Add buffer donation to benchmark

.buildkite/check-wheel-size.py

@@ -1,36 +0,0 @@
-import os
-import zipfile
-
-MAX_SIZE_MB = 200
-
-
-def print_top_10_largest_files(zip_file):
-    with zipfile.ZipFile(zip_file, 'r') as z:
-        file_sizes = [(f, z.getinfo(f).file_size) for f in z.namelist()]
-        file_sizes.sort(key=lambda x: x[1], reverse=True)
-        for f, size in file_sizes[:10]:
-            print(f"{f}: {size/(1024*1024)} MBs uncompressed.")
-
-
-def check_wheel_size(directory):
-    for root, _, files in os.walk(directory):
-        for f in files:
-            if f.endswith(".whl"):
-                wheel_path = os.path.join(root, f)
-                wheel_size = os.path.getsize(wheel_path)
-                wheel_size_mb = wheel_size / (1024 * 1024)
-                if wheel_size_mb > MAX_SIZE_MB:
-                    print(
-                        f"Wheel {wheel_path} is too large ({wheel_size_mb} MB) "
-                        f"compare to the allowed size ({MAX_SIZE_MB} MB).")
-                    print_top_10_largest_files(wheel_path)
-                    return 1
-                else:
-                    print(f"Wheel {wheel_path} is within the allowed size "
-                          f"({wheel_size_mb} MB).")
-    return 0
-
-
-if __name__ == "__main__":
-    import sys
-    sys.exit(check_wheel_size(sys.argv[1]))

.buildkite/run-amd-test.sh

@@ -1,73 +1,38 @@
-# This script runs test inside the corresponding ROCm docker container.
+# This script build the ROCm docker image and run the API server inside the container.
+# It serves a sanity check for compilation and basic model usage.
 set -ex
 
 # Print ROCm version
-echo "--- ROCm info"
 rocminfo
 
-# cleanup older docker images
-cleanup_docker() {
-  # Get Docker's root directory
-  docker_root=$(docker info -f '{{.DockerRootDir}}')
-  if [ -z "$docker_root" ]; then
-    echo "Failed to determine Docker root directory."
-    exit 1
-  fi
-  echo "Docker root directory: $docker_root"
-  # Check disk usage of the filesystem where Docker's root directory is located
-  disk_usage=$(df "$docker_root" | tail -1 | awk '{print $5}' | sed 's/%//')
-  # Define the threshold
-  threshold=70
-  if [ "$disk_usage" -gt "$threshold" ]; then
-    echo "Disk usage is above $threshold%. Cleaning up Docker images and volumes..."
-    # Remove dangling images (those that are not tagged and not used by any container)
-    docker image prune -f
-    # Remove unused volumes
-    docker volume prune -f
-    echo "Docker images and volumes cleanup completed."
-  else
-    echo "Disk usage is below $threshold%. No cleanup needed."
-  fi
-}
+# Try building the docker image
+docker build -t rocm -f Dockerfile.rocm .
 
-# Call the cleanup docker function
-cleanup_docker
-
-echo "--- Resetting GPUs"
-
-echo "reset" > /opt/amdgpu/etc/gpu_state
-
-while true; do
-        sleep 3
-        if grep -q clean /opt/amdgpu/etc/gpu_state; then
-                echo "GPUs state is \"clean\""
-                break
-        fi
-done
-
-echo "--- Building container"
-sha=$(git rev-parse --short HEAD)
-image_name=rocm_${sha}
-container_name=rocm_${sha}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)
-docker build \
-        -t ${image_name} \
-        -f Dockerfile.rocm \
-        --progress plain \
-        .
-
-remove_docker_container() {
-   docker rm -f ${container_name} || docker image rm -f ${image_name} || true
-}
+# Setup cleanup
+remove_docker_container() { docker rm -f rocm || true; }
 trap remove_docker_container EXIT
+remove_docker_container
 
-echo "--- Running container"
+# Run the image
+docker run --device /dev/kfd --device /dev/dri --network host --name rocm rocm python3 -m vllm.entrypoints.api_server &
 
-docker run \
-        --device /dev/kfd --device /dev/dri \
-        --network host \
-        --rm \
-        -e HF_TOKEN \
-        --name ${container_name} \
-        ${image_name} \
-        /bin/bash -c "${@}"
+# Wait for the server to start
+wait_for_server_to_start() {
+    timeout=300
+    counter=0
 
+    while [ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:8000/health)" != "200" ]; do
+        sleep 1
+        counter=$((counter + 1))
+        if [ $counter -ge $timeout ]; then
+            echo "Timeout after $timeout seconds"
+            break
+        fi
+    done
+}
+wait_for_server_to_start
+
+# Test a simple prompt
+curl -X POST -H "Content-Type: application/json" \
+    localhost:8000/generate \
+    -d '{"prompt": "San Francisco is a"}'

.buildkite/run-benchmarks.sh

@@ -9,10 +9,10 @@ cd "$(dirname "${BASH_SOURCE[0]}")/.."
 (which wget && which curl) || (apt-get update && apt-get install -y wget curl)
 
 # run python-based benchmarks and upload the result to buildkite
-python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
+python3 benchmarks/benchmark_latency.py 2>&1 | tee benchmark_latency.txt
 bench_latency_exit_code=$?
 
-python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
+python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 2>&1 | tee benchmark_throughput.txt
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
@@ -53,11 +53,6 @@ echo '```' >> benchmark_results.md
 tail -n 20 benchmark_serving.txt >> benchmark_results.md # last 20 lines
 echo '```' >> benchmark_results.md
 
-# if the agent binary is not found, skip uploading the results, exit 0
-if [ ! -f /workspace/buildkite-agent ]; then
-    exit 0
-fi
-
 # upload the results to buildkite
 /workspace/buildkite-agent annotate --style "info" --context "benchmark-results" < benchmark_results.md
 
@@ -74,5 +69,4 @@ if [ $bench_serving_exit_code -ne 0 ]; then
     exit $bench_serving_exit_code
 fi
 
-rm ShareGPT_V3_unfiltered_cleaned_split.json
-/workspace/buildkite-agent artifact upload "*.json"
+/workspace/buildkite-agent artifact upload openai-*.json

.buildkite/run-cpu-test.sh

@@ -11,4 +11,4 @@ trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and launch offline inference
-docker run --network host --env VLLM_CPU_KVCACHE_SPACE=1 --name cpu-test cpu-test python3 vllm/examples/offline_inference.py
+docker run --network host --env VLLM_CPU_KVCACHE_SPACE=1 --name cpu-test cpu-test python3 examples/offline_inference.py

.buildkite/run-neuron-test.sh

@@ -1,51 +0,0 @@
-# This script build the Neuron docker image and run the API server inside the container.
-# It serves a sanity check for compilation and basic model usage.
-set -e
-
-# Try building the docker image
-aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 763104351884.dkr.ecr.us-west-2.amazonaws.com
-
-# prune old image and containers to save disk space, and only once a day
-# by using a timestamp file in tmp.
-if [ -f /tmp/neuron-docker-build-timestamp ]; then
-    last_build=$(cat /tmp/neuron-docker-build-timestamp)
-    current_time=$(date +%s)
-    if [ $((current_time - last_build)) -gt 86400 ]; then
-        docker system prune -f
-        echo $current_time > /tmp/neuron-docker-build-timestamp
-    fi
-else
-    echo $(date +%s) > /tmp/neuron-docker-build-timestamp
-fi
-
-docker build -t neuron -f Dockerfile.neuron .
-
-# Setup cleanup
-remove_docker_container() { docker rm -f neuron || true; }
-trap remove_docker_container EXIT
-remove_docker_container
-
-# Run the image
-docker run --device=/dev/neuron0 --device=/dev/neuron1 --network host --name neuron neuron python3 -m vllm.entrypoints.api_server \
-       --model TinyLlama/TinyLlama-1.1B-Chat-v1.0 --max-num-seqs 8 --max-model-len 128 --block-size 128 --device neuron --tensor-parallel-size 2 &
-
-# Wait for the server to start
-wait_for_server_to_start() {
-    timeout=300
-    counter=0
-
-    while [ "$(curl -s -o /dev/null -w ''%{http_code}'' localhost:8000/health)" != "200" ]; do
-        sleep 1
-        counter=$((counter + 1))
-        if [ $counter -ge $timeout ]; then
-            echo "Timeout after $timeout seconds"
-            break
-        fi
-    done
-}
-wait_for_server_to_start
-
-# Test a simple prompt
-curl -X POST -H "Content-Type: application/json" \
-    localhost:8000/generate \
-    -d '{"prompt": "San Francisco is a"}'

.buildkite/test-pipeline.yaml

@@ -5,155 +5,100 @@
 
 steps:
 - label: Regression Test
-  mirror_hardwares: [amd]
   command: pytest -v -s test_regression.py
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: AsyncEngine Test
-  #mirror_hardwares: [amd]
   command: pytest -v -s async_engine
 
 - label: Basic Correctness Test
-  mirror_hardwares: [amd]
   commands:
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_basic_correctness.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_basic_correctness.py
+  - VLLM_ATTENTION_BACKEND=ROCM_FLASH pytest -v -s basic_correctness/test_basic_correctness.py
   - VLLM_ATTENTION_BACKEND=XFORMERS pytest -v -s basic_correctness/test_chunked_prefill.py
   - VLLM_ATTENTION_BACKEND=FLASH_ATTN pytest -v -s basic_correctness/test_chunked_prefill.py
-  - VLLM_TEST_ENABLE_ARTIFICIAL_PREEMPT=1 pytest -v -s basic_correctness/test_preemption.py
+  - VLLM_ATTENTION_BACKEND=ROCM_FLASH pytest -v -s basic_correctness/test_chunked_prefill.py
 
 - label: Core Test
-  mirror_hardwares: [amd]
   command: pytest -v -s core
 
 - label: Distributed Comm Ops Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s distributed/test_comm_ops.py
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  command: pytest -v -s test_comm_ops.py
+  working_dir: "/vllm-workspace/tests/distributed"
+  num_gpus: 2 # only support 1 or 2 for now.
 
 - label: Distributed Tests
-  mirror_hardwares: [amd]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 2
+  working_dir: "/vllm-workspace/tests/distributed"
+  num_gpus: 2 # only support 1 or 2 for now.
   commands:
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_basic_distributed_correctness.py
-  - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py
-  - pytest -v -s spec_decode/e2e/test_integration_dist.py 
-
-- label: Distributed Tests (Multiple Groups)
-  #mirror_hardwares: [amd]
-  working_dir: "/vllm-workspace/tests"
-  num_gpus: 4
-  commands:
-  - pytest -v -s distributed/test_pynccl.py
+  - pytest -v -s test_pynccl.py
+  - TEST_DIST_MODEL=facebook/opt-125m pytest -v -s test_basic_distributed_correctness.py
+  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf pytest -v -s test_basic_distributed_correctness.py
+  - TEST_DIST_MODEL=facebook/opt-125m pytest -v -s test_chunked_prefill_distributed.py
+  - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf pytest -v -s test_chunked_prefill_distributed.py
 
 - label: Engine Test
-  mirror_hardwares: [amd]
-  command: pytest -v -s engine tokenization test_sequence.py test_config.py test_logger.py
+  command: pytest -v -s engine tokenization test_sequence.py test_config.py
 
 - label: Entrypoints Test
-  mirror_hardwares: [amd]
-
   commands:
-  - pytest -v -s test_inputs.py
-  - pytest -v -s entrypoints -m llm
-  - pytest -v -s entrypoints -m openai
+  # these tests have to be separated, because each one will allocate all posible GPU memory
+  - pytest -v -s entrypoints --ignore=entrypoints/test_server_oot_registration.py
+  - pytest -v -s entrypoints/test_server_oot_registration.py
 
 - label: Examples Test
   working_dir: "/vllm-workspace/examples"
-  mirror_hardwares: [amd]
   commands:
     # install aws cli for llava_example.py
-    # install tensorizer for tensorize_vllm_model.py
-    - pip install awscli tensorizer
+    - pip install awscli
     - python3 offline_inference.py
     - python3 offline_inference_with_prefix.py
     - python3 llm_engine_example.py
     - python3 llava_example.py
-    - python3 tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
 
 - label: Kernels Test %N
-  #mirror_hardwares: [amd]
   command: pytest -v -s kernels --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 4
 
 - label: Models Test
-  #mirror_hardwares: [amd]
   commands:
     - bash ../.buildkite/download-images.sh
-    - pytest -v -s models --ignore=models/test_llava.py
+    - pytest -v -s models --ignore=models/test_llava.py --ignore=models/test_mistral.py
 
 - label: Llava Test
-  mirror_hardwares: [amd]
   commands:
     - bash ../.buildkite/download-images.sh
     - pytest -v -s models/test_llava.py
 
 - label: Prefix Caching Test
-  mirror_hardwares: [amd]
   commands:
     - pytest -v -s prefix_caching
 
 - label: Samplers Test
-  #mirror_hardwares: [amd]
   command: pytest -v -s samplers
 
 - label: LogitsProcessor Test
-  mirror_hardwares: [amd]
   command: pytest -v -s test_logits_processor.py
 
-- label: Utils Test
-  command: pytest -v -s test_utils.py
-
 - label: Worker Test
-  mirror_hardwares: [amd]
   command: pytest -v -s worker
 
 - label: Speculative decoding tests
-  #mirror_hardwares: [amd]
   command: pytest -v -s spec_decode
 
 - label: LoRA Test %N
-  #mirror_hardwares: [amd]
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --ignore=lora/test_long_context.py
+  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
   parallelism: 4
 
-- label: LoRA Long Context (Distributed)
-  #mirror_hardwares: [amd]
-  num_gpus: 4
-  # This test runs llama 13B, so it is required to run on 4 GPUs.
-  commands:
-    # Temporarily run this way because we cannot clean up GPU mem usage
-    # for multi GPU tests.
-    # TODO(sang): Fix it.
-    - pytest -v -s lora/test_long_context.py::test_rotary_emb_replaced
-    - pytest -v -s lora/test_long_context.py::test_batched_rope_kernel
-    - pytest -v -s lora/test_long_context.py::test_self_consistency
-    - pytest -v -s lora/test_long_context.py::test_quality
-    - pytest -v -s lora/test_long_context.py::test_max_len
-
 - label: Tensorizer Test
-  #mirror_hardwares: [amd]
-  command: apt-get install curl libsodium23 && pytest -v -s tensorizer_loader
+  command: apt-get install curl libsodium23 && pytest -v -s tensorizer
 
 - label: Metrics Test
-  mirror_hardwares: [amd]
   command: pytest -v -s metrics
 
-- label: Quantization Test
-  #mirror_hardwares: [amd]
-  command: pytest -v -s quantization
-
 - label: Benchmarks
   working_dir: "/vllm-workspace/.buildkite"
-  mirror_hardwares: [amd]
   commands:
   - pip install aiohttp
   - bash run-benchmarks.sh

.buildkite/test-template.j2

@@ -3,8 +3,16 @@
 {% set default_working_dir = "/vllm-workspace/tests" %}
 
 steps:
+  - label: "AMD Test"
+    agents:
+      queue: amd
+    command: bash .buildkite/run-amd-test.sh
+
+  - label: "CPU Test"
+    command: bash .buildkite/run-cpu-test.sh
+
   - label: ":docker: build image"
-    commands: 
+    commands:
       - "docker build --build-arg max_jobs=16 --tag {{ docker_image }} --target test --progress plain ."
       - "docker push {{ docker_image }}"
     env:
@@ -13,35 +21,8 @@ steps:
       automatic:
         - exit_status: -1  # Agent was lost
           limit: 5
-        - exit_status: -10  # Agent was lost
-          limit: 5
   - wait
 
-  - group: "AMD Tests"
-    depends_on: ~
-    steps:
-    {% for step in steps %}
-    {% if step.mirror_hardwares and "amd" in step.mirror_hardwares %}
-      - label: "AMD: {{ step.label }}"
-        agents:
-          queue: amd
-        command: bash .buildkite/run-amd-test.sh "cd {{ (step.working_dir or default_working_dir) | safe  }} ; {{ step.command  or (step.commands | join(" ; ")) | safe }}"
-        env:
-          DOCKER_BUILDKIT: "1"
-    {% endif %}
-    {% endfor %}
-
-  - label: "Neuron Test"
-    depends_on: ~
-    agents:
-      queue: neuron
-    command: bash .buildkite/run-neuron-test.sh
-    soft_fail: true
-
-  - label: "Intel Test"
-    depends_on: ~
-    command: bash .buildkite/run-cpu-test.sh
-
   {% for step in steps %}
   - label: "{{ step.label }}"
     agents:
@@ -54,14 +35,9 @@ steps:
       automatic:
         - exit_status: -1  # Agent was lost
           limit: 5
-        - exit_status: -10  # Agent was lost
-          limit: 5
     plugins:
       - kubernetes:
           podSpec:
-            {% if step.num_gpus %}
-            priorityClassName: gpu-priority-cls-{{ step.num_gpus }}
-            {% endif %}
             volumes:
               - name: dshm
                 emptyDir:

.clang-format

@@ -1,26 +0,0 @@
-BasedOnStyle: Google
-UseTab: Never
-IndentWidth: 2
-ColumnLimit: 80
-
-# Force pointers to the type for C++.
-DerivePointerAlignment: false
-PointerAlignment: Left
-
-# Reordering #include statements can (and currently will) introduce errors
-SortIncludes: false
-
-# Style choices
-AlignConsecutiveAssignments: false
-AlignConsecutiveDeclarations: false
-IndentPPDirectives: BeforeHash
-
-IncludeCategories:
-  - Regex:           '^<'
-    Priority:        4
-  - Regex:           '^"(llvm|llvm-c|clang|clang-c|mlir|mlir-c)/'
-    Priority:        3
-  - Regex:           '^"(qoda|\.\.)/'
-    Priority:        2
-  - Regex:           '.*'
-    Priority:        1

.github/ISSUE_TEMPLATE/200-installation.yml

@@ -18,7 +18,6 @@ body:
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```
-      It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
       ```text
       The output of `python collect_env.py`

.github/ISSUE_TEMPLATE/300-usage.yml

@@ -18,7 +18,6 @@ body:
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```
-      It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
       ```text
       The output of `python collect_env.py`

.github/ISSUE_TEMPLATE/400-bug report.yml

@@ -18,7 +18,6 @@ body:
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```
-      It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
       ```text
       The output of `python collect_env.py`
@@ -58,10 +57,6 @@ body:
       If the code is too long (hopefully, it isn't), feel free to put it in a public gist and link it in the issue: https://gist.github.com.
 
       Please also paste or describe the results you observe instead of the expected results. If you observe an error, please paste the error message including the **full** traceback of the exception. It may be relevant to wrap error messages in ```` ```triple quotes blocks``` ````.
-
-      Please set the environment variable `export VLLM_LOGGING_LEVEL=DEBUG` to turn on more logging to help debugging potential issues.
-
-      If you experienced crashes or hangs, it would be helpful to run vllm with `export VLLM_TRACE_FUNCTION=1` . All the function calls in vllm will be recorded. Inspect these log files, and tell which function crashes or hangs.
     placeholder: |
       A clear and concise description of what the bug is.
 

.github/ISSUE_TEMPLATE/700-performance discussion.yml

@@ -39,7 +39,6 @@ body:
       # For security purposes, please feel free to check the contents of collect_env.py before running it.
       python collect_env.py
       ```
-      It is suggested to download and execute the latest script, as vllm might frequently update the diagnosis information needed for accurately and quickly responding to issues.
     value: |
       ```text
       The output of `python collect_env.py`

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -1,49 +0,0 @@
-name: 💬 Request for comments (RFC).
-description: Ask for feedback on major architectural changes or design choices.
-title: "[RFC]: "
-labels: ["RFC"]
-
-body:
-- type: markdown
-  attributes:
-    value: >
-      #### Please take a look at previous [RFCs](https://github.com/vllm-project/vllm/issues?q=label%3ARFC+sort%3Aupdated-desc) for reference.
-- type: textarea
-  attributes:
-    label: Motivation.
-    description: >
-      The motivation of the RFC.
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: Proposed Change.
-    description: >
-      The proposed change of the RFC.
-  validations:
-    required: true
-- type: textarea
-  attributes:
-    label: Feedback Period.
-    description: >
-      The feedback period of the RFC. Usually at least one week.
-  validations:
-    required: false
-- type: textarea
-  attributes:
-    label: CC List.
-    description: >
-      The list of people you want to CC.
-  validations:
-    required: false
-- type: textarea
-  attributes:
-    label: Any Other Things.
-    description: >
-      Any other things you would like to mention.
-  validations:
-    required: false
-- type: markdown
-  attributes:
-    value: >
-      Thanks for contributing 🎉!

.github/workflows/clang-format.yml

@@ -1,42 +0,0 @@
-name: clang-format
-
-on:
-  # Trigger the workflow on push or pull request,
-  # but only for the main branch
-  push:
-    branches:
-      - main
-  pull_request:
-    branches:
-      - main
-
-jobs:
-  clang-format:
-    runs-on: ubuntu-latest
-    strategy:
-      matrix:
-        python-version: ["3.11"]
-    steps:
-    - uses: actions/checkout@v2
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
-      with:
-        python-version: ${{ matrix.python-version }}
-    - name: Install dependencies
-      run: |
-        python -m pip install --upgrade pip
-        pip install clang-format==18.1.5
-    - name: Running clang-format
-      run: |
-        EXCLUDES=(
-            'csrc/moe/topk_softmax_kernels.cu'
-            'csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu'
-            'csrc/punica/bgmv/bgmv_config.h'
-            'csrc/punica/bgmv/bgmv_impl.cuh'
-            'csrc/punica/bgmv/vec_dtypes.cuh'
-            'csrc/punica/punica_ops.cu'
-            'csrc/punica/type_convert.h'
-        )
-        find csrc/ \( -name '*.h' -o -name '*.cpp' -o -name '*.cu' -o -name '*.cuh' \) -print \
-            | grep -vFf <(printf "%s\n" "${EXCLUDES[@]}") \
-            | xargs clang-format --dry-run --Werror

.github/workflows/mypy.yaml

@@ -32,19 +32,19 @@ jobs:
         pip install types-setuptools
     - name: Mypy
       run: |
-        mypy vllm/attention --config-file pyproject.toml
-        mypy vllm/core --config-file pyproject.toml
-        mypy vllm/distributed --config-file pyproject.toml
-        mypy vllm/entrypoints --config-file pyproject.toml
-        mypy vllm/executor --config-file pyproject.toml
-        mypy vllm/usage --config-file pyproject.toml
-        mypy vllm/*.py --config-file pyproject.toml
-        mypy vllm/transformers_utils --config-file pyproject.toml
-        mypy vllm/engine  --config-file pyproject.toml
-        mypy vllm/worker --config-file pyproject.toml
-        mypy vllm/spec_decode --config-file pyproject.toml
-        mypy vllm/model_executor  --config-file pyproject.toml
-        mypy vllm/lora --config-file pyproject.toml
-        mypy vllm/logging --config-file pyproject.toml
-        mypy vllm/model_executor --config-file pyproject.toml
+        mypy vllm/attention/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/core/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/distributed/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/entrypoints/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/executor/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/usage/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/*.py --follow-imports=skip --config-file pyproject.toml
+        mypy vllm/transformers_utils/*.py --follow-imports=skip --config-file pyproject.toml
+
+        # TODO(sang): Follow up
+        # mypy vllm/engine/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/worker/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/spec_decoding/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/model_executor/*.py --follow-imports=skip --config-file pyproject.toml
+        # mypy vllm/lora/*.py --follow-imports=skip --config-file pyproject.toml
 

.github/workflows/publish.yml

@@ -49,19 +49,13 @@ jobs:
       matrix:
           os: ['ubuntu-20.04']
           python-version: ['3.8', '3.9', '3.10', '3.11']
-          pytorch-version: ['2.3.0']  # Must be the most recent version that meets requirements-cuda.txt.
+          pytorch-version: ['2.2.1']  # Must be the most recent version that meets requirements-cuda.txt.
           cuda-version: ['11.8', '12.1']
 
     steps:
       - name: Checkout
         uses: actions/checkout@v3
 
-      - name: Setup ccache
-        uses: hendrikmuhs/ccache-action@v1.2
-        with:
-          create-symlink: true
-          key: ${{ github.job }}-${{ matrix.python-version }}-${{ matrix.cuda-version }}
-
       - name: Set up Linux Env
         if: ${{ runner.os == 'Linux' }}
         run: |
@@ -82,8 +76,6 @@ jobs:
 
       - name: Build wheel
         shell: bash
-        env:
-          CMAKE_BUILD_TYPE: Release # do not compile with debug symbol to reduce wheel size
         run: |
           bash -x .github/workflows/scripts/build.sh ${{ matrix.python-version }} ${{ matrix.cuda-version }}
           wheel_name=$(ls dist/*whl | xargs -n 1 basename)

.github/workflows/scripts/create_release.js

@@ -8,7 +8,7 @@ module.exports = async (github, context, core) => {
 			generate_release_notes: true,
 			name: process.env.RELEASE_TAG,
 			owner: context.repo.owner,
-			prerelease: true,
+			prerelease: false,
 			repo: context.repo.repo,
 			tag_name: process.env.RELEASE_TAG,
 		});

.gitignore

@@ -70,8 +70,6 @@ instance/
 
 # Sphinx documentation
 docs/_build/
-docs/source/getting_started/examples/*.rst
-!**/*.template.rst
 
 # PyBuilder
 .pybuilder/

CMakeLists.txt

@@ -31,7 +31,7 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx940;gfx941;gfx942;gfx1030;gfx11
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.3.0")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.2.1")
 set(TORCH_SUPPORTED_VERSION_ROCM_5X "2.0.1")
 set(TORCH_SUPPORTED_VERSION_ROCM_6X "2.1.1")
 
@@ -167,47 +167,15 @@ set(VLLM_EXT_SRC
   "csrc/layernorm_kernels.cu"
   "csrc/quantization/squeezellm/quant_cuda_kernel.cu"
   "csrc/quantization/gptq/q_gemm.cu"
-  "csrc/quantization/compressed_tensors/int8_quant_kernels.cu"
-  "csrc/quantization/fp8/common.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/moe_align_block_size_kernels.cu"
   "csrc/pybind.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  include(FetchContent)
-  SET(CUTLASS_ENABLE_HEADERS_ONLY=ON)
-  FetchContent_Declare(
-        cutlass
-        GIT_REPOSITORY https://github.com/nvidia/cutlass.git
-        # CUTLASS 3.5.0
-        GIT_TAG 7d49e6c7e2f8896c47f586706e67e1fb215529dc
-  )
-  FetchContent_MakeAvailable(cutlass)
-
   list(APPEND VLLM_EXT_SRC
-    "csrc/quantization/aqlm/gemm_kernels.cu"
     "csrc/quantization/awq/gemm_kernels.cu"
-    "csrc/quantization/marlin/dense/marlin_cuda_kernel.cu"
-    "csrc/quantization/marlin/sparse/marlin_24_cuda_kernel.cu"
-    "csrc/quantization/gptq_marlin/gptq_marlin.cu"
-    "csrc/quantization/gptq_marlin/gptq_marlin_repack.cu"
-    "csrc/custom_all_reduce.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_entry.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c2x.cu"
-    "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu")
-
-  #
-  # The CUTLASS kernels for Hopper require sm90a to be enabled.
-  # This is done via the below gencode option, BUT that creates kernels for both sm90 and sm90a.
-  # That adds an extra 17MB to compiled binary, so instead we selectively enable it.
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0)
-    set_source_files_properties(
-          "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu"
-          PROPERTIES
-          COMPILE_FLAGS
-          "-gencode arch=compute_90a,code=sm_90a")
-  endif()
-
+    "csrc/quantization/marlin/marlin_cuda_kernel.cu"
+    "csrc/custom_all_reduce.cu")
 endif()
 
 define_gpu_extension_target(
@@ -217,7 +185,6 @@ define_gpu_extension_target(
   SOURCES ${VLLM_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
-  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   WITH_SOABI)
 
 #
@@ -243,13 +210,24 @@ define_gpu_extension_target(
 
 set(VLLM_PUNICA_EXT_SRC
   "csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu"
+  "csrc/punica/bgmv/bgmv_bf16_bf16_fp16.cu"
+  "csrc/punica/bgmv/bgmv_bf16_fp16_bf16.cu"
+  "csrc/punica/bgmv/bgmv_bf16_fp16_fp16.cu"
   "csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu"
+  "csrc/punica/bgmv/bgmv_bf16_fp32_fp16.cu"
+  "csrc/punica/bgmv/bgmv_fp16_bf16_bf16.cu"
+  "csrc/punica/bgmv/bgmv_fp16_bf16_fp16.cu"
+  "csrc/punica/bgmv/bgmv_fp16_fp16_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu"
+  "csrc/punica/bgmv/bgmv_fp16_fp32_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu"
   "csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu"
+  "csrc/punica/bgmv/bgmv_fp32_bf16_fp16.cu"
+  "csrc/punica/bgmv/bgmv_fp32_fp16_bf16.cu"
   "csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu"
-  "csrc/punica/punica_ops.cu"
-  "csrc/punica/punica_pybind.cpp")
+  "csrc/punica/bgmv/bgmv_fp32_fp32_bf16.cu"
+  "csrc/punica/bgmv/bgmv_fp32_fp32_fp16.cu"
+  "csrc/punica/punica_ops.cc")
 
 #
 # Copy GPU compilation flags+update for punica
@@ -273,9 +251,6 @@ if (${VLLM_GPU_LANG} STREQUAL "CUDA")
     endif()
   endforeach()
   message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
-elseif(${VLLM_GPU_LANG} STREQUAL "HIP")
-  set(VLLM_PUNICA_GPU_ARCHES ${VLLM_GPU_ARCHES})
-  message(STATUS "Punica target arches: ${VLLM_PUNICA_GPU_ARCHES}")
 endif()
 
 if (VLLM_PUNICA_GPU_ARCHES)
@@ -310,6 +285,11 @@ add_custom_target(default)
 if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   message(STATUS "Enabling C extension.")
   add_dependencies(default _C)
+endif()
+
+if(VLLM_GPU_LANG STREQUAL "CUDA")
+  message(STATUS "Enabling moe extension.")
+  add_dependencies(default _moe_C)
 
   # Enable punica if -DVLLM_INSTALL_PUNICA_KERNELS=ON or
   # VLLM_INSTALL_PUNICA_KERNELS is set in the environment and
@@ -320,8 +300,3 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
     add_dependencies(default _punica_C)
   endif()
 endif()
-
-if(VLLM_GPU_LANG STREQUAL "CUDA")
-  message(STATUS "Enabling moe extension.")
-  add_dependencies(default _moe_C)
-endif()

Dockerfile

@@ -1,13 +1,9 @@
 # The vLLM Dockerfile is used to construct vLLM image that can be directly used
 # to run the OpenAI compatible server.
 
-# Please update any changes made here to
-# docs/source/dev/dockerfile/dockerfile.rst and
-# docs/source/assets/dev/dockerfile-stages-dependency.png
-
 #################### BASE BUILD IMAGE ####################
 # prepare basic build environment
-FROM nvidia/cuda:12.4.1-devel-ubuntu22.04 AS dev
+FROM nvidia/cuda:12.1.0-devel-ubuntu22.04 AS dev
 
 RUN apt-get update -y \
     && apt-get install -y python3-pip git
@@ -16,7 +12,7 @@ RUN apt-get update -y \
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
 # this won't be needed for future versions of this docker image
 # or future versions of triton.
-RUN ldconfig /usr/local/cuda-12.4/compat/
+RUN ldconfig /usr/local/cuda-12.1/compat/
 
 WORKDIR /workspace
 
@@ -75,15 +71,34 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/pip \
     python3 setup.py bdist_wheel --dist-dir=dist
 
-# check the size of the wheel, we cannot upload wheels larger than 100MB
-COPY .buildkite/check-wheel-size.py check-wheel-size.py
-RUN python3 check-wheel-size.py dist
-
+# the `vllm_nccl` package must be installed from source distribution
+# pip is too smart to store a wheel in the cache, and other CI jobs
+# will directly use the wheel from the cache, which is not what we want.
+# we need to remove it manually
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip cache remove vllm_nccl*
 #################### EXTENSION Build IMAGE ####################
 
+#################### FLASH_ATTENTION Build IMAGE ####################
+FROM dev as flash-attn-builder
+# max jobs used for build
+ARG max_jobs=2
+ENV MAX_JOBS=${max_jobs}
+# flash attention version
+ARG flash_attn_version=v2.5.6
+ENV FLASH_ATTN_VERSION=${flash_attn_version}
+
+WORKDIR /usr/src/flash-attention-v2
+
+# Download the wheel or build it if a pre-compiled release doesn't exist
+RUN pip --verbose wheel flash-attn==${FLASH_ATTN_VERSION} \
+    --no-build-isolation --no-deps --no-cache-dir
+
+#################### FLASH_ATTENTION Build IMAGE ####################
+
 #################### vLLM installation IMAGE ####################
 # image with vLLM installed
-FROM nvidia/cuda:12.4.1-base-ubuntu22.04 AS vllm-base
+FROM nvidia/cuda:12.1.0-base-ubuntu22.04 AS vllm-base
 WORKDIR /vllm-workspace
 
 RUN apt-get update -y \
@@ -93,12 +108,16 @@ RUN apt-get update -y \
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
 # this won't be needed for future versions of this docker image
 # or future versions of triton.
-RUN ldconfig /usr/local/cuda-12.4/compat/
+RUN ldconfig /usr/local/cuda-12.1/compat/
 
 # install vllm wheel first, so that torch etc will be installed
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
     --mount=type=cache,target=/root/.cache/pip \
     pip install dist/*.whl --verbose
+
+RUN --mount=type=bind,from=flash-attn-builder,src=/usr/src/flash-attention-v2,target=/usr/src/flash-attention-v2 \
+    --mount=type=cache,target=/root/.cache/pip \
+    pip install /usr/src/flash-attention-v2/*.whl --no-cache-dir
 #################### vLLM installation IMAGE ####################
 
 

Dockerfile.cpu

@@ -17,6 +17,4 @@ RUN pip install -v -r requirements-cpu.txt --extra-index-url https://download.py
 
 RUN VLLM_TARGET_DEVICE=cpu python3 setup.py install
 
-WORKDIR /workspace/
-
 CMD ["/bin/bash"]

Dockerfile.neuron

@@ -1,36 +0,0 @@
-# default base image
-ARG BASE_IMAGE="763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-inference-neuronx:2.1.1-neuronx-py310-sdk2.17.0-ubuntu20.04"
-
-FROM $BASE_IMAGE
-
-RUN echo "Base image is $BASE_IMAGE"
-
-# Install some basic utilities
-RUN apt-get update && apt-get install python3 python3-pip -y
-
-### Mount Point ###
-# When launching the container, mount the code directory to /app
-ARG APP_MOUNT=/app
-VOLUME [ ${APP_MOUNT} ]
-WORKDIR ${APP_MOUNT}
-
-RUN python3 -m pip install --upgrade pip
-RUN python3 -m pip install --no-cache-dir fastapi ninja tokenizers pandas
-RUN python3 -m pip install sentencepiece transformers==4.36.2 -U
-RUN python3 -m pip install transformers-neuronx --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
-RUN python3 -m pip install --pre neuronx-cc==2.12.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
-
-COPY ./vllm /app/vllm/vllm
-COPY ./setup.py /app/vllm/setup.py
-COPY ./requirements-common.txt /app/vllm/requirements-common.txt
-COPY ./requirements-neuron.txt /app/vllm/requirements-neuron.txt
-
-RUN cd /app/vllm \
-    && python3 -m pip install -U -r requirements-neuron.txt
-
-ENV VLLM_BUILD_WITH_NEURON 1
-RUN cd /app/vllm \
-    && pip install -e . \
-    && cd ..
-
-CMD ["/bin/bash"]

Dockerfile.rocm

@@ -14,7 +14,7 @@ RUN echo "Base image is $BASE_IMAGE"
 ARG FA_GFX_ARCHS="gfx90a;gfx942"
 RUN echo "FA_GFX_ARCHS is $FA_GFX_ARCHS"
 
-ARG FA_BRANCH="ae7928c"
+ARG FA_BRANCH="3d2b6f5"
 RUN echo "FA_BRANCH is $FA_BRANCH"
 
 # whether to build flash-attention
@@ -46,7 +46,7 @@ RUN apt-get update && apt-get install -y \
 
 ### Mount Point ###
 # When launching the container, mount the code directory to /app
-ARG APP_MOUNT=/vllm-workspace
+ARG APP_MOUNT=/app
 VOLUME [ ${APP_MOUNT} ]
 WORKDIR ${APP_MOUNT}
 
@@ -89,26 +89,21 @@ RUN if [ "$BUILD_TRITON" = "1" ]; then \
     && cd ../..; \
     fi
 
-WORKDIR /vllm-workspace
-COPY . .
+COPY ./ /app/vllm
 
-#RUN python3 -m pip install pynvml # to be removed eventually
 RUN python3 -m pip install --upgrade pip numba
+RUN python3 -m pip install xformers==0.0.23 --no-deps
 
-# make sure punica kernels are built (for LoRA)
-ENV VLLM_INSTALL_PUNICA_KERNELS=1
-# Workaround for ray >= 2.10.0
-ENV RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES=1
-
-ENV VLLM_NCCL_SO_PATH=/opt/rocm/lib/librccl.so
-
-RUN --mount=type=cache,target=/root/.cache/pip \
-    pip install -U -r requirements-rocm.txt \
-    && patch /opt/rocm/include/hip/amd_detail/amd_hip_bf16.h ./rocm_patch/rocm_bf16.patch \
+RUN cd /app \
+    && cd vllm \
+    && pip install -U -r requirements-rocm.txt \
+    && if [ "$BUILD_FA" = "1" ]; then \
+       bash patch_xformers.rocm.sh; fi \
+    && patch /opt/rocm/include/hip/amd_detail/amd_hip_bf16.h /app/vllm/rocm_patch/rocm_bf16.patch \
     && python3 setup.py install \
-    && cp build/lib.linux-x86_64-cpython-39/vllm/_C.cpython-39-x86_64-linux-gnu.so vllm/ \
-    && cp build/lib.linux-x86_64-cpython-39/vllm/_punica_C.cpython-39-x86_64-linux-gnu.so vllm/ \
     && cd ..
 
+RUN python3 -m pip install --upgrade pip
+RUN python3 -m pip install --no-cache-dir ray[all]==2.9.3
 
 CMD ["/bin/bash"]

MANIFEST.in

@@ -1,9 +1,6 @@
 include LICENSE
 include requirements-common.txt
 include requirements-cuda.txt
-include requirements-rocm.txt
-include requirements-neuron.txt
-include requirements-cpu.txt
 include CMakeLists.txt
 
 recursive-include cmake *

README.md

@@ -14,17 +14,6 @@ Easy, fast, and cheap LLM serving for everyone
 
 </p>
 
----
-
-**The Fourth vLLM Bay Area Meetup (June 11th 5:30pm-8pm PT)**
-
-We are thrilled to announce our fourth vLLM Meetup!
-The vLLM team will share recent updates and roadmap.
-We will also have vLLM collaborators from BentoML and Cloudflare coming up to the stage to discuss their experience in deploying LLMs with vLLM.
-Please register [here](https://lu.ma/agivllm) and join us!
-
----
-
 *Latest News* 🔥
 - [2024/04] We hosted [the third vLLM meetup](https://robloxandvllmmeetup2024.splashthat.com/) with Roblox! Please find the meetup slides [here](https://docs.google.com/presentation/d/1A--47JAK4BJ39t954HyTkvtfwn0fkqtsL8NGFuslReM/edit?usp=sharing).
 - [2024/01] We hosted [the second vLLM meetup](https://lu.ma/ygxbpzhl) in SF! Please find the meetup slides [here](https://docs.google.com/presentation/d/12mI2sKABnUw5RBWXDYY-HtHth4iMSNcEoQ10jDQbxgA/edit?usp=sharing).
@@ -62,14 +51,40 @@ vLLM is flexible and easy to use with:
 - (Experimental) Prefix caching support
 - (Experimental) Multi-lora support
 
-vLLM seamlessly supports most popular open-source models on HuggingFace, including:
-- Transformer-like LLMs (e.g., Llama)
-- Mixture-of-Expert LLMs (e.g., Mixtral)
-- Multi-modal LLMs (e.g., LLaVA)
+vLLM seamlessly supports many Hugging Face models, including the following architectures:
 
-Find the full list of supported models [here](https://docs.vllm.ai/en/latest/models/supported_models.html).
-
-## Getting Started
+- Aquila & Aquila2 (`BAAI/AquilaChat2-7B`, `BAAI/AquilaChat2-34B`, `BAAI/Aquila-7B`, `BAAI/AquilaChat-7B`, etc.)
+- Baichuan & Baichuan2 (`baichuan-inc/Baichuan2-13B-Chat`, `baichuan-inc/Baichuan-7B`, etc.)
+- BLOOM (`bigscience/bloom`, `bigscience/bloomz`, etc.)
+- ChatGLM (`THUDM/chatglm2-6b`, `THUDM/chatglm3-6b`, etc.)
+- Command-R (`CohereForAI/c4ai-command-r-v01`, etc.)
+- DBRX (`databricks/dbrx-base`, `databricks/dbrx-instruct` etc.)
+- DeciLM (`Deci/DeciLM-7B`, `Deci/DeciLM-7B-instruct`, etc.)
+- Falcon (`tiiuae/falcon-7b`, `tiiuae/falcon-40b`, `tiiuae/falcon-rw-7b`, etc.)
+- Gemma (`google/gemma-2b`, `google/gemma-7b`, etc.)
+- GPT-2 (`gpt2`, `gpt2-xl`, etc.)
+- GPT BigCode (`bigcode/starcoder`, `bigcode/gpt_bigcode-santacoder`, etc.)
+- GPT-J (`EleutherAI/gpt-j-6b`, `nomic-ai/gpt4all-j`, etc.)
+- GPT-NeoX (`EleutherAI/gpt-neox-20b`, `databricks/dolly-v2-12b`, `stabilityai/stablelm-tuned-alpha-7b`, etc.)
+- InternLM (`internlm/internlm-7b`, `internlm/internlm-chat-7b`, etc.)
+- InternLM2 (`internlm/internlm2-7b`, `internlm/internlm2-chat-7b`, etc.)
+- Jais (`core42/jais-13b`, `core42/jais-13b-chat`, `core42/jais-30b-v3`, `core42/jais-30b-chat-v3`, etc.)
+- LLaMA & LLaMA-2 (`meta-llama/Llama-2-70b-hf`, `lmsys/vicuna-13b-v1.3`, `young-geng/koala`, `openlm-research/open_llama_13b`, etc.)
+- MiniCPM (`openbmb/MiniCPM-2B-sft-bf16`, `openbmb/MiniCPM-2B-dpo-bf16`, etc.)
+- Mistral (`mistralai/Mistral-7B-v0.1`, `mistralai/Mistral-7B-Instruct-v0.1`, etc.)
+- Mixtral (`mistralai/Mixtral-8x7B-v0.1`, `mistralai/Mixtral-8x7B-Instruct-v0.1`, `mistral-community/Mixtral-8x22B-v0.1`, etc.)
+- MPT (`mosaicml/mpt-7b`, `mosaicml/mpt-30b`, etc.)
+- OLMo (`allenai/OLMo-1B`, `allenai/OLMo-7B`, etc.)
+- OPT (`facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc.)
+- Orion (`OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc.)
+- Phi (`microsoft/phi-1_5`, `microsoft/phi-2`, etc.)
+- Qwen (`Qwen/Qwen-7B`, `Qwen/Qwen-7B-Chat`, etc.)
+- Qwen2 (`Qwen/Qwen1.5-7B`, `Qwen/Qwen1.5-7B-Chat`, etc.)
+- Qwen2MoE (`Qwen/Qwen1.5-MoE-A2.7B`, `Qwen/Qwen1.5-MoE-A2.7B-Chat`, etc.)
+- StableLM(`stabilityai/stablelm-3b-4e1t`, `stabilityai/stablelm-base-alpha-7b-v2`, etc.)
+- Starcoder2(`bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc.)
+- Xverse (`xverse/XVERSE-7B-Chat`, `xverse/XVERSE-13B-Chat`, `xverse/XVERSE-65B-Chat`, etc.)
+- Yi (`01-ai/Yi-6B`, `01-ai/Yi-34B`, etc.)
 
 Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/getting_started/installation.html#build-from-source):
 
@@ -77,7 +92,9 @@ Install vLLM with pip or [from source](https://vllm.readthedocs.io/en/latest/get
 pip install vllm
 ```
 
-Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to learn more.
+## Getting Started
+
+Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to get started.
 - [Installation](https://vllm.readthedocs.io/en/latest/getting_started/installation.html)
 - [Quickstart](https://vllm.readthedocs.io/en/latest/getting_started/quickstart.html)
 - [Supported Models](https://vllm.readthedocs.io/en/latest/models/supported_models.html)
@@ -87,32 +104,6 @@ Visit our [documentation](https://vllm.readthedocs.io/en/latest/) to learn more.
 We welcome and value any contributions and collaborations.
 Please check out [CONTRIBUTING.md](./CONTRIBUTING.md) for how to get involved.
 
-## Sponsors
-
-vLLM is a community project. Our compute resources for development and testing are supported by the following organizations. Thank you for your support!
-
-<!-- Note: Please sort them in alphabetical order. -->
-<!-- Note: Please keep these consistent with docs/source/community/sponsors.md -->
-
-- a16z
-- AMD
-- Anyscale
-- AWS
-- Crusoe Cloud
-- Databricks
-- DeepInfra
-- Dropbox
-- Lambda Lab
-- NVIDIA
-- Replicate
-- Roblox
-- RunPod
-- Trainy
-- UC Berkeley
-- UC San Diego
-
-We also have an official fundraising venue through [OpenCollective](https://opencollective.com/vllm). We plan to use the fund to support the development, maintenance, and adoption of vLLM.
-
 ## Citation
 
 If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs/2309.06180):

benchmarks/backend_request_func.py

@@ -89,9 +89,6 @@ async def async_request_tgi(
                     output.latency = most_recent_timestamp - st
                     output.success = True
                     output.generated_text = data["generated_text"]
-                else:
-                    output.error = response.reason or ""
-                    output.success = False
         except Exception:
             output.success = False
             exc_info = sys.exc_info()
@@ -138,7 +135,6 @@ async def async_request_trt_llm(
                                               "data:")
 
                         data = json.loads(chunk)
-                        output.generated_text += data["text_output"]
                         timestamp = time.perf_counter()
                         # First token
                         if ttft == 0.0:
@@ -153,6 +149,7 @@ async def async_request_trt_llm(
                         most_recent_timestamp = timestamp
 
                     output.latency = most_recent_timestamp - st
+                    output.generated_text = json.loads(data)["text_output"]
                     output.success = True
 
                 else:
@@ -279,9 +276,6 @@ async def async_request_openai_completions(
                     output.generated_text = generated_text
                     output.success = True
                     output.latency = latency
-                else:
-                    output.error = response.reason or ""
-                    output.success = False
         except Exception:
             output.success = False
             exc_info = sys.exc_info()

benchmarks/bench_cache_write.py

@@ -0,0 +1,148 @@
+import functools
+import time
+from typing import Tuple
+
+import chex
+import jax
+import jax.numpy as jnp
+
+_PAD_SLOT_ID = -1
+
+
+@jax.jit
+def write_to_kv_cache1(
+    key: jax.Array,             # [batch_size, seq_len, num_heads, head_size]
+    value: jax.Array,           # [batch_size, seq_len, num_heads, head_size]
+    k_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    v_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    slot_mapping: jax.Array,    # [batch_size, seq_len]
+) -> Tuple[jax.Array, jax.Array]:
+    num_heads = key.shape[-2]
+    head_size = key.shape[-1]
+
+    key = key.reshape(-1, num_heads, head_size)
+    key = key.transpose((1, 0, 2))
+    value = value.reshape(-1, num_heads, head_size)
+    value = value.transpose((1, 0, 2))
+
+    k_cache = k_cache.at[:, slot_mapping.reshape(-1), :].set(key)
+    v_cache = v_cache.at[:, slot_mapping.reshape(-1), :].set(value)
+    return k_cache, v_cache
+
+
+@functools.partial(jax.jit, donate_argnums=(2, 3))
+def write_to_kv_cache2(
+    key: jax.Array,             # [batch_size, seq_len, num_heads, head_size]
+    value: jax.Array,           # [batch_size, seq_len, num_heads, head_size]
+    k_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    v_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    slot_mapping: jax.Array,    # [batch_size, seq_len]
+) -> Tuple[jax.Array, jax.Array]:
+    batch_size = slot_mapping.shape[0]
+
+    def cond(val: _IteratorState):
+        return val.idx < batch_size
+
+    def body(val: _IteratorState):
+        k_cache, v_cache = _write_seq_to_kv_cache(
+            key[val.idx],
+            value[val.idx],
+            val.k_cache,
+            val.v_cache,
+            slot_mapping[val.idx],
+        )
+        val.k_cache = k_cache
+        val.v_cache = v_cache
+        val.idx += 1
+        return val
+
+    iterator = _IteratorState(idx=0, k_cache=k_cache, v_cache=v_cache)
+    iterator = jax.lax.while_loop(cond, body, iterator)
+    return iterator.k_cache, iterator.v_cache
+
+
+@functools.partial(jax.jit, donate_argnums=(2, 3))
+def _write_seq_to_kv_cache(
+    key: jax.Array,             # [seq_len, num_heads, head_size]
+    value: jax.Array,           # [seq_len, num_heads, head_size]
+    k_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    v_cache: jax.Array,         # [num_heads, num_blocks * block_size, head_size]
+    slot_mapping: jax.Array,    # [seq_len]
+) -> Tuple[jax.Array, jax.Array]:
+    seq_len = slot_mapping.shape[0]
+    num_heads, _, head_size = k_cache.shape
+    # Reshape to match the rank of kv_cache.
+    key = key.reshape(seq_len, num_heads, 1, head_size)
+    value = value.reshape(seq_len, num_heads, 1, head_size)
+
+    def cond(val: _IteratorState):
+        return jnp.logical_and(
+            val.idx < seq_len, slot_mapping[val.idx] != _PAD_SLOT_ID)
+
+    def body(val: _IteratorState):
+        slot_idx = slot_mapping[val.idx]
+        val.k_cache = jax.lax.dynamic_update_slice(
+            val.k_cache,
+            key[val.idx],
+            (0, slot_idx, 0),
+        )
+        val.v_cache = jax.lax.dynamic_update_slice(
+            val.v_cache,
+            value[val.idx],
+            (0, slot_idx, 0),
+        )
+        val.idx += 1
+        return val
+
+    iterator = _IteratorState(idx=0, k_cache=k_cache, v_cache=v_cache)
+    iterator = jax.lax.while_loop(cond, body, iterator)
+    return iterator.k_cache, iterator.v_cache
+
+
+@chex.dataclass
+class _IteratorState:
+
+    idx: jnp.int32
+    k_cache: jnp.ndarray       # [num_heads, num_blocks, block_size, head_size]
+    v_cache: jnp.ndarray       # [num_heads, num_blocks, block_size, head_size]
+
+
+def benchmark_write_to_kv_cache(
+    batch_size: int,
+    seq_len: int,
+    num_kv_heads: int,
+    head_size: int,
+    num_blocks: int,
+    block_size: int,
+    version: int = 1,
+):
+    if version == 1:
+        f = write_to_kv_cache1
+    elif version == 2:
+        f = write_to_kv_cache2
+    else:
+        raise ValueError(f"Invalid version: {version}")
+
+    rng_key = jax.random.PRNGKey(0)
+    key = jax.random.normal(rng_key, (batch_size, seq_len, num_kv_heads, head_size), dtype=jnp.bfloat16)
+    value = jax.random.normal(rng_key, (batch_size, seq_len, num_kv_heads, head_size), dtype=jnp.bfloat16)
+    k_cache = jax.random.normal(rng_key, (num_kv_heads, num_blocks * block_size, head_size), dtype=jnp.bfloat16)
+    v_cache = jax.random.normal(rng_key, (num_kv_heads, num_blocks * block_size, head_size), dtype=jnp.bfloat16)
+    slot_mapping = jax.random.randint(rng_key, (batch_size, seq_len), 0, num_blocks * block_size, dtype=jnp.int32)
+
+    # For JIT compilation.
+    k_cache, v_cache = f(key, value, k_cache, v_cache, slot_mapping)
+    k_cache.block_until_ready()
+
+    start = time.time()
+    for _ in range(100):
+        k_cache, v_cache = f(key, value, k_cache, v_cache, slot_mapping)
+    k_cache.block_until_ready()
+    end = time.time()
+    print(f"Time taken: {(end - start) * 10:.2f} ms")
+
+
+if __name__ == "__main__":
+    for num_blocks in [16, 256, 512, 1024, 2048, 8192, 16384]:
+        print(f"Benchmarking Write to KV Cache w/ {num_blocks} blocks")
+        benchmark_write_to_kv_cache(16, 256, 16, 256, num_blocks, 16, version=1)

benchmarks/bench_paged_attn.py

@@ -0,0 +1,101 @@
+import argparse
+import functools
+import time
+
+import jax
+import jax.numpy as jnp
+from jax.experimental.pallas.ops.tpu.paged_attention import paged_attention
+
+BLOCK_SIZE = 16
+MAX_NUM_BLOCKS_PER_SEQ = 512
+
+
+@functools.partial(jax.jit, static_argnums=(6, 7))
+def paged_attn(
+    q: jax.Array,               # [batch, 1, num_heads, head_size]
+    k_cache: jax.Array,         # [num_kv_heads, num_blocks * block_size, head_size]
+    v_cache: jax.Array,         # [num_kv_heads, num_blocks * block_size, head_size]
+    sm_scale: float,
+    block_tables: jax.Array,    # [batch, max_num_blocks_per_batch]
+    context_lens: jax.Array,    # [batch]
+    block_size: int,
+    pages_per_compute_block: int,
+) -> jax.Array:                 # [batch, 1, num_heads, head_size]
+    q = q.squeeze(1)
+    q = q * sm_scale
+
+    head_size = q.shape[-1]
+    num_slots = k_cache.shape[-2]
+    k_cache = k_cache.reshape(-1, num_slots // block_size, block_size, head_size)
+    v_cache = v_cache.reshape(-1, num_slots // block_size, block_size, head_size)
+
+    output = paged_attention(
+        q,
+        k_cache,
+        v_cache,
+        context_lens,
+        block_tables,
+        pages_per_compute_block=pages_per_compute_block,
+    )
+    return output.reshape(q.shape[0], 1, q.shape[1], q.shape[2])
+
+
+def benchmark_paged_attn(
+    batch_size: int,
+    num_heads: int,
+    num_kv_heads: int,
+    head_size: int,
+    context_len: int,
+    num_blocks: int,
+    block_size: int,
+    pages_per_compute_block: int,
+):
+    rng_key = jax.random.PRNGKey(0)
+    query = jax.random.normal(rng_key, (batch_size, 1, num_heads, head_size), dtype=jnp.bfloat16)
+    k_cache = jax.random.normal(rng_key, (num_kv_heads, num_blocks * block_size, head_size), dtype=jnp.bfloat16)
+    v_cache = jax.random.normal(rng_key, (num_kv_heads, num_blocks * block_size, head_size), dtype=jnp.bfloat16)
+    sm_scale = head_size ** -0.5
+    block_tables = jax.random.randint(rng_key, (batch_size, MAX_NUM_BLOCKS_PER_SEQ), 0, num_blocks, dtype=jnp.int32)
+    context_lens = jnp.array([context_len] * batch_size, dtype=jnp.int32)
+
+    # For JIT compilation.
+    output = paged_attn(query, k_cache, v_cache, sm_scale, block_tables, context_lens, block_size, pages_per_compute_block)
+    output.block_until_ready()
+
+    start = time.time()
+    for _ in range(100):
+        output = paged_attn(query, k_cache, v_cache, sm_scale, block_tables, context_lens, block_size, pages_per_compute_block)
+    output.block_until_ready()
+    end = time.time()
+
+    print(f"Time taken: {(end - start) * 10000:.2f} us")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--batch-size", type=int, default=8)
+    parser.add_argument("--num-heads", type=int, default=16)
+    parser.add_argument("--num-kv-heads", type=int, default=16)
+    parser.add_argument("--head-size", type=int, default=256)
+    parser.add_argument("--context-len", type=int, default=512)
+    parser.add_argument("--num-blocks", type=int, default=2048)
+    args = parser.parse_args()
+    print(args)
+
+    for block_size in [16, 32, 64, 128]:
+        for pages_per_compute_block in [1, 2, 4, 8, 16, 32, 64, 128]:
+            if pages_per_compute_block > MAX_NUM_BLOCKS_PER_SEQ:
+                continue
+            if block_size * pages_per_compute_block > 1024:
+                continue
+            print(f"block_size {block_size}, pages_per_compute_block: {pages_per_compute_block}")
+            benchmark_paged_attn(
+                args.batch_size,
+                args.num_heads,
+                args.num_kv_heads,
+                args.head_size,
+                args.context_len,
+                args.num_blocks,
+                block_size,
+                pages_per_compute_block,
+            )

benchmarks/benchmark_latency.py

@@ -1,17 +1,14 @@
 """Benchmark the latency of processing a single batch of requests."""
 import argparse
-import json
 import time
 from pathlib import Path
-from typing import List, Optional
+from typing import Optional
 
 import numpy as np
 import torch
 from tqdm import tqdm
 
 from vllm import LLM, SamplingParams
-from vllm.inputs import PromptStrictInputs
-from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 
 
 def main(args: argparse.Namespace):
@@ -20,8 +17,6 @@ def main(args: argparse.Namespace):
     # NOTE(woosuk): If the request cannot be processed in a single batch,
     # the engine will automatically process the request in multiple batches.
     llm = LLM(model=args.model,
-              speculative_model=args.speculative_model,
-              num_speculative_tokens=args.num_speculative_tokens,
               tokenizer=args.tokenizer,
               quantization=args.quantization,
               tensor_parallel_size=args.tensor_parallel_size,
@@ -32,11 +27,9 @@ def main(args: argparse.Namespace):
               quantization_param_path=args.quantization_param_path,
               device=args.device,
               ray_workers_use_nsight=args.ray_workers_use_nsight,
-              use_v2_block_manager=args.use_v2_block_manager,
               enable_chunked_prefill=args.enable_chunked_prefill,
               download_dir=args.download_dir,
-              block_size=args.block_size,
-              gpu_memory_utilization=args.gpu_memory_utilization)
+              block_size=args.block_size)
 
     sampling_params = SamplingParams(
         n=args.n,
@@ -50,9 +43,7 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_inputs: List[PromptStrictInputs] = [{
-        "prompt_token_ids": batch
-    } for batch in dummy_prompt_token_ids.tolist()]
+    dummy_prompt_token_ids = dummy_prompt_token_ids.tolist()
 
     def run_to_completion(profile_dir: Optional[str] = None):
         if profile_dir:
@@ -63,13 +54,13 @@ def main(args: argparse.Namespace):
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
                         str(profile_dir))) as p:
-                llm.generate(dummy_inputs,
+                llm.generate(prompt_token_ids=dummy_prompt_token_ids,
                              sampling_params=sampling_params,
                              use_tqdm=False)
             print(p.key_averages())
         else:
             start_time = time.perf_counter()
-            llm.generate(dummy_inputs,
+            llm.generate(prompt_token_ids=dummy_prompt_token_ids,
                          sampling_params=sampling_params,
                          use_tqdm=False)
             end_time = time.perf_counter()
@@ -101,28 +92,16 @@ def main(args: argparse.Namespace):
     for percentage, percentile in zip(percentages, percentiles):
         print(f'{percentage}% percentile latency: {percentile} seconds')
 
-    # Output JSON results if specified
-    if args.output_json:
-        results = {
-            "avg_latency": np.mean(latencies),
-            "latencies": latencies.tolist(),
-            "percentiles": dict(zip(percentages, percentiles.tolist())),
-        }
-        with open(args.output_json, "w") as f:
-            json.dump(results, f, indent=4)
-
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(
         description='Benchmark the latency of processing a single batch of '
         'requests till completion.')
     parser.add_argument('--model', type=str, default='facebook/opt-125m')
-    parser.add_argument('--speculative-model', type=str, default=None)
-    parser.add_argument('--num-speculative-tokens', type=int, default=None)
     parser.add_argument('--tokenizer', type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=[*QUANTIZATION_METHODS, None],
+                        choices=['awq', 'gptq', 'squeezellm', None],
                         default=None)
     parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
     parser.add_argument('--input-len', type=int, default=32)
@@ -157,13 +136,15 @@ if __name__ == '__main__':
                         action='store_true',
                         help='enforce eager mode and disable CUDA graph')
     parser.add_argument(
-        '--kv-cache-dtype',
+        "--kv-cache-dtype",
         type=str,
-        choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
-        default="auto",
-        help='Data type for kv cache storage. If "auto", will use model '
-        'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
-        'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
+        choices=['auto', 'fp8'],
+        default='auto',
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type. '
+        'FP8_E5M2 (without scaling) is only supported on cuda version greater '
+        'than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for '
+        'common inference criteria.')
     parser.add_argument(
         '--quantization-param-path',
         type=str,
@@ -199,7 +180,6 @@ if __name__ == '__main__':
         action='store_true',
         help='If True, the prefill requests can be chunked based on the '
         'max_num_batched_tokens')
-    parser.add_argument('--use-v2-block-manager', action='store_true')
     parser.add_argument(
         "--ray-workers-use-nsight",
         action='store_true',
@@ -210,16 +190,5 @@ if __name__ == '__main__':
                         default=None,
                         help='directory to download and load the weights, '
                         'default to the default cache dir of huggingface')
-    parser.add_argument(
-        '--output-json',
-        type=str,
-        default=None,
-        help='Path to save the latency results in JSON format.')
-    parser.add_argument('--gpu-memory-utilization',
-                        type=float,
-                        default=0.9,
-                        help='the fraction of GPU memory to be used for '
-                        'the model executor, which can range from 0 to 1.'
-                        'If unspecified, will use the default value of 0.9.')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_prefix_caching.py

@@ -16,22 +16,20 @@ def test_prefix(llm=None, sampling_params=None, prompts=None):
 
 
 def main(args):
-    llm = LLM(model=args.model,
+    llm = LLM(model="baichuan-inc/Baichuan2-13B-Chat",
               tokenizer_mode='auto',
               trust_remote_code=True,
               enforce_eager=True,
-              use_v2_block_manager=args.use_v2_block_manager,
-              tensor_parallel_size=args.tensor_parallel_size,
               enable_prefix_caching=args.enable_prefix_caching)
 
     num_prompts = 100
     prompts = [PROMPT] * num_prompts
-    sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
+    sampling_params = SamplingParams(temperature=0, max_tokens=100)
 
     print("------warm up------")
     test_prefix(
         llm=llm,
-        prompts=prompts,
+        prompts=prompts[:1],
         sampling_params=sampling_params,
     )
 
@@ -47,16 +45,8 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser(
         description='Benchmark the performance with or without automatic '
         'prefix caching.')
-    parser.add_argument('--model',
-                        type=str,
-                        default='baichuan-inc/Baichuan2-13B-Chat')
-    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
-    parser.add_argument('--output-len', type=int, default=10)
     parser.add_argument('--enable-prefix-caching',
                         action='store_true',
                         help='enable prefix caching')
-    parser.add_argument('--use-v2-block-manager',
-                        action='store_true',
-                        help='Use BlockSpaceMangerV2')
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_serving.py

@@ -17,10 +17,6 @@ On the client side, run:
         --dataset-path <path to dataset> \
         --request-rate <request_rate> \ # By default <request_rate> is inf
         --num-prompts <num_prompts> # By default <num_prompts> is 1000
-        
-    when using tgi backend, add
-        --endpoint /generate_stream
-    to the end of the command above.
 """
 import argparse
 import asyncio
@@ -31,7 +27,7 @@ import time
 import warnings
 from dataclasses import dataclass
 from datetime import datetime
-from typing import AsyncGenerator, List, Optional, Tuple
+from typing import AsyncGenerator, List, Tuple
 
 import numpy as np
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
@@ -62,11 +58,7 @@ def sample_sharegpt_requests(
     dataset_path: str,
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int] = None,
 ) -> List[Tuple[str, int, int]]:
-    if fixed_output_len is not None and fixed_output_len < 4:
-        raise ValueError("output_len too small")
-
     # Load the dataset.
     with open(dataset_path) as f:
         dataset = json.load(f)
@@ -76,32 +68,38 @@ def sample_sharegpt_requests(
     dataset = [(data["conversations"][0]["value"],
                 data["conversations"][1]["value"]) for data in dataset]
 
-    # Shuffle the dataset.
-    random.shuffle(dataset)
+    # some of these will be filtered out, so sample more than we need
+    sampled_indices = random.sample(range(len(dataset)),
+                                    int(num_requests * 1.2))
+    dataset = [dataset[i] for i in sampled_indices]
 
-    # Filter out sequences that are too long or too short
-    filtered_dataset: List[Tuple[str, int, int]] = []
+    # Tokenize the prompts and completions.
+    prompts = [prompt for prompt, _ in dataset]
+    prompt_token_ids = tokenizer(prompts).input_ids
+    completions = [completion for _, completion in dataset]
+    completion_token_ids = tokenizer(completions).input_ids
+    tokenized_dataset = []
     for i in range(len(dataset)):
-        if len(filtered_dataset) == num_requests:
-            break
+        output_len = len(completion_token_ids[i])
+        tokenized_dataset.append((prompts[i], prompt_token_ids[i], output_len))
 
-        # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
-        prompt_token_ids = tokenizer(prompt).input_ids
-        completion = dataset[i][1]
-        completion_token_ids = tokenizer(completion).input_ids
+    # Filter out too long sequences.
+    filtered_dataset: List[Tuple[str, int, int]] = []
+    for prompt, prompt_token_ids, output_len in tokenized_dataset:
         prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
         if prompt_len < 4 or output_len < 4:
             # Prune too short sequences.
+            # This is because TGI causes errors when the input or output length
+            # is too short.
             continue
         if prompt_len > 1024 or prompt_len + output_len > 2048:
             # Prune too long sequences.
             continue
         filtered_dataset.append((prompt, prompt_len, output_len))
 
-    return filtered_dataset
+    # Sample the requests.
+    sampled_requests = random.sample(filtered_dataset, num_requests)
+    return sampled_requests
 
 
 def sample_sonnet_requests(
@@ -215,11 +213,6 @@ def calculate_metrics(
         else:
             actual_output_lens.append(0)
 
-    if completed == 0:
-        warnings.warn(
-            "All requests failed. This is likely due to a misconfiguration "
-            "on the benchmark arguments.",
-            stacklevel=2)
     metrics = BenchmarkMetrics(
         completed=completed,
         total_input=total_input,
@@ -231,9 +224,9 @@ def calculate_metrics(
         1000,  # ttfts is empty if streaming is not supported by backend
         median_ttft_ms=np.median(ttfts or 0) * 1000,
         p99_ttft_ms=np.percentile(ttfts or 0, 99) * 1000,
-        mean_tpot_ms=np.mean(tpots or 0) * 1000,
-        median_tpot_ms=np.median(tpots or 0) * 1000,
-        p99_tpot_ms=np.percentile(tpots or 0, 99) * 1000,
+        mean_tpot_ms=np.mean(tpots) * 1000,
+        median_tpot_ms=np.median(tpots) * 1000,
+        p99_tpot_ms=np.percentile(tpots, 99) * 1000,
     )
 
     return metrics, actual_output_lens
@@ -255,24 +248,6 @@ async def benchmark(
     else:
         raise ValueError(f"Unknown backend: {backend}")
 
-    print("Starting initial single prompt test run...")
-    test_prompt, test_prompt_len, test_output_len = input_requests[0]
-    test_input = RequestFuncInput(
-        model=model_id,
-        prompt=test_prompt,
-        api_url=api_url,
-        prompt_len=test_prompt_len,
-        output_len=test_output_len,
-        best_of=best_of,
-        use_beam_search=use_beam_search,
-    )
-    test_output = await request_func(request_func_input=test_input)
-    if not test_output.success:
-        raise ValueError(
-            "Initial test run failed - Please make sure benchmark arguments "
-            f"are correctly specified. Error: {test_output.error}")
-    else:
-        print("Initial test run completed. Starting main benchmark run...")
     print(f"Traffic request rate: {request_rate}")
 
     pbar = None if disable_tqdm else tqdm(total=len(input_requests))
@@ -386,7 +361,6 @@ def main(args: argparse.Namespace):
             dataset_path=args.dataset,
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
-            fixed_output_len=args.sharegpt_output_len,
         )
 
     elif args.dataset_name == "sharegpt":
@@ -394,7 +368,6 @@ def main(args: argparse.Namespace):
             dataset_path=args.dataset_path,
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
-            fixed_output_len=args.sharegpt_output_len,
         )
 
     elif args.dataset_name == "sonnet":
@@ -551,12 +524,6 @@ if __name__ == "__main__":
         default=1000,
         help="Number of prompts to process.",
     )
-    parser.add_argument(
-        "--sharegpt-output-len",
-        type=int,
-        default=None,
-        help="Output length for each request. Overrides the output length "
-        "from the ShareGPT dataset.")
     parser.add_argument(
         "--sonnet-input-len",
         type=int,

benchmarks/benchmark_throughput.py

@@ -10,8 +10,6 @@ from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
 
-from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
-
 
 def sample_requests(
     dataset_path: str,
@@ -103,22 +101,25 @@ def run_vllm(
     )
 
     # Add the requests to the engine.
-    prompts = []
-    sampling_params = []
     for prompt, _, output_len in requests:
-        prompts.append(prompt)
-        sampling_params.append(
-            SamplingParams(
-                n=n,
-                temperature=0.0 if use_beam_search else 1.0,
-                top_p=1.0,
-                use_beam_search=use_beam_search,
-                ignore_eos=True,
-                max_tokens=output_len,
-            ))
+        sampling_params = SamplingParams(
+            n=n,
+            temperature=0.0 if use_beam_search else 1.0,
+            top_p=1.0,
+            use_beam_search=use_beam_search,
+            ignore_eos=True,
+            max_tokens=output_len,
+        )
+        # FIXME(woosuk): Do not use internal method.
+        llm._add_request(
+            prompt=prompt,
+            prompt_token_ids=None,
+            sampling_params=sampling_params,
+        )
 
     start = time.perf_counter()
-    llm.generate(prompts, sampling_params, use_tqdm=True)
+    # FIXME(woosuk): Do not use internal method.
+    llm._run_engine(use_tqdm=True)
     end = time.perf_counter()
     return end - start
 
@@ -242,18 +243,6 @@ def main(args: argparse.Namespace):
     print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
           f"{total_num_tokens / elapsed_time:.2f} tokens/s")
 
-    # Output JSON results if specified
-    if args.output_json:
-        results = {
-            "elapsed_time": elapsed_time,
-            "num_requests": len(requests),
-            "total_num_tokens": total_num_tokens,
-            "requests_per_second": len(requests) / elapsed_time,
-            "tokens_per_second": total_num_tokens / elapsed_time,
-        }
-        with open(args.output_json, "w") as f:
-            json.dump(results, f, indent=4)
-
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Benchmark the throughput.")
@@ -278,7 +267,7 @@ if __name__ == "__main__":
     parser.add_argument("--tokenizer", type=str, default=None)
     parser.add_argument('--quantization',
                         '-q',
-                        choices=[*QUANTIZATION_METHODS, None],
+                        choices=['awq', 'gptq', 'squeezellm', None],
                         default=None)
     parser.add_argument("--tensor-parallel-size", "-tp", type=int, default=1)
     parser.add_argument("--n",
@@ -323,13 +312,15 @@ if __name__ == "__main__":
                         action="store_true",
                         help="enforce eager execution")
     parser.add_argument(
-        '--kv-cache-dtype',
+        "--kv-cache-dtype",
         type=str,
-        choices=['auto', 'fp8', 'fp8_e5m2', 'fp8_e4m3'],
+        choices=["auto", "fp8"],
         default="auto",
-        help='Data type for kv cache storage. If "auto", will use model '
-        'data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. '
-        'ROCm (AMD GPU) supports fp8 (=fp8_e4m3)')
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type. '
+        'FP8_E5M2 (without scaling) is only supported on cuda version greater '
+        'than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for '
+        'common inference criteria.')
     parser.add_argument(
         '--quantization-param-path',
         type=str,
@@ -344,7 +335,7 @@ if __name__ == "__main__":
         "--device",
         type=str,
         default="cuda",
-        choices=["cuda", "cpu"],
+        choices=["cuda", "cpu", "tpu"],
         help='device type for vLLM execution, supporting CUDA and CPU.')
     parser.add_argument(
         "--enable-prefix-caching",
@@ -363,11 +354,6 @@ if __name__ == "__main__":
                         default=None,
                         help='directory to download and load the weights, '
                         'default to the default cache dir of huggingface')
-    parser.add_argument(
-        '--output-json',
-        type=str,
-        default=None,
-        help='Path to save the throughput results in JSON format.')
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model

benchmarks/kernels/benchmark_aqlm.py

@@ -1,302 +0,0 @@
-import argparse
-import os
-import sys
-from typing import Optional
-
-import torch
-import torch.nn.functional as F
-
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.aqlm import (
-    dequantize_weight, generic_dequantize_gemm, get_int_dtype,
-    optimized_dequantize_gemm)
-
-os.environ['CUDA_VISIBLE_DEVICES'] = '0'
-
-
-def torch_mult(
-        input: torch.Tensor,  #  [..., in_features]
-        weights: torch.Tensor,
-        scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-) -> torch.Tensor:
-    output = F.linear(input, weights)
-    return output
-
-
-def dequant_out_scale(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    if bias is None:
-        output = F.linear(input, weights, bias)
-        orig_shape = output.shape
-        flattened_output = output.view(-1, output.size(-1))
-        f_scales = scales.view(-1, scales.shape[0])
-        b_scales = f_scales.expand(flattened_output.shape[0], -1)
-        flattened_output *= b_scales
-        return flattened_output.view(orig_shape)
-    else:
-        b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-            -1, weights.shape[1])
-        weights *= b_scales
-        return F.linear(input, weights, bias)
-
-
-def dequant_weight_scale(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    b_scales = scales.view(scales.shape[:-3] + (-1, )).expand(
-        -1, weights.shape[1])
-    weights *= b_scales
-    return F.linear(input, weights, bias)
-
-
-def dequant_no_scale(
-    input: torch.Tensor,  #  [..., in_features]
-    codes: torch.IntTensor,  #  [num_out_groups, num_in_groups, num_codebooks]
-    codebooks: torch.
-    Tensor,  #  [num_codebooks, codebook_size, out_group_size, in_group_size]
-    scales: torch.Tensor,  #  [num_out_groups, 1, 1, 1]
-    output_partition_sizes: torch.IntTensor,
-    bias: Optional[torch.Tensor],
-) -> torch.Tensor:
-
-    weights = ops.aqlm_dequant(codes, codebooks, output_partition_sizes)
-
-    return F.linear(input, weights, bias)
-
-
-# Compare the optimized 1x16 and 2x8 cuda decompression/dequant kernels against
-# the generic pytorch version.
-# Just visual comparison.
-def dequant_test(k: int, parts: torch.tensor, nbooks: int, bits: int) -> None:
-
-    n = parts.sum().item()
-
-    device = torch.device('cuda:0')
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(-code_range,
-                          code_range,
-                          size=(n, k // ingroups, nbooks),
-                          dtype=get_int_dtype(bits),
-                          device=device)
-
-    codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-                            dtype=torch.float16,
-                            device=device)
-
-    count = 0
-    for index in range(16):
-        for i in range(8):
-            for book in range(nbooks):
-                codebooks[book, index, 0, i] = count * (10**book)
-            count += 1
-
-    print("codes shape", codes.shape)
-
-    for i in range(16):
-        for book in range(nbooks):
-            codes[0, i, book] = i
-            codes[0, -i, book] = i
-
-    weights = dequantize_weight(codes, codebooks, None)
-    weights2 = ops.aqlm_dequant(codes, codebooks, parts)
-
-    print("weights shape:", weights.shape)
-    print("weights2 shape:", weights2.shape)
-
-    print("weights are:", weights)
-    print("weights2 are:", weights2)
-
-    print("first 128 weights are", weights[0, 0:128].to(torch.int32))
-    print("first 128 weights2 are:", weights2[0, 0:128].to(torch.int32))
-
-    print("last 128 weights are", weights[0, -128:])
-    print("last 128 weights2 are:", weights2[0, -128:])
-
-
-def main():
-
-    parser = argparse.ArgumentParser(description="Benchmark aqlm performance.")
-
-    # Add arguments
-    parser.add_argument("--nbooks",
-                        type=int,
-                        default=1,
-                        help="Number of codebooks (default: 1)")
-    parser.add_argument("--bits",
-                        type=int,
-                        default=16,
-                        help="Number of bits per code element (default: 16)")
-    parser.add_argument(
-        "--test",
-        type=bool,
-        default=False,
-        help="Run the decompression/dequant tester rather than benchmarking "
-        "(default: False)")
-
-    # Parse the arguments
-    args = parser.parse_args()
-
-    # Extract values
-    nbooks = args.nbooks
-    bits = args.bits
-
-    if args.test:
-        dequant_test(4096, torch.tensor((4096, )), nbooks, bits)
-        return
-
-    # Otherwise, benchmark.
-    methods = [
-        ops.aqlm_gemm,
-        dequant_out_scale,
-        generic_dequantize_gemm,
-        optimized_dequantize_gemm,
-        dequant_weight_scale,
-        torch_mult,
-        dequant_no_scale,
-    ]
-
-    filename = f"./aqlm_benchmark_{nbooks}x{bits}.csv"
-    print(f"writing benchmarks to file {filename}")
-    with open(filename, "w") as f:
-        sys.stdout = f
-
-        print('m | k | n | n parts', end='')
-        for method in methods:
-            print(f" | {method.__name__.replace('_', ' ')} (µs)", end='')
-        print('')
-
-        # These are reasonable prefill sizes.
-        ksandpartions = ((4096, (4096, 4096, 4096)), (4096, (4096, )),
-                         (4096, (11008, 11008)), (11008, (4096, )))
-
-        # reasonable ranges for m.
-        for m in [
-                1, 2, 4, 8, 10, 12, 14, 16, 24, 32, 48, 52, 56, 64, 96, 112,
-                128, 256, 512, 1024, 1536, 2048, 3072, 4096
-        ]:
-            print(f'{m}', file=sys.__stdout__)
-            for ksp in ksandpartions:
-                run_grid(m, ksp[0], torch.tensor(ksp[1]), nbooks, bits,
-                         methods)
-
-        sys.stdout = sys.__stdout__
-
-
-def run_grid(m: int, k: int, parts: torch.tensor, nbooks: int, bits: int,
-             methods):
-
-    # I didn't see visible improvements from increasing these, but feel free :)
-    num_warmup_trials = 1
-    num_trials = 1
-
-    num_calls = 100
-
-    # warmup.
-    for method in methods:
-        for _ in range(num_warmup_trials):
-            run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-    n = parts.sum().item()
-    print(f'{m} | {k} | {n} | {parts.tolist()}', end='')
-
-    for method in methods:
-        best_time_us = 1e20
-        for _ in range(num_trials):
-            kernel_dur_ms = run_timing(
-                num_calls=num_calls,
-                m=m,
-                k=k,
-                parts=parts,
-                nbooks=nbooks,
-                bits=bits,
-                method=method,
-            )
-
-            kernel_dur_us = 1000 * kernel_dur_ms
-
-            if kernel_dur_us < best_time_us:
-                best_time_us = kernel_dur_us
-
-        print(f' | {kernel_dur_us:.0f}', end='')
-
-    print('')
-
-
-def run_timing(num_calls: int, m: int, k: int, parts: torch.tensor,
-               nbooks: int, bits: int, method) -> float:
-
-    n = parts.sum().item()
-
-    device = torch.device('cuda:0')
-
-    input = torch.randn((1, m, k), dtype=torch.float16, device=device)
-
-    code_range = (1 << bits) // 2
-    ingroups = 8
-
-    codes = torch.randint(-code_range,
-                          code_range,
-                          size=(n, k // ingroups, nbooks),
-                          dtype=get_int_dtype(bits),
-                          device=device)
-
-    codebooks = torch.randn(size=(parts.shape[0] * nbooks, 1 << bits, 1, 8),
-                            dtype=torch.float16,
-                            device=device)
-
-    scales = torch.randn(size=(n, 1, 1, 1), dtype=torch.float16, device=device)
-
-    # for comparison to just a pytorch mult.
-    weights = torch.randn((n, k), dtype=torch.float16, device=device)
-
-    start_event = torch.cuda.Event(enable_timing=True)
-    end_event = torch.cuda.Event(enable_timing=True)
-
-    start_event.record()
-
-    if method is torch_mult:
-        for i in range(num_calls):
-            torch_mult(input, weights, scales)
-    else:
-        for i in range(num_calls):
-            method(input, codes, codebooks, scales, parts, None)
-
-    end_event.record()
-    end_event.synchronize()
-
-    dur_ms = start_event.elapsed_time(end_event) / num_calls
-    return dur_ms
-
-
-if __name__ == "__main__":
-    sys.exit(main())

benchmarks/kernels/benchmark_marlin.py

@@ -1,233 +0,0 @@
-import argparse
-
-import torch
-import torch.utils.benchmark as benchmark
-from benchmark_shapes import WEIGHT_SHAPES
-
-from vllm import _custom_ops as ops
-from vllm.model_executor.layers.quantization.gptq_marlin import (
-    GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N,
-    GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS)
-from vllm.model_executor.layers.quantization.gptq_marlin_24 import (
-    GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N,
-    GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS)
-from vllm.model_executor.layers.quantization.utils.marlin_utils import (
-    MarlinWorkspace, marlin_24_quantize, marlin_quantize)
-from vllm.model_executor.layers.quantization.utils.quant_utils import (
-    gptq_pack, quantize_weights, sort_weights)
-
-DEFAULT_MODELS = ["meta-llama/Llama-2-7b-hf/TP1"]
-DEFAULT_BATCH_SIZES = [1, 16, 32, 64, 128, 256, 512]
-
-ACT_ORDER_OPTS = [False, True]
-K_FULL_OPTS = [False, True]
-
-
-def bench_run(results, model, act_order, is_k_full, num_bits, group_size,
-              size_m, size_k, size_n):
-    label = "Quant Matmul"
-
-    sub_label = ("{}, act={} k_full={}, b={}, g={}, "
-                 "MKN=({}x{}x{})".format(model, act_order, is_k_full, num_bits,
-                                         group_size, size_m, size_k, size_n))
-
-    print(f"Testing: {sub_label}")
-
-    a = torch.randn(size_m, size_k).to(torch.half).cuda()
-    b = torch.rand(size_k, size_n).to(torch.half).cuda()
-
-    a_tmp = (torch.zeros(size_m, size_k).to(torch.half).cuda())
-
-    # Marlin quant
-    (
-        marlin_w_ref,
-        marlin_q_w,
-        marlin_s,
-        marlin_g_idx,
-        marlin_sort_indices,
-        marlin_rand_perm,
-    ) = marlin_quantize(b, num_bits, group_size, act_order)
-
-    # Marlin_24 quant
-    (marlin_24_w_ref, marlin_24_q_w_comp, marlin_24_meta,
-     marlin_24_s) = marlin_24_quantize(b, num_bits, group_size)
-
-    # GPTQ quant
-    (w_ref, q_w, s, g_idx,
-     rand_perm) = quantize_weights(b, num_bits, group_size, act_order)
-    q_w_gptq = gptq_pack(q_w, num_bits, size_k, size_n)
-
-    # For act_order, sort the "weights" and "g_idx"
-    # so that group ids are increasing
-    repack_sort_indices = torch.empty(0, dtype=torch.int, device=b.device)
-    if act_order:
-        (q_w, g_idx, repack_sort_indices) = sort_weights(q_w, g_idx)
-
-    # Prepare
-    marlin_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N,
-                                       GPTQ_MARLIN_MAX_PARALLEL)
-
-    marlin_24_workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_24_MIN_THREAD_N,
-                                          GPTQ_MARLIN_24_MAX_PARALLEL)
-
-    globals = {
-        # Gen params
-        "num_bits": num_bits,
-        "group_size": group_size,
-        "size_m": size_m,
-        "size_n": size_n,
-        "size_k": size_k,
-        "a": a,
-        "a_tmp": a_tmp,
-        # Marlin params
-        "marlin_w_ref": marlin_w_ref,
-        "marlin_q_w": marlin_q_w,
-        "marlin_s": marlin_s,
-        "marlin_g_idx": marlin_g_idx,
-        "marlin_sort_indices": marlin_sort_indices,
-        "marlin_rand_perm": marlin_rand_perm,
-        "marlin_workspace": marlin_workspace,
-        "is_k_full": is_k_full,
-        # Marlin_24 params
-        "marlin_24_w_ref": marlin_24_w_ref,
-        "marlin_24_q_w_comp": marlin_24_q_w_comp,
-        "marlin_24_meta": marlin_24_meta,
-        "marlin_24_s": marlin_24_s,
-        "marlin_24_workspace": marlin_24_workspace,
-        # GPTQ params
-        "q_w_gptq": q_w_gptq,
-        "repack_sort_indices": repack_sort_indices,
-        # Kernels
-        "gptq_marlin_gemm": ops.gptq_marlin_gemm,
-        "gptq_marlin_24_gemm": ops.gptq_marlin_24_gemm,
-        "gptq_marlin_repack": ops.gptq_marlin_repack,
-    }
-
-    min_run_time = 1
-
-    # Warmup pytorch
-    for i in range(5):
-        torch.matmul(a, marlin_w_ref)
-
-    results.append(
-        benchmark.Timer(
-            stmt="torch.matmul(a, marlin_w_ref)",
-            globals=globals,
-            label=label,
-            sub_label=sub_label,
-            description="pytorch_gemm",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-    results.append(
-        benchmark.Timer(
-            stmt=
-            "output = gptq_marlin_gemm(a, marlin_q_w, marlin_s, marlin_g_idx, marlin_sort_indices, marlin_workspace.scratch, num_bits, size_m, size_n, size_k, is_k_full)",  # noqa: E501
-            globals=globals,
-            label=label,
-            sub_label=sub_label,
-            description="gptq_marlin_gemm",
-        ).blocked_autorange(min_run_time=min_run_time))
-
-    if (num_bits in GPTQ_MARLIN_24_SUPPORTED_NUM_BITS
-            and group_size in GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES):
-        results.append(
-            benchmark.Timer(
-                stmt=
-                "output = gptq_marlin_24_gemm(a, marlin_24_q_w_comp, marlin_24_meta, marlin_24_s, marlin_24_workspace.scratch, num_bits, size_m, size_n, size_k)",  # noqa: E501
-                globals=globals,
-                label=label,
-                sub_label=sub_label,
-                description="gptq_marlin_24_gemm",
-            ).blocked_autorange(min_run_time=min_run_time))
-
-    results.append(
-        benchmark.Timer(
-            stmt=
-            "q_res = gptq_marlin_repack(q_w_gptq, repack_sort_indices, size_k, size_n, num_bits)",  # noqa: E501
-            globals=globals,
-            label=label,
-            sub_label=sub_label,
-            description="gptq_marlin_repack",
-        ).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 = []
-
-    for model in args.models:
-        for layer in WEIGHT_SHAPES[model]:
-            size_k = layer[0]
-            size_n = layer[1]
-
-            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 act_order in ACT_ORDER_OPTS:
-                if len(args.limit_act_order
-                       ) > 0 and act_order not in args.limit_act_order:
-                    continue
-
-                for is_k_full in K_FULL_OPTS:
-                    if len(args.limit_k_full
-                           ) > 0 and is_k_full not in args.limit_k_full:
-                        continue
-
-                    for num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS:
-                        if len(args.limit_num_bits
-                               ) > 0 and num_bits not in args.limit_num_bits:
-                            continue
-
-                        for group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES:
-                            if len(
-                                    args.limit_group_size
-                            ) > 0 and group_size not in args.limit_group_size:
-                                continue
-
-                            # For act_order, the group_size must be less than
-                            # size_k
-                            if act_order and (group_size == size_k
-                                              or group_size == -1):
-                                continue
-
-                            for size_m in args.batch_sizes:
-                                bench_run(results, model, act_order, is_k_full,
-                                          num_bits, group_size, size_m, size_k,
-                                          size_n)
-
-    compare = benchmark.Compare(results)
-    compare.print()
-
-
-# For quick benchmarking use:
-#   python benchmark_marlin.py --batch-sizes 1 16 32 --limit-k 4096 --limit-n 4096 --limit-group-size 128 --limit-num-bits 4 --limit-act-order 0 --limit-k-full 1 # noqa E501
-#
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description="Benchmark Marlin across specified models/shapes/batches")
-    parser.add_argument(
-        "--models",
-        nargs="+",
-        type=str,
-        default=DEFAULT_MODELS,
-        choices=WEIGHT_SHAPES.keys(),
-    )
-    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-group-size", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-num-bits", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-act-order", nargs="+", type=int, default=[])
-    parser.add_argument("--limit-k-full", nargs="+", type=int, default=[])
-
-    args = parser.parse_args()
-    main(args)

benchmarks/kernels/benchmark_mixtral_moe.py

@@ -1,4 +1,3 @@
-import argparse
 import json
 import os
 import sys
@@ -6,70 +5,68 @@ import sys
 import torch
 import torch.nn.functional as F
 import triton
-from tqdm import tqdm
 
 from vllm.model_executor.layers.fused_moe import (fused_moe,
                                                   get_config_file_name)
 
+os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
-def main(model, tp_size, gpu, dtype: str):
-    os.environ['CUDA_VISIBLE_DEVICES'] = str(gpu)
+
+def main():
     method = fused_moe
     for bs in [
             1, 2, 4, 8, 16, 24, 32, 48, 64, 96, 128, 256, 512, 1024, 1536,
             2048, 3072, 4096
     ]:
-        run_grid(bs,
-                 model=model,
-                 method=method,
-                 gpu=gpu,
-                 tp_size=tp_size,
-                 dtype=dtype)
+        run_grid(bs, method=method)
 
 
-def run_grid(bs, model, method, gpu, tp_size, dtype: str):
-    if model == '8x7B':
-        d_model = 4096
-        model_intermediate_size = 14336
-        num_layers = 32
-    elif model == '8x22B':
-        d_model = 6144
-        model_intermediate_size = 16384
-        num_layers = 56
-    else:
-        raise ValueError(f'Unsupported Mixtral model {model}')
+def run_grid(bs, method):
+    d_model = 4096
     num_total_experts = 8
     top_k = 2
-    # tp_size = 2
+    tp_size = 2
+    model_intermediate_size = 14336
+    num_layers = 32
     num_calls = 100
 
     num_warmup_trials = 1
     num_trials = 1
 
     configs = []
+    if bs <= 16:
+        BLOCK_SIZES_M = [16]
+    elif bs <= 32:
+        BLOCK_SIZES_M = [16, 32]
+    elif bs <= 64:
+        BLOCK_SIZES_M = [16, 32, 64]
+    elif bs <= 128:
+        BLOCK_SIZES_M = [16, 32, 64, 128]
+    else:
+        BLOCK_SIZES_M = [16, 32, 64, 128, 256]
 
     for block_size_n in [32, 64, 128, 256]:
-        for block_size_m in [16, 32, 64, 128, 256]:
+        for block_size_m in BLOCK_SIZES_M:
             for block_size_k in [64, 128, 256]:
                 for group_size_m in [1, 16, 32, 64]:
                     for num_warps in [4, 8]:
-                        for num_stages in [2, 3, 4, 5]:
-                            configs.append({
-                                "BLOCK_SIZE_M": block_size_m,
-                                "BLOCK_SIZE_N": block_size_n,
-                                "BLOCK_SIZE_K": block_size_k,
-                                "GROUP_SIZE_M": group_size_m,
-                                "num_warps": num_warps,
-                                "num_stages": num_stages,
-                            })
+                        configs.append({
+                            "BLOCK_SIZE_M": block_size_m,
+                            "BLOCK_SIZE_N": block_size_n,
+                            "BLOCK_SIZE_K": block_size_k,
+                            "GROUP_SIZE_M": group_size_m,
+                            "num_warps": num_warps,
+                            "num_stages": 4,
+                        })
 
     best_config = None
     best_time_us = 1e20
 
-    print(f'{tp_size=} {bs=}')
-
-    for config in tqdm(configs):
+    for config in configs:
+        print(f'{tp_size=} {bs=}')
+        print(f'{config}')
         # warmup
+        print('warming up')
         try:
             for _ in range(num_warmup_trials):
                 run_timing(
@@ -82,12 +79,12 @@ def run_grid(bs, model, method, gpu, tp_size, dtype: str):
                     model_intermediate_size=model_intermediate_size,
                     method=method,
                     config=config,
-                    dtype=dtype,
                 )
         except triton.runtime.autotuner.OutOfResources:
             continue
 
         # trial
+        print('benchmarking')
         for _ in range(num_trials):
             kernel_dur_ms = run_timing(
                 num_calls=num_calls,
@@ -99,7 +96,6 @@ def run_grid(bs, model, method, gpu, tp_size, dtype: str):
                 model_intermediate_size=model_intermediate_size,
                 method=method,
                 config=config,
-                dtype=dtype,
             )
 
             kernel_dur_us = 1000 * kernel_dur_ms
@@ -109,18 +105,16 @@ def run_grid(bs, model, method, gpu, tp_size, dtype: str):
                 best_config = config
                 best_time_us = kernel_dur_us
 
-                tqdm.write(
-                    f'{kernel_dur_us=:.1f} {model_dur_ms=:.1f}'
-                    f' {bs=} {tp_size=} {top_k=} {num_total_experts=} '
-                    f'{d_model=} {model_intermediate_size=} {num_layers=}')
+            print(f'{kernel_dur_us=:.1f} {model_dur_ms=:.1f}'
+                  f' {bs=} {tp_size=} {top_k=} {num_total_experts=} '
+                  f'{d_model=} {model_intermediate_size=} {num_layers=}')
 
     print("best_time_us", best_time_us)
     print("best_config", best_config)
 
     # holds Dict[str, Dict[str, int]]
     filename = get_config_file_name(num_total_experts,
-                                    model_intermediate_size // tp_size,
-                                    "float8" if dtype == "float8" else None)
+                                    model_intermediate_size // tp_size)
     print(f"writing config to file {filename}")
     existing_content = {}
     if os.path.exists(filename):
@@ -134,48 +128,27 @@ def run_grid(bs, model, method, gpu, tp_size, dtype: str):
 
 def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
                top_k: int, tp_size: int, model_intermediate_size: int, method,
-               config, dtype: str) -> float:
+               config) -> float:
     shard_intermediate_size = model_intermediate_size // tp_size
 
     hidden_states = torch.rand(
         (bs, d_model),
         device="cuda:0",
-        dtype=torch.float16,
+        dtype=torch.bfloat16,
     )
 
-    w1 = torch.rand(
+    ws = torch.rand(
         (num_total_experts, 2 * shard_intermediate_size, d_model),
         device=hidden_states.device,
         dtype=hidden_states.dtype,
     )
 
-    w2 = torch.rand(
+    w2s = torch.rand(
         (num_total_experts, d_model, shard_intermediate_size),
         device=hidden_states.device,
         dtype=hidden_states.dtype,
     )
 
-    w1_scale = None
-    w2_scale = None
-    a1_scale = None
-    a2_scale = None
-
-    if dtype == "float8":
-        w1 = w1.to(torch.float8_e4m3fn)
-        w2 = w2.to(torch.float8_e4m3fn)
-        w1_scale = torch.ones(num_total_experts,
-                              device=hidden_states.device,
-                              dtype=torch.float32)
-        w2_scale = torch.ones(num_total_experts,
-                              device=hidden_states.device,
-                              dtype=torch.float32)
-        a1_scale = torch.ones(1,
-                              device=hidden_states.device,
-                              dtype=torch.float32)
-        a2_scale = torch.ones(1,
-                              device=hidden_states.device,
-                              dtype=torch.float32)
-
     gating_output = F.softmax(torch.rand(
         (num_calls, bs, num_total_experts),
         device=hidden_states.device,
@@ -190,18 +163,13 @@ def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
     for i in range(num_calls):
         hidden_states = method(
             hidden_states=hidden_states,
-            w1=w1,
-            w2=w2,
-            w1_scale=w1_scale,
-            w2_scale=w2_scale,
-            a1_scale=a1_scale,
-            a2_scale=a2_scale,
+            w1=ws,
+            w2=w2s,
             gating_output=gating_output[i],
             topk=2,
             renormalize=True,
             inplace=True,
             override_config=config,
-            use_fp8=dtype == "float8",
         )
     end_event.record()
     end_event.synchronize()
@@ -211,29 +179,4 @@ def run_timing(num_calls: int, bs: int, d_model: int, num_total_experts: int,
 
 
 if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        prog='benchmark_mixtral_moe',
-        description='Benchmark and tune the fused_moe kernel',
-    )
-    parser.add_argument(
-        '--dtype',
-        type=str,
-        default='auto',
-        choices=['float8', 'float16'],
-        help='Data type used for fused_moe kernel computations',
-    )
-    parser.add_argument('--model',
-                        type=str,
-                        default='8x7B',
-                        choices=['8x7B', '8x22B'],
-                        help='The Mixtral model to benchmark')
-    parser.add_argument('--tp-size',
-                        type=int,
-                        default=2,
-                        help='Tensor paralleli size')
-    parser.add_argument('--gpu',
-                        type=int,
-                        default=0,
-                        help="GPU ID for benchmarking")
-    args = parser.parse_args()
-    sys.exit(main(args.model, args.tp_size, args.gpu, args.dtype))
+    sys.exit(main())

benchmarks/kernels/benchmark_paged_attention.py

@@ -16,7 +16,7 @@ PARTITION_SIZE = 512
 def main(
     version: str,
     num_seqs: int,
-    seq_len: int,
+    context_len: int,
     num_query_heads: int,
     num_kv_heads: int,
     head_size: int,
@@ -48,12 +48,12 @@ def main(
                                    dtype=torch.float,
                                    device=device)
 
-    seq_lens = [seq_len for _ in range(num_seqs)]
-    max_seq_len = max(seq_lens)
-    seq_lens = torch.tensor(seq_lens, dtype=torch.int, device=device)
+    context_lens = [context_len for _ in range(num_seqs)]
+    max_context_len = max(context_lens)
+    context_lens = torch.tensor(context_lens, dtype=torch.int, device=device)
 
     # Create the block tables.
-    max_num_blocks_per_seq = (max_seq_len + block_size - 1) // block_size
+    max_num_blocks_per_seq = (max_context_len + block_size - 1) // block_size
     block_tables = []
     for _ in range(num_seqs):
         block_table = [
@@ -77,7 +77,8 @@ def main(
     # Prepare for the paged attention kernel.
     output = torch.empty_like(query)
     if version == "v2":
-        num_partitions = ((max_seq_len + PARTITION_SIZE - 1) // PARTITION_SIZE)
+        num_partitions = ((max_context_len + PARTITION_SIZE - 1) //
+                          PARTITION_SIZE)
         tmp_output = torch.empty(
             size=(num_seqs, num_query_heads, num_partitions, head_size),
             dtype=output.dtype,
@@ -109,9 +110,9 @@ def main(
                     num_kv_heads,
                     scale,
                     block_tables,
-                    seq_lens,
+                    context_lens,
                     block_size,
-                    max_seq_len,
+                    max_context_len,
                     alibi_slopes,
                     kv_cache_dtype,
                     kv_scale,
@@ -128,9 +129,9 @@ def main(
                     num_kv_heads,
                     scale,
                     block_tables,
-                    seq_lens,
+                    context_lens,
                     block_size,
-                    max_seq_len,
+                    max_context_len,
                     alibi_slopes,
                     kv_cache_dtype,
                     kv_scale,
@@ -165,12 +166,12 @@ if __name__ == '__main__':
                         choices=["v1", "v2"],
                         default="v2")
     parser.add_argument("--batch-size", type=int, default=8)
-    parser.add_argument("--seq_len", type=int, default=4096)
+    parser.add_argument("--context-len", type=int, default=4096)
     parser.add_argument("--num-query-heads", type=int, default=64)
     parser.add_argument("--num-kv-heads", type=int, default=8)
     parser.add_argument("--head-size",
                         type=int,
-                        choices=[64, 80, 96, 112, 128, 192, 256],
+                        choices=[64, 80, 96, 112, 128, 256],
                         default=128)
     parser.add_argument("--block-size", type=int, choices=[16, 32], default=16)
     parser.add_argument("--use-alibi", action="store_true")
@@ -183,11 +184,13 @@ if __name__ == '__main__':
     parser.add_argument(
         "--kv-cache-dtype",
         type=str,
-        choices=["auto", "fp8", "fp8_e5m2", "fp8_e4m3"],
+        choices=["auto", "fp8"],
         default="auto",
-        help="Data type for kv cache storage. If 'auto', will use model "
-        "data type. CUDA 11.8+ supports fp8 (=fp8_e4m3) and fp8_e5m2. "
-        "ROCm (AMD GPU) supports fp8 (=fp8_e4m3)")
+        help=
+        'Data type for kv cache storage. If "auto", will use model data type. '
+        'FP8_E5M2 (without scaling) is only supported on cuda version greater '
+        'than 11.8. On ROCm (AMD GPU), FP8_E4M3 is instead supported for '
+        'common inference criteria.')
     args = parser.parse_args()
     print(args)
 
@@ -196,7 +199,7 @@ if __name__ == '__main__':
     main(
         version=args.version,
         num_seqs=args.batch_size,
-        seq_len=args.seq_len,
+        context_len=args.context_len,
         num_query_heads=args.num_query_heads,
         num_kv_heads=args.num_kv_heads,
         head_size=args.head_size,

benchmarks/kernels/benchmark_rope.py

@@ -93,7 +93,7 @@ if __name__ == '__main__':
     parser.add_argument("--num-heads", type=int, default=8)
     parser.add_argument("--head-size",
                         type=int,
-                        choices=[64, 80, 96, 112, 128, 192, 256],
+                        choices=[64, 80, 96, 112, 128, 256],
                         default=128)
     parser.add_argument("--rotary-dim", type=int, choices=[16, 32], default=32)
     parser.add_argument("--dtype",

benchmarks/kernels/benchmark_shapes.py

@@ -1,75 +0,0 @@
-WEIGHT_SHAPES = {
-    "ideal": [[4 * 256 * 32, 256 * 32]],
-    "mistralai/Mistral-7B-v0.1/TP1": [
-        [4096, 6144],
-        [4096, 4096],
-        [4096, 28672],
-        [14336, 4096],
-    ],
-    "mistralai/Mistral-7B-v0.1/TP2": [
-        [4096, 3072],
-        [2048, 4096],
-        [4096, 14336],
-        [7168, 4096],
-    ],
-    "mistralai/Mistral-7B-v0.1/TP4": [
-        [4096, 1536],
-        [1024, 4096],
-        [4096, 7168],
-        [3584, 4096],
-    ],
-    "meta-llama/Llama-2-7b-hf/TP1": [
-        [4096, 12288],
-        [4096, 4096],
-        [4096, 22016],
-        [11008, 4096],
-    ],
-    "meta-llama/Llama-2-7b-hf/TP2": [
-        [4096, 6144],
-        [2048, 4096],
-        [4096, 11008],
-        [5504, 4096],
-    ],
-    "meta-llama/Llama-2-7b-hf/TP4": [
-        [4096, 3072],
-        [1024, 4096],
-        [4096, 5504],
-        [2752, 4096],
-    ],
-    "meta-llama/Llama-2-13b-hf/TP1": [
-        [5120, 15360],
-        [5120, 5120],
-        [5120, 27648],
-        [13824, 5120],
-    ],
-    "meta-llama/Llama-2-13b-hf/TP2": [
-        [5120, 7680],
-        [2560, 5120],
-        [5120, 13824],
-        [6912, 5120],
-    ],
-    "meta-llama/Llama-2-13b-hf/TP4": [
-        [5120, 3840],
-        [1280, 5120],
-        [5120, 6912],
-        [3456, 5120],
-    ],
-    "meta-llama/Llama-2-70b-hf/TP1": [
-        [8192, 10240],
-        [8192, 8192],
-        [8192, 57344],
-        [28672, 8192],
-    ],
-    "meta-llama/Llama-2-70b-hf/TP2": [
-        [8192, 5120],
-        [4096, 8192],
-        [8192, 28672],
-        [14336, 8192],
-    ],
-    "meta-llama/Llama-2-70b-hf/TP4": [
-        [8192, 2560],
-        [2048, 8192],
-        [8192, 14336],
-        [7168, 8192],
-    ],
-}

benchmarks/launch_tgi_server.sh

@@ -4,7 +4,7 @@ PORT=8000
 MODEL=$1
 TOKENS=$2
 
-docker run -e HF_TOKEN=$HF_TOKEN --gpus all --shm-size 1g -p $PORT:80 \
+docker run --gpus all --shm-size 1g -p $PORT:80 \
            -v $PWD/data:/data \
            ghcr.io/huggingface/text-generation-inference:1.4.0 \
            --model-id $MODEL \

benchmarks/overheads/benchmark_hashing.py

@@ -1,63 +0,0 @@
-import argparse
-import cProfile
-import pstats
-
-from vllm import LLM, SamplingParams
-
-# A very long prompt, total number of tokens is about 15k.
-LONG_PROMPT = ["You are an expert in large language models, aren't you?"
-               ] * 1000
-LONG_PROMPT = ' '.join(LONG_PROMPT)
-
-
-def main(args):
-    llm = LLM(
-        model=args.model,
-        enforce_eager=True,
-        enable_prefix_caching=True,
-        tensor_parallel_size=args.tensor_parallel_size,
-        use_v2_block_manager=args.use_v2_block_manager,
-    )
-
-    sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
-    profiler = cProfile.Profile()
-
-    print("------warm up------")
-    for i in range(3):
-        output = llm.generate(LONG_PROMPT, sampling_params)
-        print(output[0].outputs[0].text)
-
-    print("------start generating------")
-    for i in range(3):
-        profiler.runctx('llm.generate(LONG_PROMPT, sampling_params)',
-                        globals(), locals())
-
-    # analyze the runtime of hashing function
-    stats = pstats.Stats(profiler)
-    stats.sort_stats('cumulative')
-    total_time = 0
-    total_calls = 0
-    for func in stats.stats:
-        if 'hash_of_block' in func[2]:
-            total_time = stats.stats[func][3]
-            total_calls = stats.stats[func][0]
-    percentage = (total_time / stats.total_tt) * 100
-    print(f"Hashing took {total_time:.2f} seconds,"
-          f"{percentage:.2f}% of the total runtime.")
-
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(
-        description='Benchmark the performance of hashing function in'
-        'automatic prefix caching.')
-    parser.add_argument('--model', type=str, default='lmsys/longchat-7b-16k')
-    parser.add_argument('--tensor-parallel-size', '-tp', type=int, default=1)
-    parser.add_argument('--output-len', type=int, default=10)
-    parser.add_argument('--enable-prefix-caching',
-                        action='store_true',
-                        help='enable prefix caching')
-    parser.add_argument('--use-v2-block-manager',
-                        action='store_true',
-                        help='Use BlockSpaceMangerV2')
-    args = parser.parse_args()
-    main(args)

cmake/utils.cmake

@@ -99,7 +99,7 @@ function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG)
       "Failed to determine torch nvcc compiler flags")
 
     if (CUDA_VERSION VERSION_GREATER_EQUAL 11.8)
-      list(APPEND GPU_FLAGS "-DENABLE_FP8")
+      list(APPEND GPU_FLAGS "-DENABLE_FP8_E5M2")
     endif()
     if (CUDA_VERSION VERSION_GREATER_EQUAL 12.0)
       list(REMOVE_ITEM GPU_FLAGS
@@ -119,7 +119,7 @@ function (get_torch_gpu_compiler_flags OUT_GPU_FLAGS GPU_LANG)
 
     list(APPEND GPU_FLAGS
       "-DUSE_ROCM"
-      "-DENABLE_FP8"
+      "-DENABLE_FP8_E4M3"
       "-U__HIP_NO_HALF_CONVERSIONS__"
       "-U__HIP_NO_HALF_OPERATORS__"
       "-fno-gpu-rdc")

collect_env.py

@@ -63,7 +63,6 @@ DEFAULT_CONDA_PATTERNS = {
     "magma",
     "triton",
     "optree",
-    "nccl",
 }
 
 DEFAULT_PIP_PATTERNS = {
@@ -74,7 +73,6 @@ DEFAULT_PIP_PATTERNS = {
     "triton",
     "optree",
     "onnx",
-    "nccl",
 }
 
 

csrc/activation_kernels.cu

@@ -10,11 +10,11 @@
 namespace vllm {
 
 // Activation and gating kernel template.
-template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
 __global__ void act_and_mul_kernel(
-    scalar_t* __restrict__ out,          // [..., d]
-    const scalar_t* __restrict__ input,  // [..., 2, d]
-    const int d) {
+  scalar_t* __restrict__ out,               // [..., d]
+  const scalar_t* __restrict__ input,       // [..., 2, d]
+  const int d) {
   const int64_t token_idx = blockIdx.x;
   for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
     const scalar_t x = VLLM_LDG(&input[token_idx * 2 * d + idx]);
@@ -23,66 +23,72 @@ __global__ void act_and_mul_kernel(
   }
 }
 
-template <typename T>
+template<typename T>
 __device__ __forceinline__ T silu_kernel(const T& x) {
   // x * sigmoid(x)
-  return (T)(((float)x) / (1.0f + expf((float)-x)));
+  return (T) (((float) x) / (1.0f + expf((float) -x)));
 }
 
-template <typename T>
+template<typename T>
 __device__ __forceinline__ T gelu_kernel(const T& x) {
   // Equivalent to PyTorch GELU with 'none' approximation.
   // Refer to:
   // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L36-L38
-  const float f = (float)x;
+  const float f = (float) x;
   constexpr float ALPHA = M_SQRT1_2;
-  return (T)(f * 0.5f * (1.0f + ::erf(f * ALPHA)));
+  return (T) (f * 0.5f * (1.0f + ::erf(f * ALPHA)));
 }
 
-template <typename T>
+template<typename T>
 __device__ __forceinline__ T gelu_tanh_kernel(const T& x) {
   // Equivalent to PyTorch GELU with 'tanh' approximation.
   // Refer to:
   // https://github.com/pytorch/pytorch/blob/8ac9b20d4b090c213799e81acf48a55ea8d437d6/aten/src/ATen/native/cuda/ActivationGeluKernel.cu#L25-L30
-  const float f = (float)x;
+  const float f = (float) x;
   constexpr float BETA = M_SQRT2 * M_2_SQRTPI * 0.5f;
   constexpr float KAPPA = 0.044715;
   float x_cube = f * f * f;
   float inner = BETA * (f + KAPPA * x_cube);
-  return (T)(0.5f * f * (1.0f + ::tanhf(inner)));
+  return (T) (0.5f * f * (1.0f + ::tanhf(inner)));
 }
 
-}  // namespace vllm
+} // namespace vllm
 
 // Launch activation and gating kernel.
-#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                            \
-  int d = input.size(-1) / 2;                                            \
-  int64_t num_tokens = input.numel() / input.size(-1);                   \
-  dim3 grid(num_tokens);                                                 \
-  dim3 block(std::min(d, 1024));                                         \
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));      \
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();          \
-  VLLM_DISPATCH_FLOATING_TYPES(                                          \
-      input.scalar_type(), "act_and_mul_kernel", [&] {                   \
-        vllm::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>>             \
-            <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),       \
-                                         input.data_ptr<scalar_t>(), d); \
-      });
+#define LAUNCH_ACTIVATION_GATE_KERNEL(KERNEL)                                             \
+  int d = input.size(-1) / 2;                                                             \
+  int64_t num_tokens = input.numel() / input.size(-1);                                    \
+  dim3 grid(num_tokens);                                                                  \
+  dim3 block(std::min(d, 1024));                                                          \
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));                       \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                           \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                           \
+    input.scalar_type(),                                                                  \
+    "act_and_mul_kernel",                                                                 \
+    [&] {                                                                                 \
+      vllm::act_and_mul_kernel<scalar_t, KERNEL<scalar_t>><<<grid, block, 0, stream>>>(   \
+        out.data_ptr<scalar_t>(),                                                         \
+        input.data_ptr<scalar_t>(),                                                       \
+        d);                                                                               \
+    });
 
-void silu_and_mul(torch::Tensor& out,    // [..., d]
-                  torch::Tensor& input)  // [..., 2 * d]
+void silu_and_mul(
+  torch::Tensor& out,      // [..., d]
+  torch::Tensor& input)    // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::silu_kernel);
 }
 
-void gelu_and_mul(torch::Tensor& out,    // [..., d]
-                  torch::Tensor& input)  // [..., 2 * d]
+void gelu_and_mul(
+  torch::Tensor& out,      // [..., d]
+  torch::Tensor& input)    // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_kernel);
 }
 
-void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
-                       torch::Tensor& input)  // [..., 2 * d]
+void gelu_tanh_and_mul(
+  torch::Tensor& out,      // [..., d]
+  torch::Tensor& input)    // [..., 2 * d]
 {
   LAUNCH_ACTIVATION_GATE_KERNEL(vllm::gelu_tanh_kernel);
 }
@@ -90,11 +96,11 @@ void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
 namespace vllm {
 
 // Element-wise activation kernel template.
-template <typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
+template<typename scalar_t, scalar_t (*ACT_FN)(const scalar_t&)>
 __global__ void activation_kernel(
-    scalar_t* __restrict__ out,          // [..., d]
-    const scalar_t* __restrict__ input,  // [..., d]
-    const int d) {
+  scalar_t* __restrict__ out,               // [..., d]
+  const scalar_t* __restrict__ input,       // [..., d]
+  const int d) {
   const int64_t token_idx = blockIdx.x;
   for (int64_t idx = threadIdx.x; idx < d; idx += blockDim.x) {
     const scalar_t x = VLLM_LDG(&input[token_idx * d + idx]);
@@ -102,49 +108,54 @@ __global__ void activation_kernel(
   }
 }
 
-}  // namespace vllm
+} // namespace vllm
 
 // Launch element-wise activation kernel.
-#define LAUNCH_ACTIVATION_KERNEL(KERNEL)                                       \
-  int d = input.size(-1);                                                      \
-  int64_t num_tokens = input.numel() / d;                                      \
-  dim3 grid(num_tokens);                                                       \
-  dim3 block(std::min(d, 1024));                                               \
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));            \
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                \
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "activation_kernel", [&] { \
-    vllm::activation_kernel<scalar_t, KERNEL<scalar_t>>                        \
-        <<<grid, block, 0, stream>>>(out.data_ptr<scalar_t>(),                 \
-                                     input.data_ptr<scalar_t>(), d);           \
-  });
+#define LAUNCH_ACTIVATION_KERNEL(KERNEL)                                                  \
+  int d = input.size(-1);                                                                 \
+  int64_t num_tokens = input.numel() / d;                                                 \
+  dim3 grid(num_tokens);                                                                  \
+  dim3 block(std::min(d, 1024));                                                          \
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));                       \
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();                           \
+  VLLM_DISPATCH_FLOATING_TYPES(                                                           \
+    input.scalar_type(),                                                                  \
+    "activation_kernel",                                                                  \
+    [&] {                                                                                 \
+      vllm::activation_kernel<scalar_t, KERNEL<scalar_t>><<<grid, block, 0, stream>>>(    \
+        out.data_ptr<scalar_t>(),                                                         \
+        input.data_ptr<scalar_t>(),                                                       \
+        d);                                                                               \
+    });
 
 namespace vllm {
 
-template <typename T>
+template<typename T>
 __device__ __forceinline__ T gelu_new_kernel(const T& x) {
-  const float x3 = (float)(x * x * x);
-  const T t = (T)tanhf((T)(0.79788456f * (float)(x + (T)(0.044715f * x3))));
-  return ((T)0.5) * x * (((T)1.0) + t);
+  const float x3 = (float) (x * x * x);
+  const T t = (T) tanhf((T) (0.79788456f * (float) (x + (T) (0.044715f * x3))));
+  return ((T) 0.5) * x * (((T) 1.0) + t);
 }
 
-template <typename T>
+template<typename T>
 __device__ __forceinline__ T gelu_fast_kernel(const T& x) {
-  const float f = (float)x;
-  const T t =
-      (T)tanhf(((T)(f * 0.79788456f)) * (((T)1.0) + (T)(0.044715f * f) * x));
-  return ((T)0.5) * x * (((T)1.0) + t);
+  const float f = (float) x;
+  const T t = (T) tanhf(((T) (f * 0.79788456f)) * (((T) 1.0) + (T) (0.044715f * f) * x));
+  return ((T) 0.5) * x * (((T) 1.0) + t);
 }
 
-}  // namespace vllm
+} // namespace vllm
 
-void gelu_new(torch::Tensor& out,    // [..., d]
-              torch::Tensor& input)  // [..., d]
+void gelu_new(
+  torch::Tensor& out,     // [..., d]
+  torch::Tensor& input)   // [..., d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_new_kernel);
 }
 
-void gelu_fast(torch::Tensor& out,    // [..., d]
-               torch::Tensor& input)  // [..., d]
+void gelu_fast(
+  torch::Tensor& out,     // [..., d]
+  torch::Tensor& input)   // [..., d]
 {
   LAUNCH_ACTIVATION_KERNEL(vllm::gelu_fast_kernel);
 }

csrc/attention/attention_generic.cuh

@@ -1,6 +1,5 @@
 /*
- * Adapted from
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
+ * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
  * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
@@ -23,31 +22,31 @@
 namespace vllm {
 
 // A vector type to store Q, K, V elements.
-template <typename T, int VEC_SIZE>
+template<typename T, int VEC_SIZE>
 struct Vec {};
 
 // A vector type to store FP32 accumulators.
-template <typename T>
+template<typename T>
 struct FloatVec {};
 
 // Template vector operations.
-template <typename Acc, typename A, typename B>
+template<typename Acc, typename A, typename B>
 inline __device__ Acc mul(A a, B b);
 
-template <typename T>
+template<typename T>
 inline __device__ float sum(T v);
 
-template <typename T>
+template<typename T>
 inline __device__ float dot(T a, T b) {
   return sum(mul<T, T, T>(a, b));
 }
 
-template <typename A, typename T>
+template<typename A, typename T>
 inline __device__ float dot(T a, T b) {
   return sum(mul<A, T, T>(a, b));
 }
 
-template <typename T>
+template<typename T>
 inline __device__ void zero(T& dst) {
   constexpr int WORDS = sizeof(T) / 4;
   union {
@@ -62,4 +61,4 @@ inline __device__ void zero(T& dst) {
   dst = tmp.raw;
 }
 
-}  // namespace vllm
+} // namespace vllm

csrc/attention/attention_utils.cuh

@@ -1,6 +1,5 @@
 /*
- * Adapted from
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
+ * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
  * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
@@ -27,7 +26,7 @@
 namespace vllm {
 
 // Q*K^T operation.
-template <int THREAD_GROUP_SIZE, typename Vec, int N>
+template<int THREAD_GROUP_SIZE, typename Vec, int N>
 inline __device__ float qk_dot_(const Vec (&q)[N], const Vec (&k)[N]) {
   using A_vec = typename FloatVec<Vec>::Type;
   // Compute the parallel products for Q*K^T (treat vector lanes separately).
@@ -46,12 +45,12 @@ inline __device__ float qk_dot_(const Vec (&q)[N], const Vec (&k)[N]) {
   return qk;
 }
 
-template <typename T, int THREAD_GROUP_SIZE>
+template<typename T, int THREAD_GROUP_SIZE>
 struct Qk_dot {
-  template <typename Vec, int N>
+  template<typename Vec, int N>
   static inline __device__ float dot(const Vec (&q)[N], const Vec (&k)[N]) {
     return qk_dot_<THREAD_GROUP_SIZE>(q, k);
   }
 };
 
-}  // namespace vllm
+} // namespace vllm

csrc/attention/dtype_bfloat16.cuh

@@ -1,8 +1,6 @@
 /*
- * Adapted from
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
- * and
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
+ * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
+ * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
  * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
@@ -30,8 +28,8 @@
   #include <hip/hip_bf16.h>
   #include <hip/hip_fp16.h>
 
-typedef __hip_bfloat162 __nv_bfloat162;
-typedef __hip_bfloat16 __nv_bfloat16;
+  typedef __hip_bfloat162 __nv_bfloat162;
+  typedef __hip_bfloat16 __nv_bfloat16;
 #endif
 
 #include <stdint.h>
@@ -52,37 +50,37 @@ struct bf16_8_t {
 };
 
 // BF16 vector types for Q, K, V.
-template <>
+template<>
 struct Vec<__nv_bfloat16, 1> {
   using Type = __nv_bfloat16;
 };
-template <>
+template<>
 struct Vec<__nv_bfloat16, 2> {
   using Type = __nv_bfloat162;
 };
-template <>
+template<>
 struct Vec<__nv_bfloat16, 4> {
   using Type = bf16_4_t;
 };
-template <>
+template<>
 struct Vec<__nv_bfloat16, 8> {
   using Type = bf16_8_t;
 };
 
 // FP32 accumulator vector types corresponding to Vec.
-template <>
+template<>
 struct FloatVec<__nv_bfloat16> {
   using Type = float;
 };
-template <>
+template<>
 struct FloatVec<__nv_bfloat162> {
   using Type = float2;
 };
-template <>
+template<>
 struct FloatVec<bf16_4_t> {
   using Type = Float4_;
 };
-template <>
+template<>
 struct FloatVec<bf16_8_t> {
   using Type = Float8_;
 };
@@ -110,9 +108,9 @@ inline __device__ __nv_bfloat16 add(__nv_bfloat16 a, __nv_bfloat16 b) {
   assert(false);
 #else
   #ifndef USE_ROCM
-  return a + b;
+    return a + b;
   #else
-  return __hadd(a, b);
+    return __hadd(a, b);
   #endif
 #endif
 }
@@ -163,7 +161,7 @@ inline __device__ Float8_ add(bf16_8_t a, Float8_ fb) {
 }
 
 // Vector multiplication.
-template <>
+template<>
 inline __device__ __nv_bfloat16 mul(__nv_bfloat16 a, __nv_bfloat16 b) {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
   assert(false);
@@ -172,7 +170,7 @@ inline __device__ __nv_bfloat16 mul(__nv_bfloat16 a, __nv_bfloat16 b) {
 #endif
 }
 
-template <>
+template<>
 inline __device__ __nv_bfloat162 mul(__nv_bfloat162 a, __nv_bfloat162 b) {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
   assert(false);
@@ -181,12 +179,12 @@ inline __device__ __nv_bfloat162 mul(__nv_bfloat162 a, __nv_bfloat162 b) {
 #endif
 }
 
-template <>
+template<>
 inline __device__ __nv_bfloat162 mul(__nv_bfloat16 a, __nv_bfloat162 b) {
   return mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(bf162bf162(a), b);
 }
 
-template <>
+template<>
 inline __device__ bf16_4_t mul(bf16_4_t a, bf16_4_t b) {
   bf16_4_t c;
   c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
@@ -194,7 +192,7 @@ inline __device__ bf16_4_t mul(bf16_4_t a, bf16_4_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ bf16_4_t mul(__nv_bfloat16 a, bf16_4_t b) {
   __nv_bfloat162 s = bf162bf162(a);
   bf16_4_t c;
@@ -203,7 +201,7 @@ inline __device__ bf16_4_t mul(__nv_bfloat16 a, bf16_4_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ bf16_8_t mul(bf16_8_t a, bf16_8_t b) {
   bf16_8_t c;
   c.x = mul<__nv_bfloat162, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
@@ -213,7 +211,7 @@ inline __device__ bf16_8_t mul(bf16_8_t a, bf16_8_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ bf16_8_t mul(__nv_bfloat16 a, bf16_8_t b) {
   __nv_bfloat162 s = bf162bf162(a);
   bf16_8_t c;
@@ -224,26 +222,26 @@ inline __device__ bf16_8_t mul(__nv_bfloat16 a, bf16_8_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ float mul(__nv_bfloat16 a, __nv_bfloat16 b) {
   float fa = __bfloat162float(a);
   float fb = __bfloat162float(b);
   return fa * fb;
 }
 
-template <>
+template<>
 inline __device__ float2 mul(__nv_bfloat162 a, __nv_bfloat162 b) {
   float2 fa = bf1622float2(a);
   float2 fb = bf1622float2(b);
   return mul<float2, float2, float2>(fa, fb);
 }
 
-template <>
+template<>
 inline __device__ float2 mul(__nv_bfloat16 a, __nv_bfloat162 b) {
   return mul<float2, __nv_bfloat162, __nv_bfloat162>(bf162bf162(a), b);
 }
 
-template <>
+template<>
 inline __device__ Float4_ mul(bf16_4_t a, bf16_4_t b) {
   Float4_ fc;
   fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
@@ -251,7 +249,7 @@ inline __device__ Float4_ mul(bf16_4_t a, bf16_4_t b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float4_ mul(__nv_bfloat16 a, bf16_4_t b) {
   __nv_bfloat162 s = bf162bf162(a);
   Float4_ fc;
@@ -260,7 +258,7 @@ inline __device__ Float4_ mul(__nv_bfloat16 a, bf16_4_t b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float8_ mul(bf16_8_t a, bf16_8_t b) {
   Float8_ fc;
   fc.x = mul<float2, __nv_bfloat162, __nv_bfloat162>(a.x, b.x);
@@ -270,7 +268,7 @@ inline __device__ Float8_ mul(bf16_8_t a, bf16_8_t b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float8_ mul(__nv_bfloat16 a, bf16_8_t b) {
   __nv_bfloat162 s = bf162bf162(a);
   Float8_ fc;
@@ -282,8 +280,7 @@ inline __device__ Float8_ mul(__nv_bfloat16 a, bf16_8_t b) {
 }
 
 // Vector fused multiply-add.
-inline __device__ __nv_bfloat162 fma(__nv_bfloat162 a, __nv_bfloat162 b,
-                                     __nv_bfloat162 c) {
+inline __device__ __nv_bfloat162 fma(__nv_bfloat162 a, __nv_bfloat162 b, __nv_bfloat162 c) {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
   assert(false);
 #else
@@ -291,8 +288,7 @@ inline __device__ __nv_bfloat162 fma(__nv_bfloat162 a, __nv_bfloat162 b,
 #endif
 }
 
-inline __device__ __nv_bfloat162 fma(__nv_bfloat16 a, __nv_bfloat162 b,
-                                     __nv_bfloat162 c) {
+inline __device__ __nv_bfloat162 fma(__nv_bfloat16 a, __nv_bfloat162 b, __nv_bfloat162 c) {
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 800
   assert(false);
 #else
@@ -383,23 +379,23 @@ inline __device__ Float8_ fma(__nv_bfloat16 a, bf16_8_t b, Float8_ fc) {
 }
 
 // Vector sum.
-template <>
+template<>
 inline __device__ float sum(__nv_bfloat16 v) {
   return __bfloat162float(v);
 }
 
-template <>
+template<>
 inline __device__ float sum(__nv_bfloat162 v) {
   float2 vf = bf1622float2(v);
   return vf.x + vf.y;
 }
 
-template <>
+template<>
 inline __device__ float sum(bf16_4_t v) {
   return sum(v.x) + sum(v.y);
 }
 
-template <>
+template<>
 inline __device__ float sum(bf16_8_t v) {
   return sum(v.x) + sum(v.y) + sum(v.z) + sum(v.w);
 }
@@ -452,4 +448,4 @@ inline __device__ void zero(__nv_bfloat16& dst) {
 #endif
 }
 
-}  // namespace vllm
+} // namespace vllm

csrc/attention/dtype_float16.cuh

@@ -1,8 +1,6 @@
 /*
- * Adapted from
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
- * and
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
+ * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
+ * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
  * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
@@ -32,37 +30,37 @@
 namespace vllm {
 
 // FP16 vector types for Q, K, V.
-template <>
+template<>
 struct Vec<uint16_t, 1> {
   using Type = uint16_t;
 };
-template <>
+template<>
 struct Vec<uint16_t, 2> {
   using Type = uint32_t;
 };
-template <>
+template<>
 struct Vec<uint16_t, 4> {
   using Type = uint2;
 };
-template <>
+template<>
 struct Vec<uint16_t, 8> {
   using Type = uint4;
 };
 
 // FP32 accumulator vector types corresponding to Vec.
-template <>
+template<>
 struct FloatVec<uint16_t> {
   using Type = float;
 };
-template <>
+template<>
 struct FloatVec<uint32_t> {
   using Type = float2;
 };
-template <>
+template<>
 struct FloatVec<uint2> {
   using Type = Float4_;
 };
-template <>
+template<>
 struct FloatVec<uint4> {
   using Type = Float8_;
 };
@@ -75,8 +73,8 @@ inline __device__ uint32_t h0_h0(uint16_t a) {
   return b;
 #else
   union {
-    uint32_t u32;
-    uint16_t u16[2];
+   uint32_t u32;
+   uint16_t u16[2];
   } tmp;
   tmp.u16[0] = a;
   tmp.u16[1] = a;
@@ -132,12 +130,10 @@ inline __device__ uint32_t float2_to_half2(float2 f) {
   } tmp;
 #ifndef USE_ROCM
   #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
-  asm volatile("cvt.rn.f16x2.f32 %0, %1, %2;\n"
-               : "=r"(tmp.u32)
-               : "f"(f.y), "f"(f.x));
+    asm volatile("cvt.rn.f16x2.f32 %0, %1, %2;\n" : "=r"(tmp.u32) : "f"(f.y), "f"(f.x));
   #else
-  asm volatile("cvt.rn.f16.f32 %0, %1;\n" : "=h"(tmp.u16[0]) : "f"(f.x));
-  asm volatile("cvt.rn.f16.f32 %0, %1;\n" : "=h"(tmp.u16[1]) : "f"(f.y));
+    asm volatile("cvt.rn.f16.f32 %0, %1;\n" : "=h"(tmp.u16[0]) : "f"(f.x));
+    asm volatile("cvt.rn.f16.f32 %0, %1;\n" : "=h"(tmp.u16[1]) : "f"(f.y));
   #endif
 #else
   tmp.u16[0] = float_to_half(f.x);
@@ -205,7 +201,7 @@ inline __device__ Float8_ add(uint4 a, Float8_ fb) {
 }
 
 // Vector multiplication.
-template <>
+template<>
 inline __device__ uint16_t mul(uint16_t a, uint16_t b) {
   uint16_t c;
 #ifndef USE_ROCM
@@ -216,7 +212,7 @@ inline __device__ uint16_t mul(uint16_t a, uint16_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ uint32_t mul(uint32_t a, uint32_t b) {
   uint32_t c;
 #ifndef USE_ROCM
@@ -227,12 +223,12 @@ inline __device__ uint32_t mul(uint32_t a, uint32_t b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ uint32_t mul(uint16_t a, uint32_t b) {
   return mul<uint32_t, uint32_t, uint32_t>(h0_h0(a), b);
 }
 
-template <>
+template<>
 inline __device__ uint2 mul(uint2 a, uint2 b) {
   uint2 c;
   c.x = mul<uint32_t, uint32_t, uint32_t>(a.x, b.x);
@@ -240,7 +236,7 @@ inline __device__ uint2 mul(uint2 a, uint2 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ uint2 mul(uint16_t a, uint2 b) {
   uint32_t s = h0_h0(a);
   uint2 c;
@@ -249,7 +245,7 @@ inline __device__ uint2 mul(uint16_t a, uint2 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ uint4 mul(uint4 a, uint4 b) {
   uint4 c;
   c.x = mul<uint32_t, uint32_t, uint32_t>(a.x, b.x);
@@ -259,7 +255,7 @@ inline __device__ uint4 mul(uint4 a, uint4 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ uint4 mul(uint16_t a, uint4 b) {
   uint32_t s = h0_h0(a);
   uint4 c;
@@ -270,26 +266,26 @@ inline __device__ uint4 mul(uint16_t a, uint4 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ float mul(uint16_t a, uint16_t b) {
   float fa = half_to_float(a);
   float fb = half_to_float(b);
   return fa * fb;
 }
 
-template <>
+template<>
 inline __device__ float2 mul(uint32_t a, uint32_t b) {
   float2 fa = half2_to_float2(a);
   float2 fb = half2_to_float2(b);
   return mul<float2, float2, float2>(fa, fb);
 }
 
-template <>
+template<>
 inline __device__ float2 mul(uint16_t a, uint32_t b) {
   return mul<float2, uint32_t, uint32_t>(h0_h0(a), b);
 }
 
-template <>
+template<>
 inline __device__ Float4_ mul(uint2 a, uint2 b) {
   Float4_ fc;
   fc.x = mul<float2, uint32_t, uint32_t>(a.x, b.x);
@@ -297,7 +293,7 @@ inline __device__ Float4_ mul(uint2 a, uint2 b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float4_ mul(uint16_t a, uint2 b) {
   uint32_t s = h0_h0(a);
   Float4_ fc;
@@ -306,7 +302,7 @@ inline __device__ Float4_ mul(uint16_t a, uint2 b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float8_ mul(uint4 a, uint4 b) {
   Float8_ fc;
   fc.x = mul<float2, uint32_t, uint32_t>(a.x, b.x);
@@ -316,7 +312,7 @@ inline __device__ Float8_ mul(uint4 a, uint4 b) {
   return fc;
 }
 
-template <>
+template<>
 inline __device__ Float8_ mul(uint16_t a, uint4 b) {
   uint32_t s = h0_h0(a);
   Float8_ fc;
@@ -331,13 +327,9 @@ inline __device__ Float8_ mul(uint16_t a, uint4 b) {
 inline __device__ uint32_t fma(uint32_t a, uint32_t b, uint32_t c) {
   uint32_t d;
 #ifndef USE_ROCM
-  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
-               : "=r"(d)
-               : "r"(a), "r"(b), "r"(c));
+  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(d) : "r"(a), "r"(b), "r"(c));
 #else
-  asm volatile("v_pk_fma_f16 %0, %1, %2, %3;\n"
-               : "=v"(d)
-               : "v"(a), "v"(b), "v"(c));
+  asm volatile("v_pk_fma_f16 %0, %1, %2, %3;\n" : "=v"(d) : "v"(a), "v"(b), "v"(c));
 #endif
   return d;
 }
@@ -431,24 +423,24 @@ inline __device__ Float8_ fma(uint16_t a, uint4 b, Float8_ fc) {
 }
 
 // Vector sum.
-template <>
+template<>
 inline __device__ float sum(uint16_t v) {
   return half_to_float(v);
 }
 
-template <>
+template<>
 inline __device__ float sum(uint32_t v) {
   float2 tmp = half2_to_float2(v);
   return tmp.x + tmp.y;
 }
 
-template <>
+template<>
 inline __device__ float sum(uint2 v) {
   uint32_t c = add(v.x, v.y);
   return sum(c);
 }
 
-template <>
+template<>
 inline __device__ float sum(uint4 v) {
   uint32_t c = add(v.x, v.y);
   c = add(c, v.z);
@@ -478,9 +470,13 @@ inline __device__ void from_float(uint4& dst, Float8_ src) {
 }
 
 // From float16 to float32.
-inline __device__ float to_float(uint16_t u) { return half_to_float(u); }
+inline __device__ float to_float(uint16_t u) {
+  return half_to_float(u);
+}
 
-inline __device__ float2 to_float(uint32_t u) { return half2_to_float2(u); }
+inline __device__ float2 to_float(uint32_t u) {
+  return half2_to_float2(u);
+}
 
 inline __device__ Float4_ to_float(uint2 u) {
   Float4_ tmp;
@@ -499,6 +495,8 @@ inline __device__ Float8_ to_float(uint4 u) {
 }
 
 // Zero-out a variable.
-inline __device__ void zero(uint16_t& dst) { dst = uint16_t(0); }
+inline __device__ void zero(uint16_t& dst) {
+  dst = uint16_t(0);
+}
 
-}  // namespace vllm
+} // namespace vllm

csrc/attention/dtype_float32.cuh

@@ -1,8 +1,6 @@
 /*
- * Adapted from
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
- * and
- * https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
+ * Adapted from https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention/decoder_masked_multihead_attention_template.hpp
+ * and https://github.com/NVIDIA/FasterTransformer/blob/release/v5.3_tag/src/fastertransformer/kernels/decoder_masked_multihead_attention_utils.h
  * Copyright (c) 2023, The vLLM team.
  * Copyright (c) 2020-2023, NVIDIA CORPORATION.  All rights reserved.
  *
@@ -40,35 +38,37 @@ struct Float8_ {
 };
 
 // FP32 vector types for Q, K, V.
-template <>
+template<>
 struct Vec<float, 1> {
   using Type = float;
 };
-template <>
+template<>
 struct Vec<float, 2> {
   using Type = float2;
 };
-template <>
+template<>
 struct Vec<float, 4> {
   using Type = float4;
 };
 
 // FP32 accumulator vector types corresponding to Vec.
-template <>
+template<>
 struct FloatVec<float> {
   using Type = float;
 };
-template <>
+template<>
 struct FloatVec<float2> {
   using Type = float2;
 };
-template <>
+template<>
 struct FloatVec<float4> {
   using Type = float4;
 };
 
 // Vector addition.
-inline __device__ float add(float a, float b) { return a + b; }
+inline __device__ float add(float a, float b) {
+  return a + b;
+}
 
 inline __device__ float2 add(float2 a, float2 b) {
   float2 c;
@@ -87,12 +87,12 @@ inline __device__ float4 add(float4 a, float4 b) {
 }
 
 // Vector multiplication.
-template <>
+template<>
 inline __device__ float mul<float, float>(float a, float b) {
   return a * b;
 }
 
-template <>
+template<>
 inline __device__ float2 mul(float2 a, float2 b) {
   float2 c;
   c.x = a.x * b.x;
@@ -100,7 +100,7 @@ inline __device__ float2 mul(float2 a, float2 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ float2 mul(float a, float2 b) {
   float2 c;
   c.x = a * b.x;
@@ -108,7 +108,7 @@ inline __device__ float2 mul(float a, float2 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ float4 mul(float4 a, float4 b) {
   float4 c;
   c.x = a.x * b.x;
@@ -118,7 +118,7 @@ inline __device__ float4 mul(float4 a, float4 b) {
   return c;
 }
 
-template <>
+template<>
 inline __device__ float4 mul(float a, float4 b) {
   float4 c;
   c.x = a * b.x;
@@ -129,7 +129,9 @@ inline __device__ float4 mul(float a, float4 b) {
 }
 
 // Vector fused multiply-add.
-inline __device__ float fma(float a, float b, float c) { return a * b + c; }
+inline __device__ float fma(float a, float b, float c) {
+  return a * b + c;
+}
 
 inline __device__ float2 fma(float2 a, float2 b, float2 c) {
   float2 d;
@@ -180,33 +182,35 @@ inline __device__ Float8_ fma(float a, Float8_ b, Float8_ c) {
 }
 
 // Vector sum.
-template <>
+template<>
 inline __device__ float sum(float v) {
   return v;
 }
 
-template <>
+template<>
 inline __device__ float sum(float2 v) {
   return v.x + v.y;
 }
 
-template <>
+template<>
 inline __device__ float sum(float4 v) {
   return v.x + v.y + v.z + v.w;
 }
 
-template <>
+template<>
 inline __device__ float sum(Float4_ v) {
   return v.x.x + v.x.y + v.y.x + v.y.y;
 }
 
-template <>
+template<>
 inline __device__ float sum(Float8_ v) {
   return v.x.x + v.x.y + v.y.x + v.y.y + v.z.x + v.z.y + v.w.x + v.w.y;
 }
 
 // Vector dot product.
-inline __device__ float dot(float a, float b) { return a * b; }
+inline __device__ float dot(float a, float b) {
+  return a * b;
+}
 
 inline __device__ float dot(float2 a, float2 b) {
   float2 c = mul<float2, float2, float2>(a, b);
@@ -228,24 +232,42 @@ inline __device__ float dot(Float8_ a, Float8_ b) {
 }
 
 // From float to float.
-inline __device__ void from_float(float& dst, float src) { dst = src; }
+inline __device__ void from_float(float& dst, float src) {
+  dst = src;
+}
 
-inline __device__ void from_float(float2& dst, float2 src) { dst = src; }
+inline __device__ void from_float(float2& dst, float2 src) {
+  dst = src;
+}
 
-inline __device__ void from_float(float4& dst, float4 src) { dst = src; }
+inline __device__ void from_float(float4& dst, float4 src) {
+  dst = src;
+}
 
 // From float to float.
-inline __device__ float to_float(float u) { return u; }
+inline __device__ float to_float(float u) {
+  return u;
+}
 
-inline __device__ float2 to_float(float2 u) { return u; }
+inline __device__ float2 to_float(float2 u) {
+  return u;
+}
 
-inline __device__ float4 to_float(float4 u) { return u; }
+inline __device__ float4 to_float(float4 u) {
+  return u;
+}
 
-inline __device__ Float4_ to_float(Float4_ u) { return u; }
+inline __device__ Float4_ to_float(Float4_ u) {
+  return u;
+}
 
-inline __device__ Float8_ to_float(Float8_ u) { return u; }
+inline __device__ Float8_ to_float(Float8_ u) {
+  return u;
+}
 
 // Zero-out a variable.
-inline __device__ void zero(float& dst) { dst = 0.f; }
+inline __device__ void zero(float& dst) {
+  dst = 0.f;
+}
 
-}  // namespace vllm
+} // namespace vllm

csrc/attention/dtype_fp8.cuh

@@ -3,39 +3,33 @@
 #include "attention_generic.cuh"
 
 #include <stdint.h>
-#ifdef ENABLE_FP8
-  #ifndef USE_ROCM
-    #include <cuda_fp8.h>
-  #endif  // USE_ROCM
-#endif    // ENABLE_FP8
+#ifdef ENABLE_FP8_E5M2
+#include <cuda_fp8.h>
+#endif
 
 namespace vllm {
-
-enum class Fp8KVCacheDataType {
-  kAuto = 0,
-  kFp8E4M3 = 1,
-  kFp8E5M2 = 2,
-};
-
+#if defined(ENABLE_FP8_E5M2) || defined(ENABLE_FP8_E4M3)
 // fp8 vector types for quantization of kv cache
-template <>
+
+template<>
 struct Vec<uint8_t, 1> {
-  using Type = uint8_t;
+    using Type = uint8_t;
 };
 
-template <>
+template<>
 struct Vec<uint8_t, 2> {
-  using Type = uint16_t;
+    using Type = uint16_t;
 };
 
-template <>
+template<>
 struct Vec<uint8_t, 4> {
-  using Type = uint32_t;
+    using Type = uint32_t;
 };
 
-template <>
+template<>
 struct Vec<uint8_t, 8> {
-  using Type = uint2;
+    using Type = uint2;
 };
+#endif // ENABLE_FP8_E5M2
 
-}  // namespace vllm
+} // namespace vllm

csrc/cache.h

@@ -5,24 +5,26 @@
 #include <map>
 #include <vector>
 
-void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
-                 const torch::Tensor& block_mapping);
+void swap_blocks(
+  torch::Tensor& src,
+  torch::Tensor& dst,
+  const std::map<int64_t, int64_t>& block_mapping);
 
-void copy_blocks(std::vector<torch::Tensor>& key_caches,
-                 std::vector<torch::Tensor>& value_caches,
-                 const torch::Tensor& block_mapping);
+void copy_blocks(
+  std::vector<torch::Tensor>& key_caches,
+  std::vector<torch::Tensor>& value_caches,
+  const std::map<int64_t, std::vector<int64_t>>& block_mapping);
 
-void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
-                       torch::Tensor& key_cache, torch::Tensor& value_cache,
-                       torch::Tensor& slot_mapping,
-                       const std::string& kv_cache_dtype, const float kv_scale);
-
-void reshape_and_cache_flash(torch::Tensor& key, torch::Tensor& value,
-                             torch::Tensor& key_cache,
-                             torch::Tensor& value_cache,
-                             torch::Tensor& slot_mapping,
-                             const std::string& kv_cache_dtype);
+void reshape_and_cache(
+  torch::Tensor& key,
+  torch::Tensor& value,
+  torch::Tensor& key_cache,
+  torch::Tensor& value_cache,
+  torch::Tensor& slot_mapping,
+  const std::string& kv_cache_dtype,
+  const float kv_scale);
 
 // Just for unittest
-void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
-                 const float scale, const std::string& kv_cache_dtype);
+void convert_fp8(
+  torch::Tensor& src_cache,
+  torch::Tensor& dst_cache);

csrc/cache_kernels.cu

@@ -4,11 +4,10 @@
 
 #include "cuda_compat.h"
 #include "dispatch_utils.h"
-
-#ifdef USE_ROCM
-  #include "quantization/fp8/amd/quant_utils.cuh"
-#else
-  #include "quantization/fp8/nvidia/quant_utils.cuh"
+#if defined(ENABLE_FP8_E5M2)
+#include "quantization/fp8_e5m2_kvcache/quant_utils.cuh"
+#elif defined(ENABLE_FP8_E4M3)
+#include "quantization/fp8/amd_detail/quant_utils.cuh"
 #endif
 
 #include <algorithm>
@@ -18,17 +17,20 @@
 
 #ifdef USE_ROCM
   #include <hip/hip_bf16.h>
-typedef __hip_bfloat16 __nv_bfloat16;
+  typedef __hip_bfloat16 __nv_bfloat16;
 #endif
 
-void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
-                 const torch::Tensor& block_mapping) {
+void swap_blocks(
+  torch::Tensor& src,
+  torch::Tensor& dst,
+  const std::map<int64_t, int64_t>& block_mapping) {
   torch::Device src_device = src.device();
   torch::Device dst_device = dst.device();
   cudaMemcpyKind memcpy_type;
   if (src_device.is_cuda() && dst_device.is_cuda()) {
-    TORCH_CHECK(src_device.index() == dst_device.index(),
-                "src and dst must be on the same GPU");
+    TORCH_CHECK(
+      src_device.index() == dst_device.index(),
+      "src and dst must be on the same GPU");
     memcpy_type = cudaMemcpyDeviceToDevice;
   } else if (src_device.is_cuda() && dst_device.is_cpu()) {
     memcpy_type = cudaMemcpyDeviceToHost;
@@ -38,44 +40,41 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
     TORCH_CHECK(false, "Invalid device combination");
   }
 
-  // NOTE(youkaichao): keep in mind that `block_mapping` should be
-  // a cpu tensor, otherwise every `item` call will require a gpu-cpu
-  // synchronization.
-  TORCH_CHECK(block_mapping.device().is_cpu(), "block_mapping must be on CPU");
-
-  char* src_ptr = static_cast<char*>(src.data_ptr());
-  char* dst_ptr = static_cast<char*>(dst.data_ptr());
+  char *src_ptr = static_cast<char*>(src.data_ptr());
+  char *dst_ptr = static_cast<char*>(dst.data_ptr());
 
   const int64_t block_size_in_bytes = src.element_size() * src[0].numel();
-  const at::cuda::OptionalCUDAGuard device_guard(
-      src_device.is_cuda() ? src_device : dst_device);
+  const at::cuda::OptionalCUDAGuard device_guard(src_device.is_cuda() ? src_device : dst_device);
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   // NOTE(woosuk): This can be slow if the number of blocks is large.
-  const int64_t num_blocks = block_mapping.size(0);
-  for (size_t i = 0; i < num_blocks; i++) {
-    int64_t src_block_number = block_mapping[i][0].item<int64_t>();
-    int64_t dst_block_number = block_mapping[i][1].item<int64_t>();
+  for (const auto& pair : block_mapping) {
+    int64_t src_block_number = pair.first;
+    int64_t dst_block_number = pair.second;
     int64_t src_offset = src_block_number * block_size_in_bytes;
     int64_t dst_offset = dst_block_number * block_size_in_bytes;
-    cudaMemcpyAsync(dst_ptr + dst_offset, src_ptr + src_offset,
-                    block_size_in_bytes, memcpy_type, stream);
+    cudaMemcpyAsync(
+      dst_ptr + dst_offset,
+      src_ptr + src_offset,
+      block_size_in_bytes,
+      memcpy_type,
+      stream);
   }
 }
 
 namespace vllm {
 
 // Grid: (num_layers, num_pairs)
-template <typename scalar_t>
-__global__ void copy_blocks_kernel(int64_t* key_cache_ptrs,
-                                   int64_t* value_cache_ptrs,
-                                   const int64_t* __restrict__ block_mapping,
-                                   const int numel_per_block) {
+template<typename scalar_t>
+__global__ void copy_blocks_kernel(
+  int64_t* key_cache_ptrs,
+  int64_t* value_cache_ptrs,
+  const int64_t* __restrict__ block_mapping,
+  const int numel_per_block) {
   const int layer_idx = blockIdx.x;
   const int pair_idx = blockIdx.y;
 
   scalar_t* key_cache = reinterpret_cast<scalar_t*>(key_cache_ptrs[layer_idx]);
-  scalar_t* value_cache =
-      reinterpret_cast<scalar_t*>(value_cache_ptrs[layer_idx]);
+  scalar_t* value_cache = reinterpret_cast<scalar_t*>(value_cache_ptrs[layer_idx]);
   int64_t src_block_number = block_mapping[2 * pair_idx];
   int64_t dst_block_number = block_mapping[2 * pair_idx + 1];
 
@@ -93,11 +92,12 @@ __global__ void copy_blocks_kernel(int64_t* key_cache_ptrs,
   }
 }
 
-}  // namespace vllm
+} // namespace vllm
 
-void copy_blocks(std::vector<torch::Tensor>& key_caches,
-                 std::vector<torch::Tensor>& value_caches,
-                 const torch::Tensor& block_mapping) {
+void copy_blocks(
+  std::vector<torch::Tensor>& key_caches,
+  std::vector<torch::Tensor>& value_caches,
+  const std::map<int64_t, std::vector<int64_t>>& block_mapping) {
   int num_layers = key_caches.size();
   TORCH_CHECK(num_layers == value_caches.size());
   if (num_layers == 0) {
@@ -111,23 +111,29 @@ void copy_blocks(std::vector<torch::Tensor>& key_caches,
   int64_t key_cache_ptrs[num_layers];
   int64_t value_cache_ptrs[num_layers];
   for (int layer_idx = 0; layer_idx < num_layers; ++layer_idx) {
-    key_cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(key_caches[layer_idx].data_ptr());
-    value_cache_ptrs[layer_idx] =
-        reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
+    key_cache_ptrs[layer_idx] = reinterpret_cast<int64_t>(key_caches[layer_idx].data_ptr());
+    value_cache_ptrs[layer_idx] = reinterpret_cast<int64_t>(value_caches[layer_idx].data_ptr());
   }
-
-  // block_mapping is a 2D tensor with shape (num_pairs, 2).
-  int num_pairs = block_mapping.size(0);
+  // Create block mapping array.
+  std::vector<int64_t> block_mapping_vec;
+  for (const auto& pair : block_mapping) {
+    int64_t src_block_number = pair.first;
+    for (int64_t dst_block_number : pair.second) {
+      block_mapping_vec.push_back(src_block_number);
+      block_mapping_vec.push_back(dst_block_number);
+    }
+  }
+  int64_t* block_mapping_array = block_mapping_vec.data();
+  int num_pairs = block_mapping_vec.size() / 2;
 
   // Move the data structures to the GPU.
   // NOTE: This synchronizes the CPU and GPU.
-  torch::Tensor key_cache_ptrs_tensor =
-      torch::from_blob(key_cache_ptrs, {num_layers}, torch::kInt64)
-          .to(cache_device);
-  torch::Tensor value_cache_ptrs_tensor =
-      torch::from_blob(value_cache_ptrs, {num_layers}, torch::kInt64)
-          .to(cache_device);
+  torch::Tensor key_cache_ptrs_tensor = torch::from_blob(
+    key_cache_ptrs, {num_layers}, torch::kInt64).to(cache_device);
+  torch::Tensor value_cache_ptrs_tensor = torch::from_blob(
+    value_cache_ptrs, {num_layers}, torch::kInt64).to(cache_device);
+  torch::Tensor block_mapping_tensor = torch::from_blob(
+    block_mapping_array, {2 * num_pairs}, torch::kInt64).to(cache_device);
 
   // Launch the kernel.
   const int numel_per_block = key_caches[0][0].numel();
@@ -136,28 +142,31 @@ void copy_blocks(std::vector<torch::Tensor>& key_caches,
   const at::cuda::OptionalCUDAGuard device_guard(cache_device);
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(
-      key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
-        vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            key_cache_ptrs_tensor.data_ptr<int64_t>(),
-            value_cache_ptrs_tensor.data_ptr<int64_t>(),
-            block_mapping.data_ptr<int64_t>(), numel_per_block);
-      }));
+    key_caches[0].scalar_type(), "copy_blocks_kernel", ([&] {
+      vllm::copy_blocks_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        key_cache_ptrs_tensor.data_ptr<int64_t>(),
+        value_cache_ptrs_tensor.data_ptr<int64_t>(),
+        block_mapping_tensor.data_ptr<int64_t>(),
+        numel_per_block);
+    }));
 }
 
 namespace vllm {
 
-template <typename scalar_t, typename cache_t, Fp8KVCacheDataType kv_dt>
+template<typename scalar_t, typename cache_t, bool is_fp8_kv_cache>
 __global__ void reshape_and_cache_kernel(
-    const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
-    const scalar_t* __restrict__ value,  // [num_tokens, num_heads, head_size]
-    cache_t* __restrict__ key_cache,     // [num_blocks, num_heads, head_size/x,
-                                         // block_size, x]
-    cache_t* __restrict__ value_cache,   // [num_blocks, num_heads, head_size,
-                                         // block_size]
-    const int64_t* __restrict__ slot_mapping,  // [num_tokens]
-    const int key_stride, const int value_stride, const int num_heads,
-    const int head_size, const int block_size, const int x,
-    const float kv_scale) {
+  const scalar_t* __restrict__ key,           // [num_tokens, num_heads, head_size]
+  const scalar_t* __restrict__ value,         // [num_tokens, num_heads, head_size]
+  cache_t* __restrict__ key_cache,            // [num_blocks, num_heads, head_size/x, block_size, x]
+  cache_t* __restrict__ value_cache,          // [num_blocks, num_heads, head_size, block_size]
+  const int64_t* __restrict__ slot_mapping,   // [num_tokens]
+  const int key_stride,
+  const int value_stride,
+  const int num_heads,
+  const int head_size,
+  const int block_size,
+  const int x,
+  const float kv_scale) {
   const int64_t token_idx = blockIdx.x;
   const int64_t slot_idx = slot_mapping[token_idx];
   if (slot_idx < 0) {
@@ -178,84 +187,60 @@ __global__ void reshape_and_cache_kernel(
     const int x_idx = head_offset / x;
     const int x_offset = head_offset % x;
 
-    const int64_t tgt_key_idx =
-        block_idx * num_heads * (head_size / x) * block_size * x +
-        head_idx * (head_size / x) * block_size * x + x_idx * block_size * x +
-        block_offset * x + x_offset;
-    const int64_t tgt_value_idx =
-        block_idx * num_heads * head_size * block_size +
-        head_idx * head_size * block_size + head_offset * block_size +
-        block_offset;
+    const int64_t tgt_key_idx = block_idx * num_heads * (head_size / x) * block_size * x
+                                + head_idx * (head_size / x) * block_size * x
+                                + x_idx * block_size * x
+                                + block_offset * x
+                                + x_offset;
+    const int64_t tgt_value_idx = block_idx * num_heads * head_size * block_size
+                                  + head_idx * head_size * block_size
+                                  + head_offset * block_size
+                                  + block_offset;
     scalar_t tgt_key = key[src_key_idx];
     scalar_t tgt_value = value[src_value_idx];
-    if constexpr (kv_dt == Fp8KVCacheDataType::kAuto) {
+    if constexpr (is_fp8_kv_cache) {
+#if defined(ENABLE_FP8_E5M2)
+      key_cache[tgt_key_idx] = fp8_e5m2_unscaled::vec_conversion<uint8_t, scalar_t>(tgt_key);
+      value_cache[tgt_value_idx] = fp8_e5m2_unscaled::vec_conversion<uint8_t, scalar_t>(tgt_value);
+#elif defined(ENABLE_FP8_E4M3)
+      key_cache[tgt_key_idx] = fp8_e4m3::scaled_vec_conversion<uint8_t, scalar_t>(tgt_key, kv_scale);
+      value_cache[tgt_value_idx] = fp8_e4m3::scaled_vec_conversion<uint8_t, scalar_t>(tgt_value, kv_scale);
+#else
+      assert(false);
+#endif
+    } else {
       key_cache[tgt_key_idx] = tgt_key;
       value_cache[tgt_value_idx] = tgt_value;
-    } else {
-      key_cache[tgt_key_idx] =
-          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_key, kv_scale);
-      value_cache[tgt_value_idx] =
-          fp8::scaled_convert<cache_t, scalar_t, kv_dt>(tgt_value, kv_scale);
     }
   }
 }
 
-template <typename scalar_t>
-__global__ void reshape_and_cache_flash_kernel(
-    const scalar_t* __restrict__ key,    // [num_tokens, num_heads, head_size]
-    const scalar_t* __restrict__ value,  // [num_tokens, num_heads, head_size]
-    scalar_t* __restrict__ k_cache,      // [num_blocks, block_size, num_heads,
-                                         // head_size]
-    scalar_t* __restrict__ v_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 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
-  if (slot_idx < 0) {
-    return;
-  }
-  const int64_t block_idx = slot_idx / block_size;
-  const int64_t block_offset = slot_idx % block_size;
-  const int n = num_heads * head_size;
-  for (int i = threadIdx.x; i < n; i += blockDim.x) {
-    const int64_t src_key_idx = token_idx * key_stride + i;
-    const int64_t src_value_idx = token_idx * value_stride + i;
-    const int head_idx = i / head_size;
-    const int head_offset = i % head_size;
-    const int64_t tgt_value_idx = block_idx * block_stride +
-                                  block_offset * num_heads * head_size +
-                                  head_idx * head_size + head_offset;
-    k_cache[tgt_value_idx] = key[src_key_idx];
-    v_cache[tgt_value_idx] = value[src_value_idx];
-  }
-}
-}  // namespace vllm
+} // namespace vllm
 
-// KV_T is the stored data type of kv-cache.
-// CACHE_T is the data type of key and value tensors.
-// KV_DTYPE is the real data type of kv-cache.
-#define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, KV_DTYPE)               \
-  vllm::reshape_and_cache_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>(), key_stride, value_stride, \
-          num_heads, head_size, block_size, x, kv_scale);
+#define CALL_RESHAPE_AND_CACHE(KV_T, CACHE_T, IS_FP8_KV_CACHE)                                     \
+  vllm::reshape_and_cache_kernel<KV_T, CACHE_T, IS_FP8_KV_CACHE><<<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>(),                                                              \
+    key_stride,                                                                                    \
+    value_stride,                                                                                  \
+    num_heads,                                                                                     \
+    head_size,                                                                                     \
+    block_size,                                                                                    \
+    x,                                                                                             \
+    kv_scale);
 
 void reshape_and_cache(
-    torch::Tensor& key,    // [num_tokens, num_heads, head_size]
-    torch::Tensor& value,  // [num_tokens, num_heads, head_size]
-    torch::Tensor&
-        key_cache,  // [num_blocks, num_heads, head_size/x, block_size, x]
-    torch::Tensor&
-        value_cache,  // [num_blocks, num_heads, head_size, block_size]
-    torch::Tensor& slot_mapping,  // [num_tokens]
-    const std::string& kv_cache_dtype, const float kv_scale) {
+  torch::Tensor& key,           // [num_tokens, num_heads, head_size]
+  torch::Tensor& value,         // [num_tokens, num_heads, head_size]
+  torch::Tensor& key_cache,     // [num_blocks, num_heads, head_size/x, block_size, x]
+  torch::Tensor& value_cache,   // [num_blocks, num_heads, head_size, block_size]
+  torch::Tensor& slot_mapping,  // [num_tokens]
+  const std::string& kv_cache_dtype,
+  const float kv_scale)
+{
   int num_tokens = key.size(0);
   int num_heads = key.size(1);
   int head_size = key.size(2);
@@ -269,78 +254,66 @@ void reshape_and_cache(
   dim3 block(std::min(num_heads * head_size, 512));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-
-  DISPATCH_BY_KV_CACHE_DTYPE(key.dtype(), kv_cache_dtype,
-                             CALL_RESHAPE_AND_CACHE)
-}
-
-void reshape_and_cache_flash(
-    torch::Tensor& key,      // [num_tokens, num_heads, head_size]
-    torch::Tensor& value,    // [num_tokens, num_heads, head_size]
-    torch::Tensor& k_cache,  // [num_blocks, block_size, num_heads, head_size]
-    torch::Tensor& v_cache,  // [num_blocks, block_size, num_heads, head_size]
-    torch::Tensor& slot_mapping,  // [num_tokens]
-    const std::string& kv_cache_dtype) {
-  // FIXME: only support auto datatype, does not support fp8
-  if (kv_cache_dtype != "auto") {
+  if (kv_cache_dtype == "auto") {
+    if (key.dtype() == at::ScalarType::Float) {
+      CALL_RESHAPE_AND_CACHE(float, float, false);
+    } else if (key.dtype() == at::ScalarType::Half) {
+      CALL_RESHAPE_AND_CACHE(uint16_t, uint16_t, false);
+    } else if (key.dtype() == at::ScalarType::BFloat16) {
+      CALL_RESHAPE_AND_CACHE(__nv_bfloat16, __nv_bfloat16, false);
+    }
+  } else if (kv_cache_dtype == "fp8") {
+    if (key.dtype() == at::ScalarType::Float) {
+      CALL_RESHAPE_AND_CACHE(float, uint8_t, true);
+    } else if (key.dtype() == at::ScalarType::Half) {
+      CALL_RESHAPE_AND_CACHE(uint16_t, uint8_t, true);
+    } else if (key.dtype() == at::ScalarType::BFloat16) {
+      CALL_RESHAPE_AND_CACHE(__nv_bfloat16, uint8_t, true);
+    }
+  } else {
     TORCH_CHECK(false, "Unsupported data type of kv cache: ", kv_cache_dtype);
   }
-  int num_tokens = key.size(0);
-  int num_heads = key.size(1);
-  int head_size = key.size(2);
-  int block_size = k_cache.size(1);
-
-  int key_stride = key.stride(0);
-  int value_stride = value.stride(0);
-  int block_stride = k_cache.stride(0);
-  TORCH_CHECK(k_cache.stride(0) == v_cache.stride(0));
-
-  dim3 grid(num_tokens);
-  dim3 block(std::min(num_heads * head_size, 512));
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(key));
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      key.scalar_type(), "reshape_and_cache_flash", [&] {
-        vllm::reshape_and_cache_flash_kernel<scalar_t>
-            <<<grid, block, 0, stream>>>(
-                key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
-                k_cache.data_ptr<scalar_t>(), v_cache.data_ptr<scalar_t>(),
-                slot_mapping.data_ptr<int64_t>(), block_stride, key_stride,
-                value_stride, num_heads, head_size, block_size);
-      });
 }
 
 namespace vllm {
 
-template <typename Tout, typename Tin, Fp8KVCacheDataType kv_dt>
-__global__ void convert_fp8_kernel(const Tin* __restrict__ src_cache,
-                                   Tout* __restrict__ dst_cache,
-                                   const float kv_scale,
-                                   const int64_t block_stride) {
+template<typename Tout, typename Tin>
+__global__ void convert_fp8_kernel(
+  const Tin* __restrict__ src_cache,
+  Tout* __restrict__ dst_cache,
+  const int64_t block_stride) {
   const int64_t block_idx = blockIdx.x;
   for (int i = threadIdx.x; i < block_stride; i += blockDim.x) {
     int64_t idx = block_idx * block_stride + i;
-    dst_cache[idx] =
-        fp8::scaled_convert<Tout, Tin, kv_dt>(src_cache[idx], kv_scale);
+#if defined(ENABLE_FP8_E5M2)
+    dst_cache[idx] = fp8_e5m2_unscaled::vec_conversion<Tout, Tin>(src_cache[idx]);
+#elif defined(ENABLE_FP8_E4M3)
+    dst_cache[idx] = fp8_e4m3::vec_conversion<Tout, Tin>(src_cache[idx]);
+#else
+    assert(false);
+#endif
   }
 }
 
-}  // namespace vllm
+} // namespace vllm
 
-#define CALL_CONVERT_FP8(Tout, Tin, KV_DTYPE)                                \
-  vllm::convert_fp8_kernel<Tout, Tin, KV_DTYPE><<<grid, block, 0, stream>>>( \
-      reinterpret_cast<Tin*>(src_cache.data_ptr()),                          \
-      reinterpret_cast<Tout*>(dst_cache.data_ptr()), kv_scale, block_stride);
+#define CALL_CONVERT_FP8(Tout, Tin)                                 \
+  vllm::convert_fp8_kernel<Tout, Tin><<<grid, block, 0, stream>>>(  \
+    reinterpret_cast<Tin*>(src_cache.data_ptr()),                   \
+    reinterpret_cast<Tout*>(dst_cache.data_ptr()),                  \
+    block_stride);
 
-// Only for testing.
-void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
-                 const float kv_scale, const std::string& kv_cache_dtype) {
+void convert_fp8(
+  torch::Tensor& src_cache,
+  torch::Tensor& dst_cache)
+{
   torch::Device src_device = src_cache.device();
   torch::Device dst_device = dst_cache.device();
   TORCH_CHECK(src_device.is_cuda(), "src must be on a GPU")
   TORCH_CHECK(dst_device.is_cuda(), "dst must be on a GPU")
-  TORCH_CHECK(src_device.index() == dst_device.index(),
-              "src and dst must be on the same GPU");
+  TORCH_CHECK(
+    src_device.index() == dst_device.index(),
+    "src and dst must be on the same GPU");
   at::cuda::OptionalCUDAGuard device_guard(src_device);
 
   int64_t num_blocks = src_cache.size(0);
@@ -350,37 +323,17 @@ void convert_fp8(torch::Tensor& dst_cache, torch::Tensor& src_cache,
   dim3 block(std::min(block_stride, int64_t(512)));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  if (kv_cache_dtype == "auto") {
-    if (src_cache.dtype() == at::ScalarType::Float) {
-      CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kAuto);
-    } else if (src_cache.dtype() == at::ScalarType::Half) {
-      CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kAuto);
-    } else if (src_cache.dtype() == at::ScalarType::BFloat16) {
-      CALL_CONVERT_FP8(uint8_t, __nv_bfloat16, vllm::Fp8KVCacheDataType::kAuto);
-    } else if (dst_cache.dtype() == at::ScalarType::Float) {
-      CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
-    } else if (dst_cache.dtype() == at::ScalarType::Half) {
-      CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
-    } else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
-      CALL_CONVERT_FP8(__nv_bfloat16, uint8_t, vllm::Fp8KVCacheDataType::kAuto);
-    }
-  } else if (kv_cache_dtype == "fp8" || kv_cache_dtype == "fp8_e4m3") {
-    if (src_cache.dtype() == at::ScalarType::Float) {
-      CALL_CONVERT_FP8(uint8_t, float, vllm::Fp8KVCacheDataType::kFp8E4M3);
-    } else if (src_cache.dtype() == at::ScalarType::Half) {
-      CALL_CONVERT_FP8(uint8_t, uint16_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
-    } else if (src_cache.dtype() == at::ScalarType::BFloat16) {
-      CALL_CONVERT_FP8(uint8_t, __nv_bfloat16,
-                       vllm::Fp8KVCacheDataType::kFp8E4M3);
-    } else if (dst_cache.dtype() == at::ScalarType::Float) {
-      CALL_CONVERT_FP8(float, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
-    } else if (dst_cache.dtype() == at::ScalarType::Half) {
-      CALL_CONVERT_FP8(uint16_t, uint8_t, vllm::Fp8KVCacheDataType::kFp8E4M3);
-    } else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
-      CALL_CONVERT_FP8(__nv_bfloat16, uint8_t,
-                       vllm::Fp8KVCacheDataType::kFp8E4M3);
-    }
-  } else {
-    TORCH_CHECK(false, "Unsupported data type: ", kv_cache_dtype);
+  if (src_cache.dtype() == at::ScalarType::Float) {
+    CALL_CONVERT_FP8(uint8_t, float);
+  } else if (src_cache.dtype() == at::ScalarType::Half) {
+    CALL_CONVERT_FP8(uint8_t, uint16_t);
+  } else if (src_cache.dtype() == at::ScalarType::BFloat16) {
+    CALL_CONVERT_FP8(uint8_t, __nv_bfloat16);
+  } else if (dst_cache.dtype() == at::ScalarType::Float) {
+    CALL_CONVERT_FP8(float, uint8_t);
+  } else if (dst_cache.dtype() == at::ScalarType::Half) {
+    CALL_CONVERT_FP8(uint16_t, uint8_t);
+  } else if (dst_cache.dtype() == at::ScalarType::BFloat16) {
+    CALL_CONVERT_FP8(__nv_bfloat16, uint8_t);
   }
 }

csrc/cpu/activation.cpp

@@ -1,10 +1,10 @@
 #include "cpu_types.hpp"
 
 namespace {
-template <typename scalar_t, vec_op::FP32Vec8 (*func)(const vec_op::FP32Vec8&),
+template <typename scalar_t, vec_op::FP32Vec8 (*func)(const vec_op::FP32Vec8 &),
           bool is_gated>
-void activation_kernel(int num_tokens, int d, scalar_t* __restrict__ input,
-                       scalar_t* __restrict__ output) {
+void activation_kernel(int num_tokens, int d, scalar_t *__restrict__ input,
+                       scalar_t *__restrict__ output) {
   using scalar_vec_t = vec_op::vec_t<scalar_t>;
   constexpr int VEC_ELEM_NUM = scalar_vec_t::get_elem_num();
 
@@ -34,13 +34,13 @@ void activation_kernel(int num_tokens, int d, scalar_t* __restrict__ input,
   }
 }
 
-FORCE_INLINE vec_op::FP32Vec8 silu_act(const vec_op::FP32Vec8& x) {
+FORCE_INLINE vec_op::FP32Vec8 silu_act(const vec_op::FP32Vec8 &x) {
   const vec_op::FP32Vec8 zeros(0.0);
   const vec_op::FP32Vec8 ones(1.0);
   return x / (ones + (zeros - x).exp());
 }
 
-FORCE_INLINE vec_op::FP32Vec8 gelu_new_act(const vec_op::FP32Vec8& x) {
+FORCE_INLINE vec_op::FP32Vec8 gelu_new_act(const vec_op::FP32Vec8 &x) {
   const vec_op::FP32Vec8 ones(1.0);
   const vec_op::FP32Vec8 w1(0.79788456f);
   const vec_op::FP32Vec8 w2(0.044715f);
@@ -50,7 +50,7 @@ FORCE_INLINE vec_op::FP32Vec8 gelu_new_act(const vec_op::FP32Vec8& x) {
   return w3 * x * (ones + t);
 }
 
-FORCE_INLINE vec_op::FP32Vec8 gelu_fast_act(const vec_op::FP32Vec8& x) {
+FORCE_INLINE vec_op::FP32Vec8 gelu_fast_act(const vec_op::FP32Vec8 &x) {
   const vec_op::FP32Vec8 ones(1.0);
   const vec_op::FP32Vec8 w1(0.79788456f);
   const vec_op::FP32Vec8 w2(0.044715f);
@@ -59,14 +59,14 @@ FORCE_INLINE vec_op::FP32Vec8 gelu_fast_act(const vec_op::FP32Vec8& x) {
   return w3 * x * (ones + t);
 }
 
-FORCE_INLINE vec_op::FP32Vec8 gelu_act(const vec_op::FP32Vec8& x) {
+FORCE_INLINE vec_op::FP32Vec8 gelu_act(const vec_op::FP32Vec8 &x) {
   const vec_op::FP32Vec8 ones(1.0);
   const vec_op::FP32Vec8 w1(M_SQRT1_2);
   const vec_op::FP32Vec8 w2(0.5);
   return x * w2 * (ones + (x * w1).er());
 }
 
-FORCE_INLINE vec_op::FP32Vec8 gelu_tanh_act(const vec_op::FP32Vec8& x) {
+FORCE_INLINE vec_op::FP32Vec8 gelu_tanh_act(const vec_op::FP32Vec8 &x) {
   const vec_op::FP32Vec8 ones(1.0);
   const vec_op::FP32Vec8 w1(M_SQRT2 * M_2_SQRTPI * 0.5);
   const vec_op::FP32Vec8 w2(0.5);
@@ -75,36 +75,40 @@ FORCE_INLINE vec_op::FP32Vec8 gelu_tanh_act(const vec_op::FP32Vec8& x) {
   const vec_op::FP32Vec8 inner = w1 * (x + x_3 * w3);
   return x * w2 * (ones + inner.tanh());
 }
-};  // namespace
+}; // namespace
 
-void silu_and_mul(torch::Tensor& out, torch::Tensor& input) {
+void silu_and_mul(torch::Tensor &out, torch::Tensor &input) {
   int num_tokens = input.numel() / input.size(-1);
   int d = input.size(-1) / 2;
 
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "silu_and_mul_impl", [&] {
-    CPU_KERNEL_GUARD_IN(silu_and_mul_impl)
-    activation_kernel<scalar_t, silu_act, true>(
-        num_tokens, d, input.data_ptr<scalar_t>(), out.data_ptr<scalar_t>());
-    CPU_KERNEL_GUARD_OUT(silu_and_mul_impl)
-  });
+  VLLM_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "silu_and_mul_impl", [&] {
+        CPU_KERNEL_GUARD_IN(silu_and_mul_impl)
+        activation_kernel<scalar_t, silu_act, true>(num_tokens, d,
+                                                    input.data_ptr<scalar_t>(),
+                                                    out.data_ptr<scalar_t>());
+        CPU_KERNEL_GUARD_OUT(silu_and_mul_impl)
+      });
 }
 
-void gelu_and_mul(torch::Tensor& out,    // [..., d]
-                  torch::Tensor& input)  // [..., 2 * d]
+void gelu_and_mul(torch::Tensor &out,   // [..., d]
+                      torch::Tensor &input) // [..., 2 * d]
 {
   int num_tokens = input.numel() / input.size(-1);
   int d = input.size(-1) / 2;
 
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "gelu_and_mul_impl", [&] {
-    CPU_KERNEL_GUARD_IN(gelu_and_mul_impl)
-    activation_kernel<scalar_t, gelu_act, true>(
-        num_tokens, d, input.data_ptr<scalar_t>(), out.data_ptr<scalar_t>());
-    CPU_KERNEL_GUARD_OUT(gelu_and_mul_impl)
-  });
+  VLLM_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "gelu_and_mul_impl", [&] {
+        CPU_KERNEL_GUARD_IN(gelu_and_mul_impl)
+        activation_kernel<scalar_t, gelu_act, true>(num_tokens, d,
+                                                    input.data_ptr<scalar_t>(),
+                                                    out.data_ptr<scalar_t>());
+        CPU_KERNEL_GUARD_OUT(gelu_and_mul_impl)
+      });
 }
 
-void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
-                       torch::Tensor& input)  // [..., 2 * d]
+void gelu_tanh_and_mul(torch::Tensor &out,   // [..., d]
+                           torch::Tensor &input) // [..., 2 * d]
 {
   int num_tokens = input.numel() / input.size(-1);
   int d = input.size(-1) / 2;
@@ -119,7 +123,7 @@ void gelu_tanh_and_mul(torch::Tensor& out,    // [..., d]
       });
 }
 
-void gelu_new(torch::Tensor& out, torch::Tensor& input) {
+void gelu_new(torch::Tensor &out, torch::Tensor &input) {
   int num_tokens = input.numel() / input.size(-1);
   int d = input.size(-1);
 
@@ -131,7 +135,7 @@ void gelu_new(torch::Tensor& out, torch::Tensor& input) {
   });
 }
 
-void gelu_fast(torch::Tensor& out, torch::Tensor& input) {
+void gelu_fast(torch::Tensor &out, torch::Tensor &input) {
   int num_tokens = input.numel() / input.size(-1);
   int d = input.size(-1);
 

csrc/cpu/attention.cpp

@@ -2,8 +2,7 @@
 
 namespace {
 
-template <typename scalar_t>
-struct KernelVecType {
+template <typename scalar_t> struct KernelVecType {
   using q_load_vec_type = void;
   using q_vec_type = void;
   using k_load_vec_type = void;
@@ -12,8 +11,7 @@ struct KernelVecType {
   using v_load_vec_type = void;
 };
 
-template <>
-struct KernelVecType<float> {
+template <> struct KernelVecType<float> {
   using q_load_vec_type = vec_op::FP32Vec4;
   using q_vec_type = vec_op::FP32Vec16;
   using k_load_vec_type = vec_op::FP32Vec16;
@@ -23,8 +21,7 @@ struct KernelVecType<float> {
 };
 
 #ifdef __AVX512BF16__
-template <>
-struct KernelVecType<c10::BFloat16> {
+template <> struct KernelVecType<c10::BFloat16> {
   using q_load_vec_type = vec_op::BF16Vec8;
   using q_vec_type = vec_op::BF16Vec32;
   using k_load_vec_type = vec_op::BF16Vec32;
@@ -33,8 +30,7 @@ struct KernelVecType<c10::BFloat16> {
   using v_load_vec_type = vec_op::BF16Vec16;
 };
 #else
-template <>
-struct KernelVecType<c10::BFloat16> {
+template <> struct KernelVecType<c10::BFloat16> {
   using q_load_vec_type = vec_op::BF16Vec8;
   using q_vec_type = vec_op::FP32Vec16;
   using k_load_vec_type = vec_op::BF16Vec16;
@@ -45,7 +41,7 @@ struct KernelVecType<c10::BFloat16> {
 #endif
 
 template <typename T>
-FORCE_INLINE std::pair<T, T> reduceSoftmax(T* data, const int size,
+FORCE_INLINE std::pair<T, T> reduceSoftmax(T *data, const int size,
                                            const int capacity) {
   T max = data[0];
   for (int i = 1; i < size; ++i) {
@@ -71,15 +67,14 @@ FORCE_INLINE std::pair<T, T> reduceSoftmax(T* data, const int size,
 }
 
 template <typename T>
-FORCE_INLINE std::pair<T, T> reduceSoftmaxAlibi(T* data, const int size,
-                                                const int capacity,
-                                                const float alibi_slope,
-                                                const int start_index,
-                                                const int seq_len) {
-  data[0] += alibi_slope * (start_index - seq_len + 1);
+FORCE_INLINE std::pair<T, T>
+reduceSoftmaxAlibi(T *data, const int size, const int capacity,
+                   const float alibi_slope, const int start_index,
+                   const int context_len) {
+  data[0] += alibi_slope * (start_index - context_len + 1);
   T max = data[0];
   for (int i = 1; i < size; ++i) {
-    T qk = data[i] + alibi_slope * (start_index + i - seq_len + 1);
+    T qk = data[i] + alibi_slope * (start_index + i - context_len + 1);
     data[i] = qk;
     max = max >= qk ? max : qk;
   }
@@ -103,7 +98,7 @@ FORCE_INLINE std::pair<T, T> reduceSoftmaxAlibi(T* data, const int size,
 }
 
 template <typename T>
-FORCE_INLINE void reducePartitonSoftmax(const T* max_data, T* sum_data,
+FORCE_INLINE void reducePartitonSoftmax(const T *max_data, T *sum_data,
                                         const int size) {
   T max = max_data[0];
   for (int i = 1; i < size; ++i) {
@@ -137,9 +132,9 @@ struct reduceQKBlockKernel {
   static_assert(k_load_vec_type::get_elem_num() % x == 0);
   static_assert(q_load_vec_type::get_elem_num() * sizeof(scalar_t) == 16);
 
-  FORCE_INLINE static void call(const scalar_t* __restrict__ q,
-                                const scalar_t* __restrict__ k_block,
-                                float* __restrict__ logits, float scale,
+  FORCE_INLINE static void call(const scalar_t *__restrict__ q,
+                                const scalar_t *__restrict__ k_block,
+                                float *__restrict__ logits, float scale,
                                 const int token_num) {
     const int group_num = (token_num + TOKEN_PER_GROUP - 1) / TOKEN_PER_GROUP;
 
@@ -201,8 +196,8 @@ struct reduceQKBlockKernel {
 
 template <typename scalar_t, int HEAD_SIZE, int BLOCK_SIZE,
           int HEAD_PARTITION_SIZE, typename acc_t>
-FORCE_INLINE void reduceValueBlock(const float* prob, const scalar_t* v_block,
-                                   acc_t&& acc) {
+FORCE_INLINE void reduceValueBlock(const float *prob, const scalar_t *v_block,
+                                   acc_t &&acc) {
   using v_load_vec_type = typename KernelVecType<scalar_t>::v_load_vec_type;
   constexpr int ELEM_NUM = v_load_vec_type::get_elem_num();
   static_assert(BLOCK_SIZE == ELEM_NUM);
@@ -214,65 +209,66 @@ FORCE_INLINE void reduceValueBlock(const float* prob, const scalar_t* v_block,
     acc[head_elem_idx] = acc[head_elem_idx] + prob_vec * fp32_v_vec;
   });
 }
-};  // namespace
+}; // namespace
 
 // Paged attention v1
 namespace {
 template <typename scalar_t, int HEAD_SIZE, int BLOCK_SIZE>
 struct paged_attention_v1_impl {
-  static void call(
-      scalar_t* __restrict__ out,            // [num_seqs, num_heads, head_size]
-      const scalar_t* __restrict__ q,        // [num_seqs, num_heads, head_size]
-      const scalar_t* __restrict__ k_cache,  // [num_blocks, num_kv_heads,
+  static void
+  call(scalar_t *__restrict__ out,           // [num_seqs, num_heads, head_size]
+       const scalar_t *__restrict__ q,       // [num_seqs, num_heads, head_size]
+       const scalar_t *__restrict__ k_cache, // [num_blocks, num_kv_heads,
                                              // head_size/x, block_size, x]
-      const scalar_t* __restrict__ v_cache,  // [num_blocks, num_kv_heads,
+       const scalar_t *__restrict__ v_cache, // [num_blocks, num_kv_heads,
                                              // head_size, block_size]
-      const int num_kv_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 float* __restrict__ alibi_slopes,  // [num_heads]
-      const int q_stride, const int kv_block_stride, const int kv_head_stride,
-      const int num_seqs, const int num_heads) {
+       const int num_kv_heads, const float scale,
+       const int
+           *__restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
+       const int *__restrict__ context_lens, // [num_seqs]
+       const int max_num_blocks_per_seq,
+       const float *__restrict__ alibi_slopes, // [num_heads]
+       const int q_stride, const int kv_block_stride, const int kv_head_stride,
+       const int num_seqs, const int num_heads) {
     constexpr int x = 16 / sizeof(scalar_t);
     const int num_queries_per_kv = num_heads / num_kv_heads;
 
     static_assert(BLOCK_SIZE == 16);
 
-    int max_seq_len = max_num_blocks_per_seq * BLOCK_SIZE;
-    int max_seq_len_padded = (max_seq_len + 15) & 0xFFFFFFF0;
-    TORCH_CHECK((max_seq_len_padded * sizeof(float)) % 64 == 0);
+    int max_context_len = max_num_blocks_per_seq * BLOCK_SIZE;
+    int max_context_len_padded = (max_context_len + 15) & 0xFFFFFFF0;
+    TORCH_CHECK((max_context_len_padded * sizeof(float)) % 64 == 0);
 
     const int parallel_work_item_num = omp_get_max_threads();
 
     size_t logits_bytes =
-        parallel_work_item_num * max_seq_len_padded * sizeof(float);
-    float* logits = (float*)std::aligned_alloc(
-        64, logits_bytes);  // Cacheline alignment for each context token.
-                            // [parallel_work_item_num, max_seq_len_padded]
+        parallel_work_item_num * max_context_len_padded * sizeof(float);
+    float *logits = (float *)std::aligned_alloc(
+        64, logits_bytes); // Cacheline alignment for each context token.
+                           // [parallel_work_item_num, max_context_len_padded]
 
 #pragma omp parallel for collapse(2) schedule(dynamic, 1)
     for (int seq_idx = 0; seq_idx < num_seqs; ++seq_idx) {
       for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
-        int seq_len = seq_lens[seq_idx];
-        const int* seq_block_table =
+        int context_len = context_lens[seq_idx];
+        const int *seq_block_table =
             block_tables + max_num_blocks_per_seq * seq_idx;
-        const int block_num = (seq_len + BLOCK_SIZE - 1) / BLOCK_SIZE;
+        const int block_num = (context_len + BLOCK_SIZE - 1) / BLOCK_SIZE;
         const int64_t kv_head_idx = head_idx / num_queries_per_kv;
-        const scalar_t* __restrict__ q_vec_ptr =
+        const scalar_t *__restrict__ q_vec_ptr =
             q + seq_idx * q_stride + head_idx * HEAD_SIZE;
-        const int last_block_token_num = seq_len - (block_num - 1) * BLOCK_SIZE;
-        float* __restrict__ thread_block_logits =
-            logits + omp_get_thread_num() * max_seq_len_padded;
+        const int last_block_token_num =
+            context_len - (block_num - 1) * BLOCK_SIZE;
+        float *__restrict__ thread_block_logits =
+            logits + omp_get_thread_num() * max_context_len_padded;
 
         // Compute logits
         for (int block_idx = 0; block_idx < block_num; ++block_idx) {
           const int64_t physical_block_idx = seq_block_table[block_idx];
-          const scalar_t* __restrict__ k_block_cache_ptr =
+          const scalar_t *__restrict__ k_block_cache_ptr =
               k_cache + physical_block_idx * kv_block_stride +
               kv_head_idx * kv_head_stride;
-          float* __restrict__ head_block_logits =
+          float *__restrict__ head_block_logits =
               thread_block_logits + block_idx * BLOCK_SIZE;
 
           reduceQKBlockKernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, x>::call(
@@ -282,11 +278,12 @@ struct paged_attention_v1_impl {
 
         // Compute softmax
         if (alibi_slopes) {
-          reduceSoftmaxAlibi(thread_block_logits, seq_len,
+          reduceSoftmaxAlibi(thread_block_logits, context_len,
                              block_num * BLOCK_SIZE, alibi_slopes[head_idx], 0,
-                             seq_len);
+                             context_len);
         } else {
-          reduceSoftmax(thread_block_logits, seq_len, block_num * BLOCK_SIZE);
+          reduceSoftmax(thread_block_logits, context_len,
+                        block_num * BLOCK_SIZE);
         }
 
         // Compute value
@@ -296,14 +293,14 @@ struct paged_attention_v1_impl {
         for (int head_part_idx = 0; head_part_idx < head_partition_num;
              ++head_part_idx) {
           vec_op::FP32Vec16 accums[head_elem_num_per_partition];
-          scalar_t* __restrict__ out_ptr =
+          scalar_t *__restrict__ out_ptr =
               out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE +
               head_part_idx * head_elem_num_per_partition;
           for (int block_idx = 0; block_idx < block_num; ++block_idx) {
             const int64_t physical_block_idx = seq_block_table[block_idx];
-            const float* __restrict__ prob_vec_ptr =
+            const float *__restrict__ prob_vec_ptr =
                 thread_block_logits + block_idx * BLOCK_SIZE;
-            const scalar_t* __restrict__ v_block_cache_ptr =
+            const scalar_t *__restrict__ v_block_cache_ptr =
                 v_cache + physical_block_idx * kv_block_stride +
                 kv_head_idx * kv_head_stride +
                 BLOCK_SIZE * head_part_idx * head_elem_num_per_partition;
@@ -314,7 +311,7 @@ struct paged_attention_v1_impl {
             if (block_idx != block_num - 1) {
               const int64_t next_physical_block_idx =
                   seq_block_table[block_idx + 1];
-              const scalar_t* __restrict__ next_v_block_cache_ptr =
+              const scalar_t *__restrict__ next_v_block_cache_ptr =
                   v_cache + next_physical_block_idx * kv_block_stride +
                   kv_head_idx * kv_head_stride +
                   BLOCK_SIZE * head_part_idx * head_elem_num_per_partition;
@@ -343,16 +340,16 @@ struct paged_attention_v1_impl {
 #define LAUNCH_V1_ATTENTION_KERNEL(T, HEAD_SIZE, BLOCK_SIZE)                   \
   paged_attention_v1_impl<T, HEAD_SIZE, BLOCK_SIZE>::call(                     \
       out_ptr, query_ptr, key_cache_ptr, value_cache_ptr, num_kv_heads, scale, \
-      block_tables_ptr, seq_lens_ptr, max_num_blocks_per_seq,                  \
+      block_tables_ptr, context_lens_ptr, max_num_blocks_per_seq,              \
       alibi_slopes_ptr, q_stride, kv_block_stride, kv_head_stride, num_seqs,   \
       num_heads);
 
 template <typename T, int BLOCK_SIZE>
 void paged_attention_v1_impl_launcher(
-    torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int max_seq_len,
-    const c10::optional<torch::Tensor>& alibi_slopes) {
+    torch::Tensor &out, torch::Tensor &query, torch::Tensor &key_cache,
+    torch::Tensor &value_cache, int num_kv_heads, float scale,
+    torch::Tensor &block_tables, torch::Tensor &context_lens,
+    int max_context_len, const c10::optional<torch::Tensor> &alibi_slopes) {
   int num_seqs = query.size(0);
   int num_heads = query.size(1);
   int head_size = query.size(2);
@@ -362,73 +359,68 @@ void paged_attention_v1_impl_launcher(
   int kv_head_stride = key_cache.stride(1);
 
   // NOTE: alibi_slopes is optional.
-  const float* alibi_slopes_ptr =
+  const float *alibi_slopes_ptr =
       alibi_slopes
-          ? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
+          ? reinterpret_cast<const float *>(alibi_slopes.value().data_ptr())
           : nullptr;
 
-  T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
-  T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
-  T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
-  T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
-  int* block_tables_ptr = block_tables.data_ptr<int>();
-  int* seq_lens_ptr = seq_lens.data_ptr<int>();
+  T *out_ptr = reinterpret_cast<T *>(out.data_ptr());
+  T *query_ptr = reinterpret_cast<T *>(query.data_ptr());
+  T *key_cache_ptr = reinterpret_cast<T *>(key_cache.data_ptr());
+  T *value_cache_ptr = reinterpret_cast<T *>(value_cache.data_ptr());
+  int *block_tables_ptr = block_tables.data_ptr<int>();
+  int *context_lens_ptr = context_lens.data_ptr<int>();
 
   switch (head_size) {
-    case 64:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 64, BLOCK_SIZE);
-      break;
-    case 80:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 80, BLOCK_SIZE);
-      break;
-    case 96:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 96, BLOCK_SIZE);
-      break;
-    case 112:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 112, BLOCK_SIZE);
-      break;
-    case 128:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 128, BLOCK_SIZE);
-      break;
-    case 192:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 192, BLOCK_SIZE);
-      break;
-    case 256:
-      LAUNCH_V1_ATTENTION_KERNEL(T, 256, BLOCK_SIZE);
-      break;
-    default:
-      TORCH_CHECK(false, "Unsupported head size: ", head_size);
-      break;
+  case 64:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 64, BLOCK_SIZE);
+    break;
+  case 80:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 80, BLOCK_SIZE);
+    break;
+  case 96:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 96, BLOCK_SIZE);
+    break;
+  case 112:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 112, BLOCK_SIZE);
+    break;
+  case 128:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 128, BLOCK_SIZE);
+    break;
+  case 256:
+    LAUNCH_V1_ATTENTION_KERNEL(T, 256, BLOCK_SIZE);
+    break;
+  default:
+    TORCH_CHECK(false, "Unsupported head size: ", head_size);
+    break;
   }
 }
 
-#define CALL_V1_KERNEL_LAUNCHER(T, BLOCK_SIZE)                               \
-  paged_attention_v1_impl_launcher<T, BLOCK_SIZE>(                           \
-      out, query, key_cache, value_cache, num_kv_heads, scale, block_tables, \
-      seq_lens, max_seq_len, alibi_slopes);
+#define CALL_V1_KERNEL_LAUNCHER(T, BLOCK_SIZE)                                 \
+  paged_attention_v1_impl_launcher<T, BLOCK_SIZE>(                             \
+      out, query, key_cache, value_cache, num_kv_heads, scale, block_tables,   \
+      context_lens, max_context_len, alibi_slopes);
 
-#define CALL_V1_KERNEL_LAUNCHER_BLOCK_SIZE(T)                     \
-  switch (block_size) {                                           \
-    case 16:                                                      \
-      CALL_V1_KERNEL_LAUNCHER(T, 16);                             \
-      break;                                                      \
-    default:                                                      \
-      TORCH_CHECK(false, "Unsupported block size: ", block_size); \
-      break;                                                      \
+#define CALL_V1_KERNEL_LAUNCHER_BLOCK_SIZE(T)                                  \
+  switch (block_size) {                                                        \
+  case 16:                                                                     \
+    CALL_V1_KERNEL_LAUNCHER(T, 16);                                            \
+    break;                                                                     \
+  default:                                                                     \
+    TORCH_CHECK(false, "Unsupported block size: ", block_size);                \
+    break;                                                                     \
   }
-}  // namespace
+} // namespace
 
-void paged_attention_v1(
-    torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int block_size,
-    int max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
-    const std::string& kv_cache_dtype, float kv_scale, const int tp_rank,
-    const int blocksparse_local_blocks, const int blocksparse_vert_stride,
-    const int blocksparse_block_size, const int blocksparse_head_sliding_step) {
+void paged_attention_v1(torch::Tensor &out, torch::Tensor &query,
+                        torch::Tensor &key_cache, torch::Tensor &value_cache,
+                        int num_kv_heads, float scale,
+                        torch::Tensor &block_tables,
+                        torch::Tensor &context_lens, int block_size,
+                        int max_context_len,
+                        const c10::optional<torch::Tensor> &alibi_slopes,
+                        const std::string &kv_cache_dtype, float kv_scale) {
   TORCH_CHECK(kv_scale == 1.0f);
-  TORCH_CHECK(blocksparse_vert_stride <= 1,
-              "CPU backend does not support blocksparse attention yet.");
   VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "paged_attention_v1_impl",
                                [&] {
                                  CPU_KERNEL_GUARD_IN(paged_attention_v1_impl)
@@ -442,24 +434,23 @@ namespace {
 template <typename scalar_t, int HEAD_SIZE, int BLOCK_SIZE, int PARTITION_SIZE>
 struct paged_attention_v2_impl {
   static void call(
-      scalar_t* __restrict__ out,            // [num_seqs, num_heads, head_size]
-      float* __restrict__ exp_sums,          // [num_seqs, num_heads,
-                                             // max_num_partitions]
-      float* __restrict__ max_logits,        // [num_seqs, num_heads,
-                                             // max_num_partitions]
-      scalar_t* __restrict__ tmp_out,        // [num_seqs, num_heads,
-                                             // max_num_partitions, head_size]
-      const scalar_t* __restrict__ q,        // [num_seqs, num_heads, head_size]
-      const scalar_t* __restrict__ k_cache,  // [num_blocks, num_kv_heads,
-                                             // head_size/x, block_size, x]
-      const scalar_t* __restrict__ v_cache,  // [num_blocks, num_kv_heads,
-                                             // head_size, block_size]
+      scalar_t *__restrict__ out,   // [num_seqs, num_heads, head_size]
+      float *__restrict__ exp_sums, // [num_seqs, num_heads, max_num_partitions]
+      float
+          *__restrict__ max_logits, // [num_seqs, num_heads, max_num_partitions]
+      scalar_t *__restrict__ tmp_out,       // [num_seqs, num_heads,
+                                            // max_num_partitions, head_size]
+      const scalar_t *__restrict__ q,       // [num_seqs, num_heads, head_size]
+      const scalar_t *__restrict__ k_cache, // [num_blocks, num_kv_heads,
+                                            // head_size/x, block_size, x]
+      const scalar_t *__restrict__ v_cache, // [num_blocks, num_kv_heads,
+                                            // head_size, block_size]
       const int num_kv_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
+          *__restrict__ block_tables, // [num_seqs, max_num_blocks_per_seq]
+      const int *__restrict__ context_lens, // [num_seqs]
       const int max_num_blocks_per_seq,
-      const float* __restrict__ alibi_slopes,  // [num_heads]
+      const float *__restrict__ alibi_slopes, // [num_heads]
       const int q_stride, const int kv_block_stride, const int kv_head_stride,
       const int num_seqs, const int num_heads, const int max_num_partitions) {
     constexpr int x = 16 / sizeof(scalar_t);
@@ -474,25 +465,27 @@ struct paged_attention_v2_impl {
       for (int partition_idx = 0; partition_idx < max_num_partitions;
            ++partition_idx) {
         for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
-          const int seq_len = seq_lens[seq_idx];
+          const int context_len = context_lens[seq_idx];
           const int start_token_idx = partition_idx * PARTITION_SIZE;
 
-          if (start_token_idx >= seq_len) continue;
+          if (start_token_idx >= context_len)
+            continue;
 
           const int partition_num =
-              (seq_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
+              (context_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
           const bool no_reduce = (partition_num == 1);
-          const int token_num =
-              (std::min(seq_len, start_token_idx + PARTITION_SIZE) -
+          const int context_token_num =
+              (std::min(context_len, start_token_idx + PARTITION_SIZE) -
                start_token_idx);
-          const int block_num = (token_num + BLOCK_SIZE - 1) / BLOCK_SIZE;
+          const int block_num =
+              (context_token_num + BLOCK_SIZE - 1) / BLOCK_SIZE;
           const int last_block_token_num =
-              token_num - (block_num - 1) * BLOCK_SIZE;
-          const int* seq_block_table = block_tables +
+              context_token_num - (block_num - 1) * BLOCK_SIZE;
+          const int *seq_block_table = block_tables +
                                        max_num_blocks_per_seq * seq_idx +
                                        start_token_idx / BLOCK_SIZE;
           const int64_t kv_head_idx = head_idx / num_queries_per_kv;
-          const scalar_t* __restrict__ q_vec_ptr =
+          const scalar_t *__restrict__ q_vec_ptr =
               q + seq_idx * q_stride + head_idx * HEAD_SIZE;
 
           float logits[PARTITION_SIZE] __attribute__((aligned(64))) = {0};
@@ -500,10 +493,10 @@ struct paged_attention_v2_impl {
           // Compute logits
           for (int block_idx = 0; block_idx < block_num; ++block_idx) {
             const int64_t physical_block_idx = seq_block_table[block_idx];
-            const scalar_t* __restrict__ k_block_cache_ptr =
+            const scalar_t *__restrict__ k_block_cache_ptr =
                 k_cache + physical_block_idx * kv_block_stride +
                 kv_head_idx * kv_head_stride;
-            float* __restrict__ head_block_logits =
+            float *__restrict__ head_block_logits =
                 logits + block_idx * BLOCK_SIZE;
 
             reduceQKBlockKernel<scalar_t, HEAD_SIZE, BLOCK_SIZE, x>::call(
@@ -514,16 +507,16 @@ struct paged_attention_v2_impl {
           std::pair<float, float> max_and_sum;
           if (alibi_slopes) {
             max_and_sum = reduceSoftmaxAlibi(
-                logits, token_num, block_num * BLOCK_SIZE,
-                alibi_slopes[head_idx], start_token_idx, seq_len);
+                logits, context_token_num, block_num * BLOCK_SIZE,
+                alibi_slopes[head_idx], start_token_idx, context_len);
           } else {
-            max_and_sum =
-                reduceSoftmax(logits, token_num, block_num * BLOCK_SIZE);
+            max_and_sum = reduceSoftmax(logits, context_token_num,
+                                        block_num * BLOCK_SIZE);
           }
 
-          auto&& [max_logit, exp_sum] = max_and_sum;
+          auto &&[max_logit, exp_sum] = max_and_sum;
 
-          scalar_t* __restrict__ output_buffer = nullptr;
+          scalar_t *__restrict__ output_buffer = nullptr;
           if (!no_reduce) {
             auto idx = seq_idx * num_heads * max_num_partitions +
                        head_idx * max_num_partitions + partition_idx;
@@ -545,13 +538,13 @@ struct paged_attention_v2_impl {
           for (int head_part_idx = 0; head_part_idx < head_partition_num;
                ++head_part_idx) {
             vec_op::FP32Vec16 accums[head_elem_num_per_partition];
-            scalar_t* __restrict__ out_ptr =
+            scalar_t *__restrict__ out_ptr =
                 output_buffer + head_part_idx * head_elem_num_per_partition;
             for (int block_idx = 0; block_idx < block_num; ++block_idx) {
               const int64_t physical_block_idx = seq_block_table[block_idx];
-              const float* __restrict__ prob_vec_ptr =
+              const float *__restrict__ prob_vec_ptr =
                   logits + block_idx * BLOCK_SIZE;
-              const scalar_t* __restrict__ v_block_cache_ptr =
+              const scalar_t *__restrict__ v_block_cache_ptr =
                   v_cache + physical_block_idx * kv_block_stride +
                   kv_head_idx * kv_head_stride +
                   BLOCK_SIZE * head_part_idx * head_elem_num_per_partition;
@@ -562,7 +555,7 @@ struct paged_attention_v2_impl {
               if (block_idx != block_num - 1) {
                 const int64_t next_physical_block_idx =
                     seq_block_table[block_idx + 1];
-                const scalar_t* __restrict__ next_v_block_cache_ptr =
+                const scalar_t *__restrict__ next_v_block_cache_ptr =
                     v_cache + next_physical_block_idx * kv_block_stride +
                     kv_head_idx * kv_head_stride +
                     BLOCK_SIZE * head_part_idx * head_elem_num_per_partition;
@@ -590,11 +583,12 @@ struct paged_attention_v2_impl {
 #pragma omp parallel for collapse(2) schedule(static, 1)
     for (int seq_idx = 0; seq_idx < num_seqs; ++seq_idx) {
       for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
-        const int seq_len = seq_lens[seq_idx];
+        const int context_len = context_lens[seq_idx];
         const int partition_num =
-            (seq_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
+            (context_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
 
-        if (partition_num == 1) continue;
+        if (partition_num == 1)
+          continue;
 
         reducePartitonSoftmax(
             max_logits + seq_idx * num_heads * max_num_partitions +
@@ -609,29 +603,30 @@ struct paged_attention_v2_impl {
     using v_load_vec_type = typename KernelVecType<scalar_t>::v_load_vec_type;
     static_assert(v_load_vec_type::get_elem_num() == BLOCK_SIZE);
     constexpr int head_elem_num_per_group =
-        16;  // Note: didn't align with the cacheline size, due to some
-             // HEAD_SIZE didn't align with 64 bytes
+        16; // Note: didn't align with the cacheline size, due to some HEAD_SIZE
+            // didn't align with 64 bytes
     static_assert(HEAD_SIZE % head_elem_num_per_group == 0);
     constexpr int head_group_num = HEAD_SIZE / head_elem_num_per_group;
-    const float* __restrict__ rescale_factors = exp_sums;
+    const float *__restrict__ rescale_factors = exp_sums;
 #pragma omp parallel for collapse(3) schedule(static, 1)
     for (int seq_idx = 0; seq_idx < num_seqs; ++seq_idx) {
       for (int head_idx = 0; head_idx < num_heads; ++head_idx) {
         for (int group_idx = 0; group_idx < head_group_num; ++group_idx) {
-          const int seq_len = seq_lens[seq_idx];
+          const int context_len = context_lens[seq_idx];
           const int partition_num =
-              (seq_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
+              (context_len + PARTITION_SIZE - 1) / PARTITION_SIZE;
 
-          if (partition_num == 1) continue;
+          if (partition_num == 1)
+            continue;
 
-          const float* __restrict__ seq_head_rescale_factors =
+          const float *__restrict__ seq_head_rescale_factors =
               rescale_factors + seq_idx * num_heads * max_num_partitions +
               head_idx * max_num_partitions;
-          const scalar_t* __restrict__ seq_head_tmp_out =
+          const scalar_t *__restrict__ seq_head_tmp_out =
               tmp_out + seq_idx * num_heads * max_num_partitions * HEAD_SIZE +
               head_idx * max_num_partitions * HEAD_SIZE +
               group_idx * head_elem_num_per_group;
-          scalar_t* __restrict__ seq_head_output =
+          scalar_t *__restrict__ seq_head_output =
               out + seq_idx * num_heads * HEAD_SIZE + head_idx * HEAD_SIZE +
               group_idx * head_elem_num_per_group;
 
@@ -650,21 +645,21 @@ struct paged_attention_v2_impl {
   }
 };
 
-#define LAUNCH_V2_ATTENTION_KERNEL(T, HEAD_SIZE, BLOCK_SIZE)                 \
-  paged_attention_v2_impl<T, HEAD_SIZE, BLOCK_SIZE, PARTITION_SIZE>::call(   \
-      out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, query_ptr,         \
-      key_cache_ptr, value_cache_ptr, num_kv_heads, scale, block_tables_ptr, \
-      seq_lens_ptr, max_num_blocks_per_seq, alibi_slopes_ptr, q_stride,      \
-      kv_block_stride, kv_head_stride, num_seqs, num_heads,                  \
+#define LAUNCH_V2_ATTENTION_KERNEL(T, HEAD_SIZE, BLOCK_SIZE)                   \
+  paged_attention_v2_impl<T, HEAD_SIZE, BLOCK_SIZE, PARTITION_SIZE>::call(     \
+      out_ptr, exp_sums_ptr, max_logits_ptr, tmp_out_ptr, query_ptr,           \
+      key_cache_ptr, value_cache_ptr, num_kv_heads, scale, block_tables_ptr,   \
+      context_lens_ptr, max_num_blocks_per_seq, alibi_slopes_ptr, q_stride,    \
+      kv_block_stride, kv_head_stride, num_seqs, num_heads,                    \
       max_num_partitions);
 
 template <typename T, int BLOCK_SIZE, int PARTITION_SIZE = 512>
 void paged_attention_v2_impl_launcher(
-    torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
-    torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int block_size,
-    int max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes) {
+    torch::Tensor &out, torch::Tensor &exp_sums, torch::Tensor &max_logits,
+    torch::Tensor &tmp_out, torch::Tensor &query, torch::Tensor &key_cache,
+    torch::Tensor &value_cache, int num_kv_heads, float scale,
+    torch::Tensor &block_tables, torch::Tensor &context_lens, int block_size,
+    int max_context_len, const c10::optional<torch::Tensor> &alibi_slopes) {
   int num_seqs = query.size(0);
   int num_heads = query.size(1);
   int head_size = query.size(2);
@@ -675,78 +670,73 @@ void paged_attention_v2_impl_launcher(
   int max_num_partitions = exp_sums.size(-1);
 
   // NOTE: alibi_slopes is optional.
-  const float* alibi_slopes_ptr =
+  const float *alibi_slopes_ptr =
       alibi_slopes
-          ? reinterpret_cast<const float*>(alibi_slopes.value().data_ptr())
+          ? reinterpret_cast<const float *>(alibi_slopes.value().data_ptr())
           : nullptr;
 
-  T* out_ptr = reinterpret_cast<T*>(out.data_ptr());
-  float* exp_sums_ptr = reinterpret_cast<float*>(exp_sums.data_ptr());
-  float* max_logits_ptr = reinterpret_cast<float*>(max_logits.data_ptr());
-  T* tmp_out_ptr = reinterpret_cast<T*>(tmp_out.data_ptr());
-  T* query_ptr = reinterpret_cast<T*>(query.data_ptr());
-  T* key_cache_ptr = reinterpret_cast<T*>(key_cache.data_ptr());
-  T* value_cache_ptr = reinterpret_cast<T*>(value_cache.data_ptr());
-  int* block_tables_ptr = block_tables.data_ptr<int>();
-  int* seq_lens_ptr = seq_lens.data_ptr<int>();
+  T *out_ptr = reinterpret_cast<T *>(out.data_ptr());
+  float *exp_sums_ptr = reinterpret_cast<float *>(exp_sums.data_ptr());
+  float *max_logits_ptr = reinterpret_cast<float *>(max_logits.data_ptr());
+  T *tmp_out_ptr = reinterpret_cast<T *>(tmp_out.data_ptr());
+  T *query_ptr = reinterpret_cast<T *>(query.data_ptr());
+  T *key_cache_ptr = reinterpret_cast<T *>(key_cache.data_ptr());
+  T *value_cache_ptr = reinterpret_cast<T *>(value_cache.data_ptr());
+  int *block_tables_ptr = block_tables.data_ptr<int>();
+  int *context_lens_ptr = context_lens.data_ptr<int>();
 
   switch (head_size) {
-    case 64:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 64, BLOCK_SIZE);
-      break;
-    case 80:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 80, BLOCK_SIZE);
-      break;
-    case 96:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 96, BLOCK_SIZE);
-      break;
-    case 112:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 112, BLOCK_SIZE);
-      break;
-    case 128:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 128, BLOCK_SIZE);
-      break;
-    case 192:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 192, BLOCK_SIZE);
-      break;
-    case 256:
-      LAUNCH_V2_ATTENTION_KERNEL(T, 256, BLOCK_SIZE);
-      break;
-    default:
-      TORCH_CHECK(false, "Unsupported head size: ", head_size);
-      break;
+  case 64:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 64, BLOCK_SIZE);
+    break;
+  case 80:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 80, BLOCK_SIZE);
+    break;
+  case 96:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 96, BLOCK_SIZE);
+    break;
+  case 112:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 112, BLOCK_SIZE);
+    break;
+  case 128:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 128, BLOCK_SIZE);
+    break;
+  case 256:
+    LAUNCH_V2_ATTENTION_KERNEL(T, 256, BLOCK_SIZE);
+    break;
+  default:
+    TORCH_CHECK(false, "Unsupported head size: ", head_size);
+    break;
   }
 }
 
-#define CALL_V2_KERNEL_LAUNCHER(T, BLOCK_SIZE)                              \
-  paged_attention_v2_impl_launcher<T, BLOCK_SIZE>(                          \
-      out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache,    \
-      num_kv_heads, scale, block_tables, seq_lens, block_size, max_seq_len, \
-      alibi_slopes);
+#define CALL_V2_KERNEL_LAUNCHER(T, BLOCK_SIZE)                                 \
+  paged_attention_v2_impl_launcher<T, BLOCK_SIZE>(                             \
+      out, exp_sums, max_logits, tmp_out, query, key_cache, value_cache,       \
+      num_kv_heads, scale, block_tables, context_lens, block_size,             \
+      max_context_len, alibi_slopes);
 
-#define CALL_V2_KERNEL_LAUNCHER_BLOCK_SIZE(T)                     \
-  switch (block_size) {                                           \
-    case 16:                                                      \
-      CALL_V2_KERNEL_LAUNCHER(T, 16);                             \
-      break;                                                      \
-    default:                                                      \
-      TORCH_CHECK(false, "Unsupported block size: ", block_size); \
-      break;                                                      \
+#define CALL_V2_KERNEL_LAUNCHER_BLOCK_SIZE(T)                                  \
+  switch (block_size) {                                                        \
+  case 16:                                                                     \
+    CALL_V2_KERNEL_LAUNCHER(T, 16);                                            \
+    break;                                                                     \
+  default:                                                                     \
+    TORCH_CHECK(false, "Unsupported block size: ", block_size);                \
+    break;                                                                     \
   }
-}  // namespace
+} // namespace
 
-void paged_attention_v2(
-    torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
-    torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int block_size,
-    int max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
-    const std::string& kv_cache_dtype, float kv_scale, const int tp_rank,
-    const int blocksparse_local_blocks, const int blocksparse_vert_stride,
-    const int blocksparse_block_size, const int blocksparse_head_sliding_step) {
+void paged_attention_v2(torch::Tensor &out, torch::Tensor &exp_sums,
+                        torch::Tensor &max_logits, torch::Tensor &tmp_out,
+                        torch::Tensor &query, torch::Tensor &key_cache,
+                        torch::Tensor &value_cache, int num_kv_heads,
+                        float scale, torch::Tensor &block_tables,
+                        torch::Tensor &context_lens, int block_size,
+                        int max_context_len,
+                        const c10::optional<torch::Tensor> &alibi_slopes,
+                        const std::string &kv_cache_dtype, float kv_scale) {
   TORCH_CHECK(kv_scale == 1.0f);
-  TORCH_CHECK(blocksparse_vert_stride <= 1,
-              "CPU backend does not support blocksparse attention yet.");
   VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "paged_attention_v2_impl",
                                [&] {
                                  CPU_KERNEL_GUARD_IN(paged_attention_v2_impl)

csrc/cpu/cache.cpp

@@ -5,26 +5,25 @@
 
 namespace {
 template <typename scalar_t>
-void copy_blocks_cpu_impl(std::vector<torch::Tensor>& key_caches,
-                          std::vector<torch::Tensor>& value_caches,
-                          const torch::Tensor& mapping_pairs,
-                          const int element_num_per_block,
-                          const int layer_num) {
-  const size_t pair_num = mapping_pairs.size(0);
+void copy_blocks_cpu_impl(
+    std::vector<torch::Tensor> &key_caches,
+    std::vector<torch::Tensor> &value_caches,
+    const std::vector<std::pair<int64_t, int64_t>> mapping_pairs,
+    const int element_num_per_block, const int layer_num) {
+  const size_t pair_num = mapping_pairs.size();
   const size_t block_bytes = sizeof(scalar_t) * element_num_per_block;
 #pragma omp parallel for collapse(2)
   for (int layer = 0; layer < layer_num; ++layer) {
     for (size_t pair = 0; pair < pair_num; ++pair) {
-      int64_t source_offset =
-          element_num_per_block * mapping_pairs[pair][0].item<int64_t>();
+      int64_t source_offset = element_num_per_block * mapping_pairs[pair].first;
       int64_t target_offset =
-          element_num_per_block * mapping_pairs[pair][1].item<int64_t>();
-      scalar_t* key_cache_ptr = key_caches[layer].data_ptr<scalar_t>();
-      scalar_t* source_ptr = key_cache_ptr + source_offset;
-      scalar_t* target_ptr = key_cache_ptr + target_offset;
+          element_num_per_block * mapping_pairs[pair].second;
+      scalar_t *key_cache_ptr = key_caches[layer].data_ptr<scalar_t>();
+      scalar_t *source_ptr = key_cache_ptr + source_offset;
+      scalar_t *target_ptr = key_cache_ptr + target_offset;
       std::memcpy(target_ptr, source_ptr, block_bytes);
 
-      scalar_t* value_cache_ptr = value_caches[layer].data_ptr<scalar_t>();
+      scalar_t *value_cache_ptr = value_caches[layer].data_ptr<scalar_t>();
       source_ptr = value_cache_ptr + source_offset;
       target_ptr = value_cache_ptr + target_offset;
       std::memcpy(target_ptr, source_ptr, block_bytes);
@@ -34,9 +33,9 @@ void copy_blocks_cpu_impl(std::vector<torch::Tensor>& key_caches,
 
 template <typename scalar_t>
 void reshape_and_cache_cpu_impl(
-    const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-    scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
-    const int64_t* __restrict__ slot_mapping, const int num_tokens,
+    const scalar_t *__restrict__ key, const scalar_t *__restrict__ value,
+    scalar_t *__restrict__ key_cache, scalar_t *__restrict__ value_cache,
+    const int64_t *__restrict__ slot_mapping, const int num_tokens,
     const int key_stride, const int value_stride, const int num_heads,
     const int head_size, const int block_size, const int x) {
   const int block_elem_num = num_heads * head_size * block_size;
@@ -49,14 +48,14 @@ void reshape_and_cache_cpu_impl(
         int src_key_head_idx = token_idx * key_stride + head_idx * head_size;
         int src_value_head_idx =
             token_idx * value_stride + head_idx * head_size;
-        const scalar_t* src_key_head_ptr = key + src_key_head_idx;
-        const scalar_t* src_value_head_ptr = value + src_value_head_idx;
+        const scalar_t *src_key_head_ptr = key + src_key_head_idx;
+        const scalar_t *src_value_head_ptr = value + src_value_head_idx;
         const int64_t block_index = slot_idx / block_size;
         const int64_t block_offset = slot_idx % block_size;
-        scalar_t* target_key_head_ptr = key_cache +
+        scalar_t *target_key_head_ptr = key_cache +
                                         block_elem_num * block_index +
                                         head_idx * block_size * head_size;
-        scalar_t* target_value_head_ptr = value_cache +
+        scalar_t *target_value_head_ptr = value_cache +
                                           block_elem_num * block_index +
                                           head_idx * block_size * head_size;
 
@@ -80,31 +79,39 @@ void reshape_and_cache_cpu_impl(
     }
   }
 }
-};  // namespace
+}; // namespace
 
-void copy_blocks(std::vector<torch::Tensor>& key_caches,
-                 std::vector<torch::Tensor>& value_caches,
-                 const torch::Tensor& block_mapping) {
-  unsigned num_layers = key_caches.size();
+void copy_blocks(std::vector<torch::Tensor> &key_caches,
+                 std::vector<torch::Tensor> &value_caches,
+                 const std::map<int64_t, std::vector<int64_t>> &block_mapping) {
+  int num_layers = key_caches.size();
   TORCH_CHECK(num_layers == value_caches.size());
   if (num_layers == 0) {
     return;
   }
 
+  std::vector<std::pair<int64_t, int64_t>> mapping_pairs;
+  mapping_pairs.reserve(block_mapping.size());
+  for (const auto &pair : block_mapping) {
+    for (const auto &dst : pair.second) {
+      mapping_pairs.emplace_back(pair.first, dst);
+    }
+  }
+
   const int element_num_per_block = key_caches[0][0].numel();
   VLLM_DISPATCH_FLOATING_TYPES(
       key_caches[0].scalar_type(), "copy_blocks_cpu_impl", [&] {
         CPU_KERNEL_GUARD_IN(copy_blocks_cpu_impl)
-        copy_blocks_cpu_impl<scalar_t>(key_caches, value_caches, block_mapping,
+        copy_blocks_cpu_impl<scalar_t>(key_caches, value_caches, mapping_pairs,
                                        element_num_per_block, num_layers);
         CPU_KERNEL_GUARD_OUT(copy_blocks_cpu_impl)
       });
 }
 
-void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
-                       torch::Tensor& key_cache, torch::Tensor& value_cache,
-                       torch::Tensor& slot_mapping,
-                       const std::string& kv_cache_dtype, float kv_scale) {
+void reshape_and_cache(torch::Tensor &key, torch::Tensor &value,
+                       torch::Tensor &key_cache, torch::Tensor &value_cache,
+                       torch::Tensor &slot_mapping,
+                       const std::string &kv_cache_dtype, float kv_scale) {
   TORCH_CHECK(kv_scale == 1.0f);
 
   int num_tokens = key.size(0);
@@ -128,7 +135,7 @@ void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
       });
 }
 
-void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
-                 const torch::Tensor& block_mapping) {
+void swap_blocks(torch::Tensor &src, torch::Tensor &dst,
+                 const std::map<int64_t, int64_t> &block_mapping) {
   TORCH_CHECK(false, "swap_blocks is unsupported on CPU.")
 }

csrc/cpu/layernorm.cpp

@@ -2,10 +2,10 @@
 
 namespace {
 template <typename scalar_t>
-void rms_norm_impl(scalar_t* __restrict__ out,
-                   const scalar_t* __restrict__ input,
-                   const scalar_t* __restrict__ weight, const float epsilon,
-                   const int num_tokens, const int hidden_size) {
+void rms_norm_impl(scalar_t *__restrict__ out,
+                       const scalar_t *__restrict__ input,
+                       const scalar_t *__restrict__ weight, const float epsilon,
+                       const int num_tokens, const int hidden_size) {
   using scalar_vec_t = vec_op::vec_t<scalar_t>;
   constexpr int VEC_ELEM_NUM = scalar_vec_t::get_elem_num();
   TORCH_CHECK(hidden_size % VEC_ELEM_NUM == 0);
@@ -41,11 +41,11 @@ void rms_norm_impl(scalar_t* __restrict__ out,
 }
 
 template <typename scalar_t>
-void fused_add_rms_norm_impl(scalar_t* __restrict__ input,
-                             scalar_t* __restrict__ residual,
-                             const scalar_t* __restrict__ weight,
-                             const float epsilon, const int num_tokens,
-                             const int hidden_size) {
+void fused_add_rms_norm_impl(scalar_t *__restrict__ input,
+                                 scalar_t *__restrict__ residual,
+                                 const scalar_t *__restrict__ weight,
+                                 const float epsilon, const int num_tokens,
+                                 const int hidden_size) {
   using scalar_vec_t = vec_op::vec_t<scalar_t>;
   constexpr int VEC_ELEM_NUM = scalar_vec_t::get_elem_num();
   TORCH_CHECK(hidden_size % VEC_ELEM_NUM == 0);
@@ -85,24 +85,24 @@ void fused_add_rms_norm_impl(scalar_t* __restrict__ input,
     }
   }
 }
-}  // namespace
+} // namespace
 
-void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
-              float epsilon) {
+void rms_norm(torch::Tensor &out, torch::Tensor &input,
+                  torch::Tensor &weight, float epsilon) {
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 
   VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_impl", [&] {
     CPU_KERNEL_GUARD_IN(rms_norm_impl)
     rms_norm_impl(out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
-                  weight.data_ptr<scalar_t>(), epsilon, num_tokens,
-                  hidden_size);
+                      weight.data_ptr<scalar_t>(), epsilon, num_tokens,
+                      hidden_size);
     CPU_KERNEL_GUARD_OUT(rms_norm_impl)
   });
 }
 
-void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
-                        torch::Tensor& weight, float epsilon) {
+void fused_add_rms_norm(torch::Tensor &input, torch::Tensor &residual,
+                            torch::Tensor &weight, float epsilon) {
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 

csrc/cpu/pos_encoding.cpp

@@ -4,21 +4,22 @@
 namespace {
 template <typename scalar_t>
 void rotary_embedding_impl(
-    const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
-                                            // [num_tokens]
-    scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
-                                   /// head_size] or [num_tokens, num_heads,
-                                   /// head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // head_size] or [num_tokens, num_kv_heads,
-                                 // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
+    const int64_t
+        *__restrict__ positions, // [batch_size, seq_len] or [num_tokens]
+    scalar_t
+        *__restrict__ query, /// [batch_size, seq_len, num_heads, head_size] or
+                             /// [num_tokens, num_heads, head_size]
+    scalar_t
+        *__restrict__ key, // [batch_size, seq_len, num_kv_heads, head_size] or
+                           // [num_tokens, num_kv_heads, head_size]
+    const scalar_t
+        *__restrict__ cos_sin_cache, // [max_position, 2, rot_dim // 2]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
     const int num_heads, const int num_kv_heads, const int head_size,
     const int num_tokens) {
   using scalar_vec_t = vec_op::vec_t<scalar_t>;
   constexpr int VEC_ELEM_NUM = scalar_vec_t::get_elem_num();
+  constexpr int ELEM_SIZE = sizeof(scalar_t);
 
   const int embed_dim = rot_dim / 2;
   TORCH_CHECK(embed_dim % VEC_ELEM_NUM == 0);
@@ -26,7 +27,7 @@ void rotary_embedding_impl(
 #pragma omp parallel for
   for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
     int64_t pos = positions[token_idx];
-    const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
+    const scalar_t *cache_ptr = cos_sin_cache + pos * rot_dim;
 
     for (int i = 0; i < num_heads; ++i) {
       const int head_idx = i;
@@ -94,16 +95,16 @@ void rotary_embedding_impl(
 
 template <typename scalar_t>
 void rotary_embedding_gptj_impl(
-    const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
-                                            // [num_tokens]
-    scalar_t* __restrict__ query,           /// [batch_size, seq_len, num_heads,
-                                   /// head_size] or [num_tokens, num_heads,
-                                   /// head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // head_size] or [num_tokens, num_kv_heads,
-                                 // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
+    const int64_t
+        *__restrict__ positions, // [batch_size, seq_len] or [num_tokens]
+    scalar_t
+        *__restrict__ query, /// [batch_size, seq_len, num_heads, head_size] or
+                             /// [num_tokens, num_heads, head_size]
+    scalar_t
+        *__restrict__ key, // [batch_size, seq_len, num_kv_heads, head_size] or
+                           // [num_tokens, num_kv_heads, head_size]
+    const scalar_t
+        *__restrict__ cos_sin_cache, // [max_position, 2, rot_dim // 2]
     const int rot_dim, const int64_t query_stride, const int64_t key_stride,
     const int num_heads, const int num_kv_heads, const int head_size,
     const int num_tokens) {
@@ -113,13 +114,13 @@ void rotary_embedding_gptj_impl(
   for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
     for (int i = 0; i < num_heads; ++i) {
       int64_t pos = positions[token_idx];
-      const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
-      const scalar_t* cos_cache_ptr = cache_ptr;
-      const scalar_t* sin_cache_ptr = cache_ptr + embed_dim;
+      const scalar_t *cache_ptr = cos_sin_cache + pos * rot_dim;
+      const scalar_t *cos_cache_ptr = cache_ptr;
+      const scalar_t *sin_cache_ptr = cache_ptr + embed_dim;
       const int head_idx = i;
       const int64_t token_head =
           token_idx * query_stride + head_idx * head_size;
-      scalar_t* head_query = token_head + query;
+      scalar_t *head_query = token_head + query;
       for (int j = 0; j < embed_dim; j += 1) {
         const int rot_offset = j;
         const int x_index = 2 * rot_offset;
@@ -141,12 +142,12 @@ void rotary_embedding_gptj_impl(
   for (int token_idx = 0; token_idx < num_tokens; ++token_idx) {
     for (int i = 0; i < num_kv_heads; ++i) {
       int64_t pos = positions[token_idx];
-      const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
-      const scalar_t* cos_cache_ptr = cache_ptr;
-      const scalar_t* sin_cache_ptr = cache_ptr + embed_dim;
+      const scalar_t *cache_ptr = cos_sin_cache + pos * rot_dim;
+      const scalar_t *cos_cache_ptr = cache_ptr;
+      const scalar_t *sin_cache_ptr = cache_ptr + embed_dim;
       const int head_idx = i;
       const int64_t token_head = token_idx * key_stride + head_idx * head_size;
-      scalar_t* head_key = key + token_head;
+      scalar_t *head_key = key + token_head;
       for (int j = 0; j < embed_dim; j += 1) {
         const int rot_offset = j;
         const int x_index = 2 * rot_offset;
@@ -164,11 +165,11 @@ void rotary_embedding_gptj_impl(
     }
   }
 }
-};  // namespace
+}; // namespace
 
-void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                      torch::Tensor& key, int head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox) {
+void rotary_embedding(torch::Tensor &positions, torch::Tensor &query,
+                          torch::Tensor &key, int head_size,
+                          torch::Tensor &cos_sin_cache, bool is_neox) {
   int num_tokens = query.numel() / query.size(-1);
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;

csrc/cpu/pybind.cpp

@@ -8,37 +8,66 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   pybind11::module ops = m.def_submodule("ops", "vLLM custom operators");
 
   // Attention ops
-  ops.def("paged_attention_v1", &paged_attention_v1,
-          "Compute the attention between an input query and the cached "
-          "keys/values using PagedAttention.");
-  ops.def("paged_attention_v2", &paged_attention_v2, "PagedAttention V2.");
+  ops.def(
+    "paged_attention_v1",
+    &paged_attention_v1,
+    "Compute the attention between an input query and the cached keys/values using PagedAttention.");
+  ops.def(
+    "paged_attention_v2",
+    &paged_attention_v2,
+    "PagedAttention V2.");
 
   // Activation ops
-  ops.def("silu_and_mul", &silu_and_mul, "Activation function used in SwiGLU.");
-  ops.def("gelu_and_mul", &gelu_and_mul,
-          "Activation function used in GeGLU with `none` approximation.");
-  ops.def("gelu_tanh_and_mul", &gelu_tanh_and_mul,
-          "Activation function used in GeGLU with `tanh` approximation.");
-  ops.def("gelu_new", &gelu_new, "GELU implementation used in GPT-2.");
-  ops.def("gelu_fast", &gelu_fast, "Approximate GELU implementation.");
+  ops.def(
+    "silu_and_mul",
+    &silu_and_mul,
+    "Activation function used in SwiGLU.");
+  ops.def(
+    "gelu_and_mul",
+    &gelu_and_mul,
+    "Activation function used in GeGLU with `none` approximation.");
+  ops.def(
+    "gelu_tanh_and_mul",
+    &gelu_tanh_and_mul,
+    "Activation function used in GeGLU with `tanh` approximation.");
+  ops.def(
+    "gelu_new",
+    &gelu_new,
+    "GELU implementation used in GPT-2.");
+  ops.def(
+    "gelu_fast",
+    &gelu_fast,
+    "Approximate GELU implementation.");
 
   // Layernorm
-  ops.def("rms_norm", &rms_norm,
-          "Apply Root Mean Square (RMS) Normalization to the input tensor.");
+  ops.def(
+    "rms_norm",
+    &rms_norm,
+    "Apply Root Mean Square (RMS) Normalization to the input tensor.");
 
-  ops.def("fused_add_rms_norm", &fused_add_rms_norm,
-          "In-place fused Add and RMS Normalization");
+  ops.def(
+    "fused_add_rms_norm",
+    &fused_add_rms_norm,
+    "In-place fused Add and RMS Normalization");
 
   // Rotary embedding
-  ops.def("rotary_embedding", &rotary_embedding,
-          "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
+  ops.def(
+    "rotary_embedding",
+    &rotary_embedding,
+    "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
 
   // Cache ops
   pybind11::module cache_ops = m.def_submodule("cache_ops", "vLLM cache ops");
-  cache_ops.def("swap_blocks", &swap_blocks,
-                "Swap in (out) the cache blocks from src to dst");
-  cache_ops.def("copy_blocks", &copy_blocks,
-                "Copy the cache blocks from src to dst");
-  cache_ops.def("reshape_and_cache", &reshape_and_cache,
-                "Reshape the key and value tensors and cache them");
+  cache_ops.def(
+    "swap_blocks",
+    &swap_blocks,
+    "Swap in (out) the cache blocks from src to dst");
+  cache_ops.def(
+    "copy_blocks",
+    &copy_blocks,
+    "Copy the cache blocks from src to dst");
+  cache_ops.def(
+    "reshape_and_cache",
+    &reshape_and_cache,
+    "Reshape the key and value tensors and cache them");
 }

csrc/cuda_compat.h

@@ -1,7 +1,7 @@
 #pragma once
 
 #ifdef USE_ROCM
-  #include <hip/hip_runtime.h>
+#include <hip/hip_runtime.h>
 #endif
 
 #ifndef USE_ROCM
@@ -17,8 +17,7 @@
 #endif
 
 #ifndef USE_ROCM
-  #define VLLM_SHFL_XOR_SYNC(var, lane_mask) \
-    __shfl_xor_sync(uint32_t(-1), var, lane_mask)
+  #define VLLM_SHFL_XOR_SYNC(var, lane_mask) __shfl_xor_sync(uint32_t(-1), var, lane_mask)
 #else
   #define VLLM_SHFL_XOR_SYNC(var, lane_mask) __shfl_xor(var, lane_mask)
 #endif
@@ -29,13 +28,6 @@
   #define VLLM_SHFL_SYNC(var, src_lane) __shfl(var, src_lane)
 #endif
 
-#ifndef USE_ROCM
-  #define VLLM_SHFL_DOWN_SYNC(var, lane_delta) \
-    __shfl_down_sync(uint32_t(-1), var, lane_delta)
-#else
-  #define VLLM_SHFL_DOWN_SYNC(var, lane_delta) __shfl_down(var, lane_delta)
-#endif
-
 #ifndef USE_ROCM
   #define VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(FUNC, VAL) \
     cudaFuncSetAttribute(FUNC, cudaFuncAttributeMaxDynamicSharedMemorySize, VAL)
@@ -43,3 +35,4 @@
   #define VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(FUNC, VAL) \
     hipFuncSetAttribute(FUNC, hipFuncAttributeMaxDynamicSharedMemorySize, VAL)
 #endif
+

csrc/cuda_utils.h

@@ -2,6 +2,9 @@
 
 #include <torch/extension.h>
 
-int get_device_attribute(int attribute, int device_id);
+int get_device_attribute(
+    int attribute,
+    int device_id);
 
-int get_max_shared_memory_per_block_device_attribute(int device_id);
+int get_max_shared_memory_per_block_device_attribute(
+    int device_id);

csrc/cuda_utils_kernels.cu

@@ -2,28 +2,34 @@
   #include <hip/hip_runtime.h>
   #include <hip/hip_runtime_api.h>
 #endif
-int get_device_attribute(int attribute, int device_id) {
-  int device, value;
-  if (device_id < 0) {
-    cudaGetDevice(&device);
-  } else {
-    device = device_id;
-  }
-  cudaDeviceGetAttribute(&value, static_cast<cudaDeviceAttr>(attribute),
-                         device);
-  return value;
+int get_device_attribute(
+    int attribute,
+    int device_id)
+{
+    int device, value;
+    if (device_id < 0) {
+        cudaGetDevice(&device);
+    }
+    else {
+        device = device_id;
+    }
+    cudaDeviceGetAttribute(&value, static_cast<cudaDeviceAttr>(attribute), device);
+    return value;
 }
 
-int get_max_shared_memory_per_block_device_attribute(int device_id) {
-  int attribute;
-  // https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html
-  // cudaDevAttrMaxSharedMemoryPerBlockOptin = 97 if not is_hip() else 74
+
+int get_max_shared_memory_per_block_device_attribute(
+    int device_id)
+{
+int attribute;    
+// https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html
+// cudaDevAttrMaxSharedMemoryPerBlockOptin = 97 if not is_hip() else 74
 
 #ifdef USE_ROCM
-  attribute = hipDeviceAttributeMaxSharedMemoryPerBlock;
+    attribute = hipDeviceAttributeMaxSharedMemoryPerBlock;
 #else
-  attribute = cudaDevAttrMaxSharedMemoryPerBlockOptin;
+    attribute = cudaDevAttrMaxSharedMemoryPerBlockOptin;
 #endif
 
-  return get_device_attribute(attribute, device_id);
+    return get_device_attribute(attribute, device_id);
 }

csrc/custom_all_reduce.cu

@@ -7,11 +7,11 @@
 
 // fake pointer type
 using fptr_t = uint64_t;
-static_assert(sizeof(void*) == sizeof(fptr_t));
+static_assert(sizeof(void *) == sizeof(fptr_t));
 
-fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
-                      const std::vector<std::string>& handles,
-                      const std::vector<int64_t>& offsets, int rank,
+fptr_t init_custom_ar(torch::Tensor &meta, torch::Tensor &rank_data,
+                      const std::vector<std::string> &handles,
+                      const std::vector<int64_t> &offsets, int rank,
                       bool full_nvlink) {
   int world_size = offsets.size();
   if (world_size > 8)
@@ -29,7 +29,7 @@ fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
     std::memcpy(&ipc_handles[i], handles[i].data(), sizeof(cudaIpcMemHandle_t));
   }
   return (fptr_t) new vllm::CustomAllreduce(
-      reinterpret_cast<vllm::Signal*>(meta.data_ptr()), rank_data.data_ptr(),
+      reinterpret_cast<vllm::Signal *>(meta.data_ptr()), rank_data.data_ptr(),
       rank_data.numel(), ipc_handles, offsets, rank, full_nvlink);
 }
 
@@ -49,13 +49,13 @@ fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
  * 5. A[None].expand(2, -1, -1, -1): Not OK
  * 6. A[:, 1:, 1:]: Not OK
  */
-bool _is_weak_contiguous(torch::Tensor& t) {
+bool _is_weak_contiguous(torch::Tensor &t) {
   return t.is_contiguous() ||
          (t.storage().nbytes() - t.storage_offset() * t.element_size() ==
           t.numel() * t.element_size());
 }
 
-bool should_custom_ar(torch::Tensor& inp, int max_size, int world_size,
+bool should_custom_ar(torch::Tensor &inp, int max_size, int world_size,
                       bool full_nvlink) {
   auto inp_size = inp.numel() * inp.element_size();
   // custom allreduce requires input byte size to be multiples of 16
@@ -67,27 +67,28 @@ bool should_custom_ar(torch::Tensor& inp, int max_size, int world_size,
   return false;
 }
 
-void _all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
+void _all_reduce(fptr_t _fa, torch::Tensor &inp, torch::Tensor &out,
                  cudaStream_t stream) {
-  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  auto fa = reinterpret_cast<vllm::CustomAllreduce *>(_fa);
   TORCH_CHECK(_is_weak_contiguous(out));
   switch (out.scalar_type()) {
     case at::ScalarType::Float: {
-      fa->allreduce<float>(stream, reinterpret_cast<float*>(inp.data_ptr()),
-                           reinterpret_cast<float*>(out.data_ptr()),
+      fa->allreduce<float>(stream, reinterpret_cast<float *>(inp.data_ptr()),
+                           reinterpret_cast<float *>(out.data_ptr()),
                            out.numel());
       break;
     }
     case at::ScalarType::Half: {
-      fa->allreduce<half>(stream, reinterpret_cast<half*>(inp.data_ptr()),
-                          reinterpret_cast<half*>(out.data_ptr()), out.numel());
+      fa->allreduce<half>(stream, reinterpret_cast<half *>(inp.data_ptr()),
+                          reinterpret_cast<half *>(out.data_ptr()),
+                          out.numel());
       break;
     }
 #if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
     case at::ScalarType::BFloat16: {
       fa->allreduce<nv_bfloat16>(
-          stream, reinterpret_cast<nv_bfloat16*>(inp.data_ptr()),
-          reinterpret_cast<nv_bfloat16*>(out.data_ptr()), out.numel());
+          stream, reinterpret_cast<nv_bfloat16 *>(inp.data_ptr()),
+          reinterpret_cast<nv_bfloat16 *>(out.data_ptr()), out.numel());
       break;
     }
 #endif
@@ -97,7 +98,7 @@ void _all_reduce(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out,
   }
 }
 
-void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out) {
+void all_reduce_reg(fptr_t _fa, torch::Tensor &inp, torch::Tensor &out) {
   const at::cuda::OptionalCUDAGuard device_guard(device_of(inp));
   auto stream = c10::cuda::getCurrentCUDAStream().stream();
   TORCH_CHECK_EQ(inp.scalar_type(), out.scalar_type());
@@ -105,8 +106,8 @@ void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out) {
   _all_reduce(_fa, inp, out, stream);
 }
 
-void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer,
-                      torch::Tensor& out) {
+void all_reduce_unreg(fptr_t _fa, torch::Tensor &inp, torch::Tensor &reg_buffer,
+                      torch::Tensor &out) {
   const at::cuda::OptionalCUDAGuard device_guard(device_of(inp));
   auto stream = c10::cuda::getCurrentCUDAStream().stream();
 
@@ -121,27 +122,27 @@ void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer,
 }
 
 void dispose(fptr_t _fa) {
-  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  auto fa = reinterpret_cast<vllm::CustomAllreduce *>(_fa);
   delete fa;
 }
 
 int meta_size() { return sizeof(vllm::Signal); }
 
-void register_buffer(fptr_t _fa, torch::Tensor& t,
-                     const std::vector<std::string>& handles,
-                     const std::vector<int64_t>& offsets) {
-  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+void register_buffer(fptr_t _fa, torch::Tensor &t,
+                     const std::vector<std::string> &handles,
+                     const std::vector<int64_t> &offsets) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce *>(_fa);
   fa->register_buffer(handles, offsets, t.data_ptr());
 }
 
 std::pair<std::vector<uint8_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(
     fptr_t _fa) {
-  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+  auto fa = reinterpret_cast<vllm::CustomAllreduce *>(_fa);
   return fa->get_graph_buffer_ipc_meta();
 }
 
-void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
-                            const std::vector<std::vector<int64_t>>& offsets) {
-  auto fa = reinterpret_cast<vllm::CustomAllreduce*>(_fa);
+void register_graph_buffers(fptr_t _fa, const std::vector<std::string> &handles,
+                            const std::vector<std::vector<int64_t>> &offsets) {
+  auto fa = reinterpret_cast<vllm::CustomAllreduce *>(_fa);
   fa->register_graph_buffers(handles, offsets);
 }

csrc/custom_all_reduce.cuh

@@ -31,9 +31,9 @@ struct Signal {
   alignas(128) uint32_t end[kMaxBlocks][8];
 };
 
-struct __align__(16) RankData { const void* __restrict__ ptrs[8]; };
+struct __align__(16) RankData { const void *__restrict__ ptrs[8]; };
 
-struct __align__(16) RankSignals { volatile Signal* signals[8]; };
+struct __align__(16) RankSignals { volatile Signal *signals[8]; };
 
 // like std::array, but aligned
 template <typename T, int sz>
@@ -68,11 +68,11 @@ DINLINE half downcast_s(float val) {
 // scalar add functions
 // for some reason when compiling with Pytorch, the + operator for half and
 // bfloat is disabled so we call the intrinsics directly
-DINLINE half& assign_add(half& a, half b) {
+DINLINE half &assign_add(half &a, half b) {
   a = __hadd(a, b);
   return a;
 }
-DINLINE float& assign_add(float& a, float b) { return a += b; }
+DINLINE float &assign_add(float &a, float b) { return a += b; }
 
 #if (__CUDA_ARCH__ >= 800 || !defined(__CUDA_ARCH__))
 DINLINE float upcast_s(nv_bfloat16 val) { return __bfloat162float(val); }
@@ -80,14 +80,14 @@ template <>
 DINLINE nv_bfloat16 downcast_s(float val) {
   return __float2bfloat16(val);
 }
-DINLINE nv_bfloat16& assign_add(nv_bfloat16& a, nv_bfloat16 b) {
+DINLINE nv_bfloat16 &assign_add(nv_bfloat16 &a, nv_bfloat16 b) {
   a = __hadd(a, b);
   return a;
 }
 #endif
 
 template <typename T, int N>
-DINLINE array_t<T, N>& packed_assign_add(array_t<T, N>& a, array_t<T, N> b) {
+DINLINE array_t<T, N> &packed_assign_add(array_t<T, N> &a, array_t<T, N> b) {
 #pragma unroll
   for (int i = 0; i < N; i++) {
     assign_add(a.data[i], b.data[i]);
@@ -128,7 +128,7 @@ DINLINE O downcast(array_t<float, O::size> val) {
 // prior memory accesses. Note: volatile writes will not be reordered against
 // other volatile writes.
 template <int ngpus>
-DINLINE void start_sync(const RankSignals& sg, volatile Signal* self_sg,
+DINLINE void start_sync(const RankSignals &sg, volatile Signal *self_sg,
                         int rank) {
   if (threadIdx.x < ngpus) {
     // reset flag for next time
@@ -137,7 +137,8 @@ DINLINE void start_sync(const RankSignals& sg, volatile Signal* self_sg,
     // Latency = 1 p2p write
     sg.signals[threadIdx.x]->start[blockIdx.x][rank] = 1;
     // wait until we got true from all ranks
-    while (!self_sg->start[blockIdx.x][threadIdx.x]);
+    while (!self_sg->start[blockIdx.x][threadIdx.x])
+      ;
   }
   __syncthreads();
 }
@@ -146,13 +147,13 @@ DINLINE void start_sync(const RankSignals& sg, volatile Signal* self_sg,
 // 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 end_sync(const RankSignals& sg, volatile Signal* self_sg,
+DINLINE void end_sync(const RankSignals &sg, volatile Signal *self_sg,
                       int rank) {
   __syncthreads();
   // eliminate the case that prior writes are not visible after signals become
   // visible. Note that I did not managed to make this happen through a lot of
   // testing. Might be the case that hardware provides stronger guarantee than
-  // the memory model.
+  // the memory model. 
   if constexpr (!final_sync) __threadfence_system();
   if (threadIdx.x < ngpus) {
     // reset flag for next time
@@ -161,13 +162,14 @@ DINLINE void end_sync(const RankSignals& sg, volatile Signal* self_sg,
     // Latency = 1 p2p write
     sg.signals[threadIdx.x]->end[blockIdx.x][rank] = 1;
     // wait until we got true from all ranks
-    while (!self_sg->end[blockIdx.x][threadIdx.x]);
+    while (!self_sg->end[blockIdx.x][threadIdx.x])
+      ;
   }
   if constexpr (!final_sync) __syncthreads();
 }
 
 template <typename P, int ngpus, typename A>
-DINLINE P packed_reduce(const P* ptrs[], int idx) {
+DINLINE P packed_reduce(const P *ptrs[], int idx) {
   A tmp = upcast(ptrs[0][idx]);
 #pragma unroll
   for (int i = 1; i < ngpus; i++) {
@@ -178,8 +180,8 @@ DINLINE P packed_reduce(const P* ptrs[], int idx) {
 
 template <typename T, int ngpus>
 __global__ void __launch_bounds__(512, 1)
-    cross_device_reduce_1stage(RankData* _dp, RankSignals sg,
-                               volatile Signal* self_sg, T* __restrict__ result,
+    cross_device_reduce_1stage(RankData *_dp, RankSignals sg,
+                               volatile Signal *self_sg, T *__restrict__ result,
                                int rank, int size) {
   using P = typename packed_t<T>::P;
   using A = typename packed_t<T>::A;
@@ -190,20 +192,21 @@ __global__ void __launch_bounds__(512, 1)
   // 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);
+    ((P *)result)[idx] =
+        packed_reduce<P, ngpus, A>((const P **)&dp.ptrs[0], idx);
   }
   end_sync<ngpus, true>(sg, self_sg, rank);
 }
 
 template <typename P>
-DINLINE P* get_tmp_buf(volatile Signal* sg) {
-  return (P*)(((Signal*)sg) + 1);
+DINLINE P *get_tmp_buf(volatile Signal *sg) {
+  return (P *)(((Signal *)sg) + 1);
 }
 
 template <typename T, int ngpus>
 __global__ void __launch_bounds__(512, 1)
-    cross_device_reduce_2stage(RankData* _dp, RankSignals sg,
-                               volatile Signal* self_sg, T* __restrict__ result,
+    cross_device_reduce_2stage(RankData *_dp, RankSignals sg,
+                               volatile Signal *self_sg, T *__restrict__ result,
                                int rank, int size) {
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = gridDim.x * blockDim.x;
@@ -213,12 +216,12 @@ __global__ void __launch_bounds__(512, 1)
   int start = rank * part;
   int end = rank == ngpus - 1 ? size : start + part;
   int largest_part = part + size % ngpus;
-  const P* ptrs[ngpus];
-  P* tmps[ngpus];
+  const P *ptrs[ngpus];
+  P *tmps[ngpus];
 #pragma unroll
   for (int i = 0; i < ngpus; i++) {
     int target = (rank + i) % ngpus;
-    ptrs[i] = (const P*)_dp->ptrs[target];
+    ptrs[i] = (const P *)_dp->ptrs[target];
     tmps[i] = get_tmp_buf<P>(sg.signals[target]);
   }
   auto tmp_out = tmps[0];
@@ -240,7 +243,7 @@ __global__ void __launch_bounds__(512, 1)
       int gather_from_rank = ((rank + i) % ngpus);
       if (gather_from_rank == ngpus - 1 || idx < part) {
         int dst_idx = gather_from_rank * part + idx;
-        ((P*)result)[dst_idx] = tmps[i][idx];
+        ((P *)result)[dst_idx] = tmps[i][idx];
       }
     }
   }
@@ -258,14 +261,14 @@ class CustomAllreduce {
 
   // below are device pointers
   RankSignals sg_;
-  std::unordered_map<void*, RankData*> buffers_;
-  Signal* self_sg_;
+  std::unordered_map<void *, RankData *> buffers_;
+  Signal *self_sg_;
 
   // stores the registered device pointers from all ranks
   RankData *d_rank_data_base_, *d_rank_data_end_;
-  std::vector<void*> graph_unreg_buffers_;
+  std::vector<void *> graph_unreg_buffers_;
   // a map from IPC handles to opened IPC pointers
-  std::map<IPC_KEY, char*> ipc_handles_;
+  std::map<IPC_KEY, char *> ipc_handles_;
 
   /**
    * meta is a pointer to device metadata and temporary buffer for allreduce.
@@ -276,22 +279,22 @@ class CustomAllreduce {
    * note: this class does not own any device memory. Any required buffers
    * are passed in from the constructor
    */
-  CustomAllreduce(Signal* meta, void* rank_data, size_t rank_data_sz,
-                  const cudaIpcMemHandle_t* handles,
-                  const std::vector<int64_t>& offsets, int rank,
+  CustomAllreduce(Signal *meta, void *rank_data, size_t rank_data_sz,
+                  const cudaIpcMemHandle_t *handles,
+                  const std::vector<int64_t> &offsets, int rank,
                   bool full_nvlink = true)
       : rank_(rank),
         world_size_(offsets.size()),
         full_nvlink_(full_nvlink),
         self_sg_(meta),
-        d_rank_data_base_(reinterpret_cast<RankData*>(rank_data)),
+        d_rank_data_base_(reinterpret_cast<RankData *>(rank_data)),
         d_rank_data_end_(d_rank_data_base_ + rank_data_sz / sizeof(RankData)) {
     for (int i = 0; i < world_size_; i++) {
-      Signal* rank_sg;
+      Signal *rank_sg;
       if (i != rank_) {
-        char* handle = open_ipc_handle(&handles[i]);
+        char *handle = open_ipc_handle(&handles[i]);
         handle += offsets[i];
-        rank_sg = (Signal*)handle;
+        rank_sg = (Signal *)handle;
       } else {
         rank_sg = self_sg_;
       }
@@ -299,13 +302,13 @@ class CustomAllreduce {
     }
   }
 
-  char* open_ipc_handle(const void* ipc_handle) {
+  char *open_ipc_handle(const void *ipc_handle) {
     auto [it, new_handle] =
-        ipc_handles_.insert({*((IPC_KEY*)ipc_handle), nullptr});
+        ipc_handles_.insert({*((IPC_KEY *)ipc_handle), nullptr});
     if (new_handle) {
-      char* ipc_ptr;
-      CUDACHECK(cudaIpcOpenMemHandle((void**)&ipc_ptr,
-                                     *((const cudaIpcMemHandle_t*)ipc_handle),
+      char *ipc_ptr;
+      CUDACHECK(cudaIpcOpenMemHandle((void **)&ipc_ptr,
+                                     *((const cudaIpcMemHandle_t *)ipc_handle),
                                      cudaIpcMemLazyEnablePeerAccess));
       it->second = ipc_ptr;
     }
@@ -320,7 +323,7 @@ class CustomAllreduce {
     std::vector<int64_t> offsets(num_buffers);
     for (int i = 0; i < num_buffers; i++) {
       auto ptr = graph_unreg_buffers_[i];
-      void* base_ptr;
+      void *base_ptr;
       // note: must share the base address of each allocation, or we get wrong
       // address
       if (cuPointerGetAttribute(&base_ptr,
@@ -328,8 +331,8 @@ class CustomAllreduce {
                                 (CUdeviceptr)ptr) != CUDA_SUCCESS)
         throw std::runtime_error("failed to get pointer attr");
       CUDACHECK(cudaIpcGetMemHandle(
-          (cudaIpcMemHandle_t*)&handles[i * handle_sz], base_ptr));
-      offsets[i] = ((char*)ptr) - ((char*)base_ptr);
+          (cudaIpcMemHandle_t *)&handles[i * handle_sz], base_ptr));
+      offsets[i] = ((char *)ptr) - ((char *)base_ptr);
     }
     return std::make_pair(handles, offsets);
   }
@@ -341,13 +344,13 @@ class CustomAllreduce {
           std::to_string(d_rank_data_base_ + num - d_rank_data_end_));
   }
 
-  void register_buffer(const std::vector<std::string>& handles,
-                       const std::vector<int64_t>& offsets, void* self) {
+  void register_buffer(const std::vector<std::string> &handles,
+                       const std::vector<int64_t> &offsets, void *self) {
     check_rank_data_capacity();
     RankData data;
     for (int i = 0; i < world_size_; i++) {
       if (i != rank_) {
-        char* handle = open_ipc_handle(handles[i].data());
+        char *handle = open_ipc_handle(handles[i].data());
         handle += offsets[i];
         data.ptrs[i] = handle;
       } else {
@@ -368,17 +371,17 @@ class CustomAllreduce {
   // 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) {
+      const std::vector<std::string> &handles,
+      const std::vector<std::vector<int64_t>> &offsets) {
     auto num_buffers = graph_unreg_buffers_.size();
     check_rank_data_capacity(num_buffers);
     std::vector<RankData> rank_data(num_buffers);
     for (int i = 0; i < num_buffers; i++) {
       auto self_ptr = graph_unreg_buffers_[i];
-      auto& rd = rank_data[i];
+      auto &rd = rank_data[i];
       for (int j = 0; j < world_size_; j++) {
         if (j != rank_) {
-          char* handle =
+          char *handle =
               open_ipc_handle(&handles[j][i * sizeof(cudaIpcMemHandle_t)]);
           handle += offsets[j][i];
           rd.ptrs[j] = handle;
@@ -402,7 +405,7 @@ class CustomAllreduce {
    * will cause contention on NVLink bus.
    */
   template <typename T>
-  void allreduce(cudaStream_t stream, T* input, T* output, int size,
+  void allreduce(cudaStream_t stream, T *input, T *output, int size,
                  int threads = 512, int block_limit = 36) {
     auto d = packed_t<T>::P::size;
     if (size % d != 0)
@@ -415,7 +418,7 @@ class CustomAllreduce {
                                std::to_string(kMaxBlocks) + ". Got " +
                                std::to_string(block_limit));
 
-    RankData* ptrs;
+    RankData *ptrs;
     cudaStreamCaptureStatus status;
     CUDACHECK(cudaStreamIsCapturing(stream, &status));
     if (status == cudaStreamCaptureStatusActive) {

csrc/custom_all_reduce_test.cu

@@ -48,7 +48,7 @@ __global__ void dummy_kernel() {
 }
 
 template <typename T>
-__global__ void set_data(T* data, int size, int myRank) {
+__global__ void set_data(T *data, int size, int myRank) {
   for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
        idx += gridDim.x * blockDim.x) {
     data[idx] = myRank * 0.11f;
@@ -56,8 +56,8 @@ __global__ void set_data(T* data, int size, int myRank) {
 }
 
 template <typename T>
-__global__ void convert_data(const T* data1, const T* data2, double* fdata1,
-                             double* fdata2, int size) {
+__global__ void convert_data(const T *data1, const T *data2, double *fdata1,
+                             double *fdata2, int size) {
   for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
        idx += gridDim.x * blockDim.x) {
     fdata1[idx] = data1[idx];
@@ -65,7 +65,7 @@ __global__ void convert_data(const T* data1, const T* data2, double* fdata1,
   }
 }
 
-__global__ void init_rand(curandState_t* state, int size, int nRanks) {
+__global__ void init_rand(curandState_t *state, int size, int nRanks) {
   for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
        idx += gridDim.x * blockDim.x) {
     for (int i = 0; i < nRanks; i++) {
@@ -75,7 +75,7 @@ __global__ void init_rand(curandState_t* state, int size, int nRanks) {
 }
 
 template <typename T>
-__global__ void gen_data(curandState_t* state, T* data, double* ground_truth,
+__global__ void gen_data(curandState_t *state, T *data, double *ground_truth,
                          int myRank, int nRanks, int size) {
   for (int idx = blockIdx.x * blockDim.x + threadIdx.x; idx < size;
        idx += gridDim.x * blockDim.x) {
@@ -91,9 +91,9 @@ __global__ void gen_data(curandState_t* state, T* data, double* ground_truth,
 }
 
 template <typename T>
-void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
+void run(int myRank, int nRanks, ncclComm_t &comm, int threads, int block_limit,
          int data_size, bool performance_test) {
-  T* result;
+  T *result;
   cudaStream_t stream;
   CUDACHECK(cudaStreamCreateWithFlags(&stream, cudaStreamNonBlocking));
   CUDACHECK(cudaMalloc(&result, data_size * sizeof(T)));
@@ -101,8 +101,8 @@ void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
 
   cudaIpcMemHandle_t self_data_handle;
   cudaIpcMemHandle_t data_handles[8];
-  vllm::Signal* buffer;
-  T* self_data_copy;
+  vllm::Signal *buffer;
+  T *self_data_copy;
   /**
    * Allocate IPC buffer
    *
@@ -125,22 +125,22 @@ void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
                          MPI_BYTE, data_handles, sizeof(cudaIpcMemHandle_t),
                          MPI_BYTE, MPI_COMM_WORLD));
 
-  void* rank_data;
+  void *rank_data;
   size_t rank_data_sz = 16 * 1024 * 1024;
   CUDACHECK(cudaMalloc(&rank_data, rank_data_sz));
   std::vector<int64_t> offsets(nRanks, 0);
   vllm::CustomAllreduce fa(buffer, rank_data, rank_data_sz, data_handles,
                            offsets, myRank);
-  auto* self_data =
-      reinterpret_cast<T*>(reinterpret_cast<char*>(buffer) +
-                           sizeof(vllm::Signal) + data_size * sizeof(T));
+  auto *self_data =
+      reinterpret_cast<T *>(reinterpret_cast<char *>(buffer) +
+                            sizeof(vllm::Signal) + data_size * sizeof(T));
   // hack buffer registration
   {
     std::vector<std::string> handles;
     handles.reserve(nRanks);
     for (int i = 0; i < nRanks; i++) {
-      char* begin = (char*)&data_handles[i];
-      char* end = (char*)&data_handles[i + 1];
+      char *begin = (char *)&data_handles[i];
+      char *end = (char *)&data_handles[i + 1];
       handles.emplace_back(begin, end);
     }
     std::vector<int64_t> offsets(nRanks,
@@ -148,9 +148,9 @@ void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
     fa.register_buffer(handles, offsets, self_data);
   }
 
-  double* ground_truth;
+  double *ground_truth;
   CUDACHECK(cudaMallocHost(&ground_truth, data_size * sizeof(double)));
-  curandState_t* states;
+  curandState_t *states;
   CUDACHECK(cudaMalloc(&states, sizeof(curandState_t) * nRanks * data_size));
   init_rand<<<108, 1024, 0, stream>>>(states, data_size, nRanks);
   gen_data<T><<<108, 1024, 0, stream>>>(states, self_data, ground_truth, myRank,
@@ -287,7 +287,7 @@ void run(int myRank, int nRanks, ncclComm_t& comm, int threads, int block_limit,
   CUDACHECK(cudaStreamDestroy(stream));
 }
 
-int main(int argc, char** argv) {
+int main(int argc, char **argv) {
   int nRanks, myRank;
   MPICHECK(MPI_Init(&argc, &argv));
   MPICHECK(MPI_Comm_rank(MPI_COMM_WORLD, &myRank));
@@ -296,7 +296,7 @@ int main(int argc, char** argv) {
   ncclUniqueId id;
   ncclComm_t comm;
   if (myRank == 0) ncclGetUniqueId(&id);
-  MPICHECK(MPI_Bcast(static_cast<void*>(&id), sizeof(id), MPI_BYTE, 0,
+  MPICHECK(MPI_Bcast(static_cast<void *>(&id), sizeof(id), MPI_BYTE, 0,
                      MPI_COMM_WORLD));
   NCCLCHECK(ncclCommInitRank(&comm, nRanks, id, myRank));
 

csrc/dispatch_utils.h

@@ -6,30 +6,32 @@
 
 #include <torch/extension.h>
 
-#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
-  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
-  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)              \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)       \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
 
-#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
-  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...)             \
+  AT_DISPATCH_SWITCH(                                             \
+    TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
 
-#define VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(...)   \
-  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
-  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)     \
-  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
+#define VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(...)     \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)       \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)   \
   AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)
 
-#define VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(TYPE, NAME, ...) \
-  AT_DISPATCH_SWITCH(TYPE, NAME,                               \
-                     VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(__VA_ARGS__))
-
-#define VLLM_DISPATCH_CASE_INTEGRAL_TYPES(...)         \
-  AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)  \
-  AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)  \
-  AT_DISPATCH_CASE(at::ScalarType::Short, __VA_ARGS__) \
-  AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__)   \
+#define VLLM_DISPATCH_FLOATING_AND_BYTE_TYPES(TYPE, NAME, ...)           \
+  AT_DISPATCH_SWITCH(                                                    \
+    TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_AND_BYTE_TYPES(__VA_ARGS__))
+    
+#define VLLM_DISPATCH_CASE_INTEGRAL_TYPES(...)             \
+  AT_DISPATCH_CASE(at::ScalarType::Byte, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)      \
+  AT_DISPATCH_CASE(at::ScalarType::Short, __VA_ARGS__)     \
+  AT_DISPATCH_CASE(at::ScalarType::Int, __VA_ARGS__)       \
   AT_DISPATCH_CASE(at::ScalarType::Long, __VA_ARGS__)
 
-#define VLLM_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...) \
-  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__))
+#define VLLM_DISPATCH_INTEGRAL_TYPES(TYPE, NAME, ...)             \
+  AT_DISPATCH_SWITCH(                                             \
+    TYPE, NAME, VLLM_DISPATCH_CASE_INTEGRAL_TYPES(__VA_ARGS__))

csrc/layernorm_kernels.cu

@@ -11,24 +11,26 @@
   #include <hip/hip_bf16.h>
   #include <hip/hip_fp16.h>
 
-using __nv_bfloat16 = __hip_bfloat16;
-using __nv_bfloat162 = __hip_bfloat162;
+  using __nv_bfloat16 = __hip_bfloat16;
+  using __nv_bfloat162 = __hip_bfloat162;
 #endif
 
 namespace vllm {
 
 // TODO(woosuk): Further optimize this kernel.
-template <typename scalar_t>
+template<typename scalar_t>
 __global__ void rms_norm_kernel(
-    scalar_t* __restrict__ out,           // [..., hidden_size]
-    const scalar_t* __restrict__ input,   // [..., hidden_size]
-    const scalar_t* __restrict__ weight,  // [hidden_size]
-    const float epsilon, const int num_tokens, const int hidden_size) {
+  scalar_t* __restrict__ out,             // [..., hidden_size]
+  const scalar_t* __restrict__ input,     // [..., hidden_size]
+  const scalar_t* __restrict__ weight,    // [hidden_size]
+  const float epsilon,
+  const int num_tokens,
+  const int hidden_size) {
   __shared__ float s_variance;
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    const float x = (float)input[blockIdx.x * hidden_size + idx];
+    const float x = (float) input[blockIdx.x * hidden_size + idx];
     variance += x * x;
   }
   variance = blockReduceSum<float>(variance);
@@ -38,12 +40,12 @@ __global__ void rms_norm_kernel(
   __syncthreads();
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)input[blockIdx.x * hidden_size + idx];
-    out[blockIdx.x * hidden_size + idx] =
-        ((scalar_t)(x * s_variance)) * weight[idx];
+    float x = (float) input[blockIdx.x * hidden_size + idx];
+    out[blockIdx.x * hidden_size + idx] = ((scalar_t) (x * s_variance)) * weight[idx];
   }
 }
 
+
 /* Converter structs for the conversion from torch types to HIP/CUDA types,
    and the associated type conversions within HIP/CUDA. These helpers need
    to be implemented for now because the relevant type conversion
@@ -52,68 +54,51 @@ __global__ void rms_norm_kernel(
 
    Each struct should have the member static constexpr bool `exists`:
    If false, the optimized kernel is not used for the corresponding torch type.
-   If true, the struct should be fully defined as shown in the examples below.
+   If true, the struct should be fully defined as shown in the examples below. 
  */
-template <typename torch_type>
-struct _typeConvert {
-  static constexpr bool exists = false;
-};
+template<typename torch_type>
+struct _typeConvert { static constexpr bool exists = false; };
 
 #if defined(USE_ROCM) || (defined(CUDA_VERSION) && (CUDA_VERSION >= 12000))
 // CUDA < 12.0 runs into issues with packed type conversion
-template <>
+template<>
 struct _typeConvert<c10::Half> {
   static constexpr bool exists = true;
   using hip_type = __half;
   using packed_hip_type = __half2;
 
   __device__ static inline float convert(hip_type x) { return __half2float(x); }
-  __device__ static inline float2 convert(packed_hip_type x) {
-    return __half22float2(x);
-  }
-  __device__ static inline hip_type convert(float x) {
-    return __float2half_rn(x);
-  }
-  __device__ static inline packed_hip_type convert(float2 x) {
-    return __float22half2_rn(x);
-  }
+  __device__ static inline float2 convert(packed_hip_type x) { return __half22float2(x); }
+  __device__ static inline hip_type convert(float x) { return __float2half_rn(x); }
+  __device__ static inline packed_hip_type convert(float2 x) { return __float22half2_rn(x); }
 };
 
-  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
 // CUDA_ARCH < 800 does not have BF16 support
 // TODO: Add in ROCm support once public headers handle bf16 maturely
-template <>
+template<>
 struct _typeConvert<c10::BFloat16> {
   static constexpr bool exists = true;
   using hip_type = __nv_bfloat16;
   using packed_hip_type = __nv_bfloat162;
 
-  __device__ static inline float convert(hip_type x) {
-    return __bfloat162float(x);
-  }
-  __device__ static inline float2 convert(packed_hip_type x) {
-    return __bfloat1622float2(x);
-  }
-  __device__ static inline hip_type convert(float x) {
-    return __float2bfloat16(x);
-  }
-  __device__ static inline packed_hip_type convert(float2 x) {
-    return __float22bfloat162_rn(x);
-  }
+  __device__ static inline float convert(hip_type x) { return __bfloat162float(x); }
+  __device__ static inline float2 convert(packed_hip_type x) { return __bfloat1622float2(x); }
+  __device__ static inline hip_type convert(float x) { return __float2bfloat16(x); }
+  __device__ static inline packed_hip_type convert(float2 x) { return __float22bfloat162_rn(x); }
 };
-  #endif  // defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
-#endif    // defined(USE_ROCM) || (defined(CUDA_VERSION) && (CUDA_VERSION >=
-          // 12000))
+#endif // defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+#endif // defined(USE_ROCM) || (defined(CUDA_VERSION) && (CUDA_VERSION >= 12000))
 
 /* Vector POD struct to generate vectorized and packed FP16/BF16 ops
    for appropriate specializations of fused_add_rms_norm_kernel.
    Only functions that are necessary in that kernel are implemented.
    Alignment to 16 bytes is required to use 128-bit global memory ops.
  */
-template <typename scalar_t, int width>
+template<typename scalar_t, int width>
 struct alignas(16) _f16Vec {
-  /* Not theoretically necessary that width is a power of 2 but should
-     almost always be the case for optimization purposes */
+  /* Not theoretically necessary that width is a power of 2 but should 
+     almost always be the case for optimization purposes */ 
   static_assert(width > 0 && (width & (width - 1)) == 0,
                 "Width is not a positive power of 2!");
   using Converter = _typeConvert<scalar_t>;
@@ -123,49 +108,51 @@ struct alignas(16) _f16Vec {
 
   __device__ _f16Vec& operator+=(const _f16Vec<scalar_t, width>& other) {
     if constexpr (width % 2 == 0) {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; i += 2) {
-        T2 temp{data[i], data[i + 1]};
-        temp += T2{other.data[i], other.data[i + 1]};
+        T2 temp{data[i], data[i+1]};
+        temp += T2{other.data[i], other.data[i+1]};
         data[i] = temp.x;
-        data[i + 1] = temp.y;
+        data[i+1] = temp.y;
       }
     } else {
-#pragma unroll
-      for (int i = 0; i < width; ++i) data[i] += other.data[i];
+      #pragma unroll
+      for (int i = 0; i < width; ++i)
+        data[i] += other.data[i];
     }
     return *this;
   }
 
   __device__ _f16Vec& operator*=(const _f16Vec<scalar_t, width>& other) {
     if constexpr (width % 2 == 0) {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; i += 2) {
-        T2 temp{data[i], data[i + 1]};
-        temp *= T2{other.data[i], other.data[i + 1]};
+        T2 temp{data[i], data[i+1]};
+        temp *= T2{other.data[i], other.data[i+1]};
         data[i] = temp.x;
-        data[i + 1] = temp.y;
+        data[i+1] = temp.y;
       }
     } else {
-#pragma unroll
-      for (int i = 0; i < width; ++i) data[i] *= other.data[i];
+      #pragma unroll
+      for (int i = 0; i < width; ++i)
+        data[i] *= other.data[i];
     }
     return *this;
   }
 
   __device__ _f16Vec& operator*=(const float scale) {
     if constexpr (width % 2 == 0) {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; i += 2) {
-        float2 temp_f = Converter::convert(T2{data[i], data[i + 1]});
+        float2 temp_f = Converter::convert(T2{data[i], data[i+1]});
         temp_f.x *= scale;
         temp_f.y *= scale;
         T2 temp = Converter::convert(temp_f);
         data[i] = temp.x;
-        data[i + 1] = temp.y;
+        data[i+1] = temp.y;
       }
     } else {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; ++i) {
         float temp = Converter::convert(data[i]) * scale;
         data[i] = Converter::convert(temp);
@@ -177,13 +164,13 @@ struct alignas(16) _f16Vec {
   __device__ float sum_squares() const {
     float result = 0.0f;
     if constexpr (width % 2 == 0) {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; i += 2) {
-        float2 z = Converter::convert(T2{data[i], data[i + 1]});
+        float2 z = Converter::convert(T2{data[i], data[i+1]});
         result += z.x * z.x + z.y * z.y;
       }
     } else {
-#pragma unroll
+      #pragma unroll
       for (int i = 0; i < width; ++i) {
         float x = Converter::convert(data[i]);
         result += x * x;
@@ -197,13 +184,15 @@ struct alignas(16) _f16Vec {
    Additional optimizations we can make in this case are
    packed and vectorized operations, which help with the
    memory latency bottleneck. */
-template <typename scalar_t, int width>
-__global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
-fused_add_rms_norm_kernel(
-    scalar_t* __restrict__ input,         // [..., hidden_size]
-    scalar_t* __restrict__ residual,      // [..., hidden_size]
-    const scalar_t* __restrict__ weight,  // [hidden_size]
-    const float epsilon, const int num_tokens, const int hidden_size) {
+template<typename scalar_t, int width>
+__global__ std::enable_if_t<
+  (width > 0) && _typeConvert<scalar_t>::exists> fused_add_rms_norm_kernel(
+  scalar_t* __restrict__ input,           // [..., hidden_size]
+  scalar_t* __restrict__ residual,        // [..., hidden_size]
+  const scalar_t* __restrict__ weight,    // [hidden_size]
+  const float epsilon,
+  const int num_tokens,
+  const int hidden_size) {
   // Sanity checks on our vector struct and type-punned pointer arithmetic
   static_assert(std::is_pod_v<_f16Vec<scalar_t, width>>);
   static_assert(sizeof(_f16Vec<scalar_t, width>) == sizeof(scalar_t) * width);
@@ -214,12 +203,9 @@ fused_add_rms_norm_kernel(
   /* These and the argument pointers are all declared `restrict` as they are
      not aliased in practice. Argument pointers should not be dereferenced
      in this kernel as that would be undefined behavior */
-  auto* __restrict__ input_v =
-      reinterpret_cast<_f16Vec<scalar_t, width>*>(input);
-  auto* __restrict__ residual_v =
-      reinterpret_cast<_f16Vec<scalar_t, width>*>(residual);
-  auto* __restrict__ weight_v =
-      reinterpret_cast<const _f16Vec<scalar_t, width>*>(weight);
+  auto* __restrict__ input_v = reinterpret_cast<_f16Vec<scalar_t, width>*>(input);
+  auto* __restrict__ residual_v = reinterpret_cast<_f16Vec<scalar_t, width>*>(residual);
+  auto* __restrict__ weight_v = reinterpret_cast<const _f16Vec<scalar_t, width>*>(weight);
 
   for (int idx = threadIdx.x; idx < vec_hidden_size; idx += blockDim.x) {
     int id = blockIdx.x * vec_hidden_size + idx;
@@ -229,11 +215,10 @@ fused_add_rms_norm_kernel(
     residual_v[id] = temp;
   }
   /* Keep the following if-else block in sync with the
-     calculation of max_block_size in fused_add_rms_norm */
+     calculation of max_block_size in fused_add_rms_norm */ 
   if (num_tokens < 256) {
     variance = blockReduceSum<float, 1024>(variance);
-  } else
-    variance = blockReduceSum<float, 256>(variance);
+  } else variance = blockReduceSum<float, 256>(variance);
   if (threadIdx.x == 0) {
     s_variance = rsqrtf(variance / hidden_size + epsilon);
   }
@@ -248,50 +233,52 @@ fused_add_rms_norm_kernel(
   }
 }
 
+
 /* Generic fused_add_rms_norm_kernel
    The width field is not used here but necessary for other specializations.
  */
-template <typename scalar_t, int width>
-__global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
-fused_add_rms_norm_kernel(
-    scalar_t* __restrict__ input,         // [..., hidden_size]
-    scalar_t* __restrict__ residual,      // [..., hidden_size]
-    const scalar_t* __restrict__ weight,  // [hidden_size]
-    const float epsilon, const int num_tokens, const int hidden_size) {
+template<typename scalar_t, int width>
+__global__ std::enable_if_t<
+  (width == 0) || !_typeConvert<scalar_t>::exists> fused_add_rms_norm_kernel(
+  scalar_t* __restrict__ input,           // [..., hidden_size]
+  scalar_t* __restrict__ residual,        // [..., hidden_size]
+  const scalar_t* __restrict__ weight,    // [hidden_size]
+  const float epsilon,
+  const int num_tokens,
+  const int hidden_size) {
   __shared__ float s_variance;
   float variance = 0.0f;
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
     scalar_t z = input[blockIdx.x * hidden_size + idx];
     z += residual[blockIdx.x * hidden_size + idx];
-    float x = (float)z;
+    float x = (float) z;
     variance += x * x;
     residual[blockIdx.x * hidden_size + idx] = z;
   }
   /* Keep the following if-else block in sync with the
-     calculation of max_block_size in fused_add_rms_norm */
+     calculation of max_block_size in fused_add_rms_norm */ 
   if (num_tokens < 256) {
     variance = blockReduceSum<float, 1024>(variance);
-  } else
-    variance = blockReduceSum<float, 256>(variance);
+  } else variance = blockReduceSum<float, 256>(variance);
   if (threadIdx.x == 0) {
     s_variance = rsqrtf(variance / hidden_size + epsilon);
   }
   __syncthreads();
 
   for (int idx = threadIdx.x; idx < hidden_size; idx += blockDim.x) {
-    float x = (float)residual[blockIdx.x * hidden_size + idx];
-    input[blockIdx.x * hidden_size + idx] =
-        ((scalar_t)(x * s_variance)) * weight[idx];
+    float x = (float) residual[blockIdx.x * hidden_size + idx];
+    input[blockIdx.x * hidden_size + idx] = ((scalar_t) (x * s_variance)) * weight[idx];
   }
 }
 
-}  // namespace vllm
+} // namespace vllm
 
-void rms_norm(torch::Tensor& out,     // [..., hidden_size]
-              torch::Tensor& input,   // [..., hidden_size]
-              torch::Tensor& weight,  // [hidden_size]
-              float epsilon) {
+void rms_norm(
+  torch::Tensor& out,      // [..., hidden_size]
+  torch::Tensor& input,    // [..., hidden_size]
+  torch::Tensor& weight,   // [hidden_size]
+  float epsilon) {
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 
@@ -299,27 +286,40 @@ void rms_norm(torch::Tensor& out,     // [..., hidden_size]
   dim3 block(std::min(hidden_size, 1024));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
-    vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
-        out.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
-        weight.data_ptr<scalar_t>(), epsilon, num_tokens, hidden_size);
-  });
+  VLLM_DISPATCH_FLOATING_TYPES(
+    input.scalar_type(),
+    "rms_norm_kernel",
+    [&] {
+      vllm::rms_norm_kernel<scalar_t><<<grid, block, 0, stream>>>(
+        out.data_ptr<scalar_t>(),
+        input.data_ptr<scalar_t>(),
+        weight.data_ptr<scalar_t>(),
+        epsilon,
+        num_tokens,
+        hidden_size);
+    });
 }
 
-#define LAUNCH_FUSED_ADD_RMS_NORM(width)                                       \
-  VLLM_DISPATCH_FLOATING_TYPES(                                                \
-      input.scalar_type(), "fused_add_rms_norm_kernel", [&] {                  \
-        vllm::fused_add_rms_norm_kernel<scalar_t, width>                       \
-            <<<grid, block, 0, stream>>>(input.data_ptr<scalar_t>(),           \
-                                         residual.data_ptr<scalar_t>(),        \
-                                         weight.data_ptr<scalar_t>(), epsilon, \
-                                         num_tokens, hidden_size);             \
-      });
+#define LAUNCH_FUSED_ADD_RMS_NORM(width)              \
+  VLLM_DISPATCH_FLOATING_TYPES(                       \
+    input.scalar_type(),                              \
+    "fused_add_rms_norm_kernel",                      \
+    [&] {                                             \
+      vllm::fused_add_rms_norm_kernel                 \
+      <scalar_t, width><<<grid, block, 0, stream>>>(  \
+        input.data_ptr<scalar_t>(),                   \
+        residual.data_ptr<scalar_t>(),                \
+        weight.data_ptr<scalar_t>(),                  \
+        epsilon,                                      \
+        num_tokens,                                   \
+        hidden_size);                                 \
+    });
 
-void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
-                        torch::Tensor& residual,  // [..., hidden_size]
-                        torch::Tensor& weight,    // [hidden_size]
-                        float epsilon) {
+void fused_add_rms_norm(
+  torch::Tensor& input,    // [..., hidden_size]
+  torch::Tensor& residual, // [..., hidden_size]
+  torch::Tensor& weight,   // [hidden_size]
+  float epsilon) {
   int hidden_size = input.size(-1);
   int num_tokens = input.numel() / hidden_size;
 
@@ -342,8 +342,8 @@ void fused_add_rms_norm(torch::Tensor& input,     // [..., hidden_size]
   auto inp_ptr = reinterpret_cast<std::uintptr_t>(input.data_ptr());
   auto res_ptr = reinterpret_cast<std::uintptr_t>(residual.data_ptr());
   auto wt_ptr = reinterpret_cast<std::uintptr_t>(weight.data_ptr());
-  bool ptrs_are_aligned =
-      inp_ptr % 16 == 0 && res_ptr % 16 == 0 && wt_ptr % 16 == 0;
+  bool ptrs_are_aligned = inp_ptr % 16 == 0 && res_ptr % 16 == 0 \
+                          && wt_ptr % 16 == 0;
   if (ptrs_are_aligned && hidden_size % 8 == 0) {
     LAUNCH_FUSED_ADD_RMS_NORM(8);
   } else {

csrc/moe/moe_ops.cpp

@@ -3,6 +3,5 @@
 #include <torch/extension.h>
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("topk_softmax", &topk_softmax,
-        "Apply topk softmax to the gating outputs.");
+  m.def("topk_softmax", &topk_softmax, "Apply topk softmax to the gating outputs.");
 }

csrc/moe/moe_ops.h

@@ -2,6 +2,8 @@
 
 #include <torch/extension.h>
 
-void topk_softmax(torch::Tensor& topk_weights, torch::Tensor& topk_indices,
-                  torch::Tensor& token_expert_indices,
-                  torch::Tensor& gating_output);
+void topk_softmax(
+  torch::Tensor& topk_weights,
+  torch::Tensor& topk_indices,
+  torch::Tensor& token_expert_indices,
+  torch::Tensor& gating_output);

csrc/moe_align_block_size_kernels.cu

@@ -7,128 +7,119 @@
 #include "cuda_compat.h"
 #include "dispatch_utils.h"
 
-#define CEILDIV(x, y) (((x) + (y) - 1) / (y))
+#define CEILDIV(x,y) (((x) + (y) - 1) / (y))
 
 namespace vllm {
 
 namespace {
-__device__ __forceinline__ int32_t index(int32_t total_col, int32_t row,
-                                         int32_t col) {
-  // don't worry about overflow because num_experts is relatively small
-  return row * total_col + col;
+__device__ __forceinline__ int32_t index(int32_t total_col, int32_t row, int32_t col) {
+    // don't worry about overflow because num_experts is relatively small
+    return row * total_col + col;
+}
 }
-}  // namespace
 
 template <typename scalar_t>
-__global__ void moe_align_block_size_kernel(scalar_t* __restrict__ topk_ids,
-                                            int32_t* sorted_token_ids,
-                                            int32_t* expert_ids,
-                                            int32_t* total_tokens_post_pad,
-                                            int32_t num_experts,
-                                            int32_t block_size, size_t numel) {
-  const size_t tokens_per_thread = CEILDIV(numel, blockDim.x);
-  const size_t start_idx = threadIdx.x * tokens_per_thread;
+__global__ void moe_align_block_size_kernel(scalar_t *__restrict__ topk_ids, 
+                                int32_t *sorted_token_ids, 
+                                int32_t *expert_ids, 
+                                int32_t *total_tokens_post_pad,
+                                int32_t num_experts, 
+                                int32_t block_size, 
+                                size_t numel) {
+    const size_t tokens_per_thread = CEILDIV(numel, blockDim.x);
+    const size_t start_idx = threadIdx.x * tokens_per_thread;
 
-  extern __shared__ int32_t shared_mem[];
+    extern __shared__ int32_t shared_mem[];
 
-  int32_t* tokens_cnts =
-      shared_mem;  // 2d tensor with shape (num_experts + 1, num_experts)
-  int32_t* cumsum =
-      shared_mem + (num_experts + 1) *
-                       num_experts;  // 1d tensor with shape (num_experts + 1)
+    int32_t* tokens_cnts = shared_mem; // 2d tensor with shape (num_experts + 1, num_experts)
+    int32_t* cumsum = shared_mem + (num_experts + 1) * num_experts; // 1d tensor with shape (num_experts + 1)
 
-  for (int i = 0; i < num_experts; ++i) {
-    tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0;
-  }
-
-  /**
-   * In the first step we compute token_cnts[thread_index + 1][expert_index],
-   * which counts how many tokens in the token shard of thread_index are
-   * assigned to expert expert_index.
-   */
-  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
-    ++tokens_cnts[index(num_experts, threadIdx.x + 1, topk_ids[i])];
-  }
-
-  __syncthreads();
-
-  // For each expert we accumulate the token counts from the different threads.
-  tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0;
-  for (int i = 1; i <= blockDim.x; ++i) {
-    tokens_cnts[index(num_experts, i, threadIdx.x)] +=
-        tokens_cnts[index(num_experts, i - 1, threadIdx.x)];
-  }
-
-  __syncthreads();
-
-  // We accumulate the token counts of all experts in thread 0.
-  if (threadIdx.x == 0) {
-    cumsum[0] = 0;
-    for (int i = 1; i <= num_experts; ++i) {
-      cumsum[i] = cumsum[i - 1] +
-                  CEILDIV(tokens_cnts[index(num_experts, blockDim.x, i - 1)],
-                          block_size) *
-                      block_size;
+    for (int i = 0; i < num_experts; ++i) {
+        tokens_cnts[index(num_experts, threadIdx.x + 1, i)] = 0;
     }
-    *total_tokens_post_pad = cumsum[num_experts];
-  }
 
-  __syncthreads();
+    /**
+    * In the first step we compute token_cnts[thread_index + 1][expert_index],
+    * which counts how many tokens in the token shard of thread_index are assigned
+    * to expert expert_index.
+    */
+    for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
+        ++tokens_cnts[index(num_experts, threadIdx.x + 1, topk_ids[i])]; 
+    }
 
-  /**
-   * For each expert, each thread processes the tokens of the corresponding
-   * blocks and stores the corresponding expert_id for each block.
-   */
-  for (int i = cumsum[threadIdx.x]; i < cumsum[threadIdx.x + 1];
-       i += block_size) {
-    expert_ids[i / block_size] = threadIdx.x;
-  }
+    __syncthreads();
 
-  /**
-   * Each thread processes a token shard, calculating the index of each token
-   * after sorting by expert number. Given the example topk_ids =
-   * [0,1,2,1,2,3,0,3,4] and block_size = 4, then the output would be [0, 6, *,
-   * *, 1, 3, *, *, 2, 4, *, *, 5, 7, *, *, 8, *, *, *], where * represents a
-   * padding value(preset in python).
-   */
-  for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
-    int32_t expert_id = topk_ids[i];
-    /** The cumsum[expert_id] stores the starting index of the tokens that the
-     * expert with expert_id needs to process, and
-     * tokens_cnts[threadIdx.x][expert_id] stores the indices of the tokens
-     * processed by the expert with expert_id within the current thread's token
-     * shard.
-     */
-    int32_t rank_post_pad =
-        tokens_cnts[index(num_experts, threadIdx.x, expert_id)] +
-        cumsum[expert_id];
-    sorted_token_ids[rank_post_pad] = i;
-    ++tokens_cnts[index(num_experts, threadIdx.x, expert_id)];
-  }
+    // For each expert we accumulate the token counts from the different threads.
+    tokens_cnts[index(num_experts, 0, threadIdx.x)] = 0;
+    for (int i = 1; i <= blockDim.x; ++i) {
+        tokens_cnts[index(num_experts, i, threadIdx.x)] += tokens_cnts[index(num_experts, i-1, threadIdx.x)];
+    }
+
+    __syncthreads();
+    
+    // We accumulate the token counts of all experts in thread 0.
+    if (threadIdx.x == 0) {
+        cumsum[0] = 0;
+        for (int i = 1; i <= num_experts; ++i) {
+            cumsum[i] = cumsum[i-1] + CEILDIV(tokens_cnts[index(num_experts, blockDim.x, i - 1)], block_size) * block_size;
+        }
+        *total_tokens_post_pad = cumsum[num_experts];
+    }
+
+    __syncthreads();
+
+    /**
+    * For each expert, each thread processes the tokens of the corresponding blocks
+    * and stores the corresponding expert_id for each block.
+    */
+    for (int i = cumsum[threadIdx.x];i < cumsum[threadIdx.x + 1];i += block_size) {
+        expert_ids[i / block_size] = threadIdx.x;
+    }
+    
+    /**
+    * Each thread processes a token shard, calculating the index of each token after
+    * sorting by expert number. Given the example topk_ids = [0,1,2,1,2,3,0,3,4] and
+    * block_size = 4, then the output would be [0, 6, *, *, 1, 3, *, *, 2, 4, *, *, 5, 7, *, *, 8, *, *, *],
+    * where * represents a padding value(preset in python).
+    */
+    for (int i = start_idx; i < numel && i < start_idx + tokens_per_thread; ++i) {
+        int32_t expert_id = topk_ids[i];
+        /** The cumsum[expert_id] stores the starting index of the tokens that the
+        * expert with expert_id needs to process, and tokens_cnts[threadIdx.x][expert_id]
+        * stores the indices of the tokens processed by the expert with expert_id within
+        * the current thread's token shard.
+        */
+        int32_t rank_post_pad = tokens_cnts[index(num_experts, threadIdx.x, expert_id)] + cumsum[expert_id];
+        sorted_token_ids[rank_post_pad] = i;
+        ++tokens_cnts[index(num_experts, threadIdx.x, expert_id)];
+    }
+}
 }
-}  // namespace vllm
 
-void moe_align_block_size(torch::Tensor topk_ids, int num_experts,
-                          int block_size, torch::Tensor sorted_token_ids,
-                          torch::Tensor experts_ids,
-                          torch::Tensor num_tokens_post_pad) {
-  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_INTEGRAL_TYPES(
-      topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
-        // calc needed amount of shared mem for `tokens_cnts` and `cumsum`
-        // tensors
-        const int32_t shared_mem =
-            ((num_experts + 1) * num_experts + (num_experts + 1)) *
-            sizeof(int32_t);
+void moe_align_block_size(
+    torch::Tensor topk_ids,
+    int num_experts,
+    int block_size,
+    torch::Tensor sorted_token_ids,
+    torch::Tensor experts_ids,
+    torch::Tensor num_tokens_post_pad) {
+    const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+    VLLM_DISPATCH_INTEGRAL_TYPES(
+        topk_ids.scalar_type(), "moe_align_block_size_kernel", [&] {
+        // calc needed amount of shared mem for `tokens_cnts` and `cumsum` tensors
+        const int32_t shared_mem = ((num_experts + 1) * num_experts + (num_experts + 1)) * sizeof(int32_t);
 
         // set dynamic shared mem
         auto kernel = vllm::moe_align_block_size_kernel<scalar_t>;
-        AT_CUDA_CHECK(VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize(
-            (void*)kernel, shared_mem));
+        AT_CUDA_CHECK(
+            VLLM_DevFuncAttribute_SET_MaxDynamicSharedMemorySize((void *)kernel, shared_mem));
         kernel<<<1, num_experts, shared_mem, stream>>>(
-            topk_ids.data_ptr<scalar_t>(), sorted_token_ids.data_ptr<int32_t>(),
-            experts_ids.data_ptr<int32_t>(),
-            num_tokens_post_pad.data_ptr<int32_t>(), num_experts, block_size,
+            topk_ids.data_ptr<scalar_t>(),
+            sorted_token_ids.data_ptr<int32_t>(), 
+            experts_ids.data_ptr<int32_t>(), 
+            num_tokens_post_pad.data_ptr<int32_t>(), 
+            num_experts,
+            block_size,
             topk_ids.numel());
-      });
+    });
 }

csrc/ops.h

@@ -3,139 +3,159 @@
 #include <torch/extension.h>
 
 void paged_attention_v1(
-    torch::Tensor& out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int block_size,
-    int max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
-    const std::string& kv_cache_dtype, float kv_scale, const int tp_rank,
-    const int blocksparse_local_blocks, const int blocksparse_vert_stride,
-    const int blocksparse_block_size, const int blocksparse_head_sliding_step);
+  torch::Tensor& out,
+  torch::Tensor& query,
+  torch::Tensor& key_cache,
+  torch::Tensor& value_cache,
+  int num_kv_heads,
+  float scale,
+  torch::Tensor& block_tables,
+  torch::Tensor& context_lens,
+  int block_size,
+  int max_context_len,
+  const c10::optional<torch::Tensor>& alibi_slopes,
+  const std::string& kv_cache_dtype,
+  float kv_scale);
 
 void paged_attention_v2(
-    torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,
-    torch::Tensor& tmp_out, torch::Tensor& query, torch::Tensor& key_cache,
-    torch::Tensor& value_cache, int num_kv_heads, float scale,
-    torch::Tensor& block_tables, torch::Tensor& seq_lens, int block_size,
-    int max_seq_len, const c10::optional<torch::Tensor>& alibi_slopes,
-    const std::string& kv_cache_dtype, float kv_scale, const int tp_rank,
-    const int blocksparse_local_blocks, const int blocksparse_vert_stride,
-    const int blocksparse_block_size, const int blocksparse_head_sliding_step);
+  torch::Tensor& out,
+  torch::Tensor& exp_sums,
+  torch::Tensor& max_logits,
+  torch::Tensor& tmp_out,
+  torch::Tensor& query,
+  torch::Tensor& key_cache,
+  torch::Tensor& value_cache,
+  int num_kv_heads,
+  float scale,
+  torch::Tensor& block_tables,
+  torch::Tensor& context_lens,
+  int block_size,
+  int max_context_len,
+  const c10::optional<torch::Tensor>& alibi_slopes,
+  const std::string& kv_cache_dtype,
+  float kv_scale);
 
-void rms_norm(torch::Tensor& out, torch::Tensor& input, torch::Tensor& weight,
-              float epsilon);
+void rms_norm(
+  torch::Tensor& out,
+  torch::Tensor& input,
+  torch::Tensor& weight,
+  float epsilon);
 
-void fused_add_rms_norm(torch::Tensor& input, torch::Tensor& residual,
-                        torch::Tensor& weight, float epsilon);
+void fused_add_rms_norm(
+  torch::Tensor& input,
+  torch::Tensor& residual,
+  torch::Tensor& weight,
+  float epsilon);
 
-void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                      torch::Tensor& key, int head_size,
-                      torch::Tensor& cos_sin_cache, bool is_neox);
+void rotary_embedding(
+  torch::Tensor& positions,
+  torch::Tensor& query,
+  torch::Tensor& key,
+  int head_size,
+  torch::Tensor& cos_sin_cache,
+  bool is_neox);
 
-void batched_rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
-                              torch::Tensor& key, int head_size,
-                              torch::Tensor& cos_sin_cache, bool is_neox,
-                              int rot_dim,
-                              torch::Tensor& cos_sin_cache_offsets);
+void batched_rotary_embedding(
+  torch::Tensor& positions,
+  torch::Tensor& query,
+  torch::Tensor& key,
+  int head_size,
+  torch::Tensor& cos_sin_cache,
+  bool is_neox,
+  int rot_dim,
+  torch::Tensor& cos_sin_cache_offsets);
 
-void silu_and_mul(torch::Tensor& out, torch::Tensor& input);
+void silu_and_mul(
+  torch::Tensor& out,
+  torch::Tensor& input);
 
-void gelu_and_mul(torch::Tensor& out, torch::Tensor& input);
+void gelu_and_mul(
+  torch::Tensor& out,
+  torch::Tensor& input);
 
-void gelu_tanh_and_mul(torch::Tensor& out, torch::Tensor& input);
+void gelu_tanh_and_mul(
+  torch::Tensor& out,
+  torch::Tensor& input);
 
-void gelu_new(torch::Tensor& out, torch::Tensor& input);
+void gelu_new(
+  torch::Tensor& out,
+  torch::Tensor& input);
 
-void gelu_fast(torch::Tensor& out, torch::Tensor& input);
+void gelu_fast(
+  torch::Tensor& out,
+  torch::Tensor& input);
 
 #ifndef USE_ROCM
-torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
-                        const torch::Tensor& codebooks,
-                        const torch::Tensor& scales,
-                        const torch::Tensor& codebook_partition_sizes,
-                        const std::optional<torch::Tensor>& bias);
+torch::Tensor awq_gemm(
+  torch::Tensor _in_feats,
+  torch::Tensor _kernel,
+  torch::Tensor _scaling_factors,
+  torch::Tensor _zeros,
+  int split_k_iters);
 
-torch::Tensor aqlm_dequant(const torch::Tensor& codes,
-                           const torch::Tensor& codebooks,
-                           const torch::Tensor& codebook_partition_sizes);
-
-torch::Tensor awq_gemm(torch::Tensor _in_feats, torch::Tensor _kernel,
-                       torch::Tensor _scaling_factors, torch::Tensor _zeros,
-                       int split_k_iters);
-
-torch::Tensor awq_dequantize(torch::Tensor _kernel,
-                             torch::Tensor _scaling_factors,
-                             torch::Tensor _zeros, int split_k_iters, int thx,
-                             int thy);
-
-torch::Tensor marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
-                          torch::Tensor& b_scales, torch::Tensor& workspace,
-                          int64_t size_m, int64_t size_n, int64_t size_k);
-
-torch::Tensor gptq_marlin_24_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
-                                  torch::Tensor& b_meta,
-                                  torch::Tensor& b_scales,
-                                  torch::Tensor& workspace, int64_t num_bits,
-                                  int64_t size_m, int64_t size_n,
-                                  int64_t size_k);
-
-torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
-                               torch::Tensor& b_scales, torch::Tensor& g_idx,
-                               torch::Tensor& perm, torch::Tensor& workspace,
-                               int64_t num_bits, int64_t size_m, int64_t size_n,
-                               int64_t size_k, bool is_k_full);
-
-torch::Tensor gptq_marlin_repack(torch::Tensor& b_q_weight, torch::Tensor& perm,
-                                 int64_t size_k, int64_t size_n,
-                                 int64_t num_bits);
-
-int cutlass_scaled_mm_dq(torch::Tensor& out, torch::Tensor const& a,
-                         torch::Tensor const& b, torch::Tensor const& a_scales,
-                         torch::Tensor const& b_scales);
+torch::Tensor awq_dequantize(
+    torch::Tensor _kernel,
+    torch::Tensor _scaling_factors,
+    torch::Tensor _zeros,
+    int split_k_iters,
+    int thx,
+    int thy);
 
+torch::Tensor marlin_gemm(
+    torch::Tensor& a, 
+    torch::Tensor& b_q_weight,
+    torch::Tensor& b_scales, 
+    torch::Tensor& workspace,
+    int64_t size_m, 
+    int64_t size_n, 
+    int64_t size_k);
 #endif
 
-void static_scaled_int8_quant(torch::Tensor& out, torch::Tensor& input,
-                              float scale);
+void squeezellm_gemm(
+  torch::Tensor vec,
+  torch::Tensor mat,
+  torch::Tensor mul,
+  torch::Tensor lookup_table);
 
-void squeezellm_gemm(torch::Tensor vec, torch::Tensor mat, torch::Tensor mul,
-                     torch::Tensor lookup_table);
+torch::Tensor gptq_gemm(
+  torch::Tensor a,
+  torch::Tensor b_q_weight,
+  torch::Tensor b_gptq_qzeros,
+  torch::Tensor b_gptq_scales,
+  torch::Tensor b_g_idx,
+  bool use_exllama,
+  int bit);
 
-torch::Tensor gptq_gemm(torch::Tensor a, torch::Tensor b_q_weight,
-                        torch::Tensor b_gptq_qzeros,
-                        torch::Tensor b_gptq_scales, torch::Tensor b_g_idx,
-                        bool use_exllama, int bit);
+void gptq_shuffle(
+  torch::Tensor q_weight,
+  torch::Tensor q_perm,
+  int bit);
 
-void gptq_shuffle(torch::Tensor q_weight, torch::Tensor q_perm, int bit);
-
-void static_scaled_fp8_quant(torch::Tensor& out, torch::Tensor& input,
-                             torch::Tensor& scale);
-
-void dynamic_scaled_fp8_quant(torch::Tensor& out, torch::Tensor& input,
-                              torch::Tensor& scale);
-
-void moe_align_block_size(torch::Tensor topk_ids, int num_experts,
-                          int block_size, torch::Tensor sorted_token_ids,
-                          torch::Tensor experts_ids,
-                          torch::Tensor num_tokens_post_pad);
+void moe_align_block_size(
+  torch::Tensor topk_ids,
+  int num_experts,
+  int block_size,
+  torch::Tensor sorted_token_ids,
+  torch::Tensor experts_ids,
+  torch::Tensor num_tokens_post_pad);
 
 #ifndef USE_ROCM
 using fptr_t = uint64_t;
-fptr_t init_custom_ar(torch::Tensor& meta, torch::Tensor& rank_data,
-                      const std::vector<std::string>& handles,
-                      const std::vector<int64_t>& offsets, int rank,
+fptr_t init_custom_ar(torch::Tensor &meta, torch::Tensor &rank_data,
+                    const std::vector<std::string> &handles,
+                    const std::vector<int64_t> &offsets, int rank,
+                    bool full_nvlink);
+bool should_custom_ar(torch::Tensor &inp, int max_size, int world_size,
                       bool full_nvlink);
-bool should_custom_ar(torch::Tensor& inp, int max_size, int world_size,
-                      bool full_nvlink);
-void all_reduce_reg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& out);
-void all_reduce_unreg(fptr_t _fa, torch::Tensor& inp, torch::Tensor& reg_buffer,
-                      torch::Tensor& out);
+void all_reduce_reg(fptr_t _fa, torch::Tensor &inp, torch::Tensor &out);
+void all_reduce_unreg(fptr_t _fa, torch::Tensor &inp, torch::Tensor &reg_buffer,
+                      torch::Tensor &out);
 void dispose(fptr_t _fa);
 int meta_size();
-void register_buffer(fptr_t _fa, torch::Tensor& t,
-                     const std::vector<std::string>& handles,
-                     const std::vector<int64_t>& offsets);
-std::pair<std::vector<uint8_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(
-    fptr_t _fa);
-void register_graph_buffers(fptr_t _fa, const std::vector<std::string>& handles,
-                            const std::vector<std::vector<int64_t>>& offsets);
+void register_buffer(fptr_t _fa, torch::Tensor &t,
+                     const std::vector<std::string> &handles,
+                     const std::vector<int64_t> &offsets);
+std::pair<std::vector<uint8_t>, std::vector<int64_t>> get_graph_buffer_ipc_meta(fptr_t _fa);
+void register_graph_buffers(fptr_t _fa, const std::vector<std::string> &handles,
+                            const std::vector<std::vector<int64_t>> &offsets);
 #endif

csrc/pos_encoding_kernels.cu

@@ -7,10 +7,14 @@
 
 namespace vllm {
 
-template <typename scalar_t, bool IS_NEOX>
+template<typename scalar_t, bool IS_NEOX>
 inline __device__ void apply_token_rotary_embedding(
-    scalar_t* __restrict__ arr, const scalar_t* __restrict__ cos_ptr,
-    const scalar_t* __restrict__ sin_ptr, int rot_offset, int embed_dim) {
+  scalar_t* __restrict__ arr,
+  const scalar_t* __restrict__ cos_ptr,
+  const scalar_t* __restrict__ sin_ptr,
+  int rot_offset,
+  int embed_dim)
+{
   int x_index, y_index;
   scalar_t cos, sin;
   if (IS_NEOX) {
@@ -33,17 +37,19 @@ inline __device__ void apply_token_rotary_embedding(
   arr[y_index] = y * cos + x * sin;
 }
 
-template <typename scalar_t, bool IS_NEOX>
+template<typename scalar_t, bool IS_NEOX>
 inline __device__ void apply_rotary_embedding(
-    scalar_t* __restrict__ query,  // [batch_size, seq_len, num_heads,
-                                   // head_size] or [num_tokens, num_heads,
-                                   // head_size]
-    scalar_t* __restrict__ key,    // [batch_size, seq_len, num_kv_heads,
-                                   // head_size] or [num_tokens, num_kv_heads,
-                                   // head_size]
-    const scalar_t* cache_ptr, const int head_size, const int num_heads,
-    const int num_kv_heads, const int rot_dim, const int token_idx,
-    const int64_t query_stride, const int64_t key_stride) {
+  scalar_t* __restrict__ query,                 // [batch_size, seq_len, num_heads, head_size] or [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key,                   // [batch_size, seq_len, num_kv_heads, head_size] or [num_tokens, num_kv_heads, head_size]
+  const scalar_t* cache_ptr,
+  const int head_size,
+  const int num_heads,
+  const int num_kv_heads,
+  const int rot_dim,
+  const int token_idx,
+  const int64_t query_stride,
+  const int64_t key_stride)
+{
   const int embed_dim = rot_dim / 2;
   const scalar_t* cos_ptr = cache_ptr;
   const scalar_t* sin_ptr = cache_ptr + embed_dim;
@@ -53,8 +59,8 @@ inline __device__ void apply_rotary_embedding(
     const int head_idx = i / embed_dim;
     const int64_t token_head = token_idx * query_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
-    apply_token_rotary_embedding<scalar_t, IS_NEOX>(
-        query + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
+    apply_token_rotary_embedding<scalar_t, IS_NEOX>(query + token_head, cos_ptr,
+                                              sin_ptr, rot_offset, embed_dim);
   }
 
   const int nk = num_kv_heads * embed_dim;
@@ -62,74 +68,62 @@ inline __device__ void apply_rotary_embedding(
     const int head_idx = i / embed_dim;
     const int64_t token_head = token_idx * key_stride + head_idx * head_size;
     const int rot_offset = i % embed_dim;
-    apply_token_rotary_embedding<scalar_t, IS_NEOX>(
-        key + token_head, cos_ptr, sin_ptr, rot_offset, embed_dim);
+    apply_token_rotary_embedding<scalar_t, IS_NEOX>(key + token_head, cos_ptr,
+                                              sin_ptr, rot_offset, embed_dim);
   }
 }
 
-template <typename scalar_t, bool IS_NEOX>
+template<typename scalar_t, bool IS_NEOX>
 __global__ void rotary_embedding_kernel(
-    const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
-                                            // [num_tokens]
-    scalar_t* __restrict__ query,           // [batch_size, seq_len, num_heads,
-                                   // head_size] or [num_tokens, num_heads,
-                                   // head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // head_size] or [num_tokens, num_kv_heads,
-                                 // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
-    const int rot_dim, const int64_t query_stride, const int64_t key_stride,
-    const int num_heads, const int num_kv_heads, const int head_size) {
+  const int64_t* __restrict__ positions,        // [batch_size, seq_len] or [num_tokens]
+  scalar_t* __restrict__ query,                 // [batch_size, seq_len, num_heads, head_size] or [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key,                   // [batch_size, seq_len, num_kv_heads, head_size] or [num_tokens, num_kv_heads, head_size]
+  const scalar_t* __restrict__ cos_sin_cache,   // [max_position, 2, rot_dim // 2]
+  const int rot_dim,
+  const int64_t query_stride,
+  const int64_t key_stride,
+  const int num_heads,
+  const int num_kv_heads,
+  const int head_size) {
   // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
   const scalar_t* cache_ptr = cos_sin_cache + pos * rot_dim;
 
-  apply_rotary_embedding<scalar_t, IS_NEOX>(
-      query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride);
+  apply_rotary_embedding<scalar_t, IS_NEOX>(query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim, token_idx, query_stride, key_stride);
 }
 
-template <typename scalar_t, bool IS_NEOX>
+template<typename scalar_t, bool IS_NEOX>
 __global__ void batched_rotary_embedding_kernel(
-    const int64_t* __restrict__ positions,  // [batch_size, seq_len] or
-                                            // [num_tokens]
-    scalar_t* __restrict__ query,           // [batch_size, seq_len, num_heads,
-                                   // head_size] or [num_tokens, num_heads,
-                                   // head_size]
-    scalar_t* __restrict__ key,  // [batch_size, seq_len, num_kv_heads,
-                                 // head_size] or [num_tokens, num_kv_heads,
-                                 // head_size]
-    const scalar_t* __restrict__ cos_sin_cache,  // [max_position, 2, rot_dim //
-                                                 // 2]
-    const int64_t* __restrict__ cos_sin_cache_offsets,  // [batch_size, seq_len]
-                                                        // or [num_tokens]
-    const int rot_dim, const int64_t query_stride, const int64_t key_stride,
-    const int num_heads, const int num_kv_heads, const int head_size) {
+  const int64_t* __restrict__ positions,              // [batch_size, seq_len] or [num_tokens]
+  scalar_t* __restrict__ query,                       // [batch_size, seq_len, num_heads, head_size] or [num_tokens, num_heads, head_size]
+  scalar_t* __restrict__ key,                         // [batch_size, seq_len, num_kv_heads, head_size] or [num_tokens, num_kv_heads, head_size]
+  const scalar_t* __restrict__ cos_sin_cache,         // [max_position, 2, rot_dim // 2]
+  const int64_t* __restrict__ cos_sin_cache_offsets,  // [batch_size, seq_len] or [num_tokens]
+  const int rot_dim,
+  const int64_t query_stride,
+  const int64_t key_stride,
+  const int num_heads,
+  const int num_kv_heads,
+  const int head_size) {
   // Each thread block is responsible for one token.
   const int token_idx = blockIdx.x;
   int64_t pos = positions[token_idx];
   int64_t cos_sin_cache_offset = cos_sin_cache_offsets[token_idx];
-  const scalar_t* cache_ptr =
-      cos_sin_cache + (cos_sin_cache_offset + pos) * rot_dim;
+  const scalar_t* cache_ptr = cos_sin_cache + (cos_sin_cache_offset + pos) * rot_dim;
 
-  apply_rotary_embedding<scalar_t, IS_NEOX>(
-      query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim,
-      token_idx, query_stride, key_stride);
+  apply_rotary_embedding<scalar_t, IS_NEOX>(query, key, cache_ptr, head_size, num_heads, num_kv_heads, rot_dim, token_idx, query_stride, key_stride);
 }
 
-}  // namespace vllm
+} // namespace vllm
 
 void rotary_embedding(
-    torch::Tensor& positions,  // [batch_size, seq_len] or [num_tokens]
-    torch::Tensor& query,  // [batch_size, seq_len, num_heads * head_size] or
-                           // [num_tokens, num_heads * head_size]
-    torch::Tensor& key,    // [batch_size, seq_len, num_kv_heads * head_size] or
-                           // [num_tokens, num_kv_heads * head_size]
-    int head_size,
-    torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
-    bool is_neox) {
+  torch::Tensor& positions,         // [batch_size, seq_len] or [num_tokens]
+  torch::Tensor& query,             // [batch_size, seq_len, num_heads * head_size] or [num_tokens, num_heads * head_size]
+  torch::Tensor& key,               // [batch_size, seq_len, num_kv_heads * head_size] or [num_tokens, num_kv_heads * head_size]
+  int head_size,
+  torch::Tensor& cos_sin_cache,     // [max_position, rot_dim]
+  bool is_neox) {
   int64_t num_tokens = query.numel() / query.size(-1);
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;
@@ -141,21 +135,36 @@ void rotary_embedding(
   dim3 block(std::min(num_heads * rot_dim / 2, 512));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
-    if (is_neox) {
-      vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
-          positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-          key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(), rot_dim,
-          query_stride, key_stride, num_heads, num_kv_heads, head_size);
-    } else {
-      vllm::rotary_embedding_kernel<scalar_t, false>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              rot_dim, query_stride, key_stride, num_heads, num_kv_heads,
-              head_size);
-    }
-  });
+  VLLM_DISPATCH_FLOATING_TYPES(
+    query.scalar_type(),
+    "rotary_embedding",
+    [&] {
+      if (is_neox) {
+        vllm::rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      } else {
+        vllm::rotary_embedding_kernel<scalar_t, false><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      }
+    });
 }
 
 /*
@@ -163,15 +172,14 @@ Batched version of rotary embedding, pack multiple LoRAs together
 and process in batched manner.
 */
 void batched_rotary_embedding(
-    torch::Tensor& positions,  // [batch_size, seq_len] or [num_tokens]
-    torch::Tensor& query,  // [batch_size, seq_len, num_heads * head_size] or
-                           // [num_tokens, num_heads * head_size]
-    torch::Tensor& key,    // [batch_size, seq_len, num_kv_heads * head_size] or
-                           // [num_tokens, num_kv_heads * head_size]
-    int head_size,
-    torch::Tensor& cos_sin_cache,  // [max_position, rot_dim]
-    bool is_neox, int rot_dim,
-    torch::Tensor& cos_sin_cache_offsets  // [num_tokens]
+  torch::Tensor& positions,         // [batch_size, seq_len] or [num_tokens]
+  torch::Tensor& query,             // [batch_size, seq_len, num_heads * head_size] or [num_tokens, num_heads * head_size]
+  torch::Tensor& key,               // [batch_size, seq_len, num_kv_heads * head_size] or [num_tokens, num_kv_heads * head_size]
+  int head_size,
+  torch::Tensor& cos_sin_cache,     // [max_position, rot_dim]
+  bool is_neox,
+  int rot_dim,
+  torch::Tensor& cos_sin_cache_offsets // [num_tokens]
 ) {
   int64_t num_tokens = cos_sin_cache_offsets.size(0);
   int num_heads = query.size(-1) / head_size;
@@ -183,21 +191,36 @@ void batched_rotary_embedding(
   dim3 block(std::min(num_heads * rot_dim / 2, 512));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(query));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(query.scalar_type(), "rotary_embedding", [&] {
-    if (is_neox) {
-      vllm::batched_rotary_embedding_kernel<scalar_t, true>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, num_heads, num_kv_heads, head_size);
-    } else {
-      vllm::batched_rotary_embedding_kernel<scalar_t, false>
-          <<<grid, block, 0, stream>>>(
-              positions.data_ptr<int64_t>(), query.data_ptr<scalar_t>(),
-              key.data_ptr<scalar_t>(), cos_sin_cache.data_ptr<scalar_t>(),
-              cos_sin_cache_offsets.data_ptr<int64_t>(), rot_dim, query_stride,
-              key_stride, num_heads, num_kv_heads, head_size);
-    }
-  });
+  VLLM_DISPATCH_FLOATING_TYPES(
+    query.scalar_type(),
+    "rotary_embedding",
+    [&] {
+      if (is_neox) {
+        vllm::batched_rotary_embedding_kernel<scalar_t, true><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          cos_sin_cache_offsets.data_ptr<int64_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      } else {
+        vllm::batched_rotary_embedding_kernel<scalar_t, false><<<grid, block, 0, stream>>>(
+          positions.data_ptr<int64_t>(),
+          query.data_ptr<scalar_t>(),
+          key.data_ptr<scalar_t>(),
+          cos_sin_cache.data_ptr<scalar_t>(),
+          cos_sin_cache_offsets.data_ptr<int64_t>(),
+          rot_dim,
+          query_stride,
+          key_stride,
+          num_heads,
+          num_kv_heads,
+          head_size);
+      }
+    });
 }

csrc/punica/bgmv/bgmv_bf16_bf16_bf16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, nv_bfloat16, nv_bfloat16)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_bfloat16, nv_bfloat16, nv_bfloat16)

csrc/punica/bgmv/bgmv_bf16_bf16_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, nv_bfloat16, nv_half)

csrc/punica/bgmv/bgmv_bf16_fp16_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, nv_half, nv_bfloat16)

csrc/punica/bgmv/bgmv_bf16_fp16_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, nv_half, nv_half)

csrc/punica/bgmv/bgmv_bf16_fp32_bf16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, float, nv_bfloat16)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_bfloat16, float, nv_bfloat16)

csrc/punica/bgmv/bgmv_bf16_fp32_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_bfloat16, float, nv_half)

csrc/punica/bgmv/bgmv_config.h

@@ -28,7 +28,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 2752) \
     f(in_T, out_T, W_T, narrow, 2816) \
     f(in_T, out_T, W_T, narrow, 3072) \
-    f(in_T, out_T, W_T, narrow, 3328) \
     f(in_T, out_T, W_T, narrow, 3456) \
     f(in_T, out_T, W_T, narrow, 3584) \
     f(in_T, out_T, W_T, narrow, 4096) \
@@ -37,7 +36,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 5504) \
     f(in_T, out_T, W_T, narrow, 5632) \
     f(in_T, out_T, W_T, narrow, 6144) \
-    f(in_T, out_T, W_T, narrow, 6400) \
     f(in_T, out_T, W_T, narrow, 6848) \
     f(in_T, out_T, W_T, narrow, 6912) \
     f(in_T, out_T, W_T, narrow, 7168) \
@@ -55,7 +53,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 22016) \
     f(in_T, out_T, W_T, narrow, 24576) \
     f(in_T, out_T, W_T, narrow, 27392) \
-    f(in_T, out_T, W_T, narrow, 27648) \
     f(in_T, out_T, W_T, narrow, 28672) \
     f(in_T, out_T, W_T, narrow, 32000) \
     f(in_T, out_T, W_T, narrow, 32256) \
@@ -63,7 +60,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     f(in_T, out_T, W_T, narrow, 32768) \
     f(in_T, out_T, W_T, narrow, 33024) \
     f(in_T, out_T, W_T, narrow, 36864) \
-    f(in_T, out_T, W_T, narrow, 43264) \
     f(in_T, out_T, W_T, narrow, 49152) \
     f(in_T, out_T, W_T, narrow, 64000) \
     f(in_T, out_T, W_T, narrow, 64256) \
@@ -77,77 +73,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
 // Keep above in sync with vllm/lora/layers::LogitsProcessorWithLoRA
 // and vllm/tests/lora/test_punica.py
 
-// Used for defining kernels going from the variety of 
-// dim in to the narrow dim out
-    // Using it for the fully sharded column 
-    // parallel LoRA A which splits the rank dim
-#define FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, narrow) \
-    f(in_T, out_T, W_T, 128, narrow) \
-    f(in_T, out_T, W_T, 256, narrow) \
-    f(in_T, out_T, W_T, 512, narrow) \
-    f(in_T, out_T, W_T, 640, narrow) \
-    f(in_T, out_T, W_T, 768, narrow) \
-    f(in_T, out_T, W_T, 1024, narrow) \
-    f(in_T, out_T, W_T, 1152, narrow) \
-    f(in_T, out_T, W_T, 1280, narrow) \
-    f(in_T, out_T, W_T, 1536, narrow) \
-    f(in_T, out_T, W_T, 1728, narrow) \
-    f(in_T, out_T, W_T, 1792, narrow) \
-    f(in_T, out_T, W_T, 2048, narrow) \
-    f(in_T, out_T, W_T, 2304, narrow) \
-    f(in_T, out_T, W_T, 2560, narrow) \
-    f(in_T, out_T, W_T, 2752, narrow) \
-    f(in_T, out_T, W_T, 2816, narrow) \
-    f(in_T, out_T, W_T, 3072, narrow) \
-    f(in_T, out_T, W_T, 3328, narrow) \
-    f(in_T, out_T, W_T, 3456, narrow) \
-    f(in_T, out_T, W_T, 3584, narrow) \
-    f(in_T, out_T, W_T, 4096, narrow) \
-    f(in_T, out_T, W_T, 4608, narrow) \
-    f(in_T, out_T, W_T, 5120, narrow) \
-    f(in_T, out_T, W_T, 5504, narrow) \
-    f(in_T, out_T, W_T, 5632, narrow) \
-    f(in_T, out_T, W_T, 6144, narrow) \
-    f(in_T, out_T, W_T, 6400, narrow) \
-    f(in_T, out_T, W_T, 6848, narrow) \
-    f(in_T, out_T, W_T, 6912, narrow) \
-    f(in_T, out_T, W_T, 7168, narrow) \
-    f(in_T, out_T, W_T, 8192, narrow) \
-    f(in_T, out_T, W_T, 9216, narrow) \
-    f(in_T, out_T, W_T, 10240, narrow) \
-    f(in_T, out_T, W_T, 11008, narrow) \
-    f(in_T, out_T, W_T, 12288, narrow) \
-    f(in_T, out_T, W_T, 13696, narrow) \
-    f(in_T, out_T, W_T, 13824, narrow) \
-    f(in_T, out_T, W_T, 14336, narrow) \
-    f(in_T, out_T, W_T, 15360, narrow) \
-    f(in_T, out_T, W_T, 16384, narrow) \
-    f(in_T, out_T, W_T, 20480, narrow) \
-    f(in_T, out_T, W_T, 22016, narrow) \
-    f(in_T, out_T, W_T, 24576, narrow) \
-    f(in_T, out_T, W_T, 27392, narrow) \
-    f(in_T, out_T, W_T, 27648, narrow) \
-    f(in_T, out_T, W_T, 28672, narrow) \
-    f(in_T, out_T, W_T, 32000, narrow) \
-    f(in_T, out_T, W_T, 32256, narrow) \
-    f(in_T, out_T, W_T, 32512, narrow) \
-    f(in_T, out_T, W_T, 32768, narrow) \
-    f(in_T, out_T, W_T, 33024, narrow) \
-    f(in_T, out_T, W_T, 36864, narrow) \
-    f(in_T, out_T, W_T, 43264, narrow) \
-    f(in_T, out_T, W_T, 49152, narrow) \
-    f(in_T, out_T, W_T, 64000, narrow) \
-    f(in_T, out_T, W_T, 64256, narrow) \
-    f(in_T, out_T, W_T, 64512, narrow) \
-    f(in_T, out_T, W_T, 102400, narrow) \
-    f(in_T, out_T, W_T, 102656, narrow) \
-    f(in_T, out_T, W_T, 102912, narrow) \
-    f(in_T, out_T, W_T, 128000, narrow) \
-    f(in_T, out_T, W_T, 128256, narrow) \
-    f(in_T, out_T, W_T, 128512, narrow) \
-// Keep above in sync with vllm/lora/layers::SamplerWithLoRA
-
-
 // Keep this in sync with vllm/config::LoRAConfig
 #define FOR_BGMV_WIDE_NARROW(f, in_T, out_T, W_T) \
     FOR_BGMV_WIDE(f, in_T, out_T, W_T, 8)  \
@@ -155,14 +80,4 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
     FOR_BGMV_WIDE(f, in_T, out_T, W_T, 32) \
     FOR_BGMV_WIDE(f, in_T, out_T, W_T, 64)
 
-
-#define FOR_INST_BGMV_WIDE_NARROW(f, in_T, out_T, W_T) \
-    FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 1) \
-    FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 2) \
-    FOR_INST_BGMV_NARROW(f, in_T, out_T, W_T, 4) \
-    f(in_T, out_T, W_T, 8, 64) \
-    f(in_T, out_T, W_T, 16, 64) \
-    f(in_T, out_T, W_T, 32, 64) \
-    f(in_T, out_T, W_T, 64, 64)
-
 // clang-format on

csrc/punica/bgmv/bgmv_fp16_bf16_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, nv_bfloat16, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp16_bf16_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, nv_bfloat16, nv_half)

csrc/punica/bgmv/bgmv_fp16_fp16_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, nv_half, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp16_fp16_fp16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, nv_half, nv_half)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_half, nv_half, nv_half)

csrc/punica/bgmv/bgmv_fp16_fp32_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, float, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp16_fp32_fp16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, nv_half, float, nv_half)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, nv_half, float, nv_half)

csrc/punica/bgmv/bgmv_fp32_bf16_bf16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_bfloat16, nv_bfloat16)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, float, nv_bfloat16, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp32_bf16_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_bfloat16, nv_half)

csrc/punica/bgmv/bgmv_fp32_fp16_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_half, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp32_fp16_fp16.cu

@@ -2,4 +2,3 @@
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, nv_half, nv_half)
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, float, nv_half, nv_half)

csrc/punica/bgmv/bgmv_fp32_fp32_bf16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, float, nv_bfloat16)

csrc/punica/bgmv/bgmv_fp32_fp32_fp16.cu

@@ -0,0 +1,4 @@
+#include "bgmv_config.h"
+#include "bgmv_impl.cuh"
+
+FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, float, float, nv_half)

csrc/punica/bgmv/bgmv_impl.cuh

@@ -1,14 +1,8 @@
 #pragma once
 
 #include <ATen/cuda/CUDAContext.h>
-#ifndef USE_ROCM
 #include <cooperative_groups.h>
-#else
-#include <hip/hip_cooperative_groups.h>
-#endif
-#ifndef USE_ROCM
 #include <cuda/pipeline>
-#endif
 #include <cuda_runtime.h>
 #include <iostream>
 #include <stdio.h>
@@ -17,24 +11,6 @@
 
 namespace cg = cooperative_groups;
 
-#ifdef USE_ROCM
-template <size_t len>
-__host__ __device__
-inline void* memcpy_blocking(void *dst, const void *src) {
-  // Does not handle the case of long datatypes
-  char *d = reinterpret_cast<char *>(dst);
-  const char *s = reinterpret_cast<const char *>(src);
-  size_t i = 0;
-#pragma unroll
-  for (i = 0; i < len; ++i) {
-    d[i] = s[i];
-  }
-  return dst;
-}
-#endif
-
-#ifndef USE_ROCM
-
 // nthrs = (32, 4)
 template <int feat_in, int feat_out, size_t vec_size, size_t X_copy_size,
           size_t W_copy_size, int tx, int ty, int tz, typename in_T,
@@ -165,81 +141,6 @@ bgmv_shrink_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
   }
 }
 
-#else
-
-template <int feat_in, int feat_out, size_t vec_size, size_t X_copy_size,
-          size_t W_copy_size, int tx, int ty, int tz, typename in_T,
-          typename out_T, typename W_T>
-__global__ void
-bgmv_shrink_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
-                   const W_T *__restrict__ W,
-                   const int64_t *__restrict__ indicies, int64_t y_offset,
-                   int64_t full_y_size, int64_t num_layers, int64_t layer_idx,
-                   float scale) {
-  size_t batch_idx = blockIdx.y;
-  int64_t idx = indicies[batch_idx] * num_layers + layer_idx;
-  if (idx < 0) {
-    return;
-  }
-
-  size_t j = blockIdx.x;
-  constexpr size_t tile_size = tx * ty * vec_size;
-  constexpr size_t num_tiles = (feat_in + tile_size - 1) / tile_size;
-  __shared__ float y_warpwise[ty];
-
-  float y = 0;
-  vec_t<in_T, vec_size> x_vec;
-  vec_t<W_T, vec_size> w_vec;
-  size_t tile_idx;
-
-#pragma unroll
-  for (tile_idx = 0; tile_idx < num_tiles; ++tile_idx) {
-    if (tile_idx * tile_size + (threadIdx.y * tx + threadIdx.x + 1) * vec_size - 1 < feat_in) {
-      x_vec.load(X + (batch_idx * feat_in) +
-                     tile_idx * tile_size +
-                     (threadIdx.y * tx + threadIdx.x) * vec_size);
-      w_vec.load(W + (idx * feat_out + j) * feat_in +
-                     tile_idx * tile_size +
-                     (threadIdx.y * tx + threadIdx.x) * vec_size);
-    }
-
-    float sum = 0.f;
-#pragma unroll
-    for (size_t i = 0; i < vec_size; ++i) {
-      sum += convert_type<W_T, float>(w_vec[i]) * convert_type<in_T, float>(x_vec[i]) * scale;
-    }
-#pragma unroll
-    for (size_t offset = tx / 2; offset > 0; offset /= 2) {
-      sum += VLLM_SHFL_DOWN_SYNC(sum, offset);
-    }
-
-    __syncthreads();
-
-    if (tile_idx * tile_size + (threadIdx.y * tx + threadIdx.x + 1) * vec_size - 1 < feat_in) {
-      y += sum;
-    }
-  }
-
-  if (threadIdx.x == 0) {
-    y_warpwise[threadIdx.y] = y;
-  }
-  __syncthreads();
-
-  float y_write = 0.f;
-#pragma unroll
-  for (size_t i = 0; i < ty; ++i) {
-    y_write += y_warpwise[i];
-  }
- 
-  // write Y;
-  if (threadIdx.x == 0 && threadIdx.y == 0) {
-    size_t y_idx = batch_idx * full_y_size + y_offset + j;
-    Y[y_idx] = vllm_add<out_T>(Y[y_idx], convert_type<float, out_T>(y_write));
-  }
-}
-
-#endif
-
 // nthrs = (2, 16, 4)
 template <int feat_in, int feat_out, size_t vec_size, int tx, int ty, int tz,
           typename in_T, typename out_T, typename W_T>
@@ -271,11 +172,7 @@ bgmv_expand_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
   float sum = 0.f;
 #pragma unroll
   for (size_t i = 0; i < vec_size; ++i) {
-#ifndef USE_ROCM
     sum += float(w_vec[i]) * float(x_vec[i]) * scale;
-#else
-    sum += convert_type<W_T, float>(w_vec[i]) * convert_type<in_T, float>(x_vec[i]) * scale;
-#endif
   }
 
   cg::thread_block_tile g = cg::tiled_partition<tx>(block);
@@ -286,14 +183,8 @@ bgmv_expand_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
   sum = g.shfl(sum, 0);
 
   if (threadIdx.x == 0) {
-#ifndef USE_ROCM
     Y[batch_idx * full_y_size + y_offset + tile_idx * (tz * ty) +
       threadIdx.z * ty + threadIdx.y] += static_cast<out_T>(sum);
-#else
-    size_t y_idx = batch_idx * full_y_size + y_offset + tile_idx * (tz * ty) +
-                   threadIdx.z * ty + threadIdx.y;
-    Y[y_idx] = vllm_add<out_T>(Y[y_idx], convert_type<float, out_T>(sum));
-#endif
   }
 }
 
@@ -308,7 +199,7 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
   constexpr int tz = 4;
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  if constexpr (feat_in <= feat_out) {
+  if constexpr (feat_in < feat_out) {
     static_assert(feat_in % vec_size == 0);
     constexpr int tx = feat_in / vec_size;
 
@@ -345,7 +236,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
                                         scale);
     }
   } else {
-#ifndef USE_ROCM
     static_assert(feat_in % (vec_size * 32) == 0 ||
                   feat_in % (vec_size * 16) == 0 ||
                   feat_in % (vec_size * 8) == 0);
@@ -389,50 +279,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
                                         full_y_size, num_layers, layer_idx,
                                         scale);
     }
-#else
-    constexpr size_t rocm_warp_size = warpSize;
-
-#define CHECK_INPUT_TILEABLE_BY(vec_size_) \
-    feat_in % (rocm_warp_size * vec_size_) == 0
-
-#define LAUNCH_BGMV_SHRINK_KERNELS_ROCM(factor_, vec_size_, tx_, ty_)       \
-    if constexpr (CHECK_INPUT_TILEABLE_BY(factor_)) {                       \
-      constexpr size_t vec_size_shrink = vec_size_;                         \
-      constexpr int tx = tx_;                                               \
-      constexpr int ty = ty_;                                               \
-      dim3 nblks(feat_out, batch_size);                                     \
-      dim3 nthrs(tx, ty);                                                   \
-      bgmv_shrink_kernel<feat_in, feat_out, vec_size_shrink,                \
-                          vec_size_shrink * sizeof(in_T),                   \
-                          vec_size_shrink * sizeof(W_T),                    \
-                          tx, ty, tz>                                       \
-          <<<nblks, nthrs, 0, stream>>>(Y, X, W, indicies, y_offset,        \
-                                        full_y_size, num_layers, layer_idx, \
-                                        scale);                             \
-    }
-
-    static_assert(CHECK_INPUT_TILEABLE_BY(32) ||
-                  CHECK_INPUT_TILEABLE_BY(16) ||
-                  CHECK_INPUT_TILEABLE_BY( 8) ||
-                  CHECK_INPUT_TILEABLE_BY( 4) ||
-                  CHECK_INPUT_TILEABLE_BY( 2) ||
-                  CHECK_INPUT_TILEABLE_BY( 1));
-    
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM(32, vec_size, rocm_warp_size, 32/vec_size)
-    else
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM(16, vec_size, rocm_warp_size, 16/vec_size)
-    else
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 8, vec_size, rocm_warp_size,  8/vec_size)
-    else
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 4, vec_size, rocm_warp_size/(vec_size/4), vec_size/4)
-    else
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 2, vec_size, rocm_warp_size/(vec_size/2), vec_size/2)
-    else
-    LAUNCH_BGMV_SHRINK_KERNELS_ROCM( 1, vec_size, rocm_warp_size/(vec_size/1), vec_size/1)
-
-#undef CHECK_INPUT_TILEABLE_BY
-#undef LAUNCH_BGMV_SHRINK_KERNELS_ROCM
-#endif
   }
 }
 
@@ -443,9 +289,6 @@ void bgmv_kernel(out_T *__restrict__ Y, const in_T *__restrict__ X,
       int64_t y_offset, int64_t full_y_size, int64_t batch_size,               \
       int64_t num_layers, int64_t layer_idx, float scale);
 
-#define INST_BGMV_ONESIDE(in_T, out_T, W_T, feat_in, feat_out)                 \
-  INST_BGMV(feat_in, feat_out, in_T, out_T, W_T)
-
 #define INST_BGMV_TWOSIDE(in_T, out_T, W_T, narrow, wide)                      \
   INST_BGMV(narrow, wide, in_T, out_T, W_T)                                    \
   INST_BGMV(wide, narrow, in_T, out_T, W_T)

csrc/punica/bgmv/generator.py

@@ -10,7 +10,6 @@ TEMPLATE = """
 #include "bgmv_impl.cuh"
 
 FOR_BGMV_WIDE_NARROW(INST_BGMV_TWOSIDE, {input_dtype}, {output_dtype}, {weight_dtype})
-FOR_INST_BGMV_WIDE_NARROW(INST_BGMV_ONESIDE, {input_dtype}, {output_dtype}, {weight_dtype})
 """.lstrip()  # noqa: E501
 
 for input_dtype in DTYPES:
@@ -19,26 +18,6 @@ for input_dtype in DTYPES:
             if weight_dtype == "fp32":
                 # FP32 weights are not supported.
                 continue
-            if output_dtype == "fp32":
-                # LoRA A matrix.
-                if input_dtype != weight_dtype:
-                    # NOTE(woosuk): While Punica supports the case where the
-                    # input and weight dtypes are different, we only generate
-                    # the kernels the same dtypes to reduce the binary size.
-                    continue
-            elif input_dtype == "fp32":
-                # LoRA B matrix.
-                if output_dtype != weight_dtype:
-                    # NOTE(woosuk): While Punica supports the case where the
-                    # output and weight dtypes are different, we only generate
-                    # the kernels the same dtypes to reduce the binary size.
-                    continue
-            elif not (input_dtype == output_dtype == weight_dtype):
-                # NOTE(woosuk): While Punica supports mixed data types for
-                # input, output, and weight, we only generate the kernels with
-                # the same data types to reduce the binary size.
-                continue
-
             kernel_definition = TEMPLATE.format(
                 input_dtype=DTYPE_MAP[input_dtype],
                 output_dtype=DTYPE_MAP[output_dtype],

csrc/punica/bgmv/vec_dtypes.cuh

@@ -1,6 +1,8 @@
 #ifndef VEC_DTYPES_CUH_
 #define VEC_DTYPES_CUH_
 
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
 #ifdef FLASHINFER_USE_FP8
 #include <cuda_fp8.h>
 #endif
@@ -8,9 +10,6 @@
 
 #include <type_traits>
 
-#include "../type_convert.h"
-#include "../../cuda_compat.h"
-
 #define FLASHINFER_INLINE \
   inline __attribute__((always_inline)) __device__ __host__
 

csrc/punica/punica_ops.cc

@@ -1,11 +1,12 @@
+#include <cuda_bf16.h>
+#include <cuda_fp16.h>
 #include <torch/extension.h>
 #include <c10/cuda/CUDAGuard.h>
 #include <cstdint>
 
-#include "type_convert.h"
-#include "../cuda_compat.h"
 #include "bgmv/bgmv_config.h"
 
+namespace {
 
 //====== utils ======
 
@@ -49,23 +50,6 @@ inline bool launch_bgmv_kernel(out_T *Y, const in_T *X, const W_T *W,
                                int64_t y_offset, int64_t full_y_size,
                                int64_t batch_size, int64_t num_layers,
                                int64_t layer_idx, float scale) {
-  // NOTE(woosuk): While Punica supports various combinations of input/output
-  // data types, we limit the supported data types to reduce the binary size.
-  constexpr bool is_input_float = std::is_same<in_T, float>::value;
-  constexpr bool is_output_float = std::is_same<out_T, float>::value;
-  if (is_input_float) {
-    if (!std::is_same<out_T, W_T>::value) {
-      return false;
-    }
-  } else if (is_output_float) {
-    if (!std::is_same<in_T, W_T>::value) {
-      return false;
-    }
-  } else if (!(std::is_same<in_T, W_T>::value &&
-               std::is_same<out_T, W_T>::value)) {
-    return false;
-  }
-
   switch (pack_u32(in_features, out_features)) {
 #define CASE_ONESIDE(_in_T, _out_T, _W_T, feat_in, feat_out)                   \
   case pack_u32(feat_in, feat_out):                                            \
@@ -78,12 +62,12 @@ inline bool launch_bgmv_kernel(out_T *Y, const in_T *X, const W_T *W,
   CASE_ONESIDE(in_T, out_T, W_T, wide, narrow)
 
     FOR_BGMV_WIDE_NARROW(CASE, _, _, _)
-    FOR_INST_BGMV_WIDE_NARROW(CASE_ONESIDE, _, _, _)
 #undef CASE
 #undef CASE_ONESIDE
   default:
     return false;
   }
+
   return true;
 }
 
@@ -567,3 +551,15 @@ void dispatch_bgmv_low_level(torch::Tensor y, torch::Tensor x, torch::Tensor w,
   TORCH_CHECK(ok, "No suitable kernel.", " h_in=", h_in, " h_out=", h_out,
               " dtype=", x.scalar_type(), " out_dtype=", y.scalar_type());
 }
+
+} // namespace
+
+//====== pybind ======
+
+#define DEFINE_pybind(name) m.def(#name, &name, #name);
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("dispatch_bgmv", &dispatch_bgmv, "dispatch_bgmv");
+  m.def("dispatch_bgmv_low_level", &dispatch_bgmv_low_level,
+        "dispatch_bgmv_low_level");
+}

csrc/punica/punica_ops.h

@@ -1,11 +0,0 @@
-#pragma once
-
-#include <torch/extension.h>
-
-void dispatch_bgmv(torch::Tensor y, torch::Tensor x, torch::Tensor w,
-                   torch::Tensor indicies, int64_t layer_idx, float scale);
-
-void dispatch_bgmv_low_level(torch::Tensor y, torch::Tensor x, torch::Tensor w,
-                             torch::Tensor indicies, int64_t layer_idx,
-                             float scale, int64_t h_in, int64_t h_out,
-                             int64_t y_offset);

csrc/punica/punica_pybind.cpp

@@ -1,13 +0,0 @@
-#include <torch/extension.h>
-
-#include "punica_ops.h"
-
-//====== pybind ======
-
-#define DEFINE_pybind(name) m.def(#name, &name, #name);
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
-  m.def("dispatch_bgmv", &dispatch_bgmv, "dispatch_bgmv");
-  m.def("dispatch_bgmv_low_level", &dispatch_bgmv_low_level,
-        "dispatch_bgmv_low_level");
-}

csrc/punica/type_convert.h

@@ -1,82 +0,0 @@
-#ifndef CSRC__PUNICA__TYPE_CONVERT_H__
-#define CSRC__PUNICA__TYPE_CONVERT_H__
-
-#ifndef USE_ROCM
-
-#include <cuda_bf16.h>
-#include <cuda_fp16.h>
-
-#else
-
-#include <hip/hip_bf16.h>
-#include <hip/hip_fp16.h>
-
-#define __TYPE_CONVERT__HOST_DEVICE__ __host__ __device__
-
-typedef __half nv_half;
-typedef __hip_bfloat16 nv_bfloat16;
-typedef __hip_bfloat162 nv_bfloat162;
-
-__TYPE_CONVERT__HOST_DEVICE__
-inline __hip_bfloat162 make_bfloat162(__hip_bfloat16 val) {
-  return __hip_bfloat162{val, val};
-}
-
-__TYPE_CONVERT__HOST_DEVICE__
-inline __hip_bfloat162 make_bfloat162(__hip_bfloat16 vall, __hip_bfloat16 valr) {
-  return __hip_bfloat162{vall, valr};
-}
-
-template <typename T_src, typename T_dst>
-__TYPE_CONVERT__HOST_DEVICE__
-inline T_dst convert_type(T_src val) {
-  return static_cast<T_dst>(val);
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline float convert_type<__half, float>(__half val) {
-  return __half2float(val);
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline __half convert_type<float, __half>(float val) {
-  return __float2half(val);
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline float convert_type<__hip_bfloat16, float>(__hip_bfloat16 val) {
-  return __bfloat162float(val);
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline __hip_bfloat16 convert_type<float, __hip_bfloat16>(float val) {
-  return __float2bfloat16(val);
-}
-
-template <typename T>
-__TYPE_CONVERT__HOST_DEVICE__
-inline T vllm_add(T a, T b) {
-  return a + b;
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline __half vllm_add<__half>(__half a, __half b) {
-  return __hadd(a, b);
-}
-
-template <>
-__TYPE_CONVERT__HOST_DEVICE__
-inline __hip_bfloat16 vllm_add<__hip_bfloat16>(__hip_bfloat16 a, __hip_bfloat16 b) {
-  return __hadd(a, b);
-}
-
-#undef __TYPE_CONVERT__HOST_DEVICE__
-
-#endif // USE_ROCM
-
-#endif // CSRC__PUNICA__TYPE_CONVERT_H__

csrc/pybind.cpp

@@ -8,90 +8,104 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   pybind11::module ops = m.def_submodule("ops", "vLLM custom operators");
 
   // Attention ops
-  ops.def("paged_attention_v1", &paged_attention_v1,
-          "Compute the attention between an input query and the cached "
-          "keys/values using PagedAttention.");
-  ops.def("paged_attention_v2", &paged_attention_v2, "PagedAttention V2.");
+  ops.def(
+    "paged_attention_v1",
+    &paged_attention_v1,
+    "Compute the attention between an input query and the cached keys/values using PagedAttention.");
+  ops.def(
+    "paged_attention_v2",
+    &paged_attention_v2,
+    "PagedAttention V2.");
 
   // Activation ops
-  ops.def("silu_and_mul", &silu_and_mul, "Activation function used in SwiGLU.");
-  ops.def("gelu_and_mul", &gelu_and_mul,
-          "Activation function used in GeGLU with `none` approximation.");
-  ops.def("gelu_tanh_and_mul", &gelu_tanh_and_mul,
-          "Activation function used in GeGLU with `tanh` approximation.");
-  ops.def("gelu_new", &gelu_new, "GELU implementation used in GPT-2.");
-  ops.def("gelu_fast", &gelu_fast, "Approximate GELU implementation.");
+  ops.def(
+    "silu_and_mul",
+    &silu_and_mul,
+    "Activation function used in SwiGLU.");
+  ops.def(
+    "gelu_and_mul",
+    &gelu_and_mul,
+    "Activation function used in GeGLU with `none` approximation.");
+  ops.def(
+    "gelu_tanh_and_mul",
+    &gelu_tanh_and_mul,
+    "Activation function used in GeGLU with `tanh` approximation.");
+  ops.def(
+    "gelu_new",
+    &gelu_new,
+    "GELU implementation used in GPT-2.");
+  ops.def(
+    "gelu_fast",
+    &gelu_fast,
+    "Approximate GELU implementation.");
 
   // Layernorm
-  ops.def("rms_norm", &rms_norm,
-          "Apply Root Mean Square (RMS) Normalization to the input tensor.");
+  ops.def(
+    "rms_norm",
+    &rms_norm,
+    "Apply Root Mean Square (RMS) Normalization to the input tensor.");
 
-  ops.def("fused_add_rms_norm", &fused_add_rms_norm,
-          "In-place fused Add and RMS Normalization");
+  ops.def(
+    "fused_add_rms_norm",
+    &fused_add_rms_norm,
+    "In-place fused Add and RMS Normalization");
 
   // Rotary embedding
-  ops.def("rotary_embedding", &rotary_embedding,
-          "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
+  ops.def(
+    "rotary_embedding",
+    &rotary_embedding,
+    "Apply GPT-NeoX or GPT-J style rotary embedding to query and key");
 
-  ops.def("batched_rotary_embedding", &batched_rotary_embedding,
-          "Apply GPT-NeoX or GPT-J style rotary embedding to query and key "
-          "(supports multiple loras)");
+  ops.def(
+    "batched_rotary_embedding",
+    &batched_rotary_embedding,
+    "Apply GPT-NeoX or GPT-J style rotary embedding to query and key (supports multiple loras)");
 
 // Quantization ops
 #ifndef USE_ROCM
-  ops.def("aqlm_gemm", &aqlm_gemm, "Quantized GEMM for AQLM");
-  ops.def("aqlm_dequant", &aqlm_dequant, "Decompression method for AQLM");
   ops.def("awq_gemm", &awq_gemm, "Quantized GEMM for AWQ");
-  ops.def("marlin_gemm", &marlin_gemm,
-          "Marlin (Dense) Optimized Quantized GEMM for GPTQ");
-  ops.def("gptq_marlin_24_gemm", &gptq_marlin_24_gemm,
-          "Marlin_24 (Sparse) Optimized Quantized GEMM for GPTQ");
-  ops.def("gptq_marlin_gemm", &gptq_marlin_gemm,
-          "gptq_marlin Optimized Quantized GEMM for GPTQ");
-  ops.def("gptq_marlin_repack", &gptq_marlin_repack,
-          "gptq_marlin repack from GPTQ");
+  ops.def("marlin_gemm", &marlin_gemm, "Marlin Optimized Quantized GEMM for GPTQ");
   ops.def("awq_dequantize", &awq_dequantize, "Dequantization for AWQ");
-  ops.def("cutlass_scaled_mm_dq", &cutlass_scaled_mm_dq,
-          "CUTLASS w8a8 GEMM, supporting symmetric per-tensor or "
-          "per-row/column quantization.");
 #endif
-
+ 
   ops.def("gptq_gemm", &gptq_gemm, "Quantized GEMM for GPTQ");
   ops.def("gptq_shuffle", &gptq_shuffle, "Post processing for GPTQ");
   ops.def("squeezellm_gemm", &squeezellm_gemm, "Quantized GEMM for SqueezeLLM");
-  ops.def("static_scaled_fp8_quant", &static_scaled_fp8_quant,
-          "Compute FP8 quantized tensor for given scaling factor");
-  ops.def("dynamic_scaled_fp8_quant", &dynamic_scaled_fp8_quant,
-          "Compute FP8 quantized tensor and scaling factor");
-  ops.def("moe_align_block_size", &moe_align_block_size,
-          "Aligning the number of tokens to be processed by each expert such "
-          "that it is divisible by the block size.");
-
-  ops.def("static_scaled_int8_quant", &static_scaled_int8_quant,
-          "Compute int8 quantized tensor for given scaling factor");
+  ops.def(
+    "moe_align_block_size",
+    &moe_align_block_size,
+    "Aligning the number of tokens to be processed by each expert such that it is divisible by the block size.");
 
   // Cache ops
   pybind11::module cache_ops = m.def_submodule("cache_ops", "vLLM cache ops");
-  cache_ops.def("swap_blocks", &swap_blocks,
-                "Swap in (out) the cache blocks from src to dst");
-  cache_ops.def("copy_blocks", &copy_blocks,
-                "Copy the cache blocks from src to dst");
-  cache_ops.def("reshape_and_cache", &reshape_and_cache,
-                "Reshape the key and value tensors and cache them");
-  cache_ops.def("reshape_and_cache_flash", &reshape_and_cache_flash,
-                "Reshape the key and value tensors and cache them");
-  cache_ops.def("convert_fp8", &convert_fp8,
-                "Convert the key and value cache to fp8 data type");
+  cache_ops.def(
+    "swap_blocks",
+    &swap_blocks,
+    "Swap in (out) the cache blocks from src to dst");
+  cache_ops.def(
+    "copy_blocks",
+    &copy_blocks,
+    "Copy the cache blocks from src to dst");
+  cache_ops.def(
+    "reshape_and_cache",
+    &reshape_and_cache,
+    "Reshape the key and value tensors and cache them");
+  cache_ops.def(
+    "convert_fp8",
+    &convert_fp8,
+    "Convert the key and value cache to fp8 data type");
 
   // Cuda utils
-  pybind11::module cuda_utils =
-      m.def_submodule("cuda_utils", "vLLM cuda utils");
-  cuda_utils.def("get_device_attribute", &get_device_attribute,
-                 "Gets the specified device attribute.");
+  pybind11::module cuda_utils = m.def_submodule("cuda_utils", "vLLM cuda utils");
+  cuda_utils.def(
+    "get_device_attribute",
+    &get_device_attribute,
+    "Gets the specified device attribute.");
 
-  cuda_utils.def("get_max_shared_memory_per_block_device_attribute",
-                 &get_max_shared_memory_per_block_device_attribute,
-                 "Gets the maximum shared memory per block device attribute.");
+  cuda_utils.def(
+    "get_max_shared_memory_per_block_device_attribute",
+    &get_max_shared_memory_per_block_device_attribute,
+    "Gets the maximum shared memory per block device attribute.");
 
 #ifndef USE_ROCM
   // Custom all-reduce kernels
@@ -108,4 +122,5 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   custom_ar.def("register_graph_buffers", &register_graph_buffers,
                 "register_graph_buffers");
 #endif
+
 }

csrc/quantization/aqlm/gemm_kernels.cu

@@ -1,598 +0,0 @@
-/*
- * Modified by Neural Magic
- * Adapted from https://github.com/Vahe1994/AQLM
- *
- * 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 <cuda.h>
-#include <cuda_fp16.h>
-#include <cuda_runtime.h>
-#include <torch/extension.h>
-#include <c10/cuda/CUDAStream.h>
-#include <c10/cuda/CUDAGuard.h>
-
-#include <iostream>
-#include <cstdlib>
-
-namespace vllm {
-namespace aqlm {
-
-__global__ void Code1x16MatVec(
-    const int4* __restrict__ A, const int4* __restrict__ B,
-    int4* __restrict__ C, const int4* __restrict__ codebook, const int prob_m,
-    const int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  int b_gl_rd = 0;
-  int c_gl_wr = a_gl_rd;
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-
-  __shared__ int4 sh_b[32 * 9];
-  float res = 0;
-
-  int iters = (prob_k / 8 + 8 * 32 - 1) / (8 * 32);
-  while (iters--) {
-    // We pad shared memory to avoid bank conflicts during reads
-    __syncthreads();
-    for (int i = threadIdx.x; i < 32 * 8; i += blockDim.x) {
-      if (b_gl_rd + i < prob_k / 8) sh_b[9 * (i / 8) + i % 8] = B[b_gl_rd + i];
-    }
-    __syncthreads();
-    b_gl_rd += 32 * 8;
-
-    int b_sh_rd = 9 * (threadIdx.x % 32);
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint16_t* enc = reinterpret_cast<const uint16_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        uint32_t dec[4];
-        // We bypass the L1 cache to avoid massive amounts of memory streaming
-        // that doesn't actually help us; this brings > 2x speedup.
-        asm volatile("ld.cg.global.v4.u32 {%0, %1, %2, %3}, [%4];"
-                     : "=r"(dec[0]), "=r"(dec[1]), "=r"(dec[2]), "=r"(dec[3])
-                     : "l"((void*)&codebook[enc[i]]));
-        half2* a = reinterpret_cast<half2*>(&dec);
-        half2* b = reinterpret_cast<half2*>(&sh_b[b_sh_rd]);
-        half2 res2 = {};
-#pragma unroll
-        for (int j = 0; j < 4; j++) res2 = __hfma2(a[j], b[j], res2);
-        res += __half2float(res2.x) + __half2float(res2.y);
-        b_sh_rd++;
-      }
-      a_gl_rd += 32;
-    }
-  }
-
-  if (pred) {
-#pragma unroll
-    for (int i = 16; i > 0; i /= 2) res += __shfl_down_sync(0xffffffff, res, i);
-    if (threadIdx.x % 32 == 0)
-      reinterpret_cast<__half*>(C)[c_gl_wr] = __float2half(res);
-  }
-}
-
-__global__ void Code2x8MatVec(
-    const int4* __restrict__ A, const int4* __restrict__ B,
-    int4* __restrict__ C, const int4* __restrict__ codebook, int prob_m,
-    int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  int b_gl_rd = 0;
-  int c_gl_wr = a_gl_rd;
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-  int lane = threadIdx.x % 8;
-
-  extern __shared__ int4 sh[];
-  int4* sh_b = sh;
-  int4* sh_code = sh_b + 32 * 9;
-  int4* sh_code0 = sh_code;
-  int4* sh_code1 = sh_code + 256 * 8;
-
-  for (int i = threadIdx.x; i < 2 * 256; i += blockDim.x) {
-    int4 dec = codebook[i];
-#pragma unroll
-    for (int j = 0; j < 8; j++) sh_code[8 * i + (j + lane) % 8] = dec;
-  }
-  __syncthreads();
-
-  float res = 0;
-
-  int iters = (prob_k / 8 + 8 * 32 - 1) / (8 * 32);
-  while (iters--) {
-    // We pad shared memory to avoid bank conflicts during reads
-    __syncthreads();
-    for (int i = threadIdx.x; i < 32 * 8; i += blockDim.x) {
-      if (b_gl_rd + i < prob_k / 8) sh_b[9 * (i / 8) + i % 8] = B[b_gl_rd + i];
-    }
-    __syncthreads();
-    b_gl_rd += 32 * 8;
-
-    int b_sh_rd = 9 * (threadIdx.x % 32);
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint8_t* enc = reinterpret_cast<const uint8_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        half2* a0 =
-            reinterpret_cast<half2*>(&sh_code0[8 * enc[2 * i + 0] + lane]);
-        half2* a1 =
-            reinterpret_cast<half2*>(&sh_code1[8 * enc[2 * i + 1] + lane]);
-        half2* b = reinterpret_cast<half2*>(&sh_b[b_sh_rd]);
-        half2 res2 = {};
-#pragma unroll
-        for (int j = 0; j < 4; j++)
-          res2 = __hfma2(__hadd2(a0[j], a1[j]), b[j], res2);
-        res += __half2float(res2.x) + __half2float(res2.y);
-        b_sh_rd++;
-      }
-      a_gl_rd += 32;
-    }
-  }
-
-  if (pred) {
-#pragma unroll
-    for (int i = 16; i > 0; i /= 2) res += __shfl_down_sync(0xffffffff, res, i);
-    if (threadIdx.x % 32 == 0)
-      reinterpret_cast<__half*>(C)[c_gl_wr] = __float2half(res);
-  }
-}
-
-__global__ void Code1x16Dequant(
-    const int4* __restrict__ A, int4* __restrict__ C,
-    const int4* __restrict__ codebook, int prob_m, int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long, sums to m.
-    const int codebook_stride     // as int4
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-
-  int c_gl_stride = prob_k / 8;
-  int c_gl_wr = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  c_gl_wr = c_gl_stride * c_gl_wr + (threadIdx.x % 32) * 8;
-
-  int iters = (prob_k / 8 - 1) / (8 * 32) + 1;
-  while (iters--) {
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint16_t* enc = reinterpret_cast<const uint16_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        int4 chunk;
-        auto dec = reinterpret_cast<uint32_t*>(&chunk);
-        // We bypass the L1 cache to avoid massive amounts of memory streaming
-        // that doesn't actually help us; this brings > 2x speedup.
-        asm volatile("ld.cg.global.v4.u32 {%0, %1, %2, %3}, [%4];"
-                     : "=r"(dec[0]), "=r"(dec[1]), "=r"(dec[2]), "=r"(dec[3])
-                     : "l"((void*)&codebook[enc[i]]));
-
-        C[a_gl_rd * 8 + i] = chunk;
-      }
-    }
-    a_gl_rd += 32;
-  }
-}
-
-__global__ void Code2x8Dequant(
-    const int4* __restrict__ A, int4* __restrict__ C,
-    const int4* __restrict__ codebook, int prob_m, int prob_k,
-    const int4
-        codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                           // most 3 long, corresponds to cols.
-    const int codebook_stride  // as int4
-) {
-  int a_gl_stride = prob_k / 8 / 8;
-  int a_gl_rd = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  bool pred = a_gl_rd < prob_m;
-
-  if (pred) {
-    // advance to the correct codebook, this easy because we only multiply one
-    // column of the codebook.
-    auto codebook_size = &codebook_a_sizes.x;
-    while (a_gl_rd >= *codebook_size) {
-      codebook += codebook_stride;
-      ++codebook_size;
-    }
-  }
-
-  a_gl_rd = a_gl_stride * a_gl_rd + threadIdx.x % 32;
-  int a_gl_end = a_gl_rd + a_gl_stride - threadIdx.x % 32;
-  int lane = threadIdx.x % 8;
-
-  int c_gl_stride = prob_k / 8;
-  int c_gl_wr = (blockDim.x / 32) * blockIdx.x + (threadIdx.x / 32);
-  c_gl_wr = c_gl_stride * c_gl_wr + (threadIdx.x % 32) * 8;
-
-  extern __shared__ int4 sh[];
-  int4* sh_code = sh;
-  int4* sh_code0 = sh_code;
-  int4* sh_code1 = sh_code + 256 * 8;
-
-  for (int i = threadIdx.x; i < 2 * 256; i += blockDim.x) {
-    int4 dec = codebook[i];
-#pragma unroll
-    for (int j = 0; j < 8; j++) sh_code[8 * i + (j + lane) % 8] = dec;
-  }
-  __syncthreads();
-
-  float res = 0;
-
-  int iters = (prob_k / 8 - 1) / (8 * 32) + 1;
-  while (iters--) {
-    if (pred && a_gl_rd < a_gl_end) {
-      const uint8_t* enc = reinterpret_cast<const uint8_t*>(&A[a_gl_rd]);
-#pragma unroll
-      for (int i = 0; i < 8; i++) {
-        int4 chunk;
-        half2* a0 =
-            reinterpret_cast<half2*>(&sh_code0[8 * enc[2 * i + 0] + lane]);
-        half2* a1 =
-            reinterpret_cast<half2*>(&sh_code1[8 * enc[2 * i + 1] + lane]);
-#pragma unroll
-        for (int j = 0; j < 4; j++)
-          reinterpret_cast<half2*>(&chunk)[j] = __hadd2(a0[j], a1[j]);
-        C[a_gl_rd * 8 + i] = chunk;
-      }
-    }
-    a_gl_rd += 32;
-  }
-}
-
-inline int ceildiv(int a, int b) { return (a + b - 1) / b; }
-
-const int THREAD_M = 16;
-
-void code1x16_matvec_cuda(const void* __restrict__ A,
-                          const void* __restrict__ B, void* __restrict__ C,
-                          const void* __restrict__ codebook, int prob_m,
-                          int prob_k, const int4 codebook_a_sizes,
-                          const int codebook_stride) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code1x16MatVec<<<blocks, threads, 16 * 32 * 9, stream>>>(
-      (const int4*)A, (const int4*)B, (int4*)C, (const int4*)codebook, prob_m,
-      prob_k, codebook_a_sizes, codebook_stride);
-}
-
-void code2x8_matvec_cuda(const void* __restrict__ A, const void* __restrict__ B,
-                         void* __restrict__ C,
-                         const void* __restrict__ codebook, int prob_m,
-                         int prob_k, const int4 codebook_a_sizes,
-                         const int codebook_stride) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  int shared = 16 * (2 * 256 * 8 + 32 * 9);
-  cudaFuncSetAttribute(Code2x8MatVec,
-                       cudaFuncAttributeMaxDynamicSharedMemorySize, shared);
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code2x8MatVec<<<blocks, threads, shared, stream>>>(
-      (const int4*)A, (const int4*)B, (int4*)C, (const int4*)codebook, prob_m,
-      prob_k, codebook_a_sizes, codebook_stride);
-}
-
-void code1x16_dequant_cuda(
-    const void* __restrict__ A, void* __restrict__ C,
-    const void* __restrict__ codebook, int prob_m, int prob_k,
-    const int4 codebook_a_sizes,  // cumulative sizes of A spanning each
-                                  // codebook, at most 3 long.
-    const int codebook_stride     // as int4.
-) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-  Code1x16Dequant<<<blocks, threads, 0, stream>>>(
-      (const int4*)A, (int4*)C, (const int4*)codebook, prob_m, prob_k,
-      codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                         // most 3 long.
-      codebook_stride    // as int4.
-  );
-}
-
-// Dequantizes the code and codebook into weights.
-void code2x8_dequant_cuda(
-    const void* __restrict__ A, void* __restrict__ C,
-    const void* __restrict__ codebook, int prob_m, int prob_k,
-    const int4
-        codebook_a_sizes,  // cumulative sizes of A spanning each codebook, at
-                           // most 3 long, corresponds to cols.
-    const int codebook_stride  // as int4
-) {
-  int sms;
-  cudaDeviceGetAttribute(&sms, cudaDevAttrMultiProcessorCount, 0);
-  int waves = 0;
-  int thread_m;
-  do {
-    waves++;
-    thread_m = ceildiv(prob_m, waves * sms);
-  } while (thread_m > THREAD_M);
-
-  int blocks = ceildiv(prob_m, thread_m);
-  int threads = 32 * thread_m;
-  int shared = 16 * (2 * 256 * 8 + 32 * 9);
-  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
-
-  cudaFuncSetAttribute(Code2x8Dequant,
-                       cudaFuncAttributeMaxDynamicSharedMemorySize, shared);
-  Code2x8Dequant<<<blocks, threads, shared, stream>>>(
-      (const int4*)A, (int4*)C, (const int4*)codebook, prob_m, prob_k,
-      codebook_a_sizes, codebook_stride);
-}
-
-int codebook_stride(const torch::Tensor& codebooks) {
-  return codebooks.stride(0) * codebooks.element_size() / sizeof(int4);
-}
-
-void code1x16_matvec(
-    const torch::Tensor& A, const torch::Tensor& B, torch::Tensor& C,
-    const torch::Tensor& codebook,
-    const int4 codebook_a_sizes  // cumulative sizes of A spanning each
-                                 // codebook, at most 3 long.
-) {
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(A));
-  int prob_m = C.size(0);
-  int prob_k = B.size(0);
-
-  code1x16_matvec_cuda(A.data_ptr(), B.data_ptr(), C.data_ptr(),
-                       codebook.data_ptr(), prob_m, prob_k, codebook_a_sizes,
-                       codebook_stride(codebook));
-}
-
-torch::Tensor code1x16_matmat(const torch::Tensor& input,
-                              const torch::Tensor& codes,
-                              const torch::Tensor& codebooks,
-                              const torch::Tensor& scales,
-                              const int4 codebook_a_sizes,
-                              const std::optional<torch::Tensor>& bias) {
-  auto input_sizes = input.sizes();
-  auto out_features = codes.size(0) * codebooks.size(2);
-  auto flat_input = input.reshape({-1, input.size(-1)});
-  auto flat_output = torch::empty(
-      {flat_input.size(0), out_features},
-      torch::TensorOptions().dtype(input.dtype()).device(input.device()));
-
-  for (int i = 0; i < flat_input.size(0); ++i) {
-    auto input_vec = flat_input.index({i});
-    auto output_vec = flat_output.index({i});
-    code1x16_matvec(codes.squeeze(2), input_vec, output_vec, codebooks,
-                    codebook_a_sizes);
-  }
-  flat_output *= scales.flatten().unsqueeze(0);
-
-  if (bias.has_value()) {
-    flat_output += bias->unsqueeze(0);
-  }
-
-  auto output_sizes = input_sizes.vec();
-  output_sizes.pop_back();
-  output_sizes.push_back(-1);
-  auto output = flat_output.reshape(output_sizes);
-  return output;
-}
-
-void code2x8_matvec(const torch::Tensor& A, const torch::Tensor& B,
-                    torch::Tensor& C, const torch::Tensor& codebook,
-                    const int4 codebook_a_sizes) {
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(A));
-  int prob_m = C.size(0);
-  int prob_k = B.size(0);
-  code2x8_matvec_cuda(A.data_ptr(), B.data_ptr(), C.data_ptr(),
-                      codebook.data_ptr(), prob_m, prob_k, codebook_a_sizes,
-                      2 * codebook_stride(codebook));
-}
-
-torch::Tensor code2x8_matmat(const torch::Tensor& input,
-                             const torch::Tensor& codes,
-                             const torch::Tensor& codebooks,
-                             const torch::Tensor& scales,
-                             const int4 codebook_a_sizes,
-                             const std::optional<torch::Tensor>& bias) {
-  auto input_sizes = input.sizes();
-  auto out_features = codes.size(0) * codebooks.size(2);
-  auto flat_input = input.reshape({-1, input.size(-1)});
-  auto flat_output = torch::empty(
-      {flat_input.size(0), out_features},
-      torch::TensorOptions().dtype(input.dtype()).device(input.device()));
-
-  for (int i = 0; i < flat_input.size(0); ++i) {
-    auto input_vec = flat_input.index({i});
-    auto output_vec = flat_output.index({i});
-    code2x8_matvec(codes.squeeze(2), input_vec, output_vec, codebooks,
-                   codebook_a_sizes);
-  }
-  flat_output *= scales.flatten().unsqueeze(0);
-  if (bias.has_value()) {
-    flat_output += bias->unsqueeze(0);
-  }
-
-  auto output_sizes = input_sizes.vec();
-  output_sizes.pop_back();
-  output_sizes.push_back(-1);
-  auto output = flat_output.reshape(output_sizes);
-  return output;
-}
-
-// Accumulate the partition sizes.
-int4 accumulate_sizes(const torch::Tensor& codebook_partition_sizes) {
-  int4 cumulative_sizes;
-  auto cumulative_size = &cumulative_sizes.x;
-  int i = 0;
-  int last = 0;
-  assert(codebook_partition_sizes.size(0) <= 4);
-  for (; i < codebook_partition_sizes.size(0); ++i, ++cumulative_size) {
-    *cumulative_size = codebook_partition_sizes[i].item<int>() + last;
-    last = *cumulative_size;
-  }
-  // fill in the rest with unreachable.
-  for (; i < 4; ++i, ++cumulative_size) {
-    *cumulative_size = last * 10;
-  }
-  return cumulative_sizes;
-}
-
-}  // namespace aqlm
-}  // namespace vllm
-
-torch::Tensor aqlm_gemm(const torch::Tensor& input, const torch::Tensor& codes,
-                        const torch::Tensor& codebooks,
-                        const torch::Tensor& scales,
-                        const torch::Tensor& codebook_partition_sizes,
-                        const std::optional<torch::Tensor>& bias) {
-  int4 cumulative_sizes =
-      vllm::aqlm::accumulate_sizes(codebook_partition_sizes);
-
-  int const nbooks = codebooks.size(0) / codebook_partition_sizes.size(0);
-  int const entries = codebooks.size(1);
-
-  if (nbooks == 1 && entries == (1 << 16)) {
-    return vllm::aqlm::code1x16_matmat(input, codes, codebooks, scales,
-                                       cumulative_sizes, bias);
-  }
-  if (nbooks == 2 && entries == (1 << 8)) {
-    return vllm::aqlm::code2x8_matmat(input, codes, codebooks, scales,
-                                      cumulative_sizes, bias);
-  }
-
-  TORCH_CHECK(false, "AQLM with ", nbooks, " codebooks and ", entries,
-              " entries is not currently supported.")
-  return {};
-}
-
-torch::Tensor aqlm_dequant(const torch::Tensor& codes,
-                           const torch::Tensor& codebooks,
-                           const torch::Tensor& codebook_partition_sizes) {
-  int4 cumulative_sizes =
-      vllm::aqlm::accumulate_sizes(codebook_partition_sizes);
-
-  int const nbooks = codebooks.size(0) / codebook_partition_sizes.size(0);
-  int const entries = codebooks.size(1);
-
-  const at::cuda::OptionalCUDAGuard device_guard(device_of(codes));
-  int rows = codes.size(1);
-  int cols = codes.size(0);
-
-  auto in_features = codes.size(1) * 8;
-  auto out_features = codes.size(0);
-
-  assert(out_features = codebook_partition_sizes.sum().item<int>());
-
-  auto weights = torch::empty({out_features, in_features},
-                              torch::TensorOptions()
-                                  .dtype(codebooks.dtype())
-                                  .device(codebooks.device()));
-
-  if (nbooks == 1 && entries == (1 << 16)) {
-    vllm::aqlm::code1x16_dequant_cuda(codes.data_ptr(), weights.data_ptr(),
-                                      codebooks.data_ptr(), out_features,
-                                      in_features, cumulative_sizes,
-                                      vllm::aqlm::codebook_stride(codebooks));
-
-    // if you wanted to flip to scaling the weights, (though it's 30%-ish slower
-    // and not consistent with gemv implementation.) weights *=
-    // scales.index({"...", 0, 0});
-
-    return weights;
-  }
-
-  if (nbooks == 2 && entries == (1 << 8)) {
-    vllm::aqlm::code2x8_dequant_cuda(codes.data_ptr(), weights.data_ptr(),
-                                     codebooks.data_ptr(), out_features,
-                                     in_features, cumulative_sizes,
-                                     vllm::aqlm::codebook_stride(codebooks));
-
-    // if you wanted to flip to scaling the weights, (though it's 30%-ish slower
-    // and not consistent with gemv implementation) weights *=
-    // scales.index({"...", 0, 0});
-
-    return weights;
-  }
-
-  TORCH_CHECK(false, "AQLM with ", nbooks, " codebooks and ", entries,
-              " entries is not currently supported.")
-  return {};
-}

csrc/quantization/awq/dequantize.cuh

@@ -1,11 +1,11 @@
 /*
 Adapted from https://github.com/mit-han-lab/llm-awq
-Modified from NVIDIA FasterTransformer:
-https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
+Modified from NVIDIA FasterTransformer: https://github.com/NVIDIA/FasterTransformer/blob/main/src/fastertransformer/cutlass_extensions/include/cutlass_extensions/interleaved_numeric_conversion.h
 @article{lin2023awq,
-  title={AWQ: Activation-aware Weight Quantization for LLM Compression and
-Acceleration}, author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang,
-Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023}
+  title={AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration},
+  author={Lin, Ji and Tang, Jiaming and Tang, Haotian and Yang, Shang and Dang, Xingyu and Han, Song},
+  journal={arXiv},
+  year={2023}
 }
 */
 
@@ -14,88 +14,74 @@ Shang and Dang, Xingyu and Han, Song}, journal={arXiv}, year={2023}
 namespace vllm {
 namespace awq {
 
-__device__ uint4 dequantize_s4_to_fp16x2(uint32_t const& source) {
+__device__ uint4 dequantize_s4_to_fp16x2(uint32_t const& source)
+{
 #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 750
   assert(false);
 #else
-  uint4 result;
+    uint4 result;
 
-  uint32_t* h = reinterpret_cast<uint32_t*>(&result);
-  uint32_t const i4s = reinterpret_cast<uint32_t const&>(source);
+    uint32_t*      h   = reinterpret_cast<uint32_t*>(&result);
+    uint32_t const i4s = reinterpret_cast<uint32_t const&>(source);
 
-  // First, we extract the i4s and construct an intermediate fp16 number.
-  static constexpr uint32_t immLut = (0xf0 & 0xcc) | 0xaa;
-  static constexpr uint32_t BOTTOM_MASK = 0x000f000f;
-  static constexpr uint32_t TOP_MASK = 0x00f000f0;
-  static constexpr uint32_t I4s_TO_F16s_MAGIC_NUM = 0x64006400;
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint32_t immLut                = (0xf0 & 0xcc) | 0xaa;
+    static constexpr uint32_t BOTTOM_MASK           = 0x000f000f;
+    static constexpr uint32_t TOP_MASK              = 0x00f000f0;
+    static constexpr uint32_t I4s_TO_F16s_MAGIC_NUM = 0x64006400;
 
-  // Note that the entire sequence only requires 1 shift instruction. This is
-  // thanks to the register packing format and the fact that we force our
-  // integers to be unsigned, and account for this in the fp16 subtractions. In
-  // addition, I exploit the fact that sub and fma have the same throughput in
-  // order to convert elt_23 and elt_67 to fp16 without having to shift them to
-  // the bottom bits before hand.
+    // Note that the entire sequence only requires 1 shift instruction. This is thanks to the register packing
+    // format and the fact that we force our integers to be unsigned, and account for this in the fp16 subtractions.
+    // In addition, I exploit the fact that sub and fma have the same throughput in order to convert elt_23 and
+    // elt_67 to fp16 without having to shift them to the bottom bits before hand.
 
-  // Shift right by 8 to now consider elt_45 and elt_67. Issue first to hide RAW
-  // dependency if we issue immediately before required.
-  const uint32_t top_i4s = i4s >> 8;
-  // Extract elt_01 - (i4s & 0x000f000f) | 0x64006400
-  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
-               : "=r"(h[0])
-               : "r"(i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM),
-                 "n"(immLut));
-  // Extract elt_23 (i4s & 0x00f000f0) | 0x64006400
-  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
-               : "=r"(h[1])
-               : "r"(i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM),
-                 "n"(immLut));
-  // Extract elt_45 (top_i4s & 0x000f000f) | 0x64006400
-  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
-               : "=r"(h[2])
-               : "r"(top_i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM),
-                 "n"(immLut));
-  // Extract elt_67 (top_i4s & 0x00f000f0) | 0x64006400
-  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
-               : "=r"(h[3])
-               : "r"(top_i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM),
-                 "n"(immLut));
+    // Shift right by 8 to now consider elt_45 and elt_67. Issue first to hide RAW dependency if we issue
+    // immediately before required.
+    const uint32_t top_i4s = i4s >> 8;
+    // Extract elt_01 - (i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[0])
+                    : "r"(i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_23 (i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[1])
+                    : "r"(i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_45 (top_i4s & 0x000f000f) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[2])
+                    : "r"(top_i4s), "n"(BOTTOM_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
+    // Extract elt_67 (top_i4s & 0x00f000f0) | 0x64006400
+    asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+                    : "=r"(h[3])
+                    : "r"(top_i4s), "n"(TOP_MASK), "n"(I4s_TO_F16s_MAGIC_NUM), "n"(immLut));
 
-  // I use inline PTX below because I am not sure if the compiler will emit
-  // float2half instructions if I use the half2 ctor. In this case, I chose
-  // performance reliability over code readability.
+    // I use inline PTX below because I am not sure if the compiler will emit float2half instructions if I use the
+    // half2 ctor. In this case, I chose performance reliability over code readability.
 
-  // This is the half2 {1032, 1032} represented as an integer.
-  // static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64086408;
-  // Haotian: subtract {1024, 1024} instead, we do not need to map to [-8, 7]
-  static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64006400;
-  // This is the half2 {1 / 16, 1 / 16} represented as an integer.
-  static constexpr uint32_t ONE_SIXTEENTH = 0x2c002c00;
-  // This is the half2 {-72, -72} represented as an integer.
-  // static constexpr uint32_t NEG_72 = 0xd480d480;
-  // Haotian: Let's use {-64, -64}.
-  static constexpr uint32_t NEG_64 = 0xd400d400;
+    // This is the half2 {1032, 1032} represented as an integer.
+    // static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64086408;
+    // Haotian: subtract {1024, 1024} instead, we do not need to map to [-8, 7]
+    static constexpr uint32_t FP16_TOP_MAGIC_NUM = 0x64006400;
+    // This is the half2 {1 / 16, 1 / 16} represented as an integer.
+    static constexpr uint32_t ONE_SIXTEENTH = 0x2c002c00;
+    // This is the half2 {-72, -72} represented as an integer.
+    // static constexpr uint32_t NEG_72 = 0xd480d480;
+    // Haotian: Let's use {-64, -64}.
+    static constexpr uint32_t NEG_64 = 0xd400d400;
 
-  // Finally, we construct the output numbers.
-  // Convert elt_01
-  asm volatile("sub.f16x2 %0, %1, %2;\n"
-               : "=r"(h[0])
-               : "r"(h[0]), "r"(FP16_TOP_MAGIC_NUM));
-  // Convert elt_23
-  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
-               : "=r"(h[1])
-               : "r"(h[1]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
-  // Convert elt_45
-  asm volatile("sub.f16x2 %0, %1, %2;\n"
-               : "=r"(h[2])
-               : "r"(h[2]), "r"(FP16_TOP_MAGIC_NUM));
-  // Convert elt_67
-  asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n"
-               : "=r"(h[3])
-               : "r"(h[3]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+    // Finally, we construct the output numbers.
+    // Convert elt_01
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[0]) : "r"(h[0]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_23
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[1]) : "r"(h[1]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
+    // Convert elt_45
+    asm volatile("sub.f16x2 %0, %1, %2;\n" : "=r"(h[2]) : "r"(h[2]), "r"(FP16_TOP_MAGIC_NUM));
+    // Convert elt_67
+    asm volatile("fma.rn.f16x2 %0, %1, %2, %3;\n" : "=r"(h[3]) : "r"(h[3]), "r"(ONE_SIXTEENTH), "r"(NEG_64));
 
-  return result;
+    return result;
 #endif
 }
 
-}  // namespace awq
-}  // namespace vllm
+} // namespace awq
+} // namespace vllm