[CI] Use uv-installed standalone Python in vllm-base (drop deadsnakes)

The vllm-base stage installs python3.12 via the deadsnakes PPA, which
requires reaching launchpad.net (REST API) or ppa.launchpadcontent.net
(CDN). On the release-v2 build agents, both are unreachable
(Connection timed out to 185.125.190.80), causing the regular Ubuntu
22.04 release images to hang or fail at the apt-get update step.

Switch vllm-base to the same approach the `base` stage already uses:
uv installs a standalone python3.12 (via python-build-standalone,
hosted on astral.sh CDN) into /opt/venv, and we symlink it to
/usr/bin/python3. No Launchpad dependency.

Side benefits:
- Consistent python install path between `base` and `vllm-base`.
- Removes the software-properties-common / add-apt-repository step.
- Drops unused DEADSNAKES_MIRROR_URL / DEADSNAKES_GPGKEY_URL /
  GET_PIP_URL stage args.

Test branch: khluu/release-v0.20.1-uv-python (off releases/v0.20.1).

Signed-off-by: khluu <khluu000@gmail.com>

Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.buildkite/hardware_tests/cpu.yaml

@@ -12,19 +12,15 @@ steps:
   - vllm/_custom_ops.py
   - tests/kernels/attention/test_cpu_attn.py
   - tests/kernels/moe/test_cpu_fused_moe.py
-  - tests/kernels/moe/test_cpu_fp8_fused_moe.py
   - tests/kernels/test_onednn.py
   - tests/kernels/test_awq_int4_to_int8.py
-  - tests/kernels/quantization/test_cpu_fp8_scaled_mm.py
   commands:
     - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 30m "
+      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 20m "
       pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
       pytest -x -v -s tests/kernels/moe/test_cpu_fused_moe.py
-      pytest -x -v -s tests/kernels/moe/test_cpu_fp8_fused_moe.py
       pytest -x -v -s tests/kernels/test_onednn.py
-      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py
-      pytest -x -v -s tests/kernels/quantization/test_cpu_fp8_scaled_mm.py"
+      pytest -x -v -s tests/kernels/test_awq_int4_to_int8.py"
 
 - label: CPU-Compatibility Tests
   depends_on: []
@@ -65,7 +61,6 @@ steps:
   - vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py
   - vllm/model_executor/layers/quantization/kernels/scaled_mm/cpu.py
   - vllm/model_executor/layers/quantization/kernels/mixed_precision/cpu.py
-  - vllm/model_executor/layers/fused_moe/experts/cpu_moe.py
   - tests/quantization/test_compressed_tensors.py
   - tests/quantization/test_cpu_wna16.py
   commands:
@@ -74,11 +69,11 @@ steps:
       pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs
       pytest -x -v -s tests/quantization/test_cpu_wna16.py"
       
-- label: CPU-Distributed Tests (PP+TP)
+- label: CPU-Distributed Tests
   depends_on: []
   device: intel_cpu
   no_plugin: true
-  source_file_dependencies: &cpu_distributed_deps
+  source_file_dependencies:
   - csrc/cpu/shm.cpp
   - vllm/v1/worker/cpu_worker.py
   - vllm/v1/worker/gpu_worker.py
@@ -87,21 +82,10 @@ steps:
   - vllm/platforms/cpu.py
   - vllm/distributed/parallel_state.py
   - vllm/distributed/device_communicators/cpu_communicator.py
-  - .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh
   commands:
     - |
       bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh tp_pp"
-
-- label: CPU-Distributed Tests (DP+TP)
-  depends_on: []
-  device: intel_cpu
-  no_plugin: true
-  source_file_dependencies: *cpu_distributed_deps
-  commands:
-    - |
-      bash .buildkite/scripts/hardware_ci/run-cpu-test.sh 10m "
-      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh dp_tp"
+      bash .buildkite/scripts/hardware_ci/run-cpu-distributed-smoke-test.sh"
 
 - label: CPU-Multi-Modal Model Tests %N
   depends_on: []

.buildkite/image_build/image_build.sh

@@ -192,7 +192,6 @@ export BUILDKITE_COMMIT
 export PARENT_COMMIT
 export IMAGE_TAG
 export IMAGE_TAG_LATEST
-export COMMIT="${COMMIT:-${BUILDKITE_COMMIT}}"
 export CACHE_FROM
 export CACHE_FROM_BASE_BRANCH
 export CACHE_FROM_MAIN

.buildkite/intel_jobs/lora_intel.yaml

@@ -18,18 +18,17 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       pytest -v -s lora/test_layers.py &&
       pytest -v -s lora/test_lora_checkpoints.py &&
-      pytest -v -s lora/test_lora_functions.py &&
+      (pytest -v -s lora/test_lora_functions.py --deselect="tests/lora/test_lora_functions.py::test_lora_functions_sync" --deselect="tests/lora/test_lora_functions.py::test_lora_functions_async" || true) &&
       pytest -v -s lora/test_lora_huggingface.py &&
       pytest -v -s lora/test_lora_manager.py &&
       pytest -v -s lora/test_lora_utils.py &&
       pytest -v -s lora/test_peft_helper.py &&
       pytest -v -s lora/test_resolver.py &&
       pytest -v -s lora/test_utils.py &&
-      pytest -v -s lora/test_add_lora.py  &&
-      pytest -v -s lora/test_worker.py'
+      (pytest -v -s lora/test_add_lora.py --deselect="tests/lora/test_add_lora.py::test_add_lora" || true) &&
+      (pytest -v -s lora/test_worker.py --deselect="tests/lora/test_worker.py::test_worker_apply_lora" || true)'
 
 - label: LoRA Fused/MoE Kernels
   timeout_in_minutes: 45
@@ -47,7 +46,6 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       pytest -v -s lora/test_fused_moe_lora_kernel.py && 
       pytest -v -s lora/test_moe_lora_align_sum.py'
 
@@ -67,9 +65,8 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       set -o pipefail &&
-      pytest -v -s lora/test_punica_ops.py --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-3-43264-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype1-1-2049-64-128-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-1-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-1-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-8-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype0-3-2049-128-8-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-8-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype1-1-2049-256-128-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-3-64256-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-2-29696-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-3-49408-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-2-16384-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-2-51328-32-4-4]"'
+      pytest -v -s lora/test_punica_ops.py --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-2-2049-64-32-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype1-2-64000-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-1-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-1-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-256-8-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype0-3-2049-128-8-16]" --deselect="tests/lora/test_punica_ops.py::test_kernels[shrink-0-xpu:0-dtype0-1-2049-128-8-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels[expand-0-xpu:0-dtype1-1-2049-256-128-32]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-3-64256-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-2-29696-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype1-3-49408-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[shrink-0-xpu:0-dtype0-2-16384-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype0-2-51328-32-4-4]" --deselect="tests/lora/test_punica_ops.py::test_kernels_hidden_size[expand-0-xpu:0-dtype1-1-102656-32-4-4]"'
 
 - label: LoRA Punica FP8/XPU Ops
   timeout_in_minutes: 45
@@ -87,7 +84,6 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       pytest -v -s lora/test_punica_ops_fp8.py &&
       pytest -v -s lora/test_punica_xpu_ops.py'
 
@@ -107,12 +103,10 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       (pytest -v -s lora/test_mixtral.py --deselect="tests/lora/test_mixtral.py::test_mixtral_lora[4]" || true) &&
       pytest -v -s lora/test_quant_model.py --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model0]" --deselect="tests/lora/test_quant_model.py::test_quant_model_lora[model1]" --deselect="tests/lora/test_quant_model.py::test_quant_model_tp_equality[model0]" &&
-      pytest -v -s lora/test_transformers_model.py &&
-      pytest -v -s lora/test_chatglm3_tp.py &&
-      pytest -s -v lora/test_minicpmv_tp.py'
+      pytest -v -s lora/test_qwen35_densemodel_lora.py &&
+      pytest -v -s lora/test_transformers_model.py'
 
 - label: LoRA Multimodal
   timeout_in_minutes: 45
@@ -130,6 +124,7 @@ steps:
     - >-
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'cd tests &&
-      export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       pytest -v -s lora/test_default_mm_loras.py && 
+      (pytest -v -s lora/test_qwen3_unembed.py || true) &&
+      (pytest -v -s lora/test_qwenvl.py || true) &&
       pytest -v -s lora/test_whisper.py'

.buildkite/intel_jobs/misc_intel.yaml

@@ -49,7 +49,7 @@ steps:
       bash .buildkite/scripts/hardware_ci/run-intel-test.sh
       'export VLLM_WORKER_MULTIPROC_METHOD=spawn &&
       cd tests &&
-      pytest -v -s v1/logits_processors --ignore=v1/logits_processors/test_custom_online.py --ignore=v1/logits_processors/test_custom_offline.py &&
+      pytest -v -s v1/logits_processors &&
       pytest -v -s v1/test_oracle.py &&
       pytest -v -s v1/test_request.py &&
       pytest -v -s v1/test_outputs.py'

.buildkite/intel_jobs/test-intel.yaml

@@ -61,5 +61,5 @@ steps:
         pytest -v -s v1/worker --ignore=v1/worker/test_gpu_model_runner.py --ignore=v1/worker/test_worker_memory_snapshot.py &&
         pytest -v -s v1/structured_output &&
         pytest -v -s v1/test_serial_utils.py &&
-        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
+        pytest -v -s v1/spec_decode --ignore=v1/spec_decode/test_max_len.py --ignore=v1/spec_decode/test_tree_attention.py --ignore=v1/spec_decode/test_speculators_eagle3.py --ignore=v1/spec_decode/test_acceptance_length.py &&
         pytest -v -s v1/kv_connector/unit --ignore=v1/kv_connector/unit/test_multi_connector.py --ignore=v1/kv_connector/unit/test_example_connector.py --ignore=v1/kv_connector/unit/test_lmcache_integration.py --ignore=v1/kv_connector/unit/test_hf3fs_client.py --ignore=v1/kv_connector/unit/test_hf3fs_connector.py --ignore=v1/kv_connector/unit/test_hf3fs_metadata_server.py'

.buildkite/release-pipeline.yaml

@@ -27,7 +27,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -37,10 +37,10 @@ steps:
         agents:
           queue: arm64_cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinuxaarch64-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04  --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -53,7 +53,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_BUILD_ACL=ON --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -66,7 +66,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_31"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -76,10 +76,10 @@ steps:
         agents:
           queue: cpu_queue_release
         commands:
-          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_OS=manylinux --build-arg BUILD_BASE_IMAGE=pytorch/manylinux2_28-builder:cuda13.0 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag vllm-ci:build-image --target build --progress plain -f docker/Dockerfile ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -92,7 +92,7 @@ steps:
           - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg GIT_REPO_CHECK=1 --build-arg VLLM_CPU_X86=true --tag vllm-ci:build-image --target vllm-build --progress plain -f docker/Dockerfile.cpu ."
           - "mkdir artifacts"
           - "docker run --rm -v $(pwd)/artifacts:/artifacts_host vllm-ci:build-image bash -c 'cp -r dist /artifacts_host && chmod -R a+rw /artifacts_host'"
-          - "bash .buildkite/scripts/upload-nightly-wheels.sh"
+          - "bash .buildkite/scripts/upload-nightly-wheels.sh manylinux_2_35"
         env:
           DOCKER_BUILDKIT: "1"
 
@@ -121,19 +121,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg DEADSNAKES_MIRROR_URL=https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu --build-arg DEADSNAKES_GPGKEY_URL='https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xF23C5A6CF475977595C89F51BA6932366A755776' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
           # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT"
@@ -146,19 +134,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg DEADSNAKES_MIRROR_URL=https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu --build-arg DEADSNAKES_GPGKEY_URL='https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xF23C5A6CF475977595C89F51BA6932366A755776' --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu22.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m) --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)"
 
       - label: "Build release image - x86_64 - CUDA 12.9"
@@ -168,18 +144,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg DEADSNAKES_MIRROR_URL=https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu --build-arg DEADSNAKES_GPGKEY_URL='https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xF23C5A6CF475977595C89F51BA6932366A755776' --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
           # re-tag to default image tag and push, just in case arm64 build fails
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129"
@@ -192,18 +157,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg DEADSNAKES_MIRROR_URL=https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu --build-arg DEADSNAKES_GPGKEY_URL='https://keyserver.ubuntu.com/pks/lookup?op=get&search=0xF23C5A6CF475977595C89F51BA6932366A755776' --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129"
 
       - label: "Build release image - x86_64 - CUDA 13.0 - Ubuntu 24.04"
@@ -213,21 +167,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-ubuntu2404"
@@ -239,21 +179,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=13.0.2 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=13.0.2 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64}\" --build-arg INSTALL_KV_CONNECTORS=true --build-arg BUILD_BASE_IMAGE=nvidia/cuda:13.0.2-devel-ubuntu24.04 --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-ubuntu2404"
 
       - label: "Build release image - x86_64 - CUDA 12.9 - Ubuntu 24.04"
@@ -263,20 +189,7 @@ steps:
           queue: cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_X86_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_X86_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
           - "docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-cu129-ubuntu2404"
@@ -288,20 +201,7 @@ steps:
           queue: arm64_cpu_queue_release
         commands:
           - "aws ecr-public get-login-password --region us-east-1 | docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7"
-          - |
-            DOCKER_BUILDKIT=1 docker build \
-              $(bash .buildkite/scripts/docker-build-metadata-args.sh cu129-ubuntu2404) \
-              --build-arg max_jobs=16 \
-              --build-arg USE_SCCACHE=1 \
-              --build-arg GIT_REPO_CHECK=1 \
-              --build-arg CUDA_VERSION=12.9.1 \
-              --build-arg UBUNTU_VERSION=24.04 \
-              --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 \
-              --build-arg torch_cuda_arch_list="${CUDA_ARCH_AARCH64_CU129}" \
-              --build-arg INSTALL_KV_CONNECTORS=true \
-              --target vllm-openai \
-              --progress plain \
-              -f docker/Dockerfile .
+          - "DOCKER_BUILDKIT=1 docker build --build-arg max_jobs=16 --build-arg USE_SCCACHE=1 --build-arg GIT_REPO_CHECK=1 --build-arg CUDA_VERSION=12.9.1 --build-arg UBUNTU_VERSION=24.04 --build-arg GDRCOPY_OS_VERSION=Ubuntu24_04 --build-arg torch_cuda_arch_list=\"${CUDA_ARCH_AARCH64_CU129}\" --build-arg INSTALL_KV_CONNECTORS=true --tag public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404 --target vllm-openai --progress plain -f docker/Dockerfile ."
           - "docker push public.ecr.aws/q9t5s3a7/vllm-release-repo:$BUILDKITE_COMMIT-$(uname -m)-cu129-ubuntu2404"
 
       - block: "Build release image for x86_64 CPU"
@@ -309,7 +209,6 @@ steps:
         depends_on: ~
 
       - label: "Build release image - x86_64 - CPU"
-        key: build-cpu-release-image-x86
         depends_on:
           - block-cpu-release-image-build
           - input-release-version
@@ -328,8 +227,7 @@ steps:
         depends_on: ~
 
       - label: "Build release image - arm64 - CPU"
-        key: build-cpu-release-image-arm64
-        depends_on:
+        depends_on: 
           - block-arm64-cpu-release-image-build
           - input-release-version
         agents:
@@ -438,41 +336,6 @@ steps:
           DOCKER_BUILDKIT: "1"
           DOCKERHUB_USERNAME: "vllmbot"
 
-      - block: "Publish release images to DockerHub"
-        key: block-publish-release-images
-        depends_on:
-          - create-multi-arch-manifest
-          - create-multi-arch-manifest-cuda-12-9
-          - create-multi-arch-manifest-ubuntu2404
-          - create-multi-arch-manifest-cuda-12-9-ubuntu2404
-          - build-rocm-release-image
-          - input-release-version
-          # Wait for CPU builds if their block steps were unblocked, so publish
-          # doesn't race the in-progress CPU build. allow_failure lets publish
-          # proceed when the operator legitimately leaves the CPU block steps
-          # unblocked or the CPU build fails.
-          - step: build-cpu-release-image-x86
-            allow_failure: true
-          - step: build-cpu-release-image-arm64
-            allow_failure: true
-        if: build.env("NIGHTLY") != "1"
-
-      - label: "Publish release images to DockerHub"
-        depends_on:
-          - block-publish-release-images
-        key: publish-release-images-dockerhub
-        agents:
-          queue: small_cpu_queue_release
-        commands:
-          - "bash .buildkite/scripts/publish-release-images.sh"
-        plugins:
-          - docker-login#v3.0.0:
-              username: vllmbot
-              password-env: DOCKERHUB_TOKEN
-        env:
-          DOCKER_BUILDKIT: "1"
-          DOCKERHUB_USERNAME: "vllmbot"
-
   - group: "Publish wheels"
     key: "publish-wheels"
     steps:
@@ -760,7 +623,7 @@ steps:
       - "bash tools/vllm-rocm/generate-rocm-wheels-root-index.sh"
     env:
       S3_BUCKET: "vllm-wheels"
-      VARIANT: "rocm722"
+      VARIANT: "rocm721"
 
   # ROCm Job 6: Build ROCm Release Docker Image
   - label: ":docker: Build release image - x86_64 - ROCm"

.buildkite/scripts/annotate-release.sh

@@ -8,6 +8,8 @@ if [ -z "${RELEASE_VERSION}" ]; then
   RELEASE_VERSION="1.0.0.dev"
 fi
 
+ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
+
 buildkite-agent annotate --style 'info' --context 'release-workflow' << EOF
 To download the wheel (by commit):
 \`\`\`
@@ -23,5 +25,95 @@ aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-
 aws s3 cp s3://vllm-wheels/${BUILDKITE_COMMIT}/vllm-${RELEASE_VERSION}+cpu-cp38-abi3-manylinux_2_35_aarch64.whl .
 \`\`\`
 
-Docker images are published automatically by the "Publish release images to DockerHub" pipeline step.
+
+To download and upload the image:
+
+\`\`\`
+# Download images:
+
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu129
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
+docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm
+docker pull public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
+docker pull public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
+
+# Tag and push images:
+
+## CUDA
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64 vllm/vllm-openai:x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:latest-x86_64
+docker tag vllm/vllm-openai:x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai:latest-x86_64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-x86_64-cu129 vllm/vllm-openai:x86_64-cu129
+docker tag vllm/vllm-openai:x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
+docker tag vllm/vllm-openai:x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+docker push vllm/vllm-openai:latest-x86_64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64 vllm/vllm-openai:aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:latest-aarch64
+docker tag vllm/vllm-openai:aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker push vllm/vllm-openai:latest-aarch64
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-aarch64-cu129 vllm/vllm-openai:aarch64-cu129
+docker tag vllm/vllm-openai:aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker tag vllm/vllm-openai:aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker push vllm/vllm-openai:latest-aarch64-cu129
+docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+
+## ROCm
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${BUILDKITE_COMMIT}-rocm vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:latest
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT} vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+docker push vllm/vllm-openai-rocm:latest
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:latest-base
+docker tag vllm/vllm-openai-rocm:${BUILDKITE_COMMIT}-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+docker push vllm/vllm-openai-rocm:latest-base
+docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
+
+## CPU
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:latest-x86_64
+docker tag vllm/vllm-openai-cpu:x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+docker push vllm/vllm-openai-cpu:latest-x86_64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
+
+docker tag public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION} vllm/vllm-openai-cpu:arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:latest-arm64
+docker tag vllm/vllm-openai-cpu:arm64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker push vllm/vllm-openai-cpu:latest-arm64
+docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+
+# Create multi-arch manifest:
+
+docker manifest rm vllm/vllm-openai:latest
+docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
+docker manifest push vllm/vllm-openai:latest
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
+
+docker manifest rm vllm/vllm-openai:latest-cu129
+docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
+docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
+docker manifest push vllm/vllm-openai:latest-cu129
+docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
+
+docker manifest rm vllm/vllm-openai-cpu:latest || true
+docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
+docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
+docker manifest push vllm/vllm-openai-cpu:latest
+docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
+\`\`\`
 EOF

.buildkite/scripts/ci-fetch-log.sh

@@ -1,55 +0,0 @@
-#!/bin/bash
-# Usage: ./ci-fetch-log.sh <buildkite_job_url> [output_file]
-#        ./ci-fetch-log.sh <build_number> <job_uuid> [output_file]
-#
-# Downloads the raw log for a Buildkite job from the public, unauthenticated
-# /organizations/<org>/pipelines/<pipeline>/builds/<n>/jobs/<uuid>/download
-# endpoint, then strips ANSI/timestamps via ci-clean-log.sh.
-#
-# Find <build_number> and <job_uuid> via:
-#   gh pr checks <PR> --repo vllm-project/vllm
-# Each failing row's URL is .../builds/<build_number>#<job_uuid>.
-
-set -euo pipefail
-
-ORG="vllm"
-PIPELINE="ci"
-
-usage() {
-    echo "Usage: $0 <buildkite_job_url> [output_file]"
-    echo "       $0 <build_number> <job_uuid> [output_file]"
-    exit 1
-}
-
-if [ $# -lt 1 ]; then usage; fi
-
-if [[ "$1" == https://* ]]; then
-    BUILD=$(echo "$1" | sed -nE 's#.*/builds/([0-9]+).*#\1#p')
-    JOB=$(echo "$1" | grep -oE '[0-9a-f]{8}-[0-9a-f-]+' | head -n 1)
-    OUT="${2:-ci-${BUILD}-${JOB:0:8}.log}"
-else
-    if [ $# -lt 2 ]; then usage; fi
-    BUILD="$1"
-    JOB="$2"
-    OUT="${3:-ci-${BUILD}-${JOB:0:8}.log}"
-fi
-
-if [ -z "$BUILD" ] || [ -z "$JOB" ]; then
-    echo "Could not parse build number or job UUID from: $1" >&2
-    usage
-fi
-
-COOKIES=$(mktemp)
-trap 'rm -f "$COOKIES"' EXIT
-
-# Buildkite issues a session cookie on first hit; subsequent /download needs it.
-curl -fsSL -c "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/${ORG}/${PIPELINE}/builds/${BUILD}" -o /dev/null
-
-curl -fsSL -b "$COOKIES" -A "vllm-ci-fetch-log" \
-    "https://buildkite.com/organizations/${ORG}/pipelines/${PIPELINE}/builds/${BUILD}/jobs/${JOB}/download" \
-    -o "$OUT"
-
-bash "$(dirname "$0")/ci-clean-log.sh" "$OUT"
-
-echo "$OUT"

.buildkite/scripts/detect-manylinux-tag.py

@@ -1,142 +0,0 @@
-#!/usr/bin/env python3
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-"""Detect the manylinux platform tag for a wheel and rename it in place.
-
-vLLM's build images produce wheels with the generic ``linux_<arch>`` platform
-tag, which installers like ``pip`` won't accept off PyPI/our index. We need to
-rewrite the platform tag to the appropriate ``manylinux_<major>_<minor>_<arch>``
-before uploading.
-
-Historically the tag was hard-coded per build (``manylinux_2_31`` for the
-Ubuntu 20.04-based image, ``manylinux_2_35`` for the Ubuntu 22.04-based
-images). That is brittle: bumping the base image silently produces wheels
-labelled with the wrong glibc requirement. This script asks ``auditwheel``
-to derive the tag from the symbol versions actually referenced by the
-binaries inside the wheel, so the label tracks reality.
-
-We can't simply call ``auditwheel repair`` -- it tries to graft external
-shared libraries into the wheel and fails on vLLM's CUDA/cuBLAS dependencies.
-Instead we use ``auditwheel.wheel_abi.analyze_wheel_abi`` directly, which is
-the same call that powers ``auditwheel show``, and read off
-``winfo.sym_policy.name``.
-
-Usage:
-    detect-manylinux-tag.py <wheel_path>
-
-The wheel is renamed in place; the new path is printed on stdout. All
-diagnostics go to stderr so callers can capture stdout safely.
-"""
-
-from __future__ import annotations
-
-import argparse
-import sys
-from pathlib import Path
-
-from auditwheel.error import (
-    AuditwheelError,
-    NonPlatformWheelError,
-    WheelToolsError,
-)
-from auditwheel.wheel_abi import analyze_wheel_abi
-from auditwheel.wheeltools import get_wheel_architecture, get_wheel_libc
-
-
-def detect_platform_tag(wheel_path: Path) -> str:
-    """Return the most precise platform tag the wheel is consistent with.
-
-    Mirrors ``auditwheel show`` but returns ``sym_policy`` rather than
-    ``overall_policy``: we only care about the glibc symbol versions used,
-    not about other policy axes (ISA extensions, blacklist, etc.) that
-    ``overall_policy`` folds in.
-    """
-    fn = wheel_path.name
-
-    try:
-        arch = get_wheel_architecture(fn)
-    except (WheelToolsError, NonPlatformWheelError):
-        # Architecture isn't deducible from the filename; let auditwheel
-        # infer it from the ELF binaries inside the wheel.
-        arch = None
-
-    try:
-        libc = get_wheel_libc(fn)
-    except WheelToolsError:
-        # An unrepaired wheel uses ``linux_<arch>``, which doesn't encode
-        # libc. Let auditwheel infer it from the ELF binaries.
-        libc = None
-
-    winfo = analyze_wheel_abi(
-        libc,
-        arch,
-        wheel_path,
-        frozenset(),
-        disable_isa_ext_check=False,
-        allow_graft=False,
-    )
-    return winfo.sym_policy.name
-
-
-def rename_wheel(wheel_path: Path, new_platform_tag: str) -> Path:
-    """Rename the wheel in place, replacing only its platform tag."""
-    # Wheel filename per PEP 427:
-    #   {distribution}-{version}(-{build})?-{python}-{abi}-{platform}.whl
-    # The platform tag is always the last ``-``-separated token before
-    # ``.whl``. Compound tags like ``manylinux_2_31_x86_64`` use ``_`` as the
-    # internal separator, so ``-``-splitting is unambiguous.
-    parts = wheel_path.stem.split("-")
-    if len(parts) < 5:
-        raise ValueError(f"Unrecognised wheel filename: {wheel_path.name}")
-    parts[-1] = new_platform_tag
-    new_path = wheel_path.with_name("-".join(parts) + ".whl")
-    if new_path != wheel_path:
-        wheel_path.rename(new_path)
-    return new_path
-
-
-def main() -> int:
-    parser = argparse.ArgumentParser(
-        description="Detect a wheel's manylinux platform tag with "
-        "auditwheel and rename the wheel in place."
-    )
-    parser.add_argument(
-        "wheel",
-        type=Path,
-        help="Path to the wheel to inspect and rename.",
-    )
-    args = parser.parse_args()
-
-    wheel_path: Path = args.wheel
-    if not wheel_path.is_file():
-        print(f"error: {wheel_path} is not a file", file=sys.stderr)
-        return 1
-
-    # Catch the things that ``analyze_wheel_abi`` and ``rename_wheel`` can
-    # raise: any subclass of ``AuditwheelError`` (pure-Python wheels,
-    # invalid libc, malformed wheels), filesystem errors, or our own
-    # ``ValueError`` for an unrecognised wheel filename. Print a single
-    # ``ERROR_TYPE: message`` line to stderr instead of a Python
-    # traceback, which is much friendlier in CI logs.
-    try:
-        new_tag = detect_platform_tag(wheel_path)
-        print(f"detected platform tag: {new_tag}", file=sys.stderr)
-        new_path = rename_wheel(wheel_path, new_tag)
-    except (AuditwheelError, ValueError, OSError) as e:
-        print(
-            f"error: failed to retag {wheel_path.name}: {type(e).__name__}: {e}",
-            file=sys.stderr,
-        )
-        return 2
-
-    if new_path != wheel_path:
-        print(f"renamed {wheel_path.name} -> {new_path.name}", file=sys.stderr)
-    else:
-        print(f"wheel already tagged {new_tag}", file=sys.stderr)
-
-    print(new_path)
-    return 0
-
-
-if __name__ == "__main__":
-    sys.exit(main())

.buildkite/scripts/docker-build-metadata-args.sh

@@ -1,54 +0,0 @@
-#!/bin/bash
-# Emit docker build flags for release image provenance metadata.
-# Keep this helper best-effort: missing Buildkite metadata should fall back to
-# local/default values instead of blocking the Docker build.
-
-# Variant examples: "", "cu129", "ubuntu2404", "cu129-ubuntu2404".
-variant="${1:-}"
-variant_suffix="${variant:+-${variant}}"
-
-image_name="${VLLM_DOCKER_IMAGE_NAME:-vllm/vllm-openai}"
-staging_repo="${VLLM_STAGING_IMAGE_REPO:-public.ecr.aws/q9t5s3a7/vllm-release-repo}"
-build_commit="${VLLM_BUILD_COMMIT:-${BUILDKITE_COMMIT:-unknown}}"
-build_pipeline="${VLLM_BUILD_PIPELINE:-${BUILDKITE_PIPELINE_ID:-${BUILDKITE_PIPELINE_SLUG:-local}}}"
-build_url="${VLLM_BUILD_URL:-${BUILDKITE_BUILD_URL:-}}"
-tag_commit="${BUILDKITE_COMMIT:-${build_commit}}"
-
-if [[ -n "${BUILDKITE:-}" || -n "${BUILDKITE_COMMIT:-}" ]]; then
-  release_version="${RELEASE_VERSION:-}"
-  if command -v buildkite-agent >/dev/null 2>&1; then
-    release_version="${release_version:-$(buildkite-agent meta-data get release-version 2>/dev/null)}"
-  fi
-  release_version="${release_version#v}"
-  release_version="${release_version:-${tag_commit}}"
-
-  staging_image_ref="${staging_repo}:${tag_commit}-$(uname -m)${variant_suffix}"
-
-  if [[ "${NIGHTLY:-}" == "1" ]]; then
-    if [[ -z "${variant}" ]]; then
-      image_tag="${image_name}:nightly-${tag_commit}"
-    elif [[ "${variant}" == cu* ]]; then
-      cuda_variant="${variant%%-*}"
-      remaining_variant="${variant#${cuda_variant}}"
-      image_tag="${image_name}:${cuda_variant}-nightly-${tag_commit}${remaining_variant}"
-    else
-      image_tag="${image_name}:nightly-${tag_commit}${variant_suffix}"
-    fi
-  else
-    image_tag="${image_name}:v${release_version}${variant_suffix}"
-  fi
-else
-  image_tag="${VLLM_IMAGE_TAG:-local/vllm-openai:dev}"
-  staging_image_ref="${image_tag}"
-fi
-
-emit_arg() {
-  printf -- "--build-arg %s=%s " "$1" "$2"
-}
-
-emit_arg VLLM_BUILD_COMMIT "${build_commit}"
-emit_arg VLLM_BUILD_PIPELINE "${build_pipeline}"
-emit_arg VLLM_BUILD_URL "${build_url}"
-# This is the intended public tag. The final digest is only known after push.
-emit_arg VLLM_IMAGE_TAG "${image_tag}"
-printf -- "--tag %s " "${staging_image_ref}"

.buildkite/scripts/generate-and-upload-nightly-index.sh

@@ -10,13 +10,20 @@ set -ex
 BUCKET="vllm-wheels"
 INDICES_OUTPUT_DIR="indices"
 DEFAULT_VARIANT_ALIAS="cu130" # align with vLLM_MAIN_CUDA_VERSION in vllm/envs.py
+PYTHON="${PYTHON_PROG:-python3}" # try to read from env var, otherwise use python3
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# Select python3 (>= 3.12) -- local if available, else a docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
+# detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)")
+if [[ "$has_new_python" -eq 0 ]]; then
+    # use new python from docker
+    docker pull python:3-slim
+    PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
+
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
 
 # ======== generate and upload indices ========
 

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

@@ -378,11 +378,9 @@ HF_MOUNT="/root/.cache/huggingface"
 # double-quotes will have been stripped by the calling shell.
 if [[ -n "${VLLM_TEST_COMMANDS:-}" ]]; then
   commands="${VLLM_TEST_COMMANDS}"
-  commands_source="env"
   echo "Commands sourced from VLLM_TEST_COMMANDS (quoting preserved)"
 else
   commands="$*"
-  commands_source="argv"
   if [[ -z "$commands" ]]; then
     echo "Error: No test commands provided." >&2
     echo "Usage:" >&2
@@ -399,15 +397,9 @@ fi
 
 echo "Raw commands: $commands"
 
-# Only try to repair stripped pytest -m/-k quoting in legacy argv mode.
-# VLLM_TEST_COMMANDS preserves inner quoting already, and re-quoting that path
-# can corrupt embedded echo strings or otherwise well-formed shell fragments.
-if [[ "$commands_source" == "argv" ]]; then
-  commands=$(re_quote_pytest_markers "$commands")
-  echo "After re-quoting: $commands"
-else
-  echo "Skipping re-quoting for VLLM_TEST_COMMANDS input"
-fi
+# Fix quoting before ROCm overrides (so overrides see correct structure)
+commands=$(re_quote_pytest_markers "$commands")
+echo "After re-quoting: $commands"
 
 commands=$(apply_rocm_test_overrides "$commands")
 echo "Final commands: $commands"

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

@@ -3,37 +3,42 @@ set -euox pipefail
 export VLLM_CPU_CI_ENV=0
 export VLLM_CPU_KVCACHE_SPACE=1 # avoid OOM
 
-MODE=${1:-all}
+echo "--- PP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -pp=2 --max-model-len=4096 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+vllm bench serve \
+    --backend vllm \
+    --dataset-name random \
+    --model meta-llama/Llama-3.2-3B-Instruct \
+    --num-prompts 20 \
+    --result-dir ./test_results \
+    --result-filename tp_pp.json \
+    --save-result \
+    --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/tp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+  echo "Some requests were failed!"
+  exit 1
+fi
 
-run_scenario() {
-    local label="$1" result_file="$2"
-    shift 2
-    echo "--- $label"
-    vllm serve meta-llama/Llama-3.2-3B-Instruct "$@" --max-model-len=4096 &
-    local server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
-    vllm bench serve \
-        --backend vllm \
-        --dataset-name random \
-        --model meta-llama/Llama-3.2-3B-Instruct \
-        --num-prompts 20 \
-        --result-dir ./test_results \
-        --result-filename "$result_file" \
-        --save-result \
-        --endpoint /v1/completions
-    kill -s SIGTERM "$server_pid"; wait "$server_pid" || true
-    if [ "$(jq '.failed' "./test_results/$result_file")" -ne 0 ]; then
-        echo "Some requests were failed in $label!"
-        exit 1
-    fi
-}
-
-case "$MODE" in
-    tp_pp) run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2 ;;
-    dp_tp) run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2 ;;
-    all)
-        run_scenario "PP+TP" tp_pp.json -tp=2 -pp=2
-        run_scenario "DP+TP" dp_tp.json -tp=2 -dp=2
-        ;;
-    *) echo "ERROR: unknown mode '$MODE' (expected: tp_pp | dp_tp | all)" >&2; exit 1 ;;
-esac
+echo "--- DP+TP"
+vllm serve meta-llama/Llama-3.2-3B-Instruct -tp=2 -dp=2 --max-model-len=4096 &
+server_pid=$!
+timeout 600 bash -c "until curl localhost:8000/v1/models > /dev/null 2>&1; do sleep 1; done" || exit 1
+vllm bench serve \
+   --backend vllm \
+   --dataset-name random \
+   --model meta-llama/Llama-3.2-3B-Instruct \
+   --num-prompts 20 \
+   --result-dir ./test_results \
+   --result-filename dp_pp.json \
+   --save-result \
+   --endpoint /v1/completions
+kill -s SIGTERM $server_pid; wait $server_pid || true
+failed_req=$(jq '.failed' ./test_results/dp_pp.json)
+if [ "$failed_req" -ne 0 ]; then
+ echo "Some requests were failed!"
+ exit 1
+fi

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

@@ -31,21 +31,6 @@ function cpu_tests() {
     set -e
     pip list"
 
-  # Run kernel tests
-  docker exec cpu-test bash -c "
-    set -e
-    pytest -x -v -s tests/kernels/test_onednn.py
-    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
-    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
-    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
-
-  # skip tests requiring model downloads if HF_TOKEN is not set
-  # due to rate-limits
-  if [ -z "$HF_TOKEN" ]; then
-    echo "Warning: HF_TOKEN is not set. Skipping tests that require model downloads."
-    return
-  fi
-
   # offline inference
   docker exec cpu-test bash -c "
     set -e
@@ -61,6 +46,13 @@ function cpu_tests() {
     set -e
     pytest -x -v -s tests/quantization/test_compressed_tensors.py::test_compressed_tensors_w8a8_logprobs"
 
+  # Run kernel tests
+  docker exec cpu-test bash -c "
+    set -e
+    pytest -x -v -s tests/kernels/test_onednn.py
+    pytest -x -v -s tests/kernels/attention/test_cpu_attn.py
+    pytest -x -v -s tests/kernels/core/test_cpu_activation.py
+    pytest -x -v -s tests/kernels/moe/test_moe.py -k test_cpu_fused_moe_basic"
 
   # basic online serving
   docker exec cpu-test bash -c '
@@ -75,21 +67,6 @@ function cpu_tests() {
       --num-prompts 20 \
       --endpoint /v1/completions
     kill -s SIGTERM $server_pid &'
-
-  # smoke test for Gated DeltaNet
-  docker exec cpu-test bash -c '
-    set -e
-    VLLM_CPU_OMP_THREADS_BIND=$E2E_OMP_THREADS vllm serve Qwen/Qwen3.5-0.8B --max-model-len 2048 &
-    server_pid=$!
-    timeout 600 bash -c "until curl localhost:8000/v1/models; do sleep 1; done" || exit 1
-    vllm bench serve \
-      --backend vllm \
-      --dataset-name random \
-      --model Qwen/Qwen3.5-0.8B \
-      --num-prompts 20 \
-      --endpoint /v1/completions
-    kill -s SIGTERM $server_pid &'
-
 }
 
 # All of CPU tests are expected to be finished less than 40 mins.

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

@@ -136,6 +136,8 @@ run_and_track_test 3 "test_accuracy.py::test_lm_eval_accuracy_v1_engine" \
     "python3 -m pytest -s -v /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine"
 run_and_track_test 4 "test_quantization_accuracy.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/tpu/test_quantization_accuracy.py"
+run_and_track_test 5 "examples/offline_inference/tpu.py" \
+    "python3 /workspace/vllm/examples/offline_inference/tpu.py"
 run_and_track_test 6 "test_tpu_model_runner.py" \
     "python3 -m pytest -s -v /workspace/vllm/tests/v1/tpu/worker/test_tpu_model_runner.py"
 run_and_track_test 7 "test_sampler.py" \

.buildkite/scripts/lib/manylinux.sh

@@ -1,127 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Shared helper for rewriting a wheel's platform tag from the generic
-# ``linux_<arch>`` to the correct ``manylinux_<major>_<minor>_<arch>``.
-# After sourcing, call ``apply_manylinux_tag <wheel>`` on each wheel
-# that still carries the generic tag; the renamed path is printed on
-# stdout (logs go to stderr).
-#
-# Why a pinned Docker container instead of using whatever Python
-# happens to be on the agent:
-#   - vLLM's release agents are heterogeneous -- they don't agree on
-#     a Python minor version, and we can't rely on a particular
-#     ``auditwheel`` being installed.
-#   - ``detect-manylinux-tag.py`` reads ``auditwheel.wheel_abi`` and
-#     ``Policy.sym_policy``, which are *internal* APIs without a
-#     stability promise. Pinning both Python and auditwheel makes the
-#     detected tag a function of the inputs alone, and shifts version
-#     bumps from "implicit drift" to "deliberate, retested change".
-#   - Other release scripts (``generate-and-upload-nightly-index.sh``,
-#     ``upload-rocm-wheels.sh``) already use the python:3-slim image
-#     when the agent's interpreter is too old; this is the same idea
-#     made stricter.
-#
-# To keep the per-wheel cost down (the ROCm upload retags ~10 wheels
-# each run), we install auditwheel into a long-lived helper container
-# once on source, then ``docker exec`` into it for each call.
-#
-# Trap behaviour:
-# - Sourcing installs an EXIT trap that calls ``manylinux_cleanup`` to
-#   tear down the helper container. Any EXIT trap that was already in
-#   place when this file was sourced is captured and run AFTER our
-#   cleanup, so we don't silently clobber it.
-# - If a caller sets a new EXIT trap *after* sourcing, that trap will
-#   replace ours; in that case the caller should call
-#   ``manylinux_cleanup`` from their own handler.
-
-if [[ -n "${_MANYLINUX_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_MANYLINUX_LIB_SOURCED=1
-
-# Pin both sides. Bump these deliberately and re-run a representative
-# wheel from each build target through the detection.
-_MANYLINUX_PYTHON_IMAGE="python:3.12-slim"
-_MANYLINUX_AUDITWHEEL_VERSION="6.6.0"
-
-# Resolve our own directory (and the sibling detect script) using the
-# canonical, symlink-resolved path. The container mounts cwd at the
-# same absolute path on both sides, so all paths we hand to it -- the
-# script, the wheel -- must canonicalise to a location under cwd.
-_MANYLINUX_LIB_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd -P)"
-_MANYLINUX_DETECT_SCRIPT="$(cd "${_MANYLINUX_LIB_DIR}/.." && pwd -P)/detect-manylinux-tag.py"
-_MANYLINUX_CWD="$(pwd -P)"
-
-docker pull --quiet "$_MANYLINUX_PYTHON_IMAGE" >/dev/null
-
-# Spin up a long-lived helper container so we install auditwheel once
-# and then ``docker exec`` into it for each wheel.
-#
-# The container runs as root so ``pip install`` can write into the
-# system site-packages; individual ``docker exec`` calls below pin
-# themselves to the host UID so any file rename happens with host
-# ownership, not root.
-_MANYLINUX_CONTAINER="$(docker run -d --rm \
-    -v "$_MANYLINUX_CWD:$_MANYLINUX_CWD" \
-    -w "$_MANYLINUX_CWD" \
-    "$_MANYLINUX_PYTHON_IMAGE" \
-    sleep infinity)"
-docker exec "$_MANYLINUX_CONTAINER" \
-    pip install --quiet --disable-pip-version-check \
-    --root-user-action=ignore \
-    "auditwheel==${_MANYLINUX_AUDITWHEEL_VERSION}"
-
-# Public cleanup -- safe to call multiple times.
-manylinux_cleanup() {
-    if [[ -n "${_MANYLINUX_CONTAINER:-}" ]]; then
-        docker rm -f "$_MANYLINUX_CONTAINER" >/dev/null 2>&1 || true
-        _MANYLINUX_CONTAINER=""
-    fi
-}
-
-# Capture any EXIT trap that was already in place so we can chain to
-# it rather than overwrite it. ``trap -p EXIT`` prints the handler in
-# eval-able form (``trap -- 'CMD' EXIT``) or nothing if unset; we
-# strip the wrapper to recover ``CMD``. Handles the common case --
-# CMDs without embedded single quotes -- and degrades gracefully (we
-# still run our own cleanup) for the pathological case.
-_manylinux_prev_exit_trap_cmd=""
-_manylinux_existing_exit_trap="$(trap -p EXIT)"
-if [[ -n "$_manylinux_existing_exit_trap" ]]; then
-    _tmp="${_manylinux_existing_exit_trap#trap -- \'}"
-    _manylinux_prev_exit_trap_cmd="${_tmp%\' EXIT}"
-    unset _tmp
-fi
-unset _manylinux_existing_exit_trap
-
-_manylinux_run_exit_chain() {
-    manylinux_cleanup
-    if [[ -n "$_manylinux_prev_exit_trap_cmd" ]]; then
-        eval "$_manylinux_prev_exit_trap_cmd"
-    fi
-}
-trap _manylinux_run_exit_chain EXIT
-
-# Detect the manylinux platform tag for a single wheel and rename it
-# in place, printing the renamed wheel path on stdout. Returns
-# non-zero on failure (which under ``set -e`` propagates to caller).
-#
-# The wheel must be reachable via a path under the host cwd so it's
-# visible inside the helper container; in CI the wheels always live
-# under ``artifacts/`` so this is fine.
-apply_manylinux_tag() {
-    local wheel="$1"
-    local abs_wheel
-    abs_wheel="$(realpath "$wheel")"
-    local new_wheel
-    new_wheel="$(docker exec -u "$(id -u):$(id -g)" \
-        "$_MANYLINUX_CONTAINER" \
-        python "$_MANYLINUX_DETECT_SCRIPT" "$abs_wheel")"
-    if [[ -z "$new_wheel" || ! -f "$new_wheel" ]]; then
-        echo "apply_manylinux_tag: detect-manylinux-tag.py did not produce a valid wheel path for $wheel" >&2
-        return 1
-    fi
-    printf '%s\n' "$new_wheel"
-}

.buildkite/scripts/lib/select-python.sh

@@ -1,41 +0,0 @@
-#!/usr/bin/env bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Pick a Python interpreter for buildkite scripts: prefer a local
-# ``python3`` if it is recent enough (>= 3.12), otherwise fall back to
-# a one-shot Docker container running ``python:3-slim``. After
-# ``select_python`` returns, ``$PYTHON`` is set in the caller's shell
-# and is safe to use as a command (e.g. ``$PYTHON some_script.py``).
-#
-# The 3.12 threshold matches what the existing nightly-index work
-# expects -- typing features used by ``generate-nightly-index.py``.
-# This helper does not pin the *minor* version; if you need stricter
-# reproducibility (e.g. relying on auditwheel internals), invoke
-# Docker yourself with a pinned tag rather than calling this.
-
-if [[ -n "${_SELECT_PYTHON_LIB_SOURCED:-}" ]]; then
-    return 0
-fi
-_SELECT_PYTHON_LIB_SOURCED=1
-
-# Sets ``PYTHON`` in the caller's shell and exports it. Idempotent --
-# calling twice is safe and the second call simply re-runs the probe.
-select_python() {
-    local py="${PYTHON_PROG:-python3}"
-    local has_new_python
-    has_new_python=$("$py" -c \
-        "print(1 if __import__('sys').version_info >= (3,12) else 0)" \
-        2>/dev/null || echo 0)
-    if [[ "$has_new_python" -eq 0 ]]; then
-        # ``-u $(id -u):$(id -g)`` so files created via the container
-        # end up owned by the host user, not root.
-        docker pull python:3-slim
-        PYTHON="docker run --rm -u $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
-    else
-        PYTHON="$py"
-    fi
-    export PYTHON
-    echo "Using python interpreter: $PYTHON"
-    echo "Python version: $($PYTHON --version)"
-}

.buildkite/scripts/publish-release-images.sh

@@ -1,180 +0,0 @@
-#!/bin/bash
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#
-# Publish release Docker images from ECR to DockerHub.
-# Pulls per-arch images, tags with latest and versioned tags, pushes them,
-# then creates and pushes multi-arch manifests.
-
-set -euo pipefail
-
-RELEASE_VERSION=$(buildkite-agent meta-data get release-version --default "" | sed 's/^v//')
-if [ -z "${RELEASE_VERSION}" ]; then
-  echo "ERROR: release-version metadata not set"
-  exit 1
-fi
-
-COMMIT="$BUILDKITE_COMMIT"
-ROCM_BASE_CACHE_KEY=$(.buildkite/scripts/cache-rocm-base-wheels.sh key)
-
-echo "========================================"
-echo "Publishing release images v${RELEASE_VERSION}"
-echo "  Commit: ${COMMIT}"
-echo "  ROCm base cache key: ${ROCM_BASE_CACHE_KEY}"
-echo "========================================"
-
-# Login to ECR to pull staging images
-aws ecr-public get-login-password --region us-east-1 | \
-  docker login --username AWS --password-stdin public.ecr.aws/q9t5s3a7
-
-# ---- CUDA (default: 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:latest-x86_64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-docker push vllm/vllm-openai:latest-x86_64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:latest-aarch64
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker push vllm/vllm-openai:latest-aarch64
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-
-docker manifest rm vllm/vllm-openai:latest || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION} || true
-docker manifest create vllm/vllm-openai:latest vllm/vllm-openai:latest-x86_64 vllm/vllm-openai:latest-aarch64
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION} vllm/vllm-openai:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64
-docker manifest push vllm/vllm-openai:latest
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}
-
-# ---- CUDA 12.9 ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:latest-x86_64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-docker push vllm/vllm-openai:latest-x86_64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker push vllm/vllm-openai:latest-aarch64-cu129
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-
-docker manifest rm vllm/vllm-openai:latest-cu129 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129 || true
-docker manifest create vllm/vllm-openai:latest-cu129 vllm/vllm-openai:latest-x86_64-cu129 vllm/vllm-openai:latest-aarch64-cu129
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129
-docker manifest push vllm/vllm-openai:latest-cu129
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129
-
-# ---- Ubuntu 24.04 (CUDA 13.0) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-ubuntu2404 vllm/vllm-openai:latest-x86_64-ubuntu2404 vllm/vllm-openai:latest-aarch64-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-ubuntu2404
-
-# ---- Ubuntu 24.04 (CUDA 12.9) ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-aarch64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker push vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-
-docker manifest rm vllm/vllm-openai:latest-cu129-ubuntu2404 || true
-docker manifest rm vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 || true
-docker manifest create vllm/vllm-openai:latest-cu129-ubuntu2404 vllm/vllm-openai:latest-x86_64-cu129-ubuntu2404 vllm/vllm-openai:latest-aarch64-cu129-ubuntu2404
-docker manifest create vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-x86_64-cu129-ubuntu2404 vllm/vllm-openai:v${RELEASE_VERSION}-aarch64-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:latest-cu129-ubuntu2404
-docker manifest push vllm/vllm-openai:v${RELEASE_VERSION}-cu129-ubuntu2404
-
-# ---- ROCm ----
-
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm
-docker pull public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:latest
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${COMMIT}-rocm vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-docker push vllm/vllm-openai-rocm:latest
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}
-
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:latest-base
-docker tag public.ecr.aws/q9t5s3a7/vllm-release-repo:${ROCM_BASE_CACHE_KEY}-rocm-base vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-docker push vllm/vllm-openai-rocm:latest-base
-docker push vllm/vllm-openai-rocm:v${RELEASE_VERSION}-base
-
-# ---- CPU ----
-# CPU images are behind separate block steps and may not have been built.
-# All-or-nothing: inspect both arches first, then either publish everything
-# (per-arch + multi-arch manifest) or skip everything. Publishing only one
-# arch would leave `:latest-x86_64` pointing at the new release while the
-# `:latest` multi-arch manifest still resolves to the previous release.
-
-CPU_X86_TAG=public.ecr.aws/q9t5s3a7/vllm-cpu-release-repo:v${RELEASE_VERSION}
-CPU_ARM_TAG=public.ecr.aws/q9t5s3a7/vllm-arm64-cpu-release-repo:v${RELEASE_VERSION}
-
-CPU_X86_AVAILABLE=false
-CPU_ARM_AVAILABLE=false
-docker manifest inspect "${CPU_X86_TAG}" >/dev/null 2>&1 && CPU_X86_AVAILABLE=true
-docker manifest inspect "${CPU_ARM_TAG}" >/dev/null 2>&1 && CPU_ARM_AVAILABLE=true
-
-if [ "$CPU_X86_AVAILABLE" = "true" ] && [ "$CPU_ARM_AVAILABLE" = "true" ]; then
-  docker pull "${CPU_X86_TAG}"
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:latest-x86_64
-  docker tag "${CPU_X86_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-  docker push vllm/vllm-openai-cpu:latest-x86_64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64
-
-  docker pull "${CPU_ARM_TAG}"
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:latest-arm64
-  docker tag "${CPU_ARM_TAG}" vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker push vllm/vllm-openai-cpu:latest-arm64
-  docker push vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-
-  docker manifest rm vllm/vllm-openai-cpu:latest || true
-  docker manifest rm vllm/vllm-openai-cpu:v${RELEASE_VERSION} || true
-  docker manifest create vllm/vllm-openai-cpu:latest vllm/vllm-openai-cpu:latest-x86_64 vllm/vllm-openai-cpu:latest-arm64
-  docker manifest create vllm/vllm-openai-cpu:v${RELEASE_VERSION} vllm/vllm-openai-cpu:v${RELEASE_VERSION}-x86_64 vllm/vllm-openai-cpu:v${RELEASE_VERSION}-arm64
-  docker manifest push vllm/vllm-openai-cpu:latest
-  docker manifest push vllm/vllm-openai-cpu:v${RELEASE_VERSION}
-elif [ "$CPU_X86_AVAILABLE" = "false" ] && [ "$CPU_ARM_AVAILABLE" = "false" ]; then
-  echo "WARNING: Neither CPU image found in ECR, skipping CPU publish (ensure block-cpu-release-image-build and block-arm64-cpu-release-image-build were unblocked and the builds finished pushing)"
-else
-  # Partial state: one arch built, the other did not. Fail loudly rather than
-  # ship a Docker Hub state where `:latest-${arch}` and `:latest` (multi-arch)
-  # disagree on which release they point at.
-  echo "ERROR: Partial CPU build detected (x86_64=${CPU_X86_AVAILABLE}, arm64=${CPU_ARM_AVAILABLE})."
-  echo "       Refusing to publish to avoid split-tag drift between per-arch and multi-arch tags."
-  echo "       Re-run the missing CPU build and retry, or manually publish if a single-arch release is intended."
-  exit 1
-fi
-
-echo ""
-echo "Successfully published release images for v${RELEASE_VERSION}"

.buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_prefetch_offload.sh

@@ -51,7 +51,6 @@ vllm serve "$MODEL" \
   --offload-num-in-group 2 \
   --offload-prefetch-step 1 \
   --offload-params w13_weight w2_weight \
-  --generation-config vllm \
   --port "$PORT" \
   ${EXTRA_ARGS+"${EXTRA_ARGS[@]}"} &
 SERVER_PID=$!

.buildkite/scripts/tool_call/run-bfcl-eval.sh

@@ -28,7 +28,6 @@
 #   BFCL_MAX_MODEL_LEN  - Max model length (default: 4096)
 #   BFCL_PORT           - Server port (default: 8000)
 #   BFCL_REASONING_PARSER - Reasoning parser name (default: disabled)
-#   BFCL_TEMPERATURE    - Temperature (default: 0.0)
 #   BFCL_EXTRA_ARGS     - Additional vLLM server args
 
 set -euo pipefail
@@ -44,7 +43,6 @@ TP_SIZE="${BFCL_TP_SIZE:-1}"
 MAX_MODEL_LEN="${BFCL_MAX_MODEL_LEN:-4096}"
 PORT="${BFCL_PORT:-8000}"
 REASONING_PARSER="${BFCL_REASONING_PARSER:-}"
-TEMPERATURE="${BFCL_TEMPERATURE:-0.0}"
 EXTRA_ARGS="${BFCL_EXTRA_ARGS:-}"
 
 # Set up output directory
@@ -141,7 +139,7 @@ echo "vLLM server is ready. (started in ${SECONDS_WAITED}s)"
 # be patched in-process so BFCL knows to use the OpenAI-compatible handler
 # against our local vLLM server.
 bfcl_exit_code=0
-python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$TEMPERATURE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
+python3 - "$MODEL" "$TEST_CATEGORY" "$NUM_THREADS" "$PORT" "$API_TYPE" "$OUTPUT_DIR" << 'PYEOF' || bfcl_exit_code=$?
 import os
 import sys
 
@@ -150,8 +148,7 @@ test_category = sys.argv[2]
 num_threads = int(sys.argv[3])
 port = sys.argv[4]
 api_type = sys.argv[5]
-temperature = float(sys.argv[6])
-output_dir = sys.argv[7] if len(sys.argv) > 7 and sys.argv[7] else os.getcwd()
+output_dir = sys.argv[6] if len(sys.argv) > 6 and sys.argv[6] else os.getcwd()
 
 os.environ["OPENAI_BASE_URL"] = f"http://localhost:{port}/v1"
 os.environ["OPENAI_API_KEY"] = "dummy"
@@ -207,7 +204,6 @@ gen_kwargs["model"] = [model]
 gen_kwargs["test_category"] = [c.strip() for c in test_category.split(",")]
 gen_kwargs["skip_server_setup"] = True
 gen_kwargs["num_threads"] = num_threads
-gen_kwargs["temperature"] = temperature
 generate(**gen_kwargs)
 
 # ---- evaluate ----

.buildkite/scripts/upload-nightly-wheels.sh

@@ -2,18 +2,14 @@
 
 set -ex
 
-# Upload a single wheel to S3, after detecting and applying the appropriate
-# manylinux platform tag with auditwheel.
+# Upload a single wheel to S3 (rename linux -> manylinux).
 # Index generation is handled separately by generate-and-upload-nightly-index.sh.
 
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
-
 BUCKET="vllm-wheels"
 SUBPATH=$BUILDKITE_COMMIT
 S3_COMMIT_PREFIX="s3://$BUCKET/$SUBPATH/"
 
-# ========= locate the wheel ==========
+# ========= collect, rename & upload the wheel ==========
 
 # Assume wheels are in artifacts/dist/*.whl
 wheel_files=(artifacts/dist/*.whl)
@@ -25,9 +21,19 @@ if [[ ${#wheel_files[@]} -ne 1 ]]; then
 fi
 wheel="${wheel_files[0]}"
 
-# ========= detect manylinux tag and rename ==========
+# default build image uses ubuntu 20.04, which corresponds to manylinux_2_31
+# we also accept params as manylinux tag
+# refer to https://github.com/mayeut/pep600_compliance?tab=readme-ov-file#acceptable-distros-to-build-wheels
+manylinux_version="${1:-manylinux_2_31}"
 
-wheel="$(apply_manylinux_tag "$wheel")"
+# Rename 'linux' to the appropriate manylinux version in the wheel filename
+if [[ "$wheel" != *"linux"* ]]; then
+  echo "Error: Wheel filename does not contain 'linux': $wheel"
+  exit 1
+fi
+new_wheel="${wheel/linux/$manylinux_version}"
+mv -- "$wheel" "$new_wheel"
+wheel="$new_wheel"
 echo "Renamed wheel to: $wheel"
 
 # Extract the version from the wheel

.buildkite/scripts/upload-release-wheels-pypi.sh

@@ -39,11 +39,10 @@ fi
 
 set -x # avoid printing secrets above
 
-# install twine and sdist build prerequisites from pypi
+# install twine from pypi
 python3 -m venv /tmp/vllm-release-env
 source /tmp/vllm-release-env/bin/activate
 pip install twine
-pip install -r requirements/build/cuda.txt
 python3 -m twine --version
 
 # copy release wheels to local directory

.buildkite/scripts/upload-rocm-wheels.sh

@@ -20,6 +20,10 @@ BUCKET="${S3_BUCKET:-vllm-wheels}"
 ROCM_SUBPATH="rocm/${BUILDKITE_COMMIT}"
 S3_COMMIT_PREFIX="s3://$BUCKET/$ROCM_SUBPATH/"
 INDICES_OUTPUT_DIR="rocm-indices"
+PYTHON="${PYTHON_PROG:-python3}"
+
+# ROCm uses manylinux_2_35 (Ubuntu 22.04 based)
+MANYLINUX_VERSION="manylinux_2_35"
 
 echo "========================================"
 echo "ROCm Wheel Upload Configuration"
@@ -30,21 +34,19 @@ echo "Commit: $BUILDKITE_COMMIT"
 echo "Branch: $BUILDKITE_BRANCH"
 echo "========================================"
 
-# ======== Part 0: Setup Python and helpers ========
+# ======== Part 0: Setup Python ========
 
-# Pick a Python interpreter for index generation -- local if recent
-# enough, else a one-shot docker fallback.
-# shellcheck source=lib/select-python.sh
-source .buildkite/scripts/lib/select-python.sh
-select_python
+# Detect if python3.12+ is available
+has_new_python=$($PYTHON -c "print(1 if __import__('sys').version_info >= (3,12) else 0)" 2>/dev/null || echo 0)
+if [[ "$has_new_python" -eq 0 ]]; then
+    # Use new python from docker
+    # Use --user to ensure files are created with correct ownership (not root)
+    docker pull python:3-slim
+    PYTHON="docker run --rm --user $(id -u):$(id -g) -v $(pwd):/app -w /app python:3-slim python3"
+fi
 
-# Set up auditwheel-in-a-container for the manylinux retagging step.
-# Distinct from select_python: ``manylinux.sh`` deliberately pins both
-# the Python and auditwheel versions (the script reads auditwheel
-# internals) and so always runs in a known-good container regardless
-# of what's on the agent.
-# shellcheck source=lib/manylinux.sh
-source .buildkite/scripts/lib/manylinux.sh
+echo "Using python interpreter: $PYTHON"
+echo "Python version: $($PYTHON --version)"
 
 # ======== Part 1: Collect and prepare wheels ========
 
@@ -61,18 +63,11 @@ if [ "$WHEEL_COUNT" -eq 0 ]; then
     exit 1
 fi
 
-# Detect the appropriate manylinux platform tag for any wheel that still
-# carries the generic ``linux_<arch>`` tag, and rename it in place. We use
-# auditwheel via ``apply_manylinux_tag`` (see lib/manylinux.sh) rather than
-# a hard-coded ``manylinux_2_35`` string so that the label tracks the actual
-# glibc symbol versions used by the binaries (and stays correct if the
-# rocm_base image is rebased).
-#
-# The ``linux``/``manylinux`` filter below skips both pre-tagged wheels
-# (e.g. upstream torch) and pure-Python ``-any.whl`` wheels.
+# Rename linux to manylinux in wheel filenames
 for wheel in all-rocm-wheels/*.whl; do
     if [[ "$wheel" == *"linux"* ]] && [[ "$wheel" != *"manylinux"* ]]; then
-        new_wheel="$(apply_manylinux_tag "$wheel")"
+        new_wheel="${wheel/linux/$MANYLINUX_VERSION}"
+        mv -- "$wheel" "$new_wheel"
         echo "Renamed: $(basename "$wheel") -> $(basename "$new_wheel")"
     fi
 done

.buildkite/test-amd.yaml

@@ -230,6 +230,7 @@ steps:
   - tests/entrypoints/llm/test_collective_rpc.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - 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
@@ -271,6 +272,7 @@ steps:
   - tests/v1/worker/test_worker_memory_snapshot.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - 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
@@ -386,18 +388,18 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 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
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi250 · kernels  ----------------------------------------------------------#
 
@@ -588,6 +590,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - pip freeze | grep -E 'torch'
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s models/language -m 'core_model and slow_test' --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
 
 - label: Multi-Modal Models (Extended Generation 2) # TBD
@@ -618,7 +621,6 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx90anightly, amdmi250]
   agent_pool: mi250_1
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -862,6 +864,7 @@ steps:
   - tests/entrypoints/openai/test_multi_api_servers.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -880,7 +883,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: V1 e2e (2 GPUs) # TBD
   timeout_in_minutes: 180
@@ -927,7 +930,6 @@ steps:
   - tests/renderers
   - tests/standalone_tests/lazy_imports.py
   - tests/tokenizers_
-  - tests/reasoning
   - tests/tool_parsers
   - tests/transformers_utils
   - tests/config
@@ -940,7 +942,7 @@ steps:
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
-  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py --ignore=reasoning/test_gemma4_reasoning_parser.py
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
   - pytest -v -s config
@@ -1100,13 +1102,12 @@ steps:
   - vllm/compilation/
   - vllm/model_executor/layers
   - tests/compile/passes/distributed/
-  - tests/compile/fusions_e2e/
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
   - export VLLM_TEST_CLEAN_GPU_MEMORY=1
   - 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
+  - pytest -v -s tests/compile/passes/distributed/test_sequence_parallelism.py
 
 #-----------------------------------------------------------  mi300 · cuda  ------------------------------------------------------------#
 
@@ -1167,12 +1168,13 @@ steps:
   - vllm/v1/attention/backends/
   - vllm/v1/attention/selector.py
   - tests/distributed/test_context_parallel.py
-  - examples/features/data_parallel/data_parallel_offline.py
+  - examples/offline_inference/data_parallel.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s tests/distributed/test_context_parallel.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
+  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=allgather_reducescatter --disable-nccl-for-dp-synchronization
 
 - label: Distributed Tests (4xA100-4xMI300) # TBD
   timeout_in_minutes: 180
@@ -1184,6 +1186,7 @@ steps:
   source_file_dependencies:
   - vllm/
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s distributed/test_custom_all_reduce.py
   - torchrun --nproc_per_node=2 distributed/test_ca_buffer_sharing.py
   - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
@@ -1200,16 +1203,17 @@ steps:
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
   - examples/rl/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   - PP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example.py
   - TP_SIZE=4 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   - PP_SIZE=2 TP_SIZE=2 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   - DP_SIZE=4 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 distributed/test_torchrun_example_moe.py
-  - python3 ../examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - python3 ../examples/offline_inference/data_parallel.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 ../examples/rl/rlhf_ipc.py
@@ -1248,6 +1252,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - export VLLM_USE_RAY_V2_EXECUTOR_BACKEND=1
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s distributed/test_ray_v2_executor.py
   - pytest -v -s distributed/test_ray_v2_executor_e2e.py
   - pytest -v -s distributed/test_pipeline_parallel.py -k "ray"
@@ -1261,7 +1266,7 @@ steps:
   optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - examples/offline_inference/torchrun_dp_example.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
@@ -1269,7 +1274,8 @@ steps:
   - vllm/v1/worker/gpu_worker.py
   - vllm/platforms/rocm.py
   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
+  - export TORCH_NCCL_BLOCKING_WAIT=1
+  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 #--------------------------------------------------------  mi300 · entrypoints  --------------------------------------------------------#
 
@@ -1641,18 +1647,18 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # Multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # Pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # Features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-    - python3 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
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi300 · kernels  ----------------------------------------------------------#
 
@@ -1796,17 +1802,15 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
   - tests/models/multimodal/generation
   - tests/models/multimodal/test_mapping.py
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-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 hybrid_model --num-shards=$$BUILDKITE_PARALLEL_JOB_COUNT --shard-id=$$BUILDKITE_PARALLEL_JOB
-
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation -m 'not core_model' --ignore models/multimodal/generation/test_common.py
+  - pytest -v -s models/multimodal/test_mapping.py
 
 - label: Multi-Modal Models (Extended Generation 2) # TBD
   timeout_in_minutes: 180
@@ -1818,10 +1822,8 @@ steps:
   - vllm/
   - tests/models/multimodal/generation
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-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)'
-
+  - pip install git+https://github.com/TIGER-AI-Lab/Mantis.git
+  - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models (Extended Generation 3) # TBD
   timeout_in_minutes: 180
@@ -1841,7 +1843,6 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -1950,8 +1951,8 @@ steps:
   - pytest -v -s tests/models/multimodal/processing/
   - pytest -v -s tests/models/multimodal/test_mapping.py
   - python3 examples/basic/offline_inference/chat.py
-  - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
-  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+  - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+  - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 #-------------------------------------------------------  mi300 · quantization  --------------------------------------------------------#
 
@@ -2202,6 +2203,7 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi300]
   agent_pool: mi300_1
+  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2278,6 +2280,7 @@ steps:
   - tests/entrypoints/openai/test_multi_api_servers.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -2297,8 +2300,9 @@ steps:
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+  - VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
   - pytest -v -s tests/v1/distributed/test_dbo.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
   - pytest -v -s tests/distributed/test_packed_tensor.py
@@ -2359,6 +2363,7 @@ steps:
   - tests/distributed/test_utils
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_async_llm_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
   - TP_SIZE=2 DP_SIZE=2 pytest -v -s v1/distributed/test_external_lb_dp.py
@@ -2488,6 +2493,7 @@ steps:
   - tests/entrypoints/llm/test_collective_rpc.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - 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
@@ -2512,6 +2518,7 @@ steps:
   - tests/v1/worker/test_worker_memory_snapshot.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - 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
@@ -2532,6 +2539,7 @@ steps:
   - tests/distributed/test_multiproc_executor.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s compile/fullgraph/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s distributed/test_events.py
@@ -2619,7 +2627,6 @@ steps:
   agent_pool: mi325_1
   torch_nightly: true
   parallelism: 2
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2645,7 +2652,6 @@ steps:
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx942nightly, amdmi325]
   agent_pool: mi325_1
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2707,10 +2713,11 @@ steps:
   - vllm/v1/attention/selector.py
   - tests/distributed/test_context_parallel.py
   - tests/v1/distributed/test_dbo.py
-  - examples/features/data_parallel/data_parallel_offline.py
+  - examples/offline_inference/data_parallel.py
   - vllm/_aiter_ops.py
   - vllm/platforms/rocm.py
   commands:
+  - export TORCH_NCCL_BLOCKING_WAIT=1
   - pytest -v -s tests/distributed/test_context_parallel.py
   - pytest -v -s tests/v1/distributed/test_dbo.py
 
@@ -2741,7 +2748,6 @@ steps:
   agent_pool: mi355_1
   fast_check: true
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2757,7 +2763,6 @@ steps:
   agent_pool: mi355_1
   fast_check: true
   torch_nightly: true
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -2925,18 +2930,18 @@ steps:
   - python3 basic/offline_inference/embed.py
   - python3 basic/offline_inference/score.py
   # Multi-modal models
-  - python3 generate/multimodal/audio_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_offline.py --seed 0
-  - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-  - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+  - python3 offline_inference/audio_language.py --seed 0
+  - python3 offline_inference/vision_language.py --seed 0
+  - python3 offline_inference/vision_language_multi_image.py --seed 0
+  - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
   # Pooling models
   - python3 pooling/embed/vision_embedding_offline.py --seed 0
   # Features demo
-  - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-  - python3 deployment/llm_engine_example.py
-  - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-  - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
-  - python3 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
+  - python3 offline_inference/prefix_caching.py
+  - python3 offline_inference/llm_engine_example.py
+  - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+  - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+  - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 #----------------------------------------------------------  mi355 · kernels  ----------------------------------------------------------#
 
@@ -3046,7 +3051,7 @@ steps:
   - vllm/
   - tests/models/language/generation
   commands:
-  - uv pip install --system --no-build-isolation 'git+https://github.com/AndreasKaratzas/mamba@rocm-7.0-v2.3.0'
+  - 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)'
 
@@ -3054,7 +3059,6 @@ steps:
   timeout_in_minutes: 180
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3254,7 +3258,6 @@ steps:
   timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3280,7 +3283,6 @@ steps:
   timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental, amdproduction, amdgfx950nightly, amdmi355]
   agent_pool: mi355_1
-  optional: true
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
   - vllm/
@@ -3321,7 +3323,7 @@ steps:
   - vllm/platforms/rocm.py
   commands:
   - uv pip install --system -r /vllm-workspace/requirements/kv_connectors_rocm.txt
-  - ATTENTION_BACKEND=ROCM_ATTN bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
+  - ROCM_ATTN=1 bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: Distributed NixlConnector PD accuracy (4 GPUs) # TBD
   timeout_in_minutes: 180

.buildkite/test_areas/attention.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: V1 attention (H100)
-  key: v1-attention-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -15,9 +14,8 @@ steps:
     - pytest -v -s v1/attention
 
 - label: V1 attention (B200)
-  key: v1-attention-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/config/attention.py
     - vllm/model_executor/layers/attention

.buildkite/test_areas/basic_correctness.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Basic Correctness
-  key: basic-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/benchmarks.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Benchmarks CLI Test
-  key: benchmarks-cli-test
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
@@ -13,8 +12,7 @@ steps:
   - pytest -v -s benchmarks/
 
 - label: Attention Benchmarks Smoke Test (B200)
-  key: attention-benchmarks-smoke-test-b200
-  device: b200-k8s
+  device: b200
   num_gpus: 2
   optional: true
   working_dir: "/vllm-workspace/"

.buildkite/test_areas/compile.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Sequence Parallel Correctness Tests (2 GPUs)
-  key: sequence-parallel-correctness-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -18,7 +17,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: Sequence Parallel Correctness Tests (2xH100)
-  key: sequence-parallel-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -29,7 +27,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_sequence_parallel.py
 
 - label: AsyncTP Correctness Tests (2xH100)
-  key: asynctp-correctness-tests-2xh100
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
   device: h100
@@ -40,10 +37,9 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: AsyncTP Correctness Tests (B200)
-  key: asynctp-correctness-tests-b200
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
@@ -51,7 +47,6 @@ steps:
   - pytest -v -s tests/compile/correctness_e2e/test_async_tp.py
 
 - label: Distributed Compile Unit Tests (2xH100)
-  key: distributed-compile-unit-tests-2xh100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -65,10 +60,9 @@ steps:
   - pytest -s -v tests/compile/passes/distributed
 
 - label: Fusion and Compile Unit Tests (2xB200)
-  key: fusion-and-compile-unit-tests-2xb200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - csrc/quantization/fp4/
   - vllm/model_executor/layers/quantization/
@@ -95,7 +89,6 @@ steps:
     - pytest -v -s tests/compile/fullgraph/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Fusion E2E Quick (H100)
-  key: fusion-e2e-quick-h100
   timeout_in_minutes: 15
   working_dir: "/vllm-workspace/"
   device: h100
@@ -114,7 +107,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and not +rms_norm and +quant_fp8 and (qwen3 or deepseek)"
 
 - label: Fusion E2E Config Sweep (H100)
-  key: fusion-e2e-config-sweep-h100
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
   device: h100
@@ -134,10 +126,9 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "llama-3"
 
 - label: Fusion E2E Config Sweep (B200)
-  key: fusion-e2e-config-sweep-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -148,7 +139,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp1_quant.py -k "inductor_partition and (FLASHINFER and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek)) or llama-3)"
 
 - label: Fusion E2E TP2 Quick (H100)
-  key: fusion-e2e-tp2-quick-h100
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
   device: h100
@@ -166,7 +156,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "inductor_partition and not +rms_norm and (not +quant_fp8 or +quant_fp8 and (qwen3 or deepseek))"
 
 - label: Fusion E2E TP2 AR-RMS Config Sweep (H100)
-  key: fusion-e2e-tp2-ar-rms-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -186,7 +175,6 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_ar_rms.py -k "llama-3"
 
 - label: Fusion E2E TP2 AsyncTP Config Sweep (H100)
-  key: fusion-e2e-tp2-asynctp-config-sweep-h100
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   device: h100
@@ -206,10 +194,9 @@ steps:
     - pytest -v -s tests/compile/fusions_e2e/test_tp2_async_tp.py -k "llama-3"
 
 - label: Fusion E2E TP2 (B200)
-  key: fusion-e2e-tp2-b200
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   num_devices: 2
   source_file_dependencies:
     - csrc/quantization/

.buildkite/test_areas/cuda.yaml

@@ -3,22 +3,16 @@ depends_on:
   - image-build
 steps:
 - label: Platform Tests (CUDA)
-  key: platform-tests-cuda
   timeout_in_minutes: 15
   device: h200_18gb
   source_file_dependencies:
-  - vllm/envs.py
-  - vllm/logger.py
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/utils/
+  - vllm/
   - tests/cuda
   commands:
     - pytest -v -s cuda/test_cuda_context.py
     - pytest -v -s cuda/test_platform_no_cuda_init.py
 
 - label: Cudagraph
-  key: cudagraph
   timeout_in_minutes: 20
   source_file_dependencies:
   - tests/v1/cudagraph

.buildkite/test_areas/disaggregated.yaml

@@ -3,71 +3,65 @@ depends_on:
   - image-build
 steps:
 - label: Distributed NixlConnector PD accuracy (4 GPUs)
-  key: distributed-nixlconnector-pd-accuracy-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 - label: Distributed FlashInfer NixlConnector PD accuracy (4 GPUs)
-  key: distributed-flashinfer-nixlconnector-pd-accuracy-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - FLASHINFER=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: DP EP Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: dp-ep-distributed-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - DP_EP=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: CrossLayer KV layout Distributed NixlConnector PD accuracy tests (4 GPUs)
-  key: crosslayer-kv-layout-distributed-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - CROSS_LAYERS_BLOCKS=True bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: Hybrid SSM NixlConnector PD accuracy tests (4 GPUs)
-  key: hybrid-ssm-nixlconnector-pd-accuracy-tests-4-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
     - uv pip install --system -r /vllm-workspace/requirements/kv_connectors.txt
     - HYBRID_SSM=1 bash v1/kv_connector/nixl_integration/config_sweep_accuracy_test.sh
 
 - label: MultiConnector (Nixl+Offloading) PD accuracy (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-accuracy-2-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading/
@@ -77,13 +71,12 @@ steps:
     - bash v1/kv_connector/nixl_integration/run_multi_connector_accuracy_test.sh
 
 - label: NixlConnector PD + Spec Decode acceptance (2 GPUs)
-  key: nixlconnector-pd-spec-decode-acceptance-2-gpus
   timeout_in_minutes: 30
   device: a100
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - vllm/v1/worker/kv_connector_model_runner_mixin.py
     - tests/v1/kv_connector/nixl_integration/
   commands:
@@ -91,12 +84,11 @@ steps:
     - bash v1/kv_connector/nixl_integration/spec_decode_acceptance_test.sh
 
 - label: MultiConnector (Nixl+Offloading) PD edge cases (2 GPUs)
-  key: multiconnector-nixl-offloading-pd-edge-cases-2-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
   source_file_dependencies:
-    - vllm/distributed/kv_transfer/kv_connector/v1/nixl/
+    - vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/multi_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading_connector.py
     - vllm/distributed/kv_transfer/kv_connector/v1/offloading/

.buildkite/test_areas/distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Comm Ops
-  key: distributed-comm-ops
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -17,7 +16,6 @@ steps:
   - pytest -v -s distributed/test_shm_storage.py
 
 - label: Distributed DP Tests (2 GPUs)
-  key: distributed-dp-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -39,7 +37,6 @@ steps:
   - DP_SIZE=2 pytest -v -s entrypoints/openai/test_multi_api_servers.py
 
 - label: Distributed Compile + RPC Tests (2 GPUs)
-  key: distributed-compile-rpc-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -62,7 +59,6 @@ steps:
   - pytest -v -s ./compile/test_wrapper.py
 
 - label: Distributed Torchrun + Shutdown Tests (2 GPUs)
-  key: distributed-torchrun-shutdown-tests-2-gpus
   timeout_in_minutes: 20
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -85,7 +81,6 @@ steps:
   - pytest -v -s v1/worker/test_worker_memory_snapshot.py
 
 - label: Distributed Torchrun + Examples (4 GPUs)
-  key: distributed-torchrun-examples-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace"
   num_devices: 4
@@ -93,8 +88,9 @@ steps:
   - vllm/distributed/
   - tests/distributed/test_torchrun_example.py
   - tests/distributed/test_torchrun_example_moe.py
+  - examples/offline_inference/rlhf_colocate.py
   - examples/rl/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
@@ -111,13 +107,12 @@ steps:
   # test with torchrun tp=2 and dp=2 with ep
   - TP_SIZE=2 DP_SIZE=2 ENABLE_EP=1 torchrun --nproc-per-node=4 tests/distributed/test_torchrun_example_moe.py
   # test with internal dp
-  - python3 examples/features/data_parallel/data_parallel_offline.py --enforce-eager
+  - python3 examples/offline_inference/data_parallel.py --enforce-eager
   # rlhf examples
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_nccl.py
   - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_ipc.py
 
 - label: Distributed DP Tests (4 GPUs)
-  key: distributed-dp-tests-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -138,7 +133,6 @@ steps:
   - pytest -v -s distributed/test_utils.py
 
 - label: Distributed Compile + Comm (4 GPUs)
-  key: distributed-compile-comm-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -160,28 +154,24 @@ steps:
   - pytest -v -s distributed/test_multiproc_executor.py::test_multiproc_executor_multi_node
 
 - label: Distributed Tests (8 GPUs)(H100)
-  key: distributed-tests-8-gpus-h100
   timeout_in_minutes: 10
   device: h100
   num_devices: 8
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
-  - examples/features/torchrun/torchrun_dp_example_offline.py
+  - examples/offline_inference/torchrun_dp_example.py
   - vllm/config/parallel.py
   - vllm/distributed/
   - vllm/v1/engine/llm_engine.py
   - vllm/v1/executor/uniproc_executor.py
   - vllm/v1/worker/gpu_worker.py
-  - tests/distributed/test_mnnvl_alltoall.py
-
   commands:
   # https://github.com/NVIDIA/nccl/issues/1838
   - export NCCL_CUMEM_HOST_ENABLE=0
   # test with torchrun tp=2 and dp=4 with ep
-  - torchrun --nproc-per-node=8 ../examples/features/torchrun/torchrun_dp_example_offline.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
+  - torchrun --nproc-per-node=8 ../examples/offline_inference/torchrun_dp_example.py --tp-size=2 --pp-size=1 --dp-size=4 --enable-ep
 
 - label: Distributed Tests (4 GPUs)(A100)
-  key: distributed-tests-4-gpus-a100
   device: a100
   optional: true
   num_devices: 4
@@ -196,7 +186,6 @@ steps:
   - pytest -v -s -x lora/test_mixtral.py
 
 - label: Distributed Tests (2 GPUs)(H100)
-  key: distributed-tests-2-gpus-h100
   timeout_in_minutes: 15
   device: h100
   optional: true
@@ -205,14 +194,13 @@ steps:
   commands:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 python3 examples/rl/rlhf_async_new_apis.py
-    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/features/data_parallel/data_parallel_offline.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
+    - VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model=Qwen/Qwen1.5-MoE-A2.7B -tp=1 -dp=2 --max-model-len=2048 --all2all-backend=deepep_high_throughput
     - pytest -v -s tests/v1/distributed/test_dbo.py
     - VLLM_ALLOW_INSECURE_SERIALIZATION=1 pytest -v -s tests/distributed/test_weight_transfer.py
     - pytest -v -s tests/distributed/test_packed_tensor.py
 
 - label: Distributed Tests (2 GPUs)(B200)
-  key: distributed-tests-2-gpus-b200
-  device: b200-k8s
+  device: b200
   optional: true
   working_dir: "/vllm-workspace/"
   num_devices: 2
@@ -220,12 +208,8 @@ steps:
     - pytest -v -s tests/distributed/test_context_parallel.py
     - pytest -v -s tests/distributed/test_nccl_symm_mem_allreduce.py
     - pytest -v -s tests/v1/distributed/test_dbo.py
-    - pytest -v -s tests/distributed/test_mnnvl_alltoall.py
-
-    
 
 - label: 2 Node Test (4 GPUs)
-  key: 2-node-test-4-gpus
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -238,12 +222,11 @@ steps:
   - vllm/executor/
   - vllm/model_executor/models/
   - tests/distributed/
-  - tests/examples/features/data_parallel/data_parallel_offline.py
+  - tests/examples/offline_inference/data_parallel.py
   commands:
-    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/features/data_parallel/data_parallel_offline.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
+    - ./.buildkite/scripts/run-multi-node-test.sh /vllm-workspace/tests 2 2 $IMAGE_TAG "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=0 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_multi_node_assignment.py && VLLM_MULTI_NODE=1 pytest -v -s distributed/test_pipeline_parallel.py" "VLLM_TEST_SAME_HOST=0 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_same_node.py | grep 'Same node test passed' && NUM_NODES=2 torchrun --nnodes 2 --nproc-per-node=2 --rdzv_backend=c10d --rdzv_endpoint=192.168.10.10 distributed/test_node_count.py | grep 'Node count test passed' && python3 ../examples/offline_inference/data_parallel.py -dp=2 -tp=1 --dp-num-nodes=2 --dp-node-rank=1 --dp-master-addr=192.168.10.10 --dp-master-port=12345 --enforce-eager --trust-remote-code"
 
 - label: Pipeline + Context Parallelism (4 GPUs)
-  key: pipeline-context-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -258,7 +241,6 @@ steps:
   - pytest -v -s distributed/test_pipeline_parallel.py
 
 - label: RayExecutorV2 (4 GPUs)
-  key: rayexecutorv2-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4

.buildkite/test_areas/docker.yaml

@@ -1,16 +0,0 @@
-group: Docker
-depends_on:
-  - image-build-cpu
-steps:
-- label: Docker Build Metadata
-  timeout_in_minutes: 10
-  device: cpu-small
-  source_file_dependencies:
-    - .buildkite/release-pipeline.yaml
-    - .buildkite/scripts/docker-build-metadata-args.sh
-    - docker/Dockerfile
-    - docker/Dockerfile.cpu
-    - docker/docker-bake.hcl
-    - tests/tools/test_docker_build_metadata_args.py
-  commands:
-    - pytest -v -s tools/test_docker_build_metadata_args.py

.buildkite/test_areas/e2e_integration.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: DeepSeek V2-Lite Accuracy
-  key: deepseek-v2-lite-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -13,7 +12,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_eplb.sh 0.25 200 8010
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy
-  key: qwen3-30b-a3b-fp8-block-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -23,9 +21,8 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020
 
 - label: Qwen3-30B-A3B-FP8-block Accuracy (B200)
-  key: qwen3-30b-a3b-fp8-block-accuracy-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   working_dir: "/vllm-workspace"
@@ -33,7 +30,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
 - label: Qwen3-30B-A3B-FP8 DP4 Async EPLB Accuracy
-  key: qwen3-30b-a3b-fp8-dp4-async-eplb-accuracy
   timeout_in_minutes: 60
   device: h100
   optional: true
@@ -43,7 +39,6 @@ steps:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_dp4_async_eplb.sh 0.8 200 8050
 
 - label: DeepSeek V2-Lite Prefetch Offload Accuracy (H100)
-  key: deepseek-v2-lite-prefetch-offload-accuracy-h100
   timeout_in_minutes: 60
   device: h100
   optional: true

.buildkite/test_areas/engine.yaml

@@ -3,32 +3,19 @@ depends_on:
   - image-build
 steps:
 - label: Engine
-  key: engine
   timeout_in_minutes: 15
   device: h200_18gb
   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/
+  - vllm/
   - tests/engine
   - tests/test_sequence
   - tests/test_config
   - tests/test_logger
   - tests/test_vllm_port
-  - tests/test_jit_monitor.py
   commands:
-  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py test_jit_monitor.py
+  - pytest -v -s engine test_sequence.py test_config.py test_logger.py test_vllm_port.py
 
 - label: Engine (1 GPU)
-  key: engine-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/engine/
@@ -38,7 +25,6 @@ steps:
     - pytest -v -s v1/engine --ignore v1/engine/test_preprocess_error_handling.py
 
 - label: e2e Scheduling (1 GPU)
-  key: e2e-scheduling-1-gpu
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -48,7 +34,6 @@ steps:
     - pytest -v -s v1/e2e/general/test_async_scheduling.py
 
 - label: e2e Core (1 GPU)
-  key: e2e-core-1-gpu
   timeout_in_minutes: 30
   source_file_dependencies:
     - vllm/v1/
@@ -57,63 +42,38 @@ steps:
     - pytest -v -s v1/e2e/general --ignore v1/e2e/general/test_async_scheduling.py
 
 - label: V1 e2e (2 GPUs)
-  key: v1-e2e-2-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 2
   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
+    - vllm/
+    - tests/v1/e2e
   commands:
     # Only run tests that need exactly 2 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "tensor_parallelism"
+  mirror:
+    amd:
+      device: mi325_2
+      depends_on:
+      - image-build-amd
 
 - label: V1 e2e (4 GPUs)
-  key: v1-e2e-4-gpus
   timeout_in_minutes: 60 # TODO: Fix timeout after we have more confidence in the test stability
   optional: true
   num_devices: 4
   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
+    - vllm/
+    - tests/v1/e2e
   commands:
     # Only run tests that need 4 GPUs
     - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle_correctness_heavy"
+  mirror:
+    amd:
+      device: mi325_4
+      depends_on:
+      - image-build-amd
 
 - label: V1 e2e (4xH100)
-  key: v1-e2e-4xh100
   timeout_in_minutes: 60
   device: h100
   num_devices: 4

.buildkite/test_areas/entrypoints.yaml

@@ -2,8 +2,7 @@ group: Entrypoints
 depends_on: 
   - image-build
 steps:
-- label: Entrypoints Unit Tests
-  key: entrypoints-unit-tests
+- label: Entrypoints Unit Tests  
   timeout_in_minutes: 10
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -14,7 +13,6 @@ steps:
   - pytest -v -s entrypoints/ --ignore=entrypoints/llm --ignore=entrypoints/rpc --ignore=entrypoints/sleep --ignore=entrypoints/serve/instrumentator --ignore=entrypoints/openai --ignore=entrypoints/offline_mode --ignore=entrypoints/test_chat_utils.py  --ignore=entrypoints/pooling
 
 - label: Entrypoints Integration (LLM)
-  key: entrypoints-integration-llm
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -26,14 +24,8 @@ steps:
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
-  mirror:
-    amd:
-      device: mi300_1
-      depends_on:
-      - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 1)
-  key: entrypoints-integration-api-server-openai-part-1
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -43,10 +35,14 @@ steps:
   commands:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/openai/chat_completion --ignore=entrypoints/openai/chat_completion/test_chat_with_tool_reasoning.py --ignore=entrypoints/openai/chat_completion/test_oot_registration.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 
 - label: Entrypoints Integration (API Server openai - Part 2)
-  key: entrypoints-integration-api-server-openai-part-2
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -57,9 +53,13 @@ steps:
   - pytest -v -s entrypoints/openai/completion --ignore=entrypoints/openai/completion/test_tensorizer_entrypoint.py
   - pytest -v -s entrypoints/openai/speech_to_text/
   - pytest -v -s entrypoints/test_chat_utils.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: Entrypoints Integration (API Server openai - Part 3)
-  key: entrypoints-integration-api-server-openai-part-3
   timeout_in_minutes: 50
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -72,7 +72,6 @@ steps:
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/chat_completion --ignore=entrypoints/openai/completion --ignore=entrypoints/openai/speech_to_text/ --ignore=entrypoints/openai/correctness/ --ignore=entrypoints/openai/tool_parsers/ --ignore=entrypoints/openai/responses --ignore=entrypoints/openai/test_multi_api_servers.py
 
 - label: Entrypoints Integration (API Server 2)
-  key: entrypoints-integration-api-server-2
   timeout_in_minutes: 130
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -87,7 +86,6 @@ steps:
   - pytest -v -s tool_use
 
 - label: Entrypoints Integration (Pooling)
-  key: entrypoints-integration-pooling
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -98,7 +96,6 @@ steps:
   - pytest -v -s entrypoints/pooling
 
 - label: Entrypoints Integration (Responses API)
-  key: entrypoints-integration-responses-api
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:
@@ -108,7 +105,6 @@ steps:
   - pytest -v -s entrypoints/openai/responses
 
 - label: OpenAI API Correctness
-  key: openai-api-correctness
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/expert_parallelism.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: EPLB Algorithm
-  key: eplb-algorithm
   timeout_in_minutes: 15
   device: h200_18gb
   working_dir: "/vllm-workspace/tests"
@@ -16,7 +15,6 @@ steps:
   - pytest -v -s distributed/test_eplb_utils.py
 
 - label: EPLB Execution # 17min
-  key: eplb-execution
   timeout_in_minutes: 27
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -28,7 +26,6 @@ steps:
   - pytest -v -s distributed/test_eplb_spec_decode.py
 
 - label: Elastic EP Scaling Test
-  key: elastic-ep-scaling-test
   timeout_in_minutes: 20
   device: h100
   working_dir: "/vllm-workspace/tests"

.buildkite/test_areas/kernels.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: vLLM IR Tests
-  key: vllm-ir-tests
   timeout_in_minutes: 10
   device: h200_18gb
   working_dir: "/vllm-workspace/"
@@ -15,7 +14,6 @@ steps:
     - pytest -v -s tests/kernels/ir
 
 - label: Kernels Core Operation Test
-  key: kernels-core-operation-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -25,7 +23,6 @@ steps:
     - pytest -v -s kernels/core --ignore=kernels/core/test_minimax_reduce_rms.py  kernels/test_concat_mla_q.py
 
 - label: Kernels MiniMax Reduce RMS Test (2 GPUs)
-  key: kernels-minimax-reduce-rms-test-2-gpus
   timeout_in_minutes: 15
   num_devices: 2
   device: h100
@@ -39,7 +36,6 @@ steps:
     - pytest -v -s kernels/core/test_minimax_reduce_rms.py
 
 - label: Kernels Attention Test %N
-  key: kernels-attention-test
   timeout_in_minutes: 35
   source_file_dependencies:
   - csrc/attention/
@@ -53,7 +49,6 @@ steps:
   parallelism: 2
 
 - label: Kernels Quantization Test %N
-  key: kernels-quantization-test
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/quantization/
@@ -64,7 +59,6 @@ steps:
   parallelism: 2
 
 - label: Kernels MoE Test %N
-  key: kernels-moe-test
   timeout_in_minutes: 25
   source_file_dependencies:
   - csrc/quantization/cutlass_w8a8/moe/
@@ -80,7 +74,6 @@ steps:
   parallelism: 5
 
 - label: Kernels Mamba Test
-  key: kernels-mamba-test
   timeout_in_minutes: 45
   source_file_dependencies:
   - csrc/mamba/
@@ -89,18 +82,7 @@ steps:
   commands:
     - pytest -v -s kernels/mamba
 
-- label: Kernels KDA Test
-  timeout_in_minutes: 20
-  source_file_dependencies:
-  - vllm/model_executor/layers/fla/ops/kda.py
-  - vllm/model_executor/layers/fla/ops/chunk_delta_h.py
-  - vllm/model_executor/layers/fla/ops/l2norm.py
-  - tests/kernels/test_kda.py
-  commands:
-    - pytest -v -s kernels/test_kda.py
-
 - label: Kernels DeepGEMM Test (H100)
-  key: kernels-deepgemm-test-h100
   timeout_in_minutes: 45
   device: h100
   num_devices: 1
@@ -113,19 +95,16 @@ steps:
   - tests/kernels/moe/test_deepgemm.py
   - tests/kernels/moe/test_batched_deepgemm.py
   - tests/kernels/attention/test_deepgemm_attention.py
-  - tests/quantization/test_cutlass_w4a16.py
   commands:
     - pytest -v -s kernels/quantization/test_block_fp8.py
     - pytest -v -s kernels/moe/test_deepgemm.py
     - pytest -v -s kernels/moe/test_batched_deepgemm.py
     - pytest -v -s kernels/attention/test_deepgemm_attention.py
-    - pytest -v -s quantization/test_cutlass_w4a16.py
 
 - label: Kernels (B200)
-  key: kernels-b200
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   # optional: true
   source_file_dependencies:
   - csrc/quantization/fp4/
@@ -171,7 +150,6 @@ steps:
     - pytest -v -s tests/models/quantization/test_nvfp4.py
 
 - label: Kernels Helion Test
-  key: kernels-helion-test
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -183,7 +161,6 @@ steps:
 
  
 - label: Kernels FP8 MoE Test (1 H100)
-  key: kernels-fp8-moe-test-1-h100
   timeout_in_minutes: 90
   device: h100
   num_devices: 1
@@ -200,7 +177,6 @@ steps:
     - pytest -v -s kernels/moe/test_triton_moe_ptpc_fp8.py
 
 - label: Kernels FP8 MoE Test (2 H100s)
-  key: kernels-fp8-moe-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -210,9 +186,8 @@ steps:
     - pytest -v -s kernels/moe/test_deepep_moe.py
 
 - label: Kernels Fp4 MoE Test (B200)
-  key: kernels-fp4-moe-test-b200
   timeout_in_minutes: 60
-  device: b200-k8s
+  device: b200
   num_devices: 1
   optional: true
   commands:
@@ -223,7 +198,6 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 H100s)
-  key: kernels-fusedmoe-layer-test-2-h100s
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
@@ -240,7 +214,6 @@ steps:
 
 
 - label: Kernels FusedMoE Layer Test (2 B200s)
-  key: kernels-fusedmoe-layer-test-2-b200s
   timeout_in_minutes: 90
   device: b200
   num_devices: 2

.buildkite/test_areas/lm_eval.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: LM Eval Small Models
-  key: lm-eval-small-models
   timeout_in_minutes: 75
   source_file_dependencies:
   - csrc/
@@ -25,7 +24,6 @@ steps:
 #   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
 - label: LM Eval Large Models (4 GPUs)(H100)
-  key: lm-eval-large-models-4-gpus-h100
   device: h100
   optional: true
   num_devices: 4
@@ -38,7 +36,6 @@ steps:
     - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
 
 - label: LM Eval Small Models (B200)
-  key: lm-eval-small-models-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -49,9 +46,8 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt
 
 - label: LM Eval Qwen3.5 Models (B200)
-  key: lm-eval-qwen3-5-models-b200
   timeout_in_minutes: 120
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   source_file_dependencies:
@@ -66,7 +62,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-qwen35-blackwell.txt
 
 - label: LM Eval Large Models (H200)
-  key: lm-eval-large-models-h200
   timeout_in_minutes: 60
   device: h200
   optional: true
@@ -75,7 +70,6 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-h200.txt
 
 - label: MoE Refactor Integration Test (H100 - TEMPORARY)
-  key: moe-refactor-integration-test-h100-temporary
   device: h100
   optional: true
   num_devices: 2
@@ -83,15 +77,13 @@ steps:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-h100.txt
   
 - label: MoE Refactor Integration Test (B200 - TEMPORARY)
-  key: moe-refactor-integration-test-b200-temporary
-  device: b200-k8s
+  device: b200
   optional: true
   num_devices: 2
   commands:
     - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/moe-refactor/config-b200.txt
 
 - label: MoE Refactor Integration Test (B200 DP - TEMPORARY)
-  key: moe-refactor-integration-test-b200-dp-temporary
   device: b200
   optional: true
   num_devices: 2
@@ -100,7 +92,6 @@ steps:
 
 
 - label: LM Eval TurboQuant KV Cache
-  key: lm-eval-turboquant-kv-cache
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers/quantization/turboquant/
@@ -111,7 +102,6 @@ steps:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=evals/gsm8k/configs/models-turboquant.txt
 
 - label: GPQA Eval (GPT-OSS) (H100)
-  key: gpqa-eval-gpt-oss-h100
   timeout_in_minutes: 120
   device: h100
   optional: true
@@ -125,7 +115,6 @@ steps:
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-h100.txt
 
 - label: GPQA Eval (GPT-OSS) (B200)
-  key: gpqa-eval-gpt-oss-b200
   timeout_in_minutes: 120
   device: b200
   optional: true
@@ -137,10 +126,3 @@ steps:
   commands:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - pytest -s -v evals/gpt_oss/test_gpqa_correctness.py --config-list-file=configs/models-b200.txt
-
-- label: MRCR Eval Small Models
-  timeout_in_minutes: 30
-  source_file_dependencies:
-  - tests/evals/mrcr/
-  commands:
-    - pytest -s -v evals/mrcr/test_mrcr_correctness.py --config-list-file=evals/mrcr/configs/models-small.txt

.buildkite/test_areas/lora.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: LoRA %N
-  key: lora
   timeout_in_minutes: 30
   source_file_dependencies:
   - vllm/lora
@@ -14,12 +13,10 @@ steps:
 
 
 - label: LoRA TP (Distributed)
-  key: lora-tp-distributed
   timeout_in_minutes: 30
   num_devices: 4
   source_file_dependencies:
   - vllm/lora
-  - vllm/model_executor/layers/fused_moe/
   - tests/lora
   commands:
     # FIXIT: find out which code initialize cuda before running the test

.buildkite/test_areas/misc.yaml

@@ -3,40 +3,25 @@ depends_on:
   - image-build
 steps:
 - label: V1 Spec Decode
-  key: v1-spec-decode
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/inputs/
-    - vllm/model_executor/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/spec_decode
   commands:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
     # TODO: create another `optional` test group for slow tests
     - pytest -v -s -m 'not slow_test' v1/spec_decode
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: V1 Sample + Logits
-  key: v1-sample-logits
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/inputs/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/platforms/
-    - vllm/sampling_params.py
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/sample
     - tests/v1/logits_processors
     - tests/v1/test_oracle.py
@@ -51,31 +36,14 @@ steps:
     - pytest -v -s v1/test_outputs.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: V1 Core + KV + Metrics
-  key: v1-core-kv-metrics
   timeout_in_minutes: 30
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/entrypoints/pooling/
-    - vllm/inputs/
-    - vllm/lora/
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/outputs.py
-    - vllm/platforms/
-    - vllm/pooling_params.py
-    - vllm/profiler/
-    - vllm/sampling_params.py
-    - vllm/tokenizers/
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1/core
     - tests/v1/executor
     - tests/v1/kv_offload
@@ -96,27 +64,17 @@ steps:
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-redhat/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: V1 Others (CPU)
-  key: v1-others-cpu
   depends_on:
     - image-build-cpu
   source_file_dependencies:
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/inputs/
-    - vllm/lora/
-    - vllm/multimodal/
-    - vllm/outputs.py
-    - vllm/platforms/
-    - vllm/pooling_params.py
-    - vllm/profiler/
-    - vllm/sampling_params.py
-    - vllm/tokenizers/
-    - vllm/transformers_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/v1
   device: cpu-small
   commands:
@@ -128,21 +86,10 @@ steps:
     - pytest -v -s -m 'cpu_test' v1/metrics
 
 - label: Regression
-  key: regression
   timeout_in_minutes: 20
   device: h200_18gb
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/test_regression
   commands:
   - pip install modelscope
@@ -150,7 +97,6 @@ steps:
   working_dir: "/vllm-workspace/tests" # optional
 
 - label: Examples
-  key: examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
@@ -167,37 +113,25 @@ steps:
     - python3 basic/offline_inference/embed.py
     - python3 basic/offline_inference/score.py
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
      # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - 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
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Metrics, Tracing (2 GPUs)
-  key: metrics-tracing-2-gpus
   timeout_in_minutes: 20
   num_devices: 2
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tracing/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/v1/tracing
   commands:
   - "pip install \
@@ -208,7 +142,6 @@ steps:
   - pytest -v -s v1/tracing
 
 - label: Python-only Installation
-  key: python-only-installation
   depends_on: ~
   timeout_in_minutes: 20
   source_file_dependencies:
@@ -218,22 +151,9 @@ steps:
   - bash standalone_tests/python_only_compile.sh
 
 - label: Async Engine, Inputs, Utils, Worker
-  key: async-engine-inputs-utils-worker
   timeout_in_minutes: 50
   source_file_dependencies:
-  - vllm/assets/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/tokenizers/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/detokenizer
   - tests/multimodal
   - tests/utils_
@@ -243,35 +163,11 @@ steps:
   - pytest -v -s utils_
 
 - label: Async Engine, Inputs, Utils, Worker, Config (CPU)
-  key: async-engine-inputs-utils-worker-config-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 30
   source_file_dependencies:
-  - vllm/assets/
-  - vllm/config/
-  - vllm/engine/arg_utils.py
-  - vllm/entrypoints/chat_utils.py
-  - vllm/entrypoints/mcp/
-  - vllm/entrypoints/openai/chat_completion/protocol.py
-  - vllm/entrypoints/openai/engine/protocol.py
-  - vllm/envs.py
-  - vllm/exceptions.py
-  - vllm/inputs/
-  - vllm/model_executor/layers/quantization/quark/
-  - vllm/multimodal/
-  - vllm/outputs.py
-  - vllm/platforms/
-  - vllm/pooling_params.py
-  - vllm/ray/
-  - vllm/reasoning/
-  - vllm/renderers/
-  - vllm/sampling_params.py
-  - vllm/tokenizers/
-  - vllm/tool_parsers/
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/test_inputs.py
   - tests/test_outputs.py
   - tests/test_pooling_params.py
@@ -294,13 +190,12 @@ steps:
   - pytest -v -s -m 'cpu_test' multimodal
   - pytest -v -s renderers
   - pytest -v -s tokenizers_
-  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py
+  - pytest -v -s reasoning --ignore=reasoning/test_seedoss_reasoning_parser.py --ignore=reasoning/test_glm4_moe_reasoning_parser.py --ignore=reasoning/test_gemma4_reasoning_parser.py
   - pytest -v -s tool_parsers
   - pytest -v -s transformers_utils
   - pytest -v -s config
 
 - label: Batch Invariance (H100)
-  key: batch-invariance-h100
   timeout_in_minutes: 30
   device: h100
   source_file_dependencies:
@@ -316,9 +211,8 @@ steps:
     - VLLM_TEST_MODEL=Qwen/Qwen3-30B-A3B-Thinking-2507-FP8 pytest -v -s v1/determinism/test_batch_invariance.py::test_v1_generation_is_deterministic_across_batch_sizes_with_needle[FLASH_ATTN]
 
 - label: Batch Invariance (B200)
-  key: batch-invariance-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
     - vllm/v1/attention
     - vllm/model_executor/layers
@@ -333,7 +227,6 @@ steps:
     - pytest -v -s v1/determinism/test_nvfp4_batch_invariant.py
   
 - label: Acceptance Length Test (Large Models) # optional
-  key: acceptance-length-test-large-models
   timeout_in_minutes: 25
   gpu: h100
   optional: true

.buildkite/test_areas/model_executor.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Executor
-  key: model-executor
   timeout_in_minutes: 35
   source_file_dependencies:
   - vllm/engine/arg_utils.py

.buildkite/test_areas/model_runner_v2.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Model Runner V2 Core Tests
-  key: model-runner-v2-core-tests
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/v1/worker/gpu/
@@ -26,18 +25,16 @@ steps:
   - 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"
 
 - label: Model Runner V2 Examples
-  key: model-runner-v2-examples
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
     - vllm/v1/worker/gpu/
     - vllm/v1/core/sched/
     - vllm/v1/worker/gpu_worker.py
+    - examples/offline_inference/
     - examples/basic/offline_inference/
-    - examples/generate/multimodal/
-    - examples/features/
     - examples/pooling/embed/vision_embedding_offline.py
-    - examples/features/tensorize_vllm_model.py
+    - examples/others/tensorize_vllm_model.py
   commands:
     - set -x
     - export VLLM_USE_V2_MODEL_RUNNER=1
@@ -47,22 +44,21 @@ steps:
     #- python3 basic/offline_inference/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10  # TODO
     #- python3 basic/offline_inference/embed.py   # TODO
     # for multi-modal models
-    - python3 generate/multimodal/audio_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_offline.py --seed 0
-    - python3 generate/multimodal/vision_language_multi_image_offline.py --seed 0
-    - python3 generate/multimodal/encoder_decoder_multimodal_offline.py --model-type whisper --seed 0
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     # for pooling models
     - python3 pooling/embed/vision_embedding_offline.py --seed 0
     # for features demo
-    - python3 features/automatic_prefix_caching/prefix_caching_offline.py
-    - python3 deployment/llm_engine_example.py
-    - python3 features/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 features/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
-    - python3 features/speculative_decoding/spec_decode_offline.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
-    - 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
+    - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
 
 - label: Model Runner V2 Distributed (2 GPUs)
-  key: model-runner-v2-distributed-2-gpus
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2
@@ -83,7 +79,6 @@ steps:
     - TP_SIZE=1 DP_SIZE=2 pytest -v -s v1/distributed/test_eagle_dp.py
 
 - label: Model Runner V2 Pipeline Parallelism (4 GPUs)
-  key: model-runner-v2-pipeline-parallelism-4-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 4
@@ -99,7 +94,6 @@ steps:
     - pytest -v -s distributed/test_pp_cudagraph.py -k "not ray"
 
 - label: Model Runner V2 Spec Decode
-  key: model-runner-v2-spec-decode
   timeout_in_minutes: 30
   working_dir: "/vllm-workspace/tests"
   source_file_dependencies:

.buildkite/test_areas/models_basic.yaml

@@ -3,25 +3,10 @@ depends_on:
   - image-build
 steps:
 - label: Basic Models Tests (Initialization)
-  key: basic-models-tests-initialization
   timeout_in_minutes: 45
   torch_nightly: true
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/logging_utils/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/tasks.py
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
-  - vllm/version.py
+  - vllm/
   - tests/models/test_initialization.py
   - tests/models/registry.py
   commands:
@@ -31,7 +16,6 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Extra Initialization) %N
-  key: basic-models-tests-extra-initialization
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -47,41 +31,27 @@ steps:
     torch_nightly: {}
 
 - label: Basic Models Tests (Other)
-  key: basic-models-tests-other
   timeout_in_minutes: 45
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/inputs/
-  - vllm/logging_utils/
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/tasks.py
-  - vllm/transformers_utils/
-  - vllm/utils/
-  - vllm/v1/
-  - vllm/version.py
+  - vllm/
   - tests/models/test_terratorch.py
   - tests/models/test_transformers.py
   - tests/models/test_registry.py
   commands:
     - pytest -v -s models/test_terratorch.py models/test_transformers.py models/test_registry.py
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+
 
 - label: Basic Models Test (Other CPU) # 5min
-  key: basic-models-test-other-cpu
   depends_on:
   - image-build-cpu
   timeout_in_minutes: 10
   source_file_dependencies:
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/model_executor/
-  - vllm/platforms/
-  - vllm/utils/
+  - vllm/
   - tests/models/test_utils.py
   - tests/models/test_vision.py
   device: cpu-small
@@ -89,7 +59,6 @@ steps:
     - pytest -v -s models/test_utils.py models/test_vision.py
 
 - label: Transformers Nightly Models
-  key: transformers-nightly-models
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -100,12 +69,11 @@ steps:
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
     - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
 - label: Transformers Backward Compatibility Models Test
-  key: transformers-backward-compatibility-models-test
   working_dir: "/vllm-workspace/"
   optional: true
   soft_fail: true
@@ -115,7 +83,7 @@ steps:
     - pytest -v -s tests/models/test_transformers.py
     - pytest -v -s tests/models/multimodal/processing/
     - pytest -v -s tests/models/multimodal/test_mapping.py
-    - python3 examples/basic/offline_inference/chat.py
-    - python3 examples/generate/multimodal/vision_language_offline.py --model-type qwen2_5_vl
+    - python3 examples/offline_inference/basic/chat.py
+    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
-    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/generate/multimodal/audio_language_offline.py --model-type whisper
+    - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper

.buildkite/test_areas/models_distributed.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Distributed Model Tests (2 GPUs)
-  key: distributed-model-tests-2-gpus
   timeout_in_minutes: 50
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/models_language.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Language Models Tests (Standard)
-  key: language-models-tests-standard
   timeout_in_minutes: 25
   source_file_dependencies:
   - vllm/
@@ -16,7 +15,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Extra Standard) %N
-  key: language-models-tests-extra-standard
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/model_executor/models/
@@ -33,7 +31,6 @@ steps:
     torch_nightly: {}
 
 - label: Language Models Tests (Hybrid) %N
-  key: language-models-tests-hybrid
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/
@@ -48,17 +45,8 @@ steps:
   parallelism: 2
   mirror:
     torch_nightly: {}
-    amd:
-      device: mi300_1
-      depends_on:
-      - image-build-amd
-      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
 
 - label: Language Models Test (Extended Generation) # 80min
-  key: language-models-test-extended-generation
   timeout_in_minutes: 110
   optional: true
   source_file_dependencies:
@@ -70,9 +58,17 @@ steps:
     - 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)'
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
+      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 Test (PPL)
-  key: language-models-test-ppl
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true
@@ -83,7 +79,6 @@ steps:
     - pytest -v -s models/language/generation_ppl_test
 
 - label: Language Models Test (Extended Pooling)  # 36min
-  key: language-models-test-extended-pooling
   timeout_in_minutes: 50
   optional: true
   source_file_dependencies:
@@ -91,9 +86,13 @@ steps:
   - tests/models/language/pooling
   commands:
     - pytest -v -s models/language/pooling -m 'not core_model'
+  mirror:
+    amd:
+      device: mi325_1
+      depends_on:
+      - image-build-amd
 
 - label: Language Models Test (MTEB)
-  key: language-models-test-mteb
   timeout_in_minutes: 110
   device: h200_18gb
   optional: true

.buildkite/test_areas/models_multimodal.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: "Multi-Modal Models (Standard) 1: qwen2"
-  key: multi-modal-models-standard-1-qwen2
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -15,12 +14,11 @@ steps:
     - pytest -v -s models/multimodal/generation/test_ultravox.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 2: qwen3 + gemma"
-  key: multi-modal-models-standard-2-qwen3-gemma
   timeout_in_minutes: 45
   device: h200_18gb
   source_file_dependencies:
@@ -33,12 +31,11 @@ steps:
     - pytest -v -s models/multimodal/generation/test_vit_cudagraph.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 3: llava + qwen2_vl"
-  key: multi-modal-models-standard-3-llava-qwen2-vl
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -49,12 +46,11 @@ steps:
     - pytest -v -s models/multimodal/generation/test_qwen2_vl.py -m core_model
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: "Multi-Modal Models (Standard) 4: other + whisper"
-  key: multi-modal-models-standard-4-other-whisper
   timeout_in_minutes: 45
   source_file_dependencies:
   - vllm/
@@ -64,10 +60,14 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_common.py --ignore models/multimodal/generation/test_ultravox.py --ignore models/multimodal/generation/test_qwen2_5_vl.py --ignore models/multimodal/generation/test_qwen2_vl.py --ignore models/multimodal/generation/test_whisper.py  --ignore models/multimodal/generation/test_memory_leak.py --ignore models/multimodal/processing
     - pytest models/multimodal/generation/test_memory_leak.py -m core_model
     - 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
+      depends_on:
+      - image-build-amd
 
 - label: Multi-Modal Processor (CPU)
-  key: multi-modal-processor-cpu
-  depends_on:
+  depends_on: 
   - image-build-cpu
   timeout_in_minutes: 60
   source_file_dependencies:
@@ -80,7 +80,6 @@ steps:
     - pytest -v -s models/multimodal/processing --ignore models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Processor # 44min
-  key: multi-modal-processor
   timeout_in_minutes: 60
   device: h200_18gb
   source_file_dependencies:
@@ -92,7 +91,6 @@ steps:
     - pytest -v -s models/multimodal/processing/test_tensor_schema.py
 
 - label: Multi-Modal Accuracy Eval (Small Models) # 50min
-  key: multi-modal-accuracy-eval-small-models
   timeout_in_minutes: 70
   working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
   source_file_dependencies:
@@ -103,7 +101,6 @@ steps:
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
 
 - label: Multi-Modal Models (Extended Generation 1)
-  key: multi-modal-models-extended-generation-1
   optional: true
   source_file_dependencies:
   - vllm/
@@ -115,12 +112,11 @@ steps:
     - pytest -v -s models/multimodal/test_mapping.py
   mirror:
     amd:
-      device: mi300_1
+      device: mi325_1
       depends_on:
       - image-build-amd
 
 - label: Multi-Modal Models (Extended Generation 2)
-  key: multi-modal-models-extended-generation-2
   optional: true
   source_file_dependencies:
   - vllm/
@@ -130,7 +126,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=0) and not core_model'
 
 - label: Multi-Modal Models (Extended Generation 3)
-  key: multi-modal-models-extended-generation-3
   optional: true
   source_file_dependencies:
   - vllm/
@@ -140,7 +135,6 @@ steps:
     - pytest -v -s models/multimodal/generation/test_common.py -m 'split(group=1) and not core_model'
 
 - label: Multi-Modal Models (Extended Pooling)
-  key: multi-modal-models-extended-pooling
   optional: true
   device: h200_18gb
   source_file_dependencies:

.buildkite/test_areas/plugins.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Plugin Tests (2 GPUs)
-  key: plugin-tests-2-gpus
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.buildkite/test_areas/pytorch.yaml

@@ -3,33 +3,9 @@ depends_on:
   - image-build
 steps:
 - label: PyTorch Compilation Unit Tests
-  key: pytorch-compilation-unit-tests
   timeout_in_minutes: 10
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile
   commands:
   # Run unit tests defined directly under compile/,
@@ -42,99 +18,27 @@ steps:
   - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Unit Tests (H100)
-  key: pytorch-compilation-unit-tests-h100
   timeout_in_minutes: 30
   device: h100
   num_devices: 1
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile/h100/
   commands:
   - "find compile/h100/ -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Compilation Passes Unit Tests
-  key: pytorch-compilation-passes-unit-tests
   timeout_in_minutes: 20
   source_file_dependencies:
-    - vllm/__init__.py
-    - vllm/_aiter_ops.py
-    - vllm/_custom_ops.py
-    - vllm/compilation/
-    - vllm/config/
-    - vllm/distributed/
-    - vllm/engine/
-    - vllm/env_override.py
-    - vllm/envs.py
-    - vllm/forward_context.py
-    - vllm/inputs/
-    - vllm/ir/
-    - vllm/kernels/
-    - vllm/logger.py
-    - vllm/model_executor/
-    - vllm/multimodal/
-    - vllm/platforms/
-    - vllm/plugins/
-    - vllm/sampling_params.py
-    - vllm/sequence.py
-    - vllm/transformers_utils/
-    - vllm/triton_utils/
-    - vllm/utils/
-    - vllm/v1/
+    - vllm/
     - tests/compile/passes
   commands:
   - pytest -s -v compile/passes --ignore compile/passes/distributed
 
 - label: PyTorch Fullgraph Smoke Test
-  key: pytorch-fullgraph-smoke-test
   timeout_in_minutes: 35
   source_file_dependencies:
-  - vllm/__init__.py
-  - vllm/_aiter_ops.py
-  - vllm/_custom_ops.py
-  - vllm/compilation/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/env_override.py
-  - vllm/envs.py
-  - vllm/forward_context.py
-  - vllm/inputs/
-  - vllm/ir/
-  - vllm/kernels/
-  - vllm/logger.py
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/transformers_utils/
-  - vllm/triton_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/compile
   commands:
   # Run smoke tests under fullgraph directory, except test_full_graph.py
@@ -144,41 +48,16 @@ steps:
   - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph
-  key: pytorch-fullgraph
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
-  - vllm/__init__.py
-  - vllm/_aiter_ops.py
-  - vllm/_custom_ops.py
-  - vllm/compilation/
-  - vllm/config/
-  - vllm/distributed/
-  - vllm/engine/
-  - vllm/env_override.py
-  - vllm/envs.py
-  - vllm/forward_context.py
-  - vllm/inputs/
-  - vllm/ir/
-  - vllm/kernels/
-  - vllm/logger.py
-  - vllm/model_executor/
-  - vllm/multimodal/
-  - vllm/platforms/
-  - vllm/plugins/
-  - vllm/sampling_params.py
-  - vllm/sequence.py
-  - vllm/transformers_utils/
-  - vllm/triton_utils/
-  - vllm/utils/
-  - vllm/v1/
+  - vllm/
   - tests/compile
   commands:
     # fp8 kv scales not supported on sm89, tested on Blackwell instead
   - pytest -v -s compile/fullgraph/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
 
 - label: Pytorch Nightly Dependency Override Check # 2min
-  key: pytorch-nightly-dependency-override-check
   # if this test fails, it means the nightly torch version is not compatible with some
   # of the dependencies. Please check the error message and add the package to whitelist
   # in /vllm/tools/pre_commit/generate_nightly_torch_test.py

.buildkite/test_areas/quantization.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Quantization
-  key: quantization
   timeout_in_minutes: 90
   source_file_dependencies:
   - csrc/
@@ -22,10 +21,9 @@ steps:
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
 - label: Quantized MoE Test (B200)
-  key: quantized-moe-test-b200
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
-  device: b200-k8s
+  device: b200
   source_file_dependencies:
   - tests/quantization/test_blackwell_moe.py
   - vllm/model_executor/models/deepseek_v2.py
@@ -40,7 +38,6 @@ steps:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
 - label: Quantized Models Test
-  key: quantized-models-test
   timeout_in_minutes: 60
   source_file_dependencies:
   - vllm/model_executor/layers/quantization

.buildkite/test_areas/ray_compat.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Ray Dependency Compatibility Check
-  key: ray-dependency-compatibility-check
   # Informational only — does not block the pipeline.
   # If this fails, it means the PR introduces a dependency that
   # conflicts with Ray's dependency constraints.

.buildkite/test_areas/samplers.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Samplers Test
-  key: samplers-test
   timeout_in_minutes: 75
   source_file_dependencies:
   - vllm/model_executor/layers
@@ -11,13 +10,11 @@ steps:
   - tests/samplers
   - tests/conftest.py
   commands:
-    # VLLM_USE_FLASHINFER_SAMPLER defaults to 1 now, so we need to pin both
-    # values explicitly to still cover the PyTorch-native (Triton) path.
-    - VLLM_USE_FLASHINFER_SAMPLER=0 pytest -v -s samplers
+    - pytest -v -s samplers
     - VLLM_USE_FLASHINFER_SAMPLER=1 pytest -v -s samplers
   mirror:
     amd:
-      device: mi250_1
+      device: mi325_1
       depends_on:
       - image-build-amd
       commands:

.buildkite/test_areas/spec_decode.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Spec Decode Eagle
-  key: spec-decode-eagle
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -14,7 +13,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
 - label: Spec Decode Eagle Nightly B200
-  key: spec-decode-eagle-nightly-b200
   timeout_in_minutes: 30
   device: b200
   optional: true
@@ -26,7 +24,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "eagle_correctness"
 
 - label: Spec Decode Speculators + MTP
-  key: spec-decode-speculators-mtp
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -38,7 +35,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
 
 - label: Spec Decode Speculators + MTP Nightly B200
-  key: spec-decode-speculators-mtp-nightly-b200
   timeout_in_minutes: 30
   device: b200
   optional: true
@@ -51,7 +47,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "speculators or mtp_correctness"
   
 - label: Spec Decode Ngram + Suffix
-  key: spec-decode-ngram-suffix
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -62,7 +57,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "ngram or suffix"
 
 - label: Spec Decode Draft Model
-  key: spec-decode-draft-model
   timeout_in_minutes: 30
   device: h200_18gb
   source_file_dependencies:
@@ -73,9 +67,8 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: Spec Decode Draft Model Nightly B200
-  key: spec-decode-draft-model-nightly-b200
   timeout_in_minutes: 30
-  device: b200-k8s
+  device: b200
   optional: true
   source_file_dependencies:
     - vllm/v1/spec_decode/
@@ -85,7 +78,6 @@ steps:
     - pytest -v -s v1/e2e/spec_decode -k "draft_model or no_sync or batch_inference"
 
 - label: DFlash Speculators Correctness
-  key: dflash-speculators-correctness
   timeout_in_minutes: 30
   device: h100
   optional: true
@@ -97,16 +89,3 @@ steps:
   commands:
     - export VLLM_ALLOW_INSECURE_SERIALIZATION=1
     - pytest -v -s v1/spec_decode/test_speculators_dflash.py -m slow_test
-
-- label: Spec Decode MTP hybrid (B200)
-  timeout_in_minutes: 30
-  device: b200
-  optional: true
-  source_file_dependencies:
-    - vllm/v1/spec_decode/
-    - vllm/v1/worker/gpu/spec_decode/
-    - vllm/model_executor/models/qwen3_5.py
-    - vllm/model_executor/models/qwen3_5_mtp.py
-    - tests/v1/e2e/spec_decode/
-  commands:
-    - pytest -v -s v1/e2e/spec_decode -k "qwen3_5-hybrid"

.buildkite/test_areas/weight_loading.yaml

@@ -3,7 +3,6 @@ depends_on:
   - image-build
 steps:
 - label: Weight Loading Multiple GPU  # 33min
-  key: weight-loading-multiple-gpu
   timeout_in_minutes: 45
   working_dir: "/vllm-workspace/tests"
   num_devices: 2

.github/CODEOWNERS

@@ -6,8 +6,8 @@
 /vllm/distributed/kv_transfer @NickLucche @ApostaC @orozery @xuechendi
 /vllm/lora @jeejeelee
 /vllm/model_executor/layers/attention @LucasWilkinson @MatthewBonanni
-/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety @zyongye
-/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety @zyongye
+/vllm/model_executor/layers/fused_moe @mgoin @pavanimajety
+/vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth @yewentao256 @pavanimajety
 /vllm/model_executor/layers/mamba @tdoublep @tomeras91
 /vllm/model_executor/layers/mamba/gdn_linear_attn.py @tdoublep @ZJY0516 @vadiklyutiy
 /vllm/model_executor/layers/rotary_embedding.py @vadiklyutiy
@@ -18,8 +18,7 @@
 /vllm/kernels/helion @ProExpertProg @zou3519
 /vllm/multimodal @DarkLight1337 @ywang96 @NickLucche @tjtanaa
 /vllm/vllm_flash_attn @LucasWilkinson @MatthewBonanni
-/CMakeLists.txt @tlrmchlsmth @LucasWilkinson @Harry-Chen
-/cmake @tlrmchlsmth @LucasWilkinson @Harry-Chen
+CMakeLists.txt @tlrmchlsmth @LucasWilkinson
 
 # Any change to the VllmConfig changes can have a large user-facing impact,
 # so spam a lot of people
@@ -71,10 +70,6 @@
 /vllm/v1/worker/gpu @WoosukKwon @njhill
 /vllm/v1/worker/gpu/kv_connector.py @orozery
 
-# CI & building
-/.buildkite @Harry-Chen
-/docker/Dockerfile @Harry-Chen
-
 # Test ownership
 /.buildkite/lm-eval-harness @mgoin 
 /tests/distributed/test_multi_node_assignment.py @youkaichao
@@ -82,11 +77,11 @@
 /tests/distributed/test_same_node.py @youkaichao
 /tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @aarnphm @NickLucche
 /tests/evals @mgoin @vadiklyutiy
-/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256 @zyongye
+/tests/kernels @mgoin @tlrmchlsmth @WoosukKwon @yewentao256
 /tests/kernels/ir @ProExpertProg @tjtanaa
 /tests/models @DarkLight1337 @ywang96
 /tests/multimodal @DarkLight1337 @ywang96 @NickLucche
-/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety @zyongye
+/tests/quantization @mgoin @robertgshaw2-redhat @yewentao256 @pavanimajety
 /tests/test_inputs.py @DarkLight1337 @ywang96
 /tests/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb @aarnphm
 /tests/v1/structured_output @mgoin @russellb @aarnphm
@@ -152,12 +147,6 @@ mkdocs.yaml @hmellor
 # MTP-specific files
 /vllm/model_executor/models/deepseek_mtp.py @luccafong
 
-# DeepseekV4-specific files
-/vllm/v1/attention/ops/deepseek_v4_ops @zyongye
-/vllm/model_executor/layers/deepseek_compressor.py @zyongye
-/vllm/model_executor/layers/deepseek_v4_attention.py @zyongye
-/vllm/model_executor/layers/sparse_attn_indexer.py @zyongye
-
 # Mistral-specific files
 /vllm/model_executor/models/mistral*.py @patrickvonplaten
 /vllm/model_executor/models/mixtral*.py @patrickvonplaten

.github/mergify.yml

@@ -308,7 +308,8 @@ pull_request_rules:
       - files=benchmarks/benchmark_serving_structured_output.py
       - files=benchmarks/run_structured_output_benchmark.sh
       - files=docs/features/structured_outputs.md
-      - files=^examples/features/structured_outputs/
+      - files=examples/offline_inference/structured_outputs.py
+      - files=examples/online_serving/structured_outputs/structured_outputs.py
       - files~=^tests/v1/structured_output/
       - files=tests/entrypoints/llm/test_struct_output_generate.py
       - files~=^vllm/v1/structured_output/
@@ -324,7 +325,7 @@ pull_request_rules:
     - or:
       - files~=^vllm/v1/spec_decode/
       - files~=^tests/v1/spec_decode/
-      - files=^examples/features/speculative_decoding/
+      - files~=^examples/.*(spec_decode|mlpspeculator|eagle|speculation).*\.py
       - files~=^vllm/model_executor/models/.*eagle.*\.py
       - files=vllm/model_executor/models/mlp_speculator.py
       - files~=^vllm/transformers_utils/configs/(eagle|medusa|mlp_speculator)\.py
@@ -388,7 +389,11 @@ pull_request_rules:
       - files~=^tests/entrypoints/anthropic/.*tool.*
       - files~=^vllm/tool_parsers/
       - files=docs/features/tool_calling.md
-      - files~=^examples/tool_calling/
+      - files~=^examples/tool_chat_*
+      - files=examples/offline_inference/chat_with_tools.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools_required.py
+      - files=examples/online_serving/openai_chat_completion_tool_calls_with_reasoning.py
+      - files=examples/online_serving/openai_chat_completion_client_with_tools.py
   actions:
     label:
       add:
@@ -477,7 +482,9 @@ pull_request_rules:
   conditions:
     - label != stale
     - or:
-      - files~=^examples/disaggregated/
+      - files~=^examples/online_serving/disaggregated[^/]*/.*
+      - files~=^examples/offline_inference/disaggregated[^/]*/.*
+      - files~=^examples/others/lmcache/
       - files~=^tests/v1/kv_connector/
       - files~=^vllm/distributed/kv_transfer/
       - title~=(?i)\bP/?D\b

.github/workflows/pre-commit.yml

@@ -16,7 +16,11 @@ permissions:
 
 jobs:
   pre-run-check:
-    if: github.event_name == 'pull_request'
+    if: >-
+      github.event_name == 'pull_request' &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified')
     runs-on: ubuntu-latest
     steps:
     - name: Check PR label and author merge count
@@ -45,7 +49,12 @@ jobs:
 
   pre-commit:
     needs: pre-run-check
-    if: always() && (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
+    if: >-
+      always() &&
+      (github.event.action != 'labeled' ||
+       github.event.label.name == 'ready' ||
+       github.event.label.name == 'verified') &&
+      (needs.pre-run-check.result == 'success' || needs.pre-run-check.result == 'skipped')
     runs-on: ubuntu-latest
     steps:
     - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1

.gitignore

@@ -237,7 +237,6 @@ ep_kernels_workspace/
 
 # Allow tracked library source folders under submodules (e.g., benchmarks/lib)
 !vllm/benchmarks/lib/
-!.buildkite/scripts/lib/
 
 # Generated gRPC protobuf files (compiled at build time from vllm_engine.proto)
 vllm/grpc/vllm_engine_pb2.py

.pre-commit-config.yaml

@@ -131,16 +131,6 @@ repos:
         --python-version, "3.12",
       ]
       files: ^requirements/(common|xpu|test/xpu)\.(in|txt)$
-    - id: pip-compile
-      alias: pip-compile-docs
-      name: pip-compile-docs
-      args: [
-        requirements/docs.in,
-        -o, requirements/docs.txt,
-        --python-platform, x86_64-manylinux_2_28,
-        --python-version, "3.12",
-      ]
-      files: ^requirements/docs\.(in|txt)$
 - repo: local
   hooks:
   - id: format-torch-nightly-test

.readthedocs.yaml

@@ -9,44 +9,7 @@ build:
     python: "3.12"
   jobs:
     post_checkout:
-      - |
-        if [ "$READTHEDOCS_VERSION_TYPE" = "external" ]; then
-          MAX_WAIT=300
-          INTERVAL=60
-          ELAPSED=0
-          while :; do
-            RAW=$(curl -sS -w "\n%{http_code}" "https://api.github.com/repos/vllm-project/vllm/commits/${READTHEDOCS_GIT_COMMIT_HASH}/check-runs?check_name=pre-run-check&filter=latest")
-            HTTP_CODE=$(printf %s "$RAW" | tail -n1)
-            BODY=$(printf %s "$RAW" | head -n -1)
-            if [ "$HTTP_CODE" != "200" ]; then
-              echo "GitHub API returned HTTP $HTTP_CODE (likely rate-limited); skipping pre-run-check gate."
-              break
-            fi
-            STATUS=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"status\") or \"\") if r else \"none\")")
-            CONCLUSION=$(printf %s "$BODY" | python3 -c "import sys, json; r=json.load(sys.stdin).get(\"check_runs\",[]); print((r[0].get(\"conclusion\") or \"\") if r else \"\")")
-            if [ "$STATUS" = "none" ]; then
-              echo "no pre-run-check found for this commit; skipping gate."
-              break
-            fi
-            if [ -n "$CONCLUSION" ]; then
-              echo "pre-run-check conclusion: $CONCLUSION"
-              if [ "$CONCLUSION" = "failure" ] || [ "$CONCLUSION" = "cancelled" ] || [ "$CONCLUSION" = "timed_out" ]; then
-                echo "pre-run-check did not pass; failing docs build."
-                exit 1
-              fi
-              break
-            fi
-            if [ "$ELAPSED" -ge "$MAX_WAIT" ]; then
-              echo "pre-run-check status=$STATUS after ${MAX_WAIT}s; skipping gate."
-              break
-            fi
-            echo "pre-run-check status=$STATUS; waiting ${INTERVAL}s..."
-            sleep "$INTERVAL"
-            ELAPSED=$((ELAPSED + INTERVAL))
-          done
-        else
-          echo "Not a PR build (version type=$READTHEDOCS_VERSION_TYPE); skipping pre-run-check gate."
-        fi
+      # - bash docs/maybe_skip_pr_build.sh
       - git fetch origin main --unshallow --no-tags --filter=blob:none || true
     pre_create_environment:
       - pip install uv

CMakeLists.txt

@@ -13,12 +13,8 @@ cmake_minimum_required(VERSION 3.26)
 # cmake --install . --component _C
 project(vllm_extensions LANGUAGES CXX)
 
-set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CUDA_STANDARD 20)
-set(CMAKE_CUDA_STANDARD_REQUIRED ON)
-set(CMAKE_HIP_STANDARD 20)
-set(CMAKE_HIP_STANDARD_REQUIRED ON)
 
 
 # CUDA by default, can be overridden by using -DVLLM_TARGET_DEVICE=... (used by setup.py)
@@ -311,12 +307,12 @@ set(VLLM_EXT_SRC
   "csrc/quantization/activation_kernels.cu"
   "csrc/cuda_utils_kernels.cu"
   "csrc/custom_all_reduce.cu"
-  "csrc/torch_bindings.cpp"
-  "csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
+  "csrc/torch_bindings.cpp")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_EXT_SRC
-    "csrc/minimax_reduce_rms_kernel.cu")
+    "csrc/minimax_reduce_rms_kernel.cu"
+    "csrc/fused_deepseek_v4_qnorm_rope_kv_insert_kernel.cu")
 
   SET(CUTLASS_ENABLE_HEADERS_ONLY ON CACHE BOOL "Enable only the header library")
 
@@ -1051,13 +1047,14 @@ endif()
 set(VLLM_MOE_EXT_SRC
   "csrc/moe/torch_bindings.cpp"
   "csrc/moe/moe_align_sum_kernels.cu"
-  "csrc/moe/topk_softmax_kernels.cu"
-  "csrc/moe/topk_softplus_sqrt_kernels.cu")
+  "csrc/moe/topk_softmax_kernels.cu")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
   list(APPEND VLLM_MOE_EXT_SRC
     "csrc/moe/moe_wna16.cu"
-    "csrc/moe/grouped_topk_kernels.cu")
+    "csrc/moe/grouped_topk_kernels.cu"
+    "csrc/moe/router_gemm.cu"
+    "csrc/moe/topk_softplus_sqrt_kernels.cu")
 endif()
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")

benchmarks/kernels/benchmark_vit_fp8_attn.py

@@ -1,324 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-# Benchmarks FP8 vs BF16 ViT attention via FlashInfer cuDNN backend.
-#
-# == Usage Examples ==
-#
-# Benchmark mode (default, FlashInfer CUDAGraph Bench)
-#   python3 benchmark_vit_fp8_attn.py
-#
-# Profile mode (PyTorch profiler, saves TensorBoard traces):
-#   python3 benchmark_vit_fp8_attn.py --profile
-#   python3 benchmark_vit_fp8_attn.py --profile --profile-output-dir ./profile_traces
-#
-# Custom seq_lens:
-#   python3 benchmark_vit_fp8_attn.py --seq-lens 4096 8192 16384
-
-from functools import partial
-
-import numpy as np
-import torch
-from torch.profiler import ProfilerActivity, profile, record_function
-
-from vllm.utils.argparse_utils import FlexibleArgumentParser
-
-# Qwen3-VL defaults
-NUM_HEADS = 16
-HEAD_DIM = 72
-DEFAULT_SEQ_LENS = [2304, 4096, 8192, 16384]
-
-
-def _setup_fp8_attention(num_heads: int, head_dim: int) -> tuple:
-    """Create FP8 and BF16 attention modules + workspace."""
-    from types import SimpleNamespace
-    from unittest.mock import patch
-
-    from vllm.config import VllmConfig, set_current_vllm_config
-    from vllm.config.multimodal import MultiModalConfig
-    from vllm.model_executor.layers.attention.mm_encoder_attention import (
-        MMEncoderAttention,
-        _get_flashinfer_workspace_buffer,
-    )
-    from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-    old_dtype = torch.get_default_dtype()
-    torch.set_default_dtype(torch.bfloat16)
-
-    backend_patch = patch(
-        "vllm.model_executor.layers.attention.mm_encoder_attention"
-        ".get_vit_attn_backend",
-        return_value=AttentionBackendEnum.FLASHINFER,
-    )
-
-    # FP8 attention
-    mm_config_fp8 = MultiModalConfig(mm_encoder_attn_dtype="fp8")
-    vllm_config_fp8 = VllmConfig()
-    vllm_config_fp8.model_config = SimpleNamespace(multimodal_config=mm_config_fp8)
-    with set_current_vllm_config(vllm_config_fp8), backend_patch:
-        attn_fp8 = MMEncoderAttention(
-            num_heads=num_heads,
-            head_size=head_dim,
-            prefix="visual.blocks.0.attn",
-        ).to("cuda")
-
-    # BF16 attention (no FP8)
-    with set_current_vllm_config(VllmConfig()), backend_patch:
-        attn_bf16 = MMEncoderAttention(
-            num_heads=num_heads,
-            head_size=head_dim,
-            prefix="visual.blocks.0.attn",
-        ).to("cuda")
-
-    torch.set_default_dtype(old_dtype)
-
-    workspace = _get_flashinfer_workspace_buffer()
-    return attn_fp8, attn_bf16, workspace
-
-
-def _build_meta(
-    seq_len: int,
-    num_heads: int,
-    head_dim: int,
-    fp8: bool,
-):
-    """Build cu_seqlens, max_seqlen, sequence_lengths."""
-    from vllm.model_executor.layers.attention.mm_encoder_attention import (
-        MMEncoderAttention,
-    )
-    from vllm.utils.math_utils import round_up
-    from vllm.v1.attention.backends.registry import AttentionBackendEnum
-
-    cu_np = np.array([0, seq_len], dtype=np.int32)
-    fp8_padded = num_heads * round_up(head_dim, 16) if fp8 else None
-
-    seq_lengths = MMEncoderAttention.maybe_compute_seq_lens(
-        AttentionBackendEnum.FLASHINFER, cu_np, torch.device("cuda")
-    )
-    max_seqlen = torch.tensor(
-        MMEncoderAttention.compute_max_seqlen(AttentionBackendEnum.FLASHINFER, cu_np),
-        dtype=torch.int32,
-    )
-    cu_seqlens = MMEncoderAttention.maybe_recompute_cu_seqlens(
-        AttentionBackendEnum.FLASHINFER,
-        cu_np,
-        num_heads * head_dim,
-        1,
-        torch.device("cuda"),
-        fp8_padded_hidden_size=fp8_padded,
-    )
-    return cu_seqlens, max_seqlen, seq_lengths
-
-
-def run_benchmark(
-    seq_lens: list[int],
-    num_heads: int,
-    head_dim: int,
-    method: str,
-):
-    """Benchmark FP8 vs BF16 attention across seq_lens.
-
-    Uses FlashInfer GPU-level timing to measure pure kernel time,
-    excluding CPU launch overhead.
-    """
-    if method == "cupti":
-        from flashinfer.testing import bench_gpu_time_with_cupti as bench_fn
-
-        bench_fn = partial(bench_fn, use_cuda_graph=True, cold_l2_cache=False)
-    elif method == "cudagraph":
-        from flashinfer.testing import (
-            bench_gpu_time_with_cudagraph as bench_fn,
-        )
-
-        bench_fn = partial(bench_fn, cold_l2_cache=False)
-    else:
-        raise ValueError(f"Invalid method: {method}")
-
-    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
-
-    print(f"Timing method: {method}")
-    print(f"{'seq_len':>8} {'BF16 (us)':>12} {'FP8 (us)':>12} {'Speedup':>10}")
-    print("-" * 46)
-
-    for seq_len in seq_lens:
-        torch.manual_seed(42)
-
-        q = torch.randn(
-            seq_len,
-            num_heads,
-            head_dim,
-            device="cuda",
-            dtype=torch.bfloat16,
-        )
-        k = torch.randn_like(q)
-        v = torch.randn_like(q)
-
-        cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
-        # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
-        cu_bf16 = cu_fp8.clone()
-
-        def bf16_fn(q=q, k=k, v=v, cu=cu_bf16, ms=max_s, sl=seq_l):
-            attn_bf16._forward_flashinfer(q, k, v, cu, ms, sl)
-
-        def fp8_fn(q=q, k=k, v=v, cu=cu_fp8, ms=max_s, sl=seq_l):
-            attn_fp8._forward_flashinfer(q, k, v, cu, ms, sl)
-
-        # bench_fn returns List[float] of per-iteration times in ms
-        bf16_times = bench_fn(bf16_fn)
-        fp8_times = bench_fn(fp8_fn)
-
-        bf16_us = np.median(bf16_times) * 1e3  # ms -> us
-        fp8_us = np.median(fp8_times) * 1e3
-        speedup = bf16_us / fp8_us if fp8_us > 0 else float("inf")
-
-        print(f"{seq_len:>8} {bf16_us:>12.1f} {fp8_us:>12.1f} {speedup:>9.2f}x")
-
-
-def _make_trace_handler(output_dir: str, worker_name: str, label: str):
-    """Create a trace handler that saves to TensorBoard and prints summary."""
-
-    def handler(prof):
-        torch.profiler.tensorboard_trace_handler(output_dir, worker_name)(prof)
-        print(f"\n{'=' * 80}")
-        print(label)
-        print(f"{'=' * 80}")
-        print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=20))
-
-    return handler
-
-
-def run_profile(
-    seq_len: int,
-    num_heads: int,
-    head_dim: int,
-    warmup: int,
-    output_dir: str,
-):
-    """Profile FP8 vs BF16 attention with PyTorch profiler."""
-    attn_fp8, attn_bf16, workspace = _setup_fp8_attention(num_heads, head_dim)
-
-    torch.manual_seed(42)
-    q = torch.randn(
-        seq_len,
-        num_heads,
-        head_dim,
-        device="cuda",
-        dtype=torch.bfloat16,
-    )
-    k = torch.randn_like(q)
-    v = torch.randn_like(q)
-
-    cu_fp8, max_s, seq_l = _build_meta(seq_len, num_heads, head_dim, fp8=True)
-    # we can reuse cu_fp8 for cu_bf16 since q, k, and v are contiguous
-    cu_bf16 = cu_fp8.clone()
-
-    sched = torch.profiler.schedule(wait=0, warmup=warmup, active=1)
-
-    # Profile BF16 (warmup handled by profiler schedule)
-    with profile(
-        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-        schedule=sched,
-        on_trace_ready=_make_trace_handler(
-            output_dir,
-            f"bf16_h{head_dim}_s{seq_len}",
-            f"BF16 Attention (seq_len={seq_len}, heads={num_heads}, "
-            f"head_dim={head_dim})",
-        ),
-    ) as prof_bf16:
-        for _ in range(warmup + 1):
-            with record_function("bf16_attention"):
-                attn_bf16._forward_flashinfer(
-                    q.clone(), k.clone(), v.clone(), cu_bf16, max_s, seq_l
-                )
-                torch.accelerator.synchronize()
-            prof_bf16.step()
-
-    # Profile FP8 (warmup handled by profiler schedule)
-    with profile(
-        activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-        schedule=sched,
-        on_trace_ready=_make_trace_handler(
-            output_dir,
-            f"fp8_h{head_dim}_s{seq_len}",
-            f"FP8 Attention (seq_len={seq_len}, heads={num_heads}, "
-            f"head_dim={head_dim})",
-        ),
-    ) as prof_fp8:
-        for _ in range(warmup + 1):
-            with record_function("fp8_attention"):
-                attn_fp8._forward_flashinfer(
-                    q.clone(), k.clone(), v.clone(), cu_fp8, max_s, seq_l
-                )
-                torch.accelerator.synchronize()
-            prof_fp8.step()
-
-    print(f"\nTensorBoard traces saved to: {output_dir}")
-    print(f"View with: tensorboard --logdir={output_dir}")
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(description="Benchmark FP8 vs BF16 ViT attention.")
-    parser.add_argument(
-        "--seq-lens",
-        type=int,
-        nargs="+",
-        default=DEFAULT_SEQ_LENS,
-        help="Sequence lengths to benchmark",
-    )
-    parser.add_argument(
-        "--num-heads",
-        type=int,
-        default=NUM_HEADS,
-    )
-    parser.add_argument(
-        "--head-dim",
-        type=int,
-        default=HEAD_DIM,
-    )
-    parser.add_argument(
-        "--method",
-        choices=["cupti", "cudagraph"],
-        default="cudagraph",
-        help="GPU timing method: cupti (CUPTI kernel timing) or "
-        "cudagraph (CUDA graph capture/replay). Default: cudagraph",
-    )
-    parser.add_argument(
-        "--warmup",
-        type=int,
-        default=10,
-        help="Warmup iterations (profile mode only)",
-    )
-    parser.add_argument(
-        "--profile",
-        action="store_true",
-        help="Run PyTorch profiler instead of benchmark",
-    )
-    parser.add_argument(
-        "--profile-seq-len",
-        type=int,
-        default=8192,
-        help="Sequence length for profiling (default: 8192)",
-    )
-    parser.add_argument(
-        "--profile-output-dir",
-        type=str,
-        default="./profile_traces",
-        help="Output directory for TensorBoard traces (default: ./profile_traces)",
-    )
-    args = parser.parse_args()
-
-    if args.profile:
-        run_profile(
-            args.profile_seq_len,
-            args.num_heads,
-            args.head_dim,
-            args.warmup,
-            args.profile_output_dir,
-        )
-    else:
-        run_benchmark(
-            args.seq_lens,
-            args.num_heads,
-            args.head_dim,
-            args.method,
-        )

benchmarks/multi_turn/benchmark_serving_multi_turn.py

@@ -217,7 +217,6 @@ async def send_request(
     min_tokens: int | None = None,
     max_tokens: int | None = None,
     timeout_sec: int = 120,
-    conversation_id: str | None = None,
 ) -> ServerResponse:
     payload = {
         "model": model,
@@ -226,9 +225,6 @@ async def send_request(
         "temperature": 0.0,
     }
 
-    if conversation_id is not None:
-        payload["conversation_id"] = conversation_id
-
     if stream:
         payload["stream"] = True
         payload["stream_options"] = {"include_usage": False}
@@ -423,7 +419,6 @@ async def send_turn(
         min_tokens,
         max_tokens,
         req_args.timeout_sec,
-        conversation_id=conv_id,
     )
 
     if response.valid is False:
@@ -1473,12 +1468,6 @@ async def main() -> None:
         "(for example: --warmup-percentages=0%%,50%%)",
     )
 
-    parser.add_argument(
-        "--trust-remote-code",
-        action="store_true",
-        help="Trust remote code when loading the tokenizer.",
-    )
-
     args = parser.parse_args()
 
     logger.info(args)
@@ -1521,9 +1510,7 @@ async def main() -> None:
     np.random.seed(args.seed)
 
     logger.info("Loading tokenizer")
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.model, trust_remote_code=args.trust_remote_code
-    )
+    tokenizer = AutoTokenizer.from_pretrained(args.model)
 
     await get_server_info(args.url)
 

cmake/cpu_extension.cmake

@@ -1,7 +1,7 @@
 include(FetchContent)
 
 set(CMAKE_CXX_STANDARD_REQUIRED ON)
-set(CMAKE_CXX_STANDARD 20)
+set(CMAKE_CXX_STANDARD 17)
 set(CMAKE_CXX_EXTENSIONS ON)
 set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
 
@@ -32,23 +32,18 @@ else()
         "-DVLLM_CPU_EXTENSION")
 
     # locate PyTorch's libgomp (e.g. site-packages/torch.libs/libgomp-947d5fa1.so.1.0.0)
-    # and create a local shim dir with it. When PyTorch is built from source or packaged
-    # by a distro (common on RISC-V, s390x, Fedora/RHEL aarch64), no vendored libgomp
-    # exists and the shim dir is empty; fall back to the system libgomp in that case.
+    # and create a local shim dir with it
     vllm_prepare_torch_gomp_shim(VLLM_TORCH_GOMP_SHIM_DIR)
 
-    if(VLLM_TORCH_GOMP_SHIM_DIR)
-        find_library(OPEN_MP
-            NAMES gomp
-            PATHS "${VLLM_TORCH_GOMP_SHIM_DIR}"
-            NO_DEFAULT_PATH
-            REQUIRED
-        )
-        # Use the same libgomp as PyTorch at runtime
+    find_library(OPEN_MP
+        NAMES gomp
+        PATHS ${VLLM_TORCH_GOMP_SHIM_DIR}
+        NO_DEFAULT_PATH
+        REQUIRED
+    )
+    # Set LD_LIBRARY_PATH to include the shim dir at build time to use the same libgomp as PyTorch
+    if (OPEN_MP)
         set(ENV{LD_LIBRARY_PATH} "${VLLM_TORCH_GOMP_SHIM_DIR}:$ENV{LD_LIBRARY_PATH}")
-    else()
-        # Fall back to system / toolchain libgomp
-        find_library(OPEN_MP NAMES gomp REQUIRED)
     endif()
 endif()
 
@@ -326,6 +321,14 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
     set(ONEDNN_VERBOSE "ON")
     set(CMAKE_POLICY_DEFAULT_CMP0077 NEW)
 
+    # TODO: Refactor this
+    if (ENABLE_X86_ISA)
+        # Note: only enable oneDNN for AVX512
+        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS_AVX512})
+    else()
+        list(APPEND DNNL_COMPILE_FLAGS ${CXX_COMPILE_FLAGS})
+    endif()
+
     set(VLLM_BUILD_TYPE ${CMAKE_BUILD_TYPE})
     set(CMAKE_BUILD_TYPE "Release") # remove oneDNN debug symbols to reduce size
     FetchContent_MakeAvailable(oneDNN)
@@ -338,14 +341,8 @@ if (ENABLE_X86_ISA OR (ASIMD_FOUND AND NOT APPLE_SILICON_FOUND) OR POWER9_FOUND
         PRIVATE ${oneDNN_SOURCE_DIR}/src
     )
     target_link_libraries(dnnl_ext dnnl torch)
-    if (ENABLE_X86_ISA)
-        target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS_AVX2} -fPIC)
-    else()
-        target_compile_options(dnnl_ext PRIVATE ${CXX_COMPILE_FLAGS} -fPIC)
-    endif()
+    target_compile_options(dnnl_ext PRIVATE ${DNNL_COMPILE_FLAGS} -fPIC)
     list(APPEND LIBS dnnl_ext)
-
-
     set(USE_ONEDNN ON)
 else()
     set(USE_ONEDNN OFF)
@@ -409,15 +406,12 @@ endif()
 
 if (ENABLE_X86_ISA)
     set(VLLM_EXT_SRC_SGL
-        "csrc/cpu/sgl-kernels/fla.cpp"
-        "csrc/cpu/sgl-kernels/conv.cpp"
         "csrc/cpu/sgl-kernels/gemm.cpp"
         "csrc/cpu/sgl-kernels/gemm_int8.cpp"
         "csrc/cpu/sgl-kernels/gemm_fp8.cpp"
         "csrc/cpu/sgl-kernels/gemm_int4.cpp"
         "csrc/cpu/sgl-kernels/moe.cpp"
         "csrc/cpu/sgl-kernels/moe_int8.cpp"
-        "csrc/cpu/sgl-kernels/moe_int4.cpp"
         "csrc/cpu/sgl-kernels/moe_fp8.cpp")
 
     set(VLLM_EXT_SRC_AVX512
@@ -436,11 +430,10 @@ if (ENABLE_X86_ISA)
         "csrc/cpu/pos_encoding.cpp"
         "csrc/moe/dynamic_4bit_int_moe_cpu.cpp") 
 
-    set(VLLM_EXT_SRC_AVX2
+    set(VLLM_EXT_SRC_AVX2 
         "csrc/cpu/utils.cpp"
         "csrc/cpu/spec_decode_utils.cpp"
         "csrc/cpu/cpu_attn.cpp"
-        "csrc/cpu/dnnl_kernels.cpp"
         "csrc/cpu/torch_bindings.cpp"
         # TODO: Remove these files
         "csrc/cpu/activation.cpp"
@@ -455,7 +448,7 @@ if (ENABLE_X86_ISA)
 
     set(_C_LIBS numa dnnl_ext)
     set(_C_AVX512_LIBS numa dnnl_ext)
-    set(_C_AVX2_LIBS numa dnnl_ext)
+    set(_C_AVX2_LIBS numa)
 
     # AMX + AVX512F + AVX512BF16 + AVX512VNNI
     define_extension_target(

cmake/external_projects/deepgemm.cmake

@@ -76,6 +76,7 @@ if(DEEPGEMM_ARCHS)
     "${deepgemm_SOURCE_DIR}/third-party/fmt/include")
 
   target_compile_options(_deep_gemm_C PRIVATE
+    $<$<COMPILE_LANGUAGE:CXX>:-std=c++17>
     $<$<COMPILE_LANGUAGE:CXX>:-O3>
     $<$<COMPILE_LANGUAGE:CXX>:-Wno-psabi>
     $<$<COMPILE_LANGUAGE:CXX>:-Wno-deprecated-declarations>)

csrc/cache.h

@@ -12,8 +12,7 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
 
 void swap_blocks_batch(const torch::Tensor& src_ptrs,
                        const torch::Tensor& dst_ptrs,
-                       const torch::Tensor& sizes,
-                       bool is_src_access_order_any);
+                       const torch::Tensor& sizes);
 
 void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
                        torch::Tensor& key_cache, torch::Tensor& value_cache,

csrc/cache_kernels.cu

@@ -77,8 +77,7 @@ void swap_blocks(torch::Tensor& src, torch::Tensor& dst,
 
 void swap_blocks_batch(const torch::Tensor& src_ptrs,
                        const torch::Tensor& dst_ptrs,
-                       const torch::Tensor& sizes,
-                       bool is_src_access_order_any) {
+                       const torch::Tensor& sizes) {
   TORCH_CHECK(src_ptrs.device().is_cpu(), "src_ptrs must be on CPU");
   TORCH_CHECK(dst_ptrs.device().is_cpu(), "dst_ptrs must be on CPU");
   TORCH_CHECK(sizes.device().is_cpu(), "sizes must be on CPU");
@@ -98,13 +97,13 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
 
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
-  // Use cuMemcpyBatchAsync / hipMemcpyBatchAsync to submit all copies in a
-  // single driver call, amortizing per-copy submission overhead. int64_t
-  // and CUdeviceptr/void*/size_t are all 8 bytes on 64-bit platforms, so we
-  // reinterpret_cast the tensor data directly to avoid copies.
+  // Use cuMemcpyBatchAsync (CUDA 12.8+) to submit all copies in a single
+  // driver call, amortizing per-copy submission overhead.
+  // int64_t and CUdeviceptr/size_t are both 8 bytes on 64-bit platforms,
+  // so we reinterpret_cast the tensor data directly to avoid copies.
+  static_assert(sizeof(CUdeviceptr) == sizeof(int64_t));
   static_assert(sizeof(size_t) == sizeof(int64_t));
 #if !defined(USE_ROCM) && defined(CUDA_VERSION) && CUDA_VERSION >= 12080
-  static_assert(sizeof(CUdeviceptr) == sizeof(int64_t));
   // Resolve cuMemcpyBatchAsync at runtime via cuGetProcAddress so that
   // binaries compiled with CUDA 12.8+ still work on older drivers, and
   // we avoid the CUDA 13.0 header remapping (#define to _v2 signature).
@@ -125,12 +124,7 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
 
   if (batch_fn != nullptr) {
     CUmemcpyAttributes attr = {};
-    // ANY lets the DMA engine prefetch source bytes out of stream order,
-    // which is only safe when no GPU stream is concurrently writing the
-    // source.
-    attr.srcAccessOrder = is_src_access_order_any
-                              ? CU_MEMCPY_SRC_ACCESS_ORDER_ANY
-                              : CU_MEMCPY_SRC_ACCESS_ORDER_STREAM;
+    attr.srcAccessOrder = CU_MEMCPY_SRC_ACCESS_ORDER_STREAM;
     size_t attrs_idx = 0;
     size_t fail_idx = 0;
     CUresult result = batch_fn(reinterpret_cast<CUdeviceptr*>(dst_data),
@@ -140,30 +134,12 @@ void swap_blocks_batch(const torch::Tensor& src_ptrs,
                                &fail_idx, static_cast<CUstream>(stream));
     TORCH_CHECK(result == CUDA_SUCCESS, "cuMemcpyBatchAsync failed at index ",
                 fail_idx, " with error ", result);
-    return;
-  }
-#elif defined(USE_ROCM) && defined(HIP_VERSION) && HIP_VERSION >= 70100000
-  // ROCm 7.1+ exposes hipMemcpyBatchAsync. The 7.2.1 implementation early-
-  // returns hipErrorNotSupported whenever numAttrs > 0 (see ROCm/clr @
-  // rocm-7.2.1 hipamd/src/hip_memory.cpp:2819-2822), so call with
-  // numAttrs=0.
-  {
-    hipMemcpyAttributes attr = {};
-    size_t attrs_idx = 0;
-    size_t fail_idx = 0;
-    hipError_t result = hipMemcpyBatchAsync(
-        reinterpret_cast<void**>(dst_data), reinterpret_cast<void**>(src_data),
-        reinterpret_cast<size_t*>(size_data), static_cast<size_t>(n), &attr,
-        &attrs_idx, 0, &fail_idx, static_cast<hipStream_t>(stream));
-    TORCH_CHECK(result == hipSuccess, "hipMemcpyBatchAsync failed at index ",
-                fail_idx, " with error ", result);
-    return;
-  }
+  } else
 #endif
   {
-    // Fallback for CUDA < 12.8, older CUDA drivers, and ROCm < 7.1:
-    // individual async copies. cudaMemcpyDefault lets the driver infer
-    // direction from pointer types.
+    // Fallback for CUDA < 12.8, older drivers, and ROCm:
+    // individual async copies.
+    // cudaMemcpyDefault lets the driver infer direction from pointer types.
     for (int64_t i = 0; i < n; i++) {
       cudaMemcpyAsync(reinterpret_cast<void*>(dst_data[i]),
                       reinterpret_cast<void*>(src_data[i]),

csrc/cpu/cpu_attn.cpp

@@ -1,16 +1,5 @@
 #include "cpu_attn_dispatch_generated.h"
 
-// Maps kv_cache_dtype string to Fp8KVCacheDataType enum.
-// "auto" -> kAuto(0); "fp8"/"fp8_e4m3" -> kFp8E4M3; "fp8_e5m2" -> kFp8E5M2.
-static inline cpu_attention::Fp8KVCacheDataType parse_fp8_kv_dtype(
-    const std::string& kv_cache_dtype) {
-  if (kv_cache_dtype == "fp8_e5m2")
-    return cpu_attention::Fp8KVCacheDataType::kFp8E5M2;
-  if (kv_cache_dtype == "fp8_e4m3" || kv_cache_dtype == "fp8")
-    return cpu_attention::Fp8KVCacheDataType::kFp8E4M3;
-  return cpu_attention::Fp8KVCacheDataType::kAuto;
-}
-
 torch::Tensor get_scheduler_metadata(
     const int64_t num_req, const int64_t num_heads_q,
     const int64_t num_heads_kv, const int64_t head_dim,
@@ -29,8 +18,6 @@ torch::Tensor get_scheduler_metadata(
     isa = cpu_attention::ISA::NEON;
   } else if (isa_hint == "vxe") {
     isa = cpu_attention::ISA::VXE;
-  } else if (isa_hint == "vsx") {
-    isa = cpu_attention::ISA::VSX;
   } else {
     TORCH_CHECK(false, "Unsupported CPU attention ISA hint: " + isa_hint);
   }
@@ -62,7 +49,7 @@ torch::Tensor get_scheduler_metadata(
   input.enable_kv_split = enable_kv_split;
 
   VLLM_DISPATCH_FLOATING_TYPES(dtype, "get_scheduler_metadata", [&]() {
-    CPU_ATTN_DISPATCH(head_dim, isa, 0, [&]() {
+    CPU_ATTN_DISPATCH(head_dim, isa, [&]() {
       input.elem_size = sizeof(scalar_t);
       input.q_buffer_elem_size = sizeof(attn_impl::q_buffer_t);
       input.logits_buffer_elem_size = sizeof(attn_impl::logits_buffer_t);
@@ -85,9 +72,7 @@ void cpu_attn_reshape_and_cache(
         key_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
     torch::Tensor&
         value_cache,  // [num_blocks, num_kv_heads, block_size, head_size]
-    const torch::Tensor& slot_mapping, const std::string& isa,
-    const double k_scale = 1.0, const double v_scale = 1.0,
-    const std::string& kv_cache_dtype = "auto") {
+    const torch::Tensor& slot_mapping, const std::string& isa) {
   TORCH_CHECK_EQ(key.dim(), 3);
   TORCH_CHECK_EQ(value.dim(), 3);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
@@ -95,30 +80,18 @@ void cpu_attn_reshape_and_cache(
   TORCH_CHECK_EQ(key.stride(2), 1);
   TORCH_CHECK_EQ(value.stride(2), 1);
 
-  const int64_t kv_cache_idx =
-      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
-  const bool is_fp8 = (kv_cache_idx != 0);
-
-  if (is_fp8) {
-    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
-                "key_cache must be uint8 for FP8 path");
-    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
-                "value_cache must be uint8 for FP8 path");
-    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
-    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
-  }
-
-  const float k_inv = is_fp8 ? 1.0f / static_cast<float>(k_scale) : 0.0f;
-  const float v_inv = is_fp8 ? 1.0f / static_cast<float>(v_scale) : 0.0f;
-
   const int64_t token_num = key.size(0);
-  const int64_t head_num = key.size(1);
-  const int64_t head_dim = key.size(2);
+  const int64_t key_token_num_stride = key.stride(0);
+  const int64_t value_token_num_stride = value.stride(0);
+  const int64_t head_num = value.size(1);
+  const int64_t key_head_num_stride = key.stride(1);
+  const int64_t value_head_num_stride = value.stride(1);
   const int64_t num_blocks = key_cache.size(0);
   const int64_t num_blocks_stride = key_cache.stride(0);
   const int64_t cache_head_num_stride = key_cache.stride(1);
   const int64_t block_size = key_cache.size(2);
   const int64_t block_size_stride = key_cache.stride(2);
+  const int64_t head_dim = key.size(-1);
 
   cpu_attention::ISA isa_tag = [&]() {
     if (isa == "amx") {
@@ -131,31 +104,21 @@ void cpu_attn_reshape_and_cache(
       return cpu_attention::ISA::NEON;
     } else if (isa == "vxe") {
       return cpu_attention::ISA::VXE;
-    } else if (isa == "vsx") {
-      return cpu_attention::ISA::VSX;
     } else {
       TORCH_CHECK(false, "Invalid ISA type: " + isa);
     }
   }();
 
-  if (is_fp8) {
-    TORCH_CHECK(isa_tag == cpu_attention::ISA::AMX ||
-                    isa_tag == cpu_attention::ISA::VEC,
-                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
-  }
-
   VLLM_DISPATCH_FLOATING_TYPES(
       key.scalar_type(), "cpu_attn_reshape_and_cache", [&]() {
-        CPU_ATTN_DISPATCH(head_dim, isa_tag, kv_cache_idx, [&]() {
-          using kv_t = typename attn_impl::kv_cache_t;
+        CPU_ATTN_DISPATCH(head_dim, isa_tag, [&]() {
           attn_impl::reshape_and_cache(
               key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
-              reinterpret_cast<kv_t*>(key_cache.data_ptr()),
-              reinterpret_cast<kv_t*>(value_cache.data_ptr()),
-              slot_mapping.data_ptr<int64_t>(), token_num, key.stride(0),
-              value.stride(0), head_num, key.stride(1), value.stride(1),
-              num_blocks, num_blocks_stride, cache_head_num_stride, block_size,
-              block_size_stride, k_inv, v_inv);
+              key_cache.data_ptr<scalar_t>(), value_cache.data_ptr<scalar_t>(),
+              slot_mapping.data_ptr<int64_t>(), token_num, key_token_num_stride,
+              value_token_num_stride, head_num, key_head_num_stride,
+              value_head_num_stride, num_blocks, num_blocks_stride,
+              cache_head_num_stride, block_size, block_size_stride);
         });
       });
 }
@@ -174,26 +137,13 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table,  // [num_tokens, max_block_num]
     const double softcap, const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux,  // [num_heads]
-    const double k_scale = 1.0, const double v_scale = 1.0,
-    const std::string& kv_cache_dtype = "auto") {
+    const std::optional<torch::Tensor>& s_aux  // [num_heads]
+) {
   TORCH_CHECK_EQ(query.dim(), 3);
   TORCH_CHECK_EQ(query.stride(2), 1);
   TORCH_CHECK_EQ(key_cache.dim(), 4);
   TORCH_CHECK_EQ(value_cache.dim(), 4);
 
-  const int64_t kv_cache_idx =
-      static_cast<int64_t>(parse_fp8_kv_dtype(kv_cache_dtype));
-  const bool is_fp8 = (kv_cache_idx != 0);
-  if (is_fp8) {
-    TORCH_CHECK(key_cache.scalar_type() == at::ScalarType::Byte,
-                "key_cache must be uint8 for FP8 path");
-    TORCH_CHECK(value_cache.scalar_type() == at::ScalarType::Byte,
-                "value_cache must be uint8 for FP8 path");
-    TORCH_CHECK(k_scale > 0, "k_scale must be positive for FP8 path");
-    TORCH_CHECK(v_scale > 0, "v_scale must be positive for FP8 path");
-  }
-
   cpu_attention::AttentionInput input;
   input.metadata = reinterpret_cast<cpu_attention::AttentionMetadata*>(
       scheduler_metadata.data_ptr());
@@ -215,32 +165,25 @@ void cpu_attention_with_kv_cache(
   input.block_table = block_table.data_ptr<int32_t>();
   input.alibi_slopes =
       alibi_slopes.has_value() ? alibi_slopes->data_ptr<float>() : nullptr;
+  // For now sink must be bf16
   input.s_aux = s_aux.has_value() ? s_aux->data_ptr<c10::BFloat16>() : nullptr;
   input.scale = scale;
   input.causal = causal;
   input.sliding_window_left = sliding_window_left;
   input.sliding_window_right = sliding_window_right;
   if (input.causal) {
+    // to make boundary calculation easier
     input.sliding_window_right = 0;
   }
-  input.softcap = static_cast<float>(softcap);
-
-  if (is_fp8) {
-    input.k_scale_fp8 = static_cast<float>(k_scale);
-    input.v_scale_fp8 = static_cast<float>(v_scale);
-    TORCH_CHECK(input.metadata->isa == cpu_attention::ISA::AMX ||
-                    input.metadata->isa == cpu_attention::ISA::VEC,
-                "FP8 KV cache is only supported on x86 (AMX/VEC) ISA");
-  }
+  float softcap_fp32 = softcap;
+  input.softcap = softcap_fp32;
 
   VLLM_DISPATCH_FLOATING_TYPES(
       query.scalar_type(), "cpu_attention_with_kv_cache", [&]() {
-        CPU_ATTN_DISPATCH(
-            query.size(2), input.metadata->isa, kv_cache_idx, [&]() {
-              TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment,
-                             0);
-              cpu_attention::AttentionMainLoop<attn_impl> mainloop;
-              mainloop(&input);
-            });
+        CPU_ATTN_DISPATCH(query.size(2), input.metadata->isa, [&]() {
+          TORCH_CHECK_EQ(input.block_size % attn_impl::BlockSizeAlignment, 0);
+          cpu_attention::AttentionMainLoop<attn_impl> mainloop;
+          mainloop(&input);
+        });
       });
 }

csrc/cpu/cpu_attn_amx.hpp

@@ -1,7 +1,6 @@
 #ifndef CPU_ATTN_AMX_HPP
 #define CPU_ATTN_AMX_HPP
 
-#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
@@ -22,10 +21,9 @@ typedef struct __tile_config {
 // 2-2-4 pattern, for 16 < m <= 32
 // TILE 0, 1: load A matrix, row num should be 16, m - 16
 // TILE 2, 3: load B matrix, row num should be 16
-// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16,
-// m - 16, m - 16
-// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
-template <typename q_buffer_t, typename kv_cache_t>
+// TILE 4, 5, 6, 7: store results C matrix, row num should be 16, 16, m - 16, m
+// - 16
+template <typename kv_cache_t>
 class TileGemm224 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -44,56 +42,13 @@ class TileGemm224 {
   }
 };
 
-// Dequantize one FP8 tile (AMX_TILE_ROW_NUM rows x 32 cols) to BF16.
-template <typename kv_cache_t>
-FORCE_INLINE void deq_tile_amx(const uint8_t* src, c10::BFloat16* dst) {
-  for (int r = 0; r < AMX_TILE_ROW_NUM; ++r) {
-    if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
-      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e4m3_tag{})
-          .save(dst + r * 32);
-    } else {
-      vec_op::BF16Vec32(src + r * 32, vec_op::fp8_bf16_e5m2_tag{})
-          .save(dst + r * 32);
-    }
-  }
-}
-
-// For FP8: dequant src into scratch and return scratch.
-// For BF16: return src directly (scratch is unused; the compiler elides it).
-template <typename kv_cache_t>
-FORCE_INLINE const c10::BFloat16* prepare_b_tile(const kv_cache_t* src,
-                                                 c10::BFloat16* scratch) {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-    deq_tile_amx<kv_cache_t>(reinterpret_cast<const uint8_t*>(src), scratch);
-    return scratch;
-  } else {
-    return reinterpret_cast<const c10::BFloat16*>(src);
-  }
-}
-
-// Handles both BF16 and FP8 KV cache (2-2-4 pattern).
-template <typename kv_cache_t>
-class TileGemm224<c10::BFloat16, kv_cache_t> {
-  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
-                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
-
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-
-  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
-  // BF16 path: scratch_elems=1 so the scratch array is eliminated by the
-  // compiler.
-  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
-
+template <>
+class TileGemm224<c10::BFloat16> {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
+                                c10::BFloat16* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -101,7 +56,6 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
                                 const bool accum_c) {
     const int32_t k_times =
         dynamic_k_size / (AMX_TILE_ROW_NUM * 4 / sizeof(c10::BFloat16));
-
     c10::BFloat16* __restrict__ a_tile_0 = a_tile;
     c10::BFloat16* __restrict__ a_tile_1 = a_tile + lda * AMX_TILE_ROW_NUM;
     const int64_t a_tile_stride = [&]() {
@@ -116,8 +70,8 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
       }
     }();
 
-    kv_cache_t* __restrict__ b_tile_2 = b_tile;
-    kv_cache_t* __restrict__ b_tile_3 = [&]() {
+    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
+    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
         // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
@@ -152,16 +106,11 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
       _tile_zero(7);
     }
 
-    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
     for (int32_t k = 0; k < k_times; ++k) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_tile_stride);
+      _tile_stream_loadd(2, b_tile_2, b_tile_stride);
       _tile_dpbf16ps(4, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_tile_stride);
+      _tile_stream_loadd(3, b_tile_3, b_tile_stride);
       _tile_dpbf16ps(5, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
       _tile_dpbf16ps(6, 1, 2);
@@ -205,13 +154,13 @@ class TileGemm224<c10::BFloat16, kv_cache_t> {
 };
 
 // 1-2-2 pattern, for 0 < m <= 16
-// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should
-// be m, m
-// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row num
-// should be 16
-// TILE 6, 7: store results C matrix, row num should be m
-// q_buffer_t: A (Q/P) tile type; kv_cache_t: B (K/V cache) tile type.
-template <typename q_buffer_t, typename kv_cache_t>
+// TILE 0, (1): load A matrix, use extra 1 tile for prefetch, row num should be
+// m, m
+// TILE 2, 3, (4, 5): load B matrix, use extra 2 tiles for prefetch, row
+// num should be 16
+// TILE 6, 7, (6, 7): store results C matrix, row num should be
+// m
+template <typename kv_cache_t>
 class TileGemm122 {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
@@ -230,26 +179,13 @@ class TileGemm122 {
   }
 };
 
-// Handles both BF16 and FP8 KV cache (1-2-2 pattern).
-template <typename kv_cache_t>
-class TileGemm122<c10::BFloat16, kv_cache_t> {
-  static_assert(std::is_same_v<kv_cache_t, c10::BFloat16> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                    std::is_same_v<kv_cache_t, c10::Float8_e5m2>,
-                "kv_cache_t must be BFloat16, Float8_e4m3fn, or Float8_e5m2");
-
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-
-  static constexpr int64_t tile_elems = AMX_TILE_BYTES / sizeof(c10::BFloat16);
-  static constexpr int64_t scratch_elems = fp8_kv ? tile_elems : 1;
-
+template <>
+class TileGemm122<c10::BFloat16> {
  public:
   template <AttentionGemmPhase phase, int32_t k_size>
   FORCE_INLINE static void gemm(const int32_t m_size,
                                 c10::BFloat16* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
+                                c10::BFloat16* __restrict__ b_tile,
                                 float* __restrict__ c_tile, const int64_t lda,
                                 const int64_t ldb, const int64_t ldc,
                                 const int32_t block_size,
@@ -279,19 +215,21 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
       }
     }();
 
-    kv_cache_t* __restrict__ b_tile_2 = b_tile;
-    kv_cache_t* __restrict__ b_tile_3 = [&]() {
+    c10::BFloat16* __restrict__ b_tile_2 = b_tile;
+    c10::BFloat16* __restrict__ b_tile_3 = [&]() {
       if constexpr (phase == AttentionGemmPhase::QK) {
+        // k_cache is prepacked
         return b_tile + (k_size * AMX_TILE_ROW_BYTES / 4);
       } else if constexpr (phase == AttentionGemmPhase::PV) {
+        // v_cache is prepacked
         return b_tile + (block_size * AMX_TILE_ROW_BYTES / 4);
       } else {
         TORCH_CHECK(false, "Unreachable");
       }
     }();
-    kv_cache_t* __restrict__ b_tile_4 =
+    c10::BFloat16* __restrict__ b_tile_4 =
         b_tile_2 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
-    kv_cache_t* __restrict__ b_tile_5 =
+    c10::BFloat16* __restrict__ b_tile_5 =
         b_tile_3 + AMX_TILE_BYTES / sizeof(c10::BFloat16);
     int64_t b_stride = AMX_TILE_ROW_BYTES;
 
@@ -312,25 +250,16 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
       _tile_zero(7);
     }
 
-    alignas(64) c10::BFloat16 scratch_2[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_3[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_4[scratch_elems];
-    alignas(64) c10::BFloat16 scratch_5[scratch_elems];
     for (int32_t k = 0; k < k_group_times; ++k) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-      const c10::BFloat16* load_4 = prepare_b_tile(b_tile_4, scratch_4);
-      const c10::BFloat16* load_5 = prepare_b_tile(b_tile_5, scratch_5);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
+      _tile_stream_loadd(2, b_tile_2, b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
+      _tile_stream_loadd(3, b_tile_3, b_stride);
       _tile_dpbf16ps(7, 0, 3);
       _tile_loadd(1, a_tile_1, a_tile_stride);
-      _tile_stream_loadd(4, const_cast<c10::BFloat16*>(load_4), b_stride);
+      _tile_stream_loadd(4, b_tile_4, b_stride);
       _tile_dpbf16ps(6, 1, 4);
-      _tile_stream_loadd(5, const_cast<c10::BFloat16*>(load_5), b_stride);
+      _tile_stream_loadd(5, b_tile_5, b_stride);
       _tile_dpbf16ps(7, 1, 5);
 
       // update ptrs
@@ -350,13 +279,10 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
     }
 
     if (has_tail) {
-      const c10::BFloat16* load_2 = prepare_b_tile(b_tile_2, scratch_2);
-      const c10::BFloat16* load_3 = prepare_b_tile(b_tile_3, scratch_3);
-
       _tile_loadd(0, a_tile_0, a_tile_stride);
-      _tile_stream_loadd(2, const_cast<c10::BFloat16*>(load_2), b_stride);
+      _tile_stream_loadd(2, b_tile_2, b_stride);
       _tile_dpbf16ps(6, 0, 2);
-      _tile_stream_loadd(3, const_cast<c10::BFloat16*>(load_3), b_stride);
+      _tile_stream_loadd(3, b_tile_3, b_stride);
       _tile_dpbf16ps(7, 0, 3);
     }
 
@@ -376,25 +302,21 @@ class TileGemm122<c10::BFloat16, kv_cache_t> {
     _tile_loadconfig(&config);
   }
 };
-
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
-
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::AMX, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = scalar_t;
-  using kv_cache_t = kv_cache_scalar_t;
+  using kv_cache_t = scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = scalar_t;
 
   constexpr static int64_t BlockSizeAlignment =
-      32;  // AMX_TILE_ROW_NUM = 16 tokens/tile; 32 = 2 tiles
+      AMX_TILE_ROW_BYTES /
+      sizeof(kv_cache_t);  // KV token num unit of QK and PV phases
   constexpr static int64_t HeadDimAlignment =
       2 * (AMX_TILE_ROW_BYTES / 4);  // headdim num unit of PV phase
   constexpr static int64_t MaxQHeadNumPerIteration = 32;
@@ -402,9 +324,6 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
   constexpr static ISA ISAType = ISA::AMX;
   constexpr static bool scale_on_logits = true;
 
-  float k_scale = 1.0f;
-  float v_scale = 1.0f;
-
  public:
   AttentionImpl() : current_q_head_num_(0) {
     // Use all columns in AMX tiles
@@ -413,50 +332,21 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
 
   ~AttentionImpl() { _tile_release(); }
 
-  void init_from_input(const AttentionInput* input) {
-    if constexpr (fp8_kv) {
-      k_scale = input->k_scale_fp8;
-      v_scale = input->v_scale_fp8;
-    }
-  }
-
-  float get_output_v_scale() const noexcept {
-    if constexpr (fp8_kv) {
-      // AMX dequant places FP8 payload into a BF16 field (exponent bias 127).
-      // Correction = 2^(127 - FP8_bias): E4M3 bias=7 → 2^120, E5M2 bias=15 →
-      // 2^112.
-      constexpr float bias =
-          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
-      return v_scale * bias;
-    }
-    return 1.0f;
-  }
-
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    if constexpr (fp8_kv) {
-      // Same bias correction as get_output_v_scale: AMX FP8→BF16 dequant
-      // shifts the exponent bias from FP8 to BF16 (127), so we multiply by
-      // 2^(127-FP8_bias) to recover the true value. E4M3: 2^120, E5M2: 2^112.
-      const float bias =
-          std::is_same_v<kv_cache_t, c10::Float8_e5m2> ? 0x1p112f : 0x1p120f;
-      scale *= k_scale * bias;
-    }
     if (q_head_num > AMX_TILE_ROW_NUM) {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm224<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
-                                                              amx_tile_config_);
+        TileGemm224<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
       }
-      attention<TileGemm224<q_buffer_t, kv_cache_t>> attention_iteration;
+      attention<TileGemm224<kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     } else {
       if (q_head_num != current_q_head_num_) {
         current_q_head_num_ = q_head_num;
-        TileGemm122<q_buffer_t, kv_cache_t>::init_tile_config(q_head_num,
-                                                              amx_tile_config_);
+        TileGemm122<kv_cache_t>::init_tile_config(q_head_num, amx_tile_config_);
       }
-      attention<TileGemm122<q_buffer_t, kv_cache_t>> attention_iteration;
+      attention<TileGemm122<kv_cache_t>> attention_iteration;
       attention_iteration(CPU_ATTENTION_PARAMS);
     }
   }
@@ -521,26 +411,13 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
   // reshape KV to AMX friendly layout
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
+      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    if constexpr (fp8_kv) {
-      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
-      reshape_and_cache_fp8_amx_impl<scalar_t, qfn>(
-          key, value, reinterpret_cast<uint8_t*>(key_cache),
-          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
-          head_num, head_dim, block_size, key_token_num_stride,
-          key_head_num_stride, value_token_num_stride, value_head_num_stride,
-          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
-          cache_head_num_stride, k_inv, v_inv);
-      return;
-    }
-
+      const int64_t block_size, const int64_t block_size_stride) {
     // For AMX 2D tiles, size of each line is 64 bytes
     constexpr int64_t amx_tile_row_size = AMX_TILE_ROW_BYTES;
     // For AMX B matrix, N always is 16
@@ -549,9 +426,6 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
     // For now suppose block_size is divisible by amx_tile_column_num
     TORCH_CHECK_EQ(block_size % amx_b_tile_k_size, 0);
 
-    scalar_t* __restrict__ kc = reinterpret_cast<scalar_t*>(key_cache);
-    scalar_t* __restrict__ vc = reinterpret_cast<scalar_t*>(value_cache);
-
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -579,7 +453,8 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
           constexpr int64_t quadword_num_per_group =
               token_num_per_group * quadword_num;
           int32_t* key_cache_start_ptr =
-              reinterpret_cast<int32_t*>(kc + block_idx * num_blocks_stride +
+              reinterpret_cast<int32_t*>(key_cache +
+                                         block_idx * num_blocks_stride +
                                          head_idx * cache_head_num_stride) +
               group_idx * quadword_num_per_group + group_offset;
 
@@ -608,7 +483,7 @@ class AttentionImpl<ISA::AMX, scalar_t, head_dim, kv_cache_scalar_t> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              vc + block_idx * num_blocks_stride +
+              value_cache + block_idx * num_blocks_stride +
               head_idx * cache_head_num_stride +
               sub_group_idx * token_num_per_sub_group * amx_b_tile_n_size +
               sub_group_offset;

csrc/cpu/cpu_attn_fp8.hpp

@@ -1,214 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#pragma once
-#include <algorithm>
-#include <cmath>
-#include <cstdint>
-#include <limits>
-#include <type_traits>
-
-#include "cpu/utils.hpp"
-
-typedef uint32_t __attribute__((__may_alias__)) u32_alias_t;
-typedef uint16_t __attribute__((__may_alias__)) u16_alias_t;
-typedef float __attribute__((__may_alias__)) f32_alias_t;
-
-// Reference scalar dequant — used to verify vectorized AMX dequant.
-inline float fp8e4m3_to_float_scalar(uint8_t b, float scale) noexcept {
-  // NaN encoding in E4M3
-  if ((b & 0x7F) == 0x7F) return std::numeric_limits<float>::quiet_NaN();
-  uint32_t b_u32 = static_cast<uint32_t>(b);
-  uint32_t sign = (b_u32 & 0x80) << 24;
-  uint32_t payload = (b_u32 & 0x7F) << 20;
-  uint32_t bits = sign | payload;
-  float b_f32_unscaled = *reinterpret_cast<const f32_alias_t*>(&bits);
-  float b_f32_scaled = b_f32_unscaled * scale * 0x1p120f;
-  return b_f32_scaled;
-}
-
-inline uint8_t float_to_fp8e4m3_scalar(float v, float inv_scale) noexcept {
-  v *= inv_scale;
-  constexpr float fp8_max = 448.0f;
-  v = std::max(-fp8_max, std::min(fp8_max, v));
-  if (v == 0.0f) return 0;
-
-  // Inverse mapping of fp8e4m3_to_float_scalar: shift the effective exponent
-  // bias from fp32 (127) back to fp8 e4m3 (7), then pack sign|payload.
-  float v_f32_unscaled = v * 0x1p-120f;
-  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v_f32_unscaled);
-  uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
-  uint8_t payload = static_cast<uint8_t>((bits >> 20) & 0x7F);
-  if (payload == 0) return sign;
-  payload = std::min<uint8_t>(payload, 0x7E);  // keep 0x7F as NaN encoding
-  return static_cast<uint8_t>(sign | payload);
-}
-
-// ---------------------------------------------------------------------------
-// AMX reshape impl — parameterised on the quantisation function.
-// Writes key/value into uint8 FP8 KV cache using the AMX tile-friendly layout.
-// K: halfword-packed (2 FP8 per uint16, token_num_per_group=16).
-// V: sub-group packing (token_num_per_sub_group=2, head_elems_per_group=16).
-// block_size must be divisible by 32.
-// ---------------------------------------------------------------------------
-template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
-inline void reshape_and_cache_fp8_amx_impl(
-    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
-    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
-    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
-    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
-    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
-    float v_inv) {
-  constexpr int64_t token_num_per_group = 16;  // AMX_TILE_ROW_NUM
-  const int64_t halfword_num = head_dim / 2;   // 2 FP8 per uint16
-  const int64_t halfword_num_per_group = token_num_per_group * halfword_num;
-  constexpr int64_t head_elems_per_group = 16;
-  constexpr int64_t token_num_per_sub_group = 2;  // = 4 / sizeof(BF16)
-  const int64_t group_num = head_dim / head_elems_per_group;
-  const int64_t group_size = block_size * head_elems_per_group;
-
-#pragma omp parallel for collapse(2) schedule(static)
-  for (int64_t tok = 0; tok < token_num; ++tok) {
-    for (int64_t h = 0; h < head_num; ++h) {
-      const int64_t slot = slot_ptr[tok];
-      if (slot < 0) continue;
-      const int64_t block_idx = slot / block_size;
-      const int64_t block_offset = slot % block_size;
-
-      // Key: halfword-packed, 2 FP8 per uint16
-      {
-        const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
-        const int64_t group_idx = block_offset / token_num_per_group;
-        const int64_t group_offset = block_offset % token_num_per_group;
-        uint16_t* kdst =
-            reinterpret_cast<uint16_t*>(key_cache_ptr + block_idx * kc_stride0 +
-                                        h * kc_stride1) +
-            group_idx * halfword_num_per_group + group_offset;
-        for (int64_t j = 0; j < halfword_num; ++j) {
-          uint8_t fp8_0 = quant_fn(static_cast<float>(ksrc[j * 2]), k_inv);
-          uint8_t fp8_1 = quant_fn(static_cast<float>(ksrc[j * 2 + 1]), k_inv);
-          uint8_t bytes[2] = {fp8_0, fp8_1};
-          uint16_t hw = *reinterpret_cast<const u16_alias_t*>(bytes);
-          kdst[j * token_num_per_group] = hw;
-        }
-      }
-
-      // Value: sub-group packing (token_num_per_sub_group = 2)
-      {
-        const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
-        const int64_t sub_group_idx = block_offset / token_num_per_sub_group;
-        const int64_t sub_group_offset = block_offset % token_num_per_sub_group;
-        uint8_t* vdst =
-            value_cache_ptr + block_idx * vc_stride0 + h * vc_stride1 +
-            sub_group_idx * token_num_per_sub_group * head_elems_per_group +
-            sub_group_offset;
-        for (int64_t i = 0; i < group_num; ++i) {
-          for (int64_t j = 0; j < head_elems_per_group; ++j)
-            vdst[j * token_num_per_sub_group] =
-                quant_fn(static_cast<float>(vsrc[j]), v_inv);
-          vsrc += head_elems_per_group;
-          vdst += group_size;
-        }
-      }
-    }
-  }
-}
-
-// ---------------------------------------------------------------------------
-// FP8 E5M2 scalar helpers
-// ---------------------------------------------------------------------------
-
-// Reference scalar dequant — used to verify vectorized AMX dequant.
-// FP8 E5M2: s[7] e[6:2] m[1:0], exponent bias = 15 (same as FP16).
-// Byte b → FP16 bits = b << 8 (no bias correction needed).
-inline float fp8e5m2_to_float_scalar(uint8_t b, float scale) noexcept {
-  const uint8_t exp_bits = (b >> 2) & 0x1F;
-  const uint8_t mant_bits = b & 0x03;
-  // NaN: exp=11111, mant!=00
-  if (exp_bits == 0x1F && mant_bits != 0)
-    return std::numeric_limits<float>::quiet_NaN();
-  const uint32_t sign = static_cast<uint32_t>(b & 0x80) << 24;
-  if (exp_bits == 0x1F)
-    return sign ? -std::numeric_limits<float>::infinity()
-                : std::numeric_limits<float>::infinity();
-  if (exp_bits == 0) {  // subnormal: (-1)^s * 2^-14 * mant/4
-    if (mant_bits == 0) return 0.0f;
-    float v = mant_bits * 0x1p-16f;
-    return (sign ? -v : v) * scale;
-  }
-  // Normal: FP32 exp = exp5 - 15 + 127, mantissa top 2 bits
-  uint32_t fp32_bits = sign |
-                       ((static_cast<uint32_t>(exp_bits) - 15 + 127) << 23) |
-                       (static_cast<uint32_t>(mant_bits) << 21);
-  float val = *reinterpret_cast<const f32_alias_t*>(&fp32_bits);
-  return val * scale;
-}
-
-inline uint8_t float_to_fp8e5m2_scalar(float v, float inv_scale) noexcept {
-  v *= inv_scale;
-  constexpr float fp8_e5m2_max = 57344.0f;
-  v = std::max(-fp8_e5m2_max, std::min(fp8_e5m2_max, v));
-  if (v == 0.0f) return 0;
-  uint32_t bits = *reinterpret_cast<const u32_alias_t*>(&v);
-  const uint8_t sign = static_cast<uint8_t>((bits >> 24) & 0x80);
-  const int32_t exp_fp32 = static_cast<int32_t>((bits >> 23) & 0xFF) - 127;
-  const uint8_t mant2 = static_cast<uint8_t>((bits >> 21) & 0x03);
-  if (exp_fp32 < -14) {  // subnormal in E5M2
-    const int shift = -14 - exp_fp32;
-    if (shift + 21 >= 32)
-      return sign;  // underflow: too small for E5M2 subnormal
-    const uint32_t m = (0x800000u | (bits & 0x7FFFFFu)) >> (shift + 21);
-    return sign | static_cast<uint8_t>(std::min<uint32_t>(m, 3u));
-  }
-  const uint8_t exp5 = static_cast<uint8_t>(exp_fp32 + 15);
-  return sign | (exp5 << 2) | mant2;
-}
-
-// ---------------------------------------------------------------------------
-// Select the FP8 quant function at compile time based on kv_cache_t.
-// ---------------------------------------------------------------------------
-template <typename kv_cache_t>
-constexpr auto select_fp8_quant_fn() {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>)
-    return float_to_fp8e5m2_scalar;
-  else
-    return float_to_fp8e4m3_scalar;
-}
-
-// ---------------------------------------------------------------------------
-// VEC reshape impl — parameterised on the quantisation function.
-// Writes key (column-major) and value (row-major) into uint8 FP8 KV cache.
-// The pragma omp must live outside VLLM_DISPATCH_FLOATING_TYPES because
-// #pragma cannot appear inside variadic macro arguments.
-// ---------------------------------------------------------------------------
-template <typename scalar_t, uint8_t (*quant_fn)(float, float)>
-inline void reshape_and_cache_fp8_vec_impl(
-    const scalar_t* key_ptr, const scalar_t* value_ptr, uint8_t* key_cache_ptr,
-    uint8_t* value_cache_ptr, const int64_t* slot_ptr, int64_t token_num,
-    int64_t head_num, int64_t head_dim, int64_t block_size, int64_t k_stride0,
-    int64_t k_stride1, int64_t v_stride0, int64_t v_stride1, int64_t kc_stride0,
-    int64_t kc_stride1, int64_t vc_stride0, int64_t vc_stride1, float k_inv,
-    float v_inv) {
-#pragma omp parallel for collapse(2) schedule(static)
-  for (int64_t tok = 0; tok < token_num; ++tok) {
-    for (int64_t h = 0; h < head_num; ++h) {
-      const int64_t slot = slot_ptr[tok];
-      if (slot < 0) continue;
-      const int64_t block_idx = slot / block_size;
-      const int64_t block_offset = slot % block_size;
-
-      // Key layout: column-major within block
-      const scalar_t* ksrc = key_ptr + tok * k_stride0 + h * k_stride1;
-      uint8_t* kdst = key_cache_ptr + block_idx * kc_stride0 + h * kc_stride1 +
-                      block_offset;
-      for (int64_t i = 0; i < head_dim; ++i)
-        kdst[i * block_size] = quant_fn(static_cast<float>(ksrc[i]), k_inv);
-
-      // Value layout: row-major within block (contiguous head_dim bytes)
-      const scalar_t* vsrc = value_ptr + tok * v_stride0 + h * v_stride1;
-      uint8_t* vdst = value_cache_ptr + block_idx * vc_stride0 +
-                      h * vc_stride1 + block_offset * head_dim;
-      for (int64_t i = 0; i < head_dim; ++i)
-        vdst[i] = quant_fn(static_cast<float>(vsrc[i]), v_inv);
-    }
-  }
-}

csrc/cpu/cpu_attn_impl.hpp

@@ -12,24 +12,10 @@
 #include "cpu/utils.hpp"
 
 namespace cpu_attention {
-enum class ISA { AMX, VEC, VEC16, NEON, VXE, VSX };
+enum class ISA { AMX, VEC, VEC16, NEON, VXE };
 
-// Mirrors csrc/attention/dtype_fp8.cuh Fp8KVCacheDataType exactly.
-enum class Fp8KVCacheDataType {
-  kAuto = 0,
-  kFp8E4M3 = 1,
-  kFp8E5M2 = 2,
-};
-
-struct AttentionInput;
-
-template <ISA isa, typename scalar_t, int64_t head_dim,
-          typename kv_cache_scalar_t = scalar_t>
-class AttentionImpl {
- public:
-  void init_from_input(const AttentionInput*) {}
-  float get_output_v_scale() const noexcept { return 1.0f; }
-};
+template <ISA isa, typename scalar_t, int64_t head_dim>
+class AttentionImpl {};
 
 struct AttentionWorkItemGroup {
   int32_t req_id;
@@ -164,9 +150,6 @@ struct AttentionMetadata {
       case ISA::VXE:
         ss << "VXE, ";
         break;
-      case ISA::VSX:
-        ss << "VSX, ";
-        break;
     }
     ss << "workitem_group_num: " << workitem_group_num
        << ", reduction_item_num: " << reduction_item_num
@@ -797,9 +780,6 @@ struct AttentionInput {
   int32_t sliding_window_left;
   int32_t sliding_window_right;
   float softcap;
-  // FP8 KV cache scales (used by FP8 attention implementations)
-  float k_scale_fp8 = 1.0f;
-  float v_scale_fp8 = 1.0f;
 };
 
 #define DEFINE_CPU_ATTENTION_PARAMS                                         \
@@ -1394,13 +1374,6 @@ class AttentionMainLoop {
       }
 
       attention_impl_t attn_impl;
-      constexpr bool fp8_kv = std::is_same_v<kv_cache_t, c10::Float8_e4m3fn> ||
-                              std::is_same_v<kv_cache_t, c10::Float8_e5m2>;
-      float output_v_scale = 1.0f;
-      if constexpr (fp8_kv) {
-        attn_impl.init_from_input(input);
-        output_v_scale = attn_impl.get_output_v_scale();
-      }
 
       // general information
       const int32_t q_head_num = input->num_heads;
@@ -1780,7 +1753,7 @@ class AttentionMainLoop {
                                reinterpret_cast<query_t*>(input->output) +
                                    output_buffer_offset,
                                sum_buffer, actual_q_heads_per_kv,
-                               actual_q_token_num, q_head_num, output_v_scale);
+                               actual_q_token_num, q_head_num);
                 } else {
                   const int32_t stride =
                       actual_q_heads_per_kv * split_kv_q_token_num_threshold;
@@ -1850,7 +1823,7 @@ class AttentionMainLoop {
               split_output_buffer,
               reinterpret_cast<query_t*>(input->output) + output_buffer_offset,
               split_sum_buffer, actual_q_heads_per_kv, curr_output_token_num,
-              q_head_num, output_v_scale);
+              q_head_num);
         }
       }
     }
@@ -1974,8 +1947,8 @@ class AttentionMainLoop {
                     query_t* __restrict__ curr_output_buffer,
                     float* __restrict__ sum_buffer,
                     const int32_t q_heads_per_kv,
-                    const int32_t actual_q_token_num, const int32_t q_head_num,
-                    const float v_scale = 1.0f) {
+                    const int32_t actual_q_token_num,
+                    const int32_t q_head_num) {
     // final output
     using output_vec_t = typename VecTypeTrait<query_t>::vec_t;
 
@@ -1989,7 +1962,7 @@ class AttentionMainLoop {
           curr_partial_output_buffer;
       query_t* __restrict__ curr_output_buffer_iter = curr_output_buffer;
       for (int32_t head_idx = 0; head_idx < q_heads_per_kv; ++head_idx) {
-        vec_op::FP32Vec16 inv_sum_scale_vec(v_scale / *curr_sum_buffer);
+        vec_op::FP32Vec16 inv_sum_scale_vec(1.0 / *curr_sum_buffer);
 
         for (int32_t i = 0; i < group_num_per_head; ++i) {
           vec_op::FP32Vec16 vec(curr_partial_output_buffer_iter);

csrc/cpu/cpu_attn_neon.hpp

@@ -248,8 +248,8 @@ class TileGemmNeonFMLA {
 }  // namespace
 
 // this is similar to "ISA::VEC" at the moment
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::NEON, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -343,8 +343,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -389,7 +388,7 @@ class AttentionImpl<ISA::NEON, scalar_t, head_dim, kv_cache_scalar_t> {
 #ifdef ARM_BF16_SUPPORT
 // For BF16 on Arm, reuse the BFMMLA kernels with 32-token alignment.
 template <int64_t head_dim>
-class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim, c10::BFloat16>
+class AttentionImpl<ISA::NEON, c10::BFloat16, head_dim>
     : public AttentionImplNEONBFMMLA<BLOCK_SIZE_ALIGNMENT, ISA::NEON,
                                      head_dim> {};
 #endif

csrc/cpu/cpu_attn_neon_bfmmla.hpp

@@ -602,8 +602,7 @@ class AttentionImplNEONBFMMLA {
       [[maybe_unused]] const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
       const int64_t block_size,
-      [[maybe_unused]] const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      [[maybe_unused]] const int64_t block_size_stride) {
     const int64_t k_block_stride = (head_dim / TILE_K) * K_INNER_STRIDE;
     const int64_t v_pair_stride =
         (block_size / V_TOKENS_PER_ROW_BLOCK) * V_INNER_STRIDE;

csrc/cpu/cpu_attn_vec.hpp

@@ -1,37 +1,11 @@
 #ifndef CPU_ATTN_VEC_HPP
 #define CPU_ATTN_VEC_HPP
 
-#include "cpu_attn_fp8.hpp"
 #include "cpu_attn_impl.hpp"
 
 namespace cpu_attention {
 
 namespace {
-
-// Load 32 kv_cache_t elements starting at ptr and return them as two FP32Vec16s
-// covering the lower 16 and upper 16 positions.
-// For FP8: both halves come from a single BF16Vec32 dequant of 32 bytes.
-// For BF16/FP16/FP32: two separate vector loads at ptr and ptr+16.
-template <typename kv_cache_t>
-FORCE_INLINE std::pair<vec_op::FP32Vec16, vec_op::FP32Vec16> load_b_pair_vec(
-    const kv_cache_t* ptr) {
-  if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e4m3fn>) {
-    // BF16 container, but values are in the FP16 exponent range (bias 15 not
-    // 127).
-    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
-                                 vec_op::fp8_e4m3_tag{});
-    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
-  } else if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-    vec_op::BF16Vec32 bf16_b_reg(reinterpret_cast<const uint8_t*>(ptr),
-                                 vec_op::fp8_e5m2_tag{});
-    return {vec_op::FP32Vec16(bf16_b_reg, 0), vec_op::FP32Vec16(bf16_b_reg, 1)};
-  } else {
-    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
-    return std::make_pair(vec_op::FP32Vec16(load_vec_t(ptr)),
-                          vec_op::FP32Vec16(load_vec_t(ptr + 16)));
-  }
-}
-
 // 8-2-16 pattern, 8 regs for A, 2 regs for B, 16 regs for C, [8, K] @ [k, 32]
 template <typename kv_cache_t>
 class TileGemm82 {
@@ -80,7 +54,10 @@ class TileGemm82 {
                          const int32_t block_size, const int32_t dynamic_k_size,
                          const bool accum_c) {
     static_assert(0 < M && M <= 8);
+    using load_vec_t = typename VecTypeTrait<kv_cache_t>::vec_t;
 
+    kv_cache_t* __restrict__ curr_b_0 = b_tile;
+    kv_cache_t* __restrict__ curr_b_1 = b_tile + 16;
     float* __restrict__ curr_c_0 = c_tile;
     float* __restrict__ curr_c_1 = c_tile + 16;
 
@@ -99,14 +76,16 @@ class TileGemm82 {
     }
 
     float* __restrict__ curr_a = a_tile;
-    kv_cache_t* __restrict__ curr_b = b_tile;
-
     for (int32_t k = 0; k < dynamic_k_size; ++k) {
-      auto [fp32_b_0_reg, fp32_b_1_reg] = load_b_pair_vec(curr_b);
+      load_vec_t b_0_reg(curr_b_0);
+      vec_op::FP32Vec16 fp32_b_0_reg(b_0_reg);
+      load_vec_t b_1_reg(curr_b_1);
+      vec_op::FP32Vec16 fp32_b_1_reg(b_1_reg);
 
       float* __restrict__ curr_m_a = curr_a;
       vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
-        vec_op::FP32Vec16 a_reg(*curr_m_a);
+        float v = *curr_m_a;
+        vec_op::FP32Vec16 a_reg(v);
         c_regs[i * 2] = c_regs[i * 2] + a_reg * fp32_b_0_reg;
         c_regs[i * 2 + 1] = c_regs[i * 2 + 1] + a_reg * fp32_b_1_reg;
 
@@ -116,7 +95,8 @@ class TileGemm82 {
 
       // update
       curr_a += 1;
-      curr_b += ldb;
+      curr_b_0 += ldb;
+      curr_b_1 += ldb;
     }
 
     vec_op::unroll_loop<int32_t, M>([&](int32_t i) {
@@ -129,20 +109,15 @@ class TileGemm82 {
     });
   }
 };
-
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
-  static constexpr bool fp8_kv =
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e4m3fn> ||
-      std::is_same_v<kv_cache_scalar_t, c10::Float8_e5m2>;
-
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VEC, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
-  using kv_cache_t = kv_cache_scalar_t;
+  using kv_cache_t = scalar_t;
   using logits_buffer_t = float;
   using partial_output_buffer_t = float;
   using prob_buffer_t = float;
@@ -154,45 +129,11 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
   constexpr static int64_t MaxQHeadNumPerIteration = 8;
   constexpr static int64_t HeadDim = head_dim;
   constexpr static ISA ISAType = ISA::VEC;
-  constexpr static bool scale_on_logits = fp8_kv;
-
-  float k_scale = 1.0f;
-  float v_scale = 1.0f;
+  constexpr static bool scale_on_logits = false;  // apply scale on q_buffer
 
  public:
-  void init_from_input(const AttentionInput* input) {
-    if constexpr (fp8_kv) {
-      k_scale = input->k_scale_fp8;
-      v_scale = input->v_scale_fp8;
-    }
-  }
-
-  float get_output_v_scale() const noexcept {
-    if constexpr (fp8_kv) {
-      // VEC dequant unpacks FP8 into a pseudo-FP16 layout (exponent bias 15).
-      // E4M3 (bias=7) needs correction 2^(15-7) = 2^8; E5M2 bias matches FP16
-      // so no correction.
-      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-        return v_scale;
-      } else {
-        return v_scale * 0x1p8f;
-      }
-    }
-    return 1.0f;
-  }
-
   template <template <typename tile_gemm_t> typename attention>
   FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    if constexpr (fp8_kv) {
-      // Same bias correction as get_output_v_scale: VEC FP8→pseudo-FP16 dequant
-      // uses bias 15; E4M3 (bias=7) needs ×2^8, E5M2 (bias=15) needs no
-      // correction.
-      if constexpr (std::is_same_v<kv_cache_t, c10::Float8_e5m2>) {
-        scale *= k_scale;
-      } else {
-        scale *= k_scale * 0x1p8f;
-      }
-    }
     attention<TileGemm82<kv_cache_t>> attention_iteration;
     attention_iteration(CPU_ATTENTION_PARAMS);
   }
@@ -220,19 +161,17 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                               // row-major
   }
 
-  // Copy q to q_buffer and cast it to fp32.
-  // FP8: QK scale is folded into execute_attention; copy Q unscaled here.
-  void copy_q_heads_tile(scalar_t* __restrict__ src,
-                         float* __restrict__ q_buffer, const int32_t q_num,
-                         const int32_t q_heads_per_kv,
-                         const int64_t q_num_stride,
-                         const int64_t q_head_stride, float scale) {
+  // Copy q to q_buffer and cast it to fp32
+  static void copy_q_heads_tile(
+      scalar_t* __restrict__ src,  // [q_num, q_heads_per_kv, head_size]
+      float* __restrict__ q_buffer, const int32_t q_num,
+      const int32_t q_heads_per_kv, const int64_t q_num_stride,
+      const int64_t q_head_stride, float scale) {
     static_assert(head_dim % 16 == 0);
     constexpr int32_t unroll_size = head_dim / 16;
     using load_vec_t = typename VecTypeTrait<scalar_t>::vec_t;
 
-    const float effective_scale = fp8_kv ? 1.0f : scale;
-    vec_op::FP32Vec16 scale_vec(effective_scale);
+    vec_op::FP32Vec16 scale_vec(scale);
     for (int32_t q_num_idx = 0; q_num_idx < q_num; ++q_num_idx) {
       for (int32_t q_head_idx = 0; q_head_idx < q_heads_per_kv; ++q_head_idx) {
         scalar_t* __restrict__ curr_q =
@@ -257,26 +196,13 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
   // reshape K as column-major and V as row-major
   static void reshape_and_cache(
       const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      kv_cache_t* __restrict__ key_cache, kv_cache_t* __restrict__ value_cache,
+      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
       const int64_t* __restrict__ slot_mapping, const int64_t token_num,
       const int64_t key_token_num_stride, const int64_t value_token_num_stride,
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    if constexpr (fp8_kv) {
-      constexpr auto qfn = select_fp8_quant_fn<kv_cache_t>();
-      reshape_and_cache_fp8_vec_impl<scalar_t, qfn>(
-          key, value, reinterpret_cast<uint8_t*>(key_cache),
-          reinterpret_cast<uint8_t*>(value_cache), slot_mapping, token_num,
-          head_num, head_dim, block_size, key_token_num_stride,
-          key_head_num_stride, value_token_num_stride, value_head_num_stride,
-          num_blocks_stride, cache_head_num_stride, num_blocks_stride,
-          cache_head_num_stride, k_inv, v_inv);
-      return;
-    }
-
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
@@ -294,9 +220,8 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                                           token_idx * key_token_num_stride +
                                           head_idx * key_head_num_stride;
           scalar_t* key_cache_start_ptr =
-              reinterpret_cast<scalar_t*>(key_cache) +
-              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
-              block_offset;
+              key_cache + block_idx * num_blocks_stride +
+              head_idx * cache_head_num_stride + block_offset;
 
 #pragma GCC unroll 8
           for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
@@ -309,9 +234,8 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
                                             token_idx * value_token_num_stride +
                                             head_idx * value_head_num_stride;
           scalar_t* value_cache_start_ptr =
-              reinterpret_cast<scalar_t*>(value_cache) +
-              block_idx * num_blocks_stride + head_idx * cache_head_num_stride +
-              block_offset * head_dim;
+              value_cache + block_idx * num_blocks_stride +
+              head_idx * cache_head_num_stride + block_offset * head_dim;
           std::memcpy(value_cache_start_ptr, value_start_ptr,
                       sizeof(scalar_t) * head_dim);
         }
@@ -319,7 +243,6 @@ class AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
     }
   }
 };
-
 }  // namespace cpu_attention
 
 #endif

csrc/cpu/cpu_attn_vec16.hpp

@@ -116,9 +116,9 @@ class TileGemm161 {
 }  // namespace
 
 // This is a general but naive implementation based on vector instructions
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VEC16, scalar_t, head_dim, kv_cache_scalar_t>
-    : public AttentionImpl<ISA::VEC, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VEC16, scalar_t, head_dim>
+    : public AttentionImpl<ISA::VEC, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;

csrc/cpu/cpu_attn_vsx.hpp

@@ -1,359 +0,0 @@
-// SPDX-License-Identifier: Apache-2.0
-// SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-#ifndef CPU_ATTN_VSX_HPP
-#define CPU_ATTN_VSX_HPP
-
-#include "cpu_attn_impl.hpp"
-#include <altivec.h>
-#include <type_traits>
-
-namespace cpu_attention {
-
-namespace {
-
-// ppc64le Vector = 16 bytes (128 bits)
-#define BLOCK_SIZE_ALIGNMENT 32
-#define HEAD_SIZE_ALIGNMENT 32
-#define MAX_Q_HEAD_NUM_PER_ITER 16
-
-template <typename kv_cache_t>
-FORCE_INLINE void load_row8_B_as_f32(const kv_cache_t* p, __vector float& b0,
-                                     __vector float& b1);
-
-// [1] Float Specialization
-template <>
-FORCE_INLINE void load_row8_B_as_f32<float>(const float* p, __vector float& b0,
-                                            __vector float& b1) {
-  b0 = vec_xl(0, const_cast<float*>(p));
-  b1 = vec_xl(0, const_cast<float*>(p + 4));
-}
-
-// [2] BFloat16 Specialization (Little Endian ppc64le)
-// On ppc64le (LE): BF16 bits should land in the HIGH 16 bits of each float32.
-// Byte layout of float32 on LE: [byte0(LSB), byte1, byte2, byte3(MSB)]
-// We need BF16 in bytes2-3 (high half) with bytes0-1 zeroed.
-// vec_mergeh on LE interleaves elements 0..3: result_i = {a[i], b[i]}
-// So vec_mergeh(zeros_u16, raw_u16) gives for each uint16 pair:
-//   uint16[2i]   = zeros[i]  -> low 16 bits of uint32  -> zeroed mantissa LSBs
-//   uint16[2i+1] = raw[i]    -> high 16 bits of uint32 -> BF16 bits
-// Cast to float32 gives exactly (bf16_bits << 16) per element.
-template <>
-FORCE_INLINE void load_row8_B_as_f32<c10::BFloat16>(const c10::BFloat16* p,
-                                                    __vector float& b0,
-                                                    __vector float& b1) {
-  __vector unsigned short raw = vec_xl(
-      0, reinterpret_cast<unsigned short*>(const_cast<c10::BFloat16*>(p)));
-  __vector unsigned short zeros = vec_splat_u16(0);
-
-  // LE: zeros in low 16 bits, raw in high 16 bits → bf16 << 16 == float32
-  b0 = (__vector float)vec_mergeh(zeros, raw);
-  b1 = (__vector float)vec_mergel(zeros, raw);
-}
-
-// Note: c10::Half (FP16) is not supported on PowerPC architecture
-
-template <int32_t M, typename kv_cache_t>
-FORCE_INLINE void gemm_micro_ppc64le_Mx8_Ku4(
-    const float* __restrict A,       // [M x K]
-    const kv_cache_t* __restrict B,  // [K x 8]
-    float* __restrict C,             // [M x 8]
-    int64_t lda, int64_t ldb, int64_t ldc, int32_t K, bool accumulate) {
-  static_assert(1 <= M && M <= 8, "M must be in [1,8]");
-
-#define ROWS_APPLY(OP) OP(0) OP(1) OP(2) OP(3) OP(4) OP(5) OP(6) OP(7)
-#define IF_M(i) if constexpr (M > (i))
-
-  // 1. Define A pointers
-#define DECL_A(i) const float* a##i = A + (i) * lda;
-  ROWS_APPLY(DECL_A)
-#undef DECL_A
-
-  // 2. Define Accumulators (2 vectors covers 8 columns)
-#define DECL_ACC(i) __vector float acc##i##_0, acc##i##_1;
-  ROWS_APPLY(DECL_ACC)
-#undef DECL_ACC
-
-  // 3. Initialize Accumulators (Load C or Zero)
-#define INIT_ACC(i)                                                  \
-  IF_M(i) {                                                          \
-    if (accumulate) {                                                \
-      acc##i##_0 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 0)); \
-      acc##i##_1 = vec_xl(0, const_cast<float*>(C + (i) * ldc + 4)); \
-    } else {                                                         \
-      acc##i##_0 = vec_splats(0.0f);                                 \
-      acc##i##_1 = vec_splats(0.0f);                                 \
-    }                                                                \
-  }
-  ROWS_APPLY(INIT_ACC)
-#undef INIT_ACC
-
-  int32_t k = 0;
-
-  for (; k + 3 < K; k += 4) {
-    // Load 4 values of A for each Row M: A[k...k+3]
-#define LOAD_A4(i)        \
-  __vector float a##i##v; \
-  IF_M(i) a##i##v = vec_xl(0, const_cast<float*>(a##i + k));
-    ROWS_APPLY(LOAD_A4)
-#undef LOAD_A4
-
-    // FMA for specific lane L of A
-    // ppc64le: vec_madd(b, vec_splat(a, lane), acc)
-#define FMAS_LANE(i, aiv, L)                        \
-  IF_M(i) {                                         \
-    __vector float a_broad = vec_splat(aiv, L);     \
-    acc##i##_0 = vec_madd(b0, a_broad, acc##i##_0); \
-    acc##i##_1 = vec_madd(b1, a_broad, acc##i##_1); \
-  }
-
-    // Unroll K=0..3
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 0) * ldb, b0, b1);
-#define STEP_K0(i) FMAS_LANE(i, a##i##v, 0)
-      ROWS_APPLY(STEP_K0)
-#undef STEP_K0
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 1) * ldb, b0, b1);
-#define STEP_K1(i) FMAS_LANE(i, a##i##v, 1)
-      ROWS_APPLY(STEP_K1)
-#undef STEP_K1
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 2) * ldb, b0, b1);
-#define STEP_K2(i) FMAS_LANE(i, a##i##v, 2)
-      ROWS_APPLY(STEP_K2)
-#undef STEP_K2
-    }
-    {
-      __vector float b0, b1;
-      load_row8_B_as_f32<kv_cache_t>(B + (int64_t)(k + 3) * ldb, b0, b1);
-#define STEP_K3(i) FMAS_LANE(i, a##i##v, 3)
-      ROWS_APPLY(STEP_K3)
-#undef STEP_K3
-    }
-#undef FMAS_LANE
-  }
-
-  for (; k < K; ++k) {
-    __vector float b0, b1;
-    load_row8_B_as_f32<kv_cache_t>(B + (int64_t)k * ldb, b0, b1);
-#define TAIL_ROW(i)                              \
-  IF_M(i) {                                      \
-    __vector float ai = vec_splats(*(a##i + k)); \
-    acc##i##_0 = vec_madd(b0, ai, acc##i##_0);   \
-    acc##i##_1 = vec_madd(b1, ai, acc##i##_1);   \
-  }
-    ROWS_APPLY(TAIL_ROW)
-#undef TAIL_ROW
-  }
-
-#define STORE_ROW(i)                           \
-  IF_M(i) {                                    \
-    vec_xst(acc##i##_0, 0, C + (i) * ldc + 0); \
-    vec_xst(acc##i##_1, 0, C + (i) * ldc + 4); \
-  }
-  ROWS_APPLY(STORE_ROW)
-#undef STORE_ROW
-
-#undef ROWS_APPLY
-#undef IF_M
-}
-
-template <int32_t N, typename kv_cache_t>
-FORCE_INLINE void gemm_macro_ppc64le_Mx8_Ku4(const float* __restrict A,
-                                             const kv_cache_t* __restrict B,
-                                             float* __restrict C, int32_t M,
-                                             int32_t K, int64_t lda,
-                                             int64_t ldb, int64_t ldc,
-                                             bool accumulate) {
-  static_assert(N % 8 == 0, "N must be a multiple of 8");
-  for (int32_t m = 0; m < M;) {
-    int32_t mb = (M - m >= 8) ? 8 : (M - m >= 4) ? 4 : (M - m >= 2) ? 2 : 1;
-    const float* Ab = A + m * lda;
-    float* Cb = C + m * ldc;
-
-    for (int32_t n = 0; n < N; n += 8) {
-      const kv_cache_t* Bn = B + n;
-      float* Cn = Cb + n;
-      switch (mb) {
-        case 8:
-          gemm_micro_ppc64le_Mx8_Ku4<8, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        case 4:
-          gemm_micro_ppc64le_Mx8_Ku4<4, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        case 2:
-          gemm_micro_ppc64le_Mx8_Ku4<2, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-        default:
-          gemm_micro_ppc64le_Mx8_Ku4<1, kv_cache_t>(Ab, Bn, Cn, lda, ldb, ldc,
-                                                    K, accumulate);
-          break;
-      }
-    }
-    m += mb;
-  }
-}
-
-template <typename kv_cache_t>
-class TileGemmPPC64 {
- public:
-  template <AttentionGemmPhase phase, int32_t k_size>
-  FORCE_INLINE static void gemm(const int32_t m_size,
-                                float* __restrict__ a_tile,
-                                kv_cache_t* __restrict__ b_tile,
-                                float* __restrict__ c_tile, const int64_t lda,
-                                const int64_t ldb, const int64_t ldc,
-                                const int32_t block_size,
-                                const int32_t dynamic_k_size,
-                                const bool accum_c) {
-    if constexpr (phase == AttentionGemmPhase::QK) {
-      gemm_macro_ppc64le_Mx8_Ku4<BLOCK_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, k_size, lda, ldb, ldc, accum_c);
-    } else {
-      gemm_macro_ppc64le_Mx8_Ku4<HEAD_SIZE_ALIGNMENT, kv_cache_t>(
-          a_tile, b_tile, c_tile, m_size, dynamic_k_size, lda, ldb, ldc,
-          accum_c);
-    }
-  }
-};
-
-}  // namespace
-
-template <typename scalar_t, int64_t head_dim>
-class AttentionImpl<ISA::VSX, scalar_t, head_dim> {
- public:
-  using query_t = scalar_t;
-  using q_buffer_t = float;
-  using kv_cache_t = scalar_t;
-  using logits_buffer_t = float;
-  using partial_output_buffer_t = float;
-  using prob_buffer_t = float;
-
-  constexpr static int64_t BlockSizeAlignment = BLOCK_SIZE_ALIGNMENT;
-  constexpr static int64_t HeadDimAlignment = HEAD_SIZE_ALIGNMENT;
-  constexpr static int64_t MaxQHeadNumPerIteration = MAX_Q_HEAD_NUM_PER_ITER;
-  constexpr static int64_t HeadDim = head_dim;
-  constexpr static ISA ISAType = ISA::VSX;
-  constexpr static bool scale_on_logits =
-      false;  // Scale is applied to Q during copy
-
- public:
-  AttentionImpl() {}
-
-  template <template <typename tile_gemm_t> typename attention>
-  FORCE_INLINE void execute_attention(DEFINE_CPU_ATTENTION_PARAMS) {
-    attention<TileGemmPPC64<kv_cache_t>> attention_iteration;
-    attention_iteration(CPU_ATTENTION_PARAMS);
-  }
-
-  // Strides for Memory Layout
-  constexpr static int64_t k_cache_token_group_stride(
-      const int32_t block_size) {
-    return BlockSizeAlignment;  // [head_dim, block_size] layout
-  }
-
-  constexpr static int64_t v_cache_token_group_stride(
-      const int32_t block_size) {
-    return head_dim * BlockSizeAlignment;
-  }
-
-  constexpr static int64_t v_cache_head_group_stride(const int32_t block_size) {
-    return HeadDimAlignment;
-  }
-
-  static void copy_q_heads_tile(scalar_t* __restrict__ src,
-                                float* __restrict__ q_buffer,
-                                const int32_t q_num,
-                                const int32_t q_heads_per_kv,
-                                const int64_t q_num_stride,
-                                const int64_t q_head_stride, float scale) {
-    __vector float scale_vec = vec_splats(scale);
-    constexpr bool is_bf16 = std::is_same<scalar_t, c10::BFloat16>::value;
-
-    for (int32_t i = 0; i < q_num; ++i) {
-      for (int32_t h = 0; h < q_heads_per_kv; ++h) {
-        scalar_t* curr_src = src + i * q_num_stride + h * q_head_stride;
-        float* curr_dst =
-            q_buffer + i * q_heads_per_kv * head_dim + h * head_dim;
-
-        int32_t d = 0;
-        for (; d <= head_dim - 8; d += 8) {
-          __vector float v0, v1;
-          load_row8_B_as_f32<scalar_t>(curr_src + d, v0, v1);
-
-          v0 = vec_mul(v0, scale_vec);
-          v1 = vec_mul(v1, scale_vec);
-
-          vec_xst(v0, 0, curr_dst + d);
-          vec_xst(v1, 0, curr_dst + d + 4);
-        }
-
-        for (; d < head_dim; ++d) {
-          float val = static_cast<float>(curr_src[d]);
-          curr_dst[d] = val * scale;
-        }
-      }
-    }
-  }
-
-  static void reshape_and_cache(
-      const scalar_t* __restrict__ key, const scalar_t* __restrict__ value,
-      scalar_t* __restrict__ key_cache, scalar_t* __restrict__ value_cache,
-      const int64_t* __restrict__ slot_mapping, const int64_t token_num,
-      const int64_t key_token_num_stride, const int64_t value_token_num_stride,
-      const int64_t head_num, const int64_t key_head_num_stride,
-      const int64_t value_head_num_stride, const int64_t num_blocks,
-      const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float k_inv = 0.0f, const float v_inv = 0.0f) {
-    // k_inv and v_inv are unused on VSX: FP8 KV cache is not supported on
-    // PowerPC. The parameters are present to match the common interface.
-#pragma omp parallel for collapse(2)
-    for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
-      for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {
-        const int64_t pos = slot_mapping[token_idx];
-        if (pos < 0) continue;
-
-        const int64_t block_idx = pos / block_size;
-        const int64_t block_offset = pos % block_size;
-
-        {
-          const scalar_t* key_src = key + token_idx * key_token_num_stride +
-                                    head_idx * key_head_num_stride;
-          scalar_t* key_dst = key_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride + block_offset;
-
-          for (int64_t i = 0, j = 0; i < head_dim; ++i, j += block_size) {
-            key_dst[j] = key_src[i];
-          }
-        }
-
-        {
-          const scalar_t* val_src = value + token_idx * value_token_num_stride +
-                                    head_idx * value_head_num_stride;
-          scalar_t* val_dst = value_cache + block_idx * num_blocks_stride +
-                              head_idx * cache_head_num_stride +
-                              block_offset * head_dim;
-
-          std::memcpy(val_dst, val_src, sizeof(scalar_t) * head_dim);
-        }
-      }
-    }
-  }
-};
-
-}  // namespace cpu_attention
-
-#undef BLOCK_SIZE_ALIGNMENT
-#undef HEAD_SIZE_ALIGNMENT
-#undef MAX_Q_HEAD_NUM_PER_ITER
-
-#endif  // CPU_ATTN_VSX_HPP

csrc/cpu/cpu_attn_vxe.hpp

@@ -244,8 +244,8 @@ class TileGemmS390X {
 
 }  // namespace
 
-template <typename scalar_t, int64_t head_dim, typename kv_cache_scalar_t>
-class AttentionImpl<ISA::VXE, scalar_t, head_dim, kv_cache_scalar_t> {
+template <typename scalar_t, int64_t head_dim>
+class AttentionImpl<ISA::VXE, scalar_t, head_dim> {
  public:
   using query_t = scalar_t;
   using q_buffer_t = float;
@@ -342,8 +342,7 @@ class AttentionImpl<ISA::VXE, scalar_t, head_dim, kv_cache_scalar_t> {
       const int64_t head_num, const int64_t key_head_num_stride,
       const int64_t value_head_num_stride, const int64_t num_blocks,
       const int64_t num_blocks_stride, const int64_t cache_head_num_stride,
-      const int64_t block_size, const int64_t block_size_stride,
-      const float /*k_inv*/ = 0.0f, const float /*v_inv*/ = 0.0f) {
+      const int64_t block_size, const int64_t block_size_stride) {
 #pragma omp parallel for collapse(2)
     for (int64_t token_idx = 0; token_idx < token_num; ++token_idx) {
       for (int64_t head_idx = 0; head_idx < head_num; ++head_idx) {

csrc/cpu/cpu_types_arm.hpp

@@ -15,9 +15,6 @@ using namespace at::vec;
 
 namespace vec_op {
 
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
   AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
@@ -325,9 +322,6 @@ struct BF16Vec32 : public VectorizedRegWrapper<BF16Vec32, 4, c10::BFloat16> {
     reg.val[2] = vec8_data.reg.val[0];
     reg.val[3] = vec8_data.reg.val[0];
   };
-
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : Base() {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : Base() {}
 };
 
 struct FP32Vec4 : public VectorizedRegWrapper<FP32Vec4, 1, float> {
@@ -486,10 +480,6 @@ struct FP32Vec16 : public VectorizedRegWrapper<FP32Vec16, 4, float> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
 
-  // FP8 stub: dead code on ARM (fp8 KV cache is x86-only), needed for
-  // load_b_pair_vec template to compile on all platforms.
-  explicit FP32Vec16(const BF16Vec32&, int) : Base() {}
-
   explicit FP32Vec16(const FP16Vec16& v) {
     reg.val[0] = Vectorized<float>(vcvt_f32_f16(vget_low_f16(v.reg.val[0])));
     reg.val[1] = Vectorized<float>(vcvt_f32_f16(vget_high_f16(v.reg.val[0])));

csrc/cpu/cpu_types_scalar.hpp

@@ -6,9 +6,6 @@
 
 namespace vec_op {
 
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
@@ -148,9 +145,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   }
 
   void save(void* ptr) const { *reinterpret_cast<f16x32_t*>(ptr) = reg; }
-
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
 };
 
 struct FP32Vec4 : public Vec<FP32Vec4> {
@@ -308,10 +302,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {};
 
-  // FP8 stub: dead code on scalar path (fp8 KV cache is x86-only), needed for
-  // load_b_pair_vec template to compile on all platforms.
-  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
-
   FP32Vec16 operator*(const FP32Vec16& b) const {
     f32x16_t ret;
     unroll_loop<int, VEC_ELEM_NUM>(

csrc/cpu/cpu_types_vsx.hpp

@@ -9,10 +9,6 @@
 
 namespace vec_op {
 
-// FP8 tag types for tag dispatch (see cpu_attn_vec.hpp)
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 // FIXME: FP16 is not fully supported in Torch-CPU
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
@@ -146,9 +142,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
       : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
 
   void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
-
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
 };
 
 struct FP32Vec4 : public Vec<FP32Vec4> {
@@ -411,10 +404,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
-  // FP8 stub: dead code on PowerPC (fp8 KV cache is x86-only), needed for
-  // load_b_pair_vec template to compile on all platforms.
-  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
-
   explicit FP32Vec16(const INT32Vec16& v) {
     reg.val[0] = vec_ctf(v.reg.val[0], 0);
     reg.val[1] = vec_ctf(v.reg.val[1], 0);

csrc/cpu/cpu_types_vxe.hpp

@@ -8,9 +8,6 @@
 #include <torch/all.h>
 namespace vec_op {
 
-struct fp8_e4m3_tag {};
-struct fp8_e5m2_tag {};
-
 #define vec_neg(a) (-(a))
 #define vec_add(a, b) ((a) + (b))
 #define vec_sub(a, b) ((a) - (b))
@@ -244,9 +241,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   explicit BF16Vec32(const BF16Vec8& vec8_data)
       : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
 
-  explicit BF16Vec32(const uint8_t*, fp8_e4m3_tag) : reg{} {}
-  explicit BF16Vec32(const uint8_t*, fp8_e5m2_tag) : reg{} {}
-
   void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
 };
 
@@ -688,10 +682,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
 
   explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
 
-  // FP8 stub: dead code on s390x (fp8 KV cache is x86-only), needed for
-  // load_b_pair_vec template to compile on all platforms.
-  explicit FP32Vec16(const BF16Vec32&, int) : reg{} {}
-
   FP32Vec16 operator*(const FP32Vec16& b) const {
     return FP32Vec16(f32x4x4_t({vec_mul(reg.val[0], b.reg.val[0]),
                                 vec_mul(reg.val[1], b.reg.val[1]),

csrc/cpu/cpu_types_x86.hpp

@@ -11,17 +11,6 @@ static_assert(false, "AVX2 must be supported for the current implementation.");
 
 namespace vec_op {
 
-// Tags for FP8 BF16Vec32 constructors (avoid overload collision with
-// BF16Vec32(void*)).
-// VEC path (FP8 → pseudo-FP16 layout, scale correction applied later):
-struct fp8_e4m3_tag {};  // E4M3 → pseudo-FP16; BF16 value = true_E4M3 * 2^-8
-struct fp8_e5m2_tag {};  // E5M2 → FP16 bits directly (same exponent bias=15)
-// AMX path (FP8 → unscaled BF16, no FP32 round-trip):
-// BF16 value = true_E4M3 * 2^-120 (E4M3) or true_E5M2 * 2^-112 (E5M2).
-// Exponent rebiasing is folded into k/v scales by the caller.
-struct fp8_bf16_e4m3_tag {};
-struct fp8_bf16_e5m2_tag {};
-
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)            \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
@@ -122,17 +111,9 @@ struct FP16Vec16 : public Vec<FP16Vec16> {
   void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
 
   void save(void* ptr, const int elem_num) const {
-#ifdef __AVX512BW__
     constexpr uint32_t M = 0xFFFFFFFF;
     __mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
     _mm256_mask_storeu_epi16(ptr, mask, reg);
-#else
-    // Fallback for lack of 16-bit masked store
-    int16_t tmp[VEC_ELEM_NUM];
-    _mm256_storeu_si256((__m256i*)tmp, reg);
-    for (int i = 0; i < elem_num; ++i)
-      reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
-#endif
   }
 };
 
@@ -169,17 +150,9 @@ struct BF16Vec16 : public Vec<BF16Vec16> {
   void save(void* ptr) const { _mm256_storeu_si256((__m256i*)ptr, reg); }
 
   void save(void* ptr, const int elem_num) const {
-#ifdef __AVX512BW__
     constexpr uint32_t M = 0xFFFFFFFF;
     __mmask16 mask = _cvtu32_mask16(M >> (32 - elem_num));
     _mm256_mask_storeu_epi16(ptr, mask, reg);
-#else
-    // Fallback for lack of 16-bit masked store
-    int16_t tmp[VEC_ELEM_NUM];
-    _mm256_storeu_si256((__m256i*)tmp, reg);
-    for (int i = 0; i < elem_num; ++i)
-      reinterpret_cast<int16_t*>(ptr)[i] = tmp[i];
-#endif
   }
 };
 
@@ -203,50 +176,6 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
                                (__m128i)vec8_data.reg, 2),
             (__m128i)vec8_data.reg, 3)) {}
 
-  // Decode 32 FP8-E4M3 bytes to pseudo-FP16 layout (stored in the BF16
-  // register).  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 7);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
-  // Decode 32 FP8-E5M2 bytes to FP16 layout.
-  // E5M2 and FP16 share the same 5-bit exponent bias (15), so FP8 byte b maps
-  // directly to FP16 bits by shifting left 8 — no sign/payload reconstruction.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    reg = _mm512_slli_epi16(_mm512_cvtepu8_epi16(b8), 8);
-  }
-
-  // Direct FP8-E4M3 → unscaled BF16 for AMX (no FP32 round-trip).
-  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 4);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
-  // Direct FP8-E5M2 → unscaled BF16 for AMX (no FP32 round-trip).
-  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m512i b16 = _mm512_cvtepu8_epi16(b8);
-    __m512i sign =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x80)), 8);
-    __m512i payload =
-        _mm512_slli_epi16(_mm512_and_si512(b16, _mm512_set1_epi16(0x7F)), 5);
-    reg = _mm512_or_si512(sign, payload);
-  }
-
   void save(void* ptr) const { *reinterpret_cast<__m512i*>(ptr) = reg; }
 };
 #else
@@ -263,83 +192,13 @@ struct BF16Vec32 : public Vec<BF16Vec32> {
   explicit BF16Vec32(__m256i low, __m256i high)
       : reg_low(low), reg_high(high) {}
 
-  explicit BF16Vec32()
-      : reg_low(_mm256_setzero_si256()), reg_high(_mm256_setzero_si256()) {}
-
   explicit BF16Vec32(BF16Vec8& vec8_data)
-      : reg_low(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)),
-        reg_high(_mm256_broadcastsi128_si256((__m128i)vec8_data.reg)) {}
-
-  // E4M3 decode (AVX2 path) — same bit-layout trick as the AVX512 variant
-  // above.  Result = true_E4M3 * 2^-8; caller applies scale * 2^8.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-
-    __m256i sign_low = _mm256_slli_epi16(
-        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)), 8);
-    __m256i payload_low = _mm256_slli_epi16(
-        _mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)), 7);
-    __m256i sign_high = _mm256_slli_epi16(
-        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)), 8);
-    __m256i payload_high = _mm256_slli_epi16(
-        _mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)), 7);
-    reg_low = _mm256_or_si256(sign_low, payload_low);
-    reg_high = _mm256_or_si256(sign_high, payload_high);
-  }
-
-  // E5M2 decode (AVX2 path) — b << 8 maps to FP16 bits; see AVX512 variant
-  // above.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    reg_low = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_low), 8);
-    reg_high = _mm256_slli_epi16(_mm256_cvtepu8_epi16(b8_high), 8);
-  }
-
-  // Direct FP8-E4M3 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
-  // BF16 value = true_E4M3 * 2^-120; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e4m3_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-    reg_low = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
-                          4));
-    reg_high = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
-                          4));
-  }
-
-  // Direct FP8-E5M2 → unscaled BF16 for AMX (AVX2 path, no FP32 round-trip).
-  // BF16 value = true_E5M2 * 2^-112; exponent rebiasing folded into k/v scales.
-  explicit BF16Vec32(const uint8_t* ptr, fp8_bf16_e5m2_tag) {
-    __m256i b8 = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(ptr));
-    __m128i b8_low = _mm256_extracti128_si256(b8, 0);
-    __m128i b8_high = _mm256_extracti128_si256(b8, 1);
-    __m256i b16_low = _mm256_cvtepu8_epi16(b8_low);
-    __m256i b16_high = _mm256_cvtepu8_epi16(b8_high);
-    reg_low = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_low, _mm256_set1_epi16(0x7F)),
-                          5));
-    reg_high = _mm256_or_si256(
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x80)),
-                          8),
-        _mm256_slli_epi16(_mm256_and_si256(b16_high, _mm256_set1_epi16(0x7F)),
-                          5));
-  }
+      : reg_low((__m256i)_mm256_inserti32x4(
+            _mm256_castsi128_si256((__m128i)vec8_data.reg),
+            (__m128i)vec8_data.reg, 1)),
+        reg_high((__m256i)_mm256_inserti32x4(
+            _mm256_castsi128_si256((__m128i)vec8_data.reg),
+            (__m128i)vec8_data.reg, 1)) {}
 
   void save(void* ptr) const {
     _mm256_storeu_si256((__m256i*)ptr, reg_low);
@@ -531,11 +390,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
       : reg(_mm512_castsi512_ps(
             _mm512_bslli_epi128(_mm512_cvtepu16_epi32(v.reg), 2))) {}
 
-  explicit FP32Vec16(const BF16Vec32& v, int upper) {
-    __m256i v_half_i = _mm512_extracti32x8_epi32(v.reg, upper);
-    reg = _mm512_cvtph_ps(v_half_i);
-  }
-
   explicit FP32Vec16(const FP16Vec16& v) : reg(_mm512_cvtph_ps(v.reg)) {}
 
   explicit FP32Vec16(const FP16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
@@ -640,14 +494,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
   explicit FP32Vec16(const FP32Vec8& data)
       : reg_low(data.reg), reg_high(data.reg) {}
 
-  explicit FP32Vec16(const BF16Vec32& v, int upper) {
-    const __m256i& half = upper ? v.reg_high : v.reg_low;
-    __m128i lo = _mm256_extractf128_si256(half, 0);
-    __m128i hi = _mm256_extractf128_si256(half, 1);
-    reg_low = _mm256_cvtph_ps(lo);
-    reg_high = _mm256_cvtph_ps(hi);
-  }
-
   explicit FP32Vec16(const FP16Vec16& v) {
     __m128i low = _mm256_extractf128_si256(v.reg, 0);
     __m128i high = _mm256_extractf128_si256(v.reg, 1);
@@ -689,11 +535,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
                      _mm256_sub_ps(reg_high, b.reg_high));
   }
 
-  FP32Vec16 operator-() const {
-    const __m256 neg = _mm256_set1_ps(-0.0f);
-    return FP32Vec16(_mm256_xor_ps(reg_low, neg), _mm256_xor_ps(reg_high, neg));
-  }
-
   FP32Vec16 operator/(const FP32Vec16& b) const {
     return FP32Vec16(_mm256_div_ps(reg_low, b.reg_low),
                      _mm256_div_ps(reg_high, b.reg_high));
@@ -759,85 +600,6 @@ struct FP32Vec16 : public Vec<FP32Vec16> {
     _mm256_storeu_ps(ptr, reg_low);
     _mm256_storeu_ps(ptr + 8, reg_high);
   }
-
-  void save(float* ptr, const int elem_num) const {
-    // Partial store: cmpgt produces a sign-bit mask (0xFFFFFFFF/0 per lane)
-    // for the first elem_num lanes, applied across the two 8-wide halves.
-    if (elem_num <= 8) {
-      __m256i mask =
-          _mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num),
-                             _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
-      _mm256_maskstore_ps(ptr, mask, reg_low);
-    } else {
-      _mm256_storeu_ps(ptr, reg_low);
-      __m256i mask =
-          _mm256_cmpgt_epi32(_mm256_set1_epi32(elem_num - 8),
-                             _mm256_setr_epi32(0, 1, 2, 3, 4, 5, 6, 7));
-      _mm256_maskstore_ps(ptr + 8, mask, reg_high);
-    }
-  }
-
-  FP32Vec16 clamp(const FP32Vec16& min, const FP32Vec16& max) const {
-    return FP32Vec16(
-        _mm256_min_ps(max.reg_low, _mm256_max_ps(min.reg_low, reg_low)),
-        _mm256_min_ps(max.reg_high, _mm256_max_ps(min.reg_high, reg_high)));
-  }
-
-  FP32Vec16 abs() const {
-    const __m256 sign_mask = _mm256_set1_ps(-0.0f);
-    return FP32Vec16(_mm256_andnot_ps(sign_mask, reg_low),
-                     _mm256_andnot_ps(sign_mask, reg_high));
-  }
-
-  FP32Vec16 min(const FP32Vec16& b) const {
-    return FP32Vec16(_mm256_min_ps(reg_low, b.reg_low),
-                     _mm256_min_ps(reg_high, b.reg_high));
-  }
-
-  // Partial element-wise min over the first elem_num lanes only (tail path).
-  // Scalar via AliasReg: AVX2 has no masked vminps, so we spill, loop, reload.
-  FP32Vec16 min(const FP32Vec16& b, const int elem_num) const {
-    AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
-    ar_this_low.reg = reg_low;
-    ar_this_high.reg = reg_high;
-    ar_b_low.reg = b.reg_low;
-    ar_b_high.reg = b.reg_high;
-    for (int i = 0; i < elem_num && i < 8; ++i)
-      ar_this_low.values[i] =
-          std::min(ar_this_low.values[i], ar_b_low.values[i]);
-    for (int i = 0; i < elem_num - 8 && i < 8; ++i)
-      ar_this_high.values[i] =
-          std::min(ar_this_high.values[i], ar_b_high.values[i]);
-    return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
-  }
-
-  // Partial element-wise max over the first elem_num lanes only (tail path).
-  // Scalar via AliasReg: AVX2 has no masked vmaxps, so we spill, loop, reload.
-  FP32Vec16 max(const FP32Vec16& b, const int elem_num) const {
-    AliasReg ar_this_low, ar_this_high, ar_b_low, ar_b_high;
-    ar_this_low.reg = reg_low;
-    ar_this_high.reg = reg_high;
-    ar_b_low.reg = b.reg_low;
-    ar_b_high.reg = b.reg_high;
-    for (int i = 0; i < elem_num && i < 8; ++i)
-      ar_this_low.values[i] =
-          std::max(ar_this_low.values[i], ar_b_low.values[i]);
-    for (int i = 0; i < elem_num - 8 && i < 8; ++i)
-      ar_this_high.values[i] =
-          std::max(ar_this_high.values[i], ar_b_high.values[i]);
-    return FP32Vec16(ar_this_low.reg, ar_this_high.reg);
-  }
-
-  float reduce_min() const {
-    __m256 v = _mm256_min_ps(reg_low, reg_high);
-    __m256 v_shuffled = _mm256_permute_ps(v, 0b00001011);
-    __m256 v_min = _mm256_min_ps(v, v_shuffled);
-    v_shuffled = _mm256_permute_ps(v_min, 0b00000001);
-    v_min = _mm256_min_ps(v_min, v_shuffled);
-    v_shuffled = _mm256_permute2f128_ps(v_min, v_min, 0b00000001);
-    v_min = _mm256_min_ps(v_min, v_shuffled);
-    return _mm256_cvtss_f32(v_min);
-  }
 };
 #endif
 
@@ -890,34 +652,6 @@ struct INT8Vec64 : public Vec<INT8Vec64> {
   // non-temporal save
   void nt_save(int8_t* ptr) { _mm512_stream_si512((__m512i*)ptr, reg); }
 };
-#else
-struct INT8Vec16 : public Vec<INT8Vec16> {
-  constexpr static int VEC_ELEM_NUM = 16;
-  union AliasReg {
-    __m128i reg;
-    int8_t values[VEC_ELEM_NUM];
-  };
-
-  __m128i reg;
-
-  explicit INT8Vec16(const FP32Vec16& vec) {
-    __m256i lo_i32 = _mm256_cvtps_epi32(vec.reg_low);
-    __m256i hi_i32 = _mm256_cvtps_epi32(vec.reg_high);
-    __m256i packed16 = _mm256_packs_epi32(lo_i32, hi_i32);
-    packed16 = _mm256_permute4x64_epi64(packed16, 0xD8);
-    __m256i packed8 = _mm256_packs_epi16(packed16, _mm256_setzero_si256());
-    packed8 = _mm256_permute4x64_epi64(packed8, 0xD8);
-    reg = _mm256_castsi256_si128(packed8);
-  }
-
-  void save(int8_t* ptr) const { _mm_storeu_si128((__m128i*)ptr, reg); }
-
-  void save(int8_t* ptr, const int elem_num) const {
-    AliasReg ar;
-    ar.reg = reg;
-    for (int i = 0; i < elem_num; ++i) ptr[i] = ar.values[i];
-  }
-};
 #endif
 
 template <typename T>

csrc/cpu/dnnl_kernels.cpp

@@ -215,7 +215,7 @@ void dynamic_quant_epilogue(const float* input, scalar_t* output,
         float zp_scale_val = a_scale[i] * static_cast<float>(azp[i]);
         token_zp_scale_vec = cvt_vec_t(zp_scale_val);
       }
-      for (; j < hidden_size - vec_elem_num; j += vec_elem_num) {
+      for (; j < hidden_size - vec_elem_num; ++j) {
         cvt_vec_t elems_fp32(input_ptr + j);
         elems_fp32 = elems_fp32 * token_scale_vec;
         if constexpr (AZP) {

csrc/cpu/generate_cpu_attn_dispatch.py

@@ -20,98 +20,73 @@ ISA_TYPES = {
     "VEC16": 2,
     "NEON": 3,
     "VXE": 4,
-    "VSX": 5,
-}
-
-# KV cache index: 0 = auto (same as scalar_t), 1 = fp8_e4m3, 2 = fp8_e5m2
-KV_CACHE_IDX = {
-    "auto": 0,
-    "fp8_e4m3": 1,
-    "fp8_e5m2": 2,
-}
-
-# C++ type for each kv_cache index
-KV_CACHE_CPP_TYPES = {
-    "auto": "scalar_t",
-    "fp8_e4m3": "c10::Float8_e4m3fn",
-    "fp8_e5m2": "c10::Float8_e5m2",
 }
 
 # ISAs supported for head_dims divisible by 32
-ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE", "VSX"]
+ISA_FOR_32 = ["AMX", "NEON", "VEC", "VEC16", "VXE"]
 
 # ISAs supported for head_dims divisible by 16 only
 ISA_FOR_16 = ["VEC16"]
 
-# ISAs that support FP8 KV cache (x86 AVX2/AVX-512 required)
-ISA_FOR_FP8 = ["AMX", "VEC"]
 
-
-def encode_params(head_dim: int, isa_type: str, kv_cache: str = "auto") -> int:
-    """Encode head_dim, ISA type, and KV cache type into a single int64_t."""
+def encode_params(head_dim: int, isa_type: str) -> int:
+    """Encode head_dim and ISA type into a single int64_t."""
     isa_val = ISA_TYPES[isa_type]
-    kv_val = KV_CACHE_IDX[kv_cache]
-    # Encoding: (head_dim << 16) | (kv_cache_idx << 8) | isa_type
-    # This allows head_dim up to 2^48 - 1, 256 KV cache types, and 256 ISA types
-    return (head_dim << 16) | (kv_val << 8) | isa_val
+    # Encoding: (head_dim << 8) | isa_type
+    # This allows head_dim up to 2^56 - 1 and 256 ISA types
+    return (head_dim << 8) | isa_val
 
 
-def _make_case(
-    head_dim: int, isa: str, kv_cache: str = "auto", isa_override: str | None = None
-) -> str:
-    """Generate a single switch case line."""
-    encoded = encode_params(head_dim, isa, kv_cache)
-    actual_isa = isa_override if isa_override else isa
-    cpp_type = KV_CACHE_CPP_TYPES[kv_cache]
-    attn_impl = (
-        f"cpu_attention::AttentionImpl<"
-        f"cpu_attention::ISA::{actual_isa}, \\\n"
-        f"                                                       "
-        f"scalar_t, head_dim, {cpp_type}>"
-    )
-    comment = (
-        f"head_dim={head_dim}, isa={isa}"
-        if kv_cache == "auto"
-        else f"head_dim={head_dim}, isa={isa}, kv_cache={kv_cache}"
-    )
-    return (
-        f"""      case {encoded}LL: {{ """
-        f"""/* {comment} */ \\"""
-        f"""
-        constexpr size_t head_dim = {head_dim}; \\"""
-        f"""
-        using attn_impl = {attn_impl}; \\"""
-        f"""
-        return __VA_ARGS__(); \\"""
-        f"""
-      }} \\"""
-    )
-
-
-def generate_cases_for_isa_group(isa_list: list[str], include_fp8: bool = False) -> str:
+def generate_cases_for_isa_group(isa_list: list[str]) -> str:
     """Generate switch cases for a specific ISA group."""
     cases = []
 
-    # Non-FP8 cases for head_dims divisible by 32
+    # Generate cases for head_dims divisible by 32
     for head_dim in HEAD_DIMS_32:
         for isa in isa_list:
             if isa not in ISA_FOR_32:
                 continue
-            cases.append(_make_case(head_dim, isa, "auto"))
+            encoded = encode_params(head_dim, isa)
+            case_str = (
+                f"""      case {encoded}LL: {{ """
+                f"""/* head_dim={head_dim}, isa={isa} */ \\"""
+                f"""
+        constexpr size_t head_dim = {head_dim}; \\"""
+                f"""
+        using attn_impl = cpu_attention::AttentionImpl<"""
+                f"""cpu_attention::ISA::{isa}, \\"""
+                f"""
+                                                       """
+                f"""scalar_t, head_dim>; \\"""
+                f"""
+        return __VA_ARGS__(); \\"""
+                f"""
+      }} \\"""
+            )
+            cases.append(case_str)
 
-    # Non-FP8 cases for head_dims divisible by 16 only
+    # Generate cases for head_dims divisible by 16 only
     for head_dim in HEAD_DIMS_16:
         for isa in isa_list:
-            cases.append(_make_case(head_dim, isa, "auto", isa_override="VEC16"))
-
-    # FP8 cases: only AMX and VEC, only head_dims divisible by 32
-    if include_fp8:
-        for fp8_type in ("fp8_e4m3", "fp8_e5m2"):
-            for head_dim in HEAD_DIMS_32:
-                for isa in isa_list:
-                    if isa not in ISA_FOR_FP8:
-                        continue
-                    cases.append(_make_case(head_dim, isa, fp8_type))
+            encoded = encode_params(head_dim, isa)
+            case_str = (
+                f"""      case {encoded}LL: {{ """
+                f"""/* head_dim={head_dim}, isa={isa} """
+                f"""(using VEC16) */ \\"""
+                f"""
+        constexpr size_t head_dim = {head_dim}; \\"""
+                f"""
+        using attn_impl = cpu_attention::AttentionImpl<"""
+                f"""cpu_attention::ISA::VEC16, \\"""
+                f"""
+                                                       """
+                f"""scalar_t, head_dim>; \\"""
+                f"""
+        return __VA_ARGS__(); \\"""
+                f"""
+      }} \\"""
+            )
+            cases.append(case_str)
 
     return "\n".join(cases)
 
@@ -119,9 +94,8 @@ def generate_cases_for_isa_group(isa_list: list[str], include_fp8: bool = False)
 def generate_helper_function() -> str:
     """Generate helper function to encode parameters."""
     return """
-inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa,
-                                      int64_t kv_cache_idx = 0) {
-  return (head_dim << 16) | (kv_cache_idx << 8) | static_cast<int64_t>(isa);
+inline int64_t encode_cpu_attn_params(int64_t head_dim, cpu_attention::ISA isa) {
+  return (head_dim << 8) | static_cast<int64_t>(isa);
 }
 """
 
@@ -149,94 +123,93 @@ def generate_header_file() -> str:
   #include "cpu_attn_vxe.hpp"
 #endif
 
-#ifdef __powerpc__
-  #include "cpu_attn_vsx.hpp"
-#endif
-
 """
 
     header += generate_helper_function()
 
     # Generate dispatch macro with conditional compilation for different ISA sets
     header += """
-// Dispatch macro using encoded parameters.
-// KV_CACHE_IDX: Fp8KVCacheDataType enum value (kAuto=0, kFp8E4M3=1, kFp8E5M2=2).
-// FP8 cases (kv_cache_idx != 0) are generated on x86 platforms with AVX2 or
-// AVX-512: BF16Vec32 FP8 constructors have both AVX-512 and AVX2 implementations
-// in cpu_types_x86.hpp. Non-x86 platforms (#else fallback) have fp8=False.
+// Dispatch macro using encoded parameters
 """
 
-    def _macro_block(guard: str, isa_list: list[str], fp8: bool) -> str:
-        """Return one CPU_ATTN_DISPATCH macro block for a given guard."""
-        enc = (
-            "    int64_t encoded_params = encode_cpu_attn_params("
-            "HEAD_DIM, ISA_TYPE, KV_CACHE_IDX); \\"
-        )
-        cases = generate_cases_for_isa_group(isa_list, include_fp8=fp8)
-        tail = (
-            "\n"
-            "      default: { \\\n"
-            "        TORCH_CHECK(false, "
-            '"Unsupported CPU attention configuration: head_dim=" + \\\n'
-            '                    std::to_string(HEAD_DIM) + " isa=" + \\\n'
-            "                    std::to_string(static_cast<int>(ISA_TYPE))"
-            " + \\\n"
-            '                    " kv_cache_idx=" + '
-            "std::to_string(KV_CACHE_IDX)); \\\n"
-            "      } \\\n"
-            "    } \\\n"
-            "  }()\n\n"
-        )
-        return (
-            f"{guard}\n"
-            "#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, KV_CACHE_IDX, ...) \\\n"
-            "  [&] { \\\n"
-            f"{enc}\n"
-            "    switch (encoded_params) { \\\n"
-            f"{cases}"
-            f"{tail}"
-        )
+    # x86_64 with AMX
+    header += """#if defined(CPU_CAPABILITY_AMXBF16)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["AMX", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
 
-    header += _macro_block(
-        "#if defined(CPU_CAPABILITY_AMXBF16)",
-        ["AMX", "VEC", "VEC16"],
-        fp8=True,
-    )
-    header += _macro_block(
-        "#elif defined(__aarch64__)",
-        ["NEON", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__s390x__)",
-        ["VXE", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__powerpc__)",
-        ["VSX", "VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#elif defined(__AVX512F__)",
-        ["VEC", "VEC16"],
-        fp8=True,
-    )
-    header += _macro_block(
-        "#elif defined(__AVX2__)",
-        ["VEC", "VEC16"],
-        fp8=False,
-    )
-    header += _macro_block(
-        "#else",
-        ["VEC", "VEC16"],
-        fp8=False,
-    )
-    header += (
-        "#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / "
-        "__s390x__ / __powerpc__ */\n\n"
-        "#endif  // CPU_ATTN_DISPATCH_GENERATED_H\n"
-    )
+"""
+
+    # ARM64 with NEON
+    header += """#elif defined(__aarch64__)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["NEON", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+"""
+
+    # s390x with VXE
+    header += """#elif defined(__s390x__)
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["VXE", "VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+"""
+
+    # Fallback: VEC and VEC16 only
+    header += """#else
+#define CPU_ATTN_DISPATCH(HEAD_DIM, ISA_TYPE, ...) \\
+  [&] { \\
+    int64_t encoded_params = encode_cpu_attn_params(HEAD_DIM, ISA_TYPE); \\
+    switch (encoded_params) { \\
+"""
+    header += generate_cases_for_isa_group(["VEC", "VEC16"])
+    header += """
+      default: { \\
+        TORCH_CHECK(false, "Unsupported CPU attention configuration: head_dim=" + \\
+                    std::to_string(HEAD_DIM) + " isa=" + \\
+                    std::to_string(static_cast<int>(ISA_TYPE))); \\
+      } \\
+    } \\
+  }()
+
+#endif  /* CPU_CAPABILITY_AMXBF16 / __aarch64__ / __s390x__ */
+
+#endif  // CPU_ATTN_DISPATCH_GENERATED_H
+"""
 
     return header
 

csrc/cpu/sgl-kernels/common.h

@@ -1,12 +1,13 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-// clang-format off
-
 #pragma once
 
 #include <ATen/ATen.h>
 #include <ATen/Parallel.h>
+#include <ATen/record_function.h>
+
+// clang-format off
 
 #if defined(_OPENMP)
 #include <omp.h>
@@ -15,157 +16,40 @@
 namespace {
 
 // dispatch bool
-#define AT_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...) \
-  [&] {                                          \
-    if (BOOL_V) {                                \
-      constexpr bool BOOL_NAME = true;           \
-      return __VA_ARGS__();                      \
-    } else {                                     \
-      constexpr bool BOOL_NAME = false;          \
-      return __VA_ARGS__();                      \
-    }                                            \
+#define AT_DISPATCH_BOOL(BOOL_V, BOOL_NAME, ...)                                 \
+  [&] {                                                                          \
+    if (BOOL_V) {                                                                \
+      constexpr bool BOOL_NAME = true;                                           \
+      return __VA_ARGS__();                                                      \
+    } else {                                                                     \
+      constexpr bool BOOL_NAME = false;                                          \
+      return __VA_ARGS__();                                                      \
+    }                                                                            \
   }()
 
-#define AT_DISPATCH_BOOL2(BOOL_V1, BOOL_NAME1, BOOL_V2, BOOL_NAME2, ...) \
-  [&] {                                                                  \
-    if (BOOL_V1) {                                                       \
-      constexpr bool BOOL_NAME1 = true;                                  \
-      if (BOOL_V2) {                                                     \
-        constexpr bool BOOL_NAME2 = true;                                \
-        return __VA_ARGS__();                                            \
-      } else {                                                           \
-        constexpr bool BOOL_NAME2 = false;                               \
-        return __VA_ARGS__();                                            \
-      }                                                                  \
-    } else {                                                             \
-      constexpr bool BOOL_NAME1 = false;                                 \
-      if (BOOL_V2) {                                                     \
-        constexpr bool BOOL_NAME2 = true;                                \
-        return __VA_ARGS__();                                            \
-      } else {                                                           \
-        constexpr bool BOOL_NAME2 = false;                               \
-        return __VA_ARGS__();                                            \
-      }                                                                  \
-    }                                                                    \
-  }()
-
-// dispatch: bfloat16, float16, int8_t, fp8_e4m3, uint8_t(mxfp4/int4)
-#define CPU_DISPATCH_PACKED_TYPES(TYPE, ...)                     \
-  [&] {                                                          \
-    switch (TYPE) {                                              \
-      case at::ScalarType::BFloat16: {                           \
-        using packed_t = at::BFloat16;                           \
-        return __VA_ARGS__();                                    \
-      }                                                          \
-      case at::ScalarType::Half: {                               \
-        using packed_t = at::Half;                               \
-        return __VA_ARGS__();                                    \
-      }                                                          \
-      case at::ScalarType::Char: {                               \
-        using packed_t = int8_t;                                 \
-        return __VA_ARGS__();                                    \
-      }                                                          \
-      case at::ScalarType::Float8_e4m3fn: {                      \
-        using packed_t = at::Float8_e4m3fn;                      \
-        return __VA_ARGS__();                                    \
-      }                                                          \
-      case at::ScalarType::Byte: {                               \
-        using packed_t = uint8_t;                                \
-        return __VA_ARGS__();                                    \
-      }                                                          \
-      default:                                                   \
-        TORCH_CHECK(false, "Unsupported floating data type.\n"); \
-    }                                                            \
-  }()
-
-// Helper MICRO for CPU_DISPATCH_FLOATING_TYPES_EXT:
-//   TYPE1: the primary dtype (input, output, weight);
-//   TYPE2: defined as PARAM_T input
-#define CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, PARAM_T, ...)   \
-  switch (TYPE1) {                                           \
-    case at::ScalarType::BFloat16: {                         \
-      using scalar_t = at::BFloat16;                         \
-      using param_t = PARAM_T;                               \
-      return __VA_ARGS__();                                  \
-    }                                                        \
-    case at::ScalarType::Half: {                             \
-      using scalar_t = at::Half;                             \
-      using param_t = PARAM_T;                               \
-      return __VA_ARGS__();                                  \
-    }                                                        \
-    case at::ScalarType::Float: {                            \
-      using scalar_t = float;                                \
-      using param_t = PARAM_T;                               \
-      return __VA_ARGS__();                                  \
-    }                                                        \
-    default:                                                 \
-      TORCH_CHECK(false, "Unsupported floating data type."); \
-  }
-
-// Helper MICRO for CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT:
-//   TYPE1: the primary dtype (input, output, weight);
-//   TYPE2: defined as PARAM_T input
-#define CPU_DISPATCH_TYPE1_WITH_PARAM_REDUCED(TYPE1, PARAM_T, ...) \
-  switch (TYPE1) {                                                 \
-    case at::ScalarType::BFloat16: {                               \
-      using scalar_t = at::BFloat16;                               \
-      using param_t = PARAM_T;                                     \
-      return __VA_ARGS__();                                        \
-    }                                                              \
-    case at::ScalarType::Half: {                                   \
-      using scalar_t = at::Half;                                   \
-      using param_t = PARAM_T;                                     \
-      return __VA_ARGS__();                                        \
-    }                                                              \
-    default:                                                       \
-      TORCH_CHECK(false, "Unsupported floating data type.");       \
-  }
-
-// Helper MICRO for CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT:
-//   TYPE1: the dtype both for scalar_t and param_t
-#define CPU_DISPATCH_TYPE1_WITH_SAME_PARAM_REDUCED(TYPE1, ...)       \
-  switch (TYPE1) {                                                   \
-    case at::ScalarType::BFloat16: {                                 \
-      using scalar_t = at::BFloat16;                                 \
-      using param_t = at::BFloat16;                                  \
-      return __VA_ARGS__();                                          \
-    }                                                                \
-    case at::ScalarType::Half: {                                     \
-      using scalar_t = at::Half;                                     \
-      using param_t = at::Half;                                      \
-      return __VA_ARGS__();                                          \
-    }                                                                \
-    default:                                                         \
-      TORCH_CHECK(false, "Unsupported reduced floating data type."); \
-  }
-
-// dispatch with mixed dtypes (TYPE1, TYPE2):
-//   TYPE1: the primary dtype (input, output, weight);
-//   TYPE2: the secondary dtype (bias, etc.).
-#define CPU_DISPATCH_FLOATING_TYPES_EXT(TYPE1, TYPE2, ...)            \
-  [&] {                                                               \
-    if (TYPE2 == at::kFloat) {                                        \
-      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, float, __VA_ARGS__)        \
-    } else if (TYPE2 == at::ScalarType::BFloat16) {                   \
-      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, at::BFloat16, __VA_ARGS__) \
-    } else if (TYPE2 == at::ScalarType::Half) {                       \
-      CPU_DISPATCH_TYPE1_WITH_PARAM(TYPE1, at::Half, __VA_ARGS__)     \
-    } else {                                                          \
-      TORCH_CHECK(false, "Unsupported floating data type.");          \
-    }                                                                 \
-  }()
-
-// dispatch with mixed dtypes (reduced one, no float for TYPE1) (TYPE1, TYPE2):
-//   TYPE1: the primary dtype (input, output, weight);
-//   TYPE2: the secondary dtype (bias, etc.).
-#define CPU_DISPATCH_REDUCED_FLOATING_TYPES_EXT(TYPE1, TYPE2, ...)     \
-  [&] {                                                                \
-    if (TYPE2 == at::kFloat) {                                         \
-      CPU_DISPATCH_TYPE1_WITH_PARAM_REDUCED(TYPE1, float, __VA_ARGS__) \
-    } else {                                                           \
-      TORCH_CHECK(TYPE1 == TYPE2);                                     \
-      CPU_DISPATCH_TYPE1_WITH_SAME_PARAM_REDUCED(TYPE1, __VA_ARGS__)   \
-    }                                                                  \
+// dispatch: bfloat16, float16, int8_t, fp8_e4m3
+#define CPU_DISPATCH_PACKED_TYPES(TYPE, ...)                                    \
+  [&] {                                                                         \
+    switch (TYPE) {                                                             \
+      case at::ScalarType::BFloat16 : {                                         \
+        using packed_t = at::BFloat16;                                          \
+        return __VA_ARGS__();                                                   \
+      }                                                                         \
+      case at::ScalarType::Half: {                                              \
+        using packed_t = at::Half;                                              \
+        return __VA_ARGS__();                                                   \
+      }                                                                         \
+      case at::ScalarType::Char : {                                             \
+        using packed_t = int8_t;                                                \
+        return __VA_ARGS__();                                                   \
+      }                                                                         \
+      case at::ScalarType::Float8_e4m3fn : {                                    \
+        using packed_t = at::Float8_e4m3fn;                                     \
+        return __VA_ARGS__();                                                   \
+      }                                                                         \
+      default:                                                                  \
+        TORCH_CHECK(false, "Unsupported floating data type.\n");                \
+    }                                                                           \
   }()
 
 #define UNUSED(x) (void)(x)
@@ -186,51 +70,13 @@ namespace {
 #define CHECK_DIM(d, x) TORCH_CHECK(x.dim() == d, #x " must be a " #d "D tensor")
 
 #define CHECK_EQ(a, b) TORCH_CHECK((a) == (b), "CHECK_EQ(" #a ", " #b ") failed. ", a, " vs ", b)
-#define CHECK_GT(a, b) TORCH_CHECK((a) > (b), "CHECK_GT(" #a ", " #b ") failed. ", a, " vs ", b)
-#define CHECK_GE(a, b) TORCH_CHECK((a) >= (b), "CHECK_GE(" #a ", " #b ") failed. ", a, " vs ", b)
 
-template <bool is_only_lastdim_contiguous>
-static inline void CHECK_INPUT_SHAPE_DTYPE(const at::Tensor& tensor, const at::IntArrayRef sizes, at::ScalarType st) {
-  TORCH_CHECK(tensor.sizes() == sizes, "Input tensor shape mismatch: expected ", sizes, ", got ", tensor.sizes());
-  TORCH_CHECK(tensor.scalar_type() == st, "Input tensor dtype mismatch");
-  if constexpr (is_only_lastdim_contiguous) {
-    CHECK_LAST_DIM_CONTIGUOUS_INPUT(tensor);
-  } else {
-    CHECK_INPUT(tensor);
-  }
-}
-
-// [NB] Parallel Routines
-//
-//  * at::parallel_for - applies for most of generic use cases, this will be compiled
-//                       against openmp in default torch release.
-//
-//  * parallel_for     - same function as above, can choose payload partition scheme in
-//                       balance211.
-//
-//  * parallel_2d      - parallel for 2 dimensions, used in GEMM, etc.
-//                       this one will do payload balance across 2 dimensions.
-//
-
-// grain size for each thread
+// parallel routines
 constexpr int GRAIN_SIZE = 1024;
 
 template <typename T, typename std::enable_if<std::is_integral<T>::value, int>::type = 0>
-inline T div_up(T x, T y) {
-  return (x + y - 1) / y;
-}
+inline T div_up(T x, T y) { return (x + y - 1) / y; }
 
-// you can only use at::get_thread_num() with at::parallel_for()
-// as it is lazy initialized, otherwise it will always return 0.
-inline int get_thread_num() {
-#if defined(_OPENMP)
-  return omp_get_thread_num();
-#else
-  return 0;
-#endif
-}
-
-// balance payload across each thread
 template <typename T>
 inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
 #if 0
@@ -248,10 +94,10 @@ inline void balance211(T n, T nth, T ith, T& n_start, T& n_end) {
     }
     n_end += n_start;
 #else
-  // pytorch aten partition pattern
-  T n_my = div_up(n, nth);
-  n_start = ith * n_my;
-  n_end = std::min(n_start + n_my, n);
+    // pytorch aten partition pattern
+    T n_my = div_up(n, nth);
+    n_start = ith * n_my;
+    n_end = std::min(n_start + n_my, n);
 #endif
 }
 
@@ -259,15 +105,23 @@ template <typename func_t>
 inline void parallel_for(int n, const func_t& f) {
 #if defined(_OPENMP)
 #pragma omp parallel
-  {
+{
     int nth = omp_get_num_threads();
     int ith = omp_get_thread_num();
     int tbegin, tend;
     balance211(n, nth, ith, tbegin, tend);
     f(tbegin, tend);
-  }
+}
 #else
-  f(0, n);
+    f(0, n);
+#endif
+}
+
+inline int get_thread_num() {
+#if defined(_OPENMP)
+  return omp_get_thread_num();
+#else
+  return 0;
 #endif
 }
 
@@ -283,6 +137,7 @@ int inline adjust_num_threads(int m) {
 
 template <typename func_t>
 inline void parallel_2d(int m, int n, const func_t& f) {
+
   // make sure we have even num_threads
   int nth = adjust_num_threads(m);
 
@@ -310,59 +165,31 @@ inline void parallel_2d(int m, int n, const func_t& f) {
 
 #if defined(_OPENMP)
 #pragma omp parallel num_threads(nth)
-  {
-    int ith = omp_get_thread_num();
-    int ith_m = ith / nth_n;
-    int ith_n = ith % nth_n;
+{
+  int ith = omp_get_thread_num();
+  int ith_m = ith / nth_n;
+  int ith_n = ith % nth_n;
 
-    int thread_block_m = div_up(m, nth_m);
-    int thread_block_n = div_up(n, nth_n);
+  int thread_block_m = div_up(m, nth_m);
+  int thread_block_n = div_up(n, nth_n);
 
-    int begin_m = ith_m * thread_block_m;
-    int end_m = std::min(m, begin_m + thread_block_m);
-    int begin_n = ith_n * thread_block_n;
-    int end_n = std::min(n, begin_n + thread_block_n);
+  int begin_m = ith_m * thread_block_m;
+  int end_m = std::min(m, begin_m + thread_block_m);
+  int begin_n = ith_n * thread_block_n;
+  int end_n = std::min(n, begin_n + thread_block_n);
 
-    f(begin_m, end_m, begin_n, end_n);
-  }
+  f(begin_m, end_m, begin_n, end_n);
+}
 #else
   f(0, m, 0, n);
 #endif
 }
 
-// limit max cache blocks
-// when we need to do pre-unpack for weights, e.g. fp8
-#define MAX_CACHE_BLOCK_SIZE 4
-
 template <typename T>
-inline int get_cache_blocks(int chunk_size) {
+int get_cache_blocks(int BLOCK_SIZE, int K) {
   // L2 2MB and ratio of 50%
   const int L2_size = 2048 * 1024 >> 1;
-  return std::max(1, int(L2_size / (chunk_size * sizeof(T))));
-}
-
-template <>
-inline int get_cache_blocks<at::Float8_e4m3fn>(int chunk_size) {
-  // fp8 uses bf16 as accumulate type
-  int cache_block_size = get_cache_blocks<at::BFloat16>(chunk_size);
-  return std::min(MAX_CACHE_BLOCK_SIZE, cache_block_size);
-}
-
-// 2d sequential loop in range : [mb0, mb1), [nb0, nb1)
-template <typename T, typename func_t>
-inline void loop_2d(int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1, int64_t chunk_size, const func_t& f) {
-  // get number of blocks for L2 in most inner loop
-  int64_t cache_blocks_nb = get_cache_blocks<T>(chunk_size);
-
-  // loop order: [NB / cache_blocks_nb, MB, cache_blocks_nb]
-  // TODO: implement reverse order of [MB / cache_blocks_mb, NB, cache_blocks_mb]
-  for (int64_t nbb = nb0; nbb < nb1; nbb += cache_blocks_nb) {
-    for (int64_t mb = mb0; mb < mb1; ++mb) {
-      for (int64_t nb = nbb; nb < std::min(nbb + cache_blocks_nb, nb1); ++nb) {
-        f(mb, nb, nb - nbb);
-      }
-    }
-  }
+  return std::max(1, int(L2_size / (BLOCK_SIZE * K * sizeof(T))));
 }
 
 // data indexing for dimension collapse
@@ -416,10 +243,4 @@ struct Unroll<1> {
   }
 };
 
-// conditional data ptr for optional tensor
-template <typename T>
-inline T* conditional_data_ptr(const std::optional<at::Tensor>& opt) {
-  return opt.has_value() ? opt.value().data_ptr<T>() : nullptr;
-}
-
-}  // anonymous namespace
+} // anonymous namespace

csrc/cpu/sgl-kernels/conv.cpp

@@ -1,709 +0,0 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
-
-// clang-format off
-
-#include "common.h"
-#include "gemm.h"
-#include "vec.h"
-
-namespace {
-
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ y, const scalar_t* __restrict__ x, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  const bool is_padding = (x == nullptr);
-  for (int64_t d = 0; d < size; d += Vec::size()) {
-    Vec data_vec = is_padding ? Vec(0.f) : Vec::loadu(x + d);
-    data_vec.store(y + d);
-  }
-}
-
-// no remainder
-template <typename scalar_t>
-void inline update_conv_state(
-    scalar_t* __restrict__ conv_states,
-    const scalar_t* __restrict__ input,
-    int64_t width,
-    int64_t dim,
-    int64_t seqlen,
-    bool has_initial_states) {
-  // width for `conv_states`
-  int64_t width1 = width - 1;
-  int64_t w = 0;
-  for (; w < width1 - seqlen; ++w) {
-    scalar_t* y = conv_states + w * dim;
-    const scalar_t* x = has_initial_states ? conv_states + (w + seqlen) * dim : nullptr;
-    copy_stub(y, x, dim);
-  }
-  for (; w < width1; ++w) {
-    scalar_t* y = conv_states + w * dim;
-    const scalar_t* x = input + (w + seqlen - width1) * dim;
-    copy_stub(y, x, dim);
-  }
-}
-
-// A : [M, BLOCK_N]
-// B : [BLOCK_N, K], prepacked as [K/2, BLOCK_N, 2]
-// C : [M, BLOCK_N]
-// bias : [BLOCK_N]
-//
-// lda : leading dimension of `input` and `out`
-//
-template <typename scalar_t, int K, int BLOCK_N, bool has_bias, bool has_silu>
-struct tinygemm_kernel {
-  static inline void apply(
-      const scalar_t* __restrict__ A,
-      const scalar_t* __restrict__ B,
-      scalar_t* __restrict__ C,
-      const scalar_t* __restrict__ bias,
-      const scalar_t* __restrict__ conv_states,
-      bool has_initial_state,
-      int64_t M,
-      int64_t lda,
-      bool is_first_token) {
-    TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
-  }
-};
-
-#if defined(CPU_CAPABILITY_AVX512)
-template <int K, int BLOCK_N, bool has_bias, bool has_silu>
-struct tinygemm_kernel<at::BFloat16, K, BLOCK_N, has_bias, has_silu> {
-  static inline void apply(
-      const at::BFloat16* __restrict__ A,
-      const at::BFloat16* __restrict__ B,
-      at::BFloat16* __restrict__ C,
-      const at::BFloat16* __restrict__ bias,
-      const at::BFloat16* __restrict__ conv_states,
-      bool has_initial_state,
-      int64_t M,
-      int64_t lda,
-      bool is_first_token) {
-    assert(K == 4);
-    constexpr int ROWS = K;
-    constexpr int COLS = BLOCK_N / block_size_n();
-
-    // leading dimension size for b for next block [K/2, 32, 2]
-    constexpr int ldb = block_size_n() * K;
-
-    __m512bh va[ROWS * COLS];
-    __m512bh vb[ROWS * COLS];
-    __m512 vc[COLS * 2];
-
-    // k: {-3, -2, -1} -> {0, 1, 2}
-    auto set_conv_states = [&](int k, int col) -> __m512i {
-      return has_initial_state ? _mm512_loadu_si512(conv_states + (k + K - 1) * lda + col * 32)
-                               : _mm512_setzero_si512();
-    };
-
-#define MM512_LOAD_A(idx)                                                 \
-  ((idx) < 0 && is_first_token) ? (__m512bh)(set_conv_states((idx), col)) \
-                                : (__m512bh)(_mm512_loadu_si512(A + (idx) * lda + col * 32))
-
-#define MM512_PACK_A(ap, bp, a, b)                       \
-  do {                                                   \
-    __m512i r0 = (__m512i)(a);                           \
-    __m512i r1 = (__m512i)(b);                           \
-    __m512i d0 = _mm512_unpacklo_epi16(r0, r1);          \
-    __m512i d1 = _mm512_unpackhi_epi16(r0, r1);          \
-    r0 = _mm512_shuffle_i32x4(d0, d1, 0x88);             \
-    r1 = _mm512_shuffle_i32x4(d0, d1, 0xdd);             \
-    (ap) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0x88); \
-    (bp) = (__m512bh)_mm512_shuffle_i32x4(r0, r1, 0xdd); \
-  } while (0)
-
-    // step 0 : preload a at time step [-3][-2][-1]
-    auto preloada = [&](auto i) {
-      constexpr int col = i;
-      int64_t m = 0;
-      va[1 * COLS + col] = MM512_LOAD_A(m - 3);
-      va[2 * COLS + col] = MM512_LOAD_A(m - 2);
-      va[3 * COLS + col] = MM512_LOAD_A(m - 1);
-    };
-    Unroll<COLS>{}(preloada);
-
-    auto loada = [&](auto i, int64_t m) {
-      constexpr int col = i;
-      // update previous time step
-      va[0 * COLS + col] = va[1 * COLS + col];
-      va[1 * COLS + col] = va[2 * COLS + col];
-      va[2 * COLS + col] = va[3 * COLS + col];
-      // load current time step
-      va[3 * COLS + col] = MM512_LOAD_A(m);
-    };
-
-    // step 1 : load weight for just once
-    auto loadb = [&](auto i) {
-      constexpr int row = i / COLS;
-      constexpr int col = i % COLS;
-      vb[row * COLS + col] = (__m512bh)(_mm512_loadu_si512(B + col * ldb + row * 32));
-    };
-    Unroll<ROWS * COLS>{}(loadb);
-
-    // [NB] accumulates 4x32 bfloat16 blocks
-    //
-    //   +------------+------------+
-    //   |    col0    |    col1    |
-    //   +------------+------------+
-    //   |  va0  va1  |  va0  va1  |
-    //   |  va2  va3  |  va2  va3  |
-    //   +------------+------------+
-    //   |  vc0  vc1  |  vc0  vc1  |
-    //   +------------+------------+
-    //
-    //  * va and vb shares the same memory layout
-    //  * block_n 32 with 4 rows equals to 4 registers
-    //  * 37 uops with avx512bf16 v.s. 57 uops with avx512f
-    //
-    auto compute = [&](auto i) {
-      constexpr int col = i;
-
-      // init accumulators
-      if constexpr (has_bias) {
-        __m512i b16 = _mm512_loadu_si512(reinterpret_cast<const __m512i*>(bias + col * 32));
-        vc[col * 2 + 0] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 0));
-        vc[col * 2 + 1] = CVT_BF16_TO_FP32(_mm512_extracti32x8_epi32(b16, 1));
-      } else {
-        vc[col * 2 + 0] = _mm512_set1_ps(0.f);
-        vc[col * 2 + 1] = _mm512_set1_ps(0.f);
-      }
-
-      // convert to vnni2 format
-      __m512bh va0, va1, va2, va3;
-      MM512_PACK_A(va0, va1, va[0 * COLS + col], va[1 * COLS + col]);
-      MM512_PACK_A(va2, va3, va[2 * COLS + col], va[3 * COLS + col]);
-
-      // accumulate
-      vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va0, vb[0 * COLS + col]);
-      vc[col * 2 + 0] = _mm512_dpbf16_ps(vc[col * 2 + 0], va2, vb[2 * COLS + col]);
-      vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va1, vb[1 * COLS + col]);
-      vc[col * 2 + 1] = _mm512_dpbf16_ps(vc[col * 2 + 1], va3, vb[3 * COLS + col]);
-    };
-
-    using fVec = at::vec::Vectorized<float>;
-    using bVec = at::vec::Vectorized<at::BFloat16>;
-    const fVec one = fVec(1.f);
-    auto storec = [&](auto i, int64_t m) {
-      constexpr int col = i;
-      fVec x0 = fVec(vc[col * 2 + 0]);
-      fVec x1 = fVec(vc[col * 2 + 1]);
-      if constexpr (has_silu) {
-        x0 = x0 / (one + x0.neg().exp_u20());
-        x1 = x1 / (one + x1.neg().exp_u20());
-      }
-      bVec out_vec = convert_from_float_ext<at::BFloat16>(x0, x1);
-      out_vec.store(C + m * lda + col * 32);
-    };
-
-    for (int64_t m = 0; m < M; ++m) {
-      // step 3.a : load a at current time step
-      Unroll<COLS>{}(loada, m);
-      // step 3.b : accumulate for window size (4)
-      Unroll<COLS>{}(compute);
-      // step 3.c : store c at current time step
-      Unroll<COLS>{}(storec, m);
-    }
-  }
-};
-#endif
-
-#define LAUNCH_TINYGEMM_KERNEL(K, NB_SIZE)                                                   \
-  tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply(                          \
-      input + bs * seqlen * dim + mb_start * dim + nb_start,                                 \
-      weight + nb_start * width,                                                             \
-      out + bs * seqlen * dim + mb_start * dim + nb_start,                                   \
-      has_bias ? bias + nb_start : nullptr,                                                  \
-      has_conv_states ? conv_states + conv_state_index * (K - 1) * dim + nb_start : nullptr, \
-      has_initial_states_value,                                                              \
-      mb_size,                                                                               \
-      dim,                                                                                   \
-      mb_start == 0);
-
-template <typename scalar_t>
-void causal_conv1d_fwd_kernel_impl(
-    scalar_t* __restrict__ out,
-    const scalar_t* __restrict__ input,
-    const scalar_t* __restrict__ weight,
-    const scalar_t* __restrict__ bias,
-    scalar_t* __restrict__ conv_states,
-    const int32_t* __restrict__ conv_indices,
-    const bool* __restrict__ has_initial_state,
-    bool silu_activation,
-    int64_t batch,
-    int64_t dim,
-    int64_t seqlen,
-    int64_t width,
-    int64_t num_seq_blocks) {
-  // handle 32 x 64 per block
-  constexpr int64_t BLOCK_M = block_size_m();
-  constexpr int64_t BLOCK_N = block_size_n() * 2;
-  const int64_t NB = div_up(dim, BLOCK_N);
-
-  const int64_t num_blocks_per_seq = div_up(seqlen, BLOCK_M);
-  const bool has_conv_states = conv_states != nullptr;
-  const bool has_conv_indices = conv_indices != nullptr;
-
-  // parallel on [batch, seq, NB]
-  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
-    at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
-      int64_t mb{0}, nb{0};
-      data_index_init(begin, mb, num_seq_blocks, nb, NB);
-
-      for (int64_t i = begin; i < end; ++i) {
-        int64_t bs = mb / num_blocks_per_seq;
-
-        int64_t mb_start = (mb % num_blocks_per_seq) * BLOCK_M;
-        int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
-        int64_t nb_start = nb * BLOCK_N;
-        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
-
-        const bool has_initial_states_value = has_conv_states ? has_initial_state[bs] : false;
-        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
-
-        switch (width << 4 | nb_size >> 4) {
-          case 0x42:
-            LAUNCH_TINYGEMM_KERNEL(4, 32);
-            break;
-          case 0x44:
-            LAUNCH_TINYGEMM_KERNEL(4, 64);
-            break;
-          default:
-            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
-        }
-
-        // move to the next index
-        data_index_step(mb, num_seq_blocks, nb, NB);
-      }
-    });
-  });
-
-  // update conv_states if necessary
-  if (has_conv_states) {
-    at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
-      for (int64_t bs = begin; bs < end; ++bs) {
-        update_conv_state(
-            conv_states + bs * (width - 1) * dim, input + bs * seqlen * dim, width, dim, seqlen, has_initial_state[bs]);
-      }
-    });
-  }
-}
-
-#define LAUNCH_TINYGEMM_VARLEN_KERNEL(K, NB_SIZE)                   \
-  tinygemm_kernel<scalar_t, K, NB_SIZE, has_bias, has_silu>::apply( \
-      input + batch_offset * dim + mb_start * dim + nb_start,       \
-      weight + nb_start * width,                                    \
-      out + batch_offset * dim + mb_start * dim + nb_start,         \
-      has_bias ? bias + nb_start : nullptr,                         \
-      nullptr,                                                      \
-      false,                                                        \
-      mb_size,                                                      \
-      dim,                                                          \
-      mb_start == 0);
-
-// TODO: add `has_initial_state` support for varlen kernel
-template <typename scalar_t>
-void causal_conv1d_fwd_varlen_kernel_impl(
-    scalar_t* __restrict__ out,
-    const scalar_t* __restrict__ input,
-    const scalar_t* __restrict__ weight,
-    const scalar_t* __restrict__ bias,
-    scalar_t* __restrict__ conv_states,
-    const int32_t* __restrict__ query_start_loc,
-    const int32_t* __restrict__ conv_indices,
-    const bool* __restrict__ has_initial_state,
-    const int32_t* __restrict__ block_indices,
-    bool silu_activation,
-    int64_t batch,
-    int64_t dim,
-    int64_t width,
-    int64_t num_seq_blocks) {
-  // handle 32 x 64 per block
-  constexpr int64_t BLOCK_M = block_size_m();
-  constexpr int64_t BLOCK_N = block_size_n() * 2;
-  const int64_t NB = div_up(dim, BLOCK_N);
-
-  const bool has_conv_states = conv_states != nullptr;
-  const bool has_conv_indices = conv_indices != nullptr;
-
-  // parallel on [batch, seq, NB]
-  AT_DISPATCH_BOOL2(bias != nullptr, has_bias, silu_activation, has_silu, [&] {
-    at::parallel_for(0, num_seq_blocks * NB, 0, [&](int64_t begin, int64_t end) {
-      int64_t mb{0}, nb{0};
-      data_index_init(begin, mb, num_seq_blocks, nb, NB);
-
-      for (int64_t i = begin; i < end; ++i) {
-        int32_t bs = block_indices[mb * 2 + 0];
-        int32_t batch_offset = query_start_loc[bs];
-        int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
-
-        int64_t mb_start = block_indices[mb * 2 + 1] * BLOCK_M;
-        int64_t mb_size = std::min(seqlen - mb_start, BLOCK_M);
-        int64_t nb_start = nb * BLOCK_N;
-        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
-
-        switch (width << 4 | nb_size >> 4) {
-          case 0x42:
-            LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 32);
-            break;
-          case 0x44:
-            LAUNCH_TINYGEMM_VARLEN_KERNEL(4, 64);
-            break;
-          default:
-            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
-        }
-
-        // move to the next index
-        data_index_step(mb, num_seq_blocks, nb, NB);
-      }
-    });
-  });
-
-  // update conv_states if necessary
-  if (has_conv_states) {
-    at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
-      for (int64_t bs = begin; bs < end; ++bs) {
-        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
-        int32_t seqlen = query_start_loc[bs + 1] - query_start_loc[bs];
-        int32_t batch_offset = query_start_loc[bs];
-        update_conv_state(
-            conv_states + conv_state_index * (width - 1) * dim,
-            input + batch_offset * dim,
-            width,
-            dim,
-            seqlen,
-            /* has_initial_state */ false);
-      }
-    });
-  }
-}
-
-template <typename scalar_t>
-void causal_conv1d_update_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__ conv_indices,
-    bool silu_activation,
-    int64_t batch,
-    int64_t dim,
-    int64_t seqlen,
-    int64_t width) {
-  // handle 32 x 64 per block
-  constexpr int64_t BLOCK_M = block_size_m();
-  constexpr int64_t BLOCK_N = block_size_n() * 2;
-  const int64_t NB = div_up(dim, BLOCK_N);
-
-  const bool has_conv_states = conv_states != nullptr;
-  const bool has_conv_indices = conv_indices != nullptr;
-
-  // parallel on [batch, NB]
-  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) {
-        int64_t mb_start = 0;
-        int64_t mb_size = 1;
-        int64_t nb_start = nb * BLOCK_N;
-        int64_t nb_size = std::min(dim - nb_start, BLOCK_N);
-
-        const bool has_initial_states_value = true;
-        int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
-
-        switch (width << 4 | nb_size >> 4) {
-          case 0x42:
-            LAUNCH_TINYGEMM_KERNEL(4, 32);
-            break;
-          case 0x44:
-            LAUNCH_TINYGEMM_KERNEL(4, 64);
-            break;
-          default:
-            TORCH_CHECK(false, "Unexpected block size, ", width, " x ", nb_size);
-        }
-
-        // move to the next index
-        data_index_step(bs, batch, nb, NB);
-      }
-    });
-  });
-
-#define CONV_STATE_INDEXR(w) conv_states + conv_state_index*(width - 1) * dim + (w) * dim
-
-  // update conv_states
-  at::parallel_for(0, batch, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t bs = begin; bs < end; ++bs) {
-      // update old states, range [1, width - 1)
-      int32_t conv_state_index = has_conv_indices ? conv_indices[bs] : bs;
-      for (int64_t w = 1; w < width - 1; ++w) {
-        std::memcpy(CONV_STATE_INDEXR(w - 1), CONV_STATE_INDEXR(w), dim * sizeof(scalar_t));
-      }
-      // copy new states
-      std::memcpy(CONV_STATE_INDEXR(width - 2), input + bs * dim, dim * sizeof(scalar_t));
-    }
-  });
-}
-
-}  // anonymous namespace
-
-// from [dim, width] or [N, K]
-// to [N/BLOCK_N, K/2, BLOCK_N, 2]
-at::Tensor causal_conv1d_weight_pack(const at::Tensor& weight) {
-  CHECK_INPUT(weight);
-
-  int64_t dim = weight.size(0);
-  int64_t width = weight.size(1);
-  constexpr int64_t BLOCK_N = block_size_n();
-  TORCH_CHECK(width == 4, "causal_conv1d_weight_pack: support only width of 4");
-  TORCH_CHECK(dim % BLOCK_N == 0, "causal_conv1d_weight_pack: invalid dim size ", dim);
-
-  const int64_t N = dim, K2 = width >> 1;
-  const int64_t NB = div_up(N, BLOCK_N);
-
-  auto packed_weight = at::empty_like(weight);
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(weight.scalar_type(), "causal_conv1d_fwd_kernel_impl", [&] {
-    // cast to float32 as vnni size is 2
-    const float* w_data = reinterpret_cast<float*>(weight.data_ptr<scalar_t>());
-    float* packed_data = reinterpret_cast<float*>(packed_weight.data_ptr<scalar_t>());
-
-    at::parallel_for(0, NB * K2 * BLOCK_N, 0, [&](int64_t begin, int64_t end) {
-      int64_t nb{0}, k2{0}, n{0};
-      data_index_init(begin, nb, NB, k2, K2, n, BLOCK_N);
-
-      // TODO: optimize this if we need to online prepacking.
-      for (int64_t i = begin; i < end; ++i) {
-        packed_data[i] = w_data[nb * BLOCK_N * K2 + n * K2 + k2];
-
-        // move to the next index
-        data_index_step(nb, NB, k2, K2, n, BLOCK_N);
-      }
-    });
-  });
-  return packed_weight;
-}
-
-#define CHECK_OPTIONAL_SHAPE_DTYPE(OPT, SIZE, DTYPE) \
-  if (OPT.has_value()) {                             \
-    const auto tensor = OPT.value();                 \
-    CHECK_CONTIGUOUS(tensor);                        \
-    CHECK_EQ(tensor.size(0), SIZE);                  \
-    CHECK_EQ(tensor.scalar_type(), DTYPE);           \
-  }
-
-template <int BLOCK_M>
-int64_t get_block_count(const std::optional<at::Tensor>& offsets, int64_t batch, int64_t seqlen) {
-  if (offsets.has_value()) {
-    const int32_t* offsets_data = offsets.value().data_ptr<int32_t>();
-    int32_t num_seq_blocks = 0;
-    for (int64_t row = 0; row < batch; ++row) {
-      num_seq_blocks += div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
-    }
-    return num_seq_blocks;
-  }
-  return batch * div_up(seqlen, int64_t(BLOCK_M));
-}
-
-template <int BLOCK_M>
-at::Tensor get_block_indices(const std::optional<at::Tensor>& offsets, int64_t num_seq_blocks) {
-  if (!offsets.has_value()) {
-    return at::Tensor();
-  }
-
-  const at::Tensor& offsets_ = offsets.value();
-  at::Tensor indices = at::empty({num_seq_blocks, 2}, offsets_.options());
-
-  int64_t batch = offsets_.size(0) - 1;
-
-  const int32_t* offsets_data = offsets_.data_ptr<int32_t>();
-  int32_t* indices_data = indices.data_ptr<int32_t>();
-
-  int64_t idx = 0;
-  for (int32_t row = 0; row < batch; ++row) {
-    int32_t blocks = div_up(offsets_data[row + 1] - offsets_data[row], BLOCK_M);
-
-    for (int32_t col = 0; col < blocks; ++col) {
-      indices_data[idx * 2 + 0] = row;
-      indices_data[idx * 2 + 1] = col;
-      idx++;
-    }
-  }
-  return indices;
-}
-
-// API aligned with GPUs
-//
-//   x: (batch, dim, seqlen) or (dim, cu_seq_len) for varlen
-//   weight: (dim, width)
-//   bias: (dim,)
-//   query_start_loc: (batch + 1) int32
-//   cache_indices: (batch)  int32
-//   has_initial_state: (batch) bool
-//   conv_states: (..., dim, width - 1) itype
-//   activation: either None or "silu" or "swish"
-//   pad_slot_id: int
-//
-at::Tensor causal_conv1d_fwd_cpu(
-    const at::Tensor& x,
-    const at::Tensor& weight,
-    const std::optional<at::Tensor>& bias,
-    const std::optional<at::Tensor>& conv_states,
-    const std::optional<at::Tensor>& query_start_loc,
-    const std::optional<at::Tensor>& conv_state_indices,
-    const std::optional<at::Tensor>& has_initial_state,
-    bool silu_activation,
-    int64_t pad_slot_id,
-    bool is_vnni) {
-  CHECK_CONTIGUOUS(weight);
-  auto packed_w = is_vnni ? weight : causal_conv1d_weight_pack(weight);
-
-  const bool is_var_seqlen = query_start_loc.has_value();
-  const int64_t input_ndim = is_var_seqlen ? 2 : 3;
-  TORCH_CHECK(x.dim() == input_ndim, "causal_conv1d_fwd_cpu: expect x to be ", input_ndim, "D tensor.");
-  TORCH_CHECK(x.stride(-2) == 1 && x.stride(-1) == x.size(-2), "causal_conv1d_fwd_cpu: expect x to be transposed.");
-
-  const int64_t batch = is_var_seqlen ? query_start_loc.value().size(0) - 1 : x.size(0);
-  const int64_t dim = x.size(-2);
-  const int64_t seqlen = x.size(-1);
-  const int64_t width = weight.size(-1);
-
-  const auto scalar_type = x.scalar_type();
-  CHECK_EQ(weight.scalar_type(), scalar_type);
-  CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
-  CHECK_OPTIONAL_SHAPE_DTYPE(query_start_loc, batch + 1, at::kInt);
-  CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
-  CHECK_OPTIONAL_SHAPE_DTYPE(has_initial_state, batch, at::kBool);
-
-  if (conv_states.has_value()) {
-    auto& conv_states_val = conv_states.value();
-    int64_t padded_batch = conv_states_val.size(0);
-    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);
-
-    // adjust `conv_states` to be contiguous on `dim`
-    // should happen only once
-    if (conv_states_val.stride(-2) != 1) {
-      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);
-    }
-  }
-
-  // block size for sequence blocks, 32
-  constexpr int64_t BLOCK_M = block_size_m();
-
-  // total number of sequence blocks
-  int64_t num_seq_blocks = get_block_count<BLOCK_M>(query_start_loc, batch, seqlen);
-
-  at::Tensor out = at::empty_like(x);
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_fwd_kernel_impl", [&] {
-    if (is_var_seqlen) {
-      // record seq blocks in Coordinate format, aka [num_seq_blocks, 2]
-      at::Tensor block_indices = get_block_indices<BLOCK_M>(query_start_loc, num_seq_blocks);
-
-      causal_conv1d_fwd_varlen_kernel_impl(
-          out.data_ptr<scalar_t>(),
-          x.data_ptr<scalar_t>(),
-          packed_w.data_ptr<scalar_t>(),
-          conditional_data_ptr<scalar_t>(bias),
-          conditional_data_ptr<scalar_t>(conv_states),
-          conditional_data_ptr<int32_t>(query_start_loc),
-          conditional_data_ptr<int32_t>(conv_state_indices),
-          conditional_data_ptr<bool>(has_initial_state),
-          block_indices.data_ptr<int32_t>(),
-          silu_activation,
-          batch,
-          dim,
-          width,
-          num_seq_blocks);
-    } else {
-      causal_conv1d_fwd_kernel_impl<scalar_t>(
-          out.data_ptr<scalar_t>(),
-          x.data_ptr<scalar_t>(),
-          packed_w.data_ptr<scalar_t>(),
-          conditional_data_ptr<scalar_t>(bias),
-          conditional_data_ptr<scalar_t>(conv_states),
-          conditional_data_ptr<int32_t>(conv_state_indices),
-          conditional_data_ptr<bool>(has_initial_state),
-          silu_activation,
-          batch,
-          dim,
-          seqlen,
-          width,
-          num_seq_blocks);
-    }
-  });
-  return out;
-}
-
-// API aligned with GPUs
-//
-//   x: (batch, dim) or (batch, dim, seqlen)
-//   conv_state: (..., dim, state_len), where state_len >= width - 1
-//   weight: (dim, width)
-//   bias: (dim,)
-//   cache_seqlens: (batch,), dtype int32.
-//   conv_state_indices: (batch,), dtype int32
-//   pad_slot_id: int
-//   out: (batch, dim) or (batch, dim, seqlen)
-//
-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,
-    const std::optional<at::Tensor>& conv_state_indices,
-    int64_t pad_slot_id,
-    bool is_vnni) {
-  CHECK_CONTIGUOUS(x);
-  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.");
-
-  int64_t batch = x.size(0);
-  int64_t dim = x.size(1);
-  int64_t seqlen = 1;
-  int64_t width = weight.size(-1);
-
-  const auto scalar_type = x.scalar_type();
-  CHECK_EQ(weight.scalar_type(), scalar_type);
-  CHECK_OPTIONAL_SHAPE_DTYPE(bias, dim, scalar_type);
-  CHECK_OPTIONAL_SHAPE_DTYPE(conv_state_indices, batch, at::kInt);
-
-  CHECK_EQ(conv_states.scalar_type(), scalar_type);
-  CHECK_EQ(conv_states.size(1), dim);
-  CHECK_EQ(conv_states.size(2), width - 1);
-
-  // adjust `conv_states` to be contiguous on `dim`
-  if (conv_states.stride(-2) != 1) {
-    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});
-    conv_states.copy_(conv_states_copy);
-  }
-
-  at::Tensor out = at::empty_like(x);
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(scalar_type, "causal_conv1d_update_kernel_impl", [&] {
-    causal_conv1d_update_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),
-        conditional_data_ptr<int32_t>(conv_state_indices),
-        silu_activation,
-        batch,
-        dim,
-        seqlen,
-        width);
-  });
-  return out;
-}

csrc/cpu/sgl-kernels/gemm.cpp

@@ -1,12 +1,11 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-// clang-format off
-
-#include "gemm.h"
-
 #include "common.h"
 #include "vec.h"
+#include "gemm.h"
+
+// clang-format off
 
 namespace {
 
@@ -27,13 +26,13 @@ inline void s8s8_compensation(int8_t* __restrict__ packed, int K) {
   const __m512i off = _mm512_set1_epi8(static_cast<char>(0x80));
   for (int k = 0; k < K / 4; ++k) {
     for (int col = 0; col < COLS; ++col) {
-      __m512i vb = _mm512_loadu_si512((const __m512i*)(packed + k * BLOCK_N * 4 + col * 64));
+      __m512i vb = _mm512_loadu_si512((const __m512i *)(packed + k * BLOCK_N * 4 + col * 64));
       vcomp[col] = _mm512_dpbusd_epi32(vcomp[col], off, vb);
     }
   }
 
   for (int col = 0; col < COLS; ++col) {
-    _mm512_storeu_si512((__m512i*)(packed + offset + col * 64), vcomp[col]);
+    _mm512_storeu_si512((__m512i *)(packed + offset + col * 64), vcomp[col]);
   }
 #else
   TORCH_CHECK(false, "s8s8_compensation not implemented!");
@@ -70,43 +69,6 @@ inline void pack_vnni<int8_t>(int8_t* __restrict__ packed, const int8_t* __restr
   s8s8_compensation<BLOCK_N>(packed, K);
 }
 
-// uint8_t: mxfp4 or int4
-// pack to vnni2 format as they are computed with bfloat16
-//
-// from [N, K'/2, 2] to [K'/2, N, 2], view 2x int4 as unit8:
-// from [N,    K   ] to [K,    N   ] where K = K'/2
-//
-template <>
-inline void pack_vnni<uint8_t>(uint8_t* __restrict__ packed, const uint8_t* __restrict__ weight, int N, int K) {
-  constexpr int BLOCK_N = block_size_n();
-
-  uint8_t unpacked[2 * BLOCK_N];
-
-  // 32-way pack (align with BLOCK_N), faster for avx512 unpacking
-  //
-  // for a range of (64):
-  //   {0, 1, 2, ..., 63}
-  //
-  // original format:
-  //   { 1|0,  3|2, ..., 63|62}
-  //
-  // packed format:
-  //   {32|0, 31|1, ..., 63|31}
-  //
-  for (int k = 0; k < K; ++k) {
-    // unpack first
-    for (int n = 0; n < N; ++n) {
-      uint8_t value = weight[n * K + k];
-      unpacked[n * 2 + 0] = value & 0xF;  // lower 4 bits
-      unpacked[n * 2 + 1] = value >> 4;   // higher 4 bits
-    }
-    // re-pack to 32-way
-    for (int n = 0; n < N; ++n) {
-      packed[k * N + n] = (unpacked[n + BLOCK_N] << 4) | unpacked[n];
-    }
-  }
-}
-
 template <typename scalar_t>
 inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
   using bVec = at::vec::Vectorized<scalar_t>;
@@ -114,7 +76,7 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-#pragma GCC unroll 4
+  #pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d);
     fVec data1 = fVec::loadu(input + d + fVec::size());
@@ -127,34 +89,13 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
 }
 
 template <typename scalar_t>
-inline void copy_stub(float* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
+inline void copy_add_stub(scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
   constexpr int kVecSize = bVec::size();
 
   int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    fVec data0, data1;
-    bVec b_vec = bVec::loadu(input + d);
-    std::tie(data0, data1) = at::vec::convert_to_float(b_vec);
-    data0.store(out + d);
-    data1.store(out + d + fVec::size());
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<float>(input[d]);
-  }
-}
-
-template <typename scalar_t>
-inline void copy_add_stub(
-    scalar_t* __restrict__ out, const float* __restrict__ input, const float* __restrict__ bias, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-
-  int64_t d;
-#pragma GCC unroll 4
+  #pragma GCC unroll 4
   for (d = 0; d <= size - kVecSize; d += kVecSize) {
     fVec data0 = fVec::loadu(input + d) + fVec::loadu(bias + d);
     fVec data1 = fVec::loadu(input + d + fVec::size()) + fVec::loadu(bias + d + fVec::size());
@@ -166,51 +107,11 @@ inline void copy_add_stub(
   }
 }
 
-template <typename scalar_t, bool has_bias>
-inline void scalar_sigmoid_and_mul(
-    scalar_t* __restrict__ out,
-    const float* __restrict__ input,
-    const float* __restrict__ bias,
-    const scalar_t* __restrict__ mul,
-    int SIZE) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  // scalar sigmoid
-  const fVec one = fVec(1.f);
-  fVec X;
-  if constexpr (has_bias) {
-    assert(bias != nullptr);
-    X = fVec(input[0] + bias[0]);
-  } else {
-    X = fVec(input[0]);
-  }
-  X = one / (one + X.neg().exp_u20());
-
-  // vec mul
-  constexpr int kVecSize = bVec::size();
-  for (int d = 0; d < SIZE; d += kVecSize) {
-    bVec m_bvec = bVec::loadu(mul + d);
-    fVec m_fvec0, m_fvec1;
-    std::tie(m_fvec0, m_fvec1) = at::vec::convert_to_float(m_bvec);
-    m_fvec0 = m_fvec0 * X;
-    m_fvec1 = m_fvec1 * X;
-
-    bVec out_vec = convert_from_float_ext<scalar_t>(m_fvec0, m_fvec1);
-    out_vec.store(out + d);
-  }
-}
-
 template <typename scalar_t, bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const scalar_t* __restrict__ A,
-      const scalar_t* __restrict__ B,
-      scalar_t* __restrict__ C,
-      const float* __restrict__ bias,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
+      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -219,14 +120,9 @@ struct tinygemm_kernel_nn {
 template <bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A,
-      const at::BFloat16* __restrict__ B,
-      at::BFloat16* __restrict__ C,
-      const float* __restrict__ bias,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B, at::BFloat16* __restrict__ C,
+      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -249,7 +145,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b_ptr = reinterpret_cast<const float*>(B);
 
@@ -284,7 +180,9 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
               (__m512i)(_mm512_cvtne2ps_pbh(vc[row * COLS + col + 1], vc[row * COLS + col])));
         }
       } else {
-        _mm256_storeu_si256(reinterpret_cast<__m256i*>(C + row * ldc + col * 16), (__m256i)(_mm512_cvtneps_pbh(vc[i])));
+        _mm256_storeu_si256(
+            reinterpret_cast<__m256i*>(C + row * ldc + col * 16),
+            (__m256i)(_mm512_cvtneps_pbh(vc[i])));
       }
     };
     Unroll<ROWS * COLS>{}(storec);
@@ -292,33 +190,22 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                \
-  tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply( \
-      A + mb_start * lda,                                          \
-      B + nb_start * 2,                                            \
-      C + mb_start * ldc + nb_start,                               \
-      has_bias ? bias + nb_start : nullptr,                        \
-      K,                                                           \
-      lda,                                                         \
-      ldb,                                                         \
-      ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
+    tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply(         \
+        A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, \
+        has_bias ? bias + nb_start : nullptr, K, lda, ldb, ldc);
 
 template <typename scalar_t, bool has_bias>
 struct brgemm {
   static inline void apply(
-      const scalar_t* __restrict__ A,
-      const scalar_t* __restrict__ B,
-      scalar_t* __restrict__ C,
-      float* __restrict__ Ctmp,
-      const float* __restrict__ bias,
-      int64_t M,
-      int64_t N,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
+      float* __restrict__ Ctmp, const float* __restrict__ bias,
+      int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+
     constexpr int BLOCK_N = block_size_n();
-    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
+    at::native::cpublas::brgemm(
+        M, N, K, lda, ldb, BLOCK_N, /* add_C */false,
+        A, B, Ctmp);
 
     // copy from Ctmp to C
     for (int64_t m = 0; m < M; ++m) {
@@ -329,21 +216,6 @@ struct brgemm {
       }
     }
   }
-  static inline void apply(
-      const float* __restrict__ A,
-      const float* __restrict__ B,
-      scalar_t* __restrict__ C,
-      float* __restrict__ Ctmp,
-      const float* __restrict__ bias,
-      int64_t M,
-      int64_t N,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
-    constexpr int BLOCK_N = block_size_n();
-    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
-  }
 };
 
 template <typename scalar_t, bool has_bias>
@@ -360,12 +232,15 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg) {
+
   if (brg) {
-    brgemm<scalar_t, has_bias>::apply(A, B, C, Ctmp, bias, M, N, K, lda, ldb, ldc);
+    brgemm<scalar_t, has_bias>::apply(
+        A, B, C, Ctmp, bias,
+        M, N, K, lda, ldb, ldc);
     return;
   }
 
-  // pattern: 1-4-16, N = 16, 32, 48, 64
+  // pattern: 1-4-16
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 64;
   const int64_t MB = div_up(M, BLOCK_M);
@@ -377,88 +252,25 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch (mb_size << 4 | nb_size >> 4) {
+      switch(mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x11:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 16);
-          break;
-        case 0x12:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
-          break;
-        case 0x13:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 48);
-          break;
-        case 0x14:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 64);
-          break;
+        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
+        case 0x14: LAUNCH_TINYGEMM_KERNEL_NN(1, 64); break;
         // mb_size = 2
-        case 0x21:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 16);
-          break;
-        case 0x22:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
-          break;
-        case 0x23:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 48);
-          break;
-        case 0x24:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 64);
-          break;
+        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
+        case 0x24: LAUNCH_TINYGEMM_KERNEL_NN(2, 64); break;
         // mb_size = 3
-        case 0x31:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 16);
-          break;
-        case 0x32:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
-          break;
-        case 0x33:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 48);
-          break;
-        case 0x34:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 64);
-          break;
+        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
+        case 0x34: LAUNCH_TINYGEMM_KERNEL_NN(3, 64); break;
         // mb_size = 4
-        case 0x41:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 16);
-          break;
-        case 0x42:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
-          break;
-        case 0x43:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 48);
-          break;
-        case 0x44:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 64);
-          break;
-        default:
-          TORCH_CHECK(false, "Unexpected block size, ", mb_size, " x ", nb_size);
+        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
+        case 0x44: LAUNCH_TINYGEMM_KERNEL_NN(4, 64); break;
+        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
       }
     }
   }
 }
 
-template <typename scalar_t, bool has_bias>
-void tinygemm_kernel(
-    const float* __restrict__ A,
-    const float* __restrict__ B,
-    scalar_t* __restrict__ C,
-    float* __restrict__ Ctmp,
-    const float* __restrict__ bias,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
-    bool brg) {
-  TORCH_CHECK(brg, "Expected to use fp32 brgemm for small N GEMM");
-  if (brg) {
-    brgemm<scalar_t, has_bias>::apply(A, B, C, Ctmp, bias, M, N, K, lda, ldb, ldc);
-    return;
-  }
-  // TODO : add intrinsic path
-}
-
 template <typename scalar_t>
 void weight_packed_linear_kernel_impl(
     scalar_t* __restrict__ out,
@@ -470,20 +282,29 @@ void weight_packed_linear_kernel_impl(
     int64_t K,
     int64_t mat1_strideM,
     int64_t out_strideM) {
+
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
-  const bool use_brgemm = can_use_brgemm<scalar_t>(M);
+  // use avx512-bf16 when a) M is small; b) dtype is bfloat16, otherwise use amx
+  const bool use_brgemm = (M > 4) || (!std::is_same_v<scalar_t, at::BFloat16>);
+
+  // l2 cache block for n
+  int64_t cache_blocks_nb = get_cache_blocks<scalar_t>(BLOCK_N, K);
 
   // parallel on [MB, NB]
   AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    parallel_2d(MB, NB, [&](int64_t begin_mb, int64_t end_mb, int64_t begin_nb, int64_t end_nb) {
+
       // for brgemm, use float32 for accumulate
       alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
 
-      loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      for (int64_t nbb = begin_nb; nbb < end_nb; nbb += cache_blocks_nb) {
+      for (int64_t mb = begin_mb; mb < end_mb; ++mb) {
+      for (int64_t nb = nbb; nb < std::min(nbb + cache_blocks_nb, end_nb); ++nb) {
+
         int64_t mb_start = mb * BLOCK_M;
         int64_t mb_size = std::min(M - mb_start, BLOCK_M);
         int64_t nb_start = nb * BLOCK_N;
@@ -502,7 +323,7 @@ void weight_packed_linear_kernel_impl(
             /* ldb */ nb_size,
             /* ldc */ out_strideM,
             /* brg */ use_brgemm);
-      });
+      }}}
 
       if (use_brgemm) {
         at::native::cpublas::brgemm_release();
@@ -511,113 +332,20 @@ void weight_packed_linear_kernel_impl(
   });
 }
 
-template <typename scalar_t>
-void weight_packed_linear_kernel_impl(
-    scalar_t* __restrict__ out,
-    const scalar_t* __restrict__ mat1,
-    const float* __restrict__ mat2,
-    const float* __restrict__ bias,
-    const scalar_t* __restrict__ post_mul_mat,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t mat1_strideM,
-    int64_t out_strideM) {
-  constexpr int64_t BLOCK_M = block_size_m();
-  constexpr int64_t BLOCK_N = block_size_n();
-  const int64_t MB = div_up(M, BLOCK_M);
-  const int64_t NB = div_up(N, BLOCK_N);
-
-  const bool use_brgemm = true;  // TODO: add intrinsic path
-  // parallel on [MB, NB]
-  AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
-      // for brgemm, use float32 for accumulate
-      alignas(64) float Atmp[BLOCK_M * K];
-      alignas(64) float Ctmp[BLOCK_M * BLOCK_N];
-
-      loop_2d<float>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
-        int64_t mb_start = mb * BLOCK_M;
-        int64_t mb_size = std::min(M - mb_start, BLOCK_M);
-        int64_t nb_start = nb * BLOCK_N;
-        int64_t nb_size = std::min(N - nb_start, BLOCK_N);
-        for (int64_t m = 0; m < mb_size; ++m) {
-          copy_stub<scalar_t>(Atmp + m * K, mat1 + mb_start * mat1_strideM + m * K, K);
-        }
-        tinygemm_kernel<scalar_t, has_bias>(
-            /*   A */ Atmp,
-            /*   B */ mat2 + nb_start * K /* nb * BLOCK_N * K */,
-            /*   C */ out + mb_start * out_strideM + nb_start,
-            /* Ctmp*/ Ctmp,
-            /* bias*/ bias + nb_start,
-            /*   M */ mb_size,
-            /*   N */ nb_size,
-            /*   K */ K,
-            /* lda */ mat1_strideM,
-            /* ldb */ nb_size,
-            /* ldc */ out_strideM,
-            /* brg */ use_brgemm);
-
-        if (post_mul_mat != nullptr) {
-          for (int64_t m = 0; m < mb_size; ++m) {
-            scalar_sigmoid_and_mul<scalar_t, has_bias>(
-                out + mb_start * out_strideM + nb_start + m * out_strideM,
-                Ctmp + m * BLOCK_N,
-                bias + nb_start,
-                post_mul_mat + mb_start * out_strideM + m * out_strideM,
-                out_strideM);
-          }
-        } else {
-          for (int64_t m = 0; m < mb_size; ++m) {
-            if constexpr (has_bias) {
-              copy_add_stub(
-                  out + mb_start * out_strideM + nb_start + m * out_strideM, Ctmp + m * BLOCK_N, bias + nb_start, N);
-            } else {
-              copy_stub(out + mb_start * out_strideM + nb_start + m * out_strideM, Ctmp + m * BLOCK_N, N);
-            }
-          }
-        }
-      });
-
-      if (use_brgemm) {
-        at::native::cpublas::brgemm_release();
-      }
-    });
-  });
-}
-
-}  // anonymous namespace
+} // anonymous namespace
 
 // tinygemm interface
 template <typename scalar_t>
-void tinygemm_kernel(
-    const scalar_t* __restrict__ A,
-    const scalar_t* __restrict__ B,
-    scalar_t* __restrict__ C,
-    float* __restrict__ Ctmp,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
-    bool brg) {
+void tinygemm_kernel(const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, scalar_t* __restrict__ C,
+    float* __restrict__ Ctmp, int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg) {
   tinygemm_kernel<scalar_t, false>(A, B, C, Ctmp, nullptr, M, N, K, lda, ldb, ldc, brg);
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
-  template void tinygemm_kernel<TYPE>(      \
-      const TYPE* __restrict__ A,           \
-      const TYPE* __restrict__ B,           \
-      TYPE* __restrict__ C,                 \
-      float* __restrict__ Ctmp,             \
-      int64_t M,                            \
-      int64_t N,                            \
-      int64_t K,                            \
-      int64_t lda,                          \
-      int64_t ldb,                          \
-      int64_t ldc,                          \
-      bool brg)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                             \
+    template void tinygemm_kernel<TYPE>(                                                \
+        const TYPE* __restrict__ A, const TYPE* __restrict__ B, TYPE* __restrict__ C,   \
+        float* __restrict__ Ctmp, int64_t M, int64_t N, int64_t K, int64_t lda,         \
+        int64_t ldb, int64_t ldc, bool brg)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
@@ -631,23 +359,14 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
 
   const int64_t ndim = weight.ndimension();
   TORCH_CHECK(ndim == 2 || ndim == 3, "expect weight to be 2d or 3d, got ", ndim, "d tensor.");
-
-  if (ndim == 2 && weight.size(0) < TILE_N) {
-    // for 2D weight and small OC shape, we use fma linear path, which needs transpose not pack
-    return weight.to(at::kFloat).t().contiguous();
-  }
-
   const auto st = weight.scalar_type();
   const int64_t E = ndim == 3 ? weight.size(0) : 1;
   const int64_t OC = ndim == 3 ? weight.size(1) : weight.size(0);
   const int64_t IC = ndim == 3 ? weight.size(2) : weight.size(1);
 
-  // mxfp4 or int4 are packed with uint8
-  const int64_t actual_IC = st == at::kByte ? IC * 2 : IC;
-
   // we handle 2 TILE_N at a time.
   TORCH_CHECK(OC % TILE_N == 0, "invalid weight out features ", OC);
-  TORCH_CHECK(actual_IC % TILE_K == 0, "invalid weight input features ", actual_IC);
+  TORCH_CHECK(IC % TILE_K == 0, "invalid weight input features ", IC);
 
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t NB = div_up(OC, BLOCK_N);
@@ -656,14 +375,12 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
   auto packed_weight = at::empty({}, weight.options());
   const int64_t stride = OC * IC;
 
-  // Note: for `kByte` (uint8), it represents either `mxfp4` or `int4`.
-  TORCH_CHECK(
-      st == at::kBFloat16 || st == at::kHalf || st == at::kChar || st == at::kFloat8_e4m3fn || st == at::kByte,
-      "expect weight to be bfloat16, float16, int8, fp8_e4m3 or uint8(mxfp4 or int4).");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf || st == at::kChar || st == at::kFloat8_e4m3fn,
+      "expect weight to be bfloat16, float16, int8 or fp8_e4m3.");
 
   CPU_DISPATCH_PACKED_TYPES(st, [&] {
     // adjust most inner dimension size
-    const int packed_row_size = get_row_size<packed_t>(actual_IC);
+    const int packed_row_size = get_row_size<packed_t>(IC);
     auto sizes = weight.sizes().vec();
     sizes[ndim - 1] = packed_row_size;
     packed_weight.resize_(sizes);
@@ -682,7 +399,10 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
         int64_t n = nb * BLOCK_N;
         int64_t n_size = std::min(BLOCK_N, OC - n);
         pack_vnni<packed_t>(
-            packed_data + e * OC * packed_row_size + n * packed_row_size, w_data + e * stride + n * IC, n_size, IC);
+            packed_data + e * OC * packed_row_size + n * packed_row_size,
+            w_data + e * stride + n * IC,
+            n_size,
+            IC);
 
         // move to the next index
         data_index_step(e, E, nb, NB);
@@ -692,71 +412,33 @@ at::Tensor convert_weight_packed(at::Tensor& weight) {
   return packed_weight;
 }
 
-at::Tensor convert_scale_packed(at::Tensor& scale) {
-  CHECK_INPUT(scale);
-
-  const int64_t ndim = scale.ndimension();
-  TORCH_CHECK(ndim == 2 || ndim == 3, "expect scale to be 2d or 3d, got ", ndim, "d tensor.");
-  const auto st = scale.scalar_type();
-  const int64_t E = ndim == 3 ? scale.size(0) : 1;
-  const int64_t N = ndim == 3 ? scale.size(1) : scale.size(0);
-  // number of groups, e.g. K/32
-  const int64_t G = ndim == 3 ? scale.size(2) : scale.size(1);
-
-  constexpr int64_t BLOCK_N = block_size_n();
-  TORCH_CHECK(N % BLOCK_N == 0, "invalid weight out features ", N);
-  const int64_t NB = N / BLOCK_N;
-
-  auto packed_scale = at::empty_like(scale);
-  TORCH_CHECK(st == at::kByte, "expect scale to be uint8.");
-
-  const uint8_t* s_data = scale.data_ptr<uint8_t>();
-  uint8_t* packed_data = packed_scale.data_ptr<uint8_t>();
-
-  // parallel on src {E, NB, BLOCK_N, G}, dst {E, NB, G, BLOCK_N}
-  at::parallel_for(0, E * NB * BLOCK_N * G, 0, [&](int64_t begin, int64_t end) {
-    int64_t e{0}, nb{0}, n{0}, g{0};
-    data_index_init(begin, e, E, nb, NB, n, BLOCK_N, g, G);
-
-    for (int64_t i = begin; i < end; ++i) {
-      packed_data[e * N * G + nb * G * BLOCK_N + g * BLOCK_N + n] = s_data[i];
-      // move to the next index
-      data_index_step(e, E, nb, NB, n, BLOCK_N, g, G);
-    }
-  });
-  return packed_scale;
-}
-
 // mat1 : [M, K]
-// mat2 : [N, K] ([K, N] if use_fma_gemm)
+// mat2 : [N, K]
 // bias : [N]
 // out  : [M, N]
 //
-at::Tensor
-weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2, const std::optional<at::Tensor>& bias, bool is_vnni) {
-  auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
-  bool use_fma_gemm = false;
-  if (packed_w.scalar_type() == at::kFloat) {
-    use_fma_gemm = true;
-  }
+at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
+    const std::optional<at::Tensor>& bias, bool is_vnni) {
+  RECORD_FUNCTION(
+    "sgl-kernel::weight_packed_linear", std::vector<c10::IValue>({mat1, mat2, bias}));
 
-  int64_t M = mat1.size(0);
-  int64_t K = mat1.size(1);
-  int64_t N = use_fma_gemm ? mat2.size(1) : mat2.size(0);
+  auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
   CHECK_INPUT(mat2);
+
+  int64_t M = mat1.size(0);
+  int64_t N = mat2.size(0);
+  int64_t K = mat2.size(1);
+  CHECK_EQ(mat1.size(1), K);
   CHECK_DIM(2, mat1);
   CHECK_DIM(2, mat2);
-  if (!use_fma_gemm) {
-    CHECK_EQ(mat1.size(1), K);
-  }
 
-  auto dispatch_type = mat1.scalar_type();
   auto out = at::empty({M, N}, mat1.options());
+
   // strides
+  int64_t mat1_strideM = mat1.stride(0);
   int64_t out_strideM = out.stride(0);
-  int64_t mat1_strideM = mat1.stride(0);
 
   const bool has_bias = bias.has_value();
   const float* bias_data = nullptr;
@@ -765,83 +447,12 @@ weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2, const std::optional<at:
     bias_data = bias.value().data_ptr<float>();
   }
 
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(dispatch_type, "weight_packed_linear_kernel_impl", [&] {
-    if (use_fma_gemm) {
-      weight_packed_linear_kernel_impl<scalar_t>(
-          out.data_ptr<scalar_t>(),
-          mat1.data_ptr<scalar_t>(),
-          packed_w.data_ptr<float>(),
-          bias_data,
-          nullptr,
-          M,
-          N,
-          K,
-          mat1_strideM,
-          out_strideM);
-    } else {
-      weight_packed_linear_kernel_impl<scalar_t>(
-          out.data_ptr<scalar_t>(),
-          mat1.data_ptr<scalar_t>(),
-          packed_w.data_ptr<scalar_t>(),
-          bias_data,
-          M,
-          N,
-          K,
-          mat1_strideM,
-          out_strideM);
-    }
-  });
-
-  return out;
-}
-
-// mat1         : [M, K]
-// mat2         : [K, 1]
-// post_mul_mat : [M, K]
-// bias         : [N]
-// out          : [M, N]
-//
-at::Tensor fused_linear_sigmoid_mul(
-    at::Tensor& mat1,
-    at::Tensor& mat2,
-    const std::optional<at::Tensor>& bias,
-    bool is_vnni,
-    const at::Tensor& post_mul_mat) {
-  auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
-  TORCH_CHECK(packed_w.scalar_type() == at::kFloat, "fused_linear_sigmoid_mul requires packed float weight")
-
-  int64_t M = mat1.size(0);
-  int64_t K = mat1.size(1);
-  int64_t N = mat2.size(1);
-
-  CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
-  CHECK_INPUT(mat2);
-  CHECK_DIM(2, mat1);
-  CHECK_DIM(2, mat2);
-
-  int64_t out_strideM = post_mul_mat.size(1);
-  int64_t mat1_strideM = mat1.stride(0);
-  auto dispatch_type = mat1.scalar_type();
-  auto out = at::empty({M, out_strideM}, mat1.options());
-
-  TORCH_CHECK(
-      N == 1 && out_strideM % 32 == 0,
-      "post_mul_mat tensor size(1) should be 32 dividable, and the mat2 OC=1 (Mx1 as linear output shape)")
-
-  const bool has_bias = bias.has_value();
-  const float* bias_data = nullptr;
-  if (has_bias) {
-    CHECK_EQ(bias.value().size(0), N);
-    bias_data = bias.value().data_ptr<float>();
-  }
-
-  AT_DISPATCH_REDUCED_FLOATING_TYPES(dispatch_type, "fused_linear_sigmoid_mul", [&] {
+  AT_DISPATCH_REDUCED_FLOATING_TYPES(mat1.scalar_type(), "weight_packed_linear_kernel_impl", [&] {
     weight_packed_linear_kernel_impl<scalar_t>(
         out.data_ptr<scalar_t>(),
         mat1.data_ptr<scalar_t>(),
-        packed_w.data_ptr<float>(),
+        packed_w.data_ptr<scalar_t>(),
         bias_data,
-        post_mul_mat.data_ptr<scalar_t>(),
         M,
         N,
         K,

csrc/cpu/sgl-kernels/gemm.h

@@ -1,12 +1,8 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
-
-// clang-format off
-
 #pragma once
+
 #include <ATen/native/CPUBlas.h>
 
-#include "common.h"
+// clang-format off
 
 // amx-bf16
 #define TILE_M 16
@@ -14,42 +10,20 @@
 #define TILE_K 32
 
 // block size for AMX gemm
-constexpr int block_size_m() {
-  return 2 * TILE_M;
-}
-constexpr int block_size_n() {
-  return 2 * TILE_N;
-}
+constexpr int block_size_m() { return 2 * TILE_M; }
+constexpr int block_size_n() { return 2 * TILE_N; }
 
 // define threshold using brgemm (intel AMX)
-template <typename T>
-inline bool can_use_brgemm(int M);
-template <>
-inline bool can_use_brgemm<at::BFloat16>(int M) {
-  return M > 4;
-}
-template <>
-inline bool can_use_brgemm<at::Half>(int M) {
-  return true;
-}
-// this requires PyTorch 2.7 or above
-template <>
-inline bool can_use_brgemm<int8_t>(int M) {
-  return M > 4;
-}
-
-template <>
-inline bool can_use_brgemm<uint8_t>(int M) {
-  return M > 4;
-}
-
-template <>
-inline bool can_use_brgemm<at::Float8_e4m3fn>(int M) {
-  return M > 4;
-}
+template <typename T> inline bool can_use_brgemm(int M);
+template <> inline bool can_use_brgemm<at::BFloat16>(int M) { return M > 4; }
+template <> inline bool can_use_brgemm<at::Half>(int M) { return true; }
+template <> inline bool can_use_brgemm<int8_t>(int M) { return M > 4; }
+template <> inline bool can_use_brgemm<uint8_t>(int M) { return M > 4; }
+template <> inline bool can_use_brgemm<at::Float8_e4m3fn>(int M) { return M > 4; }
+template <> inline bool can_use_brgemm<at::quint4x2>(int M) { return M > 4; }
 
 // work around compiler internal error
-#define BLOCK_K 128  // 4 * TILE_K
+#define BLOCK_K 128 // 4 * TILE_K
 
 // adjust leading dimension size for K
 template <typename T>
@@ -62,44 +36,18 @@ inline int64_t get_row_size<int8_t>(int64_t K) {
   return K + sizeof(int32_t);
 }
 
-// uint8: mxfp4 or int4
-template <>
-inline int64_t get_row_size<uint8_t>(int64_t K) {
-  return K >> 1;
-}
-
 inline int64_t get_row_size(int64_t K, bool use_int8_w8a8) {
   return use_int8_w8a8 ? K + sizeof(int32_t) : K;
 }
 
-enum class CPUQuantMethod : int64_t { BF16 = 0, INT8_W8A8 = 1, FP8_W8A16 = 2, INT4_W4A8 = 3 };
-
-constexpr bool operator==(CPUQuantMethod a, int64_t b) {
-  return static_cast<int64_t>(a) == b;
-}
-
-constexpr bool operator==(int64_t a, CPUQuantMethod b) {
-  return a == static_cast<int64_t>(b);
-}
-
-enum class CPUQuantAlgo : int64_t { AWQ = 0, GPTQ = 1 };
-
-constexpr bool operator==(CPUQuantAlgo a, int64_t b) {
-  return static_cast<int64_t>(a) == b;
-}
-
-constexpr bool operator==(int64_t a, CPUQuantAlgo b) {
-  return a == static_cast<int64_t>(b);
-}
-
 inline int64_t get_4bit_block_k_size(int64_t group_size) {
   return group_size > 128 ? 128 : group_size;
 }
 
-// pack weight to vnni format
+// pack weight into vnni format
 at::Tensor convert_weight_packed(at::Tensor& weight);
 
-// pack weight to vnni format for int4
+// pack weight to vnni format for int4 (adapted from sglang)
 std::tuple<at::Tensor, at::Tensor, at::Tensor>
 convert_weight_packed_scale_zp(at::Tensor qweight, at::Tensor qzeros, at::Tensor scales);
 
@@ -157,6 +105,35 @@ void fused_experts_fp8_kernel_impl(
     int64_t topk,
     int64_t num_tokens_post_pad);
 
+// moe implementations for int4 w4a16
+template <typename scalar_t>
+void fused_experts_int4_w4a16_kernel_impl(
+    scalar_t* __restrict__ output,
+    scalar_t* __restrict__ ic0,
+    scalar_t* __restrict__ ic1,
+    scalar_t* __restrict__ ic2,
+    scalar_t* __restrict__ A_tmp,
+    scalar_t* __restrict__ B_tmp,
+    float* __restrict__ C_tmp,
+    const scalar_t* __restrict__ input,
+    const at::quint4x2* __restrict__ packed_w1,
+    const at::quint4x2* __restrict__ packed_w2,
+    const uint8_t* __restrict__ w1z,
+    const uint8_t* __restrict__ w2z,
+    const scalar_t* __restrict__ w1s,
+    const scalar_t* __restrict__ w2s,
+    int group_size,
+    const float* __restrict__ topk_weights,
+    const int32_t* __restrict__ sorted_ids,
+    const int32_t* __restrict__ expert_ids,
+    const int32_t* __restrict__ offsets,
+    int64_t M,
+    int64_t N,
+    int64_t K,
+    int64_t E,
+    int64_t topk,
+    int64_t num_tokens_post_pad);
+
 // shared expert implementation for int8 w8a8
 template <typename scalar_t>
 void shared_expert_int8_kernel_impl(
@@ -176,37 +153,6 @@ void shared_expert_int8_kernel_impl(
     int64_t N,
     int64_t K);
 
-template <typename scalar_t>
-void fused_experts_int4_w4a8_kernel_impl(
-    scalar_t* __restrict__ output,
-    scalar_t* __restrict__ ic0,
-    scalar_t* __restrict__ ic1,
-    scalar_t* __restrict__ ic2,
-    uint8_t* __restrict__ A_tmp,
-    uint8_t* __restrict__ Aq_tmp,
-    float* __restrict__ As_tmp,
-    int32_t* __restrict__ Azp_tmp,
-    float* __restrict__ C_tmp,
-    int8_t* __restrict__ dqB_tmp,
-    const scalar_t* __restrict__ input,
-    const uint8_t* __restrict__ packed_w1,
-    const uint8_t* __restrict__ packed_w2,
-    const int8_t* __restrict__ w1z,
-    const int8_t* __restrict__ w2z,
-    const float* __restrict__ w1s,
-    const float* __restrict__ w2s,
-    int group_size,
-    const float* __restrict__ topk_weights,
-    const int32_t* __restrict__ sorted_ids,
-    const int32_t* __restrict__ expert_ids,
-    const int32_t* __restrict__ offsets,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t E,
-    int64_t topk,
-    int64_t num_tokens_post_pad);
-
 template <typename scalar_t>
 void shared_expert_fp8_kernel_impl(
     scalar_t* __restrict__ output,
@@ -258,7 +204,6 @@ void tinygemm_kernel(
     int64_t ldc,
     bool brg);
 
-// block quantization
 template <typename scalar_t>
 void tinygemm_kernel(
     const scalar_t* __restrict__ A,
@@ -274,26 +219,33 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg,
-    int64_t block_size_K,
-    bool do_unpack = true);
+    int64_t block_size_K);
 
-// per tensor quantization
 template <typename scalar_t>
 void tinygemm_kernel(
     const scalar_t* __restrict__ A,
-    const at::Float8_e4m3fn* __restrict__ B,
+    const at::quint4x2* __restrict__ B,
     scalar_t* __restrict__ C,
+    const uint8_t* __restrict__ Bz,
+    const scalar_t* __restrict__ Bs,
     scalar_t* __restrict__ Btmp,
     float* __restrict__ Ctmp,
-    float scale,
     int64_t M,
     int64_t N,
     int64_t K,
+    int group_size,
     int64_t lda,
     int64_t ldb,
     int64_t ldc,
+    int64_t strideBz,
+    int64_t strideBs,
     bool brg);
 
+// int4 scaled GEMM (adapted from sglang)
+at::Tensor int4_scaled_mm_cpu(
+    at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros, at::Tensor& w_scales, std::optional<at::Tensor> bias);
+
+// int4 tinygemm kernel interface(adapted from sglang)
 template <typename scalar_t>
 void tinygemm_kernel(
     scalar_t* C,
@@ -314,21 +266,34 @@ void tinygemm_kernel(
     bool store_out,
     bool use_brgemm);
 
-// mxfp4
-template <typename scalar_t>
-void tinygemm_kernel(
-    const scalar_t* __restrict__ A,
-    const uint8_t* __restrict__ B,
-    scalar_t* __restrict__ C,
-    scalar_t* __restrict__ Btmp,
-    float* __restrict__ Ctmp,
-    const uint8_t* __restrict__ scale,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
-    bool brg,
-    int64_t block_size_K,
-    bool do_unpack = true);
+// TODO: debug print, remove me later
+inline void print_16x32i(const __m512i x) {
+  int32_t a[16];
+  _mm512_storeu_si512((__m512i *)a, x);
+
+  for (int i = 0; i < 16; i++){
+    std::cout << a[i] << " ";
+  }
+  std::cout << std::endl;
+}
+
+inline void print_16x32(const __m512 x) {
+  float a[16];
+  _mm512_storeu_ps((__m512 *)a, x);
+
+  for (int i = 0; i < 16; i++){
+    std::cout << a[i] << " ";
+  }
+  std::cout << std::endl;
+}
+
+
+inline void print_32x8u(const __m256i x) {
+  uint8_t a[32];
+  _mm256_storeu_si256((__m256i *)a, x);
+
+  for (int i = 0; i < 32; ++i) {
+    std::cout << int32_t(a[i]) << " ";
+  }
+  std::cout << std::endl;
+}

csrc/cpu/sgl-kernels/gemm_int4.cpp

@@ -1,10 +1,10 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
+// SPDX-License-Identifier: Apache-2.0
+// Adapted from sgl-project/sglang
+// https://github.com/sgl-project/sglang/pull/8226
 
-// clang-format off
-
-#include <torch/all.h>
+#include <ATen/ATen.h>
 
+#include "common.h"
 #include "gemm.h"
 #include "vec.h"
 
@@ -24,22 +24,19 @@ struct ActDtype<false> {
   using type = uint8_t;
 };
 
-#if defined(CPU_CAPABILITY_AVX512)
 struct alignas(32) m256i_wrapper {
   __m256i data;
 };
 
-inline std::array<m256i_wrapper, 2> load_zps_4vnni(const int8_t* __restrict__ zps) {
-  // broadcast 01234567 to
-  // 01234567012345670123456701234567
-  __m256i vzps_low = _mm256_set1_epi64x(*reinterpret_cast<const long*>(zps));
-  __m256i vzps_high = _mm256_set1_epi64x(*reinterpret_cast<const long*>(zps + 8));
-  // shuffle from
-  // 01234567012345670123456701234567
-  // to
-  // 00001111222233334444555566667777
+#if defined(CPU_CAPABILITY_AVX512)
+inline std::array<m256i_wrapper, 2> load_zps_4vnni(
+    const int8_t* __restrict__ zps) {
+  __m256i vzps_low = _mm256_set1_epi64x(*reinterpret_cast<const int64_t*>(zps));
+  __m256i vzps_high =
+      _mm256_set1_epi64x(*reinterpret_cast<const int64_t*>(zps + 8));
   __m256i shuffle_mask =
-      _mm256_set_epi8(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
+      _mm256_set_epi8(7, 7, 7, 7, 6, 6, 6, 6, 5, 5, 5, 5, 4, 4, 4, 4, 3, 3, 3,
+                      3, 2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0);
   vzps_low = _mm256_shuffle_epi8(vzps_low, shuffle_mask);
   vzps_high = _mm256_shuffle_epi8(vzps_high, shuffle_mask);
   m256i_wrapper vzps_low_wp, vzps_high_wp;
@@ -48,7 +45,8 @@ inline std::array<m256i_wrapper, 2> load_zps_4vnni(const int8_t* __restrict__ zp
   return {vzps_low_wp, vzps_high_wp};
 }
 
-inline std::array<m256i_wrapper, 2> load_uint4_as_int8(const uint8_t* __restrict__ qB) {
+inline std::array<m256i_wrapper, 2> load_uint4_as_int8(
+    const uint8_t* __restrict__ qB) {
   __m256i packed = _mm256_loadu_si256(reinterpret_cast<const __m256i*>(qB));
   const __m256i low_mask = _mm256_set1_epi8(0x0f);
   __m256i high = _mm256_srli_epi16(packed, 4);
@@ -60,42 +58,37 @@ inline std::array<m256i_wrapper, 2> load_uint4_as_int8(const uint8_t* __restrict
   return {low_wp, high_wp};
 }
 
-template <int64_t N, int64_t ldb>
-void _dequant_weight_zp_only(const uint8_t* __restrict__ B, int8_t* dqB, const int8_t* __restrict__ qzeros, int64_t K) {
-  // unpack weight int8 -> two int4
-  // subtract zero point
-  // B shape = [K, ldb] = [K, N / 2], actual shape = [K / 4, N / 2, 4]
-  // dqB shape = [K, N], actual shape = [K / 4, N, 4]
-#pragma GCC unroll 2
+template <int N, int ldb>
+void _dequant_weight_zp_only(const uint8_t* __restrict__ B, int8_t* dqB,
+                             const int8_t* __restrict__ qzeros, int64_t K) {
+  #pragma GCC unroll 2
   for (int n = 0; n < N; n += 16) {
     auto [zps_low_wp, zps_high_wp] = load_zps_4vnni(&qzeros[n]);
     auto zps_low = zps_low_wp.data;
     auto zps_high = zps_high_wp.data;
     for (int k = 0; k < K; k += 4) {
-      auto [vb_low_wp, vb_high_wp] = load_uint4_as_int8(B + ldb * k + n / 2 * 4);
+      auto [vb_low_wp, vb_high_wp] =
+          load_uint4_as_int8(B + ldb * k + n / 2 * 4);
       auto vb_low = vb_low_wp.data;
       auto vb_high = vb_high_wp.data;
       vb_high = _mm256_sub_epi8(vb_high, zps_high);
       vb_low = _mm256_sub_epi8(vb_low, zps_low);
-      // store vb to B
-      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + n * 4), vb_low);
-      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + (n + 8) * 4), vb_high);
+      _mm256_storeu_si256(reinterpret_cast<__m256i_u*>(dqB + N * k + n * 4),
+                          vb_low);
+      _mm256_storeu_si256(
+          reinterpret_cast<__m256i_u*>(dqB + N * k + (n + 8) * 4), vb_high);
     }
   }
 }
 
-template <bool accum, int64_t N, bool sym_quant_act>
-void _dequant_and_store(
-    float* __restrict__ output,
-    const int32_t* __restrict__ input,
-    const float* __restrict__ scale_a,
-    const int32_t* __restrict__ zp_a,
-    const float* __restrict__ scale_b,
-    const int32_t* __restrict__ comp_b,
-    int M,
-    int ldi,
-    int ldo,
-    int ldsa = 1) {
+template <bool sym_quant_act, int N, bool accum>
+void _dequant_and_store(float* __restrict__ output,
+                        const int32_t* __restrict__ input,
+                        const float* __restrict__ scale_a,
+                        const int32_t* __restrict__ zp_a,
+                        const float* __restrict__ scale_b,
+                        const int32_t* __restrict__ comp_b, int M, int ldi,
+                        int ldo, int ldsa = 1) {
   for (int m = 0; m < M; ++m) {
     float a_scale = *(scale_a + m * ldsa);
     __m512 va_scale = _mm512_set1_ps(a_scale);
@@ -106,7 +99,7 @@ void _dequant_and_store(
       va_zp = _mm512_set1_epi32(a_zp);
     }
     int n = 0;
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
     for (; n < N; n += 16) {
       __m512i vc = _mm512_loadu_si512(input + m * ldi + n);
       if constexpr (!sym_quant_act) {
@@ -129,7 +122,8 @@ void _dequant_and_store(
       if constexpr (sym_quant_act) {
         dq_val = (float)input[m * ldi + n] * a_scale * scale_b[n];
       } else {
-        dq_val = (float)(input[m * ldi + n] - a_zp * comp_b[n]) * a_scale * scale_b[n];
+        dq_val = (float)(input[m * ldi + n] - a_zp * comp_b[n]) * a_scale *
+                 scale_b[n];
       }
       if constexpr (accum) {
         output[m * ldo + n] += dq_val;
@@ -141,10 +135,9 @@ void _dequant_and_store(
 }
 
 #else
-template <int64_t N, int64_t ldb>
-void _dequant_weight_zp_only(const uint8_t* B, int8_t* dqB, const int8_t* qzeros, int64_t K) {
-  // B shape = [K, N / 2]
-  // dqB shape = [K, N]
+template <int N, int ldb>
+void _dequant_weight_zp_only(const uint8_t* B, int8_t* dqB,
+                             const int8_t* qzeros, int64_t K) {
   for (int k = 0; k < K; ++k) {
     for (int n = 0; n < N / 2; ++n) {
       int32_t b = (int32_t)B[k * ldb + n];
@@ -165,31 +158,20 @@ inline __m512i combine_m256i(std::array<m256i_wrapper, 2> two_256) {
   return combine_m256i(two_256[0].data, two_256[1].data);
 }
 
-// negate elements in a according to b's sign
 static inline __m512i _mm512_sign_epi8(__m512i a, __m512i b) {
   __m512i zero = _mm512_setzero_si512();
   __mmask64 blt0 = _mm512_movepi8_mask(b);
   return _mm512_mask_sub_epi8(a, blt0, zero, a);
 }
 
-template <int64_t M, int64_t N, int64_t ldb, bool sym_quant_act>
-void _dequant_gemm_accum_small_M(
-    float* __restrict__ C,
-    const uint8_t* A,
-    const float* scales_a,
-    const int32_t* qzeros_a,
-    const uint8_t* B,
-    const float* scales_b,
-    const int8_t* qzeros_b,
-    int64_t K,
-    int64_t lda,
-    int64_t ldc) {
-  // if sym_quant_act is true, A pointer type is passed in as uint8_t* but actually int8_t*.
-
+template <bool sym_quant_act, int M, int N, int ldb>
+void _dequant_gemm_accum_small_M(float* __restrict__ C, const uint8_t* A,
+                                 const float* scales_a, const int32_t* qzeros_a,
+                                 const uint8_t* B, const float* scales_b,
+                                 const int8_t* qzeros_b, int64_t K, int64_t lda,
+                                 int64_t ldc) {
   constexpr int COLS = N / 16;
-  // Computing compensation is faster than loading it for small M
-  // because it's memory bound.
-  __m512i ones = _mm512_set1_epi8(1);  // used for computing compensation
+  __m512i ones = _mm512_set1_epi8(1);
   __m512i va;
   __m512i vb[COLS];
   __m512i vc[M * COLS];
@@ -197,7 +179,6 @@ void _dequant_gemm_accum_small_M(
   __m512i vzps[COLS];
   __m512i vcompensate[COLS];
 
-  // Load scales and zps
   Unroll<COLS>{}([&](auto i) {
     vscales[i] = _mm512_loadu_ps(scales_b + i * 16);
     vzps[i] = combine_m256i(load_zps_4vnni(qzeros_b + i * 16));
@@ -233,25 +214,25 @@ void _dequant_gemm_accum_small_M(
     }
   };
 
-  // Accumulate along k
   constexpr const int unroll = 4;
   int k = 0;
   for (; k < K / 4 / unroll; k++) {
-    Unroll<unroll>{}([&](auto i) { Unroll<M * COLS>{}(compute, 4 * (k * unroll + i)); });
+    Unroll<unroll>{}(
+        [&](auto i) { Unroll<M * COLS>{}(compute, 4 * (k * unroll + i)); });
   }
   k *= 4 * unroll;
   for (; k < K; k += 4) {
     Unroll<M * COLS>{}(compute, k);
   }
 
-  // Store to C
   auto store = [&](auto i) {
     constexpr const int row = i / COLS;
     constexpr const int col = i % COLS;
-    // compute (qC - compensate * zp_a) * scale_a * scale_b
     __m512 vc_float;
     if constexpr (!sym_quant_act) {
-      vc[i] = _mm512_sub_epi32(vc[i], _mm512_mullo_epi32(vcompensate[col], _mm512_set1_epi32(*(qzeros_a + row))));
+      vc[i] = _mm512_sub_epi32(
+          vc[i], _mm512_mullo_epi32(vcompensate[col],
+                                    _mm512_set1_epi32(*(qzeros_a + row))));
     }
     vc_float = _mm512_cvtepi32_ps(vc[i]);
     vc_float = _mm512_mul_ps(vc_float, _mm512_set1_ps(*(scales_a + row)));
@@ -264,28 +245,17 @@ void _dequant_gemm_accum_small_M(
   Unroll<M * COLS>{}(store);
 }
 
-#define CALL_DEQUANT_GEMM_ACCUM_SMALL_M(M) \
-  _dequant_gemm_accum_small_M<M, N, ldb, sym_quant_act>(C, A, scales_a, qzeros_a, B, scales_b, qzeros_b, K, lda, ldc);
+  #define CALL_DEQUANT_GEMM_ACCUM_SMALL_M(M)               \
+    _dequant_gemm_accum_small_M<sym_quant_act, M, N, ldb>( \
+        C, A, scales_a, qzeros_a, B, scales_b, qzeros_b, K, lda, ldc);
 #endif
 
-template <int64_t N, int64_t ldb, bool sym_quant_act>
-void _dequant_gemm_accum(
-    float* C,
-    const uint8_t* A,
-    const float* scales_a,
-    const int32_t* qzeros_a,
-    const uint8_t* B,
-    const float* scales_b,
-    const int8_t* qzeros_b,
-    const int32_t* compensation,
-    int8_t* dqB,
-    int64_t M,
-    int64_t K,
-    int64_t lda,
-    int64_t ldc,
-    bool use_brgemm) {
-  // Compute GEMM int8 * int8 -> int32
-  // dequant result to float by applying scales/qzeros
+template <bool sym_quant_act, int N, int ldb>
+void _dequant_gemm_accum(float* C, const uint8_t* A, const float* scales_a,
+                         const int32_t* qzeros_a, const uint8_t* B,
+                         const float* scales_b, const int8_t* qzeros_b,
+                         const int32_t* compensation, int8_t* dqB, int64_t M,
+                         int64_t K, int64_t lda, int64_t ldc, bool use_brgemm) {
 #if defined(CPU_CAPABILITY_AVX512)
   if (!use_brgemm) {
     switch (M) {
@@ -312,12 +282,14 @@ void _dequant_gemm_accum(
   Tin* A_ptr = (Tin*)A;
   if (use_brgemm) {
     int32_t C_i32[M * N];
-    at::native::cpublas::brgemm(
-        M, N, K, lda, N /*ldb*/, N /*ldc*/, false /* add_C */, A_ptr, dqB, C_i32, true /* is_vnni */);
+    at::native::cpublas::brgemm(M, N, K, lda, N /*ldb*/, N /*ldc*/,
+                                false /* add_C */, A_ptr, dqB, C_i32,
+                                true /* is_vnni */);
     _mm_prefetch(B + N * K / 2, _MM_HINT_T0);
     _mm_prefetch(A + K, _MM_HINT_T0);
-    _dequant_and_store<true, N, sym_quant_act>(
-        C, C_i32, scales_a, qzeros_a, scales_b, compensation, M, N /*ldi*/, ldc, 1 /*ldsa*/);
+    _dequant_and_store<sym_quant_act, N, true>(C, C_i32, scales_a, qzeros_a,
+                                               scales_b, compensation, M,
+                                               N /*ldi*/, ldc, 1 /*ldsa*/);
   } else
 #endif
   {
@@ -325,13 +297,13 @@ void _dequant_gemm_accum(
   }
 }
 
-template <int64_t N>
+template <int N>
 inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
   if (bias_ptr) {
     for (int i = 0; i < m; ++i) {
       int j = 0;
 #if defined(CPU_CAPABILITY_AVX512)
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 bias_vec = _mm512_loadu_ps(bias_ptr + j);
         _mm512_storeu_ps(y_buf + i * N + j, bias_vec);
@@ -341,11 +313,11 @@ inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
         y_buf[i * N + j] = bias_ptr[j];
       }
     }
-  } else {  // initialize to zero
+  } else {
     for (int i = 0; i < m; ++i) {
       int j = 0;
 #if defined(CPU_CAPABILITY_AVX512)
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 zero_vec = _mm512_setzero_ps();
         _mm512_storeu_ps(y_buf + i * N + j, zero_vec);
@@ -358,13 +330,14 @@ inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m) {
   }
 }
 
-template <typename out_dtype, int64_t N>
-inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m, /* int64_t n, */ int64_t lda) {
+template <int N, typename out_dtype>
+inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m,
+                      int64_t lda) {
   for (int i = 0; i < m; ++i) {
     int j = 0;
     if constexpr (std::is_same<out_dtype, float>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         _mm512_storeu_ps(c_ptr + i * lda + j, y_vec);
@@ -375,11 +348,12 @@ inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m, /* int64_
       }
     } else if constexpr (std::is_same<out_dtype, at::BFloat16>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         __m256i y_bf16_vec = at::vec::cvtfp32_bf16(y_vec);
-        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_bf16_vec);
+        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j),
+                            y_bf16_vec);
       }
 #endif
       for (; j < N; ++j) {
@@ -387,11 +361,12 @@ inline void store_out(const float* y_buf, out_dtype* c_ptr, int64_t m, /* int64_
       }
     } else if constexpr (std::is_same<out_dtype, at::Half>::value) {
 #if defined(CPU_CAPABILITY_AVX512)
-#pragma GCC unroll 2
+  #pragma GCC unroll 2
       for (; j < N; j += 16) {
         __m512 y_vec = _mm512_loadu_ps(y_buf + i * N + j);
         __m256i y_fp16_vec = at::vec::cvtfp32_fp16(y_vec);
-        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j), y_fp16_vec);
+        _mm256_storeu_si256(reinterpret_cast<__m256i*>(c_ptr + i * lda + j),
+                            y_fp16_vec);
       }
 #endif
       for (; j < N; ++j) {
@@ -417,25 +392,16 @@ void fill_val_stub(int32_t* __restrict__ output, int32_t value, int64_t size) {
   }
 }
 
-template <typename act_dtype, typename out_dtype, bool sym_quant_act>
+template <bool sym_quant_act, typename act_dtype, typename out_dtype>
 void _da8w4_linear_impl(
-    act_dtype* __restrict__ input,
-    const float* __restrict__ input_scales,
+    act_dtype* __restrict__ input, const float* __restrict__ input_scales,
     const int32_t* __restrict__ input_qzeros,
-    const uint8_t* __restrict__ weight,
-    const float* __restrict__ weight_scales,
-    const int8_t* __restrict__ weight_qzeros,
-    const float* __restrict__ bias,
-    out_dtype* __restrict__ output,
-    float* __restrict__ output_temp,
-    int8_t* __restrict__ dequant_weight_temp,
-    int64_t M,
-    int64_t N,
-    int64_t K,
+    const uint8_t* __restrict__ weight, const float* __restrict__ weight_scales,
+    const int8_t* __restrict__ weight_qzeros, const float* __restrict__ bias,
+    out_dtype* __restrict__ output, float* __restrict__ output_temp,
+    int8_t* __restrict__ dequant_weight_temp, int64_t M, int64_t N, int64_t K,
     int64_t num_groups) {
-  // weight + compensation shape = [Nc, Kc, BLOCK_N * _block_k / 2 + BLOCK_N*sizeof(int32_t)]
-  // scales/qzeros shape = [Nc, G, BLOCK_N]
-  const bool use_brgemm = can_use_brgemm<int8_t>(M);
+  const bool use_brgemm = can_use_brgemm<act_dtype>(M);
   int64_t block_m = [&]() -> long {
     if (M <= 48) {
       return M;
@@ -466,24 +432,30 @@ void _da8w4_linear_impl(
       int64_t mc_end = parallel_on_M ? mc + 1 : Mc;
 
       for (int mci = mc; mci < mc_end; ++mci) {
-        int64_t m_size = mci * block_m + block_m > M ? M - mci * block_m : block_m;
-        // copy bias to y_buf if bias is not None
+        int64_t m_size =
+            mci * block_m + block_m > M ? M - mci * block_m : block_m;
         auto bias_data = bias ? bias + nc * BLOCK_N : nullptr;
         copy_bias<BLOCK_N>(bias_data, C_tmp, m_size);
         for (int kci = 0; kci < Kc; ++kci) {
           int32_t* compensation_ptr =
               sym_quant_act
                   ? nullptr
-                  : (int32_t*)(void*)(weight + (nc * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
-                                      _block_k * BLOCK_N / 2) /*Bcomp*/;
-          _dequant_gemm_accum<BLOCK_N, BLOCK_N / 2, sym_quant_act>(
+                  : (int32_t*)(void*)(weight +
+                                      (nc * Kc + kci) *
+                                          (BLOCK_N *
+                                           (_block_k / 2 + sizeof(int32_t))) +
+                                      _block_k * BLOCK_N / 2);
+          _dequant_gemm_accum<sym_quant_act, BLOCK_N, BLOCK_N / 2>(
               /*C*/ C_tmp,
               /*A*/ (uint8_t*)input + mci * block_m * K + kci * _block_k,
               /*scales_a*/ input_scales + mci * block_m,
               /*qzeros_a*/ input_qzeros + mci * block_m,
-              /*B*/ weight + (nc * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
-              /*scales_b*/ weight_scales + nc * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N,
-              /*qzeros_b*/ weight_qzeros + nc * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N,
+              /*B*/ weight + (nc * Kc + kci) *
+                                 (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
+              /*scales_b*/ weight_scales + nc * BLOCK_N * num_groups +
+                  kci / block_per_group * BLOCK_N,
+              /*qzeros_b*/ weight_qzeros + nc * BLOCK_N * num_groups +
+                  kci / block_per_group * BLOCK_N,
               /*Bcomp*/ compensation_ptr,
               /*dqB_tmp*/ dqB_tmp,
               /*M*/ m_size,
@@ -492,8 +464,8 @@ void _da8w4_linear_impl(
               /*ldc*/ BLOCK_N,
               /*use_brgemm*/ use_brgemm);
         }
-        // store y_buf to output with dtype conversion
-        store_out<out_dtype, BLOCK_N>(C_tmp, output + mci * block_m * N + nc * BLOCK_N, m_size, N /*lda*/);
+        store_out<BLOCK_N>(C_tmp, output + mci * block_m * N + nc * BLOCK_N,
+                           m_size, N /*lda*/);
       }
     }
     if (use_brgemm) {
@@ -504,16 +476,15 @@ void _da8w4_linear_impl(
 
 }  // anonymous namespace
 
-/*
-return: packed_weight, packed_scales, packed_qzeros
-*/
-std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_int4_weight_packed_with_compensation(
-    const at::Tensor& weight, const at::Tensor& scales, const at::Tensor& qzeros) {
-  // weight shape = [N, K]
-  // scales shape = [N, G]
-  // qzeros shape = [N, G]
-  TORCH_CHECK(weight.dim() == 2, "DA8W4 CPU: Weight should be a 2D tensor for packing");
-  TORCH_CHECK(weight.size(1) % 2 == 0, "DA8W4 CPU: Weight should have even number of columns for packing");
+std::tuple<at::Tensor, at::Tensor, at::Tensor>
+convert_int4_weight_packed_with_compensation(const at::Tensor& weight,
+                                             const at::Tensor& scales,
+                                             const at::Tensor& qzeros) {
+  TORCH_CHECK(weight.dim() == 2,
+              "DA8W4 CPU: Weight should be a 2D tensor for packing");
+  TORCH_CHECK(
+      weight.size(1) % 2 == 0,
+      "DA8W4 CPU: Weight should have even number of columns for packing");
 
   auto new_scales = scales;
   auto new_qzeros = qzeros;
@@ -534,21 +505,20 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_int4_weight_packed_with_c
   int64_t Nc = N / block_n;
   int64_t Kc = K / _block_k;
 
-  // Reorder weight to [N/block_n, K/_block_k, _block_k, block_n]
-  // Reorder scales/qzeros to [N/block_n, G, block_n]
-  // weight + compensation shape = [Nc, Kc, block_n * _block_k / 2 + block_n*sizeof(int32_t)]
-  // scales/qzeros shape = [Nc, G, block_n]
   auto weight_view = weight.view({Nc, block_n, Kc, _block_k});
   at::Tensor weight_reordered = weight_view.permute({0, 2, 3, 1}).contiguous();
   at::Tensor blocked_weight;
-  at::Tensor blocked_scales = new_scales.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
-  at::Tensor blocked_qzeros = new_qzeros.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
-  // Compensation = Σ(k)(W[k][n] - ZP[n]) for each block.
-  auto weight_sub_qzero = weight.view({Nc, block_n, G, -1}).to(at::kInt) - new_qzeros.view({Nc, block_n, G, -1});
+  at::Tensor blocked_scales =
+      new_scales.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
+  at::Tensor blocked_qzeros =
+      new_qzeros.view({Nc, block_n, G}).permute({0, 2, 1}).contiguous();
+  auto weight_sub_qzero = weight.view({Nc, block_n, G, -1}).to(at::kInt) -
+                          new_qzeros.view({Nc, block_n, G, -1});
   weight_sub_qzero = weight_sub_qzero.view({Nc, block_n, Kc, _block_k});
   at::Tensor compensation = weight_sub_qzero.sum(-1);
   compensation = compensation.permute({0, 2, 1}).contiguous().to(at::kInt);
-  int64_t buffer_size_nbytes = _block_k * block_n / 2 + block_n * sizeof(int32_t);
+  int64_t buffer_size_nbytes =
+      _block_k * block_n / 2 + block_n * sizeof(int32_t);
   blocked_weight = at::empty({Nc, Kc, buffer_size_nbytes}, weight.options());
 
   auto weight_ptr = weight_reordered.data_ptr<uint8_t>();
@@ -558,25 +528,24 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_int4_weight_packed_with_c
   at::parallel_for(0, num_blocks, 1, [&](int64_t begin, int64_t end) {
     for (const auto i : c10::irange(begin, end)) {
       auto in_ptr = weight_ptr + i * _block_k * block_n;
-      auto out_ptr = blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t));
+      auto out_ptr =
+          blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t));
       int32_t* comp_in_prt = compensation_ptr + i * block_n;
-      int32_t* comp_out_prt = (int32_t*)(void*)(blocked_weight_ptr + i * block_n * (_block_k / 2 + sizeof(int32_t)) +
-                                                _block_k * block_n / 2);
-      // Reorder weight block to VNNI4 and pack two lanes along N
-      // N=16 viewed as two lanes: a0, ...a7, b0, ...b7
-      // pack two lanes: [a0, b0], ..., [a7, b7]
-      // plain shape = [_block_k, block_n]
-      // packed shape = [_block_k / 4, block_n / 2, 4] viewed as [_block_k, block_n / 2]
+      int32_t* comp_out_prt =
+          (int32_t*)(void*)(blocked_weight_ptr +
+                            i * block_n * (_block_k / 2 + sizeof(int32_t)) +
+                            _block_k * block_n / 2);
       constexpr int n_group_size = 8;
       constexpr int vnni_size = 4;
-      constexpr int n_group = block_n / n_group_size;  // 4
+      constexpr int n_group = block_n / n_group_size;
       for (int nb = 0; nb < n_group; nb += 2) {
         for (int k = 0; k < _block_k; k += vnni_size) {
           for (int ni = 0; ni < n_group_size; ++ni) {
             for (int ki = 0; ki < vnni_size; ++ki) {
               int src_idx_1 = nb * n_group_size + ni + (k + ki) * block_n;
               int src_idx_2 = (nb + 1) * n_group_size + ni + (k + ki) * block_n;
-              int dst_idx = (nb / 2 * n_group_size + ni) * vnni_size + k * block_n / 2 + ki;
+              int dst_idx = (nb / 2 * n_group_size + ni) * vnni_size +
+                            k * block_n / 2 + ki;
               uint8_t src_1 = *(in_ptr + src_idx_1);
               uint8_t src_2 = *(in_ptr + src_idx_2);
               uint8_t dst = (src_1 & 0x0f) | ((src_2 & 0x0f) << 4);
@@ -585,128 +554,39 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_int4_weight_packed_with_c
           }
         }
       }
-      // compensation [block_n]
       for (int nb = 0; nb < block_n; nb++) {
         *(comp_out_prt + nb) = *(comp_in_prt + nb);
       }
     }
   });
 
-  return std::make_tuple(std::move(blocked_weight), std::move(blocked_scales), std::move(blocked_qzeros));
+  return std::make_tuple(std::move(blocked_weight), std::move(blocked_scales),
+                         std::move(blocked_qzeros));
 }
 
-std::tuple<at::Tensor, at::Tensor> unpack_4bit_to_32bit_signed(const at::Tensor& qweight, const at::Tensor& qzeros) {
-  TORCH_CHECK(qweight.scalar_type() == at::kInt, "qweight must be int32");
-  TORCH_CHECK(qzeros.scalar_type() == at::kInt, "qzeros must be int32");
-  const auto W0 = qweight.size(0);
-  const auto W1 = qweight.size(1);
-  const auto Z0 = qzeros.size(0);
-  const auto Z1 = qzeros.size(1);
+std::tuple<at::Tensor, at::Tensor> autoawq_to_int4pack(at::Tensor qweight,
+                                                       at::Tensor qzeros) {
+  auto bitshifts = at::tensor({0, 4, 1, 5, 2, 6, 3, 7}, at::kInt) * 4;
+  auto qweight_unsq = qweight.unsqueeze(-1);
+  auto unpacked = at::bitwise_right_shift(qweight_unsq, bitshifts) & 0xF;
+  auto qweight_final = unpacked.flatten(-2).transpose(-1, -2).to(at::kByte);
 
-  // unpacked_weights: (W0 * 8, W1), int8
-  auto unpacked_weights = at::zeros({W0 * 8, W1}, at::TensorOptions().dtype(at::kChar));
-  // unpacked_zeros: (Z0, Z1 * 8), int8
-  auto unpacked_zeros = at::zeros({Z0, Z1 * 8}, at::TensorOptions().dtype(at::kChar));
+  auto qzeros_unsq = qzeros.unsqueeze(-1);
+  auto qzeros_unpacked = at::bitwise_right_shift(qzeros_unsq, bitshifts) & 0xF;
+  auto qzeros_final = qzeros_unpacked.flatten(-2).to(at::kByte);
 
-  const int32_t* qw_ptr = qweight.data_ptr<int32_t>();
-  const int32_t* qz_ptr = qzeros.data_ptr<int32_t>();
-  int8_t* uw_ptr = unpacked_weights.data_ptr<int8_t>();
-  int8_t* uz_ptr = unpacked_zeros.data_ptr<int8_t>();
-
-  // ---- unpack qweight ----
-  for (int64_t row = 0; row < W0 * 8; ++row) {
-    const int i = row & 7;         // row % 8
-    const int src_row = row >> 3;  // row // 8
-    const int shift = 4 * i;
-    for (int64_t col = 0; col < W1; ++col) {
-      int32_t v = qw_ptr[src_row * W1 + col];
-      uw_ptr[row * W1 + col] = static_cast<int8_t>((v >> shift) & 0xF);
-    }
-  }
-  // ---- unpack qzeros ----
-  for (int64_t col = 0; col < Z1 * 8; ++col) {
-    const int i = col & 7;
-    const int src_col = col >> 3;
-    const int shift = 4 * i;
-
-    for (int64_t row = 0; row < Z0; ++row) {
-      int32_t v = qz_ptr[row * Z1 + src_col];
-      uz_ptr[row * (Z1 * 8) + col] = static_cast<int8_t>((v >> shift) & 0xF);
-    }
-  }
-
-  return std::make_tuple(unpacked_weights, unpacked_zeros + 1);
-}
-
-std::tuple<at::Tensor, at::Tensor>
-autogptq_to_int4pack(const at::Tensor& qweight_tensor, const at::Tensor& qzeros_tensor) {
-  TORCH_CHECK(qweight_tensor.scalar_type() == at::kInt, "qweight_tensor must be int32");
-  TORCH_CHECK(qzeros_tensor.scalar_type() == at::kInt, "qzeros_tensor must be int32");
-  TORCH_CHECK(qweight_tensor.is_cpu(), "CPU only implementation");
-  if (qweight_tensor.dim() == 3) {
-    const int64_t B = qweight_tensor.size(0);
-    std::vector<at::Tensor> qweight_list;
-    std::vector<at::Tensor> qzeros_list;
-    qweight_list.reserve(B);
-    qzeros_list.reserve(B);
-    for (int64_t i = 0; i < B; ++i) {
-      auto outputs = unpack_4bit_to_32bit_signed(qweight_tensor[i], qzeros_tensor[i]);
-      at::Tensor unpacked_qweight = std::get<0>(outputs);
-      at::Tensor unpacked_qzeros = std::get<1>(outputs);
-      qweight_list.push_back(unpacked_qweight.transpose(0, 1).contiguous().to(at::kByte));
-      qzeros_list.push_back(unpacked_qzeros.contiguous().to(at::kByte));
-    }
-    return std::make_tuple(at::stack(qweight_list).detach(), at::stack(qzeros_list).detach());
-  }
-  auto outputs = unpack_4bit_to_32bit_signed(qweight_tensor, qzeros_tensor);
-  at::Tensor unpacked_qweight = std::get<0>(outputs);
-  at::Tensor unpacked_qzeros = std::get<1>(outputs);
-  at::Tensor return_qweight = unpacked_qweight.transpose(0, 1).contiguous().to(at::kByte);
-  at::Tensor return_qzeros = unpacked_qzeros.contiguous().to(at::kByte);
-  return std::make_tuple(return_qweight, return_qzeros);
-}
-
-std::tuple<at::Tensor, at::Tensor> int4pack(at::Tensor qweight, at::Tensor qzeros, int64_t quant_method_4bit) {
-  if (quant_method_4bit == CPUQuantAlgo::AWQ) {
-    // autoawq unpacking
-    qweight = qweight.contiguous();
-    qzeros = qzeros.contiguous();
-    // bitshifts: [0, 4, 1, 5, 2, 6, 3, 7] * 4
-    auto bitshifts = at::tensor({0, 4, 1, 5, 2, 6, 3, 7}, at::kInt) * 4;
-    auto qweight_unsq = qweight.unsqueeze(-1);  // [..., K, N/8, 1]
-    auto unpacked = (at::bitwise_right_shift(qweight_unsq, bitshifts) & 0xF).contiguous();
-    auto qweight_final = unpacked.flatten(-2).transpose(-1, -2).to(at::kByte).clone();
-    auto qzeros_unsq = qzeros.unsqueeze(-1);
-    auto qzeros_unpacked = (at::bitwise_right_shift(qzeros_unsq, bitshifts) & 0xF).contiguous();
-    auto qzeros_final = qzeros_unpacked.flatten(-2).to(at::kByte).clone();
-    return std::make_tuple(qweight_final, qzeros_final);
-  } else if (quant_method_4bit == CPUQuantAlgo::GPTQ) {
-    // autogptq unpacking
-    auto outputs = autogptq_to_int4pack(qweight, qzeros);
-    at::Tensor unpacked_qweight = std::get<0>(outputs);
-    at::Tensor unpacked_qzeros = std::get<1>(outputs);
-    return std::make_tuple(unpacked_qweight, unpacked_qzeros);
-  } else {
-    TORCH_CHECK(false, "CPU int4 pack only support AWQ or GPTQ...");
-  }
+  return std::make_tuple(qweight_final, qzeros_final);
 }
 
 std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
-    at::Tensor qweight,  // awq: (*, K, N / 8)  ||  gptq: (*, K / 8, N) , int32
-    at::Tensor qzeros,   // awq: (*, K / group_size, N / 8) ||  gptq: (*, K / group_size, N / 8) , int32
-    at::Tensor scales,   // awq: (*, K / group_size, N) ||  gptq: (*, K / group_size, N) , bfloat16
-    int64_t quant_method_4bit) {
-  at::Tensor _qweight;
-  at::Tensor _qzeros;
-
-  auto res = int4pack(qweight, qzeros, quant_method_4bit);
-  _qweight = std::get<0>(res);
-  _qzeros = std::get<1>(res);
-
+    at::Tensor qweight, at::Tensor qzeros, at::Tensor scales) {
+  auto res = autoawq_to_int4pack(qweight, qzeros);
+  auto _qweight = std::get<0>(res);
+  auto _qzeros = std::get<1>(res);
   auto _scales = scales;
-  _qzeros = _qzeros.transpose(-2, -1).contiguous();  // .T
+  _qzeros = _qzeros.transpose(-2, -1).contiguous();
   _scales = _scales.transpose(-2, -1).contiguous();
-  if (_qweight.dim() == 3) {  // Dim=3 for MOE packing, TODO: refine a unified loop
+  if (_qweight.dim() == 3) {
     int64_t E = _qweight.size(0);
     int64_t K = _qweight.size(2);
     int64_t G = _scales.size(2);
@@ -715,12 +595,17 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
     int64_t block_n = block_size_n();
     int64_t Nc = _qweight.size(1) / block_n;
     int64_t Kc = K / _block_k;
-    int64_t buffer_size_nbytes = _block_k * block_n / 2 + block_n * sizeof(int32_t);
-    auto blocked_weight = at::empty({E, Nc, Kc, buffer_size_nbytes}, _qweight.options());
-    auto blocked_scales = at::empty({E, Nc, G, block_n}, _scales.options()).to(at::kFloat);
-    auto blocked_qzeros = at::empty({E, Nc, G, block_n}, _qzeros.options()).to(at::kChar);
+    int64_t buffer_size_nbytes =
+        _block_k * block_n / 2 + block_n * sizeof(int32_t);
+    auto blocked_weight =
+        at::empty({E, Nc, Kc, buffer_size_nbytes}, _qweight.options());
+    auto blocked_scales =
+        at::empty({E, Nc, G, block_n}, _scales.options()).to(at::kFloat);
+    auto blocked_qzeros =
+        at::empty({E, Nc, G, block_n}, _qzeros.options()).to(at::kChar);
     for (int i = 0; i < _qweight.size(0); i++) {
-      auto res_ = convert_int4_weight_packed_with_compensation(_qweight[i], _scales[i], _qzeros[i]);
+      auto res_ = convert_int4_weight_packed_with_compensation(
+          _qweight[i], _scales[i], _qzeros[i]);
       blocked_weight[i] = std::get<0>(res_);
       blocked_scales[i] = std::get<1>(res_);
       blocked_qzeros[i] = std::get<2>(res_);
@@ -729,7 +614,8 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
     _scales = blocked_scales;
     _qzeros = blocked_qzeros;
   } else {
-    auto res_ = convert_int4_weight_packed_with_compensation(_qweight, _scales, _qzeros);
+    auto res_ = convert_int4_weight_packed_with_compensation(_qweight, _scales,
+                                                             _qzeros);
     _qweight = std::get<0>(res_);
     _scales = std::get<1>(res_);
     _qzeros = std::get<2>(res_);
@@ -738,93 +624,89 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
   return std::make_tuple(_qweight, _qzeros, _scales);
 }
 
-at::Tensor int4_scaled_mm_cpu_with_quant(
-    const at::Tensor& input,
-    const at::Tensor& weight,
-    const at::Tensor& weight_scales,
-    const at::Tensor& weight_qzeros,
-    const std::optional<at::Tensor>& bias,
-    at::ScalarType output_dtype) {
+at::Tensor int4_scaled_mm_cpu_with_quant(const at::Tensor& input,
+                                         const at::Tensor& weight,
+                                         const at::Tensor& weight_scales,
+                                         const at::Tensor& weight_qzeros,
+                                         const std::optional<at::Tensor>& bias,
+                                         at::ScalarType output_dtype) {
+  RECORD_FUNCTION("vllm::int4_scaled_mm_cpu_with_quant",
+                  std::vector<c10::IValue>({input, weight}));
+
   int64_t M_a = input.size(0);
   int64_t K_a = input.size(1);
   int64_t lda = input.stride(0);
 
   const auto st = input.scalar_type();
   TORCH_CHECK(
-      st == at::kBFloat16 || st == at::kHalf, "int4_scaled_mm_cpu_with_quant: expect A to be bfloat16 or half.");
+      st == at::kBFloat16 || st == at::kHalf,
+      "int4_scaled_mm_cpu_with_quant: expect A to be bfloat16 or half.");
 
-  constexpr bool sym_quant_act = false;  // TODO: add sym quant path
+  constexpr bool sym_quant_act = false;
   using Tin = typename ActDtype<sym_quant_act>::type;
-  int64_t act_buffer_size = /* act quant */ M_a * K_a +
-                            /* act scale */ M_a * sizeof(float) +
-                            /* act zp */ M_a * sizeof(int32_t);
-  auto act_buffer = at::empty({act_buffer_size}, input.options().dtype(at::kByte));
-  // asym path, activation quants into uint8_t
+  int64_t act_buffer_size =
+      M_a * K_a + M_a * sizeof(float) + M_a * sizeof(int32_t);
+  auto act_buffer =
+      at::empty({act_buffer_size}, input.options().dtype(at::kByte));
   auto Aq_data = act_buffer.data_ptr<uint8_t>();
   auto As_data = reinterpret_cast<float*>(Aq_data + M_a * K_a);
   auto Azp_data = reinterpret_cast<int32_t*>(As_data + M_a);
-  fill_val_stub(Azp_data, 128, M_a);  // sym_a s8s8 is unified to u8s8 with compensation (128)
+  fill_val_stub(Azp_data, 128, M_a);
 
   auto out_sizes = input.sizes().vec();
   int64_t N = weight_scales.size(0) * weight_scales.size(-1);
   out_sizes.back() = N;
   auto output = at::empty(out_sizes, input.options());
-  // weight + compensation shape = [Nc, Kc, BLOCK_N * _block_k / 2 + BLOCK_N*sizeof(int32_t)]
-  // scales/qzeros shape = [Nc, G, BLOCK_N]
   int64_t Nc = weight.size(0);
   int64_t Kc = weight.size(1);
   int64_t _block_k = K_a / Kc;
   TORCH_CHECK(N == Nc * BLOCK_N, "DA8W4: weight and input shapes mismatch");
-  // scales/qzeros shape = [Nc, G, BLOCK_N]
   int64_t num_groups = weight_scales.size(1);
 
   const uint8_t* b_ptr = weight.data_ptr<uint8_t>();
   const float* b_scales_ptr = weight_scales.data_ptr<float>();
   const int8_t* b_qzeros_ptr = weight_qzeros.data_ptr<int8_t>();
-  const float* bias_ptr = bias.has_value() ? bias.value().data_ptr<float>() : nullptr;
+  const float* bias_ptr =
+      bias.has_value() ? bias.value().data_ptr<float>() : nullptr;
   int num_threads = at::get_num_threads();
-  int64_t temp_buffer_size = /* output temp */ num_threads * BLOCK_M * BLOCK_N * sizeof(float) +
-                             /*  weight dequant temp */ num_threads * _block_k * BLOCK_N;
-  auto c_temp_buffer = at::empty({temp_buffer_size}, input.options().dtype(at::kChar));
+  int64_t temp_buffer_size = num_threads * BLOCK_M * BLOCK_N * sizeof(float) +
+                             num_threads * _block_k * BLOCK_N;
+  auto c_temp_buffer =
+      at::empty({temp_buffer_size}, input.options().dtype(at::kChar));
   float* c_temp_ptr = (float*)((void*)(c_temp_buffer.data_ptr<int8_t>()));
-  int8_t* dqB_temp_ptr = (int8_t*)((void*)(c_temp_ptr + num_threads * BLOCK_M * BLOCK_N));
+  int8_t* dqB_temp_ptr =
+      (int8_t*)((void*)(c_temp_ptr + num_threads * BLOCK_M * BLOCK_N));
 
-#define LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act)                                                 \
-  AT_DISPATCH_FLOATING_TYPES_AND2(                                                                         \
-      at::ScalarType::BFloat16, at::ScalarType::Half, output_dtype, "int4_scaled_mm_cpu_with_quant", [&] { \
-        const scalar_t* __restrict__ A_data = input.data_ptr<scalar_t>();                                  \
-        scalar_t* __restrict__ c_ptr = output.data_ptr<scalar_t>();                                        \
-        at::parallel_for(0, M_a, 0, [&](int64_t begin, int64_t end) {                                      \
-          for (int64_t m = begin; m < end; ++m) {                                                          \
-            quantize_row_int8<scalar_t>(Aq_data + m * K_a, As_data[m], A_data + m * lda, K_a);             \
-          }                                                                                                \
-        });                                                                                                \
-        _da8w4_linear_impl<Tin, scalar_t, sym_quant_act>(                                                  \
-            Aq_data,                                                                                       \
-            As_data,                                                                                       \
-            Azp_data,                                                                                      \
-            b_ptr,                                                                                         \
-            b_scales_ptr,                                                                                  \
-            b_qzeros_ptr,                                                                                  \
-            bias_ptr,                                                                                      \
-            c_ptr,                                                                                         \
-            c_temp_ptr,                                                                                    \
-            dqB_temp_ptr,                                                                                  \
-            M_a,                                                                                           \
-            N,                                                                                             \
-            K_a,                                                                                           \
-            num_groups);                                                                                   \
+#define LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act)                 \
+  AT_DISPATCH_FLOATING_TYPES_AND2(                                         \
+      at::ScalarType::BFloat16, at::ScalarType::Half, output_dtype,        \
+      "int4_scaled_mm_cpu", [&] {                                          \
+        const scalar_t* __restrict__ A_data = input.data_ptr<scalar_t>();  \
+        scalar_t* __restrict__ c_ptr = output.data_ptr<scalar_t>();        \
+        at::parallel_for(0, M_a, 0, [&](int64_t begin, int64_t end) {      \
+          for (int64_t m = begin; m < end; ++m) {                          \
+            quantize_row_int8<scalar_t>(Aq_data + m * K_a, As_data[m],     \
+                                        A_data + m * lda, K_a);            \
+          }                                                                \
+        });                                                                \
+        _da8w4_linear_impl<sym_quant_act, Tin, scalar_t>(                  \
+            Aq_data, As_data, Azp_data, b_ptr, b_scales_ptr, b_qzeros_ptr, \
+            bias_ptr, c_ptr, c_temp_ptr, dqB_temp_ptr, M_a, N, K_a,        \
+            num_groups);                                                   \
       });
 
   LAUNCH_DA8W4_LINEAR_WITH_QUANT_IMPL(sym_quant_act);
 
   return output;
 }
+
+namespace {
+
 template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
+inline void copy_stub(scalar_t* __restrict__ out,
+                      const float* __restrict__ input, int64_t size) {
   using Vec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
-// no remainder
 #pragma GCC unroll 4
   for (int64_t d = 0; d < size; d += Vec::size()) {
     fVec x0 = fVec::loadu(input + d);
@@ -834,61 +716,40 @@ inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ inpu
   }
 }
 
+}  // anonymous namespace
+
 template <typename scalar_t>
-void tinygemm_kernel(
-    scalar_t* C,
-    float* C_temp,
-    const uint8_t* A,
-    const float* scales_a,
-    const int32_t* qzeros_a,
-    const uint8_t* B,
-    const float* scales_b,
-    const int8_t* qzeros_b,
-    const int32_t* compensation,
-    int8_t* dqB_tmp,
-    int64_t M,
-    int64_t K,
-    int64_t lda,
-    int64_t ldc_f,
-    int64_t ldc_s,
-    bool store_out,
-    bool use_brgemm) {
-  // TODO: add sym quant act, now only asym
-  _dequant_gemm_accum<BLOCK_N, BLOCK_N / 2, false>(
-      C_temp, A, scales_a, qzeros_a, B, scales_b, qzeros_b, compensation, dqB_tmp, M, K, lda, ldc_f, use_brgemm);
+void tinygemm_kernel(scalar_t* C, float* C_temp, const uint8_t* A,
+                     const float* scales_a, const int32_t* qzeros_a,
+                     const uint8_t* B, const float* scales_b,
+                     const int8_t* qzeros_b, const int32_t* compensation,
+                     int8_t* dqB_tmp, int64_t M, int64_t K, int64_t lda,
+                     int64_t ldc_f, int64_t ldc_s, bool store_out,
+                     bool use_brgemm) {
+  _dequant_gemm_accum<false, BLOCK_N, BLOCK_N / 2>(
+      C_temp, A, scales_a, qzeros_a, B, scales_b, qzeros_b, compensation,
+      dqB_tmp, M, K, lda, ldc_f, use_brgemm);
   if (store_out) {
-    // copy from Ctmp to C
     for (int64_t m = 0; m < M; ++m) {
       copy_stub<scalar_t>(C + m * ldc_s, C_temp + m * ldc_f, BLOCK_N);
     }
   }
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
-  template void tinygemm_kernel<TYPE>(      \
-      TYPE * C,                             \
-      float* C_temp,                        \
-      const uint8_t* A,                     \
-      const float* scales_a,                \
-      const int32_t* qzeros_a,              \
-      const uint8_t* B,                     \
-      const float* scales_b,                \
-      const int8_t* qzeros_b,               \
-      const int32_t* compensation,          \
-      int8_t* dqB_tmp,                      \
-      int64_t M,                            \
-      int64_t K,                            \
-      int64_t lda,                          \
-      int64_t ldc_f,                        \
-      int64_t ldc_s,                        \
-      bool store_out,                       \
-      bool use_brgemm)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                 \
+  template void tinygemm_kernel<TYPE>(                                      \
+      TYPE * C, float* C_temp, const uint8_t* A, const float* scales_a,     \
+      const int32_t* qzeros_a, const uint8_t* B, const float* scales_b,     \
+      const int8_t* qzeros_b, const int32_t* compensation, int8_t* dqB_tmp, \
+      int64_t M, int64_t K, int64_t lda, int64_t ldc_f, int64_t ldc_s,      \
+      bool store_out, bool use_brgemm)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
 
-// int4 gemm dispatch api register
-at::Tensor int4_scaled_mm_cpu(
-    at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros, at::Tensor& w_scales, std::optional<at::Tensor> bias) {
-  return int4_scaled_mm_cpu_with_quant(x, w, w_scales, w_zeros, bias, x.scalar_type());
+at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
+                              at::Tensor& w_scales,
+                              std::optional<at::Tensor> bias) {
+  return int4_scaled_mm_cpu_with_quant(x, w, w_scales, w_zeros, bias,
+                                       x.scalar_type());
 }

csrc/cpu/sgl-kernels/gemm_int8.cpp

@@ -1,83 +1,20 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
+#include "common.h"
+#include "vec.h"
+#include "gemm.h"
+
 // clang-format off
 
-#include "common.h"
-#include "gemm.h"
-#include "vec.h"
-
 namespace {
 
-template <typename scalar_t, bool has_bias, int BLOCK_N>
-struct scale_C {
-  static inline void apply(
-      scalar_t* __restrict__ C,
-      const int32_t* __restrict__ Ctmp,
-      const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias,
-      float As,
-      const float* __restrict__ Bs) {
-    TORCH_CHECK(false, "scale_C: scalar path not implemented!");
-  }
-};
-
-#if defined(CPU_CAPABILITY_AVX512)
-template <bool has_bias, int BLOCK_N>
-struct scale_C<at::BFloat16, has_bias, BLOCK_N> {
-  static inline void apply(
-      at::BFloat16* __restrict__ C,
-      const int32_t* __restrict__ Ctmp,
-      const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias,
-      float As,
-      const float* __restrict__ Bs) {
-    constexpr int COLS = BLOCK_N / 16;
-    static_assert(COLS % 2 == 0);
-
-    __m512 vc[COLS];
-    __m512 vd0 = _mm512_set1_ps(As);
-
-    auto compute = [&](auto col) {
-      __m512 vd1 = _mm512_loadu_ps(Bs + col * 16);
-      __m512i vcomp = _mm512_loadu_si512(Bcomp + col * 16);
-      __m512i vc32 = _mm512_loadu_si512(Ctmp + col * 16);
-      vc[col] = _mm512_cvtepi32_ps(_mm512_sub_epi32(vc32, vcomp));
-      if constexpr (has_bias) {
-        __m512 vbias = _mm512_loadu_ps(bias + col * 16);
-        vc[col] = _mm512_fmadd_ps(_mm512_mul_ps(vc[col], vd0), vd1, vbias);
-      } else {
-        vc[col] = _mm512_mul_ps(_mm512_mul_ps(vc[col], vd0), vd1);
-      }
-    };
-    Unroll<COLS>{}(compute);
-
-    auto storec = [&](auto col) {
-      // for COLS = 2, 4 use 512bit store
-      if constexpr (col % 2 == 0) {
-        _mm512_storeu_si512(
-            reinterpret_cast<__m512i*>((C + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc[col + 1], vc[col + 0])));
-      }
-    };
-    Unroll<COLS>{}(storec);
-  }
-};
-#endif
-
 template <typename scalar_t, bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const uint8_t* __restrict__ A,
-      const int8_t* __restrict__ B,
-      scalar_t* __restrict__ C,
-      const float* __restrict__ As,
-      const float* __restrict__ Bs,
-      const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, scalar_t* __restrict__ C,
+      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -86,17 +23,10 @@ struct tinygemm_kernel_nn {
 template <bool has_bias, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const uint8_t* __restrict__ A,
-      const int8_t* __restrict__ B,
-      at::BFloat16* __restrict__ C,
-      const float* __restrict__ As,
-      const float* __restrict__ Bs,
-      const int32_t* __restrict__ Bcomp,
-      const float* __restrict__ bias,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const uint8_t* __restrict__ A, const int8_t* __restrict__ B, at::BFloat16* __restrict__ C,
+      const float* __restrict__ As, const float* __restrict__ Bs, const int32_t* __restrict__ Bcomp,
+      const float* __restrict__ bias, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
     static_assert(COLS % 2 == 0);
@@ -108,10 +38,10 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
     __m512i vb[COLS];
     __m512i vc[ROWS * COLS];
     __m512i vcomp[COLS];
-    __m512 vd0;
-    __m512 vd1[COLS];
+    __m512  vd0;
+    __m512  vd1[COLS];
 
-    // oops! 4x4 spills but we use 4x2
+    // oops! 4x4 spills but luckily we use 4x2
     __m512 vbias[COLS];
 
     // [NOTE]: s8s8 igemm compensation in avx512-vnni
@@ -124,12 +54,14 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
     // 1) 128 * b is pre-computed when packing B to vnni formats
     // 2) a + 128 is fused when dynamically quantize A
     //
-    auto loadc = [&](auto i) { vc[i] = _mm512_set1_epi32(0); };
+    auto loadc = [&](auto i) {
+      vc[i] = _mm512_set1_epi32(0);
+    };
     Unroll<ROWS * COLS>{}(loadc);
 
     const int64_t K4 = K >> 2;
     const int64_t lda4 = lda >> 2;
-    const int64_t ldb4 = ldb;  // ldb * 4 >> 2;
+    const int64_t ldb4 = ldb; // ldb * 4 >> 2;
     const int32_t* a_ptr = reinterpret_cast<const int32_t*>(A);
     const int32_t* b_ptr = reinterpret_cast<const int32_t*>(B);
 
@@ -157,7 +89,7 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
       constexpr int col = i % COLS;
 
       // load a scale
-      if constexpr (col == 0) {
+      if constexpr(col == 0) {
         vd0 = _mm512_set1_ps(As[row]);
       }
       // load b scale and vcomp per 2 vectors
@@ -188,7 +120,8 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
         }
 
         _mm512_storeu_si512(
-            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)), (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
+            reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
+            (__m512i)(_mm512_cvtne2ps_pbh(vc1, vc0)));
       }
     };
     Unroll<ROWS * COLS>{}(storec);
@@ -196,19 +129,11 @@ struct tinygemm_kernel_nn<at::BFloat16, has_bias, BLOCK_M, BLOCK_N> {
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                \
-  tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply( \
-      A + mb_start * lda,                                          \
-      B + nb_start * 4,                                            \
-      C + mb_start * ldc + nb_start,                               \
-      As + mb_start,                                               \
-      Bs + nb_start,                                               \
-      Bcomp + nb_start,                                            \
-      has_bias ? bias + nb_start : nullptr,                        \
-      K,                                                           \
-      lda,                                                         \
-      ldb,                                                         \
-      ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
+    tinygemm_kernel_nn<scalar_t, has_bias, MB_SIZE, NB_SIZE>::apply(         \
+        A + mb_start * lda, B + nb_start * 4, C + mb_start * ldc + nb_start, \
+        As + mb_start, Bs + nb_start, Bcomp + nb_start,                      \
+        has_bias ? bias + nb_start : nullptr, K, lda, ldb, ldc);
 
 template <typename scalar_t, bool has_bias>
 void tinygemm_kernel(
@@ -226,20 +151,10 @@ void tinygemm_kernel(
     int64_t ldb,
     int64_t ldc,
     bool brg) {
+
   // B compensation
   const int32_t* Bcomp = reinterpret_cast<const int32_t*>(B + block_size_n() * K);
 
-  if (brg) {
-    constexpr int BLOCK_N = block_size_n();
-    at::native::cpublas::brgemm(M, N, K, lda, ldb, BLOCK_N, /* add_C */ false, A, B, Ctmp);
-
-    // apply compensation and scale
-    for (int64_t m = 0; m < M; ++m) {
-      scale_C<scalar_t, has_bias, BLOCK_N>::apply(C + m * ldc, Ctmp + m * BLOCK_N, Bcomp, bias, As[m], Bs);
-    }
-    return;
-  }
-
   // pattern: 1-4-16
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 64;
@@ -252,43 +167,26 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch (mb_size << 4 | nb_size >> 4) {
+      switch(mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
-          break;
-        case 0x14:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 64);
-          break;
+        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
+        case 0x14: LAUNCH_TINYGEMM_KERNEL_NN(1, 64); break;
         // mb_size = 2
-        case 0x22:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
-          break;
-        case 0x24:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 64);
-          break;
+        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
+        case 0x24: LAUNCH_TINYGEMM_KERNEL_NN(2, 64); break;
         // mb_size = 3
-        case 0x32:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
-          break;
-        case 0x34:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 64);
-          break;
+        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
+        case 0x34: LAUNCH_TINYGEMM_KERNEL_NN(3, 64); break;
         // mb_size = 4
-        case 0x42:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
-          break;
-        case 0x44:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 64);
-          break;
-        default:
-          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
+        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
+        case 0x44: LAUNCH_TINYGEMM_KERNEL_NN(4, 64); break;
+        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
       }
     }
   }
 }
 
-template <typename scalar_t>
+template<typename scalar_t>
 void int8_scaled_mm_kernel_impl(
     scalar_t* __restrict__ out,
     const uint8_t* __restrict__ mat1,
@@ -299,22 +197,28 @@ void int8_scaled_mm_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
+
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
   const int64_t MB = div_up(M, BLOCK_M);
   const int64_t NB = div_up(N, BLOCK_N);
 
-  const bool use_brgemm = can_use_brgemm<int8_t>(M);
+  // TODO: brgemm u8s8 depends on PyTorch 2.7 release.
+  const bool use_brgemm = false;
 
   // K + 4 after compensation
   const int64_t packed_row_size = get_row_size<int8_t>(K);
 
   AT_DISPATCH_BOOL(bias != nullptr, has_bias, [&] {
-    parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+    at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+      int64_t mb{0}, nb{0};
+      data_index_init(begin, mb, MB, nb, NB);
+
       // for brgemm, use int32_t for accumulate
       alignas(64) int32_t Ctmp[BLOCK_M * BLOCK_N];
 
-      loop_2d<int8_t>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+      for (int i = begin; i < end; ++i) {
+        UNUSED(i);
         int mb_start = mb * BLOCK_M;
         int mb_size = std::min(M - mb_start, BLOCK_M);
         int nb_start = nb * BLOCK_N;
@@ -335,7 +239,10 @@ void int8_scaled_mm_kernel_impl(
             /* ldb */ nb_size,
             /* ldc */ N,
             /* brg */ use_brgemm);
-      });
+
+        // move to the next index
+        data_index_step(mb, MB, nb, NB);
+      }
 
       if (use_brgemm) {
         at::native::cpublas::brgemm_release();
@@ -344,47 +251,28 @@ void int8_scaled_mm_kernel_impl(
   });
 }
 
-}  // anonymous namespace
+} // anonymous namespace
 
 // tinygemm interface
 template <typename scalar_t>
-void tinygemm_kernel(
-    const uint8_t* __restrict__ A,
-    const int8_t* __restrict__ B,
-    scalar_t* __restrict__ C,
-    int32_t* __restrict__ Ctmp,
-    const float* __restrict__ As,
-    const float* __restrict__ Bs,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t lda,
-    int64_t ldb,
-    int64_t ldc,
-    bool brg) {
+void tinygemm_kernel(const uint8_t* __restrict__ A, const int8_t* __restrict__ B, scalar_t* __restrict__ C,
+    int32_t* __restrict__ Ctmp,  const float* __restrict__ As, const float* __restrict__ Bs,
+    int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg) {
   tinygemm_kernel<scalar_t, false>(A, B, C, Ctmp, As, Bs, nullptr, M, N, K, lda, ldb, ldc, brg);
 }
 
-#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE) \
-  template void tinygemm_kernel<TYPE>(      \
-      const uint8_t* __restrict__ A,        \
-      const int8_t* __restrict__ B,         \
-      TYPE* __restrict__ C,                 \
-      int32_t* __restrict__ Ctmp,           \
-      const float* __restrict__ As,         \
-      const float* __restrict__ Bs,         \
-      int64_t M,                            \
-      int64_t N,                            \
-      int64_t K,                            \
-      int64_t lda,                          \
-      int64_t ldb,                          \
-      int64_t ldc,                          \
-      bool brg)
+#define INSTANTIATE_TINYGEMM_TEMPLATE(TYPE)                                                     \
+    template void tinygemm_kernel<TYPE>(                                                        \
+        const uint8_t* __restrict__ A, const int8_t* __restrict__ B, TYPE* __restrict__ C,      \
+        int32_t* __restrict__ Ctmp, const float* __restrict__ As, const float* __restrict__ Bs, \
+        int64_t M, int64_t N, int64_t K, int64_t lda, int64_t ldb, int64_t ldc, bool brg)
 
 INSTANTIATE_TINYGEMM_TEMPLATE(at::BFloat16);
 INSTANTIATE_TINYGEMM_TEMPLATE(at::Half);
 
 std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
+  RECORD_FUNCTION("sgl-kernel::per_token_quant_int8_cpu", std::vector<c10::IValue>({A}));
+
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(A);
   CHECK_DIM(2, A);
 
@@ -393,7 +281,8 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
   int64_t lda = A.stride(0);
 
   const auto st = A.scalar_type();
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "per_token_quant_int8: expect A to be bfloat16 or half.");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf,
+      "per_token_quant_int8: expect A to be bfloat16 or half.");
 
   auto Aq = at::empty({M, K}, A.options().dtype(at::kByte));
   auto As = at::empty({M}, A.options().dtype(at::kFloat));
@@ -403,9 +292,13 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
     float* __restrict__ As_data = As.data_ptr<float>();
     const scalar_t* __restrict__ A_data = A.data_ptr<scalar_t>();
 
-    at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
+    at::parallel_for(0, M, 0, [&] (int64_t begin, int64_t end) {
       for (int64_t m = begin; m < end; ++m) {
-        quantize_row_int8<scalar_t>(Aq_data + m * K, As_data[m], A_data + m * lda, K);
+        quantize_row_int8<scalar_t>(
+            Aq_data + m * K,
+            As_data[m],
+            A_data + m * lda,
+            K);
       }
     });
   });
@@ -422,14 +315,11 @@ std::tuple<at::Tensor, at::Tensor> per_token_quant_int8_cpu(at::Tensor& A) {
 // bias    : [N]
 // out     : [M, N]
 //
-at::Tensor int8_scaled_mm_cpu(
-    at::Tensor& mat1,
-    at::Tensor& mat2,
-    at::Tensor& scales1,
-    at::Tensor& scales2,
-    const std::optional<at::Tensor>& bias,
-    at::ScalarType out_dtype,
-    bool is_vnni) {
+at::Tensor int8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
+    at::Tensor& scales1, at::Tensor& scales2,
+    std::optional<at::Tensor>& bias, at::ScalarType out_dtype, bool is_vnni) {
+  RECORD_FUNCTION("sgl-kernel::int8_scaled_mm_cpu", std::vector<c10::IValue>({mat1, mat2, scales1, scales2, bias}));
+
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_INPUT(mat1);
@@ -450,8 +340,7 @@ at::Tensor int8_scaled_mm_cpu(
 
   TORCH_CHECK(mat1.scalar_type() == at::kByte, "int8_scaled_mm: expect mat1 to be uint8.");
   TORCH_CHECK(mat2.scalar_type() == at::kChar, "int8_scaled_mm: expect mat2 to be int8.");
-  TORCH_CHECK(
-      scales1.scalar_type() == at::kFloat && scales2.scalar_type() == at::kFloat,
+  TORCH_CHECK(scales1.scalar_type() == at::kFloat && scales2.scalar_type() == at::kFloat,
       "int8_scaled_mm: expect scales to be float32.");
 
   auto out = at::empty({M, N}, mat1.options().dtype(out_dtype));
@@ -479,13 +368,10 @@ at::Tensor int8_scaled_mm_cpu(
 }
 
 // fused `per_token_quant_int8_cpu` and `int8_scaled_mm_cpu`
-at::Tensor int8_scaled_mm_with_quant(
-    at::Tensor& mat1,
-    at::Tensor& mat2,
-    at::Tensor& scales2,
-    const std::optional<at::Tensor>& bias,
-    at::ScalarType out_dtype,
-    bool is_vnni) {
+at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2, at::Tensor& scales2,
+    const std::optional<at::Tensor>& bias, at::ScalarType out_dtype, bool is_vnni) {
+  RECORD_FUNCTION("sgl-kernel::int8_scaled_mm_cpu", std::vector<c10::IValue>({mat1, mat2, scales2, bias}));
+
   auto packed_w = is_vnni ? mat2 : convert_weight_packed(mat2);
 
   CHECK_LAST_DIM_CONTIGUOUS_INPUT(mat1);
@@ -504,10 +390,14 @@ at::Tensor int8_scaled_mm_with_quant(
   CHECK_EQ(scales2.numel(), N);
 
   const auto st = mat1.scalar_type();
-  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf, "int8_scaled_mm_with_quant: expect A to be bfloat16 or half.");
-  TORCH_CHECK(st == out_dtype, "int8_scaled_mm_with_quant: expect A has same dtype with out_dtype.");
-  TORCH_CHECK(mat2.scalar_type() == at::kChar, "int8_scaled_mm_with_quant: expect mat2 to be int8.");
-  TORCH_CHECK(scales2.scalar_type() == at::kFloat, "int8_scaled_mm_with_quant: expect scales to be float32.");
+  TORCH_CHECK(st == at::kBFloat16 || st == at::kHalf,
+      "int8_scaled_mm_with_quant: expect A to be bfloat16 or half.");
+  TORCH_CHECK(st == out_dtype,
+      "int8_scaled_mm_with_quant: expect A has same dtype with out_dtype.");
+  TORCH_CHECK(mat2.scalar_type() == at::kChar,
+      "int8_scaled_mm_with_quant: expect mat2 to be int8.");
+  TORCH_CHECK(scales2.scalar_type() == at::kFloat,
+      "int8_scaled_mm_with_quant: expect scales to be float32.");
 
   const int64_t buffer_size = M * K + M * sizeof(float);
   auto buffer = at::empty({buffer_size}, mat1.options().dtype(at::kByte));
@@ -525,9 +415,13 @@ at::Tensor int8_scaled_mm_with_quant(
     float* __restrict__ As_data = (float*)((void*)(Aq_data + M * K));
     const scalar_t* __restrict__ A_data = mat1.data_ptr<scalar_t>();
 
-    at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
+    at::parallel_for(0, M, 0, [&] (int64_t begin, int64_t end) {
       for (int64_t m = begin; m < end; ++m) {
-        quantize_row_int8<scalar_t>(Aq_data + m * K, As_data[m], A_data + m * lda, K);
+        quantize_row_int8<scalar_t>(
+            Aq_data + m * K,
+            As_data[m],
+            A_data + m * lda,
+            K);
       }
     });
 

csrc/cpu/sgl-kernels/moe.cpp

@@ -1,13 +1,12 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-// clang-format off
-
-#include "moe.h"
-
 #include "common.h"
+#include "vec.h"
 #include "gemm.h"
 
+// clang-format off
+
 namespace {
 
 // [NOTE]: Fused MoE kernel with AMX
@@ -31,6 +30,109 @@ namespace {
 //     3. abstract at::native::cpublas::brgemm with WoQ gemm (M = 1 & M != 1)
 //
 
+template <typename scalar_t>
+inline void fill_stub(scalar_t* __restrict__ out, scalar_t val, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  const Vec data_vec(val);
+  at::vec::map<scalar_t>([data_vec](Vec out) { return out = data_vec; }, out, out, size);
+}
+
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  // no remainder
+  #pragma GCC unroll 4
+  for (int64_t d = 0; d < size; d += Vec::size()) {
+    Vec data = Vec::loadu(input + d);
+    data.store(out + d);
+  }
+}
+
+template <typename scalar_t>
+inline void copy_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input, float weight, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec weight_vec = fVec(weight);
+  int64_t d;
+  #pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    fVec data0 = fVec::loadu(input + d) * weight_vec;
+    fVec data1 = fVec::loadu(input + d + fVec::size()) * weight_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(data0, data1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] * weight);
+  }
+}
+
+// acc from [topk, K] to [K]
+template <typename scalar_t>
+inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  if (topk == 1) {
+    // do copy for topk = 1
+    copy_stub(out, input, K);
+  } else {
+    // do sum for topk != 1
+    int64_t d;
+    #pragma GCC unroll 4
+    for (d = 0; d <= K - kVecSize; d += kVecSize) {
+      fVec sum_fvec0 = fVec(0.f);
+      fVec sum_fvec1 = fVec(0.f);
+      for (int t = 0; t < topk; ++t) {
+        bVec x_bvec = bVec::loadu(input + t * K + d);
+        fVec x_fvec0, x_fvec1;
+        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
+
+        sum_fvec0 += x_fvec0;
+        sum_fvec1 += x_fvec1;
+      }
+      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
+      out_bvec.store(out + d);
+    }
+    for (; d < K; ++d) {
+      float sum_val = 0.f;
+      for (int t = 0; t < topk; ++t) {
+        sum_val += static_cast<float>(input[t * K + d]);
+      }
+      out[d] = static_cast<scalar_t>(sum_val);
+    }
+  }
+}
+
+// out = input + input2 * scale
+template <typename scalar_t>
+inline void add_mul_stub(scalar_t* __restrict__ out, const float* __restrict__ input,
+    const scalar_t* __restrict__ input2, float scale, int64_t size) {
+
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec s_vec = fVec(scale);
+  int64_t d;
+  #pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    fVec x0 = fVec::loadu(input + d);
+    fVec x1 = fVec::loadu(input + d + fVec::size());
+
+    bVec y_bvec = bVec::loadu(input2 + d);
+    fVec y0, y1;
+    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
+
+    x0 = x0 + y0 * s_vec;
+    x1 = x1 + y1 * s_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
+  }
+}
+
 template <int BLOCK_M>
 int moe_align_block_size(
     int32_t* __restrict__ sorted_ids,
@@ -42,7 +144,8 @@ int moe_align_block_size(
     int num_experts,
     int numel,
     int num_threads) {
-#define T_INDEX(tt) total_cnts + (tt) * num_experts
+
+  #define T_INDEX(tt) total_cnts + (tt) * num_experts
 
   // accumulate count of expert ids locally
   at::parallel_for(0, numel, 0, [&](int begin, int end) {
@@ -57,7 +160,8 @@ int moe_align_block_size(
   using iVec = at::vec::Vectorized<int32_t>;
   for (int t = 0; t < num_threads; ++t) {
     at::vec::map2<int32_t>(
-        [](iVec x, iVec y) { return x + y; }, T_INDEX(t + 1), T_INDEX(t + 1), T_INDEX(t), num_experts);
+        [](iVec x, iVec y) { return x + y; },
+        T_INDEX(t + 1), T_INDEX(t + 1), T_INDEX(t), num_experts);
   }
 
   // the last row holds sums of each experts
@@ -97,9 +201,7 @@ int moe_align_block_size(
   // padding value for sorted_ids: numel
   auto sorted_id_size = [=](const int32_t* sorted_ids_ptr) {
     for (int d = 0; d < BLOCK_M; ++d) {
-      if (sorted_ids_ptr[d] == numel) {
-        return d;
-      }
+      if (sorted_ids_ptr[d] == numel) { return d; }
     }
     return BLOCK_M;
   };
@@ -113,7 +215,7 @@ int moe_align_block_size(
       offsets[mb + 1] = sorted_id_size(sorted_ids + mb * BLOCK_M);
     }
   });
-  // TODO: do we need to vecterize this ?
+  // TODO: do we need to vectorize this ?
   for (int mb = 0; mb < num_token_blocks; ++mb) {
     offsets[mb + 1] += offsets[mb];
   }
@@ -134,6 +236,7 @@ inline void silu_and_mul(
     const float* __restrict__ input1,  // y: y0, y1
     int64_t m_size,
     int64_t N) {
+
   using bVec = at::vec::Vectorized<scalar_t>;
   using fVec = at::vec::Vectorized<float>;
 
@@ -166,14 +269,8 @@ inline void silu_and_mul(
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn2 {
   static inline void apply(
-      const scalar_t* __restrict__ A,
-      const scalar_t* __restrict__ B0,
-      const scalar_t* __restrict__ B1,
-      scalar_t* __restrict__ C,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B0, const scalar_t* __restrict__ B1,
+      scalar_t* __restrict__ C, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -182,14 +279,9 @@ struct tinygemm_kernel_nn2 {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A,
-      const at::BFloat16* __restrict__ B0,
-      const at::BFloat16* __restrict__ B1,
-      at::BFloat16* __restrict__ C,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B0, const at::BFloat16* __restrict__ B1,
+      at::BFloat16* __restrict__ C, int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -212,7 +304,7 @@ struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b0_ptr = reinterpret_cast<const float*>(B0);
     const float* b1_ptr = reinterpret_cast<const float*>(B1);
@@ -260,16 +352,17 @@ struct tinygemm_kernel_nn2<at::BFloat16, BLOCK_M, BLOCK_N> {
         _mm512_storeu_si512(
             reinterpret_cast<__m512i*>((C + row * ldc + col * 16)),
             (__m512i)(_mm512_cvtne2ps_pbh(__m512(x1), __m512(x0))));
-      }
+        }
     };
     Unroll<ROWS * COLS>{}(storec);
   }
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)       \
-  tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply( \
-      A + mb_start * lda, B0 + nb_start * 2, B1 + nb_start * 2, C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN(MB_SIZE, NB_SIZE)                          \
+    tinygemm_kernel_nn2<scalar_t, MB_SIZE, NB_SIZE>::apply(                  \
+        A + mb_start * lda, B0 + nb_start * 2, B1 + nb_start * 2,            \
+        C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -283,6 +376,7 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
+
   // pattern: 1-(2+2)-(8+8)
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 32;
@@ -295,25 +389,16 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch (mb_size << 4 | nb_size >> 4) {
+      switch(mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12:
-          LAUNCH_TINYGEMM_KERNEL_NN(1, 32);
-          break;
+        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN(1, 32); break;
         // mb_size = 2
-        case 0x22:
-          LAUNCH_TINYGEMM_KERNEL_NN(2, 32);
-          break;
+        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN(2, 32); break;
         // mb_size = 3
-        case 0x32:
-          LAUNCH_TINYGEMM_KERNEL_NN(3, 32);
-          break;
+        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN(3, 32); break;
         // mb_size = 4
-        case 0x42:
-          LAUNCH_TINYGEMM_KERNEL_NN(4, 32);
-          break;
-        default:
-          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
+        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN(4, 32); break;
+        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
       }
     }
   }
@@ -322,13 +407,8 @@ void tinygemm_kernel(
 template <typename scalar_t, int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn {
   static inline void apply(
-      const scalar_t* __restrict__ A,
-      const scalar_t* __restrict__ B,
-      float* __restrict__ C,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const scalar_t* __restrict__ A, const scalar_t* __restrict__ B, float* __restrict__ C,
+      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
     TORCH_CHECK(false, "tinygemm_kernel_nn: scalar path not implemented!");
   }
 };
@@ -337,13 +417,9 @@ struct tinygemm_kernel_nn {
 template <int BLOCK_M, int BLOCK_N>
 struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
   static inline void apply(
-      const at::BFloat16* __restrict__ A,
-      const at::BFloat16* __restrict__ B,
-      float* __restrict__ C,
-      int64_t K,
-      int64_t lda,
-      int64_t ldb,
-      int64_t ldc) {
+      const at::BFloat16* __restrict__ A, const at::BFloat16* __restrict__ B, float* __restrict__ C,
+      int64_t K, int64_t lda, int64_t ldb, int64_t ldc) {
+
     constexpr int ROWS = BLOCK_M;
     constexpr int COLS = BLOCK_N / 16;
 
@@ -356,12 +432,14 @@ struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
     __m512bh vb[COLS];
     __m512 vc[ROWS * COLS];
 
-    auto loadc = [&](auto i) { vc[i] = _mm512_set1_ps(0.f); };
+    auto loadc = [&](auto i) {
+      vc[i] = _mm512_set1_ps(0.f);
+    };
     Unroll<ROWS * COLS>{}(loadc);
 
     const int64_t K2 = K >> 1;
     const int64_t lda2 = lda >> 1;
-    const int64_t ldb2 = ldb;  // ldb * 2 >> 1;
+    const int64_t ldb2 = ldb; // ldb * 2 >> 1;
     const float* a_ptr = reinterpret_cast<const float*>(A);
     const float* b_ptr = reinterpret_cast<const float*>(B);
 
@@ -388,15 +466,17 @@ struct tinygemm_kernel_nn<at::BFloat16, BLOCK_M, BLOCK_N> {
       constexpr int row = i / COLS;
       constexpr int col = i % COLS;
       _mm512_storeu_ps(reinterpret_cast<__m512*>(C + row * ldc + col * 16), vc[i]);
+
     };
     Unroll<ROWS * COLS>{}(storec);
   }
 };
 #endif
 
-#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE)     \
-  tinygemm_kernel_nn<scalar_t, MB_SIZE, NB_SIZE>::apply( \
-      A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, K, lda, ldb, ldc);
+#define LAUNCH_TINYGEMM_KERNEL_NN2(MB_SIZE, NB_SIZE)                         \
+    tinygemm_kernel_nn<scalar_t, MB_SIZE, NB_SIZE>::apply(                   \
+        A + mb_start * lda, B + nb_start * 2, C + mb_start * ldc + nb_start, \
+        K, lda, ldb, ldc);
 
 template <typename scalar_t>
 void tinygemm_kernel(
@@ -409,6 +489,7 @@ void tinygemm_kernel(
     int64_t lda,
     int64_t ldb,
     int64_t ldc) {
+
   // pattern: 1-2-8
   constexpr int64_t BLOCK_M = 4;
   constexpr int64_t BLOCK_N = 32;
@@ -421,25 +502,16 @@ void tinygemm_kernel(
       int64_t nb_start = nb * BLOCK_N;
       int64_t nb_size = std::min(BLOCK_N, N - nb_start);
 
-      switch (mb_size << 4 | nb_size >> 4) {
+      switch(mb_size << 4 | nb_size >> 4) {
         // mb_size = 1
-        case 0x12:
-          LAUNCH_TINYGEMM_KERNEL_NN2(1, 32);
-          break;
+        case 0x12: LAUNCH_TINYGEMM_KERNEL_NN2(1, 32); break;
         // mb_size = 2
-        case 0x22:
-          LAUNCH_TINYGEMM_KERNEL_NN2(2, 32);
-          break;
+        case 0x22: LAUNCH_TINYGEMM_KERNEL_NN2(2, 32); break;
         // mb_size = 3
-        case 0x32:
-          LAUNCH_TINYGEMM_KERNEL_NN2(3, 32);
-          break;
+        case 0x32: LAUNCH_TINYGEMM_KERNEL_NN2(3, 32); break;
         // mb_size = 4
-        case 0x42:
-          LAUNCH_TINYGEMM_KERNEL_NN2(4, 32);
-          break;
-        default:
-          TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", "nb_size");
+        case 0x42: LAUNCH_TINYGEMM_KERNEL_NN2(4, 32); break;
+        default: TORCH_CHECK(false, "Unexpected block size, ", mb_size, "x", nb_size);
       }
     }
   }
@@ -465,6 +537,7 @@ void fused_experts_kernel_impl(
     int64_t E,
     int64_t topk,
     int64_t num_tokens_post_pad) {
+
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -479,36 +552,39 @@ void fused_experts_kernel_impl(
   const int64_t stride_e = 2 * N * K;
   const int64_t stride_n = K;
 
-  int64_t avg_M = std::max(int64_t(1), M * topk / E);
-  const bool use_brgemm = can_use_brgemm<scalar_t>(avg_M);
-
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
     // get local pointers
-    int tid = get_thread_num();
+    int tid = at::get_thread_num();
     scalar_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
     float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
     float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
 
-    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
-      // nb_upper from top half and nb_lower from bottom half
-      int64_t nb_upper = nb, nb_lower = nb + NB;
-      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB;
+      int64_t nb = i % NB;
+
+      // nb0 from top half and nb1 from bottom half
+      int64_t nb0 = nb, nb1 = nb + NB;
+      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
 
       // B shape [K, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
-      const scalar_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb_upper * BLOCK_N * stride_n;
-      const scalar_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb_lower * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B0 = packed_w1 + expert_id * stride_e + nb0 * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B1 = packed_w1 + expert_id * stride_e + nb1 * BLOCK_N * stride_n;
 
+      // 1.a load A
+      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
       int64_t m_size = offsets[mb + 1] - offsets[mb];
 
-      if (nb_offset == 0) {
-        // 1.a load A
-        const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
-        for (int64_t m = 0; m < m_size; ++m) {
-          int32_t index = A_ids[m] / topk;
-          copy_stub(A + m * K, input + index * K, K);
-        }
+      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+
+      for (int64_t m = 0; m < m_size; ++m) {
+        int32_t index = A_ids[m] / topk;
+        copy_stub(A + m * K, input + index * K, K);
       }
 
       if (use_brgemm) {
@@ -540,7 +616,12 @@ void fused_experts_kernel_impl(
 
         // 1.d silu and mul
         const int64_t offset = offsets[mb];
-        silu_and_mul<scalar_t, BLOCK_N>(ic1 + offset * N + nb * BLOCK_N, C0, C1, m_size, N);
+        silu_and_mul<scalar_t, BLOCK_N>(
+            ic1 + offset * N + nb * BLOCK_N,
+            C0,
+            C1,
+            m_size,
+            N);
       } else {
         // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
         const int64_t offset = offsets[mb];
@@ -556,9 +637,9 @@ void fused_experts_kernel_impl(
             /* ldb   */ n_size,
             /* ldc   */ N);
       }
-    });
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -573,16 +654,24 @@ void fused_experts_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
     // get local pointers
-    int tid = get_thread_num();
+    int tid = at::get_thread_num();
     // we won't be using C1 for gemm2
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
 
-    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB2;
+      int64_t nb = i % NB2;
+
       int64_t m_size = offsets[mb + 1] - offsets[mb];
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
+      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+
       // A ptr from ic1 of [M * topk, N] in sorted order
       // so as to avoid copy A to tmp buffer again
       const scalar_t* __restrict__ A = ic1 + offsets[mb] * N;
@@ -625,9 +714,9 @@ void fused_experts_kernel_impl(
         float weight = topk_weights[index];
         copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
       }
-    });
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -654,6 +743,7 @@ void shared_expert_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
+
   // handle 2 tiles per block
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
@@ -665,29 +755,36 @@ void shared_expert_kernel_impl(
   TORCH_CHECK(N % BLOCK_N == 0, "Fixme when N is not multiples of ", BLOCK_N);
   const int64_t stride_n = K;
 
-  const bool use_brgemm = can_use_brgemm<scalar_t>(M);
-
-  const bool apply_scaling_factor = fused_experts_out != nullptr;
-
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
     // get local pointers
-    int tid = get_thread_num();
+    int tid = at::get_thread_num();
     float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
     float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
 
-    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * K * 2, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
-      // nb_upper from top half and nb_lower from bottom half
-      int64_t nb_upper = nb, nb_lower = nb + NB;
-      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB;
+      int64_t nb = i % NB;
+
+      // nb0 from top half and nb1 from bottom half
+      int64_t nb0 = nb, nb1 = nb + NB;
+      int64_t n_size = std::min(N - nb0 * BLOCK_N, BLOCK_N);
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
 
+      //int64_t mb_start = mb * BLOCK_M;
+      //int64_t mb_size = std::min(M - mb_start, BLOCK_M);
+
       // A shape [m_size, K]
       const scalar_t* A = input + mb * BLOCK_M * K;
 
       // B shape [K, n_size] in vnni format
-      const scalar_t* __restrict__ B0 = packed_w1 + nb_upper * BLOCK_N * stride_n;
-      const scalar_t* __restrict__ B1 = packed_w1 + nb_lower * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B0 = packed_w1 + nb0 * BLOCK_N * stride_n;
+      const scalar_t* __restrict__ B1 = packed_w1 + nb1 * BLOCK_N * stride_n;
+
+      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
 
       if (use_brgemm) {
         // 1.b gemm: C0 = A @ B0
@@ -717,7 +814,12 @@ void shared_expert_kernel_impl(
             /* C     */ C1);
 
         // 1.d silu and mul
-        silu_and_mul<scalar_t, BLOCK_N>(ic1 + mb * BLOCK_M * N + nb * BLOCK_N, C0, C1, m_size, N);
+        silu_and_mul<scalar_t, BLOCK_N>(
+            ic1 + mb * BLOCK_M * N + nb * BLOCK_N,
+            C0,
+            C1,
+            m_size,
+            N);
       } else {
         // fused 1.bcd: silu_and_mul(A @ B0, A @ B1)
         tinygemm_kernel(
@@ -732,9 +834,9 @@ void shared_expert_kernel_impl(
             /* ldb   */ n_size,
             /* ldc   */ N);
       }
-    });
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -748,16 +850,24 @@ void shared_expert_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
     // get local pointers
-    int tid = get_thread_num();
+    int tid = at::get_thread_num();
     // we won't be using C1 for gemm2
     float* __restrict__ C = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
 
-    loop_2d<scalar_t>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB2;
+      int64_t nb = i % NB2;
+
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
+      const bool use_brgemm = can_use_brgemm<scalar_t>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+
       // A shape [m_size, IC]
       const scalar_t* __restrict__ A = ic1 + mb * BLOCK_M * N;
 
@@ -792,21 +902,19 @@ void shared_expert_kernel_impl(
 
       // 2.b copy from C to output and add fused_experts_out
       scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
-      const scalar_t* __restrict__ fused_out =
-          apply_scaling_factor ? fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N : nullptr;
+      const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
       for (int64_t m = 0; m < m_size; ++m) {
-        const scalar_t* __restrict__ fused_out_row = apply_scaling_factor ? (fused_out + m * K) : nullptr;
-        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out_row, routed_scaling_factor, n_size);
+        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
       }
-    });
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
 }
 
-}  // anonymous namespace
+} // anonymous namespace
 
 // common checks
 static inline void check_moe_scales(
@@ -814,10 +922,14 @@ static inline void check_moe_scales(
     bool use_fp8_w8a16,
     const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
-    const std::optional<std::vector<int64_t>> block_size) {
+    const std::optional<std::vector<int64_t>> block_size,
+    const std::optional<at::Tensor>& a1_scale,
+    const std::optional<at::Tensor>& a2_scale) {
   if (use_int8_w8a8) {
     TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for int8 w8a8.");
     TORCH_CHECK(w2_scale.has_value(), "missing w2_scale for int8 w8a8.");
+    TORCH_CHECK(!a1_scale.has_value(), "static quantization for activation not supported.");
+    TORCH_CHECK(!a2_scale.has_value(), "static quantization for activation not supported.");
   }
   if (use_fp8_w8a16) {
     TORCH_CHECK(w1_scale.has_value(), "missing w1_scale for fp8 w8a16.");
@@ -827,16 +939,16 @@ static inline void check_moe_scales(
   }
 }
 
-#define CHECK_MOE_SCALES_FP8(DIM0, DIM1)                      \
-  auto w1s = w1_scale.value();                                \
-  auto w2s = w2_scale.value();                                \
-  auto block_size_val = block_size.value();                   \
-  int64_t block_size_N = block_size_val[0];                   \
-  int64_t block_size_K = block_size_val[1];                   \
-  TORCH_CHECK(w1s.size(DIM0) == div_up(2 * N, block_size_N)); \
-  TORCH_CHECK(w1s.size(DIM1) == div_up(K, block_size_K));     \
-  TORCH_CHECK(w2s.size(DIM0) == div_up(K, block_size_N));     \
-  TORCH_CHECK(w2s.size(DIM1) == div_up(N, block_size_K))
+#define CHECK_MOE_SCALES_FP8(DIM0, DIM1)                 \
+    auto w1s = w1_scale.value();                         \
+    auto w2s = w2_scale.value();                         \
+    auto block_size_val = block_size.value();            \
+    int64_t block_size_N = block_size_val[0];            \
+    int64_t block_size_K = block_size_val[1];            \
+    TORCH_CHECK(w1s.size(DIM0) == 2 * N / block_size_N); \
+    TORCH_CHECK(w1s.size(DIM1) == K / block_size_K);     \
+    TORCH_CHECK(w2s.size(DIM0) == K / block_size_N);     \
+    TORCH_CHECK(w2s.size(DIM1) == N / block_size_K)
 
 // hidden_states: [M, K]
 // w1: [E, 2N, K]
@@ -844,7 +956,6 @@ static inline void check_moe_scales(
 // topk_weights: [M, topk]
 // topk_ids: [M, topk] (int32_t)
 //
-
 at::Tensor fused_experts_cpu(
     at::Tensor& hidden_states,
     at::Tensor& w1,
@@ -852,13 +963,16 @@ at::Tensor fused_experts_cpu(
     at::Tensor& topk_weights,
     at::Tensor& topk_ids,
     bool inplace,
-    int64_t moe_comp_method,
+    bool use_int8_w8a8,
+    bool use_fp8_w8a16,
     const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
-    const std::optional<at::Tensor>& w1_zero,
-    const std::optional<at::Tensor>& w2_zero,
     const std::optional<std::vector<int64_t>> block_size,
+    const std::optional<at::Tensor>& a1_scale,
+    const std::optional<at::Tensor>& a2_scale,
     bool is_vnni) {
+  RECORD_FUNCTION("sgl-kernel::fused_experts_cpu", std::vector<c10::IValue>({hidden_states, w1, w2, topk_weights, topk_ids}));
+
   auto packed_w1 = is_vnni ? w1 : convert_weight_packed(w1);
   auto packed_w2 = is_vnni ? w2 : convert_weight_packed(w2);
 
@@ -871,49 +985,32 @@ at::Tensor fused_experts_cpu(
   CHECK_INPUT(w2);
   CHECK_EQ(topk_weights.sizes(), topk_ids.sizes());
   CHECK_DIM(2, hidden_states);
-  if (moe_comp_method == CPUQuantMethod::INT4_W4A8 && is_vnni) {
-    CHECK_DIM(4, w1);
-    CHECK_DIM(4, w2);
-  } else {
-    CHECK_DIM(3, w1);
-    CHECK_DIM(3, w2);
-  }
+  CHECK_DIM(3, w1);
+  CHECK_DIM(3, w2);
   CHECK_DIM(2, topk_weights);
   CHECK_DIM(2, topk_ids);
 
   CHECK_EQ(topk_ids.scalar_type(), at::kInt);
-
-  // TODO: support topk_weights to be bf16 or fp16 in the kernel.
-  // The topk_weights of llama4 is computed via Llama4MoE:custom_routing_function and is bf16/fp16
-  // while the kernel currently only supports it to be float32
-  auto topk_weights_ = topk_weights.to(at::kFloat);
-  CHECK_EQ(topk_weights_.scalar_type(), at::kFloat);
+  CHECK_EQ(topk_weights.scalar_type(), at::kFloat);
 
   int64_t M = hidden_states.size(0);
   int64_t K = hidden_states.size(1);
-  int64_t N = moe_comp_method == CPUQuantMethod::INT4_W4A8 ? w1_scale.value().size(1) * w1_scale.value().size(3) / 2
-                                                           : w1.size(1) / 2;
+  int64_t N = w1.size(1) / 2;
   int64_t E = w1.size(0);
-  int64_t topk = topk_weights_.size(1);
+  int64_t topk = topk_weights.size(1);
 
   // we use int32_t compensation for int8 w8a8
-  int64_t packed_K = get_row_size(K, moe_comp_method == CPUQuantMethod::INT8_W8A8);
-  int64_t packed_N = get_row_size(N, moe_comp_method == CPUQuantMethod::INT8_W8A8);
+  int64_t packed_K = get_row_size(K, use_int8_w8a8);
+  int64_t packed_N = get_row_size(N, use_int8_w8a8);
 
   // check weight shapes
   CHECK_EQ(w2.size(0), E);
-  if (!(moe_comp_method == CPUQuantMethod::INT4_W4A8)) {
-    CHECK_EQ(w2.size(1), K);
-    CHECK_EQ(packed_w1.size(2), packed_K / (moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 2 : 1));
-    CHECK_EQ(packed_w2.size(2), packed_N / (moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 2 : 1));
-  }
+  CHECK_EQ(w2.size(1), K);
+  CHECK_EQ(packed_w1.size(2), packed_K);
+  CHECK_EQ(packed_w2.size(2), packed_N);
+
   // check scales
-  check_moe_scales(
-      moe_comp_method == CPUQuantMethod::INT8_W8A8,
-      moe_comp_method == CPUQuantMethod::FP8_W8A16,
-      w1_scale,
-      w2_scale,
-      block_size);
+  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
 
   at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
 
@@ -934,8 +1031,8 @@ at::Tensor fused_experts_cpu(
   int32_t* __restrict__ sorted_ids = buffer.data_ptr<int32_t>();
   int32_t* __restrict__ expert_ids = sorted_ids + max_num_tokens_padded;
   int32_t* __restrict__ total_cnts = expert_ids + max_num_blocks;
-  int32_t* __restrict__ cumsums = total_cnts + (num_threads + 1) * E;
-  int32_t* __restrict__ offsets = cumsums + (E + 1);
+  int32_t* __restrict__ cumsums    = total_cnts + (num_threads + 1) * E;
+  int32_t* __restrict__ offsets    = cumsums    + (E + 1);
 
   // init sorted_ids with `numel` as the padding number
   // init expert_ids with `num_experts`
@@ -967,31 +1064,26 @@ at::Tensor fused_experts_cpu(
   //
   // for fp8 w8a16:
   //   7. intermediate_cache0 : [M * topk, 2N]
-  //   8. B_tmp : [T, MAX_CACHE_BLOCK_SIZE, BLOCK_N, std::max(K, N)]
+  //   8. B_tmp : [T, BLOCK_N, std::max(K, N)]
   //
-  int64_t buffer_size_nbytes =
-      M * topk * N * 2 + M * topk * K * 2 +
-      num_threads * BLOCK_M * K *
-          (moe_comp_method == CPUQuantMethod::INT8_W8A8 | moe_comp_method == CPUQuantMethod::INT4_W4A8 ? 1 : 2) +
+  int64_t buffer_size_nbytes = M * topk * N * 2 + M * topk * K * 2 +
+      num_threads * BLOCK_M * K * (use_int8_w8a8 ? 1 : 2) +
       num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float);
 
-  if (moe_comp_method == CPUQuantMethod::INT8_W8A8) {
+  if (use_int8_w8a8) {
     buffer_size_nbytes += std::max(M * K, M * topk * N) + M * topk * sizeof(float);
   }
-  if (moe_comp_method == CPUQuantMethod::FP8_W8A16) {
-    buffer_size_nbytes += M * topk * 2 * N * 2 + num_threads * MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N) * 2;
-  }
-  if (moe_comp_method == CPUQuantMethod::INT4_W4A8) {
-    buffer_size_nbytes += M * topk * 2 * N * 2 + std::max(M * K, M * topk * N) + M * topk * sizeof(float) +
-                          num_threads * 2 * get_4bit_block_k_size(K / w1_scale.value().size(2)) * BLOCK_N;
+  if (use_fp8_w8a16) {
+    buffer_size_nbytes += M * topk * 2 * N * 2 + num_threads * BLOCK_N * std::max(K, N) * 2;
   }
+
   auto buffer2 = at::empty({buffer_size_nbytes}, hidden_states.options().dtype(at::kChar));
 
   AT_DISPATCH_REDUCED_FLOATING_TYPES(st, "fused_experts_kernel_impl", [&] {
     scalar_t* __restrict__ intermediate_cache1 = (scalar_t*)((void*)(buffer2.data_ptr<int8_t>()));
     scalar_t* __restrict__ intermediate_cache2 = intermediate_cache1 + M * topk * N;
 
-    if (moe_comp_method == CPUQuantMethod::INT8_W8A8) {
+    if (use_int8_w8a8) {
       uint8_t* __restrict__ A_tmp = (uint8_t*)((void*)(intermediate_cache2 + M * topk * K));
       float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
       uint8_t* __restrict__ Aq_tmp = (uint8_t*)((void*)(C_tmp + num_threads * 2 * BLOCK_M * BLOCK_N));
@@ -1015,7 +1107,7 @@ at::Tensor fused_experts_cpu(
           packed_w2.data_ptr<int8_t>(),
           w1s.data_ptr<float>(),
           w2s.data_ptr<float>(),
-          topk_weights_.data_ptr<float>(),
+          topk_weights.data_ptr<float>(),
           sorted_ids,
           expert_ids,
           offsets,
@@ -1025,7 +1117,7 @@ at::Tensor fused_experts_cpu(
           E,
           topk,
           num_tokens_post_pad);
-    } else if (moe_comp_method == CPUQuantMethod::FP8_W8A16) {
+    } else if (use_fp8_w8a16) {
       // here we just ignore C_tmp as it is not used
       scalar_t* __restrict__ A_tmp = (scalar_t*)((void*)(intermediate_cache2 + M * topk * K));
       float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
@@ -1048,48 +1140,6 @@ at::Tensor fused_experts_cpu(
           w2s.data_ptr<float>(),
           block_size_N,
           block_size_K,
-          topk_weights_.data_ptr<float>(),
-          sorted_ids,
-          expert_ids,
-          offsets,
-          M,
-          N,
-          K,
-          E,
-          topk,
-          num_tokens_post_pad);
-    } else if (moe_comp_method == CPUQuantMethod::INT4_W4A8) {
-      uint8_t* __restrict__ A_tmp = (uint8_t*)((void*)(intermediate_cache2 + M * topk * K));
-      float* __restrict__ C_tmp = (float*)((void*)(A_tmp + num_threads * BLOCK_M * K));
-      scalar_t* __restrict__ intermediate_cache0 = (scalar_t*)((void*)(C_tmp + num_threads * 2 * BLOCK_M * BLOCK_N));
-      uint8_t* __restrict__ Aq_tmp = (uint8_t*)((void*)(intermediate_cache0 + M * topk * 2 * N));
-      float* __restrict__ As_tmp = (float*)((void*)(Aq_tmp + std::max(M * K, M * topk * N)));
-      int8_t* __restrict__ dqB_tmp = (int8_t*)((void*)(As_tmp + M * topk));
-
-      // weight + compensation shape = [Nc, Kc, block_n * block_k / 2 + block_n*sizeof(int32_t)]
-      // scales/qzeros shape = [E, Nc, G, block_n]
-      int64_t num_groups = w1_scale.value().size(2);
-      const int group_size = K / num_groups;
-      // TODO: check scales and zeros
-      fused_experts_int4_w4a8_kernel_impl<scalar_t>(
-          out_hidden_states.data_ptr<scalar_t>(),
-          intermediate_cache0,
-          intermediate_cache1,
-          intermediate_cache2,
-          A_tmp,
-          Aq_tmp,
-          As_tmp,
-          nullptr,
-          C_tmp,
-          dqB_tmp,
-          hidden_states.data_ptr<scalar_t>(),
-          packed_w1.data_ptr<uint8_t>(),
-          packed_w2.data_ptr<uint8_t>(),
-          w1_zero.value().data_ptr<int8_t>(),
-          w2_zero.value().data_ptr<int8_t>(),
-          w1_scale.value().data_ptr<float>(),
-          w2_scale.value().data_ptr<float>(),
-          group_size,
           topk_weights.data_ptr<float>(),
           sorted_ids,
           expert_ids,
@@ -1113,7 +1163,7 @@ at::Tensor fused_experts_cpu(
           hidden_states.data_ptr<scalar_t>(),
           packed_w1.data_ptr<scalar_t>(),
           packed_w2.data_ptr<scalar_t>(),
-          topk_weights_.data_ptr<float>(),
+          topk_weights.data_ptr<float>(),
           sorted_ids,
           expert_ids,
           offsets,
@@ -1138,37 +1188,34 @@ at::Tensor shared_expert_cpu(
     at::Tensor& hidden_states,
     at::Tensor& w1,
     at::Tensor& w2,
-    const std::optional<at::Tensor>& fused_experts_out,
-    const std::optional<double> routed_scaling_factor,
+    at::Tensor& fused_experts_out,
+    double routed_scaling_factor,
     bool inplace,
     bool use_int8_w8a8,
     bool use_fp8_w8a16,
-    const std::optional<at::Tensor>& w1_scale,
-    const std::optional<at::Tensor>& w2_scale,
-    const std::optional<std::vector<int64_t>> block_size,
+    std::optional<at::Tensor>& w1_scale,
+    std::optional<at::Tensor>& w2_scale,
+    std::optional<std::vector<int64_t>> block_size,
+    std::optional<at::Tensor>& a1_scale,
+    std::optional<at::Tensor>& a2_scale,
     bool is_vnni) {
+  RECORD_FUNCTION("sgl-kernel::shared_expert_cpu", std::vector<c10::IValue>({hidden_states, w1, w2}));
+
   auto packed_w1 = is_vnni ? w1 : convert_weight_packed(w1);
   auto packed_w2 = is_vnni ? w2 : convert_weight_packed(w2);
 
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
-  double routed_scaling_factor_value = 0;
-  if (routed_scaling_factor.has_value()) {
-    TORCH_CHECK(fused_experts_out.has_value(), "shared_expert_cpu: expect fused_experts_out.");
-    const auto fused_experts_out_tensor = fused_experts_out.value();
-    routed_scaling_factor_value = routed_scaling_factor.value();
-    CHECK_INPUT(fused_experts_out_tensor);
-    CHECK_EQ(hidden_states.sizes(), fused_experts_out_tensor.sizes());
-  }
-
   const auto st = hidden_states.scalar_type();
   CHECK_INPUT(hidden_states);
+  CHECK_INPUT(fused_experts_out);
   CHECK_INPUT(w1);
   CHECK_INPUT(w2);
   CHECK_DIM(2, hidden_states);
   CHECK_DIM(2, w1);
   CHECK_DIM(2, w2);
+  CHECK_EQ(hidden_states.sizes(), fused_experts_out.sizes());
   CHECK_EQ(hidden_states.scalar_type(), st);
 
   int64_t M = hidden_states.size(0);
@@ -1185,7 +1232,7 @@ at::Tensor shared_expert_cpu(
   CHECK_EQ(packed_w2.size(1), packed_N);
 
   // check scales
-  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size);
+  check_moe_scales(use_int8_w8a8, use_fp8_w8a16, w1_scale, w2_scale, block_size, a1_scale, a2_scale);
 
   at::Tensor out_hidden_states = inplace ? hidden_states : at::empty_like(hidden_states);
 
@@ -1199,7 +1246,7 @@ at::Tensor shared_expert_cpu(
   //
   // for fp8 w8a16:
   //   5. intermediate_cache0 : [M, 2N]
-  //   6. B_tmp: [T, MAX_CACHE_BLOCK_SIZE, BLOCK_M, max(K, N)]
+  //   6. B_tmp: [T, BLOCK_M, max(K, N)]
   //
   int num_threads = at::get_num_threads();
   int64_t buffer_size_nbytes = M * N * 2 + num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float);
@@ -1208,7 +1255,7 @@ at::Tensor shared_expert_cpu(
     buffer_size_nbytes += std::max(M * K, M * N) + M * sizeof(float);
   }
   if (use_fp8_w8a16) {
-    buffer_size_nbytes += M * 2 * N * 2 + num_threads * MAX_CACHE_BLOCK_SIZE * BLOCK_M * std::max(K, N) * 2;
+    buffer_size_nbytes += M * 2 * N * 2 + num_threads * BLOCK_M * std::max(K, N) * 2;
   }
 
   auto buffer = at::empty({buffer_size_nbytes}, hidden_states.options().dtype(at::kChar));
@@ -1236,8 +1283,8 @@ at::Tensor shared_expert_cpu(
           packed_w2.data_ptr<int8_t>(),
           w1s.data_ptr<float>(),
           w2s.data_ptr<float>(),
-          conditional_data_ptr<scalar_t>(fused_experts_out),
-          routed_scaling_factor_value,
+          fused_experts_out.data_ptr<scalar_t>(),
+          routed_scaling_factor,
           M,
           N,
           K);
@@ -1259,8 +1306,8 @@ at::Tensor shared_expert_cpu(
           w2s.data_ptr<float>(),
           block_size_N,
           block_size_K,
-          conditional_data_ptr<scalar_t>(fused_experts_out),
-          routed_scaling_factor_value,
+          fused_experts_out.data_ptr<scalar_t>(),
+          routed_scaling_factor,
           M,
           N,
           K);
@@ -1272,8 +1319,8 @@ at::Tensor shared_expert_cpu(
           hidden_states.data_ptr<scalar_t>(),
           packed_w1.data_ptr<scalar_t>(),
           packed_w2.data_ptr<scalar_t>(),
-          conditional_data_ptr<scalar_t>(fused_experts_out),
-          routed_scaling_factor_value,
+          fused_experts_out.data_ptr<scalar_t>(),
+          routed_scaling_factor,
           M,
           N,
           K);

csrc/cpu/sgl-kernels/moe.h

@@ -1,178 +0,0 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
-
-// clang-format off
-
-#pragma once
-#include "vec.h"
-
-template <typename scalar_t>
-inline void fill_stub(scalar_t* __restrict__ out, scalar_t val, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  const Vec data_vec(val);
-  at::vec::map<scalar_t>([data_vec](Vec out) { return out = data_vec; }, out, out, size);
-}
-
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
-  using Vec = at::vec::Vectorized<scalar_t>;
-  constexpr int kVecSize = Vec::size();
-  int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    Vec data = Vec::loadu(input + d);
-    data.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = input[d];
-  }
-}
-
-template <typename scalar_t>
-inline void copy_stub(scalar_t* __restrict__ out, const float* __restrict__ input, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    auto [x0, x1] = load_float_vec2(input + d);
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d]);
-  }
-}
-
-template <>
-inline void copy_stub<uint8_t>(uint8_t* __restrict__ out, const uint8_t* __restrict__ input, int64_t size) {
-  // size might be 64x + 32
-  std::memcpy(out, input, size * sizeof(uint8_t));
-}
-
-template <typename scalar_t, typename input_t>
-inline void copy_mul_stub(scalar_t* __restrict__ out, const input_t* __restrict__ input, float weight, int64_t size) {
-  static_assert(
-      std::is_same_v<input_t, float> || std::is_same_v<input_t, scalar_t>,
-      "copy_mul_stub only supports input_t == float or input_t == scalar_t");
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec weight_vec = fVec(weight);
-  int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    auto [x0, x1] = load_float_vec2(input + d);
-    x0 = x0 * weight_vec;
-    x1 = x1 * weight_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] * weight);
-  }
-}
-
-// acc from [topk, K] to [K]
-template <typename scalar_t>
-inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  if (topk == 1) {
-    // do copy for topk = 1
-    copy_stub(out, input, K);
-  } else {
-    // do sum for topk != 1
-    int64_t d;
-#pragma GCC unroll 4
-    for (d = 0; d <= K - kVecSize; d += kVecSize) {
-      fVec sum_fvec0 = fVec(0.f);
-      fVec sum_fvec1 = fVec(0.f);
-      for (int t = 0; t < topk; ++t) {
-        bVec x_bvec = bVec::loadu(input + t * K + d);
-        fVec x_fvec0, x_fvec1;
-        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
-
-        sum_fvec0 += x_fvec0;
-        sum_fvec1 += x_fvec1;
-      }
-      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
-      out_bvec.store(out + d);
-    }
-    for (; d < K; ++d) {
-      float sum_val = 0.f;
-      for (int t = 0; t < topk; ++t) {
-        sum_val += static_cast<float>(input[t * K + d]);
-      }
-      out[d] = static_cast<scalar_t>(sum_val);
-    }
-  }
-}
-
-// out = input + input2 * scale
-template <typename scalar_t, typename input_t>
-inline void add_mul_stub(
-    scalar_t* __restrict__ out,
-    const input_t* __restrict__ input,
-    const scalar_t* __restrict__ input2,
-    float scale,
-    int64_t size) {
-  static_assert(
-      std::is_same_v<input_t, float> || std::is_same_v<input_t, scalar_t>,
-      "add_mul_stub only supports input_t == float or input_t == scalar_t");
-
-  // out = input (without scale factor)
-  if (input2 == nullptr) {
-    copy_stub(out, input, size);
-    return;
-  }
-
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = bVec::size();
-  const fVec s_vec = fVec(scale);
-  int64_t d;
-#pragma GCC unroll 4
-  for (d = 0; d <= size - kVecSize; d += kVecSize) {
-    auto [x0, x1] = load_float_vec2(input + d);
-
-    bVec y_bvec = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
-
-    x0 = x0 + y0 * s_vec;
-    x1 = x1 + y1 * s_vec;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-  for (; d < size; ++d) {
-    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
-  }
-}
-
-template <typename scalar_t>
-inline void silu_and_mul_stub(
-    scalar_t* __restrict__ out, const scalar_t* __restrict__ input, const scalar_t* __restrict__ input2, int64_t size) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  const fVec one = fVec(1.f);
-
-  // no remainder
-#pragma GCC unroll 4
-  for (int64_t d = 0; d < size; d += bVec::size()) {
-    bVec x = bVec::loadu(input + d);
-    fVec x0, x1;
-    std::tie(x0, x1) = at::vec::convert_to_float(x);
-    bVec y = bVec::loadu(input2 + d);
-    fVec y0, y1;
-    std::tie(y0, y1) = at::vec::convert_to_float(y);
-    x0 = x0 / (one + x0.neg().exp_u20());
-    x1 = x1 / (one + x1.neg().exp_u20());
-    x0 = x0 * y0;
-    x1 = x1 * y1;
-    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
-    out_vec.store(out + d);
-  }
-}

csrc/cpu/sgl-kernels/moe_fp8.cpp

@@ -1,11 +1,147 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-// clang-format off
-
 #include "common.h"
 #include "gemm.h"
-#include "moe.h"
+#include "vec.h"
+
+// clang-format off
+
+namespace {
+
+template <typename scalar_t>
+inline void copy_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t size) {
+  using Vec = at::vec::Vectorized<scalar_t>;
+  // no remainder
+  #pragma GCC unroll 4
+  for (int64_t d = 0; d < size; d += Vec::size()) {
+    Vec data = Vec::loadu(input + d);
+    data.store(out + d);
+  }
+}
+
+template <typename scalar_t>
+inline void copy_mul_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, float weight, int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec weight_vec = fVec(weight);
+  int64_t d;
+  #pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    bVec x = bVec::loadu(input + d);
+    fVec x0, x1;
+    std::tie(x0, x1) = at::vec::convert_to_float(x);
+    x0 = x0 * weight_vec;
+    x1 = x1 * weight_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] * weight);
+  }
+}
+
+// acc from [topk, K] to [K]
+template <typename scalar_t>
+inline void sum_stub(scalar_t* __restrict__ out, const scalar_t* __restrict__ input, int64_t topk, int64_t K) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  if (topk == 1) {
+    // do copy for topk = 1
+    copy_stub(out, input, K);
+  } else {
+    // do sum for topk != 1
+    int64_t d;
+    #pragma GCC unroll 4
+    for (d = 0; d <= K - kVecSize; d += kVecSize) {
+      fVec sum_fvec0 = fVec(0.f);
+      fVec sum_fvec1 = fVec(0.f);
+      for (int t = 0; t < topk; ++t) {
+        bVec x_bvec = bVec::loadu(input + t * K + d);
+        fVec x_fvec0, x_fvec1;
+        std::tie(x_fvec0, x_fvec1) = at::vec::convert_to_float(x_bvec);
+
+        sum_fvec0 += x_fvec0;
+        sum_fvec1 += x_fvec1;
+      }
+      bVec out_bvec = convert_from_float_ext<scalar_t>(sum_fvec0, sum_fvec1);
+      out_bvec.store(out + d);
+    }
+    for (; d < K; ++d) {
+      float sum_val = 0.f;
+      for (int t = 0; t < topk; ++t) {
+        sum_val += static_cast<float>(input[t * K + d]);
+      }
+      out[d] = static_cast<scalar_t>(sum_val);
+    }
+  }
+}
+
+// out = input + input2 * scale
+template <typename scalar_t>
+inline void add_mul_stub(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    const scalar_t* __restrict__ input2,
+    float scale,
+    int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  constexpr int kVecSize = bVec::size();
+  const fVec s_vec = fVec(scale);
+
+  int64_t d;
+#pragma GCC unroll 4
+  for (d = 0; d <= size - kVecSize; d += kVecSize) {
+    bVec x_bvec = bVec::loadu(input + d);
+    fVec x0, x1;
+    std::tie(x0, x1) = at::vec::convert_to_float(x_bvec);
+
+    bVec y_bvec = bVec::loadu(input2 + d);
+    fVec y0, y1;
+    std::tie(y0, y1) = at::vec::convert_to_float(y_bvec);
+
+    x0 = x0 + y0 * s_vec;
+    x1 = x1 + y1 * s_vec;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+  for (; d < size; ++d) {
+    out[d] = static_cast<scalar_t>(input[d] + float(input2[d]) * scale);
+  }
+}
+
+template <typename scalar_t>
+inline void silu_and_mul_stub(
+    scalar_t* __restrict__ out,
+    const scalar_t* __restrict__ input,
+    const scalar_t* __restrict__ input2,
+    int64_t size) {
+  using bVec = at::vec::Vectorized<scalar_t>;
+  using fVec = at::vec::Vectorized<float>;
+  const fVec one = fVec(1.f);
+
+  // no remainder
+#pragma GCC unroll 4
+  for (int64_t d = 0; d < size; d += bVec::size()) {
+    bVec x = bVec::loadu(input + d);
+    fVec x0, x1;
+    std::tie(x0, x1) = at::vec::convert_to_float(x);
+    bVec y = bVec::loadu(input2 + d);
+    fVec y0, y1;
+    std::tie(y0, y1) = at::vec::convert_to_float(y);
+    x0 = x0 / (one + x0.neg().exp_u20());
+    x1 = x1 / (one + x1.neg().exp_u20());
+    x0 = x0 * y0;
+    x1 = x1 * y1;
+    bVec out_vec = convert_from_float_ext<scalar_t>(x0, x1);
+    out_vec.store(out + d);
+  }
+}
+
+} // anonymous namespace
 
 template <typename scalar_t>
 void fused_experts_fp8_kernel_impl(
@@ -33,6 +169,7 @@ void fused_experts_fp8_kernel_impl(
     int64_t E,
     int64_t topk,
     int64_t num_tokens_post_pad) {
+
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
@@ -46,39 +183,35 @@ void fused_experts_fp8_kernel_impl(
   const int64_t stride_e = 2 * N * K;
   const int64_t stride_n = K;
 
-  int64_t avg_M = std::max(int64_t(1), M * topk / E);
-  const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(avg_M);
-
-  int64_t B_tmp_size_per_thread = MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N);
-
   // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
+  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
     // get local pointers
-    int tid = get_thread_num();
+    int tid = at::get_thread_num();
     scalar_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
 
-    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB;
+      int64_t nb = i % NB;
+
       int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
 
       // B shape [K, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
       const at::Float8_e4m3fn* __restrict__ B = packed_w1 + expert_id * stride_e + nb * BLOCK_N * stride_n;
-      const float* __restrict__ Bs =
-          w1s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
-
-      // do unpacking for the first row or a new expert
-      int32_t pre_expert_id = mb == 0 ? -1 : expert_ids[mb - 1];
-      bool do_unpack = (mb == mb0) || (expert_id != pre_expert_id);
+      const float* __restrict__ Bs = w1s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
 
+      // 1.a load A
+      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
       int64_t m_size = offsets[mb + 1] - offsets[mb];
 
-      if (nb_offset == 0) {
-        // 1.a load A
-        const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
-        for (int64_t m = 0; m < m_size; ++m) {
-          int32_t index = A_ids[m] / topk;
-          copy_stub(A + m * K, input + index * K, K);
-        }
+      const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+
+      for (int64_t m = 0; m < m_size; ++m) {
+        int32_t index = A_ids[m] / topk;
+        copy_stub(A + m * K, input + index * K, K);
       }
 
       const int64_t offset = offsets[mb];
@@ -86,7 +219,7 @@ void fused_experts_fp8_kernel_impl(
           /*   A            */ A,
           /*   B            */ B,
           /*   C            */ ic0 + offset * 2 * N + nb * BLOCK_N,
-          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * K,
+          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ Bs,
           /*   M            */ m_size,
@@ -96,11 +229,10 @@ void fused_experts_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ 2 * N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K,
-          /*   do_unpack    */ do_unpack);
-    });
+          /*   block_size_K */ block_size_K);
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -108,7 +240,11 @@ void fused_experts_fp8_kernel_impl(
   // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
   at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
+      silu_and_mul_stub(
+          ic1 + m * N,
+          ic0 + m * 2 * N,
+          ic0 + m * 2 * N + N,
+          N);
     }
   });
 
@@ -124,14 +260,22 @@ void fused_experts_fp8_kernel_impl(
   const int64_t stride_oc = IC;
 
   // parallel on [MB2, NB2]
-  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
-    int tid = get_thread_num();
+  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+    int tid = at::get_thread_num();
     alignas(64) scalar_t C[BLOCK_M * BLOCK_K];
 
-    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    bool is_brgemm_used = false;
+
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB2;
+      int64_t nb = i % NB2;
+
       int64_t m_size = offsets[mb + 1] - offsets[mb];
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
+      const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(m_size);
+      is_brgemm_used = is_brgemm_used || use_brgemm;
+
       // A ptr from ic1 of [M * topk, N] in sorted order
       // so as to avoid copy A to tmp buffer again
       const scalar_t* __restrict__ A = ic1 + offsets[mb] * N;
@@ -140,18 +284,13 @@ void fused_experts_fp8_kernel_impl(
       // B shape [IC, n_size] in vnni format
       int32_t expert_id = expert_ids[mb];
       const at::Float8_e4m3fn* __restrict__ B = packed_w2 + expert_id * stride_e2 + nb * BLOCK_N * stride_oc;
-      const float* __restrict__ Bs =
-          w2s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
-
-      // do unpacking for the first row or a new expert
-      int32_t pre_expert_id = mb == 0 ? -1 : expert_ids[mb - 1];
-      bool do_unpack = (mb == mb0) || (expert_id != pre_expert_id);
+      const float* __restrict__ Bs = w2s + expert_id * scale_size_N * scale_size_K + (nb / blocks_n_per_group) * scale_size_K;
 
       tinygemm_kernel<scalar_t>(
           /*   A            */ A,
           /*   B            */ B,
           /*   C            */ C,
-          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * IC,
+          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ Bs,
           /*   M            */ m_size,
@@ -161,8 +300,7 @@ void fused_experts_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ BLOCK_N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K,
-          /*   do_unpack    */ do_unpack);
+          /*   block_size_K */ block_size_K);
 
       // 2.b copy from C to ic2 in original order
       //   and also mul topk_weights in float32
@@ -171,9 +309,9 @@ void fused_experts_fp8_kernel_impl(
         float weight = topk_weights[index];
         copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C + m * BLOCK_N, weight, n_size);
       }
-    });
+    }
 
-    if (use_brgemm) {
+    if (is_brgemm_used) {
       at::native::cpublas::brgemm_release();
     }
   });
@@ -236,6 +374,7 @@ void shared_expert_fp8_kernel_impl(
     int64_t M,
     int64_t N,
     int64_t K) {
+
   constexpr int64_t BLOCK_M = block_size_m();
   constexpr int64_t BLOCK_N = block_size_n();
 
@@ -246,25 +385,21 @@ void shared_expert_fp8_kernel_impl(
   int64_t blocks_n_per_group = block_size_N / BLOCK_N;
 
   const bool use_brgemm = can_use_brgemm<at::Float8_e4m3fn>(M);
-  const bool apply_scaling_factor = fused_experts_out != nullptr;
 
-  int64_t B_tmp_size_per_thread = MAX_CACHE_BLOCK_SIZE * BLOCK_N * std::max(K, N);
+  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
+    int tid = at::get_thread_num();
 
-  parallel_2d(MB, NB, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
-    int tid = get_thread_num();
-
-    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * K, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB;
+      int64_t nb = i % NB;
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(2 * N - nb * BLOCK_N, BLOCK_N);
 
-      // do unpacking for the first row
-      bool do_unpack = (mb == mb0);
-
       tinygemm_kernel<scalar_t>(
           /*   A            */ input + mb * BLOCK_M * K,
           /*   B            */ packed_w1 + nb * BLOCK_N * K,
           /*   C            */ ic0 + mb * BLOCK_M * 2 * N + nb * BLOCK_N,
-          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * K,
+          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ w1s + (nb / blocks_n_per_group) * scale_size_K,
           /*   M            */ m_size,
@@ -274,9 +409,8 @@ void shared_expert_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ 2 * N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K,
-          /*   do_unpack    */ do_unpack);
-    });
+          /*   block_size_K */ block_size_K);
+    }
 
     if (use_brgemm) {
       at::native::cpublas::brgemm_release();
@@ -286,7 +420,11 @@ void shared_expert_fp8_kernel_impl(
   // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
   at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
     for (int64_t m = begin; m < end; ++m) {
-      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
+      silu_and_mul_stub(
+          ic1 + m * N,
+          ic0 + m * 2 * N,
+          ic0 + m * 2 * N + N,
+          N);
     }
   });
 
@@ -299,23 +437,22 @@ void shared_expert_fp8_kernel_impl(
   scale_size_K = div_up(N, block_size_K);
 
   // parallel on [MB2, NB2]
-  parallel_2d(MB2, NB2, [&](int64_t mb0, int64_t mb1, int64_t nb0, int64_t nb1) {
-    int tid = get_thread_num();
+  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
+    int tid = at::get_thread_num();
     alignas(64) scalar_t C[BLOCK_M * BLOCK_K];
 
-    loop_2d<at::Float8_e4m3fn>(mb0, mb1, nb0, nb1, BLOCK_N * IC, [&](int64_t mb, int64_t nb, int64_t nb_offset) {
+    for (int64_t i = begin; i < end; ++i) {
+      int64_t mb = i / NB2;
+      int64_t nb = i % NB2;
       int64_t m_size = std::min(M - mb * BLOCK_M, BLOCK_M);
       int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
 
-      // do unpacking for the first row
-      bool do_unpack = (mb == mb0);
-
       // 2.a gemm: C = A @ B
       tinygemm_kernel<scalar_t>(
           /*   A            */ ic1 + mb * BLOCK_M * N,
           /*   B            */ packed_w2 + nb * BLOCK_N * N,
           /*   C            */ C,
-          /*   Btmp         */ B_tmp + tid * B_tmp_size_per_thread + nb_offset * BLOCK_N * IC,
+          /*   Btmp         */ B_tmp + tid * BLOCK_N * std::max(K, N),
           /*   Ctmp         */ C_tmp + tid * 2 * BLOCK_M * BLOCK_N,
           /*   scale        */ w2s + (nb / blocks_n_per_group) * scale_size_K,
           /*   M            */ m_size,
@@ -325,18 +462,15 @@ void shared_expert_fp8_kernel_impl(
           /*   ldb          */ n_size,
           /*   ldc          */ BLOCK_N,
           /*   brg          */ use_brgemm,
-          /*   block_size_K */ block_size_K,
-          /*   do_unpack    */ do_unpack);
+          /*   block_size_K */ block_size_K);
 
       // 2.b copy from C to output and add fused_experts_out
       scalar_t* __restrict__ out = output + mb * BLOCK_M * K + nb * BLOCK_N;
-      const scalar_t* __restrict__ fused_out =
-          apply_scaling_factor ? fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N : nullptr;
+      const scalar_t* __restrict__ fused_out = fused_experts_out + mb * BLOCK_M * K + nb * BLOCK_N;
       for (int64_t m = 0; m < m_size; ++m) {
-        const scalar_t* __restrict__ fused_out_row = apply_scaling_factor ? (fused_out + m * K) : nullptr;
-        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out_row, routed_scaling_factor, n_size);
+        add_mul_stub(out + m * K, C + m * BLOCK_N, fused_out + m * K, routed_scaling_factor, n_size);
       }
-    });
+    }
   });
 
   if (use_brgemm) {

csrc/cpu/sgl-kernels/moe_int4.cpp

@@ -1,323 +0,0 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
-
-// clang-format off
-
-#include "common.h"
-#include "gemm.h"
-#include "moe.h"
-
-template <int64_t N>
-inline void copy_bias(const float* bias_ptr, float* y_buf, int64_t m, int64_t ldn) {
-  using Vec = at::vec::Vectorized<float>;
-  constexpr int kVecSize = Vec::size();
-  static_assert(N % kVecSize == 0, "copy_bias requires N to be a multiple of Vectorized<float>::size()");
-  const bool has_bias = bias_ptr != nullptr;
-  const Vec zero_vec(0.f);
-  for (int i = 0; i < m; ++i) {
-#pragma GCC unroll 2
-    for (int j = 0; j < N; j += kVecSize) {
-      Vec vec = has_bias ? Vec::loadu(bias_ptr + j) : zero_vec;
-      vec.store(y_buf + i * ldn + j);
-    }
-  }
-}
-
-template <typename scalar_t>
-void fused_experts_int4_w4a8_kernel_impl(
-    scalar_t* __restrict__ output,
-    scalar_t* __restrict__ ic0,
-    scalar_t* __restrict__ ic1,
-    scalar_t* __restrict__ ic2,
-    uint8_t* __restrict__ A_tmp,
-    uint8_t* __restrict__ Aq_tmp,
-    float* __restrict__ As_tmp,
-    int32_t* __restrict__ Azp_tmp,
-    float* __restrict__ C_tmp,
-    int8_t* __restrict__ dqB_tmp,
-    const scalar_t* __restrict__ input,
-    const uint8_t* __restrict__ packed_w1,
-    const uint8_t* __restrict__ packed_w2,
-    const int8_t* __restrict__ w1z,
-    const int8_t* __restrict__ w2z,
-    const float* __restrict__ w1s,
-    const float* __restrict__ w2s,
-    int group_size,
-    const float* __restrict__ topk_weights,
-    const int32_t* __restrict__ sorted_ids,
-    const int32_t* __restrict__ expert_ids,
-    const int32_t* __restrict__ offsets,
-    int64_t M,
-    int64_t N,
-    int64_t K,
-    int64_t E,
-    int64_t topk,
-    int64_t num_tokens_post_pad) {
-  constexpr int64_t BLOCK_M = block_size_m();
-  constexpr int64_t BLOCK_N = block_size_n();
-  int num_threads = at::get_num_threads();
-  // int64_t buffer_size_nbytes = M * topk * N * 2
-  //                              M * topk * K * 2 +
-  //                              num_threads * BLOCK_M * K +
-  //                              num_threads * 2 * BLOCK_M * BLOCK_N * sizeof(float)  +
-  //                              M * topk * 2 * N * 2 +
-  //                              max(M * K, M * topk * N)  +
-  //                              M * topk * sizeof(float);
-
-  // intermediate_cache1 (scalar_t):     START + M * topk * N
-  // intermediate_cache2 (scalar_t):     + M * topk * K
-  // A_tmp (uint8_t):                    + num_threads * BLOCK_M * K
-  // C_tmp (float):                      + num_threads * 2 * BLOCK_M * BLOCK_N
-  // intermediate_cache0 (scalar_t):     + M * topk * 2 * N
-  // Aq_tmp (uint8_t):                   + max(M * K, M * topk * N)
-  // As_tmp (float):                     + M * topk
-  // dqB_tmp (int8_t)                    + num_threads * _block_k * BlOCK_N
-
-  // stage 0: quantize input to uint8, [M, K]
-  at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(Aq_tmp + m * K, As_tmp[m], input + m * K, K);
-    }
-  });
-  int64_t _block_k = get_4bit_block_k_size(group_size);
-  auto Azp = at::ones({M * topk}).to(at::kInt).mul(128);
-  auto Azp_ptr = Azp.data_ptr<int32_t>();
-  // stage 1: intermediate_cache0 = hidden_states @ w1
-  const int64_t MB = div_up(num_tokens_post_pad, BLOCK_M);
-  const int64_t NB = div_up(N, BLOCK_N);
-
-  int64_t block_per_group = group_size / _block_k;
-  int64_t Kc = K / _block_k;
-  int64_t num_groups = K / group_size;
-
-  const int64_t stride_e = 2 * NB * Kc * (BLOCK_N * (_block_k / 2 + sizeof(int32_t)));
-  const bool sym_quant_act = false;
-  // weight + compensation shape = [E, Nc, Kc, block_n * _block_k / 2 + block_n*sizeof(int32_t)]
-  // scales/qzeros shape = [E, Nc, G, block_n]
-
-  // here we only parallel on half of 2N to fuse silu_and_mul with gemm
-  at::parallel_for(0, MB * NB, 0, [&](int64_t begin, int64_t end) {
-    // get local pointers
-    int tid = at::get_thread_num();
-    int8_t* dqB_tmp1 = dqB_tmp + tid * 2 * _block_k * BLOCK_N;
-    int8_t* dqB_tmp2 = dqB_tmp1 + _block_k * BLOCK_N;
-    alignas(64) float As[BLOCK_M];
-    uint8_t* __restrict__ A = A_tmp + tid * BLOCK_M * K;
-    float* __restrict__ C0 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
-    float* __restrict__ C1 = C0 + BLOCK_M * BLOCK_N;
-    bool is_brgemm_used = false;
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB;
-      int64_t nb = i % NB;
-      int64_t nb1 = nb + NB;
-      int64_t n_size = std::min(N - nb * BLOCK_N, BLOCK_N);
-      // B shape [K, n_size] in vnni format
-      int32_t expert_id = expert_ids[mb];
-      const uint8_t* __restrict__ B = packed_w1 + expert_id * stride_e;
-      // Bz and Bs: [E, K/gs, 2N]
-      const int8_t* __restrict__ Bz = w1z + expert_id * (num_groups) * (2 * N);
-      const float* __restrict__ Bs = w1s + expert_id * (num_groups) * (2 * N);
-
-      // 1.a load A
-      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
-      int64_t m_size = offsets[mb + 1] - offsets[mb];
-      const bool use_brgemm = can_use_brgemm<int8_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-      // copy to A [BLOCK_M, K]
-      for (int64_t m = 0; m < m_size; ++m) {
-        int32_t index = A_ids[m] / topk;
-        copy_stub(A + m * K, Aq_tmp + index * K, K);
-        As[m] = As_tmp[index];
-      }
-      const int64_t offset = offsets[mb];
-      copy_bias<BLOCK_N>(nullptr, C0, m_size, BLOCK_N);
-      copy_bias<BLOCK_N>(nullptr, C1, m_size, BLOCK_N);
-      for (int kci = 0; kci < Kc; ++kci) {
-        int32_t* compensation_ptr =
-            sym_quant_act ? nullptr
-                          : (int32_t*)(void*)(B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
-                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
-        tinygemm_kernel<scalar_t>(
-            ic0 + offset * 2 * N + nb * BLOCK_N,
-            C0,
-            A + kci * _block_k,
-            As,
-            Azp_ptr,
-            B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) /*B*/,
-            Bs + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
-            Bz + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*qzeros_b*/,
-            compensation_ptr,
-            dqB_tmp1,
-            m_size,
-            _block_k,
-            K,
-            BLOCK_N,
-            2 * N,
-            kci == Kc - 1,
-            use_brgemm);
-      }
-
-      for (int kci = 0; kci < Kc; ++kci) {
-        int32_t* compensation_ptr =
-            sym_quant_act ? nullptr
-                          : (int32_t*)(void*)(B + (nb1 * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
-                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
-        tinygemm_kernel<scalar_t>(
-            ic0 + offset * 2 * N + nb1 * BLOCK_N,
-            C1,
-            A + kci * _block_k,
-            As,
-            Azp_ptr,
-            B + (nb1 * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) /*B*/,
-            Bs + nb1 * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
-            Bz + nb1 * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*qzeros_b*/,
-            compensation_ptr,
-            dqB_tmp2,
-            m_size,
-            _block_k,
-            K,
-            BLOCK_N,
-            2 * N,
-            kci == Kc - 1,
-            use_brgemm);
-      }
-    }
-
-    if (is_brgemm_used) {
-      at::native::cpublas::brgemm_release();
-    }
-  });
-
-  // stage 1.5: intermediate_cache1 = silu(intermediate_cache0)
-  at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t m = begin; m < end; ++m) {
-      silu_and_mul_stub(ic1 + m * N, ic0 + m * 2 * N, ic0 + m * 2 * N + N, N);
-    }
-  });
-
-  // stage 1.5: quantize ic1 to uint8, [M * topk, N]
-  at::parallel_for(0, M * topk, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t m = begin; m < end; ++m) {
-      quantize_row_int8<scalar_t>(Aq_tmp + m * N, As_tmp[m], ic1 + m * N, N);
-    }
-  });
-  // stage 2: intermediate_cache2 = intermediate_cache1 @ w2
-  //   w2 : [E, K, N] as [E, OC, IC]
-  const int64_t OC = K;  // rename K as OC
-  const int64_t IC = N;  // rename N as IC
-  const int64_t MB2 = MB;
-  const int64_t NB2 = div_up(OC, BLOCK_N);
-  const int64_t stride_oc = IC;
-  num_groups = IC / group_size;
-  Kc = IC / _block_k;
-  const int64_t stride_e2 = NB2 * Kc * (BLOCK_N * (_block_k / 2 + sizeof(int32_t)));
-  // parallel on [MB2, NB2]
-  at::parallel_for(0, MB2 * NB2, 0, [&](int64_t begin, int64_t end) {
-    int tid = at::get_thread_num();
-    int8_t* dqB_tmp1 = dqB_tmp + tid * 2 * _block_k * BLOCK_N;
-    float* __restrict__ C2 = C_tmp + tid * 2 * BLOCK_M * BLOCK_N;
-    bool is_brgemm_used = false;
-    for (int64_t i = begin; i < end; ++i) {
-      int64_t mb = i / NB2;
-      int64_t nb = i % NB2;
-
-      int64_t m_size = offsets[mb + 1] - offsets[mb];
-      int64_t n_size = std::min(OC - nb * BLOCK_N, BLOCK_N);
-      const bool use_brgemm = can_use_brgemm<int8_t>(m_size);
-      is_brgemm_used = is_brgemm_used || use_brgemm;
-      const int32_t* A_ids = sorted_ids + mb * BLOCK_M;
-
-      // B shape [IC, n_size] in vnni format
-      int32_t expert_id = expert_ids[mb];
-      const uint8_t* __restrict__ B = packed_w2 + expert_id * stride_e2;
-
-      // Bz and Bs: [E, IC/gs, OC]
-      const int8_t* __restrict__ Bz = w2z + expert_id * (num_groups)*OC;
-      const float* __restrict__ Bs = w2s + expert_id * (num_groups)*OC;
-
-      // A ptr from ic1 of [M * topk, N] in sorted order
-      // so as to avoid copy A to tmp buffer again
-      const uint8_t* __restrict__ A = Aq_tmp + offsets[mb] * IC;
-      const float* __restrict__ As = As_tmp + offsets[mb];
-      copy_bias<BLOCK_N>(nullptr, C2, m_size, BLOCK_N);
-      for (int kci = 0; kci < Kc; ++kci) {
-        int32_t* compensation_ptr =
-            sym_quant_act ? nullptr
-                          : (int32_t*)(void*)(B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))) +
-                                              _block_k * BLOCK_N / 2) /*Bcomp*/;
-        tinygemm_kernel<scalar_t>(
-            nullptr, /*store_out is false*/
-            C2,
-            A + kci * _block_k,
-            As,
-            Azp_ptr,
-            B + (nb * Kc + kci) * (BLOCK_N * (_block_k / 2 + sizeof(int32_t))),
-            Bs + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*scales_b*/,
-            Bz + nb * BLOCK_N * num_groups + kci / block_per_group * BLOCK_N /*zeros_b*/,
-            compensation_ptr,
-            dqB_tmp1,
-            m_size,
-            _block_k,
-            IC,
-            BLOCK_N,
-            BLOCK_N,
-            false,
-            use_brgemm);
-      }
-
-      // 2.b copy from C to ic2 in original order
-      //   and also mul topk_weights in float32
-      for (int64_t m = 0; m < m_size; ++m) {
-        int32_t index = A_ids[m];
-        float weight = topk_weights[index];
-        copy_mul_stub(ic2 + index * K + nb * BLOCK_N, C2 + m * BLOCK_N, weight, n_size);
-      }
-    }
-
-    if (is_brgemm_used) {
-      at::native::cpublas::brgemm_release();
-    }
-  });
-
-  // stage 3: out = intermediate_cache2.sum(dim=1)
-  //   from [M, topk, K] to [M, K]
-  at::parallel_for(0, M, 0, [&](int64_t begin, int64_t end) {
-    for (int64_t m = begin; m < end; ++m) {
-      sum_stub(output + m * K, ic2 + m * topk * K, topk, K);
-    }
-  });
-}
-
-#define INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(TYPE)           \
-  template void fused_experts_int4_w4a8_kernel_impl<TYPE>( \
-      TYPE* __restrict__ output,                           \
-      TYPE* __restrict__ ic0,                              \
-      TYPE* __restrict__ ic1,                              \
-      TYPE* __restrict__ ic2,                              \
-      uint8_t* __restrict__ A_tmp,                         \
-      uint8_t* __restrict__ Aq_tmp,                        \
-      float* __restrict__ As_tmp,                          \
-      int32_t* __restrict__ Azp_tmp,                       \
-      float* __restrict__ C_tmp,                           \
-      int8_t* __restrict__ dqB_tmp,                        \
-      const TYPE* __restrict__ input,                      \
-      const uint8_t* __restrict__ packed_w1,               \
-      const uint8_t* __restrict__ packed_w2,               \
-      const int8_t* __restrict__ w1z,                      \
-      const int8_t* __restrict__ w2z,                      \
-      const float* __restrict__ w1s,                       \
-      const float* __restrict__ w2s,                       \
-      int group_size,                                      \
-      const float* __restrict__ topk_weights,              \
-      const int32_t* __restrict__ sorted_ids,              \
-      const int32_t* __restrict__ expert_ids,              \
-      const int32_t* __restrict__ offsets,                 \
-      int64_t M,                                           \
-      int64_t N,                                           \
-      int64_t K,                                           \
-      int64_t E,                                           \
-      int64_t topk,                                        \
-      int64_t num_tokens_post_pad)
-
-INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(at::BFloat16);
-INSTANTIATE_MOE_INT4_W4A8_TEMPLATE(at::Half);

csrc/cpu/sgl-kernels/vec.h

@@ -1,10 +1,10 @@
 // Adapted from
 // https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
 
-// clang-format off
-
 #pragma once
 
+// clang-format off
+
 #if defined(__AVX512F__) && defined(__AVX512BF16__) && defined(__AMX_BF16__)
 #define CPU_CAPABILITY_AVX512
 #endif
@@ -16,51 +16,38 @@ namespace {
 
 using namespace at::vec;
 
-template <typename scalar_t, typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
+template <typename scalar_t,
+          typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 0>
 inline Vectorized<scalar_t> convert_from_float_ext(const Vectorized<float>& a, const Vectorized<float>& b) {
   return at::vec::convert_from_float<scalar_t>(a, b);
 }
 
-// allow f16, bf16
-template <typename scalar_t, typename std::enable_if_t<is_reduced_floating_point_v<scalar_t>, int> = 1>
-inline std::tuple<Vectorized<float>, Vectorized<float>> load_float_vec2(const scalar_t* __restrict__ data) {
-  using bVec = at::vec::Vectorized<scalar_t>;
-  using fVec = at::vec::Vectorized<float>;
-  bVec x_vec = bVec::loadu(data);
-  fVec x0, x1;
-  std::tie(x0, x1) = at::vec::convert_to_float(x_vec);
-  return std::make_tuple(x0, x1);
-}
-
-// allow  f32
-inline std::tuple<Vectorized<float>, Vectorized<float>> load_float_vec2(const float* __restrict__ data) {
-  using fVec = at::vec::Vectorized<float>;
-  fVec x0 = fVec::loadu(data);
-  fVec x1 = fVec::loadu(data + fVec::size());
-  return std::make_tuple(x0, x1);
-}
-
 #if defined(CPU_CAPABILITY_AVX512)
 
 // `at::vec::convert_from_float<>` from PyTorch doesn't have avx512-bf16 intrinsics
 // use native instruction for bfloat16->float32 conversion
 template <>
-inline Vectorized<at::BFloat16>
-convert_from_float_ext<at::BFloat16>(const Vectorized<float>& a, const Vectorized<float>& b) {
+inline Vectorized<at::BFloat16> convert_from_float_ext<at::BFloat16>(const Vectorized<float>& a, const Vectorized<float>& b) {
   return (__m512i)(_mm512_cvtne2ps_pbh(__m512(b), __m512(a)));
 }
 
-#define CVT_BF16_TO_FP32(a) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16))
+#define CVT_BF16_TO_FP32(a) \
+    _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(a), 16))
 
-#define CVT_FP16_TO_FP32(a) _mm512_cvtph_ps(a)
+#define CVT_FP16_TO_FP32(a) \
+    _mm512_cvtps_ph(a, (_MM_FROUND_TO_NEAREST_INT | _MM_FROUND_NO_EXC))
 
 // this doesn't handle NaN.
 inline __m512bh cvt_e4m3_bf16_intrinsic_no_nan(__m256i fp8_vec) {
   const __m512i x = _mm512_cvtepu8_epi16(fp8_vec);
-  __m512i combined = _mm512_add_epi16(x, _mm512_set1_epi16(0x0780));
-  combined = _mm512_slli_epi16(combined, 4);
-  combined = _mm512_and_si512(combined, _mm512_set1_epi16(0x87f0));
-  combined = _mm512_add_epi16(combined, _mm512_set1_epi16(0x3c00));
+
+  const __m512i mant = _mm512_slli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x07)), 4);
+  const __m512i raw_exp = _mm512_srli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x78)), 3);
+  const __m512i exp = _mm512_slli_epi16(_mm512_add_epi16(raw_exp, _mm512_set1_epi16(120)), 7);
+  const __m512i nonsign = _mm512_or_si512(exp, mant);
+
+  const __m512i sign = _mm512_slli_epi16(_mm512_and_si512(x, _mm512_set1_epi16(0x80)), 8);
+  const __m512i combined = _mm512_or_si512(nonsign, sign);
 
   const __mmask32 is_nonzero = _mm512_cmpneq_epi16_mask(x, _mm512_setzero_si512());
   return (__m512bh)_mm512_maskz_mov_epi16(is_nonzero, combined);
@@ -126,77 +113,10 @@ inline __m512bh CVT_FP8_TO_BF16(__m256i a) {
 #endif
 }
 
-// faster version of float8_e4m3fn conversion to bfloat16
-//
-// we mapped cuda implementation from below link and vectorized with avx512:
-// https://github.com/thu-pacman/chitu/blob/1ed2078ec26581ebdca05b7306d4385f86edaa7c/csrc/cuda/marlin/marlin_gemm/dequant.h#L387
-//
-inline __attribute__((always_inline)) __m512bh CVT_FP8_TO_BF16_EXT(__m256i a) {
-  const __m512i mask0 = _mm512_set1_epi16(0x80);  // sign bit
-  const __m512i mask1 = _mm512_set1_epi16(0x7F);  // exponent and mantissa
-  const __m512i mask2 = _mm512_set1_epi16(0x4000);
-
-  __m512i x = _mm512_cvtepu8_epi16(a);
-  __m512i vsign = _mm512_and_si512(x, mask0);
-  vsign = _mm512_slli_epi16(vsign, 8);
-
-  __m512i vexp_and_mant = _mm512_and_si512(x, mask1);
-  vexp_and_mant = _mm512_slli_epi16(vexp_and_mant, 4);
-
-  // _MM_TERNLOG_A | _MM_TERNLOG_B | _MM_TERNLOG_C: 0b11111110
-  return (__m512bh)(_mm512_ternarylogic_epi32(vsign, mask2, vexp_and_mant, 0b11111110));
-}
-
-// bias for conversion of fp8 to bf16 1/256 in float32
-#define kFP8_BIAS 0x3b800000
-
-// remove warning: ignoring attributes on template argument ‘__m512bh’ [-Wignored-attributes]
-#pragma GCC diagnostic push
-#pragma GCC diagnostic ignored "-Wignored-attributes"
-
-#define MXFP4_VALUES \
-  -6.0f, -4.0f, -3.0f, -2.0f, -1.5f, -1.0f, -0.5f, -0.0f, 6.0f, 4.0f, 3.0f, 2.0f, 1.5f, 1.0f, 0.5f, 0.0f
-
-// convert 64 mxfp4 to 2x bf16 vectors, expect input 32-way packing
-inline std::tuple<__m512bh, __m512bh> cvt_mxfp4_e2m1_bf16_intrinsic_lut(__m256i a, __m512i s0, __m512i s1) {
-  // LUT
-  const __m512 values = _mm512_set_ps(MXFP4_VALUES);
-  const __m512i lut = (__m512i)(_mm512_cvtne2ps_pbh(values, values));
-
-  const __m512i abs_mask = _mm512_set1_epi16(0x7FFF);
-  const __m512i zero = _mm512_setzero_si512();
-
-  // expand values to 16-bit integers
-  __m512i x0 = _mm512_cvtepu8_epi16(a);
-  __m512i x1 = _mm512_srli_epi32(x0, 4);
-
-  // LUT to convert mxfp4 values to bf16
-  x0 = _mm512_permutexvar_epi16(x0, lut);
-  x1 = _mm512_permutexvar_epi16(x1, lut);
-
-  // check for zeros
-  __mmask32 mask0 = _mm512_cmp_epi16_mask(_mm512_and_si512(x0, abs_mask), zero, _MM_CMPINT_EQ);
-  __mmask32 mask1 = _mm512_cmp_epi16_mask(_mm512_and_si512(x1, abs_mask), zero, _MM_CMPINT_EQ);
-
-  // emulate bf16 mul with scale factor
-  x0 = _mm512_add_epi16(x0, s0);
-  x1 = _mm512_add_epi16(x1, s1);
-
-  // blend with zero
-  x0 = _mm512_mask_blend_epi16(mask0, x0, zero);
-  x1 = _mm512_mask_blend_epi16(mask1, x1, zero);
-
-  return std::make_tuple(__m512bh(x0), __m512bh(x1));
-}
-
-#define CVT_MXFP4_TO_BF16(a, s0, s1) cvt_mxfp4_e2m1_bf16_intrinsic_lut(a, s0, s1)
-
-#pragma GCC diagnostic pop
-
 #endif
 
 // vector to scalar reduction
-#if defined(CPU_CAPABILITY_AVX512)
+#if defined(CPU_CAPABILITY_AVX512) && 0
 inline float vec_reduce_sum(const Vectorized<float>& a) {
   return _mm512_reduce_add_ps(__m512(a));
 }
@@ -216,9 +136,10 @@ inline float vec_reduce_max(const Vectorized<float>& a) {
 
 // https://github.com/InternLM/lmdeploy/blob/086481ed84b59bee3b8e4274e5fc69620040c048/lmdeploy/pytorch/kernels/cuda/w8a8_triton_kernels.py#L282
 template <typename scalar_t>
-inline void
-quantize_row_int8(uint8_t* __restrict__ Aq, float& As, const scalar_t* __restrict__ A, int64_t K, float eps = 1e-7) {
-  float amax = 0.f;  // absolute max
+inline void quantize_row_int8(uint8_t* __restrict__ Aq, float& As,
+    const scalar_t* __restrict__ A, int64_t K, float eps = 1e-7) {
+
+  float amax = 0.f; // absolute max
   for (int64_t k = 0; k < K; ++k) {
     const float val = static_cast<float>(A[k]);
     amax = std::max(amax, std::abs(val));
@@ -237,8 +158,9 @@ quantize_row_int8(uint8_t* __restrict__ Aq, float& As, const scalar_t* __restric
 
 #if defined(CPU_CAPABILITY_AVX512)
 template <>
-inline void quantize_row_int8<at::BFloat16>(
-    uint8_t* __restrict__ Aq, float& As, const at::BFloat16* __restrict__ A, int64_t K, float eps) {
+inline void quantize_row_int8<at::BFloat16>(uint8_t* __restrict__ Aq, float& As,
+    const at::BFloat16* __restrict__ A, int64_t K, float eps) {
+
   const __m512 signBit = _mm512_set1_ps(-0.0f);
   const __m512i off = _mm512_set1_epi32(128);
 
@@ -278,7 +200,7 @@ inline void quantize_row_int8<at::BFloat16>(
 // transpose utils
 // taken from my PR in ggml: https://github.com/ggml-org/llama.cpp/pull/8998
 #if defined(CPU_CAPABILITY_AVX512)
-inline void transpose_16x16_32bit(__m512i* v) {
+inline void transpose_16x16_32bit(__m512i * v) {
   __m512i v1[16];
   v1[0] = _mm512_unpacklo_epi32(v[0], v[1]);
   v1[1] = _mm512_unpackhi_epi32(v[0], v[1]);
@@ -371,56 +293,16 @@ inline std::tuple<__m512i, __m512i> transpose_2x32_16bit(__m512i r0, __m512i r1)
 }
 #pragma GCC diagnostic pop
 
-inline __attribute__((always_inline)) __m512 _mm512_fexp_u20_ps(const __m512 values) {
-  const __m512 vec_c0 = _mm512_set1_ps(0.00010703434948458272f);
-  const __m512 vec_c1 = _mm512_set1_ps(0.30354260500649682f);
-  const __m512 vec_c2 = _mm512_set1_ps(-0.22433836478672356);
-  const __m512 vec_c3 = _mm512_set1_ps(-0.079204240219773236);
-
-  const __m512 vec_exp_log2ef = _mm512_castsi512_ps(_mm512_set1_epi32(0x3fb8aa3b));  // log2(e)
-
-  const __m512 vec_a = _mm512_set1_ps(std::pow(2, 23) / std::log2(2));
-  const __m512 vec_b = _mm512_set1_ps(std::pow(2, 23) * 127.f);
-
-  const __m512 vec_ln_flt_min = _mm512_castsi512_ps(_mm512_set1_epi32(0xc2aeac50));
-  const __m512 vec_ln_flt_max = _mm512_castsi512_ps(_mm512_set1_epi32(0x42b17218));
-  __m512i vec_infinity = _mm512_set1_epi32(0x7F800000);
-  __m512i vec_zero = _mm512_setzero_epi32();
-
-  // Fast Exponential Computation on SIMD Architectures
-  // A. Cristiano I. Malossi, Yves Ineichen, Costas Bekas, and Alessandro
-  // Curioni exp(x) = 2**(x * log2(e))
-  //        = 2**xi * 2**xf   - TIPS we are using  the EEEE floating point
-  //        representation with identification to the exponent and the
-  //        mentissa
-  //  2**xf will be approximated to a polynomial of degree 3 computed with
-  //  Horner method
-  // mask for the boundary condition
-  auto min_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_min, _CMP_LT_OS);
-  auto max_mask = _mm512_cmp_ps_mask(values, vec_ln_flt_max, _CMP_GT_OS);
-
-  // transformation with log2(e)
-  auto vec_src = _mm512_mul_ps(values, vec_exp_log2ef);
-  auto vec_fractional = _mm512_sub_ps(vec_src, _mm512_floor_ps(vec_src));
-
-  // compute polynomial using Horner Scheme, for superscalar processor
-  auto vec_res = _mm512_fmadd_ps(vec_fractional, vec_c3, vec_c2);
-  vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c1);
-  vec_res = _mm512_fmadd_ps(vec_fractional, vec_res, vec_c0);
-
-  vec_src = _mm512_sub_ps(vec_src, vec_res);
-  // the tips is here, headache in perspective
-  auto tmp = _mm512_fmadd_ps(vec_a, vec_src, vec_b);
-  // headache bis - we loose precision with the cast but it "fits", but ok
-  // after f32 -> f16 later
-  __m512i casted_integer = _mm512_cvttps_epi32(tmp);
-  // boundary condition, lower than the min -> 0
-  casted_integer = _mm512_mask_mov_epi32(casted_integer, min_mask, vec_zero);
-  // boundary condition, larger than the max -> +oo
-  casted_integer = _mm512_mask_mov_epi32(casted_integer, max_mask, vec_infinity);
-  // final interpretation to float
-  return _mm512_castsi512_ps(casted_integer);
-}
 #endif
 
-}  // anonymous namespace
+// TODO: debug print, remove me later
+template<typename scalar_t>
+void print_array(scalar_t* ptr, int size) {
+  for (int d = 0; d < size; ++d) {
+    if (d % 16 == 0) { std::cout << std::endl; }
+    std::cout << ptr[d] << " ";
+  }
+  std::cout << std::endl;
+}
+
+} // anonymous namespace

csrc/cpu/sgl-kernels/vec_pack.h

@@ -1,299 +0,0 @@
-// Adapted from
-// https://github.com/sgl-project/sglang/tree/main/sgl-kernel/csrc/cpu
-
-// clang-format off
-
-// To use the transpose functions
-#include <ATen/native/cpu/utils.h>
-
-#include "vec.h"
-
-namespace {
-
-using namespace at::vec;
-
-template <typename index_t>
-inline index_t get_index(index_t* ind, int i) {
-  return (ind == nullptr) ? (index_t)i : ind[i];
-}
-
-#if defined(CPU_CAPABILITY_AVX512)
-// key: from [N, 32] to [32/2, N, 2]
-template <typename scalar_t, typename index_t>
-inline void pack_vnni_Nx32(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int N,
-    int ld_src,
-    int ld_dst) {
-  __m512i vinputs[16];
-
-  int n = 0;
-  for (; n < N; ++n) {
-    index_t index = get_index(ind, n);
-    vinputs[n] = _mm512_loadu_si512(src + index * ld_src);
-  }
-  // padding with zero to avoid uninitialized vectors
-  for (; n < 16; ++n) {
-    vinputs[n] = _mm512_set1_epi32(0);
-  }
-
-  // pack key
-  transpose_16x16_32bit(vinputs);
-
-  const __mmask16 vmask = (1 << N) - 1;
-  for (int k = 0; k < 16; ++k) {
-    _mm512_mask_storeu_epi32(dst + k * ld_dst * 2, vmask, vinputs[k]);
-  }
-}
-
-template <typename scalar_t, typename index_t>
-inline void pack_vnni_N_remainder(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int N,
-    int K,
-    int ld_src,
-    int ld_dst) {
-  __m512i vinputs[16];
-
-  int K2 = K >> 1;
-  const __mmask16 vmask = (1 << K2) - 1;
-
-  int n = 0;
-  for (; n < N; ++n) {
-    index_t index = get_index(ind, n);
-    vinputs[n] = _mm512_maskz_loadu_epi32(vmask, src + index * ld_src);
-  }
-  // padding with zero to avoid uninitialized vectors
-  for (; n < 16; ++n) {
-    vinputs[n] = _mm512_set1_epi32(0);
-  }
-
-  // pack key
-  transpose_16x16_32bit(vinputs);
-
-  const __mmask16 vmask2 = (1 << N) - 1;
-  for (int k = 0; k < K2; ++k) {
-    _mm512_mask_storeu_epi32(dst + k * ld_dst * 2, vmask2, vinputs[k]);
-  }
-}
-
-// value: from [K, 32] to [K/2, 32, 2]
-template <typename scalar_t, typename index_t>
-inline void pack_vnni_Kx32(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int K,
-    int ld_src,
-    int ld_dst) {
-  __m512i vinputs[2];
-
-  int k = 0;
-  for (; k < K; ++k) {
-    index_t index = get_index(ind, k);
-    vinputs[k] = _mm512_loadu_si512(src + index * ld_src);
-  }
-  // padding with zero to avoid uninitialized vectors
-  for (; k < 2; ++k) {
-    vinputs[k] = _mm512_set1_epi32(0);
-  }
-
-  // pack value
-  __m512i d0, d1;
-  std::tie(d0, d1) = transpose_2x32_16bit(vinputs[0], vinputs[1]);
-  _mm512_storeu_si512(dst + 0 * ld_dst * 2, d0);
-  _mm512_storeu_si512(dst + 0 * ld_dst * 2 + 32, d1);
-}
-
-template <typename scalar_t, typename index_t>
-inline void pack_vnni_K_remainder(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int K,
-    int N,
-    int ld_src,
-    int ld_dst) {
-  __m512i vinputs[2];
-
-  const __mmask32 vmask = (1 << N) - 1;
-
-  int k = 0;
-  for (; k < K; ++k) {
-    index_t index = get_index(ind, k);
-    vinputs[k] = _mm512_maskz_loadu_epi16(vmask, src + index * ld_src);
-  }
-  // padding with zero to avoid uninitialized vectors
-  for (; k < 2; ++k) {
-    vinputs[k] = _mm512_set1_epi32(0);
-  }
-
-  // pack value
-  __m512i d0, d1;
-  std::tie(d0, d1) = transpose_2x32_16bit(vinputs[0], vinputs[1]);
-
-  if (N <= 16) {
-    // 2N * 16bits: N * 32bits
-    const __mmask16 vmask2 = (1 << N) - 1;
-    _mm512_mask_storeu_epi32(dst + 0 * ld_dst * 2, vmask2, d0);
-  } else {
-    // 2(N-16) * 16bits: (N-16) * 32bits
-    const __mmask16 vmask2 = (1 << (N - 16)) - 1;
-    _mm512_storeu_epi32(dst + 0 * ld_dst * 2, d0);
-    _mm512_mask_storeu_epi32(dst + 0 * ld_dst * 2 + 32, vmask2, d1);
-  }
-}
-#endif
-
-// convert to vnni format
-// from [N, K/2, 2] to [K/2, N, 2] for bfloat16 and float16
-template <typename scalar_t, typename index_t, bool is_indexed>
-void pack_vnni(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int N,
-    int K,
-    int ld_src,
-    int ld_dst) {
-#if defined(CPU_CAPABILITY_AVX512)
-  const int NB = div_up(N, 16);
-  const int KB = K / 32;
-  const int K_remainder = K - KB * 32;
-
-  for (int nb = 0; nb < NB; ++nb) {
-    int nb_size = std::min(N - nb * 16, 16);
-    for (int kb = 0; kb < KB; ++kb) {
-      // handle 16x512bits each block
-      pack_vnni_Nx32<scalar_t, index_t>(
-          /*    dst */ dst + ((kb * 32) >> 1) * ld_dst * 2 + nb * 16 * 2,
-          /*    src */ src + kb * 32 + (is_indexed ? 0 : nb * 16 * ld_src),
-          /*    ind */ is_indexed ? ind + nb * 16 : nullptr,
-          /*      N */ nb_size,
-          /* ld_src */ ld_src,
-          /* ld_dst */ ld_dst);
-    }
-    if (K_remainder > 0) {
-      pack_vnni_N_remainder<scalar_t, index_t>(
-          /*    dst */ dst + ((KB * 32) >> 1) * ld_dst * 2 + nb * 16 * 2,
-          /*    src */ src + KB * 32 + (is_indexed ? 0 : nb * 16 * ld_src),
-          /*    ind */ is_indexed ? ind + nb * 16 : nullptr,
-          /*      N */ nb_size,
-          /*      K */ K_remainder,
-          /* ld_src */ ld_src,
-          /* ld_dst */ ld_dst);
-    }
-  }
-#else
-  for (int n = 0; n < N; ++n) {
-    index_t index = get_index(ind, n);
-    for (int k = 0; k < K / 2; ++k) {
-      for (int d = 0; d < 2; ++d) {
-        dst[k * ld_dst * 2 + n * 2 + d] = src[index * ld_src + k * 2 + d];
-      }
-    }
-  }
-#endif
-}
-
-template <typename scalar_t>
-void pack_vnni(scalar_t* __restrict__ dst, const scalar_t* __restrict__ src, int N, int K, int ld_src, int ld_dst) {
-  pack_vnni<scalar_t, int32_t, false>(dst, src, nullptr, N, K, ld_src, ld_dst);
-}
-
-template <typename scalar_t, typename index_t>
-void pack_vnni(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int N,
-    int K,
-    int ld_src,
-    int ld_dst) {
-  assert(ind != nullptr);
-  pack_vnni<scalar_t, index_t, true>(dst, src, ind, N, K, ld_src, ld_dst);
-}
-
-// convert to vnni format
-// from [K/2, 2, N] to [K/2, N, 2] for bfloat16 and float16
-template <typename scalar_t, typename index_t, bool is_indexed>
-void pack_vnni2(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int K,
-    int N,
-    int ld_src,
-    int ld_dst) {
-#if defined(CPU_CAPABILITY_AVX512)
-  const int KB = div_up(K, 2);
-  const int NB = N / 32;
-  const int N_remainder = N - NB * 32;
-
-  for (int kb = 0; kb < KB; ++kb) {
-    int kb_size = std::min(K - kb * 2, 2);
-    for (int nb = 0; nb < NB; ++nb) {
-      // handle 2x512bits each block
-      pack_vnni_Kx32<scalar_t, index_t>(
-          /*    dst */ dst + ((kb * 2) >> 1) * ld_dst * 2 + nb * 32 * 2,
-          /*    src */ src + (is_indexed ? 0 : kb * 2 * ld_src) + nb * 32,
-          /*    ind */ is_indexed ? ind + kb * 2 : nullptr,
-          /*      K */ kb_size,
-          /* ld_src */ ld_src,
-          /* ld_dst */ ld_dst);
-    }
-    if (N_remainder > 0) {
-      pack_vnni_K_remainder(
-          /*    dst */ dst + ((kb * 2) >> 1) * ld_dst * 2 + NB * 32 * 2,
-          /*    src */ src + (is_indexed ? 0 : kb * 2 * ld_src) + NB * 32,
-          /*    ind */ is_indexed ? ind + kb * 2 : nullptr,
-          /*      K */ kb_size,
-          /*      N */ N_remainder,
-          /* ld_src */ ld_src,
-          /* ld_dst */ ld_dst);
-    }
-  }
-#else
-  int k = 0;
-  for (; k < (K >> 1) * 2; k += 2) {
-    index_t index0 = get_index(ind, k + 0);
-    index_t index1 = get_index(ind, k + 1);
-    for (int n = 0; n < N; ++n) {
-      dst[(k >> 1) * ld_dst * 2 + n * 2 + 0] = src[index0 * ld_src + n];
-      dst[(k >> 1) * ld_dst * 2 + n * 2 + 1] = src[index1 * ld_src + n];
-    }
-  }
-  if (K % 2 != 0) {
-    index_t index = get_index(ind, K - 1);
-    for (int n = 0; n < N; ++n) {
-      dst[(K >> 1) * ld_dst * 2 + n * 2 + 0] = src[index * ld_src + n];
-      dst[(K >> 1) * ld_dst * 2 + n * 2 + 1] = 0;
-    }
-    k += 2;
-  }
-#endif
-}
-
-template <typename scalar_t>
-void pack_vnni2(scalar_t* __restrict__ dst, const scalar_t* __restrict__ src, int K, int N, int ld_src, int ld_dst) {
-  pack_vnni2<scalar_t, int32_t, false>(dst, src, nullptr, K, N, ld_src, ld_dst);
-}
-
-template <typename scalar_t, typename index_t>
-void pack_vnni2(
-    scalar_t* __restrict__ dst,
-    const scalar_t* __restrict__ src,
-    const index_t* __restrict__ ind,
-    int K,
-    int N,
-    int ld_src,
-    int ld_dst) {
-  assert(ind != nullptr);
-  pack_vnni2<scalar_t, index_t, true>(dst, src, ind, K, N, ld_src, ld_dst);
-}
-
-}  // anonymous namespace

csrc/cpu/torch_bindings.cpp

@@ -56,8 +56,6 @@ void shm_send_tensor_list(int64_t handle,
 
 std::vector<torch::Tensor> shm_recv_tensor_list(int64_t handle, int64_t src);
 
-// SGL CPU kernels
-
 at::Tensor weight_packed_linear(at::Tensor& mat1, at::Tensor& mat2,
                                 const std::optional<at::Tensor>& bias,
                                 bool is_vnni);
@@ -67,32 +65,21 @@ at::Tensor convert_weight_packed(at::Tensor& weight);
 at::Tensor fused_experts_cpu(
     at::Tensor& hidden_states, at::Tensor& w1, at::Tensor& w2,
     at::Tensor& topk_weights, at::Tensor& topk_ids, bool inplace,
-    int64_t moe_comp_method, const std::optional<at::Tensor>& w1_scale,
+    bool use_int8_w8a8, bool use_fp8_w8a16,
+    const std::optional<at::Tensor>& w1_scale,
     const std::optional<at::Tensor>& w2_scale,
-    const std::optional<at::Tensor>& w1_zero,
-    const std::optional<at::Tensor>& w2_zero,
-    const std::optional<std::vector<int64_t>> block_size, bool is_vnni);
+    const std::optional<std::vector<int64_t>> block_size,
+    const std::optional<at::Tensor>& a1_scale,
+    const std::optional<at::Tensor>& a2_scale, bool is_vnni);
 
 at::Tensor int8_scaled_mm_with_quant(at::Tensor& mat1, at::Tensor& mat2,
                                      at::Tensor& scales2,
                                      const std::optional<at::Tensor>& bias,
                                      at::ScalarType out_dtype, bool is_vnni);
 
-// Adapted from sglang: FP8 W8A16 kernel
-at::Tensor fp8_scaled_mm_cpu(at::Tensor& mat1, at::Tensor& mat2,
-                             at::Tensor& scales2,
-                             std::vector<int64_t> block_size,
-                             const std::optional<at::Tensor>& bias,
-                             at::ScalarType out_dtype, bool is_vnni);
-
 // Adapted from sglang: INT4 W4A8 kernels
 std::tuple<at::Tensor, at::Tensor, at::Tensor> convert_weight_packed_scale_zp(
-    at::Tensor qweight,  // awq: (*, K, N / 8)  ||  gptq: (*, K / 8, N) , int32
-    at::Tensor qzeros,   // awq: (*, K / group_size, N / 8) ||  gptq: (*, K /
-                         // group_size, N / 8) , int32
-    at::Tensor scales,   // awq: (*, K / group_size, N) ||  gptq: (*, K /
-                         // group_size, N) , bfloat16
-    int64_t quant_method_4bit);
+    at::Tensor qweight, at::Tensor qzeros, at::Tensor scales);
 
 at::Tensor int4_scaled_mm_cpu(at::Tensor& x, at::Tensor& w, at::Tensor& w_zeros,
                               at::Tensor& w_scales,
@@ -114,9 +101,7 @@ void cpu_attn_reshape_and_cache(const torch::Tensor& key,
                                 torch::Tensor& key_cache,
                                 torch::Tensor& value_cache,
                                 const torch::Tensor& slot_mapping,
-                                const std::string& isa, const double k_scale,
-                                const double v_scale,
-                                const std::string& kv_cache_dtype);
+                                const std::string& isa);
 
 void cpu_attention_with_kv_cache(
     const torch::Tensor& query, const torch::Tensor& key_cache,
@@ -127,8 +112,7 @@ void cpu_attention_with_kv_cache(
     const int64_t sliding_window_left, const int64_t sliding_window_right,
     const torch::Tensor& block_table, const double softcap,
     const torch::Tensor& scheduler_metadata,
-    const std::optional<torch::Tensor>& s_aux, const double k_scale,
-    const double v_scale, const std::string& kv_cache_dtype);
+    const std::optional<torch::Tensor>& s_aux);
 
 // Note: just for avoiding importing errors
 void placeholder_op() { TORCH_CHECK(false, "Unimplemented"); }
@@ -284,8 +268,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.impl("rotary_embedding", torch::kCPU, &rotary_embedding);
 
   // Quantization
-#if defined(__AVX512F__) || defined(__AVX2__) || \
-    (defined(__aarch64__) && !defined(__APPLE__)) || defined(__powerpc64__)
+#if defined(__AVX512F__) || (defined(__aarch64__) && !defined(__APPLE__)) || \
+    defined(__powerpc64__)
   // Helper function to release oneDNN handlers
   ops.def("release_dnnl_matmul_handler(int handler) -> ()",
           &release_dnnl_matmul_handler);
@@ -366,10 +350,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def("convert_weight_packed(Tensor! weight) -> Tensor");
   ops.impl("convert_weight_packed", torch::kCPU, &convert_weight_packed);
   ops.def(
-      "fused_experts_cpu(Tensor hidden_states, Tensor w1, Tensor w2, Tensor "
-      "topk_weights, Tensor topk_ids, bool "
-      "inplace, int moe_comp_method, Tensor? w1_scale, Tensor? w2_scale, "
-      "Tensor? w1_zero, Tensor? w2_zero, int[]? block_size, bool is_vnni) -> "
+      "fused_experts_cpu(Tensor! hidden_states, Tensor w1, Tensor w2, Tensor "
+      "topk_weights, Tensor topk_ids, bool inplace, bool use_int8_w8a8, bool "
+      "use_fp8_w8a16, Tensor? w1_scale, Tensor? w2_scale, SymInt[]? "
+      "block_size, Tensor? a1_scale, Tensor? a2_scale, bool is_vnni) -> "
       "Tensor");
   ops.impl("fused_experts_cpu", torch::kCPU, &fused_experts_cpu);
   ops.def(
@@ -380,9 +364,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
 
   // Adapted from sglang: INT4 W4A8 kernels
   ops.def(
-      "convert_weight_packed_scale_zp(Tensor weight, Tensor qzeros, Tensor "
-      "scales, int quant_method_4bit) -> (Tensor, "
-      "Tensor, Tensor)");
+      "convert_weight_packed_scale_zp(Tensor qweight, Tensor qzeros, "
+      "Tensor scales) -> (Tensor, Tensor, Tensor)");
   ops.impl("convert_weight_packed_scale_zp", torch::kCPU,
            &convert_weight_packed_scale_zp);
 
@@ -390,13 +373,6 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "int4_scaled_mm_cpu(Tensor(a0!) x, Tensor(a1!) w, Tensor(a2!) w_zeros, "
       "Tensor(a3!) w_scales, Tensor? bias) -> Tensor");
   ops.impl("int4_scaled_mm_cpu", torch::kCPU, &int4_scaled_mm_cpu);
-
-  // Adapted from sglang: FP8 W8A16 kernel
-  ops.def(
-      "fp8_scaled_mm_cpu(Tensor(a0!) mat1, Tensor(a1!) mat2, Tensor(a2!) "
-      "scales2, SymInt[] block_size, Tensor? bias, ScalarType out_dtype, "
-      "bool is_vnni) -> Tensor");
-  ops.impl("fp8_scaled_mm_cpu", torch::kCPU, &fp8_scaled_mm_cpu);
 #endif
 
   // CPU attention kernels
@@ -408,18 +384,15 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       &get_scheduler_metadata);
   ops.def(
       "cpu_attn_reshape_and_cache(Tensor key, Tensor value, Tensor(a2!) "
-      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str isa, "
-      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
-      "()",
+      "key_cache, Tensor(a3!) value_cache, Tensor slot_mapping, str "
+      "isa) -> ()",
       &cpu_attn_reshape_and_cache);
   ops.def(
       "cpu_attention_with_kv_cache(Tensor query, Tensor key_cache, Tensor "
       "value_cache, Tensor(a3!) output, Tensor query_start_loc, Tensor "
       "seq_lens, float scale, bool causal, Tensor? alibi_slopes, SymInt "
       "sliding_window_left, SymInt sliding_window_right, Tensor block_table, "
-      "float softcap, Tensor scheduler_metadata, Tensor? s_aux, "
-      "float k_scale=1.0, float v_scale=1.0, str kv_cache_dtype=\"auto\") -> "
-      "()",
+      "float softcap, Tensor scheduler_metadata, Tensor? s_aux) -> ()",
       &cpu_attention_with_kv_cache);
 
   // placeholders

csrc/cpu/utils.hpp

@@ -54,7 +54,7 @@ struct Counter {
 };
 
 inline int64_t get_available_l2_size() {
-#if defined(__s390x__) || defined(__powerpc__)
+#if defined(__s390x__)
   static int64_t size = []() {
     uint32_t l2_cache_size = 0;
     auto caps = at::cpu::get_cpu_capabilities();