64,8 +67,8 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
       token_scale = block_absmax_val_maybe;
     }
     // token scale computation
-    token_scale = max(token_scale / quant_type_max_v<fp8_type>,
-                      min_scaling_factor<fp8_type>::val());
+    token_scale = max(token_scale / fp8_e4m3_adjusted_max_v<fp8_type>,
+                      min_scaling_factor);
     scale[token_idx] = token_scale;
   }
   __syncthreads();

csrc/quantization/fp8/common.cuh

@@ -1,12 +1,20 @@
 #pragma once
 
 #include "quantization/vectorization.cuh"
-#include "quantization/utils.cuh"
 
 #include <cmath>
+#include <c10/core/ScalarType.h>
 
-#ifdef USE_ROCM
+#ifndef USE_ROCM
+  #include <c10/util/Float8_e4m3fn.h>
+  #define MAYBE_HOST_DEVICE C10_HOST_DEVICE
+#else
+  #include <ATen/hip/HIPContext.h>
+  #include <c10/util/Float8_e4m3fn.h>
+  #include <c10/util/Float8_e4m3fnuz.h>
   #include "amd/quant_utils.cuh"
+  // ROCm doesn't seem to need C10_HOST_DEVICE for static constexpr
+  #define MAYBE_HOST_DEVICE
 #endif
 
 // Determines the preferred FP8 type for the current platform.
@@ -23,6 +31,29 @@ static bool is_fp8_ocp() {
 #endif
 }
 
+template <typename T>
+struct fp8_e4m3_adjusted_max;
+
+template <>
+struct fp8_e4m3_adjusted_max<c10::Float8_e4m3fn> {
+  static constexpr c10::Float8_e4m3fn val() {
+    return std::numeric_limits<c10::Float8_e4m3fn>::max();
+  }
+};
+
+// Using the default max value from pytorch (240.0 0x7F) will cause accuracy
+// issues when running dynamic quantization. Here use 224.0 0x7E for rocm.
+template <>
+struct fp8_e4m3_adjusted_max<c10::Float8_e4m3fnuz> {
+  static constexpr c10::Float8_e4m3fnuz val() {
+    return c10::Float8_e4m3fnuz(0x7E, c10::Float8_e4m3fnuz::from_bits());
+  }
+};
+
+template <typename T>
+MAYBE_HOST_DEVICE static constexpr T fp8_e4m3_adjusted_max_v =
+    fp8_e4m3_adjusted_max<T>::val();
+
 namespace vllm {
 
 __device__ __forceinline__ float atomicMaxFloat(float* addr, float value) {
@@ -45,8 +76,8 @@ __device__ __forceinline__ fp8_type scaled_fp8_conversion(float const val,
     x = val / scale;
   }
 
-  float r =
-      fmax(-quant_type_max_v<fp8_type>, fmin(x, quant_type_max_v<fp8_type>));
+  float r = fmax(-fp8_e4m3_adjusted_max_v<fp8_type>,
+                 fmin(x, fp8_e4m3_adjusted_max_v<fp8_type>));
 #ifndef USE_ROCM
   return static_cast<fp8_type>(r);
 #else
@@ -92,7 +123,7 @@ __global__ void segmented_max_reduction(float* __restrict__ scale,
   // Finally, since cache[0] contains the maximum for this thread block,
   // atomically write the max to the target location
   if (threadIdx.x == 0) {
-    atomicMaxFloat(scale, cache[0] / quant_type_max_v<fp8_type>);
+    atomicMaxFloat(scale, cache[0] / fp8_e4m3_adjusted_max_v<fp8_type>);
   }
 }
 

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

@@ -14,7 +14,8 @@ __device__ void rms_norm_dynamic_per_token_quant_vec(
     float* __restrict__ scales,           // [num_tokens]
     scalar_t const* __restrict__ input,   // [..., hidden_size]
     scalar_t const* __restrict__ weight,  // [hidden_size]
-    float const* scale_ub, float const var_epsilon, int32_t const hidden_size,
+    float const* scale_ub, float const var_epsilon,
+    float const min_scaling_factor, int32_t const hidden_size,
     scalar_t* __restrict__ residual = nullptr) {
   float rms = 0.0f;
   float token_scale = 0.0f;
@@ -26,8 +27,8 @@ __device__ void rms_norm_dynamic_per_token_quant_vec(
   // Compute scale
   vllm::vectorized::compute_dynamic_per_token_scales<scalar_t, scalar_out_t,
                                                      has_residual>(
-      &token_scale, scales, input, weight, rms, scale_ub, hidden_size,
-      residual);
+      &token_scale, scales, input, weight, rms, scale_ub, min_scaling_factor,
+      hidden_size, residual);
 
   // RMS Norm + Quant
   if constexpr (std::is_same_v<scalar_out_t, int8_t>) {
@@ -49,7 +50,8 @@ __global__ void rms_norm_dynamic_per_token_quant_kernel(
     float* __restrict__ scales,           // [num_tokens]
     scalar_t const* __restrict__ input,   // [..., hidden_size]
     scalar_t const* __restrict__ weight,  // [hidden_size]
-    float const* scale_ub, float const var_epsilon, int32_t const hidden_size,
+    float const* scale_ub, float const var_epsilon,
+    float const min_scaling_factor, int32_t const hidden_size,
     scalar_t* __restrict__ residual = nullptr) {
   // For vectorization, token_input and token_output pointers need to be
   // aligned at 8-byte and 4-byte addresses respectively.
@@ -58,8 +60,8 @@ __global__ void rms_norm_dynamic_per_token_quant_kernel(
   if (can_vectorize) {
     return rms_norm_dynamic_per_token_quant_vec<scalar_t, scalar_out_t,
                                                 has_residual>(
-        out, scales, input, weight, scale_ub, var_epsilon, hidden_size,
-        residual);
+        out, scales, input, weight, scale_ub, var_epsilon, min_scaling_factor,
+        hidden_size, residual);
   }
 
   float rms = 0.0f;
@@ -70,8 +72,8 @@ __global__ void rms_norm_dynamic_per_token_quant_kernel(
                                             var_epsilon, residual);
   // Compute Scale
   vllm::compute_dynamic_per_token_scales<scalar_t, scalar_out_t, has_residual>(
-      &token_scale, scales, input, weight, rms, scale_ub, hidden_size,
-      residual);
+      &token_scale, scales, input, weight, rms, scale_ub, min_scaling_factor,
+      hidden_size, residual);
 
   // RMS Norm + Quant
   if constexpr (std::is_same_v<scalar_out_t, int8_t>) {
@@ -103,6 +105,11 @@ void rms_norm_dynamic_per_token_quant_dispatch(
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
+  const float min_scaling_factor =
+      out.dtype() == torch::kInt8
+          ? std::numeric_limits<float>::epsilon()
+          : 1.0f / (std::numeric_limits<c10::Float8_e4m3fn>::max() * 512.f);
+
   if (residual.has_value()) {
     VLLM_DISPATCH_QUANT_TYPES(
         out.scalar_type(), "rms_norm_dynamic_per_token_quant_kernel", [&] {
@@ -112,7 +119,8 @@ void rms_norm_dynamic_per_token_quant_dispatch(
                   out.data_ptr<scalar_t>(), scales.data_ptr<float>(),
                   input.data_ptr<scalar_in_t>(), weight.data_ptr<scalar_in_t>(),
                   scale_ub.has_value() ? scale_ub->data_ptr<float>() : nullptr,
-                  var_epsilon, hidden_size, residual->data_ptr<scalar_in_t>());
+                  var_epsilon, min_scaling_factor, hidden_size,
+                  residual->data_ptr<scalar_in_t>());
         });
 
   } else {
@@ -124,7 +132,7 @@ void rms_norm_dynamic_per_token_quant_dispatch(
                   out.data_ptr<scalar_t>(), scales.data_ptr<float>(),
                   input.data_ptr<scalar_in_t>(), weight.data_ptr<scalar_in_t>(),
                   scale_ub.has_value() ? scale_ub->data_ptr<float>() : nullptr,
-                  var_epsilon, hidden_size, nullptr);
+                  var_epsilon, min_scaling_factor, hidden_size, nullptr);
         });
   }
 }

csrc/quantization/fused_kernels/layernorm_utils.cuh

@@ -5,7 +5,6 @@
  */
 
 #include "quantization/vectorization.cuh"
-#include "quantization/utils.cuh"
 #include "quant_conversions.cuh"
 
 #ifndef USE_ROCM
@@ -52,11 +51,11 @@ __device__ void compute_dynamic_per_token_scales(
     float* __restrict__ token_scale, float* __restrict__ all_token_scales,
     scalar_t const* __restrict__ input, scalar_t const* __restrict__ weight,
     float const rms, float const* __restrict__ scale_ub,
-    int32_t const hidden_size,
+    float const min_scaling_factor, int32_t const hidden_size,
     scalar_t const* __restrict__ residual = nullptr) {
   int64_t const token_offset = blockIdx.x * static_cast<int64_t>(hidden_size);
   ;
-  constexpr scalar_out_t qmax{quant_type_max_v<scalar_out_t>};
+  constexpr scalar_out_t qmax{std::numeric_limits<scalar_out_t>::max()};
 
   float block_absmax_val_maybe = 0.0f;
   for (auto i = threadIdx.x; i < hidden_size; i += blockDim.x) {
@@ -84,7 +83,7 @@ __device__ void compute_dynamic_per_token_scales(
       scale = block_absmax_val_maybe;
     }
     // token scale computation
-    scale = max(scale / qmax, min_scaling_factor<scalar_out_t>::val());
+    scale = max(scale / qmax, min_scaling_factor);
     s_token_scale = scale;                 // Shared memory store
     all_token_scales[blockIdx.x] = scale;  // Global output store
   }
@@ -185,7 +184,7 @@ __device__ void compute_dynamic_per_token_scales(
     float* __restrict__ token_scale, float* __restrict__ all_token_scales,
     scalar_t const* __restrict__ input, scalar_t const* __restrict__ weight,
     float const rms, float const* __restrict__ scale_ub,
-    int32_t const hidden_size,
+    float const min_scaling_factor, int32_t const hidden_size,
     scalar_t const* __restrict__ residual = nullptr) {
   int64_t const token_offset = blockIdx.x * static_cast<int64_t>(hidden_size);
   ;
@@ -201,7 +200,7 @@ __device__ void compute_dynamic_per_token_scales(
         reinterpret_cast<vec4_t<scalar_t> const*>(&residual[token_offset]);
   }
 
-  constexpr scalar_out_t qmax{quant_type_max_v<scalar_out_t>};
+  constexpr scalar_out_t qmax{std::numeric_limits<scalar_out_t>::max()};
 
   int32_t const num_vec_elems = hidden_size >> 2;
   float block_absmax_val_maybe = 0.0f;
@@ -249,7 +248,7 @@ __device__ void compute_dynamic_per_token_scales(
       scale = block_absmax_val_maybe;
     }
     // token scale computation
-    scale = max(scale / qmax, min_scaling_factor<scalar_out_t>::val());
+    scale = max(scale / qmax, min_scaling_factor);
     s_token_scale = scale;                 // shared memory store
     all_token_scales[blockIdx.x] = scale;  // global output store
   }

csrc/quantization/fused_kernels/quant_conversions.cuh

@@ -33,8 +33,8 @@ static __device__ __forceinline__ int8_t float_to_int8_rn(float const x) {
 
 template <typename fp8_type>
 static __device__ __forceinline__ fp8_type float_to_fp8(float const x) {
-  float const r =
-      fmax(-quant_type_max_v<fp8_type>, fmin(x, quant_type_max_v<fp8_type>));
+  float const r = fmax(-fp8_e4m3_adjusted_max_v<fp8_type>,
+                       fmin(x, fp8_e4m3_adjusted_max_v<fp8_type>));
   return static_cast<fp8_type>(r);
 }
 

csrc/quantization/gguf/dequantize.cuh

@@ -94,8 +94,8 @@ static __global__ void dequantize_block(const void * __restrict__ vx, dst_t * __
     dfloat2 v;
     dequantize_kernel(vx, ib, iqs, v);
 
-    y[iybs + iqs + 0]        = convert_from_half<dst_t>(v.x);
-    y[iybs + iqs + y_offset] = convert_from_half<dst_t>(v.y);
+    y[iybs + iqs + 0]        = v.x;
+    y[iybs + iqs + y_offset] = v.y;
 }
 
 template<typename dst_t>
@@ -114,10 +114,10 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t
 
     half dall = __low2half(x[i].dm);
     half dmin = __high2half(x[i].dm);
-    y[l+ 0] = convert_from_half<dst_t>(__hsub(__hmul(dall, __int2half_rn((x[i].scales[is+0] & 0xF) * ((q >> 0) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+0] >> 4))));
-    y[l+32] = convert_from_half<dst_t>(__hsub(__hmul(dall, __int2half_rn((x[i].scales[is+2] & 0xF) * ((q >> 2) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+2] >> 4))));
-    y[l+64] = convert_from_half<dst_t>(__hsub(__hmul(dall, __int2half_rn((x[i].scales[is+4] & 0xF) * ((q >> 4) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+4] >> 4))));
-    y[l+96] = convert_from_half<dst_t>(__hsub(__hmul(dall, __int2half_rn((x[i].scales[is+6] & 0xF) * ((q >> 6) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+6] >> 4))));
+    y[l+ 0] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+0] & 0xF) * ((q >> 0) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+0] >> 4)));
+    y[l+32] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+2] & 0xF) * ((q >> 2) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+2] >> 4)));
+    y[l+64] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+4] & 0xF) * ((q >> 4) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+4] >> 4)));
+    y[l+96] = __hsub(__hmul(dall, __int2half_rn((x[i].scales[is+6] & 0xF) * ((q >> 6) & 3))), __hmul(dmin,  __int2half_rn(x[i].scales[is+6] >> 4)));
 }
 
 template<typename dst_t>
@@ -148,9 +148,7 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t
     const uint8_t * q = x[i].qs + 32*n;
     const uint8_t * hm = x[i].hmask;
 
-    for (int l = l0; l < l0+4; ++l) {
-        y[l] = convert_from_half<dst_t>(__hmul(dl,  __int2half_rn((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4))));
-    }
+    for (int l = l0; l < l0+4; ++l) y[l] = __hmul(dl,  __int2half_rn((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)));
 }
 
 static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) {
@@ -190,8 +188,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t
     const half d2 = __hmul(dall, __int2half_rn(sc));
     const half m2 = __hmul(dmin, __int2half_rn(m));
     for (int l = 0; l < n; ++l) {
-        y[l + 0] = convert_from_half<dst_t>(__hsub(__hmul(d1, __int2half_rn(q[l] & 0xF)), m1));
-        y[l +32] = convert_from_half<dst_t>(__hsub(__hmul(d2,  __int2half_rn(q[l] >> 4)), m2));
+        y[l + 0] = __hsub(__hmul(d1, __int2half_rn(q[l] & 0xF)), m1);
+        y[l +32] = __hsub(__hmul(d2,  __int2half_rn(q[l] >> 4)), m2);
     }
 }
 
@@ -222,11 +220,11 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t
     const half d2 = __hmul(dall, __int2half_rn(sc)); const half m2 = __hmul(dmin, __int2half_rn(m));
 
     uint8_t   hm  = 1 << (2*il);
-    y[ 0] = convert_from_half<dst_t>(__hsub(__hmul(d1, __int2half_rn((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0))), m1));
-    y[ 1] = convert_from_half<dst_t>(__hsub(__hmul(d1, __int2half_rn((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0))), m1));
+    y[ 0] = __hsub(__hmul(d1, __int2half_rn((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0))), m1);
+    y[ 1] = __hsub(__hmul(d1, __int2half_rn((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0))), m1);
     hm <<= 1;
-    y[32] = convert_from_half<dst_t>(__hsub(__hmul(d2, __int2half_rn((ql[0] >>  4) + (qh[0] & hm ? 16 : 0))), m2));
-    y[33] = convert_from_half<dst_t>(__hsub(__hmul(d2, __int2half_rn((ql[1] >>  4) + (qh[1] & hm ? 16 : 0))), m2));
+    y[32] = __hsub(__hmul(d2, __int2half_rn((ql[0] >>  4) + (qh[0] & hm ? 16 : 0))), m2);
+    y[33] = __hsub(__hmul(d2, __int2half_rn((ql[1] >>  4) + (qh[1] & hm ? 16 : 0))), m2);
 }
 
 template<typename dst_t>
@@ -249,10 +247,10 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t
     const uint8_t   qh = x[i].qh[32*ip + il];
     const int8_t  * sc = x[i].scales + is;
 
-    y[ 0] = convert_from_half<dst_t>(__hmul(d, __int2half_rn(sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32))));
-    y[32] = convert_from_half<dst_t>(__hmul(d, __int2half_rn(sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32))));
-    y[64] = convert_from_half<dst_t>(__hmul(d, __int2half_rn(sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32))));
-    y[96] = convert_from_half<dst_t>(__hmul(d, __int2half_rn(sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32))));
+    y[ 0] = __hmul(d, __int2half_rn(sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32)));
+    y[32] = __hmul(d, __int2half_rn(sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32)));
+    y[64] = __hmul(d, __int2half_rn(sc[4] * ((int8_t)((ql[ 0]  >> 4) | (((qh >> 4) & 3) << 4)) - 32)));
+    y[96] = __hmul(d, __int2half_rn(sc[6] * ((int8_t)((ql[32]  >> 4) | (((qh >> 6) & 3) << 4)) - 32)));
 }
 
 template<typename dst_t>
@@ -271,7 +269,7 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds
     const uint32_t aux32 = q2[2] | (q2[3] << 16);
     const float d = __half2float(x[i].d) * (0.5f + (aux32 >> 28)) * 0.25f;
     const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
 }
 
 template<typename dst_t>
@@ -288,7 +286,7 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst
     const uint8_t  * grid = (const uint8_t *)(iq2xs_grid + (q2[il] & 511));
     const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
     const uint8_t signs = ksigns_iq2xs[q2[il] >> 9];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
 
 }
 
@@ -305,7 +303,7 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_
     const uint8_t * grid = (const uint8_t *)(iq2s_grid + (x[i].qs[4*ib+il] | ((x[i].qh[ib] << (8-2*il)) & 0x300)));
     const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f;
     const uint8_t signs = x[i].qs[QK_K/8+4*ib+il];
-    for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f);
+    for (int j = 0; j < 8; ++j) y[j] = __float2half(d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f));
 }
 
 template<typename dst_t>
@@ -326,8 +324,8 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds
     const float d = __half2float(x[i].d) * (0.5f + (aux32 >> 28)) * 0.5f;
     const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127];
     for (int j = 0; j < 4; ++j) {
-        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
-        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+        y[j+0] = __float2half(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
+        y[j+4] = __float2half(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
     }
 }
 
@@ -347,8 +345,8 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_
     const float d = __half2float(x[i].d) * (0.5f + ((x[i].scales[ib/2] >> 4*(ib%2)) & 0xf)) * 0.5f;
     const uint8_t signs = x[i].signs[4*ib + il];
     for (int j = 0; j < 4; ++j) {
-        y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f);
-        y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f);
+        y[j+0] = __float2half(d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f));
+        y[j+4] = __float2half(d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f));
     }
 }
 
@@ -369,7 +367,7 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_
     grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
     grid32[0] &= 0x0f0f0f0f;
     for (int j = 0; j < 8; ++j) {
-        y[j] = d * (q[j] + delta);
+        y[j] = __float2half(d * (q[j] + delta));
     }
 }
 
@@ -394,7 +392,7 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_
     grid32[1] = (grid32[0] >> 4) & 0x0f0f0f0f;
     grid32[0] &= 0x0f0f0f0f;
     for (int j = 0; j < 8; ++j) {
-        y[j] = d * (q[j] + delta);
+        y[j] = __float2half(d * (q[j] + delta));
     }
 }
 
@@ -411,8 +409,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst
     const uint8_t  * q4 = x[ib].qs + 4*il;
     const float d = __half2float(x[ib].d);
     for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
-        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+        y[j+ 0] = __float2half(d * kvalues_iq4nl[q4[j] & 0xf]);
+        y[j+16] = __float2half(d * kvalues_iq4nl[q4[j] >>  4]);
     }
 
 }
@@ -429,8 +427,8 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst
     const uint8_t  * q4 = x[i].qs + 16*ib + 4*il;
     const float d = __half2float(x[i].d) * ((((x[i].scales_l[ib/2] >> 4*(ib%2)) & 0xf) | (((x[i].scales_h >> 2*ib) & 3) << 4)) - 32);
     for (int j = 0; j < 4; ++j) {
-        y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf];
-        y[j+16] = d * kvalues_iq4nl[q4[j] >>  4];
+        y[j+ 0] = __float2half(d * kvalues_iq4nl[q4[j] & 0xf]);
+        y[j+16] = __float2half(d * kvalues_iq4nl[q4[j] >>  4]);
     }
 }
 
@@ -524,8 +522,7 @@ static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int k,
     dequantize_block_iq4_xs<<<nb, 32, 0, stream>>>(vx, y);
 }
 
-template<typename dst_t>
-static to_cuda_ggml_t<dst_t> ggml_get_to_cuda(int64_t type) {
+static to_fp16_cuda_t ggml_get_to_fp16_cuda(int64_t type) {
     switch (type) {
         case 2:
             return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;

csrc/quantization/gguf/ggml-common.h

@@ -1063,8 +1063,7 @@ static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -
 typedef half dfloat; // dequantize float
 typedef half2 dfloat2;
 typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v);
-template<typename dst_t>
-using to_cuda_ggml_t = void (*)(const void * __restrict__ x, dst_t * __restrict__ y, int k, cudaStream_t stream);
+typedef void (*to_fp16_cuda_t)(const void * __restrict__ x, dfloat * __restrict__ y, int k, cudaStream_t stream);
 typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs);
 typedef void (*allocate_tiles_cuda_t)(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc);
 typedef void (*load_tiles_cuda_t)(
@@ -1076,25 +1075,6 @@ typedef float (*vec_dot_q_mul_mat_cuda_t)(
 
 // Utility function
 
-template<typename dst_t>
-static __device__ __forceinline__ dst_t convert_from_half(half val) {
-    return val;
-}
-
-template<>
-__device__ __forceinline__ c10::BFloat16 convert_from_half<c10::BFloat16>(half val) {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
-    return __float2bfloat16(__half2float(val));
-#else
-    return __half2float(val);
-#endif  // defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
-}
-
-template<>
-__device__ __forceinline__ float convert_from_half<float>(half val) {
-    return __half2float(val);
-}
-
 #if defined(USE_ROCM)
 
 #ifndef __has_builtin

csrc/quantization/gguf/gguf_kernel.cu

@@ -71,19 +71,14 @@ static void quantize_row_q8_1_cuda(const scalar_t* x, void* vy, const int kx,
 }
 
 torch::Tensor ggml_dequantize(torch::Tensor W,  // quant weight
-                              int64_t type, int64_t m, int64_t n,
-                              std::optional<at::ScalarType> const& dtype) {
+                              int64_t type, int64_t m, int64_t n) {
   const at::cuda::OptionalCUDAGuard device_guard(device_of(W));
-  auto dtype_ = dtype.value_or(torch::kFloat16);
-  auto options = torch::TensorOptions().dtype(dtype_).device(W.device());
+  auto options =
+      torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
   at::Tensor DW = torch::empty({m, n}, options);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-
-  VLLM_DISPATCH_FLOATING_TYPES(DW.scalar_type(), "ggml_dequantize", [&] {
-    auto to_cuda = ggml_get_to_cuda<scalar_t>(type);
-    to_cuda((void*)W.data_ptr(), (scalar_t*)DW.data_ptr(), m * n, stream);
-  });
-
+  const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(type);
+  to_fp16_cuda((void*)W.data_ptr(), (half*)DW.data_ptr(), m * n, stream);
   return DW;
 }
 

csrc/quantization/gguf/moe.cuh

@@ -129,7 +129,7 @@ static __device__ __forceinline__ void moe_q(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q4_0 8
+  #define MOE_X_Q4_0 64
   #define MOE_Y_Q4_0 128
   #define NWARPS_Q4_0 8
 #else
@@ -190,7 +190,7 @@ static void ggml_moe_q4_0_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q4_1 8
+  #define MOE_X_Q4_1 64
   #define MOE_Y_Q4_1 128
   #define NWARPS_Q4_1 8
 #else
@@ -251,7 +251,7 @@ static void ggml_moe_q4_1_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q5_0 8
+  #define MOE_X_Q5_0 64
   #define MOE_Y_Q5_0 128
   #define NWARPS_Q5_0 8
 #else
@@ -312,7 +312,7 @@ static void ggml_moe_q5_0_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q5_1 8
+  #define MOE_X_Q5_1 64
   #define MOE_Y_Q5_1 128
   #define NWARPS_Q5_1 8
 #else
@@ -373,7 +373,7 @@ static void ggml_moe_q5_1_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q8_0 8
+  #define MOE_X_Q8_0 64
   #define MOE_Y_Q8_0 128
   #define NWARPS_Q8_0 8
 #else
@@ -434,7 +434,7 @@ static void ggml_moe_q8_0_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q2_K 8
+  #define MOE_X_Q2_K 64
   #define MOE_Y_Q2_K 128
   #define NWARPS_Q2_K 8
 #else
@@ -495,7 +495,7 @@ static void ggml_moe_q2_K_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q3_K 8
+  #define MOE_X_Q3_K 64
   #define MOE_Y_Q3_K 128
   #define NWARPS_Q3_K 8
 #else
@@ -556,7 +556,7 @@ static void ggml_moe_q3_K_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q4_K 8
+  #define MOE_X_Q4_K 64
   #define MOE_Y_Q4_K 128
   #define NWARPS_Q4_K 8
 #else
@@ -617,7 +617,7 @@ static void ggml_moe_q4_K_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q5_K 8
+  #define MOE_X_Q5_K 64
   #define MOE_Y_Q5_K 128
   #define NWARPS_Q5_K 8
 #else
@@ -678,7 +678,7 @@ static void ggml_moe_q5_K_q8_1_cuda(
 }
 
 #if defined(USE_ROCM)
-  #define MOE_X_Q6_K 8
+  #define MOE_X_Q6_K 64
   #define MOE_Y_Q6_K 128
   #define NWARPS_Q6_K 8
 #else

csrc/quantization/gptq_marlin/gptq_marlin.cu

@@ -1785,7 +1785,7 @@ __global__ void Marlin(
             <<<blocks, NUM_THREADS, max_shared_mem, stream>>>(                 \
                 A_ptr, B_ptr, C_ptr, C_tmp_ptr, s_ptr, zp_ptr, g_idx_ptr,      \
                 num_groups, prob_m, prob_n, prob_k, lda, locks,                \
-                part_use_atomic_add, use_fp32_reduce);                         \
+                use_atomic_add, use_fp32_reduce);                              \
       }                                                                        \
     }
 
@@ -2215,10 +2215,6 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
       thread_m_blocks = exec_cfg.max_m_blocks;
     }
 
-    // atomic add reduce have better performance only when m * n is small
-    bool part_use_atomic_add =
-        use_atomic_add && div_ceil(prob_m, 64) * prob_n <= 2048;
-
     if (false) {
     }
     GPTQ_CALL_IF(vllm::kU4B8, 16, 4, 256)

csrc/quantization/utils.cuh

@@ -1,59 +0,0 @@
-#pragma once
-
-/**
- * Quantization utilities including:
- *   Adjusted maximum values for qtypes.
- *   Minimum scaling factors for qtypes.
- */
-
-#include <cmath>
-#include <torch/types.h>
-
-#ifndef USE_ROCM
-  #include <c10/util/Float8_e4m3fn.h>
-  #define MAYBE_HOST_DEVICE C10_HOST_DEVICE
-#else
-  #include <ATen/hip/HIPContext.h>
-  #include <c10/util/Float8_e4m3fn.h>
-  #include <c10/util/Float8_e4m3fnuz.h>
-  // ROCm doesn't seem to need C10_HOST_DEVICE for static constexpr
-  #define MAYBE_HOST_DEVICE
-#endif
-
-template <typename T,
-          typename = std::enable_if_t<std::is_same_v<T, c10::Float8_e4m3fn> ||
-                                      std::is_same_v<T, c10::Float8_e4m3fnuz> ||
-                                      std::is_same_v<T, int8_t>>>
-struct quant_type_max {
-  static constexpr T val() { return std::numeric_limits<T>::max(); }
-};
-
-// Using the default max value from pytorch (240.0 0x7F) will cause accuracy
-// issues when running dynamic quantization. Here use 224.0 0x7E for rocm.
-template <>
-struct quant_type_max<c10::Float8_e4m3fnuz> {
-  static constexpr c10::Float8_e4m3fnuz val() {
-    return c10::Float8_e4m3fnuz(0x7E, c10::Float8_e4m3fnuz::from_bits());
-  }
-};
-
-template <typename T>
-MAYBE_HOST_DEVICE static constexpr T quant_type_max_v =
-    quant_type_max<T>::val();
-
-template <typename T,
-          typename = std::enable_if_t<std::is_same_v<T, c10::Float8_e4m3fn> ||
-                                      std::is_same_v<T, c10::Float8_e4m3fnuz> ||
-                                      std::is_same_v<T, int8_t>>>
-struct min_scaling_factor {
-  C10_DEVICE C10_ALWAYS_INLINE static float val() {
-    return 1.0f / (quant_type_max_v<T> * 512.0f);
-  }
-};
-
-template <>
-struct min_scaling_factor<int8_t> {
-  C10_DEVICE C10_ALWAYS_INLINE static float val() {
-    return std::numeric_limits<float>::epsilon();
-  }
-};

csrc/rocm/attention.cu

@@ -272,7 +272,6 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const float scale,    
     const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
     const int* __restrict__ context_lens,   // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes, // [num_heads]
     const int q_stride,
@@ -292,13 +291,6 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   const int rowid = laneid / 16;
 
   const auto seq_idx = blockIdx.x;
-  // NOTE queries with sequence len > 1 are prefills and taken care by another
-  // kernel.
-  if (query_start_loc_ptr != nullptr &&
-      (query_start_loc_ptr[seq_idx + 1] - query_start_loc_ptr[seq_idx]) != 1) {
-    return;
-  }
-
   const auto partition_idx = blockIdx.y;
 
   constexpr int T_PAR_SIZE = 256;  // token partition size set to 256
@@ -385,10 +377,9 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
   // fetch Q in shared across warps and then write to registers
   const int local_qhead_idx = 4 * warpid + rowid;
   const int global_qhead_idx = wg_start_head_idx + local_qhead_idx;
-  const int64_t query_start_off = static_cast<int64_t>(
-      query_start_loc_ptr ? query_start_loc_ptr[seq_idx] : seq_idx);
+  const int64_t seq_idx64 = static_cast<int64_t>(seq_idx);
   const scalar_t* q_ptr =
-      q + query_start_off * q_stride + global_qhead_idx * HEAD_SIZE;
+      q + seq_idx64 * q_stride + global_qhead_idx * HEAD_SIZE;
 
   const int qhead_element = lane16id * CONTIGUOUS_SCALAR_ELEMS_16B;
   if ((local_qhead_idx < GQA_RATIO) && (qhead_element < HEAD_SIZE)) {
@@ -786,7 +777,6 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const float scale,
     const int* __restrict__ block_tables,   // [num_seqs, max_num_blocks_per_seq]
     const int* __restrict__ context_lens,   // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,   // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes, // [num_heads]
     const int q_stride,
@@ -804,12 +794,6 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
   const int lane4id = laneid % 4;
 
   const auto seq_idx = blockIdx.x;
-  // NOTE queries with sequence len > 1 are prefills and taken care by another
-  // kernel.
-  if (query_start_loc_ptr != nullptr &&
-      (query_start_loc_ptr[seq_idx + 1] - query_start_loc_ptr[seq_idx] != 1)) {
-    return;
-  }
   const auto partition_idx = blockIdx.y;
   const auto partition_size = blockDim.x;
   const auto max_num_partitions = gridDim.y;
@@ -898,11 +882,9 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     }
 
     // fetch q elements
-    // every 4 lanes fetch 8 elems, so warp fetches 8*16 = 128 elemsc
-    const int64_t query_start_off = static_cast<int64_t>(
-        query_start_loc_ptr ? query_start_loc_ptr[seq_idx] : seq_idx);
+    // every 4 lanes fetch 8 elems, so warp fetches 8*16 = 128 elems
     const scalar_t* q_ptr =
-        q + query_start_off * q_stride + wg_start_head_idx * HEAD_SIZE;
+        q + seq_idx * q_stride + wg_start_head_idx * HEAD_SIZE;
     const _B16x8* q_ptrh8 = reinterpret_cast<const _B16x8*>(q_ptr);
     const int qhead_elemh8 = laneid / 4;
 
@@ -1285,19 +1267,10 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads,
                                            // max_num_partitions, head_size]
     const int* __restrict__ context_lens,  // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions) {
   const auto num_heads = gridDim.x;
   const auto head_idx = blockIdx.x;
   const auto seq_idx = blockIdx.y;
-
-  // NOTE queries with sequence len > 1 are prefills and taken care by another
-  // kernel.
-  if (query_start_loc_ptr != nullptr &&
-      (query_start_loc_ptr[seq_idx + 1] - query_start_loc_ptr[seq_idx] != 1)) {
-    return;
-  }
-
   const int context_len = context_lens[seq_idx];
   const int num_partitions = DIVIDE_ROUND_UP(context_len, PARTITION_SIZE);
   [[maybe_unused]] constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
@@ -1466,9 +1439,7 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
       __fdividef(1.0f, shared_global_exp_sum + 1e-6f);
   acc *= inv_global_exp_sum;
 
-  const int64_t query_start_off = static_cast<int64_t>(
-      query_start_loc_ptr ? query_start_loc_ptr[seq_idx] : seq_idx);
-  OUTT* out_ptr = out + query_start_off * num_heads * HEAD_SIZE +
+  OUTT* out_ptr = out + static_cast<int64_t>(seq_idx) * num_heads * HEAD_SIZE +
                   static_cast<int64_t>(head_idx) * HEAD_SIZE;
   if constexpr (std::is_same<OUTT, bit8_t>::value) {
     out_ptr[threadIdx.x] =
@@ -1495,7 +1466,6 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const float scale,
     const int* __restrict__ block_tables,    // [num_seqs, max_num_blocks_per_seq]
     const int* __restrict__ context_lens,    // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
     const int q_stride,
@@ -1522,7 +1492,6 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_QKV_mfma4_kernel(
     const float scale,
     const int* __restrict__ block_tables,    // [num_seqs, max_num_blocks_per_seq]
     const int* __restrict__ context_lens,    // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_blocks_per_seq,
     const float* __restrict__ alibi_slopes,  // [num_heads]
     const int q_stride,
@@ -1546,7 +1515,6 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
     const float* __restrict__ max_logits,  // [num_seqs, num_heads, max_num_partitions]
     const scalar_t* __restrict__ tmp_out,  // [num_seqs, num_heads, max_num_partitions, head_size]
     const int* __restrict__ context_lens,  // [num_seqs]
-    const int* __restrict__ query_start_loc_ptr,  // [num_seqs]
     const int max_num_partitions) {
   UNREACHABLE_CODE
 }
@@ -1554,34 +1522,34 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
 
 #endif  // defined(__HIP__MI300_MI250__) TODO: Add NAVI support
 
-#define LAUNCH_CUSTOM_ATTENTION_MFMA16(GQA_RATIO)                              \
-  paged_attention_ll4mi_QKV_mfma16_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE,  \
-                                          HEAD_SIZE, NTHR, ALIBI_ENABLED,      \
-                                          GQA_RATIO>                           \
-      <<<grid, block, 0, stream>>>(                                            \
-          query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,      \
-          block_tables_ptr, context_lens_ptr, query_start_loc_ptr,             \
-          max_num_blocks_per_seq, alibi_slopes_ptr, q_stride, kv_block_stride, \
-          kv_head_stride, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr,  \
-          max_ctx_blocks, k_scale_ptr, v_scale_ptr);
+#define LAUNCH_CUSTOM_ATTENTION_MFMA16(GQA_RATIO)                             \
+  paged_attention_ll4mi_QKV_mfma16_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE, \
+                                          HEAD_SIZE, NTHR, ALIBI_ENABLED,     \
+                                          GQA_RATIO>                          \
+      <<<grid, block, 0, stream>>>(                                           \
+          query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,     \
+          block_tables_ptr, context_lens_ptr, max_num_blocks_per_seq,         \
+          alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride,        \
+          exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr, max_ctx_blocks, \
+          k_scale_ptr, v_scale_ptr);
 
-#define LAUNCH_CUSTOM_ATTENTION_MFMA4(GQA_RATIO)                               \
-  paged_attention_ll4mi_QKV_mfma4_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE,   \
-                                         HEAD_SIZE, NTHR, ALIBI_ENABLED,       \
-                                         GQA_RATIO>                            \
-      <<<grid, block, 0, stream>>>(                                            \
-          query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,      \
-          block_tables_ptr, context_lens_ptr, query_start_loc_ptr,             \
-          max_num_blocks_per_seq, alibi_slopes_ptr, q_stride, kv_block_stride, \
-          kv_head_stride, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr,  \
-          max_ctx_blocks, k_scale_ptr, v_scale_ptr);
+#define LAUNCH_CUSTOM_ATTENTION_MFMA4(GQA_RATIO)                              \
+  paged_attention_ll4mi_QKV_mfma4_kernel<T, KVT, KV_DTYPE, OUTT, BLOCK_SIZE,  \
+                                         HEAD_SIZE, NTHR, ALIBI_ENABLED,      \
+                                         GQA_RATIO>                           \
+      <<<grid, block, 0, stream>>>(                                           \
+          query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale,     \
+          block_tables_ptr, context_lens_ptr, max_num_blocks_per_seq,         \
+          alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride,        \
+          exp_sums_ptr, max_logits_ptr, tmp_out_ptr, out_ptr, max_ctx_blocks, \
+          k_scale_ptr, v_scale_ptr);
 
 #define LAUNCH_CUSTOM_REDUCTION(NPAR_LOOPS)                          \
   paged_attention_ll4mi_reduce_kernel<T, OUTT, HEAD_SIZE, HEAD_SIZE, \
                                       PARTITION_SIZE, NPAR_LOOPS>    \
       <<<reduce_grid, reduce_block, 0, stream>>>(                    \
           out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr,        \
-          context_lens_ptr, query_start_loc_ptr, max_num_partitions);
+          context_lens_ptr, max_num_partitions);
 
 template <typename T, typename KVT, vllm::Fp8KVCacheDataType KV_DTYPE,
           int BLOCK_SIZE, int HEAD_SIZE, typename OUTT, int PARTITION_SIZE_OLD,
@@ -1591,10 +1559,9 @@ void paged_attention_custom_launcher(
     torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
     torch::Tensor& value_cache, const int num_kv_heads, float scale,
     torch::Tensor& block_tables, torch::Tensor& context_lens,
-    const std::optional<torch::Tensor>& query_start_loc, int max_context_len,
-    const std::optional<torch::Tensor>& alibi_slopes, torch::Tensor& k_scale,
-    torch::Tensor& v_scale) {
-  int num_seqs = block_tables.size(0);
+    int max_context_len, const std::optional<torch::Tensor>& alibi_slopes,
+    torch::Tensor& k_scale, torch::Tensor& v_scale) {
+  int num_seqs = query.size(0);
   int num_heads = query.size(1);
   int head_size = query.size(2);
   int max_num_blocks_per_seq = block_tables.size(1);
@@ -1602,13 +1569,6 @@ void paged_attention_custom_launcher(
   int kv_block_stride = key_cache.stride(0);
   int kv_head_stride = key_cache.stride(1);
 
-  // NOTE: query start location is optional for V0 decode should not be used.
-  // If batch contains mix of prefills and decode, prefills should be skipped.
-  const int* query_start_loc_ptr =
-      query_start_loc
-          ? reinterpret_cast<const int*>(query_start_loc.value().data_ptr())
-          : nullptr;
-
   // NOTE: alibi_slopes is optional.
   const float* alibi_slopes_ptr =
       alibi_slopes
@@ -1740,8 +1700,8 @@ void paged_attention_custom_launcher(
   paged_attention_custom_launcher<T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE, T, \
                                   PSIZE, ALIBI_ENABLED>(                    \
       out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache,    \
-      num_kv_heads, scale, block_tables, context_lens, query_start_loc,     \
-      max_context_len, alibi_slopes, k_scale, v_scale);
+      num_kv_heads, scale, block_tables, context_lens, max_context_len,     \
+      alibi_slopes, k_scale, v_scale);
 
 #define CALL_CUSTOM_LAUNCHER_ALIBI(T, KVT, KV_DTYPE, BLK_SIZE, HEAD_SIZE,      \
                                    PSIZE)                                      \
@@ -1790,7 +1750,6 @@ void paged_attention(
     double scale,
     torch::Tensor& block_tables, // [num_seqs, max_num_blocks_per_seq]
     torch::Tensor& context_lens, // [num_seqs]
-    const std::optional<torch::Tensor>& query_start_loc, // [num_seqs]
     int64_t block_size, int64_t max_context_len,
     const std::optional<torch::Tensor>& alibi_slopes,
     const std::string& kv_cache_dtype, torch::Tensor& k_scale,

csrc/rocm/ops.h

@@ -7,9 +7,8 @@ void paged_attention(torch::Tensor& out, torch::Tensor& exp_sums,
                      torch::Tensor& query, torch::Tensor& key_cache,
                      torch::Tensor& value_cache, int64_t num_kv_heads,
                      double scale, torch::Tensor& block_tables,
-                     torch::Tensor& context_lens,
-                     const std::optional<torch::Tensor>& query_start_loc,
-                     int64_t block_size, int64_t max_context_len,
+                     torch::Tensor& context_lens, int64_t block_size,
+                     int64_t max_context_len,
                      const std::optional<torch::Tensor>& alibi_slopes,
                      const std::string& kv_cache_dtype, torch::Tensor& k_scale,
                      torch::Tensor& v_scale);

csrc/rocm/torch_bindings.cpp

@@ -23,9 +23,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, rocm_ops) {
       "                Tensor query, Tensor key_cache,"
       "                Tensor value_cache, int num_kv_heads,"
       "                float scale, Tensor block_tables,"
-      "                Tensor context_lens,"
-      "                Tensor? query_start_loc,"
-      "                int block_size,"
+      "                Tensor context_lens, int block_size,"
       "                int max_context_len,"
       "                Tensor? alibi_slopes,"
       "                str kv_cache_dtype,"

csrc/torch_bindings.cpp

@@ -31,10 +31,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("weak_ref_tensor(Tensor input) -> Tensor");
   ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
 
-  ops.def("get_cuda_view_from_cpu_tensor(Tensor cpu_tensor) -> Tensor");
-  ops.impl("get_cuda_view_from_cpu_tensor", torch::kCPU,
-           &get_cuda_view_from_cpu_tensor);
-
   // Attention ops
   // Compute the attention between an input query and the cached
   // keys/values using PagedAttention.
@@ -64,21 +60,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    int blocksparse_head_sliding_step) -> ()");
   ops.impl("paged_attention_v2", torch::kCUDA, &paged_attention_v2);
 
-#ifndef USE_ROCM
-  // Merge attn states
-  // Implements section 2.2 of https://www.arxiv.org/pdf/2501.01005
-  // can be used to combine partial attention results (in the split-KV case)
-  ops.def(
-      "merge_attn_states("
-      "    Tensor! output,"
-      "    Tensor!? output_lse,"
-      "    Tensor prefix_output,"
-      "    Tensor prefix_lse,"
-      "    Tensor suffix_output,"
-      "    Tensor suffix_lse) -> ()");
-  ops.impl("merge_attn_states", torch::kCUDA, &merge_attn_states);
-#endif
-
   // Activation ops
   // Activation function used in SwiGLU.
   ops.def("silu_and_mul(Tensor! out, Tensor input) -> ()");
@@ -130,6 +111,19 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       ") -> ()");
   ops.impl("advance_step_flashinfer", torch::kCUDA, &advance_step_flashinfer);
 
+  ops.def(
+      "block_table_appends(Tensor append_row_indices, "
+      "Tensor append_row_indices_cpu, Tensor append_cumsums, "
+      "Tensor append_cumsums_cpu, Tensor append_block_ids, "
+      "Tensor append_block_ids_cpu, Tensor! block_table, int num_appends, "
+      "int total_num_append_blocks) -> ()");
+  ops.impl("block_table_appends", torch::kCUDA, &block_table_appends);
+
+  ops.def(
+      "block_table_moves(Tensor src_dst_n, Tensor src_dst_n_cpu, "
+      "Tensor! block_table, int num_moves) -> ()");
+  ops.impl("block_table_moves", torch::kCUDA, &block_table_moves);
+
   // Layernorm
   // Apply Root Mean Square (RMS) Normalization to the input tensor.
   ops.def(
@@ -310,9 +304,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 #endif
 
   // Dequantization for GGML.
-  ops.def(
-      "ggml_dequantize(Tensor W, int type, SymInt m, SymInt n, ScalarType? "
-      "dtype) -> Tensor");
+  ops.def("ggml_dequantize(Tensor W, int type, SymInt m, SymInt n) -> Tensor");
   ops.impl("ggml_dequantize", torch::kCUDA, &ggml_dequantize);
 
   // mmvq kernel for GGML.
@@ -386,35 +378,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("cutlass_scaled_mm_supports_fp8(int cuda_device_capability) -> bool");
   ops.impl("cutlass_scaled_mm_supports_fp8", &cutlass_scaled_mm_supports_fp8);
 
-  // Check if cutlass grouped gemm is supported for CUDA devices of the given
-  // capability
-  ops.def("cutlass_group_gemm_supported(int cuda_device_capability) -> bool");
-  ops.impl("cutlass_group_gemm_supported", &cutlass_group_gemm_supported);
-
-  // CUTLASS w8a8 grouped GEMM
-  ops.def(
-      "cutlass_moe_mm(Tensor! out_tensors, Tensor a_tensors, Tensor b_tensors, "
-      "               Tensor a_scales, Tensor b_scales, Tensor expert_offsets, "
-      "               Tensor problem_sizes, Tensor a_strides, "
-      "               Tensor b_strides, Tensor c_strides) -> ()",
-      {stride_tag});
-  ops.impl("cutlass_moe_mm", torch::kCUDA, &cutlass_moe_mm);
-
-  // A function that computes data required to run fused MoE with w8a8 grouped
-  // GEMM. It takes topk_ids as an input, and computes expert_offsets
-  // (token start indices of each expert). In addition to this, it computes
-  // problem sizes for each expert's multiplication used by the two mms called
-  // from fused MoE operation, and arrays with permutations required to shuffle
-  // and de-shuffle the input/output of the fused operation.
-  ops.def(
-      "get_cutlass_moe_mm_data(Tensor topk_ids, Tensor! expert_offsets, "
-      "                        Tensor! problem_sizes1, Tensor! problem_sizes2, "
-      "                        Tensor! input_permutation, "
-      "                        Tensor! output_permutation, int num_experts, "
-      "                        int n, int k) -> ()",
-      {stride_tag});
-  ops.impl("get_cutlass_moe_mm_data", torch::kCUDA, &get_cutlass_moe_mm_data);
-
   // Check if cutlass scaled_mm supports block quantization (used by DeepSeekV3)
   ops.def(
       "cutlass_scaled_mm_supports_block_fp8(int cuda_device_capability) -> "
@@ -631,11 +594,12 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cuda_utils), cuda_utils) {
                   &get_max_shared_memory_per_block_device_attribute);
 }
 
+#ifndef USE_ROCM
 TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _custom_ar), custom_ar) {
   // Custom all-reduce kernels
   custom_ar.def(
       "init_custom_ar(int[] ipc_tensors, Tensor rank_data, "
-      "int rank, bool fully_connected) -> int");
+      "int rank, bool full_nvlink) -> int");
   custom_ar.impl("init_custom_ar", torch::kCUDA, &init_custom_ar);
   custom_ar.def(
       "all_reduce(int fa, Tensor inp, Tensor! out, int reg_buffer, "
@@ -648,13 +612,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _custom_ar), custom_ar) {
   custom_ar.def("register_buffer", &register_buffer);
   custom_ar.def("get_graph_buffer_ipc_meta", &get_graph_buffer_ipc_meta);
   custom_ar.def("register_graph_buffers", &register_graph_buffers);
-
-  custom_ar.def("allocate_shared_buffer_and_handle",
-                &allocate_shared_buffer_and_handle);
-  custom_ar.def("open_mem_handle(Tensor mem_handle) -> int", &open_mem_handle);
-  custom_ar.impl("open_mem_handle", torch::kCPU, &open_mem_handle);
-
-  custom_ar.def("free_shared_buffer", &free_shared_buffer);
 }
+#endif
 
 REGISTER_EXTENSION(TORCH_EXTENSION_NAME)

docker/Dockerfile.cpu

@@ -1,141 +0,0 @@
-# This vLLM Dockerfile is used to construct image that can build and run vLLM on x86 CPU platform.
-#
-# Build targets:
-#   vllm-openai (default): used for serving deployment
-#   vllm-test: used for CI tests
-#   vllm-dev: used for development
-#
-# Build arguments:
-#   PYTHON_VERSION=3.12 (default)|3.11|3.10|3.9
-#   VLLM_CPU_DISABLE_AVX512=false (default)|true
-#
-
-######################### BASE IMAGE #########################
-FROM ubuntu:22.04 AS base
-
-WORKDIR /workspace/
-
-ARG PYTHON_VERSION=3.12
-ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
-
-ENV LD_PRELOAD=""
-
-# Install minimal dependencies and uv
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt-get update -y \
-    && apt-get install -y --no-install-recommends ccache git curl wget ca-certificates \
-        gcc-12 g++-12 libtcmalloc-minimal4 libnuma-dev ffmpeg libsm6 libxext6 libgl1 \
-    && update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12 \
-    && curl -LsSf https://astral.sh/uv/install.sh | sh
-
-ENV CCACHE_DIR=/root/.cache/ccache
-ENV CMAKE_CXX_COMPILER_LAUNCHER=ccache
-
-ENV PATH="/root/.local/bin:$PATH"
-ENV VIRTUAL_ENV="/opt/venv"
-ENV UV_PYTHON_INSTALL_DIR=/opt/uv/python
-RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
-ENV PATH="$VIRTUAL_ENV/bin:$PATH"
-
-ENV UV_HTTP_TIMEOUT=500
-
-# Install Python dependencies 
-ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
-ENV UV_INDEX_STRATEGY="unsafe-best-match"
-ENV UV_LINK_MODE="copy"
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
-    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
-    uv pip install --upgrade pip && \
-    uv pip install -r requirements/cpu.txt
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install intel-openmp==2024.2.1 intel_extension_for_pytorch==2.6.0
-
-ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/opt/venv/lib/libiomp5.so:$LD_PRELOAD"
-
-RUN echo 'ulimit -c 0' >> ~/.bashrc
-
-######################### BUILD IMAGE #########################
-FROM base AS vllm-build
-
-ARG GIT_REPO_CHECK=0
-# Support for building with non-AVX512 vLLM: docker build --build-arg VLLM_CPU_DISABLE_AVX512="true" ...
-ARG VLLM_CPU_DISABLE_AVX512
-ENV VLLM_CPU_DISABLE_AVX512=${VLLM_CPU_DISABLE_AVX512}
-
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
-    uv pip install -r requirements/build.txt
-
-COPY . .
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel 
-
-######################### DEV IMAGE #########################
-FROM vllm-build AS vllm-dev
-
-WORKDIR /workspace/vllm
-
-RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
-    --mount=type=cache,target=/var/lib/apt,sharing=locked \
-    apt-get install -y --no-install-recommends vim numactl
-
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install -e tests/vllm_test_utils 
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py develop 
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install -r requirements/dev.txt && \
-    pre-commit install --hook-type pre-commit --hook-type commit-msg
-
-ENTRYPOINT ["bash"]
-
-######################### TEST IMAGE #########################
-FROM base AS vllm-test
-
-WORKDIR /workspace/
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,src=requirements/test.txt,target=requirements/test.txt \
-    uv pip install -r requirements/test.txt
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \
-    uv pip install dist/*.whl
-
-ADD ./tests/ ./tests/
-ADD ./examples/ ./examples/
-ADD ./benchmarks/ ./benchmarks/
-
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install -e tests/vllm_test_utils 
-
-ENTRYPOINT ["bash"]
-
-######################### RELEASE IMAGE #########################
-FROM base AS vllm-openai
-
-WORKDIR /workspace/
-
-RUN --mount=type=cache,target=/root/.cache/uv \
-    --mount=type=cache,target=/root/.cache/ccache \
-    --mount=type=bind,from=vllm-build,src=/workspace/vllm/dist,target=dist \
-    uv pip install dist/*.whl
-
-ENTRYPOINT ["python3", "-m", "vllm.entrypoints.openai.api_server"]

docs/README.md

@@ -2,42 +2,19 @@
 
 ## Build the docs
 
-- Make sure in `docs` directory
-
-```bash
-cd docs
-```
-
-- Install the dependencies:
-
 ```bash
+# Install dependencies.
 pip install -r ../requirements/docs.txt
-```
 
-- Clean the previous build (optional but recommended):
-
-```bash
+# Build the docs.
 make clean
-```
-
-- Generate the HTML documentation:
-
-```bash
 make html
 ```
 
 ## Open the docs with your browser
 
-- Serve the documentation locally:
-
 ```bash
 python -m http.server -d build/html/
 ```
 
-This will start a local server at http://localhost:8000. You can now open your browser and view the documentation.
-
-If port 8000 is already in use, you can specify a different port, for example:
-
-```bash
-python -m http.server 3000 -d build/html/
-```
+Launch your browser and open localhost:8000.

docs/source/_static/custom.js

@@ -10,8 +10,8 @@ document.addEventListener("DOMContentLoaded", function () {
     script.setAttribute("runllm-keyboard-shortcut", "Mod+j"); // cmd-j or ctrl-j to open the widget.
     script.setAttribute("runllm-name", "vLLM");
     script.setAttribute("runllm-position", "BOTTOM_RIGHT");
-    script.setAttribute("runllm-position-y", "120px");
-    script.setAttribute("runllm-position-x", "20px");
+    script.setAttribute("runllm-position-y", "20%");
+    script.setAttribute("runllm-position-x", "3%");
     script.setAttribute("runllm-assistant-id", "207");
   
     script.async = true;

docs/source/community/meetups.md

@@ -4,9 +4,6 @@
 
 We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
 
-- [Asia Developer Day](https://www.sginnovate.com/event/limited-availability-morning-evening-slots-remaining-inaugural-vllm-asia-developer-day), April 3rd 2025. [[Slides]](https://docs.google.com/presentation/d/19cp6Qu8u48ihB91A064XfaXruNYiBOUKrBxAmDOllOo/edit?usp=sharing).
-- [vLLM x Ollama Inference Night](https://lu.ma/vllm-ollama), March 27th 2025. [[Slides]](https://docs.google.com/presentation/d/16T2PDD1YwRnZ4Tu8Q5r6n53c5Lr5c73UV9Vd2_eBo4U/edit?usp=sharing).
-- [The first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg), March 16th 2025. [[Slides]](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit?usp=sharing).
 - [The East Coast vLLM Meetup](https://lu.ma/7mu4k4xx), March 11th 2025. [[Slides]](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0)
 - [The ninth vLLM meetup](https://lu.ma/h7g3kuj9), with Meta, February 27th 2025. [[Slides]](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing)
 - [The eighth vLLM meetup](https://lu.ma/zep56hui), with Google Cloud, January 22nd 2025. [[Slides]](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing)

docs/source/community/sponsors.md

@@ -22,7 +22,6 @@ Compute Resources:
 - Databricks
 - DeepInfra
 - Google Cloud
-- Intel
 - Lambda Lab
 - Nebius
 - Novita AI

docs/source/conf.py

@@ -103,11 +103,6 @@ myst_url_schemes = {
         "title": "Pull Request #{{path}}",
         "classes": ["github"],
     },
-    "gh-project": {
-        "url": "https://github.com/orgs/vllm-project/projects/{{path}}",
-        "title": "Project #{{path}}",
-        "classes": ["github"],
-    },
     "gh-dir": {
         "url": "https://github.com/vllm-project/vllm/tree/main/{{path}}",
         "title": "{{path}}",

docs/source/contributing/dockerfile/dockerfile.md

@@ -1,6 +1,6 @@
 # Dockerfile
 
-We provide a <gh-file:docker/Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
+We provide a <gh-file:Dockerfile> to construct the image for running an OpenAI compatible server with vLLM.
 More information about deploying with Docker can be found [here](#deployment-docker).
 
 Below is a visual representation of the multi-stage Dockerfile. The build graph contains the following nodes:
@@ -28,7 +28,7 @@ The edges of the build graph represent:
   > Commands to regenerate the build graph (make sure to run it **from the \`root\` directory of the vLLM repository** where the dockerfile is present):
   >
   > ```bash
-  > dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename docker/Dockerfile
+  > dockerfilegraph -o png --legend --dpi 200 --max-label-length 50 --filename Dockerfile
   > ```
   >
   > or in case you want to run it directly with the docker image:
@@ -43,7 +43,7 @@ The edges of the build graph represent:
   >    --output png \
   >    --dpi 200 \
   >    --max-label-length 50 \
-  >    --filename docker/Dockerfile \
+  >    --filename Dockerfile \
   >    --legend
   > ```
   >

docs/source/contributing/model/multimodal.md

@@ -79,17 +79,6 @@ Further update the model as follows:
             return inputs_embeds
     ```
 
-- Implement {meth}`~vllm.model_executor.models.interfaces.SupportsMultiModal.get_language_model` getter to provide stable access to the underlying language model.
-
-    ```python
-    class YourModelForImage2Seq(nn.Module):
-        ...
-
-        def get_language_model(self) -> torch.nn.Module:
-            # Change `language_model` according to your implementation.
-            return self.language_model
-    ```
-
 - Once the above steps are done, update the model class with the {class}`~vllm.model_executor.models.interfaces.SupportsMultiModal` interface.
 
   ```diff
@@ -121,21 +110,17 @@ def get_supported_mm_limits(self) -> Mapping[str, Optional[int]]:
     return {"image": None, "video": 1}
 ```
 
-## 3. Specify dummy inputs
+### Maximum number of placeholder feature tokens
 
-Then, inherit {class}`~vllm.multimodal.profiling.BaseDummyInputsBuilder` to construct dummy inputs for
-HF processing as well as memory profiling.
+Also, override the abstract method {meth}`~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item`
+to return the maximum number of placeholder feature tokens per input item for each modality.
 
-### For memory profiling
+When calling the model, the output embeddings from the visual encoder are assigned to the input positions
+containing placeholder feature tokens. Therefore, the number of placeholder feature tokens should be equal
+to the size of the output embeddings.
 
-Override the abstract method {meth}`~vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_processor_inputs`
-to construct dummy inputs for memory profiling. This dummy input should result in the worst-case memory usage of
-the model so that vLLM can reserve the correct amount of memory for it.
-
-Assuming that the memory usage increases with the number of tokens, the dummy input can be constructed to maximize the number of output embeddings, which is the same number as placeholder feature tokens.
-
-::::{tab-set}
-:::{tab-item} Basic example: LLaVA
+:::::{tab-set}
+::::{tab-item} Basic example: LLaVA
 :sync: llava
 
 Looking at the code of HF's `LlavaForConditionalGeneration`:
@@ -244,7 +229,7 @@ def get_num_image_tokens(
 ```
 
 Notice that the number of image tokens doesn't depend on the image width and height.
-We can simply use a dummy `image_size`:
+So, we can calculate the maximum number of image tokens using any image size:
 
 ```python
 def get_image_size_with_most_features(self) -> ImageSize:
@@ -252,35 +237,33 @@ def get_image_size_with_most_features(self) -> ImageSize:
     width = height = hf_config.image_size
     return ImageSize(width=width, height=height)
 
-def get_dummy_processor_inputs(
-    self,
-    seq_len: int,
-    mm_counts: Mapping[str, int],
-) -> ProcessorInputs:
-    num_images = mm_counts.get("image", 0)
+def get_max_image_tokens(self) -> int:
+    target_width, target_height = self.get_image_size_with_most_features()
 
-    processor = self.info.get_hf_processor()
-    image_token = processor.image_token
-  
-    hf_config = self.get_hf_config()
-    target_width, target_height = self.info.get_image_size_with_most_features()
-
-    mm_data = {
-        "image":
-        self._get_dummy_images(width=target_width,
-                               height=target_height,
-                               num_images=num_images)
-    }
-
-    return ProcessorInputs(
-        prompt_text=image_token * num_images,
-        mm_data=mm_data,
+    return self.get_num_image_tokens(
+        image_width=target_width,
+        image_height=target_height,
     )
 ```
 
+And thus, we can override the method as:
+
+```python
+def get_mm_max_tokens_per_item(
+    self,
+    seq_len: int,
+    mm_counts: Mapping[str, int],
+) -> Mapping[str, int]:
+    return {"image": self.get_max_image_tokens()}
+```
+
+:::{note}
+Our [actual code](gh-file:vllm/model_executor/models/llava.py) is more abstracted to support vision encoders other than CLIP.
 :::
 
-:::{tab-item} No input placeholders: Fuyu
+::::
+
+::::{tab-item} Non-consecutive feature tokens: Fuyu
 :sync: fuyu
 
 Looking at the code of HF's `FuyuForCausalLM`:
@@ -400,16 +383,188 @@ num_patches_per_dim_w = image_width // patch_width
 num_patches = num_patches_per_dim_h * num_patches_per_dim_w
 ```
 
-These image patches correspond to placeholder tokens (`|SPEAKER|`). So, we just need to maximize the number of image patches. Since input images are first resized
-to fit within `image_processor.size`, we can maximize the number of image patches by inputting an image with size equal to `image_processor.size`.
+We can calculate this in vLLM using this code:
+
+```python
+def get_num_image_patches(
+    self,
+    *,
+    image_width: int,
+    image_height: int,
+) -> int:
+    image_processor = self.get_image_processor()
+    target_width = image_processor.size["width"]
+    target_height = image_processor.size["height"]
+    patch_width = image_processor.patch_size["width"]
+    patch_height = image_processor.patch_size["height"]
+
+    if not (image_width <= target_width and image_height <= target_height):
+        height_scale_factor = target_height / image_height
+        width_scale_factor = target_width / image_width
+        optimal_scale_factor = min(height_scale_factor, width_scale_factor)
+
+        image_height = int(image_height * optimal_scale_factor)
+        image_width = int(image_width * optimal_scale_factor)
+
+    ncols = math.ceil(image_width / patch_width)
+    nrows = math.ceil(image_height / patch_height)
+    return ncols * nrows
+```
+
+These image patches correspond to placeholder tokens (`|SPEAKER|`). However, the processor also
+inserts newline tokens (`|NEWLINE|`) as shown here:
+
+```python
+# https://github.com/huggingface/transformers/blob/v4.48.3/src/transformers/models/fuyu/image_processing_fuyu.py#L654-L670
+tensor_of_image_ids = torch.full(
+    [num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
+)
+patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
+assert num_patches == patches.shape[0]
+
+if variable_sized:
+    # Now terminate each line with |NEWLINE|.
+    tensor_of_image_ids = tensor_of_image_ids.reshape(-1, image_width // patch_width)
+    newline_ids = torch.full(
+        [tensor_of_image_ids.shape[0], 1],
+        image_newline_id,
+        dtype=torch.int32,
+        device=image_input.device,
+    )
+    tensor_of_image_ids = torch.cat([tensor_of_image_ids, newline_ids], dim=1)
+    tensor_of_image_ids = tensor_of_image_ids.reshape(-1)
+```
+
+So, the layout of tokens for an image is:
+
+```
+|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
+|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
+...
+|SPEAKER||SPEAKER|...|SPEAKER||NEWLINE|
+```
+
+This makes the placeholder tokens non-consecutive in the prompt.
+Since vLLM requires the feature tokens to be consecutive, **we also treat the newline tokens as feature tokens**.
+
+So overall, the total number of feature tokens is
+
+```python
+def get_num_image_tokens(
+    self,
+    *,
+    image_width: int,
+    image_height: int,
+) -> int:
+    image_processor = self.get_image_processor()
+    target_width = image_processor.size["width"]
+    target_height = image_processor.size["height"]
+    patch_width = image_processor.patch_size["width"]
+    patch_height = image_processor.patch_size["height"]
+
+    if not (image_width <= target_width and image_height <= target_height):
+        height_scale_factor = target_height / image_height
+        width_scale_factor = target_width / image_width
+        optimal_scale_factor = min(height_scale_factor, width_scale_factor)
+
+        image_height = int(image_height * optimal_scale_factor)
+        image_width = int(image_width * optimal_scale_factor)
+
+    ncols = math.ceil(image_width / patch_width)
+    nrows = math.ceil(image_height / patch_height)
+    return (ncols + 1) * nrows
+```
+
+To calculate the maximum number of image tokens, recall that input images are first resized
+to fit within `image_processor.size`. The maximum possible dimensions of the image before
+being converted into patches is therefore equal to `image_processor.size`.
 
 ```python
 def get_image_size_with_most_features(self) -> ImageSize:
     image_processor = self.get_image_processor()
     return ImageSize(width=image_processor.size["width"],
                         height=image_processor.size["height"])
+
+def get_max_image_tokens(self) -> int:
+    target_width, target_height = self.get_image_size_with_most_features()
+
+    return self.get_num_image_tokens(
+        image_width=target_width,
+        image_height=target_height,
+    )
 ```
 
+And thus, we can override the method as:
+
+```python
+def get_mm_max_tokens_per_item(
+    self,
+    seq_len: int,
+    mm_counts: Mapping[str, int],
+) -> Mapping[str, int]:
+    return {"image": self.get_max_image_tokens()}
+```
+
+:::{note}
+Our [actual code](gh-file:vllm/model_executor/models/fuyu.py) returns `ncols` and `nrows` directly instead of the total token count.
+This is because `ncols` and `nrows` are used to specify the layout of the feature tokens (as shown in Step 4 of this guide).
+:::
+
+::::
+:::::
+
+## 3. Specify dummy inputs
+
+Then, inherit {class}`~vllm.multimodal.profiling.BaseDummyInputsBuilder` to construct dummy inputs for
+HF processing as well as memory profiling.
+
+### For memory profiling
+
+Override the abstract method {meth}`~vllm.multimodal.profiling.BaseDummyInputsBuilder.get_dummy_processor_inputs`
+to construct dummy inputs for memory profiling. This dummy input should result in the worst-case memory usage of
+the model so that vLLM can reserve the correct amount of memory for it.
+
+Assuming that the memory usage increases with the number of tokens, the dummy input can be constructed based
+on the code for {meth}`~vllm.multimodal.processing.BaseProcessingInfo.get_mm_max_tokens_per_item`.
+
+::::{tab-set}
+:::{tab-item} Basic example: LLaVA
+:sync: llava
+
+Making use of the `get_image_size_with_most_features` method implemented in Step 2:
+
+```python
+def get_dummy_processor_inputs(
+    self,
+    seq_len: int,
+    mm_counts: Mapping[str, int],
+) -> ProcessorInputs:
+    num_images = mm_counts.get("image", 0)
+
+    processor = self.info.get_hf_processor()
+    image_token = processor.image_token
+  
+    hf_config = self.get_hf_config()
+    target_width, target_height = self.info.get_image_size_with_most_features()
+
+    mm_data = {
+        "image":
+        self._get_dummy_images(width=target_width,
+                               height=target_height,
+                               num_images=num_images)
+    }
+
+    return ProcessorInputs(
+        prompt_text=image_token * num_images,
+        mm_data=mm_data,
+    )
+```
+
+:::
+
+:::{tab-item} No input placeholders: Fuyu
+:sync: fuyu
+
 Fuyu does not expect image placeholders in the inputs to HF processor, so
 the dummy prompt text is empty regardless of the number of images.
 Otherwise, the logic of this method is very similar to LLaVA:
@@ -705,8 +860,8 @@ prompt_tokens, prompts_length = _tokenize_prompts_with_image_and_batch(
 )
 ```
 
-To assign the vision embeddings to only the image tokens, instead of a string
-you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`:
+To accommodate this, instead of a string you can return an instance of {class}`~vllm.multimodal.processing.PromptUpdateDetails`
+with different `full` and `feature` attributes:
 
 ```python
 hf_config = self.info.get_hf_config()
@@ -724,9 +879,9 @@ def get_replacement_fuyu(item_idx: int):
     image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                     [_NEWLINE_TOKEN_ID]) * nrows
 
-    return PromptUpdateDetails.select_token_id(
-        image_tokens + [bos_token_id],
-        embed_token_id=_IMAGE_TOKEN_ID,
+    return PromptUpdateDetails(
+        full=image_tokens + [bos_token_id],
+        features=image_tokens,
     )
 ```
 
@@ -759,9 +914,9 @@ def _get_prompt_updates(
         image_tokens = ([_IMAGE_TOKEN_ID] * ncols +
                         [_NEWLINE_TOKEN_ID]) * nrows
 
-        return PromptUpdateDetails.select_token_id(
-            image_tokens + [bos_token_id],
-            embed_token_id=_IMAGE_TOKEN_ID,
+        return PromptUpdateDetails(
+            full=image_tokens + [bos_token_id],
+            features=image_tokens,
         )
 
     return [

docs/source/contributing/overview.md

@@ -11,15 +11,6 @@ We also believe in the power of community support; thus, answering queries, offe
 
 Finally, one of the most impactful ways to support us is by raising awareness about vLLM. Talk about it in your blog posts and highlight how it's driving your incredible projects. Express your support on social media if you're using vLLM, or simply offer your appreciation by starring our repository!
 
-## Job Board
-
-Unsure on where to start? Check out the following links for tasks to work on:
-
-- [Good first issues](https://github.com/vllm-project/vllm/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22good%20first%20issue%22)
-  - [Selected onboarding tasks](gh-project:6)
-- [New model requests](https://github.com/vllm-project/vllm/issues?q=is%3Aissue%20state%3Aopen%20label%3A%22new%20model%22)
-  - [Models with multi-modal capabilities](gh-project:10)
-
 ## License
 
 See <gh-file:LICENSE>.
@@ -44,12 +35,6 @@ pre-commit run --all-files
 pytest tests/
 ```
 
-:::{tip}
-Since the <gh-file:docker/Dockerfile> ships with Python 3.12, all tests in CI (except `mypy`) are run with Python 3.12.
-
-Therefore, we recommend developing with Python 3.12 to minimise the chance of your local environment clashing with our CI environment.
-:::
-
 :::{note}
 Currently, the repository is not fully checked by `mypy`.
 :::

docs/source/deployment/docker.md

@@ -34,11 +34,11 @@ If you need to use those dependencies (having accepted the license terms),
 create a custom Dockerfile on top of the base image with an extra layer that installs them:
 
 ```Dockerfile
-FROM vllm/vllm-openai:v0.8.3
+FROM vllm/vllm-openai:v0.8.0
 
-# e.g. install the `audio` optional dependencies
+# e.g. install the `audio` and `video` optional dependencies
 # NOTE: Make sure the version of vLLM matches the base image!
-RUN uv pip install --system vllm[audio]==0.8.3
+RUN uv pip install vllm[audio,video]==0.8.0
 ```
 
 :::
@@ -52,7 +52,7 @@ with an extra layer that installs their code from source:
 ```Dockerfile
 FROM vllm/vllm-openai:latest
 
-RUN uv pip install --system git+https://github.com/huggingface/transformers.git
+RUN uv pip install git+https://github.com/huggingface/transformers.git
 ```
 
 :::
@@ -61,11 +61,11 @@ RUN uv pip install --system git+https://github.com/huggingface/transformers.git
 
 ## Building vLLM's Docker Image from Source
 
-You can build and run vLLM from source via the provided <gh-file:docker/Dockerfile>. To build vLLM:
+You can build and run vLLM from source via the provided <gh-file:Dockerfile>. To build vLLM:
 
 ```console
 # optionally specifies: --build-arg max_jobs=8 --build-arg nvcc_threads=2
-DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai --file docker/Dockerfile
+DOCKER_BUILDKIT=1 docker build . --target vllm-openai --tag vllm/vllm-openai
 ```
 
 :::{note}
@@ -92,7 +92,6 @@ Keep an eye on memory usage with parallel jobs as it can be substantial (see exa
 # Example of building on Nvidia GH200 server. (Memory usage: ~15GB, Build time: ~1475s / ~25 min, Image size: 6.93GB)
 $ python3 use_existing_torch.py
 $ DOCKER_BUILDKIT=1 docker build . \
-  --file docker/Dockerfile \
   --target vllm-openai \
   --platform "linux/arm64" \
   -t vllm/vllm-gh200-openai:latest \

docs/source/deployment/k8s.md

@@ -46,7 +46,6 @@ metadata:
 type: Opaque
 data:
   token: $(HF_TOKEN)
-EOF
 ```
 
 Next, start the vLLM server as a Kubernetes Deployment and Service: