[Doc]: improve CPU(x86) build-wheel-from-source section (#25617)

Signed-off-by: Kosseila (CloudThrill) <klouddude@gmail.com>

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

@@ -86,10 +86,6 @@ if [[ $commands == *"pytest -v -s models/test_registry.py"* ]]; then
   commands=${commands//"pytest -v -s models/test_registry.py"/"pytest -v -s models/test_registry.py -k 'not BambaForCausalLM and not GritLM and not Mamba2ForCausalLM and not Zamba2ForCausalLM'"}
 fi
 
-if [[ $commands == *"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"* ]]; then
-  commands=${commands//"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2'"/"VLLM_USE_V1=0 pytest -v -s models/test_initialization.py -k 'not llama4 and not plamo2 and not BambaForCausalLM and not Gemma2ForCausalLM and not Grok1ModelForCausalLM and not Zamba2ForCausalLM and not Gemma2Model and not GritLM'"}
-fi
-
 if [[ $commands == *"pytest -v -s compile/test_basic_correctness.py"* ]]; then
   commands=${commands//"pytest -v -s compile/test_basic_correctness.py"/"VLLM_USE_TRITON_FLASH_ATTN=0 pytest -v -s compile/test_basic_correctness.py"}
 fi

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

@@ -58,11 +58,8 @@ function cpu_tests() {
     # pytest -x -v -s tests/kernels/attention/test_cache.py -m cpu_model
     # pytest -x -v -s tests/kernels/attention/test_mla_decode_cpu.py -m cpu_model
 
-    # Note: disable Bart until supports V1
-    pytest -x -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
-    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model \
-                --ignore=tests/models/language/generation/test_bart.py
+    pytest -x -v -s tests/models/language/generation -m cpu_model
+    VLLM_CPU_SGL_KERNEL=1 pytest -x -v -s tests/models/language/generation -m cpu_model
 
     pytest -x -v -s tests/models/language/pooling -m cpu_model
     pytest -x -v -s tests/models/multimodal/generation \

.buildkite/scripts/hardware_ci/run-tpu-v1-test-part2.sh

@@ -62,7 +62,7 @@ echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
     && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
-    && python3 -m pip install --progress-bar off hf-transfer
+    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1

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

@@ -62,7 +62,7 @@ echo "--- Installing Python dependencies ---"
 python3 -m pip install --progress-bar off git+https://github.com/thuml/depyf.git \
     && python3 -m pip install --progress-bar off pytest pytest-asyncio tpu-info \
     && python3 -m pip install --progress-bar off "lm-eval @ git+https://github.com/EleutherAI/lm-evaluation-harness.git@206b7722158f58c35b7ffcd53b035fdbdda5126d" \
-    && python3 -m pip install --progress-bar off hf-transfer
+    && python3 -m pip install --progress-bar off hf-transfer tblib==3.1.0
 echo "--- Python dependencies installed ---"
 export VLLM_USE_V1=1
 export VLLM_XLA_CHECK_RECOMPILATION=1

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

@@ -35,7 +35,7 @@ docker run \
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 -O3 -O.cudagraph_mode=NONE
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend ray
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager -tp 2 --distributed-executor-backend mp
-    VLLM_ATTENTION_BACKEND=TRITON_ATTN_VLLM_V1 python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
+    VLLM_ATTENTION_BACKEND=TRITON_ATTN python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m --block-size 64 --enforce-eager
     cd tests
     pytest -v -s v1/core
     pytest -v -s v1/engine

.buildkite/scripts/run-prime-rl-test.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Setup script for Prime-RL integration tests
+# This script prepares the environment for running Prime-RL tests with nightly vLLM
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+REPO_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+PRIME_RL_REPO="https://github.com/PrimeIntellect-ai/prime-rl.git"
+PRIME_RL_DIR="${REPO_ROOT}/prime-rl"
+
+echo "Setting up Prime-RL integration test environment..."
+
+# Clean up any existing Prime-RL directory
+if [ -d "${PRIME_RL_DIR}" ]; then
+    echo "Removing existing Prime-RL directory..."
+    rm -rf "${PRIME_RL_DIR}"
+fi
+
+# Install UV if not available
+if ! command -v uv &> /dev/null; then
+    echo "Installing UV package manager..."
+    curl -LsSf https://astral.sh/uv/install.sh | sh
+    source $HOME/.local/bin/env
+fi
+
+# Clone Prime-RL repository at specific branch for reproducible tests
+PRIME_RL_BRANCH="integ-vllm-main"
+echo "Cloning Prime-RL repository at branch: ${PRIME_RL_BRANCH}..."
+git clone --branch "${PRIME_RL_BRANCH}" --single-branch "${PRIME_RL_REPO}" "${PRIME_RL_DIR}"
+cd "${PRIME_RL_DIR}"
+
+echo "Setting up UV project environment..."
+export UV_PROJECT_ENVIRONMENT=/usr/local
+ln -s /usr/bin/python3 /usr/local/bin/python
+
+# Remove vllm pin from pyproject.toml
+echo "Removing vllm pin from pyproject.toml..."
+sed -i '/vllm==/d' pyproject.toml
+
+# Sync Prime-RL dependencies
+echo "Installing Prime-RL dependencies..."
+uv sync --inexact && uv sync --inexact --all-extras
+
+# Verify installation
+echo "Verifying installations..."
+uv run python -c "import vllm; print(f'vLLM version: {vllm.__version__}')"
+uv run python -c "import prime_rl; print('Prime-RL imported successfully')"
+
+echo "Prime-RL integration test environment setup complete!"
+
+echo "Running Prime-RL integration tests..."
+export WANDB_MODE=offline # this makes this test not require a WANDB_API_KEY
+uv run pytest -vs tests/integration/test_rl.py -m gpu
+
+echo "Prime-RL integration tests completed!"

.buildkite/test-pipeline.yaml

@@ -6,24 +6,28 @@
 # to generate the final pipeline yaml file.
 
 # Documentation
-# label(str): the name of the test. emoji allowed.
-# fast_check(bool): whether to run this on each commit on fastcheck pipeline.
-# torch_nightly(bool): whether to run this on vllm against torch nightly pipeline.
-# fast_check_only(bool): run this test on fastcheck pipeline only
-# optional(bool): never run this test by default (i.e. need to unblock manually) unless it's scheduled nightly run.
+# label(str): the name of the test. emojis allowed.
+# fast_check(bool): whether to run this on each commit on the fastcheck pipeline.
+# torch_nightly(bool): whether to run this on vllm against the torch nightly pipeline.
+# fast_check_only(bool): run this test on the fastcheck pipeline only
+# optional(bool): never run this test by default (i.e. need to unblock manually) unless it's a scheduled nightly run.
+# soft_fail(bool): allow this step to fail without failing the entire pipeline (useful for flaky or experimental tests).
 # command(str): the single command to run for tests. incompatible with commands.
-# commands(list): the list of commands to run for test. incompatbile with command.
-# mirror_hardwares(list): the list of hardwares to run the test on as well. currently only supports [amd]
-# gpu(str): override the GPU selection for the test. default is on L4 GPUs. currently only supports a100
-# num_gpus(int): override the number of GPUs for the test. default to 1 GPU. currently support 2,4.
-# num_nodes(int): whether to simulate multi-node setup by launch multiple containers on one host,
-#     in this case, commands must be specified. the first command runs on first host, the second
+# commands(list): the list of commands to run for the test. incompatible with command.
+# mirror_hardwares(list): the list of hardware to run the test on as well. currently only supports [amdexperimental]
+# gpu(str): override the GPU selection for the test. default is L4 GPUs. supports a100, b200, h200
+# num_gpus(int): override the number of GPUs for the test. defaults to 1 GPU. currently supports 2,4.
+# num_nodes(int): whether to simulate multi-node setup by launching multiple containers on one host,
+#     in this case, commands must be specified. the first command runs on the first host, the second
 #     command runs on the second host.
-# working_dir(str): specify the place where command should execute, default to /vllm-workspace/tests
-# source_file_dependencies(list): the list of prefix to opt-in the test for, if empty, the test will always run.
+# timeout_in_minutes(int): sets a timeout for the step in minutes. if not specified, uses the default timeout.
+# parallelism(int): number of parallel jobs to run for this step. enables test sharding using $$BUILDKITE_PARALLEL_JOB
+#     and $$BUILDKITE_PARALLEL_JOB_COUNT environment variables.
+# working_dir(str): specify the place where the command should execute, default to /vllm-workspace/tests
+# source_file_dependencies(list): the list of prefixes to opt-in the test for, if empty, the test will always run.
 
 # When adding a test
-# - If the test belong to an existing group, add it there
+# - If the test belongs to an existing group, add it there
 # - If the test is short, add to any existing step
 # - If the test takes more than 10min, then it is okay to create a new step.
 #   Note that all steps execute in parallel.
@@ -110,7 +114,7 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
-  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
+  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
 - label: Entrypoints Integration Test (API Server) # 100min
   timeout_in_minutes: 130
@@ -148,7 +152,6 @@ steps:
   num_gpus: 4
   source_file_dependencies:
   - vllm/distributed/
-  - vllm/core/
   - tests/distributed/test_utils
   - tests/distributed/test_pynccl
   - tests/distributed/test_events
@@ -161,12 +164,20 @@ steps:
   - tests/v1/test_internal_lb_dp.py
   - tests/v1/test_hybrid_lb_dp.py
   - tests/v1/engine/test_engine_core_client.py
+  - tests/distributed/test_symm_mem_allreduce.py
   commands:
-  # test with tp=2 and external_dp=2
-  - VLLM_USE_V1=0 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  # test with torchrun tp=2 and external_dp=2
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
-  # test with tp=2 and pp=2
+  # test with torchrun tp=2 and pp=2
   - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
+  # test with torchrun tp=4 and dp=1
+  - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  # test with torchrun tp=2, pp=2 and dp=1
+  - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  # test with torchrun tp=1 and dp=4 with ep
+  - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
+  # test with torchrun tp=2 and dp=2 with ep
+  - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   # test with internal dp
   - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
@@ -178,6 +189,7 @@ steps:
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
+  - pytest -v -s distributed/test_symm_mem_allreduce.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
   - pushd ../examples/offline_inference
@@ -288,10 +300,12 @@ steps:
     - pytest -v -s v1/spec_decode
     - pytest -v -s v1/kv_connector/unit
     - pytest -v -s v1/metrics
+    - pytest -v -s v1/test_kv_sharing.py
+    - pytest -v -s v1/test_metrics_reader.py
+    - pytest -v -s v1/test_oracle.py
+    - pytest -v -s v1/test_request.py
     - pytest -v -s v1/test_serial_utils.py
     - pytest -v -s v1/test_utils.py
-    - pytest -v -s v1/test_oracle.py
-    - pytest -v -s v1/test_metrics_reader.py
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
@@ -314,12 +328,13 @@ steps:
     - python3 offline_inference/vision_language.py --seed 0
     - python3 offline_inference/vision_language_pooling.py --seed 0
     - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - VLLM_USE_V1=0 python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     - python3 offline_inference/basic/classify.py
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
-    - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
 
 - label: Platform Tests (CUDA) # 4min
   timeout_in_minutes: 15
@@ -757,8 +772,9 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/audio_language.py --model-type whisper
     - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    # Whisper needs spawn method to avoid deadlock
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 - label: Blackwell Test # 38 min
   timeout_in_minutes: 60
@@ -856,26 +872,28 @@ steps:
     - NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed'
     - python3 ../examples/offline_inference/data_parallel.py --dp-size=2 --tp-size=1 --node-size=2 --node-rank=1 --master-addr=192.168.10.10 --master-port=12345 --enforce-eager --trust-remote-code
 
-- label: Distributed Tests (2 GPUs) # 110min
-  timeout_in_minutes: 150
+- label: Distributed Tests (2 GPUs) # 68min
+  timeout_in_minutes: 90
   mirror_hardwares: [amdexperimental]
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
   source_file_dependencies:
+  - vllm/compilation/
   - vllm/distributed/
   - vllm/engine/
   - vllm/executor/
-  - vllm/model_executor/models/
-  - tests/distributed/
-  - vllm/compilation
   - vllm/worker/worker_base.py
-  - vllm/worker/worker.py
-  - vllm/worker/model_runner.py
-  - entrypoints/llm/test_collective_rpc.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/compile/test_basic_correctness.py
+  - tests/compile/test_wrapper.py
+  - tests/distributed/
+  - tests/entrypoints/llm/test_collective_rpc.py
   - tests/v1/test_async_llm_dp.py
   - tests/v1/test_external_lb_dp.py
   - tests/v1/entrypoints/openai/test_multi_api_servers.py
-  - vllm/v1/engine/
+  - tests/v1/shutdown
+  - tests/v1/worker/test_worker_memory_snapshot.py
   commands:
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/test_external_lb_dp.py
@@ -884,19 +902,29 @@ steps:
   - pytest -v -s ./compile/test_basic_correctness.py
   - pytest -v -s ./compile/test_wrapper.py
   - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - pytest -v -s distributed/test_sequence_parallel.py
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
+  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
+
+- label: Distributed Model Tests (2 GPUs) # 37min
+  timeout_in_minutes: 50
+  mirror_hardwares: [amdexperimental]
+  working_dir: "/vllm-workspace/tests"
+  num_gpus: 2
+  source_file_dependencies:
+  - vllm/model_executor/model_loader/sharded_state_loader.py
+  - vllm/model_executor/models/
+  - tests/basic_correctness/
+  - tests/model_executor/model_loader/test_sharded_state_loader.py
+  - tests/models/
+  commands:
   - TARGET_TEST_SUITE=L4 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
+  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py
   # Avoid importing model tests that cause CUDA reinitialization error
   - pytest models/test_transformers.py -v -s -m 'distributed(num_gpus=2)'
   - pytest models/language -v -s -m 'distributed(num_gpus=2)'
   - pytest models/multimodal -v -s -m 'distributed(num_gpus=2)' --ignore models/multimodal/generation/test_whisper.py
   - VLLM_WORKER_MULTIPROC_METHOD=spawn pytest models/multimodal/generation/test_whisper.py -v -s -m 'distributed(num_gpus=2)'
-  # test sequence parallel
-  - pytest -v -s distributed/test_sequence_parallel.py
-  # this test fails consistently.
-  # TODO: investigate and fix
-  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s models/multimodal/generation/test_maverick.py
 
 - label: Plugin Tests (2 GPUs) # 40min
   timeout_in_minutes: 60
@@ -1030,3 +1058,16 @@ steps:
   num_gpus: 2
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
+    - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
+
+##### RL Integration Tests #####
+- label: Prime-RL Integration Test # 15min
+  timeout_in_minutes: 30
+  optional: true
+  num_gpus: 2
+  working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/
+  - .buildkite/scripts/run-prime-rl-test.sh
+  commands:
+    - bash .buildkite/scripts/run-prime-rl-test.sh

.github/CODEOWNERS

@@ -4,11 +4,8 @@
 # This lists cover the "core" components of vLLM that require careful review
 /vllm/attention @LucasWilkinson
 /vllm/attention/backends/abstract.py @WoosukKwon @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/core @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
-/vllm/engine/llm_engine.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/executor/executor_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
 /vllm/worker/worker_base.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @22quinn
-/vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/fused_moe @mgoin
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill @NickLucche
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256
@@ -66,18 +63,26 @@ CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 /tests/models/test_transformers.py @hmellor
 
 # Docs
-/docs @hmellor
+/docs/mkdocs @hmellor
+/docs/**/*.yml @hmellor
+/requirements/docs.txt @hmellor
+.readthedocs.yaml @hmellor
 mkdocs.yaml @hmellor
 
+# Linting
+.markdownlint.yaml @hmellor
+.pre-commit-config.yaml @hmellor
+/tools/pre_commit @hmellor
+
 # CPU
-/vllm/v1/worker/^cpu @bigPYJ1151
+/vllm/v1/worker/cpu* @bigPYJ1151
 /csrc/cpu @bigPYJ1151
 /vllm/platforms/cpu.py @bigPYJ1151
 /cmake/cpu_extension.cmake @bigPYJ1151
 /docker/Dockerfile.cpu @bigPYJ1151
 
 # Intel GPU
-/vllm/v1/worker/^xpu @jikunshang
+/vllm/v1/worker/xpu* @jikunshang
 /vllm/platforms/xpu.py @jikunshang
 /docker/Dockerfile.xpu @jikunshang
 

.github/ISSUE_TEMPLATE/750-RFC.yml

@@ -43,10 +43,6 @@ body:
       Any other things you would like to mention.
   validations:
     required: false
-- type: markdown
-  attributes:
-    value: >
-      Thanks for contributing 🎉! The vLLM core team hosts a biweekly RFC review session at 9:30AM Pacific Time, while most RFCs can be discussed online, you can optionally sign up for a slot to discuss your RFC online [here](https://docs.google.com/document/d/1CiLVBZeIVfR7_PNAKVSusxpceywkoOOB78qoWqHvSZc/edit).
 - type: checkboxes
   id: askllm
   attributes:

.pre-commit-config.yaml

@@ -49,7 +49,7 @@ repos:
   rev: 0.6.17
   hooks:
     - id: pip-compile
-      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128]
+      args: [requirements/test.in, -o, requirements/test.txt, --index-strategy, unsafe-best-match, --torch-backend, cu128, --python-platform, x86_64-manylinux_2_28]
       files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
@@ -60,38 +60,32 @@ repos:
     files: ^requirements/test\.(in|txt)$
   - id: mypy-local
     name: Run mypy for local Python installation
-    entry: tools/mypy.sh 0 "local"
-    language: python
-    types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests, pydantic]
+    entry: python tools/pre_commit/mypy.py 0 "local"
     stages: [pre-commit] # Don't run in CI
+    <<: &mypy_common
+      language: python
+      types_or: [python, pyi]
+      require_serial: true
+      additional_dependencies: [mypy==1.11.1, regex, types-cachetools, types-setuptools, types-PyYAML, types-requests, types-torch, pydantic]
   - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.9
-    entry: tools/mypy.sh 1 "3.9"
-    language: python
-    types: [python]
-    additional_dependencies: *mypy_deps
+    entry: python tools/pre_commit/mypy.py 1 "3.9"
+    <<: *mypy_common
     stages: [manual] # Only run in CI
   - id: mypy-3.10 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.10
-    entry: tools/mypy.sh 1 "3.10"
-    language: python
-    types: [python]
-    additional_dependencies: *mypy_deps
+    entry: python tools/pre_commit/mypy.py 1 "3.10"
+    <<: *mypy_common
     stages: [manual] # Only run in CI
   - id: mypy-3.11 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.11
-    entry: tools/mypy.sh 1 "3.11"
-    language: python
-    types: [python]
-    additional_dependencies: *mypy_deps
+    entry: python tools/pre_commit/mypy.py 1 "3.11"
+    <<: *mypy_common
     stages: [manual] # Only run in CI
   - id: mypy-3.12 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.12
-    entry: tools/mypy.sh 1 "3.12"
-    language: python
-    types: [python]
-    additional_dependencies: *mypy_deps
+    entry: python tools/pre_commit/mypy.py 1 "3.12"
+    <<: *mypy_common
     stages: [manual] # Only run in CI
   - id: shellcheck
     name: Lint shell scripts
@@ -155,11 +149,10 @@ repos:
     additional_dependencies: [regex]
   - id: check-pickle-imports
     name: Prevent new pickle/cloudpickle imports
-    entry: python tools/check_pickle_imports.py
+    entry: python tools/pre_commit/check_pickle_imports.py
     language: python
     types: [python]
-    pass_filenames: false
-    additional_dependencies: [pathspec, regex]
+    additional_dependencies: [regex]
   - id: validate-config
     name: Validate configuration has default values and that each field has a docstring
     entry: python tools/validate_config.py

.readthedocs.yaml

@@ -13,6 +13,7 @@ build:
 
 mkdocs:
   configuration: mkdocs.yaml
+  fail_on_warning: true
 
 # Optionally declare the Python requirements required to build your docs
 python:

benchmarks/auto_tune/auto_tune.sh

@@ -103,10 +103,15 @@ start_server() {
         VLLM_USE_V1=1 VLLM_SERVER_DEV_MODE=1 \
             vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
     fi
+    local server_pid=$!
 
     # wait for 10 minutes...
     server_started=0
     for i in {1..60}; do
+        # This line checks whether the server is still alive or not,
+        # since that we should always have permission to send signal to the server process.
+        kill -0 $server_pid 2> /dev/null || break
+
         RESPONSE=$(curl -s -X GET "http://0.0.0.0:8004/health" -w "%{http_code}" -o /dev/stdout)
         STATUS_CODE=$(echo "$RESPONSE" | tail -n 1)
         if [[ "$STATUS_CODE" -eq 200 ]]; then
@@ -118,7 +123,7 @@ start_server() {
     done
 
     if (( ! server_started )); then
-        echo "server did not start within 10 minutes. Please check server log at $vllm_log".
+        echo "server did not start within 10 minutes or crashed. Please check server log at $vllm_log".
         return 1
     else
         return 0

benchmarks/benchmark_ngram_proposer.py

@@ -1,17 +1,31 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import gc
+import time
+from unittest import mock
 
 import numpy as np
 from tabulate import tabulate
 
 from benchmark_utils import TimeCollector
-from vllm.config import ModelConfig, SpeculativeConfig, VllmConfig
+from vllm.config import (
+    CacheConfig,
+    DeviceConfig,
+    LoadConfig,
+    ModelConfig,
+    ParallelConfig,
+    SchedulerConfig,
+    SpeculativeConfig,
+    VllmConfig,
+)
+from vllm.platforms import current_platform
 from vllm.utils import FlexibleArgumentParser
 from vllm.v1.spec_decode.ngram_proposer import NgramProposer
+from vllm.v1.worker.gpu_input_batch import InputBatch
+from vllm.v1.worker.gpu_model_runner import GPUModelRunner
 
 
-def main(args):
+def benchmark_propose(args):
     rows = []
     for max_ngram in args.max_ngram:
         collector = TimeCollector(TimeCollector.US)
@@ -69,10 +83,88 @@ def main(args):
     )
 
 
+def benchmark_batched_propose(args):
+    NUM_SPECULATIVE_TOKENS_NGRAM = 10
+    PROMPT_LOOKUP_MIN = 5
+    PROMPT_LOOKUP_MAX = 15
+    MAX_MODEL_LEN = int(1e7)
+    DEVICE = current_platform.device_type
+
+    model_config = ModelConfig(model="facebook/opt-125m", runner="generate")
+
+    speculative_config = SpeculativeConfig(
+        target_model_config=model_config,
+        target_parallel_config=ParallelConfig(),
+        method="ngram",
+        num_speculative_tokens=NUM_SPECULATIVE_TOKENS_NGRAM,
+        prompt_lookup_max=PROMPT_LOOKUP_MAX,
+        prompt_lookup_min=PROMPT_LOOKUP_MIN,
+    )
+
+    vllm_config = VllmConfig(
+        model_config=model_config,
+        cache_config=CacheConfig(),
+        speculative_config=speculative_config,
+        device_config=DeviceConfig(device=current_platform.device_type),
+        parallel_config=ParallelConfig(),
+        load_config=LoadConfig(),
+        scheduler_config=SchedulerConfig(),
+    )
+
+    # monkey patch vllm.v1.worker.gpu_model_runner.get_pp_group
+    mock_pp_group = mock.MagicMock()
+    mock_pp_group.world_size = 1
+    with mock.patch(
+        "vllm.v1.worker.gpu_model_runner.get_pp_group", return_value=mock_pp_group
+    ):
+        runner = GPUModelRunner(vllm_config, DEVICE)
+
+        # hack max model len
+        runner.max_model_len = MAX_MODEL_LEN
+        runner.drafter.max_model_len = MAX_MODEL_LEN
+
+        dummy_input_batch = InputBatch(
+            max_num_reqs=args.num_req,
+            max_model_len=MAX_MODEL_LEN,
+            max_num_batched_tokens=args.num_req * args.num_token,
+            device=DEVICE,
+            pin_memory=False,
+            vocab_size=256000,
+            block_sizes=[16],
+        )
+        dummy_input_batch._req_ids = list(str(id) for id in range(args.num_req))
+        dummy_input_batch.spec_decode_unsupported_reqs = ()
+        dummy_input_batch.num_tokens_no_spec = [args.num_token] * args.num_req
+        dummy_input_batch.token_ids_cpu = np.random.randint(
+            0, 20, (args.num_req, args.num_token)
+        )
+
+        runner.input_batch = dummy_input_batch
+
+        sampled_token_ids = [[0]] * args.num_req
+
+        print("Starting benchmark")
+        # first run is warmup so ignore it
+        for _ in range(args.num_iteration):
+            start = time.time()
+            runner.drafter.propose(
+                sampled_token_ids,
+                dummy_input_batch.req_ids,
+                dummy_input_batch.num_tokens_no_spec,
+                dummy_input_batch.token_ids_cpu,
+                dummy_input_batch.spec_decode_unsupported_reqs,
+            )
+            end = time.time()
+            print(f"Iteration time (s): {end - start}")
+
+
 def invoke_main() -> None:
     parser = FlexibleArgumentParser(
         description="Benchmark the performance of N-gram speculative decode drafting"
     )
+    parser.add_argument(
+        "--batched", action="store_true", help="consider time to prepare batch"
+    )  # noqa: E501
     parser.add_argument(
         "--num-iteration",
         type=int,
@@ -105,8 +197,17 @@ def invoke_main() -> None:
         help="Number of speculative tokens to generate",
     )
     args = parser.parse_args()
-    main(args)
+
+    if not args.batched:
+        benchmark_propose(args)
+    else:
+        benchmark_batched_propose(args)
 
 
+"""
+# Example command lines:
+# time python3 benchmarks/benchmark_ngram_proposer.py
+# time python3 benchmarks/benchmark_ngram_proposer.py --batched --num-iteration 4 --num-token 1000000 --num-req 128
+"""  # noqa: E501
 if __name__ == "__main__":
     invoke_main()  # pragma: no cover

benchmarks/benchmark_serving_structured_output.py

@@ -449,7 +449,8 @@ async def benchmark(
     def prepare_extra_body(request) -> dict:
         extra_body = {}
         # Add the schema to the extra_body
-        extra_body[request.structure_type] = request.schema
+        extra_body["structured_outputs"] = {}
+        extra_body["structured_outputs"][request.structure_type] = request.schema
         return extra_body
 
     print("Starting initial single prompt test run...")

benchmarks/kernels/bench_nvfp4_gemm.py

@@ -3,6 +3,7 @@
 import argparse
 import copy
 import itertools
+import os
 
 import torch
 from weight_shapes import WEIGHT_SHAPES
@@ -23,21 +24,45 @@ PROVIDER_CFGS = {
     "torch-bf16": dict(enabled=True),
     "nvfp4": dict(no_a_quant=False, enabled=True),
     "nvfp4-noquant": dict(no_a_quant=True, enabled=True),
+    "fbgemm-nvfp4": dict(fbgemm=True, no_a_quant=False, enabled=True),
+    "fbgemm-nvfp4-noquant": dict(fbgemm=True, no_a_quant=True, enabled=True),
 }
 
+_needs_fbgemm = any(
+    v.get("fbgemm", False) for v in PROVIDER_CFGS.values() if v.get("enabled", False)
+)
+if _needs_fbgemm:
+    try:
+        from fbgemm_gpu.experimental.gemm.triton_gemm.fp4_quantize import (
+            triton_scale_nvfp4_quant,
+        )
+    except ImportError:
+        print(
+            "WARNING: FBGEMM providers are enabled but fbgemm_gpu is not installed. "
+            "These providers will be skipped. Please install fbgemm_gpu with: "
+            "'pip install fbgemm-gpu-genai' to run them."
+        )
+        # Disable FBGEMM providers so the benchmark can run.
+        for cfg in PROVIDER_CFGS.values():
+            if cfg.get("fbgemm"):
+                cfg["enabled"] = False
+
 _enabled = [k for k, v in PROVIDER_CFGS.items() if v["enabled"]]
 
 
-def _quant_weight_nvfp4(b: torch.Tensor, device: str):
+def _quant_weight_nvfp4(b: torch.Tensor, device: str, cfg):
     # Compute global scale for weight
     b_amax = torch.abs(b).max().to(torch.float32)
     b_global_scale = FLOAT8_E4M3_MAX * FLOAT4_E2M1_MAX / b_amax
-    b_fp4, scale_b_fp4 = ops.scaled_fp4_quant(b, b_global_scale)
+    if "fbgemm" in cfg and cfg["fbgemm"]:
+        b_fp4, scale_b_fp4 = triton_scale_nvfp4_quant(b, b_global_scale)
+    else:
+        b_fp4, scale_b_fp4 = ops.scaled_fp4_quant(b, b_global_scale)
     return b_fp4, scale_b_fp4, b_global_scale
 
 
 def build_nvfp4_runner(cfg, a, b, dtype, device):
-    b_fp4, scale_b_fp4, b_global_scale = _quant_weight_nvfp4(b, device)
+    b_fp4, scale_b_fp4, b_global_scale = _quant_weight_nvfp4(b, device, cfg)
 
     # Compute global scale for activation
     # NOTE: This is generally provided ahead-of-time by the model checkpoint.
@@ -46,6 +71,35 @@ def build_nvfp4_runner(cfg, a, b, dtype, device):
 
     # Alpha for the GEMM operation
     alpha = 1.0 / (a_global_scale * b_global_scale)
+    if "fbgemm" in cfg and cfg["fbgemm"]:
+        if cfg["no_a_quant"]:
+            a_fp4, scale_a_fp4 = triton_scale_nvfp4_quant(a, a_global_scale)
+
+            def run():
+                return torch.ops.fbgemm.f4f4bf16(
+                    a_fp4,
+                    b_fp4,
+                    scale_a_fp4,
+                    scale_b_fp4,
+                    global_scale=alpha,
+                    use_mx=False,
+                )
+
+            return run
+        else:
+
+            def run():
+                a_fp4, scale_a_fp4 = triton_scale_nvfp4_quant(a, a_global_scale)
+                return torch.ops.fbgemm.f4f4bf16(
+                    a_fp4,
+                    b_fp4,
+                    scale_a_fp4,
+                    scale_b_fp4,
+                    global_scale=alpha,
+                    use_mx=False,
+                )
+
+            return run
 
     if cfg["no_a_quant"]:
         # Pre-quantize activation
@@ -130,10 +184,13 @@ if __name__ == "__main__":
 
     for K, N, model in prepare_shapes(args):
         print(f"{model}, N={N} K={K}, BF16 vs NVFP4 GEMMs TFLOP/s:")
+        save_dir = f"bench_nvfp4_res_n{N}_k{K}"
+        os.makedirs(save_dir, exist_ok=True)
+
         benchmark.run(
             print_data=True,
             show_plots=True,
-            save_path=f"bench_nvfp4_res_n{N}_k{K}",
+            save_path=save_dir,
             N=N,
             K=K,
         )

benchmarks/kernels/bench_per_token_quant_fp8.py

@@ -51,7 +51,7 @@ def calculate_diff(
 ):
     """Calculate the difference between Inductor and CUDA implementations."""
     device = torch.device("cuda")
-    x = torch.rand((batch_size * hidden_size, 4096), dtype=dtype, device=device)
+    x = torch.randn((batch_size, hidden_size), dtype=dtype, device=device)
 
     quant_fp8 = QuantFP8(False, group_shape, column_major_scales=False)
 
@@ -59,23 +59,25 @@ def calculate_diff(
     torch_eager_out, torch_eager_scale = quant_fp8.forward_native(x)
     cuda_out, cuda_scale = quant_fp8.forward_cuda(x)
 
-    out_allclose = lambda o1, o2: torch.allclose(
-        o1.to(torch.float32),
-        o2.to(torch.float32),
-        rtol=1e-3,
-        atol=1e-5,
-    )
-    scale_allclose = lambda s1, s2: torch.allclose(s1, s2, rtol=1e-3, atol=1e-5)
-
-    if (
-        out_allclose(cuda_out, torch_out)
-        and scale_allclose(cuda_scale, torch_scale)
-        and out_allclose(cuda_out, torch_eager_out)
-        and scale_allclose(cuda_scale, torch_eager_scale)
-    ):
+    try:
+        torch.testing.assert_close(
+            cuda_out.to(torch.float32),
+            torch_out.to(torch.float32),
+            rtol=1e-3,
+            atol=1e-5,
+        )
+        torch.testing.assert_close(cuda_scale, torch_scale, rtol=1e-3, atol=1e-5)
+        torch.testing.assert_close(
+            cuda_out.to(torch.float32),
+            torch_eager_out.to(torch.float32),
+            rtol=1e-3,
+            atol=1e-5,
+        )
+        torch.testing.assert_close(cuda_scale, torch_eager_scale, rtol=1e-3, atol=1e-5)
         print("✅ All implementations match")
-    else:
+    except AssertionError as e:
         print("❌ Implementations differ")
+        print(e)
 
 
 configs = []
@@ -91,7 +93,7 @@ def benchmark_quantization(
 ):
     device = torch.device("cuda")
 
-    x = torch.randn(batch_size * hidden_size, 4096, device=device, dtype=dtype)
+    x = torch.randn(batch_size, hidden_size, device=device, dtype=dtype)
 
     quantiles = [0.5, 0.2, 0.8]
     quant_fp8 = QuantFP8(False, group_shape, column_major_scales=col_major)
@@ -157,21 +159,21 @@ if __name__ == "__main__":
     )
     parser.add_argument("-c", "--check", action="store_true")
     parser.add_argument(
-        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="half"
+        "--dtype", type=str, choices=["half", "bfloat16", "float"], default="bfloat16"
     )
     parser.add_argument(
         "--hidden-sizes",
         type=int,
         nargs="+",
-        default=None,
-        help="Hidden sizes to benchmark (default: 1,16,64,128,256,512,1024,2048,4096)",
+        default=[896, 1024, 2048, 4096, 7168],
+        help="Hidden sizes to benchmark",
     )
     parser.add_argument(
         "--batch-sizes",
         type=int,
         nargs="+",
-        default=None,
-        help="Batch sizes to benchmark (default: 1,16,32,64,128)",
+        default=[1, 16, 128, 512, 1024],
+        help="Batch sizes to benchmark",
     )
     parser.add_argument(
         "--group-sizes",
@@ -192,8 +194,8 @@ if __name__ == "__main__":
 
     dtype = STR_DTYPE_TO_TORCH_DTYPE[args.dtype]
 
-    hidden_sizes = args.hidden_sizes or [1, 16, 64, 128, 256, 512, 1024, 2048, 4096]
-    batch_sizes = args.batch_sizes or [1, 16, 32, 64, 128]
+    hidden_sizes = args.hidden_sizes
+    batch_sizes = args.batch_sizes
 
     if args.group_sizes is not None:
         group_shapes = []

benchmarks/kernels/benchmark_cutlass_moe_fp8.py

@@ -0,0 +1,406 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+Benchmark the performance of the cutlass_moe_fp8 kernel vs the triton_moe
+kernel. Both kernels take in fp8 quantized weights and 16-bit activations,
+but use different quantization strategies and backends.
+"""
+
+import nvtx
+import torch
+
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.fused_moe.config import fp8_w8a8_moe_quant_config
+from vllm.model_executor.layers.fused_moe.cutlass_moe import cutlass_moe_fp8
+from vllm.model_executor.layers.fused_moe.fused_moe import fused_experts, fused_topk
+from vllm.platforms import current_platform
+from vllm.utils import FlexibleArgumentParser
+
+# Weight shapes for different models: [num_experts, topk, hidden_size,
+# intermediate_size]
+WEIGHT_SHAPES_MOE = {
+    "mixtral-8x7b": [
+        [8, 2, 4096, 14336],
+    ],
+    "deepseek-v2": [
+        [160, 6, 5120, 12288],
+    ],
+    "custom-small": [
+        [8, 2, 2048, 7168],
+    ],
+    "glm45-fp8": [
+        [128, 8, 4096, 1408],
+    ],
+    "Llama-4-Maverick-17B-128E-Instruct-FP8": [
+        [128, 1, 5120, 8192],
+    ],
+}
+
+DEFAULT_MODELS = [
+    "mixtral-8x7b",
+]
+
+DEFAULT_BATCH_SIZES = [4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048]
+DEFAULT_TP_SIZES = [1]
+
+PER_ACT_TOKEN_OPTS = [False, True]
+PER_OUT_CH_OPTS = [False, True]
+
+FP8_DTYPE = current_platform.fp8_dtype()
+
+
+def bench_run(
+    results: list,
+    model: str,
+    num_experts: int,
+    topk: int,
+    per_act_token: bool,
+    per_out_ch: bool,
+    mkn: tuple[int, int, int],
+):
+    (m, k, n) = mkn
+
+    dtype = torch.half
+    device = "cuda"
+
+    # Create input activations
+    a = torch.randn((m, k), device=device, dtype=dtype) / 10
+
+    # Create weights
+    w1 = torch.randn((num_experts, 2 * n, k), device=device, dtype=dtype) / 10
+    w2 = torch.randn((num_experts, k, n), device=device, dtype=dtype) / 10
+
+    # Create FP8 quantized weights and scales for both kernels
+    w1_fp8q = torch.empty((num_experts, 2 * n, k), device=device, dtype=FP8_DTYPE)
+    w2_fp8q = torch.empty((num_experts, k, n), device=device, dtype=FP8_DTYPE)
+
+    # Create scales based on quantization strategy
+    if per_out_ch:
+        # Per-channel quantization
+        w1_scale = torch.empty(
+            (num_experts, 2 * n, 1), device=device, dtype=torch.float32
+        )
+        w2_scale = torch.empty((num_experts, k, 1), device=device, dtype=torch.float32)
+    else:
+        # Per-tensor quantization
+        w1_scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+        w2_scale = torch.empty((num_experts, 1, 1), device=device, dtype=torch.float32)
+
+    # Quantize weights
+    for expert in range(num_experts):
+        if per_out_ch:
+            # Per-channel quantization - not yet implemented properly
+            # For now, fall back to per-tensor quantization
+            w1_fp8q[expert], w1_scale_temp = ops.scaled_fp8_quant(w1[expert])
+            w2_fp8q[expert], w2_scale_temp = ops.scaled_fp8_quant(w2[expert])
+            # Expand scalar scales to the expected per-channel shape
+            w1_scale[expert] = w1_scale_temp.expand(2 * n, 1)
+            w2_scale[expert] = w2_scale_temp.expand(k, 1)
+        else:
+            # Per-tensor quantization
+            w1_fp8q[expert], w1_scale_temp = ops.scaled_fp8_quant(w1[expert])
+            w2_fp8q[expert], w2_scale_temp = ops.scaled_fp8_quant(w2[expert])
+            # Store scalar scales in [1, 1] tensors
+            w1_scale[expert, 0, 0] = w1_scale_temp
+            w2_scale[expert, 0, 0] = w2_scale_temp
+
+    # Prepare weights for CUTLASS (no transpose needed)
+    w1_fp8q_cutlass = w1_fp8q  # Keep original [E, 2N, K]
+    w2_fp8q_cutlass = w2_fp8q  # Keep original [E, K, N]
+
+    # Create router scores and get topk
+    score = torch.randn((m, num_experts), device=device, dtype=dtype)
+    topk_weights, topk_ids, _ = fused_topk(a, score, topk, renormalize=False)
+
+    # WORKAROUND: CUTLASS MoE FP8 has issues with per-token quantization
+    # Force per-tensor quantization for all cases to match working e2e setup
+    a1_scale = torch.full((), 1e-2, device=device, dtype=torch.float32)
+    a2_scale = torch.full((), 1e-2, device=device, dtype=torch.float32)
+
+    # Force per-tensor quantization for all cases
+    per_act_token = False
+
+    # Create stride tensors for CUTLASS
+    ab_strides1 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+    ab_strides2 = torch.full((num_experts,), n, dtype=torch.int64, device=device)
+    c_strides1 = torch.full((num_experts,), 2 * n, dtype=torch.int64, device=device)
+    c_strides2 = torch.full((num_experts,), k, dtype=torch.int64, device=device)
+
+    def run_triton_moe(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            fused_experts(
+                a,
+                w1,
+                w2,
+                topk_weights,
+                topk_ids,
+                quant_config=quant_config,
+            )
+
+    def run_cutlass_moe_fp8(
+        a: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_weights: torch.Tensor,
+        topk_ids: torch.Tensor,
+        ab_strides1: torch.Tensor,
+        ab_strides2: torch.Tensor,
+        c_strides1: torch.Tensor,
+        c_strides2: torch.Tensor,
+        w1_scale: torch.Tensor,
+        w2_scale: torch.Tensor,
+        a1_scale: torch.Tensor,
+        a2_scale: torch.Tensor,
+        num_repeats: int,
+    ):
+        quant_config = fp8_w8a8_moe_quant_config(
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            a1_scale=a1_scale,
+            a2_scale=a2_scale,
+            per_act_token_quant=per_act_token,
+            per_out_ch_quant=per_out_ch,
+        )
+
+        for _ in range(num_repeats):
+            with nvtx.annotate("cutlass_moe_fp8", color="blue"):
+                cutlass_moe_fp8(
+                    a=a,
+                    w1_q=w1,
+                    w2_q=w2,
+                    topk_weights=topk_weights,
+                    topk_ids=topk_ids,
+                    ab_strides1=ab_strides1,
+                    ab_strides2=ab_strides2,
+                    c_strides1=c_strides1,
+                    c_strides2=c_strides2,
+                    quant_config=quant_config,
+                    activation="silu",
+                    global_num_experts=num_experts,
+                )
+
+    # Pre-create quantization config to avoid creating it inside CUDA graph
+    quant_config = fp8_w8a8_moe_quant_config(
+        w1_scale=w1_scale,
+        w2_scale=w2_scale,
+        a1_scale=a1_scale,
+        a2_scale=a2_scale,
+        per_act_token_quant=per_act_token,
+        per_out_ch_quant=per_out_ch,
+    )
+
+    # Create CUDA graphs for CUTLASS (match benchmark_moe.py pattern exactly)
+    cutlass_stream = torch.cuda.Stream()
+    cutlass_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(cutlass_graph, stream=cutlass_stream):
+        # Capture 10 invocations like benchmark_moe.py
+        for _ in range(10):
+            cutlass_moe_fp8(
+                a=a,
+                w1_q=w1_fp8q_cutlass,
+                w2_q=w2_fp8q_cutlass,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                ab_strides1=ab_strides1,
+                ab_strides2=ab_strides2,
+                c_strides1=c_strides1,
+                c_strides2=c_strides2,
+                quant_config=quant_config,
+                activation="silu",
+                global_num_experts=num_experts,
+            )
+    torch.cuda.synchronize()
+
+    # Create CUDA graphs for Triton (match benchmark_moe.py pattern exactly)
+    triton_stream = torch.cuda.Stream()
+    triton_graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(triton_graph, stream=triton_stream):
+        # Capture 10 invocations like benchmark_moe.py
+        for _ in range(10):
+            fused_experts(
+                a,
+                w1_fp8q,
+                w2_fp8q,
+                topk_weights,
+                topk_ids,
+                quant_config=quant_config,
+            )
+    torch.cuda.synchronize()
+
+    def bench_cuda_graph(graph, num_warmup=5, num_iters=100):
+        """Benchmark CUDA graph using events like benchmark_moe.py"""
+        # Warmup
+        for _ in range(num_warmup):
+            graph.replay()
+        torch.cuda.synchronize()
+
+        # Timing
+        start_event = torch.cuda.Event(enable_timing=True)
+        end_event = torch.cuda.Event(enable_timing=True)
+
+        latencies = []
+        for _ in range(num_iters):
+            torch.cuda.synchronize()
+            start_event.record()
+            graph.replay()
+            end_event.record()
+            end_event.synchronize()
+            latencies.append(start_event.elapsed_time(end_event))
+
+        # Divide by 10 since graph contains 10 calls
+        return sum(latencies) / (num_iters * 10)
+
+    # Benchmark parameters
+    num_warmup = 5
+    num_iters = 100
+
+    # Benchmark only CUDA graphs (more reliable and faster)
+    # Benchmark Triton MoE with CUDA graphs
+    triton_graph_time = bench_cuda_graph(
+        triton_graph, num_warmup=num_warmup, num_iters=num_iters
+    )
+
+    # Benchmark CUTLASS MoE with CUDA graphs
+    cutlass_graph_time = bench_cuda_graph(
+        cutlass_graph, num_warmup=num_warmup, num_iters=num_iters
+    )
+
+    # Convert ms to us and return results
+    triton_time_us = triton_graph_time * 1000
+    cutlass_time_us = cutlass_graph_time * 1000
+
+    return {
+        "batch_size": m,
+        "triton_time_us": triton_time_us,
+        "cutlass_time_us": cutlass_time_us,
+    }
+
+
+def main(args):
+    print("Benchmarking models:")
+    for i, model in enumerate(args.models):
+        print(f"[{i}]  {model}")
+
+    all_results = []
+
+    for model in args.models:
+        for tp in args.tp_sizes:
+            for layer in WEIGHT_SHAPES_MOE[model]:
+                num_experts = layer[0]
+                topk = layer[1]
+                size_k = layer[2]
+                size_n = layer[3] // tp
+
+                if len(args.limit_k) > 0 and size_k not in args.limit_k:
+                    continue
+
+                if len(args.limit_n) > 0 and size_n not in args.limit_n:
+                    continue
+
+                for per_act_token in args.per_act_token_opts:
+                    for per_out_ch in args.per_out_ch_opts:
+                        print(
+                            f"\n=== {model}, experts={num_experts}, topk={topk},"
+                            f"per_act={per_act_token}, per_out_ch={per_out_ch} ==="
+                        )
+
+                        config_results = []
+                        for size_m in args.batch_sizes:
+                            mkn = (size_m, size_k, size_n)
+                            result = bench_run(
+                                [],  # Not used anymore
+                                model,
+                                num_experts,
+                                topk,
+                                per_act_token,
+                                per_out_ch,
+                                mkn,
+                            )
+                            if result:
+                                config_results.append(result)
+
+                        # Print results table for this configuration
+                        if config_results:
+                            print(
+                                f"\n{'Batch Size':<12}"
+                                f"{'Triton (us)':<15}"
+                                f"{'CUTLASS (us)':<15}"
+                            )
+                            print("-" * 45)
+                            for result in config_results:
+                                print(
+                                    f"{result['batch_size']:<12}"
+                                    f"{result['triton_time_us']:<15.2f}"
+                                    f"{result['cutlass_time_us']:<15.2f}"
+                                )
+
+                            all_results.extend(config_results)
+
+    print(f"\nTotal benchmarks completed: {len(all_results)}")
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser(
+        description="""Benchmark CUTLASS FP8 MOE vs Triton FP8 FUSED MOE
+         across specified models/shapes/batches
+
+        Example usage:
+        python benchmark_cutlass_moe_fp8.py  \
+            --model "Llama-4-Maverick-17B-128E-Instruct-FP8"  \
+            --tp-sizes 8 \
+            --batch-size 2 4 8  \
+            --per-act-token-opts false \
+            --per-out-ch-opts false
+
+        """
+    )
+    parser.add_argument(
+        "--models",
+        nargs="+",
+        type=str,
+        default=DEFAULT_MODELS,
+        choices=WEIGHT_SHAPES_MOE.keys(),
+    )
+    parser.add_argument("--tp-sizes", nargs="+", type=int, default=DEFAULT_TP_SIZES)
+    parser.add_argument(
+        "--batch-sizes", nargs="+", type=int, default=DEFAULT_BATCH_SIZES
+    )
+    parser.add_argument("--limit-k", nargs="+", type=int, default=[])
+    parser.add_argument("--limit-n", nargs="+", type=int, default=[])
+    parser.add_argument(
+        "--per-act-token-opts",
+        nargs="+",
+        type=lambda x: x.lower() == "true",
+        default=[False, True],
+        help="Per-activation token quantization options (true/false)",
+    )
+    parser.add_argument(
+        "--per-out-ch-opts",
+        nargs="+",
+        type=lambda x: x.lower() == "true",
+        default=[False, True],
+        help="Per-output channel quantization options (true/false)",
+    )
+
+    args = parser.parse_args()
+    main(args)

benchmarks/kernels/benchmark_device_communicators.py

@@ -7,6 +7,10 @@ Benchmark script for device communicators:
 CustomAllreduce (oneshot, twoshot), PyNcclCommunicator,
 and SymmMemCommunicator (multimem, two-shot).
 
+for NCCL symmetric memory you need to set the environment variables
+NCCL_NVLS_ENABLE=1 NCCL_CUMEM_ENABLE=1 VLLM_USE_NCCL_SYMM_MEM=1, otherwise NCCL does
+not use fast NVLS implementation for all reduce.
+
 Usage:
     torchrun --nproc_per_node=<N> benchmark_device_communicators.py [options]
 
@@ -26,7 +30,13 @@ import torch.distributed as dist
 from torch.distributed import ProcessGroup
 
 from vllm.distributed.device_communicators.custom_all_reduce import CustomAllreduce
-from vllm.distributed.device_communicators.pynccl import PyNcclCommunicator
+from vllm.distributed.device_communicators.pynccl import (
+    PyNcclCommunicator,
+    register_nccl_symmetric_ops,
+)
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    set_graph_pool_id,
+)
 from vllm.distributed.device_communicators.symm_mem import SymmMemCommunicator
 from vllm.logger import init_logger
 from vllm.utils import FlexibleArgumentParser
@@ -98,6 +108,7 @@ class CommunicatorBenchmark:
             )
             if not self.pynccl_comm.disabled:
                 logger.info("Rank %s: PyNcclCommunicator initialized", self.rank)
+                register_nccl_symmetric_ops(self.pynccl_comm)
             else:
                 logger.info("Rank %s: PyNcclCommunicator disabled", self.rank)
                 self.pynccl_comm = None
@@ -194,6 +205,15 @@ class CommunicatorBenchmark:
                     None,  # no env variable needed
                 )
             )
+            communicators.append(
+                (
+                    "pynccl-symm",
+                    lambda t: torch.ops.vllm.all_reduce_symmetric_with_copy(t),
+                    lambda t: True,  # Always available if initialized
+                    nullcontext(),
+                    None,  # no env variable needed
+                )
+            )
 
         if self.symm_mem_comm_multimem is not None:
             comm = self.symm_mem_comm_multimem
@@ -271,7 +291,9 @@ class CommunicatorBenchmark:
                 # Capture the graph using context manager
                 with context:
                     graph = torch.cuda.CUDAGraph()
-                    with torch.cuda.graph(graph):
+                    graph_pool = torch.cuda.graph_pool_handle()
+                    set_graph_pool_id(graph_pool)
+                    with torch.cuda.graph(graph, pool=graph_pool):
                         for _ in range(CUDA_GRAPH_CAPTURE_CYCLES):
                             allreduce_fn(graph_input)
 

benchmarks/kernels/benchmark_lora.py

@@ -79,9 +79,9 @@ def make_rand_lora_weight_tensor(
 
 
 def make_rand_tensors(
-    a_shape: tuple[int],
-    b_shape: tuple[int],
-    c_shape: tuple[int],
+    a_shape: tuple[int, ...],
+    b_shape: tuple[int, ...],
+    c_shape: tuple[int, ...],
     a_dtype: torch.dtype,
     b_dtype: torch.dtype,
     c_dtype: torch.dtype,
@@ -243,7 +243,7 @@ class OpType(Enum):
         lora_rank: int,
         num_loras: int,
         num_slices: int,
-    ) -> tuple[tuple[int], tuple[int], tuple[int]]:
+    ) -> tuple[tuple[int, ...], tuple[int, ...], tuple[int, ...]]:
         """
         Given num_slices, return the shapes of the A, B, and C matrices
         in A x B = C, for the op_type

benchmarks/kernels/benchmark_reshape_and_cache_flash.py

@@ -9,6 +9,9 @@ import torch
 from tabulate import tabulate
 
 from vllm import _custom_ops as ops
+from vllm.attention.ops.triton_reshape_and_cache_flash import (
+    triton_reshape_and_cache_flash,
+)
 from vllm.logger import init_logger
 from vllm.platforms import current_platform
 from vllm.utils import (
@@ -31,6 +34,8 @@ def run_benchmark(
     kv_cache_dtype: str,
     kv_cache_layout: str,
     num_iters: int,
+    implementation: str,
+    benchmark_mode: str,
     device: str = "cuda",
 ) -> float:
     """Return latency (seconds) for given num_tokens."""
@@ -38,6 +43,14 @@ def run_benchmark(
     if kv_cache_dtype == "fp8" and head_size % 16:
         raise ValueError("fp8 kv-cache requires head_size to be a multiple of 16.")
 
+    if implementation not in ("cuda", "triton"):
+        raise ValueError(
+            f"Unsupported implementation: {implementation}. "
+            "Only 'cuda' and 'triton' are supported."
+        )
+    if implementation == "triton" and kv_cache_layout == "HND":
+        return float("nan")  # Triton does not support HND layout yet.
+
     current_platform.seed_everything(42)
     torch.set_default_device(device)
 
@@ -65,27 +78,49 @@ def run_benchmark(
         cache_layout=kv_cache_layout,
     )
     key_cache, value_cache = key_caches[0], value_caches[0]
+    # to free unused memory
+    del key_caches, value_caches
 
     # compute per-kernel scaling factors for fp8 conversion (if used).
     k_scale = (key.amax() / 64.0).to(torch.float32)
     v_scale = (value.amax() / 64.0).to(torch.float32)
 
+    if implementation == "cuda":
+        function_under_test = lambda: ops.reshape_and_cache_flash(
+            key,  # noqa: F821
+            value,  # noqa: F821
+            key_cache,  # noqa: F821
+            value_cache,  # noqa: F821
+            slot_mapping,  # noqa: F821
+            kv_cache_dtype,
+            k_scale,
+            v_scale,
+        )
+    else:
+        function_under_test = lambda: triton_reshape_and_cache_flash(
+            key,  # noqa: F821
+            value,  # noqa: F821
+            key_cache,  # noqa: F821
+            value_cache,  # noqa: F821
+            slot_mapping,  # noqa: F821
+            kv_cache_dtype,
+            k_scale,
+            v_scale,
+        )
+    if benchmark_mode == "cudagraph":
+        g = torch.cuda.CUDAGraph()
+        with torch.cuda.graph(g):
+            function_under_test()
+        torch.cuda.synchronize()
+        function_under_test = lambda: g.replay()
+
     def run_cuda_benchmark(n_iters: int) -> float:
         nonlocal key, value, key_cache, value_cache, slot_mapping
         torch.cuda.synchronize()
         start = time.perf_counter()
         for _ in range(n_iters):
-            ops.reshape_and_cache_flash(
-                key,
-                value,
-                key_cache,
-                value_cache,
-                slot_mapping,
-                kv_cache_dtype,
-                k_scale,
-                v_scale,
-            )
-        torch.cuda.synchronize()
+            function_under_test()
+            torch.cuda.synchronize()
         end = time.perf_counter()
         return (end - start) / n_iters
 
@@ -116,10 +151,16 @@ def main(args):
                 kv_cache_dtype=args.kv_cache_dtype,
                 kv_cache_layout=layout,
                 num_iters=args.iters,
+                implementation=args.implementation,
+                benchmark_mode=args.mode,
                 device="cuda",
             )
             rows.append([n_tok, layout, f"{lat * 1e6:.3f}"])
 
+    print(
+        f"Benchmark results for implementation {args.implementation}"
+        f" (measuring with {args.mode}):"
+    )
     print(tabulate(rows, headers=["num_tokens", "layout", "latency (µs)"]))
 
 
@@ -151,6 +192,21 @@ if __name__ == "__main__":
     )
 
     parser.add_argument("--iters", type=int, default=100)
+
+    parser.add_argument(
+        "--implementation",
+        type=str,
+        choices=["cuda", "triton"],
+        default="cuda",
+    )
+
+    parser.add_argument(
+        "--mode",
+        type=str,
+        choices=["cudagraph", "no_graph"],
+        default="cudagraph",
+    )
+
     args = parser.parse_args()
 
     main(args)

benchmarks/kernels/benchmark_w8a8_block_fp8.py

@@ -11,13 +11,13 @@ from datetime import datetime
 from typing import Any
 
 import torch
-import triton
 from tqdm import tqdm
 
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
     _w8a8_block_fp8_matmul,
 )
 from vllm.platforms import current_platform
+from vllm.triton_utils import triton
 from vllm.utils import FlexibleArgumentParser
 
 mp.set_start_method("spawn", force=True)

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -8,12 +8,16 @@ import torch
 
 from vllm import _custom_ops as ops
 from vllm.model_executor.layers.quantization.utils.fp8_utils import (
-    get_col_major_tma_aligned_tensor,
     per_token_group_quant_fp8,
     w8a8_block_fp8_matmul,
 )
 from vllm.triton_utils import triton
-from vllm.utils.deep_gemm import calc_diff, fp8_gemm_nt, per_block_cast_to_fp8
+from vllm.utils.deep_gemm import (
+    calc_diff,
+    fp8_gemm_nt,
+    get_col_major_tma_aligned_tensor,
+    per_block_cast_to_fp8,
+)
 
 
 def benchmark_shape(m: int,

cmake/cpu_extension.cmake

@@ -101,6 +101,7 @@ else()
     find_isa(${CPUINFO} "asimd" ASIMD_FOUND) # Check for ARM NEON support
     find_isa(${CPUINFO} "bf16" ARM_BF16_FOUND) # Check for ARM BF16 support
     find_isa(${CPUINFO} "S390" S390_FOUND)
+    find_isa(${CPUINFO} "v" RVV_FOUND) # Check for RISC-V RVV support
 endif()
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
@@ -177,8 +178,14 @@ elseif (S390_FOUND)
         "-mzvector"
         "-march=native"
         "-mtune=native")
+elseif (CMAKE_SYSTEM_PROCESSOR MATCHES "riscv64")
+    if(RVV_FOUND)
+	    message(FAIL_ERROR "Can't support rvv now.")
+    else()
+        list(APPEND CXX_COMPILE_FLAGS "-march=rv64gc")
+    endif()
 else()
-    message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA or ARMv8 support.")
+    message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA, ARMv8 or RISC-V support.")
 endif()
 
 #
@@ -258,7 +265,8 @@ set(VLLM_EXT_SRC
     "csrc/cpu/layernorm.cpp"
     "csrc/cpu/mla_decode.cpp"
     "csrc/cpu/pos_encoding.cpp"
-    "csrc/cpu/torch_bindings.cpp")
+    "csrc/cpu/torch_bindings.cpp"
+    "csrc/moe/dynamic_4bit_int_moe_cpu.cpp")
 
 if (AVX512_FOUND AND NOT AVX512_DISABLED)
     set(VLLM_EXT_SRC

csrc/attention/mla/cutlass_sm100_mla/device/sm100_mla.hpp

@@ -135,10 +135,10 @@ public:
     max_splits = min(16, max_splits);
 
     // TODO: This avoids a hang when the batch size larger than 1 and 
-    // there is more than 4 kv_splits. 
+    // there is more than 1 kv_splits. 
     // Discuss with NVIDIA how this can be fixed.
     if (B > 1) {
-      max_splits = min(2, max_splits);
+      max_splits = min(1, max_splits);
     }
     
     // printf("    max_splits = %d\n", max_splits);

csrc/cpu/cpu_types.hpp

@@ -14,7 +14,8 @@
   // arm implementation
   #include "cpu_types_arm.hpp"
 #else
-  #warning "unsupported vLLM cpu implementation"
+  #warning "unsupported vLLM cpu implementation, vLLM will compile with scalar"
+  #include "cpu_types_scalar.hpp"
 #endif
 
 #ifdef _OPENMP

csrc/cpu/cpu_types_scalar.hpp

@@ -0,0 +1,513 @@
+#include <cmath>
+#include <cstdint>
+#include <cstring>
+#include <torch/all.h>
+#include "float_convert.hpp"
+
+namespace vec_op {
+
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
+  AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)
+
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
+
+#ifndef CPU_OP_GUARD
+  #define CPU_KERNEL_GUARD_IN(NAME)
+  #define CPU_KERNEL_GUARD_OUT(NAME)
+#else
+  #define CPU_KERNEL_GUARD_IN(NAME) \
+    std::cout << #NAME << " invoked." << std::endl;
+  #define CPU_KERNEL_GUARD_OUT(NAME) \
+    std::cout << #NAME << " exit." << std::endl;
+#endif
+
+#define FORCE_INLINE __attribute__((always_inline)) inline
+
+#define __max(a, b) ((a) > (b) ? (a) : (b))
+#define __min(a, b) ((a) < (b) ? (a) : (b))
+#define __abs(a) ((a) < (0) ? (0 - a) : (a))
+
+typedef struct f16x8_t {
+  uint16_t val[8];
+} f16x8_t;
+
+typedef struct f16x16_t {
+  uint16_t val[16];
+} f16x16_t;
+
+typedef struct f16x32_t {
+  uint16_t val[32];
+} f16x32_t;
+
+typedef struct f32x4_t {
+  float val[4];
+} f32x4_t;
+
+typedef struct f32x8_t {
+  float val[8];
+} f32x8_t;
+
+typedef struct f32x16_t {
+  float val[16];
+} f32x16_t;
+
+namespace {
+template <typename T, T... indexes, typename F>
+constexpr void unroll_loop_item(std::integer_sequence<T, indexes...>, F&& f) {
+  (f(std::integral_constant<T, indexes>{}), ...);
+};
+};  // namespace
+
+template <typename T, T count, typename F,
+          typename = std::enable_if_t<std::is_invocable_v<F, T> > >
+constexpr void unroll_loop(F&& f) {
+  unroll_loop_item(std::make_integer_sequence<T, count>{}, std::forward<F>(f));
+}
+
+template <typename T>
+struct Vec {
+  constexpr static int get_elem_num() { return T::VEC_ELEM_NUM; }
+};
+
+struct FP32Vec8;
+struct FP32Vec16;
+
+struct FP16Vec8 : public Vec<FP16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  f16x8_t reg;
+
+  explicit FP16Vec8(const void* ptr)
+      : reg(*reinterpret_cast<const f16x8_t*>(ptr)) {};
+
+  explicit FP16Vec8(const FP32Vec8&);
+
+  void save(void* ptr) const { *reinterpret_cast<f16x8_t*>(ptr) = reg; }
+};
+
+struct FP16Vec16 : public Vec<FP16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  f16x16_t reg;
+
+  explicit FP16Vec16(const void* ptr)
+      : reg(*reinterpret_cast<const f16x16_t*>(ptr)) {};
+
+  explicit FP16Vec16(const FP32Vec16&);
+
+  void save(void* ptr) const { *reinterpret_cast<f16x16_t*>(ptr) = reg; }
+
+  void save(void* ptr, const int elem_num) const {
+    int num = __min(elem_num, VEC_ELEM_NUM);
+    std::memcpy(ptr, &(reg.val[0]), num * sizeof(uint16_t));
+  }
+};
+
+struct BF16Vec8 : public Vec<BF16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  f16x8_t reg;
+
+  explicit BF16Vec8(const void* ptr)
+      : reg(*reinterpret_cast<const f16x8_t*>(ptr)) {};
+
+  explicit BF16Vec8(const FP32Vec8&);
+
+  void save(void* ptr) const { *reinterpret_cast<f16x8_t*>(ptr) = reg; }
+};
+
+struct BF16Vec16 : public Vec<BF16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  f16x16_t reg;
+
+  explicit BF16Vec16(const void* ptr)
+      : reg(*reinterpret_cast<const f16x16_t*>(ptr)) {};
+
+  explicit BF16Vec16(const FP32Vec16&);
+
+  void save(void* ptr) const { *reinterpret_cast<f16x16_t*>(ptr) = reg; }
+
+  void save(void* ptr, const int elem_num) const {
+    int num = __min(elem_num, VEC_ELEM_NUM);
+    std::memcpy(ptr, &(reg.val[0]), num * sizeof(uint16_t));
+  }
+};
+
+struct BF16Vec32 : public Vec<BF16Vec32> {
+  constexpr static int VEC_ELEM_NUM = 32;
+  f16x32_t reg;
+
+  explicit BF16Vec32(const void* ptr)
+      : reg(*reinterpret_cast<const f16x32_t*>(ptr)) {};
+
+  explicit BF16Vec32(f16x32_t data) : reg(data) {};
+
+  explicit BF16Vec32(BF16Vec8& vec8_data) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = vec8_data.reg.val[i % BF16Vec8::VEC_ELEM_NUM];
+    }
+  }
+
+  void save(void* ptr) const { *reinterpret_cast<f16x32_t*>(ptr) = reg; }
+};
+
+struct FP32Vec4 : public Vec<FP32Vec4> {
+  constexpr static int VEC_ELEM_NUM = 4;
+
+  f32x4_t reg;
+
+  explicit FP32Vec4(float v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = v;
+    }
+  }
+
+  explicit FP32Vec4() {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = 0.0f;
+    }
+  }
+
+  explicit FP32Vec4(const float* ptr)
+      : reg(*reinterpret_cast<const f32x4_t*>(ptr)) {};
+
+  explicit FP32Vec4(f32x4_t data) : reg(data) {};
+
+  explicit FP32Vec4(const FP32Vec4& data) : reg(data.reg) {};
+};
+
+struct FP32Vec8 : public Vec<FP32Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+
+  f32x8_t reg;
+
+  explicit FP32Vec8(float v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = v;
+    }
+  }
+
+  explicit FP32Vec8() {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = 0.0f;
+    }
+  }
+
+  explicit FP32Vec8(const float* ptr)
+      : reg(*reinterpret_cast<const f32x8_t*>(ptr)) {};
+
+  explicit FP32Vec8(f32x8_t data) : reg(data) {};
+
+  explicit FP32Vec8(const FP32Vec8& data) : reg(data.reg) {};
+
+  explicit FP32Vec8(const FP16Vec8& v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = fp16_to_float(v.reg.val[i]);
+    }
+  }
+
+  FP32Vec8(const BF16Vec8& v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = bf16_to_float(v.reg.val[i]);
+    }
+  }
+
+  float reduce_sum() const {
+    float result = 0;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result += reg.val[i];
+    }
+    return result;
+  }
+
+  FP32Vec8 exp() const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = expf(reg.val[i]);
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 tanh() const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = tanhf(reg.val[i]);
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 er() const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = erf(reg.val[i]);
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 operator*(const FP32Vec8& b) const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = reg.val[i] * b.reg.val[i];
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 operator+(const FP32Vec8& b) const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = reg.val[i] + b.reg.val[i];
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 operator-(const FP32Vec8& b) const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = reg.val[i] - b.reg.val[i];
+    }
+    return FP32Vec8(ret);
+  }
+
+  FP32Vec8 operator/(const FP32Vec8& b) const {
+    f32x8_t ret;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      ret.val[i] = reg.val[i] / b.reg.val[i];
+    }
+    return FP32Vec8(ret);
+  }
+
+  void save(void* ptr) const { *reinterpret_cast<f32x8_t*>(ptr) = reg; }
+};
+
+struct FP32Vec16 : public Vec<FP32Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  f32x16_t reg;
+
+  explicit FP32Vec16(float v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = v;
+    }
+  }
+
+  explicit FP32Vec16() {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = 0.0f;
+    }
+  }
+
+  explicit FP32Vec16(const float* ptr)
+      : reg(*reinterpret_cast<const f32x16_t*>(ptr)) {};
+
+  explicit FP32Vec16(f32x16_t data) : reg(data) {};
+
+  FP32Vec16(const FP32Vec4& data) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = data.reg.val[i % FP32Vec4::VEC_ELEM_NUM];
+    }
+  }
+
+  FP32Vec16(const FP32Vec8& data) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = data.reg.val[i % FP32Vec8::VEC_ELEM_NUM];
+    }
+  }
+
+  FP32Vec16(const FP32Vec16& data) : reg(data.reg) {};
+
+  explicit FP32Vec16(const FP16Vec16& v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = fp16_to_float(v.reg.val[i]);
+    }
+  }
+
+  explicit FP32Vec16(const BF16Vec16& v) {
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      reg.val[i] = bf16_to_float(v.reg.val[i]);
+    }
+  }
+
+  explicit FP32Vec16(const FP16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
+
+  FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
+
+  FP32Vec16 operator*(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = reg.val[i] * b.reg.val[i];
+    }
+    return result;
+  }
+
+  FP32Vec16 operator+(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = reg.val[i] + b.reg.val[i];
+    }
+    return result;
+  }
+
+  FP32Vec16 operator-(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = reg.val[i] - b.reg.val[i];
+    }
+    return result;
+  }
+
+  FP32Vec16 operator/(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = reg.val[i] / b.reg.val[i];
+    }
+    return result;
+  }
+
+  FP32Vec16 max(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = __max(reg.val[i], b.reg.val[i]);
+    }
+    return result;
+  }
+
+  FP32Vec16 min(const FP32Vec16& b) const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = __min(reg.val[i], b.reg.val[i]);
+    }
+    return result;
+  }
+
+  FP32Vec16 abs() const {
+    FP32Vec16 result(0.0f);
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result.reg.val[i] = __abs(reg.val[i]);
+    }
+    return result;
+  }
+
+  float reduce_sum() const {
+    float result = 0.0f;
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result += reg.val[i];
+    }
+    return result;
+  }
+
+  float reduce_max() const {
+    float result = reg.val[0];
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result = __max(reg.val[i], result);
+    }
+    return result;
+  }
+
+  float reduce_min() const {
+    float result = reg.val[0];
+    for (int i = 0; i < VEC_ELEM_NUM; ++i) {
+      result = __min(reg.val[i], result);
+    }
+    return result;
+  }
+
+  template <int group_size>
+  float reduce_sub_sum(int idx) {
+    static_assert(VEC_ELEM_NUM % group_size == 0);
+    float sum = 0.0;
+    int start = idx * group_size;
+    int end = (idx + 1) * group_size;
+
+    for (; (start < VEC_ELEM_NUM) && (start < end); ++start) {
+      sum += reg.val[start];
+    }
+
+    return sum;
+  }
+
+  void save(void* ptr) const { *reinterpret_cast<f32x16_t*>(ptr) = reg; }
+};
+
+template <typename T>
+struct VecType {
+  using vec_type = void;
+};
+
+template <typename T>
+using vec_t = typename VecType<T>::vec_type;
+
+template <>
+struct VecType<float> {
+  using vec_type = FP32Vec8;
+};
+
+template <>
+struct VecType<c10::Half> {
+  using vec_type = FP16Vec8;
+};
+
+template <>
+struct VecType<c10::BFloat16> {
+  using vec_type = BF16Vec8;
+};
+
+template <typename T>
+void storeFP32(float v, T* ptr) {
+  *ptr = v;
+}
+
+/*
+template <> inline void storeFP32<c10::Half>(float v, c10::Half *ptr) {
+  c10::Half __attribute__((__may_alias__)) *v_ptr =
+      reinterpret_cast<c10::Half *>(&v);
+  *ptr = *(v_ptr + 1);
+}
+*/
+
+template <>
+inline void storeFP32<c10::Half>(float v, c10::Half* ptr) {
+  uint16_t fp16 = float_to_fp16(v);
+  *reinterpret_cast<uint16_t*>(ptr) = fp16;
+}
+
+template <>
+inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
+  c10::BFloat16 __attribute__((__may_alias__))* v_ptr =
+      reinterpret_cast<c10::BFloat16*>(&v);
+  *ptr = *(v_ptr + 1);
+}
+
+inline FP16Vec16::FP16Vec16(const FP32Vec16& v) {
+  int i = 0;
+  for (i = 0; i < FP16Vec16::VEC_ELEM_NUM; ++i) {
+    reg.val[i] = float_to_fp16(v.reg.val[i]);
+  }
+}
+
+inline FP16Vec8 ::FP16Vec8(const FP32Vec8& v) {
+  int i = 0;
+  for (i = 0; i < FP16Vec8::VEC_ELEM_NUM; ++i) {
+    reg.val[i] = float_to_fp16(v.reg.val[i]);
+  }
+}
+
+inline void fma(FP32Vec16& acc, FP32Vec16& a, FP32Vec16& b) {
+  acc = acc + a * b;
+}
+
+inline BF16Vec8::BF16Vec8(const FP32Vec8& v) {
+  int i = 0;
+  for (i = 0; i < BF16Vec8::VEC_ELEM_NUM; ++i) {
+    reg.val[i] = float_to_bf16(v.reg.val[i]);
+  }
+}
+
+inline BF16Vec16::BF16Vec16(const FP32Vec16& v) {
+  int i = 0;
+  for (i = 0; i < BF16Vec16::VEC_ELEM_NUM; ++i) {
+    reg.val[i] = float_to_bf16(v.reg.val[i]);
+  }
+}
+
+inline void prefetch(const void* addr) { __builtin_prefetch(addr, 0, 3); }
+
+};  // namespace vec_op

csrc/cpu/float_convert.hpp

@@ -0,0 +1,106 @@
+
+static float bf16_to_float(uint16_t bf16) {
+  uint32_t bits = static_cast<uint32_t>(bf16) << 16;
+  float fp32;
+  std::memcpy(&fp32, &bits, sizeof(fp32));
+  return fp32;
+}
+
+static uint16_t float_to_bf16(float fp32) {
+  uint32_t bits;
+  std::memcpy(&bits, &fp32, sizeof(fp32));
+  return static_cast<uint16_t>(bits >> 16);
+}
+
+/************************************************
+ * Copyright (c) 2015 Princeton Vision Group
+ * Licensed under the MIT license.
+ * Codes below copied from
+ * https://github.com/PrincetonVision/marvin/tree/master/tools/tensorIO_matlab
+ *************************************************/
+static uint16_t float_to_fp16(float fp32) {
+  uint16_t fp16;
+
+  unsigned x;
+  unsigned u, remainder, shift, lsb, lsb_s1, lsb_m1;
+  unsigned sign, exponent, mantissa;
+
+  std::memcpy(&x, &fp32, sizeof(fp32));
+  u = (x & 0x7fffffff);
+
+  // Get rid of +NaN/-NaN case first.
+  if (u > 0x7f800000) {
+    fp16 = 0x7fffU;
+    return fp16;
+  }
+
+  sign = ((x >> 16) & 0x8000);
+
+  // Get rid of +Inf/-Inf, +0/-0.
+  if (u > 0x477fefff) {
+    fp16 = sign | 0x7c00U;
+    return fp16;
+  }
+  if (u < 0x33000001) {
+    fp16 = (sign | 0x0000);
+    return fp16;
+  }
+
+  exponent = ((u >> 23) & 0xff);
+  mantissa = (u & 0x7fffff);
+
+  if (exponent > 0x70) {
+    shift = 13;
+    exponent -= 0x70;
+  } else {
+    shift = 0x7e - exponent;
+    exponent = 0;
+    mantissa |= 0x800000;
+  }
+  lsb = (1 << shift);
+  lsb_s1 = (lsb >> 1);
+  lsb_m1 = (lsb - 1);
+
+  // Round to nearest even.
+  remainder = (mantissa & lsb_m1);
+  mantissa >>= shift;
+  if (remainder > lsb_s1 || (remainder == lsb_s1 && (mantissa & 0x1))) {
+    ++mantissa;
+    if (!(mantissa & 0x3ff)) {
+      ++exponent;
+      mantissa = 0;
+    }
+  }
+
+  fp16 = (sign | (exponent << 10) | mantissa);
+
+  return fp16;
+}
+
+static float fp16_to_float(uint16_t fp16) {
+  unsigned sign = ((fp16 >> 15) & 1);
+  unsigned exponent = ((fp16 >> 10) & 0x1f);
+  unsigned mantissa = ((fp16 & 0x3ff) << 13);
+  int temp;
+  float fp32;
+  if (exponent == 0x1f) { /* NaN or Inf */
+    mantissa = (mantissa ? (sign = 0, 0x7fffff) : 0);
+    exponent = 0xff;
+  } else if (!exponent) { /* Denorm or Zero */
+    if (mantissa) {
+      unsigned int msb;
+      exponent = 0x71;
+      do {
+        msb = (mantissa & 0x400000);
+        mantissa <<= 1; /* normalize */
+        --exponent;
+      } while (!msb);
+      mantissa &= 0x7fffff; /* 1.mantissa is implicit */
+    }
+  } else {
+    exponent += 0x70;
+  }
+  temp = ((sign << 31) | (exponent << 23) | mantissa);
+  std::memcpy(&fp32, &temp, sizeof(temp));
+  return fp32;
+}

csrc/cpu/torch_bindings.cpp

@@ -88,8 +88,18 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "    int tp_rank, int blocksparse_local_blocks,"
       "    int blocksparse_vert_stride, int blocksparse_block_size,"
       "    int blocksparse_head_sliding_step) -> ()");
+
   ops.impl("paged_attention_v1", torch::kCPU, &paged_attention_v1);
 
+  ops.def(
+      "dynamic_4bit_int_moe("
+      "Tensor x, Tensor topk_ids, Tensor topk_weights,"
+      "Tensor w13_packed, Tensor w2_packed, int H, int I, int I2,"
+      "int group_size, bool apply_router_weight_on_input, int activation_kind"
+      ") -> Tensor");
+
+  ops.impl("dynamic_4bit_int_moe", torch::kCPU, &dynamic_4bit_int_moe_cpu);
+
   // PagedAttention V2.
   ops.def(
       "paged_attention_v2("

csrc/launch_bounds_utils.h

@@ -0,0 +1,38 @@
+#pragma once
+
+#include <cuda_runtime_api.h>
+#include <algorithm>
+
+// maximum blocks per SM cap
+#ifndef VLLM_LAUNCH_BLOCKS_CAP
+  #define VLLM_LAUNCH_BLOCKS_CAP 4
+#endif
+
+// compile-time estimate of max threads per SM for launch bounds.
+#ifndef VLLM_MAX_THREADS_PER_SM
+  #if defined(__CUDA_ARCH__) && __CUDA_ARCH__ < 300
+    #define VLLM_MAX_THREADS_PER_SM 1536
+  #else
+    #define VLLM_MAX_THREADS_PER_SM 2048
+  #endif
+#endif
+
+// compute the number of blocks per SM to request in __launch_bounds__
+#define VLLM_BLOCKS_DIV(VAL) (VLLM_MAX_THREADS_PER_SM / (VAL))
+#define VLLM_CLAMP_BLOCKS_PER_SM(VAL) \
+  (((VAL) <= 0)                       \
+       ? 1                            \
+       : (((VAL) < VLLM_LAUNCH_BLOCKS_CAP) ? (VAL) : VLLM_LAUNCH_BLOCKS_CAP))
+#define VLLM_BLOCKS_PER_SM(BLOCK_THREADS) \
+  VLLM_CLAMP_BLOCKS_PER_SM(VLLM_BLOCKS_DIV(BLOCK_THREADS))
+
+// runtime-time helper to compute blocks/SM
+static inline int vllm_runtime_blocks_per_sm(int block_threads) {
+  int device = -1;
+  cudaGetDevice(&device);
+  int max_threads_per_sm = VLLM_MAX_THREADS_PER_SM;
+  cudaDeviceGetAttribute(&max_threads_per_sm,
+                         cudaDevAttrMaxThreadsPerMultiProcessor, device);
+  int blocks = (block_threads > 0) ? (max_threads_per_sm / block_threads) : 1;
+  return VLLM_CLAMP_BLOCKS_PER_SM(blocks);
+}

csrc/moe/dynamic_4bit_int_moe_cpu.cpp

@@ -0,0 +1,156 @@
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/all.h>
+
+// _dyn_quant_matmul_4bit is only available on AArch64.
+#if defined(__aarch64__)
+  #include <ATen/ops/_dyn_quant_matmul_4bit.h>
+#endif
+
+inline torch::Tensor mm(const torch::Tensor& a, const torch::Tensor& packed_w,
+                        int64_t group_size_eff, int64_t in_features,
+                        int64_t out_features) {
+#if defined(__aarch64__)
+  return at::_ops::_dyn_quant_matmul_4bit::call(a, packed_w, group_size_eff,
+                                                in_features, out_features);
+#else
+  TORCH_CHECK(false,
+              "dynamic 4-bit int MoE path requires AArch64 (ARM64); "
+              "_dyn_quant_matmul_4bit is unavailable on this architecture");
+  return {};
+#endif
+}
+
+enum ActivationKind : int64_t {
+  SwiGLU_Gu = 0,  // act = SiLU(g) * u
+  SwiGLUOAI = 1,  // act = SiLU(u) * g
+  SiLU = 2        // SiLU
+};
+
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind) {
+  TORCH_CHECK(x.dim() == 2, "x must be 2D");
+  TORCH_CHECK(topk_ids.dim() == 2 && topk_weights.dim() == 2,
+              "topk tensors must be [T, K]");
+  TORCH_CHECK(
+      w13_packed.size(0) == w2_packed.size(0),
+      "w13_packed and w2_packed must have same number of experts in dim 0");
+  TORCH_CHECK(I2 == 2 * I, "I2 must equal 2*I");
+
+  const int64_t T = x.size(0);
+  const int64_t K = topk_ids.size(1);
+  const int64_t E = w13_packed.size(0);
+  const int64_t N = T * K;
+
+  auto x_c = x.contiguous();
+  auto ids_c = topk_ids.contiguous();
+  auto gates_c = topk_weights.to(at::kFloat).contiguous();
+
+  // bucketing tokens -> experts
+  c10::SmallVector<int64_t, 64> counts(
+      E, 0);  // Small vector uses stack allocation
+  {
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    for (int64_t i = 0; i < N; ++i) {
+      const int64_t e_id = ids_ptr[i];
+      TORCH_CHECK(0 <= e_id && e_id < E, "expert id out of range");
+      counts[e_id]++;
+    }
+  }
+  c10::SmallVector<int64_t, 65> offsets(E + 1, 0);  // ( E +1 )
+  for (int64_t e = 0; e < E; ++e) offsets[e + 1] = offsets[e] + counts[e];
+
+  auto expert_tokens = at::empty({offsets[E]}, ids_c.options());
+  auto expert_gates = at::empty({offsets[E]}, gates_c.options());
+  {
+    c10::SmallVector<int64_t, 64> cursor(E, 0);
+    const auto* ids_ptr = ids_c.data_ptr<int64_t>();
+    const auto* gts_ptr = gates_c.data_ptr<float>();
+    auto* tok_ptr = expert_tokens.data_ptr<int64_t>();
+    auto* gate_ptr = expert_gates.data_ptr<float>();
+
+    for (int64_t t = 0; t < T; ++t) {
+      const int64_t base = t * K;
+      for (int64_t k = 0; k < K; ++k) {
+        const int64_t idx = base + k;
+        const int64_t e = ids_ptr[idx];
+        const int64_t p = offsets[e] + (cursor[e]++);
+        tok_ptr[p] = t;
+        gate_ptr[p] = gts_ptr[idx];
+      }
+    }
+  }
+
+  const int64_t g_eff_13 = (group_size != -1) ? group_size : H;
+  const int64_t g_eff_2 = (group_size != -1) ? group_size : I;
+
+  // Per-expert outputs filled in parallel
+  std::vector<torch::Tensor> y_list(E);
+  y_list.resize(E);
+
+  at::parallel_for(0, E, 1, [&](int64_t e_begin, int64_t e_end) {
+    for (int64_t e = e_begin; e < e_end; ++e) {
+      const int64_t te = counts[e];
+      if (te == 0) {
+        y_list[e] = at::empty({0, H}, x_c.options());
+        continue;
+      }
+
+      const int64_t start = offsets[e];
+
+      auto sel_tokens =
+          expert_tokens.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+      auto gates_e =
+          expert_gates.narrow(/*dim=*/0, /*start=*/start, /*length=*/te);
+
+      auto x_e = x_c.index_select(/*dim=*/0, sel_tokens);
+
+      if (apply_router_weight_on_input) {
+        x_e = x_e.mul(gates_e.unsqueeze(1));
+      }
+
+      auto w13_e = w13_packed.select(/*dim=*/0, e);
+      auto w2_e = w2_packed.select(/*dim=*/0, e);
+
+      // W13
+      auto y13 =
+          mm(x_e, w13_e, g_eff_13, /*in_features=*/H, /*out_features=*/I2);
+
+      auto g_part = y13.narrow(/*dim=*/1, /*start=*/0, /*length=*/I);
+      auto u_part = y13.narrow(/*dim=*/1, /*start=*/I, /*length=*/I);
+
+      torch::Tensor act;
+      if (activation_kind == ActivationKind::SwiGLUOAI) {  // SwiGLUOAI
+        constexpr double kAlpha = 1.702;                   // GPT-OSS default
+        constexpr double kLimit = 7.0;                     // GPT-OSS default
+        auto gate_c = at::clamp_max(g_part, kLimit);
+        auto up_c = at::clamp(u_part, -kLimit, kLimit);
+        auto glu = gate_c.mul(at::sigmoid(gate_c.mul(kAlpha)));
+        act = up_c.add(1.0).mul(glu);
+      } else {  // SiLU , SwiGLU_GU, vLLM maps silu to SiluAndMul()
+        act = at::silu(g_part).mul(u_part);
+      }
+
+      // W2
+      auto y = mm(act, w2_e, g_eff_2, /*in_features=*/I, /*out_features=*/H);
+
+      if (!apply_router_weight_on_input) {
+        y = y.mul(gates_e.unsqueeze(1));
+      }
+
+      // Store per-expert result
+      y_list[e] = y;
+    }
+  });
+
+  // Concatenate all expert outputs to match expert_tokens order
+  auto Y_all = at::cat(y_list, /*dim=*/0);
+  auto out = at::zeros({T, H}, x.options());
+  out =
+      at::index_add(out, /*dim=*/0, /*index=*/expert_tokens, /*source=*/Y_all);
+
+  return out;
+}

csrc/moe/grouped_topk_kernels.cu

@@ -418,6 +418,15 @@ __device__ inline T neg_inf() {
   return cuda_cast<T, float>(-cuda::std::numeric_limits<float>::infinity());
 }
 
+template <typename T>
+__device__ inline bool is_finite(const T val) {
+#if (__CUDACC_VER_MAJOR__ * 10000 + __CUDACC_VER_MINOR__ * 100 >= 120800)
+  return cuda::std::isfinite(val);
+#else
+  return isfinite(cuda_cast<float, T>(val));
+#endif
+}
+
 template <typename T>
 __device__ void topk_with_k2(T* output, T const* input,
                              cg::thread_block_tile<32> const& tile,
@@ -533,7 +542,7 @@ __global__ void group_idx_and_topk_idx_kernel(
     // calculate group_idx
     int32_t target_num_min = WARP_SIZE - n_group + topk_group;
     // The check is necessary to avoid abnormal input
-    if (lane_id < n_group && cuda::std::isfinite(group_scores[lane_id])) {
+    if (lane_id < n_group && is_finite(group_scores[lane_id])) {
       value = group_scores[lane_id];
     }
 
@@ -568,11 +577,10 @@ __global__ void group_idx_and_topk_idx_kernel(
         int32_t offset = i_group * num_experts_per_group;
         for (int32_t i = lane_id; i < align_num_experts_per_group;
              i += WARP_SIZE) {
-          T candidates =
-              (i < num_experts_per_group) &&
-                      cuda::std::isfinite(scores_with_bias[offset + i])
-                  ? scores_with_bias[offset + i]
-                  : neg_inf<T>();
+          T candidates = (i < num_experts_per_group) &&
+                                 is_finite(scores_with_bias[offset + i])
+                             ? scores_with_bias[offset + i]
+                             : neg_inf<T>();
           queue.add(candidates, offset + i);
         }
         if (group_scores[i_group] == topk_group_value) {

csrc/moe/moe_align_sum_kernels.cu

@@ -44,6 +44,9 @@ __global__ void moe_align_block_size_kernel(
 
   for (size_t i = tid; i < numel; i += stride) {
     int expert_id = topk_ids[i];
+    if (expert_id >= num_experts) {
+      continue;
+    }
     int warp_idx = expert_id / experts_per_warp;
     int expert_offset = expert_id % experts_per_warp;
     atomicAdd(&shared_counts[warp_idx * experts_per_warp + expert_offset], 1);
@@ -95,12 +98,15 @@ template <typename scalar_t>
 __global__ void count_and_sort_expert_tokens_kernel(
     const scalar_t* __restrict__ topk_ids,
     int32_t* __restrict__ sorted_token_ids, int32_t* __restrict__ cumsum_buffer,
-    size_t numel) {
+    size_t numel, int32_t num_experts) {
   const size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
   const size_t stride = blockDim.x * gridDim.x;
 
   for (size_t i = tid; i < numel; i += stride) {
     int32_t expert_id = topk_ids[i];
+    if (expert_id >= num_experts) {
+      continue;
+    }
     int32_t rank_post_pad = atomicAdd(&cumsum_buffer[expert_id], 1);
     sorted_token_ids[rank_post_pad] = i;
   }
@@ -269,7 +275,7 @@ void moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
           sort_kernel<<<actual_blocks, block_threads, 0, stream>>>(
               topk_ids.data_ptr<scalar_t>(),
               sorted_token_ids.data_ptr<int32_t>(),
-              cumsum_buffer.data_ptr<int32_t>(), topk_ids.numel());
+              cumsum_buffer.data_ptr<int32_t>(), topk_ids.numel(), num_experts);
         }
       });
 }

csrc/ops.h

@@ -328,6 +328,12 @@ void selective_scan_fwd(const torch::Tensor& u, const torch::Tensor& delta,
                         const std::optional<torch::Tensor>& has_initial_state,
                         const torch::Tensor& ssm_states, int64_t pad_slot_id);
 
+torch::Tensor dynamic_4bit_int_moe_cpu(
+    torch::Tensor x, torch::Tensor topk_ids, torch::Tensor topk_weights,
+    torch::Tensor w13_packed, torch::Tensor w2_packed, int64_t H, int64_t I,
+    int64_t I2, int64_t group_size, bool apply_router_weight_on_input,
+    int64_t activation_kind);
+
 using fptr_t = int64_t;
 fptr_t init_custom_ar(const std::vector<int64_t>& fake_ipc_ptrs,
                       torch::Tensor& rank_data, int64_t rank,

csrc/quantization/activation_kernels.cu

@@ -23,9 +23,14 @@
 typedef __hip_bfloat162 __nv_bfloat162;
 typedef __hip_bfloat16 __nv_bfloat16;
 typedef __hip_bfloat16_raw __nv_bfloat16_raw;
-
+  #if defined(HIP_FP8_TYPE_OCP)
 typedef __hip_fp8_e4m3 __nv_fp8_e4m3;
 typedef __hip_fp8x4_e4m3 __nv_fp8x4_e4m3;
+  #else
+// ROCm 6.2 fallback: only *_fnuz types exist
+typedef __hip_fp8_e4m3_fnuz __nv_fp8_e4m3;
+typedef __hip_fp8x4_e4m3_fnuz __nv_fp8x4_e4m3;
+  #endif
 #endif
 
 #include "core/registration.h"

csrc/quantization/fp4/activation_nvfp4_quant_fusion_kernels.cu

@@ -26,6 +26,7 @@
 #include "dispatch_utils.h"
 
 #include "cuda_utils.h"
+#include "launch_bounds_utils.h"
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
@@ -63,7 +64,7 @@ __inline__ __device__ PackedVec<Type> compute_silu_mul(PackedVec<Type>& vec,
 
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
-__global__ void __launch_bounds__(1024, 4)
+__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
     silu_mul_cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                              float const* SFScale, uint32_t* out,
                              uint32_t* SFout) {
@@ -131,7 +132,8 @@ void silu_and_mul_nvfp4_quant_sm1xxa(torch::Tensor& output,  // [..., d]
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   auto stream = at::cuda::getCurrentCUDAStream(input.get_device());
   dim3 block(std::min(int(n / ELTS_PER_THREAD), 1024));
-  int const numBlocksPerSM = 2048 / block.x;
+  int const numBlocksPerSM =
+      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
   dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
 
   VLLM_DISPATCH_HALF_TYPES(

csrc/quantization/fp4/nvfp4_experts_quant.cu

@@ -26,12 +26,13 @@
 #include "dispatch_utils.h"
 
 #include "nvfp4_utils.cuh"
+#include "launch_bounds_utils.h"
 
 namespace vllm {
 
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
-__global__ void __launch_bounds__(512, 4)
+__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                     float const* SFScale, uint32_t* out, uint32_t* SFout,
                     uint32_t* input_offset_by_experts,
@@ -129,7 +130,7 @@ __global__ void __launch_bounds__(512, 4)
 
 // Kernel for LARGE_M_TOPK = true (large m_topk optimized version)
 template <class Type, bool UE8M0_SF = false, bool SMALL_NUM_EXPERTS = false>
-__global__ void __launch_bounds__(1024, 4)
+__global__ void __launch_bounds__(1024, VLLM_BLOCKS_PER_SM(1024))
     cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                     float const* SFScale, uint32_t* out, uint32_t* SFout,
                     uint32_t* input_offset_by_experts,
@@ -233,8 +234,9 @@ void quant_impl(void* output, void* output_scale, void* input,
   int const workSizePerRow = k / ELTS_PER_THREAD;
   int const totalWorkSize = m_topk * workSizePerRow;
   dim3 block(std::min(workSizePerRow, 512));
-  // Get number of blocks per SM (assume we can fully utilize the SM).
-  int const numBlocksPerSM = 2048 / block.x;
+  // Get number of blocks per SM
+  int const numBlocksPerSM =
+      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
   dim3 grid(std::min(static_cast<int>((totalWorkSize + block.x - 1) / block.x),
                      multiProcessorCount * numBlocksPerSM));
   while (grid.x <= multiProcessorCount && block.x > 64) {

csrc/quantization/fp4/nvfp4_quant_kernels.cu

@@ -26,13 +26,14 @@
 #include "dispatch_utils.h"
 
 #include "cuda_utils.h"
+#include "launch_bounds_utils.h"
 #include "nvfp4_utils.cuh"
 
 namespace vllm {
 
 // Use UE4M3 by default.
 template <class Type, bool UE8M0_SF = false>
-__global__ void __launch_bounds__(512, 4)
+__global__ void __launch_bounds__(512, VLLM_BLOCKS_PER_SM(512))
     cvt_fp16_to_fp4(int32_t numRows, int32_t numCols, Type const* in,
                     float const* SFScale, uint32_t* out, uint32_t* SFout) {
   using PackedVec = PackedVec<Type>;
@@ -75,8 +76,9 @@ void invokeFP4Quantization(int m, int n, T const* input, float const* SFScale,
   // Grid, Block size.
   // Each thread converts 8 values.
   dim3 block(std::min(int(n / ELTS_PER_THREAD), 512));
-  // Get number of blocks per SM (assume we can fully utilize the SM).
-  int const numBlocksPerSM = 2048 / block.x;
+  // Get number of blocks per SM
+  int const numBlocksPerSM =
+      vllm_runtime_blocks_per_sm(static_cast<int>(block.x));
   dim3 grid(std::min(int(m), multiProcessorCount * numBlocksPerSM));
 
   // Launch the cvt kernel.

csrc/quantization/fp8/per_token_group_quant.cu

@@ -12,8 +12,8 @@
 #include "../vectorization_utils.cuh"
 #include "../../dispatch_utils.h"
 
-__device__ __forceinline__ float GroupReduceMax(float val, const int tid) {
-  unsigned mask = 0xffff;
+__device__ __forceinline__ float GroupReduceMax(float val) {
+  unsigned mask = threadIdx.x % 32 >= 16 ? 0xffff0000 : 0x0000ffff;
 
   val = fmaxf(val, __shfl_xor_sync(mask, val, 8));
   val = fmaxf(val, __shfl_xor_sync(mask, val, 4));
@@ -86,7 +86,7 @@ __global__ void per_token_group_quant_8bit_kernel(
       threads_per_group,  // stride in group
       scalar_op_cache);   // scalar handler
 
-  local_absmax = GroupReduceMax(local_absmax, lane_id);
+  local_absmax = GroupReduceMax(local_absmax);
 
   float y_s = local_absmax / max_8bit;
   if constexpr (SCALE_UE8M0) {

csrc/rocm/attention.cu

@@ -25,6 +25,12 @@
 #include "../attention/dtype_fp8.cuh"
 #include "../quantization/fp8/amd/quant_utils.cuh"
 
+// ROCm 6.2 compatibility: map OCP fp8 types to FNUZ variants if OCP is absent
+#if !defined(HIP_FP8_TYPE_OCP)
+using __hip_fp8_e4m3 = __hip_fp8_e4m3_fnuz;
+using __hip_fp8_e5m2 = __hip_fp8_e5m2_fnuz;
+#endif
+
 #if defined(__HIPCC__) && \
     (defined(__gfx90a__) || defined(__gfx942__) || defined(__gfx950__))
   #define __HIP__GFX9__

csrc/rocm/ops.h

@@ -5,11 +5,14 @@
 torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
                     const int64_t rows_per_block);
 
-torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
+torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
+                       const std::optional<at::Tensor>& in_bias,
                        const int64_t CuCount);
 
-void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
-               at::Tensor& scale_a, at::Tensor& scale_b, const int64_t CuCount);
+void wvSplitKQ(const at::Tensor& in_a, const at::Tensor& in_b,
+               const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
+               const at::Tensor& scale_a, const at::Tensor& scale_b,
+               const int64_t CuCount);
 
 void paged_attention(
     torch::Tensor& out, torch::Tensor& exp_sums, torch::Tensor& max_logits,

csrc/rocm/skinny_gemms.cu

@@ -292,8 +292,9 @@ torch::Tensor LLMM1(at::Tensor& in_a, at::Tensor& in_b,
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
-    wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
-                     const scalar_t* __restrict__ A, scalar_t* C,
+    wvSplitK_hf_sml_(const int K, const int M, const int Bx, const int By,
+                     const scalar_t* B, const scalar_t* __restrict__ A,
+                     const scalar_t* __restrict__ BIAS, scalar_t* C,
                      const int _WvPrGrp, const int CuCount) {
   constexpr int max_lds_len = LDS_SIZE / 2;
   #if defined(__HIP__MI3XX__)
@@ -484,7 +485,14 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            // if (commitColumn[i]) C[m + i + n * M] = __float2half(sum[n][i]);
+            if constexpr (std::is_same_v<scalar_t, half>) {
+              if (BIAS)
+                sum[n][i] += __half2float(BIAS[(m + i) % Bx + (n % By) * M]);
+            } else if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+              if (BIAS)
+                sum[n][i] +=
+                    __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
+            }
             C[m + i + n * M] = __float2s<scalar_t>(sum[n][i]);
           }
         }
@@ -529,7 +537,9 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            // if (commitColumn[i]) C[n + i + m * N] = __float2half(sum[n][i]);
+            if (BIAS)
+              sum4[n][i][0] +=
+                  __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
             C[m + i + n * M] = __float2bfloat16(sum4[n][i][0]);
           }
         }
@@ -541,8 +551,10 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
 #else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
-__global__ void wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
-                                 const scalar_t* __restrict__ A, scalar_t* C,
+__global__ void wvSplitK_hf_sml_(const int K, const int M, const int Bx,
+                                 const int By, const scalar_t* B,
+                                 const scalar_t* __restrict__ A,
+                                 const scalar_t* __restrict__ BIAS, scalar_t* C,
                                  const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
@@ -553,8 +565,9 @@ __global__ void wvSplitK_hf_sml_(const int K, const int M, const scalar_t* B,
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
-    wvSplitK_hf_(const int K, const int M, const scalar_t* B,
-                 const scalar_t* __restrict__ A, scalar_t* C,
+    wvSplitK_hf_(const int K, const int M, const int Bx, const int By,
+                 const scalar_t* B, const scalar_t* __restrict__ A,
+                 const scalar_t* __restrict__ BIAS, scalar_t* C,
                  const int _WvPrGrp, const int CuCount) {
   constexpr int max_lds_len = LDS_SIZE / 2;
   #if defined(__HIP__MI3XX__)
@@ -772,8 +785,17 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            if (commitColumn[i])
+            if (commitColumn[i]) {
+              if constexpr (std::is_same_v<scalar_t, half>) {
+                if (BIAS)
+                  sum[n][i] += __half2float(BIAS[(m + i) % Bx + (n % By) * M]);
+              } else if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+                if (BIAS)
+                  sum[n][i] +=
+                      __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
+              }
               C[m + i + n * M] = __float2s<scalar_t>(sum[n][i]);
+            }
           }
         }
       }
@@ -818,8 +840,12 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            // if (commitColumn[i]) C[n + i + m * N] = __float2half(sum[n][i]);
-            C[m + i + n * M] = __float2bfloat16(sum4[n][i][0]);
+            if (commitColumn[i]) {
+              if (BIAS)
+                sum4[n][i][0] +=
+                    __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
+              C[m + i + n * M] = __float2bfloat16(sum4[n][i][0]);
+            }
           }
         }
       }
@@ -842,8 +868,10 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
 #else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
-__global__ void wvSplitK_hf_(const int K, const int M, const scalar_t* B,
-                             const scalar_t* __restrict__ A, scalar_t* C,
+__global__ void wvSplitK_hf_(const int K, const int M, const int Bx,
+                             const int By, const scalar_t* B,
+                             const scalar_t* __restrict__ A,
+                             const scalar_t* __restrict__ BIAS, scalar_t* C,
                              const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
@@ -854,8 +882,9 @@ __global__ void wvSplitK_hf_(const int K, const int M, const scalar_t* B,
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
-    wvSplitK_hf_big_(const int K, const int M, const scalar_t* B,
-                     const scalar_t* __restrict__ A, scalar_t* C,
+    wvSplitK_hf_big_(const int K, const int M, const int Bx, const int By,
+                     const scalar_t* B, const scalar_t* __restrict__ A,
+                     const scalar_t* __restrict__ BIAS, scalar_t* C,
                      const int _WvPrGrp, const int CuCount) {
   constexpr int max_lds_len = LDS_SIZE / 2;
   #if defined(__HIP__MI3XX__)
@@ -1124,8 +1153,17 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            if (commitColumn[i])
+            if (commitColumn[i]) {
+              if constexpr (std::is_same_v<scalar_t, half>) {
+                if (BIAS)
+                  sum[n][i] += __half2float(BIAS[(m + i) % Bx + (n % By) * M]);
+              } else if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+                if (BIAS)
+                  sum[n][i] +=
+                      __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
+              }
               C[m + i + n * M] = __float2s<scalar_t>(sum[n][i]);
+            }
           }
         }
       }
@@ -1166,8 +1204,12 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       if (threadIdx.x == 63) {
         for (int n = 0; n < N; n++) {
           for (int i = 0; i < YTILE; i++) {
-            // if (commitColumn[i]) C[n + i + m * N] = __float2half(sum[n][i]);
-            C[m + i + n * M] = __float2bfloat16(sum4[n][i][0]);
+            if (commitColumn[i]) {
+              if (BIAS)
+                sum4[n][i][0] +=
+                    __bfloat162float(BIAS[(m + i) % Bx + (n % By) * M]);
+              C[m + i + n * M] = __float2bfloat16(sum4[n][i][0]);
+            }
           }
         }
       }
@@ -1190,8 +1232,10 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
 #else   // !defined(__HIP__GFX9__) TODO: Add NAVI support
 template <typename scalar_t, int THRDS, int YTILE, int WvPrGrp, int A_CHUNK,
           int UNRL, int N>
-__global__ void wvSplitK_hf_big_(const int K, const int M, const scalar_t* B,
-                                 const scalar_t* __restrict__ A, scalar_t* C,
+__global__ void wvSplitK_hf_big_(const int K, const int M, const int Bx,
+                                 const int By, const scalar_t* B,
+                                 const scalar_t* __restrict__ A,
+                                 const scalar_t* __restrict__ BIAS, scalar_t* C,
                                  const int _WvPrGrp, const int CuCount) {
   UNREACHABLE_CODE
 }
@@ -1226,11 +1270,20 @@ int mindiv(int N, int div1, int div2) {
   return rtn;
 }
 
-torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
+torch::Tensor wvSplitK(const at::Tensor& in_a, const at::Tensor& in_b,
+                       const std::optional<at::Tensor>& in_bias,
                        const int64_t CuCount) {
   auto M_in = in_a.size(0);
   auto K_in = in_a.size(1);
   auto N_in = in_b.size(0);
+  auto Bx_in =
+      (in_bias.has_value() && in_bias->numel() > 0)
+          ? (in_bias->sizes().size() == 2) ? in_bias->size(1) : in_bias->size(0)
+          : 1;
+  auto By_in = (in_bias.has_value() && in_bias->numel() > 0 &&
+                in_bias->sizes().size() == 2)
+                   ? in_bias->size(0)
+                   : 1;
 
   TORCH_CHECK(in_a.dtype() == in_b.dtype());
   TORCH_CHECK(K_in % 8 == 0, "k % 8 == 0");
@@ -1254,18 +1307,18 @@ torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
     if ((K_in * N_in <= max_lds_len) && (M_in % _YTILEs == 0)) {              \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEs, _WvPrGrp);              \
       wvSplitK_hf_sml_<fptype, 64, _YTILEs, _WvPrGrp, 8, _UNRLs, _N>          \
-          <<<grid, block, 0, stream>>>(K_in, M_in, af4, bf4, c, __wvPrGrp,    \
-                                       CuCount);                              \
+          <<<grid, block, 0, stream>>>(K_in, M_in, Bx_in, By_in, af4, bf4,    \
+                                       biasf4, c, __wvPrGrp, CuCount);        \
     } else if (K_in * N_in <= max_lds_len * 1.2) {                            \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEm, _WvPrGrp);              \
       wvSplitK_hf_<fptype, 64, _YTILEm, _WvPrGrp, 8, _UNRLm, _N>              \
-          <<<grid, block, 0, stream>>>(K_in, M_in, af4, bf4, c, __wvPrGrp,    \
-                                       CuCount);                              \
+          <<<grid, block, 0, stream>>>(K_in, M_in, Bx_in, By_in, af4, bf4,    \
+                                       biasf4, c, __wvPrGrp, CuCount);        \
     } else {                                                                  \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEb, _WvPrGrp);              \
       wvSplitK_hf_big_<fptype, 64, _YTILEb, _WvPrGrp, 8, _UNRLb, _N>          \
-          <<<grid, block, 0, stream>>>(K_in, M_in, af4, bf4, c, __wvPrGrp,    \
-                                       CuCount);                              \
+          <<<grid, block, 0, stream>>>(K_in, M_in, Bx_in, By_in, af4, bf4,    \
+                                       biasf4, c, __wvPrGrp, CuCount);        \
     }                                                                         \
   }
 
@@ -1273,6 +1326,10 @@ torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
     using fptype = typename scalar<scalar_t>::type;
     fptype* af4 = reinterpret_cast<fptype*>(in_a.data_ptr());
     const fptype* bf4 = reinterpret_cast<const fptype*>(in_b.data_ptr());
+    const fptype* biasf4 =
+        (in_bias.has_value() && in_bias->numel() > 0)
+            ? reinterpret_cast<const fptype*>(in_bias->data_ptr())
+            : nullptr;
     fptype* c = reinterpret_cast<fptype*>(out_c.data_ptr());
     switch (N_in) {
       case 1:
@@ -1300,8 +1357,9 @@ torch::Tensor wvSplitK(at::Tensor& in_a, at::Tensor& in_b,
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
-    wvSplitKQ_hf_sml_(const int K, const int Kp, const int M, const fp8_t* B,
-                      const fp8_t* __restrict__ A, scalar_t* C,
+    wvSplitKQ_hf_sml_(const int K, const int Kp, const int M, const int Bx,
+                      const int By, const fp8_t* B, const fp8_t* __restrict__ A,
+                      const scalar_t* __restrict__ BIAS, scalar_t* C,
                       const float* __restrict__ s_A,
                       const float* __restrict__ s_B, const int _WvPrGrp,
                       const int CuCount) {
@@ -1453,7 +1511,17 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
     if (threadIdx.x == 0) {
       for (int n = 0; n < N; n++) {
         for (int y = 0; y < YTILE; y++) {
-          C[m + y + n * M] = __float2s<scalar_t>(sum[n][y][0] * sA * sB);
+          if (y + m >= M) break;  // To avoid mem access fault.
+          sum[n][y][0] *= sA * sB;
+          if constexpr (std::is_same_v<scalar_t, half>) {
+            if (BIAS)
+              sum[n][y][0] += __half2float(BIAS[(m + y) % Bx + (n % By) * M]);
+          } else if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+            if (BIAS)
+              sum[n][y][0] +=
+                  __bfloat162float(BIAS[(m + y) % Bx + (n % By) * M]);
+          }
+          C[m + y + n * M] = __float2s<scalar_t>(sum[n][y][0]);  // * sA * sB);
         }
       }
     }
@@ -1465,7 +1533,9 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_sml_(const int K, const int Kp, const int M,
-                                  const fp8_t* B, const fp8_t* __restrict__ A,
+                                  const int Bx, const int By, const fp8_t* B,
+                                  const fp8_t* __restrict__ A,
+                                  const scalar_t* __restrict__ BIAS,
                                   scalar_t* C, const float* __restrict__ s_A,
                                   const float* __restrict__ s_B,
                                   const int _WvPrGrp, const int CuCount) {
@@ -1477,8 +1547,9 @@ __global__ void wvSplitKQ_hf_sml_(const int K, const int Kp, const int M,
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void __launch_bounds__(WvPrGrp* THRDS)
-    wvSplitKQ_hf_(const int K, const int Kp, const int M, const fp8_t* B,
-                  const fp8_t* __restrict__ A, scalar_t* C,
+    wvSplitKQ_hf_(const int K, const int Kp, const int M, const int Bx,
+                  const int By, const fp8_t* B, const fp8_t* __restrict__ A,
+                  const scalar_t* __restrict__ BIAS, scalar_t* C,
                   const float* __restrict__ s_A, const float* __restrict__ s_B,
                   const int _WvPrGrp, const int CuCount) {
   constexpr int max_lds_len = LDS_SIZE;
@@ -1626,7 +1697,16 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
       for (int n = 0; n < N; n++) {
         for (int y = 0; y < YTILE; y++) {
           if (y + m >= M) break;  // To avoid mem access fault.
-          C[m + y + n * M] = __float2s<scalar_t>(sum[n][y][0] * sA * sB);
+          sum[n][y][0] *= sA * sB;
+          if constexpr (std::is_same_v<scalar_t, half>) {
+            if (BIAS)
+              sum[n][y][0] += __half2float(BIAS[(m + y) % Bx + (n % By) * M]);
+          } else if constexpr (std::is_same_v<scalar_t, __hip_bfloat16>) {
+            if (BIAS)
+              sum[n][y][0] +=
+                  __bfloat162float(BIAS[(m + y) % Bx + (n % By) * M]);
+          }
+          C[m + y + n * M] = __float2s<scalar_t>(sum[n][y][0]);
         }
       }
     }
@@ -1638,16 +1718,19 @@ __global__ void __launch_bounds__(WvPrGrp* THRDS)
 template <typename scalar_t, typename fp8_t, int THRDS, int YTILE, int WvPrGrp,
           int A_CHUNK, int UNRL, int N>
 __global__ void wvSplitKQ_hf_(const int K, const int Kp, const int M,
-                              const fp8_t* B, const fp8_t* __restrict__ A,
-                              scalar_t* C, const float* __restrict__ s_A,
+                              const int Bx, const int By, const fp8_t* B,
+                              const fp8_t* __restrict__ A,
+                              const scalar_t* __restrict__ BIAS, scalar_t* C,
+                              const float* __restrict__ s_A,
                               const float* __restrict__ s_B, const int _WvPrGrp,
                               const int CuCount) {
   UNREACHABLE_CODE
 }
 #endif  // defined(__HIP__MI3XX__) TODO: Add NAVI support
 
-void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
-               at::Tensor& scale_a, at::Tensor& scale_b,
+void wvSplitKQ(const at::Tensor& in_a, const at::Tensor& in_b,
+               const std::optional<at::Tensor>& in_bias, at::Tensor& out_c,
+               const at::Tensor& scale_a, const at::Tensor& scale_b,
                const int64_t CuCount) {
   static c10::ScalarType kFp8Type = is_fp8_ocp()
                                         ? c10::ScalarType::Float8_e4m3fn
@@ -1656,6 +1739,15 @@ void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
   auto K_in = in_a.size(1);
   auto N_in = in_b.size(0);
   auto Kp_in = in_a.stride(0);
+  auto Bx_in =
+      (in_bias.has_value() && in_bias->numel() > 0)
+          ? (in_bias->sizes().size() == 2) ? in_bias->size(1) : in_bias->size(0)
+          : 1;
+  auto By_in = (in_bias.has_value() && in_bias->numel() > 0 &&
+                in_bias->sizes().size() == 2)
+                   ? in_bias->size(0)
+                   : 1;
+
   TORCH_CHECK(K_in % 16 == 0, "k % 16 == 0");
   TORCH_CHECK(in_a.dtype() == in_b.dtype() && in_a.dtype() == kFp8Type);
   TORCH_CHECK(out_c.dtype() == torch::kFloat16 ||
@@ -1673,13 +1765,15 @@ void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
     if ((K_in * N_in <= max_lds_len) && (M_in % _YTILEs == 0)) {               \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEs, _WvPrGrp);               \
       wvSplitKQ_hf_sml_<fptype, fp8_t, 64, _YTILEs, _WvPrGrp, 16, _UNRLs, _N>  \
-          <<<grid, block, 0, stream>>>(K_in, Kp_in, M_in, a_ptr, b_ptr, c_ptr, \
-                                       s_a, s_b, __wvPrGrp, CuCount);          \
+          <<<grid, block, 0, stream>>>(K_in, Kp_in, M_in, Bx_in, By_in, a_ptr, \
+                                       b_ptr, bias_ptr, c_ptr, s_a, s_b,       \
+                                       __wvPrGrp, CuCount);                    \
     } else {                                                                   \
       int __wvPrGrp = mindiv(M_in, CuCount * _YTILEm, _WvPrGrp);               \
       wvSplitKQ_hf_<fptype, fp8_t, 64, _YTILEm, _WvPrGrp, 16, _UNRLm, _N>      \
-          <<<grid, block, 0, stream>>>(K_in, Kp_in, M_in, a_ptr, b_ptr, c_ptr, \
-                                       s_a, s_b, __wvPrGrp, CuCount);          \
+          <<<grid, block, 0, stream>>>(K_in, Kp_in, M_in, Bx_in, By_in, a_ptr, \
+                                       b_ptr, bias_ptr, c_ptr, s_a, s_b,       \
+                                       __wvPrGrp, CuCount);                    \
     }                                                                          \
   }
 
@@ -1691,6 +1785,9 @@ void wvSplitKQ(at::Tensor& in_a, at::Tensor& in_b, at::Tensor& out_c,
     VLLM_DISPATCH_FP8_TYPES(in_a.scalar_type(), "wvSplitKQ", [&] {
       auto a_ptr = in_a.data_ptr<fp8_t>();
       auto b_ptr = in_b.data_ptr<fp8_t>();
+      auto bias_ptr = (in_bias.has_value() && in_bias->numel() > 0)
+                          ? reinterpret_cast<fptype*>(in_bias->data_ptr())
+                          : nullptr;
       switch (N_in) {
         case 1:
           WVSPLITKQ(16, 2, 2, 2, 2, 2, 2, 1)

csrc/rocm/torch_bindings.cpp

@@ -22,13 +22,14 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, rocm_ops) {
 
   // Custom gemm op for skinny matrix-matrix multiplication
   rocm_ops.def(
-      "wvSplitK(Tensor in_a, Tensor in_b, int CuCount) -> "
+      "wvSplitK(Tensor in_a, Tensor in_b, Tensor? in_bias, int CuCount) -> "
       "Tensor");
   rocm_ops.impl("wvSplitK", torch::kCUDA, &wvSplitK);
 
   // wvSplitK for fp8
   rocm_ops.def(
-      "wvSplitKQ(Tensor in_a, Tensor in_b, Tensor! out_c, Tensor scale_a, "
+      "wvSplitKQ(Tensor in_a, Tensor in_b, Tensor? in_bias, Tensor! out_c, "
+      "Tensor scale_a, "
       "          Tensor scale_b, int CuCount) -> ()");
   rocm_ops.impl("wvSplitKQ", torch::kCUDA, &wvSplitKQ);
 

docker/Dockerfile.cpu

@@ -114,9 +114,6 @@ WORKDIR /workspace/vllm
 RUN --mount=type=bind,src=requirements/test.in,target=requirements/test.in \
     cp requirements/test.in requirements/cpu-test.in && \
     sed -i '/mamba_ssm/d' requirements/cpu-test.in && \
-    sed -i 's/^torch==.*/torch==2.6.0/g' requirements/cpu-test.in && \
-    sed -i 's/torchaudio.*/torchaudio/g' requirements/cpu-test.in && \
-    sed -i 's/torchvision.*/torchvision/g' requirements/cpu-test.in && \
     uv pip compile requirements/cpu-test.in -o requirements/cpu-test.txt --index-strategy unsafe-best-match --torch-backend cpu
 
 RUN --mount=type=cache,target=/root/.cache/uv \

docker/Dockerfile.rocm_base

@@ -65,8 +65,6 @@ ARG PYTORCH_BRANCH
 ARG PYTORCH_VISION_BRANCH
 ARG PYTORCH_REPO
 ARG PYTORCH_VISION_REPO
-ARG FA_BRANCH
-ARG FA_REPO
 RUN git clone ${PYTORCH_REPO} pytorch
 RUN cd pytorch && git checkout ${PYTORCH_BRANCH} && \
     pip install -r requirements.txt && git submodule update --init --recursive \
@@ -77,14 +75,20 @@ RUN git clone ${PYTORCH_VISION_REPO} vision
 RUN cd vision && git checkout ${PYTORCH_VISION_BRANCH} \
     && python3 setup.py bdist_wheel --dist-dir=dist \
     && pip install dist/*.whl
+RUN mkdir -p /app/install && cp /app/pytorch/dist/*.whl /app/install \
+    && cp /app/vision/dist/*.whl /app/install
+
+FROM base AS build_fa
+ARG FA_BRANCH
+ARG FA_REPO
+RUN --mount=type=bind,from=build_pytorch,src=/app/install/,target=/install \
+    pip install /install/*.whl
 RUN git clone ${FA_REPO}
 RUN cd flash-attention \
     && git checkout ${FA_BRANCH} \
     && git submodule update --init \
     && GPU_ARCHS=$(echo ${PYTORCH_ROCM_ARCH} | sed -e 's/;gfx1[0-9]\{3\}//g') python3 setup.py bdist_wheel --dist-dir=dist
-RUN mkdir -p /app/install && cp /app/pytorch/dist/*.whl /app/install \
-    && cp /app/vision/dist/*.whl /app/install \
-    && cp /app/flash-attention/dist/*.whl /app/install
+RUN mkdir -p /app/install && cp /app/flash-attention/dist/*.whl /app/install
 
 FROM base AS build_aiter
 ARG AITER_BRANCH
@@ -103,6 +107,8 @@ FROM base AS debs
 RUN mkdir /app/debs
 RUN --mount=type=bind,from=build_triton,src=/app/install/,target=/install \
     cp /install/*.whl /app/debs
+RUN --mount=type=bind,from=build_fa,src=/app/install/,target=/install \
+    cp /install/*.whl /app/debs
 RUN --mount=type=bind,from=build_amdsmi,src=/app/install/,target=/install \
     cp /install/*.whl /app/debs
 RUN --mount=type=bind,from=build_pytorch,src=/app/install/,target=/install \
@@ -111,13 +117,7 @@ RUN --mount=type=bind,from=build_aiter,src=/app/install/,target=/install \
     cp /install/*.whl /app/debs
 
 FROM base AS final
-RUN --mount=type=bind,from=build_triton,src=/app/install/,target=/install \
-    pip install /install/*.whl
-RUN --mount=type=bind,from=build_amdsmi,src=/app/install/,target=/install \
-    pip install /install/*.whl
-RUN --mount=type=bind,from=build_pytorch,src=/app/install/,target=/install \
-    pip install /install/*.whl
-RUN --mount=type=bind,from=build_aiter,src=/app/install/,target=/install \
+RUN --mount=type=bind,from=debs,src=/app/debs,target=/install \
     pip install /install/*.whl
 
 ARG BASE_IMAGE

docs/configuration/optimization.md

@@ -139,9 +139,9 @@ there is relatively little gain from TP. On the other hand, TP incurs significan
 overhead because of all-reduce being performed after every layer.
 
 Given this, it may be advantageous to instead shard the batched input data using TP, essentially
-performing batch-level DP. This has been shown to improve the throughput by around 10% for
+performing batch-level DP. This has been shown to improve the throughput and TTFT by around 10% for
 `tensor_parallel_size=8`. For vision encoders that use hardware-unoptimized Conv3D operations,
-batch-level DP can provide another 40% increase to throughput compared to regular TP.
+batch-level DP can provide another 40% improvement compared to regular TP.
 
 Nevertheless, since the weights of the multi-modal encoder are replicated across each TP rank,
 there will be a minor increase in memory consumption and may cause OOM if you can barely fit the model already.
@@ -172,14 +172,15 @@ Batch-level DP needs to be implemented on a per-model basis,
 and enabled by setting `supports_encoder_tp_data = True` in the model class.
 Regardless, you need to set `mm_encoder_tp_mode="data"` in engine arguments to use this feature.
 
-Known supported models:
+Known supported models (with corresponding benchmarks):
 
-- GLM-4.5V GLM-4.1V (<gh-pr:23168>)
+- dots_ocr (<gh-pr:25466>)
+- GLM-4.1V or above (<gh-pr:23168>)
 - InternVL (<gh-pr:23909>)
 - Kimi-VL (<gh-pr:23817>)
 - Llama4 (<gh-pr:18368>)
 - MiniCPM-V-2.5 or above (<gh-pr:23327>, <gh-pr:23948>)
-- Qwen2.5-VL (<gh-pr:22742>)
+- Qwen2-VL or above (<gh-pr:22742>, <gh-pr:24955>, <gh-pr:25445>)
 - Step3 (<gh-pr:22697>)
 
 ## Input Processing

docs/contributing/benchmarks.md

@@ -680,7 +680,7 @@ vllm bench serve \
   --save-result \
   --result-dir ~/vllm_benchmark_results \
   --save-detailed \
-  --endpoint /v1/chat/completion
+  --endpoint /v1/chat/completions
 ```
 
 ##### Videos (ShareGPT4Video)
@@ -707,7 +707,7 @@ vllm bench serve \
   --save-result \
   --result-dir ~/vllm_benchmark_results \
   --save-detailed \
-  --endpoint /v1/chat/completion
+  --endpoint /v1/chat/completions
 ```
 
 ##### Synthetic Random Images (random-mm)

docs/features/README.md

@@ -36,22 +36,23 @@ th:not(:first-child) {
 }
 </style>
 
-| Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | [SD](spec_decode.md) | CUDA graph | [pooling](../models/pooling_models.md) | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | <abbr title="Logprobs">logP</abbr> | <abbr title="Prompt Logprobs">prmpt logP</abbr> | <abbr title="Async Output Processing">async output</abbr> | multi-step | <abbr title="Multimodal Inputs">mm</abbr> | best-of | beam-search |
-|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
-| [CP][chunked-prefill] | ✅ | | | | | | | | | | | | | |
-| [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | |
-| [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | |
-| [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | | | | | | | | | | |
-| CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | |
-| [pooling](../models/pooling_models.md) | 🟠\* | 🟠\* | ✅ | ❌ | ✅ | ✅ | | | | | | | | |
-| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | |
-| <abbr title="Logprobs">logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | |
-| <abbr title="Prompt Logprobs">prmpt logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | | | | | |
-| <abbr title="Async Output Processing">async output</abbr> | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | | | | |
-| multi-step | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | | | |
-| [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | |
-| best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | |
-| beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ |
+| Feature | [CP][chunked-prefill] | [APC](automatic_prefix_caching.md) | [LoRA](lora.md) | [SD](spec_decode.md) | CUDA graph | [pooling](../models/pooling_models.md) | <abbr title="Encoder-Decoder Models">enc-dec</abbr> | <abbr title="Logprobs">logP</abbr> | <abbr title="Prompt Logprobs">prmpt logP</abbr> | <abbr title="Async Output Processing">async output</abbr> | multi-step | <abbr title="Multimodal Inputs">mm</abbr> | best-of | beam-search | [prompt-embeds](prompt_embeds.md) |
+|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
+| [CP][chunked-prefill] | ✅ | | | | | | | | | | | | | | |
+| [APC](automatic_prefix_caching.md) | ✅ | ✅ | | | | | | | | | | | | | |
+| [LoRA](lora.md) | ✅ | ✅ | ✅ | | | | | | | | | | | | |
+| [SD](spec_decode.md) | ✅ | ✅ | ❌ | ✅ | | | | | | | | | | | |
+| CUDA graph | ✅ | ✅ | ✅ | ✅ | ✅ | | | | | | | | | | |
+| [pooling](../models/pooling_models.md) | 🟠\* | 🟠\* | ✅ | ❌ | ✅ | ✅ | | | | | | | | | |
+| <abbr title="Encoder-Decoder Models">enc-dec</abbr> | ❌ | [❌](gh-issue:7366) | ❌ | [❌](gh-issue:7366) | ✅ | ✅ | ✅ | | | | | | | | |
+| <abbr title="Logprobs">logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | | | | | | | |
+| <abbr title="Prompt Logprobs">prmpt logP</abbr> | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ✅ | | | | | | |
+| <abbr title="Async Output Processing">async output</abbr> | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | | | | | |
+| multi-step | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | | | | |
+| [mm](multimodal_inputs.md) | ✅ | ✅ | [🟠](gh-pr:4194)<sup>^</sup> | ❔ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❔ | ✅ | | | |
+| best-of | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ✅ | ✅ | | |
+| beam-search | ✅ | ✅ | ✅ | [❌](gh-issue:6137) | ✅ | ❌ | ✅ | ✅ | ✅ | ❔ | [❌](gh-issue:7968) | ❔ | ✅ | ✅ | |
+| [prompt-embeds](prompt_embeds.md) | ✅ | [❌](gh-issue:25096) | ? | ❌ | ✅ | ❌ | ❌ | ✅ | ❌ | ? | ? | ❌ | ? | ? | ✅ |
 
 \* Chunked prefill and prefix caching are only applicable to last-token pooling.  
 <sup>^</sup> LoRA is only applicable to the language backbone of multimodal models.
@@ -76,3 +77,4 @@ th:not(:first-child) {
 | multi-step                                                | ✅                  | ✅        | ✅        | ✅     | ✅        | [❌](gh-issue:8477) | ✅     | ❌ |
 | best-of                                                   | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
 | beam-search                                               | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ✅     | ❌ |
+| [prompt-embeds](prompt_embeds.md)                         | ✅                  | ✅        | ✅        | ✅     | ✅        | ✅                  | ?     | [❌](gh-issue:25097) |

docs/features/disagg_prefill.md

@@ -23,7 +23,7 @@ Now supports 5 types of connectors:
 
 - **SharedStorageConnector**: refer to <gh-file:examples/offline_inference/disaggregated-prefill-v1/run.sh> for the example usage of SharedStorageConnector disaggregated prefilling.
 - **LMCacheConnectorV1**: refer to <gh-file:examples/others/lmcache/disagg_prefill_lmcache_v1/disagg_example_nixl.sh> for the example usage of LMCacheConnectorV1 disaggregated prefilling which uses NIXL as the underlying KV transmission.
-- **NixlConnector**: refer to <gh-file:tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh> for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv.
+- **NixlConnector**: refer to <gh-file:tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh> for the example usage of NixlConnector disaggregated prefilling which support fully async send/recv. For detailed usage guide, see [NixlConnector Usage Guide](nixl_connector_usage.md).
 - **P2pNcclConnector**: refer to <gh-file:examples/online_serving/disaggregated_serving_p2p_nccl_xpyd/disagg_example_p2p_nccl_xpyd.sh> for the example usage of P2pNcclConnector disaggregated prefilling.
 - **MultiConnector**: take advantage of the kv_connector_extra_config: dict[str, Any] already present in KVTransferConfig to stash all the connectors we want in an ordered list of kwargs.such as:
 
@@ -31,6 +31,18 @@ Now supports 5 types of connectors:
   --kv-transfer-config '{"kv_connector":"MultiConnector","kv_role":"kv_both","kv_connector_extra_config":{"connectors":[{"kv_connector":"NixlConnector","kv_role":"kv_both"},{"kv_connector":"SharedStorageConnector","kv_role":"kv_both","kv_connector_extra_config":{"shared_storage_path":"local_storage"}}]}}'
   ```
 
+For NixlConnector, you may also specify one or multiple NIXL_Backend. Such as:
+
+  ```bash
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_buffer_device":"cuda", "kv_connector_extra_config":{"backends":["UCX", "GDS"]}}'
+  ```
+
+- **OffloadingConnector**: enable offloading of KV data to CPU memory, customizing the CPU block size (in tokens) and number of blocks to allocate (per worker):
+
+  ```bash
+  --kv-transfer-config '{"kv_connector":"OffloadingConnector","kv_role":"kv_both","kv_connector_extra_config":{"block_size": 64, "num_cpu_blocks": 1000}}'
+  ```
+
 ## Benchmarks
 
 Please refer to <gh-file:benchmarks/disagg_benchmarks> for disaggregated prefilling benchmarks.

docs/features/nixl_connector_usage.md

@@ -0,0 +1,159 @@
+# NixlConnector Usage Guide
+
+NixlConnector is a high-performance KV cache transfer connector for vLLM's disaggregated prefilling feature. It provides fully asynchronous send/receive operations using the NIXL library for efficient cross-process KV cache transfer.
+
+## Prerequisites
+
+### Installation
+
+Install the NIXL library: `uv pip install nixl`, as a quick start.
+
+- Refer to [NIXL official repository](https://github.com/ai-dynamo/nixl) for more installation instructions
+- The specified required NIXL version can be found in [requirements/kv_connectors.txt](gh-file:requirements/kv_connectors.txt) and other relevant config files
+
+### Transport Configuration
+
+NixlConnector uses NIXL library for underlying communication, which supports multiple transport backends. UCX (Unified Communication X) is the primary default transport library used by NIXL. Configure transport environment variables:
+
+```bash
+# Example UCX configuration, adjust according to your enviroment
+export UCX_TLS=all  # or specify specific transports like "rc,ud,sm,^cuda_ipc" ..etc
+export UCX_NET_DEVICES=all  # or specify network devices like "mlx5_0:1,mlx5_1:1"
+```
+
+!!! tip
+    When using UCX as the transport backend, NCCL environment variables (like `NCCL_IB_HCA`, `NCCL_SOCKET_IFNAME`) are not applicable to NixlConnector, so configure UCX-specific environment variables instead of NCCL variables.
+
+## Basic Usage (on the same host)
+
+### Producer (Prefiller) Configuration
+
+Start a prefiller instance that produces KV caches
+
+```bash
+# 1st GPU as prefiller
+CUDA_VISIBLE_DEVICES=0 \
+UCX_NET_DEVICES=all \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
+vllm serve Qwen/Qwen3-0.6B \
+  --port 8100 \
+  --enforce-eager \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+```
+
+### Consumer (Decoder) Configuration
+
+Start a decoder instance that consumes KV caches:
+
+```bash
+# 2nd GPU as decoder
+CUDA_VISIBLE_DEVICES=1 \
+UCX_NET_DEVICES=all \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5601 \
+vllm serve Qwen/Qwen3-0.6B \
+  --port 8200 \
+  --enforce-eager \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}'
+```
+
+### Proxy Server
+
+Use a proxy server to route requests between prefiller and decoder:
+
+```bash
+python tests/v1/kv_connector/nixl_integration/toy_proxy_server.py \
+  --port 8192 \
+  --prefiller-hosts localhost \
+  --prefiller-ports 8100 \
+  --decoder-hosts localhost \
+  --decoder-ports 8200
+```
+
+## Environment Variables
+
+- `VLLM_NIXL_SIDE_CHANNEL_PORT`: Port for NIXL handshake communication
+    - Default: 5600
+    - **Required for both prefiller and decoder instances**
+    - Each vLLM worker needs a unique port on its host; using the same port number across different hosts is fine
+    - For TP/DP deployments, each worker's port on a node is computed as: base_port + dp_rank * tp_size + tp_rank (e.g., with `--tensor-parallel-size=4` and base_port=5600, tp_rank 0..3 use ports 5600, 5601, 5602, 5603 on that node).
+    - Used for the initial NIXL handshake between the prefiller and the decoder
+
+- `VLLM_NIXL_SIDE_CHANNEL_HOST`: Host for side channel communication
+    - Default: "localhost"
+    - Set when prefiller and decoder are on different machines
+    - Connection info is passed via KVTransferParams from prefiller to decoder for handshake
+
+- `VLLM_NIXL_ABORT_REQUEST_TIMEOUT`: Timeout (in seconds) for automatically releasing the prefiller’s KV cache for a particular request. (Optional)
+    - Default: 120
+    - If a request is aborted and the decoder has not yet read the KV-cache blocks through the nixl channel, the prefill instance will release its KV-cache blocks after this timeout to avoid holding them indefinitely.
+
+## Multi-Instance Setup
+
+### Multiple Prefiller Instances on Different Machines
+
+```bash
+# Prefiller 1 on Machine A (example IP: ${IP1})
+VLLM_NIXL_SIDE_CHANNEL_HOST=${IP1} \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
+UCX_NET_DEVICES=all \
+vllm serve Qwen/Qwen3-0.6B --port 8000 \
+  --tensor-parallel-size 8 \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'
+
+# Prefiller 2 on Machine B (example IP: ${IP2})
+VLLM_NIXL_SIDE_CHANNEL_HOST=${IP2} \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
+UCX_NET_DEVICES=all \
+vllm serve Qwen/Qwen3-0.6B --port 8000 \
+  --tensor-parallel-size 8 \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_producer"}'
+```
+
+### Multiple Decoder Instances on Different Machines
+
+```bash
+# Decoder 1 on Machine C (example IP: ${IP3})
+VLLM_NIXL_SIDE_CHANNEL_HOST=${IP3} \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
+UCX_NET_DEVICES=all \
+vllm serve Qwen/Qwen3-0.6B --port 8000 \
+  --tensor-parallel-size 8 \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'
+
+# Decoder 2 on Machine D (example IP: ${IP4})
+VLLM_NIXL_SIDE_CHANNEL_HOST=${IP4} \
+VLLM_NIXL_SIDE_CHANNEL_PORT=5600 \
+UCX_NET_DEVICES=all \
+vllm serve Qwen/Qwen3-0.6B --port 8000 \
+  --tensor-parallel-size 8 \
+  --kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_consumer"}'
+```
+
+### Proxy for Multiple Instances
+
+```bash
+python tests/v1/kv_connector/nixl_integration/toy_proxy_server.py \
+  --port 8192 \
+  --prefiller-hosts ${IP1} ${IP2} \
+  --prefiller-ports 8000 8000 \
+  --decoder-hosts ${IP3} ${IP4} \
+  --decoder-ports 8000 8000
+```
+
+### KV Role Options
+
+- **kv_producer**: For prefiller instances that generate KV caches
+- **kv_consumer**: For decoder instances that consume KV caches from prefiller
+- **kv_both**: Enables symmetric functionality where the connector can act as both producer and consumer. This provides flexibility for experimental setups and scenarios where the role distinction is not predetermined.
+
+!!! tip
+    NixlConnector currently does not distinguish `kv_role`; the actual prefiller/decoder roles are determined by the upper-level proxy (e.g., `toy_proxy_server.py` using `--prefiller-hosts` and `--decoder-hosts`).
+    Therefore, `kv_role` in `--kv-transfer-config` is effectively a placeholder and does not affect NixlConnector's behavior.
+
+## Example Scripts/Code
+
+Refer to these example scripts in the vLLM repository:
+
+- [run_accuracy_test.sh](gh-file:tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh)
+- [toy_proxy_server.py](gh-file:tests/v1/kv_connector/nixl_integration/toy_proxy_server.py)
+- [test_accuracy.py](gh-file:tests/v1/kv_connector/nixl_integration/test_accuracy.py)

docs/features/prompt_embeds.md

@@ -6,9 +6,6 @@ This page teaches you how to pass prompt embedding inputs to vLLM.
 
 The traditional flow of text data for a Large Language Model goes from text to token ids (via a tokenizer) then from token ids to prompt embeddings. For a traditional decoder-only model (such as meta-llama/Llama-3.1-8B-Instruct), this step of converting token ids to prompt embeddings happens via a look-up from a learned embedding matrix, but the model is not limited to processing only the embeddings corresponding to its token vocabulary.
 
-!!! note
-    Prompt embeddings are currently only supported in the v0 engine.
-
 ## Offline Inference
 
 To input multi-modal data, follow this schema in [vllm.inputs.EmbedsPrompt][]:

docs/features/structured_outputs.md

@@ -6,6 +6,17 @@ vLLM supports the generation of structured outputs using
 This document shows you some examples of the different options that are
 available to generate structured outputs.
 
+!!! warning
+    If you are still using the following deprecated API fields, please update your code to use `structured_outputs` as demonstrated in the rest of this document:
+
+    - `guided_json` -> `{"structured_outputs": {"json": ...}}` or `StructuredOutputsParams(json=...)`
+    - `guided_regex` -> `{"structured_outputs": {"regex": ...}}` or `StructuredOutputsParams(regex=...)`
+    - `guided_choice` -> `{"structured_outputs": {"choice": ...}}` or `StructuredOutputsParams(choice=...)`
+    - `guided_grammar` -> `{"structured_outputs": {"grammar": ...}}` or `StructuredOutputsParams(grammar=...)`
+    - `guided_whitespace_pattern` -> `{"structured_outputs": {"whitespace_pattern": ...}}` or `StructuredOutputsParams(whitespace_pattern=...)`
+    - `structural_tag` -> `{"structured_outputs": {"structural_tag": ...}}` or `StructuredOutputsParams(structural_tag=...)`
+    - `guided_decoding_backend` -> Remove this field from your request
+
 ## Online Serving (OpenAI API)
 
 You can generate structured outputs using the OpenAI's [Completions](https://platform.openai.com/docs/api-reference/completions) and [Chat](https://platform.openai.com/docs/api-reference/chat) API.

docs/features/tool_calling.md

@@ -310,6 +310,15 @@ Flags:
 * For non-reasoning: `--tool-call-parser hunyuan_a13b`
 * For reasoning: `--tool-call-parser hunyuan_a13b --reasoning-parser hunyuan_a13b --enable_reasoning`
 
+### LongCat-Flash-Chat Models (`longcat`)
+
+Supported models:
+
+* `meituan-longcat/LongCat-Flash-Chat`
+* `meituan-longcat/LongCat-Flash-Chat-FP8`
+
+Flags: `--tool-call-parser longcat`
+
 ### GLM-4.5 Models (`glm45`)
 
 Supported models:
@@ -319,6 +328,15 @@ Supported models:
 
 Flags: `--tool-call-parser glm45`
 
+### Qwen3-Coder Models (`qwen3_xml`)
+
+Supported models:
+
+* `Qwen/Qwen3-480B-A35B-Instruct`
+* `Qwen/Qwen3-Coder-30B-A3B-Instruct`
+
+Flags: `--tool-call-parser qwen3_xml`
+
 ### Models with Pythonic Tool Calls (`pythonic`)
 
 A growing number of models output a python list to represent tool calls instead of using JSON. This has the advantage of inherently supporting parallel tool calls and removing ambiguity around the JSON schema required for tool calls. The `pythonic` tool parser can support such models.

docs/getting_started/installation/cpu/x86.inc.md

@@ -20,7 +20,80 @@ vLLM supports basic model inferencing and serving on x86 CPU platform, with data
 # --8<-- [end:pre-built-wheels]
 # --8<-- [start:build-wheel-from-source]
 
---8<-- "docs/getting_started/installation/cpu/build.inc.md"
+Install recommended compiler. We recommend to use `gcc/g++ >= 12.3.0` as the default compiler to avoid potential problems. For example, on Ubuntu 22.4, you can run:
+
+```bash
+sudo apt-get update -y
+sudo apt-get install -y gcc-12 g++-12 libnuma-dev python3-dev
+sudo update-alternatives --install /usr/bin/gcc gcc /usr/bin/gcc-12 10 --slave /usr/bin/g++ g++ /usr/bin/g++-12
+```
+
+Clone the vLLM project:
+
+```bash
+git clone https://github.com/vllm-project/vllm.git vllm_source
+cd vllm_source
+```
+
+Install the required dependencies:
+
+```bash
+uv pip install -r requirements/cpu-build.txt --torch-backend cpu
+uv pip install -r requirements/cpu.txt --torch-backend cpu
+```
+
+??? console "pip"
+    ```bash
+    pip install --upgrade pip
+    pip install -v -r requirements/cpu-build.txt --extra-index-url https://download.pytorch.org/whl/cpu
+    pip install -v -r requirements/cpu.txt --extra-index-url https://download.pytorch.org/whl/cpu
+    ```
+
+Build and install vLLM:
+
+```bash
+VLLM_TARGET_DEVICE=cpu uv pip install . --no-build-isolation
+```
+
+If you want to develop vLLM, install it in editable mode instead.
+
+```bash
+VLLM_TARGET_DEVICE=cpu uv pip install -e . --no-build-isolation
+```
+
+Optionally, build a portable wheel which you can then install elsewhere:
+
+```bash
+VLLM_TARGET_DEVICE=cpu uv build --wheel
+```
+
+```bash
+uv pip install dist/*.whl
+```
+
+??? console "pip"
+    ```bash
+    VLLM_TARGET_DEVICE=cpu python -m build --wheel --no-isolation
+    ```
+
+    ```bash
+    pip install dist/*.whl
+    ```
+
+!!! example "Troubleshooting"
+    - **NumPy ≥2.0 error**: Downgrade using `pip install "numpy<2.0"`.
+    - **CMake picks up CUDA**: Add `CMAKE_DISABLE_FIND_PACKAGE_CUDA=ON` to prevent CUDA detection during CPU builds, even if CUDA is installed.
+    - `AMD` requies at least 4th gen processors (Zen 4/Genoa) or higher to support [AVX512](https://www.phoronix.com/review/amd-zen4-avx512) to run vLLM on CPU.
+    - If you receive an error such as: `Could not find a version that satisfies the requirement torch==X.Y.Z+cpu+cpu`, consider updating [pyproject.toml](https://github.com/vllm-project/vllm/blob/main/pyproject.toml) to help pip resolve the dependency.
+    ```toml title="pyproject.toml"
+    [build-system]
+    requires = [
+      "cmake>=3.26.1",
+      ...
+      "torch==X.Y.Z+cpu"   # <-------
+    ]
+    ```
+    - If you are building vLLM from source and not using the pre-built images, remember to set `LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:$LD_PRELOAD"` on x86 machines before running vLLM.
 
 # --8<-- [end:build-wheel-from-source]
 # --8<-- [start:pre-built-images]
@@ -57,4 +130,4 @@ docker run --rm \
 
 # --8<-- [end:build-image-from-source]
 # --8<-- [start:extra-information]
-# --8<-- [end:extra-information]
+# --8<-- [end:extra-information]

docs/mkdocs/hooks/generate_argparse.py

@@ -32,8 +32,9 @@ def auto_mock(module, attr, max_mocks=50):
     for _ in range(max_mocks):
         try:
             # First treat attr as an attr, then as a submodule
-            return getattr(importlib.import_module(module), attr,
-                           importlib.import_module(f"{module}.{attr}"))
+            with patch("importlib.metadata.version", return_value="0.0.0"):
+                return getattr(importlib.import_module(module), attr,
+                               importlib.import_module(f"{module}.{attr}"))
         except importlib.metadata.PackageNotFoundError as e:
             raise e
         except ModuleNotFoundError as e:
@@ -167,5 +168,5 @@ def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
         doc_path = ARGPARSE_DOC_DIR / f"{stem}.md"
         # Specify encoding for building on Windows
         with open(doc_path, "w", encoding="utf-8") as f:
-            f.write(parser.format_help())
+            f.write(super(type(parser), parser).format_help())
         logger.info("Argparse generated: %s", doc_path.relative_to(ROOT_DIR))

docs/models/generative_models.md

@@ -4,7 +4,7 @@ vLLM provides first-class support for generative models, which covers most of LL
 
 In vLLM, generative models implement the[VllmModelForTextGeneration][vllm.model_executor.models.VllmModelForTextGeneration] interface.
 Based on the final hidden states of the input, these models output log probabilities of the tokens to generate,
-which are then passed through [Sampler][vllm.model_executor.layers.sampler.Sampler] to obtain the final text.
+which are then passed through [Sampler][vllm.v1.sample.sampler.Sampler] to obtain the final text.
 
 ## Configuration
 

docs/models/pooling_models.md

@@ -59,7 +59,7 @@ enabling the corresponding APIs:
 #### Predefined models
 
 If the [Pooler][vllm.model_executor.layers.pooler.Pooler] defined by the model accepts `pooler_config`,
-you can override some of its attributes via the `--override-pooler-config` option.
+you can override some of its attributes via the `--pooler-config` option.
 
 #### Converted models
 
@@ -75,7 +75,7 @@ the pooler assigned to each task has the following attributes by default:
 When loading [Sentence Transformers](https://huggingface.co/sentence-transformers) models,
 its Sentence Transformers configuration file (`modules.json`) takes priority over the model's defaults.
 
-You can further customize this via the `--override-pooler-config` option,
+You can further customize this via the `--pooler-config` option,
 which takes priority over both the model's and Sentence Transformers's defaults.
 
 ## Offline Inference

docs/models/supported_models.md

@@ -17,9 +17,24 @@ These models are what we list in [supported-text-models][supported-text-models]
 
 ### Transformers
 
-vLLM also supports model implementations that are available in Transformers. This does not currently work for all models, but most decoder language models and common vision language models are supported! Vision-language models currently accept only image inputs. Support for video inputs will be added in future releases.
+vLLM also supports model implementations that are available in Transformers. You should expect the performance of a Transformers model implementation used in vLLM to be within <1% of the performance of a dedicated vLLM model implementation. We call this feature the "Transformers backend".
 
-To check if the modeling backend is Transformers, you can simply do this:
+Currently, the Transformers backend works for the following:
+
+- Modalities: embedding models, language models and vision-language models*
+- Architectures: encoder-only, decoder-only
+- Attention types: full attention and/or sliding attention
+
+_*Vision-language models currently accept only image inputs. Support for video inputs will be added in a future release._
+
+If the Transformers model implementation follows all the steps in [writing a custom model](#writing-custom-models) then, when used with the Transformers backend, it will be compatible with the following features of vLLM:
+
+- All the features listed in the [compatibility matrix](../features/README.md#feature-x-feature)
+- Any combination of the following vLLM parallelisation schemes:
+    - Pipeline parallel
+    - Tensor parallel
+
+Checking if the modeling backend is Transformers is as simple as:
 
 ```python
 from vllm import LLM
@@ -27,16 +42,12 @@ llm = LLM(model=...)  # Name or path of your model
 llm.apply_model(lambda model: print(type(model)))
 ```
 
-If it is `TransformersForCausalLM` or `TransformersForMultimodalLM` then it means it's based on Transformers!
+If the printed type starts with `Transformers...` then it's using the Transformers model implementation!
 
-!!! tip
-    You can force the use of `TransformersForCausalLM` by setting `model_impl="transformers"` for [offline-inference](../serving/offline_inference.md) or `--model-impl transformers` for the [openai-compatible-server](../serving/openai_compatible_server.md).
+If a model has a vLLM implementation but you would prefer to use the Transformers implementation via the Transformers backend, set `model_impl="transformers"` for [offline inference](../serving/offline_inference.md) or `--model-impl transformers` for the [online serving](../serving/openai_compatible_server.md).
 
 !!! note
-    vLLM may not fully optimise the Transformers implementation so you may see degraded performance if comparing a native model to a Transformers model in vLLM.
-
-!!! note
-    In case of vision language models if you are loading with `dtype="auto"`, vLLM loads the whole model with config's `dtype` if it exists. In contrast the native Transformers will respect the `dtype` attribute of each backbone in the model. That might cause a slight difference in performance.
+    For vision-language models, if you are loading with `dtype="auto"`, vLLM loads the whole model with config's `dtype` if it exists. In contrast the native Transformers will respect the `dtype` attribute of each backbone in the model. That might cause a slight difference in performance.
 
 #### Custom models
 
@@ -66,10 +77,11 @@ This section details the necessary modifications to make to a Transformers compa
 To make your model compatible with the Transformers backend, it needs:
 
 1. `kwargs` passed down through all modules from `MyModel` to `MyAttention`.
+    1. If your model is encoder-only, you must also add `is_causal = False` to `MyAttention`.
 2. `MyAttention` must use `ALL_ATTENTION_FUNCTIONS` to call attention.
 3. `MyModel` must contain `_supports_attention_backend = True`.
 
-<details>
+<details class="code">
 <summary>modeling_my_model.py</summary>
 
 ```python
@@ -78,6 +90,7 @@ from transformers import PreTrainedModel
 from torch import nn
 
 class MyAttention(nn.Module):
+    is_causal = False  # Only do this for encoder-only models
 
     def forward(self, hidden_states, **kwargs):
         ...
@@ -101,13 +114,13 @@ Here is what happens in the background when this model is loaded:
 
 1. The config is loaded.
 2. `MyModel` Python class is loaded from the `auto_map` in config, and we check that the model `is_backend_compatible()`.
-3. `MyModel` is loaded into `TransformersForCausalLM` or `TransformersForMultimodalLM` (see <gh-file:vllm/model_executor/models/transformers.py>) which sets `self.config._attn_implementation = "vllm"` so that vLLM's attention layer is used.
+3. `MyModel` is loaded into one of the Transformers backend classes in <gh-file:vllm/model_executor/models/transformers.py> which sets `self.config._attn_implementation = "vllm"` so that vLLM's attention layer is used.
 
 That's it!
 
 For your model to be compatible with vLLM's tensor parallel and/or pipeline parallel features, you must add `base_model_tp_plan` and/or `base_model_pp_plan` to your model's config class:
 
-<details>
+<details class="code">
 <summary>configuration_my_model.py</summary>
 
 ```python
@@ -339,6 +352,7 @@ th {
 | `DeepseekV2ForCausalLM` | DeepSeek-V2 | `deepseek-ai/DeepSeek-V2`, `deepseek-ai/DeepSeek-V2-Chat`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `DeepseekV3ForCausalLM` | DeepSeek-V3 | `deepseek-ai/DeepSeek-V3`, `deepseek-ai/DeepSeek-R1`, `deepseek-ai/DeepSeek-V3.1`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Dots1ForCausalLM` | dots.llm1 | `rednote-hilab/dots.llm1.base`, `rednote-hilab/dots.llm1.inst`, etc. | | ✅︎ | ✅︎ |
+| `DotsOCRForCausalLM` | dots_ocr | `rednote-hilab/dots.ocr` | | ✅︎ | ✅︎ |
 | `Ernie4_5ForCausalLM` | Ernie4.5 | `baidu/ERNIE-4.5-0.3B-PT`, etc. | ✅︎ | ✅︎ | ✅︎ |
 | `Ernie4_5_MoeForCausalLM` | Ernie4.5MoE | `baidu/ERNIE-4.5-21B-A3B-PT`, `baidu/ERNIE-4.5-300B-A47B-PT`, etc. |✅︎| ✅︎ | ✅︎ |
 | `ExaoneForCausalLM` | EXAONE-3 | `LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct`, etc. | ✅︎ | ✅︎ | ✅︎ |
@@ -414,6 +428,7 @@ th {
 | `MiniMaxM1ForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-M1-40k`, `MiniMaxAI/MiniMax-M1-80k`, etc. | | | ✅︎ |
 | `MiniMaxText01ForCausalLM` | MiniMax-Text | `MiniMaxAI/MiniMax-Text-01`, etc. | | | ✅︎ |
 | `Zamba2ForCausalLM` | Zamba2 | `Zyphra/Zamba2-7B-instruct`, `Zyphra/Zamba2-2.7B-instruct`, `Zyphra/Zamba2-1.2B-instruct`, etc. | | | ✅︎ |
+| `LongcatFlashForCausalLM` | LongCat-Flash | `meituan-longcat/LongCat-Flash-Chat`, `meituan-longcat/LongCat-Flash-Chat-FP8` | ✅︎ |✅︎ | ✅︎ |
 
 Some models are supported only via the [Transformers backend](#transformers). The purpose of the table below is to acknowledge models which we officially support in this way. The logs will say that the Transformers backend is being used, and you will see no warning that this is fallback behaviour. This means that, if you have issues with any of the models listed below, please [make an issue](https://github.com/vllm-project/vllm/issues/new/choose) and we'll do our best to fix it!
 
@@ -457,7 +472,7 @@ These models primarily support the [`LLM.embed`](./pooling_models.md#llmembed) A
 
 !!! note
     `ssmits/Qwen2-7B-Instruct-embed-base` has an improperly defined Sentence Transformers config.
-    You need to manually set mean pooling by passing `--override-pooler-config '{"pooling_type": "MEAN"}'`.
+    You need to manually set mean pooling by passing `--pooler-config '{"pooling_type": "MEAN"}'`.
 
 !!! note
     For `Alibaba-NLP/gte-Qwen2-*`, you need to enable `--trust-remote-code` for the correct tokenizer to be loaded.
@@ -552,7 +567,7 @@ If your model is not in the above list, we will try to automatically convert the
 
 !!! important
     For process-supervised reward models such as `peiyi9979/math-shepherd-mistral-7b-prm`, the pooling config should be set explicitly,
-    e.g.: `--override-pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
+    e.g.: `--pooler-config '{"pooling_type": "STEP", "step_tag_id": 123, "returned_token_ids": [456, 789]}'`.
 
 #### Token Classification
 

docs/serving/expert_parallel_deployment.md

@@ -193,7 +193,7 @@ For production deployments requiring strict SLA guarantees for time-to-first-tok
 
 1. **Install gdrcopy/ucx/nixl**: For maximum performance, run the [install_gdrcopy.sh](gh-file:tools/install_gdrcopy.sh) script to install `gdrcopy` (e.g., `install_gdrcopy.sh "${GDRCOPY_OS_VERSION}" "12.8" "x64"`). You can find available OS versions [here](https://developer.download.nvidia.com/compute/redist/gdrcopy/CUDA%2012.8/). If `gdrcopy` is not installed, things will still work with a plain `pip install nixl`, just with lower performance. `nixl` and `ucx` are installed as dependencies via pip.
 
-2. **Configure Both Instances**: Add this flag to both prefill and decode instances `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}`
+2. **Configure Both Instances**: Add this flag to both prefill and decode instances `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both"}`. Noted, you may also specify one or multiple NIXL_Backend. Such as: `--kv-transfer-config '{"kv_connector":"NixlConnector","kv_role":"kv_both", "kv_connector_extra_config":{"backends":["UCX", "GDS"]}}'`
 
 3. **Client Orchestration**: Use the client-side script below to coordinate prefill/decode operations. We are actively working on routing solutions.
 

docs/usage/README.md

@@ -1,6 +1,6 @@
 # Using vLLM
 
-First, vLLM must be [installed](../getting_started/installation) for your chosen device in either a Python or Docker environment.
+First, vLLM must be [installed](../getting_started/installation/) for your chosen device in either a Python or Docker environment.
 
 Then, vLLM supports the following usage patterns:
 

examples/offline_inference/basic/chat.py

@@ -87,6 +87,7 @@ def main(args: dict):
             use_tqdm=False,
             chat_template=chat_template,
         )
+        print_outputs(outputs)
 
 
 if __name__ == "__main__":

examples/offline_inference/data_parallel.py

@@ -101,6 +101,13 @@ def parse_args():
         "--quantization",
         type=str,
     )
+    parser.add_argument(
+        "--disable-expert-parallel",
+        dest="enable_expert_parallel",
+        action="store_false",
+        help="Disable expert parallel (default: enabled).",
+    )
+    parser.set_defaults(enable_expert_parallel=True)
     return parser.parse_args()
 
 
@@ -113,6 +120,7 @@ def main(
     dp_master_port,
     GPUs_per_dp_rank,
     enforce_eager,
+    enable_expert_parallel,
     trust_remote_code,
     max_num_seqs,
     max_model_len,
@@ -168,7 +176,7 @@ def main(
         model=model,
         tensor_parallel_size=GPUs_per_dp_rank,
         enforce_eager=enforce_eager,
-        enable_expert_parallel=True,
+        enable_expert_parallel=enable_expert_parallel,
         trust_remote_code=trust_remote_code,
         max_num_seqs=max_num_seqs,
         max_model_len=max_model_len,
@@ -229,6 +237,7 @@ if __name__ == "__main__":
                 dp_master_port,
                 tp_size,
                 args.enforce_eager,
+                args.enable_expert_parallel,
                 args.trust_remote_code,
                 args.max_num_seqs,
                 args.max_model_len,

examples/offline_inference/profiling.py

@@ -1,510 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import inspect
-import json
-import os
-import sys
-from argparse import RawTextHelpFormatter
-from collections.abc import Generator
-from dataclasses import asdict, dataclass
-from typing import Any, Optional, TypeAlias
-
-import torch
-import tqdm
-
-from vllm import LLM, SamplingParams
-from vllm.engine.arg_utils import EngineArgs
-from vllm.profiler.layerwise_profile import layerwise_profile
-from vllm.utils import FlexibleArgumentParser
-
-BATCH_SIZE_DEFAULT = 1
-PROMPT_LEN_DEFAULT = 256
-
-
-@dataclass
-class ProfileContext:
-    engine_args: EngineArgs
-    prompt_len: int
-    batch_size: int
-
-    # The profiler can run in 2 modes,
-    # 1. Run profiler for user specified num_steps
-    num_steps: Optional[int] = None
-    # 2. Run profiler until all requests complete
-    complete_num_requests_per_step: Optional[int] = None
-
-    save_chrome_traces_folder: Optional[str] = None
-
-
-def get_dtype(dtype: str):
-    if dtype == "torch.float":
-        return torch.float
-    else:
-        return dtype
-
-
-OutputLen_NumReqs_Map: TypeAlias = dict[int, int]
-
-
-def compute_request_output_lengths(
-    batch_size: int, step_requests: list[int]
-) -> OutputLen_NumReqs_Map:
-    """
-    Given the number of requests, batch_size, and the number of requests
-    that each engine-step should process, step_requests, determine the
-    output lengths of the requests such that step_request is honoured.
-
-    Example:
-    if batch size = 128 and step_request = [128, 128, 96, 64, 32, 1]
-    then return,
-    {2 : 32, 3 : 32, 4 : 32, 5 : 31, 6 : 1}, meaning,
-    32 requests should have output length 2,
-    32 requests should have output length 3,
-    32 requests should have output length 4,
-    31 requests should have output length 5,
-    1 request should have output length 6.
-
-    Args:
-        batch_size (int): Number of requests submitted for profile. This is
-            args.batch_size.
-        step_requests (list[int]): step_requests[i] is the number of requests
-            that the ith engine step should process.
-
-    Returns:
-        OutputLen_NumReqs_Map : A dictionary with output-length as keys and the
-            number of requests required to have that output-length as values.
-    """
-    ol_nr: OutputLen_NumReqs_Map = {}
-
-    # Number of request that are assigned an output-length
-    num_reqs_assigned: int = 0
-    num_steps: int = len(step_requests)
-
-    # sanity check. The first step (prefill-step), must process all requests.
-    assert step_requests[0] == batch_size
-
-    # Begin assignments from the last step.
-    output_length: int = num_steps
-    for num_requests_at_step in reversed(step_requests):
-        if num_reqs_assigned == batch_size:
-            break
-
-        assert num_reqs_assigned < batch_size
-
-        # Remove the number of requests that have been determined
-        # to participate in this step and beyond.
-        num_reqs_unassigned_at_step = num_requests_at_step - num_reqs_assigned
-        assert num_reqs_unassigned_at_step >= 0
-
-        if num_reqs_unassigned_at_step > 0:
-            ol_nr[output_length] = num_reqs_unassigned_at_step
-            num_reqs_assigned += num_reqs_unassigned_at_step
-
-        output_length -= 1
-
-    # sanity checks.
-    assert sum(ol_nr.values()) == batch_size, (
-        "Number of requests in output-length assignment does not match "
-        f"batch-size.\n batch size {batch_size} - "
-        f"step requests {step_requests} - assignments {ol_nr}"
-    )
-
-    # Check that the output-length is in [1, num-steps]. Output length must be
-    # at least 1 as all requests must participate in the prefill-step.
-    assert all(ol >= 1 and ol <= num_steps for ol in ol_nr), (
-        "Output lengths of requests should be in range "
-        f"[1, num-engine-steps].\n batch size {batch_size} - "
-        f"step requests {step_requests} - assignments {ol_nr}"
-    )
-
-    return ol_nr
-
-
-def determine_requests_per_step(context: ProfileContext) -> list[int]:
-    """
-    Determine number of requests each engine step should process.
-    If context.num_steps is set, then all engine steps process the
-    same number of requests and the output list is of length
-    context.num_steps.
-
-    If context.complete_num_requests_per_step is set, then each decode step
-    processes fewer and fewer requests until there are no requests to process.
-    In this case, the output list is as big as the number of steps
-    required to process all requests.
-
-    Args:
-        context: ProfileContext object.
-
-    Returns:
-        list[int]: Number of requests to process for all engine-steps.
-         output[i], contains the number of requests that the ith step
-         should process.
-    """
-    if context.num_steps:
-        # All requests must run until num_engine_steps. This implies
-        # that their output lengths must be equal to num_engine_steps.
-        return [context.batch_size] * context.num_steps
-
-    assert (
-        context.complete_num_requests_per_step
-        and context.complete_num_requests_per_step > 0
-    ), (
-        f"Expected a positive complete_num_requests_per_step argument."
-        f"Instead got {context.complete_num_requests_per_step}"
-    )
-
-    # We start dropping after the first decode step.
-    step_requests = [
-        context.batch_size,  # prefill
-        context.batch_size,  # decode
-    ]
-
-    num_running_requests = context.batch_size
-    num_running_requests -= context.complete_num_requests_per_step
-    while num_running_requests > 0:
-        step_requests.append(num_running_requests)
-        num_running_requests -= context.complete_num_requests_per_step
-
-    if step_requests[-1] != 1:
-        # have 1 request running at the last step. This is often
-        # useful
-        step_requests.append(1)
-
-    return step_requests
-
-
-def run_profile(
-    context: ProfileContext, csv_output: Optional[str], json_output: Optional[str]
-):
-    print("Run profile with:")
-    for key, value in asdict(context).items():
-        print(f"  {key} = {value}")
-
-    requests_per_step: list[int] = determine_requests_per_step(context)
-
-    ol_nr: OutputLen_NumReqs_Map = compute_request_output_lengths(
-        context.batch_size, requests_per_step
-    )
-
-    num_steps_to_profile: int = len(requests_per_step)
-    max_output_len: int = max(ol_nr.keys())
-    assert max_output_len >= 1
-
-    # Create sampling params
-    sampling_params = SamplingParams(
-        temperature=0.8,
-        top_p=0.95,
-        # max_tokens is set on a per-request basis.
-        max_tokens=None,
-        ignore_eos=True,
-    )
-
-    # Create LLM
-    llm = LLM(**asdict(context.engine_args))
-    batch_size = context.batch_size
-    prompt_len = context.prompt_len
-
-    scheduler_config = llm.llm_engine.vllm_config.scheduler_config
-    max_model_len = llm.llm_engine.model_config.max_model_len
-    max_num_batched_tokens = scheduler_config.max_num_batched_tokens
-    max_num_seqs = scheduler_config.max_num_seqs
-
-    if batch_size * prompt_len > max_num_batched_tokens:
-        print(
-            f"ERROR: chosen batch_size * prompt_len "
-            f"({batch_size} * {prompt_len} = {batch_size * prompt_len}) is  "
-            f"larger than max_num_batched_tokens ({max_num_batched_tokens}) "
-            f"and therefore cannot be run in a single profile step, please "
-            f"choose a smaller batch size or prompt length, or increase "
-            f"--max-num-batched-tokens"
-        )
-        sys.exit(-1)
-    if batch_size > max_num_seqs:
-        print(
-            f"ERROR: chosen batch_size ({batch_size}) is larger than "
-            f"max_num_seqs ({max_num_seqs}) and therefore cannot be run in a "
-            f"single profile step, please choose a smaller batch size"
-        )
-        sys.exit(-1)
-    print(
-        "llm.llm_engine.model_config.max_model_len: ",
-        llm.llm_engine.model_config.max_model_len,
-    )
-    if prompt_len + max_output_len > llm.llm_engine.model_config.max_model_len:
-        print(
-            f"ERROR: chosen prompt_len + max_output_len ({prompt_len} + "
-            f"{max_output_len} = {prompt_len + max_output_len}) is larger "
-            f"than the model's max_model_len ({max_model_len}), please "
-            f"choose a smaller prompt_len or max_output_len, or increase "
-            f"--max-model-len"
-        )
-        sys.exit(-1)
-
-    def add_requests():
-        def get_output_len_generator() -> Generator[int, Any, Any]:
-            for output_len, num_reqs in ol_nr.items():
-                for _ in range(num_reqs):
-                    yield output_len
-
-        output_len_generator = get_output_len_generator()
-        for i in range(batch_size):
-            sampling_params.max_tokens = next(output_len_generator)
-            assert isinstance(sampling_params.max_tokens, int)
-
-            prompt_token_ids = torch.randint(
-                llm.get_tokenizer().vocab_size, size=(prompt_len,)
-            ).tolist()
-
-            llm.llm_engine.add_request(
-                request_id=f"seq{i}",
-                prompt={"prompt_token_ids": prompt_token_ids},
-                params=sampling_params,
-            )
-
-    def abort_requests():
-        for i in range(batch_size):
-            llm.llm_engine.abort_request(f"seq{i}")
-
-    # Warm up run
-    print("Warm up run ...")
-    add_requests()
-    llm.llm_engine.step()  # Prefill
-    llm.llm_engine.step()  # Decode
-    abort_requests()
-
-    print("Profile run ...")
-    add_requests()
-
-    with layerwise_profile() as prefill_prof:
-        llm.llm_engine.step()  # First step is prefill
-
-    decode_profs = []
-    for _ in tqdm.tqdm(range(num_steps_to_profile - 1)):
-        num_running_seqs = llm.llm_engine.scheduler[0].get_num_unfinished_seq_groups()
-        with layerwise_profile(num_running_seqs=num_running_seqs) as decode_prof:
-            llm.llm_engine.step()
-        decode_profs.append(decode_prof)
-
-    decode_results_list = [prof.results for prof in decode_profs]
-    prefill_results = prefill_prof.results
-    has_decode = len(decode_results_list) > 0
-
-    LINE_WIDTH = 80
-    print("=" * LINE_WIDTH)
-    print(f"= Prefill Model Table (prompt_len={prompt_len}, batch_size={batch_size})")
-    print("=" * LINE_WIDTH)
-    print()
-    prefill_results.print_model_table()
-
-    if has_decode:
-        print()
-        print("=" * LINE_WIDTH)
-        print(
-            f"= First Decode Step Model Table "
-            f"(prompt_len={prompt_len}, batch_size={batch_size})"
-        )
-        print("=" * LINE_WIDTH)
-        print()
-        decode_results_list[0].print_model_table()
-
-    print()
-    print("=" * LINE_WIDTH)
-    print(f"= Prefill Summary Table (prompt_len={prompt_len}, batch_size={batch_size})")
-    print("=" * LINE_WIDTH)
-    print()
-    prefill_results.print_summary_table()
-
-    if has_decode:
-        print()
-        print("=" * LINE_WIDTH)
-        print(
-            f"= First Decode Step Summary Table "
-            f"(prompt_len={prompt_len}, batch_size={batch_size})"
-        )
-        print("=" * LINE_WIDTH)
-        print()
-        decode_results_list[0].print_summary_table()
-
-    if csv_output:
-        csv_filename_base = (
-            csv_output[:-4] if csv_output.endswith(".csv") else csv_output
-        )
-        prefill_results.export_model_stats_table_csv(
-            csv_filename_base + "_prefill_model_table.csv"
-        )
-        prefill_results.export_summary_stats_table_csv(
-            csv_filename_base + "_prefill_summary_table.csv"
-        )
-
-        if has_decode:
-            decode_results_list[0].export_model_stats_table_csv(
-                csv_filename_base + "_decode_model_table.csv"
-            )
-            decode_results_list[0].export_summary_stats_table_csv(
-                csv_filename_base + "_decode_summary_table.csv"
-            )
-
-    if json_output:
-        cuda_devices = [
-            torch.cuda.get_device_properties(dev_idx)
-            for dev_idx in range(torch.cuda.device_count())
-        ]
-
-        json_dict = {
-            "context": {
-                "python_version": f"{sys.version}",
-                "torch_version": f"{torch.__version__}",
-                "torch_cuda_version": f"{torch.version.cuda}",
-                "cuda_devices": f"{cuda_devices}",
-                **asdict(context),
-            },
-            "prefill": prefill_results.convert_stats_to_dict(),
-        }
-
-        if has_decode:
-            for idx, dr in enumerate(decode_results_list):
-                json_dict[f"decode_{idx + 1}"] = dr.convert_stats_to_dict()
-
-        # Add .json to json_output filename if it doesn't exist already.
-        json_output_file = (
-            json_output if json_output.endswith(".json") else json_output + ".json"
-        )
-        with open(json_output_file, "w+") as f:
-            json.dump(json_dict, f, indent=2)
-        pass
-
-    if context.save_chrome_traces_folder is not None:
-        os.makedirs(context.save_chrome_traces_folder, exist_ok=True)
-        prefill_prof.profiler.export_chrome_trace(
-            context.save_chrome_traces_folder + "/prefill.json"
-        )
-        for idx, decode_prof in enumerate(decode_profs):
-            decode_prof.profiler.export_chrome_trace(
-                context.save_chrome_traces_folder + f"/decode_{idx + 1}.json"
-            )
-        print(
-            "Traces saved as prefill.json and decode_1.json, etc."
-            f" in folder {context.save_chrome_traces_folder}"
-        )
-
-
-def parse_args():
-    parser = FlexibleArgumentParser(
-        description="""
-Profile a model
-
-    example:
-    ```
-    python examples/offline_inference/profiling.py \\
-        --model neuralmagic/Meta-Llama-3.1-8B-Instruct-FP8 --batch-size 4 \\
-        --prompt-len 512 --max-num-batched-tokens 8196 --json Llama31-8b-FP8 \\
-        --enforce-eager run_num_steps -n 2
-    ```
-
-    then you can use various tools to analyze the json output
-    terminal ascii tables:
-        ```
-        python tools/profiler/print_layerwise_table.py \\
-            --json-trace Llama31-8b-FP8.json --phase prefill --table summary
-        ```
-    or create matplotlib stacked bar charts:
-        ```
-        python tools/profiler/visualize_layerwise_profile.py \\
-            --json-trace Llama31-8b-FP8.json \\
-            --output-directory profile_breakdown --plot-metric pct_cuda_time
-        ```
-""",
-        formatter_class=RawTextHelpFormatter,
-    )
-    parser.add_argument(
-        "--csv",
-        type=str,
-        default=None,
-        help="Export the results as multiple csv file. This should be the root "
-        "filename, will create <filename>_prefill_model_table.csv, "
-        "<filename>_prefill_summary_table.csv, "
-        "<filename>_decode_model_table.csv, and "
-        "<filename>_decode_summary_table.csv",
-    )
-    parser.add_argument(
-        "--json",
-        type=str,
-        default=None,
-        help="Export the results as a json file. This should be the filename",
-    )
-    parser.add_argument(
-        "--save-chrome-traces-folder",
-        type=str,
-        help="Save chrome traces for the prefill and decode "
-        "will save traces as prefill.json and decode_1.json, "
-        "etc. inside this folder",
-    )
-    parser.add_argument(
-        "--prompt-len",
-        type=int,
-        default=PROMPT_LEN_DEFAULT,
-        help=f"Length of the random prompt to use when profiling, all batched "
-        f"requests use the same prompt_len, default={PROMPT_LEN_DEFAULT}",
-    )
-    parser.add_argument(
-        "--batch-size",
-        type=int,
-        default=BATCH_SIZE_DEFAULT,
-        help=f"Number of requests to run as a single batch, "
-        f"default={BATCH_SIZE_DEFAULT}",
-    )
-
-    subparsers = parser.add_subparsers(dest="cmd")
-
-    run_num_steps_parser = subparsers.add_parser(
-        "run_num_steps", help="This variation profiles n engine.step() invocations."
-    )
-    run_num_steps_parser.add_argument(
-        "-n",
-        "--num-steps",
-        type=int,
-        help="Number of engine steps to profile.\n"
-        "Setting it to 1, profiles only the prefill step.\n"
-        "Setting it to 2, profiles the prefill and first decode step\n"
-        "Setting it to 3, profiles the prefill, 1st and 2nd decode steps\n"
-        "and so on ...",
-    )
-
-    run_to_completion_parser = subparsers.add_parser(
-        "run_to_completion",
-        help="This variation profiles all the engine.step() invocations"
-        "until the engine exhausts all submitted requests.",
-    )
-    run_to_completion_parser.add_argument(
-        "-n",
-        "--complete-num-requests-per-step",
-        type=int,
-        help="Complete complete_num_requests_per_step requests every decode step."
-        "For e.g., with batch_size 128 and complete_num_requests_per_step 32,"
-        "the profiler is run for 6 engine steps, with the steps processing, "
-        "128, 128, 96, 64, 32, 1 requests respectively.\n"
-        "Note that we tack-on a one-request step at the end as it is often "
-        "useful.",
-    )
-
-    EngineArgs.add_cli_args(parser)
-
-    return parser.parse_args()
-
-
-def main(args):
-    context = ProfileContext(
-        engine_args=EngineArgs.from_cli_args(args),
-        **{
-            k: v
-            for k, v in vars(args).items()
-            if k in inspect.signature(ProfileContext).parameters
-        },
-    )
-    run_profile(context, csv_output=args.csv, json_output=args.json)
-
-
-if __name__ == "__main__":
-    args = parse_args()
-    main(args)

examples/offline_inference/qwen_1m.py

@@ -5,7 +5,6 @@ from urllib.request import urlopen
 
 from vllm import LLM, SamplingParams
 
-os.environ["VLLM_ATTENTION_BACKEND"] = "DUAL_CHUNK_FLASH_ATTN"
 os.environ["VLLM_ALLOW_LONG_MAX_MODEL_LEN"] = "1"
 
 

examples/offline_inference/spec_decode.py

@@ -49,6 +49,7 @@ def get_custom_mm_prompts(num_prompts):
 def parse_args():
     parser = FlexibleArgumentParser()
     add_dataset_parser(parser)
+    parser.add_argument("--test", action="store_true")
     parser.add_argument(
         "--method",
         type=str,
@@ -60,6 +61,7 @@ def parse_args():
     parser.add_argument("--tp", type=int, default=1)
     parser.add_argument("--enforce-eager", action="store_true")
     parser.add_argument("--enable-chunked-prefill", action="store_true")
+    parser.add_argument("--max-model-len", type=int, default=16384)
     parser.add_argument("--temp", type=float, default=0)
     parser.add_argument("--top-p", type=float, default=1.0)
     parser.add_argument("--top-k", type=int, default=-1)
@@ -71,8 +73,7 @@ def parse_args():
     return parser.parse_args()
 
 
-def main():
-    args = parse_args()
+def main(args):
     args.endpoint_type = "openai-chat"
 
     model_dir = args.model_dir
@@ -134,7 +135,7 @@ def main():
         gpu_memory_utilization=0.8,
         speculative_config=speculative_config,
         disable_log_stats=False,
-        max_model_len=16384,
+        max_model_len=args.max_model_len,
         limit_mm_per_prompt={"image": 5},
         disable_chunked_mm_input=True,
     )
@@ -198,6 +199,39 @@ def main():
         acceptance_rate = acceptance_counts[i] / num_drafts if num_drafts > 0 else 0
         print(f"acceptance at token {i}: {acceptance_rate:.2f}")
 
+    return acceptance_length
+
 
 if __name__ == "__main__":
-    main()
+    args = parse_args()
+    acceptance_length = main(args)
+
+    if args.test:
+        # takes ~30s to run on 1xH100
+        assert args.method in ["eagle", "eagle3"]
+        assert args.tp == 1
+        assert args.num_spec_tokens == 3
+        assert args.dataset_name == "hf"
+        assert args.dataset_path == "philschmid/mt-bench"
+        assert args.num_prompts == 80
+        assert args.temp == 0
+        assert args.top_p == 1.0
+        assert args.top_k == -1
+        assert args.enable_chunked_prefill
+
+        # check acceptance length is within 2% of expected value
+        rtol = 0.02
+        expected_acceptance_length = 2.296 if args.method == "eagle" else 2.811
+
+        assert (
+            acceptance_length <= (1 + rtol) * expected_acceptance_length
+            and acceptance_length >= (1 - rtol) * expected_acceptance_length
+        ), (
+            f"acceptance_length {acceptance_length} is not "
+            f"within {rtol * 100}% of {expected_acceptance_length}"
+        )
+
+        print(
+            f"Test passed! Expected AL: "
+            f"{expected_acceptance_length}, got {acceptance_length}"
+        )

examples/offline_inference/torchrun_dp_example.py

@@ -0,0 +1,81 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""
+experimental support for data-parallel inference with torchrun
+Note the data load balancing and distribution is done out of the vllm engine,
+no internal lb supported in external_launcher mode.
+"""
+
+from vllm import LLM, SamplingParams
+
+# Create prompts, the same across all ranks
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+] * 50
+
+# Create sampling parameters, the same across all ranks
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+# Use `distributed_executor_backend="external_launcher"` so that
+# this llm engine/instance only creates one worker.
+# it is important to set an explicit seed to make sure that
+# all ranks have the same random seed, so that sampling can be
+# deterministic across ranks.
+llm = LLM(
+    model="microsoft/Phi-mini-MoE-instruct",
+    tensor_parallel_size=1,
+    data_parallel_size=2,
+    pipeline_parallel_size=1,
+    enable_expert_parallel=False,
+    distributed_executor_backend="external_launcher",
+    max_model_len=4096,
+    gpu_memory_utilization=0.6,
+    seed=1,
+)
+
+dp_rank = llm.llm_engine.vllm_config.parallel_config.data_parallel_rank
+dp_size = llm.llm_engine.vllm_config.parallel_config.data_parallel_size
+
+prompts = [
+    f"{idx}.{prompt}" for idx, prompt in enumerate(prompts) if idx % dp_size == dp_rank
+]
+
+outputs = llm.generate(prompts, sampling_params)
+
+
+# all ranks will have the same outputs
+print("-" * 50)
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}\n")
+    print("-" * 50)
+"""
+Further tips:
+
+1. to communicate control messages across all ranks, use the cpu group,
+a PyTorch ProcessGroup with GLOO backend.
+
+```python
+from vllm.distributed.parallel_state import get_world_group
+cpu_group = get_world_group().cpu_group
+torch_rank = dist.get_rank(group=cpu_group)
+if torch_rank == 0:
+    # do something for rank 0, e.g. saving the results to disk.
+```
+
+2. to communicate data across all ranks, use the model's device group,
+a PyTorch ProcessGroup with NCCL backend.
+```python
+from vllm.distributed.parallel_state import get_world_group
+device_group = get_world_group().device_group
+```
+
+3. to access the model directly in every rank, use the following code:
+```python
+llm.llm_engine.model_executor.driver_worker.worker.model_runner.model
+```
+"""

examples/offline_inference/vision_language.py

@@ -126,6 +126,23 @@ def run_chameleon(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
+# Dots-OCR
+def run_dots_ocr(questions: list[str], modality: str) -> ModelRequestData:
+    assert modality == "image"
+
+    prompts = [f"<|img|><|imgpad|><|endofimg|>{question}" for question in questions]
+    engine_args = EngineArgs(
+        model="rednote-hilab/dots.ocr",
+        limit_mm_per_prompt={modality: 1},
+        trust_remote_code=True,
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompts=prompts,
+    )
+
+
 def run_command_a_vision(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
 
@@ -1676,6 +1693,7 @@ model_example_map = {
     "aya_vision": run_aya_vision,
     "blip-2": run_blip2,
     "chameleon": run_chameleon,
+    "dots_ocr": run_dots_ocr,
     "command_a_vision": run_command_a_vision,
     "deepseek_vl_v2": run_deepseek_vl2,
     "ernie45_vl": run_ernie45_vl,

examples/online_serving/dashboards/grafana/README.md

@@ -11,9 +11,9 @@ vLLM performance and metrics.
 
 ## Dashboard Descriptions
 
-- **[performance_statistics.json](./performance_statistics.json)**: Tracks performance metrics including latency and
+- **performance_statistics.json**: Tracks performance metrics including latency and
   throughput for your vLLM service.
-- **[query_statistics.json](./query_statistics.json)**: Tracks query performance, request volume, and key
+- **query_statistics.json**: Tracks query performance, request volume, and key
   performance indicators for your vLLM service.
 
 ## Deployment Options

examples/online_serving/dashboards/perses/README.md

@@ -21,9 +21,9 @@ deployment methods:
 
 ## Dashboard Descriptions
 
-- **[performance_statistics.yaml](./performance_statistics.yaml)**: Performance metrics with aggregated latency
+- **performance_statistics.yaml**: Performance metrics with aggregated latency
   statistics
-- **[query_statistics.yaml](./query_statistics.yaml)**: Query performance and deployment metrics
+- **query_statistics.yaml**: Query performance and deployment metrics
 
 ## Deployment Options
 

examples/online_serving/openai_embedding_long_text/README.md

@@ -42,7 +42,7 @@ python client.py
 
 ### Server Configuration
 
-The key parameters for chunked processing are in the `--override-pooler-config`:
+The key parameters for chunked processing are in the `--pooler-config`:
 
 ```json
 {

examples/online_serving/openai_embedding_long_text/client.py

@@ -13,7 +13,7 @@ Prerequisites:
    
    # MEAN pooling (processes all chunks, recommended for complete coverage)
    vllm serve intfloat/multilingual-e5-large \
-     --override-pooler-config \
+     --pooler-config \
       '{"pooling_type": "MEAN", "normalize": true, ' \
       '"enable_chunked_processing": true, "max_embed_len": 3072000}' \
      --served-model-name multilingual-e5-large \
@@ -23,7 +23,7 @@ Prerequisites:
 
    # OR CLS pooling (native CLS within chunks, MEAN aggregation across chunks)
    vllm serve BAAI/bge-large-en-v1.5 \
-     --override-pooler-config \
+     --pooler-config \
       '{"pooling_type": "CLS", "normalize": true, ' \
       '"enable_chunked_processing": true, "max_embed_len": 1048576}' \
      --served-model-name bge-large-en-v1.5 \

examples/online_serving/openai_embedding_long_text/service.sh

@@ -103,7 +103,7 @@ POOLER_CONFIG="{\"pooling_type\": \"$POOLING_TYPE\", \"normalize\": true, \"enab
 vllm serve "$MODEL_NAME" \
   --tensor-parallel-size "$GPU_COUNT" \
   --enforce-eager \
-  --override-pooler-config "$POOLER_CONFIG" \
+  --pooler-config "$POOLER_CONFIG" \
   --served-model-name ${MODEL_CODE} \
   --api-key "$API_KEY" \
   --trust-remote-code \

examples/others/tensorize_vllm_model.py

@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import argparse
-import dataclasses
 import json
 import logging
 import os
@@ -327,12 +325,7 @@ def main():
 
 
     if args.command == "serialize":
-        eng_args_dict = {f.name: getattr(args, f.name) for f in
-                        dataclasses.fields(EngineArgs)}
-
-        engine_args = EngineArgs.from_cli_args(
-            argparse.Namespace(**eng_args_dict)
-        )
+        engine_args = EngineArgs.from_cli_args(args)
 
         input_dir = tensorizer_dir.rstrip('/')
         suffix = args.suffix if args.suffix else uuid.uuid4().hex

mkdocs.yaml

@@ -102,6 +102,7 @@ plugins:
           - https://numpy.org/doc/stable/objects.inv
           - https://pytorch.org/docs/stable/objects.inv
           - https://psutil.readthedocs.io/en/stable/objects.inv
+          - https://huggingface.co/docs/transformers/main/en/objects.inv
 
 markdown_extensions:
   - attr_list

pyproject.toml

@@ -70,7 +70,6 @@ line-length = 80
 "vllm/_version.py" = ["ALL"]
 # Python 3.8 typing - skip V0 code
 "vllm/attention/**/*.py" = ["UP006", "UP035"]
-"vllm/core/**/*.py" = ["UP006", "UP035"]
 "vllm/engine/**/*.py" = ["UP006", "UP035"]
 "vllm/executor/**/*.py" = ["UP006", "UP035"]
 "vllm/worker/**/*.py" = ["UP006", "UP035"]
@@ -111,28 +110,6 @@ ignore_missing_imports = true
 check_untyped_defs = true
 follow_imports = "silent"
 
-# After fixing type errors resulting from follow_imports: "skip" -> "silent",
-# move the directory here and remove it from tools/mypy.sh
-files = [
-    "vllm/*.py",
-    "vllm/assets",
-    "vllm/entrypoints",
-    "vllm/core",
-    "vllm/inputs",
-    "vllm/logging_utils",
-    "vllm/multimodal",
-    "vllm/platforms",
-    "vllm/transformers_utils",
-    "vllm/triton_utils",
-    "vllm/usage",
-]
-# TODO(woosuk): Include the code from Megatron and HuggingFace.
-exclude = [
-    "vllm/model_executor/parallel_utils/|vllm/model_executor/models/",
-    # Ignore triton kernels in ops.
-    'vllm/attention/ops/.*\.py$'
-]
-
 [tool.isort]
 skip_glob = [
     ".buildkite/*",

requirements/common.txt

@@ -24,7 +24,7 @@ outlines_core == 0.2.11
 # required for outlines backend disk cache
 diskcache == 5.6.3
 lark == 1.2.2
-xgrammar == 0.1.23; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
+xgrammar == 0.1.25; platform_machine == "x86_64" or platform_machine == "aarch64" or platform_machine == "arm64"
 typing_extensions >= 4.10
 filelock >= 3.16.1 # need to contain https://github.com/tox-dev/filelock/pull/317
 partial-json-parser # used for parsing partial JSON outputs

requirements/cpu-build.txt

@@ -1,12 +1,11 @@
-# Temporarily used for x86 CPU backend to avoid performance regression of torch>2.6.0+cpu,
-# see https://github.com/pytorch/pytorch/pull/151218
 cmake>=3.26.1
 ninja
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 setuptools-scm>=8
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch==2.6.0+cpu
+torch==2.8.0+cpu; platform_machine == "x86_64"
+torch==2.8.0; platform_machine == "ppc64le" or platform_machine == "aarch64" or platform_system == "Darwin"
 wheel
 jinja2>=3.1.6
 regex

requirements/cpu.txt

@@ -8,7 +8,7 @@ numba == 0.61.2; python_version > '3.9' and platform_machine != "s390x"
 packaging>=24.2
 setuptools>=77.0.3,<80.0.0
 --extra-index-url https://download.pytorch.org/whl/cpu
-torch==2.6.0+cpu; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
+torch==2.8.0+cpu; platform_machine == "x86_64"
 torch==2.8.0; platform_system == "Darwin"
 torch==2.8.0; platform_machine == "ppc64le" or platform_machine == "aarch64"
 
@@ -23,7 +23,7 @@ datasets # for benchmark scripts
 
 # Intel Extension for PyTorch, only for x86_64 CPUs
 intel-openmp==2024.2.1; platform_machine == "x86_64"
-intel_extension_for_pytorch==2.6.0; platform_machine == "x86_64" # torch>2.6.0+cpu has performance regression on x86 platform, see https://github.com/pytorch/pytorch/pull/151218
+intel_extension_for_pytorch==2.8.0; platform_machine == "x86_64"
 triton==3.2.0; platform_machine == "x86_64" # Triton is required for torch 2.6+cpu, as it is imported in torch.compile.
 
 # Use this to gather CPU info and optimize based on ARM Neoverse cores

requirements/test.txt

@@ -1,5 +1,5 @@
 # This file was autogenerated by uv via the following command:
-#    uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match --torch-backend cu128
+#    uv pip compile requirements/test.in -o requirements/test.txt --index-strategy unsafe-best-match --torch-backend cu128 --python-platform x86_64-manylinux_2_28
 absl-py==2.1.0
     # via rouge-score
 accelerate==1.0.1

requirements/tpu.txt

@@ -14,14 +14,4 @@ nixl==0.3.0
 tpu_info==0.4.0
 
 # Install torch_xla
---pre
---extra-index-url https://download.pytorch.org/whl/nightly/cpu
---find-links https://storage.googleapis.com/libtpu-wheels/index.html
---find-links https://storage.googleapis.com/libtpu-releases/index.html
---find-links https://storage.googleapis.com/jax-releases/jax_nightly_releases.html
---find-links https://storage.googleapis.com/jax-releases/jaxlib_nightly_releases.html
-torch==2.9.0.dev20250730
-torchvision==0.24.0.dev20250730
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.9.0.dev20250730-cp311-cp311-linux_x86_64.whl ; python_version == "3.11"
-torch_xla[tpu, pallas] @ https://storage.googleapis.com/pytorch-xla-releases/wheels/tpuvm/torch_xla-2.9.0.dev20250730-cp312-cp312-linux_x86_64.whl ; python_version == "3.12"
-
+torch_xla[tpu, pallas]==2.8.0

tests/basic_correctness/test_basic_correctness.py

@@ -11,7 +11,7 @@ from unittest.mock import Mock
 import pytest
 import torch
 
-from vllm import LLM, envs
+from vllm import LLM
 from vllm.v1.engine.llm_engine import LLMEngine as LLMEngineV1
 
 from ..conftest import HfRunner, VllmRunner
@@ -26,14 +26,6 @@ MODELS = [
 TARGET_TEST_SUITE = os.environ.get("TARGET_TEST_SUITE", "L4")
 
 
-@pytest.fixture(autouse=True)
-def v1(run_with_both_engines):
-    # Simple autouse wrapper to run both engines for each test
-    # This can be promoted up to conftest.py to run for every
-    # test in a package
-    pass
-
-
 def test_vllm_gc_ed():
     """Verify vllm instance is GC'ed when it is deleted"""
     llm = LLM("distilbert/distilgpt2")
@@ -76,17 +68,6 @@ def test_models(
     model_executor: str,
     enable_prompt_embeds: bool,
 ) -> None:
-
-    if enable_prompt_embeds and envs.is_set(
-            "VLLM_USE_V1") and envs.VLLM_USE_V1:
-        pytest.skip("enable_prompt_embeds is not supported in v1.")
-
-    if not envs.VLLM_USE_V1:
-        if async_scheduling:
-            pytest.skip("async_scheduling only supported in v1.")
-        if model_executor != "uni":
-            pytest.skip("only test uniproc executor for v0.")
-
     if backend == "XFORMERS" and model == "google/gemma-2-2b-it":
         pytest.skip(
             f"{backend} does not support gemma2 with full context length.")
@@ -164,11 +145,6 @@ def test_models_distributed(
     extra_env: dict[str, str],
     enable_prompt_embeds: bool,
 ) -> None:
-
-    if enable_prompt_embeds and envs.is_set(
-            "VLLM_USE_V1") and envs.VLLM_USE_V1:
-        pytest.skip("enable_prompt_embeds is not supported in v1.")
-
     if test_suite != TARGET_TEST_SUITE:
         pytest.skip(f"Skip test for {test_suite}")
 

tests/basic_correctness/test_cumem.py

@@ -122,11 +122,12 @@ def test_cumem_with_cudagraph():
         # sleep mode with safetensors
         ("meta-llama/Llama-3.2-1B", True),
         # sleep mode with pytorch checkpoint
-        ("facebook/opt-125m", False),
+        ("facebook/opt-125m", True),
     ])
 def test_end_to_end(monkeypatch: pytest.MonkeyPatch, model: str, use_v1: bool):
     with monkeypatch.context() as m:
-        m.setenv("VLLM_USE_V1", "1" if use_v1 else "0")
+        assert use_v1
+        m.setenv("VLLM_USE_V1", "1")
         free, total = torch.cuda.mem_get_info()
         used_bytes_baseline = total - free  # in case other process is running
         llm = LLM(model, enable_sleep_mode=True)

tests/build_cython.py

@@ -1,39 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-import Cython.Compiler.Options
-from Cython.Build import cythonize
-from setuptools import setup
-
-Cython.Compiler.Options.annotate = True
-
-infiles = []
-
-infiles += [
-    "vllm/engine/llm_engine.py",
-    "vllm/transformers_utils/detokenizer.py",
-    "vllm/engine/output_processor/single_step.py",
-    "vllm/outputs.py",
-    "vllm/engine/output_processor/stop_checker.py",
-]
-
-infiles += [
-    "vllm/core/scheduler.py",
-    "vllm/sequence.py",
-    "vllm/core/block_manager.py",
-]
-
-infiles += [
-    "vllm/model_executor/layers/sampler.py",
-    "vllm/sampling_params.py",
-    "vllm/utils/__init__.py",
-]
-
-setup(ext_modules=cythonize(infiles,
-                            annotate=False,
-                            force=True,
-                            compiler_directives={
-                                'language_level': "3",
-                                'infer_types': True
-                            }))
-
-# example usage: python3 build_cython.py build_ext --inplace

tests/compile/backend.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import weakref
 from collections.abc import Sequence
 from copy import deepcopy
 from typing import Callable, Union
@@ -10,7 +11,26 @@ from torch._ops import OpOverload
 
 from vllm.compilation.fx_utils import find_op_nodes
 from vllm.compilation.inductor_pass import InductorPass
-from vllm.config import get_current_vllm_config
+from vllm.compilation.pass_manager import with_pattern_match_debug
+from vllm.compilation.vllm_inductor_pass import VllmInductorPass
+from vllm.config import VllmConfig, get_current_vllm_config
+
+
+class LazyInitPass(InductorPass):
+    """
+    If there's a pass that we want to initialize lazily in a test,
+    we can wrap it in LazyInitPass, which will initialize the pass when invoked
+    and then immediately invoke it.
+    """
+
+    def __init__(self, pass_cls: type[VllmInductorPass],
+                 vllm_config: VllmConfig):
+        self.pass_cls = pass_cls
+        self.vllm_config = weakref.proxy(vllm_config)  # avoid cycle
+
+    def __call__(self, graph: fx.Graph) -> None:
+        self.pass_ = self.pass_cls(self.vllm_config)
+        self.pass_(graph)
 
 
 class TestBackend:
@@ -40,10 +60,16 @@ class TestBackend:
                           example_inputs,
                           config_patches=self.inductor_config)
 
+    @with_pattern_match_debug
     def post_pass(self, graph: fx.Graph):
         self.graph_pre_pass = deepcopy(graph)
+
+        VllmInductorPass.dump_prefix = 0
         for pass_ in self.custom_passes:
             pass_(graph)
+            VllmInductorPass.dump_prefix += 1
+
+        VllmInductorPass.dump_prefix = None
 
         self.graph_post_pass = deepcopy(graph)
         # assign by reference, will reflect the final state of the graph

tests/compile/piecewise/test_full_cudagraph.py

@@ -46,7 +46,10 @@ backend_configs = {
     # FA3 on Hopper
     "FA3":
     BackendConfig(name="FA3",
-                  env_vars={"VLLM_FLASH_ATTN_VERSION": "3"},
+                  env_vars={
+                      "VLLM_FLASH_ATTN_VERSION": "3",
+                      "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16",
+                  },
                   comp_config={
                       "cudagraph_mode": "FULL",
                   },
@@ -66,6 +69,7 @@ backend_configs = {
     BackendConfig(name="FlashAttentionMLA",
                   env_vars={
                       "VLLM_ATTENTION_BACKEND": "FLASH_ATTN_MLA",
+                      "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16",
                   },
                   comp_config={
                       "cudagraph_mode": "FULL_DECODE_ONLY",
@@ -89,14 +93,17 @@ backend_configs = {
     # FA2
     "FA2":
     BackendConfig(name="FA2",
-                  env_vars={"VLLM_FLASH_ATTN_VERSION": "2"},
+                  env_vars={
+                      "VLLM_FLASH_ATTN_VERSION": "2",
+                      "VLLM_FLASH_ATTN_MAX_NUM_SPLITS_FOR_CUDA_GRAPH": "16",
+                  },
                   comp_config={
                       "cudagraph_mode": "FULL",
                   }),
     # Triton Attention
     "TritonAttn":
     BackendConfig(name="TritonAttn",
-                  env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN_VLLM_V1"},
+                  env_vars={"VLLM_ATTENTION_BACKEND": "TRITON_ATTN"},
                   comp_config={
                       "cudagraph_mode": "FULL",
                   }),

tests/compile/piecewise/test_simple.py

@@ -15,6 +15,7 @@ from vllm.config import (CompilationConfig, CompilationLevel, CUDAGraphMode,
                          VllmConfig, set_current_vllm_config)
 from vllm.envs import VLLM_USE_V1
 from vllm.forward_context import BatchDescriptor, set_forward_context
+from vllm.utils import is_torch_equal_or_newer
 
 # This import automatically registers `torch.ops.silly.attention`
 from ..silly_attention import get_global_counter, reset_global_counter
@@ -50,16 +51,21 @@ class SillyModel(nn.Module):
         return x
 
 
-@pytest.mark.parametrize("use_inductor", [True, False])
-@torch.inference_mode()
-def test_simple_piecewise_compile(use_inductor):
-    assert VLLM_USE_V1
-
+def _run_simple_model(
+    splitting_ops,
+    use_inductor_graph_partition,
+    use_inductor,
+    expected_num_piecewise_graphs_seen,
+    expected_num_piecewise_capturable_graphs_seen,
+    expected_num_backend_compilations,
+    expected_num_cudagraph_captured,
+):
     vllm_config = VllmConfig(compilation_config=CompilationConfig(
         level=CompilationLevel.PIECEWISE,
         use_cudagraph=True,
         use_inductor=use_inductor,
-        splitting_ops=["silly.attention"],
+        splitting_ops=splitting_ops,
+        use_inductor_graph_partition=use_inductor_graph_partition,
         cudagraph_copy_inputs=True,
         cudagraph_capture_sizes=[1, 2],
     ))
@@ -70,11 +76,11 @@ def test_simple_piecewise_compile(use_inductor):
 
     with compilation_counter.expect(
             num_graphs_seen=1,  # one graph for the model
-            num_piecewise_graphs_seen=5,  # 2 * num_layers + 1
-            num_piecewise_capturable_graphs_seen=3,  # 1 + num_layers
-            num_backend_compilations=3,  # num_piecewise_capturable_graphs_seen
-            num_cudagraph_captured=
-            6,  # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
+            num_piecewise_graphs_seen=expected_num_piecewise_graphs_seen,
+            num_piecewise_capturable_graphs_seen=
+            expected_num_piecewise_capturable_graphs_seen,
+            num_backend_compilations=expected_num_backend_compilations,
+            num_cudagraph_captured=expected_num_cudagraph_captured,
     ), set_forward_context(None,
                            vllm_config=vllm_config):  # background context
         # warm up with background context
@@ -104,3 +110,46 @@ def test_simple_piecewise_compile(use_inductor):
             output = model(input)
         assert get_global_counter() == 2
         assert torch.allclose(output.cpu(), torch.tensor([19.0, 19.0]))
+
+
+@pytest.mark.parametrize("use_inductor", [True, False])
+@torch.inference_mode()
+def test_simple_piecewise_compile(use_inductor):
+    assert VLLM_USE_V1
+    _run_simple_model(
+        splitting_ops=["silly.attention"],
+        use_inductor_graph_partition=False,
+        use_inductor=use_inductor,
+        expected_num_piecewise_graphs_seen=5,  # 2 * num_layers + 1
+        expected_num_piecewise_capturable_graphs_seen=3,  # 1 + num_layers
+        expected_num_backend_compilations=
+        3,  # num_piecewise_capturable_graphs_seen
+        expected_num_cudagraph_captured=
+        6,  # num_cudagraph_sizes * num_piecewise_capturable_graphs_seen
+    )
+
+
+@torch.inference_mode()
+@pytest.mark.parametrize("splitting_ops", [["silly.attention"], []])
+def test_simple_inductor_graph_partition(splitting_ops):
+    assert VLLM_USE_V1
+    if not is_torch_equal_or_newer("2.9.0.dev"):
+        pytest.skip("inductor graph partition is only available "
+                    "in PyTorch 2.9+")
+
+    _run_simple_model(
+        # inductor graph partition automatically resets splitting_ops
+        # to be an empty list
+        splitting_ops=splitting_ops,
+        use_inductor_graph_partition=True,
+        use_inductor=True,
+        expected_num_piecewise_graphs_seen=
+        1,  # since not splitting at fx graph level
+        expected_num_piecewise_capturable_graphs_seen=
+        1,  # since not splitting at fx graph level
+        expected_num_backend_compilations=
+        1,  # since not splitting at fx graph level
+        expected_num_cudagraph_captured=
+        6,  # inductor graph partition still captures 6
+        # graph, same as fx graph partition.
+    )

tests/compile/silly_attention.py

@@ -60,4 +60,5 @@ direct_register_custom_op(
     mutates_args=["out"],
     fake_impl=silly_attention_fake,
     target_lib=silly_lib,
+    tags=(torch._C.Tag.cudagraph_unsafe, ),
 )

tests/compile/test_async_tp.py

@@ -294,6 +294,8 @@ def async_tp_pass_on_test_model(local_rank: int, world_size: int,
     compiled_model = torch.compile(model, backend=backend)
     compiled_model(hidden_states)
 
+    assert async_tp_pass.matched_count == 1
+
     # In pre-nodes, all gather or reduce scatter should exist,
     # fused_matmul_reduce_scatter or fused_all_gather_matmul should not
     backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)

tests/compile/test_basic_correctness.py

@@ -20,7 +20,6 @@ class TestSetting:
     tp_size: int
     attn_backend: str
     method: str
-    fullgraph: bool
 
 
 # we cannot afford testing the full Cartesian product
@@ -36,7 +35,6 @@ class TestSetting:
             tp_size=2,
             attn_backend="FLASH_ATTN",
             method="generate",
-            fullgraph=True,
         ),
         # llama model with quantization
         TestSetting(
@@ -46,7 +44,6 @@ class TestSetting:
             tp_size=1,
             attn_backend="FLASH_ATTN",
             method="generate",
-            fullgraph=True,
         ),
         # MoE model
         TestSetting(
@@ -56,7 +53,6 @@ class TestSetting:
             tp_size=2,
             attn_backend="FLASH_ATTN",
             method="generate",
-            fullgraph=True,
         ),
         # embedding model
         TestSetting(
@@ -73,7 +69,6 @@ class TestSetting:
             tp_size=1,
             attn_backend="FLASH_ATTN",
             method="encode",
-            fullgraph=True,
         ),
         TestSetting(
             model="BAAI/bge-base-en-v1.5",
@@ -82,7 +77,6 @@ class TestSetting:
             tp_size=1,
             attn_backend="FLASH_ATTN",
             method="encode",
-            fullgraph=True,
         ),
         # vision language model
         TestSetting(
@@ -92,7 +86,6 @@ class TestSetting:
             tp_size=1,
             attn_backend="FLASH_ATTN",
             method="generate_with_image",
-            fullgraph=False,
         ),
     ],
 )
@@ -109,9 +102,8 @@ def test_compile_correctness(
     tp_size = test_setting.tp_size
     attn_backend = test_setting.attn_backend
     method = test_setting.method
-    fullgraph = test_setting.fullgraph
-    if cuda_device_count_stateless() != pp_size * tp_size:
-        pytest.skip(f"Need exactly {pp_size}*{tp_size} CUDA gpus but got "
+    if cuda_device_count_stateless() < pp_size * tp_size:
+        pytest.skip(f"Need at least {pp_size}*{tp_size} CUDA gpus but got "
                     f"{cuda_device_count_stateless()}")
 
     with monkeypatch.context() as m:
@@ -149,9 +141,5 @@ def test_compile_correctness(
         ]:
             all_args.append(final_args + [f"-O{level}"])
             all_envs.append({})
-            if level != CompilationLevel.DYNAMO_ONCE and not fullgraph:
-                # "DYNAMO_ONCE" will always use fullgraph
-                all_envs[-1][
-                    "VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE"] = "0"  # type: ignore
 
         compare_all_settings(model, all_args * 3, all_envs, method=method)

tests/compile/test_config.py

@@ -4,7 +4,7 @@ import pytest
 
 import vllm
 from vllm.compilation.counter import compilation_counter
-from vllm.config import VllmConfig
+from vllm.config import CompilationConfig, VllmConfig
 from vllm.utils import _is_torch_equal_or_newer
 
 
@@ -26,6 +26,14 @@ def test_use_cudagraphs_dynamic(monkeypatch):
     assert not vllm_config.compilation_config.use_cudagraph
 
 
+def test_custom_op():
+    # proper syntax
+    _ = CompilationConfig(custom_ops=["+quant_fp8", "-silu_and_mul"])
+
+    with pytest.raises(ValueError, match="Invalid syntax '"):
+        _ = CompilationConfig(custom_ops=["quant_fp8"])
+
+
 # forked needed to workaround https://github.com/vllm-project/vllm/issues/21073
 @pytest.mark.forked
 # NB: We don't test VLLM_DISABLE_COMPILE_CACHE=0 because that depends

tests/compile/test_full_graph.py

@@ -3,6 +3,7 @@
 
 from __future__ import annotations
 
+import logging
 import tempfile
 from typing import Any, Optional, Union
 
@@ -10,9 +11,13 @@ import pytest
 import torch
 
 from tests.quantization.utils import is_quant_method_supported
+from tests.v1.attention.utils import _Backend
 from vllm import LLM, SamplingParams
-from vllm.config import CompilationConfig, CompilationLevel, PassConfig
+from vllm.attention.selector import global_force_attn_backend_context_manager
+from vllm.config import (CompilationConfig, CompilationLevel, CUDAGraphMode,
+                         PassConfig)
 from vllm.platforms import current_platform
+from vllm.utils import is_torch_equal_or_newer
 
 from ..utils import create_new_process_for_each_test
 
@@ -79,9 +84,7 @@ def test_full_graph(
 ):
     model, model_kwargs = model_info
 
-    with monkeypatch.context() as m:
-        # make sure these models can be captured in full graph mode
-        m.setenv("VLLM_TEST_DYNAMO_FULLGRAPH_CAPTURE", "1")
+    with monkeypatch.context():
         print(f"MODEL={model}")
 
         run_model(optimization_level, model, model_kwargs)
@@ -107,6 +110,18 @@ def test_full_graph(
         (CompilationConfig(level=CompilationLevel.PIECEWISE,
                            debug_dump_path=tempfile.gettempdir()),
          ("facebook/opt-125m", {})),
+    ] + [
+        # graph inductor partition
+        (
+            CompilationConfig(
+                level=CompilationLevel.PIECEWISE,
+                # inductor graph partition uses
+                # torch._C.Tag.cudagraph_unsafe to specify splitting ops
+                use_inductor_graph_partition=True,
+                cudagraph_mode=CUDAGraphMode.PIECEWISE,
+                compile_sizes=[1, 2]),
+            model) for model in models_list(all=False)
+        if is_torch_equal_or_newer("2.9.0.dev")
     ])
 # only test some of the models
 @create_new_process_for_each_test()
@@ -114,11 +129,51 @@ def test_custom_compile_config(
     compilation_config: CompilationConfig,
     model_info: tuple[str, dict[str, Any]],
 ):
+    if (compilation_config.use_inductor_graph_partition
+            and not is_torch_equal_or_newer("2.9.0.dev")):
+        pytest.skip("inductor graph partition is only available "
+                    "in PyTorch 2.9+")
+
     model, model_kwargs = model_info
     print(f"MODEL={model}")
     run_model(compilation_config, model, model_kwargs)
 
 
+def test_inductor_graph_partition_attn_fusion(caplog_vllm):
+    if not is_torch_equal_or_newer("2.9.0.dev"):
+        pytest.skip("inductor graph partition is only available "
+                    "in PyTorch 2.9+")
+
+    model = "nvidia/Llama-4-Scout-17B-16E-Instruct-FP8"
+    compilation_config = CompilationConfig(
+        level=CompilationLevel.PIECEWISE,
+        use_inductor_graph_partition=True,
+        cudagraph_mode=CUDAGraphMode.PIECEWISE,
+        custom_ops=["+quant_fp8"],
+        pass_config=PassConfig(enable_attn_fusion=True, enable_noop=True),
+    )
+    model_kwargs = {
+        "kv_cache_dtype": "fp8",
+        "max_model_len": 1024,
+    }
+    with caplog_vllm.at_level(
+            logging.DEBUG), global_force_attn_backend_context_manager(
+                _Backend.FLASHINFER):
+        run_model(compilation_config, model, model_kwargs)
+
+    try:
+        assert ("Fused quantization onto 48 attention nodes"
+                in caplog_vllm.text), caplog_vllm.text
+    except AssertionError:
+        # Note: this message is only triggered when the compilation goes
+        # through the custom pass. Due to multiple layers of cache on
+        # PyTorch side, the compilation of a graph may be cached such
+        # that custom pass directly goes through cache. In this case,
+        # we go through this branch and assert that the pass is not
+        # triggered.
+        assert "Fused quantization" not in caplog_vllm.text
+
+
 def run_model(compile_config: Union[int, CompilationConfig], model: str,
               model_kwargs: dict[str, Any]):
     prompts = [

tests/compile/test_functionalization.py

@@ -8,9 +8,10 @@ import vllm.envs as envs
 from vllm import LLM, SamplingParams
 from vllm.compilation.activation_quant_fusion import ActivationQuantFusionPass
 from vllm.compilation.fix_functionalization import FixFunctionalizationPass
-from vllm.compilation.fusion import FUSED_OPS, FusionPass
+from vllm.compilation.fusion import FUSED_OPS, RMSNormQuantFusionPass
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.config import CompilationConfig, PassConfig, VllmConfig
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
     QuantKey, kFp8DynamicTokenSym, kFp8StaticTensorSym)
@@ -58,11 +59,12 @@ def test_fix_functionalization(model: str, quant_key: QuantKey,
     vllm_config.compilation_config = CompilationConfig(
         pass_config=PassConfig(enable_fusion=do_fusion, enable_noop=True))
     noop_pass = NoOpEliminationPass(vllm_config)
-    fusion_pass = FusionPass.instance(vllm_config)
+    fusion_pass = RMSNormQuantFusionPass(vllm_config)
+    cleanup_pass = PostCleanupPass(vllm_config)
     act_quant_fusion_pass = ActivationQuantFusionPass(vllm_config)
 
-    passes = [noop_pass, fusion_pass, act_quant_fusion_pass
-              ] if do_fusion else [noop_pass]
+    passes = [noop_pass, fusion_pass, act_quant_fusion_pass, cleanup_pass
+              ] if do_fusion else [noop_pass, cleanup_pass]
     func_pass = FixFunctionalizationPass(vllm_config)
     backend_func = TestBackend(*passes, func_pass)
     backend_no_func = TestBackend(*passes)

tests/compile/test_fusion.py

@@ -4,11 +4,11 @@
 import pytest
 import torch
 
-import vllm.envs as envs
 import vllm.plugins
 from vllm.compilation.fusion import (FUSED_OPS, QUANT_OPS, FusedRMSQuantKey,
-                                     FusionPass)
+                                     RMSNormQuantFusionPass)
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.config import (CompilationConfig, CompilationLevel, PassConfig,
                          VllmConfig)
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -79,15 +79,15 @@ class TestModel(torch.nn.Module):
 
 
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
-@pytest.mark.parametrize("hidden_size", [64, 3392, 4096])
-@pytest.mark.parametrize("num_tokens", [7, 256, 533, 2048, 2049])
+@pytest.mark.parametrize("hidden_size", [64])
+@pytest.mark.parametrize("num_tokens", [257])
 @pytest.mark.parametrize("eps", [1e-5, 1e-6])
 @pytest.mark.parametrize("static", [True, False])
 # cuda_force_torch used to test torch code path on platforms that
 # cutlass_fp8_supported() == True.
 @pytest.mark.parametrize("cuda_force_torch",
                          [True, False] if cutlass_fp8_supported() else [True])
-@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda", "rocm"],
+@pytest.mark.skipif(not current_platform.is_cuda_alike(),
                     reason="Only test on CUDA and ROCm")
 def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
                               cuda_force_torch):
@@ -104,9 +104,10 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
     with vllm.config.set_current_vllm_config(vllm_config):
         # Reshape pass is needed for the fusion pass to work
         noop_pass = NoOpEliminationPass(vllm_config)
-        fusion_pass = FusionPass.instance(vllm_config)
+        fusion_pass = RMSNormQuantFusionPass(vllm_config)
+        cleanup_pass = PostCleanupPass(vllm_config)
 
-        backend = TestBackend(noop_pass, fusion_pass)
+        backend = TestBackend(noop_pass, fusion_pass, cleanup_pass)
         model = TestModel(hidden_size, eps, static, cuda_force_torch)
 
         # First dimension dynamic
@@ -128,6 +129,8 @@ def test_fusion_rmsnorm_quant(dtype, hidden_size, num_tokens, eps, static,
 
         torch.testing.assert_close(result, result2, atol=ATOL, rtol=RTOL)
 
+        assert fusion_pass.matched_count == 2
+
         # In pre-nodes, fp8 quant should be there and fused kernels should not
         backend.check_before_ops(model.ops_in_model_before())
 

tests/compile/test_fusion_all_reduce.py

@@ -9,6 +9,7 @@ import vllm.envs as envs
 from vllm.compilation.collective_fusion import AllReduceFusionPass
 from vllm.compilation.fix_functionalization import FixFunctionalizationPass
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.config import (CompilationConfig, CompilationLevel, DeviceConfig,
                          ModelConfig, PassConfig, VllmConfig)
 from vllm.distributed import tensor_model_parallel_all_reduce
@@ -215,8 +216,10 @@ def all_reduce_fusion_pass_on_test_model(local_rank: int, world_size: int,
     all_reduce_fusion_pass = AllReduceFusionPass(vllm_config)
     noop_pass = NoOpEliminationPass(vllm_config)
     func_pass = FixFunctionalizationPass(vllm_config)
+    cleanup_pass = PostCleanupPass(vllm_config)
 
-    backend = TestBackend(all_reduce_fusion_pass, noop_pass, func_pass)
+    backend = TestBackend(all_reduce_fusion_pass, noop_pass, func_pass,
+                          cleanup_pass)
 
     token_num = batch_size * seq_len
     model = test_model_cls(hidden_size, token_num)
@@ -227,6 +230,7 @@ def all_reduce_fusion_pass_on_test_model(local_rank: int, world_size: int,
     compiled_model = torch.compile(model, backend=backend)
     compiled_model(hidden_states, residual)
 
+    assert all_reduce_fusion_pass.matched_count == 1
     backend.check_before_ops(model.ops_in_model_before(), fully_replaced=False)
     backend.check_after_ops(model.ops_in_model_after())
     del all_reduce_fusion_pass

tests/compile/test_fusion_attn.py

@@ -6,18 +6,19 @@ from typing import Optional
 import pytest
 import torch._dynamo
 
-from tests.compile.backend import TestBackend
+from tests.compile.backend import LazyInitPass, TestBackend
 from tests.models.utils import check_outputs_equal
 from tests.v1.attention.utils import (BatchSpec, _Backend,
                                       create_common_attn_metadata)
 from vllm import LLM, SamplingParams
 from vllm._custom_ops import cutlass_scaled_fp4_mm, scaled_fp4_quant
-from vllm.attention import Attention
+from vllm.attention import Attention, AttentionMetadata
 from vllm.attention.selector import global_force_attn_backend_context_manager
 from vllm.compilation.fusion import QUANT_OPS
 from vllm.compilation.fusion_attn import ATTN_OP, AttnFusionPass
 from vllm.compilation.fx_utils import find_op_nodes
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.config import (CacheConfig, CompilationConfig, CompilationLevel,
                          ModelConfig, PassConfig, SchedulerConfig, VllmConfig,
                          set_current_vllm_config)
@@ -27,6 +28,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import (
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import (
     Fp8LinearOp)
 from vllm.platforms import current_platform
+from vllm.utils import is_torch_equal_or_newer
 from vllm.v1.kv_cache_interface import AttentionSpec
 
 FP8_DTYPE = current_platform.fp8_dtype()
@@ -53,8 +55,7 @@ def test_attention_fusion_v0(example_prompts, monkeypatch, model: str,
     # Use global backends
     global backend, backend_unfused
 
-    use_v1 = False  # can be made a param once V1 support added
-    monkeypatch.setenv("VLLM_USE_V1", str(int(use_v1)))
+    monkeypatch.setenv("VLLM_USE_V1", "1")
     monkeypatch.setenv("VLLM_USE_TRITON_FLASH_ATTN", str(int(use_triton_fa)))
 
     # Prompt 4 seems too open-ended, differs between fused and unfused
@@ -104,7 +105,7 @@ def test_attention_fusion_v0(example_prompts, monkeypatch, model: str,
 
     # AttnFusionPass needs attention layers to be registered in config upon init
     # so we initialize it during compilation.
-    attn_pass = lambda *args, **kw: AttnFusionPass(vllm_config)(*args, **kw)
+    attn_pass = LazyInitPass(AttnFusionPass, vllm_config)
     backend = TestBackend(NoOpEliminationPass(vllm_config), attn_pass)
     llm2 = LLM(model,
                enforce_eager=True,
@@ -197,7 +198,8 @@ class AttentionQuantPatternModel(torch.nn.Module):
             device=self.device,
         )
 
-    def build_attn_metadata(self, batch_size: int, use_hnd: bool):
+    def build_attn_metadata(self, batch_size: int, use_hnd: bool) \
+            -> AttentionMetadata:
         """Initialize attention metadata."""
 
         # Create common attn metadata
@@ -334,11 +336,16 @@ else:
                          [7, 256, 533] if current_platform.is_cuda() else [8])
 @pytest.mark.parametrize("dtype", [torch.bfloat16, torch.float16])
 @pytest.mark.parametrize("model_name, model_class", MODELS)
-@pytest.mark.parametrize("backend", [_Backend.FLASHINFER] if
-                         current_platform.is_cuda() else [_Backend.ROCM_FLASH])
+@pytest.mark.parametrize("backend",
+                         [_Backend.FLASHINFER] if current_platform.is_cuda()
+                         else [_Backend.TRITON_ATTN])
 @pytest.mark.parametrize(
     "split_attention",
     [False, True] if current_platform.is_rocm() else [False])
+# TODO(boyuan): test inductor graph partition on rocm
+@pytest.mark.parametrize(
+    "use_inductor_graph_partition",
+    [False] if current_platform.is_rocm() else [False, True])
 @pytest.mark.skipif(not current_platform.is_cuda_alike(),
                     reason="Only test ROCm or CUDA")
 @pytest.mark.skipif(not current_platform.supports_fp8(), reason="Need FP8")
@@ -352,9 +359,15 @@ def test_attention_quant_pattern(num_qo_heads: int, num_kv_heads: int,
                                  dtype: torch.dtype, model_name: str,
                                  model_class: type[AttentionQuantPatternModel],
                                  backend: _Backend, split_attention: bool,
-                                 monkeypatch, dist_init):
+                                 use_inductor_graph_partition: bool,
+                                 monkeypatch, dist_init, caplog_vllm):
     """Test AttentionStaticQuantPattern fusion pass"""
 
+    if use_inductor_graph_partition and not is_torch_equal_or_newer(
+            "2.9.0.dev"):
+        pytest.skip("inductor graph partition is only available "
+                    "in PyTorch 2.9+")
+
     monkeypatch.setenv("VLLM_USE_V1", "1")
     if split_attention:
         monkeypatch.setenv("VLLM_V1_USE_PREFILL_DECODE_ATTENTION", "1")
@@ -372,6 +385,7 @@ def test_attention_quant_pattern(num_qo_heads: int, num_kv_heads: int,
         compilation_config=CompilationConfig(
             level=CompilationLevel.PIECEWISE,
             custom_ops=["+quant_fp8"],
+            use_inductor_graph_partition=use_inductor_graph_partition,
         ),
         cache_config=CacheConfig(cache_dtype="fp8"))
 
@@ -435,15 +449,17 @@ def test_attention_quant_pattern(num_qo_heads: int, num_kv_heads: int,
 
         # Create test backend with fusion passes enabled
         noop_pass = NoOpEliminationPass(vllm_config)
-        attn_pass = lambda *args, **kw: AttnFusionPass(vllm_config)(*args, **kw
-                                                                    )
-        test_backend = TestBackend(noop_pass, attn_pass)
+        attn_pass = LazyInitPass(AttnFusionPass, vllm_config)
+        cleanup_pass = PostCleanupPass(vllm_config)
+
+        test_backend = TestBackend(noop_pass, attn_pass, cleanup_pass)
 
         # Compile model with fusion enabled
         model_compiled = torch.compile(model_fused,
                                        backend=test_backend,
                                        fullgraph=True)
         assert model_compiled.attn._o_scale_float is None
+
         result_fused_1 = model_compiled(q, k, v)
 
         if backend == _Backend.FLASHINFER:
@@ -453,6 +469,7 @@ def test_attention_quant_pattern(num_qo_heads: int, num_kv_heads: int,
             # _o_scale_float
             assert model_compiled.attn._o_scale_float is not None
             result_fused_2 = model_compiled(q, k, v)
+
             assert model_compiled.attn._o_scale_float is not None
 
             torch.testing.assert_close(result_unfused,
@@ -471,6 +488,9 @@ def test_attention_quant_pattern(num_qo_heads: int, num_kv_heads: int,
         test_backend.check_before_ops([QUANT_OPS[quant_key]],
                                       fully_replaced=True)
 
+    # access the underlying `AttnFusionPass` on the `LazyInitPass`
+    assert attn_pass.pass_.matched_count == sum(attn_fusion_supported)
+
     # Check attention ops in the graph before and after fusion
     attn_nodes_pre = list(find_op_nodes(ATTN_OP, test_backend.graph_pre_pass))
     attn_nodes_post = list(find_op_nodes(ATTN_OP,

tests/compile/test_sequence_parallelism.py

@@ -6,10 +6,12 @@ import torch
 
 import vllm.envs as envs
 from vllm.compilation.fix_functionalization import FixFunctionalizationPass
-from vllm.compilation.fusion import FusionPass
+from vllm.compilation.fusion import RMSNormQuantFusionPass
 from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.compilation.sequence_parallelism import SequenceParallelismPass
+from vllm.compilation.vllm_inductor_pass import VllmInductorPass
 from vllm.config import (CompilationConfig, DeviceConfig, ModelConfig,
                          PassConfig, VllmConfig)
 from vllm.distributed import tensor_model_parallel_all_reduce
@@ -104,7 +106,7 @@ class TestQuantModel(torch.nn.Module):
         # Initialize weights
         torch.nn.init.normal_(self.gate_proj, std=0.02)
 
-        self.fp8_linear = Fp8LinearOp(use_per_token_if_dynamic=False)
+        self.fp8_linear = Fp8LinearOp(act_quant_static=True)
 
         self.scale = torch.rand(1, dtype=torch.float32)
         # Create a weight that is compatible with torch._scaled_mm,
@@ -137,8 +139,7 @@ class TestQuantModel(torch.nn.Module):
         # layer normalization
         norm_output, residual_output = self.norm(all_reduce, residual)
 
-        # for static input quantization
-        # self.fp8_linear is initialized with use_per_token_if_dynamic=False
+        # scaled_mm with static input quantization
         fp8_linear_result = self.fp8_linear.apply(norm_output,
                                                   self.w,
                                                   self.wscale,
@@ -253,16 +254,20 @@ def sequence_parallelism_pass_on_test_model(
                                            dtype=dtype,
                                            seed=42)
 
-    sequence_parallelism_pass = SequenceParallelismPass(vllm_config)
     noop_pass = NoOpEliminationPass(vllm_config)
+    sequence_parallelism_pass = SequenceParallelismPass(vllm_config)
     func_pass = FixFunctionalizationPass(vllm_config)
+    cleanup_pass = PostCleanupPass(vllm_config)
 
-    passes_for_backend = [noop_pass, sequence_parallelism_pass]
+    passes_for_backend: list[VllmInductorPass] = \
+        [noop_pass, sequence_parallelism_pass]
 
     if enable_fusion:
-        fusion_pass = FusionPass.instance(vllm_config)
+        fusion_pass = RMSNormQuantFusionPass(vllm_config)
         passes_for_backend.append(fusion_pass)
 
+    passes_for_backend.append(cleanup_pass)
+
     backend_no_func = TestBackend(*passes_for_backend)
     backend_func = TestBackend(*passes_for_backend, func_pass)
 
@@ -279,6 +284,8 @@ def sequence_parallelism_pass_on_test_model(
     compiled_model_func = torch.compile(model, backend=backend_func)
     compiled_model_func(hidden_states, residual)
 
+    assert sequence_parallelism_pass.matched_count == 1
+
     # In pre-nodes, all reduce should be there,
     # reduce scatter and all gather should not
     backend_no_func.check_before_ops(model.ops_in_model_before())

tests/compile/test_silu_mul_quant_fusion.py

@@ -15,6 +15,7 @@ from vllm.compilation.activation_quant_fusion import (
 # yapf: enable
 from vllm.compilation.fusion import QUANT_OPS
 from vllm.compilation.noop_elimination import NoOpEliminationPass
+from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.config import CompilationConfig, PassConfig, VllmConfig
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.quantization.utils.quant_utils import (
@@ -69,6 +70,10 @@ class TestSiluMulNvfp4QuantModel(torch.nn.Module):
 
     def __init__(self, hidden_size: int, x: torch.Tensor, **kwargs):
         super().__init__()
+        from vllm.compilation.activation_quant_fusion import (
+            silu_and_mul_nvfp4_quant_supported)
+        assert silu_and_mul_nvfp4_quant_supported
+
         self.silu_and_mul = SiluAndMul()
 
         # create nvfp4 weight
@@ -127,7 +132,11 @@ def test_fusion_silu_and_mul_quant(num_tokens, hidden_size, dtype, model_class,
         pass_config=PassConfig(enable_fusion=True, enable_noop=True))
     fusion_pass = ActivationQuantFusionPass(config)
 
-    backend = TestBackend(NoOpEliminationPass(config), fusion_pass)
+    passes = [
+        NoOpEliminationPass(config), fusion_pass,
+        PostCleanupPass(config)
+    ]
+    backend = TestBackend(*passes)
     model = model_class(hidden_size=hidden_size,
                         cuda_force_torch=cuda_force_torch,
                         x=x)
@@ -151,6 +160,8 @@ def test_fusion_silu_and_mul_quant(num_tokens, hidden_size, dtype, model_class,
                                atol=atol,
                                rtol=rtol)
 
+    assert fusion_pass.matched_count == 1
+
     # In pre-nodes, quant op should be present and fused kernels should not
     backend.check_before_ops(model.ops_in_model_before())
 

tests/conftest.py

@@ -19,6 +19,7 @@ import socket
 import tempfile
 import threading
 from collections.abc import Generator
+from contextlib import nullcontext
 from enum import Enum
 from typing import Any, Callable, Optional, TypedDict, TypeVar, Union, cast
 
@@ -39,19 +40,20 @@ from vllm import LLM, SamplingParams
 from vllm.assets.audio import AudioAsset
 from vllm.assets.image import ImageAsset
 from vllm.assets.video import VideoAsset
-from vllm.config import ConvertOption, RunnerOption, _get_and_verify_dtype
+from vllm.config.model import (ConvertOption, RunnerOption,
+                               _get_and_verify_dtype)
 from vllm.connections import global_http_connection
 from vllm.distributed import (cleanup_dist_env_and_memory,
                               init_distributed_environment,
                               initialize_model_parallel)
-from vllm.inputs import (ExplicitEncoderDecoderPrompt, TextPrompt,
-                         to_enc_dec_tuple_list, zip_enc_dec_prompts)
+from vllm.inputs import TextPrompt
 from vllm.logger import init_logger
+from vllm.logprobs import Logprob
 from vllm.multimodal.utils import fetch_image
 from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
-from vllm.sequence import Logprob
 from vllm.transformers_utils.utils import maybe_model_redirect
+from vllm.utils import set_default_torch_num_threads
 
 logger = init_logger(__name__)
 
@@ -158,26 +160,6 @@ def cleanup_VLLM_USE_V1(monkeypatch):
         monkeypatch.delenv("VLLM_USE_V1")
 
 
-@pytest.fixture(params=[True, False])
-def run_with_both_engines(request, monkeypatch):
-    # Automatically runs tests twice, once with V1 and once without
-    use_v1 = request.param
-    # Tests decorated with `@skip_v1` are only run without v1
-    skip_v0 = request.node.get_closest_marker("skip_v0")
-    skip_v1 = request.node.get_closest_marker("skip_v1")
-
-    if use_v1:
-        if skip_v1:
-            pytest.skip("Skipping test on vllm V1")
-        monkeypatch.setenv('VLLM_USE_V1', '1')
-    else:
-        if skip_v0:
-            pytest.skip("Skipping test on vllm V0")
-        monkeypatch.setenv('VLLM_USE_V1', '0')
-
-    yield
-
-
 @pytest.fixture(autouse=True)
 def init_test_http_connection():
     # pytest_asyncio may use a different event loop per test
@@ -244,39 +226,6 @@ class DecoderPromptType(Enum):
     EMPTY_STR = 3
 
 
-@pytest.fixture
-def example_encoder_decoder_prompts(
-) -> dict[DecoderPromptType, list[ExplicitEncoderDecoderPrompt]]:
-    '''
-    Returns an encoder prompt list and a decoder prompt list, wherein each pair
-    of same-index entries in both lists corresponds to an (encoder prompt,
-    decoder prompt) tuple.
-
-    Returns:
-
-    * Encoder prompt list
-    * Decoder prompt list (reverse of encoder prompt list)
-    '''
-
-    encoder_prompts = []
-    for filename in _TEST_PROMPTS:
-        encoder_prompts += _read_prompts(filename)
-
-    custom_decoder_prompts = encoder_prompts[::-1]
-    empty_str_decoder_prompts = [""] * len(encoder_prompts)
-    none_decoder_prompts = [None] * len(encoder_prompts)
-
-    # NONE decoder prompt type
-    return {
-        DecoderPromptType.NONE:
-        zip_enc_dec_prompts(encoder_prompts, none_decoder_prompts),
-        DecoderPromptType.EMPTY_STR:
-        zip_enc_dec_prompts(encoder_prompts, empty_str_decoder_prompts),
-        DecoderPromptType.CUSTOM:
-        zip_enc_dec_prompts(encoder_prompts, custom_decoder_prompts),
-    }
-
-
 @pytest.fixture
 def example_long_prompts() -> list[str]:
     prompts = []
@@ -338,6 +287,35 @@ class HfRunner:
         is_cross_encoder: bool = False,
         skip_tokenizer_init: bool = False,
         auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
+        # Set this to avoid hanging issue
+        default_torch_num_threads: Optional[int] = None,
+    ) -> None:
+        init_ctx = (nullcontext() if default_torch_num_threads is None else
+                    set_default_torch_num_threads(default_torch_num_threads))
+
+        with init_ctx:
+            self._init(
+                model_name=model_name,
+                dtype=dtype,
+                model_kwargs=model_kwargs,
+                trust_remote_code=trust_remote_code,
+                is_sentence_transformer=is_sentence_transformer,
+                is_cross_encoder=is_cross_encoder,
+                skip_tokenizer_init=skip_tokenizer_init,
+                auto_cls=auto_cls,
+            )
+
+    def _init(
+        self,
+        model_name: str,
+        dtype: str = "auto",
+        *,
+        model_kwargs: Optional[dict[str, Any]] = None,
+        trust_remote_code: bool = True,
+        is_sentence_transformer: bool = False,
+        is_cross_encoder: bool = False,
+        skip_tokenizer_init: bool = False,
+        auto_cls: type[_BaseAutoModelClass] = AutoModelForCausalLM,
     ) -> None:
         model_name = maybe_model_redirect(model_name)
         self.model_name = model_name
@@ -690,68 +668,6 @@ class HfRunner:
         return [(output_ids, output_str, output_logprobs)
                 for output_ids, output_str, output_logprobs in outputs]
 
-    def generate_encoder_decoder_greedy_logprobs_limit(
-        self,
-        encoder_decoder_prompts: list[ExplicitEncoderDecoderPrompt[str, str]],
-        max_tokens: int,
-        num_logprobs: Optional[int],
-        images: Optional[PromptImageInput] = None,
-        **kwargs: Any,
-    ) -> list[TokensTextLogprobs]:
-        '''
-        Greedy logprobs generation for vLLM encoder/decoder models
-        '''
-
-        all_logprobs: list[list[dict[int, float]]] = []
-        all_output_ids: list[list[int]] = []
-        all_output_strs: list[str] = []
-
-        for i, (encoder_prompt, decoder_prompt) in enumerate(
-                to_enc_dec_tuple_list(encoder_decoder_prompts)):
-            processor_kwargs: dict[str, Any] = {
-                "text": encoder_prompt,
-                "return_tensors": "pt",
-            }
-            if images is not None and images[i] is not None:
-                processor_kwargs["images"] = images[i]
-
-            encoder_inputs = self.processor(**processor_kwargs)
-            encoder_inputs = self.wrap_device(encoder_inputs)
-
-            if decoder_prompt is None:
-                decoder_input_ids = None
-            else:
-                decoder_inputs = self.tokenizer(decoder_prompt,
-                                                return_tensors="pt")
-                decoder_input_ids = self.wrap_device(decoder_inputs.input_ids)
-
-            output = self.model.generate(
-                decoder_input_ids=decoder_input_ids,
-                use_cache=True,
-                do_sample=False,
-                max_new_tokens=max_tokens,
-                output_hidden_states=True,
-                return_dict_in_generate=True,
-                **encoder_inputs,
-                **kwargs,
-            )
-
-            (
-                seq_logprobs_lst,
-                output_len,
-            ) = self._hidden_states_to_logprobs(output.decoder_hidden_states,
-                                                num_logprobs)
-
-            all_logprobs.append(seq_logprobs_lst)
-            seq_ids = output.sequences[0]
-            output_ids = seq_ids[-output_len:]
-            all_output_ids.append(output_ids.tolist())
-            all_output_strs.append(self.tokenizer.decode(output_ids))
-
-        outputs = zip(all_output_ids, all_output_strs, all_logprobs)
-        return [(output_ids, output_str, output_logprobs)
-                for output_ids, output_str, output_logprobs in outputs]
-
     def encode(self, prompts: list[str], *args,
                **kwargs) -> list[list[torch.Tensor]]:
         return self.model.encode(prompts, *args, **kwargs)
@@ -808,26 +724,32 @@ class VllmRunner:
         enable_chunked_prefill: Optional[bool] = False,
         swap_space: int = 4,
         enforce_eager: Optional[bool] = False,
+        # Set this to avoid hanging issue
+        default_torch_num_threads: Optional[int] = None,
         **kwargs,
     ) -> None:
-        self.llm = LLM(
-            model=model_name,
-            runner=runner,
-            convert=convert,
-            tokenizer=tokenizer_name,
-            tokenizer_mode=tokenizer_mode,
-            trust_remote_code=trust_remote_code,
-            dtype=dtype,
-            seed=seed,
-            swap_space=swap_space,
-            enforce_eager=enforce_eager,
-            disable_log_stats=disable_log_stats,
-            tensor_parallel_size=tensor_parallel_size,
-            max_model_len=max_model_len,
-            block_size=block_size,
-            enable_chunked_prefill=enable_chunked_prefill,
-            **kwargs,
-        )
+        init_ctx = (nullcontext() if default_torch_num_threads is None else
+                    set_default_torch_num_threads(default_torch_num_threads))
+
+        with init_ctx:
+            self.llm = LLM(
+                model=model_name,
+                runner=runner,
+                convert=convert,
+                tokenizer=tokenizer_name,
+                tokenizer_mode=tokenizer_mode,
+                trust_remote_code=trust_remote_code,
+                dtype=dtype,
+                seed=seed,
+                swap_space=swap_space,
+                enforce_eager=enforce_eager,
+                disable_log_stats=disable_log_stats,
+                tensor_parallel_size=tensor_parallel_size,
+                max_model_len=max_model_len,
+                block_size=block_size,
+                enable_chunked_prefill=enable_chunked_prefill,
+                **kwargs,
+            )
 
     def get_inputs(
         self,
@@ -940,26 +862,6 @@ class VllmRunner:
                 if sampling_params.prompt_logprobs is None else
                 toks_str_logsprobs_prompt_logprobs)
 
-    def generate_encoder_decoder_w_logprobs(
-        self,
-        encoder_decoder_prompts: list[ExplicitEncoderDecoderPrompt[str, str]],
-        sampling_params: SamplingParams,
-    ) -> Union[list[TokensTextLogprobs],
-               list[TokensTextLogprobsPromptLogprobs]]:
-        '''
-        Logprobs generation for vLLM encoder/decoder models
-        '''
-
-        assert sampling_params.logprobs is not None
-        req_outputs = self.llm.generate(encoder_decoder_prompts,
-                                        sampling_params=sampling_params)
-        toks_str_logsprobs_prompt_logprobs = (
-            self._final_steps_generate_w_logprobs(req_outputs))
-        # Omit prompt logprobs if not required by sampling params
-        return ([x[0:-1] for x in toks_str_logsprobs_prompt_logprobs]
-                if sampling_params.prompt_logprobs is None else
-                toks_str_logsprobs_prompt_logprobs)
-
     def generate_greedy(
         self,
         prompts: Union[list[str], list[torch.Tensor]],
@@ -1037,29 +939,6 @@ class VllmRunner:
 
         return perplexities
 
-    def generate_encoder_decoder_greedy_logprobs(
-        self,
-        encoder_decoder_prompts: list[ExplicitEncoderDecoderPrompt[str, str]],
-        max_tokens: int,
-        num_logprobs: Optional[int],
-        num_prompt_logprobs: Optional[int] = None,
-        skip_special_tokens: bool = True,
-    ) -> Union[list[TokensTextLogprobs],
-               list[TokensTextLogprobsPromptLogprobs]]:
-        greedy_logprobs_params = SamplingParams(
-            temperature=0.0,
-            max_tokens=max_tokens,
-            logprobs=num_logprobs,
-            prompt_logprobs=(num_prompt_logprobs),
-            skip_special_tokens=skip_special_tokens,
-        )
-        '''
-        Greedy logprobs generation for vLLM encoder/decoder models
-        '''
-
-        return self.generate_encoder_decoder_w_logprobs(
-            encoder_decoder_prompts, greedy_logprobs_params)
-
     def generate_beam_search(
         self,
         prompts: list[str],
@@ -1124,17 +1003,7 @@ class VllmRunner:
         return [req_output.outputs.score for req_output in req_outputs]
 
     def apply_model(self, func: Callable[[nn.Module], _R]) -> list[_R]:
-        if hasattr(self.llm.llm_engine, "model_executor"):
-            # This works either in V0 or in V1 with
-            # VLLM_ENABLE_V1_MULTIPROCESSING=0
-            executor = self.llm.llm_engine.model_executor
-            return executor.apply_model(func)
-
-        # This works in V1 with VLLM_ALLOW_INSECURE_SERIALIZATION=1
-        def _apply_model(self):
-            return func(self.get_model())
-
-        return self.llm.llm_engine.collective_rpc(_apply_model)
+        return self.llm.apply_model(func)
 
     def get_llm(self) -> LLM:
         return self.llm
@@ -1210,7 +1079,7 @@ def dummy_llava_path():
                           local_dir=_dummy_llava_path,
                           ignore_patterns=[
                               "*.bin", "*.bin.index.json", "*.pt", "*.h5",
-                              "*.msgpack"
+                              "*.msgpack", "*.safetensors"
                           ])
         assert os.path.exists(json_path)
         with open(json_path) as f:
@@ -1229,7 +1098,7 @@ def dummy_gemma2_embedding_path():
                           local_dir=_dummy_gemma2_embedding_path,
                           ignore_patterns=[
                               "*.bin", "*.bin.index.json", "*.pt", "*.h5",
-                              "*.msgpack"
+                              "*.msgpack", "*.safetensors"
                           ])
         assert os.path.exists(json_path)
         with open(json_path) as f:

tests/detokenizer/test_stop_strings.py

@@ -32,10 +32,6 @@ def _test_stopping(llm: LLM,
     assert output.stop_reason == expected_reason
 
 
-def _set_async_mode(llm, is_async):
-    llm.llm_engine.scheduler[0].use_async_output_proc = is_async
-
-
 def _stop_basic(llm):
     _test_stopping(llm,
                    stop=["."],
@@ -103,40 +99,8 @@ def test_stop_strings():
     # async output processing below.
     llm = LLM(MODEL, enforce_eager=envs.VLLM_USE_V1)
 
-    if envs.VLLM_USE_V1:
-        _stop_basic(llm)
-    else:
-        _set_async_mode(llm, True)
-        _stop_basic(llm)
-
-        _set_async_mode(llm, False)
-        _stop_basic(llm)
-
-    if envs.VLLM_USE_V1:
-        _stop_multi_tokens(llm)
-    else:
-        _set_async_mode(llm, True)
-        _stop_multi_tokens(llm)
-
-        _set_async_mode(llm, False)
-        _stop_multi_tokens(llm)
-
-    if envs.VLLM_USE_V1:
-        _stop_partial_token(llm)
-    else:
-        _set_async_mode(llm, True)
-        _stop_partial_token(llm)
-
-        _set_async_mode(llm, False)
-        _stop_partial_token(llm)
-
-    if envs.VLLM_USE_V1:
-        # FIXME: this does not respect include_in_output=False
-        # _stop_token_id(llm)
-        pass
-    else:
-        _set_async_mode(llm, True)
-        _stop_token_id(llm)
-
-        _set_async_mode(llm, False)
-        _stop_token_id(llm)
+    _stop_basic(llm)
+    _stop_multi_tokens(llm)
+    _stop_partial_token(llm)
+    # FIXME: this does not respect include_in_output=False
+    # _stop_token_id(llm)

tests/distributed/test_nccl_symm_mem_allreduce.py

@@ -0,0 +1,94 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import random
+import typing
+
+import pytest
+import torch
+import torch.distributed as dist
+import torch.multiprocessing as mp
+
+import vllm.envs as envs
+from vllm.distributed import cleanup_dist_env_and_memory
+from vllm.distributed.device_communicators.cuda_communicator import (
+    CudaCommunicator)
+from vllm.distributed.device_communicators.pynccl import (
+    register_nccl_symmetric_ops)
+from vllm.distributed.device_communicators.pynccl_allocator import (
+    get_nccl_mem_pool, is_symmetric_memory_enabled)
+from vllm.distributed.parallel_state import (get_tp_group,
+                                             init_distributed_environment,
+                                             initialize_model_parallel)
+from vllm.platforms import current_platform
+from vllm.utils import update_environment_variables
+
+torch.manual_seed(42)
+random.seed(44)
+
+test_size_elements = 4 * 1024 * 1024
+
+
+def nccl_symm_mem_allreduce_worker(local_rank: int, world_size: int):
+    monkeypatch = pytest.MonkeyPatch()
+    with monkeypatch.context() as m:
+        m.delenv("CUDA_VISIBLE_DEVICES", raising=False)
+        dtype = torch.bfloat16
+        device = torch.device(f"cuda:{local_rank}")
+        torch.cuda.set_device(device)
+        torch.set_default_device(device)
+        torch.set_default_dtype(dtype)
+        update_environment_variables({
+            "RANK": str(local_rank),
+            "LOCAL_RANK": str(local_rank),
+            "WORLD_SIZE": str(world_size),
+            "MASTER_ADDR": "localhost",
+            "MASTER_PORT": "12345",
+        })
+
+        init_distributed_environment()
+        initialize_model_parallel(tensor_model_parallel_size=world_size)
+
+        cuda_communicator = typing.cast(CudaCommunicator,
+                                        get_tp_group().device_communicator)
+        pynccl_comm = cuda_communicator.pynccl_comm
+        if get_nccl_mem_pool() is None:
+            pytest.skip("NCCL allocator compilation failed "
+                        "(probably missing NCCL headers).")
+        if not is_symmetric_memory_enabled():
+            pytest.skip("NCCL symmetric memory allreduce is disabled.")
+
+        register_nccl_symmetric_ops(pynccl_comm)
+        input = torch.randint(1,
+                              23, (test_size_elements, ),
+                              dtype=dtype,
+                              device=device)
+        input_clone = input.clone()
+        output = torch.ops.vllm.all_reduce_symmetric_with_copy(input)
+        assert output is not None
+
+        group = get_tp_group().device_group
+        dist.all_reduce(input_clone, group=group)
+        torch.testing.assert_close(output, input_clone, atol=2.5, rtol=0.1)
+
+
+@pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="NCCLSymmMemAllreduce is only available for CUDA platforms.",
+)
+@pytest.mark.parametrize("world_size", [2])
+@pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"],
+                    reason="Only test on CUDA")
+def test_nccl_symm_mem_allreduce(monkeypatch: pytest.MonkeyPatch, world_size):
+    if world_size > torch.cuda.device_count():
+        pytest.skip("Not enough GPUs to run the test.")
+
+    # Enable SymmMemCommunicator
+    monkeypatch.setenv("VLLM_USE_NCCL_SYMM_MEM", "1")
+    monkeypatch.setenv("NCCL_NVLS_ENABLE", "1")
+    monkeypatch.setenv("NCCL_CUMEM_ENABLE", "1")
+
+    mp.spawn(nccl_symm_mem_allreduce_worker,
+             args=(world_size, ),
+             nprocs=world_size)
+    cleanup_dist_env_and_memory()

tests/distributed/test_pipeline_parallel.py

@@ -14,7 +14,7 @@ from typing import Literal, NamedTuple, Optional
 
 import pytest
 
-from vllm.config import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
+from vllm.config.model import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption
 from vllm.logger import init_logger
 from vllm.transformers_utils.config import get_config
 
@@ -382,7 +382,6 @@ def test_tp_language_generation(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,
@@ -410,7 +409,6 @@ def test_tp_language_embedding(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,
@@ -438,7 +436,6 @@ def test_tp_multimodal_generation(
     test_options: PPTestOptions,
     num_gpus_available,
 ):
-    pytest.skip("Skipping the test until V1 passes it.")
     _compare_tp(model_id,
                 parallel_setup,
                 distributed_backend,