[CI][ROCm] Fix test_ocp_mx_wikitext_correctness reference value (#49690)

Signed-off-by: Felix Marty <Felix.Marty@amd.com>

.buildkite/intel_jobs/benchmarks_intel.yaml

@@ -0,0 +1,26 @@
+group: Benchmarks
+depends_on: 
+  - image-build-xpu
+steps:
+- label: Benchmarks CLI Test
+  key: benchmarks-cli-test
+  timeout_in_minutes: 40
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+  - vllm/
+  - tests/benchmarks/
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s benchmarks/'

.buildkite/intel_jobs/engine_intel.yaml

@@ -2,6 +2,44 @@ group: Engine Intel
 depends_on:
   - image-build-xpu
 steps:
+- label: Engine
+  key: engine
+  timeout_in_minutes: 40
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+  - vllm/compilation/
+  - vllm/config/
+  - vllm/engine/
+  - vllm/entrypoints/logger.py
+  - vllm/envs.py
+  - vllm/logger.py
+  - vllm/logging_utils/
+  - vllm/platforms/
+  - vllm/sequence.py
+  - vllm/triton_utils/
+  - vllm/utils/
+  - tests/engine
+  - tests/test_sequence
+  - tests/test_config
+  - tests/test_logger
+  - tests/test_vllm_port
+  - tests/test_jit_monitor.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s engine/test_arg_utils.py test_sequence.py test_logger.py test_vllm_port.py test_jit_monitor.py'
+
 - label: Engine (1 GPU)
   timeout_in_minutes: 30
   device: intel_gpu
@@ -23,3 +61,41 @@ steps:
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
       pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py'
+
+- label: V1 e2e (2 GPUs)
+  timeout_in_minutes: 30
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 2+
+    mem: 16+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/compilation/
+    - vllm/config/
+    - vllm/distributed/
+    - vllm/engine/
+    - vllm/envs.py
+    - vllm/forward_context.py
+    - vllm/inputs/
+    - vllm/logger.py
+    - vllm/logging_utils/
+    - vllm/model_executor/
+    - vllm/multimodal/
+    - vllm/platforms/
+    - vllm/sampling_params.py
+    - vllm/transformers_utils/
+    - vllm/triton_utils/
+    - vllm/utils/
+    - vllm/v1/
+    - tests/v1/e2e/spec_decode
+  commands:
+  - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"'

.buildkite/intel_jobs/misc_intel.yaml

@@ -125,13 +125,13 @@ steps:
       pytest -v -s v1/kv_offload &&
       pytest -v -s v1/kv_connector/unit/test_offloading_connector.py'
 
-- label: NixlConnector PD accuracy (2 GPUs)
+- label: NixlConnector PD accuracy (4 GPUs)
   timeout_in_minutes: 60
-  num_devices: 2
+  num_devices: 4
   device: intel_gpu
   agent_tags:
     label: production
-    gpu: 2+
+    gpu: 4+
     mem: 16+
   no_plugin: true
   working_dir: "."
@@ -148,7 +148,10 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh'
+      bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh &&
+      PREFILLER_TP_SIZE=2 DECODER_TP_SIZE=1 bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh &&
+      PREFILLER_TP_SIZE=1 DECODER_TP_SIZE=2 bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh &&
+      PREFILLER_TP_SIZE=2 DECODER_TP_SIZE=2 bash v1/kv_connector/nixl_integration/run_xpu_disagg_accuracy_test.sh'
 
 - label: Regression
   key: regression
@@ -259,3 +262,25 @@ steps:
     pytest -v -s detokenizer &&
     pytest -v -s -m "not cpu_test" ./multimodal &&
     pytest -v -s utils_ --ignore=utils_/test_mem_utils.py'
+
+- label: Fusion Unit Tests
+  timeout_in_minutes: 30
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 16+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/compilation/
+    - tests/compile/passes/test_qk_norm_rope_fusion.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      pytest -v -s compile/passes/test_qk_norm_rope_fusion.py'

.buildkite/intel_jobs/model_executor_intel.yaml

@@ -0,0 +1,33 @@
+group: Model Executor Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: Model Executor (Intel)
+  key: model-executor-intel
+  timeout_in_minutes: 45
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+  - vllm/engine/arg_utils.py
+  - vllm/config/model.py
+  - vllm/model_executor
+  - tests/model_executor
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'apt-get update && apt-get install -y curl libsodium23 &&
+      pip3 install tensorizer==2.10.1 &&
+      pip3 install runai-model-streamer[s3,gcs,azure]\>=0.15.7 &&
+      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
+      export PYTHONFAULTHANDLER=1 &&
+      cd tests &&
+      pytest -v -s model_executor -m "not slow_test" --ignore="model_executor/layers/test_rocm_unquantized_gemm.py" --deselect="tests/model_executor/model_loader/test_reload.py::test_kv_scale_reload"'

.buildkite/intel_jobs/model_runner_v2_intel.yaml

@@ -8,7 +8,7 @@ steps:
   agent_tags:
     label: production
     gpu: 2+
-    mem: 16+
+    mem: 24+
   no_plugin: true
   working_dir: "."
   env:
@@ -28,7 +28,9 @@ steps:
       'export VLLM_USE_V2_MODEL_RUNNER=1 &&
       cd tests &&
       pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics" &&
+      pytest -v -s v1/e2e/general/test_context_length.py &&
       ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram" &&
+      pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0" &&
       pytest -v -s v1/e2e/general/test_min_tokens.py'
 
 - label: Model Runner V2 Examples (Intel)
@@ -60,3 +62,55 @@ steps:
       python3 basic/offline_inference/generate.py --model facebook/opt-125m &&
       python3 generate/multimodal/vision_language_offline.py --seed 0 &&
       python3 features/automatic_prefix_caching/prefix_caching_offline.py'
+
+- label: Model Runner V2 Distributed (2 GPUs)
+  timeout_in_minutes: 50
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 2+
+    mem: 16+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+    - vllm/v1/worker/gpu/
+    - vllm/v1/worker/gpu_worker.py
+    - tests/basic_correctness/test_basic_correctness.py
+    - tests/v1/distributed/test_async_llm_dp.py
+    - tests/v1/distributed/test_eagle_dp.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'export VLLM_USE_V2_MODEL_RUNNER=1 &&
+      cd tests &&
+      TARGET_TEST_SUITE=L4 pytest -v -s basic_correctness/test_basic_correctness.py -m "distributed\(num_gpus=2\)" -k "not ray and not True"'
+
+- label: Model Runner V2 Spec Decode
+  timeout_in_minutes: 50
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+  - vllm/v1/worker/gpu/
+  - vllm/v1/worker/gpu_worker.py
+  - tests/v1/spec_decode/test_max_len.py
+  - tests/v1/spec_decode/test_rejection_sampler_utils.py
+  - tests/v1/e2e/spec_decode/test_spec_decode.py
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'export VLLM_USE_V2_MODEL_RUNNER=1 &&
+      cd tests &&
+      pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py'

.buildkite/intel_jobs/samplers_intel.yaml

@@ -0,0 +1,29 @@
+group: Samplers Intel
+depends_on:
+  - image-build-xpu
+steps:
+- label: Samplers Test (FlashInfer)
+  key: samplers-test-flashinfer-intel
+  timeout_in_minutes: 40
+  device: intel_gpu
+  agent_tags:
+    label: production
+    gpu: 1+
+    mem: 24+
+  no_plugin: true
+  working_dir: "."
+  env:
+    REGISTRY: "public.ecr.aws/q9t5s3a7"
+    REPO: "vllm-ci-test-repo"
+    VLLM_TEST_DEVICE: "xpu"
+  source_file_dependencies:
+  - vllm/model_executor/layers
+  - vllm/sampling_metadata.py
+  - tests/samplers
+  - tests/conftest.py
+  - vllm/entrypoints/generate/beam_search
+  commands:
+    - >-
+      bash .buildkite/scripts/hardware_ci/run-intel-test.sh
+      'cd tests &&
+      VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers'

.buildkite/scripts/build-macos-wheel.sh

@@ -7,6 +7,9 @@
 
 set -euo pipefail
 
+# The macmini queue uses persistent checkouts, so refresh tags for setuptools-scm.
+git fetch --tags --force origin
+
 # The Rust frontend build needs protoc.
 if ! command -v protoc >/dev/null 2>&1; then
   brew install protobuf

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

@@ -35,7 +35,7 @@ set -o pipefail
 : "${PY_COLORS:=1}"
 : "${ROCM_DOCKER_TTY:=1}"
 : "${PYTHONFAULTHANDLER:=1}"
-: "${PYTEST_TIMEOUT:=2100}"
+: "${PYTEST_TIMEOUT:=2400}"
 if [[ " ${PYTEST_ADDOPTS:-} " != *" --color"* ]]; then
   PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }--color=yes"
 fi
@@ -45,9 +45,9 @@ fi
 if [[ " ${PYTEST_ADDOPTS:-} " != *" --durations-min="* ]]; then
   PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }--durations-min=1.0"
 fi
-# Dump stacks after 15 minutes, then stop an individual test after 35 minutes.
+# Dump stacks after 25 minutes, then stop an individual test after 40 minutes.
 if [[ " ${PYTEST_ADDOPTS:-} " != *" faulthandler_timeout="* ]]; then
-  PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }-o faulthandler_timeout=900"
+  PYTEST_ADDOPTS="${PYTEST_ADDOPTS:+${PYTEST_ADDOPTS} }-o faulthandler_timeout=1500"
 fi
 if [[ " ${PYTEST_ADDOPTS:-} " != *" --timeout-method="* &&
   " ${PYTEST_ADDOPTS:-} " != *" --timeout-method "* ]]; then
@@ -387,6 +387,7 @@ initialize_native_environment() {
   local job_id="${BUILDKITE_JOB_ID:-${BUILDKITE_PARALLEL_JOB:-local}}"
   local job_id_suffix=""
   local native_root=""
+  local hf_fstype=""
   local hf_mount=""
 
   if [[ "$(id -u)" -ne 0 ]]; then
@@ -400,16 +401,19 @@ initialize_native_environment() {
   native_root="/tmp/vllm-native-${job_id}"
   TMPDIR="/tmp/vllm-${job_id_suffix}/tmp"
   VLLM_RPC_BASE_PATH="/tmp"
-  : "${TORCHINDUCTOR_CACHE_DIR:=${native_root}/cache/torchinductor}"
-  : "${TRITON_CACHE_DIR:=${native_root}/cache/triton}"
-  : "${VLLM_CACHE_ROOT:=${native_root}/cache/vllm}"
-  : "${XDG_CACHE_HOME:=${native_root}/cache/xdg}"
+  TORCHINDUCTOR_CACHE_DIR="${native_root}/cache/torchinductor"
+  TRITON_CACHE_DIR="${native_root}/cache/triton"
+  VLLM_CACHE_ROOT="${native_root}/cache/vllm"
+  XDG_CACHE_HOME="${native_root}/cache/xdg"
   : "${HF_HOME:=/home/buildkite-agent/huggingface}"
+  # datasets uses POSIX locks that are unsupported by the shared HF NFS cache.
+  # Keep processed datasets job-local while retaining the persistent Hub cache.
+  HF_DATASETS_CACHE="${native_root}/cache/huggingface/datasets"
   : "${HF_HUB_DOWNLOAD_TIMEOUT:=300}"
   : "${HF_HUB_ETAG_TIMEOUT:=60}"
   export TMPDIR VLLM_RPC_BASE_PATH
   export TORCHINDUCTOR_CACHE_DIR TRITON_CACHE_DIR VLLM_CACHE_ROOT XDG_CACHE_HOME
-  export HF_HOME HF_HUB_DOWNLOAD_TIMEOUT HF_HUB_ETAG_TIMEOUT
+  export HF_HOME HF_DATASETS_CACHE HF_HUB_DOWNLOAD_TIMEOUT HF_HUB_ETAG_TIMEOUT
   export PYTORCH_ROCM_ARCH=""
 
   mkdir -p "${TMPDIR}" \
@@ -417,7 +421,10 @@ initialize_native_environment() {
     "${TRITON_CACHE_DIR}" \
     "${VLLM_CACHE_ROOT}" \
     "${XDG_CACHE_HOME}" \
-    "${HF_HOME}" || return 1
+    "${HF_HOME}" \
+    "${HF_DATASETS_CACHE}" || return 1
+
+  echo "Native compile caches: VLLM_CACHE_ROOT=${VLLM_CACHE_ROOT} TORCHINDUCTOR_CACHE_DIR=${TORCHINDUCTOR_CACHE_DIR}"
 
   if [[ "${VLLM_CI_REQUIRE_PERSISTENT_HF_CACHE:-0}" == "1" ]]; then
     if ! command -v findmnt >/dev/null 2>&1; then
@@ -430,6 +437,18 @@ initialize_native_environment() {
       return 1
     fi
   fi
+
+  if command -v findmnt >/dev/null 2>&1; then
+    hf_fstype=$(findmnt -n -T "${HF_HOME}" -o FSTYPE 2>/dev/null || true)
+  fi
+  if [[ "${hf_fstype}" == nfs || "${hf_fstype}" == nfs4 ]]; then
+    # Keep hf-xet state local and avoid vectored writes on shared NFS.
+    export HF_XET_CACHE="${native_root}/cache/hf-xet"
+    export HF_XET_HIGH_PERFORMANCE=0
+    export HF_XET_RECONSTRUCTION_USE_VECTORED_WRITE=0
+    mkdir -p "${HF_XET_CACHE}" || return 1
+    echo "Configured hf-xet for shared ${hf_fstype} cache at ${HF_HOME}"
+  fi
 }
 
 run_native_preflight() {

.buildkite/test-amd.yaml

@@ -40,7 +40,7 @@
 #####################################################################################################################################
 #                                                                                                                                   #
 # IMPORTANT:                                                                                                                        #
-#   * Currently AMD CI has MI250 agents, MI300 agents, MI325 agents, and MI355 agents. All upcoming feature improvements are        #
+#   * Currently AMD CI has MI250 agents, MI300 agents, and MI355 agents. All upcoming feature improvements are                      #
 #         tracked in: https://github.com/vllm-project/vllm/issues/34994                                                             #
 #                                                                                                                                   #
 #-----------------------------------------------------------------------------------------------------------------------------------#
@@ -81,10 +81,8 @@
 #                                                     the above test.) Also run if model initialization test file is modified.      #
 #   * [Language Models Tests (Extra Standard) %N]: Shard slow subset of standard language models tests. Only run when model         #
 #                                                  source is modified, or when specified test files are modified.                   #
-#   * [Language Models Tests (Hybrid) %N]: Install fast path packages for testing against transformers (mamba, conv1d) and to       #
-#                                          run plamo2 model in vLLM.                                                                #
-#   * [Language Models Test (Extended Generation)]: Install fast path packages for testing against transformers (mamba, conv1d)     #
-#                                                   and to run plamo2 model in vLLM.                                                #
+#   * [Language Models Tests (Hybrid) %N]: Install fast path packages for testing against transformers (mamba, conv1d).             #
+#   * [Language Models Test (Extended Generation)]: Install fast path packages for testing against transformers (mamba, conv1d).    #
 #   * [Multi-Modal Models (Standard) 1-4]:                                                                                          #
 #     - Do NOT remove `VLLM_WORKER_MULTIPROC_METHOD=spawn` setting as ROCm requires this for certain models to function.            #
 #   * [Transformers Nightly Models]: Whisper needs `VLLM_WORKER_MULTIPROC_METHOD=spawn` to avoid deadlock.                          #
@@ -171,20 +169,6 @@ steps:
   - pip install helion==1.1.0
   - pytest -v -s kernels/helion/
 
-- label: Kernels Mamba Test # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
-  agent_pool: mi250_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - csrc/mamba/
-  - tests/kernels/mamba
-  - vllm/model_executor/layers/mamba/ops
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s kernels/mamba
-
 #------------------------------------------------------  mi250 · models / basic  -------------------------------------------------------#
 
 - label: Basic Models Test (Other CPU) # TBD
@@ -207,6 +191,7 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -240,6 +225,20 @@ steps:
   - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "not qwen2 and not qwen3 and not gemma"
   - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
 
+- label: Multi-Modal Processor (CPU) %N # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
+  agent_pool: mi250_1
+  parallelism: 6
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal
+  - tests/models/registry.py
+  commands:
+  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
 #------------------------------------------------------------  mi250 · v1  -------------------------------------------------------------#
 
 - label: Batch Invariance (H100-MI250) # TBD
@@ -351,21 +350,21 @@ steps:
   commands:
   - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
-- label: V1 attention (H100-MI250) # TBD
+- label: V1 others (CPU) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - vllm/config/attention.py
-  - vllm/model_executor/layers/attention
-  - vllm/v1/attention
-  - tests/v1/attention
-  - vllm/_aiter_ops.py
-  - vllm/envs.py
-  - vllm/platforms/rocm.py
+  - vllm/
+  - tests/v1
   commands:
-  - pytest -v -s v1/attention
+  - pytest -v -s -m 'cpu_test' v1/core
+  - pytest -v -s v1/structured_output
+  - pytest -v -s v1/test_serial_utils.py
+  - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
+  - pytest -v -s -m 'cpu_test' v1/metrics
 
 #-------------------------------------------------------------  mi250 · misc  ------------------------------------------------------------#
 
@@ -408,6 +407,19 @@ steps:
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
+- label: Python-only Installation # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
+  agent_pool: mi250_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - tests/standalone_tests/python_only_compile.sh
+  - setup.py
+  - vllm/platforms/rocm.py
+  commands:
+  - bash standalone_tests/python_only_compile.sh
+
 #------------------------------------------------------------  mi250 · rust  -----------------------------------------------------------#
 
 - label: Rust Frontend Cargo Style + Clippy # TBD
@@ -445,6 +457,7 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   no_gpu: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - .buildkite/scripts/docker-build-metadata-args.sh
@@ -507,7 +520,7 @@ steps:
   - tests/models/
   commands:
   - TARGET_TEST_SUITE=MI300 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 -m '(not slow_test)'
+  - HIP_VISIBLE_DEVICES=0,1 pytest -v -s model_executor/model_loader/test_sharded_state_loader.py -m '(not slow_test)'
   - pytest models/transformers/test_backend.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 --ignore models/multimodal/generation/test_phi4siglip.py
@@ -657,6 +670,30 @@ steps:
   - VLLM_TEST_CLEAN_GPU_MEMORY=1 pytest -v -s tests/compile/passes/distributed/test_async_tp.py
   - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py::test_tp2_ar_rms_fusions
 
+- label: Distributed Compile + RPC Tests (2 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/compilation/
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/compile/test_wrapper.py
+  - tests/entrypoints/llm/test_collective_rpc.py
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s entrypoints/llm/test_collective_rpc.py
+  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
+  - pytest -v -s ./compile/test_wrapper.py
+
 #-----------------------------------------------------------  mi300 · cuda  ------------------------------------------------------------#
 
 - label: Platform Tests # TBD
@@ -679,6 +716,7 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -870,6 +908,71 @@ steps:
   commands:
   - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
+- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_2
+  num_gpus: 2
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/distributed/
+  - vllm/engine/
+  - vllm/executor/
+  - vllm/worker/worker_base.py
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/distributed/
+  - tests/v1/shutdown
+  - tests/v1/worker/test_worker_memory_snapshot.py
+  - vllm/platforms/rocm.py
+  commands:
+  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
+  - HIP_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
+  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
+
+- label: Distributed Compile + Comm (4 GPUs) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_4
+  num_gpus: 4
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/distributed/
+  - tests/distributed/test_pynccl
+  - tests/distributed/test_events
+  - tests/compile/fullgraph/test_basic_correctness.py
+  - tests/distributed/test_symm_mem_allreduce.py
+  - tests/distributed/test_multiproc_executor.py
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s compile/fullgraph/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
+  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
+
+#----------------------------------------------------------  mi300 · engine  -----------------------------------------------------------#
+
+- label: Engine # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/engine
+  - tests/test_sequence
+  - tests/test_config
+  - tests/test_logger
+  - tests/test_vllm_port
+  commands:
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
+
 #--------------------------------------------------------  mi300 · entrypoints  --------------------------------------------------------#
 
 - label: Entrypoints Unit Tests # TBD
@@ -878,6 +981,7 @@ steps:
   dind: false
   agent_pool: mi300_1
   fast_check: true
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/entrypoints
@@ -982,6 +1086,7 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  optional: true
   fast_check: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -1010,6 +1115,7 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  optional: true
   fast_check: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -1024,6 +1130,7 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  optional: true
   fast_check: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -1345,6 +1452,27 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm.txt --tp-size=8
 
+- label: LM Eval Large Models (4xH100-4xMI300) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_4
+  num_gpus: 4
+  optional: true
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  commands:
+  - export VLLM_USE_DEEP_GEMM=0
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
+
 #---------------------------------------------------------  mi300 · examples  ----------------------------------------------------------#
 
 - label: Examples # TBD
@@ -1419,11 +1547,12 @@ steps:
   commands:
   - pytest -v -s kernels/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
-- label: Kernels Core Operation Test # TBD
+- label: Kernels Core Operation Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  parallelism: 3
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - csrc/
@@ -1434,7 +1563,7 @@ steps:
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py
+  - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py kernels/test_concat_mla_q.py kernels/test_top_k_per_row.py --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
 - label: Kernels KDA Test # TBD
   timeout_in_minutes: 180
@@ -1452,6 +1581,21 @@ steps:
   commands:
   - pytest -v -s kernels/test_kda.py
 
+- label: Kernels Mamba Test # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - csrc/mamba/
+  - tests/kernels/mamba
+  - vllm/model_executor/layers/mamba/ops
+  - vllm/platforms/rocm.py
+  commands:
+  - pytest -v -s kernels/mamba
+
 - label: Kernels MoE Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -1593,7 +1737,7 @@ steps:
   - set -x
   - export VLLM_USE_V2_MODEL_RUNNER=1
   - pytest -v -s v1/engine/test_llm_engine.py -k "not test_engine_metrics"
-  - ENFORCE_EAGER=1 pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
+  - pytest -v -s v1/e2e/general/test_async_scheduling.py -k "not ngram"
   - pytest -v -s v1/e2e/general/test_context_length.py
   - pytest -v -s v1/e2e/general/test_min_tokens.py
   - pytest -v -s entrypoints/llm/test_struct_output_generate.py -k "xgrammar and not speculative_config6 and not speculative_config7 and not speculative_config8 and not speculative_config0"
@@ -1797,6 +1941,37 @@ steps:
   - pip freeze | grep -E 'torch'
   - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
+- label: Language Models Test (Extended Generation) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/language/generation
+  commands:
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
+
+- label: Language Models Tests (Hybrid) %N # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  parallelism: 2
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/language/generation
+  commands:
+  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
+  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
+  - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
 #----------------------------------------------------  mi300 · models / multimodal  ----------------------------------------------------#
 
 - label: Multi-Modal Models (Extended Generation 1) # TBD
@@ -1899,20 +2074,32 @@ steps:
   commands:
   - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
-- label: Multi-Modal Processor (CPU) %N # TBD
+- label: Multi-Modal Models (Extended Pooling) # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  optional: true
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/
+  - tests/models/multimodal/pooling
+  commands:
+  - pytest -v -s models/multimodal/pooling -m 'not core_model'
+
+- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
-  parallelism: 4
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal
-  - tests/models/registry.py
   commands:
-  - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
+  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
 
 #-----------------------------------------------------  mi300 · models / quantized  -----------------------------------------------------#
 
@@ -1933,12 +2120,12 @@ steps:
 
 #--------------------------------------------------  mi300 · models / transformers  ---------------------------------------------------#
 
-- label: Transformers Nightly Models (Shardable) %N # TBD
+- label: Transformers Nightly Models (Initialization) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
-  parallelism: 4
+  parallelism: 6
   optional: true
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
@@ -1954,6 +2141,27 @@ steps:
   commands:
   - pip install --upgrade git+https://github.com/huggingface/transformers
   - pytest -v -s tests/models/test_initialization.py --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
+
+- label: Transformers Nightly Models (Processing) %N # TBD
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
+  dind: false
+  agent_pool: mi300_1
+  parallelism: 8
+  optional: true
+  working_dir: "/vllm-workspace/"
+  source_file_dependencies:
+  - vllm/model_executor/models/
+  - vllm/model_executor/model_loader/
+  - vllm/multimodal/
+  - vllm/model_executor/layers/
+  - vllm/v1/attention/backends/
+  - vllm/v1/attention/selector.py
+  - vllm/_aiter_ops.py
+  - vllm/platforms/rocm.py
+  - tests/models/
+  commands:
+  - pip install --upgrade git+https://github.com/huggingface/transformers
   - pytest -v -s tests/models/multimodal/processing/ --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
 - label: Transformers Nightly Models (Single) # TBD
@@ -2472,11 +2680,12 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s v1/kv_connector/extract_hidden_states_integration
 
-- label: V1 attention (H100-MI300) # TBD
+- label: V1 attention (H100-MI300) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   dind: false
   agent_pool: mi300_1
+  parallelism: 2
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -2488,7 +2697,7 @@ steps:
   - vllm/envs.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/attention
+  - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
 - label: V1 Core + KV + Metrics # TBD
   timeout_in_minutes: 180
@@ -2517,23 +2726,6 @@ steps:
   # - export HSA_NO_SCRATCH_RECLAIM=1
   - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
 
-- label: V1 others (CPU) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
-  dind: false
-  agent_pool: mi300_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/v1
-  commands:
-  - pytest -v -s -m 'cpu_test' v1/core
-  - pytest -v -s v1/structured_output
-  - pytest -v -s v1/test_serial_utils.py
-  - pytest -v -s -m 'cpu_test' v1/kv_connector/unit
-  - pytest -v -s -m 'cpu_test' v1/metrics
-
 - label: V1 Sample + Logits # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
@@ -2830,195 +3022,6 @@ steps:
   commands:
   - bash weight_loading/run_model_weight_loading_test.sh -c weight_loading/models-large-amd.txt
 
-#########################################################################################################################################
-#                                                                                                                                       #
-#                                                         MI325 (gfx942) tests                                                          #
-#                                                                                                                                       #
-#########################################################################################################################################
-
-#----------------------------------------------------------  mi325 · compile  ----------------------------------------------------------#
-
-- label: Distributed Compile + RPC Tests (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/compilation/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/compile/test_wrapper.py
-  - tests/entrypoints/llm/test_collective_rpc.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s entrypoints/llm/test_collective_rpc.py
-  - pytest -v -s ./compile/fullgraph/test_basic_correctness.py
-  - pytest -v -s ./compile/test_wrapper.py
-
-#--------------------------------------------------------  mi325 · distributed  --------------------------------------------------------#
-
-- label: Distributed Torchrun + Shutdown Tests (2 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_2
-  num_gpus: 2
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/executor/
-  - vllm/worker/worker_base.py
-  - vllm/v1/engine/
-  - vllm/v1/worker/
-  - tests/distributed/
-  - tests/v1/shutdown
-  - tests/v1/worker/test_worker_memory_snapshot.py
-  - vllm/platforms/rocm.py
-  commands:
-  - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - VLLM_TEST_SAME_HOST=1 VLLM_TEST_WITH_DEFAULT_DEVICE_SET=1 torchrun --nproc-per-node=4 distributed/test_same_node.py | grep 'Same node test passed'
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s v1/shutdown
-  - pytest -v -s v1/worker/test_worker_memory_snapshot.py
-
-- label: Distributed Compile + Comm (4 GPUs) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/distributed/
-  - tests/distributed/test_pynccl
-  - tests/distributed/test_events
-  - tests/compile/fullgraph/test_basic_correctness.py
-  - tests/distributed/test_symm_mem_allreduce.py
-  - tests/distributed/test_multiproc_executor.py
-  - vllm/platforms/rocm.py
-  commands:
-  - pytest -v -s compile/fullgraph/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
-  - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
-
-#----------------------------------------------------------  mi325 · engine  -----------------------------------------------------------#
-
-- label: Engine # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/engine
-  - tests/test_sequence
-  - tests/test_config
-  - tests/test_logger
-  - tests/test_vllm_port
-  commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
-
-#-----------------------------------------------------------  mi325 · evals  -----------------------------------------------------------#
-
-- label: LM Eval Large Models (4xH100-4xMI325) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_4
-  num_gpus: 4
-  optional: true
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  - vllm/model_executor/models/
-  - vllm/model_executor/model_loader/
-  - vllm/v1/attention/backends/
-  - vllm/v1/attention/selector.py
-  - vllm/_aiter_ops.py
-  - vllm/platforms/rocm.py
-  commands:
-  - export VLLM_USE_DEEP_GEMM=0
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-rocm-fp8.txt --tp-size=4
-
-#-----------------------------------------------------  mi325 · models / language  -----------------------------------------------------#
-
-- label: Language Models Test (Extended Generation) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
-  - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
-
-- label: Language Models Tests (Hybrid) %N # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  parallelism: 2
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/language/generation
-  commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@fix-rocm-7.0-warp-size-constexpr'
-  - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
-  - pytest -v -s models/language/generation -m hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-
-#----------------------------------------------------  mi325 · models / multimodal  ----------------------------------------------------#
-
-- label: Multi-Modal Models (Extended Pooling) # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal/pooling
-  commands:
-  - pytest -v -s models/multimodal/pooling -m 'not core_model'
-
-- label: "Multi-Modal Models (Standard) 2: qwen3 + gemma" # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - vllm/
-  - tests/models/multimodal
-  commands:
-  - pytest -v -s models/multimodal/generation/test_common.py -m core_model -k "qwen3 or gemma"
-  - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
-
-#-----------------------------------------------------------  mi325 · misc  ------------------------------------------------------------#
-
-- label: Python-only Installation # TBD
-  timeout_in_minutes: 180
-  mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
-  agent_pool: mi325_1
-  optional: true
-  working_dir: "/vllm-workspace/tests"
-  source_file_dependencies:
-  - tests/standalone_tests/python_only_compile.sh
-  - setup.py
-  - vllm/platforms/rocm.py
-  commands:
-  - bash standalone_tests/python_only_compile.sh
-
 #########################################################################################################################################
 #                                                                                                                                       #
 #                                                         MI355 (gfx950) tests                                                          #
@@ -3030,6 +3033,7 @@ steps:
 - label: Attention Benchmarks Smoke Test (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   working_dir: "/vllm-workspace/"
@@ -3046,6 +3050,7 @@ steps:
 - label: Distributed Tests (2xH100-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   optional: true
@@ -3090,6 +3095,7 @@ steps:
 - label: Entrypoints Integration (API Server) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   fast_check: true
@@ -3107,6 +3113,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   optional: true
@@ -3122,6 +3129,7 @@ steps:
 - label: Entrypoints Integration (API Server OpenAI - Part 2) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   optional: true
@@ -3138,6 +3146,7 @@ steps:
 - label: Entrypoints Integration (API Server Generate) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   optional: true
@@ -3158,6 +3167,7 @@ steps:
 - label: Entrypoints Integration (Speech to Text) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   working_dir: "/vllm-workspace/tests"
@@ -3171,6 +3181,7 @@ steps:
 - label: Entrypoints Integration (Multimodal)
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   working_dir: "/vllm-workspace/tests"
@@ -3184,6 +3195,7 @@ steps:
 - label: Entrypoints Integration (Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   fast_check: true
   working_dir: "/vllm-workspace/tests"
@@ -3199,6 +3211,7 @@ steps:
 - label: GPQA Eval (GPT-OSS) (2xB200-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   optional: true
@@ -3221,6 +3234,7 @@ steps:
 - label: LM Eval Qwen3-5 Models (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   optional: true
@@ -3243,6 +3257,7 @@ steps:
 - label: LM Eval Small Models (2xB200-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   optional: true
@@ -3262,6 +3277,7 @@ steps:
 - label: Qwen3-30B-A3B-FP8-block Sync EPLB Accuracy (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   working_dir: "/vllm-workspace"
@@ -3282,6 +3298,7 @@ steps:
 - label: LM Eval Large Models (4xH100-4xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_4
   num_gpus: 4
   optional: true
@@ -3304,6 +3321,7 @@ steps:
 - label: Examples # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -3339,6 +3357,7 @@ steps:
 - label: Kernels (B200-MI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/"
   source_file_dependencies:
@@ -3364,6 +3383,7 @@ steps:
 - label: Kernels Attention Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   parallelism: 2
   working_dir: "/vllm-workspace/tests"
@@ -3381,6 +3401,7 @@ steps:
 - label: Kernels MoE Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   parallelism: 5
   working_dir: "/vllm-workspace/tests"
@@ -3401,6 +3422,7 @@ steps:
 - label: Kernels Quantization Test %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   parallelism: 2
   working_dir: "/vllm-workspace/tests"
@@ -3418,6 +3440,7 @@ steps:
 - label: Kernels FP8 MoE Test (2xH100-2xMI355) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   working_dir: "/vllm-workspace/tests"
@@ -3437,6 +3460,7 @@ steps:
 - label: Language Models Test (Extended Generation) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -3450,6 +3474,7 @@ steps:
 - label: Language Models Test (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3462,7 +3487,9 @@ steps:
 - label: Language Models Test (PPL) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/model_executor/models/qwen3_5.py
@@ -3489,6 +3516,7 @@ steps:
 - label: Language Models Tests (Standard) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -3503,6 +3531,7 @@ steps:
 - label: Multi-Modal Models (Extended Generation 1) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3517,6 +3546,7 @@ steps:
 - label: Multi-Modal Models (Extended Generation 3) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3529,6 +3559,7 @@ steps:
 - label: Multi-Modal Models (Extended Pooling) # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3541,6 +3572,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3554,6 +3586,7 @@ steps:
 - label: "Multi-Modal Models (Standard) 4: other + whisper" # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3570,6 +3603,7 @@ steps:
 - label: Quantized Models Test # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -3586,6 +3620,7 @@ steps:
 - label: Quantization # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -3602,6 +3637,7 @@ steps:
 # - label: Quantized MoE Test (B200-MI355) # TBD
 #   timeout_in_minutes: 180
 #   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+#   dind: false
 #   agent_pool: mi355_1
 #   working_dir: "/vllm-workspace/"
 #   source_file_dependencies:
@@ -3627,10 +3663,12 @@ steps:
 
 #------------------------------------------------------------  mi355 · v1  -------------------------------------------------------------#
 
-- label: V1 attention (B200-MI355) # TBD
+- label: V1 attention (B200-MI355) %N # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
+  parallelism: 2
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/config/attention.py
@@ -3641,11 +3679,12 @@ steps:
   - vllm/envs.py
   - vllm/platforms/rocm.py
   commands:
-  - pytest -v -s v1/attention
+  - pytest -v -s v1/attention --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
 
 - label: V1 Core + KV + Metrics # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3672,6 +3711,7 @@ steps:
 - label: V1 Sample + Logits # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"
@@ -3692,6 +3732,7 @@ steps:
 - label: V1 Spec Decode # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -3705,6 +3746,7 @@ steps:
 - label: Weight Loading Multiple GPU # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   num_gpus: 2
   working_dir: "/vllm-workspace/tests"
@@ -3717,6 +3759,7 @@ steps:
 - label: Weight Loading Multiple GPU - Large Models # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_2
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
@@ -3732,6 +3775,7 @@ steps:
 - label: Regression # TBD
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
+  dind: false
   agent_pool: mi355_1
   optional: true
   working_dir: "/vllm-workspace/tests"

.buildkite/test_areas/attention.yaml

@@ -16,8 +16,9 @@ steps:
   parallelism: 2
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 95
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 125
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/basic_correctness.yaml

@@ -18,7 +18,8 @@ steps:
   - pytest -v -s basic_correctness/test_cpu_offload.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 70
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd

.buildkite/test_areas/benchmarks.yaml

@@ -15,6 +15,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 40
       depends_on:
       - image-build-amd
 

.buildkite/test_areas/disaggregated.yaml

@@ -17,7 +17,7 @@ steps:
     amd:
       dind: false
       device: mi300_4
-      timeout_in_minutes: 85
+      timeout_in_minutes: 60
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -68,7 +68,7 @@ steps:
     amd:
       dind: false
       device: mi300_4
-      timeout_in_minutes: 60
+      timeout_in_minutes: 40
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -94,7 +94,7 @@ steps:
     amd:
       dind: false
       device: mi300_4
-      timeout_in_minutes: 85
+      timeout_in_minutes: 60
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -120,7 +120,7 @@ steps:
     amd:
       dind: false
       device: mi300_4
-      timeout_in_minutes: 80
+      timeout_in_minutes: 55
       depends_on:
         - image-build-amd
       source_file_dependencies:
@@ -177,7 +177,7 @@ steps:
     amd:
       dind: false
       device: mi300_2
-      timeout_in_minutes: 70
+      timeout_in_minutes: 45
       depends_on:
         - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/distributed.yaml

@@ -41,6 +41,7 @@ steps:
     amd:
       dind: false
       device: mi300_2
+      timeout_in_minutes: 45
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/engine.yaml

@@ -28,8 +28,9 @@ steps:
   - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 50
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 40
       depends_on:
       - image-build-amd
 
@@ -44,8 +45,8 @@ steps:
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 55
+      device: mi250_1
+      timeout_in_minutes: 45
       depends_on:
       - image-build-amd
 
@@ -60,8 +61,8 @@ steps:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 70
+      device: mi250_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
 
@@ -76,8 +77,8 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 60
+      device: mi250_1
+      timeout_in_minutes: 50
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -116,6 +117,7 @@ steps:
     amd:
       dind: false
       device: mi300_2
+      timeout_in_minutes: 30
       depends_on:
       - image-build-amd
 

.buildkite/test_areas/entrypoints.yaml

@@ -30,9 +30,9 @@ steps:
   - pytest -v -s entrypoints/llm/offline_mode # Needs to avoid interference with other tests
   mirror:
     amd:
-      device: mi325_1
-      # TODO(akaratza): Test after Torch >= 2.12 bump
-      soft_fail: true
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
 
@@ -52,7 +52,9 @@ steps:
   - pytest -v -s entrypoints/scale_out
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
@@ -70,7 +72,8 @@ steps:
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/correctness
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
       timeout_in_minutes: 65
       depends_on:
       - image-build-amd
@@ -90,8 +93,9 @@ steps:
   - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 80
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 70
       depends_on:
       - image-build-amd
 
@@ -113,7 +117,8 @@ steps:
   - pytest -v -s entrypoints/anthropic
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
       timeout_in_minutes: 65
       depends_on:
       - image-build-amd
@@ -176,7 +181,9 @@ steps:
   - pytest -s entrypoints/openai/correctness/
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 30
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/expert_parallelism.yaml

@@ -18,6 +18,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 30
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/fault_tolerance.yaml

@@ -0,0 +1,26 @@
+group: Fault Tolerance
+depends_on:
+  - image-build
+steps:
+- label: Fault Tolerance E2E (2xH100)
+  key: fault-tolerance-e2e-2xh100
+  timeout_in_minutes: 35
+  device: h100
+  num_devices: 2
+  working_dir: "/vllm-workspace/tests"
+  source_file_dependencies:
+  - vllm/v1/fault_tolerance/
+  - vllm/v1/worker/sentinel/
+  - vllm/entrypoints/serve/fault_tolerance/
+  - vllm/distributed/elastic_ep/
+  - vllm/distributed/device_communicators/
+  - vllm/v1/engine/
+  - vllm/v1/worker/
+  - tests/v1/fault_tolerance/
+  - tests/v1/distributed/test_external_lb_dp.py
+  commands:
+  # Base image has no nixl; install it or has_nixl_ep() skips the tests.
+  - bash /vllm-workspace/.buildkite/scripts/install-kv-connectors.sh
+  # https://github.com/NVIDIA/nccl/issues/1838
+  - export NCCL_CUMEM_HOST_ENABLE=0
+  - pytest -v -s v1/fault_tolerance/test_fault_tolerance_e2e.py

.buildkite/test_areas/kernels.yaml

@@ -80,7 +80,8 @@ steps:
   parallelism: 2
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
       timeout_in_minutes: 90
       depends_on:
       - image-build-amd
@@ -118,7 +119,9 @@ steps:
   parallelism: 2
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 120
       source_file_dependencies:
       - csrc/quantization/
       - vllm/model_executor/layers/quantization
@@ -148,8 +151,9 @@ steps:
   parallelism: 5
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 65
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 55
       source_file_dependencies:
       - csrc/quantization/cutlass_w8a8/moe/
       - csrc/moe/

.buildkite/test_areas/lm_eval.yaml

@@ -14,8 +14,9 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 55
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 45
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -141,7 +142,7 @@ steps:
     amd:
       dind: false
       device: mi300_8
-      timeout_in_minutes: 60
+      timeout_in_minutes: 40
       depends_on:
       - image-build-amd
       commands:
@@ -336,7 +337,7 @@ steps:
 
 - label: LM Eval KV-Offload (2xH100)
   key: kv-offload-medium
-  timeout_in_minutes: 30
+  timeout_in_minutes: 45
   device: h100
   num_devices: 2
   source_file_dependencies:
@@ -346,7 +347,7 @@ steps:
   - vllm/v1/simple_kv_offload/
   - tests/evals/gsm8k/test_gsm8k_offloading.py
   commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "qwen3.5-35b"
+  - pytest -s -v evals/gsm8k/test_gsm8k_offloading.py -k "qwen3.5-35b or deepseek-v2-lite"
 
 - label: LM Eval KV-Offload (4xH100)
   key: kv-offload-large

.buildkite/test_areas/lora.yaml

@@ -14,9 +14,10 @@ steps:
   parallelism: 4
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
       working_dir: "/vllm-workspace/tests"
-      timeout_in_minutes: 65
+      timeout_in_minutes: 85
       source_file_dependencies:
       - vllm/lora
       - tests/lora
@@ -46,4 +47,4 @@ steps:
     - pytest -v -s -x lora/test_qwen3_with_multi_loras.py
     - pytest -v -s -x lora/test_olmoe_tp.py
     - pytest -v -s -x lora/test_gptoss_tp.py
-    - pytest -v -s -x lora/test_qwen35_densemodel_lora.py
+    - pytest -v -s -x lora/test_qwen35_densemodel_lora.py

.buildkite/test_areas/misc.yaml

@@ -25,7 +25,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
-      timeout_in_minutes: 75
+      timeout_in_minutes: 50
       depends_on:
       - image-build-amd
 
@@ -59,7 +59,9 @@ steps:
     - pytest -v -s v1/test_outputs.py
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 70
       depends_on:
       - image-build-amd
 
@@ -113,8 +115,9 @@ steps:
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 75
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
@@ -209,7 +212,7 @@ steps:
   - vllm/multimodal
   - examples/
   commands:
-    - pip install tensorizer # for tensorizer test
+    - pip install --no-deps tensorizer # for tensorizer test
      # for basic
     - python3 basic/offline_inference/chat.py
     - python3 basic/offline_inference/generate.py --model facebook/opt-125m
@@ -233,7 +236,9 @@ steps:
     - python3 features/speculative_decoding/spec_decode_offline.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 1536
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 75
       source_file_dependencies:
       - vllm/entrypoints
       - vllm/multimodal
@@ -269,10 +274,11 @@ steps:
   - pytest -v -s v1/tracing
   mirror:
     amd:
-      device: mi325_2
+      dind: false
+      device: mi300_2
+      timeout_in_minutes: 30
       depends_on:
       - image-build-amd
-      optional: true
 
 - label: Python-only Installation
   key: python-only-installation
@@ -287,8 +293,8 @@ steps:
   - bash standalone_tests/python_only_compile.sh
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 45
+      device: mi250_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/model_executor.yaml

@@ -30,6 +30,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/model_runner_v2.yaml

@@ -41,7 +41,7 @@ steps:
   commands:
     - set -x
     - export VLLM_USE_V2_MODEL_RUNNER=1
-    - pip install tensorizer # for tensorizer test
+    - pip install --no-deps tensorizer # for tensorizer test
     - python3 basic/offline_inference/chat.py # for basic
     - python3 basic/offline_inference/generate.py --model facebook/opt-125m
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO

.buildkite/test_areas/models_basic.yaml

@@ -42,7 +42,9 @@ steps:
     - pytest -v -s models/test_terratorch.py models/transformers/test_backend.py models/test_registry.py
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 50
       depends_on:
       - image-build-amd
 

.buildkite/test_areas/models_language.yaml

@@ -17,6 +17,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 45
       depends_on:
       - image-build-amd
 
@@ -39,6 +40,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 40
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -61,7 +63,6 @@ steps:
   - tests/models/language/generation
   commands:
     # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     # Shard the hybrid language model tests that are numerically stable on Hopper.
@@ -69,8 +70,9 @@ steps:
   parallelism: 2
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 70
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       commands:
@@ -102,7 +104,6 @@ steps:
   - tests/models/language/generation
   commands:
     # Install fast path packages for testing against transformers
-    # Note: also needed to run plamo2 model in vLLM
     - uv pip install --system --no-build-isolation 'git+https://github.com/state-spaces/mamba@v2.3.0'
     - uv pip install --system --no-build-isolation 'git+https://github.com/Dao-AILab/causal-conv1d@v1.6.0'
     - pytest -v -s models/language/generation -m '(not core_model) and (not hybrid_model)'
@@ -130,8 +131,9 @@ steps:
     - pytest -v -s models/language/pooling -m 'not core_model'
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 120
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 95
       depends_on:
       - image-build-amd
 

.buildkite/test_areas/models_multimodal.yaml

@@ -14,7 +14,9 @@ steps:
     - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 65
       depends_on:
       - image-build-amd
 
@@ -31,7 +33,9 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_5_vl.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
 
@@ -47,7 +51,8 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
   mirror:
     amd:
-      device: mi325_1
+      device: mi250_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
 
@@ -65,7 +70,9 @@ steps:
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 50
       depends_on:
       - image-build-amd
 
@@ -109,6 +116,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 35
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -131,7 +139,9 @@ steps:
     - pytest -v -s models/multimodal/test_mapping.py
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 90
       depends_on:
       - image-build-amd
 
@@ -166,8 +176,9 @@ steps:
     - pytest -v -s models/multimodal/pooling -m 'not core_model'
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 75
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 60
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/pytorch.yaml

@@ -107,13 +107,6 @@ steps:
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
-  mirror:
-    amd:
-      dind: false
-      device: mi300_1
-      timeout_in_minutes: 65
-      depends_on:
-      - image-build-amd
 
 - label: PyTorch Fullgraph Smoke Test
   device: h200_35gb
@@ -236,6 +229,7 @@ steps:
     amd:
       dind: false
       device: mi300_1
+      timeout_in_minutes: 30
       depends_on:
       - image-build-amd
       source_file_dependencies:

.buildkite/test_areas/quantization.yaml

@@ -24,8 +24,7 @@ steps:
   - uv pip install --system conch-triton-kernels
   # The SM90-only checkpoint currently contains a removed weight_chan_scale
   # parameter. It was not exercised by the previous L4 job.
-  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py -k 'not test_compressed_tensors_w4a8_fp8' --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 8
+  - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py -k 'not test_compressed_tensors_w4a8_fp8'
 
 - label: Quantized Fusions
   device: h200_35gb
@@ -68,5 +67,4 @@ steps:
   - vllm/model_executor/layers/quantization
   - tests/models/quantization
   commands:
-    - pytest -v -s models/quantization --shard-id=$$BUILDKITE_PARALLEL_JOB --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT
-  parallelism: 3
+    - pytest -v -s models/quantization

.buildkite/test_areas/samplers.yaml

@@ -19,8 +19,18 @@ steps:
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi325_1
+      device: mi250_1
+      timeout_in_minutes: 40
       depends_on:
       - image-build-amd
+      source_file_dependencies:
+      - vllm/model_executor/layers
+      - vllm/sampling_metadata.py
+      - vllm/v1/sample/
+      - vllm/entrypoints/generate/beam_search/
+      - tests/samplers
+      - tests/conftest.py
+      - vllm/_aiter_ops.py
+      - vllm/platforms/rocm.py
       commands:
       - pytest -v -s samplers

.buildkite/test_areas/spec_decode.yaml

@@ -14,8 +14,9 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 60
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 55
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -53,8 +54,9 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 65
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 75
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -92,10 +94,9 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
   mirror:
     amd:
-      device: mi325_1
-      timeout_in_minutes: 55
-      # TODO(akaratza): Test after Torch >= 2.12 bump
-      soft_fail: true
+      dind: false
+      device: mi300_1
+      timeout_in_minutes: 35
       depends_on:
       - image-build-amd
       source_file_dependencies:
@@ -119,7 +120,8 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
   mirror:
     amd:
-      device: mi325_1
+      dind: false
+      device: mi300_1
       timeout_in_minutes: 55
       depends_on:
       - image-build-amd

.buildkite/test_areas/weight_loading.yaml

@@ -17,6 +17,7 @@ steps:
     amd:
       dind: false
       device: mi300_2
+      timeout_in_minutes: 35
       depends_on:
       - image-build-amd
       commands:

.github/mergify.yml

@@ -19,6 +19,7 @@ pull_request_rules:
   description: Comment on PR when pre-commit check fails
   conditions:
     - check-failure=pre-commit
+    - -check-cancelled=pre-commit
     - -closed
     - -draft
     - or:
@@ -232,6 +233,31 @@ pull_request_rules:
       add:
         - gpt-oss
 
+- name: label-kimi
+  description: Automatically apply kimi label
+  conditions:
+    - label != stale
+    - or:
+      - files~=(?i)kimi
+      - files~=(?i)moonshot
+      - title~=(?i)(?:kimi|moonshot)
+  actions:
+    label:
+      add:
+        - kimi
+
+- name: label-k3
+  description: Automatically apply k3 label (launch triage; retire after ramp-down)
+  conditions:
+    - label != stale
+    - or:
+      - files~=(?i)kimi[-_]?k3
+      - title~=(?i)(?:kimi[-\s]?k3|\bk3\b)
+  actions:
+    label:
+      add:
+        - k3
+
 - name: label-nvidia
   description: Automatically apply nvidia label
   conditions:

.github/workflows/issue_autolabel.yml

@@ -130,6 +130,25 @@ jobs:
                   },
                 ],
               },
+              kimi: {
+                keywords: [
+                  { term: "Kimi",     searchIn: "both" },
+                  { term: "Moonshot", searchIn: "both" },
+                ],
+                substrings: [
+                  { term: "moonshotai/", searchIn: "both" },
+                  { term: "kimi",        searchIn: "title" },
+                ],
+              },
+              k3: {
+                keywords: [
+                  { term: "Kimi K3", searchIn: "both" },
+                  { term: "K3",      searchIn: "title" },
+                ],
+                substrings: [
+                  { term: "moonshotai/kimi-k3", searchIn: "both" },
+                ],
+              },
               quantization: {
                 keywords: [
                   {

.gitignore

@@ -173,9 +173,6 @@ venv.bak/
 
 # mkdocs documentation
 /site
-docs/argparse
-docs/examples/*
-!docs/examples/README.md
 
 # mypy
 .mypy_cache/

.markdownlint.yaml

@@ -3,6 +3,9 @@ MD007:
 MD013: false
 MD024:
   siblings_only: true
+MD025:
+  # Allow front matter title to be different from the first heading in the document.
+  front_matter_title: ""
 MD031:
   list_items: false
 MD033: false

.pre-commit-config.yaml

@@ -260,10 +260,6 @@ repos:
     files: ^docker/(Dockerfile|versions\.json)$
     pass_filenames: false
     additional_dependencies: [dockerfile-parse]
-  - id: attention-backend-docs
-    name: Check attention backend documentation is up to date
-    entry: python tools/pre_commit/generate_attention_backend_docs.py --check
-    language: python
   - id: check-boolean-context-manager
     name: Check for boolean ops in with-statements
     entry: python tools/pre_commit/check_boolean_context_manager.py

CMakeLists.txt

@@ -114,6 +114,11 @@ find_package(Torch REQUIRED)
 # Supported NVIDIA architectures.
 # This check must happen after find_package(Torch) because that's when CMAKE_CUDA_COMPILER_VERSION gets defined
 if(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
+   CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.4)
+  # Rubin (10.7) can run SM100 family code, but CUDA 13.4 also supports
+  # targeting it directly.
+  set(CUDA_SUPPORTED_ARCHS "7.5;8.0;8.6;8.7;8.9;9.0;10.0;10.7;11.0;12.0")
+elseif(DEFINED CMAKE_CUDA_COMPILER_VERSION AND
    CMAKE_CUDA_COMPILER_VERSION VERSION_GREATER_EQUAL 13.0)
    # starting from CUDA 12.9 and Blackwell (10.0), we use family-specific targets (10.0f, 12.0f, etc)
    # to support the whole generation without specifying all sub-architectures
@@ -420,7 +425,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
 
     if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
       cuda_archs_loose_intersection(COOPERATIVE_TOPK_ARCHS
-        "9.0a;10.0f;10.1f;10.3f;11.0f;12.0f;12.1f" "${CUDA_ARCHS}")
+        "9.0a;10.0f;10.1f;10.3f;10.7f;11.0f;12.0f;12.1f" "${CUDA_ARCHS}")
     else()
       cuda_archs_loose_intersection(COOPERATIVE_TOPK_ARCHS
         "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
@@ -695,7 +700,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
 
   # DeepSeek V3 fused A GEMM kernel (requires SM 9.0+, Hopper and later)
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0f;10.7f;11.0f;12.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(DSV3_FUSED_A_GEMM_ARCHS "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
   endif()
@@ -815,7 +820,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   # The cutlass_scaled_mm kernels for Blackwell SM100 (c3x, i.e. CUTLASS 3.x)
   # require CUDA 12.8 or later
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;10.7f;11.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
   endif()
@@ -899,7 +904,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   endif()
 
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0f;10.7f;11.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
   endif()
@@ -924,7 +929,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
 
   # moe_data.cu is used by all CUTLASS MoE kernels.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0f;11.0f;12.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0f;10.7f;11.0f;12.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(CUTLASS_MOE_DATA_ARCHS "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${CUDA_ARCHS}")
   endif()
@@ -981,7 +986,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   # SM10x/11x FP4 kernels. MXFP4 experts quantization is currently compiled
   # only in this block; SM12x has separate NVFP4 matmul/MoE kernels above.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(FP4_SM100_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(FP4_SM100_ARCHS "10.0f;10.7f;11.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(FP4_SM100_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
   endif()
@@ -1047,7 +1052,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA" OR VLLM_GPU_LANG STREQUAL "HIP")
   # Runtime dispatch is gated in
   # vllm/v1/attention/backends/mla/cutlass_mla.py.
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(MLA_ARCHS "10.0f;11.0f" "${CUDA_ARCHS}")
+    cuda_archs_loose_intersection(MLA_ARCHS "10.0f;10.7f;11.0f" "${CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(MLA_ARCHS "10.0a;10.1a;10.3a" "${CUDA_ARCHS}")
   endif()

benchmarks/attention_benchmarks/benchmark.py

@@ -1358,6 +1358,10 @@ def main():
                             profile_memory=args.profile_memory,
                             warmup_ms=args.warmup_ms,
                             prefill_backend=pb,
+                            kv_lora_rank=args.kv_lora_rank,
+                            qk_nope_head_dim=args.qk_nope_head_dim,
+                            qk_rope_head_dim=args.qk_rope_head_dim,
+                            v_head_dim=args.v_head_dim,
                         )
 
                         result = run_benchmark(config)

benchmarks/kernels/benchmark_inkling_qkvr_prep.py

@@ -0,0 +1,176 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import statistics
+
+import torch
+from tabulate import tabulate
+
+from vllm.models.inkling.nvidia.ops import qkvr_prep
+from vllm.utils.argparse_utils import FlexibleArgumentParser
+
+
+def make_inputs(tokens: int, tp_size: int, is_local: bool):
+    torch.manual_seed(0)
+    num_q_heads = 64 // tp_size
+    num_kv_heads = (16 if is_local else 8) // tp_size
+    head_dim = 128
+    d_rel = 16
+    rel_extent = 512 if is_local else 1024
+    page_size = 16
+    num_blocks = (tokens + page_size - 1) // page_size
+    q_width = num_q_heads * head_dim
+    kv_width = num_kv_heads * head_dim
+    r_width = num_q_heads * d_rel
+    device = "cuda"
+
+    qkvr = torch.randn(
+        tokens,
+        q_width + 2 * kv_width + r_width,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    k_weight = torch.randn(kv_width, 4, device=device, dtype=torch.bfloat16)
+    v_weight = torch.randn_like(k_weight)
+    q_norm_weight = torch.randn(head_dim, device=device, dtype=torch.bfloat16)
+    k_norm_weight = torch.randn_like(q_norm_weight)
+    rel_proj = torch.randn(d_rel, rel_extent, device=device, dtype=torch.bfloat16)
+    conv_cache = torch.zeros(
+        num_blocks,
+        num_kv_heads,
+        page_size,
+        2 * head_dim,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    key_cache = torch.empty(
+        num_blocks,
+        page_size,
+        num_kv_heads,
+        head_dim,
+        device=device,
+        dtype=torch.bfloat16,
+    )
+    value_cache = torch.empty_like(key_cache)
+    positions = torch.arange(tokens, device=device, dtype=torch.int64)
+    block_table = torch.arange(num_blocks, device=device, dtype=torch.int32)[None]
+    seq_idx = torch.zeros(tokens, device=device, dtype=torch.int32)
+    slots = torch.arange(tokens, device=device, dtype=torch.int64)
+    query_start = torch.zeros(tokens, device=device, dtype=torch.int32)
+    log_scaling = None
+    if not is_local:
+        effective_n = (positions + 1).to(torch.float32)
+        log_scaling = 1.0 + 0.1 * torch.log(torch.clamp(effective_n / 128000, min=1.0))
+    return (
+        qkvr,
+        k_weight,
+        v_weight,
+        q_norm_weight,
+        k_norm_weight,
+        rel_proj,
+        1e-6,
+        num_q_heads,
+        num_kv_heads,
+        head_dim,
+        d_rel,
+        conv_cache,
+        key_cache,
+        value_cache,
+        positions,
+        block_table,
+        seq_idx,
+        slots,
+        query_start,
+        slots,
+        0,
+        head_dim,
+        page_size,
+        log_scaling,
+    )
+
+
+def capture(implementation, inputs):
+    outputs = []
+
+    def run():
+        outputs[:] = implementation.fused_qkvr_prep(*inputs)
+
+    stream = torch.cuda.Stream()
+    stream.wait_stream(torch.cuda.current_stream())
+    with torch.cuda.stream(stream):
+        for _ in range(3):
+            run()
+    torch.cuda.current_stream().wait_stream(stream)
+    torch.accelerator.synchronize()
+    graph = torch.cuda.CUDAGraph()
+    with torch.cuda.graph(graph):
+        run()
+    torch.accelerator.synchronize()
+    return graph, outputs
+
+
+def time_graph(graph: torch.cuda.CUDAGraph, warmup: int, repeats: int) -> float:
+    for _ in range(warmup):
+        graph.replay()
+    torch.accelerator.synchronize()
+    start = torch.cuda.Event(enable_timing=True)
+    end = torch.cuda.Event(enable_timing=True)
+    start.record()
+    for _ in range(repeats):
+        graph.replay()
+    end.record()
+    end.synchronize()
+    return start.elapsed_time(end) * 1000 / repeats
+
+
+def benchmark(inputs, args) -> float:
+    graph, _ = capture(qkvr_prep, inputs)
+    return statistics.median(
+        time_graph(graph, args.warmup, args.repeats) for _ in range(args.trials)
+    )
+
+
+@torch.inference_mode()
+def main(args):
+    rows = []
+    for tp_size in args.tp_sizes:
+        for tokens in args.tokens:
+            for is_local in (True, False):
+                triton_us = benchmark(make_inputs(tokens, tp_size, is_local), args)
+                rows.append(
+                    [
+                        tp_size,
+                        tokens,
+                        "local" if is_local else "global",
+                        triton_us,
+                    ]
+                )
+
+    print("Inkling QKVR prep (CUDA graph, median latency)")
+    print(
+        tabulate(
+            rows,
+            headers=[
+                "TP",
+                "tokens",
+                "scope",
+                "Triton (us)",
+            ],
+            floatfmt=("d", "d", "", ".2f"),
+        )
+    )
+
+
+if __name__ == "__main__":
+    parser = FlexibleArgumentParser()
+    parser.add_argument(
+        "--tokens",
+        type=int,
+        nargs="+",
+        default=[1 << power for power in range(15)],
+    )
+    parser.add_argument("--tp-sizes", type=int, nargs="+", default=[4, 8])
+    parser.add_argument("--warmup", type=int, default=20)
+    parser.add_argument("--repeats", type=int, default=200)
+    parser.add_argument("--trials", type=int, default=5)
+    main(parser.parse_args())

benchmarks/kernels/cpu/benchmark_cpu_attn.py

@@ -154,7 +154,7 @@ def main(
                 scale=scale,
                 causal=True,
                 alibi_slopes=None,
-                sliding_window=window_size,
+                sliding_window=window_size if sliding_window is not None else -1,
                 block_table=block_tables,
                 softcap=0,
                 scheduler_metadata=metadata,

benchmarks/replayssm/e2e_decode_speedup.py

@@ -0,0 +1,267 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""End-to-end autoregressive decode benchmark: ReplaySSM vs the standard SSM kernel.
+
+Loads a hybrid Mamba2 model, replicates one prompt across the batch, and times a
+long greedy decode (CUDA graphs on) once with the standard kernel and once with
+ReplaySSM, then reports the per-step / throughput speedup. The two modes run in
+separate subprocesses so each gets a clean CUDA context.
+
+The FlashInfer FP4-MoE autotuner is disabled by default (it is unstable under
+CUDA-graph capture on the pre-release Blackwell FP4 path); pass
+--no-disable-flashinfer-autotune for non-FP4 models.
+
+Examples:
+    python e2e_decode_speedup.py --model-id nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
+    python e2e_decode_speedup.py --dtype auto --buffer-len 16 \
+        --model-id nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4   # B300 NVFP4
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+import time
+
+DEFAULT_PROMPT = "My cat wrote all this CUDA code for a new language model and"
+
+MODE_LABEL = {"standard": "standard", "replayssm": "ReplaySSM"}
+
+
+def parse_args():
+    p = argparse.ArgumentParser(
+        description="E2E decode speedup: ReplaySSM vs the standard SSM kernel."
+    )
+    p.add_argument("--model-id", default="nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16")
+    p.add_argument("--prompt", default=DEFAULT_PROMPT)
+    p.add_argument("--batch-size", type=int, default=256)
+    p.add_argument("--num-steps", type=int, default=1000)
+    p.add_argument("--warmup-steps", type=int, default=128)
+    p.add_argument("--repeats", type=int, default=1)
+    p.add_argument(
+        "--buffer-len", type=int, default=16, help="ReplaySSM input-buffer length."
+    )
+    p.add_argument(
+        "--dtype",
+        default="bfloat16",
+        choices=["bfloat16", "float16", "float32", "auto"],
+    )
+    p.add_argument("--gpu-memory-utilization", type=float, default=0.9)
+    p.add_argument("--max-model-len", type=int, default=None)
+    p.add_argument(
+        "--disable-flashinfer-autotune",
+        action=argparse.BooleanOptionalAction,
+        default=True,
+        help="Disable the FlashInfer FP4-MoE autotuner (default: on). "
+        "It is unstable under CUDA-graph capture on the "
+        "pre-release Blackwell FP4 path; pass "
+        "--no-disable-flashinfer-autotune for non-FP4 models.",
+    )
+    p.add_argument(
+        "--mamba-ssm-cache-dtype",
+        default="auto",
+        choices=["auto", "float32", "float16", "bfloat16"],
+        help="SSM state dtype (both modes). 'auto' = config-driven; "
+        "'float32' = fp32 state, 'bfloat16' = s16 state.",
+    )
+    p.add_argument(
+        "--baseline-ssm-config",
+        default="",
+        help="Pin the STANDARD baseline's SSM launch config as "
+        "'bsm,nw' via override_ssm_config (forces the in-process "
+        "engine so the override reaches the kernel). Empty = off.",
+    )
+    p.add_argument(
+        "--worker",
+        choices=["standard", "replayssm"],
+        default=None,
+        help=argparse.SUPPRESS,
+    )
+    return p.parse_args()
+
+
+def resolve_max_model_len(args) -> int:
+    if args.max_model_len is not None:
+        return args.max_model_len
+    return args.num_steps + 256
+
+
+def run_worker(args):
+    # override_ssm_config is a module global; it only reaches the model if the
+    # engine runs in-process (default V1 spawns a separate EngineCore). Force it.
+    if args.worker == "standard" and args.baseline_ssm_config:
+        os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0"
+
+    import torch
+
+    from vllm import LLM, SamplingParams
+
+    mode = args.worker
+    max_model_len = resolve_max_model_len(args)
+
+    llm_kwargs = dict(
+        model=args.model_id,
+        tensor_parallel_size=1,
+        dtype=args.dtype,
+        max_model_len=max_model_len,
+        trust_remote_code=True,
+        enable_prefix_caching=False,
+        enable_chunked_prefill=False,
+        max_num_seqs=args.batch_size,
+        max_num_batched_tokens=max(max_model_len, args.batch_size * 64),
+        enforce_eager=False,
+        disable_log_stats=True,
+        gpu_memory_utilization=args.gpu_memory_utilization,
+        # SSM state dtype (applies to both standard and ReplaySSM).
+        mamba_ssm_cache_dtype=args.mamba_ssm_cache_dtype,
+    )
+    if args.disable_flashinfer_autotune:
+        # FP4-MoE autotuner is unstable under CUDA-graph capture on Blackwell;
+        # re-enable (--no-disable-flashinfer-autotune) only for non-FP4 models.
+        llm_kwargs["kernel_config"] = {"enable_flashinfer_autotune": False}
+    if mode == "replayssm":
+        llm_kwargs.update(use_replayssm=True, replayssm_buffer_len=args.buffer_len)
+
+    _ssm_cm = None
+    if mode == "standard" and args.baseline_ssm_config:
+        from vllm.model_executor.layers.mamba.ops.mamba_ssm import override_ssm_config
+
+        _bsm, _nw = (int(x) for x in args.baseline_ssm_config.split(","))
+        _ssm_cm = override_ssm_config((_bsm, _nw))
+        _ssm_cm.__enter__()  # active through LLM() graph capture + decode
+        print(
+            f"[{mode}] override_ssm_config -> (BLOCK_SIZE_M={_bsm}, num_warps={_nw})",
+            flush=True,
+        )
+
+    llm = LLM(**llm_kwargs)
+    prompts = [args.prompt] * args.batch_size
+
+    def timed_generate(n_tokens):
+        sp = SamplingParams(
+            n=1,
+            temperature=0.0,
+            ignore_eos=True,
+            min_tokens=n_tokens,
+            max_tokens=n_tokens,
+        )
+        if torch.accelerator.is_available():
+            torch.accelerator.synchronize()
+        t0 = time.perf_counter()
+        outs = llm.generate(prompts, sp, use_tqdm=False)
+        if torch.accelerator.is_available():
+            torch.accelerator.synchronize()
+        elapsed = time.perf_counter() - t0
+        produced = min(len(o.outputs[0].token_ids) for o in outs)
+        assert produced == n_tokens, f"expected {n_tokens} tokens, got {produced}"
+        return elapsed
+
+    timed_generate(args.warmup_steps)
+
+    best = None
+    for _ in range(args.repeats):
+        elapsed = timed_generate(args.num_steps)
+        tok_s = args.batch_size * args.num_steps / elapsed
+        per_step_ms = elapsed / args.num_steps * 1e3
+        print(
+            f"[{mode}] {elapsed:.3f}s  {tok_s:,.0f} tok/s  {per_step_ms:.3f} ms/step",
+            flush=True,
+        )
+        if best is None or elapsed < best["elapsed_s"]:
+            best = {
+                "mode": mode,
+                "elapsed_s": elapsed,
+                "tok_s": tok_s,
+                "per_step_ms": per_step_ms,
+            }
+
+    print("RESULT_JSON " + json.dumps(best), flush=True)
+    if _ssm_cm is not None:
+        _ssm_cm.__exit__(None, None, None)
+
+
+def run_one_mode(args, mode) -> dict:
+    cmd = [
+        sys.executable,
+        __file__,
+        "--worker",
+        mode,
+        "--model-id",
+        args.model_id,
+        "--prompt",
+        args.prompt,
+        "--batch-size",
+        str(args.batch_size),
+        "--num-steps",
+        str(args.num_steps),
+        "--warmup-steps",
+        str(args.warmup_steps),
+        "--repeats",
+        str(args.repeats),
+        "--buffer-len",
+        str(args.buffer_len),
+        "--dtype",
+        args.dtype,
+        "--gpu-memory-utilization",
+        str(args.gpu_memory_utilization),
+        "--mamba-ssm-cache-dtype",
+        args.mamba_ssm_cache_dtype,
+        "--baseline-ssm-config",
+        args.baseline_ssm_config,
+    ]
+    cmd.append(
+        "--disable-flashinfer-autotune"
+        if args.disable_flashinfer_autotune
+        else "--no-disable-flashinfer-autotune"
+    )
+    if args.max_model_len is not None:
+        cmd += ["--max-model-len", str(args.max_model_len)]
+
+    result = None
+    proc = subprocess.Popen(
+        cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, bufsize=1
+    )
+    for line in proc.stdout:
+        sys.stdout.write(line)
+        sys.stdout.flush()
+        if line.startswith("RESULT_JSON "):
+            result = json.loads(line[len("RESULT_JSON ") :])
+    proc.wait()
+    if proc.returncode != 0:
+        raise RuntimeError(f"mode '{mode}' worker exited with {proc.returncode}")
+    if result is None:
+        raise RuntimeError(f"mode '{mode}' produced no RESULT_JSON line")
+    return result
+
+
+def main():
+    args = parse_args()
+    if args.worker is not None:
+        run_worker(args)
+        return
+
+    print(
+        f"model={args.model_id}  batch_size={args.batch_size}  "
+        f"steps={args.num_steps}  buffer_len={args.buffer_len}  dtype={args.dtype}"
+    )
+
+    std = run_one_mode(args, "standard")
+    fla = run_one_mode(args, "replayssm")
+    speedup = std["per_step_ms"] / fla["per_step_ms"]
+
+    print()
+    header = f"{'mode':<10}{'ms/step':>12}{'tok/s':>16}{'wall (s)':>12}"
+    print(header)
+    print("-" * len(header))
+    for r in (std, fla):
+        print(
+            f"{MODE_LABEL[r['mode']]:<10}{r['per_step_ms']:>12.3f}"
+            f"{r['tok_s']:>16,.0f}{r['elapsed_s']:>12.3f}"
+        )
+    print("-" * len(header))
+    print(f"speedup (standard / ReplaySSM, per step): {speedup:.3f}x")
+
+
+if __name__ == "__main__":
+    main()

cmake/cpu_extension.cmake

@@ -15,6 +15,7 @@ endif()
 #
 set(ENABLE_X86_ISA $ENV{VLLM_CPU_X86})
 set(ENABLE_ARM_BF16 $ENV{VLLM_CPU_ARM_BF16})
+set(ENABLE_ARM_I8MM $ENV{VLLM_CPU_ARM_I8MM})
 set(ENABLE_RVV_BF16 $ENV{VLLM_CPU_RVV_BF16})
 
 include_directories("${CMAKE_SOURCE_DIR}/csrc")
@@ -96,12 +97,14 @@ if (MACOSX_FOUND AND CMAKE_SYSTEM_PROCESSOR STREQUAL "arm64")
     set(ENABLE_NUMA OFF)
     check_sysctl(hw.optional.neon ASIMD_FOUND)
     check_sysctl(hw.optional.arm.FEAT_BF16 ARM_BF16_FOUND)
+    check_sysctl(hw.optional.arm.FEAT_I8MM ARM_I8MM_FOUND)
 else()
     find_isa(${CPUINFO} "Power11" POWER11_FOUND)
     find_isa(${CPUINFO} "POWER10" POWER10_FOUND)
     find_isa(${CPUINFO} "POWER9" POWER9_FOUND)
     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(${CPUINFO} "i8mm" ARM_I8MM_FOUND) # Check for ARM I8MM support
     find_isa(${CPUINFO} "S390" S390_FOUND)
     find_isa(${CPUINFO} "zvfhmin" RVV_FP16_FOUND) # Check for RISC-V Vector FP16 support
     find_isa(${CPUINFO} "zvfbfmin" RVV_BF16_FOUND) # Check for RISC-V Vector BF16 support
@@ -111,6 +114,11 @@ else()
         set(ARM_BF16_FOUND ON)
         message(STATUS "ARM BF16 support enabled via VLLM_CPU_ARM_BF16 environment variable")
     endif()
+    if (ENABLE_ARM_I8MM)
+        set(ARM_I8MM_FOUND ON)
+        message(STATUS
+            "ARM I8MM support enabled via VLLM_CPU_ARM_I8MM environment variable")
+    endif()
     # Some kernels (e.g. Bianbu on Spacemit X100) do not report zvfbfmin
     # in /proc/cpuinfo despite hardware support. VLLM_CPU_RVV_BF16=1
     # overrides the detection result.
@@ -166,6 +174,11 @@ elseif (ASIMD_FOUND)
         message(WARNING "BF16 functionality is not available")
         set(MARCH_FLAGS "-march=armv8.2-a+dotprod+fp16")  
     endif()
+    if(ARM_I8MM_FOUND)
+        message(STATUS "I8MM extension detected")
+        string(APPEND MARCH_FLAGS "+i8mm")
+        add_compile_definitions(ARM_I8MM_SUPPORT)
+    endif()
     list(APPEND CXX_COMPILE_FLAGS ${MARCH_FLAGS})     
 elseif (S390_FOUND)
     message(STATUS "S390 detected")
@@ -447,8 +460,13 @@ if (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND)
         "csrc/cpu/shm.cpp"
         "csrc/cpu/activation_lut_bf16.cpp"
         "csrc/cpu/cpu_tanhf_neon.hpp"
-        "csrc/cpu/cpu_fused_moe.cpp"
         ${VLLM_EXT_SRC})
+    if (ARM_BF16_FOUND)
+        set(VLLM_EXT_SRC "csrc/cpu/cpu_fused_moe.cpp" ${VLLM_EXT_SRC})
+        if (ARM_I8MM_FOUND)
+            set(VLLM_EXT_SRC "csrc/cpu/cpu_fused_moe_int8.cpp" ${VLLM_EXT_SRC})
+        endif()
+    endif()
 endif()
 
 if (POWER9_FOUND OR POWER10_FOUND OR POWER11_FOUND)	

cmake/external_projects/deepgemm.cmake

@@ -68,6 +68,9 @@ endif()
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.9)
     list(APPEND DEEPGEMM_SUPPORT_ARCHS "10.0f")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.4)
+      list(APPEND DEEPGEMM_SUPPORT_ARCHS "10.7f")
+    endif()
   else()
     list(APPEND DEEPGEMM_SUPPORT_ARCHS "10.0a")
   endif()

cmake/external_projects/flashmla.cmake

@@ -60,6 +60,9 @@ if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.9)
     # CUDA 12.9 has introduced "Family-Specific Architecture Features"
     # this supports all compute_10x family
     list(APPEND SUPPORT_ARCHS "10.0f")
+    if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.4)
+        list(APPEND SUPPORT_ARCHS "10.7f")
+    endif()
 elseif(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 12.8)
     list(APPEND SUPPORT_ARCHS "10.0a")
 endif()
@@ -188,4 +191,3 @@ else()
     add_custom_target(_flashmla_C)
     add_custom_target(_flashmla_extension_C)
 endif()
-

cmake/external_projects/qutlass.cmake

@@ -55,7 +55,11 @@ message(STATUS "[QUTLASS] QuTLASS is available at ${qutlass_SOURCE_DIR}")
 
 if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
   cuda_archs_loose_intersection(QUTLASS_SM120_ARCHS "12.0f" "${CUDA_ARCHS}")
-  cuda_archs_loose_intersection(QUTLASS_SM100_ARCHS "10.0f" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.4)
+    cuda_archs_loose_intersection(QUTLASS_SM100_ARCHS "10.0f;10.7f" "${CUDA_ARCHS}")
+  else()
+    cuda_archs_loose_intersection(QUTLASS_SM100_ARCHS "10.0f" "${CUDA_ARCHS}")
+  endif()
 else()
   cuda_archs_loose_intersection(QUTLASS_SM120_ARCHS "12.0a;12.1a" "${CUDA_ARCHS}")
   cuda_archs_loose_intersection(QUTLASS_SM100_ARCHS "10.0a;10.3a" "${CUDA_ARCHS}")

cmake/utils.cmake

@@ -396,14 +396,24 @@ function(cuda_archs_loose_intersection OUT_CUDA_ARCHS SRC_CUDA_ARCHS TGT_CUDA_AR
   # match — e.g. SRC="12.0f" matches TGT="12.1a" since SM121 is in the SM12x
   # family. The output uses TGT's value to preserve the user's compilation flags.
   set(_CUDA_ARCHS)
+  # Resolve exact base matches before family fallbacks so a generic entry such
+  # as 10.0f cannot consume a 10.7 target that has a 10.7f source entry.
+  foreach(_arch ${_SRC_CUDA_ARCHS})
+    if(_arch MATCHES "[af]$")
+      string(REGEX REPLACE "[af]$" "" _base "${_arch}")
+      if("${_base}" IN_LIST _TGT_CUDA_ARCHS)
+        list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
+        list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}")
+        list(APPEND _CUDA_ARCHS "${_arch}")
+      endif()
+    endif()
+  endforeach()
+
   foreach(_arch ${_SRC_CUDA_ARCHS})
     if(_arch MATCHES "[af]$")
       list(REMOVE_ITEM _SRC_CUDA_ARCHS "${_arch}")
       string(REGEX REPLACE "[af]$" "" _base "${_arch}")
-      if ("${_base}" IN_LIST TGT_CUDA_ARCHS)
-        list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}")
-        list(APPEND _CUDA_ARCHS "${_arch}")
-      elseif("${_base}a" IN_LIST _TGT_CUDA_ARCHS)
+      if("${_base}a" IN_LIST _TGT_CUDA_ARCHS)
         list(REMOVE_ITEM _TGT_CUDA_ARCHS "${_base}a")
         list(APPEND _CUDA_ARCHS "${_base}a")
       elseif("${_base}f" IN_LIST _TGT_CUDA_ARCHS)
@@ -487,7 +497,7 @@ endfunction()
 
 function(cuda_archs_sm90plus OUT_CUDA_ARCHS TGT_CUDA_ARCHS)
   if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER_EQUAL 13.0)
-    cuda_archs_loose_intersection(_archs "9.0a;10.0f;11.0f;12.0f" "${TGT_CUDA_ARCHS}")
+    cuda_archs_loose_intersection(_archs "9.0a;10.0f;10.7f;11.0f;12.0f" "${TGT_CUDA_ARCHS}")
   else()
     cuda_archs_loose_intersection(_archs "9.0a;10.0a;10.1a;10.3a;12.0a;12.1a" "${TGT_CUDA_ARCHS}")
   endif()

csrc/cpu/cpu_arch_macros.h

@@ -172,4 +172,15 @@
 
 #endif  // __riscv_v
 
+// Power VSX
+#ifdef __powerpc__
+  // FP32Vec16::exp() in cpu_types_vsx.hpp delegates to FP32Vec8::exp(), which
+  // implements a vectorised 5-term minimax polynomial using VSX intrinsics.
+  #define DEFINE_FAST_EXP                                                     \
+    auto fast_exp = [&](const vec_op::FP32Vec16& vec)                         \
+                        __attribute__((always_inline)) { return vec.exp(); }; \
+    auto fast_exp_f16 = fast_exp;
+
+#endif  // __powerpc__
+
 #endif

csrc/cpu/cpu_fused_moe.cpp

@@ -1,5 +1,6 @@
 #include "cpu/cpu_types.hpp"
 #include "cpu/utils.hpp"
+#include "cpu/cpu_fused_moe_activations.hpp"
 #include "cpu/micro_gemm/cpu_micro_gemm_vec.hpp"
 #include "cpu/cpu_arch_macros.h"
 
@@ -43,193 +44,9 @@
   }()
 
 namespace {
-enum class FusedMOEAct {
-  SiluAndMul,
-  SwigluOAIAndMul,
-  GeluAndMul,
-  GeluTanhAndMul,
-};
 
-FusedMOEAct get_act_type(const std::string& act) {
-  if (act == "silu") {
-    return FusedMOEAct::SiluAndMul;
-  } else if (act == "swigluoai") {
-    return FusedMOEAct::SwigluOAIAndMul;
-  } else if (act == "gelu") {
-    return FusedMOEAct::GeluAndMul;
-  } else if (act == "gelu_tanh") {
-    return FusedMOEAct::GeluTanhAndMul;
-  } else {
-    TORCH_CHECK(false, "Invalid act type: " + act);
-  }
-}
-
-template <typename scalar_t>
-void swigluoai_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
-                       const int32_t m_size, const int32_t n_size,
-                       const int32_t input_stride,
-                       const int32_t output_stride) {
-  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
-#if !defined(__aarch64__)
-  // For GPT-OSS interleaved gate-up weights
-  alignas(64) static int32_t index[16] = {0,  2,  4,  6,  8,  10, 12, 14,
-                                          16, 18, 20, 22, 24, 26, 28, 30};
-  vec_op::INT32Vec16 index_vec(index);
-#endif
-  vec_op::FP32Vec16 gate_up_max_vec(7.0);
-  vec_op::FP32Vec16 up_min_vec(-7.0);
-  vec_op::FP32Vec16 alpha_vec(1.702);
-  vec_op::FP32Vec16 one_vec(1.0);
-
-  DEFINE_FAST_EXP
-
-  for (int32_t m = 0; m < m_size; ++m) {
-    for (int32_t n = 0; n < n_size; n += 32) {
-      // Note: AdvSIMD does not support gather loads
-#if defined(__aarch64__)
-      vec_op::FP32Vec16 gate_vec(vec_op::uninit);
-      vec_op::FP32Vec16 up_vec(vec_op::uninit);
-      vec_op::FP32Vec16::load_even_odd(input + n, gate_vec, up_vec);
-#else
-      vec_op::FP32Vec16 gate_vec(input + n, index_vec);
-      vec_op::FP32Vec16 up_vec(input + n + 1, index_vec);
-#endif
-      gate_vec = gate_vec.min(gate_up_max_vec);
-      up_vec = up_vec.clamp(up_min_vec, gate_up_max_vec);
-      auto sigmoid_vec = one_vec / (one_vec + fast_exp(-gate_vec * alpha_vec));
-      auto glu = gate_vec * sigmoid_vec;
-      auto gated_output_fp32 = (one_vec + up_vec) * glu;
-      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
-      gated_output.save(output + n / 2);
-    }
-    input += input_stride;
-    output += output_stride;
-  }
-}
-
-template <typename scalar_t>
-void silu_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
-                  const int32_t m_size, const int32_t n_size,
-                  const int32_t input_stride, const int32_t output_stride) {
-  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
-  const int32_t dim = n_size / 2;
-  float* __restrict__ gate = input;
-  float* __restrict__ up = input + dim;
-  vec_op::FP32Vec16 one_vec(1.0);
-
-  DEFINE_FAST_EXP
-
-  for (int32_t m = 0; m < m_size; ++m) {
-    for (int32_t n = 0; n < dim; n += 16) {
-      vec_op::FP32Vec16 gate_vec(gate + n);
-      vec_op::FP32Vec16 up_vec(up + n);
-      auto sigmoid_vec = one_vec / (one_vec + fast_exp(-gate_vec));
-      auto silu = gate_vec * sigmoid_vec;
-      auto gated_output_fp32 = up_vec * silu;
-      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
-      gated_output.save(output + n);
-    }
-    gate += input_stride;
-    up += input_stride;
-    output += output_stride;
-  }
-}
-
-template <typename scalar_t>
-void gelu_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
-                  const int32_t m_size, const int32_t n_size,
-                  const int32_t input_stride, const int32_t output_stride) {
-  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
-  const int32_t dim = n_size / 2;
-  float* __restrict__ gate = input;
-  float* __restrict__ up = input + dim;
-  vec_op::FP32Vec16 one_vec(1.0);
-  vec_op::FP32Vec16 w1_vec(M_SQRT1_2);
-  vec_op::FP32Vec16 w2_vec(0.5);
-  alignas(64) float temp[16];
-
-  DEFINE_FAST_EXP
-
-  for (int32_t m = 0; m < m_size; ++m) {
-    for (int32_t n = 0; n < dim; n += 16) {
-      vec_op::FP32Vec16 gate_vec(gate + n);
-      vec_op::FP32Vec16 up_vec(up + n);
-      auto er_input_vec = gate_vec * w1_vec;
-
-      er_input_vec.save(temp);
-      for (int32_t i = 0; i < 16; ++i) {
-        temp[i] = std::erf(temp[i]);
-      }
-      vec_op::FP32Vec16 er_vec(temp);
-      auto gelu = gate_vec * w2_vec * (one_vec + er_vec);
-      auto gated_output_fp32 = up_vec * gelu;
-      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
-      gated_output.save(output + n);
-    }
-    gate += input_stride;
-    up += input_stride;
-    output += output_stride;
-  }
-}
-
-template <typename scalar_t>
-void gelu_tanh_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
-                       const int32_t m_size, const int32_t n_size,
-                       const int32_t input_stride,
-                       const int32_t output_stride) {
-  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
-  const int32_t dim = n_size / 2;
-  float* __restrict__ gate = input;
-  float* __restrict__ up = input + dim;
-  vec_op::FP32Vec16 one_vec(1.0);
-  vec_op::FP32Vec16 w1_vec(0.7978845608028654);
-  vec_op::FP32Vec16 w2_vec(0.5);
-  vec_op::FP32Vec16 w3_vec(0.044715);
-
-  for (int32_t m = 0; m < m_size; ++m) {
-    for (int32_t n = 0; n < dim; n += 16) {
-      vec_op::FP32Vec16 gate_vec(gate + n);
-      vec_op::FP32Vec16 up_vec(up + n);
-      auto gate_pow3_vec = gate_vec * gate_vec * gate_vec;
-      auto inner_vec = w1_vec * (gate_vec + w3_vec * gate_pow3_vec);
-      // Note: can't use fast_exp form because diffusiongemma will generate
-      // wrong results
-      auto tanh_vec = inner_vec.tanh();
-      auto gelu_tanh = gate_vec * w2_vec * (one_vec + tanh_vec);
-      auto gated_output_fp32 = up_vec * gelu_tanh;
-      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
-      gated_output.save(output + n);
-    }
-    gate += input_stride;
-    up += input_stride;
-    output += output_stride;
-  }
-}
-
-template <typename scalar_t>
-FORCE_INLINE void apply_gated_act(const FusedMOEAct act,
-                                  float* __restrict__ input,
-                                  scalar_t* __restrict__ output,
-                                  const int32_t m, const int32_t n,
-                                  const int32_t input_stride,
-                                  const int32_t output_stride) {
-  switch (act) {
-    case FusedMOEAct::SwigluOAIAndMul:
-      swigluoai_and_mul(input, output, m, n, input_stride, output_stride);
-      return;
-    case FusedMOEAct::SiluAndMul:
-      silu_and_mul(input, output, m, n, input_stride, output_stride);
-      return;
-    case FusedMOEAct::GeluAndMul:
-      gelu_and_mul(input, output, m, n, input_stride, output_stride);
-      return;
-    case FusedMOEAct::GeluTanhAndMul:
-      gelu_tanh_and_mul(input, output, m, n, input_stride, output_stride);
-      return;
-    default:
-      TORCH_CHECK(false, "Unsupported act type.");
-  }
-}
+using cpu_fused_moe_utils::apply_gated_act;
+using cpu_fused_moe_utils::FusedMOEAct;
 
 template <typename scalar_t, typename gemm_t>
 void prepack_moe_weight_impl(scalar_t* __restrict__ weight_ptr,
@@ -817,6 +634,7 @@ void fused_moe_impl(scalar_t* __restrict__ output, scalar_t* __restrict__ input,
     }
   }
 }
+
 }  // namespace
 
 void prepack_moe_weight(
@@ -864,7 +682,7 @@ void cpu_fused_moe(
   const int32_t input_size_2 = w2.size(2);
   const int32_t output_size_2 = w2.size(1);
   const int32_t topk_num = topk_id.size(1);
-  const FusedMOEAct act_type = get_act_type(act);
+  const FusedMOEAct act_type = cpu_fused_moe_utils::get_act_type(act);
   cpu_utils::ISA isa_type = cpu_utils::get_isa(isa);
   TORCH_CHECK(!skip_weighted || topk_num == 1,
               "skip_weighted is only supported for topk=1 on CPU");

csrc/cpu/cpu_fused_moe_activations.hpp

@@ -0,0 +1,204 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+#ifndef CPU_FUSED_MOE_ACTIVATIONS_HPP
+#define CPU_FUSED_MOE_ACTIVATIONS_HPP
+
+#include <cmath>
+#include <cstdint>
+#include <string>
+
+#include "cpu/cpu_arch_macros.h"
+#include "cpu/utils.hpp"
+
+namespace cpu_fused_moe_utils {
+enum class FusedMOEAct {
+  SiluAndMul,
+  SwigluOAIAndMul,
+  GeluAndMul,
+  GeluTanhAndMul,
+};
+
+inline FusedMOEAct get_act_type(const std::string& act) {
+  if (act == "silu") {
+    return FusedMOEAct::SiluAndMul;
+  } else if (act == "swigluoai") {
+    return FusedMOEAct::SwigluOAIAndMul;
+  } else if (act == "gelu") {
+    return FusedMOEAct::GeluAndMul;
+  } else if (act == "gelu_tanh") {
+    return FusedMOEAct::GeluTanhAndMul;
+  } else {
+    TORCH_CHECK(false, "Invalid act type: " + act);
+  }
+}
+
+template <typename scalar_t>
+void swigluoai_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
+                       const int32_t m_size, const int32_t n_size,
+                       const int32_t input_stride,
+                       const int32_t output_stride) {
+  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
+#if !defined(__aarch64__)
+  // For GPT-OSS interleaved gate-up weights
+  alignas(64) static int32_t index[16] = {0,  2,  4,  6,  8,  10, 12, 14,
+                                          16, 18, 20, 22, 24, 26, 28, 30};
+  vec_op::INT32Vec16 index_vec(index);
+#endif
+  vec_op::FP32Vec16 gate_up_max_vec(7.0);
+  vec_op::FP32Vec16 up_min_vec(-7.0);
+  vec_op::FP32Vec16 alpha_vec(1.702);
+  vec_op::FP32Vec16 one_vec(1.0);
+
+  DEFINE_FAST_EXP
+
+  for (int32_t m = 0; m < m_size; ++m) {
+    for (int32_t n = 0; n < n_size; n += 32) {
+      // Note: AdvSIMD does not support gather loads
+#if defined(__aarch64__)
+      vec_op::FP32Vec16 gate_vec(vec_op::uninit);
+      vec_op::FP32Vec16 up_vec(vec_op::uninit);
+      vec_op::FP32Vec16::load_even_odd(input + n, gate_vec, up_vec);
+#else
+      vec_op::FP32Vec16 gate_vec(input + n, index_vec);
+      vec_op::FP32Vec16 up_vec(input + n + 1, index_vec);
+#endif
+      gate_vec = gate_vec.min(gate_up_max_vec);
+      up_vec = up_vec.clamp(up_min_vec, gate_up_max_vec);
+      auto sigmoid_vec = one_vec / (one_vec + fast_exp(-gate_vec * alpha_vec));
+      auto glu = gate_vec * sigmoid_vec;
+      auto gated_output_fp32 = (one_vec + up_vec) * glu;
+      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
+      gated_output.save(output + n / 2);
+    }
+    input += input_stride;
+    output += output_stride;
+  }
+}
+
+template <typename scalar_t>
+void silu_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
+                  const int32_t m_size, const int32_t n_size,
+                  const int32_t input_stride, const int32_t output_stride) {
+  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
+  const int32_t dim = n_size / 2;
+  float* __restrict__ gate = input;
+  float* __restrict__ up = input + dim;
+  vec_op::FP32Vec16 one_vec(1.0);
+
+  DEFINE_FAST_EXP
+
+  for (int32_t m = 0; m < m_size; ++m) {
+    for (int32_t n = 0; n < dim; n += 16) {
+      vec_op::FP32Vec16 gate_vec(gate + n);
+      vec_op::FP32Vec16 up_vec(up + n);
+      auto sigmoid_vec = one_vec / (one_vec + fast_exp(-gate_vec));
+      auto silu = gate_vec * sigmoid_vec;
+      auto gated_output_fp32 = up_vec * silu;
+      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
+      gated_output.save(output + n);
+    }
+    gate += input_stride;
+    up += input_stride;
+    output += output_stride;
+  }
+}
+
+template <typename scalar_t>
+void gelu_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
+                  const int32_t m_size, const int32_t n_size,
+                  const int32_t input_stride, const int32_t output_stride) {
+  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
+  const int32_t dim = n_size / 2;
+  float* __restrict__ gate = input;
+  float* __restrict__ up = input + dim;
+  vec_op::FP32Vec16 one_vec(1.0);
+  vec_op::FP32Vec16 w1_vec(M_SQRT1_2);
+  vec_op::FP32Vec16 w2_vec(0.5);
+  alignas(64) float temp[16];
+
+  DEFINE_FAST_EXP
+
+  for (int32_t m = 0; m < m_size; ++m) {
+    for (int32_t n = 0; n < dim; n += 16) {
+      vec_op::FP32Vec16 gate_vec(gate + n);
+      vec_op::FP32Vec16 up_vec(up + n);
+      auto er_input_vec = gate_vec * w1_vec;
+
+      er_input_vec.save(temp);
+      for (int32_t i = 0; i < 16; ++i) {
+        temp[i] = std::erf(temp[i]);
+      }
+      vec_op::FP32Vec16 er_vec(temp);
+      auto gelu = gate_vec * w2_vec * (one_vec + er_vec);
+      auto gated_output_fp32 = up_vec * gelu;
+      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
+      gated_output.save(output + n);
+    }
+    gate += input_stride;
+    up += input_stride;
+    output += output_stride;
+  }
+}
+
+template <typename scalar_t>
+void gelu_tanh_and_mul(float* __restrict__ input, scalar_t* __restrict__ output,
+                       const int32_t m_size, const int32_t n_size,
+                       const int32_t input_stride,
+                       const int32_t output_stride) {
+  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
+  const int32_t dim = n_size / 2;
+  float* __restrict__ gate = input;
+  float* __restrict__ up = input + dim;
+  vec_op::FP32Vec16 one_vec(1.0);
+  vec_op::FP32Vec16 w1_vec(0.7978845608028654);
+  vec_op::FP32Vec16 w2_vec(0.5);
+  vec_op::FP32Vec16 w3_vec(0.044715);
+
+  for (int32_t m = 0; m < m_size; ++m) {
+    for (int32_t n = 0; n < dim; n += 16) {
+      vec_op::FP32Vec16 gate_vec(gate + n);
+      vec_op::FP32Vec16 up_vec(up + n);
+      auto gate_pow3_vec = gate_vec * gate_vec * gate_vec;
+      auto inner_vec = w1_vec * (gate_vec + w3_vec * gate_pow3_vec);
+      // Note: can't use fast_exp form because diffusiongemma will generate
+      // wrong results
+      auto tanh_vec = inner_vec.tanh();
+      auto gelu_tanh = gate_vec * w2_vec * (one_vec + tanh_vec);
+      auto gated_output_fp32 = up_vec * gelu_tanh;
+      scalar_vec_t gated_output = scalar_vec_t(gated_output_fp32);
+      gated_output.save(output + n);
+    }
+    gate += input_stride;
+    up += input_stride;
+    output += output_stride;
+  }
+}
+
+template <typename scalar_t>
+FORCE_INLINE void apply_gated_act(const FusedMOEAct act,
+                                  float* __restrict__ input,
+                                  scalar_t* __restrict__ output,
+                                  const int32_t m, const int32_t n,
+                                  const int32_t input_stride,
+                                  const int32_t output_stride) {
+  switch (act) {
+    case FusedMOEAct::SwigluOAIAndMul:
+      swigluoai_and_mul(input, output, m, n, input_stride, output_stride);
+      return;
+    case FusedMOEAct::SiluAndMul:
+      silu_and_mul(input, output, m, n, input_stride, output_stride);
+      return;
+    case FusedMOEAct::GeluAndMul:
+      gelu_and_mul(input, output, m, n, input_stride, output_stride);
+      return;
+    case FusedMOEAct::GeluTanhAndMul:
+      gelu_tanh_and_mul(input, output, m, n, input_stride, output_stride);
+      return;
+    default:
+      TORCH_CHECK(false, "Unsupported act type.");
+  }
+}
+}  // namespace cpu_fused_moe_utils
+
+#endif

csrc/cpu/cpu_fused_moe_int8.cpp

@@ -0,0 +1,647 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+#include "cpu/cpu_arch_macros.h"
+
+#include <algorithm>
+#include <cstdint>
+#include <cstring>
+#include <optional>
+#include <string>
+
+#include "cpu/cpu_fused_moe_activations.hpp"
+#include "cpu/cpu_types.hpp"
+#include "cpu/micro_gemm/cpu_micro_gemm_impl.hpp"
+#include "cpu/utils.hpp"
+
+#if defined(ARM_I8MM_SUPPORT) && defined(ARM_BF16_SUPPORT)
+  #include "cpu/micro_gemm/cpu_micro_gemm_int8_neon.hpp"
+  #define NEON_DISPATCH(SCALAR_TYPE, ...)                                   \
+    case cpu_utils::ISA::NEON: {                                            \
+      using gemm_t =                                                        \
+          cpu_micro_gemm::MicroGemmINT8<cpu_utils::ISA::NEON, SCALAR_TYPE>; \
+      return __VA_ARGS__();                                                 \
+    }
+#else
+  #define NEON_DISPATCH(SCALAR_TYPE, ...) case cpu_utils::ISA::NEON:
+#endif
+
+#define CPU_INT8_ISA_DISPATCH_IMPL(ISA_TYPE, SCALAR_TYPE, ...) \
+  [&] {                                                        \
+    switch (ISA_TYPE) {                                        \
+      NEON_DISPATCH(SCALAR_TYPE, __VA_ARGS__)                  \
+      default: {                                               \
+        TORCH_CHECK(false, "Invalid CPU ISA type.");           \
+      }                                                        \
+    }                                                          \
+  }()
+
+namespace {
+using cpu_fused_moe_utils::apply_gated_act;
+using cpu_fused_moe_utils::FusedMOEAct;
+
+template <typename gemm_t>
+void prepack_moe_weight_int8_impl(const int8_t* __restrict__ weight_ptr,
+                                  int8_t* __restrict__ packed_weight_ptr,
+                                  const int32_t expert_num,
+                                  const int32_t output_size,
+                                  const int32_t input_size,
+                                  const int64_t expert_stride) {
+#pragma omp parallel for
+  for (int32_t e_idx = 0; e_idx < expert_num; ++e_idx) {
+    gemm_t::pack_weight(weight_ptr + expert_stride * e_idx,
+                        packed_weight_ptr + expert_stride * e_idx, output_size,
+                        input_size);
+  }
+}
+
+// INT8 MoE kernel, based on the original BF16 kernel in cpu_fused_moe.cpp
+template <typename scalar_t, typename gemm_t>
+void fused_moe_int8_impl(
+    scalar_t* __restrict__ output, const scalar_t* __restrict__ input,
+    const int8_t* __restrict__ w13, const int8_t* __restrict__ w2,
+    const float* __restrict__ w13_scales, const float* __restrict__ w2_scales,
+    scalar_t* __restrict__ w13_bias, scalar_t* __restrict__ w2_bias,
+    const float* __restrict__ topk_weights, const int32_t* __restrict__ topk_id,
+    const FusedMOEAct act_type, const int32_t token_num,
+    const int32_t expert_num, const int32_t topk_num,
+    const int32_t input_size_13, const int32_t output_size_13,
+    const int32_t input_size_2, const int32_t output_size_2,
+    const bool skip_weighted) {
+  using scalar_vec_t = typename cpu_utils::VecTypeTrait<scalar_t>::vec_t;
+  constexpr int32_t gemm_n_tile_size = gemm_t::NSize;
+  constexpr int32_t gemm_m_tile_size = gemm_t::MaxMSize;
+  constexpr int32_t min_w13_n_tile_size = 2 * gemm_n_tile_size;
+
+  TORCH_CHECK_EQ(input_size_13 % gemm_t::K, 0);
+  TORCH_CHECK_EQ(input_size_2 % gemm_t::K, 0);
+  TORCH_CHECK_EQ(output_size_13 % min_w13_n_tile_size, 0);
+  TORCH_CHECK_EQ(output_size_2 % gemm_n_tile_size, 0);
+  TORCH_CHECK_EQ(output_size_13 / 2, input_size_2);
+
+  const int32_t thread_num = cpu_utils::get_max_threads();
+  const int32_t w13_input_buffer_size = cpu_utils::round_up<64>(
+      gemm_m_tile_size * input_size_13 * sizeof(int8_t));
+  const int32_t w2_input_buffer_size =
+      cpu_utils::round_up<64>(gemm_m_tile_size * input_size_2 * sizeof(int8_t));
+
+  const int32_t w13_n_tile_size = [&]() {
+    const int64_t cache_size = cpu_utils::get_available_l2_size();
+    const int32_t n_size_cache_limit =
+        (cache_size - w13_input_buffer_size) /
+        (gemm_m_tile_size * sizeof(float) + input_size_13 * sizeof(int8_t));
+    const int32_t n_size_thread_limit =
+        output_size_13 / std::max(1, thread_num / topk_num);
+    const int32_t n_size = cpu_utils::round_down<min_w13_n_tile_size>(
+        std::min(n_size_cache_limit, n_size_thread_limit));
+    return std::max(n_size, min_w13_n_tile_size);
+  }();
+
+  const int32_t w2_n_tile_size = [&]() {
+    const int64_t cache_size = cpu_utils::get_available_l2_size();
+    const int32_t n_size_cache_limit =
+        (cache_size - w2_input_buffer_size) / (input_size_2 * sizeof(int8_t));
+    const int32_t n_size_thread_limit =
+        output_size_2 / std::max(1, thread_num / topk_num);
+    const int32_t n_size = cpu_utils::round_down<gemm_n_tile_size>(
+        std::min(n_size_cache_limit, n_size_thread_limit));
+    return std::max(n_size, gemm_n_tile_size);
+  }();
+
+  int32_t common_buffer_offset = 0;
+  const int32_t token_num_per_group_buffer_offset = common_buffer_offset;
+  common_buffer_offset += cpu_utils::round_up<64>(expert_num * sizeof(int32_t));
+  const int32_t cu_token_num_per_group_buffer_offset = common_buffer_offset;
+  common_buffer_offset +=
+      cpu_utils::round_up<64>((expert_num + 1) * sizeof(int32_t));
+  const int32_t expanded_token_num = token_num * topk_num;
+  const int32_t expand_token_id_buffer_offset = common_buffer_offset;
+  common_buffer_offset +=
+      cpu_utils::round_up<64>(expanded_token_num * sizeof(int32_t));
+  const int32_t expand_token_id_index_buffer_offset = common_buffer_offset;
+  common_buffer_offset +=
+      cpu_utils::round_up<64>(expanded_token_num * sizeof(int32_t));
+  const int32_t input_quant_buffer_offset = common_buffer_offset;
+  common_buffer_offset +=
+      cpu_utils::round_up<64>(token_num * input_size_13 * sizeof(int8_t));
+  const int32_t input_scale_buffer_offset = common_buffer_offset;
+  common_buffer_offset += cpu_utils::round_up<64>(token_num * sizeof(float));
+  const int32_t w13_gemm_output_buffer_offset = common_buffer_offset;
+  common_buffer_offset += cpu_utils::round_up<64>(
+      expanded_token_num * input_size_2 * sizeof(scalar_t));
+  const int32_t w13_output_scale_buffer_offset = common_buffer_offset;
+  common_buffer_offset +=
+      cpu_utils::round_up<64>(expanded_token_num * sizeof(float));
+  const int32_t w2_gemm_output_buffer_offset = common_buffer_offset;
+  common_buffer_offset += cpu_utils::round_up<64>(
+      expanded_token_num * output_size_2 * sizeof(float));
+
+  int32_t gemm_thread_buffer_offset = 0;
+  const int32_t gemm_input_buffer_offset = gemm_thread_buffer_offset;
+  gemm_thread_buffer_offset +=
+      std::max(w13_input_buffer_size, w2_input_buffer_size);
+  const int32_t gemm_output_buffer_offset = gemm_thread_buffer_offset;
+  gemm_thread_buffer_offset += cpu_utils::round_up<64>(
+      gemm_m_tile_size * std::max(w13_n_tile_size, w2_n_tile_size) *
+      sizeof(int32_t));
+
+  const int32_t ws_output_buffer_offset = 0;
+  const int32_t ws_thread_buffer_size =
+      cpu_utils::round_up<64>(output_size_2 * sizeof(float));
+  const int32_t thread_buffer_size =
+      std::max(gemm_thread_buffer_offset, ws_thread_buffer_size);
+  const int32_t buffer_size =
+      common_buffer_offset + thread_buffer_size * thread_num;
+  cpu_utils::ScratchPadManager::get_scratchpad_manager()->realloc(buffer_size);
+  uint8_t* common_buffer_start =
+      cpu_utils::ScratchPadManager::get_scratchpad_manager()
+          ->get_data<uint8_t>();
+  uint8_t* thread_buffer_start = common_buffer_start + common_buffer_offset;
+
+  int32_t* __restrict__ token_num_per_group_buffer = reinterpret_cast<int32_t*>(
+      common_buffer_start + token_num_per_group_buffer_offset);
+  int32_t* __restrict__ cu_token_num_per_group_buffer =
+      reinterpret_cast<int32_t*>(common_buffer_start +
+                                 cu_token_num_per_group_buffer_offset);
+  int32_t* __restrict__ expand_token_id_buffer = reinterpret_cast<int32_t*>(
+      common_buffer_start + expand_token_id_buffer_offset);
+  int32_t* __restrict__ expand_token_id_index_buffer =
+      reinterpret_cast<int32_t*>(common_buffer_start +
+                                 expand_token_id_index_buffer_offset);
+  int8_t* __restrict__ input_quant_buffer = reinterpret_cast<int8_t*>(
+      common_buffer_start + input_quant_buffer_offset);
+  float* __restrict__ input_scale_buffer =
+      reinterpret_cast<float*>(common_buffer_start + input_scale_buffer_offset);
+
+  std::memset(token_num_per_group_buffer, 0, expert_num * sizeof(int32_t));
+  for (int32_t i = 0; i < expanded_token_num; ++i) {
+    ++token_num_per_group_buffer[topk_id[i]];
+  }
+
+  int32_t token_num_sum = 0;
+  cu_token_num_per_group_buffer[0] = 0;
+  int32_t* token_index_buffer = cu_token_num_per_group_buffer + 1;
+  for (int32_t i = 0; i < expert_num; ++i) {
+    token_index_buffer[i] = token_num_sum;
+    token_num_sum += token_num_per_group_buffer[i];
+  }
+
+  for (int32_t i = 0; i < token_num; ++i) {
+    const int32_t* curr_topk_id = topk_id + i * topk_num;
+    int32_t* curr_index_buffer = expand_token_id_index_buffer + i * topk_num;
+    for (int32_t j = 0; j < topk_num; ++j) {
+      const int32_t curr_expert_id = curr_topk_id[j];
+      const int32_t curr_index = token_index_buffer[curr_expert_id]++;
+      expand_token_id_buffer[curr_index] = i;
+      curr_index_buffer[j] = curr_index;
+    }
+  }
+
+// quantize inputs
+#pragma omp parallel for
+  for (int32_t token_idx = 0; token_idx < token_num; ++token_idx) {
+    gemm_t::quantize_row(input + token_idx * input_size_13,
+                         input_quant_buffer + token_idx * input_size_13,
+                         input_scale_buffer[token_idx], input_size_13);
+  }
+
+  {
+    alignas(64) cpu_utils::Counter counter;
+    cpu_utils::Counter* counter_ptr = &counter;
+
+// w13 GEMM + act
+#pragma omp parallel for schedule(static, 1)
+    for (int32_t thread_id = 0; thread_id < thread_num; ++thread_id) {
+      const int32_t task_num_per_expert =
+          (output_size_13 + w13_n_tile_size - 1) / w13_n_tile_size;
+      const int32_t task_num = task_num_per_expert * expert_num;
+      uint8_t* __restrict__ thread_buffer =
+          thread_buffer_start + thread_id * thread_buffer_size;
+      int8_t* __restrict__ gemm_input_buffer =
+          reinterpret_cast<int8_t*>(thread_buffer + gemm_input_buffer_offset);
+      float* __restrict__ gemm_output_buffer =
+          reinterpret_cast<float*>(thread_buffer + gemm_output_buffer_offset);
+      auto* __restrict__ w13_gemm_output_buffer = reinterpret_cast<scalar_t*>(
+          common_buffer_start + w13_gemm_output_buffer_offset);
+      gemm_t gemm;
+
+      const int32_t w13_n_group_stride =
+          gemm_t::WeightOCGroupSize * input_size_13;
+      const int32_t w13_n_tile_stride = gemm_n_tile_size * input_size_13;
+
+      for (;;) {
+        const int32_t task_id = counter_ptr->acquire_counter();
+        if (task_id >= task_num) {
+          break;
+        }
+        const int32_t curr_expert_id = task_id / task_num_per_expert;
+        const int32_t curr_output_group_id = task_id % task_num_per_expert;
+        const int32_t curr_token_num =
+            token_num_per_group_buffer[curr_expert_id];
+        if (curr_token_num == 0) {
+          continue;
+        }
+
+        const int32_t actual_n_tile_size =
+            std::min(w13_n_tile_size,
+                     output_size_13 - curr_output_group_id * w13_n_tile_size);
+        const int32_t* __restrict__ curr_expand_token_id_buffer =
+            expand_token_id_buffer +
+            cu_token_num_per_group_buffer[curr_expert_id];
+        scalar_t* __restrict__ curr_w13_gemm_output_buffer =
+            w13_gemm_output_buffer +
+            cu_token_num_per_group_buffer[curr_expert_id] * input_size_2 +
+            curr_output_group_id * w13_n_tile_size / 2;
+
+        const int8_t* w13_weight_ptr_0 = nullptr;
+        const int8_t* w13_weight_ptr_1 = nullptr;
+        const float* w13_scale_ptr_0 = nullptr;
+        const float* w13_scale_ptr_1 = nullptr;
+        scalar_t* w13_bias_ptr_0 = nullptr;
+        scalar_t* w13_bias_ptr_1 = nullptr;
+        if (act_type == FusedMOEAct::SwigluOAIAndMul) {
+          const int32_t output_offset = curr_output_group_id * w13_n_tile_size;
+          w13_weight_ptr_0 = w13 +
+                             curr_expert_id * input_size_13 * output_size_13 +
+                             output_offset * input_size_13;
+          w13_weight_ptr_1 =
+              w13_weight_ptr_0 + actual_n_tile_size / 2 * input_size_13;
+          w13_scale_ptr_0 =
+              w13_scales + curr_expert_id * output_size_13 + output_offset;
+          w13_scale_ptr_1 = w13_scale_ptr_0 + actual_n_tile_size / 2;
+          if (w13_bias != nullptr) {
+            w13_bias_ptr_0 =
+                w13_bias + curr_expert_id * output_size_13 + output_offset;
+            w13_bias_ptr_1 = w13_bias_ptr_0 + actual_n_tile_size / 2;
+          }
+        } else {
+          const int32_t output_offset =
+              curr_output_group_id * (w13_n_tile_size / 2);
+          w13_weight_ptr_0 = w13 +
+                             curr_expert_id * input_size_13 * output_size_13 +
+                             output_offset * input_size_13;
+          w13_weight_ptr_1 =
+              w13_weight_ptr_0 + output_size_13 / 2 * input_size_13;
+          w13_scale_ptr_0 =
+              w13_scales + curr_expert_id * output_size_13 + output_offset;
+          w13_scale_ptr_1 = w13_scale_ptr_0 + output_size_13 / 2;
+          if (w13_bias != nullptr) {
+            w13_bias_ptr_0 =
+                w13_bias + curr_expert_id * output_size_13 + output_offset;
+            w13_bias_ptr_1 = w13_bias_ptr_0 + output_size_13 / 2;
+          }
+        }
+
+        for (int32_t token_idx = 0; token_idx < curr_token_num;
+             token_idx += gemm_m_tile_size) {
+          const int32_t actual_token_num =
+              std::min(gemm_m_tile_size, curr_token_num - token_idx);
+          const int8_t* input_rows[gemm_m_tile_size];
+          alignas(64) float input_scales[gemm_m_tile_size];
+          // gather and pack
+          for (int32_t i = 0; i < actual_token_num; ++i) {
+            const int32_t curr_token_id = curr_expand_token_id_buffer[i];
+            input_rows[i] = input_quant_buffer + curr_token_id * input_size_13;
+            input_scales[i] = input_scale_buffer[curr_token_id];
+          }
+          gemm_t::pack_input_from_rows(input_rows, gemm_input_buffer,
+                                       actual_token_num, input_size_13);
+          curr_expand_token_id_buffer += actual_token_num;
+
+          const int8_t* w13_weight_ptr_0_iter = w13_weight_ptr_0;
+          const int8_t* w13_weight_ptr_1_iter = w13_weight_ptr_1;
+          const float* w13_scale_ptr_0_iter = w13_scale_ptr_0;
+          const float* w13_scale_ptr_1_iter = w13_scale_ptr_1;
+          scalar_t* w13_bias_ptr_0_iter = w13_bias_ptr_0;
+          scalar_t* w13_bias_ptr_1_iter = w13_bias_ptr_1;
+          float* w13_output_buffer_0_iter = gemm_output_buffer;
+          float* w13_output_buffer_1_iter =
+              gemm_output_buffer + actual_n_tile_size / 2;
+
+          for (int32_t i = 0; i < actual_n_tile_size;
+               i += min_w13_n_tile_size) {
+            auto* output_0_int32 =
+                reinterpret_cast<int32_t*>(w13_output_buffer_0_iter);
+            gemm.gemm(gemm_input_buffer, w13_weight_ptr_0_iter, output_0_int32,
+                      actual_token_num, input_size_13, w13_n_group_stride,
+                      actual_n_tile_size);
+            gemm_t::dequantize_tile(output_0_int32, w13_output_buffer_0_iter,
+                                    input_scales, w13_scale_ptr_0_iter,
+                                    actual_token_num, gemm_n_tile_size,
+                                    actual_n_tile_size);
+            if (w13_bias != nullptr) {
+              cpu_micro_gemm::add_bias_epilogue<gemm_n_tile_size>(
+                  w13_output_buffer_0_iter, w13_output_buffer_0_iter,
+                  w13_bias_ptr_0_iter, actual_token_num, actual_n_tile_size,
+                  actual_n_tile_size);
+              w13_bias_ptr_0_iter += gemm_n_tile_size;
+            }
+
+            auto* output_1_int32 =
+                reinterpret_cast<int32_t*>(w13_output_buffer_1_iter);
+            gemm.gemm(gemm_input_buffer, w13_weight_ptr_1_iter, output_1_int32,
+                      actual_token_num, input_size_13, w13_n_group_stride,
+                      actual_n_tile_size);
+            gemm_t::dequantize_tile(output_1_int32, w13_output_buffer_1_iter,
+                                    input_scales, w13_scale_ptr_1_iter,
+                                    actual_token_num, gemm_n_tile_size,
+                                    actual_n_tile_size);
+            if (w13_bias != nullptr) {
+              cpu_micro_gemm::add_bias_epilogue<gemm_n_tile_size>(
+                  w13_output_buffer_1_iter, w13_output_buffer_1_iter,
+                  w13_bias_ptr_1_iter, actual_token_num, actual_n_tile_size,
+                  actual_n_tile_size);
+              w13_bias_ptr_1_iter += gemm_n_tile_size;
+            }
+
+            w13_weight_ptr_0_iter += w13_n_tile_stride;
+            w13_weight_ptr_1_iter += w13_n_tile_stride;
+            w13_scale_ptr_0_iter += gemm_n_tile_size;
+            w13_scale_ptr_1_iter += gemm_n_tile_size;
+            w13_output_buffer_0_iter += gemm_n_tile_size;
+            w13_output_buffer_1_iter += gemm_n_tile_size;
+          }
+
+          apply_gated_act(act_type, gemm_output_buffer,
+                          curr_w13_gemm_output_buffer, actual_token_num,
+                          actual_n_tile_size, actual_n_tile_size, input_size_2);
+          curr_w13_gemm_output_buffer += gemm_m_tile_size * input_size_2;
+        }
+      }
+    }
+  }
+
+  auto* __restrict__ w13_gemm_output_buffer = reinterpret_cast<scalar_t*>(
+      common_buffer_start + w13_gemm_output_buffer_offset);
+  float* __restrict__ w13_output_scale_buffer = reinterpret_cast<float*>(
+      common_buffer_start + w13_output_scale_buffer_offset);
+
+// quantize w2 inputs - in place
+#pragma omp parallel for
+  for (int32_t token_idx = 0; token_idx < expanded_token_num; ++token_idx) {
+    scalar_t* input_row = w13_gemm_output_buffer + token_idx * input_size_2;
+    int8_t* output_row = reinterpret_cast<int8_t*>(input_row);
+    gemm_t::quantize_row(input_row, output_row,
+                         w13_output_scale_buffer[token_idx], input_size_2);
+  }
+
+  {
+    alignas(64) cpu_utils::Counter counter;
+    cpu_utils::Counter* counter_ptr = &counter;
+
+// w2 gemm
+#pragma omp parallel for schedule(static, 1)
+    for (int32_t thread_id = 0; thread_id < thread_num; ++thread_id) {
+      const int32_t task_num_per_expert =
+          (output_size_2 + w2_n_tile_size - 1) / w2_n_tile_size;
+      const int32_t task_num = task_num_per_expert * expert_num;
+      uint8_t* __restrict__ thread_buffer =
+          thread_buffer_start + thread_id * thread_buffer_size;
+      int8_t* __restrict__ gemm_input_buffer =
+          reinterpret_cast<int8_t*>(thread_buffer + gemm_input_buffer_offset);
+      float* __restrict__ gemm_output_buffer =
+          reinterpret_cast<float*>(thread_buffer + gemm_output_buffer_offset);
+      float* __restrict__ w2_gemm_output_buffer = reinterpret_cast<float*>(
+          common_buffer_start + w2_gemm_output_buffer_offset);
+      gemm_t gemm;
+
+      const int32_t w2_n_group_stride =
+          gemm_t::WeightOCGroupSize * input_size_2;
+      const int32_t w2_n_tile_stride = gemm_n_tile_size * input_size_2;
+
+      for (;;) {
+        const int32_t task_id = counter_ptr->acquire_counter();
+        if (task_id >= task_num) {
+          break;
+        }
+        const int32_t curr_expert_id = task_id / task_num_per_expert;
+        const int32_t curr_output_group_id = task_id % task_num_per_expert;
+        const int32_t curr_token_num =
+            token_num_per_group_buffer[curr_expert_id];
+        if (curr_token_num == 0) {
+          continue;
+        }
+
+        const int32_t actual_n_tile_size =
+            std::min(w2_n_tile_size,
+                     output_size_2 - curr_output_group_id * w2_n_tile_size);
+        scalar_t* __restrict__ curr_w13_gemm_output_buffer =
+            w13_gemm_output_buffer +
+            cu_token_num_per_group_buffer[curr_expert_id] * input_size_2;
+        float* __restrict__ curr_w13_output_scale_buffer =
+            w13_output_scale_buffer +
+            cu_token_num_per_group_buffer[curr_expert_id];
+        float* __restrict__ curr_w2_gemm_output_buffer =
+            w2_gemm_output_buffer +
+            cu_token_num_per_group_buffer[curr_expert_id] * output_size_2 +
+            curr_output_group_id * w2_n_tile_size;
+        const int8_t* __restrict__ w2_weight_ptr =
+            w2 + curr_expert_id * output_size_2 * input_size_2 +
+            curr_output_group_id * w2_n_tile_size * input_size_2;
+        const float* __restrict__ w2_scale_ptr =
+            w2_scales + curr_expert_id * output_size_2 +
+            curr_output_group_id * w2_n_tile_size;
+        scalar_t* w2_bias_ptr = nullptr;
+        if (w2_bias != nullptr) {
+          w2_bias_ptr = w2_bias + curr_expert_id * output_size_2 +
+                        curr_output_group_id * w2_n_tile_size;
+        }
+
+        for (int32_t token_idx = 0; token_idx < curr_token_num;
+             token_idx += gemm_m_tile_size) {
+          const int32_t actual_token_num =
+              std::min(gemm_m_tile_size, curr_token_num - token_idx);
+          const int8_t* input_rows[gemm_m_tile_size];
+          alignas(64) float input_scales[gemm_m_tile_size];
+          for (int32_t i = 0; i < actual_token_num; ++i) {
+            input_rows[i] = reinterpret_cast<const int8_t*>(
+                curr_w13_gemm_output_buffer + i * input_size_2);
+            input_scales[i] = curr_w13_output_scale_buffer[i];
+          }
+          gemm_t::pack_input_from_rows(input_rows, gemm_input_buffer,
+                                       actual_token_num, input_size_2);
+
+          const int8_t* w2_weight_ptr_iter = w2_weight_ptr;
+          const float* w2_scale_ptr_iter = w2_scale_ptr;
+          scalar_t* w2_bias_ptr_iter = w2_bias_ptr;
+          float* curr_w2_gemm_output_buffer_iter = curr_w2_gemm_output_buffer;
+          for (int32_t i = 0; i < actual_n_tile_size; i += gemm_n_tile_size) {
+            auto* output_int32 = reinterpret_cast<int32_t*>(gemm_output_buffer);
+            gemm.gemm(gemm_input_buffer, w2_weight_ptr_iter, output_int32,
+                      actual_token_num, input_size_2, w2_n_group_stride,
+                      gemm_n_tile_size);
+            gemm_t::dequantize_tile(output_int32, gemm_output_buffer,
+                                    input_scales, w2_scale_ptr_iter,
+                                    actual_token_num, gemm_n_tile_size,
+                                    gemm_n_tile_size);
+            if (w2_bias != nullptr) {
+              cpu_micro_gemm::add_bias_epilogue<gemm_n_tile_size>(
+                  gemm_output_buffer, gemm_output_buffer, w2_bias_ptr_iter,
+                  actual_token_num, gemm_n_tile_size, gemm_n_tile_size);
+              w2_bias_ptr_iter += gemm_n_tile_size;
+            }
+            for (int32_t m_idx = 0; m_idx < actual_token_num; ++m_idx) {
+              std::memcpy(
+                  curr_w2_gemm_output_buffer_iter + m_idx * output_size_2,
+                  gemm_output_buffer + m_idx * gemm_n_tile_size,
+                  gemm_n_tile_size * sizeof(float));
+            }
+
+            w2_weight_ptr_iter += w2_n_tile_stride;
+            w2_scale_ptr_iter += gemm_n_tile_size;
+            curr_w2_gemm_output_buffer_iter += gemm_n_tile_size;
+          }
+
+          curr_w13_gemm_output_buffer += gemm_m_tile_size * input_size_2;
+          curr_w13_output_scale_buffer += gemm_m_tile_size;
+          curr_w2_gemm_output_buffer += gemm_m_tile_size * output_size_2;
+        }
+      }
+    }
+  }
+
+  {
+    alignas(64) cpu_utils::Counter counter;
+    cpu_utils::Counter* counter_ptr = &counter;
+
+#pragma omp parallel for schedule(static, 1)
+    for (int32_t thread_id = 0; thread_id < thread_num; ++thread_id) {
+      uint8_t* __restrict__ thread_buffer =
+          thread_buffer_start + thread_id * thread_buffer_size;
+      float* __restrict__ ws_output_buffer =
+          reinterpret_cast<float*>(thread_buffer + ws_output_buffer_offset);
+      float* __restrict__ w2_gemm_output_buffer = reinterpret_cast<float*>(
+          common_buffer_start + w2_gemm_output_buffer_offset);
+
+      for (;;) {
+        const int32_t token_id = counter_ptr->acquire_counter();
+        if (token_id >= token_num) {
+          break;
+        }
+        int32_t* __restrict__ curr_expand_token_id_index_buffer =
+            expand_token_id_index_buffer + token_id * topk_num;
+        const float* __restrict__ curr_weight =
+            topk_weights + token_id * topk_num;
+        const float first_weight = skip_weighted ? 1.0f : curr_weight[0];
+        scalar_t* __restrict__ curr_output_buffer =
+            output + token_id * output_size_2;
+
+        if (topk_num > 1) {
+          int32_t w2_output_idx = curr_expand_token_id_index_buffer[0];
+          float* w2_output_iter =
+              w2_gemm_output_buffer + w2_output_idx * output_size_2;
+          float* ws_output_buffer_iter = ws_output_buffer;
+          vec_op::FP32Vec16 weight_vec(first_weight);
+          for (int32_t i = 0; i < output_size_2; i += 16) {
+            vec_op::FP32Vec16 vec(w2_output_iter);
+            (vec * weight_vec).save(ws_output_buffer_iter);
+            w2_output_iter += 16;
+            ws_output_buffer_iter += 16;
+          }
+
+          for (int32_t idx = 1; idx < topk_num - 1; ++idx) {
+            w2_output_idx = curr_expand_token_id_index_buffer[idx];
+            w2_output_iter =
+                w2_gemm_output_buffer + w2_output_idx * output_size_2;
+            ws_output_buffer_iter = ws_output_buffer;
+            weight_vec = vec_op::FP32Vec16(curr_weight[idx]);
+            for (int32_t i = 0; i < output_size_2; i += 16) {
+              vec_op::FP32Vec16 vec(w2_output_iter);
+              vec_op::FP32Vec16 sum(ws_output_buffer_iter);
+              (sum + vec * weight_vec).save(ws_output_buffer_iter);
+              w2_output_iter += 16;
+              ws_output_buffer_iter += 16;
+            }
+          }
+
+          const int32_t last_idx = topk_num - 1;
+          w2_output_idx = curr_expand_token_id_index_buffer[last_idx];
+          w2_output_iter =
+              w2_gemm_output_buffer + w2_output_idx * output_size_2;
+          ws_output_buffer_iter = ws_output_buffer;
+          scalar_t* curr_output_buffer_iter = curr_output_buffer;
+          weight_vec = vec_op::FP32Vec16(curr_weight[last_idx]);
+          for (int32_t i = 0; i < output_size_2; i += 16) {
+            vec_op::FP32Vec16 vec(w2_output_iter);
+            vec_op::FP32Vec16 sum(ws_output_buffer_iter);
+            scalar_vec_t(sum + vec * weight_vec).save(curr_output_buffer_iter);
+            w2_output_iter += 16;
+            ws_output_buffer_iter += 16;
+            curr_output_buffer_iter += 16;
+          }
+        } else {
+          const int32_t w2_output_idx = curr_expand_token_id_index_buffer[0];
+          float* w2_output_iter =
+              w2_gemm_output_buffer + w2_output_idx * output_size_2;
+          scalar_t* curr_output_buffer_iter = curr_output_buffer;
+          vec_op::FP32Vec16 weight_vec(first_weight);
+          for (int32_t i = 0; i < output_size_2; i += 16) {
+            vec_op::FP32Vec16 vec(w2_output_iter);
+            scalar_vec_t(vec * weight_vec).save(curr_output_buffer_iter);
+            w2_output_iter += 16;
+            curr_output_buffer_iter += 16;
+          }
+        }
+      }
+    }
+  }
+}
+}  // namespace
+
+void prepack_moe_weight_int8(
+    const torch::Tensor& weight,  // [expert_num, output_size, input_size]
+    torch::Tensor& packed_weight, const std::string& isa) {
+  TORCH_CHECK(weight.is_contiguous());
+  const int32_t expert_num = weight.size(0);
+  const int32_t output_size = weight.size(1);
+  const int32_t input_size = weight.size(2);
+  const int64_t expert_stride = weight.stride(0);
+  const cpu_utils::ISA isa_type = cpu_utils::get_isa(isa);
+  TORCH_CHECK_EQ(output_size % 32, 0);
+
+  CPU_INT8_ISA_DISPATCH_IMPL(isa_type, c10::BFloat16, [&]() {
+    TORCH_CHECK_EQ(input_size % gemm_t::K, 0);
+    prepack_moe_weight_int8_impl<gemm_t>(
+        weight.data_ptr<int8_t>(), packed_weight.data_ptr<int8_t>(), expert_num,
+        output_size, input_size, expert_stride);
+  });
+}
+
+void cpu_fused_moe_int8(torch::Tensor& output, const torch::Tensor& input,
+                        const torch::Tensor& w13, const torch::Tensor& w2,
+                        const torch::Tensor& w13_scale,
+                        const torch::Tensor& w2_scale,
+                        const std::optional<torch::Tensor>& w13_bias,
+                        const std::optional<torch::Tensor>& w2_bias,
+                        const torch::Tensor& topk_weights,
+                        const torch::Tensor& topk_id, const bool skip_weighted,
+                        const std::string& act, const std::string& isa) {
+  const int32_t token_num = input.size(0);
+  const int32_t input_size_13 = input.size(1);
+  const int64_t input_stride = input.stride(0);
+  TORCH_CHECK_EQ(input_stride, input_size_13);
+  const int32_t expert_num = w13.size(0);
+  const int32_t output_size_13 = w13.size(1);
+  const int32_t input_size_2 = w2.size(2);
+  const int32_t output_size_2 = w2.size(1);
+  const int32_t topk_num = topk_id.size(1);
+  const FusedMOEAct act_type = cpu_fused_moe_utils::get_act_type(act);
+  const cpu_utils::ISA isa_type = cpu_utils::get_isa(isa);
+  TORCH_CHECK(!skip_weighted || topk_num == 1,
+              "skip_weighted is only supported for topk=1 on CPU");
+
+  VLLM_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "cpu_fused_moe_int8", [&]() {
+        CPU_INT8_ISA_DISPATCH_IMPL(isa_type, scalar_t, [&]() {
+          fused_moe_int8_impl<scalar_t, gemm_t>(
+              output.data_ptr<scalar_t>(), input.data_ptr<scalar_t>(),
+              w13.data_ptr<int8_t>(), w2.data_ptr<int8_t>(),
+              w13_scale.data_ptr<float>(), w2_scale.data_ptr<float>(),
+              w13_bias.has_value() ? w13_bias->data_ptr<scalar_t>() : nullptr,
+              w2_bias.has_value() ? w2_bias->data_ptr<scalar_t>() : nullptr,
+              topk_weights.data_ptr<float>(), topk_id.data_ptr<int32_t>(),
+              act_type, token_num, expert_num, topk_num, input_size_13,
+              output_size_13, input_size_2, output_size_2, skip_weighted);
+        });
+      });
+}

csrc/cpu/cpu_types_vsx.hpp

@@ -287,7 +287,7 @@ struct FP32Vec4 : public Vec<FP32Vec4> {
 
   explicit FP32Vec4(__vector float data) : reg(data) {}
 
-  explicit FP32Vec4(const FP32Vec4& data) : reg(data.reg) {}
+  FP32Vec4(const FP32Vec4& data) : reg(data.reg) {}
 };
 
 struct FP32Vec8 : public Vec<FP32Vec8> {
@@ -316,7 +316,7 @@ struct FP32Vec8 : public Vec<FP32Vec8> {
 
   explicit FP32Vec8(f32x4x2_t data) : reg(data) {}
 
-  explicit FP32Vec8(const FP32Vec8& data) {
+  FP32Vec8(const FP32Vec8& data) {
     reg.val[0] = data.reg.val[0];
     reg.val[1] = data.reg.val[1];
   }
@@ -593,7 +593,7 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
   explicit FP32Vec16(bool, const float* ptr) : FP32Vec16(ptr) {}
   explicit FP32Vec16(f32x4x4_t data) : reg(data) {}
 
-  explicit FP32Vec16(const FP32Vec16& data) {
+  FP32Vec16(const FP32Vec16& data) {
     reg.val[0] = data.reg.val[0];
     reg.val[1] = data.reg.val[1];
     reg.val[2] = data.reg.val[2];
@@ -747,6 +747,15 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
                                 vec_abs(reg.val[2]), vec_abs(reg.val[3])}));
   }
 
+  FP32Vec16 exp() const {
+    FP32Vec8 lo(f32x4x2_t{reg.val[0], reg.val[1]});
+    FP32Vec8 hi(f32x4x2_t{reg.val[2], reg.val[3]});
+    auto lo_e = lo.exp();
+    auto hi_e = hi.exp();
+    return FP32Vec16(f32x4x4_t{lo_e.reg.val[0], lo_e.reg.val[1],
+                               hi_e.reg.val[0], hi_e.reg.val[1]});
+  }
+
   float reduce_max() {
     __vector float max01 = vec_max(reg.val[0], reg.val[1]);
     __vector float max23 = vec_max(reg.val[2], reg.val[3]);

csrc/cpu/micro_gemm/cpu_micro_gemm_impl.hpp

@@ -31,6 +31,9 @@ class MicroGemm {
   }
 };
 
+template <cpu_utils::ISA isa, typename scalar_t>
+class MicroGemmINT8;
+
 template <int32_t n_size, typename scalar_t>
 FORCE_INLINE void default_epilogue(float* __restrict__ c_ptr,
                                    scalar_t* __restrict__ d_ptr,

csrc/cpu/micro_gemm/cpu_micro_gemm_int8_neon.hpp

@@ -0,0 +1,424 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+#ifndef CPU_MICRO_GEMM_INT8_NEON_HPP
+#define CPU_MICRO_GEMM_INT8_NEON_HPP
+
+#include <algorithm>
+#include <cstdint>
+
+#include "cpu/micro_gemm/cpu_micro_gemm_impl.hpp"
+
+#include <arm_bf16.h>
+#include <arm_neon.h>
+#include <c10/util/BFloat16.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Half.h>
+
+namespace cpu_micro_gemm {
+
+namespace neon_smmla {
+
+constexpr int32_t K = 8;
+constexpr int32_t Cols = 2;
+constexpr int32_t TileSize = K * Cols;
+
+FORCE_INLINE float32x4x2_t load_as_f32(const float* input) {
+  float32x4x2_t result;
+  result.val[0] = vld1q_f32(input);
+  result.val[1] = vld1q_f32(input + 4);
+  return result;
+}
+
+FORCE_INLINE float32x4x2_t load_as_f32(const c10::Half* input) {
+  const auto input_vec = vld1q_f16(reinterpret_cast<const float16_t*>(input));
+  float32x4x2_t result;
+  result.val[0] = vcvt_f32_f16(vget_low_f16(input_vec));
+  result.val[1] = vcvt_f32_f16(vget_high_f16(input_vec));
+  return result;
+}
+
+FORCE_INLINE float32x4x2_t load_as_f32(const c10::BFloat16* input) {
+  const auto input_vec = vld1q_bf16(reinterpret_cast<const bfloat16_t*>(input));
+  float32x4x2_t result;
+  result.val[0] = vcvt_f32_bf16(vget_low_bf16(input_vec));
+  result.val[1] = vcvt_f32_bf16(vget_high_bf16(input_vec));
+  return result;
+}
+
+FORCE_INLINE void store_acc_rowpair(const int32x4_t acc01,
+                                    const int32x4_t acc23,
+                                    const int32x4_t acc45,
+                                    const int32x4_t acc67,
+                                    int32_t* __restrict__ c_ptr,
+                                    const int64_t ldc, const int32_t m_rows) {
+  if (m_rows == 0) {
+    return;
+  }
+
+  vst1q_s32(c_ptr, vcombine_s32(vget_low_s32(acc01), vget_low_s32(acc23)));
+  vst1q_s32(c_ptr + 4, vcombine_s32(vget_low_s32(acc45), vget_low_s32(acc67)));
+
+  if (m_rows == 2) {
+    vst1q_s32(c_ptr + ldc,
+              vcombine_s32(vget_high_s32(acc01), vget_high_s32(acc23)));
+    vst1q_s32(c_ptr + ldc + 4,
+              vcombine_s32(vget_high_s32(acc45), vget_high_s32(acc67)));
+  }
+}
+
+FORCE_INLINE void gemm_micro_smmla_8x8_packed_a(
+    const int8_t* __restrict__ a_packed, const int8_t* __restrict__ b_packed,
+    int32_t* __restrict__ c_ptr, const int32_t m, const int32_t k_size,
+    const int64_t ldc) {
+  const int32x4_t zero = vdupq_n_s32(0);
+  int32x4_t acc0101 = zero, acc0123 = zero, acc0145 = zero, acc0167 = zero;
+  int32x4_t acc2301 = zero, acc2323 = zero, acc2345 = zero, acc2367 = zero;
+  int32x4_t acc4501 = zero, acc4523 = zero, acc4545 = zero, acc4567 = zero;
+  int32x4_t acc6701 = zero, acc6723 = zero, acc6745 = zero, acc6767 = zero;
+
+  const int8_t* __restrict__ a_tile = a_packed;
+  const int8_t* __restrict__ b_tile = b_packed;
+
+#pragma GCC unroll 8
+  for (int32_t k_idx = 0; k_idx < k_size; k_idx += K) {
+    const int8x16_t a_tile01 = vld1q_s8(a_tile);
+    const int8x16_t a_tile23 = vld1q_s8(a_tile + TileSize);
+    const int8x16_t a_tile45 = vld1q_s8(a_tile + 2 * TileSize);
+    const int8x16_t a_tile67 = vld1q_s8(a_tile + 3 * TileSize);
+
+    const int8x16_t b_tile01 = vld1q_s8(b_tile);
+    const int8x16_t b_tile23 = vld1q_s8(b_tile + TileSize);
+    const int8x16_t b_tile45 = vld1q_s8(b_tile + 2 * TileSize);
+    const int8x16_t b_tile67 = vld1q_s8(b_tile + 3 * TileSize);
+
+    acc0101 = vmmlaq_s32(acc0101, a_tile01, b_tile01);
+    acc2301 = vmmlaq_s32(acc2301, a_tile23, b_tile01);
+    acc4501 = vmmlaq_s32(acc4501, a_tile45, b_tile01);
+    acc6701 = vmmlaq_s32(acc6701, a_tile67, b_tile01);
+
+    acc0123 = vmmlaq_s32(acc0123, a_tile01, b_tile23);
+    acc2323 = vmmlaq_s32(acc2323, a_tile23, b_tile23);
+    acc4523 = vmmlaq_s32(acc4523, a_tile45, b_tile23);
+    acc6723 = vmmlaq_s32(acc6723, a_tile67, b_tile23);
+
+    acc0145 = vmmlaq_s32(acc0145, a_tile01, b_tile45);
+    acc2345 = vmmlaq_s32(acc2345, a_tile23, b_tile45);
+    acc4545 = vmmlaq_s32(acc4545, a_tile45, b_tile45);
+    acc6745 = vmmlaq_s32(acc6745, a_tile67, b_tile45);
+
+    acc0167 = vmmlaq_s32(acc0167, a_tile01, b_tile67);
+    acc2367 = vmmlaq_s32(acc2367, a_tile23, b_tile67);
+    acc4567 = vmmlaq_s32(acc4567, a_tile45, b_tile67);
+    acc6767 = vmmlaq_s32(acc6767, a_tile67, b_tile67);
+
+    a_tile += 4 * TileSize;
+    b_tile += 4 * TileSize;
+  }
+
+  store_acc_rowpair(acc0101, acc0123, acc0145, acc0167, c_ptr, ldc,
+                    std::min(2, m));
+  store_acc_rowpair(acc2301, acc2323, acc2345, acc2367, c_ptr + 2 * ldc, ldc,
+                    std::min(2, std::max(0, m - 2)));
+  store_acc_rowpair(acc4501, acc4523, acc4545, acc4567, c_ptr + 4 * ldc, ldc,
+                    std::min(2, std::max(0, m - 4)));
+  store_acc_rowpair(acc6701, acc6723, acc6745, acc6767, c_ptr + 6 * ldc, ldc,
+                    std::min(2, std::max(0, m - 6)));
+}
+
+FORCE_INLINE void gemm_micro_smmla_4x16_packed_a(
+    const int8_t* __restrict__ a_packed, const int8_t* __restrict__ b_packed,
+    int32_t* __restrict__ c_ptr, const int32_t m, const int32_t k_size,
+    const int64_t b_n_group_stride, const int64_t ldc) {
+  const int32_t m_rows_01 = std::min(2, m);
+  const int32_t m_rows_23 = std::min(2, std::max(0, m - 2));
+  const int32x4_t zero = vdupq_n_s32(0);
+
+  int32x4_t acc0101 = zero, acc0123 = zero, acc0145 = zero, acc0167 = zero;
+  int32x4_t acc2301 = zero, acc2323 = zero, acc2345 = zero, acc2367 = zero;
+  int32x4_t acc0189 = zero, acc011011 = zero, acc011213 = zero,
+            acc011415 = zero;
+  int32x4_t acc2389 = zero, acc231011 = zero, acc231213 = zero,
+            acc231415 = zero;
+
+  const int8_t* __restrict__ a_tile = a_packed;
+  // note: b packs 8 panels contiguously, so we need 2 b_tile ptrs
+  // for the 4x16 microkernel
+  const int8_t* __restrict__ b_tile0 = b_packed;
+  const int8_t* __restrict__ b_tile1 = b_packed + b_n_group_stride;
+
+#pragma GCC unroll 8
+  for (int32_t k_idx = 0; k_idx < k_size; k_idx += K) {
+    const int8x16_t a_tile01 = vld1q_s8(a_tile);
+    const int8x16_t a_tile23 = vld1q_s8(a_tile + TileSize);
+    const int8x16_t b_tile01 = vld1q_s8(b_tile0);
+    const int8x16_t b_tile23 = vld1q_s8(b_tile0 + TileSize);
+    const int8x16_t b_tile45 = vld1q_s8(b_tile0 + 2 * TileSize);
+    const int8x16_t b_tile67 = vld1q_s8(b_tile0 + 3 * TileSize);
+    const int8x16_t b_tile89 = vld1q_s8(b_tile1);
+    const int8x16_t b_tile1011 = vld1q_s8(b_tile1 + TileSize);
+    const int8x16_t b_tile1213 = vld1q_s8(b_tile1 + 2 * TileSize);
+    const int8x16_t b_tile1415 = vld1q_s8(b_tile1 + 3 * TileSize);
+
+    acc0101 = vmmlaq_s32(acc0101, a_tile01, b_tile01);
+    acc2301 = vmmlaq_s32(acc2301, a_tile23, b_tile01);
+    acc0123 = vmmlaq_s32(acc0123, a_tile01, b_tile23);
+    acc2323 = vmmlaq_s32(acc2323, a_tile23, b_tile23);
+
+    acc0145 = vmmlaq_s32(acc0145, a_tile01, b_tile45);
+    acc2345 = vmmlaq_s32(acc2345, a_tile23, b_tile45);
+    acc0167 = vmmlaq_s32(acc0167, a_tile01, b_tile67);
+    acc2367 = vmmlaq_s32(acc2367, a_tile23, b_tile67);
+
+    acc0189 = vmmlaq_s32(acc0189, a_tile01, b_tile89);
+    acc2389 = vmmlaq_s32(acc2389, a_tile23, b_tile89);
+    acc011011 = vmmlaq_s32(acc011011, a_tile01, b_tile1011);
+    acc231011 = vmmlaq_s32(acc231011, a_tile23, b_tile1011);
+
+    acc011213 = vmmlaq_s32(acc011213, a_tile01, b_tile1213);
+    acc231213 = vmmlaq_s32(acc231213, a_tile23, b_tile1213);
+    acc011415 = vmmlaq_s32(acc011415, a_tile01, b_tile1415);
+    acc231415 = vmmlaq_s32(acc231415, a_tile23, b_tile1415);
+
+    a_tile += 2 * TileSize;
+    b_tile0 += 4 * TileSize;
+    b_tile1 += 4 * TileSize;
+  }
+
+  // rows 0-1, columns 0-7
+  store_acc_rowpair(acc0101, acc0123, acc0145, acc0167, c_ptr, ldc, m_rows_01);
+  // rows 0-1, columns 8-15
+  store_acc_rowpair(acc0189, acc011011, acc011213, acc011415, c_ptr + 8, ldc,
+                    m_rows_01);
+  // rows 2-3, columns 0-7
+  store_acc_rowpair(acc2301, acc2323, acc2345, acc2367, c_ptr + 2 * ldc, ldc,
+                    m_rows_23);
+  // rows 2-3, columns 8-15
+  store_acc_rowpair(acc2389, acc231011, acc231213, acc231415,
+                    c_ptr + 2 * ldc + 8, ldc, m_rows_23);
+}
+
+}  // namespace neon_smmla
+
+template <typename scalar_t>
+class MicroGemmINT8<cpu_utils::ISA::NEON, scalar_t> {
+ public:
+  static constexpr int32_t K = neon_smmla::K;
+  static constexpr int32_t Mr = 8;
+  static constexpr int32_t Nr = 8;
+  static constexpr int32_t NrGemv = 16;
+  static constexpr int32_t MaxMSize = 8;
+  static constexpr int32_t NSize = 32;
+  static constexpr int32_t WeightOCGroupSize = Nr;
+  static_assert(MaxMSize % Mr == 0);
+
+  static FORCE_INLINE void quantize_row(const scalar_t* input, int8_t* output,
+                                        float& scale, const int32_t size) {
+    TORCH_CHECK_EQ(size % K, 0);
+    float32x4_t max_vec = vdupq_n_f32(0.0f);
+
+    for (int32_t i = 0; i < size; i += K) {
+      const float32x4x2_t input_vec = neon_smmla::load_as_f32(input + i);
+      max_vec = vmaxq_f32(max_vec, vabsq_f32(input_vec.val[0]));
+      max_vec = vmaxq_f32(max_vec, vabsq_f32(input_vec.val[1]));
+    }
+
+    const float abs_max = std::max(vmaxvq_f32(max_vec), 1.0e-7f);
+    scale = abs_max / 127.0f;
+    const float32x4_t inv_scale_vec = vdupq_n_f32(127.0f / abs_max);
+
+    for (int32_t i = 0; i < size; i += K) {
+      const float32x4x2_t input_vec = neon_smmla::load_as_f32(input + i);
+      const int32x4_t output_low =
+          vcvtnq_s32_f32(vmulq_f32(input_vec.val[0], inv_scale_vec));
+      const int32x4_t output_high =
+          vcvtnq_s32_f32(vmulq_f32(input_vec.val[1], inv_scale_vec));
+      const int16x8_t output_s16 =
+          vcombine_s16(vqmovn_s32(output_low), vqmovn_s32(output_high));
+      vst1_s8(output + i, vqmovn_s16(output_s16));
+    }
+  }
+
+  // with current code, fusing this into the gemm micro kernel didn't move the
+  // needle
+  static FORCE_INLINE void dequantize_tile(
+      int32_t* input, float* output, const float* __restrict__ input_scales,
+      const float* __restrict__ weight_scales, const int32_t m, const int32_t n,
+      const int32_t stride) {
+    TORCH_CHECK_EQ(n % 4, 0);
+    for (int32_t m_idx = 0; m_idx < m; ++m_idx) {
+      const float32x4_t input_scale_vec = vdupq_n_f32(input_scales[m_idx]);
+      for (int32_t n_idx = 0; n_idx < n; n_idx += 4) {
+        const int32x4_t input_vec = vld1q_s32(input + m_idx * stride + n_idx);
+        const float32x4_t weight_scale_vec = vld1q_f32(weight_scales + n_idx);
+        const float32x4_t output_vec =
+            vmulq_f32(vcvtq_f32_s32(input_vec),
+                      vmulq_f32(input_scale_vec, weight_scale_vec));
+        vst1q_f32(output + m_idx * stride + n_idx, output_vec);
+      }
+    }
+  }
+
+  // physical layout [
+  //  M / (8 or 4); Mr is 8 or 4
+  //  K / 8; K for smmla is 8
+  //  4,   ; 4 row-pairs for each 8 rows
+  //  2,   ; row-pair is 2 rows
+  //  4    ; 4 elements per row
+  // ]
+  static void pack_input_from_rows(const int8_t* const* __restrict__ rows,
+                                   int8_t* __restrict__ a_packed,
+                                   const int32_t m, const int32_t k) {
+    TORCH_CHECK(m > 0 && m <= MaxMSize);
+    TORCH_CHECK(k % K == 0);
+    const int8x8_t zero = vdup_n_s8(0);
+
+    for (int32_t row_base = 0; row_base < m; row_base += Mr) {
+      const int32_t panel_m = std::min(Mr, m - row_base);
+      const int8_t* const* panel_rows = rows + row_base;
+      int8_t* __restrict__ out = a_packed + row_base * k;
+
+      // fast path for full 8-row panels (fast path for 4-row panels didn't move
+      // the needle)
+      if (panel_m == Mr) {
+        const int8_t* __restrict__ row0 = panel_rows[0];
+        const int8_t* __restrict__ row1 = panel_rows[1];
+        const int8_t* __restrict__ row2 = panel_rows[2];
+        const int8_t* __restrict__ row3 = panel_rows[3];
+        const int8_t* __restrict__ row4 = panel_rows[4];
+        const int8_t* __restrict__ row5 = panel_rows[5];
+        const int8_t* __restrict__ row6 = panel_rows[6];
+        const int8_t* __restrict__ row7 = panel_rows[7];
+        int32_t k_idx = 0;
+        for (; k_idx + 2 * K <= k; k_idx += 2 * K) {
+          int8_t* __restrict__ block0 = out;
+          int8_t* __restrict__ block1 = out + 4 * neon_smmla::TileSize;
+
+          int8x16_t a0 = vld1q_s8(row0 + k_idx);
+          int8x16_t a1 = vld1q_s8(row1 + k_idx);
+          vst1q_s8(block0, vcombine_s8(vget_low_s8(a0), vget_low_s8(a1)));
+          vst1q_s8(block1, vcombine_s8(vget_high_s8(a0), vget_high_s8(a1)));
+
+          a0 = vld1q_s8(row2 + k_idx);
+          a1 = vld1q_s8(row3 + k_idx);
+          vst1q_s8(block0 + neon_smmla::TileSize,
+                   vcombine_s8(vget_low_s8(a0), vget_low_s8(a1)));
+          vst1q_s8(block1 + neon_smmla::TileSize,
+                   vcombine_s8(vget_high_s8(a0), vget_high_s8(a1)));
+
+          a0 = vld1q_s8(row4 + k_idx);
+          a1 = vld1q_s8(row5 + k_idx);
+          vst1q_s8(block0 + 2 * neon_smmla::TileSize,
+                   vcombine_s8(vget_low_s8(a0), vget_low_s8(a1)));
+          vst1q_s8(block1 + 2 * neon_smmla::TileSize,
+                   vcombine_s8(vget_high_s8(a0), vget_high_s8(a1)));
+
+          a0 = vld1q_s8(row6 + k_idx);
+          a1 = vld1q_s8(row7 + k_idx);
+          vst1q_s8(block0 + 3 * neon_smmla::TileSize,
+                   vcombine_s8(vget_low_s8(a0), vget_low_s8(a1)));
+          vst1q_s8(block1 + 3 * neon_smmla::TileSize,
+                   vcombine_s8(vget_high_s8(a0), vget_high_s8(a1)));
+
+          out += 8 * neon_smmla::TileSize;
+        }
+
+        for (; k_idx < k; k_idx += K) {
+          int8x8_t a0 = vld1_s8(row0 + k_idx);
+          int8x8_t a1 = vld1_s8(row1 + k_idx);
+          vst1q_s8(out, vcombine_s8(a0, a1));
+
+          a0 = vld1_s8(row2 + k_idx);
+          a1 = vld1_s8(row3 + k_idx);
+          vst1q_s8(out + neon_smmla::TileSize, vcombine_s8(a0, a1));
+
+          a0 = vld1_s8(row4 + k_idx);
+          a1 = vld1_s8(row5 + k_idx);
+          vst1q_s8(out + 2 * neon_smmla::TileSize, vcombine_s8(a0, a1));
+
+          a0 = vld1_s8(row6 + k_idx);
+          a1 = vld1_s8(row7 + k_idx);
+          vst1q_s8(out + 3 * neon_smmla::TileSize, vcombine_s8(a0, a1));
+
+          out += 4 * neon_smmla::TileSize;
+        }
+        continue;
+      }
+
+      const int32_t row_pairs = (panel_m <= 4) ? 2 : Mr / 2;
+      for (int32_t k_idx = 0; k_idx < k; k_idx += K) {
+        for (int32_t pair_idx = 0; pair_idx < row_pairs; ++pair_idx) {
+          const int32_t row_idx = pair_idx * 2;
+          const int8x8_t row0 =
+              (row_idx < panel_m) ? vld1_s8(panel_rows[row_idx] + k_idx) : zero;
+          const int8x8_t row1 = (row_idx + 1 < panel_m)
+                                    ? vld1_s8(panel_rows[row_idx + 1] + k_idx)
+                                    : zero;
+          vst1q_s8(out, vcombine_s8(row0, row1));
+          out += neon_smmla::TileSize;
+        }
+      }
+    }
+  }
+
+  // physical layout [
+  //  N / 8; Nr is 8
+  //  K / 8; K for smmla is 8
+  //  4,   ; 4 col-pairs for each 8 cols
+  //  2,   ; col-pair is 2 cols
+  //  4    ; 4 elements per col
+  // ]
+  static void pack_weight(const int8_t* __restrict__ weight,
+                          int8_t* __restrict__ packed_weight,
+                          const int32_t output_size, const int32_t input_size) {
+    TORCH_CHECK(output_size % NSize == 0);
+    TORCH_CHECK(input_size % K == 0);
+
+    for (int32_t o_idx = 0; o_idx < output_size; o_idx += Nr) {
+      int8_t* __restrict__ dst = packed_weight + o_idx * input_size;
+      for (int32_t k_idx = 0; k_idx < input_size; k_idx += K) {
+        for (int32_t pair_idx = 0; pair_idx < Nr;
+             pair_idx += neon_smmla::Cols) {
+          const int8_t* __restrict__ row0 =
+              weight + (o_idx + pair_idx) * input_size + k_idx;
+          const int8_t* __restrict__ row1 = row0 + input_size;
+          vst1q_s8(dst, vcombine_s8(vld1_s8(row0), vld1_s8(row1)));
+          dst += neon_smmla::TileSize;
+        }
+      }
+    }
+  }
+
+  void gemm(const int8_t* __restrict__ a_packed,
+            const int8_t* __restrict__ b_packed, int32_t* __restrict__ c,
+            const int32_t m, const int32_t k, const int64_t b_n_group_stride,
+            const int64_t ldc) const {
+    TORCH_CHECK(m > 0 && m <= MaxMSize);
+    TORCH_CHECK(k % K == 0);
+
+    for (int32_t n_idx = 0; n_idx < NSize; n_idx += NrGemv) {
+      const int8_t* __restrict__ b_panel = b_packed + n_idx * k;
+
+      for (int32_t row_base = 0; row_base < m; row_base += Mr) {
+        const int32_t panel_m = std::min(Mr, m - row_base);
+        const int8_t* __restrict__ a_panel = a_packed + row_base * k;
+        int32_t* __restrict__ c_panel = c + row_base * ldc + n_idx;
+
+        if (panel_m <= 4) {
+          neon_smmla::gemm_micro_smmla_4x16_packed_a(
+              a_panel, b_panel, c_panel, panel_m, k, b_n_group_stride, ldc);
+        } else {
+          neon_smmla::gemm_micro_smmla_8x8_packed_a(a_panel, b_panel, c_panel,
+                                                    panel_m, k, ldc);
+          neon_smmla::gemm_micro_smmla_8x8_packed_a(
+              a_panel, b_panel + b_n_group_stride, c_panel + Nr, panel_m, k,
+              ldc);
+        }
+      }
+    }
+  }
+};
+
+}  // namespace cpu_micro_gemm
+
+#endif

csrc/cpu/micro_gemm/cpu_micro_gemm_neon.hpp

@@ -1,3 +1,6 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
 #ifndef CPU_MICRO_GEMM_NEON_HPP
 #define CPU_MICRO_GEMM_NEON_HPP
 
@@ -16,9 +19,6 @@ namespace {
 constexpr int32_t K = 4;
 constexpr int32_t Cols = 2;
 constexpr int32_t TileSize = K * Cols;
-constexpr int32_t Mr = 8;
-constexpr int32_t Nr = 8;
-constexpr int32_t Nr_gemv = 16;
 
 // a = [a0, a1, a2, a3], b = [b0, b1, b2, b3] -> [a0, a1, b0, b1]
 FORCE_INLINE float32x4_t zip1_f32x4(const float32x4_t a, const float32x4_t b) {
@@ -132,7 +132,7 @@ FORCE_INLINE void gemm_micro_bfmmla_8x8_packed_a(
     acc6767 = vbfmmlaq_f32(acc6767, a_tile67, b_tile67);
 
     a_tile += 4 * TileSize;
-    b_tile += Nr * K;
+    b_tile += 4 * TileSize;
   }
 
   store_acc_rowpair(acc0101, acc0123, acc0145, acc0167, c_ptr, ldc,
@@ -205,8 +205,8 @@ FORCE_INLINE void gemm_micro_bfmmla_4x16_packed_a(
     acc231415 = vbfmmlaq_f32(acc231415, a_tile23, b_tile1415);
 
     a_tile += 2 * TileSize;
-    b_tile0 += Nr * K;
-    b_tile1 += Nr * K;
+    b_tile0 += 4 * TileSize;
+    b_tile1 += 4 * TileSize;
   }
 
   store_acc_rowpair(acc0101, acc0123, acc0145, acc0167, c_ptr, ldc, m_rows_01);
@@ -223,6 +223,9 @@ FORCE_INLINE void gemm_micro_bfmmla_4x16_packed_a(
 template <typename scalar_t>
 class MicroGemm<cpu_utils::ISA::NEON, scalar_t> {
  public:
+  static constexpr int32_t Mr = 8;
+  static constexpr int32_t Nr = 8;
+  static constexpr int32_t NrGemv = 16;
   static constexpr int32_t MaxMSize = 8;
   static constexpr int32_t NSize = 32;
   static constexpr int32_t WeightOCGroupSize = Nr;
@@ -246,6 +249,9 @@ class MicroGemm<cpu_utils::ISA::NEON, c10::BFloat16> {
  public:
   using scalar_t = c10::BFloat16;
 
+  static constexpr int32_t Mr = 8;
+  static constexpr int32_t Nr = 8;
+  static constexpr int32_t NrGemv = 16;
   static constexpr int32_t MaxMSize = 8;
   static constexpr int32_t NSize = 32;
   static constexpr int32_t WeightOCGroupSize = Nr;
@@ -253,7 +259,7 @@ class MicroGemm<cpu_utils::ISA::NEON, c10::BFloat16> {
 
  public:
   // physical layout [
-  //  M / 8; Mr is 8
+  //  M / (8 or 4); Mr is 8 or 4
   //  K / 4; K for bfmmla is 4
   //  4,   ; 4 row-pairs for each 8 rows
   //  2,   ; row-pair is 2 rows
@@ -439,7 +445,7 @@ class MicroGemm<cpu_utils::ISA::NEON, c10::BFloat16> {
     (void)lda;  // A is packed, so lda is not needed
     TORCH_CHECK_EQ(k % K, 0);
 
-    for (int32_t n_idx = 0; n_idx < NSize; n_idx += Nr_gemv) {
+    for (int32_t n_idx = 0; n_idx < NSize; n_idx += NrGemv) {
       const bfloat16_t* __restrict__ b_panel =
           reinterpret_cast<const bfloat16_t*>(b_ptr) + n_idx * k;
 

csrc/cpu/sgl-kernels/conv.cpp

@@ -451,6 +451,90 @@ void causal_conv1d_update_kernel_impl(
   });
 }
 
+template <typename scalar_t>
+void causal_conv1d_update_multi_kernel_impl(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    scalar_t* __restrict__ conv_states,
+    const scalar_t* __restrict__ weight,
+    const scalar_t* __restrict__ bias,
+    const int32_t* __restrict__ num_accepted_tokens,
+    const int32_t* __restrict__ conv_indices,
+    bool silu_activation,
+    int64_t batch,
+    int64_t dim,
+    int64_t seqlen,
+    int64_t width,
+    int64_t state_len,
+    int64_t conv_state_slot_stride) {
+  constexpr int64_t BLOCK_N = block_size_n() * 2;
+  const int64_t NB = div_up(dim, BLOCK_N);
+
+  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
+    at::parallel_for(0, batch * NB, 0, [&](int64_t begin, int64_t end) {
+      int64_t bs{0}, nb{0};
+      data_index_init(begin, bs, batch, nb, NB);
+
+      for (int64_t i = begin; i < end; ++i) {
+        const int64_t nb_start = nb * BLOCK_N;
+        const int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
+        const int32_t conv_state_index = conv_indices[bs];
+        const int32_t history_offset = num_accepted_tokens[bs] - 1;
+
+        switch (width << 4 | nb_size >> 4) {
+          case 0x42:
+            tinygemm_kernel<scalar_t, 4, 32, has_bias, has_silu>::apply(
+                input + bs * seqlen * dim + nb_start,
+                weight + nb_start * width,
+                out + bs * seqlen * dim + nb_start,
+                has_bias ? bias + nb_start : nullptr,
+                conv_states + conv_state_index * conv_state_slot_stride +
+                    history_offset * dim + nb_start,
+                true,
+                seqlen,
+                dim,
+                true);
+            break;
+          case 0x44:
+            tinygemm_kernel<scalar_t, 4, 64, has_bias, has_silu>::apply(
+                input + bs * seqlen * dim + nb_start,
+                weight + nb_start * width,
+                out + bs * seqlen * dim + nb_start,
+                has_bias ? bias + nb_start : nullptr,
+                conv_states + conv_state_index * conv_state_slot_stride +
+                    history_offset * dim + nb_start,
+                true,
+                seqlen,
+                dim,
+                true);
+            break;
+          default:
+            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
+        }
+
+        data_index_step(bs, batch, nb, NB);
+      }
+    });
+  });
+
+  at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
+    for (int64_t bs = begin; bs < end; ++bs) {
+      const int32_t conv_state_index = conv_indices[bs];
+      const int32_t num_accepted = num_accepted_tokens[bs];
+      scalar_t* state = conv_states + conv_state_index * conv_state_slot_stride;
+
+      std::memmove(
+          state,
+          state + num_accepted * dim,
+          (state_len - seqlen) * dim * sizeof(scalar_t));
+      std::memcpy(
+          state + (state_len - seqlen) * dim,
+          input + bs * seqlen * dim,
+          seqlen * dim * sizeof(scalar_t));
+    }
+  });
+}
+
 }  // anonymous namespace
 
 // from [dim, width] or [N, K]
@@ -545,7 +629,7 @@ at::Tensor get_block_indices(const std::optional<at::Tensor>& offsets, int64_t n
 //   query_start_loc: (batch + 1) int32
 //   cache_indices: (batch)  int32
 //   has_initial_state: (batch) bool
-//   conv_states: (..., dim, width - 1) itype
+//   conv_states: (..., dim, state_len) itype, where state_len >= width - 1
 //   activation: either None or "silu" or "swish"
 //   pad_slot_id: int
 //
@@ -586,11 +670,14 @@ at::Tensor causal_conv1d_fwd_cpu(
     CHECK_EQ(conv_states_val.scalar_type(), scalar_type);
     CHECK_GE(padded_batch, batch);
     CHECK_EQ(conv_states_val.size(1), dim);
-    CHECK_EQ(conv_states_val.size(2), width - 1);
+    const int64_t state_len = conv_states_val.size(2);
+    CHECK_GE(state_len, width - 1);
 
     // adjust `conv_states` to be contiguous on `dim`
     // should happen only once
     if (conv_states_val.stride(-2) != 1) {
+      TORCH_CHECK(state_len == width - 1,
+          "causal_conv1d_fwd_cpu: wide conv_states must be contiguous on dim.");
       auto conv_states_copy = conv_states_val.clone();
       conv_states_val.as_strided_({padded_batch, dim, width - 1}, {(width - 1) * dim, 1, dim});
       conv_states_val.copy_(conv_states_copy);
@@ -651,14 +738,14 @@ at::Tensor causal_conv1d_fwd_cpu(
 
 // API aligned with GPUs
 //
-//   x: (batch, dim) or (batch, dim, seqlen)
+//   x: (batch, dim) or (batch, seqlen, dim)
 //   conv_state: (..., dim, state_len), where state_len >= width - 1
 //   weight: (dim, width)
 //   bias: (dim,)
-//   cache_seqlens: (batch,), dtype int32.
+//   num_accepted_tokens: (batch,), dtype int32.
 //   conv_state_indices: (batch,), dtype int32
 //   pad_slot_id: int
-//   out: (batch, dim) or (batch, dim, seqlen)
+//   out: (batch, dim) or (batch, seqlen, dim)
 //
 at::Tensor causal_conv1d_update_cpu(
     const at::Tensor& x,
@@ -666,7 +753,7 @@ at::Tensor causal_conv1d_update_cpu(
     const at::Tensor& weight,
     const std::optional<at::Tensor>& bias,
     bool silu_activation,
-    const std::optional<at::Tensor>& cache_seqlens,
+    const std::optional<at::Tensor>& num_accepted_tokens,
     const std::optional<at::Tensor>& conv_state_indices,
     int64_t pad_slot_id,
     bool is_vnni) {
@@ -674,13 +761,13 @@ at::Tensor causal_conv1d_update_cpu(
   CHECK_CONTIGUOUS(weight);
   auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
 
-  // TODO: add multi-token prediction support
-  TORCH_CHECK(x.dim() == 2, "causal_conv1d_update_cpu: expect x to be 2D tensor.");
-  TORCH_CHECK(!cache_seqlens.has_value(), "causal_conv1d_update_cpu: don't support cache_seqlens.");
+  TORCH_CHECK(
+      x.dim() == 2 || x.dim() == 3,
+      "causal_conv1d_update_cpu: expect x to be 2D or 3D tensor.");
 
   int64_t batch = x.size(0);
-  int64_t dim = x.size(1);
-  int64_t seqlen = 1;
+  int64_t dim = x.dim() == 2 ? x.size(1) : x.size(2);
+  int64_t seqlen = x.dim() == 2 ? 1 : x.size(1);
   int64_t width = weight.size(-1);
 
   const auto scalar_type = x.scalar_type();
@@ -690,10 +777,84 @@ at::Tensor causal_conv1d_update_cpu(
 
   CHECK_EQ(conv_states.scalar_type(), scalar_type);
   CHECK_EQ(conv_states.size(1), dim);
-  CHECK_EQ(conv_states.size(2), width - 1);
+  const int64_t state_len = conv_states.size(2);
+  CHECK_GE(state_len, width - 1);
+
+  if (x.dim() == 3) {
+    TORCH_CHECK(
+        num_accepted_tokens.has_value(),
+        "causal_conv1d_update_cpu: num_accepted_tokens is required for 3D x.");
+    TORCH_CHECK(
+        conv_state_indices.has_value(),
+        "causal_conv1d_update_cpu: conv_state_indices is required for 3D x.");
+    CHECK_OPTIONAL_SHAPE_DTYPE(num_accepted_tokens, batch, at::kInt);
+    TORCH_CHECK(
+        width == 4,
+        "causal_conv1d_update_cpu: support only width of 4 for 3D x.");
+    TORCH_CHECK(
+        seqlen > 0,
+        "causal_conv1d_update_cpu: expect non-empty sequence for 3D x.");
+    TORCH_CHECK(
+        state_len >= seqlen,
+        "causal_conv1d_update_cpu: state_len must be >= seqlen for 3D x.");
+    TORCH_CHECK(
+        conv_states.stride(-2) == 1 && conv_states.stride(-1) == dim,
+        "causal_conv1d_update_cpu: 3D x requires SD conv_states layout.");
+
+    const int32_t* accepted_counts =
+        num_accepted_tokens.value().data_ptr<int32_t>();
+    const int32_t* indices = conv_state_indices.value().data_ptr<int32_t>();
+    const int64_t num_slots = conv_states.size(0);
+    for (int64_t bs = 0; bs < batch; ++bs) {
+      const int32_t num_accepted = accepted_counts[bs];
+      const int32_t conv_state_index = indices[bs];
+      TORCH_CHECK(
+          conv_state_index != pad_slot_id,
+          "causal_conv1d_update_cpu: 3D x does not support pad slots.");
+      TORCH_CHECK(
+          conv_state_index >= 0 && conv_state_index < num_slots,
+          "causal_conv1d_update_cpu: conv_state_indices out of range.");
+      TORCH_CHECK(
+          num_accepted >= 1 && num_accepted <= seqlen,
+          "causal_conv1d_update_cpu: num_accepted_tokens must be in [1, "
+          "seqlen].");
+      TORCH_CHECK(
+          num_accepted - 1 + width - 1 <= state_len,
+          "causal_conv1d_update_cpu: history window exceeds conv_states.");
+    }
+
+    int64_t conv_state_slot_stride = conv_states.stride(0);
+    at::Tensor out = at::empty_like(x);
+    AT_DISPATCH_REDUCED_FLOATING_TYPES(
+        scalar_type, "causal_conv1d_update_multi_kernel_impl", [&] {
+          causal_conv1d_update_multi_kernel_impl<scalar_t>(
+              out.data_ptr<scalar_t>(),
+              x.data_ptr<scalar_t>(),
+              conv_states.data_ptr<scalar_t>(),
+              packed_w.data_ptr<scalar_t>(),
+              conditional_data_ptr<scalar_t>(bias),
+              accepted_counts,
+              indices,
+              silu_activation,
+              batch,
+              dim,
+              seqlen,
+              width,
+              state_len,
+              conv_state_slot_stride);
+        });
+    return out;
+  }
+
+  TORCH_CHECK(
+      !num_accepted_tokens.has_value(),
+      "causal_conv1d_update_cpu: num_accepted_tokens is only supported for 3D "
+      "x.");
 
   // adjust `conv_states` to be contiguous on `dim`
   if (conv_states.stride(-2) != 1) {
+    TORCH_CHECK(state_len == width - 1,
+        "causal_conv1d_update_cpu: wide conv_states must be contiguous on dim.");
     int64_t num_cache_lines = conv_states.size(0);
     auto conv_states_copy = conv_states.clone();
     conv_states.as_strided_({num_cache_lines, dim, width - 1}, {(width - 1) * dim, 1, dim});

csrc/cpu/torch_bindings.cpp

@@ -147,7 +147,7 @@ at::Tensor causal_conv1d_fwd_cpu(
 at::Tensor causal_conv1d_update_cpu(
     const at::Tensor& x, const at::Tensor& conv_states,
     const at::Tensor& weight, const std::optional<at::Tensor>& bias,
-    bool silu_activation, const std::optional<at::Tensor>& cache_seqlens,
+    bool silu_activation, const std::optional<at::Tensor>& num_accepted_tokens,
     const std::optional<at::Tensor>& conv_state_indices, int64_t pad_slot_id,
     bool is_vnni);
 
@@ -207,6 +207,20 @@ void cpu_fused_moe(torch::Tensor& output, const torch::Tensor& input,
                    const torch::Tensor& topk_id, const bool skip_weighted,
                    const std::string& act, const std::string& isa);
 
+void prepack_moe_weight_int8(const torch::Tensor& weight,
+                             torch::Tensor& packed_weight,
+                             const std::string& isa);
+
+void cpu_fused_moe_int8(torch::Tensor& output, const torch::Tensor& input,
+                        const torch::Tensor& w13, const torch::Tensor& w2,
+                        const torch::Tensor& w13_scale,
+                        const torch::Tensor& w2_scale,
+                        const std::optional<torch::Tensor>& w13_bias,
+                        const std::optional<torch::Tensor>& w2_bias,
+                        const torch::Tensor& topk_weights,
+                        const torch::Tensor& topk_id, const bool skip_weighted,
+                        const std::string& act, const std::string& isa);
+
 void compute_slot_mapping_kernel_impl(const torch::Tensor query_start_loc,
                                       const torch::Tensor positions,
                                       const torch::Tensor block_table,
@@ -502,7 +516,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "causal_conv1d_update_cpu(Tensor x, Tensor(a!) conv_states, Tensor "
       "weight, Tensor? bias, bool silu_activation,"
-      "Tensor? cache_seqlens, Tensor? conv_state_indices, int pad_slot_id, "
+      "Tensor? num_accepted_tokens, Tensor? conv_state_indices, int "
+      "pad_slot_id, "
       "bool is_vnni) -> Tensor");
   ops.impl("causal_conv1d_update_cpu", torch::kCPU, &causal_conv1d_update_cpu);
 #endif
@@ -596,8 +611,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 #endif
 
   // fused moe
-#if defined(__AVX512F__) || \
-    (defined(__aarch64__) && !defined(__APPLE__) && defined(ARM_BF16_SUPPORT))
+#if defined(__AVX512F__) || (defined(ARM_BF16_SUPPORT) && !defined(__APPLE__))
   ops.def(
       "prepack_moe_weight(Tensor weight, Tensor(a1!) packed_weight, str isa) "
       "-> ()");
@@ -608,7 +622,22 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "bool skip_weighted, "
       "str act, str isa) -> ()");
   ops.impl("cpu_fused_moe", torch::kCPU, &cpu_fused_moe);
-#endif
+#endif  // #if defined(__AVX512F__) || (defined(ARM_BF16_SUPPORT) &&
+        // !defined(__APPLE__))
+#if defined(ARM_I8MM_SUPPORT) && defined(ARM_BF16_SUPPORT) && \
+    !defined(__APPLE__)
+  ops.def(
+      "prepack_moe_weight_int8(Tensor weight, Tensor(a1!) packed_weight, "
+      "str isa) -> ()");
+  ops.impl("prepack_moe_weight_int8", torch::kCPU, &prepack_moe_weight_int8);
+  ops.def(
+      "cpu_fused_moe_int8(Tensor(a0!) output, Tensor input, Tensor w13, "
+      "Tensor w2, Tensor w13_scale, Tensor w2_scale, Tensor? w13_bias, "
+      "Tensor? w2_bias, Tensor topk_weights, Tensor topk_id, bool "
+      "skip_weighted, str act, str isa) -> ()");
+  ops.impl("cpu_fused_moe_int8", torch::kCPU, &cpu_fused_moe_int8);
+#endif  // #if defined(ARM_I8MM_SUPPORT) && defined(ARM_BF16_SUPPORT) &&
+        // !defined(__APPLE__)
   ops.def(
       "mla_decode_kvcache("
       "   Tensor! out, Tensor query, Tensor kv_cache,"

csrc/quickreduce/quick_reduce.h

@@ -22,17 +22,25 @@ template <typename AllReduceKernel, typename T>
 __global__ __quickreduce_launch_bounds_two_shot__ static void
 allreduce_prototype_twoshot(T const* A, T* B, uint32_t N, uint32_t num_blocks,
                             int rank, uint8_t** dbuffer_list,
-                            uint32_t data_offset, uint32_t flag_color,
+                            uint32_t data_offset, uint32_t* d_flag_counters,
                             int64_t data_size_per_phase) {
   int block = blockIdx.x;
   int grid = gridDim.x;
 
+  // Load this block's counter from device memory and advance it on-device,
+  // so the color keeps changing across graph replays instead of being frozen.
+  uint32_t flag_color = d_flag_counters[blockIdx.x];
+
   while (block < num_blocks) {
     AllReduceKernel::run(A, B, N, block, rank, dbuffer_list, data_offset,
                          flag_color, data_size_per_phase);
     block += grid;
     flag_color++;
   }
+  // All threads compute the same final value; one writer per block is enough.
+  if (threadIdx.x == 0 && threadIdx.y == 0) {
+    d_flag_counters[blockIdx.x] = flag_color;
+  }
 }
 
 #define TWOSHOT_DISPATCH(__codec)                                           \
@@ -42,21 +50,21 @@ allreduce_prototype_twoshot(T const* A, T* B, uint32_t N, uint32_t num_blocks,
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       d_flag_counters, this->kMaxProblemSize);             \
   } else if (world_size == 4) {                                             \
     using LineCodec = __codec<T, 4>;                                        \
     using AllReduceKernel = AllReduceTwoshot<T, LineCodec, cast_bf2half>;   \
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       d_flag_counters, this->kMaxProblemSize);             \
   } else if (world_size == 8) {                                             \
     using LineCodec = __codec<T, 8>;                                        \
     using AllReduceKernel = AllReduceTwoshot<T, LineCodec, cast_bf2half>;   \
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       d_flag_counters, this->kMaxProblemSize);             \
   }
 
 // INT3 only retains good performance on TP2 (world_size == 2). On TP4/TP8
@@ -69,7 +77,7 @@ allreduce_prototype_twoshot(T const* A, T* B, uint32_t N, uint32_t num_blocks,
     hipLaunchKernelGGL((allreduce_prototype_twoshot<AllReduceKernel, T>),   \
                        dim3(grid), dim3(kBlockTwoShot), 0, stream, A, B, N, \
                        num_blocks, rank, dbuffer_list, data_offset,         \
-                       flag_color, this->kMaxProblemSize);                  \
+                       d_flag_counters, this->kMaxProblemSize);             \
   } else {                                                                  \
     throw std::runtime_error(                                               \
         "INT3 quick all-reduce is only supported for world_size == 2 "      \
@@ -94,7 +102,7 @@ struct DeviceComms {
   static int constexpr kMaxWorldSize = 8;
 
   bool initialized = false;
-  uint32_t flag_color = 1;
+  uint32_t* d_flag_counters = nullptr;
   int world_size;
   int rank;
 
@@ -128,6 +136,16 @@ struct DeviceComms {
     // Clear the flags buffer.
     HIP_CHECK(hipMemset(dbuffer, 0, flags_buffer_size));
 
+    // One flag-color counter per block, advanced by the kernel. Start at 1
+    // to stay clear of the flags buffer we just zeroed.
+    HIP_CHECK(hipMalloc(&d_flag_counters, kMaxNumBlocks * sizeof(uint32_t)));
+    {
+      std::vector<uint32_t> init_color(kMaxNumBlocks, 1u);
+      HIP_CHECK(hipMemcpy(d_flag_counters, init_color.data(),
+                          kMaxNumBlocks * sizeof(uint32_t),
+                          hipMemcpyHostToDevice));
+    }
+
     // Device-side list of IPC buffers.
     buffer_list.resize(world_size);
     HIP_CHECK(hipMalloc(&dbuffer_list, world_size * sizeof(uint8_t*)));
@@ -144,6 +162,12 @@ struct DeviceComms {
   hipIpcMemHandle_t const get_handle() { return buffer_ipc_handle; }
 
   void destroy() {
+    // Allocated before `initialized` flips true, so free it on its own guard
+    // to avoid a leak if init fails partway through.
+    if (d_flag_counters) {
+      HIP_CHECK(hipFree(d_flag_counters));
+      d_flag_counters = nullptr;
+    }
     if (initialized) {
       for (int i = 0; i < world_size; i++) {
         if (i != rank) {
@@ -211,8 +235,6 @@ struct DeviceComms {
         break;
     }
     HIP_CHECK(cudaGetLastError());
-    // Rotate the flag color.
-    flag_color += divceil(N, grid);
   }
 };
 

docker/Dockerfile

@@ -25,6 +25,10 @@
 ARG CUDA_VERSION=13.0.3
 ARG PYTHON_VERSION=3.12
 ARG UBUNTU_VERSION=22.04
+# DeepEPv2 requires NCCL >= 2.30.4 (GIN backend).
+# This version is only used for CUDA 13+ builds; CUDA 12 falls back to
+# the default NCCL version shipped with the base image.
+ARG NCCL_VERSION=2.30.7
 
 # By parameterizing the base images, we allow third-party to use their own
 # base images. One use case is hermetic builds with base images stored in
@@ -477,10 +481,17 @@ WORKDIR /workspace
 # Build DeepEP wheels
 COPY tools/ep_kernels/install_python_libraries.sh /tmp/install_python_libraries.sh
 # Defaults moved here from tools/ep_kernels/install_python_libraries.sh for centralized version management
-ARG DEEPEP_COMMIT_HASH=73b6ea4
+ARG DEEPEP_COMMIT_HASH=d4f41e4e93
 ARG NVSHMEM_VER
+ARG NCCL_VERSION
 RUN --mount=type=cache,target=/opt/uv/cache \
     mkdir -p /tmp/ep_kernels_workspace/dist && \
+    CUDA_MAJOR=$(echo $CUDA_VERSION | cut -d. -f1) && \
+    if [ "$CUDA_MAJOR" -ge 13 ] && [ -n "$NCCL_VERSION" ]; then \
+        echo "nvidia-nccl-cu${CUDA_MAJOR}==${NCCL_VERSION}" \
+            > /tmp/nccl-override.txt && \
+        export UV_OVERRIDE=/tmp/nccl-override.txt; \
+    fi && \
     export TORCH_CUDA_ARCH_LIST='9.0a 10.0a' && \
     /tmp/install_python_libraries.sh \
         --workspace /tmp/ep_kernels_workspace \
@@ -644,6 +655,7 @@ FROM ${FINAL_BASE_IMAGE} AS vllm-base
 
 ARG CUDA_VERSION
 ARG PYTHON_VERSION
+ARG NCCL_VERSION
 ARG DEADSNAKES_MIRROR_URL
 ARG DEADSNAKES_GPGKEY_URL
 ARG GET_PIP_URL
@@ -696,7 +708,6 @@ RUN apt-get update -y \
 # Install CUDA development tools for runtime JIT compilation
 # (FlashInfer, DeepGEMM, EP kernels all require compilation at runtime)
 RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
-    CUDA_VERSION_SHORT=$(echo $CUDA_VERSION | cut -d. -f1,2) && \
     apt-get update -y && \
     apt-get install -y --no-install-recommends --allow-change-held-packages \
         cuda-nvcc-${CUDA_VERSION_DASH} \
@@ -709,12 +720,6 @@ RUN CUDA_VERSION_DASH=$(echo $CUDA_VERSION | cut -d. -f1,2 | tr '.' '-') && \
         libnuma-dev \
         # numactl CLI for NUMA binding at runtime
         numactl && \
-    # Fixes nccl_allocator requiring nccl.h at runtime
-    # https://github.com/vllm-project/vllm/blob/1336a1ea244fa8bfd7e72751cabbdb5b68a0c11a/vllm/distributed/device_communicators/pynccl_allocator.py#L22
-    # NCCL packages don't use the cuda-MAJOR-MINOR naming convention,
-    # so we pin the version to match our CUDA version
-    NCCL_VER=$(apt-cache madison libnccl-dev | grep "+cuda${CUDA_VERSION_SHORT}" | head -1 | awk -F'|' '{gsub(/^ +| +$/, "", $2); print $2}') && \
-    apt-get install -y --no-install-recommends --allow-change-held-packages libnccl-dev=${NCCL_VER} libnccl2=${NCCL_VER} && \
     rm -rf /var/lib/apt/lists/*
 
 # Install uv for faster pip installs
@@ -734,6 +739,18 @@ RUN mkdir -p "${UV_PYTHON_INSTALL_DIR}" "${UV_CACHE_DIR}" \
     && chgrp -R 0 /opt/uv \
     && chmod -R g+rwX,a+rX /opt/uv
 
+# DeepEPv2 GIN requires NCCL >= 2.30.4 at both compile and runtime. torch pins
+# an older version as a transitive dep; this override forces uv to use our
+# pinned version whenever nvidia-nccl-cu* is resolved. Empty on CUDA 12 (no-op).
+RUN CUDA_MAJOR=$(echo $CUDA_VERSION | cut -d. -f1) && \
+    if [ "$CUDA_MAJOR" -ge 13 ]; then \
+        echo "nvidia-nccl-cu${CUDA_MAJOR}==${NCCL_VERSION}" \
+            > /etc/uv-overrides.txt; \
+    else \
+        touch /etc/uv-overrides.txt; \
+    fi
+ENV UV_OVERRIDE=/etc/uv-overrides.txt
+
 # ----------------------------------------------------------------------
 # Non-root support (opt-in)
 # ----------------------------------------------------------------------

docker/Dockerfile.rocm

@@ -745,6 +745,7 @@ ENV MIOPEN_DEBUG_CONV_GEMM=0
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
 ENV UCX_RMA_PPLN_ENABLE=y
+ENV UCX_ROCM_COPY_SIGPOOL_MAX_ELEMS=inf
 
 # ROCm profiler limits workaround.
 RUN echo "ROCTRACER_MAX_EVENTS=10000000" > ${COMMON_WORKDIR}/libkineto.conf
@@ -835,6 +836,7 @@ COPY --from=export_vllm /docker ${COMMON_WORKDIR}/vllm/docker
 # See: https://github.com/ROCm/rocm-libraries/issues/6266
 ENV HSA_ENABLE_IPC_MODE_LEGACY=1
 ENV UCX_RMA_PPLN_ENABLE=y
+ENV UCX_ROCM_COPY_SIGPOOL_MAX_ELEMS=inf
 
 ENV TOKENIZERS_PARALLELISM=false
 

docker/Dockerfile.rocm_base

@@ -30,7 +30,7 @@ ENV LD_LIBRARY_PATH=/opt/rocm/lib:/usr/local/lib:
 ARG PYTORCH_ROCM_ARCH=gfx90a;gfx942;gfx950;gfx1100;gfx1101;gfx1200;gfx1201;gfx1150;gfx1151
 ENV PYTORCH_ROCM_ARCH=${PYTORCH_ROCM_ARCH}
 ENV AITER_ROCM_ARCH=gfx942;gfx950
-ENV MORI_GPU_ARCHS=gfx942;gfx950
+# Note: Do not set MORI_GPU_ARCHS here, it is automatically inferred at runtime
 
 # Required for RCCL in ROCm7.1
 ENV HSA_NO_SCRATCH_RECLAIM=1

docker/versions.json

@@ -10,6 +10,9 @@
     "UBUNTU_VERSION": {
       "default": "22.04"
     },
+    "NCCL_VERSION": {
+      "default": "2.30.7"
+    },
     "BUILD_BASE_IMAGE": {
       "default": "nvidia/cuda:13.0.3-devel-ubuntu22.04"
     },
@@ -56,7 +59,7 @@
       "default": "cuda"
     },
     "DEEPEP_COMMIT_HASH": {
-      "default": "73b6ea4"
+      "default": "d4f41e4e93"
     },
     "GIT_REPO_CHECK": {
       "default": "0"

docs/.nav.yml

@@ -56,7 +56,9 @@ nav:
   - API Reference:
     - api/README.md
     - api/vllm
-  - CLI Reference: cli
+  - CLI Reference:
+    - cli/README.md
+    - vllm: cli
   - Community:
     - community/*
     - Governance: governance

docs/cli/.nav.yml

@@ -1,10 +1,8 @@
 nav:
-  - README.md
-  - serve.md
-  - chat.md
-  - complete.md
-  - run-batch.md
-  - vllm bench:
-    - bench/**/*.md
-  - vllm launch:
-    - launch/**/*.md
+  - "*.md"
+  - bench:
+    - bench/*.md
+    - sweep:
+      - bench/sweep/*.md
+  - launch:
+    - launch/*.md

docs/cli/bench/latency.md

@@ -1,9 +0,0 @@
-# vllm bench latency
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_latency.inc.md"

docs/cli/bench/mm_processor.md

@@ -1,55 +0,0 @@
-# vllm bench mm-processor
-
-## Overview
-
-`vllm bench mm-processor` profiles the multimodal input processor pipeline of
-vision-language models. It measures per-stage latency from the HuggingFace
-processor through to the encoder forward pass, helping you identify
-preprocessing bottlenecks and understand how different image resolutions or
-item counts affect end-to-end request time.
-
-The benchmark supports two data sources: synthetic random multimodal inputs
-(`random-mm`) and HuggingFace datasets (`hf`). Warmup requests are run before
-measurement to ensure stable results.
-
-## Quick Start
-
-```bash
-vllm bench mm-processor \
-  --model Qwen/Qwen2-VL-7B-Instruct \
-  --dataset-name random-mm \
-  --num-prompts 50 \
-  --random-input-len 300 \
-  --random-output-len 40 \
-  --random-mm-base-items-per-request 2 \
-  --random-mm-limit-mm-per-prompt '{"image": 3, "video": 0}' \
-  --random-mm-bucket-config '{(256, 256, 1): 0.7, (720, 1280, 1): 0.3}'
-```
-
-## Measured Stages
-
-| Stage | Description |
-| ----- | ----------- |
-| `get_mm_hashes_secs` | Time spent hashing multimodal inputs |
-| `get_cache_missing_items_secs` | Time spent looking up the processor cache |
-| `apply_hf_processor_secs` | Time spent in the HuggingFace processor |
-| `merge_mm_kwargs_secs` | Time spent merging multimodal kwargs |
-| `apply_prompt_updates_secs` | Time spent updating prompt tokens |
-| `preprocessor_total_secs` | Total preprocessing time |
-| `encoder_forward_secs` | Time spent in the encoder model forward pass |
-| `num_encoder_calls` | Number of encoder invocations per request |
-
-The benchmark also reports end-to-end latency (TTFT + decode time) per
-request. Use `--metric-percentiles` to select which percentiles to report
-(default: p99) and `--output-json` to save results.
-
-For more examples (HF datasets, warmup, JSON output), see
-[Benchmarking CLI — Multimodal Processor Benchmark](../../benchmarking/cli.md#multimodal-processor-benchmark).
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_mm_processor.inc.md"

docs/cli/bench/serve.md

@@ -1,9 +0,0 @@
-# vllm bench serve
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_serve.inc.md"

docs/cli/bench/sweep/plot.md

@@ -1,9 +0,0 @@
-# vllm bench sweep plot
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_sweep_plot.inc.md"

docs/cli/bench/sweep/plot_pareto.md

@@ -1,9 +0,0 @@
-# vllm bench sweep plot_pareto
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_sweep_plot_pareto.inc.md"

docs/cli/bench/sweep/serve.md

@@ -1,9 +0,0 @@
-# vllm bench sweep serve
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_sweep_serve.inc.md"

docs/cli/bench/sweep/serve_workload.md

@@ -1,9 +0,0 @@
-# vllm bench sweep serve_workload
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_sweep_serve_workload.inc.md"

docs/cli/bench/throughput.md

@@ -1,9 +0,0 @@
-# vllm bench throughput
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/bench_throughput.inc.md"

docs/cli/chat.md

@@ -1,5 +0,0 @@
-# vllm chat
-
-## Arguments
-
---8<-- "docs/generated/argparse/chat.inc.md"

docs/cli/complete.md

@@ -1,5 +0,0 @@
-# vllm complete
-
-## Arguments
-
---8<-- "docs/generated/argparse/complete.inc.md"

docs/cli/json_tip.inc.md

@@ -1,10 +0,0 @@
-<!-- markdownlint-disable MD041 -->
-When passing JSON CLI arguments, the following sets of arguments are equivalent:
-
-- `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`
-- `--json-arg.key1 value1 --json-arg.key2.key3 value2`
-
-Additionally, list elements can be passed individually using `+`:
-
-- `--json-arg '{"key4": ["value3", "value4", "value5"]}'`
-- `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`

docs/cli/launch/render.md

@@ -1,22 +0,0 @@
-# vllm launch render
-
-## Overview
-
-`vllm launch render` starts a GPU-less rendering server for preprocessing and
-postprocessing only.
-
-```bash
-vllm launch render meta-llama/Llama-3.2-1B-Instruct --port 8100
-```
-
-This command reuses the standard serving parser, so model, frontend,
-networking, and related CLI options follow the same conventions as
-[`vllm serve`](../serve.md).
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/launch_render.inc.md"

docs/cli/run-batch.md

@@ -1,9 +0,0 @@
-# vllm run-batch
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/run-batch.inc.md"

docs/cli/serve.md

@@ -1,9 +0,0 @@
-# vllm serve
-
-## JSON CLI Arguments
-
---8<-- "docs/cli/json_tip.inc.md"
-
-## Arguments
-
---8<-- "docs/generated/argparse/serve.inc.md"

docs/configuration/engine_args.md

@@ -11,12 +11,4 @@ Engine arguments control the behavior of the vLLM engine.
 
 The engine argument classes, [EngineArgs][vllm.engine.arg_utils.EngineArgs] and [AsyncEngineArgs][vllm.engine.arg_utils.AsyncEngineArgs], are a combination of the configuration classes defined in [vllm.config][]. Therefore, if you are interested in developer documentation, we recommend looking at these configuration classes as they are the source of truth for types, defaults and docstrings.
 
---8<-- "docs/cli/json_tip.inc.md"
-
-## `EngineArgs`
-
---8<-- "docs/generated/argparse/engine_args.inc.md"
-
-## `AsyncEngineArgs`
-
---8<-- "docs/generated/argparse/async_engine_args.inc.md"
+--8<-- "gen:engine-args"

docs/contributing/model/transcription.md

@@ -195,7 +195,7 @@ Provide a fast duration→token estimate to improve streaming usage statistics:
 The API server takes care of basic audio I/O and optional chunking before building prompts:
 
 - Resampling: Input audio is resampled to `SpeechToTextConfig.sample_rate` using `AudioResampler`.
-- Chunking: If `SpeechToTextConfig.allow_audio_chunking` is True and the duration exceeds `max_audio_clip_s`, the server splits the audio into overlapping chunks and generates a prompt per chunk. Overlap is controlled by `overlap_chunk_second`.
+- Chunking: If `SpeechToTextConfig.allow_audio_chunking` is True and the duration exceeds `max_audio_clip_s`, the server splits the audio into chunks and generates a prompt per chunk. There is no overlap between chunks, overlap_chunk_second controls the size of the search window used to find the split point.
 - Energy-aware splitting: When `min_energy_split_window_size` is set, the server finds low-energy regions to minimize cutting within words.
 
 Relevant server logic:

docs/deployment/docker.md

@@ -8,6 +8,26 @@ toc_depth: 2
 
 --8<-- "docs/getting_started/installation/gpu.md:pre-built-images"
 
+## Persist the compile cache across containers
+
+Mounting the Hugging Face cache keeps model weights across containers, but each
+new container still starts with an empty `VLLM_CACHE_ROOT` (default
+`~/.cache/vllm`) and recompiles the model's `torch.compile` artifacts. Mount a
+named volume at that path to reuse the inductor, Triton, and AOT artifacts from
+the second container onward:
+
+```bash
+docker run --rm --gpus all \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    -v vllm-cache:/root/.cache/vllm \
+    -p 8000:8000 \
+    vllm/vllm-openai:latest \
+    meta-llama/Llama-3.1-8B-Instruct
+```
+
+See [Faster Startup](../configuration/optimization.md#faster-startup) for the
+mechanism and for what invalidates the cache.
+
 ## Run as a non-root user
 
 The CUDA `vllm/vllm-openai` image runs as root by default for backward

docs/design/attention_backends.md

@@ -1,15 +1,9 @@
 # Attention Backend Feature Support
 
-This document is auto-generated by `tools/pre_commit/generate_attention_backend_docs.py`.
-It shows the feature support for each registered attention backend
-based on the checks in `AttentionBackend.validate_configuration()`.
-
-**Do not edit this file manually.** Run the following command to
-regenerate it:
-
-```bash
-python tools/pre_commit/generate_attention_backend_docs.py
-```
+The priority and feature tables on this page are auto-generated from the
+attention backend registry by
+`docs/mkdocs/gen_files/generate_attention_backends.py`, based on the checks in
+`AttentionBackend.validate_configuration()`.
 
 ## Setting the Attention Backend
 
@@ -98,40 +92,11 @@ Priority is **1 = highest** (tried first).
 
 ### Standard Attention (MHA, MQA, GQA)
 
-**Blackwell (SM 10.x):**
-
-| Priority | Backend |
-| -------- | ------- |
-| 1 | `FLASHINFER` |
-| 2 | `FLASH_ATTN` |
-| 3 | `TRITON_ATTN` |
-| 4 | `FLEX_ATTENTION` |
-| 5 | `TURBOQUANT` |
-
-**Ampere/Hopper (SM 8.x-9.x):**
-
-| Priority | Backend |
-| -------- | ------- |
-| 1 | `FLASH_ATTN` |
-| 2 | `FLASHINFER` |
-| 3 | `TRITON_ATTN` |
-| 4 | `FLEX_ATTENTION` |
-| 5 | `TURBOQUANT` |
+--8<-- "gen:priority-standard"
 
 ### MLA Attention (DeepSeek-style)
 
-**Blackwell (SM 10.x):**
-
-| Priority | Backend |
-| -------- | ------- |
-| 1 | `FLASHINFER_MLA` |
-| 2 | `TOKENSPEED_MLA` |
-| 3 | `CUTLASS_MLA` |
-| 4 | `FLASH_ATTN_MLA` |
-| 5 | `FLASHMLA` |
-| 6 | `TRITON_MLA` |
-| 7 | `FLASHINFER_MLA_SPARSE`**\*** |
-| 8 | `FLASHMLA_SPARSE` |
+--8<-- "gen:priority-mla"
 
 > **\*** For sparse MLA, FP8 KV cache always prefers `FLASHINFER_MLA_SPARSE`. With BF16 KV cache, `FLASHINFER_MLA_SPARSE` is preferred for low query-head counts (<= 16), while `FLASHMLA_SPARSE` is preferred otherwise.
 >
@@ -157,24 +122,7 @@ Priority is **1 = highest** (tried first).
 
 ## Standard Attention (MHA, MQA, GQA) Backends
 
-| Backend | Version | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | --------- | --- | --------------- | ------------ |
-| `CPU_ATTN` | | fp16, bf16, fp32 | `auto`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 112, 128, 160, 192, 224, 256, 512 | ❌ | ✅ | ❌ | ❌ | All | N/A |
-| `FLASHINFER` | Native† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ✅ | ❌ | ✅ | Decoder | 8.x-9.x |
-| `FLASHINFER` | XQA† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ❌ | ❌ | ❌ | ✅ | Decoder | 9.0 |
-| `FLASHINFER` | trtllm-gen† | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `nvfp4` | 16, 32, 64, 128, 256, 512, 1024 | 64, 128, 256, 512 | ✅ | ✅ | ❌ | ✅ | Decoder | 10.x |
-| `FLASH_ATTN` | FA2* | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ❌ | ✅ | All | ≥8.0 |
-| `FLASH_ATTN` | FA3* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | 9.x |
-| `FLASH_ATTN` | FA4* | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ✅ | ✅ | ❌ | ✅ | All | ≥10.0 |
-| `FLASH_ATTN_DIFFKV` | | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
-| `FLEX_ATTENTION` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder Only | Any |
-| `HPC_ATTN` | | fp16, bf16 | `auto`, `bfloat16`, `fp8_e4m3` | 64 | 128 | ❌ | ❌ | ❌ | ❌ | Decoder | ≥9.0 |
-| `ROCM_AITER_FA` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 16, 32 | 64, 128, 256 | ✅ | ✅ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_UNIFIED_ATTN` | | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | Any | ✅ | ❌ | ✅ | ❌ | All | N/A |
-| `ROCM_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %16 | 32, 64, 80, 96, 128, 160, 192, 224, 256 | ❌ | ✅ | ✅ | ❌ | Decoder, Encoder, Encoder Only | N/A |
-| `TRITON_ATTN` | | fp16, bf16, fp32 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2`, `int4_per_token_head`, `int8_per_token_head`, `fp8_per_token_head` | %16 | Any | ✅ | ✅ | ✅ | ❌ | All | Any |
-| `TRITON_ATTN_DIFFKV` | | fp16, bf16 | `auto`, `bfloat16` | Any | Any | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
-| `TURBOQUANT` | | fp16, bf16 | `turboquant_k8v4`, `turboquant_4bit_nc`, `turboquant_k3v4_nc`, `turboquant_3bit_nc` | 16, 32, 64, 128 | Any | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
+--8<-- "gen:table-standard"
 
 > **†** FlashInfer Native is the regular FlashInfer path. XQA is the SM90 decode path exposed through FlashInfer's TRTLLM decode API. trtllm-gen is used on SM100 and supports sinks. Disable XQA/trtllm-gen via `--attention-config.use_trtllm_attention=0`.
 >
@@ -188,9 +136,7 @@ automatic priority lists above. A lightning indexer scores KV blocks, the
 top-k blocks (plus fixed init/local blocks) are selected, and attention
 attends only to those blocks; index keys live in a separate side cache.
 
-| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | --------- | --- | --------------- | ------------ |
-| `MINIMAX_M3_SPARSE` | bf16, fp16 | `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | 128 | 128 | ❌ | ❌ | ❌ | ❌ | Decoder | Any |
+--8<-- "gen:table-minimax"
 
 ## MLA (Multi-head Latent Attention) Backends
 
@@ -203,38 +149,20 @@ To explicitly select a prefill backend, use
 Otherwise, the prefill backend is selected automatically at runtime based on
 hardware and configuration.
 
-| Backend | Description | Dtypes | Compute Cap. | Notes |
-| ------- | ----------- | ------ | ------------ | ----- |
-| `FLASH_ATTN`‡ | FlashAttention varlen (FA2/FA3/FA4) | fp16, bf16 | Any | (qk_nope_head_dim=128, qk_rope_head_dim=64, v_head_dim=128) (FA2/FA3/FA4) or (qk_nope_head_dim=64, qk_rope_head_dim=64, v_head_dim=128) (FA2/FA3/FA4) or (qk_nope_head_dim=192, qk_rope_head_dim=64, v_head_dim=256) (FA2/FA3 only) |
-| `TRTLLM_RAGGED` | TensorRT-LLM ragged attention | fp16, bf16 | 10.x | (qk_nope_head_dim=128, qk_rope_head_dim=64, v_head_dim=128) or (qk_nope_head_dim=192, qk_rope_head_dim=64, v_head_dim=256) only |
-| `FLASHINFER` | FlashInfer CUTLASS backend | fp16, bf16 | 10.x | (qk_nope_head_dim=128, qk_rope_head_dim=64, v_head_dim=128) only |
-| `TOKENSPEED_MLA` | | fp16, bf16 | 10.x | (qk_nope_head_dim=128, qk_rope_head_dim=64, v_head_dim=128) only |
+--8<-- "gen:table-mla-prefill"
 
 > **‡** Automatic selection tries FlashAttention first. On Blackwell
 > (SM100), the fallback order is TRT-LLM Ragged, FlashInfer, then
-> TokenSpeed MLA. On other GPUs, only FlashAttention is considered.
+> TokenSpeed MLA; for (qk_nope_head_dim=192, qk_rope_head_dim=64,
+> v_head_dim=256) TRT-LLM Ragged is tried before FlashAttention. On other
+> GPUs, only FlashAttention is considered.
 
 ### Decode Backends
 
 MLA decode backends are selected using the standard
 `-ac.backend=<BACKEND>` argument (e.g., `FLASHMLA`, `TRITON_MLA`).
 
-| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | ------ | --------- | --- | --------------- | ------------ |
-| `CUTLASS_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 128 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `FLASHINFER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `FLASHINFER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `FLASHINFER_MLA_SPARSE_SM120` | bf16 | `auto`, `fp8`, `fp8_e4m3`, `fp8_ds_mla` | 64, 256 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | 12.x |
-| `FLASHMLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x-10.x |
-| `FLASHMLA_SPARSE` | bf16 | `auto`, `bfloat16`, `fp8_ds_mla` | 64 | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
-| `FLASH_ATTN_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 9.x |
-| `FLASH_ATTN_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | 64 | Any | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x |
-| `ROCM_AITER_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_e5m2` | %1 | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | 1, 64 | Any | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | N/A |
-| `ROCM_AITER_TRITON_MLA` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
-| `TOKENSPEED_MLA` | fp16, bf16 | `fp8`, `fp8_e4m3` | 32, 64 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | 10.x |
-| `TRITON_MLA` | fp16, bf16 | `auto`, `float16`, `bfloat16`, `fp8`, `fp8_e4m3` | %16 | Any | ❌ | ❌ | ❌ | ❌ | ✅ | Decoder | Any |
-| `XPU_MLA_SPARSE` | fp16, bf16 | `auto`, `float16`, `bfloat16` | Any | 576 | ❌ | ❌ | ✅ | ❌ | ❌ | Decoder | Any |
+--8<-- "gen:table-mla-decode"
 
 ### DeepSeek V4 Decode Backends
 
@@ -245,8 +173,4 @@ pipeline (compressor + SWA + indexer, 256-token blocks, head 512);
 default on NVIDIA is `FLASHINFER_MLA_SPARSE_DSV4` on SM12x and
 `FLASHMLA_SPARSE_DSV4` on other supported CUDA architectures.
 
-| Backend | Dtypes | KV Dtypes | Block Sizes | Head Sizes | Sink | Non-Causal | Sparse | MM Prefix | DCP | Attention Types | Compute Cap. |
-| ------- | ------ | --------- | ----------- | ---------- | ---- | ---------- | ------ | --------- | --- | --------------- | ------------ |
-| `FLASHINFER_MLA_SPARSE_DSV4` | bf16 | `auto`, `bfloat16`, `fp8`, `fp8_e4m3`, `fp8_ds_mla` | 256 | 512 | ✅ | ❌ | ✅ | ❌ | ❌ | Decoder | 10.x, 12.x |
-| `FLASHMLA_SPARSE_DSV4` | bf16 | `auto`, `fp8_ds_mla`, `fp8` | 256 | 512 | ✅ | ❌ | ✅ | ❌ | ❌ | Decoder | 9.x-10.x |
-| `ROCM_FLASHMLA_SPARSE_DSV4` | fp16, bf16 | `auto` | Any | Any | ❌ | ❌ | ❌ | ❌ | ❌ | Decoder | N/A |
+--8<-- "gen:table-mla-v4-decode"

docs/design/cuda_graphs_multimodal.md

@@ -129,6 +129,7 @@ Models opt-in to encoder CUDA Graphs by implementing the [SupportsEncoderCudaGra
 | `DeepseekOCRForCausalLM` | `DeepSeek-OCR` | ✅︎ | ❌︎ | ✅︎ |
 | `Gemma3ForConditionalGeneration` | `Gemma3` | ✅︎ | ❌︎ | ❌︎ |
 | `Glm4vForConditionalGeneration` | `GLM-4.1V, GLM-4.6V-Flash` | ✅︎ | ✅︎ | ❌︎ |
+| `Gemma4ForConditionalGeneration` | `Gemma-4` | ✅︎ | ✅︎ | ❌︎ |
 | `InternVLChatModel` | `InternVL3.5`, `InternVL3`, `InternVL2.5`, `InternVL2` | ✅︎ | ✅︎ | ❌︎ |
 | `KimiVLForConditionalGeneration` | `Kimi-VL` | ✅︎ | ❌︎ | ❌︎ |
 | `Llama4ForConditionalGeneration` | `Llama 4` | ✅︎ | ❌︎ | ❌︎ |

docs/design/custom_op.md

@@ -122,8 +122,6 @@ For example:
 
 --8<-- "vllm/model_executor/layers/mamba/mamba_mixer2.py:mixer2_gated_rms_norm"
 
---8<-- "vllm/model_executor/models/plamo2.py:plamo2_mamba_mixer"
-
 --8<-- "vllm/model_executor/layers/mamba/short_conv.py:short_conv"
 ```
 

docs/design/fused_moe_modular_kernel.md

@@ -242,4 +242,4 @@ See [Fused MoE Kernel features](./moe_kernel_features.md#fused-moe-modular-all2a
 
 ## FusedMoEExpertsModular
 
-See [Fused MoE Kernel features](./moe_kernel_features.md#fused-moe-experts-kernels) for a list of all the available modular experts.
+See [Fused MoE Kernel features](./moe_kernel_features.md#fused-experts-kernels) for a list of all the available modular experts.

docs/design/nixl_kv_push_connector.md

@@ -47,7 +47,8 @@ sequenceDiagram
     else only one side present
         PWriter->>PWriter: stash and wait, self-poll only when blocks unmatched
     end
-    PWriter->>PWriter: ensure D handshake (one-time)
+    PWriter->>PWriter: _ensure_handshake to D (async; defer WRITE)
+    PWriter->>PWriter: handshake callback re-queues on _deferred_push_inbox, wake
     PWriter->>DWriter: NIXL WRITE direct to D GPU + completion notif
 
     note over DWorker,DWriter: D side - completion accounting
@@ -100,15 +101,20 @@ event:
    D, completion notifs after a WRITE, late-arriving ``PUSH_REG``)
    even when there is no new metadata to act on.
 3. **Handshake-completion callback** (background handshake executor
-   thread) — when a deferred D→P handshake finishes successfully, the
-   future's done-callback re-enqueues the registration onto
-   ``_reg_send_inbox`` and sets the wake so the corresponding
-   ``send_notif`` runs on the writer (we never call ``send_notif`` from
-   the executor thread). On this second pass ``_ensure_handshake``
-   returns ``None`` (the agent is now connected), so the writer sends
-   the ``PUSH_REG`` directly. If the handshake *failed*, the callback
-   fails the request instead of re-enqueuing, so there is no retry
-   loop.
+   thread) — both handshakes run on the executor and never block the
+   writer; their done-callbacks re-enqueue the deferred op and set the
+   wake, since neither ``send_notif`` nor the NIXL WRITE may run off the
+   writer thread:
+    * the **D→P** handshake (before sending ``PUSH_REG``) re-enqueues the
+      registration onto ``_reg_send_inbox``;
+    * the **P→D** handshake (before a WRITE) re-enqueues the matched
+      ``(req_id, blocks, reg_data)`` onto ``_deferred_push_inbox``.
+
+   On this second pass ``_ensure_handshake`` returns ``None`` (the agent
+   is now connected), so the writer sends the ``PUSH_REG`` / issues the
+   WRITE directly. If a handshake *failed*, the callback fails or drops
+   the request instead of re-enqueuing, so there is no retry loop (see
+   Failure handling).
 
 In addition to event-driven wakes, the writer self-polls at
 ``_PUSH_WRITER_POLL_INTERVAL_MS = 1.0`` ms while there are P-side
@@ -227,6 +233,13 @@ Two per-request timers are armed on the scheduler:
 * **D-side ``send_notif`` failure when shipping the PUSH_REG to P** —
   identical handling: ``_handle_failed_transfer`` marks the recv as
   failed.
+* **P-side handshake failure (P→D handshake before a WRITE)** — the
+  future's done-callback logs ``push_handshake_failed`` and drops the
+  request without re-queuing. It deliberately does *not* call
+  ``_handle_failed_transfer`` (there is no ``_recving_metadata`` entry to
+  invalidate on the producer side, same reasoning as the WRITE-submission
+  failure below). P's blocks are reclaimed by the ``_kv_lease_duration``
+  lease and D's stale registration by its watchdog.
 * **P-side WRITE submission failure** — the WRITE handle (if any) is
   released and ``xfer_stats.record_failed_transfer()`` bumps the
   failure counter. We deliberately do not call
@@ -246,10 +259,11 @@ existing NIXL connector:
   class — all subclasses of the existing base classes;
 * one dedicated background thread per worker;
 * a few cross-thread queues, each with a single consumer (the writer);
-  most have one producer, except ``_reg_send_inbox``, which is fed both
-  by the engine main thread (new registrations) and by the
-  handshake-completion callback (registrations replayed after their
-  D→P handshake finishes);
+  most have one producer, except the two replay queues fed by both the
+  engine main thread and a handshake-completion callback:
+  ``_reg_send_inbox`` (registrations replayed after their D→P handshake)
+  and ``_deferred_push_inbox`` (matched pushes replayed after their P→D
+  handshake);
 * one new notification type (`PUSH_REG:<msgpack>`).
 
 Behavior on the engine main thread is otherwise unchanged. The writer

docs/features/kv_offloading_usage.md

@@ -157,7 +157,7 @@ Object keys follow the same run-configuration digest scheme as the filesystem ti
 
 The P2P tier (`type: "p2p"`) shares completed KV blocks between vLLM instances over RDMA via NIXL. Each instance binds a control socket on `host:port` and exchanges blocks directly with peers — no shared filesystem required.
 
-PYTHONHASHSEED environment variable must be set to the same fixed value on all nodes.
+The `PYTHONHASHSEED` environment variable must be set to the same fixed value (e.g. `"0"`) on all nodes so that block content hashes match across instances (see [Cross-Process Sharing](#cross-process-sharing)). This is enforced: a P2P instance started without `PYTHONHASHSEED` set fails at startup, and each peer's value is verified during the connect handshake — a peer advertising a different `PYTHONHASHSEED` is rejected.
 
 | Key | Required | Default | Notes |
 | --- | --- | --- | --- |
@@ -176,6 +176,71 @@ Rather than embedding `host`/`port` in each `secondary_tiers` entry, set them on
 - `VLLM_P2P_SIDE_CHANNEL_HOST` (default `localhost`): address the P2P control socket binds to. It is used **verbatim** as both the bind address and the identity peers dial back — there is no auto-detection (this mirrors `VLLM_NIXL_SIDE_CHANNEL_HOST`). The default binds the loopback interface only, so peers on another host cannot reach it. **For any cross-host P2P deployment you must set this explicitly to the node's routable IP** (e.g. the pod IP) before launching `vllm serve` — otherwise remote peers will fail to connect. The NIXL agent name is a separate per-process identifier, so peers sharing a `host:port` never collide.
 - `VLLM_P2P_SIDE_CHANNEL_PORT` (default `5710`): base port for the P2P control socket. The port actually bound is `VLLM_P2P_SIDE_CHANNEL_PORT + data_parallel_index` — one socket per DP replica, matching NIXL (for DP=1 the offset is 0). The peer's port is passed as `remote_port` in `kv_transfer_params`; the router/EPP that selects the DP rank (e.g. via the `X-data-parallel-rank` header) computes `remote_port = base + rank`. The DP-index offset separates replicas *within* one deployment; two co-located *deployments* (a prefiller and a decoder on the same host) still need distinct base ports (e.g. decoder base `5711`) to avoid a bind collision.
 
+#### Orchestration-Layer Protocol
+
+The P2P tier does not decide *which* peer to pull from — that is the orchestration layer's job (the router/EPP and its scheduler). The orchestrator drives every transfer through a request's `kv_transfer_params` dict: it picks the request's role, allocates a unique transaction ID, and supplies the remote peer's address. All block lookup, hash matching, and NIXL transfer happen at the tier level below; the orchestrator only sets the correct role keys and enforces the allowed combinations.
+
+Every vLLM instance is a symmetric **peer**. Per request it acts as a **consumer** (pulls KV blocks from a remote peer's CPU cache instead of computing locally) or a **producer** (serves blocks from its own CPU cache to remote consumers) — or both, on the same session, for different requests. Roles are chosen per request by the keys below; there are no fixed prefiller/decoder processes.
+
+Three role keys are defined, each mapping to a sub-dict. All are optional; a request with none of them uses the tier only as a local CPU cache.
+
+Each key names the **remote counterpart** this peer transfers with (not this
+peer's own role), so the name reads as "the remote ___ I transfer with".
+
+| Key | Set on | Value fields | Meaning |
+| --- | --- | --- | --- |
+| `remote_decoder` | prefill producer request | `kv_request_id` | Peer computes KV and keeps it available in CPU cache for the remote decoder to pull. |
+| `remote_prefiller` | decode consumer request | `kv_request_id`, `remote_host`, `remote_port` | Peer pulls KV from the remote prefiller at the given address (classic P/D disaggregation). |
+| `remote_kv_source` | P2P consumer request | `kv_request_id`, `remote_host`, `remote_port` | Peer looks up and pulls whatever blocks the remote source currently holds in CPU cache. |
+
+Field semantics:
+
+- `kv_request_id` (str): unique transaction ID allocated by the orchestrator and pushed to every peer involved in the transfer; used to correlate the lookup, fetch, and transfer-done messages. The producer is implicit — it serves whatever block hashes it currently holds in its CPU cache for that ID.
+- `remote_host` (str): IP/hostname of the remote peer's control socket to query. Must be the peer's routable node IP (see [Environment Variables](#environment-variables)).
+- `remote_port` (int): the peer's bound control-socket port, i.e. `base + data_parallel_index` for the selected DP rank.
+
+Allowed and forbidden combinations:
+
+- **`remote_decoder` + `remote_kv_source`** is the only legal multi-key combination: a prefill producer may *also* act as a P2P consumer for the same request — skipping prefix prefill by pulling cached blocks from a source while still keeping its own computed blocks available for a downstream decoder.
+- Forbidden: `remote_prefiller` + `remote_decoder` (contradictory roles), `remote_prefiller` + `remote_kv_source` (two competing fetch sources), and all three together.
+
+Minimal examples (values that would appear in the request's `kv_transfer_params`):
+
+```python
+# Prefill producer — compute and keep KV for a remote decoder to pull
+kv_transfer_params = {"remote_decoder": {"kv_request_id": "<unique-transfer-id>"}}
+
+# Decode consumer — pull KV from a specific prefiller (classic P/D)
+kv_transfer_params = {
+    "remote_prefiller": {
+        "kv_request_id": "<unique-transfer-id>",
+        "remote_host": "<prefiller-node-ip>",
+        "remote_port": 5710,
+    }
+}
+
+# P2P consumer — pull whatever the source already has cached
+kv_transfer_params = {
+    "remote_kv_source": {
+        "kv_request_id": "<unique-transfer-id>",
+        "remote_host": "<source-node-ip>",
+        "remote_port": 5710,
+    }
+}
+```
+
+Runtime handshake for a P2P (or P/D) pull, once the orchestrator has set the keys above:
+
+1. Both peers already have listener threads on their control sockets (see [Environment Variables](#environment-variables)).
+2. **Lookup.** The consumer's tiering manager does per-block lookups; in P2P mode the tier returns `None` and registers the key. At `on_schedule_end` the consumer sends one **`LookupMsg`** (`kv_request_id` + block hashes) to the peer, per request, per step.
+3. The producer matches those hashes against its local CPU cache and replies with a **`LookupRespMsg`** carrying the hit block hashes.
+4. **Resolve.** Retried lookups now return hit / miss / in-flight. The consumer calls `submit_load` for hits only, allocating CPU slots only for hits.
+5. The consumer sends a **`FetchMsg`** (`kv_request_id`, block hashes, destination block indexes).
+6. The producer performs the **NIXL WRITE** transfer and sends **`TransferDone`** with a success status.
+7. On `get_finished`, hits are loaded into GPU as ordinary cache hits; misses are recomputed by the engine.
+
+In classic **P/D mode** (`remote_prefiller` set, no `remote_kv_source`), the lookup phase (steps 2–4) is skipped: the decode consumer assumes the prefiller holds all of the request's blocks, so every block `lookup()` returns an immediate hit and the consumer jumps straight to the **`FetchMsg`** in step 5. The `LookupMsg`/`LookupRespMsg` round-trip only happens in P2P mode, where the consumer does not know in advance which blocks the peer has cached.
+
 ## Tuning Tips
 
 - `cpu_bytes_to_use`: a bigger CPU tier means fewer trips to slower secondary tiers and a higher hit rate. The value is total across all workers, not per-worker. Leave headroom for the rest of the host workload.

docs/features/multimodal_inputs.md

@@ -818,16 +818,18 @@ Full example: [examples/generate/multimodal/openai_chat_completion_client_for_mu
 
 #### Video Decoding Backend
 
-vLLM decodes video bytes into frames using a selectable decoding backend. Three
+vLLM decodes video bytes into frames using a selectable decoding backend. Five
 backends are supported:
 
 - `opencv` (default): OpenCV-based decoder.
 - `pyav`: PyAV decoder.
 - `torchcodec`: TorchCodec (PyTorch-native) decoder.
+- `pynvvideocodec`: NVIDIA NVDEC-based decoder.
+- `deepstream`: NVIDIA DeepStream NVDEC-based decoder.
 
-All three backends are ultimately backed by FFmpeg. `torchcodec` lets
-you choose which FFmpeg version is used while `opencv` and `pyav` rely on
-whichever FFmpeg build they were linked against.
+The CPU backends are backed by FFmpeg. `torchcodec` lets you choose which FFmpeg
+version is used while `opencv` and `pyav` rely on whichever FFmpeg build they
+were linked against.
 
 Select the backend by passing the `backend` parameter via `--media-io-kwargs`:
 
@@ -854,6 +856,21 @@ vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
   --media-io-kwargs '{"video": {"backend": "torchcodec", "seek_mode": "approximate", "num_ffmpeg_threads": 4}}'
 ```
 
+**PyNvVideoCodec-specific parameters:**
+
+- `hw_decoders`: Maximum number of concurrent hardware decoder slots retained
+  by each API server process. It must be a positive integer and defaults to `2`,
+  which is the recommended starting point for concurrent video workloads.
+  Because vLLM reserves GPU memory for these slots at startup, this value cannot
+  be overridden per request. Benchmark before increasing it because each
+  additional slot increases the GPU memory reservation.
+
+```bash
+# Example: explicitly use the recommended 2 hardware decoders
+vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct \
+  --media-io-kwargs '{"video": {"backend": "pynvvideocodec", "hw_decoders": 2}}'
+```
+
 #### Video Frame Recovery
 
 For improved robustness when processing potentially corrupted or truncated video files, vLLM supports optional frame recovery using a dynamic window forward-scan approach. When enabled, if a target frame fails to load during sequential reading, the next successfully grabbed frame (before the next target frame) will be used in its place.

docs/features/quantization/modelopt.md

@@ -19,6 +19,20 @@ following `quantization.quant_algo` values:
 - `NVFP4`: ModelOpt NVFP4 checkpoints (use `quantization="modelopt_fp4"`).
 - `MXFP8`: ModelOpt MXFP8 checkpoints (use `quantization="modelopt_mxfp8"`).
 
+!!! note
+    For NVFP4 checkpoints, vLLM selects a GEMM kernel automatically at load
+    time from the backends available on the current platform (CUTLASS,
+    FlashInfer, Marlin, and others). On GPUs without a supported native FP4
+    GEMM kernel, vLLM falls back to weight-only (W4A16) execution via Marlin
+    and logs a warning; this may reduce throughput for compute-heavy
+    workloads. Use `--linear-backend` to override the automatic selection
+    (this replaces the deprecated `VLLM_NVFP4_GEMM_BACKEND` environment
+    variable). Values relevant to NVFP4 include `cutlass`,
+    `flashinfer_cutlass`, `flashinfer_trtllm`, `flashinfer_cudnn`, and
+    `marlin`; the full list is documented under `KernelConfig` on the
+    [Engine Arguments](../../configuration/engine_args.md) page and shown by
+    `vllm serve --help=KernelConfig`.
+
 ## Quantizing HuggingFace Models with PTQ
 
 You can quantize HuggingFace models using the example scripts provided in the Model Optimizer repository. The primary script for LLM PTQ is typically found within the `examples/llm_ptq` directory.

docs/mkdocs/gen_files/generate_argparse.py

@@ -2,6 +2,7 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import importlib.metadata
 import importlib.util
+import inspect
 import logging
 import sys
 import textwrap
@@ -10,17 +11,21 @@ from argparse import SUPPRESS, Action, HelpFormatter
 from collections.abc import Callable, Iterable
 from importlib.machinery import ModuleSpec
 from pathlib import Path
-from typing import TYPE_CHECKING, Literal
+from typing import TYPE_CHECKING
 from unittest.mock import MagicMock, patch
 
+import mkdocs_gen_files
+import regex as re
 from pydantic_core import core_schema
 
 logger = logging.getLogger("mkdocs")
 
 ROOT_DIR = Path(__file__).parent.parent.parent.parent
-ARGPARSE_DOC_DIR = ROOT_DIR / "docs/generated/argparse"
 
 sys.path.insert(0, str(ROOT_DIR))
+sys.path.insert(0, str(Path(__file__).parent))
+
+from generated_content import fill_markers  # noqa: E402
 
 
 def mock_if_no_torch(mock_module: str, mock: MagicMock):
@@ -132,8 +137,8 @@ def auto_mock(module_name: str, attr: str, max_mocks: int = 100):
 
 
 bench_latency = auto_mock("vllm.benchmarks", "latency")
-bench_mm_processor = auto_mock("vllm.benchmarks", "mm_processor")
 bench_serve = auto_mock("vllm.benchmarks", "serve")
+bench_startup = auto_mock("vllm.benchmarks", "startup")
 bench_sweep_plot = auto_mock("vllm.benchmarks.sweep.plot", "SweepPlotArgs")
 bench_sweep_plot_pareto = auto_mock(
     "vllm.benchmarks.sweep.plot_pareto", "SweepPlotParetoArgs"
@@ -142,12 +147,28 @@ bench_sweep_serve = auto_mock("vllm.benchmarks.sweep.serve", "SweepServeArgs")
 bench_sweep_serve_workload = auto_mock(
     "vllm.benchmarks.sweep.serve_workload", "SweepServeWorkloadArgs"
 )
+bench_sweep_startup = auto_mock("vllm.benchmarks.sweep.startup", "SweepStartupArgs")
 bench_throughput = auto_mock("vllm.benchmarks", "throughput")
 AsyncEngineArgs = auto_mock("vllm.engine.arg_utils", "AsyncEngineArgs")
 EngineArgs = auto_mock("vllm.engine.arg_utils", "EngineArgs")
 ChatCommand = auto_mock("vllm.entrypoints.cli.openai", "ChatCommand")
 CompleteCommand = auto_mock("vllm.entrypoints.cli.openai", "CompleteCommand")
+BenchmarkSubcommand = auto_mock(
+    "vllm.entrypoints.cli.benchmark.main", "BenchmarkSubcommand"
+)
+import_bench_subcommands = auto_mock(
+    "vllm.entrypoints.cli.benchmark.main", "_import_bench_subcommand_modules"
+)
+BenchmarkSubcommandBase = auto_mock(
+    "vllm.entrypoints.cli.benchmark.base", "BenchmarkSubcommandBase"
+)
+BenchmarkMMProcessorSubcommand = auto_mock(
+    "vllm.entrypoints.cli.benchmark.mm_processor", "BenchmarkMMProcessorSubcommand"
+)
+LaunchSubcommandBase = auto_mock("vllm.entrypoints.cli.launch", "LaunchSubcommandBase")
+launch_description = auto_mock("vllm.entrypoints.cli.launch", "DESCRIPTION")
 RenderSubcommand = auto_mock("vllm.entrypoints.cli.launch", "RenderSubcommand")
+sweep_subcommands = auto_mock("vllm.benchmarks.sweep.cli", "SUBCOMMANDS")
 openai_cli_args = auto_mock("vllm.entrypoints.openai", "cli_args")
 openai_run_batch = auto_mock("vllm.entrypoints.openai", "run_batch")
 
@@ -179,7 +200,7 @@ class MarkdownFormatter(HelpFormatter):
 
     def add_text(self, text: str):
         if text:
-            self._markdown_output.append(f"{text.strip()}\n\n")
+            self._markdown_output.append(f"{inspect.cleandoc(text)}\n\n")
 
     def add_usage(self, usage, actions, groups, prefix=None):
         pass
@@ -241,49 +262,163 @@ def create_parser(add_cli_args, **kwargs) -> FlexibleArgumentParser:
     return _parser or parser
 
 
-def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
-    logger.info("Generating argparse documentation")
-    logger.debug("Root directory: %s", ROOT_DIR.resolve())
-    logger.debug("Output directory: %s", ARGPARSE_DOC_DIR.resolve())
-
-    # Create the ARGPARSE_DOC_DIR if it doesn't exist
-    if not ARGPARSE_DOC_DIR.exists():
-        ARGPARSE_DOC_DIR.mkdir(parents=True)
-
-    # Create parsers to document
-    parsers = {
-        # Engine args
-        "engine_args": create_parser(EngineArgs.add_cli_args),
-        "async_engine_args": create_parser(
-            AsyncEngineArgs.add_cli_args, async_args_only=True
-        ),
-        # CLI
-        "serve": create_parser(openai_cli_args.make_arg_parser),
-        "chat": create_parser(ChatCommand.add_cli_args),
-        "complete": create_parser(CompleteCommand.add_cli_args),
-        "launch_render": create_parser(RenderSubcommand.add_cli_args),
-        "run-batch": create_parser(openai_run_batch.make_arg_parser),
-        # Benchmark CLI
-        "bench_latency": create_parser(bench_latency.add_cli_args),
-        "bench_mm_processor": create_parser(bench_mm_processor.add_cli_args),
-        "bench_serve": create_parser(bench_serve.add_cli_args),
-        "bench_sweep_plot": create_parser(bench_sweep_plot.add_cli_args),
-        "bench_sweep_plot_pareto": create_parser(bench_sweep_plot_pareto.add_cli_args),
-        "bench_sweep_serve": create_parser(bench_sweep_serve.add_cli_args),
-        "bench_sweep_serve_workload": create_parser(
-            bench_sweep_serve_workload.add_cli_args
-        ),
-        "bench_throughput": create_parser(bench_throughput.add_cli_args),
-    }
-
-    # Generate documentation for each parser
-    for stem, parser in parsers.items():
-        doc_path = ARGPARSE_DOC_DIR / f"{stem}.inc.md"
-        # Specify encoding for building on Windows
-        with open(doc_path, "w", encoding="utf-8") as f:
-            f.write(super(type(parser), parser).format_help())
-        logger.info("Argparse generated: %s", doc_path.relative_to(ROOT_DIR))
+def format_help(parser: FlexibleArgumentParser) -> str:
+    """Format a parser's help as markdown using `MarkdownFormatter`."""
+    return super(type(parser), parser).format_help()
 
 
-if __name__ == "__main__":
-    on_startup("build", False)
+# Absolute docs URLs are kept in the help text because they are useful in the
+# terminal. Wrap them as markdown links so the `url_schemes` hook can rewrite
+# them into doc-relative links / cross-references at render time.
+_DOCS_URL = re.compile(r"https://docs\.vllm\.ai/en/[^/\s]+/[^\s)>]+")
+
+
+def linkify_docs_urls(text: str) -> str:
+    """Wrap bare docs.vllm.ai URLs in help text as markdown links."""
+    return _DOCS_URL.sub(lambda m: f"[{m.group()}]({m.group()})", text)
+
+
+logger.info("Generating argparse documentation")
+logger.debug("Root directory: %s", ROOT_DIR.resolve())
+
+# The JSON tip is always rendered immediately before generated argument content,
+# and the generator is its only consumer, so it lives here rather than in a
+# separate snippet file. (The runtime terminal equivalent is
+# `FlexibleArgumentParser._json_tip` in vllm/utils/argparse_utils.py.)
+JSON_TIP = """## JSON CLI Arguments
+
+When passing JSON CLI arguments, the following sets of arguments are equivalent:
+
+- `--json-arg '{"key1": "value1", "key2": {"key3": "value2"}}'`
+- `--json-arg.key1 value1 --json-arg.key2.key3 value2`
+
+Additionally, list elements can be passed individually using `+`:
+
+- `--json-arg '{"key4": ["value3", "value4", "value5"]}'`
+- `--json-arg.key4+ value3 --json-arg.key4+='value4,value5'`
+
+"""
+
+# Argument sections filled into `gen:` markers on handwritten pages
+engine_args = create_parser(EngineArgs.add_cli_args)
+async_engine_args = create_parser(AsyncEngineArgs.add_cli_args, async_args_only=True)
+fill_markers(
+    "configuration/engine_args.md",
+    {
+        "engine-args": (
+            f"{JSON_TIP}## `EngineArgs`\n\n"
+            f"{linkify_docs_urls(format_help(engine_args))}"
+            f"## `AsyncEngineArgs`\n\n"
+            f"{linkify_docs_urls(format_help(async_engine_args))}"
+        )
+    },
+)
+
+# CLI reference pages generated entirely from their parser: page -> (parser, JSON tip)
+pages = {
+    "cli/serve.md": (create_parser(openai_cli_args.make_arg_parser), True),
+    "cli/chat.md": (create_parser(ChatCommand.add_cli_args), False),
+    "cli/complete.md": (create_parser(CompleteCommand.add_cli_args), False),
+    "cli/run-batch.md": (create_parser(openai_run_batch.make_arg_parser), True),
+    "cli/launch/render.md": (create_parser(RenderSubcommand.add_cli_args), True),
+    "cli/bench/latency.md": (create_parser(bench_latency.add_cli_args), True),
+    # URL kept as `mm_processor` for back-compat; command name is `mm-processor`
+    "cli/bench/mm_processor.md": (
+        create_parser(BenchmarkMMProcessorSubcommand.add_cli_args),
+        True,
+    ),
+    "cli/bench/serve.md": (create_parser(bench_serve.add_cli_args), True),
+    "cli/bench/startup.md": (create_parser(bench_startup.add_cli_args), True),
+    "cli/bench/throughput.md": (create_parser(bench_throughput.add_cli_args), True),
+    "cli/bench/sweep/plot.md": (create_parser(bench_sweep_plot.add_cli_args), True),
+    "cli/bench/sweep/plot_pareto.md": (
+        create_parser(bench_sweep_plot_pareto.add_cli_args),
+        True,
+    ),
+    "cli/bench/sweep/serve.md": (create_parser(bench_sweep_serve.add_cli_args), True),
+    "cli/bench/sweep/serve_workload.md": (
+        create_parser(bench_sweep_serve_workload.add_cli_args),
+        True,
+    ),
+    "cli/bench/sweep/startup.md": (
+        create_parser(bench_sweep_startup.add_cli_args),
+        True,
+    ),
+}
+
+# Command name for pages whose file stem differs (URL kept for back-compat).
+COMMAND_NAMES = {"cli/bench/mm_processor.md": "mm-processor"}
+
+for doc_path, (parser, json_tip) in pages.items():
+    segments = Path(doc_path).relative_to("cli").with_suffix("").parts
+    label = COMMAND_NAMES.get(doc_path, segments[-1])
+    command = " ".join([*segments[:-1], label])
+    # `title` frontmatter keeps the nav label to just this command's segment,
+    # while the H1 stays the full `vllm ...` command for the page heading.
+    content = f"---\ntitle: {label}\n---\n\n"
+    content += f"# vllm {command}\n\n"
+    if parser.description:
+        content += f"## Overview\n\n{parser.description}\n\n"
+        # Rendered above instead of at the top of the Arguments section
+        parser.description = None
+    if json_tip:
+        content += JSON_TIP
+    content += f"## Arguments\n\n{linkify_docs_urls(format_help(parser))}"
+    with mkdocs_gen_files.open(doc_path, "w") as f:
+        f.write(content)
+    logger.debug("CLI reference generated: %s", doc_path)
+
+logger.info("Total argparse docs generated: %d", len(pages) + 2)
+
+
+# --- Bare subcommand (group) pages -------------------------------------------
+# Mirror `vllm <group> --help`: an overview plus a table of child subcommands,
+# each linked to its reference page. Children are read from the CLI registries
+# so the listing can never drift from the actual subcommands. Each page is the
+# `README.md` of its command directory so it becomes that section's index and is
+# picked up by the existing nav globs.
+import_bench_subcommands()  # populate BenchmarkSubcommandBase.__subclasses__()
+bench_subcommands = BenchmarkSubcommandBase.__subclasses__()
+bench_children = [(cmd.name, cmd.help) for cmd in bench_subcommands]
+
+groups = {
+    "cli/bench/README.md": (BenchmarkSubcommand.help, bench_children),
+    "cli/launch/README.md": (
+        launch_description,
+        [(cmd.name, cmd.help) for cmd in LaunchSubcommandBase.__subclasses__()],
+    ),
+    "cli/bench/sweep/README.md": (
+        dict(bench_children).get("sweep"),
+        [(args.parser_name, args.parser_help) for args, _ in sweep_subcommands],
+    ),
+}
+
+# Doc paths that exist, so we only link a child that has a reference page.
+existing_pages = set(pages) | set(groups)
+
+
+def child_link(group_doc: str, name: str) -> str | None:
+    group_dir = Path(group_doc).parent  # cli/bench/README.md -> cli/bench
+    for stem in (name, name.replace("-", "_")):
+        # A leaf page (bench/latency.md) or a nested group index (sweep/README.md)
+        for candidate in (group_dir / f"{stem}.md", group_dir / stem / "README.md"):
+            if candidate.as_posix() in existing_pages:
+                return candidate.relative_to(group_dir).as_posix()
+    return None
+
+
+for doc_path, (overview, children) in groups.items():
+    title = "vllm " + Path(doc_path).parent.relative_to("cli").as_posix()
+    lines = [f"# {title.replace('/', ' ')}", ""]
+    if overview:
+        lines += ["## Overview", "", overview.strip(), ""]
+    lines += ["## Subcommands", "", "| Command | Description |", "| --- | --- |"]
+    for name, summary in children:
+        link = child_link(doc_path, name)
+        command = f"[`{name}`]({link})" if link else f"`{name}`"
+        lines.append(f"| {command} | {(summary or '').strip()} |")
+    with mkdocs_gen_files.open(doc_path, "w") as f:
+        f.write("\n".join(lines) + "\n")
+    logger.debug("CLI group reference generated: %s", doc_path)
+
+logger.info("CLI group reference pages generated: %d", len(groups))

docs/mkdocs/gen_files/generate_attention_backends.py

@@ -9,33 +9,28 @@ based on the checks in AttentionBackend.validate_configuration().
 
 This approach avoids requiring CUDA/ROCm/GPU libraries to be installed.
 
-When used as a pre-commit hook, this script receives filenames as arguments
-and only runs the check if any of the relevant files were modified.
+It runs as an mkdocs-gen-files script, so the page is generated at docs build
+time rather than being committed to the repository.
 """
 
-import argparse
 import ast
-import fnmatch
+import logging
 import sys
 from collections.abc import Callable
 from pathlib import Path
 from typing import Any
 
+sys.path.insert(0, str(Path(__file__).parent))
+
+from generated_content import fill_markers  # noqa: E402
+
+logger = logging.getLogger("mkdocs")
+
 # ---------------------------------------------------------------------------
 # Constants and file paths
 # ---------------------------------------------------------------------------
 
-REPO_ROOT = Path(__file__).parent.parent.parent
-
-RELEVANT_PATTERNS = [
-    "vllm/v1/attention/backends/*.py",
-    "vllm/v1/attention/backends/**/*.py",
-    "vllm/models/minimax_m3/common/sparse_attention.py",
-    "vllm/model_executor/layers/attention/mla_attention.py",
-    "vllm/platforms/cuda.py",
-    "tools/pre_commit/generate_attention_backend_docs.py",
-    "docs/design/attention_backends.md",
-]
+REPO_ROOT = Path(__file__).parent.parent.parent.parent
 
 BACKENDS_DIR = REPO_ROOT / "vllm" / "v1" / "attention" / "backends"
 REGISTRY_FILE = BACKENDS_DIR / "registry.py"
@@ -55,19 +50,6 @@ BACKEND_KV_DTYPE_EXCLUDES: dict[str, set[str]] = {
 }
 
 
-def is_relevant_file(filepath: str) -> bool:
-    """Check if a file matches any of the relevant patterns."""
-    path = Path(filepath)
-    if path.is_absolute():
-        try:
-            path = path.relative_to(REPO_ROOT)
-        except ValueError:
-            return False
-    path_str = str(path)
-
-    return any(fnmatch.fnmatch(path_str, pattern) for pattern in RELEVANT_PATTERNS)
-
-
 MLA_PREFILL_DIR = BACKENDS_DIR / "mla" / "prefill"
 MLA_PREFILL_REGISTRY_FILE = MLA_PREFILL_DIR / "registry.py"
 MLA_PREFILL_SELECTOR_FILE = MLA_PREFILL_DIR / "selector.py"
@@ -960,7 +942,7 @@ def analyze_backend(backend_name: str, class_path: str) -> dict[str, Any] | None
     try:
         tree = ast.parse(file_path.read_text())
     except Exception as e:
-        print(f"  Warning: Could not parse {file_path}: {e}", file=sys.stderr)
+        logger.warning("Could not parse %s: %s", file_path, e)
         return None
 
     class_name = class_path.rsplit(".", 1)[1]
@@ -1657,113 +1639,12 @@ def _render_table(
     return lines
 
 
-def generate_markdown_table(
-    backends: list[dict[str, Any]], title: str, is_mla_table: bool = False
-) -> str:
-    """Generate a titled markdown table from backend info."""
-    if not backends:
-        return f"## {title}\n\nNo backends found.\n"
-    has_versions = any(b.get("version") for b in backends)
-    columns = _build_columns(is_mla_table, has_versions)
-    lines = [f"## {title}", ""]
-    lines.extend(_render_table(columns, backends))
-    lines.append("")
-    return "\n".join(lines)
-
-
-# ---------------------------------------------------------------------------
-# Markdown section generators (usage, priority, legend, MLA)
-# ---------------------------------------------------------------------------
-
-
-def generate_usage_section() -> str:
-    """Generate the usage documentation section."""
-    return """## Setting the Attention Backend
-
-### Command Line
-
-There are two ways to specify the backend from the command line:
-
-**Option 1: Using `--attention-backend` (simple)**
-
-```bash
-vllm serve <model> --attention-backend FLASH_ATTN
-```
-
-**Option 2: Using `--attention-config.backend` / `-ac.backend` (structured config)**
-
-```bash
-# Dot notation
-vllm serve <model> --attention-config.backend FLASH_ATTN
-vllm serve <model> -ac.backend FLASH_ATTN
-
-# JSON format
-vllm serve <model> --attention-config '{"backend": "FLASH_ATTN"}'
-vllm serve <model> -ac '{"backend": "FLASH_ATTN"}'
-```
-
-> **Note:** `--attention-backend` and `--attention-config.backend` are mutually
-> exclusive. Use one or the other, not both.
-
-### Python API
-
-Use `AttentionConfig` with the `LLM` class:
-
-```python
-from vllm import LLM
-from vllm.config import AttentionConfig
-from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-# Method 1: Using AttentionConfig with enum
-llm = LLM(
-    model="Qwen/Qwen3-0.6B",
-    attention_config=AttentionConfig(backend=AttentionBackendEnum.FLASH_ATTN),
-)
-
-# Method 2: Using attention_backend parameter with string
-llm = LLM(
-    model="Qwen/Qwen3-0.6B",
-    attention_backend="FLASH_ATTN",
-)
-```
-
-## Backend Selection Behavior
-
-### Manual Selection
-
-When you explicitly set a backend via `--attention-backend` or `AttentionConfig`:
-
-1. The backend is **validated** against your configuration (model dtype, head
-   size, compute capability, etc.)
-2. If the backend **doesn't support** your configuration, an error is raised
-   with the specific reason
-3. If valid, the backend is used
-
-Example error when selecting an incompatible backend:
-
-```text
-ValueError: Selected backend FLASHMLA is not valid for this configuration.
-Reason: ['compute capability not supported']
-```
-
-### Automatic Selection
-
-When no backend is specified (the default):
-
-1. vLLM iterates through backends in **priority order** (see tables below)
-2. Each backend is validated against your configuration
-3. The **first compatible backend** is selected
-4. If no backend is compatible, an error is raised listing all backends and
-   their incompatibility reasons
-"""
-
-
 def _priority_table(
     title: str,
     backends: list[str],
     annotations: dict[str, str] | None = None,
 ) -> list[str]:
-    """Generate a priority table for a list of backends."""
+    """Render a priority table for a list of backends."""
 
     def _fmt(b: str) -> str:
         suffix = annotations.get(b, "") if annotations else ""
@@ -1779,102 +1660,38 @@ def _priority_table(
     ]
 
 
-def generate_priority_section(priorities: dict[str, list[str]]) -> str:
-    """Generate the priority ranking section."""
-    lines = [
-        "## Backend Priority (CUDA)",
-        "",
-        "When no backend is explicitly selected, vLLM chooses the first",
-        "compatible backend from these priority-ordered lists.",
-        "",
-        "Priority is **1 = highest** (tried first).",
-        "",
-        "### Standard Attention (MHA, MQA, GQA)",
-        "",
-    ]
-
-    sm100 = "Blackwell (SM 10.x)"
-    ampere = "Ampere/Hopper (SM 8.x-9.x)"
-
-    if "standard_sm100" in priorities:
-        lines.extend(_priority_table(sm100, priorities["standard_sm100"]))
-    if "standard_default" in priorities:
-        lines.extend(_priority_table(ampere, priorities["standard_default"]))
-
-    lines.extend(["### MLA Attention (DeepSeek-style)", ""])
-
-    mla_sm100_annotations = {
-        "FLASHINFER_MLA_SPARSE": "**\\***",
-    }
-    if "mla_sm100" in priorities:
-        lines.extend(
-            _priority_table(sm100, priorities["mla_sm100"], mla_sm100_annotations)
-        )
-    if "mla_default" in priorities:
-        lines.extend(_priority_table(ampere, priorities["mla_default"]))
-
-    if "mla_sm100" in priorities:
-        lines.append(
-            "> **\\*** For sparse MLA, FP8 KV cache always prefers "
-            "`FLASHINFER_MLA_SPARSE`. With BF16 KV cache, `FLASHINFER_MLA_SPARSE` "
-            "is preferred for low query-head counts (<= 16), while "
-            "`FLASHMLA_SPARSE` is preferred otherwise."
-        )
-        lines.append(">")
-
-    lines.append(
-        "> **Note:** ROCm and CPU platforms have their own selection logic. "
-        "See the platform-specific documentation for details."
-    )
-    lines.append("")
-
-    return "\n".join(lines)
+_SM100 = "Blackwell (SM 10.x)"
+_AMPERE = "Ampere/Hopper (SM 8.x-9.x)"
 
 
-def generate_legend() -> str:
-    """Generate a legend explaining the table columns."""
-    return """## Legend
-
-| Column | Description |
-| ------ | ----------- |
-| **Dtypes** | Supported model data types (fp16, bf16, fp32) |
-| **KV Dtypes** | Supported KV cache data types (`auto`, `fp8`, `fp8_e4m3`, etc.) |
-| **Block Sizes** | Supported KV cache block sizes (%N means multiples of N) |
-| **Head Sizes** | Supported attention head sizes |
-| **Sink** | Attention sink support (for StreamingLLM) |
-| **Non-Causal** | Non-causal (bidirectional) attention support for decoder models |
-| **Sparse** | Sparse attention support (MLA only) |
-| **MM Prefix** | Multimodal prefix full attention support |
-| **DCP** | Decode Context Parallelism support (`--decode-context-parallel-size`) |
-| **Attention Types** | Supported attention patterns (Decoder, Encoder, Enc-Dec) |
-| **Compute Cap.** | Required CUDA compute capability (N/A for non-CUDA backends) |
-
-**Symbols:** ✅ = Supported, ❌ = Not supported
-"""
-
-
-def generate_mla_section(
-    prefill_backends: list[dict[str, Any]],
-    decode_backends: list[dict[str, Any]],
-    v4_decode_backends: list[dict[str, Any]] | None = None,
+def _priority_block(
+    priorities: dict[str, list[str]],
+    sm100_key: str,
+    default_key: str,
+    sm100_annotations: dict[str, str] | None = None,
 ) -> str:
-    """Generate the complete MLA section with prefill and decode tables."""
+    """Render whichever priority tables exist for one attention category."""
+    lines: list[str] = []
+    if sm100_key in priorities:
+        lines += _priority_table(_SM100, priorities[sm100_key], sm100_annotations)
+    if default_key in priorities:
+        lines += _priority_table(_AMPERE, priorities[default_key])
+    return "\n".join(lines).strip()
+
+
+def _feature_table(backends: list[dict[str, Any]], is_mla: bool) -> str:
+    """Render a backend feature table (header, separator, one row per backend)."""
+    has_versions = any(b.get("version") for b in backends)
+    columns = _build_columns(is_mla, has_versions)
+    return "\n".join(_render_table(columns, backends))
+
+
+def _mla_prefill_table(prefill_backends: list[dict[str, Any]]) -> str:
+    """Render the MLA prefill backend table."""
     lines = [
-        "## MLA (Multi-head Latent Attention) Backends",
-        "",
-        "MLA uses separate backends for prefill and decode phases.",
-        "",
-        "### Prefill Backends",
-        "",
-        "To explicitly select a prefill backend, use",
-        "`-ac.mla_prefill_backend=<BACKEND>` (e.g., `FLASH_ATTN`, `FLASHINFER`).",
-        "Otherwise, the prefill backend is selected automatically at runtime based on",
-        "hardware and configuration.",
-        "",
         "| Backend | Description | Dtypes | Compute Cap. | Notes |",
         "| ------- | ----------- | ------ | ------------ | ----- |",
     ]
-
     for backend in prefill_backends:
         row = "| `{}`{} | {} | {} | {} | {} |".format(
             backend["name"],
@@ -1885,87 +1702,21 @@ def generate_mla_section(
             backend.get("notes", ""),
         )
         lines.append(row.replace("  ", " "))
-
-    lines.extend(
-        [
-            "",
-            "> **‡** Automatic selection tries FlashAttention first. On Blackwell",
-            "> (SM100), the fallback order is TRT-LLM Ragged, FlashInfer, then",
-            "> TokenSpeed MLA. On other GPUs, only FlashAttention is considered.",
-            "",
-            "### Decode Backends",
-            "",
-            "MLA decode backends are selected using the standard",
-            "`-ac.backend=<BACKEND>` argument (e.g., `FLASHMLA`, `TRITON_MLA`).",
-            "",
-        ]
-    )
-
-    # Reuse data-driven table rendering for decode backends
-    columns = _build_columns(is_mla=True, has_versions=False)
-    lines.extend(_render_table(columns, decode_backends))
-
-    if v4_decode_backends:
-        lines.extend(
-            [
-                "",
-                "### DeepSeek V4 Decode Backends",
-                "",
-                "DeepSeek V4 sparse MLA uses its own decode backends, selected via",
-                "`--attention-backend=<BACKEND>` (e.g., `FLASHMLA_SPARSE_DSV4`,",
-                "`FLASHINFER_MLA_SPARSE_DSV4`). They share the V4 sparse-index",
-                "pipeline (compressor + SWA + indexer, 256-token blocks, head 512);",
-                "default on NVIDIA is `FLASHINFER_MLA_SPARSE_DSV4` on SM12x and",
-                "`FLASHMLA_SPARSE_DSV4` on other supported CUDA architectures.",
-                "",
-            ]
-        )
-        lines.extend(_render_table(columns, v4_decode_backends))
-
-    lines.append("")
     return "\n".join(lines)
 
 
-def generate_minimax_section(backends: list[dict[str, Any]]) -> str:
-    """Generate the MiniMax M3 sparse attention section."""
-    lines = [
-        "## MiniMax M3 Sparse Attention Backends",
-        "",
-        'Block-sparse GQA backend used by MiniMax M3 sparse ("lightning indexer")',
-        "layers. It is wired in directly by the model and is not part of the",
-        "automatic priority lists above. A lightning indexer scores KV blocks, the",
-        "top-k blocks (plus fixed init/local blocks) are selected, and attention",
-        "attends only to those blocks; index keys live in a separate side cache.",
-        "",
-    ]
-    columns = _build_columns(is_mla=False, has_versions=False)
-    lines.extend(_render_table(columns, backends))
-    lines.append("")
-    return "\n".join(lines)
+def build_blocks() -> dict[str, str]:
+    """Build the generated table blocks keyed by their `gen:` marker name.
 
-
-# ---------------------------------------------------------------------------
-# Top-level orchestration
-# ---------------------------------------------------------------------------
-
-
-def generate_docs() -> str:
-    """Generate the complete documentation."""
+    Only the tables are generated here; the surrounding prose lives in the
+    handwritten ``docs/design/attention_backends.md`` page.
+    """
     attention_backends_map = parse_registry()
-
-    # Parse priority lists from cuda.py
     priorities = parse_cuda_priority_lists()
-
-    # Parse FlashAttention FA2/FA3 feature differences
     fa_features = parse_flash_attn_features()
-
-    # Parse FlashInfer TRTLLM feature differences (native vs TRTLLM on Blackwell)
     fi_features = parse_flashinfer_trtllm_features()
-
-    # Parse MLA prefill backends
     mla_prefill_backends = parse_mla_prefill_backends()
 
-    # Collect backend info
     all_backends = []
     for backend_name, class_path in attention_backends_map.items():
         if backend_name in SKIP_BACKENDS:
@@ -1973,17 +1724,14 @@ def generate_docs() -> str:
         info = analyze_backend(backend_name, class_path)
         if info:
             all_backends.append(info)
-
-    # Expand backends into version variants
     if fa_features:
         all_backends = _expand_flash_attn_variants(all_backends, fa_features)
     if fi_features:
         all_backends = _expand_flashinfer_variants(all_backends, fi_features)
 
-    # DeepSeek V4 (*_DSV4) decode backends and MiniMax M3 sparse backends each
-    # get their own subsection rather than mixing into the main MLA / standard
-    # tables (the ROCm V4 backend isn't flagged is_mla by the AST heuristic, so
-    # filter purely on the name).
+    # DeepSeek V4 (*_DSV4) and MiniMax M3 sparse backends get their own tables
+    # rather than mixing into the main MLA / standard tables (the ROCm V4 backend
+    # isn't flagged is_mla by the AST heuristic, so filter purely on the name).
     def _is_v4(b: dict[str, Any]) -> bool:
         return b["name"].endswith("_DSV4")
 
@@ -1999,112 +1747,21 @@ def generate_docs() -> str:
         if not b["is_mla"] and not _is_v4(b) and not _is_minimax(b)
     ]
 
-    # Generate documentation
-    script_path = "tools/pre_commit/generate_attention_backend_docs.py"
-    doc_lines = [
-        "# Attention Backend Feature Support",
-        "",
-        f"This document is auto-generated by `{script_path}`.",
-        "It shows the feature support for each registered attention backend",
-        "based on the checks in `AttentionBackend.validate_configuration()`.",
-        "",
-        "**Do not edit this file manually.** Run the following command to",
-        "regenerate it:",
-        "",
-        "```bash",
-        f"python {script_path}",
-        "```",
-        "",
-    ]
-
-    # Add usage documentation
-    doc_lines.append(generate_usage_section())
-
-    # Add priority section
-    doc_lines.append(generate_priority_section(priorities))
-
-    # Add legend and feature tables
-    doc_lines.append(generate_legend())
-    standard_title = "Standard Attention (MHA, MQA, GQA) Backends"
-    doc_lines.append(
-        generate_markdown_table(non_mla_backends, standard_title, is_mla_table=False)
-    )
-    # Add footnotes for version/variant distinctions (in table order)
-    footnotes = []
-    if fi_features:
-        footnotes.append(
-            "> **†** FlashInfer Native is the regular FlashInfer path. XQA is the "
-            "SM90 decode path exposed through FlashInfer's TRTLLM decode API. "
-            "trtllm-gen is used on SM100 and supports sinks. Disable XQA/trtllm-gen "
-            "via `--attention-config.use_trtllm_attention=0`."
-        )
-    if fa_features:
-        footnotes.append(
-            "> **\\*** Specify the FlashAttention version via "
-            "`--attention-config.flash_attn_version=2`, `3`, or `4`. "
-            "Default is FA4 on SM100+ (Blackwell), FA3 on SM90 (Hopper), "
-            "FA2 otherwise."
-        )
-    if footnotes:
-        doc_lines.append("\n>\n".join(footnotes) + "\n")
-
-    # Add MiniMax M3 sparse section (separate category after standard GQA)
-    if minimax_backends:
-        doc_lines.append(generate_minimax_section(minimax_backends))
-
-    # Add MLA section with prefill and decode backends
-    doc_lines.append(
-        generate_mla_section(mla_prefill_backends, mla_backends, v4_decode_backends)
-    )
-
-    return "\n".join(doc_lines)
+    mla_sm100_annotations = {"FLASHINFER_MLA_SPARSE": "**\\***"}
+    return {
+        "priority-standard": _priority_block(
+            priorities, "standard_sm100", "standard_default"
+        ),
+        "priority-mla": _priority_block(
+            priorities, "mla_sm100", "mla_default", mla_sm100_annotations
+        ),
+        "table-standard": _feature_table(non_mla_backends, is_mla=False),
+        "table-minimax": _feature_table(minimax_backends, is_mla=False),
+        "table-mla-prefill": _mla_prefill_table(mla_prefill_backends),
+        "table-mla-decode": _feature_table(mla_backends, is_mla=True),
+        "table-mla-v4-decode": _feature_table(v4_decode_backends, is_mla=True),
+    }
 
 
-def main():
-    parser = argparse.ArgumentParser(
-        description="Generate attention backend documentation table"
-    )
-    parser.add_argument(
-        "--output",
-        "-o",
-        type=str,
-        default=str(REPO_ROOT / "docs" / "design" / "attention_backends.md"),
-        help="Output file path (default: docs/design/attention_backends.md)",
-    )
-    parser.add_argument(
-        "--check",
-        action="store_true",
-        help="Check if the documentation is up to date (for pre-commit)",
-    )
-    parser.add_argument(
-        "files",
-        nargs="*",
-        help="Files to check (passed by pre-commit). If none are relevant, skip.",
-    )
-    args = parser.parse_args()
-
-    if args.files and not any(is_relevant_file(f) for f in args.files):
-        sys.exit(0)
-
-    output_path = Path(args.output)
-    new_content = generate_docs()
-
-    if args.check:
-        needs_update = (
-            not output_path.exists() or output_path.read_text() != new_content
-        )
-        if needs_update:
-            output_path.parent.mkdir(parents=True, exist_ok=True)
-            output_path.write_text(new_content)
-            print(f"🔄 Regenerated: {output_path}")
-            sys.exit(1)
-        print(f"✅ Up to date: {output_path}")
-        sys.exit(0)
-
-    output_path.parent.mkdir(parents=True, exist_ok=True)
-    output_path.write_text(new_content)
-    print(f"Generated: {output_path}")
-
-
-if __name__ == "__main__":
-    main()
+logger.info("Generating attention backend documentation")
+fill_markers("design/attention_backends.md", build_blocks())

docs/mkdocs/gen_files/generate_examples.py

@@ -5,16 +5,15 @@ import logging
 from dataclasses import dataclass
 from functools import cached_property
 from pathlib import Path
-from typing import Literal
 
+import mkdocs_awesome_nav.nav.directory as _nav_dir
+import mkdocs_gen_files
 import regex as re
 
 logger = logging.getLogger("mkdocs")
 
 ROOT_DIR = Path(__file__).parent.parent.parent.parent
-ROOT_DIR_RELATIVE = "../../../../.."
 EXAMPLE_DIR = ROOT_DIR / "examples"
-EXAMPLE_DOC_DIR = ROOT_DIR / "docs/examples"
 
 
 def title(text: str) -> str:
@@ -197,44 +196,38 @@ class Example:
         return content
 
 
-def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
-    # Monkey-patch dirname_to_title in awesome-nav so that sub-directory names are
-    # title-cased (e.g. "Offline Inference" instead of "Offline inference").
-    import mkdocs_awesome_nav.nav.directory as _nav_dir
+# Monkey-patch dirname_to_title in awesome-nav so that sub-directory names are
+# title-cased (e.g. "Offline Inference" instead of "Offline inference").
+_nav_dir.dirname_to_title = title
+logger.info("Generating example documentation")
+logger.debug("Root directory: %s", ROOT_DIR.resolve())
+logger.debug("Example directory: %s", EXAMPLE_DIR.resolve())
 
-    _nav_dir.dirname_to_title = title
-    logger.info("Generating example documentation")
-    logger.debug("Root directory: %s", ROOT_DIR.resolve())
-    logger.debug("Example directory: %s", EXAMPLE_DIR.resolve())
-    logger.debug("Example document directory: %s", EXAMPLE_DOC_DIR.resolve())
+categories = sorted(
+    p for p in EXAMPLE_DIR.iterdir() if p.is_dir() and not p.name.startswith(".")
+)
 
-    # Create the EXAMPLE_DOC_DIR if it doesn't exist
-    if not EXAMPLE_DOC_DIR.exists():
-        EXAMPLE_DOC_DIR.mkdir(parents=True)
+examples = []
+glob_patterns = ["*.py", "*.md", "*.sh"]
+# Find categorised examples
+for category in categories:
+    logger.info("Processing category: %s", category.stem)
+    globs = [category.glob(pattern) for pattern in glob_patterns]
+    for path in itertools.chain(*globs):
+        examples.append(Example(path, category.stem))
+    # Find examples in subdirectories
+    globs = [category.glob(f"*/{pattern}") for pattern in glob_patterns]
+    for path in itertools.chain(*globs):
+        examples.append(Example(path.parent, category.stem))
 
-    categories = sorted(p for p in EXAMPLE_DIR.iterdir() if p.is_dir())
-
-    examples = []
-    glob_patterns = ["*.py", "*.md", "*.sh"]
-    # Find categorised examples
-    for category in categories:
-        logger.info("Processing category: %s", category.stem)
-        globs = [category.glob(pattern) for pattern in glob_patterns]
-        for path in itertools.chain(*globs):
-            examples.append(Example(path, category.stem))
-        # Find examples in subdirectories
-        globs = [category.glob(f"*/{pattern}") for pattern in glob_patterns]
-        for path in itertools.chain(*globs):
-            examples.append(Example(path.parent, category.stem))
-
-    # Generate the example documentation
-    for example in sorted(examples, key=lambda e: e.path.stem):
-        example_name = f"{example.path.stem}.md"
-        doc_path = EXAMPLE_DOC_DIR / example.category / example_name
-        if not doc_path.parent.exists():
-            doc_path.parent.mkdir(parents=True)
-        # Specify encoding for building on Windows
-        with open(doc_path, "w+", encoding="utf-8") as f:
-            f.write(example.generate())
-        logger.debug("Example generated: %s", doc_path.relative_to(ROOT_DIR))
-    logger.info("Total examples generated: %d", len(examples))
+# Generate the example documentation
+for example in sorted(examples, key=lambda e: e.path.stem):
+    doc_path = f"examples/{example.category}/{example.path.stem}.md"
+    with mkdocs_gen_files.open(doc_path, "w") as f:
+        f.write(example.generate())
+    if example.main_file is not None:
+        # Point the edit button at the example's source file
+        edit_path = Path("..") / example.main_file.relative_to(ROOT_DIR)
+        mkdocs_gen_files.set_edit_path(doc_path, str(edit_path))
+    logger.debug("Example generated: %s", doc_path)
+logger.info("Total examples generated: %d", len(examples))

docs/mkdocs/gen_files/generate_metrics.py

@@ -2,27 +2,28 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import ast
 import logging
+import sys
 from pathlib import Path
-from typing import Literal
+
+sys.path.insert(0, str(Path(__file__).parent))
+
+from generated_content import fill_markers  # noqa: E402
 
 logger = logging.getLogger("mkdocs")
 
 ROOT_DIR = Path(__file__).parent.parent.parent.parent
-DOCS_DIR = ROOT_DIR / "docs"
-GENERATED_METRICS_DIR = DOCS_DIR / "generated" / "metrics"
 
-# Files to scan for metric definitions - each will generate a separate table
+# Files to scan for metric definitions - each fills a `gen:` marker in
+# docs/usage/metrics.md with its table (the section heading and any preamble
+# live in the tracked page next to the marker).
 METRIC_SOURCE_FILES = [
-    {"path": "vllm/v1/metrics/loggers.py", "output": "general.inc.md"},
-    {
-        "path": "vllm/v1/spec_decode/metrics.py",
-        "output": "spec_decode.inc.md",
-    },
+    {"path": "vllm/v1/metrics/loggers.py", "key": "metrics-general"},
+    {"path": "vllm/v1/spec_decode/metrics.py", "key": "metrics-spec-decode"},
     {
         "path": "vllm/distributed/kv_transfer/kv_connector/v1/nixl/stats.py",
-        "output": "nixl_connector.inc.md",
+        "key": "metrics-nixl",
     },
-    {"path": "vllm/v1/metrics/perf.py", "output": "perf.inc.md"},
+    {"path": "vllm/v1/metrics/perf.py", "key": "metrics-mfu"},
 ]
 
 
@@ -110,41 +111,27 @@ def generate_markdown_table(metrics: list[dict[str, str]]) -> str:
     return "\n".join(lines) + "\n"
 
 
-def on_startup(command: Literal["build", "gh-deploy", "serve"], dirty: bool):
-    """Generate metrics documentation tables from source files."""
-    logger.info("Generating metrics documentation")
+logger.info("Generating metrics documentation")
 
-    # Create generated directory if it doesn't exist
-    GENERATED_METRICS_DIR.mkdir(parents=True, exist_ok=True)
+blocks = {}
+total_metrics = 0
+for source_config in METRIC_SOURCE_FILES:
+    source_path = source_config["path"]
 
-    total_metrics = 0
-    for source_config in METRIC_SOURCE_FILES:
-        source_path = source_config["path"]
-        output_file = source_config["output"]
+    filepath = ROOT_DIR / source_path
+    if not filepath.exists():
+        raise FileNotFoundError(f"Metrics source file not found: {filepath}")
 
-        filepath = ROOT_DIR / source_path
-        if not filepath.exists():
-            raise FileNotFoundError(f"Metrics source file not found: {filepath}")
+    logger.debug("Extracting metrics from: %s", source_path)
+    metrics = extract_metrics_from_file(filepath)
+    logger.debug("Found %d metrics in %s", len(metrics), source_path)
 
-        logger.debug("Extracting metrics from: %s", source_path)
-        metrics = extract_metrics_from_file(filepath)
-        logger.debug("Found %d metrics in %s", len(metrics), source_path)
+    blocks[source_config["key"]] = generate_markdown_table(metrics).strip()
+    total_metrics += len(metrics)
 
-        # Generate and write the markdown table for this source
-        table_content = generate_markdown_table(metrics)
-        output_path = GENERATED_METRICS_DIR / output_file
-        with open(output_path, "w", encoding="utf-8") as f:
-            f.write(table_content)
-
-        total_metrics += len(metrics)
-        logger.info(
-            "Generated metrics table: %s (%d metrics)",
-            output_path.relative_to(ROOT_DIR),
-            len(metrics),
-        )
-
-    logger.info(
-        "Total metrics generated: %d across %d files",
-        total_metrics,
-        len(METRIC_SOURCE_FILES),
-    )
+fill_markers("usage/metrics.md", blocks)
+logger.info(
+    "Total metrics generated: %d across %d files",
+    total_metrics,
+    len(METRIC_SOURCE_FILES),
+)

docs/mkdocs/gen_files/generated_content.py

@@ -0,0 +1,56 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Inline build-time generated content into existing docs pages.
+
+Source pages mark where generated content goes with a snippet-style marker,
+`--8<-- "gen:<key>"`, so the insertion point is explicit and readable. The
+substitution happens here (at gen-files time, before mkdocs-gen-files shadows
+the page), not via pymdownx.snippets, so the content can be generated at build
+time without living in a real file on disk.
+
+The `gen:` prefix keeps these markers distinct from real pymdownx.snippets
+includes, and `fill_markers` fails loudly if a marker is missing or left behind
+(pymdownx.snippets would otherwise silently drop an unsubstituted marker).
+"""
+
+from pathlib import Path
+
+import mkdocs_gen_files
+import regex as re
+
+DOCS_DIR = Path(__file__).parent.parent.parent
+
+_MARKER = '--8<-- "gen:{key}"'
+_ANY_MARKER = re.compile(r'--8<-- "gen:[^"]*"')
+
+
+def fill_markers(doc_path: str, blocks: dict[str, str]) -> None:
+    """Replace `--8<-- "gen:<key>"` markers in a docs page with generated content.
+
+    Args:
+        doc_path: Docs-relative path of the source page to fill.
+        blocks: Mapping of marker key to the markdown to insert in its place.
+
+    Raises:
+        FileNotFoundError: If the source page does not exist.
+        ValueError: If an expected marker is missing, or any `gen:` marker is
+            left unsubstituted after filling.
+    """
+    source = DOCS_DIR / doc_path
+    if not source.exists():
+        raise FileNotFoundError(f"Cannot fill markers in missing page: {doc_path}")
+
+    text = source.read_text()
+    for key, content in blocks.items():
+        marker = _MARKER.format(key=key)
+        if marker not in text:
+            raise ValueError(f"{doc_path}: missing marker {marker}")
+        text = text.replace(marker, content)
+
+    if leftover := _ANY_MARKER.search(text):
+        raise ValueError(f"{doc_path}: unsubstituted marker {leftover.group()}")
+
+    with mkdocs_gen_files.open(doc_path, "w") as f:
+        f.write(text)
+    # Keep the edit button pointing at the real source page
+    mkdocs_gen_files.set_edit_path(doc_path, doc_path)

docs/mkdocs/hooks/url_schemes.py

@@ -19,6 +19,7 @@ The on_page_markdown hook passes the current page context to the preprocessor be
 each page is converted.
 """
 
+import posixpath
 from pathlib import Path
 
 import regex as re
@@ -38,18 +39,22 @@ TITLE = r"(?P<title>[^\[\]<>]+?)"
 REPO = r"(?P<repo>.+?/.+?)"
 TYPE = r"(?P<type>issues|pull|projects)"
 NUMBER = r"(?P<number>\d+)"
+VERSION = r"[^/\s]+"
 PATH = r"(?P<path>[^\s]+?)"
 FRAGMENT = r"(?P<fragment>#[^\s]+)?"
-URL = f"https://github.com/{REPO}/{TYPE}/{NUMBER}{FRAGMENT}"
+URL_GITHUB = f"https://github.com/{REPO}/{TYPE}/{NUMBER}{FRAGMENT}"
 RELATIVE = rf"(?!(https?|ftp)://|#){PATH}{FRAGMENT}"
+URL_DOCS = f"https://docs.vllm.ai/en/{VERSION}/{PATH}{FRAGMENT}"
 
 # Common titles to use for GitHub links when none is provided in the link.
 TITLES = {"issues": "Issue ", "pull": "Pull Request ", "projects": "Project "}
 
 # Regex to match GitHub issue, PR, and project links with optional titles.
-github_link = re.compile(rf"(\[{TITLE}\]\(|<){URL}(\)|>)")
+github_link = re.compile(rf"(\[{TITLE}\]\(|<){URL_GITHUB}(\)|>)")
 # Regex to match relative file links with optional titles.
 relative_link = re.compile(rf"\[{TITLE}\]\({RELATIVE}\)")
+# Regex to match absolute docs.vllm.ai links (should only exist in CLI).
+docs_link = re.compile(rf"\[{TITLE}\]\({URL_DOCS}\)")
 
 
 class UrlSchemesPreprocessor(Preprocessor):
@@ -61,7 +66,8 @@ class UrlSchemesPreprocessor(Preprocessor):
 
     def run(self, lines):
         page = self.ext.page
-        if page is None or getattr(page.file, "abs_src_path", None) is None:
+        files = self.ext.files
+        if page is None:
             return lines
 
         def replace_relative_link(match: re.Match) -> str:
@@ -70,7 +76,7 @@ class UrlSchemesPreprocessor(Preprocessor):
             """
             title = match.group("title")
             path = match.group("path")
-            path = (Path(page.file.abs_src_path).parent / path).resolve()
+            path = ((DOC_DIR / page.file.src_uri).parent / path).resolve()
             fragment = match.group("fragment") or ""
 
             # Check if the path exists and is outside the docs dir
@@ -105,9 +111,36 @@ class UrlSchemesPreprocessor(Preprocessor):
             url = f"https://github.com/{repo}/{type}/{number}{fragment}"
             return f"[{gh_icon} {title}]({url})"
 
+        def replace_docs_link(match: re.Match) -> str:
+            """Rewrite absolute docs.vllm.ai links as doc-relative links."""
+            title = match.group("title")
+            path = match.group("path").rstrip("/")
+            fragment = match.group("fragment") or ""
+
+            # vllm.config.<Class> API reference -> mkdocstrings cross-reference
+            if path == "api/vllm/config" and re.fullmatch(
+                r"#vllm\.config\.\w+", fragment
+            ):
+                ident = fragment[1:]
+                return f"[`{ident}`][{ident}]"
+
+            # Other docs pages -> link relative to the current page, but only
+            # when the target is a known docs page (real or generated); leave
+            # unknown/external URLs untouched. This is correct even when the same
+            # docstring is also rendered on its API reference page.
+            src = f"{path.removesuffix('.html')}.md"
+            if files.get_file_from_path(src) is None:
+                return match.group(0)
+            rel = posixpath.relpath(src, posixpath.dirname(page.file.src_uri))
+            # Auto-wrapped bare URLs use the URL as their title; make it readable.
+            if title.startswith("http"):
+                title = path.removesuffix(".html")
+            return f"[{title}]({rel}{fragment})"
+
         markdown = "\n".join(lines)
         markdown = github_link.sub(replace_github_link, markdown)
         markdown = relative_link.sub(replace_relative_link, markdown)
+        markdown = docs_link.sub(replace_docs_link, markdown)
         return markdown.split("\n")
 
 
@@ -116,6 +149,7 @@ class UrlSchemesExtension(Extension):
 
     def __init__(self, **kwargs):
         self.page = None
+        self.files = None
         super().__init__(**kwargs)
 
     def extendMarkdown(self, md):
@@ -138,4 +172,5 @@ def on_page_markdown(
 ) -> str:
     """Pass the current page context to the preprocessor."""
     _ext.page = page
+    _ext.files = files
     return markdown

docs/models/pooling_models/README.md

@@ -184,7 +184,7 @@ Our online Server provides endpoints that correspond to the offline APIs:
     - [Classification API](classify.md#online-serving)(`/classify`)
 - Corresponding to `LLM.score`:
     - [Score API](scoring.md#score-api) (`/score`, `/v1/score`)
-    - [Cohere Rerank API](scoring.md#rerank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
+    - [Cohere Rerank API](scoring.md#cohere-rerank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
 - Pooling API (`/pooling`) is similar to `LLM.encode`, being applicable to all types of pooling models.
 
 The following introduces the Pooling API. For other APIs, please refer to the link above.

docs/models/pooling_models/scoring.md

@@ -20,7 +20,7 @@ The score models is designed to compute similarity scores between two input prom
     - `LLM.score`
 - Online APIs:
     - [Score API](scoring.md#score-api) (`/score`, `/v1/score`)
-    - [Cohere Rerank API](scoring.md#rerank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
+    - [Cohere Rerank API](scoring.md#cohere-rerank-api) (`/rerank`, `/v1/rerank`, `/v2/rerank`)
 
 !!! note
     Only when a classification model outputs num_labels equal to 1 can it be used as a scoring model and have its scoring API enabled.

docs/models/supported_models.md

@@ -19,7 +19,7 @@ vLLM also supports model implementations that are available in Transformers. We
 
 Currently, the Transformers modeling backend works for the following:
 
-- Modalities: embedding models, language models and vision-language models*
+- Modalities: embedding models, language models, vision-language models* and audio-language models
 - Architectures: encoder-only, decoder-only, mixture-of-experts
 - Attention types: full attention and/or sliding attention
 
@@ -427,7 +427,6 @@ th {
 | `OlmoeForCausalLM` | OLMoE | `allenai/OLMoE-1B-7B-0924`, `allenai/OLMoE-1B-7B-0924-Instruct`, etc. | | ✅︎ |
 | `OPTForCausalLM` | OPT, OPT-IML | `facebook/opt-66b`, `facebook/opt-iml-max-30b`, etc. | ✅︎ | ✅︎ |
 | `OrionForCausalLM` | Orion | `OrionStarAI/Orion-14B-Base`, `OrionStarAI/Orion-14B-Chat`, etc. | | ✅︎ |
-| `OuroForCausalLM` | ouro | `ByteDance/Ouro-1.4B`, `ByteDance/Ouro-2.6B`, etc. | ✅︎ | |
 | `PanguEmbeddedForCausalLM` | openPangu-Embedded-7B | `FreedomIntelligence/openPangu-Embedded-7B-V1.1` | ✅︎ | ✅︎ |
 | `PanguProMoEV2ForCausalLM` | openpangu-pro-moe-v2 | | ✅︎ | ✅︎ |
 | `PanguUltraMoEForCausalLM` | openpangu-ultra-moe-718b-model | `FreedomIntelligence/openPangu-Ultra-MoE-718B-V1.1` | ✅︎ | ✅︎ |
@@ -435,7 +434,6 @@ th {
 | `PhiForCausalLM` | Phi | `microsoft/phi-1_5`, `microsoft/phi-2`, etc. | ✅︎ | ✅︎ |
 | `Phi3ForCausalLM` | Phi-4, Phi-3 | `microsoft/Phi-4-mini-instruct`, `microsoft/Phi-4`, `microsoft/Phi-3-mini-4k-instruct`, `microsoft/Phi-3-mini-128k-instruct`, `microsoft/Phi-3-medium-128k-instruct`, etc. | ✅︎ | ✅︎ |
 | `PhiMoEForCausalLM` | Phi-3.5-MoE | `microsoft/Phi-3.5-MoE-instruct`, etc. | ✅︎ | ✅︎ |
-| `Plamo2ForCausalLM` | PLaMo2 | `pfnet/plamo-2-1b`, `pfnet/plamo-2-8b`, etc. | ✅ | ✅︎ |
 | `Plamo3ForCausalLM` | PLaMo3 | `pfnet/plamo-3-nict-2b-base`, `pfnet/plamo-3-nict-8b-base`, etc. | ✅ | ✅︎ |
 | `Qwen2ForCausalLM` | QwQ, Qwen2 | `Qwen/QwQ-32B-Preview`, `Qwen/Qwen2-7B-Instruct`, `Qwen/Qwen2-7B`, etc. | ✅︎ | ✅︎ |
 | `Qwen2MoeForCausalLM` | Qwen2MoE | `Qwen/Qwen1.5-MoE-A2.7B`, `Qwen/Qwen1.5-MoE-A2.7B-Chat`, etc. | ✅︎ | ✅︎ |
@@ -466,6 +464,7 @@ Some models are supported only via the [Transformers modeling backend](#transfor
 | `Olmo2ForCausalLM` | OLMo2 | `allenai/OLMo-2-0425-1B`, etc. | ✅︎ | ✅︎ |
 | `SmolLM3ForCausalLM` | SmolLM3 | `HuggingFaceTB/SmolLM3-3B` | ✅︎ | ✅︎ |
 | `Starcoder2ForCausalLM` | Starcoder2 | `bigcode/starcoder2-3b`, `bigcode/starcoder2-7b`, `bigcode/starcoder2-15b`, etc. | ✅︎ | ✅︎ |
+| `VaultGemmaForCausalLM` | VaultGemma | `google/vaultgemma-1b` | ✅︎ | ✅︎ |
 
 !!! note
     Currently, the ROCm version of vLLM supports Mistral and Mixtral only for context lengths up to 4096.
@@ -608,7 +607,8 @@ Some models are supported only via the [Transformers modeling backend](#transfor
 
 | Architecture | Models | Inputs | Example HF Models | [LoRA](../features/lora.md) | [PP](../serving/parallelism_scaling.md) |
 | ------------ | ------ | ------ | ----------------- | --------------------------- | --------------------------------------- |
-| `Emu3ForConditionalGeneration` | Emu3 | T + I | `BAAI/Emu3-Chat-hf` | ✅︎ | ✅︎ |
+| `Emu3ForConditionalGeneration` | Emu3 | T + I<sup>+</sup> | `BAAI/Emu3-Chat-hf` | ✅︎ | ✅︎ |
+| `VibeVoiceAsrForConditionalGeneration` | VibeVoice-ASR | T + A<sup>+</sup> | `microsoft/VibeVoice-ASR-HF` | ✅︎ | ✅︎ |
 
 <sup>^</sup> You need to set the architecture name via `--hf-overrides` to match the one in vLLM.</br>
 <sup>E</sup> Pre-computed embeddings can be inputted for this modality.</br>

docs/pre_run_check.sh

@@ -28,7 +28,7 @@ DOCS_PATHS=(
   docs/                       # Actual docs content
   examples/                   # Examples are rendered in docs
   vllm/                       # API & CLI reference
-  requirements/test/cuda.txt  # CLI reference (see docs/mkdocs/hooks/generate_argparse.py)
+  requirements/test/cuda.txt  # CLI reference (see docs/mkdocs/gen_files/generate_argparse.py)
   mkdocs.yaml                 # Affects build process
   .readthedocs.yaml           # Affects build process
   requirements/docs.txt       # Affects build process

docs/serving/expert_parallel_deployment.md

@@ -12,6 +12,11 @@ Before using EP, you need to install the necessary dependencies. We are actively
 2. **Install DeepGEMM library**: Follow the [official instructions](https://github.com/deepseek-ai/DeepGEMM#installation).
 3. **For disaggregated serving**: Install `gdrcopy` by running the [`install_gdrcopy.sh`](../../tools/install_gdrcopy.sh) script (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/).
 
+!!! note "NCCL version (CUDA 13+)"
+    The `deepep_v2` backend requires NCCL >= 2.30.4. PyTorch ships an older
+    NCCL, so you must upgrade it before building or running DeepEP. See the
+    [EP kernels guide](../../tools/ep_kernels) for instructions.
+
 ### Backend Selection Guide
 
 vLLM provides multiple communication backends for EP. Use `--all2all-backend` to select one: