Merge branch 'main' into lwilkinson/machete-end2end

.buildkite/run-amd-test.sh

@@ -100,6 +100,7 @@ if [[ $commands == *" entrypoints/openai "* ]]; then
   --ignore=entrypoints/openai/test_accuracy.py \
   --ignore=entrypoints/openai/test_audio.py \
   --ignore=entrypoints/openai/test_encoder_decoder.py \
+  --ignore=entrypoints/openai/test_embedding.py \
   --ignore=entrypoints/openai/test_oot_registration.py "}
 fi
 

.github/workflows/scripts/build.sh

@@ -15,5 +15,6 @@ $python_executable -m pip install -r requirements-cuda.txt
 export MAX_JOBS=1
 # Make sure release wheels are built for the following architectures
 export TORCH_CUDA_ARCH_LIST="7.0 7.5 8.0 8.6 8.9 9.0+PTX"
+export VLLM_FA_CMAKE_GPU_ARCHES="80-real;90-real"
 # Build
 $python_executable setup.py bdist_wheel --dist-dir=dist

.gitignore

@@ -1,6 +1,9 @@
 # vllm commit id, generated by setup.py
 vllm/commit_id.py
 
+# vllm-flash-attn built from source
+vllm/vllm_flash_attn/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -12,6 +15,8 @@ __pycache__/
 # Distribution / packaging
 .Python
 build/
+cmake-build-*/
+CMakeUserPresets.json
 develop-eggs/
 dist/
 downloads/

CMakeLists.txt

@@ -1,5 +1,16 @@
 cmake_minimum_required(VERSION 3.26)
 
+# When building directly using CMake, make sure you run the install step
+# (it places the .so files in the correct location).
+#
+# Example:
+# mkdir build && cd build
+# cmake -G Ninja -DVLLM_PYTHON_EXECUTABLE=`which python3` -DCMAKE_INSTALL_PREFIX=.. ..
+# cmake --build . --target install
+#
+# If you want to only build one target, make sure to install it manually:
+# cmake --build . --target _C
+# cmake --install . --component _C
 project(vllm_extensions LANGUAGES CXX)
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
@@ -13,6 +24,9 @@ include(${CMAKE_CURRENT_LIST_DIR}/cmake/utils.cmake)
 # Suppress potential warnings about unused manually-specified variables
 set(ignoreMe "${VLLM_PYTHON_PATH}")
 
+# Prevent installation of dependencies (cutlass) by default.
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" ALL_COMPONENTS)
+
 #
 # Supported python versions.  These versions will be searched in order, the
 # first match will be selected.  These should be kept in sync with setup.py.
@@ -70,19 +84,6 @@ endif()
 find_package(Torch REQUIRED)
 
 #
-# Add the `default` target which detects which extensions should be
-# built based on platform/architecture.  This is the same logic that
-# setup.py uses to select which extensions should be built and should
-# be kept in sync.
-#
-# The `default` target makes direct use of cmake easier since knowledge
-# of which extensions are supported has been factored in, e.g.
-#
-# mkdir build && cd build
-# cmake -G Ninja -DVLLM_PYTHON_EXECUTABLE=`which python3` -DCMAKE_LIBRARY_OUTPUT_DIRECTORY=../vllm ..
-# cmake --build . --target default
-#
-add_custom_target(default)
 message(STATUS "Enabling core extension.")
 
 # Define _core_C extension
@@ -100,8 +101,6 @@ define_gpu_extension_target(
   USE_SABI 3
   WITH_SOABI)
 
-add_dependencies(default _core_C)
-
 #
 # Forward the non-CUDA device extensions to external CMake scripts.
 #
@@ -167,6 +166,8 @@ if(NVCC_THREADS AND VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_GPU_FLAGS "--threads=${NVCC_THREADS}")
 endif()
 
+include(FetchContent)
+
 #
 # Define other extension targets
 #
@@ -190,7 +191,6 @@ set(VLLM_EXT_SRC
   "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
-  include(FetchContent)
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
   FetchContent_Declare(
         cutlass
@@ -284,6 +284,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     csrc/quantization/machete/machete_pytorch.cu)
 endif()
 
+message(STATUS "Enabling C extension.")
 define_gpu_extension_target(
   _C
   DESTINATION vllm
@@ -314,6 +315,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
       "csrc/moe/marlin_moe_ops.cu")
 endif()
 
+message(STATUS "Enabling moe extension.")
 define_gpu_extension_target(
   _moe_C
   DESTINATION vllm
@@ -324,7 +326,6 @@ define_gpu_extension_target(
   USE_SABI 3
   WITH_SOABI)
 
-
 if(VLLM_GPU_LANG STREQUAL "HIP")
   #
   # _rocm_C extension
@@ -344,16 +345,63 @@ if(VLLM_GPU_LANG STREQUAL "HIP")
     WITH_SOABI)
 endif()
 
+# vllm-flash-attn currently only supported on CUDA
+if (NOT VLLM_TARGET_DEVICE STREQUAL "cuda")
+  return()
+endif ()
 
-if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
-  message(STATUS "Enabling C extension.")
-  add_dependencies(default _C)
+#
+# Build vLLM flash attention from source
+#
+# IMPORTANT: This has to be the last thing we do, because vllm-flash-attn uses the same macros/functions as vLLM.
+# Because functions all belong to the global scope, vllm-flash-attn's functions overwrite vLLMs.
+# They should be identical but if they aren't, this is a massive footgun.
+#
+# The vllm-flash-attn install rules are nested under vllm to make sure the library gets installed in the correct place.
+# To only install vllm-flash-attn, use --component vllm_flash_attn_c.
+# If no component is specified, vllm-flash-attn is still installed.
 
-  message(STATUS "Enabling moe extension.")
-  add_dependencies(default _moe_C)
+# If VLLM_FLASH_ATTN_SRC_DIR is set, vllm-flash-attn is installed from that directory instead of downloading.
+# This is to enable local development of vllm-flash-attn within vLLM.
+# It can be set as an environment variable or passed as a cmake argument.
+# The environment variable takes precedence.
+if (DEFINED ENV{VLLM_FLASH_ATTN_SRC_DIR})
+  set(VLLM_FLASH_ATTN_SRC_DIR $ENV{VLLM_FLASH_ATTN_SRC_DIR})
 endif()
 
-if(VLLM_GPU_LANG STREQUAL "HIP")
-  message(STATUS "Enabling rocm extension.")
-  add_dependencies(default _rocm_C)
+if(VLLM_FLASH_ATTN_SRC_DIR)
+  FetchContent_Declare(vllm-flash-attn SOURCE_DIR ${VLLM_FLASH_ATTN_SRC_DIR})
+else()
+  FetchContent_Declare(
+          vllm-flash-attn
+          GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
+          GIT_TAG 013f0c4fc47e6574060879d9734c1df8c5c273bd
+          GIT_PROGRESS TRUE
+  )
 endif()
+
+# Set the parent build flag so that the vllm-flash-attn library does not redo compile flag and arch initialization.
+set(VLLM_PARENT_BUILD ON)
+
+# Make sure vllm-flash-attn install rules are nested under vllm/
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY FALSE)" COMPONENT vllm_flash_attn_c)
+install(CODE "set(OLD_CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
+install(CODE "set(CMAKE_INSTALL_PREFIX \"\${CMAKE_INSTALL_PREFIX}/vllm/\")" COMPONENT vllm_flash_attn_c)
+
+# Fetch the vllm-flash-attn library
+FetchContent_MakeAvailable(vllm-flash-attn)
+message(STATUS "vllm-flash-attn is available at ${vllm-flash-attn_SOURCE_DIR}")
+
+# Restore the install prefix
+install(CODE "set(CMAKE_INSTALL_PREFIX \"\${OLD_CMAKE_INSTALL_PREFIX}\")" COMPONENT vllm_flash_attn_c)
+install(CODE "set(CMAKE_INSTALL_LOCAL_ONLY TRUE)" COMPONENT vllm_flash_attn_c)
+
+# Copy over the vllm-flash-attn python files
+install(
+        DIRECTORY ${vllm-flash-attn_SOURCE_DIR}/vllm_flash_attn/
+        DESTINATION vllm/vllm_flash_attn
+        COMPONENT vllm_flash_attn_c
+        FILES_MATCHING PATTERN "*.py"
+)
+
+# Nothing after vllm-flash-attn, see comment about macros above

Dockerfile

@@ -48,6 +48,9 @@ RUN --mount=type=cache,target=/root/.cache/pip \
 # see https://github.com/pytorch/pytorch/pull/123243
 ARG torch_cuda_arch_list='7.0 7.5 8.0 8.6 8.9 9.0+PTX'
 ENV TORCH_CUDA_ARCH_LIST=${torch_cuda_arch_list}
+# Override the arch list for flash-attn to reduce the binary size
+ARG vllm_fa_cmake_gpu_arches='80-real;90-real'
+ENV VLLM_FA_CMAKE_GPU_ARCHES=${vllm_fa_cmake_gpu_arches}
 #################### BASE BUILD IMAGE ####################
 
 #################### WHEEL BUILD IMAGE ####################

Dockerfile.neuron

@@ -1,5 +1,5 @@
 # default base image
-ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.19.1-ubuntu20.04"
+ARG BASE_IMAGE="public.ecr.aws/neuron/pytorch-inference-neuronx:2.1.2-neuronx-py310-sdk2.20.0-ubuntu20.04"
 
 FROM $BASE_IMAGE
 
@@ -20,7 +20,7 @@ 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
+RUN python3 -m pip install --pre neuronx-cc==2.15.* --extra-index-url=https://pip.repos.neuron.amazonaws.com -U
 
 COPY ./vllm /app/vllm/vllm
 COPY ./setup.py /app/vllm/setup.py

benchmarks/benchmark_latency.py

@@ -11,7 +11,7 @@
 
 from vllm import LLM, SamplingParams
 from vllm.engine.arg_utils import DEVICE_OPTIONS, EngineArgs
-from vllm.inputs import PromptInputs
+from vllm.inputs import PromptType
 from vllm.model_executor.layers.quantization import QUANTIZATION_METHODS
 from vllm.utils import FlexibleArgumentParser
 
@@ -61,7 +61,7 @@ def main(args: argparse.Namespace):
     dummy_prompt_token_ids = np.random.randint(10000,
                                                size=(args.batch_size,
                                                      args.input_len))
-    dummy_inputs: List[PromptInputs] = [{
+    dummy_prompts: List[PromptType] = [{
         "prompt_token_ids": batch
     } for batch in dummy_prompt_token_ids.tolist()]
 
@@ -74,13 +74,13 @@ def run_to_completion(profile_dir: Optional[str] = None):
                     ],
                     on_trace_ready=torch.profiler.tensorboard_trace_handler(
                         str(profile_dir))) as p:
-                llm.generate(dummy_inputs,
+                llm.generate(dummy_prompts,
                              sampling_params=sampling_params,
                              use_tqdm=False)
             print(p.key_averages())
         else:
             start_time = time.perf_counter()
-            llm.generate(dummy_inputs,
+            llm.generate(dummy_prompts,
                          sampling_params=sampling_params,
                          use_tqdm=False)
             end_time = time.perf_counter()

cmake/utils.cmake

@@ -364,5 +364,5 @@ function (define_gpu_extension_target GPU_MOD_NAME)
     target_link_libraries(${GPU_MOD_NAME} PRIVATE ${TORCH_LIBRARIES})
   endif()
 
-  install(TARGETS ${GPU_MOD_NAME} LIBRARY DESTINATION ${GPU_DESTINATION})
+  install(TARGETS ${GPU_MOD_NAME} LIBRARY DESTINATION ${GPU_DESTINATION} COMPONENT ${GPU_MOD_NAME})
 endfunction()

collect_env.py

@@ -285,9 +285,14 @@ def summarize_vllm_build_flags():
 
 
 def get_gpu_topo(run_lambda):
+    output = None
+
     if get_platform() == 'linux':
-        return run_and_read_all(run_lambda, 'nvidia-smi topo -m')
-    return None
+        output = run_and_read_all(run_lambda, 'nvidia-smi topo -m')
+        if output is None:
+            output = run_and_read_all(run_lambda, 'rocm-smi --showtopo')
+
+    return output
 
 
 # example outputs of CPU infos

docs/source/dev/multimodal/multimodal_index.rst

@@ -8,7 +8,7 @@ Multi-Modality
 vLLM provides experimental support for multi-modal models through the :mod:`vllm.multimodal` package.
 
 Multi-modal inputs can be passed alongside text and token prompts to :ref:`supported models <supported_vlms>`
-via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptInputs`.
+via the ``multi_modal_data`` field in :class:`vllm.inputs.PromptType`.
 
 Currently, vLLM only has built-in support for image data. You can extend vLLM to process additional modalities
 by following :ref:`this guide <adding_multimodal_plugin>`.

docs/source/dev/offline_inference/llm_inputs.rst

@@ -1,7 +1,7 @@
 LLM Inputs
 ==========
 
-.. autodata:: vllm.inputs.PromptInputs
+.. autodata:: vllm.inputs.PromptType
 
 .. autoclass:: vllm.inputs.TextPrompt
     :show-inheritance:

docs/source/getting_started/amd-installation.rst

@@ -83,7 +83,7 @@ Option 2: Build from source
 
 For installing PyTorch, you can start from a fresh docker image, e.g, `rocm/pytorch:rocm6.1.2_ubuntu20.04_py3.9_pytorch_staging`, `rocm/pytorch-nightly`.
 
-Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guild in PyTorch `Getting Started <https://pytorch.org/get-started/locally/>`_
+Alternatively, you can install PyTorch using PyTorch wheels. You can check PyTorch installation guide in PyTorch `Getting Started <https://pytorch.org/get-started/locally/>`_
 
 
 1. Install `Triton flash attention for ROCm <https://github.com/ROCm/triton>`_
@@ -104,7 +104,7 @@ Alternatively, wheels intended for vLLM use can be accessed under the releases.
 
     $ cd vllm
     $ pip install -U -r requirements-rocm.txt
-    $ python setup.py develop # This may take 5-10 minutes. Currently, `pip install .`` does not work for ROCm installation
+    $ python setup.py develop # This may take 5-10 minutes. Currently, `pip install .` does not work for ROCm installation
 
 
 .. tip::

docs/source/getting_started/neuron-installation.rst

@@ -3,8 +3,8 @@
 Installation with Neuron
 ========================
 
-vLLM 0.3.3 onwards supports model inferencing and serving on AWS Trainium/Inferentia with Neuron SDK.
-At the moment Paged Attention is not supported in Neuron SDK, but naive continuous batching is supported in transformers-neuronx.
+vLLM 0.3.3 onwards supports model inferencing and serving on AWS Trainium/Inferentia with Neuron SDK with continuous batching.
+Paged Attention and Chunked Prefill are currently in development and will be available soon.
 Data types currently supported in Neuron SDK are FP16 and BF16.
 
 Requirements

docs/source/index.rst

@@ -43,7 +43,7 @@ vLLM is flexible and easy to use with:
 * Tensor parallelism and pipeline parallelism support for distributed inference
 * Streaming outputs
 * OpenAI-compatible API server
-* Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Neuron.
+* Support NVIDIA GPUs, AMD CPUs and GPUs, Intel CPUs and GPUs, PowerPC CPUs, TPU, and AWS Trainium and Inferentia Accelerators.
 * Prefix caching support
 * Multi-lora support
 

docs/source/models/supported_models.rst

@@ -127,6 +127,10 @@ Decoder-only Language Models
     - Nemotron-3, Nemotron-4, Minitron
     - :code:`nvidia/Minitron-8B-Base`, :code:`mgoin/Nemotron-4-340B-Base-hf-FP8`, etc.
     - ✅︎
+  * - :code:`OLMoEForCausalLM`
+    - OLMoE
+    - :code:`allenai/OLMoE-1B-7B-0924`, :code:`allenai/OLMoE-1B-7B-0924-Instruct`, etc.
+    -
   * - :code:`OLMoForCausalLM`
     - OLMo
     - :code:`allenai/OLMo-1B-hf`, :code:`allenai/OLMo-7B-hf`, etc.

docs/source/models/vlm.rst

@@ -27,7 +27,7 @@ The :class:`~vllm.LLM` class can be instantiated in much the same way as languag
     We have removed all vision language related CLI args in the ``0.5.1`` release. **This is a breaking change**, so please update your code to follow
     the above snippet. Specifically, ``image_feature_size`` can no longer be specified as we now calculate that internally for each model.
 
-To pass an image to the model, note the following in :class:`vllm.inputs.PromptInputs`:
+To pass an image to the model, note the following in :class:`vllm.inputs.PromptType`:
 
 * ``prompt``: The prompt should follow the format that is documented on HuggingFace.
 * ``multi_modal_data``: This is a dictionary that follows the schema defined in :class:`vllm.multimodal.MultiModalDataDict`. 

requirements-cuda.txt

@@ -8,4 +8,3 @@ torch == 2.4.0
 # These must be updated alongside torch
 torchvision == 0.19   # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
 xformers == 0.0.27.post2; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch 2.4.0
-vllm-flash-attn == 2.6.1; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch 2.4.0

requirements-neuron.txt

@@ -2,6 +2,6 @@
 -r requirements-common.txt
 
 # Dependencies for Neuron devices
-transformers-neuronx >= 0.9.0
-torch-neuronx >= 2.1.0
+transformers-neuronx >= 0.12.0
+torch-neuronx >= 2.1.2
 neuronx-cc