accepted length code

Signed-off-by: Russell Bryant <rbryant@redhat.com>

.buildkite/lm-eval-harness/configs/DeepSeek-V2-Lite-Chat.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m deepseek-ai/DeepSeek-V2-Lite-Chat -b "auto" -l 1000 -f 5 -t 2
 model_name: "deepseek-ai/DeepSeek-V2-Lite-Chat"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform.yaml

@@ -1,3 +1,4 @@
+# For hf script, without -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5
 model_name: "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-70B-Instruct.yaml

@@ -1,3 +1,4 @@
+# For hf script, without -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-70B-Instruct -b 32 -l 250 -f 5
 model_name: "meta-llama/Meta-Llama-3-70B-Instruct"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-Channelwise-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors -b auto -l 1000 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8A8-FP8-Channelwise-compressed-tensors"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform -b auto -l 1000 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-FBGEMM-nonuniform"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 1000 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1
 model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors-asym.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Asym-Per-Token-Test"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-nonuniform-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test -b auto -l 1000 -f 5 -t 1
 model_name: "nm-testing/Meta-Llama-3-8B-Instruct-nonuniform-test"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml

@@ -1,4 +1,5 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5 -t 1
+# For hf script, without -t option (tensor parallel size).
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m meta-llama/Meta-Llama-3-8B-Instruct -b 32 -l 250 -f 5
 model_name: "meta-llama/Meta-Llama-3-8B-Instruct"
 tasks:
 - name: "gsm8k"

.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-QQQ.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m HandH1998/QQQ-Llama-3-8b-g128 -b 32 -l 1000 -f 5 -t 1
 model_name: "HandH1998/QQQ-Llama-3-8b-g128"
 tasks:

.buildkite/lm-eval-harness/configs/Meta-Llama-3.2-1B-Instruct-INT8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
 model_name: "neuralmagic/Llama-3.2-1B-Instruct-quantized.w8a8"
 tasks:

.buildkite/lm-eval-harness/configs/Minitron-4B-Base-FP8.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m mgoin/Minitron-4B-Base-FP8 -b auto -l 1000 -f 5 -t 1
 model_name: "mgoin/Minitron-4B-Base-FP8"
 tasks:

.buildkite/lm-eval-harness/configs/Mixtral-8x22B-Instruct-v0.1-FP8-Dynamic.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic -b "auto" -l 250 -f 5 -t 8
 model_name: "neuralmagic/Mixtral-8x22B-Instruct-v0.1-FP8-dynamic"
 tasks:

.buildkite/lm-eval-harness/configs/Mixtral-8x7B-Instruct-v0.1-FP8.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8 -b "auto" -l 250 -f 5 -t 4
 model_name: "neuralmagic/Mixtral-8x7B-Instruct-v0.1-FP8"
 tasks:

.buildkite/lm-eval-harness/configs/Mixtral-8x7B-Instruct-v0.1.yaml

@@ -1,4 +1,5 @@
-# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5 -t 4
+# For hf script, without -t option (tensor parallel size).
+# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Mixtral-8x7B-Instruct-v0.1 -b 32 -l 250 -f 5
 model_name: "mistralai/Mixtral-8x7B-Instruct-v0.1"
 tasks:
 - name: "gsm8k"

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

@@ -0,0 +1,12 @@
+# For vllm script, with -t option (tensor parallel size).
+# 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: 0.30
+  - name: "exact_match,flexible-extract"
+    value: 0.465
+limit: 1319
+num_fewshot: 5

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-FP8W8.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-FP8W8 -b auto -l 1000 -f 5 -t 1
 model_name: "nm-testing/Qwen2-1.5B-Instruct-FP8W8"
 tasks:

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8 -b "auto" -l 1000 -f 5 -t 1
 model_name: "neuralmagic/Qwen2-1.5B-Instruct-quantized.w8a8"
 tasks:

.buildkite/lm-eval-harness/configs/Qwen2-1.5B-Instruct-W8A16-compressed-tensors.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise -b "auto" -l 1000 -f 5 -t 1
 model_name: "nm-testing/Qwen2-1.5B-Instruct-W8A16-Channelwise"
 tasks:

.buildkite/lm-eval-harness/configs/Qwen2-57B-A14-Instruct.yaml

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m Qwen/Qwen2-57B-A14B-Instruct -b "auto" -l 250 -f 5 -t 4
 model_name: "Qwen/Qwen2-57B-A14B-Instruct"
 tasks:

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

@@ -1,3 +1,4 @@
+# For vllm script, with -t option (tensor parallel size).
 # bash ./run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM -b "auto" -t 2
 model_name: "nm-testing/SparseLlama-3.1-8B-gsm8k-pruned.2of4-chnl_wts_per_tok_dyn_act_fp8-BitM"
 tasks:

.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
-Minitron-4B-Base-FP8.yaml
+Qwen1.5-MoE-W4A16-compressed-tensors.yaml
 Qwen2-1.5B-Instruct-INT8-compressed-tensors.yaml
 Qwen2-1.5B-Instruct-FP8W8.yaml
 Meta-Llama-3-8B-QQQ.yaml

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

@@ -16,7 +16,7 @@ import numpy
 import pytest
 import yaml
 
-RTOL = 0.05
+RTOL = 0.08
 TEST_DATA_FILE = os.environ.get(
     "LM_EVAL_TEST_DATA_FILE",
     ".buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct.yaml")

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

@@ -10,15 +10,24 @@ set -x
 set -o pipefail
 
 check_gpus() {
-  # check the number of GPUs and GPU type.
-  declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+  if command -v nvidia-smi; then
+    # check the number of GPUs and GPU type.
+    declare -g gpu_count=$(nvidia-smi --list-gpus | wc -l)
+  elif command -v amd-smi; then
+    declare -g gpu_count=$(amd-smi list | grep 'GPU' | wc -l)
+  fi
+
   if [[ $gpu_count -gt 0 ]]; then
     echo "GPU found."
   else
     echo "Need at least 1 GPU to run benchmarking."
     exit 1
   fi
-  declare -g gpu_type=$(nvidia-smi --query-gpu=name --format=csv,noheader | awk '{print $2}')
+  if command -v nvidia-smi; then
+    declare -g gpu_type=$(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
   echo "GPU type is $gpu_type"
 }
 
@@ -90,9 +99,15 @@ kill_gpu_processes() {
 
 
   # wait until GPU memory usage smaller than 1GB
-  while [ "$(nvidia-smi --query-gpu=memory.used --format=csv,noheader,nounits | head -n 1)" -ge 1000 ]; do
-    sleep 1
-  done
+  if command -v nvidia-smi; then
+    while [ "$(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
 
   # remove vllm config file
   rm -rf ~/.config/vllm
@@ -361,7 +376,7 @@ main() {
   # get the current IP address, required by benchmark_serving.py
   export VLLM_HOST_IP=$(hostname -I | awk '{print $1}')
   # turn of the reporting of the status of each request, to clean up the terminal output
-  export VLLM_LOG_LEVEL="WARNING"
+  export VLLM_LOGGING_LEVEL="WARNING"
 
   # prepare for benchmarking
   cd benchmarks || exit 1

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

@@ -63,10 +63,12 @@
             "model": "meta-llama/Meta-Llama-3.1-70B-Instruct",
             "disable_log_requests": "", 
             "tensor_parallel_size": 4,
-            "swap_space": 16, 
-            "speculative_model": "turboderp/Qwama-0.5B-Instruct",
-            "num_speculative_tokens": 4,
-            "speculative_draft_tensor_parallel_size": 1
+            "swap_space": 16,
+            "speculative_config": {
+                "model": "turboderp/Qwama-0.5B-Instruct",
+                "num_speculative_tokens": 4,
+                "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 ."
+      - "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 ."
       - "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/upload-wheels.sh"
+      - "bash .buildkite/scripts/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 ."
+      - "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 ."
       - "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/upload-wheels.sh"
+      - "bash .buildkite/scripts/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 ."
+      - "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 ."
       - "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/upload-wheels.sh"
+      - "bash .buildkite/scripts/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:$BUILDKITE_COMMIT --target vllm-openai --progress plain ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.4.0 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT --target vllm-openai --progress plain -f docker/Dockerfile ."
       - "docker 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:$BUILDKITE_COMMIT --progress plain -f Dockerfile.tpu ."
+      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --tag vllm/vllm-tpu:nightly --tag vllm/vllm-tpu:$BUILDKITE_COMMIT --progress plain -f docker/Dockerfile.tpu ."
       - "docker push vllm/vllm-tpu:nightly"
       - "docker push vllm/vllm-tpu:$BUILDKITE_COMMIT"
     plugins:
@@ -82,7 +82,22 @@ steps:
       queue: cpu_queue_postmerge
     commands:
       - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-      - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain -f 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:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:latest --progress plain --target vllm-openai -f docker/Dockerfile.cpu ."
       - "docker push public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:$(buildkite-agent meta-data get release-version)"
     env:
       DOCKER_BUILDKIT: "1"
+
+  - block: "Build Neuron release image"
+    key: block-neuron-release-image-build
+    depends_on: ~
+
+  - label: "Build and publish Neuron release image"
+    depends_on: block-neuron-release-image-build
+    agents:
+      queue: neuron-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-neuron-release-repo:$(buildkite-agent meta-data get release-version) --tag public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:latest --progress plain -f docker/Dockerfile.neuron ."
+      - "docker push public.ecr.aws/q9t5s3a7/vllm-neuron-release-repo:$(buildkite-agent meta-data get release-version)"
+    env:
+      DOCKER_BUILDKIT: "1"

.buildkite/run-openvino-test.sh

@@ -1,16 +0,0 @@
-#!/bin/bash
-
-# This script build the OpenVINO docker image and run the offline inference inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -ex
-
-# Try building the docker image
-docker build -t openvino-test -f Dockerfile.openvino .
-
-# Setup cleanup
-remove_docker_container() { docker rm -f openvino-test || true; }
-trap remove_docker_container EXIT
-remove_docker_container
-
-# Run the image and launch offline inference
-docker run --network host --env VLLM_OPENVINO_KVCACHE_SPACE=1 --name openvino-test openvino-test python3 /workspace/examples/offline_inference/basic/generate.py --model facebook/opt-125m

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

@@ -1,36 +0,0 @@
-#!/bin/bash
-
-set -e
-
-# Build the docker image.
-docker build -f Dockerfile.tpu -t vllm-tpu .
-
-# Set up cleanup.
-remove_docker_container() { docker rm -f tpu-test || true; }
-trap remove_docker_container EXIT
-# Remove the container that might not be cleaned up in the previous run.
-remove_docker_container
-
-# For HF_TOKEN.
-source /etc/environment
-# Run a simple end-to-end example.
-docker run --privileged --net host --shm-size=16G -it \
-    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
-    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
-    && python3 -m pip install pytest \
-    && python3 -m pip install lm_eval[api]==0.4.4 \
-    && echo TEST_1 \
-    && VLLM_USE_V1=1 python3 /workspace/vllm/tests/tpu/test_compilation.py \
-    && echo TEST_2 \
-    && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
-    && echo TEST_3 \
-    && VLLM_USE_V1=1 pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
-    && echo TEST_4 \
-    && VLLM_USE_V1=1 pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
-    && echo TEST_5 \
-    && VLLM_USE_V1=1 python3 /workspace/vllm/examples/offline_inference/tpu.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/scripts/hardware_ci/run-amd-test.sh

@@ -98,6 +98,13 @@ if [[ $commands == *" kernels "* ]]; then
   --ignore=kernels/test_machete_mm.py \
   --ignore=kernels/test_mha_attn.py \
   --ignore=kernels/test_block_fp8.py \
+  --ignore=kernels/test_cutlass_moe.py \
+  --ignore=kernels/test_mamba_ssm_ssd.py \
+  --ignore=kernels/test_attention.py \
+  --ignore=kernels/test_block_int8.py \
+  --ignore=kernels/test_fused_quant_layernorm.py \
+  --ignore=kernels/test_int8_kernel.py \
+  --ignore=kernels/test_triton_moe_ptpc_fp8.py \
   --ignore=kernels/test_permute_cols.py"
 fi
 
@@ -105,19 +112,33 @@ 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 [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
-  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
+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 "}
 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
@@ -134,9 +155,10 @@ 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 --device /dev/dri \
-        --network host \
+        --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+        --network=host \
         --shm-size=16gb \
         --rm \
         -e HIP_VISIBLE_DEVICES="${GPU}" \
@@ -163,9 +185,10 @@ if [[ $commands == *"--shard-id="* ]]; then
     fi
   done
 else
+  echo "Render devices: $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES"
   docker run \
-          --device /dev/kfd --device /dev/dri \
-          --network host \
+          --device /dev/kfd $BUILDKITE_AGENT_META_DATA_RENDER_DEVICES \
+          --network=host \
           --shm-size=16gb \
           --rm \
           -e HIP_VISIBLE_DEVICES=0 \

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

@@ -0,0 +1,45 @@
+#!/bin/bash
+
+# This script build the CPU docker image and run the offline inference inside the container.
+# It serves a sanity check for compilation and basic model usage.
+set -ex
+
+# Setup cleanup
+remove_docker_container() {
+  if [[ -n "$container_id" ]]; then
+      podman rm -f "$container_id" || true
+  fi
+  podman system prune -f
+}
+trap remove_docker_container EXIT
+remove_docker_container
+
+# Try building the docker image
+podman build -t cpu-test-ubi9-ppc -f docker/Dockerfile.ppc64le .
+
+# Run the image
+container_id=$(podman run -itd --entrypoint /bin/bash -v /tmp/:/root/.cache/huggingface --privileged=true --network host -e HF_TOKEN cpu-test-ubi9-ppc)
+
+function cpu_tests() {
+
+  # offline inference
+  podman exec -it "$container_id" bash -c "
+    set -e
+    python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m"
+
+  # Run basic model test
+  podman exec -it "$container_id" bash -c "
+    set -e
+    pip install pytest pytest-asyncio einops peft Pillow soundfile transformers_stream_generator matplotlib
+    pip install sentence-transformers datamodel_code_generator
+    pytest -v -s tests/models/embedding/language/test_cls_models.py::test_classification_models[float-jason9693/Qwen2.5-1.5B-apeach]
+    pytest -v -s tests/models/embedding/language/test_embedding.py::test_models[half-BAAI/bge-base-en-v1.5]
+    pytest -v -s tests/models/encoder_decoder/language -m cpu_model"
+}
+
+# All of CPU tests are expected to be finished less than 40 mins.
+
+export container_id
+export -f cpu_tests
+timeout 40m bash -c cpu_tests
+

.buildkite/scripts/hardware_ci/run-cpu-test-s390x.sh

@@ -10,5 +10,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Try building the docker image
-docker build -t cpu-test -f Dockerfile.ppc64le .
-
+docker build -t cpu-test -f docker/Dockerfile.s390x .

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

@@ -8,15 +8,19 @@ 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; }
+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; 
+}
 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-"$BUILDKITE_BUILD_NUMBER"
@@ -36,8 +40,8 @@ function cpu_tests() {
   # Run basic model test
   docker exec cpu-test-"$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
     pytest -v -s tests/models/embedding/language -m cpu_model
     pytest -v -s tests/models/encoder_decoder/language -m cpu_model

.buildkite/scripts/hardware_ci/run-gh200-test.sh

@@ -9,11 +9,13 @@ 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 \
   --build-arg max_jobs=66 \
   --build-arg nvcc_threads=2 \
+  --build-arg RUN_WHEEL_CHECK=false \
   --build-arg torch_cuda_arch_list="9.0+PTX" \
   --build-arg vllm_fa_cmake_gpu_arches="90-real"
 
@@ -23,6 +25,6 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and test offline inference
-docker run -e HF_TOKEN -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
+docker run -e HF_TOKEN -e VLLM_WORKER_MULTIPROC_METHOD=spawn -v /root/.cache/huggingface:/root/.cache/huggingface --name gh200-test --gpus=all --entrypoint="" gh200-test bash -c '
     python3 examples/offline_inference/basic/generate.py --model meta-llama/Llama-3.2-1B
 '

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

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

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

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

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

@@ -0,0 +1,54 @@
+#!/bin/bash
+
+set -xue
+
+# Build the docker image.
+docker build -f docker/Dockerfile.tpu -t vllm-tpu .
+
+# Set up cleanup.
+remove_docker_container() { docker rm -f tpu-test || true; }
+trap remove_docker_container EXIT
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
+
+# For HF_TOKEN.
+source /etc/environment
+# Run a simple end-to-end example.
+docker run --privileged --net host --shm-size=16G -it \
+    -e "HF_TOKEN=$HF_TOKEN" --name tpu-test \
+    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install pytest pytest-asyncio tpu-info \
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && export VLLM_XLA_CACHE_PATH= \
+    && export VLLM_USE_V1=1 \
+    && export VLLM_XLA_CHECK_RECOMPILATION=1 \
+    && echo HARDWARE \
+    && tpu-info \
+    && echo TEST_0 \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_perf.py \
+    && echo TEST_1 \
+    && pytest -v -s /workspace/vllm/tests/tpu/test_compilation.py \
+    && echo TEST_2 \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
+    && echo TEST_3 \
+    && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
+    && echo TEST_4 \
+    && pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
+    && 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_multimodal.py \
+    && echo TEST_10 \
+    && pytest -s -v /workspace/vllm/tests/v1/tpu/test_pallas.py \
+    && echo TEST_11 \
+    && pytest -s -v /workspace/vllm/tests/v1/entrypoints/llm/test_struct_output_generate.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/scripts/hardware_ci/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 Dockerfile.xpu .
+docker build -t ${image_name} -f docker/Dockerfile.xpu .
 
 # Setup cleanup
 remove_docker_container() { 

.buildkite/scripts/run-benchmarks.sh

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

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

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

.buildkite/test-pipeline.yaml

@@ -8,6 +8,7 @@
 # Documentation
 # label(str): the name of the test. emoji allowed.
 # fast_check(bool): whether to run this on each commit on fastcheck pipeline.
+# torch_nightly(bool): whether to run this on vllm against torch nightly pipeline.
 # fast_check_only(bool): run this test on fastcheck pipeline only
 # optional(bool): never run this test by default (i.e. need to unblock manually) unless it's scheduled nightly run.
 # command(str): the single command to run for tests. incompatible with commands.
@@ -70,6 +71,7 @@ steps:
 - label: Basic Correctness Test # 30min
   #mirror_hardwares: [amd]
   fast_check: true
+  torch_nightly: true
   source_file_dependencies:
   - vllm/
   - tests/basic_correctness/test_basic_correctness
@@ -104,7 +106,8 @@ steps:
 - label: Entrypoints Test # 40min
   working_dir: "/vllm-workspace/tests"
   fast_check: true
-  mirror_hardwares: [amd]
+  torch_nightly: true
+  #mirror_hardwares: [amd]
   source_file_dependencies:
   - vllm/
   - tests/entrypoints/llm
@@ -118,7 +121,7 @@ steps:
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
   - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
-  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/correctness/
+  - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py  --ignore=entrypoints/openai/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/test_openai_schema.py
   - pytest -v -s entrypoints/test_chat_utils.py
   - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
@@ -135,12 +138,14 @@ 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
@@ -153,6 +158,7 @@ steps:
   - popd
 
 - label: Metrics, Tracing Test # 10min
+  mirror_hardwares: [amd]
   num_gpus: 2
   source_file_dependencies:
   - vllm/
@@ -160,18 +166,13 @@ 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
@@ -202,12 +203,13 @@ 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
     - pytest -v -s v1/worker
     - pytest -v -s v1/structured_output
+    - pytest -v -s v1/spec_decode
+    - pytest -v -s v1/test_serial_utils.py
     - pytest -v -s v1/test_stats.py
     - pytest -v -s v1/test_utils.py
     - pytest -v -s v1/test_oracle.py
@@ -283,13 +285,22 @@ 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_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
+  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
   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
+    - pytest -v -s compile/test_sequence_parallelism.py
+
 - label: PyTorch Fullgraph Smoke Test # 9min
   source_file_dependencies:
   - vllm/
@@ -299,7 +310,6 @@ steps:
   # these tests need to be separated, cannot combine
   - pytest -v -s compile/piecewise/test_simple.py
   - pytest -v -s compile/piecewise/test_toy_llama.py
-  - pytest -v -s compile/test_pass_manager.py
 
 - label: PyTorch Fullgraph Test # 18min
   source_file_dependencies:
@@ -308,18 +318,49 @@ steps:
   commands:
   - pytest -v -s compile/test_full_graph.py
 
-- label: Kernels Test %N # 1h each
-  mirror_hardwares: [amd]
+- label: Kernels Core Operation Test
   source_file_dependencies:
   - csrc/
-  - vllm/attention
-  - tests/kernels
+  - tests/kernels/core
   commands:
-    - pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 4
+    - pytest -v -s kernels/core
+
+- label: Kernels Attention Test %N
+  source_file_dependencies:
+  - csrc/attention/
+  - vllm/attention
+  - vllm/v1/attention
+  - tests/kernels/attention
+  commands:
+    - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
+
+- label: Kernels Quantization Test %N
+  source_file_dependencies:
+  - csrc/quantization/
+  - vllm/model_executor/layers/quantization
+  - tests/kernels/quantization
+  commands:
+    - pytest -v -s kernels/quantization  --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  parallelism: 2
+
+- label: Kernels MoE Test
+  source_file_dependencies:
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  commands:
+    - pytest -v -s kernels/moe
+
+- label: Kernels Mamba Test
+  source_file_dependencies:
+  - csrc/mamba/
+  - tests/kernels/mamba
+  commands:
+    - pytest -v -s kernels/mamba
 
 - label: Tensorizer Test # 11min
-  mirror_hardwares: [amd]
+  # mirror_hardwares: [amd]
   soft_fail: true
   source_file_dependencies:
   - vllm/model_executor/model_loader
@@ -335,7 +376,14 @@ steps:
   source_file_dependencies:
   - benchmarks/
   commands:
-  - bash run-benchmarks.sh
+  - bash scripts/run-benchmarks.sh
+
+- label: Benchmarks CLI Test # 10min
+  source_file_dependencies:
+  - vllm/
+  - tests/benchmarks/
+  commands:
+  - pytest -v -s benchmarks/
 
 - label: Quantization Test # 33min
   source_file_dependencies:
@@ -370,12 +418,14 @@ steps:
 
 - label: OpenAI-Compatible Tool Use # 20 min
   fast_check: false
-  mirror_hardwares: [ amd ]
+  #mirror_hardwares: [ amd ]
   source_file_dependencies:
     - vllm/
     - tests/tool_use
+    - tests/mistral_tool_use
   commands:
     - pytest -v -s tool_use
+    - pytest -v -s mistral_tool_use
 
 #####  models test  #####
 
@@ -387,7 +437,9 @@ 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
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'llama4'
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'plamo2'
 
 - label: Language Models Test (Standard) # 32min
   #mirror_hardwares: [amd]
@@ -397,6 +449,8 @@ steps:
   - tests/models/embedding/language
   - tests/models/encoder_decoder/language
   commands:
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install causal-conv1d
     - pytest -v -s models/decoder_only/language -m 'core_model or quant_model'
     - pytest -v -s models/embedding/language -m core_model
 
@@ -408,6 +462,8 @@ steps:
   - tests/models/embedding/language
   - tests/models/encoder_decoder/language
   commands:
+    # Install causal-conv1d for plamo2 models here, as it is not compatible with pip-compile.
+    - pip install causal-conv1d
     - pytest -v -s models/decoder_only/language -m 'not core_model and not quant_model'
     - pytest -v -s models/embedding/language -m 'not core_model'
 
@@ -424,11 +480,12 @@ 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 --ignore models/decoder_only/vision_language/test_phi3v.py models/decoder_only/vision_language -m 'core_model or quant_model'
+    - pytest -v -s 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
@@ -442,10 +499,7 @@ 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'
-    # 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 --ignore models/decoder_only/vision_language/test_models.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'
@@ -461,6 +515,7 @@ 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...'
@@ -472,6 +527,7 @@ steps:
 #####  multi gpus test  #####
 
 - label: Distributed Comm Ops Test # 7min
+  mirror_hardwares: [amd]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
@@ -514,7 +570,10 @@ 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
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
@@ -525,11 +584,14 @@ steps:
   - pytest models/encoder_decoder/language/test_bart.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/encoder_decoder/vision_language/test_broadcast.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/decoder_only/vision_language/test_models.py -v -s -m 'distributed(num_gpus=2)'
+  # test sequence parallel
+  - pytest -v -s distributed/test_sequence_parallel.py
   # this test fails consistently.
   # TODO: investigate and fix
   # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
   - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
 
 - label: Plugin Tests (2 GPUs) # 40min
   working_dir: "/vllm-workspace/tests"
@@ -592,14 +654,10 @@ 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/CODEOWNERS

@@ -12,6 +12,7 @@
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
 /vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
+/vllm/vllm_flash_attn @LucasWilkinson
 CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1

.github/ISSUE_TEMPLATE/200-installation.yml

@@ -14,7 +14,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.github/ISSUE_TEMPLATE/300-usage.yml

@@ -14,7 +14,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

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

@@ -14,7 +14,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.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/adding_model.html first to understand how to add a new model.
+      #### 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.
 - type: textarea
   attributes:
     label: The model to consider.

.github/ISSUE_TEMPLATE/700-performance-discussion.yml

@@ -35,7 +35,7 @@ body:
     description: |
       Please run the following and paste the output below.
       ```sh
-      wget https://raw.githubusercontent.com/vllm-project/vllm/main/collect_env.py
+      wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```

.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>**
+**BEFORE SUBMITTING, PLEASE READ <https://docs.vllm.ai/en/latest/contributing/overview.html>** (anything written below this line will be removed by GitHub Actions)

.github/mergify.yml

@@ -19,7 +19,7 @@ pull_request_rules:
       - files~=\.buildkite/
       - files~=^cmake/
       - files=CMakeLists.txt
-      - files~=^Dockerfile
+      - files~=^docker/Dockerfile
       - files~=^requirements.*\.txt
       - files=setup.py
   actions:
@@ -55,11 +55,19 @@ pull_request_rules:
   description: Automatically apply structured-output label
   conditions:
     - or:
+      - files~=^benchmarks/structured_schemas/
+      - files=benchmarks/benchmark_serving_structured_output.py
+      - files=benchmarks/run_structured_output_benchmark.sh
+      - files=docs/source/features/structured_outputs.md
+      - files=examples/offline_inference/structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs.py
+      - files=examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
       - files~=^vllm/model_executor/guided_decoding/
       - files=tests/model_executor/test_guided_processors.py
       - files=tests/entrypoints/llm/test_guided_generate.py
-      - files=benchmarks/benchmark_serving_guided.py
-      - files=benchmarks/benchmark_guided.py
+      - files~=^tests/v1/structured_output/
+      - files=tests/v1/entrypoints/llm/test_guided_generate.py
+      - files~=^vllm/v1/structured_output/
   actions:
     label:
       add:
@@ -88,6 +96,58 @@ 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: label-tool-calling
+  description: Automatically add tool-calling label
+  conditions:
+    - or:
+      - files~=^tests/tool_use/
+      - files~=^tests/mistral_tool_use/
+      - files~=^tests/entrypoints/openai/tool_parsers/
+      - files=tests/entrypoints/openai/test_chat_with_tool_reasoning.py
+      - files~=^vllm/entrypoints/openai/tool_parsers/
+      - files=docs/source/features/tool_calling.md
+      - files=docs/source/getting_started/examples/openai_chat_completion_client_with_tools.md
+      - files=docs/source/getting_started/examples/chat_with_tools.md
+      - files~=^examples/tool_chat_*
+      - files=examples/offline_inference/chat_with_tools.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
+      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
+  actions:
+    label:
+      add:
+        - tool-calling
+
 - 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 Dockerfile.cpu -t vllm-cpu-env .
+        run: docker buildx build -f docker/Dockerfile.cpu -t vllm-cpu-env .
 
       - name: Configuration of docker images, network and namespace for the kind cluster
         run: |

.gitignore

@@ -2,7 +2,7 @@
 /vllm/_version.py
 
 # vllm-flash-attn built from source
-vllm/vllm_flash_attn/
+vllm/vllm_flash_attn/*
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -202,3 +202,6 @@ benchmarks/**/*.json
 # Linting
 actionlint
 shellcheck*/
+
+# Ingore moe/marlin_moe gen code
+csrc/moe/marlin_moe_wna16/kernel_*

.pre-commit-config.yaml

@@ -1,3 +1,6 @@
+default_install_hook_types:
+  - pre-commit
+  - commit-msg
 default_stages:
   - pre-commit # Run locally
   - manual # Run in CI
@@ -8,7 +11,6 @@ repos:
   hooks:
   - id: yapf
     args: [--in-place, --verbose]
-    additional_dependencies: [toml] # TODO: Remove when yapf is upgraded
 - repo: https://github.com/astral-sh/ruff-pre-commit
   rev: v0.9.3
   hooks:
@@ -119,6 +121,12 @@ 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;gfx1030;gfx1100;gfx1101")
+set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx950;gfx1030;gfx1100;gfx1101;gfx1200;gfx1201")
 
 #
 # Supported/expected torch versions for CUDA/ROCm.
@@ -44,7 +44,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
 #
 # 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 Dockerfile.rocm
+# versions are derived from docker/Dockerfile.rocm
 #
 set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
 set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
@@ -230,10 +230,12 @@ set(VLLM_EXT_SRC
   "csrc/cache_kernels.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"
@@ -241,6 +243,7 @@ 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")
@@ -248,7 +251,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # Set CUTLASS_REVISION manually -- its revision detection doesn't work in this case.
   # Please keep this in sync with FetchContent_Declare line below.
-  set(CUTLASS_REVISION "v3.8.0" CACHE STRING "CUTLASS revision to use")
+  set(CUTLASS_REVISION "v3.9.0" CACHE STRING "CUTLASS revision to use")
 
   # Use the specified CUTLASS source directory for compilation if VLLM_CUTLASS_SRC_DIR is provided
   if (DEFINED ENV{VLLM_CUTLASS_SRC_DIR})
@@ -266,7 +269,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
         cutlass
         GIT_REPOSITORY https://github.com/nvidia/cutlass.git
         # Please keep this in sync with CUTLASS_REVISION line above.
-        GIT_TAG v3.8.0
+        GIT_TAG v3.9.0
         GIT_PROGRESS TRUE
 
         # Speed up CUTLASS download by retrieving only the specified GIT_TAG instead of the history.
@@ -282,13 +285,13 @@ 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"
     "csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu"
     "csrc/sparse/cutlass/sparse_scaled_mm_entry.cu"
-    "csrc/cutlass_extensions/common.cpp")
+    "csrc/cutlass_extensions/common.cpp"
+    "csrc/attention/mla/cutlass_mla_entry.cu")
 
   set_gencode_flags_for_srcs(
     SRCS "${VLLM_EXT_SRC}"
@@ -461,6 +464,52 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(FP4_ARCHS)
   endif()
 
+  # CUTLASS MLA Archs and flags
+  cuda_archs_loose_intersection(MLA_ARCHS "10.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND MLA_ARCHS)
+    set(SRCS
+      "csrc/attention/mla/cutlass_mla_kernels.cu")
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${MLA_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_CUTLASS_MLA=1")
+    # Add MLA-specific include directories only to MLA source files
+    set_source_files_properties(${SRCS}
+      PROPERTIES INCLUDE_DIRECTORIES "${CUTLASS_DIR}/examples/77_blackwell_fmha;${CUTLASS_DIR}/examples/common")
+    message(STATUS "Building CUTLASS MLA for archs: ${MLA_ARCHS}")
+  else()
+    message(STATUS "Not building CUTLASS MLA as no compatible archs were found.")
+    # clear MLA_ARCHS
+    set(MLA_ARCHS)
+  endif()
+
+  # CUTLASS MoE kernels
+
+  # The MoE kernel cutlass_moe_mm requires CUDA 12.3 or later (and only works
+  # on Hopper). get_cutlass_moe_mm_data should only be compiled if it's possible
+  # to compile MoE kernels that use its output.
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${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
 
@@ -580,21 +629,51 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
   cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
-    set(MARLIN_MOE_SRC
-        "csrc/moe/marlin_kernels/marlin_moe_kernel.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4b8.cu"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku8b128.cu"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.h"
-        "csrc/moe/marlin_kernels/marlin_moe_kernel_ku4.cu"
-        "csrc/moe/marlin_moe_ops.cu")
 
+    #
+    # For the Marlin MOE kernels we automatically generate sources for various
+    # preselected input type pairs and schedules.
+    # Generate sources:
+    set(MOE_MARLIN_GEN_SCRIPT
+      ${CMAKE_CURRENT_SOURCE_DIR}/csrc/moe/marlin_moe_wna16/generate_kernels.py)
+    file(MD5 ${MOE_MARLIN_GEN_SCRIPT} MOE_MARLIN_GEN_SCRIPT_HASH)
+
+    message(STATUS "Marlin MOE generation script hash: ${MOE_MARLIN_GEN_SCRIPT_HASH}")
+    message(STATUS "Last run Marlin MOE generate script hash: $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}")
+
+    if (NOT DEFINED CACHE{MOE_MARLIN_GEN_SCRIPT_HASH}
+        OR NOT $CACHE{MOE_MARLIN_GEN_SCRIPT_HASH} STREQUAL ${MOE_MARLIN_GEN_SCRIPT_HASH})
+      execute_process(
+        COMMAND ${CMAKE_COMMAND} -E env
+        PYTHONPATH=${CMAKE_CURRENT_SOURCE_DIR}/csrc/cutlass_extensions/:${CUTLASS_DIR}/python/:${VLLM_PYTHON_PATH}:$PYTHONPATH
+          ${Python_EXECUTABLE} ${MOE_MARLIN_GEN_SCRIPT}
+        RESULT_VARIABLE moe_marlin_generation_result
+        OUTPUT_VARIABLE moe_marlin_generation_output
+        OUTPUT_FILE ${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log
+        ERROR_FILE ${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log
+      )
+
+      if (NOT moe_marlin_generation_result EQUAL 0)
+        message(FATAL_ERROR "Marlin MOE generation failed."
+                            " Result: \"${moe_marlin_generation_result}\""
+                            "\nCheck the log for details: "
+                            "${CMAKE_CURRENT_BINARY_DIR}/moe_marlin_generation.log")
+      else()
+        set(MOE_MARLIN_GEN_SCRIPT_HASH ${MOE_MARLIN_GEN_SCRIPT_HASH}
+            CACHE STRING "Last run Marlin MOE generate script hash" FORCE)
+        message(STATUS "Marlin MOE generation completed successfully.")
+      endif()
+    else()
+      message(STATUS "Marlin MOE generation script has not changed, skipping generation.")
+    endif()
+
+    file(GLOB MOE_WNAA16_MARLIN_SRC "csrc/moe/marlin_moe_wna16/*.cu")
     set_gencode_flags_for_srcs(
-      SRCS "${MARLIN_MOE_SRC}"
+      SRCS "${MOE_WNAA16_MARLIN_SRC}"
       CUDA_ARCHS "${MARLIN_MOE_ARCHS}")
 
-    list(APPEND VLLM_MOE_EXT_SRC "${MARLIN_MOE_SRC}")
+    list(APPEND VLLM_MOE_EXT_SRC ${MOE_WNAA16_MARLIN_SRC})
+
     message(STATUS "Building Marlin MOE kernels for archs: ${MARLIN_MOE_ARCHS}")
   else()
     message(STATUS "Not building Marlin MOE kernels as no compatible archs found"
@@ -619,6 +698,7 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
   #
   set(VLLM_ROCM_EXT_SRC
     "csrc/rocm/torch_bindings.cpp"
+    "csrc/rocm/skinny_gemms.cu"
     "csrc/rocm/attention.cu")
 
   define_gpu_extension_target(

Dockerfile.cpu

@@ -1,69 +0,0 @@
-# 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.openvino

@@ -1,29 +0,0 @@
-# The vLLM Dockerfile is used to construct vLLM image that can be directly used
-# to run the OpenAI compatible server.
-
-FROM ubuntu:22.04 AS dev
-
-RUN apt-get update -y && \
-    apt-get install -y \
-        git python3-pip \
-        ffmpeg libsm6 libxext6 libgl1
-WORKDIR /workspace
-
-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
-
-RUN python3 -m pip install -U pip
-# install build requirements
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements/build.txt
-# build vLLM with OpenVINO backend
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace
-
-COPY examples/ /workspace/examples
-COPY benchmarks/ /workspace/benchmarks
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-CMD ["/bin/bash"]

Dockerfile.ppc64le

@@ -1,37 +0,0 @@
-FROM mambaorg/micromamba
-ARG MAMBA_DOCKERFILE_ACTIVATE=1
-USER root
-
-ENV PATH="/usr/local/cargo/bin:$PATH:/opt/conda/bin/"
-
-RUN apt-get update -y && apt-get install -y git wget kmod curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 libssl-dev 
-
-# Some packages in requirements/cpu are installed here
-# IBM provides optimized packages for ppc64le processors in the open-ce project for mamba
-# Currently these may not be available for venv or pip directly
-RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 rust && micromamba clean --all --yes
-
-COPY ./ /workspace/vllm
-
-WORKDIR /workspace/vllm
-ARG GIT_REPO_CHECK=0
-RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
-
-RUN --mount=type=cache,target=/root/.cache/pip  \
-    RUSTFLAGS='-L /opt/conda/lib' pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
-        'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
-        -r requirements/cpu.txt \
-        xformers uvloop==0.20.0
-
-RUN --mount=type=bind,source=.git,target=.git \
-    VLLM_TARGET_DEVICE=cpu python3 setup.py install
-
-# install development dependencies (for testing)
-RUN python3 -m pip install -e tests/vllm_test_utils
-
-WORKDIR /workspace/
-
-RUN ln -s /workspace/vllm/tests && ln -s /workspace/vllm/examples && ln -s /workspace/vllm/benchmarks
-
-ENTRYPOINT ["/opt/conda/bin/python3", "-m", "vllm.entrypoints.openai.api_server"]

README.md

@@ -10,25 +10,24 @@ Easy, fast, and cheap LLM serving for everyone
 </h3>
 
 <p align="center">
-| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
+| <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://blog.vllm.ai/"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://discuss.vllm.ai"><b>User Forum</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
 ---
 
-[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.
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 - [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
+
+<details>
+<summary>Previous News</summary>
+
 - [2024/11] We hosted [the seventh vLLM meetup](https://lu.ma/h0qvrajz) with Snowflake! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing), and Snowflake team [here](https://docs.google.com/presentation/d/1qF3RkDAbOULwz9WK5TOltt2fE9t6uIc_hVNLFAaQX6A/edit?usp=sharing).
 - [2024/10] We have just created a developer slack ([slack.vllm.ai](https://slack.vllm.ai)) focusing on coordinating contributions and discussing features. Please feel free to join us there!
 - [2024/10] Ray Summit 2024 held a special track for vLLM! Please find the opening talk slides from the vLLM team [here](https://docs.google.com/presentation/d/1B_KQxpHBTRa_mDF-tR6i8rWdOU5QoTZNcEg2MKZxEHM/edit?usp=sharing). Learn more from the [talks](https://www.youtube.com/playlist?list=PLzTswPQNepXl6AQwifuwUImLPFRVpksjR) from other vLLM contributors and users!
@@ -42,8 +41,9 @@ Easy, fast, and cheap LLM serving for everyone
 - [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.
 - [2023/06] We officially released vLLM! FastChat-vLLM integration has powered [LMSYS Vicuna and Chatbot Arena](https://chat.lmsys.org) since mid-April. Check out our [blog post](https://vllm.ai).
 
----
+</details>
 
+---
 ## About
 
 vLLM is a fast and easy-to-use library for LLM inference and serving.
@@ -98,7 +98,7 @@ 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.md](./CONTRIBUTING.md) for how to get involved.
+Please check out [Contributing to vLLM](https://docs.vllm.ai/en/stable/contributing/overview.html) for how to get involved.
 
 ## Sponsors
 
@@ -121,6 +121,7 @@ Compute Resources:
 - Databricks
 - DeepInfra
 - Google Cloud
+- Intel
 - Lambda Lab
 - Nebius
 - Novita AI

benchmarks/README.md

@@ -41,29 +41,39 @@ become available.
       <td><code>synthetic</code></td>
     </tr>
     <tr>
-      <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>
+      <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>
     </tr>
     <tr>
-      <td><strong>VisionArena</strong></td>
+      <td><strong>HuggingFace-InstructCoder</strong></td>
       <td style="text-align: center;">✅</td>
       <td style="text-align: center;">✅</td>
-      <td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</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>
     </tr>
   </tbody>
 </table>
 
 ✅: supported
 
+🟡: Partial support
+
 🚧: to be supported
 
-🟡: 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`
+**Note**: HuggingFace dataset's `dataset-name` should be set to `hf`
 
 ---
 ## Example - Online Benchmark
@@ -71,8 +81,7 @@ If you need support for other dataset formats, please consider contributing.
 First start serving your model
 
 ```bash
-MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
-vllm serve ${MODEL_NAME} --disable-log-requests
+vllm serve NousResearch/Hermes-3-Llama-3.1-8B --disable-log-requests
 ```
 
 Then run the benchmarking script
@@ -80,12 +89,13 @@ 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
-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}
+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
 ```
 
 If successful, you will see the following output
@@ -122,88 +132,105 @@ 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 "${BACKEND}" \
-  --model "${MODEL_NAME}" \
-  --endpoint "/v1/chat/completions" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --hf-split "${DATASET_SPLIT}" \
-  --num-prompts "${NUM_PROMPTS}"
+  --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
 ```
 
-### HuggingFaceDataset Examples
+### InstructCoder Benchmark with Speculative Decoding
 
-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
+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
 
 ```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 "${BACKEND}" \
-  --model "${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}"
+  --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
 ```
 
 **`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 "${BACKEND}" \
-  --model "${MODEL_NAME}" \
-  --endpoint "/v1/chat/completions" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --hf-split "${DATASET_SPLIT}" \
-  --num-prompts "${NUM_PROMPTS}" \
+  --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
 ```
 
 ---
 ## 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 "${MODEL_NAME}" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --num-prompts "${NUM_PROMPTS}"
+  --model NousResearch/Hermes-3-Llama-3.1-8B \
+  --dataset-name sonnet \
+  --dataset-path vllm/benchmarks/sonnet.txt \
+  --num-prompts 10
 ```
 
 If successful, you will see the following output
@@ -217,19 +244,13 @@ 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 "${MODEL_NAME}" \
-  --backend "vllm-chat" \
-  --dataset-name "${DATASET_NAME}" \
-  --dataset-path "${DATASET_PATH}" \
-  --num-prompts "${NUM_PROMPTS}" \
-  --hf-split "${DATASET_SPLIT}"
+  --model Qwen/Qwen2-VL-7B-Instruct \
+  --backend vllm-chat \
+  --dataset-name hf \
+  --dataset-path lmarena-ai/VisionArena-Chat \
+  --num-prompts 1000 \
+  --hf-split train
 ```
 
 The `num prompt tokens` now includes image token counts
@@ -240,29 +261,83 @@ 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 "${MODEL_NAME}" \
-  --backend "${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}"
+  --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
   ```

benchmarks/backend_request_func.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 
+import io
 import json
 import os
 import sys
@@ -32,6 +33,7 @@ class RequestFuncInput:
     extra_body: Optional[dict] = None
     multi_modal_content: Optional[dict] = None
     ignore_eos: bool = False
+    language: Optional[str] = None
 
 
 @dataclass
@@ -219,7 +221,15 @@ async def async_request_deepspeed_mii(
                 if response.status == 200:
                     parsed_resp = await response.json()
                     output.latency = time.perf_counter() - st
-                    output.generated_text = parsed_resp["text"][0]
+                    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.success = True
                 else:
                     output.error = response.reason or ""
@@ -428,6 +438,110 @@ async def async_request_openai_chat_completions(
     return output
 
 
+async def async_request_openai_audio(
+    request_func_input: RequestFuncInput,
+    pbar: Optional[tqdm] = None,
+) -> RequestFuncOutput:
+    # Lazy import without PlaceholderModule to avoid vllm dep.
+    import soundfile
+    api_url = request_func_input.api_url
+    assert api_url.endswith(
+        ("transcriptions", "translations"
+         )), "OpenAI Chat Completions API URL must end with 'transcriptions' "
+    "or `translations`."
+
+    async with aiohttp.ClientSession(trust_env=True,
+                                     timeout=AIOHTTP_TIMEOUT) as session:
+        content = [{"type": "text", "text": request_func_input.prompt}]
+        payload = {
+            "model": request_func_input.model_name \
+                if request_func_input.model_name else request_func_input.model,
+            "temperature": 0.0,
+            "max_completion_tokens": request_func_input.output_len,
+            "stream": True,
+            "language": "en",
+            # Flattened due to multipart/form-data
+            "stream_include_usage": True,
+            "stream_continuous_usage_stats": True
+        }
+        if request_func_input.extra_body:
+            payload.update(request_func_input.extra_body)
+        headers = {
+            "Authorization": f"Bearer {os.environ.get('OPENAI_API_KEY')}",
+        }
+
+        # Send audio file
+        def to_bytes(y, sr):
+            buffer = io.BytesIO()
+            soundfile.write(buffer, y, sr, format="WAV")
+            buffer.seek(0)
+            return buffer
+
+        with to_bytes(*request_func_input.multi_modal_content['audio']) as f:
+            form = aiohttp.FormData()
+            form.add_field('file', f, content_type='audio/wav')
+            for key, value in payload.items():
+                form.add_field(key, str(value))
+
+            output = RequestFuncOutput()
+            output.prompt_len = request_func_input.prompt_len
+
+            generated_text = ""
+            ttft = 0.0
+            st = time.perf_counter()
+            most_recent_timestamp = st
+            try:
+                async with session.post(url=api_url,
+                                        data=form,
+                                        headers=headers) as response:
+                    if response.status == 200:
+                        async for chunk_bytes in response.content:
+                            chunk_bytes = chunk_bytes.strip()
+                            if not chunk_bytes:
+                                continue
+
+                            chunk = chunk_bytes.decode("utf-8").removeprefix(
+                                "data: ")
+                            if chunk != "[DONE]":
+                                timestamp = time.perf_counter()
+                                data = json.loads(chunk)
+
+                                if choices := data.get("choices"):
+                                    content = choices[0]["delta"].get(
+                                        "content")
+                                    # First token
+                                    if ttft == 0.0:
+                                        ttft = timestamp - st
+                                        output.ttft = ttft
+
+                                    # Decoding phase
+                                    else:
+                                        output.itl.append(
+                                            timestamp - most_recent_timestamp)
+
+                                    generated_text += content or ""
+                                elif usage := data.get("usage"):
+                                    output.output_tokens = usage.get(
+                                        "completion_tokens")
+
+                                most_recent_timestamp = timestamp
+
+                        output.generated_text = generated_text
+                        output.success = True
+                        output.latency = most_recent_timestamp - st
+                    else:
+                        output.error = response.reason or ""
+                        output.success = False
+            except Exception:
+                output.success = False
+                exc_info = sys.exc_info()
+                output.error = "".join(traceback.format_exception(*exc_info))
+
+        if pbar:
+            pbar.update(1)
+        return output
+
+
 def get_model(pretrained_model_name_or_path: str) -> str:
     if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
@@ -485,7 +599,14 @@ ASYNC_REQUEST_FUNCS = {
     "deepspeed-mii": async_request_deepspeed_mii,
     "openai": async_request_openai_completions,
     "openai-chat": async_request_openai_chat_completions,
+    "openai-audio": async_request_openai_audio,
     "tensorrt-llm": async_request_trt_llm,
     "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

@@ -23,7 +23,8 @@ from abc import ABC, abstractmethod
 from collections.abc import Mapping
 from dataclasses import dataclass
 from functools import cache
-from typing import Any, Optional, Union
+from io import BytesIO
+from typing import Any, Callable, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -63,6 +64,7 @@ class SampleRequest:
 
 class BenchmarkDataset(ABC):
     DEFAULT_SEED = 0
+    IS_MULTIMODAL = False
 
     def __init__(
         self,
@@ -187,6 +189,9 @@ class BenchmarkDataset(ABC):
         """
         if len(requests) < num_requests:
             random.seed(self.random_seed)
+            logger.info("Current number of requests: %d", len(requests))
+            logger.info("Oversampled requests to reach %d total samples.",
+                        num_requests)
             additional = random.choices(requests,
                                         k=num_requests - len(requests))
             requests.extend(additional)
@@ -239,21 +244,24 @@ def process_image(image: Any) -> Mapping[str, Any]:
     """
     Process a single image input and return a multimedia content dictionary.
 
-    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.
+    Supports three input types:
 
-    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.
+    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.
 
     Raises:
-      ValueError: If the input is neither a PIL.Image.Image nor a string.
+        ValueError: If the input is not a supported type.
     """
+    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:
@@ -272,8 +280,8 @@ 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.")
+    raise ValueError(f"Invalid image input {image}. Must be a PIL.Image.Image"
+                     " or str or dictionary with raw image bytes.")
 
 
 # -----------------------------------------------------------------------------
@@ -284,7 +292,7 @@ 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 = 1.0
+    DEFAULT_RANGE_RATIO = 0.0
     DEFAULT_INPUT_LEN = 1024
     DEFAULT_OUTPUT_LEN = 128
 
@@ -304,19 +312,32 @@ 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 [])
 
-        input_low = int(input_len * range_ratio)
-        output_low = int(output_len * range_ratio)
+        # 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_lens = np.random.randint(input_low,
-                                       input_len + 1,
+                                       input_high + 1,
                                        size=num_requests)
         output_lens = np.random.randint(output_low,
-                                        output_len + 1,
+                                        output_high + 1,
                                         size=num_requests)
         offsets = np.random.randint(0, vocab_size, size=num_requests)
 
@@ -384,6 +405,13 @@ class ShareGPTDataset(BenchmarkDataset):
                 entry["conversations"][1]["value"],
             )
 
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
             lora_request, tokenizer = self.get_random_lora_request(
                 tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
             prompt_ids = tokenizer(prompt).input_ids
@@ -468,11 +496,11 @@ class SonnetDataset(BenchmarkDataset):
 
         # Determine how many poem lines to use.
         num_input_lines = round((input_len - base_offset) / avg_len)
-        num_prefix_lines = round((prefix_len - base_offset) / avg_len)
+        num_prefix_lines = max(round((prefix_len - base_offset) / avg_len), 0)
         prefix_lines = self.data[:num_prefix_lines]
 
         samples = []
-        for _ in range(num_requests):
+        while len(samples) < num_requests:
             extra_lines = random.choices(self.data,
                                          k=num_input_lines - num_prefix_lines)
             prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
@@ -480,13 +508,14 @@ class SonnetDataset(BenchmarkDataset):
             prompt_formatted = tokenizer.apply_chat_template(
                 msg, add_generation_prompt=True, tokenize=False)
             prompt_len = len(tokenizer(prompt_formatted).input_ids)
-            samples.append(
-                SampleRequest(
-                    prompt=prompt_formatted
-                    if return_prompt_formatted else prompt,
-                    prompt_len=prompt_len,
-                    expected_output_len=output_len,
-                ))
+            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,
+                    ))
         return samples
 
 
@@ -562,48 +591,48 @@ class BurstGPTDataset(BenchmarkDataset):
 
 
 # -----------------------------------------------------------------------------
-# HuggingFace Dataset Implementation
+# HuggingFace Dataset Base Implementation
 # -----------------------------------------------------------------------------
-
-
 class HuggingFaceDataset(BenchmarkDataset):
-    """
-    Dataset class for processing a HuggingFace dataset with conversation data
-    and optional images.
-    """
+    """Base class for datasets hosted on HuggingFace."""
+
+    SUPPORTED_DATASET_PATHS: Union[set[str], dict[str, Callable]] = set()
 
     def __init__(
         self,
+        dataset_path: str,
         dataset_split: str,
         dataset_subset: Optional[str] = None,
         **kwargs,
     ) -> None:
-        super().__init__(**kwargs)
+        super().__init__(dataset_path=dataset_path, **kwargs)
+
         self.dataset_split = dataset_split
         self.dataset_subset = dataset_subset
-
         self.load_data()
 
     def load_data(self) -> None:
-        if not self.dataset_path:
-            raise ValueError("dataset_path must be provided for loading data.")
-
+        """Load data from HuggingFace datasets."""
         self.data = load_dataset(
             self.dataset_path,
             name=self.dataset_subset,
             split=self.dataset_split,
             streaming=True,
         )
-        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)
+        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'
+    }
+    IS_MULTIMODAL = True
 
     def sample(self,
                tokenizer: PreTrainedTokenizerBase,
@@ -611,10 +640,13 @@ class HuggingFaceDataset(BenchmarkDataset):
                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 self.data:
+        for item in filtered_data:
             if len(sampled_requests) >= num_requests:
                 break
             conv = item["conversations"]
@@ -659,29 +691,13 @@ class VisionArenaDataset(HuggingFaceDataset):
     """
 
     DEFAULT_OUTPUT_LEN = 128
-    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)
+    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"]
+    }
+    IS_MULTIMODAL = True
 
     def sample(
         self,
@@ -697,7 +713,11 @@ class VisionArenaDataset(HuggingFaceDataset):
         for item in self.data:
             if len(sampled_requests) >= num_requests:
                 break
-            prompt = item["turns"][0][0]["content"]
+            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)
             mm_content = process_image(item["images"][0])
             prompt_len = len(tokenizer(prompt).input_ids)
             if enable_multimodal_chat:
@@ -715,3 +735,288 @@ 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 = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# 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 i, item in enumerate(self.data):
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt, completion = item['problem'], item["solution"]
+
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            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
+
+
+# -----------------------------------------------------------------------------
+# ASR Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class ASRDataset(HuggingFaceDataset):
+    """
+    Dataset class for processing a ASR dataset for transcription.
+    Tested on the following set:
+
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+    | Dataset        | Domain                                 | Speaking Style           | hf-subset                   |
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+    | TED-LIUM       | TED talks                              | Oratory                  | release1, release2, release3|
+    |                |                                        |                          | release3-speaker-adaptation |
+    | VoxPopuli      | European Parliament                    | Oratory                  | en, de, it, fr,  ...        |
+    | LibriSpeech    | Audiobook                              | Narrated                 | "LIUM/tedlium"              |
+    | GigaSpeech     | Audiobook, podcast, YouTube            | Narrated, spontaneous    | xs, s, m, l, xl, dev, test  |
+    | SPGISpeech     | Financial meetings                     | Oratory, spontaneous     | S, M, L, dev, test          |
+    | AMI            | Meetings                               | Spontaneous              | ihm, sdm                    |
+    +----------------+----------------------------------------+--------------------------+-----------------------------+
+
+    """ # noqa: E501
+    SUPPORTED_DATASET_PATHS = {
+        "openslr/librispeech_asr", "facebook/voxpopuli", "LIUM/tedlium",
+        "edinburghcstr/ami", "speechcolab/gigaspeech", "kensho/spgispeech"
+    }
+
+    DEFAULT_OUTPUT_LEN = 128
+    IS_MULTIMODAL = True
+
+    # TODO Whisper-specific. Abstract interface when more models are supported.
+    TRANSCRIPTION_PREAMBLE = "<|startoftranscript|><|en|><|transcribe|>"\
+                              "<|notimestamps|>"
+    skip_long_audios: bool = True
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        **kwargs,
+    ) -> list:
+        import librosa
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        prompt = ASRDataset.TRANSCRIPTION_PREAMBLE
+        prompt_len = len(tokenizer(prompt).input_ids)
+        sampled_requests = []
+        skipped = 0
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            audio = item["audio"]
+            y, sr = audio["array"], audio["sampling_rate"]
+            duration_s = librosa.get_duration(y=y, sr=sr)
+            # Whisper max supported duration
+            if self.skip_long_audios and duration_s > 30:
+                skipped += 1
+                continue
+
+            mm_content = {"audio": (y, sr)}
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=mm_content,
+                ))
+        if skipped:
+            logger.warning("%d samples discarded from dataset due to" \
+                           " their length being greater than" \
+                           " what Whisper supports.", skipped)
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+class MTBenchDataset(HuggingFaceDataset):
+    """
+    MT-Bench Dataset.
+    https://huggingface.co/datasets/philschmid/mt-bench
+
+    We create a single turn dataset for MT-Bench. 
+    This is similar to Spec decoding benchmark setup in vLLM
+    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
+    """ # noqa: E501
+
+    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
+    SUPPORTED_DATASET_PATHS = {
+        "philschmid/mt-bench",
+    }
+
+    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 = item['turns'][0]
+
+            # apply template
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests
+
+
+class CNNDailyMailDataset(HuggingFaceDataset):
+    """
+    MT-Bench Dataset.
+    https://huggingface.co/datasets/philschmid/mt-bench
+
+    We create a single turn dataset for MT-Bench. 
+    This is similar to Spec decoding benchmark setup in vLLM
+    https://github.com/vllm-project/vllm/blob/9d98ab5ec/examples/offline_inference/eagle.py#L14-L18
+    """ # noqa: E501
+
+    DEFAULT_OUTPUT_LEN = 256  # avg len used in SD bench in vLLM
+    SUPPORTED_DATASET_PATHS = {
+        "abisee/cnn_dailymail",
+    }
+
+    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
+            instruction = "Could you summarize the following article, " \
+            "please reuse text from the article if possible: "
+            prompt = instruction + item['article']
+
+            # apply template
+            prompt = tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                                   add_generation_prompt=True,
+                                                   tokenize=False)
+
+            prompt_len = len(tokenizer(prompt).input_ids)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests

benchmarks/benchmark_prefix_caching.py

@@ -63,14 +63,16 @@ class Request:
     output_len: int
 
 
-def sample_tokens(tokenizer: PreTrainedTokenizerBase, length: int) -> str:
+def sample_tokens(tokenizer: PreTrainedTokenizerBase,
+                  length: int) -> list[int]:
     vocab = tokenizer.get_vocab()
+    all_special_ids = set(tokenizer.all_special_ids)
+
     # Remove the special tokens.
-    vocab = {
-        k: v
-        for k, v in vocab.items() if k not in tokenizer.all_special_ids
-    }
-    return random.choices(list(vocab.values()), k=length)
+    return random.choices(
+        [v for k, v in vocab.items() if k not in all_special_ids],
+        k=length,
+    )
 
 
 def sample_requests_from_dataset(

benchmarks/benchmark_serving.py

@@ -7,9 +7,6 @@ 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> \
@@ -37,7 +34,8 @@ from datetime import datetime
 from typing import Any, Optional
 
 import numpy as np
-from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
+from backend_request_func import (ASYNC_REQUEST_FUNCS,
+                                  OPENAI_COMPATIBLE_BACKENDS, RequestFuncInput,
                                   RequestFuncOutput)
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
@@ -52,9 +50,11 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
-                               RandomDataset, SampleRequest, ShareGPTDataset,
-                               SonnetDataset, VisionArenaDataset)
+from benchmark_dataset import (AIMODataset, ASRDataset, BurstGPTDataset,
+                               ConversationDataset, HuggingFaceDataset,
+                               InstructCoderDataset, 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 output_len is None:
+            if not output_len:
                 # 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
@@ -261,6 +261,7 @@ 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]
@@ -273,10 +274,6 @@ async def benchmark(
         input_requests[0].expected_output_len, \
             input_requests[0].multi_modal_data
 
-    if backend != "openai-chat" and test_mm_content is not None:
-        # multi-modal benchmark is only available on OpenAI Chat backend.
-        raise ValueError(
-            "Multi-modal content is only supported on 'openai-chat' backend.")
     assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
         model=model_id,
@@ -288,6 +285,7 @@ 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)
@@ -314,7 +312,8 @@ async def benchmark(
                                          output_len=test_output_len,
                                          logprobs=logprobs,
                                          multi_modal_content=test_mm_content,
-                                         ignore_eos=ignore_eos)
+                                         ignore_eos=ignore_eos,
+                                         extra_body=extra_body)
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
             print("Profiler started")
@@ -364,7 +363,8 @@ async def benchmark(
                                               output_len=output_len,
                                               logprobs=logprobs,
                                               multi_modal_content=mm_content,
-                                              ignore_eos=ignore_eos)
+                                              ignore_eos=ignore_eos,
+                                              extra_body=extra_body)
         tasks.append(
             asyncio.create_task(
                 limited_request_func(request_func_input=request_func_input,
@@ -586,19 +586,49 @@ def main(args: argparse.Namespace):
                                             return_prompt_formatted=True)
 
     elif args.dataset_name == "hf":
-        # 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)
+        # 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"
+        elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = ASRDataset
+            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.")
+
+        if (dataset_class.IS_MULTIMODAL and backend not in \
+            ["openai-chat", "openai-audio"]):
+            # multi-modal benchmark is only available on OpenAI Chat backend.
+            raise ValueError(
+                "Multi-modal content is only supported on 'openai-chat' and " \
+                "'openai-audio' backend.")
         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,
         )
 
@@ -633,6 +663,26 @@ 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()
@@ -659,10 +709,11 @@ 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
-    if args.save_result:
+    if args.save_result or args.append_result:
         result_json: dict[str, Any] = {}
 
         # Setup
@@ -683,6 +734,14 @@ def main(args: argparse.Namespace):
                     raise ValueError(
                         "Invalid metadata format. Please use KEY=VALUE format."
                     )
+        # Traffic
+        result_json["request_rate"] = (args.request_rate if args.request_rate
+                                       < float("inf") else "inf")
+        result_json["burstiness"] = args.burstiness
+        result_json["max_concurrency"] = args.max_concurrency
+
+        # Merge with benchmark result
+        result_json = {**result_json, **benchmark_result}
 
         if not args.save_detailed:
             # Remove fields with too many data points
@@ -693,15 +752,6 @@ def main(args: argparse.Namespace):
                 if field in result_json:
                     del result_json[field]
 
-        # Traffic
-        result_json["request_rate"] = (args.request_rate if args.request_rate
-                                       < float("inf") else "inf")
-        result_json["burstiness"] = args.burstiness
-        result_json["max_concurrency"] = args.max_concurrency
-
-        # Merge with benchmark result
-        result_json = {**result_json, **benchmark_result}
-
         # Save to file
         base_model_id = model_id.split("/")[-1]
         max_concurrency_str = (f"-concurrency{args.max_concurrency}"
@@ -711,7 +761,12 @@ def main(args: argparse.Namespace):
             file_name = args.result_filename
         if args.result_dir:
             file_name = os.path.join(args.result_dir, file_name)
-        with open(file_name, "w", encoding='utf-8') as outfile:
+        with open(file_name,
+                  mode="a+" if args.append_result else "w",
+                  encoding='utf-8') as outfile:
+            # Append a newline.
+            if args.append_result and outfile.tell() != 0:
+                outfile.write("\n")
             json.dump(result_json, outfile)
         save_to_pytorch_benchmark_format(args, result_json, file_name)
 
@@ -843,6 +898,11 @@ if __name__ == "__main__":
         help="When saving the results, whether to include per request "
         "information such as response, error, ttfs, tpots, etc.",
     )
+    parser.add_argument(
+        "--append-result",
+        action="store_true",
+        help="Append the benchmark result to the existing json file.",
+    )
     parser.add_argument(
         "--metadata",
         metavar="KEY=VALUE",
@@ -876,7 +936,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-seperated list of selected metrics to report percentils. "
+        help="Comma-separated 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\".")
@@ -884,7 +944,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-seperated list of percentiles for selected metrics. "
+        help="Comma-separated 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.",
@@ -951,18 +1011,23 @@ if __name__ == "__main__":
     random_group.add_argument(
         "--random-range-ratio",
         type=float,
-        default=1.0,
-        help="Range of sampled ratio of input/output length, "
-        "used only for random sampling.",
+        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)].",
     )
     random_group.add_argument(
         "--random-prefix-len",
         type=int,
         default=0,
-        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).")
+        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)]."),
+    )
 
     hf_group = parser.add_argument_group("hf dataset options")
     hf_group.add_argument("--hf-subset",
@@ -981,6 +1046,33 @@ 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

@@ -5,16 +5,13 @@ 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 xgrammar \
+        --structured-output-backend auto \
         --request-rate 10 \
         --num-prompts 1000
 
@@ -54,7 +51,7 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
-from vllm.v1.structured_output.utils import (
+from vllm.v1.structured_output.backend_xgrammar import (
     has_xgrammar_unsupported_json_features)
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -133,10 +130,11 @@ 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):
-            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
+            return f"Generate an example of a user profile given the following schema: {json.dumps(get_schema(index))}"  # noqa: E501
 
         def get_schema(index: int):
             return json_schemas[index % len(json_schemas)]
@@ -152,17 +150,17 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
 
     elif args.dataset == "grammar":
         schema = """
-            ?start: select_statement
+        root ::= select_statement
 
-            ?select_statement: "SELECT " column_list " FROM " table_name
+        select_statement ::= "SELECT " column " from " table " where " condition
 
-            ?column_list: column_name ("," column_name)*
+        column ::= "col_1 " | "col_2 "
 
-            ?table_name: identifier
+        table ::= "table_1 " | "table_2 "
 
-            ?column_name: identifier
+        condition ::= column "= " number
 
-            ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
+        number ::= "1 " | "2 "
         """
         prompt = "Generate an SQL query to show the 'username' \
             and 'email' from the 'users' table."
@@ -966,7 +964,7 @@ if __name__ == "__main__":
         "--percentile-metrics",
         type=str,
         default="ttft,tpot,itl",
-        help="Comma-seperated list of selected metrics to report percentils. "
+        help="Comma-separated 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\".")
@@ -974,7 +972,7 @@ if __name__ == "__main__":
         "--metric-percentiles",
         type=str,
         default="99",
-        help="Comma-seperated list of percentiles for selected metrics. "
+        help="Comma-separated 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.",
@@ -999,12 +997,14 @@ 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"],
-        default="xgrammar",
-        help="Backend to use for structured outputs")
+    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")
 
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -11,7 +11,9 @@ from typing import Any, Optional, Union
 
 import torch
 import uvloop
-from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
+from benchmark_dataset import (AIMODataset, BurstGPTDataset,
+                               CNNDailyMailDataset, ConversationDataset,
+                               InstructCoderDataset, MTBenchDataset,
                                RandomDataset, SampleRequest, ShareGPTDataset,
                                SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
@@ -56,9 +58,9 @@ def run_vllm(
         sampling_params.append(
             SamplingParams(
                 n=n,
-                temperature=1.0,
+                temperature=0,
                 top_p=1.0,
-                ignore_eos=True,
+                ignore_eos=False,
                 max_tokens=request.expected_output_len,
                 detokenize=not disable_detokenize,
             ))
@@ -122,9 +124,9 @@ def run_vllm_chat(
         sampling_params.append(
             SamplingParams(
                 n=n,
-                temperature=1.0,
+                temperature=0,
                 top_p=1.0,
-                ignore_eos=True,
+                ignore_eos=False,
                 max_tokens=request.expected_output_len,
                 detokenize=not disable_detokenize,
             ))
@@ -166,9 +168,9 @@ async def run_vllm_async(
             sampling_params.append(
                 SamplingParams(
                     n=n,
-                    temperature=1.0,
+                    temperature=0,
                     top_p=1.0,
-                    ignore_eos=True,
+                    ignore_eos=False,
                     max_tokens=request.expected_output_len,
                     detokenize=not disable_detokenize,
                 ))
@@ -212,14 +214,17 @@ def run_hf(
     max_prompt_len = 0
     max_output_len = 0
     for i in range(len(requests)):
-        prompt, prompt_len, output_len = requests[i]
+        prompt = requests[i].prompt
+        prompt_len = requests[i].prompt_len
+        output_len = requests[i].expected_output_len
         # 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, next_output_len = requests[i + 1]
+            next_prompt_len = requests[i + 1].prompt_len
+            next_output_len = requests[i + 1].expected_output_len
             if (max(max_prompt_len, next_prompt_len) +
                     max(max_output_len, next_output_len)) <= 2048:
                 # We can add more requests to the batch.
@@ -300,6 +305,7 @@ 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
@@ -317,18 +323,31 @@ def get_requests(args, tokenizer):
     elif args.dataset_name == "burstgpt":
         dataset_cls = BurstGPTDataset
     elif args.dataset_name == "hf":
-        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
-
+        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"
+        elif args.dataset_path in MTBenchDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = MTBenchDataset
+            common_kwargs['dataset_subset'] = None
+            common_kwargs['dataset_split'] = "train"
+        elif args.dataset_path in CNNDailyMailDataset.SUPPORTED_DATASET_PATHS:
+            dataset_cls = CNNDailyMailDataset
+            common_kwargs['dataset_subset'] = '3.0.0'
+            common_kwargs['dataset_split'] = "train"
     else:
         raise ValueError(f"Unknown dataset name: {args.dataset_name}")
     # Remove None values
@@ -462,9 +481,20 @@ 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" and args.backend != "vllm-chat":
-        raise ValueError(
-            "When --dataset-name is 'hf', backend must be 'vllm-chat'")
+    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
+                | MTBenchDataset.SUPPORTED_DATASET_PATHS
+                | CNNDailyMailDataset.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.")
 
     # --random-range-ratio: only used when dataset_name is 'random'
     if args.dataset_name != 'random' and args.random_range_ratio is not None:
@@ -505,6 +535,13 @@ def validate_args(args):
         raise ValueError(
             "Tokenizer must be the same as the model for MII backend.")
 
+    # --data-parallel is not supported currently.
+    # https://github.com/vllm-project/vllm/issues/16222
+    if args.data_parallel_size > 1:
+        raise ValueError(
+            "Data parallel is not supported in offline benchmark, \
+            please use benchmark serving instead")
+
 
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(description="Benchmark the throughput.")
@@ -576,18 +613,30 @@ 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=None,
-                        help="Number of prefix tokens per request."
-                        "This is for the RandomDataset and SonnetDataset")
+    parser.add_argument(
+        "--prefix-len",
+        type=int,
+        default=None,
+        help=f"Number of prefix tokens to be used in RandomDataset "
+        "and SonnetDataset. For RandomDataset, the total input "
+        "length is the sum of prefix-len (default: "
+        f"{RandomDataset.DEFAULT_PREFIX_LEN}) and a random context length "
+        "sampled from [input_len * (1 - range_ratio), "
+        "input_len * (1 + range_ratio)]. For SonnetDataset, "
+        f"prefix_len (default: {SonnetDataset.DEFAULT_PREFIX_LEN}) "
+        "controls how much of the input is fixed lines versus "
+        "random lines, but the total input length remains approximately "
+        "input_len tokens.")
     # random dataset
     parser.add_argument(
         "--random-range-ratio",
         type=float,
         default=None,
-        help="Range of sampled ratio of input/output length, "
-        "used only for RandomDataSet.",
+        help=f"Range ratio (default : {RandomDataset.DEFAULT_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)].",
     )
 
     # hf dtaset

benchmarks/kernels/benchmark_bitblas.py

@@ -0,0 +1,236 @@
+# SPDX-License-Identifier: Apache-2.0
+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+from vllm.model_executor.layers.quantization.utils.bitblas_utils import (
+    MINIMUM_BITBLAS_VERSION)
+
+try:
+    import bitblas
+    if bitblas.__version__ < MINIMUM_BITBLAS_VERSION:
+        raise ImportError("bitblas version is wrong. Please "
+                          f"install bitblas>={MINIMUM_BITBLAS_VERSION}")
+except ImportError as e:
+    bitblas_import_exception = e
+    raise ValueError("Trying to use the bitblas backend, but could not import"
+                     f"with the following error: {bitblas_import_exception}. "
+                     "Please install bitblas through the following command: "
+                     f"`pip install bitblas>={MINIMUM_BITBLAS_VERSION}`"
+                     ) from bitblas_import_exception
+
+from bitblas import Matmul, MatmulConfig, auto_detect_nvidia_target
+
+from vllm.utils import FlexibleArgumentParser
+
+parser = FlexibleArgumentParser(
+    description="Benchmark BitBLAS int4 on a specific target.")
+
+# Add arguments to the parser
+parser.add_argument(
+    "--target",
+    type=str,
+    default=auto_detect_nvidia_target(),
+    help="Specify the target device for benchmarking.",
+)
+parser.add_argument("--group_size",
+                    type=int,
+                    default=None,
+                    help="Group size for grouped quantization.")
+parser.add_argument(
+    "--A_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "float64", "int32", "int8"],
+    help="Data type of activation A.",
+)
+parser.add_argument(
+    "--W_dtype",
+    type=str,
+    default="int4",
+    choices=[
+        "float16",
+        "float32",
+        "float64",
+        "int32",
+        "int8",
+        "int4",
+        "int2",
+        "int1",
+        "nf4",
+        "fp4_e2m1",
+    ],
+    help="Data type of weight W.",
+)
+parser.add_argument(
+    "--accum_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "int32"],
+    help="Data type for accumulation.",
+)
+parser.add_argument(
+    "--out_dtype",
+    type=str,
+    default="float16",
+    choices=["float16", "float32", "int32", "int8"],
+    help="Data type for output.",
+)
+parser.add_argument(
+    "--layout",
+    type=str,
+    default="nt",
+    choices=["nt", "nn"],
+    help="Matrix layout, 'nt' for non-transpose A and transpose W.",
+)
+parser.add_argument("--with_bias",
+                    action="store_true",
+                    help="Include bias in the benchmark.")
+parser.add_argument(
+    "--with_scaling",
+    action="store_true",
+    help="Include scaling factor in the quantization.",
+)
+parser.add_argument("--with_zeros",
+                    action="store_true",
+                    help="Include zeros in the quantization.")
+parser.add_argument(
+    "--zeros_mode",
+    type=str,
+    default=None,
+    choices=["original", "rescale", "quantized"],
+    help="Specify the mode for calculating zeros.",
+)
+
+# Parse the arguments
+args = parser.parse_args()
+
+# Assign arguments to variables
+target = args.target
+A_dtype = args.A_dtype
+W_dtype = args.W_dtype
+accum_dtype = args.accum_dtype
+out_dtype = args.out_dtype
+layout = args.layout
+with_bias = args.with_bias
+group_size = args.group_size
+with_scaling = args.with_scaling
+with_zeros = args.with_zeros
+zeros_mode = args.zeros_mode
+
+# Define a list of shared arguments that repeat in every config
+shared_args = [
+    A_dtype,
+    W_dtype,
+    out_dtype,
+    accum_dtype,
+    layout,
+    with_bias,
+    group_size,
+    with_scaling,
+    with_zeros,
+    zeros_mode,
+]
+
+# Define just the (M, K, N) shapes in a more compact list
+shapes = [
+    # square test
+    (1, 16384, 16384),
+    # BLOOM-176B
+    (1, 43008, 14336),
+    (1, 14336, 14336),
+    (1, 57344, 14336),
+    (1, 14336, 57344),
+    # OPT-65B
+    (1, 9216, 9216),
+    (1, 36864, 9216),
+    (1, 9216, 36864),
+    (1, 22016, 8192),
+    # LLAMA-70B/65B
+    (1, 8192, 22016),
+    (1, 8192, 8192),
+    (1, 28672, 8192),
+    (1, 8192, 28672),
+    # square test
+    (16384, 16384, 16384),
+    # BLOOM-176B
+    (8192, 43008, 14336),
+    (8192, 14336, 14336),
+    (8192, 57344, 14336),
+    (8192, 14336, 57344),
+    # OPT-65B
+    (8192, 9216, 9216),
+    (8192, 36864, 9216),
+    (8192, 9216, 36864),
+    (8192, 22016, 8192),
+    # LLAMA-70B/65B
+    (8192, 8192, 22016),
+    (8192, 8192, 8192),
+    (8192, 28672, 8192),
+    (8192, 8192, 28672),
+]
+
+# Build test shapes with all the shared arguments
+test_shapes = [(MatmulConfig, Matmul, (*shape, *shared_args))
+               for shape in shapes]
+
+benchmark_sets = []
+benchmark_sets.extend(test_shapes)
+
+benchmark_results = {}
+for config_class, operator, input_args in benchmark_sets:
+    config = config_class(*input_args)
+    matmul = operator(config, target=target, enable_tuning=True)
+    kernel_latency = matmul.profile_latency()
+
+    print("Time cost is: {:.3f} ms".format(kernel_latency))
+
+    profile_config = {
+        f"{operator.__name__}-{'-'.join([str(i) for i in input_args])}": {
+            "BitBLAS_top20_latency": kernel_latency,
+        }
+    }
+
+    benchmark_results.update(profile_config)
+
+# Define headers for the table
+headers = [
+    "PrimFunc",
+    "Input Arguments",
+    "BitBLAS Top20 Latency",
+]
+
+# Calculate column widths for pretty printing
+col_widths = [0, 0, 0]
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    col_widths[0] = max(col_widths[0], len(func_name) + 2, len(headers[0]) + 2)
+    col_widths[1] = max(col_widths[1],
+                        len(input_args_str) + 2,
+                        len(headers[1]) + 2)
+    col_widths[2] = max(col_widths[2],
+                        len(f"{values['BitBLAS_top20_latency']:.3f} ms") + 2,
+                        len(headers[2]) + 2)
+    # break only if you want to measure widths from a single example;
+    # otherwise, let it loop over all items.
+
+# Print header
+for i, header in enumerate(headers):
+    headers[i] = header.ljust(col_widths[i])
+print("".join(headers))
+print("-" * sum(col_widths))
+
+# Print rows
+for config_key, values in benchmark_results.items():
+    args_split = config_key.split("-")
+    func_name = args_split[0]
+    input_args_str = "-".join(args_split[1:])
+    row = [
+        func_name,
+        input_args_str,
+        f"{values['BitBLAS_top20_latency']:.3f} ms",
+    ]
+    row_str = "".join(
+        [str(cell).ljust(col_widths[idx]) for idx, cell in enumerate(row)])
+    print(row_str)

benchmarks/kernels/benchmark_grouped_gemm_cutlass.py

@@ -0,0 +1,340 @@
+# 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_lora.py

@@ -17,8 +17,14 @@ from torch.utils.benchmark import Measurement as TMeasurement
 from utils import ArgPool, Bench, CudaGraphBenchParams
 from weight_shapes import WEIGHT_SHAPES
 
-from vllm.lora.ops.triton_ops import LoRAKernelMeta, lora_expand, lora_shrink
-from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
+from vllm.triton_utils import HAS_TRITON
+
+if HAS_TRITON:
+    from vllm.lora.ops.triton_ops import (LoRAKernelMeta, lora_expand,
+                                          lora_shrink)
+    from vllm.lora.ops.triton_ops.utils import (_LORA_A_PTR_DICT,
+                                                _LORA_B_PTR_DICT)
+
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())

benchmarks/kernels/benchmark_moe.py

@@ -30,19 +30,18 @@ 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,
-) -> 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,
+                     use_deep_gemm: bool = False) -> float:
     init_dtype = torch.float16 if use_fp8_w8a8 else dtype
     x = torch.randn(num_tokens, hidden_size, dtype=dtype)
     if use_int8_w8a16:
@@ -115,22 +114,41 @@ def benchmark_config(
     def run():
         from vllm.model_executor.layers.fused_moe import override_config
         with override_config(config):
-            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,
-            )
+            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,
+                )
 
     # JIT compilation & warmup
     run()
@@ -366,6 +384,7 @@ 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,
@@ -396,7 +415,8 @@ class BenchmarkWorker:
                                        use_fp8_w8a8,
                                        use_int8_w8a16,
                                        num_iters=100,
-                                       block_quant_shape=block_quant_shape)
+                                       block_quant_shape=block_quant_shape,
+                                       use_deep_gemm=use_deep_gemm)
         return config, kernel_time
 
     def tune(
@@ -411,6 +431,7 @@ 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")
@@ -436,7 +457,8 @@ class BenchmarkWorker:
                         use_fp8_w8a8,
                         use_int8_w8a16,
                         num_iters=20,
-                        block_quant_shape=block_quant_shape)
+                        block_quant_shape=block_quant_shape,
+                        use_deep_gemm=use_deep_gemm)
                 except triton.runtime.autotuner.OutOfResources:
                     # Some configurations may be invalid and fail to compile.
                     continue
@@ -531,6 +553,8 @@ def main(args: argparse.Namespace):
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
+        # Support for llama4
+        config = config.get_text_config()
         # Default: Mixtral.
         E = config.num_local_experts
         topk = config.num_experts_per_tok
@@ -550,6 +574,8 @@ 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)]
@@ -572,10 +598,10 @@ 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)
-             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, use_deep_gemm)
+                     for batch_size in batch_sizes])
         best_configs = {
             M: sort_config(config)
             for M, config in zip(batch_sizes, configs)
@@ -589,7 +615,7 @@ 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_fp8_w8a8, use_int8_w8a16, block_quant_shape, use_deep_gemm)
              for batch_size in batch_sizes])
 
         for batch_size, (config, kernel_time) in zip(batch_sizes, outputs):
@@ -611,6 +637,7 @@ 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

@@ -7,10 +7,13 @@ 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
@@ -193,6 +196,9 @@ 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

@@ -75,3 +75,19 @@ 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/kernels/benchmark_w8a8_block_fp8.py

@@ -0,0 +1,420 @@
+# SPDX-License-Identifier: Apache-2.0
+# Adapted from sglang quantization/tuning_block_wise_kernel.py
+
+import argparse
+import json
+import multiprocessing as mp
+import os
+import time
+from datetime import datetime
+from typing import Any
+
+import torch
+import tqdm
+import triton
+
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    _w8a8_block_fp8_matmul)
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+mp.set_start_method("spawn", force=True)
+
+assert current_platform.is_cuda(
+), "Only support tune w8a8 block fp8 kernel on CUDA device."
+
+DTYPE_MAP = {
+    "float32": torch.float32,
+    "float16": torch.float16,
+    "half": torch.half,
+    "bfloat16": torch.bfloat16,
+}
+
+
+def w8a8_block_matmul(
+    A: torch.Tensor,
+    B: torch.Tensor,
+    As: torch.Tensor,
+    Bs: torch.Tensor,
+    block_size: list[int],
+    config: dict[str, Any],
+    output_dtype: torch.dtype = torch.float16,
+) -> torch.Tensor:
+    """This function performs matrix multiplication with 
+    block-wise quantization.
+
+    It takes two input tensors `A` and `B` with scales `As` and `Bs`.
+    The output is returned in the specified `output_dtype`.
+
+    Args:
+        A: The input tensor, e.g., activation.
+        B: The input tensor, e.g., weight.
+        As: The per-token-group quantization scale for `A`.
+        Bs: The per-block quantization scale for `B`.
+        block_size: The block size for per-block quantization. 
+                    It should be 2-dim, e.g., [128, 128].
+        output_dytpe: The dtype of the returned tensor.
+
+    Returns:
+        torch.Tensor: The result of matmul.
+    """
+    assert len(block_size) == 2
+    block_n, block_k = block_size[0], block_size[1]
+
+    assert A.shape[-1] == B.shape[-1]
+    assert A.shape[:-1] == As.shape[:-1] and A.is_contiguous()
+    assert triton.cdiv(A.shape[-1], block_k) == As.shape[-1]
+    M = A.numel() // A.shape[-1]
+
+    assert B.ndim == 2 and B.is_contiguous() and Bs.ndim == 2
+    N, K = B.shape
+    assert triton.cdiv(N, block_n) == Bs.shape[0]
+    assert triton.cdiv(K, block_k) == Bs.shape[1]
+
+    C_shape = A.shape[:-1] + (N, )
+    C = A.new_empty(C_shape, dtype=output_dtype)
+
+    def grid(META):
+        return (triton.cdiv(M, META["BLOCK_SIZE_M"]) *
+                triton.cdiv(N, META["BLOCK_SIZE_N"]), )
+
+    if A.dtype == torch.float8_e4m3fn:
+        kernel = _w8a8_block_fp8_matmul
+    else:
+        raise RuntimeError(
+            "Currently, only support tune w8a8 block fp8 kernel.")
+
+    kernel[grid](
+        A,
+        B,
+        C,
+        As,
+        Bs,
+        M,
+        N,
+        K,
+        block_n,
+        block_k,
+        A.stride(-2),
+        A.stride(-1),
+        B.stride(1),
+        B.stride(0),
+        C.stride(-2),
+        C.stride(-1),
+        As.stride(-2),
+        As.stride(-1),
+        Bs.stride(1),
+        Bs.stride(0),
+        **config,
+    )
+
+    return C
+
+
+def get_configs_compute_bound():
+    configs = []
+    for num_stages in [2, 3, 4, 5]:
+        for block_m in [16, 32, 64, 128, 256]:
+            for block_k in [64, 128]:
+                for block_n in [32, 64, 128, 256]:
+                    for num_warps in [4, 8]:
+                        for group_size in [1, 16, 32, 64]:
+                            configs.append({
+                                "BLOCK_SIZE_M": block_m,
+                                "BLOCK_SIZE_N": block_n,
+                                "BLOCK_SIZE_K": block_k,
+                                "GROUP_SIZE_M": group_size,
+                                "num_warps": num_warps,
+                                "num_stages": num_stages,
+                            })
+    return configs
+
+
+def get_weight_shapes(tp_size):
+    # NOTE(HandH1998): The weight shapes only works for DeepSeek-V3.
+    # Modify them, if you tune for another different model.
+    # cannot TP
+    total = [
+        (512 + 64, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (7168, 16384),
+        (7168, 18432),
+    ]
+    # N can TP
+    n_tp = [
+        (18432 * 2, 7168),
+        ((128 + 64) * 128, 7168),
+        (128 * (128 + 128), 512),
+        (24576, 1536),
+        (12288, 7168),
+        (4096, 7168),
+    ]
+    # K can TP
+    k_tp = [(7168, 18432), (7168, 16384), (7168, 2048)]
+
+    weight_shapes = []
+    for t in total:
+        weight_shapes.append(t)
+    for n_t in n_tp:
+        new_t = (n_t[0] // tp_size, n_t[1])
+        weight_shapes.append(new_t)
+    for k_t in k_tp:
+        new_t = (k_t[0], k_t[1] // tp_size)
+        weight_shapes.append(new_t)
+    return weight_shapes
+
+
+def benchmark_config(A,
+                     B,
+                     As,
+                     Bs,
+                     block_size,
+                     config,
+                     out_dtype=torch.float16,
+                     num_iters=10):
+
+    def run():
+        w8a8_block_matmul(A, B, As, Bs, block_size, config, out_dtype)
+
+    torch.cuda.synchronize()
+    # JIT complication & warmup
+    for _ in range(5):
+        run()
+    torch.cuda.synchronize()
+
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    latencies: list[float] = []
+    for i in range(num_iters):
+        torch.cuda.synchronize()
+        start_event.record()
+        run()
+        end_event.record()
+        end_event.synchronize()
+        latencies.append(start_event.elapsed_time(end_event))
+    avg = sum(latencies) / (num_iters * 10) * 1000  # us
+    return avg
+
+
+def tune(M, N, K, block_size, out_dtype, search_space, input_type):
+    factor_for_scale = 1e-2
+
+    if input_type == "fp8":
+        fp8_info = torch.finfo(torch.float8_e4m3fn)
+        fp8_max, fp8_min = fp8_info.max, fp8_info.min
+
+        A_fp32 = (
+            (torch.rand(M, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
+            fp8_max)
+        A = A_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+
+        B_fp32 = (
+            (torch.rand(N, K, dtype=torch.float32, device="cuda") - 0.5) * 2 *
+            fp8_max)
+        B = B_fp32.clamp(min=fp8_min, max=fp8_max).to(torch.float8_e4m3fn)
+    else:
+        raise RuntimeError(
+            "Currently, only support tune w8a8 block fp8 kernel.")
+
+    block_n, block_k = block_size[0], block_size[1]
+    n_tiles = (N + block_n - 1) // block_n
+    k_tiles = (K + block_k - 1) // block_k
+
+    As = torch.rand(M, k_tiles, dtype=torch.float32,
+                    device="cuda") * factor_for_scale
+    Bs = (torch.rand(n_tiles, k_tiles, dtype=torch.float32, device="cuda") *
+          factor_for_scale)
+
+    best_config = None
+    best_time = float("inf")
+    for config in tqdm(search_space):
+        try:
+            kernel_time = benchmark_config(
+                A,
+                B,
+                As,
+                Bs,
+                block_size,
+                config,
+                out_dtype,
+                num_iters=10,
+            )
+        except triton.runtime.autotuner.OutOfResources:
+            # Some configurations may be invalid and fail to compile.
+            continue
+
+        if kernel_time < best_time:
+            best_time = kernel_time
+            best_config = config
+    now = datetime.now()
+    print(f"{now.ctime()}] Completed tuning for batch_size={M}")
+    assert best_config is not None
+    return best_config
+
+
+def save_configs(
+    N,
+    K,
+    block_n,
+    block_k,
+    configs,
+    save_path,
+    input_type="fp8",
+) -> None:
+    os.makedirs(save_path, exist_ok=True)
+    device_name = current_platform.get_device_name().replace(" ", "_")
+    json_file_name = (
+        f"N={N},K={K},device_name={device_name},dtype={input_type}_w8a8,"
+        f"block_shape=[{block_n},{block_k}].json")
+
+    config_file_path = os.path.join(save_path, json_file_name)
+    print(f"Writing best config to {config_file_path}...")
+
+    with open(config_file_path, "w") as f:
+        json.dump(configs, f, indent=4)
+        f.write("\n")
+
+
+def tune_on_gpu(args_dict):
+    """Run tuning on a specific GPU."""
+    gpu_id = args_dict["gpu_id"]
+    batch_sizes = args_dict["batch_sizes"]
+    weight_shapes = args_dict["weight_shapes"]
+    args = args_dict["args"]
+
+    torch.cuda.set_device(gpu_id)
+    print(f"Starting tuning on GPU {gpu_id} with batch sizes {batch_sizes}")
+
+    block_n = args.block_n
+    block_k = args.block_k
+    out_dtype = DTYPE_MAP[args.out_dtype]
+    save_path = args.save_path
+    input_type = args.input_type
+
+    search_space = get_configs_compute_bound()
+    search_space = [
+        config for config in search_space
+        if block_k % config["BLOCK_SIZE_K"] == 0
+    ]
+
+    start = time.time()
+    for shape in tqdm(weight_shapes, desc=f"GPU {gpu_id} - Shapes"):
+        N, K = shape[0], shape[1]
+        print(f"[GPU {gpu_id}] Tune for weight shape of `N: {N}, K: {K}`")
+        benchmark_results = [
+            tune(
+                batch_size,
+                N,
+                K,
+                [block_n, block_k],
+                out_dtype,
+                search_space,
+                input_type,
+            ) for batch_size in tqdm(batch_sizes,
+                                     desc=f"GPU {gpu_id} - Batch sizes")
+        ]
+        best_configs = {
+            M: config
+            for M, config in zip(batch_sizes, benchmark_results)
+        }
+        save_configs(N, K, block_n, block_k, best_configs, save_path,
+                     input_type)
+
+    end = time.time()
+    print(f"Tuning on GPU {gpu_id} took {end - start:.2f} seconds")
+
+
+def distribute_batch_sizes(batch_sizes, num_gpus):
+    """Distribute batch sizes across available GPUs."""
+    batches_per_gpu = []
+    for i in range(num_gpus):
+        start_idx = i * len(batch_sizes) // num_gpus
+        end_idx = (i + 1) * len(batch_sizes) // num_gpus
+        batches_per_gpu.append(batch_sizes[start_idx:end_idx])
+    return batches_per_gpu
+
+
+def main(args):
+    print(args)
+    num_gpus = torch.cuda.device_count()
+    if num_gpus == 0:
+        raise RuntimeError("No GPU available for tuning")
+    print(f"Found {num_gpus} GPUs for parallel tuning")
+
+    torch.cuda.init()
+
+    if args.batch_size is None:
+        batch_sizes = [
+            1,
+            2,
+            4,
+            8,
+            16,
+            24,
+            32,
+            48,
+            64,
+            96,
+            128,
+            256,
+            512,
+            1024,
+            1536,
+            2048,
+            3072,
+            4096,
+        ]
+    else:
+        batch_sizes = [args.batch_size]
+        num_gpus = 1  # If only one batch size, use only one GPU
+
+    weight_shapes = get_weight_shapes(args.tp_size)
+
+    batches_per_gpu = distribute_batch_sizes(batch_sizes, num_gpus)
+
+    process_args = []
+    for gpu_id in range(num_gpus):
+        process_args.append({
+            "gpu_id": gpu_id,
+            "batch_sizes": batches_per_gpu[gpu_id],
+            "weight_shapes":
+            weight_shapes,  # Each GPU processes all weight shapes
+            "args": args,
+        })
+
+    ctx = mp.get_context("spawn")
+    with ctx.Pool(num_gpus) as pool:
+        pool.map(tune_on_gpu, process_args)
+
+    print("Multi-GPU tuning completed")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="""
+Tune triton w8a8 block fp8 for DeepSeek-V3/DeepSeek-R1:
+    python3 benchmark_w8a8_block_fp8.py --tp-size 8 --input-type fp8
+Then copy to model_executor/layers/quantization/utils/configs
+        """,
+        formatter_class=argparse.RawTextHelpFormatter)
+
+    parser.add_argument("--tp-size", "-tp", type=int, default=8)
+    parser.add_argument("--input-type",
+                        type=str,
+                        choices=["fp8"],
+                        default="fp8")
+    parser.add_argument(
+        "--out-dtype",
+        type=str,
+        choices=["float32", "float16", "bfloat16", "half"],
+        default="float16",
+    )
+    parser.add_argument("--block-n", type=int, default=128)
+    parser.add_argument("--block-k", type=int, default=128)
+    parser.add_argument("--batch-size", type=int, required=False)
+    parser.add_argument("--save-path", type=str, default="./")
+    args = parser.parse_args()
+
+    main(args)

benchmarks/launch_tgi_server.sh

@@ -1,16 +0,0 @@
-#!/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"

benchmarks/run.sh

@@ -0,0 +1,213 @@
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name sonnet \
+#     --dataset-path /data/lily/batch-sd/benchmarks/sonnet.txt \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name sharegpt \
+#     --dataset-path /data/lily/ShareGPT_V3_unfiltered_cleaned_split.json  \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path likaixin/InstructCoder  \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name sonnet \
+#     --dataset-path /data/lily/batch-sd/benchmarks/sonnet.txt \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 20}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name sharegpt \
+#     --dataset-path /data/lily/ShareGPT_V3_unfiltered_cleaned_split.json \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path likaixin/InstructCoder \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path likaixin/InstructCoder \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle3", "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+python benchmarks/benchmark_throughput.py \
+    --model meta-llama/Meta-Llama-3.1-8B-Instruct\
+    --dataset-name hf \
+    --dataset-path philschmid/mt-bench  \
+    --prefix-len 0 \
+    --output-len 512 \
+    --num-prompts 200 \
+    --speculative_config '{"method": "eagle3", "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name sharegpt \
+#     --dataset-path /data/lily/ShareGPT_V3_unfiltered_cleaned_split.json \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle3", "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name sonnet \
+#     --dataset-path /data/lily/batch-sd/benchmarks/sonnet.txt \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle3", "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path likaixin/InstructCoder \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name sharegpt \
+#     --dataset-path /data/lily/ShareGPT_V3_unfiltered_cleaned_split.json \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path likaixin/InstructCoder  \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
+#     --dataset-name hf \
+#     --dataset-path AI-MO/aimo-validation-aime \
+#     --prefix-len 0 \
+#     --output-len 1024 \
+#     --num-prompts 90 \
+#     --speculative_config '{"method": "eagle3", "num_speculative_tokens": 20, "model": "yuhuili/EAGLE3-DeepSeek-R1-Distill-LLaMA-8B"}'
+
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B \
+#     --dataset-name hf \
+#     --dataset-path AI-MO/aimo-validation-aime \
+#     --prefix-len 0 \
+#     --output-len 1024 \
+#     --num-prompts 90 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name sharegpt \
+#     --dataset-path /data/lily/ShareGPT_V3_unfiltered_cleaned_split.json  \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path philschmid/mt-bench \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path philschmid/mt-bench \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path abisee/cnn_dailymail \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "eagle", "model": "yuhuili/EAGLE-LLaMA3-Instruct-8B", "num_speculative_tokens": 20}'
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path abisee/cnn_dailymail \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path philschmid/mt-bench \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 10 \
+#     --speculative_config '{"method": "eagle3", "model": "yuhuili/EAGLE3-LLaMA3.1-Instruct-8B", "num_speculative_tokens": 20}'
+
+
+# python benchmarks/benchmark_throughput.py \
+#     --model meta-llama/Meta-Llama-3.1-8B-Instruct \
+#     --dataset-name hf \
+#     --dataset-path abisee/cnn_dailymail \
+#     --prefix-len 0 \
+#     --output-len 512 \
+#     --num-prompts 200 \
+#     --speculative_config '{"method": "ngram", "num_speculative_tokens": 20, "prompt_lookup_min": 2, "prompt_lookup_max": 5}'

benchmarks/visualize/common.py

@@ -0,0 +1,63 @@
+import json
+from dataclasses import dataclass
+
+MODEL_TO_NAMES = {
+    "r1-distill-llama-8B" : "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
+    "llama3-8B" : "meta-llama/Meta-Llama-3-8B-Instruct",
+    "llama3.1-8B" : "meta-llama/Llama-3.1-8B-Instruct",
+    "llama3.1-70B" : "meta-llama/Llama-3.1-70B-Instruct",
+}
+
+@dataclass
+class AccStats:
+    lens: list[int]
+    probs: list[float] = None
+    entropies: list[float] = None
+
+    def __post_init__(self):
+        if self.probs is not None:
+            assert len(self.lens) == len(self.probs), "Length of lens and probs must match"
+        if self.entropies is not None:
+            assert len(self.lens) == len(self.entropies), "Length of lens and entropies must match"
+
+        # remove the prefill accepted lens
+        self.lens = self.lens[1:]
+
+        # remove the last proposed tokens
+        if self.probs:
+            self.probs = self.probs[:-1]
+        if self.entropies:
+            self.entropies = self.entropies[:-1]
+
+    @property
+    def length(self):
+        return len(self.lens)
+
+# def cleanup(acc_stats: AccStats) -> 
+#     # Remove the prefill phase
+#     data = data[1:]
+#     # Cap the maximum value to 10
+#     data = [min(x, 10) for x in data]
+#     return data
+
+def load_data(datapath, tokenizer, verbose=False):
+    acceptance_stats = []
+    with open(datapath, "r") as f:
+        lines = f.readlines()
+        for line in lines:  
+            data = json.loads(line)
+            stat = AccStats(
+                lens=data['acc']['acc_len'],
+                probs=data['acc'].get('acc_prob', None),
+                entropies=data['acc'].get('acc_entropy', None)
+            )
+            acceptance_stats.append(stat)
+            if verbose:
+                print("Input:", tokenizer.decode(data['prompt_token_ids']))
+                print("Output:", tokenizer.decode(data['generated_token_ids']))
+                print("=============================================")
+            
+    max_length = max(stats.length for stats in acceptance_stats)
+        
+    print(f"Load {len(acceptance_stats)} with max length {max_length}")
+    return acceptance_stats

benchmarks/visualize/vis_acc.py

@@ -0,0 +1,108 @@
+import json
+import seaborn as sns
+import matplotlib.pyplot as plt
+from transformers import AutoTokenizer
+from .common import MODEL_TO_NAMES, load_data
+import requests
+import os
+from pathlib import Path
+
+class AcceptanceStatsClient:
+    """Client for fetching and processing acceptance statistics data."""
+    
+    def __init__(self, model_name, method, dataset, data_path=None):
+        """Initialize the client with model and dataset info."""
+        self.model_name = model_name
+        self.method = method
+        self.dataset = dataset
+        
+        if data_path is None:
+            self.data_path = f"/data/lily/batch-sd/data/{model_name}/{method}_{dataset}_acceptance_stats.jsonl"
+        else:
+            self.data_path = data_path
+            
+        self.tokenizer = AutoTokenizer.from_pretrained(MODEL_TO_NAMES[model_name], use_fast=False)
+        self.acceptance_stats = None
+        
+    def load_data(self):
+        """Load the acceptance statistics from file."""
+        self.acceptance_stats = load_data(self.data_path, self.tokenizer)
+        return self.acceptance_stats
+    
+    def plot_heatmap(self, output_dir="figures"):
+        """Plot the acceptance statistics as a heatmap."""
+        if self.acceptance_stats is None:
+            self.load_data()
+            
+        fig, ax = plt.subplots(figsize=(12, 8))
+        sns.heatmap(self.acceptance_stats, cmap="YlGnBu")
+        plt.xlabel("Position")
+        plt.ylabel("Request ID")
+        
+        # Add Y-axis labels on the right
+        ax2 = ax.twinx()
+        ax2.set_ylim(ax.get_ylim())
+        ax2.set_yticks([])
+        ax2.set_ylabel("# of Accepted Tokens", labelpad=10)
+        
+        plt.title(f"Acceptance Statistics: {self.model_name} - {self.method} - {self.dataset}")
+        plt.tight_layout()
+        
+        # Create output directory if it doesn't exist
+        output_path = Path(output_dir) / self.model_name
+        os.makedirs(output_path, exist_ok=True)
+        
+        output_file = output_path / f"{self.method}_{self.dataset}_acceptance_stats.pdf"
+        plt.savefig(output_file)
+        print(f"Saved heatmap to {output_file}")
+        return fig
+    
+    def get_summary_stats(self):
+        """Get summary statistics about the acceptance data."""
+        if self.acceptance_stats is None:
+            self.load_data()
+            
+        # Calculate average acceptance rate for each position
+        avg_by_position = [sum(col)/len(col) for col in zip(*self.acceptance_stats) if sum(1 for v in col if v >= 0) > 0]
+        
+        # Calculate average acceptance rate for each request
+        avg_by_request = [sum(row)/len(row) for row in self.acceptance_stats]
+        
+        return {
+            "total_requests": len(self.acceptance_stats),
+            "max_position": len(avg_by_position),
+            "avg_acceptance_rate": sum(avg_by_request)/len(avg_by_request),
+            "avg_by_position": avg_by_position,
+            "avg_by_request": avg_by_request
+        }
+
+# Example model configuration
+model = "llama3.1-8B"
+# model = "r1-distill-llama-8B"
+method = "eagle3"
+dataset = "mtbench"
+# dataset = "aime"
+# method = "ngram"
+# dataset = "cnndailymail"
+# datapath = f"/data/lily/batch-sd/data/{model}/{method}_{dataset}_acceptance_stats.jsonl"
+datapath = "acceptance_stats.jsonl"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_TO_NAMES[model], use_fast=False)
+
+
+if __name__ == "__main__":
+    # Use the client instead of directly loading data
+    client = AcceptanceStatsClient(model, method, dataset, datapath)
+    acceptance_stats = client.load_data()
+    
+    # Get summary statistics
+    summary = client.get_summary_stats()
+    print("Summary Statistics:")
+    print(f"Total Requests: {summary['total_requests']}")
+    print(f"Max Position: {summary['max_position']}")
+    print(f"Average Acceptance Rate: {summary['avg_acceptance_rate']:.2f}")
+
+    # Create heatmap visualization
+    plot_heatmap = False
+    if plot_heatmap:
+        client.plot_heatmap()
+

benchmarks/visualize/vis_acc_diff.py

@@ -0,0 +1,69 @@
+import json
+import seaborn as sns
+import matplotlib.pyplot as plt
+from matplotlib.colors import LinearSegmentedColormap
+
+model = "llama3.1-8B"
+dataset = "instructcode"
+method1 = "ngram"
+method2 = "eagle3"
+
+def get_datapath(method):
+    datapath = f"/data/lily/batch-sd/data/{model}/{method}_{dataset}_acceptance_stats.jsonl"
+    return datapath
+
+def cleanup(data):
+    # Remove the prefill phase
+    data = data[1:]
+    # Cap the maximum value to 10
+    data = [min(x, 10) for x in data]
+    return data
+
+def load_data(datapath):
+    acceptance_stats = {}
+    with open(datapath, "r") as f:
+        lines = f.readlines()
+        for line in lines:  
+            data = json.loads(line)
+            key = hash(tuple(data['prompt_token_ids']))
+            acceptance_stats[key] = cleanup(data['acc'])
+    # Pad the acceptance stats to the same length
+    max_length = max(len(stats) for k, stats in acceptance_stats.items())
+    
+    for key in acceptance_stats:
+        acceptance_stats[key] += [-2] * (max_length - len(acceptance_stats[key]))
+        
+    print(f"Load {len(acceptance_stats)} with max length {max_length} from {datapath}")
+    return acceptance_stats
+
+def diff(acceptance_stats1, acceptance_stats2):
+    diff = {}
+    for key in acceptance_stats1:
+        if key in acceptance_stats2:
+            diff[key] = [a - b for a, b in zip(acceptance_stats1[key], acceptance_stats2[key])]
+    return diff
+
+datapath_1 = get_datapath(method1)
+datapath_2 = get_datapath(method2)
+acceptance_stats_1 = load_data(datapath_1)
+acceptance_stats_2 = load_data(datapath_2)
+acceptance_stats_diff = diff(acceptance_stats_1, acceptance_stats_2)
+
+acceptance_stats = list(acceptance_stats_diff.values())
+
+
+fig, ax = plt.subplots()
+colors = ["red", "white", "blue"]
+custom_cmap = LinearSegmentedColormap.from_list("custom", colors, N=256)
+sns.heatmap(acceptance_stats, cmap=custom_cmap, center=0)
+plt.xlabel("Position")
+plt.ylabel("Request ID")
+# Add Y-axis labels on the right
+ax2 = ax.twinx()
+ax2.set_ylim(ax.get_ylim())              # Match y-axis range
+ax2.set_yticks([])                       # Remove right tick marks if undesired
+ax2.set_ylabel("# of Accepted Tokens", labelpad=10)         # Set right y-axis label
+plt.title(f"Diff between {method2} - {method1} acceptance stats for {dataset}")
+
+plt.tight_layout()
+plt.savefig(f"figures/{model}/diff_{method2}_{method1}_{dataset}_acceptance_stats.pdf")

benchmarks/visualize/vis_prob_entropy.py

@@ -0,0 +1,38 @@
+from transformers import AutoTokenizer
+from common import MODEL_TO_NAMES, load_data
+import matplotlib.pyplot as plt
+
+
+def plot_prob_entropy(acceptance_stats, 
+                    output_path):
+    
+    acc_probs = []
+    rej_probs = []
+    for stat in acceptance_stats:
+        for i, acc_len in enumerate(stat.lens):
+            acc_probs.extend(stat.probs[i][:acc_len-1])
+            rej_probs.extend(stat.probs[i][acc_len-1:])
+
+    fig, ax = plt.subplots(figsize=(12, 8))
+    plt.hist(acc_probs, bins=100, alpha=0.5, 
+             label='Accepted Probabilities', color='green')
+    plt.hist(rej_probs, bins=100, alpha=0.5, 
+             label='Rejected Probabilities', color='red')
+    plt.xlabel('Probability')
+    plt.ylabel('Frequency')
+    plt.title('Distribution of Accepted and Rejected Probabilities')
+    plt.legend()
+    plt.tight_layout()
+    plt.savefig(output_path)
+
+
+if __name__ == "__main__":
+    datapath = "/data/lily/sd-benchmark-paper/batch-sd/acceptance_stats.jsonl"
+    model = "llama3.1-8B"
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_TO_NAMES[model], 
+                                              use_fast=False)
+    acceptance_stats = load_data(datapath, tokenizer)
+    plot_prob_entropy(acceptance_stats, output_path="prob_entropy_figures")
+
+
+

cmake/cpu_extension.cmake

@@ -33,8 +33,6 @@ endif()
 
 if(MACOSX_FOUND)
     list(APPEND CXX_COMPILE_FLAGS
-        "-Xpreprocessor"
-        "-fopenmp"
         "-DVLLM_CPU_EXTENSION")
 else()
     list(APPEND CXX_COMPILE_FLAGS
@@ -190,12 +188,14 @@ set(VLLM_EXT_SRC
     "csrc/cpu/cache.cpp"
     "csrc/cpu/utils.cpp"
     "csrc/cpu/layernorm.cpp"
+    "csrc/cpu/mla_decode.cpp"
     "csrc/cpu/pos_encoding.cpp"
     "csrc/cpu/torch_bindings.cpp")
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     set(VLLM_EXT_SRC
         "csrc/cpu/quant.cpp"
+        "csrc/cpu/shm.cpp"
         ${VLLM_EXT_SRC})
 endif()
 

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG dc9d410b3e2d6534a4c70724c2515f4def670a22
+          GIT_TAG 8798f27777fb57f447070301bf33a9f9c607f491
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/attention/merge_attn_states.cu

@@ -0,0 +1,178 @@
+#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, 0, stream>>>(                                       \
+            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,n] The log-sum-exp values for the prefix attention
+ * states.
+ * @param suffix_output [n,h,d] The suffix attention states.
+ * @param suffix_lse [h,n] 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. for float, the
+  // pack_size is 4 for half/bf16, the pack_size is 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);
+
+  const c10::cuda::OptionalCUDAGuard device_guard(prefix_output.device());
+  auto stream = at::cuda::getCurrentCUDAStream();
+
+  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/attention/mla/cutlass_mla_entry.cu

@@ -0,0 +1,38 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#if defined ENABLE_CUTLASS_MLA && ENABLE_CUTLASS_MLA
+void cutlass_mla_decode_sm100a(torch::Tensor const& out,
+                               torch::Tensor const& q_nope,
+                               torch::Tensor const& q_pe,
+                               torch::Tensor const& kv_c_and_k_pe_cache,
+                               torch::Tensor const& seq_lens,
+                               torch::Tensor const& page_table, double scale);
+#endif
+
+void cutlass_mla_decode(torch::Tensor const& out, torch::Tensor const& q_nope,
+                        torch::Tensor const& q_pe,
+                        torch::Tensor const& kv_c_and_k_pe_cache,
+                        torch::Tensor const& seq_lens,
+                        torch::Tensor const& page_table, double scale) {
+#if defined ENABLE_CUTLASS_MLA && ENABLE_CUTLASS_MLA
+  return cutlass_mla_decode_sm100a(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                   seq_lens, page_table, scale);
+#endif
+  TORCH_CHECK_NOT_IMPLEMENTED(false, "No compiled cutlass MLA");
+}

csrc/attention/mla/cutlass_mla_kernels.cu

@@ -0,0 +1,225 @@
+/*
+ * Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+#include <torch/all.h>
+
+#include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAGuard.h>
+
+#include "cute/tensor.hpp"
+
+#include "cutlass/cutlass.h"
+#include "cutlass/kernel_hardware_info.h"
+
+#include "cutlass_extensions/common.hpp"
+
+#include "device/sm100_mla.hpp"
+#include "kernel/sm100_mla_tile_scheduler.hpp"
+
+using namespace cute;
+using namespace cutlass::fmha::kernel;
+
+template <typename T, bool PersistenceOption = true>
+struct MlaSm100 {
+  using Element = T;
+  using ElementAcc = float;
+  using ElementOut = T;
+
+  using TileShape = Shape<_128, _128, Shape<_512, _64>>;
+  using TileShapeH = cute::tuple_element_t<0, TileShape>;
+  using TileShapeD = cute::tuple_element_t<2, TileShape>;
+
+  // H K (D_latent D_rope) B
+  using ProblemShape = cute::tuple<TileShapeH, int, TileShapeD, int>;
+
+  using StrideQ = cute::tuple<int64_t, _1, int64_t>;  // H D B
+  using StrideK = cute::tuple<int64_t, _1, int64_t>;  // K D B
+  using StrideO = StrideK;                            // H D B
+  using StrideLSE = cute::tuple<_1, int>;             // H B
+
+  using TileScheduler =
+      std::conditional_t<PersistenceOption, Sm100MlaPersistentTileScheduler,
+                         Sm100MlaIndividualTileScheduler>;
+
+  using FmhaKernel =
+      cutlass::fmha::kernel::Sm100FmhaMlaKernelTmaWarpspecialized<
+          TileShape, Element, ElementAcc, ElementOut, ElementAcc, TileScheduler,
+          /*kIsCpAsync=*/true>;
+  using Fmha = cutlass::fmha::device::MLA<FmhaKernel>;
+};
+
+template <typename T>
+typename T::Fmha::Arguments args_from_options(
+    at::Tensor const& out, at::Tensor const& q_nope, at::Tensor const& q_pe,
+    at::Tensor const& kv_c_and_k_pe_cache, at::Tensor const& seq_lens,
+    at::Tensor const& page_table, double scale) {
+  cutlass::KernelHardwareInfo hw_info;
+  hw_info.device_id = q_nope.device().index();
+  hw_info.sm_count =
+      cutlass::KernelHardwareInfo::query_device_multiprocessor_count(
+          hw_info.device_id);
+
+  int batches = q_nope.sizes()[0];
+  int page_count_per_seq = page_table.sizes()[1];
+  int page_count_total = kv_c_and_k_pe_cache.sizes()[0];
+  int page_size = kv_c_and_k_pe_cache.sizes()[1];
+  int max_seq_len = page_size * page_count_per_seq;
+  using TileShapeH = typename T::TileShapeH;
+  using TileShapeD = typename T::TileShapeD;
+  auto problem_shape =
+      cute::make_tuple(TileShapeH{}, max_seq_len, TileShapeD{}, batches);
+
+  auto [H, K, D, B] = problem_shape;
+  auto [D_latent, D_rope] = D;
+
+  using StrideQ = typename T::StrideQ;
+  using StrideK = typename T::StrideK;
+  using StrideO = typename T::StrideO;
+  using StrideLSE = typename T::StrideLSE;
+
+  StrideQ stride_Q_latent = cute::make_tuple(
+      static_cast<int64_t>(D_latent), _1{}, static_cast<int64_t>(H * D_latent));
+  StrideQ stride_Q_rope = cute::make_tuple(static_cast<int64_t>(D_rope), _1{},
+                                           static_cast<int64_t>(H * D_rope));
+  StrideK stride_C =
+      cute::make_tuple(static_cast<int64_t>(D_latent + D_rope), _1{},
+                       static_cast<int64_t>(page_size * (D_latent + D_rope)));
+  StrideLSE stride_PT = cute::make_stride(_1{}, page_count_per_seq);
+  StrideLSE stride_LSE = cute::make_tuple(_1{}, static_cast<int>(H));
+  StrideO stride_O = cute::make_tuple(static_cast<int64_t>(D_latent), _1{},
+                                      static_cast<int64_t>(H * D_latent));
+
+  using Element = typename T::Element;
+  using ElementOut = typename T::ElementOut;
+  using ElementAcc = typename T::ElementAcc;
+  auto Q_latent_ptr = static_cast<Element*>(q_nope.data_ptr());
+  auto Q_rope_ptr = static_cast<Element*>(q_pe.data_ptr());
+  auto C_ptr = static_cast<Element*>(kv_c_and_k_pe_cache.data_ptr());
+  auto scale_f = static_cast<float>(scale);
+  typename T::Fmha::Arguments arguments{
+      problem_shape,
+      {scale_f, Q_latent_ptr, stride_Q_latent, Q_rope_ptr, stride_Q_rope, C_ptr,
+       stride_C, C_ptr + D_latent, stride_C,
+       static_cast<int*>(seq_lens.data_ptr()),
+       static_cast<int*>(page_table.data_ptr()), stride_PT, page_count_total,
+       page_size},
+      {static_cast<ElementOut*>(out.data_ptr()), stride_O,
+       static_cast<ElementAcc*>(nullptr), stride_LSE},
+      hw_info,
+      -1,       // split_kv
+      nullptr,  // is_var_split_kv
+  };
+  // TODO(kaixih@nvidia): When split_kv=-1 and is_var_split_kv=false, we compute
+  // split_kv automatically based on batch size and sequence length to balance
+  // workload across available SMs. Consider using var_split_kv for manual
+  // control if needed.
+  T::Fmha::set_split_kv(arguments);
+  return arguments;
+}
+
+template <typename Element>
+void runMla(at::Tensor const& out, at::Tensor const& q_nope,
+            at::Tensor const& q_pe, at::Tensor const& kv_c_and_k_pe_cache,
+            at::Tensor const& seq_lens, at::Tensor const& page_table,
+            float scale, cudaStream_t stream) {
+  using MlaSm100Type = MlaSm100<Element>;
+  typename MlaSm100Type::Fmha fmha;
+  auto arguments = args_from_options<MlaSm100Type>(
+      out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens, page_table, scale);
+  size_t workspace_size = MlaSm100Type::Fmha::get_workspace_size(arguments);
+  auto const workspace_options =
+      torch::TensorOptions().dtype(torch::kUInt8).device(q_nope.device());
+  auto workspace = torch::empty(workspace_size, workspace_options);
+
+  CUTLASS_CHECK(fmha.can_implement(arguments));
+
+  CUTLASS_CHECK(fmha.initialize(arguments, workspace.data_ptr(), stream));
+
+  CUTLASS_CHECK(fmha.run(arguments, workspace.data_ptr(), stream));
+}
+
+void cutlass_mla_decode_sm100a(torch::Tensor const& out,
+                               torch::Tensor const& q_nope,
+                               torch::Tensor const& q_pe,
+                               torch::Tensor const& kv_c_and_k_pe_cache,
+                               torch::Tensor const& seq_lens,
+                               torch::Tensor const& page_table, double scale) {
+  TORCH_CHECK(q_nope.device().is_cuda(), "q_nope must be on CUDA");
+  TORCH_CHECK(q_nope.dim() == 3, "q_nope must be a 3D tensor");
+  TORCH_CHECK(q_pe.dim() == 3, "q_pe must be a 3D tensor");
+  TORCH_CHECK(kv_c_and_k_pe_cache.dim() == 3,
+              "kv_c_and_k_pe_cache must be a 3D tensor");
+  TORCH_CHECK(seq_lens.dim() == 1, "seq_lens must be a 1D tensor");
+  TORCH_CHECK(page_table.dim() == 2, "page_table must be a 2D tensor");
+  TORCH_CHECK(out.dim() == 3, "out must be a 3D tensor");
+
+  auto B_q_nope = q_nope.size(0);
+  auto H_q_nope = q_nope.size(1);
+  auto D_q_nope = q_nope.size(2);
+  auto B_q_pe = q_pe.size(0);
+  auto H_q_pe = q_pe.size(1);
+  auto D_q_pe = q_pe.size(2);
+  auto B_pt = page_table.size(0);
+  auto PAGE_NUM = page_table.size(1);
+  auto PAGE_SIZE = kv_c_and_k_pe_cache.size(1);
+  auto D_ckv = kv_c_and_k_pe_cache.size(2);
+  auto B_o = out.size(0);
+  auto H_o = out.size(1);
+  auto D_o = out.size(2);
+
+  TORCH_CHECK(D_q_nope == 512, "D_q_nope must be equal to 512");
+  TORCH_CHECK(D_q_pe == 64, "D_q_pe must be equal to 64");
+  TORCH_CHECK(D_ckv == 576, "D_ckv must be equal to 576");
+  TORCH_CHECK(H_q_nope == H_q_pe && H_q_nope == H_o && H_o == 128,
+              "H_q_nope, H_q_pe, and H_o must be equal to 128");
+  TORCH_CHECK(PAGE_SIZE > 0 && (PAGE_SIZE & (PAGE_SIZE - 1)) == 0,
+              "PAGE_SIZE must be a power of 2");
+  TORCH_CHECK(
+      B_q_nope == B_q_pe && B_q_nope == B_pt && B_q_nope == B_o,
+      "Batch dims must be same for page_table, q_nope and q_pe, and out");
+  TORCH_CHECK(PAGE_NUM % (128 / PAGE_SIZE) == 0,
+              "PAGE_NUM must be divisible by 128 / PAGE_SIZE");
+  TORCH_CHECK(D_o == 512, "D_o must be equal to 512");
+
+  TORCH_CHECK(q_nope.dtype() == at::ScalarType::Half ||
+                  q_nope.dtype() == at::ScalarType::BFloat16 ||
+                  q_nope.dtype() == at::ScalarType::Float8_e4m3fn,
+              "q_nope must be a half, bfloat16, or float8_e4m3fn tensor");
+  TORCH_CHECK(kv_c_and_k_pe_cache.dtype() == q_nope.dtype() &&
+                  q_nope.dtype() == q_pe.dtype(),
+              "kv_c_and_k_pe_cache, q_nope, and q_pe must be the same type");
+  TORCH_CHECK(seq_lens.dtype() == torch::kInt32,
+              "seq_lens must be a 32-bit integer tensor");
+  TORCH_CHECK(page_table.dtype() == torch::kInt32,
+              "page_table must be a 32-bit integer tensor");
+
+  auto in_dtype = q_nope.dtype();
+  at::cuda::CUDAGuard device_guard{(char)q_nope.get_device()};
+  const cudaStream_t stream =
+      at::cuda::getCurrentCUDAStream(q_nope.get_device());
+  if (in_dtype == at::ScalarType::Half) {
+    runMla<cutlass::half_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache, seq_lens,
+                            page_table, scale, stream);
+  } else if (in_dtype == at::ScalarType::BFloat16) {
+    runMla<cutlass::bfloat16_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                seq_lens, page_table, scale, stream);
+  } else if (in_dtype == at::ScalarType::Float8_e4m3fn) {
+    runMla<cutlass::float_e4m3_t>(out, q_nope, q_pe, kv_c_and_k_pe_cache,
+                                  seq_lens, page_table, scale, stream);
+  } else {
+    TORCH_CHECK(false, "Unsupported input data type of MLA");
+  }
+}

csrc/cache_kernels.cu

@@ -270,9 +270,10 @@ __global__ void reshape_and_cache_flash_kernel(
     cache_t* __restrict__ value_cache,   // [num_blocks, block_size, num_heads,
                                          // head_size]
     const int64_t* __restrict__ slot_mapping,  // [num_tokens]
-    const int block_stride, const int key_stride, const int value_stride,
-    const int num_heads, const int head_size, const int block_size,
-    const float* k_scale, const float* v_scale) {
+    const int64_t block_stride, const int64_t page_stride,
+    const int64_t head_stride, const int64_t key_stride,
+    const int64_t value_stride, const int num_heads, const int head_size,
+    const int block_size, const float* k_scale, const float* v_scale) {
   const int64_t token_idx = blockIdx.x;
   const int64_t slot_idx = slot_mapping[token_idx];
   // NOTE: slot_idx can be -1 if the token is padded
@@ -288,8 +289,8 @@ __global__ void reshape_and_cache_flash_kernel(
     const int head_idx = i / head_size;
     const int head_offset = i % head_size;
     const int64_t tgt_key_value_idx = block_idx * block_stride +
-                                      block_offset * num_heads * head_size +
-                                      head_idx * head_size + head_offset;
+                                      block_offset * page_stride +
+                                      head_idx * head_stride + head_offset;
     scalar_t tgt_key = key[src_key_idx];
     scalar_t tgt_value = value[src_value_idx];
     if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
@@ -396,16 +397,16 @@ void reshape_and_cache(
 // KV_T is the data type of key and value tensors.
 // CACHE_T is the stored data type of kv-cache.
 // KV_DTYPE is the real data type of kv-cache.
-#define CALL_RESHAPE_AND_CACHE_FLASH(KV_T, CACHE_T, KV_DTYPE)         \
-  vllm::reshape_and_cache_flash_kernel<KV_T, CACHE_T, KV_DTYPE>       \
-      <<<grid, block, 0, stream>>>(                                   \
-          reinterpret_cast<KV_T*>(key.data_ptr()),                    \
-          reinterpret_cast<KV_T*>(value.data_ptr()),                  \
-          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),           \
-          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),         \
-          slot_mapping.data_ptr<int64_t>(), block_stride, key_stride, \
-          value_stride, num_heads, head_size, block_size,             \
-          reinterpret_cast<const float*>(k_scale.data_ptr()),         \
+#define CALL_RESHAPE_AND_CACHE_FLASH(KV_T, CACHE_T, KV_DTYPE)             \
+  vllm::reshape_and_cache_flash_kernel<KV_T, CACHE_T, KV_DTYPE>           \
+      <<<grid, block, 0, stream>>>(                                       \
+          reinterpret_cast<KV_T*>(key.data_ptr()),                        \
+          reinterpret_cast<KV_T*>(value.data_ptr()),                      \
+          reinterpret_cast<CACHE_T*>(key_cache.data_ptr()),               \
+          reinterpret_cast<CACHE_T*>(value_cache.data_ptr()),             \
+          slot_mapping.data_ptr<int64_t>(), block_stride, page_stride,    \
+          head_stride, key_stride, value_stride, num_heads, head_size,    \
+          block_size, reinterpret_cast<const float*>(k_scale.data_ptr()), \
           reinterpret_cast<const float*>(v_scale.data_ptr()));
 
 void reshape_and_cache_flash(
@@ -432,9 +433,11 @@ void reshape_and_cache_flash(
   int head_size = key.size(2);
   int block_size = key_cache.size(1);
 
-  int key_stride = key.stride(0);
-  int value_stride = value.stride(0);
-  int block_stride = key_cache.stride(0);
+  int64_t key_stride = key.stride(0);
+  int64_t value_stride = value.stride(0);
+  int64_t block_stride = key_cache.stride(0);
+  int64_t page_stride = key_cache.stride(1);
+  int64_t head_stride = key_cache.stride(2);
   TORCH_CHECK(key_cache.stride(0) == value_cache.stride(0));
 
   dim3 grid(num_tokens);

csrc/cpu/cache.cpp

@@ -88,6 +88,48 @@ void reshape_and_cache_cpu_impl(
 }
 };  // namespace
 
+template <typename scalar_t>
+void concat_and_cache_mla_cpu_impl(
+    const scalar_t* __restrict__ kv_c,  // [num_tokens, kv_lora_rank]
+    const scalar_t* __restrict__ k_pe,  // [num_tokens, pe_dim]
+    scalar_t* __restrict__ kv_cache,  // [num_blocks, block_size, (kv_lora_rank
+                                      // + pe_dim)]
+    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
+    const int num_tokens,                      //
+    const int block_stride,                    //
+    const int entry_stride,                    //
+    const int kv_c_stride,                     //
+    const int k_pe_stride,                     //
+    const int kv_lora_rank,                    //
+    const int pe_dim,                          //
+    const int block_size                       //
+) {
+#pragma omp parallel for
+  for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
+    const int64_t slot_idx = slot_mapping[token_idx];
+    // NOTE: slot_idx can be -1 if the token is padded
+    if (slot_idx < 0) {
+      continue;
+    }
+    const int64_t block_idx = slot_idx / block_size;
+    const int64_t block_offset = slot_idx % block_size;
+
+    auto copy = [&](const scalar_t* __restrict__ src,
+                    scalar_t* __restrict__ dst, int src_stride, int dst_stride,
+                    int size, int offset) {
+      for (int i = 0; i < size; i++) {
+        const int64_t src_idx = token_idx * src_stride + i;
+        const int64_t dst_idx =
+            block_idx * block_stride + block_offset * entry_stride + i + offset;
+        dst[dst_idx] = src[src_idx];
+      }
+    };
+
+    copy(kv_c, kv_cache, kv_c_stride, block_stride, kv_lora_rank, 0);
+    copy(k_pe, kv_cache, k_pe_stride, block_stride, pe_dim, kv_lora_rank);
+  }
+}
+
 // Note: the key_caches and value_caches vectors are constant but
 // not the Tensors they contain. The vectors need to be const refs
 // in order to satisfy pytorch's C++ operator registration code.
@@ -134,6 +176,38 @@ void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
   });
 }
 
+void concat_and_cache_mla(
+    torch::Tensor& kv_c,          // [num_tokens, kv_lora_rank]
+    torch::Tensor& k_pe,          // [num_tokens, pe_dim]
+    torch::Tensor& kv_cache,      // [num_blocks, block_size, (kv_lora_rank +
+                                  // pe_dim)]
+    torch::Tensor& slot_mapping,  // [num_tokens] or [num_actual_tokens]
+    const std::string& kv_cache_dtype, torch::Tensor& scale) {
+  int num_tokens = slot_mapping.size(0);
+  int kv_lora_rank = kv_c.size(1);
+  int pe_dim = k_pe.size(1);
+  int block_size = kv_cache.size(1);
+
+  TORCH_CHECK(kv_cache.size(2) == kv_lora_rank + pe_dim);
+  TORCH_CHECK(kv_cache_dtype != "fp8");
+
+  int kv_c_stride = kv_c.stride(0);
+  int k_pe_stride = k_pe.stride(0);
+  int block_stride = kv_cache.stride(0);
+  int entry_stride = kv_cache.stride(1);
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      kv_c.scalar_type(), "concat_and_cache_mla_cpu_impl", [&] {
+        CPU_KERNEL_GUARD_IN(concat_and_cache_mla_cpu_impl)
+        concat_and_cache_mla_cpu_impl<scalar_t>(
+            kv_c.data_ptr<scalar_t>(), k_pe.data_ptr<scalar_t>(),
+            kv_cache.data_ptr<scalar_t>(), slot_mapping.data_ptr<int64_t>(),
+            num_tokens, block_stride, entry_stride, kv_c_stride, k_pe_stride,
+            kv_lora_rank, pe_dim, block_size);
+        CPU_KERNEL_GUARD_OUT(concat_and_cache_mla_cpu_impl)
+      });
+}
+
 void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
                  const torch::Tensor& block_mapping) {
   TORCH_CHECK(false, "swap_blocks is unsupported on CPU.")

csrc/cpu/cpu_types_x86.hpp

@@ -78,9 +78,14 @@ 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; }
@@ -110,9 +115,14 @@ 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; }
@@ -130,6 +140,8 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
 
   __m512i reg;
 
+  explicit BF16Vec32() : reg(_mm512_setzero_si512()) {}
+
   explicit BF16Vec32(const void* ptr) : reg((__m512i)_mm512_loadu_si512(ptr)) {}
 
   explicit BF16Vec32(__m512i data) : reg(data) {}
@@ -311,8 +323,13 @@ 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)
@@ -545,6 +562,33 @@ 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>
@@ -655,6 +699,22 @@ 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/mla_decode.cpp

@@ -0,0 +1,393 @@
+#include "cpu_types.hpp"
+#include <float.h>
+
+namespace {
+template <typename scalar_t>
+struct KernelVecType {
+  using qk_load_vec_type = void;
+  using qk_vec_type = void;
+  using v_load_vec_type = void;
+};
+
+template <>
+struct KernelVecType<float> {
+  using qk_load_vec_type = vec_op::FP32Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP32Vec16;
+};
+
+template <>
+struct KernelVecType<c10::Half> {
+#if defined(__powerpc64__) || defined(__s390x__)
+  // Power and s390x architecture-specific vector types
+  using qk_load_vec_type = vec_op::FP32Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP32Vec16;
+#else
+  // Fallback for other architectures, including x86
+  using qk_load_vec_type = vec_op::FP16Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::FP16Vec16;
+#endif
+};
+
+#ifdef __AVX512BF16__
+template <>
+struct KernelVecType<c10::BFloat16> {
+  using qk_load_vec_type = vec_op::BF16Vec32;
+  using qk_vec_type = vec_op::BF16Vec32;
+  using v_load_vec_type = vec_op::BF16Vec16;
+};
+#elif defined(__aarch64__) && !defined(ARM_BF16_SUPPORT)
+// pass
+#else
+template <>
+struct KernelVecType<c10::BFloat16> {
+  using qk_load_vec_type = vec_op::BF16Vec16;
+  using qk_vec_type = vec_op::FP32Vec16;
+  using v_load_vec_type = vec_op::BF16Vec16;
+};
+#endif
+
+template <int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE, int HEAD_UNROLL,
+          typename qk_vec_type>
+void mla_decode_block_head(
+    const qk_vec_type* __restrict__ q_vecs,          // [HEAD_UNROLL, head_dim]
+    const qk_vec_type* __restrict__ k_vecs,          // [block_size, head_dim]
+    const vec_op::FP32Vec16* __restrict v_vecs_f32,  // [block_size, v_head_dim]
+    float* __restrict__ acc_out,  // [HEAD_UNROLL, v_head_dim]
+    float* __restrict__ acc_lse,  // [HEAD_UNROLL]
+    const float scale, const int num_tokens) {
+  using f32_vec_type = vec_op::FP32Vec16;
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+  constexpr int V_NUM_ELEM = f32_vec_type::VEC_ELEM_NUM;
+
+  float logits[BLOCK_SIZE][HEAD_UNROLL] = {};  // initialize to zeros
+  float max_val[HEAD_UNROLL];
+  std::fill(max_val, max_val + HEAD_UNROLL, -FLT_MAX);
+
+  f32_vec_type acc_vec[BLOCK_SIZE][HEAD_UNROLL];
+  for (int i = 0; i < HEAD_DIM; i += QK_NUM_ELEM) {
+    // load to registers
+    qk_vec_type q_vec[HEAD_UNROLL];
+
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+      q_vec[unroll] =
+          qk_vec_type{q_vecs[(i + unroll * HEAD_DIM) / QK_NUM_ELEM]};
+
+    for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+      qk_vec_type k_vec(k_vecs[(block_offset * HEAD_DIM + i) / QK_NUM_ELEM]);
+
+#pragma unroll
+      for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+        vec_op::fma(acc_vec[block_offset][unroll], q_vec[unroll], k_vec);
+    }
+  }
+
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      const float acc = acc_vec[block_offset][unroll].reduce_sum() * scale;
+      logits[block_offset][unroll] = acc;
+      max_val[unroll] = std::max(max_val[unroll], acc);
+    }
+  }
+
+  float sum_exp[HEAD_UNROLL] = {};
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      const float val =
+          std::exp(logits[block_offset][unroll] - max_val[unroll]);
+      logits[block_offset][unroll] = val;
+      sum_exp[unroll] += val;
+    }
+  }
+
+  f32_vec_type this_out[V_HEAD_DIM / V_NUM_ELEM][HEAD_UNROLL];
+
+  for (int block_offset = 0; block_offset < num_tokens; ++block_offset) {
+    // load to registers
+    f32_vec_type scale_[HEAD_UNROLL];
+
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+      scale_[unroll] =
+          f32_vec_type{logits[block_offset][unroll] / sum_exp[unroll]};
+
+    for (int i = 0; i < V_HEAD_DIM; i += V_NUM_ELEM) {
+      f32_vec_type v_vec(
+          v_vecs_f32[(block_offset * HEAD_DIM + i) / V_NUM_ELEM]);
+
+#pragma unroll
+      for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll)
+        vec_op::fma(this_out[i / V_NUM_ELEM][unroll], v_vec, scale_[unroll]);
+    }
+  }
+
+  // merge attention state
+  // section 2.2 in https://arxiv.org/pdf/2501.01005
+  f32_vec_type prev_scale[HEAD_UNROLL];
+  f32_vec_type curr_scale[HEAD_UNROLL];
+
+#pragma unroll
+  for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+    const float prev_lse = acc_lse[unroll];
+    const float curr_lse = std::log(sum_exp[unroll]) +
+                           max_val[unroll];  // add back max_val to get true lse
+    // softmax trick
+    const float max_lse = std::max(prev_lse, curr_lse);
+    const float prev_sum_exp = std::exp(prev_lse - max_lse);
+    const float curr_sum_exp = std::exp(curr_lse - max_lse);
+
+    const float new_sum_exp = prev_sum_exp + curr_sum_exp;
+    acc_lse[unroll] = std::log(new_sum_exp) + max_lse;
+
+    prev_scale[unroll] = f32_vec_type{prev_sum_exp / new_sum_exp};
+    curr_scale[unroll] = f32_vec_type{curr_sum_exp / new_sum_exp};
+  }
+
+  for (int i = 0; i < V_HEAD_DIM; i += V_NUM_ELEM) {
+#pragma unroll
+    for (int unroll = 0; unroll < HEAD_UNROLL; ++unroll) {
+      f32_vec_type o_vec(acc_out + i + V_HEAD_DIM * unroll);
+      o_vec = o_vec * prev_scale[unroll] +
+              this_out[i / V_NUM_ELEM][unroll] * curr_scale[unroll];
+      o_vec.save(acc_out + i + V_HEAD_DIM * unroll);
+    }
+  }
+
+  q_vecs += HEAD_DIM / QK_NUM_ELEM * HEAD_UNROLL;
+  acc_out += V_HEAD_DIM * HEAD_UNROLL;
+}
+
+template <typename scalar_t, int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE,
+          typename qk_vec_type>
+void mla_decode_block(
+    const qk_vec_type* __restrict__ q_vecs,  // [num_heads, head_dim]
+    const scalar_t* __restrict__ kv_cache,   // [block_size, head_dim]
+    float* __restrict__ acc_out,             // [num_heads, v_head_dim]
+    float* __restrict__ acc_lse,             // [num_heads]
+    const int num_heads, const float scale, const int num_tokens) {
+  using qk_load_vec_type = typename KernelVecType<scalar_t>::qk_load_vec_type;
+  static_assert(
+      std::is_same<qk_vec_type,
+                   typename KernelVecType<scalar_t>::qk_vec_type>::value);
+  using v_load_vec_type = typename KernelVecType<scalar_t>::v_load_vec_type;
+  using f32_vec_type = vec_op::FP32Vec16;
+  static_assert(qk_load_vec_type::VEC_ELEM_NUM == qk_vec_type::VEC_ELEM_NUM);
+  static_assert(v_load_vec_type::VEC_ELEM_NUM == f32_vec_type::VEC_ELEM_NUM);
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+  constexpr int V_NUM_ELEM = v_load_vec_type::VEC_ELEM_NUM;
+
+  const qk_vec_type* k_vecs;
+  const f32_vec_type* v_vecs_f32;
+  float* kv_cache_f32 = nullptr;
+
+  if constexpr (!std::is_same<scalar_t, float>::value) {
+    // convert KV cache block to FP32 to reuse it across query heads and
+    // attn @ V computation, since FP16/BF16->FP32 is expensive.
+    // TODO: move malloc outside of this fn to reuse across iterations.
+    const int nbytes = BLOCK_SIZE * HEAD_DIM * sizeof(float);
+    kv_cache_f32 = static_cast<float*>(std::aligned_alloc(64, nbytes));
+
+    for (int block_offset = 0; block_offset < num_tokens; ++block_offset)
+      for (int i = 0; i < HEAD_DIM; i += V_NUM_ELEM) {
+        v_load_vec_type kv_load_vec(kv_cache + block_offset * HEAD_DIM + i);
+        f32_vec_type kv_vec_f32(kv_load_vec);
+        kv_vec_f32.save(kv_cache_f32 + block_offset * HEAD_DIM + i);
+      }
+
+    if constexpr (std::is_same<qk_load_vec_type, qk_vec_type>::value) {
+      // for AVX512_BF16, Q @ K.T uses BF16 for K (no conversion)
+      // NOTE: in this case, we only need to convert the V section to FP32.
+      // But for simplicity, we will convert the whole KV block to FP32.
+      k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache);
+    } else {
+      k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache_f32);
+    }
+
+    // attn @ V always use FP32 for V, since attn is FP32.
+    v_vecs_f32 = reinterpret_cast<const f32_vec_type*>(kv_cache_f32);
+
+  } else {
+    // KV cache is FP32. don't need to do anything.
+    k_vecs = reinterpret_cast<const qk_vec_type*>(kv_cache);
+    v_vecs_f32 = reinterpret_cast<const f32_vec_type*>(kv_cache);
+  }
+
+  // compute 2 heads at the same time to improve ILP and
+  // take advantage of register cache for K and V.
+  constexpr int HEAD_UNROLL = 2;
+  for (int iter = 0; iter < num_heads / HEAD_UNROLL; ++iter) {
+    mla_decode_block_head<HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE, HEAD_UNROLL>(
+        q_vecs, k_vecs, v_vecs_f32, acc_out, acc_lse, scale, num_tokens);
+
+    q_vecs += HEAD_UNROLL * HEAD_DIM / QK_NUM_ELEM;
+    acc_out += HEAD_UNROLL * V_HEAD_DIM;
+    acc_lse += HEAD_UNROLL;
+  }
+
+  // take care of the remaining heads
+  for (int iter = 0; iter < num_heads % HEAD_UNROLL; ++iter) {
+    mla_decode_block_head<HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE, 1>(
+        q_vecs, k_vecs, v_vecs_f32, acc_out, acc_lse, scale, num_tokens);
+
+    q_vecs += HEAD_DIM / QK_NUM_ELEM;
+    acc_out += V_HEAD_DIM;
+    acc_lse += 1;
+  }
+
+  if (kv_cache_f32 != nullptr) {
+    std::free(kv_cache_f32);
+  }
+}
+}  // namespace
+
+template <typename scalar_t, int HEAD_DIM, int V_HEAD_DIM, int BLOCK_SIZE>
+void mla_decode_kvcache_cpu_impl(
+    scalar_t* __restrict__ out,             // [num_seqs, num_heads, v_head_dim]
+    const scalar_t* __restrict__ q,         // [num_seqs, num_heads, head_dim]
+    const scalar_t* __restrict__ kv_cache,  // [num_blocks, block_size,
+                                            // head_dim]
+    const int num_heads, const float scale,
+    const int* __restrict__ block_tables,  // [num_seqs, max_num_blocks_per_seq]
+    const int* __restrict__ seq_lens,      // [num_seqs]
+    const int max_num_blocks_per_seq, const int o_stride, const int q_stride,
+    const int kv_stride, const int num_seqs) {
+  using qk_load_vec_type = typename KernelVecType<scalar_t>::qk_load_vec_type;
+  using qk_vec_type = typename KernelVecType<scalar_t>::qk_vec_type;
+  constexpr int QK_NUM_ELEM = qk_vec_type::VEC_ELEM_NUM;
+
+  // shared across threads
+  const int max_threads = omp_get_max_threads();
+  const int acc_out_nbytes =
+      max_threads * num_heads * V_HEAD_DIM * sizeof(float);
+  float* acc_out = static_cast<float*>(std::aligned_alloc(64, acc_out_nbytes));
+  std::vector<float> acc_lse(max_threads * num_heads);
+
+  // allocate memory to pre-convert query to FP32 later
+  float* q_f32;
+  constexpr bool PRE_CONVERT_QUERY =
+      !std::is_same<scalar_t, float>::value &&
+      std::is_same<qk_vec_type, vec_op::FP32Vec16>::value;
+  if constexpr (PRE_CONVERT_QUERY) {
+    const int q_f32_nbytes = num_heads * HEAD_DIM * sizeof(float);
+    q_f32 = static_cast<float*>(std::aligned_alloc(64, q_f32_nbytes));
+  }
+
+#pragma omp parallel
+  {
+    const int num_threads = omp_get_num_threads();
+    const int thread_id = omp_get_thread_num();
+    float* __restrict__ acc_out_thread =
+        acc_out + thread_id * num_heads * V_HEAD_DIM;
+    float* __restrict__ acc_lse_thread = acc_lse.data() + thread_id * num_heads;
+
+    for (int seq_idx = 0; seq_idx < num_seqs; ++seq_idx) {
+      // reset accumulator
+      std::fill(acc_out_thread, acc_out_thread + num_heads * V_HEAD_DIM, 0.0f);
+      std::fill(acc_lse_thread, acc_lse_thread + num_heads, -FLT_MAX);
+
+      const int seq_len = seq_lens[seq_idx];
+      const int block_num = (seq_len + BLOCK_SIZE - 1) / BLOCK_SIZE;
+      const int last_block_size = seq_len - (block_num - 1) * BLOCK_SIZE;
+
+      const qk_vec_type* q_vecs;
+      if constexpr (PRE_CONVERT_QUERY) {
+// pre-convert query to FP32 since FP16/BF16->FP32 is slow.
+#pragma omp for
+        for (int i = 0; i < num_heads * HEAD_DIM; i += QK_NUM_ELEM) {
+          qk_load_vec_type q_load_vec(q + seq_idx * q_stride + i);
+          qk_vec_type q_vec(q_load_vec);
+          q_vec.save(q_f32 + i);
+        }
+        q_vecs = reinterpret_cast<const qk_vec_type*>(q_f32);
+      } else {
+        q_vecs = reinterpret_cast<const qk_vec_type*>(q + seq_idx * q_stride);
+      }
+
+#pragma omp for
+      for (int block_idx = 0; block_idx < block_num; ++block_idx) {
+        const int physical_block_idx =
+            block_tables[seq_idx * max_num_blocks_per_seq + block_idx];
+        const int num_tokens =
+            block_idx < block_num - 1 ? BLOCK_SIZE : last_block_size;
+
+        mla_decode_block<scalar_t, HEAD_DIM, V_HEAD_DIM, BLOCK_SIZE>(
+            q_vecs, kv_cache + physical_block_idx * kv_stride, acc_out_thread,
+            acc_lse_thread, num_heads, scale, num_tokens);
+      }
+
+// merge attention states across threads
+// section 2.2 in https://arxiv.org/pdf/2501.01005
+// each thread is responsible for 1 head
+#pragma omp for
+      for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
+        float* acc_lse_head = acc_lse.data() + head_idx;
+        float* acc_out_head = acc_out + head_idx * V_HEAD_DIM;
+
+        float max_val = -FLT_MAX;
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          max_val = std::max(max_val, acc_lse_head[thread_id_ * num_heads]);
+        }
+
+        float sum_exp = 0.0f;
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          float val = std::exp(acc_lse_head[thread_id_ * num_heads] - max_val);
+          acc_lse_head[thread_id_ * num_heads] = val;
+          sum_exp += val;
+        }
+
+        float inv_sum = 1.0f / sum_exp;
+        float out_head[V_HEAD_DIM] = {};
+        for (int thread_id_ = 0; thread_id_ < num_threads; ++thread_id_) {
+          float scale_ = acc_lse_head[thread_id_ * num_heads] * inv_sum;
+          for (int i = 0; i < V_HEAD_DIM; ++i) {
+            out_head[i] +=
+                acc_out_head[thread_id_ * num_heads * V_HEAD_DIM + i] * scale_;
+          }
+        }
+
+        for (int i = 0; i < V_HEAD_DIM; ++i) {
+          vec_op::storeFP32(out_head[i], out + seq_idx * o_stride +
+                                             head_idx * V_HEAD_DIM + i);
+        }
+      }
+    }
+  }
+  if (PRE_CONVERT_QUERY) {
+    std::free(q_f32);
+  }
+  std::free(acc_out);
+}
+
+void mla_decode_kvcache(torch::Tensor& out, torch::Tensor& query,
+                        torch::Tensor& kv_cache, double scale,
+                        torch::Tensor& block_tables, torch::Tensor& seq_lens) {
+  const int num_seqs = query.size(0);
+  const int num_heads = query.size(1);
+  const int head_dim = query.size(2);
+  const int block_size = kv_cache.size(1);
+  const int v_head_dim = out.size(2);
+
+  const int max_num_blocks_per_seq = block_tables.size(1);
+  const int o_stride = out.stride(0);
+  const int q_stride = query.stride(0);
+  const int kv_stride = kv_cache.stride(0);
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      query.scalar_type(), "mla_decode_kvcache_cpu_impl", [&] {
+        CPU_KERNEL_GUARD_IN(mla_decode_kvcache_cpu_impl)
+        if (head_dim == 576 && v_head_dim == 512 && block_size == 16)
+          mla_decode_kvcache_cpu_impl<scalar_t, 576, 512, 16>(
+              out.data_ptr<scalar_t>(), query.data_ptr<scalar_t>(),
+              kv_cache.data_ptr<scalar_t>(), num_heads, scale,
+              block_tables.data_ptr<int>(), seq_lens.data_ptr<int>(),
+              max_num_blocks_per_seq, o_stride, q_stride, kv_stride, num_seqs);
+        else
+          TORCH_CHECK(false, "Unsupported block size: ", block_size);
+        CPU_KERNEL_GUARD_OUT(mla_decode_kvcache_cpu_impl)
+      });
+}

csrc/cpu/shm.cpp

@@ -0,0 +1,781 @@
+#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

@@ -18,6 +18,30 @@ void int8_scaled_mm_azp(torch::Tensor& c, const torch::Tensor& a,
                         const std::optional<torch::Tensor>& azp,
                         const std::optional<torch::Tensor>& bias);
 
+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
 
@@ -127,6 +151,29 @@ 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) {
@@ -150,6 +197,14 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cache_ops), cache_ops) {
       "                  str kv_cache_dtype,"
       "                  Tensor k_scale, Tensor v_scale) -> ()");
   cache_ops.impl("reshape_and_cache", torch::kCPU, &reshape_and_cache);
+
+  cache_ops.def(
+      "concat_and_cache_mla(Tensor kv_c, Tensor k_pe,"
+      "                     Tensor! kv_cache,"
+      "                     Tensor slot_mapping,"
+      "                     str kv_cache_dtype,"
+      "                     Tensor scale) -> ()");
+  cache_ops.impl("concat_and_cache_mla", torch::kCPU, &concat_and_cache_mla);
 }
 
 TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _utils), utils) {
@@ -157,4 +212,12 @@ TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _utils), utils) {
   utils.def("init_cpu_threads_env(str cpu_ids) -> str", &init_cpu_threads_env);
 }
 
+TORCH_LIBRARY_EXPAND(CONCAT(TORCH_EXTENSION_NAME, _cpu), cpu_ops) {
+  cpu_ops.def(
+      "mla_decode_kvcache("
+      "   Tensor! out, Tensor query, Tensor kv_cache,"
+      "   float scale, Tensor block_tables, Tensor seq_lens) -> ()");
+  cpu_ops.impl("mla_decode_kvcache", torch::kCPU, &mla_decode_kvcache);
+}
+
 REGISTER_EXTENSION(TORCH_EXTENSION_NAME)

csrc/cpu/utils.cpp

@@ -4,6 +4,11 @@
   #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"
 
@@ -18,7 +23,7 @@ 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(cpu_ids.c_str());
+  bitmask* omp_cpu_mask = numa_parse_cpustring_all(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

@@ -0,0 +1,39 @@
+#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 full_nvlink) {
+                      bool fully_connected) {
   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,
-                                            full_nvlink);
+                                            fully_connected);
 }
 
 /**
@@ -142,3 +142,48 @@ 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

@@ -5,6 +5,10 @@
 #include <cuda_fp16.h>
 #include <cuda_runtime.h>
 
+#if defined(USE_ROCM)
+typedef __hip_bfloat16 nv_bfloat16;
+#endif
+
 #include <iostream>
 #include <array>
 #include <limits>
@@ -12,6 +16,7 @@
 #include <unordered_map>
 #include <vector>
 
+namespace vllm {
 #define CUDACHECK(cmd)                                              \
   do {                                                              \
     cudaError_t e = cmd;                                            \
@@ -22,24 +27,37 @@
     }                                                               \
   } while (0)
 
-namespace vllm {
-
+// Maximal number of blocks in allreduce kernel.
 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 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];
+  alignas(128) FlagType start[kMaxBlocks][8];
+  alignas(128) FlagType end[kMaxBlocks][8];
+  alignas(128) FlagType _flag[kMaxBlocks];  // incremental flags for each rank
 };
 
 struct __align__(16) RankData {
-  const void* __restrict__ ptrs[8];
+  const void* ptrs[8];
 };
 
 struct __align__(16) RankSignals {
@@ -134,27 +152,29 @@ 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;
 }
 
@@ -170,37 +190,99 @@ static DINLINE FlagType ld_flag_volatile(FlagType* flag_addr) {
   return flag;
 }
 
-// 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.
+// 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;
   if (threadIdx.x < ngpus) {
-    // 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;
+    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];
     // Write the expected counter value to peer and wait for correct value from
     // peer.
-    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);
+    if constexpr (!final_sync) {
+      st_flag_release(peer_counter_ptr, flag);
+      while (ld_flag_acquire(self_counter_ptr) != flag);
     } else {
-      st_flag_volatile(peer_counter_ptr, val);
-      while (ld_flag_volatile(self_counter_ptr) != val);
+      st_flag_volatile(peer_counter_ptr, flag);
+      while (ld_flag_volatile(self_counter_ptr) != flag);
     }
   }
-  if constexpr (is_start || need_fence) __syncthreads();
+  if constexpr (!final_sync) __syncthreads();
+
+  // use one thread to update flag
+  if (threadIdx.x == 0) self_sg->_flag[blockIdx.x] = flag;
 }
 
+#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]);
@@ -220,13 +302,13 @@ __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;
-  multi_gpu_barrier<ngpus, true>(sg, self_sg, rank);
+  barrier_at_start<ngpus>(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);
   }
-  multi_gpu_barrier<ngpus, false>(sg, self_sg, rank);
+  barrier_at_end<ngpus, true>(sg, self_sg, rank);
 }
 
 template <typename P>
@@ -255,18 +337,20 @@ __global__ void __launch_bounds__(512, 1)
     tmps[i] = get_tmp_buf<P>(sg.signals[target]);
   }
   auto tmp_out = tmps[0];
-  multi_gpu_barrier<ngpus, true>(sg, self_sg, rank);
+  barrier_at_start<ngpus>(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);
   }
-  multi_gpu_barrier<ngpus, false, true>(sg, self_sg, rank);
+  barrier_at_end<ngpus>(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++) {
@@ -287,21 +371,22 @@ class CustomAllreduce {
  public:
   int rank_;
   int world_size_;
-  bool full_nvlink_;
+  // Full NVLink or xGMI connection between GPUs.
+  bool fully_connected_;
 
   RankSignals sg_;
-  // Stores an map from a pointer to its peer pointters from all ranks.
+  // Stores a map from a pointer to its peer pointers 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.
@@ -319,17 +404,18 @@ 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 full_nvlink = true)
+                  int rank, int world_size, bool fully_connected = true)
       : rank_(rank),
         world_size_(world_size),
-        full_nvlink_(full_nvlink),
+        fully_connected_(fully_connected),
         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)) {
@@ -361,8 +447,7 @@ class CustomAllreduce {
       void* base_ptr;
       // note: must share the base address of each allocation, or we get wrong
       // address
-      if (cuPointerGetAttribute(&base_ptr,
-                                CU_POINTER_ATTRIBUTE_RANGE_START_ADDR,
+      if (cuPointerGetAttribute(&base_ptr, rangeStartAddrAttr,
                                 (CUdeviceptr)ptr) != CUDA_SUCCESS)
         throw std::runtime_error("failed to get pointer attr");
       CUDACHECK(cudaIpcGetMemHandle(
@@ -396,11 +481,11 @@ 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) {
@@ -431,15 +516,15 @@ 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 = 36) {
+                 int threads = 512, int block_limit = defaultBlockLimit) {
     auto d = packed_t<T>::P::size;
     if (size % d != 0)
       throw std::runtime_error(
@@ -473,13 +558,11 @@ 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 (full_nvlink_) {                        \
+    } else if (fully_connected_) {                    \
       if ((world_size_ <= 4 && bytes < 512 * 1024) || \
           (world_size_ <= 8 && bytes < 256 * 1024)) { \
         KL(ngpus, cross_device_reduce_1stage);        \
@@ -497,7 +580,8 @@ 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_));
     }
@@ -511,10 +595,11 @@ 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${MPI_HOME} -lmpi
+ * custom_all_reduce_test -lnccl -I${MPI_HOME}/include -lmpi
  *
  * Warning: this C++ test is not designed to be very readable and was used
  * during the rapid prototyping process.
@@ -22,7 +22,15 @@
 #include "cuda_profiler_api.h"
 #include "custom_all_reduce.cuh"
 #include "mpi.h"
-#include "nccl.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
 
 #define MPICHECK(cmd)                                                  \
   do {                                                                 \
@@ -43,16 +51,29 @@
     }                                                               \
   } while (0)
 
+#ifdef USE_ROCM
 __global__ void dummy_kernel() {
-#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 700
+  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);
+  }
   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
 }
+#endif
 
 template <typename T>
 __global__ void set_data(T* data, int size, int myRank) {
@@ -121,8 +142,14 @@ 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)));
@@ -311,13 +338,18 @@ 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);
-  //   }
-  // }
+// 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
   // 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);
@@ -326,4 +358,4 @@ int main(int argc, char** argv) {
   cudaProfilerStop();
   MPICHECK(MPI_Finalize());
   return EXIT_SUCCESS;
-}
+}

csrc/cutlass_extensions/common.hpp

@@ -48,4 +48,14 @@ 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

@@ -0,0 +1,457 @@
+/***************************************************************************************************
+ * 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

@@ -1,6 +1,7 @@
 #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,
@@ -69,6 +70,16 @@ 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.
@@ -96,6 +107,14 @@ 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};
+  }
 };
 
 /*
@@ -381,4 +400,51 @@ 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-positivie index)
+        // (which occurs when `final_state_position` is a non-positive 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/moe/marlin_moe_wna16/generate_kernels.py

@@ -0,0 +1,103 @@
+# SPDX-License-Identifier: Apache-2.0
+import glob
+import itertools
+import os
+import subprocess
+
+import jinja2
+
+FILE_HEAD = """
+// auto generated by generate.py
+// clang-format off
+
+#include "kernel.h"
+#include "marlin_template.h"
+
+namespace MARLIN_NAMESPACE_NAME {
+""".strip()
+
+TEMPLATE = ("template __global__ void Marlin<"
+            "{{scalar_t}}, "
+            "{{w_type_id}}, "
+            "{{threads}}, "
+            "{{thread_m_blocks}}, "
+            "{{thread_n_blocks}}, "
+            "{{thread_k_blocks}}, "
+            "{{'true' if m_block_size_8 else 'false'}}, "
+            "{{stages}}, "
+            "{{'true' if has_act_order else 'false'}}, "
+            "{{'true' if has_zp else 'false'}}, "
+            "{{group_blocks}}, "
+            "{{'true' if is_zp_float else 'false'}}>"
+            "( MARLIN_KERNEL_PARAMS );")
+
+# int8 with zero point case (vllm::kU8) is also supported,
+# we don't add it to reduce wheel size.
+SCALAR_TYPES = ["vllm::kU4", "vllm::kU4B8", "vllm::kU8B128"]
+THREAD_CONFIGS = [(128, 128, 256), (64, 256, 256), (64, 128, 128)]
+
+THREAD_M_BLOCKS = [0.5, 1, 2, 3, 4]
+# group_blocks:
+#   = 0 : act order case
+#   = -1 : channelwise quantization
+#   > 0 : group_size=16*group_blocks
+GROUP_BLOCKS = [0, -1, 2, 4, 8]
+DTYPES = ["fp16", "bf16"]
+
+
+def remove_old_kernels():
+    for filename in glob.glob(os.path.dirname(__file__) + "/kernel_*.cu"):
+        subprocess.call(["rm", "-f", filename])
+
+
+def generate_new_kernels():
+    for scalar_type, dtype in itertools.product(SCALAR_TYPES, DTYPES):
+        has_zp = "B" not in scalar_type
+        all_template_str_list = []
+
+        for group_blocks, m_blocks, thread_configs in itertools.product(
+                GROUP_BLOCKS, THREAD_M_BLOCKS, THREAD_CONFIGS):
+
+            has_act_order = group_blocks == 0
+            if has_zp and has_act_order:
+                continue
+            if thread_configs[2] == 256:
+                if m_blocks <= 1 and thread_configs[0] != 128:
+                    continue
+                if m_blocks > 1 and thread_configs[0] != 64:
+                    continue
+
+            k_blocks = thread_configs[0] // 16
+            n_blocks = thread_configs[1] // 16
+            threads = thread_configs[2]
+
+            c_dtype = "half" if dtype == "fp16" else "nv_bfloat16"
+
+            template_str = jinja2.Template(TEMPLATE).render(
+                scalar_t=c_dtype,
+                w_type_id=scalar_type + ".id()",
+                threads=threads,
+                thread_m_blocks=max(m_blocks, 1),
+                thread_n_blocks=n_blocks,
+                thread_k_blocks=k_blocks,
+                m_block_size_8=m_blocks == 0.5,
+                stages="pipe_stages",
+                has_act_order=has_act_order,
+                has_zp=has_zp,
+                group_blocks=group_blocks,
+                is_zp_float=False,
+            )
+
+            all_template_str_list.append(template_str)
+
+        file_content = FILE_HEAD + "\n\n"
+        file_content += "\n\n".join(all_template_str_list) + "\n\n}\n"
+        filename = f"kernel_{dtype}_{scalar_type[6:].lower()}.cu"
+
+        with open(os.path.join(os.path.dirname(__file__), filename), "w") as f:
+            f.write(file_content)
+
+
+if __name__ == "__main__":
+    remove_old_kernels()
+    generate_new_kernels()