Merge remote-tracking branch 'upstream/main'

.buildkite/run-amd-test.sh

@@ -1,4 +1,4 @@
-# This script build the ROCm docker image and runs test inside it.
+# This script runs test inside the corresponding ROCm docker container.
 set -ex
 
 # Print ROCm version
@@ -19,15 +19,16 @@ done
 
 echo "--- Building container"
 sha=$(git rev-parse --short HEAD)
-container_name=rocm_${sha}
+image_name=rocm_${sha}
+container_name=rocm_${sha}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)
 docker build \
-        -t ${container_name} \
+        -t ${image_name} \
         -f Dockerfile.rocm \
         --progress plain \
         .
 
 remove_docker_container() {
-   docker rm -f ${container_name} || docker image rm -f ${container_name} || true
+   docker rm -f ${container_name} || docker image rm -f ${image_name} || true
 }
 trap remove_docker_container EXIT
 
@@ -39,6 +40,6 @@ docker run \
         --rm \
         -e HF_TOKEN \
         --name ${container_name} \
-        ${container_name} \
+        ${image_name} \
         /bin/bash -c "${@}"
 

.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 2>&1 | tee benchmark_latency.txt
+python3 benchmarks/benchmark_latency.py --output-json latency_results.json 2>&1 | tee benchmark_latency.txt
 bench_latency_exit_code=$?
 
-python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 2>&1 | tee benchmark_throughput.txt
+python3 benchmarks/benchmark_throughput.py --input-len 256 --output-len 256 --output-json throughput_results.json 2>&1 | tee benchmark_throughput.txt
 bench_throughput_exit_code=$?
 
 # run server-based benchmarks and upload the result to buildkite
@@ -74,4 +74,5 @@ if [ $bench_serving_exit_code -ne 0 ]; then
     exit $bench_serving_exit_code
 fi
 
-/workspace/buildkite-agent artifact upload openai-*.json
+rm ShareGPT_V3_unfiltered_cleaned_split.json
+/workspace/buildkite-agent artifact upload "*.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 examples/offline_inference.py
+docker run --network host --env VLLM_CPU_KVCACHE_SPACE=1 --name cpu-test cpu-test python3 vllm/examples/offline_inference.py

.buildkite/test-pipeline.yaml

@@ -5,13 +5,16 @@
 
 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
@@ -24,34 +27,41 @@ steps:
   command: pytest -v -s core
 
 - label: Distributed Comm Ops Test
-  command: pytest -v -s test_comm_ops.py
-  working_dir: "/vllm-workspace/tests/distributed"
+  #mirror_hardwares: [amd]
+  command: pytest -v -s distributed/test_comm_ops.py
+  working_dir: "/vllm-workspace/tests"
   num_gpus: 2
 
 - label: Distributed Tests
-  working_dir: "/vllm-workspace/tests/distributed"
-
-  num_gpus: 2 # only support 1 or 2 for now.
   mirror_hardwares: [amd]
-
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 2
   commands:
-  - pytest -v -s test_pynccl_library.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
+  - pytest -v -s distributed/test_pynccl_library.py
+  - 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)
-  working_dir: "/vllm-workspace/tests/distributed"
+  #mirror_hardwares: [amd]
+  working_dir: "/vllm-workspace/tests"
   num_gpus: 4
   commands:
-  - pytest -v -s test_pynccl.py
+  - pytest -v -s distributed/test_pynccl.py
 
 - label: Engine Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   command: pytest -v -s engine tokenization test_sequence.py test_config.py test_logger.py
 
 - label: Entrypoints Test
+  mirror_hardwares: [amd]
+
   commands:
   # 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
@@ -62,13 +72,16 @@ steps:
   mirror_hardwares: [amd]
   commands:
     # install aws cli for llava_example.py
-    - pip install awscli
+    # install tensorizer for tensorize_vllm_model.py
+    - pip install awscli tensorizer
     - 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
 
@@ -79,7 +92,7 @@ steps:
     - pytest -v -s models --ignore=models/test_llava.py
 
 - label: Llava Test
-  #mirror_hardwares: [amd]
+  mirror_hardwares: [amd]
   commands:
     - bash ../.buildkite/download-images.sh
     - pytest -v -s models/test_llava.py
@@ -90,6 +103,7 @@ steps:
     - pytest -v -s prefix_caching
 
 - label: Samplers Test
+  #mirror_hardwares: [amd]
   command: pytest -v -s samplers
 
 - label: LogitsProcessor Test
@@ -105,16 +119,34 @@ steps:
   command: pytest -v -s spec_decode
 
 - label: LoRA Test %N
-  command: pytest -v -s lora --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
+  #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
   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
 
 - label: Metrics Test
+  mirror_hardwares: [amd]
   command: pytest -v -s metrics
 
 - label: Quantization Test
+  #mirror_hardwares: [amd]
   command: pytest -v -s quantization
 
 - label: Benchmarks

.buildkite/test-template.j2

@@ -3,9 +3,8 @@
 {% set default_working_dir = "/vllm-workspace/tests" %}
 
 steps:
-
   - 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:

.clang-format

@@ -0,0 +1,26 @@
+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/400-bug report.yml

@@ -59,6 +59,8 @@ body:
 
       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/workflows/clang-format.yml

@@ -0,0 +1,42 @@
+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

CMakeLists.txt

@@ -167,19 +167,45 @@ 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/marlin_cuda_kernel.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/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.
+  set_source_files_properties(
+      "csrc/quantization/cutlass_w8a8/scaled_mm_dq_c3x.cu"
+      PROPERTIES
+      COMPILE_FLAGS
+      "-gencode arch=compute_90a,code=sm_90a")
+
 endif()
 
 define_gpu_extension_target(
@@ -189,6 +215,7 @@ define_gpu_extension_target(
   SOURCES ${VLLM_EXT_SRC}
   COMPILE_FLAGS ${VLLM_GPU_FLAGS}
   ARCHITECTURES ${VLLM_GPU_ARCHES}
+  INCLUDE_DIRECTORIES ${CUTLASS_INCLUDE_DIR};${CUTLASS_TOOLS_UTIL_INCLUDE_DIR}
   WITH_SOABI)
 
 #

Dockerfile.cpu

@@ -17,4 +17,6 @@ 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.rocm

@@ -92,19 +92,23 @@ RUN if [ "$BUILD_TRITON" = "1" ]; then \
 WORKDIR /vllm-workspace
 COPY . .
 
+#RUN python3 -m pip install pynvml # to be removed eventually
 RUN python3 -m pip install --upgrade pip numba
 
 # 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 \
     && 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"]