[Bugfix] Make Gemma3 MM V0 only for now (#14971)

Signed-off-by: Roger Wang <ywang@roblox.com>

.buildkite/lm-eval-harness/configs/Minitron-4B-Base-FP8.yaml

@@ -4,8 +4,8 @@ tasks:
 - name: "gsm8k"
   metrics:
   - name: "exact_match,strict-match"
-    value: 0.233
+    value: 0.231
   - name: "exact_match,flexible-extract"
-    value: 0.236
+    value: 0.22
 limit: 1000
 num_fewshot: 5

.buildkite/lm-eval-harness/test_lm_eval_correctness.py

@@ -13,6 +13,7 @@ from pathlib import Path
 
 import lm_eval
 import numpy
+import pytest
 import yaml
 
 RTOL = 0.05
@@ -46,6 +47,10 @@ def test_lm_eval_correctness():
     eval_config = yaml.safe_load(
         Path(TEST_DATA_FILE).read_text(encoding="utf-8"))
 
+    if eval_config[
+            "model_name"] == "nm-testing/Meta-Llama-3-70B-Instruct-FBGEMM-nonuniform":  #noqa: E501
+        pytest.skip("FBGEMM is currently failing on main.")
+
     # Launch eval requests.
     results = launch_lm_eval(eval_config)
 

.buildkite/nightly-benchmarks/scripts/run-nightly-benchmarks.sh

@@ -426,7 +426,7 @@ main() {
 
   pip install -U transformers
 
-  pip install -r requirements-dev.txt
+  pip install -r requirements/dev.txt
   which genai-perf
 
   # check storage

.buildkite/run-amd-test.sh

@@ -101,16 +101,30 @@ if [[ $commands == *" kernels "* ]]; then
   --ignore=kernels/test_permute_cols.py"
 fi
 
-#ignore certain Entrypoints tests
+#ignore certain Entrypoints/openai tests
 if [[ $commands == *" entrypoints/openai "* ]]; then
   commands=${commands//" entrypoints/openai "/" entrypoints/openai \
-  --ignore=entrypoints/openai/test_accuracy.py \
   --ignore=entrypoints/openai/test_audio.py \
-  --ignore=entrypoints/openai/test_encoder_decoder.py \
-  --ignore=entrypoints/openai/test_embedding.py \
-  --ignore=entrypoints/openai/test_oot_registration.py "}
+  --ignore=entrypoints/openai/test_chat.py \
+  --ignore=entrypoints/openai/test_shutdown.py \
+  --ignore=entrypoints/openai/test_completion.py \
+  --ignore=entrypoints/openai/test_sleep.py \
+  --ignore=entrypoints/openai/test_models.py \
+  --ignore=entrypoints/openai/test_prompt_validation.py "}
 fi
 
+#ignore certain Entrypoints/llm tests
+if [[ $commands == *" && pytest -v -s entrypoints/llm/test_guided_generate.py"* ]]; then
+  commands=${commands//" && pytest -v -s entrypoints/llm/test_guided_generate.py"/" "}
+fi
+
+# --ignore=entrypoints/openai/test_encoder_decoder.py \
+# --ignore=entrypoints/openai/test_embedding.py \
+# --ignore=entrypoints/openai/test_oot_registration.py
+# --ignore=entrypoints/openai/test_accuracy.py \
+# --ignore=entrypoints/openai/test_models.py <= Fails on MI250 but passes on MI300 as of 2025-03-13
+
+
 PARALLEL_JOB_COUNT=8
 # check if the command contains shard flag, we will run all shards in parallel because the host have 8 GPUs. 
 if [[ $commands == *"--shard-id="* ]]; then

.buildkite/run-cpu-test.sh

@@ -19,13 +19,14 @@ remove_docker_container
 
 # Run the image, setting --shm-size=4g for tensor parallel.
 docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE"  \
- --cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"
 docker run -itd --entrypoint /bin/bash -v ~/.cache/huggingface:/root/.cache/huggingface --cpuset-cpus="$CORE_RANGE" \
- --cpuset-mems="$NUMA_NODE" --privileged=true --network host -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2
+ --cpuset-mems="$NUMA_NODE" --privileged=true -e HF_TOKEN --env VLLM_CPU_KVCACHE_SPACE=4 --shm-size=4g --name cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2
 
 function cpu_tests() {
   set -e
   export NUMA_NODE=$2
+  export BUILDKITE_BUILD_NUMBER=$3
 
   # offline inference
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-avx2-"$NUMA_NODE" bash -c "
@@ -35,7 +36,8 @@ function cpu_tests() {
   # Run basic model test
   docker exec cpu-test-"$BUILDKITE_BUILD_NUMBER"-"$NUMA_NODE" bash -c "
     set -e
-    pip install -r vllm/requirements-test.txt
+    pip install -r vllm/requirements/test.txt
+    pip install -r vllm/requirements/cpu.txt
     pytest -v -s tests/models/decoder_only/language -m cpu_model
     pytest -v -s tests/models/embedding/language -m cpu_model
     pytest -v -s tests/models/encoder_decoder/language -m cpu_model
@@ -85,4 +87,4 @@ function cpu_tests() {
 
 # All of CPU tests are expected to be finished less than 40 mins.
 export -f cpu_tests
-timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE"
+timeout 40m bash -c "cpu_tests $CORE_RANGE $NUMA_NODE $BUILDKITE_BUILD_NUMBER"

.buildkite/run-neuron-test.sh

@@ -44,11 +44,11 @@ remove_docker_container() {
 trap remove_docker_container EXIT
 
 # Run the image
-docker run --rm -it --device=/dev/neuron0 --device=/dev/neuron1 --network host \
+docker run --rm -it --device=/dev/neuron0 --network bridge \
        -v "${HF_CACHE}:${HF_MOUNT}" \
        -e "HF_HOME=${HF_MOUNT}" \
        -v "${NEURON_COMPILE_CACHE_URL}:${NEURON_COMPILE_CACHE_MOUNT}" \
        -e "NEURON_COMPILE_CACHE_URL=${NEURON_COMPILE_CACHE_MOUNT}" \
        --name "${container_name}" \
        ${image_name} \
-       /bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/ -v --capture=tee-sys"
+       /bin/bash -c "python3 /workspace/vllm/examples/offline_inference/neuron.py && python3 -m pytest /workspace/vllm/tests/neuron/1_core/ -v --capture=tee-sys && python3 -m pytest /workspace/vllm/tests/neuron/2_core/ -v --capture=tee-sys"

.buildkite/run-tpu-test.sh

@@ -19,7 +19,6 @@ docker run --privileged --net host --shm-size=16G -it \
     vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
     && python3 -m pip install pytest \
     && python3 -m pip install lm_eval[api]==0.4.4 \
-    && pytest -v -s /workspace/vllm/tests/entrypoints/openai/test_accuracy.py \
     && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
     && python3 /workspace/vllm/tests/tpu/test_compilation.py \
     && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \

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

@@ -0,0 +1,27 @@
+#!/bin/bash
+
+set -e
+
+# Build the docker image.
+docker build -f Dockerfile.tpu -t vllm-tpu .
+
+# Set up cleanup.
+remove_docker_container() { docker rm -f tpu-test || true; }
+trap remove_docker_container EXIT
+# Remove the container that might not be cleaned up in the previous run.
+remove_docker_container
+
+# For HF_TOKEN.
+source /etc/environment
+# Run a simple end-to-end example.
+docker run --privileged --net host --shm-size=16G -it \
+    -e "HF_TOKEN=$HF_TOKEN" -e "VLLM_USE_V1=1" --name tpu-test \
+    vllm-tpu /bin/bash -c "python3 -m pip install git+https://github.com/thuml/depyf.git \
+    && python3 -m pip install pytest \
+    && python3 -m pip install lm_eval[api]==0.4.4 \
+    && pytest -v -s /workspace/vllm/tests/tpu/test_custom_dispatcher.py \
+    && pytest -v -s /workspace/vllm/tests/v1/tpu/test_basic.py \
+    && pytest -v -s /workspace/vllm/tests/entrypoints/llm/test_accuracy.py::test_lm_eval_accuracy_v1_engine \
+    && python3 /workspace/vllm/tests/tpu/test_compilation.py \
+    && python3 /workspace/vllm/tests/tpu/test_quantization_accuracy.py \
+    && python3 /workspace/vllm/examples/offline_inference/tpu.py"

.buildkite/run-xpu-test.sh

@@ -4,16 +4,27 @@
 # It serves a sanity check for compilation and basic model usage.
 set -ex
 
+image_name="xpu/vllm-ci:${BUILDKITE_COMMIT}"
+container_name="xpu_${BUILDKITE_COMMIT}_$(tr -dc A-Za-z0-9 < /dev/urandom | head -c 10; echo)"
+
 # Try building the docker image
-docker build -t xpu-test -f Dockerfile.xpu .
+docker build -t ${image_name} -f Dockerfile.xpu .
 
 # Setup cleanup
-remove_docker_container() { docker rm -f xpu-test || true; }
+remove_docker_container() { 
+  docker rm -f "${container_name}" || docker image rm -f "${image_name}" || true;
+}
 trap remove_docker_container EXIT
 remove_docker_container
 
 # Run the image and test offline inference/tensor parallel
-docker run --name xpu-test --device /dev/dri -v /dev/dri/by-path:/dev/dri/by-path --entrypoint="" xpu-test sh -c '
+docker run \
+    --device /dev/dri \
+    -v /dev/dri/by-path:/dev/dri/by-path \
+    --entrypoint="" \
+    --name "${container_name}" \
+    "${image_name}" \
+    sh -c '
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m
     python3 examples/offline_inference/basic/generate.py --model facebook/opt-125m -tp 2
 '

.buildkite/test-pipeline.yaml

@@ -35,13 +35,12 @@ steps:
   fast_check: true
   no_gpu: True
   commands:
-  - pip install -r requirements-docs.txt
+  - pip install -r ../../requirements/docs.txt
   - SPHINXOPTS=\"-W\" make html
   # Check API reference (if it fails, you may have missing mock imports)
   - grep \"sig sig-object py\" build/html/api/inference_params.html
 
 - label: Async Engine, Inputs, Utils, Worker Test # 24min
-  fast_check: true
   source_file_dependencies:
   - vllm/
   - tests/mq_llm_engine
@@ -78,6 +77,7 @@ steps:
   - tests/basic_correctness/test_preemption
   - tests/basic_correctness/test_cumem.py
   commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s basic_correctness/test_cumem.py
   - pytest -v -s basic_correctness/test_basic_correctness.py
   - pytest -v -s basic_correctness/test_cpu_offload.py
@@ -112,19 +112,19 @@ steps:
   - tests/entrypoints/test_chat_utils
   - tests/entrypoints/offline_mode
   commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -v -s entrypoints/llm --ignore=entrypoints/llm/test_lazy_outlines.py --ignore=entrypoints/llm/test_generate.py --ignore=entrypoints/llm/test_generate_multiple_loras.py --ignore=entrypoints/llm/test_guided_generate.py --ignore=entrypoints/llm/test_collective_rpc.py
   - pytest -v -s entrypoints/llm/test_lazy_outlines.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate.py # it needs a clean process
   - pytest -v -s entrypoints/llm/test_generate_multiple_loras.py # it needs a clean process
-  - pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
+  - VLLM_USE_V1=0 pytest -v -s entrypoints/llm/test_guided_generate.py # it needs a clean process
   - pytest -v -s entrypoints/openai --ignore=entrypoints/openai/test_oot_registration.py --ignore=entrypoints/openai/correctness/
   - pytest -v -s entrypoints/test_chat_utils.py
-  - pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
+  - VLLM_USE_V1=0 pytest -v -s entrypoints/offline_mode # Needs to avoid interference with other tests
 
 - label: Distributed Tests (4 GPUs) # 10min
   working_dir: "/vllm-workspace/tests"
   num_gpus: 4
-  fast_check: true
   source_file_dependencies:
   - vllm/distributed/
   - vllm/core/
@@ -136,19 +136,20 @@ steps:
   - examples/offline_inference/rlhf_colocate.py
   - tests/examples/offline_inference/data_parallel.py
   commands:
-  - VLLM_USE_V1=1 python3 ../examples/offline_inference/data_parallel.py
+  - python3 ../examples/offline_inference/data_parallel.py
   - pytest -v -s distributed/test_utils.py
   - pytest -v -s compile/test_basic_correctness.py
   - pytest -v -s distributed/test_pynccl.py
   - pytest -v -s spec_decode/e2e/test_integration_dist_tp4.py
   # TODO: create a dedicated test section for multi-GPU example tests
   # when we have multiple distributed example tests
-  - python3 ../examples/offline_inference/rlhf.py
-  - RAY_DEDUP_LOGS=0 python3 ../examples/offline_inference/rlhf_colocate.py
+  - pushd ../examples/offline_inference
+  - python3 rlhf.py
+  - RAY_DEDUP_LOGS=0 python3 rlhf_colocate.py
+  - popd
 
 - label: Metrics, Tracing Test # 10min
   num_gpus: 2
-  fast_check: true
   source_file_dependencies:
   - vllm/
   - tests/metrics
@@ -196,15 +197,19 @@ steps:
     - tests/v1
   commands:
     # split the test to avoid interference
-    - VLLM_USE_V1=1 pytest -v -s v1/core
-    - VLLM_USE_V1=1 pytest -v -s v1/engine
-    - VLLM_USE_V1=1 pytest -v -s v1/sample
-    - VLLM_USE_V1=1 pytest -v -s v1/worker
-    - VLLM_USE_V1=1 pytest -v -s v1/test_stats.py
-    - VLLM_USE_V1=1 pytest -v -s v1/test_utils.py
+    - pytest -v -s v1/core
+    - pytest -v -s v1/entrypoints
+    - pytest -v -s v1/engine
+    - pytest -v -s v1/entrypoints
+    - pytest -v -s v1/sample
+    - pytest -v -s v1/worker
+    - pytest -v -s v1/structured_output
+    - pytest -v -s v1/test_stats.py
+    - pytest -v -s v1/test_utils.py
+    - pytest -v -s v1/test_oracle.py
     # TODO: accuracy does not match, whether setting
     # VLLM_USE_FLASHINFER_SAMPLER or not on H100.
-    - VLLM_USE_V1=1 pytest -v -s v1/e2e
+    - pytest -v -s v1/e2e
     # Integration test for streaming correctness (requires special branch).
     - pip install -U git+https://github.com/robertgshaw2-neuralmagic/lm-evaluation-harness.git@streaming-api
     - pytest -v -s entrypoints/openai/correctness/test_lmeval.py::test_lm_eval_accuracy_v1_engine
@@ -222,14 +227,17 @@ steps:
     - python3 offline_inference/basic/chat.py
     - python3 offline_inference/prefix_caching.py
     - python3 offline_inference/llm_engine_example.py
-    - python3 offline_inference/vision_language.py
-    - python3 offline_inference/vision_language_multi_image.py
-    - python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
+    - python3 offline_inference/audio_language.py --seed 0
+    - python3 offline_inference/vision_language.py --seed 0
+    - python3 offline_inference/vision_language_embedding.py --seed 0
+    - python3 offline_inference/vision_language_multi_image.py --seed 0
+    - VLLM_USE_V1=0 python3 other/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 other/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder.py
+    - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
     - python3 offline_inference/basic/classify.py
     - python3 offline_inference/basic/embed.py
     - python3 offline_inference/basic/score.py
-    - python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
+    - VLLM_USE_V1=0 python3 offline_inference/profiling.py --model facebook/opt-125m run_num_steps --num-steps 2
 
 - label: Prefix Caching Test # 9min
   mirror_hardwares: [amd]
@@ -279,7 +287,6 @@ steps:
   parallelism: 4
 
 - label: PyTorch Fullgraph Smoke Test # 9min
-  fast_check: true
   source_file_dependencies:
   - vllm/
   - tests/compile
@@ -374,7 +381,8 @@ steps:
   commands:
     - pytest -v -s models/test_transformers.py
     - pytest -v -s models/test_registry.py
-    - pytest -v -s models/test_initialization.py
+    # V1 Test: https://github.com/vllm-project/vllm/issues/14531
+    - VLLM_USE_V1=0 pytest -v -s models/test_initialization.py
 
 - label: Language Models Test (Standard) # 32min
   #mirror_hardwares: [amd]
@@ -517,13 +525,12 @@ steps:
   # this test fails consistently.
   # TODO: investigate and fix
   # - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
-  - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/disagg_test.py
+  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py
+  - VLLM_USE_V1=0 CUDA_VISIBLE_DEVICES=0,1 pytest -v -s kv_transfer/test_disagg.py
 
 - label: Plugin Tests (2 GPUs) # 40min
   working_dir: "/vllm-workspace/tests"
   num_gpus: 2
-  fast_check: true
   source_file_dependencies:
   - vllm/plugins/
   - tests/plugins/

.github/CODEOWNERS

@@ -10,27 +10,32 @@
 /vllm/worker/worker.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/sampler.py @zhuohan123 @youkaichao @alexm-redhat @comaniac @njhill
 /vllm/model_executor/layers/quantization @mgoin @robertgshaw2-redhat @tlrmchlsmth
-/vllm/model_executor/guided_decoding @mgoin
+/vllm/model_executor/guided_decoding @mgoin @russellb
 /vllm/multimodal @DarkLight1337 @ywang96
 CMakeLists.txt @tlrmchlsmth
 
 # vLLM V1
 /vllm/v1 @WoosukKwon @robertgshaw2-redhat @njhill @ywang96 @comaniac @alexm-redhat
+/vllm/v1/structured_output @mgoin @russellb
 
 # Test ownership
-/tests/async_engine @njhill @robertgshaw2-redhat @simon-mo
-/tests/test_inputs.py @DarkLight1337 @ywang96
-/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo
-/tests/models @DarkLight1337 @ywang96
-/tests/multimodal @DarkLight1337 @ywang96
-/tests/prefix_caching @comaniac @KuntaiDu
-/tests/spec_decode @njhill @LiuXiaoxuanPKU
-/tests/kernels @tlrmchlsmth @WoosukKwon
-/tests/quantization @mgoin @robertgshaw2-redhat
 /.buildkite/lm-eval-harness @mgoin @simon-mo
+/tests/async_engine @njhill @robertgshaw2-redhat @simon-mo
+/tests/basic_correctness/test_chunked_prefill @rkooo567 @comaniac
 /tests/distributed/test_multi_node_assignment.py @youkaichao
 /tests/distributed/test_pipeline_parallel.py @youkaichao
 /tests/distributed/test_same_node.py @youkaichao
+/tests/entrypoints @DarkLight1337 @robertgshaw2-redhat @simon-mo
+/tests/entrypoints/llm/test_guided_generate.py @mgoin @russellb
+/tests/kernels @tlrmchlsmth @WoosukKwon
+/tests/model_executor/test_guided_processors.py @mgoin @russellb
+/tests/models @DarkLight1337 @ywang96
 /tests/multi_step @alexm-redhat @comaniac
+/tests/multimodal @DarkLight1337 @ywang96
+/tests/prefix_caching @comaniac @KuntaiDu
+/tests/quantization @mgoin @robertgshaw2-redhat
+/tests/spec_decode @njhill @LiuXiaoxuanPKU
+/tests/test_inputs.py @DarkLight1337 @ywang96
+/tests/v1/entrypoints/llm/test_struct_output_generate.py @mgoin @russellb
+/tests/v1/structured_output @mgoin @russellb
 /tests/weight_loading @mgoin @youkaichao
-/tests/basic_correctness/test_chunked_prefill @rkooo567 @comaniac

.github/mergify.yml

@@ -36,6 +36,21 @@ pull_request_rules:
       add:
         - frontend
 
+- name: label-multi-modality
+  description: Automatically apply multi-modality label
+  conditions:
+    - or:
+      - files~=^vllm/multimodal/
+      - files~=^tests/multimodal/
+      - files~=^tests/models/multimodal/
+      - files~=^tests/models/*/audio_language/
+      - files~=^tests/models/*/vision_language/
+      - files=tests/models/test_vision.py
+  actions:
+    label:
+      add:
+        - multi-modality
+
 - name: label-structured-output
   description: Automatically apply structured-output label
   conditions:

.github/workflows/publish.yml

@@ -39,7 +39,7 @@ jobs:
             const script = require('.github/workflows/scripts/create_release.js')
             await script(github, context, core)
 
-  # NOTE(simon): No longer build wheel using Github Actions. See buildkite's release workflow. 
+  # NOTE(simon): No longer build wheel using GitHub Actions. See buildkite's release workflow. 
   # wheel:
   #   name: Build Wheel
   #   runs-on: ${{ matrix.os }}
@@ -50,7 +50,7 @@ jobs:
   #     matrix:
   #         os: ['ubuntu-20.04']
   #         python-version: ['3.9', '3.10', '3.11', '3.12']
-  #         pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements-cuda.txt.
+  #         pytorch-version: ['2.4.0']  # Must be the most recent version that meets requirements/cuda.txt.
   #         cuda-version: ['11.8', '12.1']
 
   #   steps:

.github/workflows/scripts/build.sh

@@ -9,7 +9,7 @@ PATH=${cuda_home}/bin:$PATH
 LD_LIBRARY_PATH=${cuda_home}/lib64:$LD_LIBRARY_PATH
 
 # Install requirements
-$python_executable -m pip install -r requirements-build.txt -r requirements-cuda.txt
+$python_executable -m pip install -r requirements/build.txt -r requirements/cuda.txt
 
 # Limit the number of parallel jobs to avoid OOM
 export MAX_JOBS=1

.github/workflows/scripts/create_release.js

@@ -1,4 +1,4 @@
-// Uses Github's API to create the release and wait for result.
+// Uses GitHub's API to create the release and wait for result.
 // We use a JS script since github CLI doesn't provide a way to wait for the release's creation and returns immediately.
 
 module.exports = async (github, context, core) => {

.gitignore

@@ -197,7 +197,7 @@ _build/
 hip_compat.h
 
 # Benchmark dataset
-benchmarks/*.json
+benchmarks/**/*.json
 
 # Linting
 actionlint

.pre-commit-config.yaml

@@ -44,8 +44,8 @@ repos:
   rev: 0.6.2
   hooks:
     - id: pip-compile
-      args: [requirements-test.in, -o, requirements-test.txt]
-      files: ^requirements-test\.(in|txt)$
+      args: [requirements/test.in, -o, requirements/test.txt]
+      files: ^requirements/test\.(in|txt)$
 - repo: local
   hooks:
   - id: mypy-local
@@ -53,7 +53,7 @@ repos:
     entry: tools/mypy.sh 0 "local"
     language: python
     types: [python]
-    additional_dependencies: &mypy_deps [mypy==1.11.1, types-setuptools, types-PyYAML, types-requests]
+    additional_dependencies: &mypy_deps [mypy==1.11.1, types-cachetools, types-setuptools, types-PyYAML, types-requests]
     stages: [pre-commit] # Don't run in CI
   - id: mypy-3.9 # TODO: Use https://github.com/pre-commit/mirrors-mypy when mypy setup is less awkward
     name: Run mypy for Python 3.9

.readthedocs.yaml

@@ -18,4 +18,4 @@ formats: []
 # Optionally declare the Python requirements required to build your docs
 python:
   install:
-    - requirements: docs/requirements-docs.txt
+    - requirements: requirements/docs.txt

CMakeLists.txt

@@ -46,8 +46,8 @@ set(HIP_SUPPORTED_ARCHS "gfx906;gfx908;gfx90a;gfx942;gfx1030;gfx1100;gfx1101")
 # requirements.txt files and should be kept consistent.  The ROCm torch
 # versions are derived from Dockerfile.rocm
 #
-set(TORCH_SUPPORTED_VERSION_CUDA "2.5.1")
-set(TORCH_SUPPORTED_VERSION_ROCM "2.5.1")
+set(TORCH_SUPPORTED_VERSION_CUDA "2.6.0")
+set(TORCH_SUPPORTED_VERSION_ROCM "2.6.0")
 
 #
 # Try to find python package with an executable that exactly matches
@@ -319,7 +319,7 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
 
   # Only build AllSpark kernels if we are building for at least some compatible archs.
   cuda_archs_loose_intersection(ALLSPARK_ARCHS "8.0;8.6;8.7;8.9" "${CUDA_ARCHS}")
-  if (${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND ALLSPARK_ARCHS)
+  if (ALLSPARK_ARCHS)
     set(ALLSPARK_SRCS
        "csrc/quantization/gptq_allspark/allspark_repack.cu"
        "csrc/quantization/gptq_allspark/allspark_qgemm_w8a16.cu")
@@ -330,39 +330,67 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     message(STATUS "Building AllSpark kernels for archs: ${ALLSPARK_ARCHS}")
   else()
     message(STATUS "Not building AllSpark kernels as no compatible archs found"
-                   " in CUDA target architectures, or CUDA not >= 12.0")
+                   " in CUDA target architectures")
   endif()
 
+
+  set(SCALED_MM_3X_ARCHS)
   # The cutlass_scaled_mm kernels for Hopper (c3x, i.e. CUTLASS 3.x) require
-  # CUDA 12.0 or later (and only work on Hopper, 9.0a for now).
-  cuda_archs_loose_intersection(SCALED_MM_3X_ARCHS "9.0a;10.0a;10.1a;12.0a" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
+  # CUDA 12.0 or later
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
     set(SRCS
-       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x.cu"
+       "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_fp8.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm90_int8.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_azp_sm90_int8.cu"
        "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_blockwise_sm90_fp8.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
+      CUDA_ARCHS "${SCALED_MM_ARCHS}")
     list(APPEND VLLM_EXT_SRC "${SRCS}")
-    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_C3X=1")
-    message(STATUS "Building scaled_mm_c3x for archs: ${SCALED_MM_3X_ARCHS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_SM90=1")
+    # Let scaled_mm_c2x know it doesn't need to build these arches
+    list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
+    message(STATUS "Building scaled_mm_c3x_sm90 for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_3X_ARCHS)
-      message(STATUS "Not building scaled_mm_c3x as CUDA Compiler version is "
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.0 AND SCALED_MM_ARCHS)
+      message(STATUS "Not building scaled_mm_c3x_sm90 as CUDA Compiler version is "
                      "not >= 12.0, we recommend upgrading to CUDA 12.0 or "
                      "later if you intend on running FP8 quantized models on "
                      "Hopper.")
     else()
-      message(STATUS "Not building scaled_mm_c3x as no compatible archs found "
+      message(STATUS "Not building scaled_mm_c3x_sm90 as no compatible archs found "
                      "in CUDA target architectures")
     endif()
+  endif()
 
-    # clear SCALED_MM_3X_ARCHS so the scaled_mm_c2x kernels know we didn't
-    # build any 3x kernels
-    set(SCALED_MM_3X_ARCHS)
+  # The cutlass_scaled_mm kernels for Blackwell (c3x, i.e. CUTLASS 3.x) require
+  # CUDA 12.8 or later
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "10.0a;10.1a;12.0a" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
+    set(SRCS
+      "csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu"
+      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
+    )
+    set_gencode_flags_for_srcs(
+      SRCS "${SRCS}"
+      CUDA_ARCHS "${SCALED_MM_ARCHS}")
+    list(APPEND VLLM_EXT_SRC "${SRCS}")
+    list(APPEND VLLM_GPU_FLAGS "-DENABLE_SCALED_MM_SM100=1")
+    # Let scaled_mm_c2x know it doesn't need to build these arches
+    list(APPEND SCALED_MM_3X_ARCHS "${SCALED_MM_ARCHS}")
+    message(STATUS "Building scaled_mm_c3x_sm100 for archs: ${SCALED_MM_ARCHS}")
+  else()
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND SCALED_MM_ARCHS)
+      message(STATUS "Not building scaled_mm_c3x_sm100 as CUDA Compiler version is "
+                     "not >= 12.8, we recommend upgrading to CUDA 12.8 or "
+                     "later if you intend on running FP8 quantized models on "
+                     "Blackwell.")
+    else()
+      message(STATUS "Not building scaled_mm_c3x_100 as no compatible archs found "
+                     "in CUDA target architectures")
+    endif()
   endif()
 
   #
@@ -394,17 +422,18 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
   # 2:4 Sparse Kernels
 
   # The 2:4 sparse kernels cutlass_scaled_sparse_mm and cutlass_compressor
-  # require CUDA 12.2 or later (and only work on Hopper and Blackwell).
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
+  # require CUDA 12.2 or later (and only work on Hopper).
+  cuda_archs_loose_intersection(SCALED_MM_ARCHS "9.0a;" "${CUDA_ARCHS}")
+  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
     set(SRCS "csrc/sparse/cutlass/sparse_scaled_mm_c3x.cu")
     set_gencode_flags_for_srcs(
       SRCS "${SRCS}"
-      CUDA_ARCHS "${SCALED_MM_3X_ARCHS}")
+      CUDA_ARCHS "${SCALED_MM_ARCHS}")
     list(APPEND VLLM_EXT_SRC "${SRCS}")
     list(APPEND VLLM_GPU_FLAGS "-DENABLE_SPARSE_SCALED_MM_C3X=1")
-    message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_3X_ARCHS}")
+    message(STATUS "Building sparse_scaled_mm_c3x for archs: ${SCALED_MM_ARCHS}")
   else()
-    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_3X_ARCHS)
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.2 AND SCALED_MM_ARCHS)
       message(STATUS "Not building sparse_scaled_mm_c3x kernels as CUDA Compiler version is "
                      "not >= 12.2, we recommend upgrading to CUDA 12.2 or later "
                      "if you intend on running FP8 sparse quantized models on Hopper.")
@@ -432,22 +461,6 @@ if(VLLM_GPU_LANG STREQUAL "CUDA")
     set(FP4_ARCHS)
   endif()
 
-  # FP8 Blackwell Archs 
-  cuda_archs_loose_intersection(BLACKWELL_ARCHS "10.0;10.1;12.0" "${CUDA_ARCHS}")
-  if(${CMAKE_CUDA_COMPILER_VERSION} VERSION_GREATER 12.8 AND BLACKWELL_ARCHS)
-    set(SRCS 
-      "csrc/quantization/cutlass_w8a8/c3x/scaled_mm_sm100_fp8.cu"
-    )
-    set_gencode_flags_for_srcs(
-      SRCS "${SRCS}"
-      CUDA_ARCHS "${BLACKWELL_ARCHS}")
-    list(APPEND VLLM_EXT_SRC "${SRCS}")
-    message(STATUS "Building FP8 for archs: ${BLACKWELL_ARCHS}")
-  else()
-    # clear BLACKWELL_ARCHS
-    set(BLACKWELL_ARCHS)
-  endif()
-  
   #
   # Machete kernels
 
@@ -548,11 +561,23 @@ set(VLLM_MOE_EXT_SRC
   "csrc/moe/moe_align_sum_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")
+endif()
+
 set_gencode_flags_for_srcs(
   SRCS "${VLLM_MOE_EXT_SRC}"
   CUDA_ARCHS "${CUDA_ARCHS}")
 
 if(VLLM_GPU_LANG STREQUAL "CUDA")
+  set(VLLM_MOE_WNA16_SRC
+    "csrc/moe/moe_wna16.cu")
+
+  set_gencode_flags_for_srcs(
+    SRCS "${VLLM_MOE_WNA16_SRC}"
+    CUDA_ARCHS "${CUDA_ARCHS}")
+
+  list(APPEND VLLM_MOE_EXT_SRC "${VLLM_MOE_WNA16_SRC}")
   cuda_archs_loose_intersection(MARLIN_MOE_ARCHS "8.0;8.6;8.7;8.9;9.0;10.0;10.1;12.0" "${CUDA_ARCHS}")
   if (MARLIN_MOE_ARCHS)
     set(MARLIN_MOE_SRC

Dockerfile

@@ -14,22 +14,21 @@ ARG PYTHON_VERSION=3.12
 ARG TARGETPLATFORM
 ENV DEBIAN_FRONTEND=noninteractive
 
-# Install Python and other dependencies
-RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
-    && echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
-    && apt-get update -y \
-    && apt-get install -y ccache software-properties-common git curl sudo \
-    && add-apt-repository ppa:deadsnakes/ppa \
-    && apt-get update -y \
-    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
-    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
-    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
-    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
-    && python3 --version && python3 -m pip --version
-# Install uv for faster pip installs
-RUN --mount=type=cache,target=/root/.cache/uv \
-    python3 -m pip install uv
+# Install minimal dependencies and uv
+RUN apt-get update -y \
+    && apt-get install -y ccache git curl wget sudo \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Add uv to PATH
+ENV PATH="/root/.local/bin:$PATH"
+# Create venv with specified Python and activate by placing at the front of path
+ENV VIRTUAL_ENV="/opt/venv"
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
 
 # Upgrade to GCC 10 to avoid https://gcc.gnu.org/bugzilla/show_bug.cgi?id=92519
 # as it was causing spam when compiling the CUTLASS kernels
@@ -47,21 +46,19 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 
 WORKDIR /workspace
 
-# install build and runtime dependencies
-
 # arm64 (GH200) build follows the practice of "use existing pytorch" build,
 # we need to install torch and torchvision from the nightly builds first,
 # pytorch will not appear as a vLLM dependency in all of the following steps
 # after this step
 RUN --mount=type=cache,target=/root/.cache/uv \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121";  \
+        uv pip install --index-url https://download.pytorch.org/whl/nightly/cu126 "torch==2.7.0.dev20250121+cu126" "torchvision==0.22.0.dev20250121";  \
     fi
 
-COPY requirements-common.txt requirements-common.txt
-COPY requirements-cuda.txt requirements-cuda.txt
+COPY requirements/common.txt requirements/common.txt
+COPY requirements/cuda.txt requirements/cuda.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements-cuda.txt
+    uv pip install -r requirements/cuda.txt
 
 # cuda arch list used by torch
 # can be useful for both `dev` and `test`
@@ -79,15 +76,19 @@ FROM base AS build
 ARG TARGETPLATFORM
 
 # install build dependencies
-COPY requirements-build.txt requirements-build.txt
+COPY requirements/build.txt requirements/build.txt
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements-build.txt
+    uv pip install -r requirements/build.txt
 
 COPY . .
 ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
-    if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh ; fi
+    if [ "$GIT_REPO_CHECK" != "0" ]; then bash tools/check_repo.sh ; fi
 
 # max jobs used by Ninja to build extensions
 ARG max_jobs=2
@@ -124,6 +125,9 @@ RUN --mount=type=cache,target=/root/.cache/ccache \
     --mount=type=cache,target=/root/.cache/uv \
     --mount=type=bind,source=.git,target=.git  \
     if [ "$USE_SCCACHE" != "1" ]; then \
+        # Clean any existing CMake artifacts
+        rm -rf .deps && \
+        mkdir -p .deps && \
         python3 setup.py bdist_wheel --dist-dir=dist --py-limited-api=cp38; \
     fi
 
@@ -143,11 +147,15 @@ RUN if [ "$RUN_WHEEL_CHECK" = "true" ]; then \
 #################### DEV IMAGE ####################
 FROM base as dev
 
-COPY requirements-lint.txt requirements-lint.txt
-COPY requirements-test.txt requirements-test.txt
-COPY requirements-dev.txt requirements-dev.txt
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+
+COPY requirements/lint.txt requirements/lint.txt
+COPY requirements/test.txt requirements/test.txt
+COPY requirements/dev.txt requirements/dev.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements-dev.txt
+    uv pip install -r requirements/dev.txt
 #################### DEV IMAGE ####################
 
 #################### vLLM installation IMAGE ####################
@@ -163,23 +171,22 @@ ARG TARGETPLATFORM
 RUN PYTHON_VERSION_STR=$(echo ${PYTHON_VERSION} | sed 's/\.//g') && \
     echo "export PYTHON_VERSION_STR=${PYTHON_VERSION_STR}" >> /etc/environment
 
-# Install Python and other dependencies
-RUN echo 'tzdata tzdata/Areas select America' | debconf-set-selections \
-    && echo 'tzdata tzdata/Zones/America select Los_Angeles' | debconf-set-selections \
-    && apt-get update -y \
-    && apt-get install -y ccache software-properties-common git curl wget sudo vim python3-pip \
-    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 \
-    && add-apt-repository ppa:deadsnakes/ppa \
-    && apt-get update -y \
-    && apt-get install -y python${PYTHON_VERSION} python${PYTHON_VERSION}-dev python${PYTHON_VERSION}-venv libibverbs-dev \
-    && update-alternatives --install /usr/bin/python3 python3 /usr/bin/python${PYTHON_VERSION} 1 \
-    && update-alternatives --set python3 /usr/bin/python${PYTHON_VERSION} \
-    && ln -sf /usr/bin/python${PYTHON_VERSION}-config /usr/bin/python3-config \
-    && curl -sS https://bootstrap.pypa.io/get-pip.py | python${PYTHON_VERSION} \
-    && python3 --version && python3 -m pip --version
-# Install uv for faster pip installs
-RUN --mount=type=cache,target=/root/.cache/uv \
-    python3 -m pip install uv
+# Install minimal dependencies and uv
+RUN apt-get update -y \
+    && apt-get install -y ccache git curl wget sudo vim \
+    && apt-get install -y ffmpeg libsm6 libxext6 libgl1 libibverbs-dev \
+    && curl -LsSf https://astral.sh/uv/install.sh | sh
+
+# Add uv to PATH
+ENV PATH="/root/.local/bin:$PATH"
+# Create venv with specified Python and activate by placing at the front of path
+ENV VIRTUAL_ENV="/opt/venv"
+RUN uv venv --python ${PYTHON_VERSION} --seed ${VIRTUAL_ENV}
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
 
 # Workaround for https://github.com/openai/triton/issues/2507 and
 # https://github.com/pytorch/pytorch/issues/107960 -- hopefully
@@ -193,13 +200,13 @@ RUN ldconfig /usr/local/cuda-$(echo $CUDA_VERSION | cut -d. -f1,2)/compat/
 # after this step
 RUN --mount=type=cache,target=/root/.cache/uv \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
+        uv pip install --index-url https://download.pytorch.org/whl/nightly/cu124 "torch==2.6.0.dev20241210+cu124" "torchvision==0.22.0.dev20241215";  \
     fi
 
 # Install vllm wheel first, so that torch etc will be installed.
 RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist \
     --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system dist/*.whl --verbose
+    uv pip install dist/*.whl --verbose
 
 # If we need to build FlashInfer wheel before its release:
 # $ export FLASHINFER_ENABLE_AOT=1
@@ -214,9 +221,8 @@ RUN --mount=type=bind,from=build,src=/workspace/dist,target=/vllm-workspace/dist
 # $ # upload the wheel to a public location, e.g. https://wheels.vllm.ai/flashinfer/524304395bd1d8cd7d07db083859523fcaa246a4/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl
 
 RUN --mount=type=cache,target=/root/.cache/uv \
-. /etc/environment && \
 if [ "$TARGETPLATFORM" != "linux/arm64" ]; then \
-    uv pip install --system https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post1/flashinfer_python-0.2.1.post1+cu124torch2.5-cp38-abi3-linux_x86_64.whl ; \
+    uv pip install https://github.com/flashinfer-ai/flashinfer/releases/download/v0.2.1.post2/flashinfer_python-0.2.1.post2+cu124torch2.6-cp38-abi3-linux_x86_64.whl ; \
 fi
 COPY examples examples
 
@@ -224,9 +230,9 @@ COPY examples examples
 # some issues w.r.t. JIT compilation. Therefore we need to
 # install build dependencies for JIT compilation.
 # TODO: Remove this once FlashInfer AOT wheel is fixed
-COPY requirements-build.txt requirements-build.txt
+COPY requirements/build.txt requirements/build.txt
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements-build.txt
+    uv pip install -r requirements/build.txt
 
 #################### vLLM installation IMAGE ####################
 
@@ -237,17 +243,21 @@ FROM vllm-base AS test
 
 ADD . /vllm-workspace/
 
-# install development dependencies (for testing)
-RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -r requirements-dev.txt
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
 
 # install development dependencies (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system -e tests/vllm_test_utils
+    uv pip install -r requirements/dev.txt
+
+# install development dependencies (for testing)
+RUN --mount=type=cache,target=/root/.cache/uv \
+    uv pip install -e tests/vllm_test_utils
 
 # enable fast downloads from hf (for testing)
 RUN --mount=type=cache,target=/root/.cache/uv \
-    uv pip install --system hf_transfer
+    uv pip install hf_transfer
 ENV HF_HUB_ENABLE_HF_TRANSFER 1
 
 # Copy in the v1 package for testing (it isn't distributed yet)
@@ -265,12 +275,16 @@ RUN mv vllm test_docs/
 # base openai image with additional requirements, for any subsequent openai-style images
 FROM vllm-base AS vllm-openai-base
 
+# This timeout (in seconds) is necessary when installing some dependencies via uv since it's likely to time out
+# Reference: https://github.com/astral-sh/uv/pull/1694
+ENV UV_HTTP_TIMEOUT=500
+
 # install additional dependencies for openai api server
 RUN --mount=type=cache,target=/root/.cache/uv \
     if [ "$TARGETPLATFORM" = "linux/arm64" ]; then \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
+        uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.42.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     else \
-        uv pip install --system accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
+        uv pip install accelerate hf_transfer 'modelscope!=1.15.0' 'bitsandbytes>=0.45.0' 'timm==0.9.10' boto3 runai-model-streamer runai-model-streamer[s3]; \
     fi
 
 ENV VLLM_USAGE_SOURCE production-docker-image

Dockerfile.arm

@@ -26,18 +26,18 @@ WORKDIR /workspace
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements-build.txt,target=requirements-build.txt \
+    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
     pip install --upgrade pip && \
-    pip install -r requirements-build.txt
+    pip install -r requirements/build.txt
 
 FROM cpu-test-arm AS build
 
 WORKDIR /workspace/vllm
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements-common.txt,target=requirements-common.txt \
-    --mount=type=bind,src=requirements-cpu.txt,target=requirements-cpu.txt \
-    pip install -v -r requirements-cpu.txt
+    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
+    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
+    pip install -v -r requirements/cpu.txt
 
 COPY . .
 ARG GIT_REPO_CHECK=0

Dockerfile.cpu

@@ -22,25 +22,25 @@ ENV LD_PRELOAD="/usr/lib/x86_64-linux-gnu/libtcmalloc_minimal.so.4:/usr/local/li
 
 RUN echo 'ulimit -c 0' >> ~/.bashrc
 
-RUN pip install intel_extension_for_pytorch==2.5.0
+RUN pip install intel_extension_for_pytorch==2.6.0
 
 WORKDIR /workspace
 
 ARG PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu"
 ENV PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}
 RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements-build.txt,target=requirements-build.txt \
+    --mount=type=bind,src=requirements/build.txt,target=requirements/build.txt \
     pip install --upgrade pip && \
-    pip install -r requirements-build.txt
+    pip install -r requirements/build.txt
 
 FROM cpu-test-1 AS build
 
 WORKDIR /workspace/vllm
 
 RUN --mount=type=cache,target=/root/.cache/pip \
-    --mount=type=bind,src=requirements-common.txt,target=requirements-common.txt \
-    --mount=type=bind,src=requirements-cpu.txt,target=requirements-cpu.txt \
-    pip install -v -r requirements-cpu.txt
+    --mount=type=bind,src=requirements/common.txt,target=requirements/common.txt \
+    --mount=type=bind,src=requirements/cpu.txt,target=requirements/cpu.txt \
+    pip install -v -r requirements/cpu.txt
 
 COPY . .
 ARG GIT_REPO_CHECK=0

Dockerfile.hpu

@@ -4,7 +4,7 @@ COPY ./ /workspace/vllm
 
 WORKDIR /workspace/vllm
 
-RUN pip install -v -r requirements-hpu.txt
+RUN pip install -v -r requirements/hpu.txt
 
 ENV no_proxy=localhost,127.0.0.1
 ENV PT_HPU_ENABLE_LAZY_COLLECTIVES=true

Dockerfile.neuron

@@ -36,7 +36,7 @@ RUN --mount=type=bind,source=.git,target=.git \
 
 RUN python3 -m pip install -U \
         'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
-        -r requirements-neuron.txt
+        -r requirements/neuron.txt
 
 ENV VLLM_TARGET_DEVICE neuron
 RUN --mount=type=bind,source=.git,target=.git \

Dockerfile.openvino

@@ -16,7 +16,7 @@ RUN --mount=type=bind,source=.git,target=.git \
 
 RUN python3 -m pip install -U pip
 # install build requirements
-RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements-build.txt
+RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" python3 -m pip install -r /workspace/requirements/build.txt
 # build vLLM with OpenVINO backend
 RUN PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cpu" VLLM_TARGET_DEVICE="openvino" python3 -m pip install /workspace
 

Dockerfile.ppc64le

@@ -6,7 +6,7 @@ ENV PATH="/usr/local/cargo/bin:$PATH:/opt/conda/bin/"
 
 RUN apt-get update -y && apt-get install -y git wget kmod curl vim libnuma-dev libsndfile-dev libprotobuf-dev build-essential ffmpeg libsm6 libxext6 libgl1 libssl-dev 
 
-# Some packages in requirements-cpu are installed here
+# Some packages in requirements/cpu are installed here
 # IBM provides optimized packages for ppc64le processors in the open-ce project for mamba
 # Currently these may not be available for venv or pip directly
 RUN micromamba install -y -n base -c https://ftp.osuosl.org/pub/open-ce/1.11.0-p10/ -c defaults python=3.10 rust && micromamba clean --all --yes
@@ -21,7 +21,7 @@ RUN --mount=type=bind,source=.git,target=.git \
 RUN --mount=type=cache,target=/root/.cache/pip  \
     RUSTFLAGS='-L /opt/conda/lib' pip install -v --prefer-binary --extra-index-url https://repo.fury.io/mgiessing \
         'cmake>=3.26' ninja packaging 'setuptools-scm>=8' wheel jinja2 \
-        -r requirements-cpu.txt \
+        -r requirements/cpu.txt \
         xformers uvloop==0.20.0
 
 RUN --mount=type=bind,source=.git,target=.git \

Dockerfile.rocm

@@ -38,14 +38,14 @@ FROM fetch_vllm AS build_vllm
 ARG USE_CYTHON
 # Build vLLM
 RUN cd vllm \
-    && python3 -m pip install -r requirements-rocm.txt \
+    && python3 -m pip install -r requirements/rocm.txt \
     && python3 setup.py clean --all  \
     && if [ ${USE_CYTHON} -eq "1" ]; then python3 setup_cython.py build_ext --inplace; fi \
     && python3 setup.py bdist_wheel --dist-dir=dist
 FROM scratch AS export_vllm
 ARG COMMON_WORKDIR
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/dist/*.whl /
-COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/requirements*.txt /
+COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/requirements /requirements
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/benchmarks /benchmarks
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/tests /tests
 COPY --from=build_vllm ${COMMON_WORKDIR}/vllm/examples /examples
@@ -60,7 +60,8 @@ RUN python3 -m pip install --upgrade pip && rm -rf /var/lib/apt/lists/*
 # Install vLLM
 RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
     cd /install \
-    && pip install -U -r requirements-rocm.txt \
+    && pip install -U -r requirements/rocm.txt \
+    && pip install -U -r requirements/rocm-test.txt \
     && pip uninstall -y vllm \
     && pip install *.whl
 
@@ -99,7 +100,7 @@ RUN if [ ${BUILD_RPD} -eq "1" ]; then \
 # Install vLLM
 RUN --mount=type=bind,from=export_vllm,src=/,target=/install \
     cd /install \
-    && pip install -U -r requirements-rocm.txt \
+    && pip install -U -r requirements/rocm.txt \
     && pip uninstall -y vllm \
     && pip install *.whl
 

Dockerfile.s390x

@@ -0,0 +1,152 @@
+# Base UBI image for s390x architecture
+ARG BASE_UBI_IMAGE_TAG=9.5-1736404155
+ARG PYTHON_VERSION=3.12
+FROM registry.access.redhat.com/ubi9/ubi-minimal:${BASE_UBI_IMAGE_TAG} AS base
+
+# Install basic dependencies
+ARG PYTHON_VERSION
+ENV PYTHON_VERSION=${PYTHON_VERSION}
+
+WORKDIR /workspace
+
+ENV LANG=C.UTF-8 \
+    LC_ALL=C.UTF-8
+
+# Install development utilities
+RUN microdnf install -y \
+    which procps findutils tar vim git gcc gcc-gfortran g++ make patch zlib-devel \
+    libjpeg-turbo-devel libtiff-devel libpng-devel libwebp-devel freetype-devel harfbuzz-devel \
+    openssl-devel openblas openblas-devel autoconf automake libtool cmake && \
+    microdnf clean all
+
+# Python Installation
+FROM base AS python-install
+ARG PYTHON_VERSION
+
+ENV VIRTUAL_ENV=/opt/vllm
+ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+ENV PYTHON_VERSION=${PYTHON_VERSION}
+RUN microdnf install -y \
+    python${PYTHON_VERSION}-devel python${PYTHON_VERSION}-pip python${PYTHON_VERSION}-wheel  && \
+    python${PYTHON_VERSION} -m venv $VIRTUAL_ENV && pip install --no-cache -U pip wheel uv && microdnf clean all
+
+FROM python-install AS pyarrow
+
+# Build Apache Arrow
+WORKDIR /tmp
+RUN --mount=type=cache,target=/root/.cache/uv \
+    git clone https://github.com/apache/arrow.git && \
+    cd arrow/cpp && \
+    mkdir release && cd release && \
+    cmake -DCMAKE_BUILD_TYPE=Release \
+          -DCMAKE_INSTALL_PREFIX=/usr/local \
+          -DARROW_PYTHON=ON \
+          -DARROW_PARQUET=ON \
+          -DARROW_ORC=ON \
+          -DARROW_FILESYSTEM=ON \
+          -DARROW_WITH_LZ4=ON \
+          -DARROW_WITH_ZSTD=ON \
+          -DARROW_WITH_SNAPPY=ON \
+          -DARROW_JSON=ON \
+          -DARROW_CSV=ON \
+          -DARROW_DATASET=ON \
+          -DPROTOBUF_PROTOC_EXECUTABLE=/usr/bin/protoc \
+          -DARROW_DEPENDENCY_SOURCE=BUNDLED \
+          .. && \
+    make -j$(nproc) && \
+    make install && \
+    cd ../../python && \
+    export PYARROW_PARALLEL=4 && \
+    export ARROW_BUILD_TYPE=release && \
+    uv pip install -r requirements/build.txt && \
+    python setup.py build_ext --build-type=$ARROW_BUILD_TYPE --bundle-arrow-cpp bdist_wheel
+
+FROM python-install AS numa-build
+# Install numactl (needed for numa.h dependency)
+WORKDIR /tmp
+RUN curl -LO https://github.com/numactl/numactl/archive/refs/tags/v2.0.16.tar.gz && \
+    tar -xvzf v2.0.16.tar.gz && \
+    cd numactl-2.0.16 && \
+    ./autogen.sh && \
+    ./configure && \
+    make
+
+# Set include path
+ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
+
+FROM python-install AS rust
+ENV CARGO_HOME=/root/.cargo
+ENV RUSTUP_HOME=/root/.rustup
+ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
+
+RUN curl https://sh.rustup.rs -sSf | sh -s -- -y && \
+    . "$CARGO_HOME/env" && \
+    rustup default stable && \
+    rustup show
+
+FROM python-install AS torch-vision
+# Install torchvision
+ARG TORCH_VERSION=2.7.0.dev20250304
+ARG TORCH_VISION_VERSION=v0.20.1
+WORKDIR /tmp
+RUN --mount=type=cache,target=/root/.cache/uv \
+    git clone https://github.com/pytorch/vision.git && \
+    cd vision && \
+    git checkout $TORCH_VISION_VERSION && \
+    uv pip install -v torch==${TORCH_VERSION} --extra-index-url https://download.pytorch.org/whl/nightly/cpu && \
+    python setup.py bdist_wheel
+
+# Final build stage
+FROM python-install AS vllm-cpu
+ARG PYTHON_VERSION
+
+# Set correct library path for torch and numactl
+ENV LD_LIBRARY_PATH="/opt/vllm/lib64/python${PYTHON_VERSION}/site-packages/torch/lib:/usr/local/lib:$LD_LIBRARY_PATH"
+ENV C_INCLUDE_PATH="/usr/local/include:$C_INCLUDE_PATH"
+ENV UV_LINK_MODE=copy
+ENV CARGO_HOME=/root/.cargo
+ENV RUSTUP_HOME=/root/.rustup
+ENV PATH="$CARGO_HOME/bin:$RUSTUP_HOME/bin:$PATH"
+
+COPY . /workspace/vllm
+WORKDIR /workspace/vllm
+
+RUN --mount=type=bind,from=numa-build,src=/tmp/numactl-2.0.16,target=/numactl \
+    make -C /numactl install
+
+# Install dependencies, including PyTorch and Apache Arrow
+RUN --mount=type=cache,target=/root/.cache/uv \
+    --mount=type=bind,from=rust,source=/root/.cargo,target=/root/.cargo,rw \
+    --mount=type=bind,from=rust,source=/root/.rustup,target=/root/.rustup,rw \
+    --mount=type=bind,from=pyarrow,source=/tmp/arrow/python/dist,target=/tmp/arrow-wheels \
+    --mount=type=bind,from=torch-vision,source=/tmp/vision/dist,target=/tmp/vision-wheels/ \
+     sed -i '/^torch/d' requirements/build.txt && \
+     ARROW_WHL_FILE=$(ls /tmp/arrow-wheels/pyarrow-*.whl | head -n 1) && \
+     VISION_WHL_FILE=$(ls /tmp/vision-wheels/*.whl | head -n 1) && \
+    uv pip install -v \    
+        $ARROW_WHL_FILE  \
+        $VISION_WHL_FILE \
+        --extra-index-url https://download.pytorch.org/whl/nightly/cpu \
+        --index-strategy unsafe-best-match \
+        -r requirements/build.txt \
+        -r requirements/cpu.txt 
+
+# Build and install vllm
+RUN --mount=type=cache,target=/root/.cache/uv \
+    VLLM_TARGET_DEVICE=cpu python setup.py bdist_wheel && \
+    uv pip install "$(echo dist/*.whl)[tensorizer]"
+
+# setup non-root user for vllm
+RUN umask 002 && \
+    useradd --uid 2000 --gid 0 vllm && \
+    mkdir -p /home/vllm && \
+    chmod g+rwx /home/vllm
+
+COPY LICENSE /licenses/vllm.md
+COPY examples/*.jinja /app/data/template/
+
+USER 2000
+WORKDIR /home/vllm
+
+# Set the default entrypoint
+ENTRYPOINT ["python", "-m", "vllm.entrypoints.openai.api_server"]

Dockerfile.tpu

@@ -15,11 +15,14 @@ ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
     if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
 
+# Remove existing versions of dependencies
+RUN pip uninstall -y torch torch_xla torchvision
+
 ENV VLLM_TARGET_DEVICE="tpu"
 RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
     python3 -m pip install \
-        -r requirements-tpu.txt
+        -r requirements/tpu.txt
 RUN python3 setup.py develop
 
 # install development dependencies (for testing)

Dockerfile.xpu

@@ -1,4 +1,4 @@
-FROM intel/oneapi-basekit:2024.2.1-0-devel-ubuntu22.04 AS vllm-base
+FROM intel/deep-learning-essentials:2025.0.1-0-devel-ubuntu22.04 AS vllm-base
 
 RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \
     echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \
@@ -21,30 +21,22 @@ RUN apt-get update -y && \
     python3 \
     python3-dev \
     python3-pip \
-    # vim \
+    libze-intel-gpu-dev \
+    libze-intel-gpu1 \
     wget
 
 WORKDIR /workspace/vllm
-COPY requirements-xpu.txt /workspace/vllm/requirements-xpu.txt
-COPY requirements-common.txt /workspace/vllm/requirements-common.txt
+COPY requirements/xpu.txt /workspace/vllm/requirements/xpu.txt
+COPY requirements/common.txt /workspace/vllm/requirements/common.txt
 
 RUN --mount=type=cache,target=/root/.cache/pip \
     pip install --no-cache-dir \
-    -r requirements-xpu.txt
-
-RUN git clone https://github.com/intel/pti-gpu && \
-    cd pti-gpu/sdk && \
-    git checkout 6c491f07a777ed872c2654ca9942f1d0dde0a082 && \
-    mkdir build && \
-    cd build && \
-    cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/icpx_toolchain.cmake -DBUILD_TESTING=OFF .. && \
-    make -j && \
-    cmake --install . --config Release --prefix "/usr/local"
+    -r requirements/xpu.txt
 
 ENV LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/lib/"
 
 COPY . .
-ARG GIT_REPO_CHECK
+ARG GIT_REPO_CHECK=0
 RUN --mount=type=bind,source=.git,target=.git \
     if [ "$GIT_REPO_CHECK" != 0 ]; then bash tools/check_repo.sh; fi
 
@@ -54,6 +46,12 @@ RUN --mount=type=cache,target=/root/.cache/pip \
     --mount=type=bind,source=.git,target=.git \
     python3 setup.py install
 
+# Please refer xpu doc, we need manually install intel-extension-for-pytorch 2.6.10+xpu due to there are some conflict dependencies with torch 2.6.0+xpu
+# FIXME: This will be fix in ipex 2.7. just leave this here for awareness.
+RUN --mount=type=cache,target=/root/.cache/pip \
+    pip install intel-extension-for-pytorch==2.6.10+xpu \
+    --extra-index-url=https://pytorch-extension.intel.com/release-whl/stable/xpu/us/
+
 CMD ["/bin/bash"]
 
 FROM vllm-base AS vllm-openai

MANIFEST.in

@@ -1,9 +1,9 @@
 include LICENSE
-include requirements-common.txt
-include requirements-cuda.txt
-include requirements-rocm.txt
-include requirements-neuron.txt
-include requirements-cpu.txt
+include requirements/common.txt
+include requirements/cuda.txt
+include requirements/rocm.txt
+include requirements/neuron.txt
+include requirements/cpu.txt
 include CMakeLists.txt
 
 recursive-include cmake *

README.md

@@ -13,18 +13,11 @@ Easy, fast, and cheap LLM serving for everyone
 | <a href="https://docs.vllm.ai"><b>Documentation</b></a> | <a href="https://vllm.ai"><b>Blog</b></a> | <a href="https://arxiv.org/abs/2309.06180"><b>Paper</b></a> | <a href="https://x.com/vllm_project"><b>Twitter/X</b></a> | <a href="https://slack.vllm.ai"><b>Developer Slack</b></a> |
 </p>
 
----
-
-We’re excited to invite you to the first **vLLM China Meetup** on **March 16** in **Beijing**!  
-
-Join us to connect with the **vLLM team** and explore how vLLM is leveraged in **post-training, fine-tuning, and deployment**, including [verl](https://github.com/volcengine/verl), [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory), and [vllm-ascend](https://github.com/vllm-project/vllm-ascend).
-
-👉 **[Register Now](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)** to be part of the discussion!  
-
----
-
 *Latest News* 🔥
 
+- [2025/03] We hosted [the first vLLM China Meetup](https://mp.weixin.qq.com/s/n77GibL2corAtQHtVEAzfg)! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1REHvfQMKGnvz6p3Fd23HhSO4c8j5WPGZV0bKYLwnHyQ/edit#slide=id.g33fb1ff286e_0_29).
+- [2025/03] We hosted [the East Coast vLLM Meetup](https://lu.ma/7mu4k4xx)! Please find the meetup slides [here](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0).
+- [2025/02] We hosted [the ninth vLLM meetup](https://lu.ma/h7g3kuj9) with Meta! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing) and AMD [here](https://drive.google.com/file/d/1Zk5qEJIkTmlQ2eQcXQZlljAx3m9s7nwn/view?usp=sharing). The slides from Meta will not be posted.
 - [2025/01] We are excited to announce the alpha release of vLLM V1: A major architectural upgrade with 1.7x speedup! Clean code, optimized execution loop, zero-overhead prefix caching, enhanced multimodal support, and more. Please check out our blog post [here](https://blog.vllm.ai/2025/01/27/v1-alpha-release.html).
 - [2025/01] We hosted [the eighth vLLM meetup](https://lu.ma/zep56hui) with Google Cloud! Please find the meetup slides from vLLM team [here](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing), and Google Cloud team [here](https://drive.google.com/file/d/1h24pHewANyRL11xy5dXUbvRC9F9Kkjix/view?usp=sharing).
 - [2024/12] vLLM joins [pytorch ecosystem](https://pytorch.org/blog/vllm-joins-pytorch)! Easy, Fast, and Cheap LLM Serving for Everyone!
@@ -90,7 +83,7 @@ pip install vllm
 ```
 
 Visit our [documentation](https://docs.vllm.ai/en/latest/) to learn more.
-- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation/index.html)
+- [Installation](https://docs.vllm.ai/en/latest/getting_started/installation.html)
 - [Quickstart](https://docs.vllm.ai/en/latest/getting_started/quickstart.html)
 - [List of Supported Models](https://docs.vllm.ai/en/latest/models/supported_models.html)
 
@@ -150,9 +143,9 @@ If you use vLLM for your research, please cite our [paper](https://arxiv.org/abs
 
 ## Contact Us
 
-- For technical questions and feature requests, please use Github issues or discussions.
+- For technical questions and feature requests, please use GitHub issues or discussions.
 - For discussing with fellow users and coordinating contributions and development, please use Slack.
-- For security disclosures, please use Github's security advisory feature.
+- For security disclosures, please use GitHub's security advisory feature.
 - For collaborations and partnerships, please contact us at vllm-questions AT lists.berkeley.edu.
 
 ## Media Kit

benchmarks/README.md

@@ -1,29 +1,217 @@
 # Benchmarking vLLM
 
-## Downloading the ShareGPT dataset
+This README guides you through running benchmark tests with the extensive
+datasets supported on vLLM. It’s a living document, updated as new features and datasets
+become available.
 
-You can download the dataset by running:
+## Dataset Overview
+
+<table style="width:100%; border-collapse: collapse;">
+  <thead>
+    <tr>
+      <th style="width:15%; text-align: left;">Dataset</th>
+      <th style="width:10%; text-align: center;">Online</th>
+      <th style="width:10%; text-align: center;">Offline</th>
+      <th style="width:65%; text-align: left;">Data Path</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>ShareGPT</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json</code></td>
+    </tr>
+    <tr>
+      <td><strong>BurstGPT</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv</code></td>
+    </tr>
+    <tr>
+      <td><strong>Sonnet</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td>Local file: <code>benchmarks/sonnet.txt</code></td>
+    </tr>
+    <tr>
+      <td><strong>Random</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>synthetic</code></td>
+    </tr>
+    <tr>
+      <td><strong>HuggingFace</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">🟡</td>
+      <td>Specify your dataset path on HuggingFace</td>
+    </tr>
+    <tr>
+      <td><strong>VisionArena</strong></td>
+      <td style="text-align: center;">✅</td>
+      <td style="text-align: center;">✅</td>
+      <td><code>lmarena-ai/vision-arena-bench-v0.1</code> (a HuggingFace dataset)</td>
+    </tr>
+  </tbody>
+</table>
+
+✅: supported
+
+🚧: to be supported
+
+🟡: Partial support. Currently, HuggingFaceDataset only supports dataset formats
+similar to `lmms-lab/LLaVA-OneVision-Data`. If you need support for other dataset
+formats, please consider contributing.
+
+**Note**: VisionArena’s `dataset-name` should be set to `hf`
+
+---
+## Example - Online Benchmark
+
+First start serving your model
 
 ```bash
-wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+vllm serve ${MODEL_NAME} --disable-log-requests
 ```
 
-## Downloading the ShareGPT4V dataset
-
-The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
-will ignore a datapoint if the referred image is missing.
+Then run the benchmarking script
 
 ```bash
-wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
-mkdir coco -p
-wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
-unzip coco/train2017.zip -d coco/
+# download dataset
+# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+NUM_PROMPTS=10
+BACKEND="vllm"
+DATASET_NAME="sharegpt"
+DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
+python3 vllm/benchmarks/benchmark_serving.py --backend ${BACKEND} --model ${MODEL_NAME} --endpoint /v1/completions --dataset-name ${DATASET_NAME} --dataset-path ${DATASET_PATH} --num-prompts ${NUM_PROMPTS}
 ```
 
-# Downloading the BurstGPT dataset
+If successful, you will see the following output
 
-You can download the BurstGPT v1.1 dataset by running:
+```
+============ Serving Benchmark Result ============
+Successful requests:                     10        
+Benchmark duration (s):                  5.78      
+Total input tokens:                      1369      
+Total generated tokens:                  2212      
+Request throughput (req/s):              1.73      
+Output token throughput (tok/s):         382.89    
+Total Token throughput (tok/s):          619.85    
+---------------Time to First Token----------------
+Mean TTFT (ms):                          71.54     
+Median TTFT (ms):                        73.88     
+P99 TTFT (ms):                           79.49     
+-----Time per Output Token (excl. 1st token)------
+Mean TPOT (ms):                          7.91      
+Median TPOT (ms):                        7.96      
+P99 TPOT (ms):                           8.03      
+---------------Inter-token Latency----------------
+Mean ITL (ms):                           7.74      
+Median ITL (ms):                         7.70      
+P99 ITL (ms):                            8.39      
+==================================================
+```
+
+### VisionArena Benchmark for Vision Language Models
 
 ```bash
-wget https://github.com/HPMLL/BurstGPT/releases/download/v1.1/BurstGPT_without_fails_2.csv
+# need a model with vision capability here
+vllm serve Qwen/Qwen2-VL-7B-Instruct --disable-log-requests
+```
+
+```bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+BACKEND="openai-chat"
+DATASET_NAME="hf"
+DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
+DATASET_SPLIT='train'
+
+python3 vllm/benchmarks/benchmark_serving.py \
+  --backend "${BACKEND}" \
+  --model "${MODEL_NAME}" \
+  --endpoint "/v1/chat/completions" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --hf-split "${DATASET_SPLIT}" \
+  --num-prompts "${NUM_PROMPTS}"
+```
+
+---
+## Example - Offline Throughput Benchmark
+
+```bash
+MODEL_NAME="NousResearch/Hermes-3-Llama-3.1-8B"
+NUM_PROMPTS=10
+DATASET_NAME="sonnet"
+DATASET_PATH="vllm/benchmarks/sonnet.txt"
+
+python3 vllm/benchmarks/benchmark_throughput.py \
+  --model "${MODEL_NAME}" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --num-prompts "${NUM_PROMPTS}"
+```
+
+If successful, you will see the following output
+
+```
+Throughput: 7.15 requests/s, 4656.00 total tokens/s, 1072.15 output tokens/s
+Total num prompt tokens:  5014
+Total num output tokens:  1500
+```
+
+### VisionArena Benchmark for Vision Language Models
+
+``` bash
+MODEL_NAME="Qwen/Qwen2-VL-7B-Instruct"
+NUM_PROMPTS=10
+DATASET_NAME="hf"
+DATASET_PATH="lmarena-ai/vision-arena-bench-v0.1"
+DATASET_SPLIT="train"
+
+python3 vllm/benchmarks/benchmark_throughput.py \
+  --model "${MODEL_NAME}" \
+  --backend "vllm-chat" \
+  --dataset-name "${DATASET_NAME}" \
+  --dataset-path "${DATASET_PATH}" \
+  --num-prompts "${NUM_PROMPTS}" \
+  --hf-split "${DATASET_SPLIT}"
+```
+
+The `num prompt tokens` now includes image token counts
+
+```
+Throughput: 2.55 requests/s, 4036.92 total tokens/s, 326.90 output tokens/s
+Total num prompt tokens:  14527
+Total num output tokens:  1280
+```
+
+### Benchmark with LoRA Adapters
+
+``` bash
+# download dataset
+# wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
+MODEL_NAME="meta-llama/Llama-2-7b-hf"
+BACKEND="vllm"
+DATASET_NAME="sharegpt"
+DATASET_PATH="<your data path>/ShareGPT_V3_unfiltered_cleaned_split.json"
+NUM_PROMPTS=10
+MAX_LORAS=2
+MAX_LORA_RANK=8
+ENABLE_LORA="--enable-lora"
+LORA_PATH="yard1/llama-2-7b-sql-lora-test"
+
+python3 vllm/benchmarks/benchmark_throughput.py \
+  --model "${MODEL_NAME}" \
+  --backend "${BACKEND}" \
+  --dataset_path "${DATASET_PATH}" \
+  --dataset_name "${DATASET_NAME}" \
+  --num-prompts "${NUM_PROMPTS}" \
+  --max-loras "${MAX_LORAS}" \
+  --max-lora-rank "${MAX_LORA_RANK}" \
+  ${ENABLE_LORA} \
+  --lora-path "${LORA_PATH}"
   ```

benchmarks/backend_request_func.py

@@ -14,7 +14,8 @@ from tqdm.asyncio import tqdm
 from transformers import (AutoTokenizer, PreTrainedTokenizer,
                           PreTrainedTokenizerFast)
 
-from vllm.model_executor.model_loader.weight_utils import get_lock
+# NOTE(simon): do not import vLLM here so the benchmark script
+# can run without vLLM installed.
 
 AIOHTTP_TIMEOUT = aiohttp.ClientTimeout(total=6 * 60 * 60)
 
@@ -27,7 +28,6 @@ class RequestFuncInput:
     output_len: int
     model: str
     model_name: Optional[str] = None
-    best_of: int = 1
     logprobs: Optional[int] = None
     extra_body: Optional[dict] = None
     multi_modal_content: Optional[dict] = None
@@ -58,7 +58,6 @@ async def async_request_tgi(
     async with aiohttp.ClientSession(trust_env=True,
                                      timeout=AIOHTTP_TIMEOUT) as session:
         params = {
-            "best_of": request_func_input.best_of,
             "max_new_tokens": request_func_input.output_len,
             "do_sample": True,
             "temperature": 0.01,  # TGI does not accept 0.0 temperature.
@@ -130,7 +129,6 @@ async def async_request_trt_llm(
 
     async with aiohttp.ClientSession(trust_env=True,
                                      timeout=AIOHTTP_TIMEOUT) as session:
-        assert request_func_input.best_of == 1
         payload = {
             "accumulate_tokens": True,
             "text_input": request_func_input.prompt,
@@ -195,7 +193,6 @@ async def async_request_deepspeed_mii(
 ) -> RequestFuncOutput:
     async with aiohttp.ClientSession(trust_env=True,
                                      timeout=AIOHTTP_TIMEOUT) as session:
-        assert request_func_input.best_of == 1
 
         payload = {
             "prompt": request_func_input.prompt,
@@ -249,7 +246,6 @@ async def async_request_openai_completions(
                 if request_func_input.model_name else request_func_input.model,
             "prompt": request_func_input.prompt,
             "temperature": 0.0,
-            "best_of": request_func_input.best_of,
             "max_tokens": request_func_input.output_len,
             "logprobs": request_func_input.logprobs,
             "stream": True,
@@ -338,7 +334,7 @@ async def async_request_openai_chat_completions(
 ) -> RequestFuncOutput:
     api_url = request_func_input.api_url
     assert api_url.endswith(
-        "chat/completions"
+        ("chat/completions", "profile")
     ), "OpenAI Chat Completions API URL must end with 'chat/completions'."
 
     async with aiohttp.ClientSession(trust_env=True,
@@ -432,6 +428,8 @@ def get_model(pretrained_model_name_or_path: str) -> str:
     if os.getenv('VLLM_USE_MODELSCOPE', 'False').lower() == 'true':
         from modelscope import snapshot_download
 
+        from vllm.model_executor.model_loader.weight_utils import get_lock
+
         # Use file lock to prevent multiple processes from
         # downloading the same model weights at the same time.
         with get_lock(pretrained_model_name_or_path):

benchmarks/benchmark_dataset.py

@@ -0,0 +1,688 @@
+# SPDX-License-Identifier: Apache-2.0
+"""
+This module defines a framework for sampling benchmark requests from various
+datasets. Each dataset subclass of BenchmarkDataset must implement sample
+generation. Supported dataset types include:
+  - ShareGPT
+  - Random (synthetic)
+  - Sonnet
+  - BurstGPT
+  - HuggingFace
+  - VisionArena
+
+TODO: Implement CustomDataset to parse a JSON file and convert its contents into
+SampleRequest instances, similar to the approach used in ShareGPT.
+"""
+
+import base64
+import io
+import json
+import random
+from abc import ABC, abstractmethod
+from collections.abc import Mapping
+from dataclasses import dataclass
+from functools import cache
+from typing import Any, Optional, Union
+
+import numpy as np
+import pandas as pd
+from datasets import load_dataset
+from PIL import Image
+from transformers import PreTrainedTokenizerBase
+
+from vllm.lora.request import LoRARequest
+from vllm.lora.utils import get_adapter_absolute_path
+from vllm.multimodal import MultiModalDataDict
+from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
+
+# -----------------------------------------------------------------------------
+# Data Classes
+# -----------------------------------------------------------------------------
+
+
+@dataclass
+class SampleRequest:
+    """
+    Represents a single inference request for benchmarking.
+    """
+
+    prompt: Union[str, Any]
+    prompt_len: int
+    expected_output_len: int
+    multi_modal_data: Optional[Union[MultiModalDataDict, dict]] = None
+    lora_request: Optional[LoRARequest] = None
+
+
+# -----------------------------------------------------------------------------
+# Benchmark Dataset Base Class
+# -----------------------------------------------------------------------------
+
+
+class BenchmarkDataset(ABC):
+    DEFAULT_SEED = 0
+
+    # num_requests has default 1000 in both the benchmark_serving.py and
+    # benchmark_throughput.py
+
+    def __init__(
+        self,
+        dataset_path: Optional[str] = None,
+        random_seed: int = DEFAULT_SEED,
+    ) -> None:
+        """
+        Initialize the BenchmarkDataset with an optional dataset path and random
+        seed.  Args:
+            dataset_path (Optional[str]): Path to the dataset. If None, it
+            indicates that a default or random dataset might be used.
+            random_seed (int): Seed value for reproducible shuffling or
+            sampling. Defaults to DEFAULT_SEED.
+        """
+        self.dataset_path = dataset_path
+        # Set the random seed, ensuring that a None value is replaced with the
+        # default seed.
+        self.random_seed = (random_seed
+                            if random_seed is not None else self.DEFAULT_SEED)
+        self.data = None
+
+    def apply_multimodal_chat_transformation(
+            self,
+            prompt: str,
+            mm_content: Optional[MultiModalDataDict] = None) -> list[dict]:
+        """
+        Transform a prompt and optional multimodal content into a chat format.
+        This method is used for chat models that expect a specific 
+        conversation format.
+        """
+        content = [{"text": prompt, "type": "text"}]
+        if mm_content is not None:
+            content.append(mm_content)
+        return [{"role": "user", "content": content}]
+
+    def load_data(self) -> None:
+        """
+        Load data from the dataset path into self.data.
+        
+        This method must be overridden by subclasses since the method to load
+        data will vary depending on the dataset format and source.
+        
+        Raises:
+            NotImplementedError: If a subclass does not implement this method.
+        """
+        # TODO (jenniferzhao): add support for downloading data
+        raise NotImplementedError(
+            "load_data must be implemented in subclasses.")
+
+    def get_random_lora_request(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        max_loras: Optional[int] = None,
+        lora_path: Optional[str] = None,
+    ) -> tuple[Optional[LoRARequest], AnyTokenizer]:
+        """
+        Optionally select a random LoRA request and return its associated
+        tokenizer.
+        
+        This method is used when LoRA parameters are provided.  It randomly
+        selects a LoRA based on max_loras and retrieves a cached tokenizer for
+        that LoRA if available. Otherwise, it returns the base tokenizer.
+        
+        Args:
+            tokenizer (PreTrainedTokenizerBase): The base tokenizer to use if no
+            LoRA is selected.  max_loras (Optional[int]): The maximum number of
+            LoRAs available. If None, LoRA is not used.  lora_path
+            (Optional[str]): Path to the LoRA parameters on disk. If None, LoRA
+            is not used.
+        
+        Returns:
+            tuple[Optional[LoRARequest], AnyTokenizer]: A tuple where the first
+            element is a LoRARequest (or None if not applicable) and the second
+            element is the tokenizer associated with the LoRA request (or the
+            base tokenizer).
+        """
+        if max_loras is None or lora_path is None:
+            return None, tokenizer
+
+        # Generate a random LoRA ID in the range [1, max_loras].
+        lora_id = random.randint(1, max_loras)
+        lora_request = LoRARequest(
+            lora_name=str(lora_id),
+            lora_int_id=lora_id,
+            lora_path=lora_path_on_disk(lora_path),
+        )
+        if lora_id not in lora_tokenizer_cache:
+            lora_tokenizer_cache[lora_id] = get_lora_tokenizer(lora_request)
+        # Return lora_request and the cached tokenizer if available; otherwise,
+        # return the base tokenizer
+        return lora_request, lora_tokenizer_cache[lora_id] or tokenizer
+
+    @abstractmethod
+    def sample(self, tokenizer: PreTrainedTokenizerBase,
+               num_requests: int) -> list[SampleRequest]:
+        """
+        Abstract method to generate sample requests from the dataset.
+        
+        Subclasses must override this method to implement dataset-specific logic
+        for generating a list of SampleRequest objects.
+        
+        Args:
+            tokenizer (PreTrainedTokenizerBase): The tokenizer to be used
+             for processing the dataset's text.
+            num_requests (int): The number of sample requests to generate.
+        
+        Returns:
+            list[SampleRequest]: A list of sample requests generated from the
+            dataset.
+        """
+        raise NotImplementedError("sample must be implemented in subclasses.")
+
+
+# -----------------------------------------------------------------------------
+# Utility Functions and Global Caches
+# -----------------------------------------------------------------------------
+
+
+def is_valid_sequence(
+    prompt_len: int,
+    output_len: int,
+    min_len: int = 4,
+    max_prompt_len: int = 1024,
+    max_total_len: int = 2048,
+    skip_min_output_len_check: bool = False,
+) -> bool:
+    """
+    Validate a sequence based on prompt and output lengths.
+
+    Default pruning criteria are copied from the original `sample_hf_requests`
+    and `sample_sharegpt_requests` functions in benchmark_serving.py, as well as
+    from `sample_requests` in benchmark_throughput.py.
+    """
+    # Check for invalid conditions
+    prompt_too_short = prompt_len < min_len
+    output_too_short = (not skip_min_output_len_check) and (output_len
+                                                            < min_len)
+    prompt_too_long = prompt_len > max_prompt_len
+    combined_too_long = (prompt_len + output_len) > max_total_len
+
+    # Return True if none of the invalid conditions are met
+    return not (prompt_too_short or output_too_short or prompt_too_long
+                or combined_too_long)
+
+
+@cache
+def lora_path_on_disk(lora_path: str) -> str:
+    return get_adapter_absolute_path(lora_path)
+
+
+# Global cache for LoRA tokenizers.
+lora_tokenizer_cache: dict[int, AnyTokenizer] = {}
+
+
+def process_image(image: Any) -> Mapping[str, Any]:
+    """
+    Process a single image input and return a multimedia content dictionary.
+
+    For a PIL.Image.Image input:
+      - Converts the image to RGB.
+      - Saves the image as a JPEG in-memory.
+      - Encodes the JPEG data as a base64 string.
+      - Returns a dictionary with the image as a base64 data URL.
+
+    For a string input:
+      - Treats the string as a URL or file path.
+      - Prepends "file://" if the string doesn't start with "http://" or
+        "file://".
+      - Returns a dictionary with the image URL.
+
+    Raises:
+      ValueError: If the input is neither a PIL.Image.Image nor a string.
+    """
+    if isinstance(image, Image.Image):
+        image = image.convert("RGB")
+        with io.BytesIO() as image_data:
+            image.save(image_data, format="JPEG")
+            image_base64 = base64.b64encode(
+                image_data.getvalue()).decode("utf-8")
+        return {
+            "type": "image_url",
+            "image_url": {
+                "url": f"data:image/jpeg;base64,{image_base64}"
+            },
+        }
+
+    if isinstance(image, str):
+        image_url = (image if image.startswith(
+            ("http://", "file://")) else f"file://{image}")
+        return {"type": "image_url", "image_url": {"url": image_url}}
+
+    raise ValueError(
+        f"Invalid image input {image}. Must be a PIL.Image.Image or str.")
+
+
+# -----------------------------------------------------------------------------
+# Random Dataset Implementation (Synthetic Data)
+# -----------------------------------------------------------------------------
+
+
+class RandomDataset(BenchmarkDataset):
+    # Default values copied from benchmark_serving.py for the random dataset.
+    DEFAULT_PREFIX_LEN = 0
+    DEFAULT_RANGE_RATIO = 1.0
+    DEFAULT_INPUT_LEN = 1024
+    DEFAULT_OUTPUT_LEN = 128
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               prefix_len: int = DEFAULT_PREFIX_LEN,
+               range_ratio: float = DEFAULT_RANGE_RATIO,
+               input_len: int = DEFAULT_INPUT_LEN,
+               output_len: int = DEFAULT_OUTPUT_LEN,
+               **kwargs) -> list[SampleRequest]:
+
+        vocab_size = tokenizer.vocab_size
+
+        prefix_token_ids = (np.random.randint(
+            0, vocab_size, size=prefix_len).tolist() if prefix_len > 0 else [])
+
+        input_low = int(input_len * range_ratio)
+        output_low = int(output_len * range_ratio)
+
+        input_lens = np.random.randint(input_low,
+                                       input_len + 1,
+                                       size=num_requests)
+        output_lens = np.random.randint(output_low,
+                                        output_len + 1,
+                                        size=num_requests)
+        offsets = np.random.randint(0, vocab_size, size=num_requests)
+
+        requests = []
+        for i in range(num_requests):
+            inner_seq = ((offsets[i] + i + np.arange(input_lens[i])) %
+                         vocab_size).tolist()
+            token_sequence = prefix_token_ids + inner_seq
+            prompt = tokenizer.decode(token_sequence)
+            total_input_len = prefix_len + int(input_lens[i])
+            requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=total_input_len,
+                    expected_output_len=int(output_lens[i]),
+                ))
+        return requests
+
+
+# -----------------------------------------------------------------------------
+# ShareGPT Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class ShareGPTDataset(BenchmarkDataset):
+    """
+    Implements the ShareGPT dataset.  Loads data from a JSON file and generates
+    sample requests based on conversation turns.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        with open(self.dataset_path, encoding="utf-8") as f:
+            self.data = json.load(f)
+        # Filter entries with at least two conversation turns.
+        self.data = [
+            entry for entry in self.data
+            if "conversations" in entry and len(entry["conversations"]) >= 2
+        ]
+        random.seed(self.random_seed)
+        random.shuffle(self.data)
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               lora_path: Optional[str] = None,
+               max_loras: Optional[int] = None,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        samples: list = []
+        for entry in self.data:
+            if len(samples) >= num_requests:
+                break
+            prompt, completion = entry["conversations"][0]["value"],\
+                entry["conversations"][1]["value"]
+
+            lora_request, tokenizer = self.get_random_lora_request(
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
+            prompt_ids = tokenizer(prompt).input_ids
+            completion_ids = tokenizer(completion).input_ids
+            prompt_len = len(prompt_ids)
+            new_output_len = (len(completion_ids)
+                              if output_len is None else output_len)
+            if not is_valid_sequence(prompt_len,
+                                     new_output_len,
+                                     skip_min_output_len_check=output_len
+                                     is not None):
+                continue
+            if enable_multimodal_chat:
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, None)
+            samples.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=new_output_len,
+                    lora_request=lora_request,
+                ))
+        return samples
+
+
+# -----------------------------------------------------------------------------
+# Sonnet Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class SonnetDataset(BenchmarkDataset):
+    """
+    Simplified implementation of the Sonnet dataset.  Loads poem lines from a
+    text file and generates sample requests.  Default values here copied from
+    `benchmark_serving.py` for the sonnet dataset.
+    """
+
+    DEFAULT_PREFIX_LEN = 200
+    DEFAULT_INPUT_LEN = 550
+    DEFAULT_OUTPUT_LEN = 150
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self) -> None:
+        if not self.dataset_path:
+            raise ValueError("dataset_path must be provided.")
+        with open(self.dataset_path, encoding="utf-8") as f:
+            self.data = f.readlines()
+
+    def sample(self,
+               tokenizer,
+               num_requests: int,
+               prefix_len: int = DEFAULT_PREFIX_LEN,
+               input_len: int = DEFAULT_INPUT_LEN,
+               output_len: int = DEFAULT_OUTPUT_LEN,
+               return_prompt_formatted: bool = False,
+               **kwargs) -> list:
+        # Calculate average token length for a poem line.
+        tokenized_lines = [tokenizer(line).input_ids for line in self.data]
+        avg_len = sum(len(tokens)
+                      for tokens in \
+                        tokenized_lines) / len(tokenized_lines)
+
+        # Build the base prompt.
+        base_prompt = "Pick as many lines as you can from these poem lines:\n"
+        base_msg = [{"role": "user", "content": base_prompt}]
+        base_fmt = tokenizer.apply_chat_template(base_msg,
+                                                 add_generation_prompt=True,
+                                                 tokenize=False)
+        base_offset = len(tokenizer(base_fmt).input_ids)
+        if input_len <= base_offset:
+            raise ValueError(
+                f"'input_len' must be higher than the base prompt length "
+                f"({base_offset}).")
+
+        # Determine how many poem lines to use.
+        num_input_lines = round((input_len - base_offset) / avg_len)
+        num_prefix_lines = round((prefix_len - base_offset) / avg_len)
+        prefix_lines = self.data[:num_prefix_lines]
+
+        samples = []
+        for _ in range(num_requests):
+            extra_lines = random.choices(self.data,
+                                         k=num_input_lines - num_prefix_lines)
+            prompt = f"{base_prompt}{''.join(prefix_lines + extra_lines)}"
+            msg = [{"role": "user", "content": prompt}]
+            prompt_formatted = tokenizer.apply_chat_template(
+                msg, add_generation_prompt=True, tokenize=False)
+            prompt_len = len(tokenizer(prompt_formatted).input_ids)
+            samples.append(
+                SampleRequest(
+                    prompt=prompt_formatted
+                    if return_prompt_formatted else prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                ))
+        return samples
+
+
+# -----------------------------------------------------------------------------
+# BurstGPT Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class BurstGPTDataset(BenchmarkDataset):
+    """
+    Implements the BurstGPT dataset.  Loads data from a CSV file and generates
+    sample requests based on synthetic prompt generation. Only rows with Model
+    "GPT-4" and positive response tokens are used.
+    """
+
+    def __init__(self, **kwargs) -> None:
+        super().__init__(**kwargs)
+        self.load_data()
+
+    def load_data(self, ):
+        if self.dataset_path is None:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        df = pd.read_csv(self.dataset_path)
+        # Filter to keep only GPT-4 rows.
+        gpt4_df = df[df["Model"] == "GPT-4"]
+        # Remove failed requests (where Response tokens is 0 or less).
+        gpt4_df = gpt4_df[gpt4_df["Response tokens"] > 0]
+        # Sample the desired number of rows.
+        self.data = gpt4_df
+
+    def _sample_loaded_data(self, num_requests: int) -> list:
+        if num_requests <= len(self.data):
+            data = self.data.sample(n=num_requests,
+                                    random_state=self.random_seed)
+        else:
+            data = self.data.sample(
+                n=num_requests,
+                random_state=self.random_seed,
+                replace=True,
+            )
+        # Convert the dataframe to a list of lists.
+        return data.values.tolist()
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               max_loras: Optional[int] = None,
+               lora_path: Optional[str] = None,
+               **kwargs) -> list[SampleRequest]:
+        samples = []
+        data = self._sample_loaded_data(num_requests=num_requests)
+        for i in range(num_requests):
+            input_len = int(data[i][2])
+            output_len = int(data[i][3])
+            lora_req, tokenizer = self.get_random_lora_request(
+                tokenizer=tokenizer, max_loras=max_loras, lora_path=lora_path)
+            vocab_size = tokenizer.vocab_size
+            # Generate a synthetic prompt: a list of token IDs computed as (i +
+            # j) modulo vocab_size.
+            token_ids = [(i + j) % vocab_size for j in range(input_len)]
+            prompt = tokenizer.decode(token_ids)
+            samples.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=input_len,
+                    expected_output_len=output_len,
+                    lora_request=lora_req,
+                ))
+        return samples
+
+
+# -----------------------------------------------------------------------------
+# HuggingFace Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class HuggingFaceDataset(BenchmarkDataset):
+    """
+    Dataset class for processing a HuggingFace dataset with conversation data
+    and optional images.
+    """
+    DEFAULT_NUM_REQUESTS = 1000
+
+    def __init__(
+        self,
+        dataset_split: str,
+        dataset_subset: Optional[str] = None,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.dataset_split = dataset_split
+        self.dataset_subset = dataset_subset
+
+        self.load_data()
+
+    def load_data(self) -> None:
+        if not self.dataset_path:
+            raise ValueError("dataset_path must be provided for loading data.")
+
+        self.data = load_dataset(
+            self.dataset_path,
+            name=self.dataset_subset,
+            split=self.dataset_split,
+            streaming=True,
+        )
+        if self.data.features is None or "conversations" \
+            not in self.data.features:
+            raise ValueError(
+                "HuggingFaceDataset currently only supports datasets with "
+                "a 'conversations' column like lmms-lab/LLaVA-OneVision-Data. "
+                "Please consider contributing if you would like to add "
+                "support for additional dataset formats.")
+        # Shuffle and filter examples with at least 2 conversations.
+        self.data = self.data.shuffle(seed=self.random_seed).filter(
+            lambda x: len(x["conversations"]) >= 2)
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        sampled_requests = []
+        dynamic_output = output_len is None
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            conv = item["conversations"]
+            prompt, completion = conv[0]["value"], conv[1]["value"]
+
+            prompt_ids = tokenizer(prompt).input_ids
+            completion_ids = tokenizer(completion).input_ids
+            prompt_len = len(prompt_ids)
+            completion_len = len(completion_ids)
+            output_len = completion_len if dynamic_output else output_len
+            assert isinstance(output_len, int) and output_len > 0
+            if dynamic_output and not is_valid_sequence(
+                    prompt_len, completion_len):
+                continue
+            mm_content = process_image(
+                item["image"]) if "image" in item else None
+            if enable_multimodal_chat:
+                # Note: when chat is enabled the request prompt_len is no longer
+                # accurate and we will be using request output to count the
+                # actual prompt len and output len
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, mm_content)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=mm_content,
+                ))
+        return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# Vision Arena Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class VisionArenaDataset(HuggingFaceDataset):
+    """
+    Vision Arena Dataset.
+    """
+
+    DEFAULT_OUTPUT_LEN = 128
+    DEFAULT_NUM_REQUESTS = 1000
+    VISION_ARENA_DATASET_PATH = "lmarena-ai/vision-arena-bench-v0.1"
+
+    def __init__(
+        self,
+        **kwargs,
+    ) -> None:
+        super().__init__(**kwargs)
+        if self.dataset_path != self.VISION_ARENA_DATASET_PATH:
+            raise ValueError(f"Only support Vision Arena dataset.\
+                    This data path {self.dataset_path} is not valid.")
+        if self.dataset_subset is None and self.dataset_split != "train":
+            raise ValueError("Dataset split must be 'train'.")
+
+        self.load_data()
+
+    def load_data(self) -> None:
+        dataset = load_dataset(
+            self.dataset_path,
+            name=self.dataset_subset,
+            split=self.dataset_split,
+            streaming=True,
+        )
+        self.data = dataset.shuffle(seed=self.random_seed)
+
+    def sample(self,
+               tokenizer: PreTrainedTokenizerBase,
+               num_requests: int,
+               output_len: Optional[int] = None,
+               enable_multimodal_chat: bool = False,
+               **kwargs) -> list:
+        output_len = (output_len
+                      if output_len is not None else self.DEFAULT_OUTPUT_LEN)
+        sampled_requests = []
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+            prompt = item["turns"][0][0]["content"]
+            mm_content = process_image(item["images"][0])
+            prompt_len = len(tokenizer(prompt).input_ids)
+            if enable_multimodal_chat:
+                # Note: when chat is enabled the request prompt_len is no longer
+                # accurate and we will be using request output to count the
+                # actual prompt len
+                prompt = self.apply_multimodal_chat_transformation(
+                    prompt, mm_content)
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt,
+                    prompt_len=prompt_len,
+                    expected_output_len=output_len,
+                    multi_modal_data=mm_content,
+                ))
+        return sampled_requests

benchmarks/benchmark_guided.py

@@ -1,507 +0,0 @@
-# SPDX-License-Identifier: Apache-2.0
-"""Benchmark guided decoding throughput."""
-import argparse
-import dataclasses
-import json
-import os
-import random
-import time
-
-import datasets
-import pandas as pd
-import uvloop
-from transformers import AutoTokenizer, PreTrainedTokenizerBase
-
-from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
-from vllm.entrypoints.openai.api_server import (
-    build_async_engine_client_from_engine_args)
-from vllm.sampling_params import GuidedDecodingParams
-from vllm.utils import FlexibleArgumentParser, merge_async_iterators
-
-
-@dataclasses.dataclass
-class SampleRequest:
-    """A class representing a single inference request for benchmarking.
-
-    Attributes:
-        prompt: The input text prompt for the model.
-        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
-            images).
-        prompt_len: The length of the prompt in tokens.
-        expected_output_len: The expected length of the output in tokens.
-    """
-    prompt: str
-    prompt_len: int
-    expected_output_len: int
-    schema: dict
-    structure_type: str = 'json'
-    completion: str = None
-
-
-def run_vllm(requests: list[SampleRequest],
-             engine_args: EngineArgs,
-             n: int,
-             guided_decoding_rate: float = 1.0,
-             warmup: bool = False) -> float:
-    from vllm import LLM, SamplingParams
-    llm = LLM(**vars(engine_args))
-    assert all(
-        llm.llm_engine.model_config.max_model_len >= (
-            request.prompt_len + request.expected_output_len)
-        for request in requests), (
-            "Please ensure that max_model_len is greater than the sum of"
-            " prompt_len and expected_output_len for all requests.")
-
-    # Add the requests to the engine.
-    prompts: list[str] = []
-    sampling_params: list[SamplingParams] = []
-    # create a list containing random selected true or false
-    guided_decoding_req_idx = random.sample(
-        range(len(requests)), int(len(requests) * guided_decoding_rate))
-
-    if warmup:
-        print(">>>>> Running warmup prompt, for the first 5")
-        # We setup the first 5 requests to warmup FSM
-        # if using xgrammar dataset, we will skip warmup
-        warmup_requests = requests[:5]
-        for i, request in enumerate(warmup_requests):
-            prompts.append(request.prompt)
-            sampling_params.append(
-                SamplingParams(
-                    n=n,
-                    temperature=1.0,
-                    top_p=1.0,
-                    ignore_eos=True,
-                    max_tokens=request.expected_output_len,
-                    guided_decoding=GuidedDecodingParams(json=request.schema)
-                    if guided_decoding_rate > 0 else None,
-                ))
-        llm.generate(prompts, sampling_params, use_tqdm=False)
-
-    print(">>>>> Benchmark started...")
-    prompts = []
-    sampling_params = []
-    for i, request in enumerate(requests):
-        prompts.append(request.prompt)
-        sampling_params.append(
-            SamplingParams(
-                n=n,
-                temperature=1.0,
-                top_p=1.0,
-                ignore_eos=True,
-                max_tokens=request.expected_output_len,
-                guided_decoding=GuidedDecodingParams(
-                    **{request.structure_type: request.schema})
-                if i in guided_decoding_req_idx else None,
-            ))
-
-    start = time.perf_counter()
-    outputs = llm.generate(prompts, sampling_params, use_tqdm=False)
-    ret = []
-    for output, request in zip(outputs, requests):
-        generated_text = output.outputs[0].text
-        ret.append({
-            "generated": generated_text,
-            "expected": request.completion
-        })
-    end = time.perf_counter()
-    return end - start, ret
-
-
-async def run_vllm_async(
-        requests: list[SampleRequest],
-        engine_args: AsyncEngineArgs,
-        n: int,
-        guided_decoding_rate: float = 1.0,
-        warmup: bool = False,
-        disable_frontend_multiprocessing: bool = False) -> float:
-    from vllm import SamplingParams
-
-    async with build_async_engine_client_from_engine_args(
-            engine_args, disable_frontend_multiprocessing) as llm:
-
-        assert all(
-            llm.model_config.max_model_len >= (request.prompt_len +
-                                               request.expected_output_len)
-            for request in requests), (
-                "Please ensure that max_model_len is greater than the sum of"
-                " prompt_len and expected_output_len for all requests.")
-
-        # Add the requests to the engine.
-        prompts: list[str] = []
-        sampling_params: list[SamplingParams] = []
-        guided_decoding_req_idx = random.sample(
-            range(len(requests)), int(len(requests) * guided_decoding_rate))
-
-        if warmup:
-            print(">>>>>> Running warmup prompt, for the first 5")
-            # We setup the first 5 requests to warmup FSM
-            # if using xgrammar dataset, we will skip warmup
-            warmup_requests = requests[:5]
-            for i, request in enumerate(warmup_requests):
-                prompts.append(request.prompt)
-                sampling_params.append(
-                    SamplingParams(
-                        n=n,
-                        temperature=1.0,
-                        top_p=1.0,
-                        ignore_eos=True,
-                        max_tokens=request.expected_output_len,
-                        guided_decoding=GuidedDecodingParams(
-                            json=request.schema)
-                        if guided_decoding_rate > 0 else None,
-                    ))
-            generators = []
-            for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
-                generator = llm.generate(prompt, sp, request_id=f"test{i}")
-                generators.append(generator)
-            all_gens = merge_async_iterators(*generators)
-            async for i, res in all_gens:
-                pass
-
-        print(">>>>> Benchmark started...")
-        prompts = []
-        sampling_params = []
-        for i, request in enumerate(requests):
-            prompts.append(request.prompt)
-            sampling_params.append(
-                SamplingParams(
-                    n=n,
-                    temperature=1.0,
-                    top_p=1.0,
-                    ignore_eos=True,
-                    max_tokens=request.expected_output_len,
-                    guided_decoding=GuidedDecodingParams(json=request.schema)
-                    if i in guided_decoding_req_idx else None,
-                ))
-
-        generators = []
-        start_time = []
-        latencies = []
-        start = time.perf_counter()
-        for i, (prompt, sp) in enumerate(zip(prompts, sampling_params)):
-            generator = llm.generate(prompt, sp, request_id=f"test{i}")
-            generators.append(generator)
-            start_time.append(time.perf_counter())
-            latencies.append([])
-        all_gens = merge_async_iterators(*generators)
-        generated_texts = [''] * len(requests)
-        async for i, res in all_gens:
-            generated_texts[i] = res.outputs[0].text
-            lat = time.perf_counter() - start_time[i]
-            latencies[i].append(lat)
-        ret = [{
-            'generated': gt,
-            'expected': req.completion
-        } for gt, req in zip(generated_texts, requests)]
-        end = time.perf_counter()
-        first_latency = pd.Series([lat[0] * 1000 for lat in latencies])
-        next_latency = pd.Series([(lat[-1] - lat[0]) / len(lat[1:]) * 1000
-                                  for lat in latencies])
-        return end - start, ret, (first_latency, next_latency)
-
-
-def sample_requests(tokenizer: PreTrainedTokenizerBase,
-                    args: argparse.Namespace) -> list[SampleRequest]:
-    if args.dataset == 'json':
-        if args.json_schema_path is None:
-            dir_path = os.path.dirname(os.path.realpath(__file__))
-            args.json_schema_path = os.path.join(dir_path,
-                                                 "structured_schemas",
-                                                 "structured_schema_1.json")
-        with open(args.json_schema_path) as f:
-            schema = json.load(f)
-        prompt = f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
-        input_len = len(tokenizer(prompt).input_ids)
-        print(f"Input length of the prompt: {input_len} tokens")
-        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=schema,
-                          structure_type=args.structure_type)
-            for _ in range(args.num_prompts)
-        ]
-
-    elif args.dataset == "grammar":
-        schema = """
-            ?start: select_statement
-
-            ?select_statement: "SELECT " column_list " FROM " table_name
-
-            ?column_list: column_name ("," column_name)*
-
-            ?table_name: identifier
-
-            ?column_name: identifier
-
-            ?identifier: /[a-zA-Z_][a-zA-Z0-9_]*/
-        """
-        prompt = "Generate an SQL query to show the 'username' \
-            and 'email' from the 'users' table."
-
-        input_len = len(tokenizer(prompt).input_ids)
-        print(f"Input length of the prompt: {input_len} tokens")
-        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=schema,
-                          structure_type=args.structure_type)
-            for _ in range(args.num_prompts)
-        ]
-
-    elif args.dataset == "regex":
-        regex = r"\w+@\w+\.com\n"
-        args.regex = regex
-        prompt = "Generate an email address for Alan Turing, \
-            who works in Enigma. End in .com and new line. \
-                Example result: alan.turing@enigma.com\n"
-
-        input_len = len(tokenizer(prompt).input_ids)
-        print(f"Input length of the prompt: {input_len} tokens")
-        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=regex,
-                          structure_type=args.structure_type)
-            for _ in range(args.num_prompts)
-        ]
-
-    elif args.dataset == "choice":
-        choice = ["Positive", "Negative"]
-        args.choice = choice
-        prompt = "Classify this sentiment: vLLM is wonderful!"
-        input_len = len(tokenizer(prompt).input_ids)
-        print(f"Input length of the prompt: {input_len} tokens")
-        requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
-                          expected_output_len=args.output_len,
-                          schema=choice,
-                          structure_type=args.structure_type)
-            for _ in range(args.num_prompts)
-        ]
-
-    elif args.dataset == "xgrammar_bench":
-        args.warmup = False
-        requests: list[SampleRequest] = []
-        dataset = datasets.load_dataset("NousResearch/json-mode-eval",
-                                        split="train")
-        print(f"dataset has {len(dataset)} entries")
-        len_dataset = len(dataset)
-        for data_point_idx in range(args.num_prompts):
-            idx = data_point_idx
-            while idx >= len_dataset:
-                idx -= len_dataset
-            schema = dataset["schema"][idx]
-            prompt = tokenizer.apply_chat_template(dataset["prompt"][idx],
-                                                   tokenize=False)
-            input_len = len(tokenizer(prompt).input_ids)
-            completion = dataset["completion"][idx]
-
-            requests.append(
-                SampleRequest(prompt=prompt,
-                              prompt_len=input_len,
-                              expected_output_len=args.output_len,
-                              schema=schema,
-                              completion=completion))
-
-    return requests
-
-
-def evaluate(ret, args):
-
-    def _eval_correctness_json(expected, actual):
-        # extract json string from string using regex
-        import re
-        actual = actual.replace('\n', '').replace(' ', '').strip()
-        try:
-            actual = re.search(r'\{.*\}', actual).group()
-            actual = json.loads(actual)
-        except Exception:
-            return False
-
-        return True
-
-    def _eval_correctness_choice(expected, actual):
-        return actual in args.choice
-
-    def _eval_correctness_regex(expected, actual):
-        import re
-        return re.match(args.regex, actual) is not None
-
-    def _eval_correctness(expected, actual):
-        if args.structure_type == 'json':
-            return _eval_correctness_json(expected, actual)
-        elif args.structure_type == 'regex':
-            return _eval_correctness_regex(expected, actual)
-        elif args.structure_type == 'choice':
-            return _eval_correctness_choice(expected, actual)
-        else:
-            return None
-
-    scores = []
-    for res in ret:
-        score = _eval_correctness(res['expected'], res['generated'])
-        res['correctness'] = score
-        scores.append(score)
-
-    not_none_scores = [score for score in scores if score is not None]
-
-    return (sum(not_none_scores) / len(not_none_scores) *
-            100) if len(not_none_scores) > 0 else None
-
-
-def main(args: argparse.Namespace):
-    print(args)
-    random.seed(args.seed)
-
-    # async engine is working for 'regex', 'choice' and 'grammar'
-    if args.dataset == 'grammar':
-        args.structure_type = 'grammar'
-        args.async_engine = False
-    elif args.dataset == 'regex':
-        args.structure_type = 'regex'
-        args.async_engine = False
-    elif args.dataset == 'choice':
-        args.structure_type = 'choice'
-        args.async_engine = False
-    else:
-        args.structure_type = 'json'
-
-    if args.no_guided_decoding:
-        args.guided_decoding_ratio = 0
-    if args.save_results:
-        result_file_name = f'{args.guided_decoding_ratio}guided'
-        result_file_name += f"_{args.model.split('/')[-1]}"
-        result_file_name += f"_{args.dataset}"
-        result_file_name += f"_{args.num_prompts}"
-        result_file_name += f"_out{args.output_len}"
-        result_file_name += f"_async{args.async_engine}"
-        result_file_name += f"_warmup{args.warmup}"
-        result_file_name += f"_chunkedprefill{args.enable_chunked_prefill}"
-        result_file_name += ".txt"
-    else:
-        result_file_name = None
-
-    # Synthesize a prompt with the given input length.
-    tokenizer = AutoTokenizer.from_pretrained(
-        args.tokenizer, trust_remote_code=args.trust_remote_code)
-    requests = sample_requests(tokenizer, args)
-
-    if args.async_engine:
-        engine_args = AsyncEngineArgs.from_cli_args(args)
-        elapsed_time, ret, (first_latency, next_latency) = uvloop.run(
-            run_vllm_async(requests, engine_args, args.n,
-                           args.guided_decoding_ratio, args.warmup,
-                           args.disable_frontend_multiprocessing))
-    else:
-        engine_args = EngineArgs.from_cli_args(args)
-        elapsed_time, ret = run_vllm(requests, engine_args, args.n,
-                                     args.guided_decoding_ratio, args.warmup)
-        first_latency, next_latency = None, None
-
-    score = evaluate(ret, args)
-    total_num_tokens = sum(request.prompt_len + request.expected_output_len
-                           for request in requests)
-    total_output_tokens = sum(request.expected_output_len
-                              for request in requests)
-    if first_latency is not None:
-        latency_breakdown = "\nFirst token latency(msecs):\n"
-        latency_breakdown += f"{first_latency.describe()}"
-        latency_breakdown += "\nNext token latency(msecs):\n"
-        latency_breakdown += f"{next_latency.describe()}"
-    print(
-        f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
-        f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
-        f"{total_output_tokens / elapsed_time:.2f} output tokens/s",
-        f"Correct rate is {score} %",
-        f"{latency_breakdown if first_latency is not None else ''}")
-
-    # Output JSON results if specified
-    if args.output_json or result_file_name:
-        results = {
-            "elapsed_time": elapsed_time,
-            "num_requests": len(requests),
-            "total_num_tokens": total_num_tokens,
-            "total_output_tokens": total_output_tokens,
-            "requests_per_second": len(requests) / elapsed_time,
-            "tokens_per_second": f"{total_num_tokens / elapsed_time:.2f}",
-            "output_tokens_per_second":
-            f"{total_output_tokens / elapsed_time:.2f}",
-            "correct_rate(%)": score
-        }
-        results = {"outputs": ret, **results}
-        if first_latency is not None:
-            results["first_token_latency(msecs)"] = first_latency.describe(
-            ).to_dict()
-            results["next_token_latency(msecs)"] = next_latency.describe(
-            ).to_dict()
-        if args.output_json:
-            with open(args.output_json, "w") as f:
-                json.dump(results, f, indent=4)
-        elif result_file_name:
-            with open(result_file_name, "w") as f:
-                json.dump(results, f, indent=4)
-
-
-if __name__ == "__main__":
-    parser = FlexibleArgumentParser(description="Benchmark guided decoding.")
-    parser = AsyncEngineArgs.add_cli_args(parser)
-
-    parser.add_argument("--output-len",
-                        type=int,
-                        default=512,
-                        help="Output length for each request. Overrides the "
-                        "output length from the dataset.")
-    parser.add_argument(
-        "--dataset",
-        default='json',
-        choices=['json', 'grammar', 'regex', 'choice', 'xgrammar_bench'])
-    parser.add_argument("--json_schema_path",
-                        type=str,
-                        default=None,
-                        help="Path to json schema.")
-    parser.add_argument("--n",
-                        type=int,
-                        default=1,
-                        help="Number of generated sequences per prompt.")
-    parser.add_argument("--num-prompts",
-                        type=int,
-                        default=10,
-                        help="Number of prompts to process.")
-    parser.add_argument(
-        '--output-json',
-        type=str,
-        default=None,
-        help='Path to save the throughput results in JSON format.')
-    parser.add_argument("--async-engine",
-                        action='store_true',
-                        default=False,
-                        help="Use vLLM async engine rather than LLM class.")
-    parser.add_argument("--no-guided-decoding",
-                        action='store_true',
-                        default=False,
-                        help="Whether to disable JSON decoding or not.")
-    parser.add_argument("--guided-decoding-ratio",
-                        type=float,
-                        default=1.0,
-                        help="Ratio of Guided Decoding requests")
-    parser.add_argument("--disable-frontend-multiprocessing",
-                        action='store_true',
-                        default=False,
-                        help="Disable decoupled async engine frontend.")
-    parser.add_argument("--warmup",
-                        action="store_true",
-                        default=False,
-                        help="Run warmup prompts before benchmark.")
-    parser.add_argument("--save-results",
-                        action="store_true",
-                        default=False,
-                        help="save output results.")
-    args = parser.parse_args()
-    if args.tokenizer is None:
-        args.tokenizer = args.model
-    main(args)

benchmarks/benchmark_latency.py

@@ -52,6 +52,7 @@ def main(args: argparse.Namespace):
         top_p=1.0,
         ignore_eos=True,
         max_tokens=args.output_len,
+        detokenize=not args.disable_detokenize,
     )
     print(sampling_params)
     dummy_prompt_token_ids = np.random.randint(10000,
@@ -173,6 +174,12 @@ if __name__ == "__main__":
         default=None,
         help="Path to save the latency results in JSON format.",
     )
+    parser.add_argument(
+        "--disable-detokenize",
+        action="store_true",
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
+    )
 
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()

benchmarks/benchmark_prefix_caching.py

@@ -194,7 +194,9 @@ def main(args):
 
     llm = LLM(**dataclasses.asdict(engine_args))
 
-    sampling_params = SamplingParams(temperature=0, max_tokens=args.output_len)
+    sampling_params = SamplingParams(temperature=0,
+                                     max_tokens=args.output_len,
+                                     detokenize=not args.disable_detokenize)
 
     print("Testing filtered requests")
     prompts = repeat_and_sort_requests(filtered_requests,
@@ -243,6 +245,12 @@ if __name__ == "__main__":
         "subtract this length when filtering prompts. Only used "
         "when dataset-path is not provided.",
     )
+    parser.add_argument(
+        '--disable-detokenize',
+        action='store_true',
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
+    )
 
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()

benchmarks/benchmark_prioritization.py

@@ -23,7 +23,7 @@ def sample_requests(
     num_requests: int,
     tokenizer: PreTrainedTokenizerBase,
     fixed_output_len: Optional[int],
-) -> list[tuple[str, int, int]]:
+) -> list[tuple[str, int, int, int]]:
     if fixed_output_len is not None and fixed_output_len < 4:
         raise ValueError("output_len too small")
 
@@ -71,6 +71,7 @@ def run_vllm(
     requests: list[tuple[str, int, int]],
     n: int,
     engine_args: EngineArgs,
+    disable_detokenize: bool = False,
 ) -> float:
     from vllm import LLM, SamplingParams
     llm = LLM(**dataclasses.asdict(engine_args))
@@ -95,6 +96,7 @@ def run_vllm(
                 top_p=1.0,
                 ignore_eos=True,
                 max_tokens=output_len,
+                detokenize=not disable_detokenize,
             ))
 
     start = time.perf_counter()
@@ -121,7 +123,8 @@ def main(args: argparse.Namespace):
 
     if args.backend == "vllm":
         elapsed_time = run_vllm(requests, args.n,
-                                EngineArgs.from_cli_args(args))
+                                EngineArgs.from_cli_args(args),
+                                args.disable_detokenize)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
     total_num_tokens = sum(prompt_len + output_len
@@ -174,6 +177,12 @@ if __name__ == "__main__":
         type=str,
         default=None,
         help='Path to save the throughput results in JSON format.')
+    parser.add_argument(
+        '--disable-detokenize',
+        action='store_true',
+        help=("Do not detokenize responses (i.e. do not include "
+              "detokenization time in the latency measurement)"),
+    )
 
     parser = EngineArgs.add_cli_args(parser)
     args = parser.parse_args()

benchmarks/benchmark_serving.py

@@ -25,25 +25,20 @@ On the client side, run:
 """
 import argparse
 import asyncio
-import base64
 import gc
-import io
 import json
 import os
 import random
 import time
 import warnings
-from collections.abc import AsyncGenerator, Collection
+from collections.abc import AsyncGenerator, Iterable
 from dataclasses import dataclass
 from datetime import datetime
 from typing import Any, Optional
 
 import numpy as np
-import pandas as pd
 from backend_request_func import (ASYNC_REQUEST_FUNCS, RequestFuncInput,
                                   RequestFuncOutput)
-from datasets import load_dataset
-from PIL.Image import Image
 from tqdm.asyncio import tqdm
 from transformers import PreTrainedTokenizerBase
 
@@ -57,6 +52,9 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
+from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
+                               RandomDataset, SampleRequest, ShareGPTDataset,
+                               SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
 
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
@@ -92,325 +90,18 @@ class BenchmarkMetrics:
     percentiles_e2el_ms: list[tuple[float, float]]
 
 
-def sample_sharegpt_requests(
-    dataset_path: str,
-    num_requests: int,
-    tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int] = None,
-) -> list[tuple[str, int, int, None]]:
-    # Load the dataset.
-    with open(dataset_path, encoding='utf-8') as f:
-        dataset = json.load(f)
-    # Filter out the conversations with less than 2 turns.
-    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
-    # Only keep the first two turns of each conversation.
-    dataset = [(data["conversations"][0]["value"],
-                data["conversations"][1]["value"]) for data in dataset]
-
-    # Shuffle the dataset.
-    random.shuffle(dataset)
-
-    # Filter out sequences that are too long or too short
-    filtered_dataset: list[tuple[str, int, int]] = []
-    for i in range(len(dataset)):
-        if len(filtered_dataset) == num_requests:
-            break
-
-        # Tokenize the prompts and completions.
-        prompt = dataset[i][0]
-        prompt_token_ids = tokenizer(prompt).input_ids
-        completion = dataset[i][1]
-        completion_token_ids = tokenizer(completion).input_ids
-        prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
-        if prompt_len < 4 or (fixed_output_len is None and output_len < 4):
-            # Prune too short sequences.
-            continue
-        if prompt_len > 1024 or prompt_len + output_len > 2048:
-            # Prune too long sequences.
-            continue
-        filtered_dataset.append((prompt, prompt_len, output_len, None))
-
-    return filtered_dataset
-
-
-def sample_burstgpt_requests(
-    dataset_path: str,
-    num_requests: int,
-    random_seed: int,
-    tokenizer: PreTrainedTokenizerBase,
-) -> list[tuple[str, int, int, None]]:
-    df = pd.read_csv(dataset_path)
-    gpt4_df = df[df["Model"] == "GPT-4"]
-    # Remove the failed requests (i.e., response length is 0)
-    gpt4_df = gpt4_df[gpt4_df["Response tokens"] > 0]
-    # Randomly sample num_requests from the dataset
-    if num_requests <= len(gpt4_df):
-        gpt4_df = gpt4_df.sample(n=num_requests, random_state=random_seed)
-    else:
-        gpt4_df = gpt4_df.sample(n=num_requests,
-                                 random_state=random_seed,
-                                 replace=True)
-    # Convert the dataframe to a list of tuples
-    dataset = gpt4_df.values.tolist()
-    input_requests = []
-    for i in range(num_requests):
-        input_len = int(dataset[i][2])
-        output_len = int(dataset[i][3])
-        prompt = tokenizer.decode([(i + j) % tokenizer.vocab_size
-                                   for j in range(input_len)])
-        input_requests.append((prompt, input_len, output_len, None))
-    return input_requests
-
-
-def sample_sonnet_requests(
-    dataset_path: str,
-    num_requests: int,
-    input_len: int,
-    output_len: int,
-    prefix_len: int,
-    tokenizer: PreTrainedTokenizerBase,
-) -> list[tuple[str, str, int, int, None]]:
-    assert (
-        input_len > prefix_len
-    ), "'args.sonnet-input-len' must be greater than 'args.prefix-input-len'."
-
-    # Load the dataset.
-    with open(dataset_path, encoding='utf-8') as f:
-        poem_lines = f.readlines()
-
-    # Tokenize the poem lines.
-    poem_token_ids = tokenizer(poem_lines).input_ids
-    average_poem_len = sum(
-        len(token_ids) for token_ids in poem_token_ids) / len(poem_token_ids)
-
-    # Base prefix for all requests.
-    base_prompt = "Pick as many lines as you can from these poem lines:\n"
-    base_message = [{
-        "role": "user",
-        "content": base_prompt,
-    }]
-    base_prompt_formatted = tokenizer.apply_chat_template(
-        base_message, add_generation_prompt=True, tokenize=False)
-    base_prompt_offset = len(tokenizer(base_prompt_formatted).input_ids)
-
-    assert (
-        input_len > base_prompt_offset
-    ), f"Please set 'args.sonnet-input-len' higher than {base_prompt_offset}."
-    num_input_lines = round(
-        (input_len - base_prompt_offset) / average_poem_len)
-
-    # First approximately `prefix_len` number of tokens in the
-    # prompt are fixed poem lines.
-    assert (
-        prefix_len > base_prompt_offset
-    ), f"Please set 'args.sonnet-prefix-len' higher than {base_prompt_offset}."
-
-    num_prefix_lines = round(
-        (prefix_len - base_prompt_offset) / average_poem_len)
-    prefix_lines = poem_lines[:num_prefix_lines]
-
-    # Sample the rest of lines per request.
-    sampled_requests: list[tuple[str, int, int]] = []
-    for _ in range(num_requests):
-        num_lines_needed = num_input_lines - num_prefix_lines
-        sampled_lines = "".join(prefix_lines +
-                                random.choices(poem_lines, k=num_lines_needed))
-
-        prompt = f"{base_prompt}{sampled_lines}"
-        message = [
-            {
-                "role": "user",
-                "content": prompt,
-            },
-        ]
-        prompt_formatted = tokenizer.apply_chat_template(
-            message, add_generation_prompt=True, tokenize=False)
-        prompt_len = len(tokenizer(prompt_formatted).input_ids)
-        sampled_requests.append(
-            (prompt, prompt_formatted, prompt_len, output_len, None))
-
-    return sampled_requests
-
-
-def sample_vision_arena_requests(
-    dataset,
-    num_requests: int,
-    tokenizer: PreTrainedTokenizerBase,
-    fixed_output_len: Optional[int] = None,
-) -> list[tuple[str, str, int, Optional[dict[str, Collection[str]]]]]:
-    sampled_requests: list[tuple[str, int, int, dict[str,
-                                                     Collection[str]]]] = []
-    for data in dataset:
-        if len(sampled_requests) == num_requests:
-            break
-
-        prompt = data["turns"][0][0]['content']
-
-        prompt_token_ids = tokenizer(prompt).input_ids
-        if fixed_output_len is None:
-            # Default max output len is set to 128
-            print("--hf-output-len is not provided. Using default value 128.")
-            fixed_output_len = 128
-
-        prompt_len = len(prompt_token_ids)
-        output_len = fixed_output_len
-
-        assert isinstance(
-            data["images"][0],
-            Image), ("Input image format must be `PIL.Image.Image`, "
-                     f"given {type(data['image'])}.")
-        image: Image = data["images"][0]
-        image = image.convert("RGB")
-        image_data = io.BytesIO()
-        image.save(image_data, format='JPEG')
-        image_base64 = base64.b64encode(image_data.getvalue()).decode("utf-8")
-        mm_content = {
-            "type": "image_url",
-            "image_url": {
-                "url": f"data:image/jpeg;base64,{image_base64}"
-            },
-        }
-
-        sampled_requests.append((prompt, prompt_len, output_len, mm_content))
-
-    return sampled_requests
-
-
-def sample_hf_requests(
-    dataset_path: str,
-    dataset_subset: Optional[str],
-    dataset_split: str,
-    num_requests: int,
-    tokenizer: PreTrainedTokenizerBase,
-    random_seed: int,
-    fixed_output_len: Optional[int] = None,
-) -> list[tuple[str, str, int, Optional[dict[str, Collection[str]]]]]:
-
-    # Special case for vision_arena dataset
-    if dataset_path == 'lmarena-ai/vision-arena-bench-v0.1' \
-        and dataset_subset is None:
-        assert dataset_split == "train"
-        dataset = load_dataset(dataset_path,
-                               name=dataset_subset,
-                               split=dataset_split,
-                               streaming=True)
-        dataset = dataset.shuffle(seed=random_seed)
-        return sample_vision_arena_requests(dataset, num_requests, tokenizer,
-                                            fixed_output_len)
-
-    dataset = load_dataset(dataset_path,
-                           name=dataset_subset,
-                           split=dataset_split,
-                           streaming=True)
-    assert "conversations" in dataset.features, (
-        "HF Dataset must have 'conversations' column.")
-    filter_func = lambda x: len(x["conversations"]) >= 2
-    filtered_dataset = dataset.shuffle(seed=random_seed).filter(filter_func)
-    sampled_requests: list[tuple[str, int, int, dict[str,
-                                                     Collection[str]]]] = []
-    for data in filtered_dataset:
-        if len(sampled_requests) == num_requests:
-            break
-
-        # Tokenize the prompts and completions.
-        prompt = data["conversations"][0]["value"]
-        prompt_token_ids = tokenizer(prompt).input_ids
-        completion = data["conversations"][1]["value"]
-        completion_token_ids = tokenizer(completion).input_ids
-        prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
-        if fixed_output_len is None and (prompt_len < 4 or output_len < 4):
-            # Prune too short sequences.
-            continue
-        if fixed_output_len is None and \
-            (prompt_len > 1024 or prompt_len + output_len > 2048):
-            # Prune too long sequences.
-            continue
-
-        if "image" in data and isinstance(data["image"], Image):
-            image: Image = data["image"]
-            image = image.convert("RGB")
-            image_data = io.BytesIO()
-            image.save(image_data, format='JPEG')
-            image_base64 = base64.b64encode(
-                image_data.getvalue()).decode("utf-8")
-            mm_content = {
-                "type": "image_url",
-                "image_url": {
-                    "url": f"data:image/jpeg;base64,{image_base64}"
-                },
-            }
-        elif "image" in data and isinstance(data["image"], str):
-            if (data["image"].startswith("http://") or \
-                data["image"].startswith("file://")):
-                image_url = data["image"]
-            else:
-                image_url = f"file://{data['image']}"
-
-            mm_content = {
-                "type": "image_url",
-                "image_url": {
-                    "url": image_url
-                },
-            }
-        else:
-            mm_content = None
-
-        sampled_requests.append((prompt, prompt_len, output_len, mm_content))
-
-    return sampled_requests
-
-
-def sample_random_requests(
-    prefix_len: int,
-    input_len: int,
-    output_len: int,
-    num_prompts: int,
-    range_ratio: float,
-    tokenizer: PreTrainedTokenizerBase,
-) -> list[tuple[str, int, int]]:
-    prefix_token_ids = np.random.randint(0,
-                                         tokenizer.vocab_size,
-                                         size=prefix_len).tolist()
-
-    input_lens = np.random.randint(
-        int(input_len * range_ratio),
-        input_len + 1,
-        size=num_prompts,
-    )
-    output_lens = np.random.randint(
-        int(output_len * range_ratio),
-        output_len + 1,
-        size=num_prompts,
-    )
-    offsets = np.random.randint(0, tokenizer.vocab_size, size=num_prompts)
-    input_requests = []
-    for i in range(num_prompts):
-        prompt = tokenizer.decode(prefix_token_ids +
-                                  [(offsets[i] + i + j) % tokenizer.vocab_size
-                                   for j in range(input_lens[i])])
-
-        input_requests.append((prompt, int(prefix_len + input_lens[i]),
-                               int(output_lens[i]), None))
-
-    return input_requests
-
-
 async def get_request(
-    input_requests: list[tuple[str, int, int]],
+    input_requests: list[SampleRequest],
     request_rate: float,
     burstiness: float = 1.0,
-) -> AsyncGenerator[tuple[str, int, int], None]:
+) -> AsyncGenerator[SampleRequest, None]:
     """
     Asynchronously generates requests at a specified rate
     with OPTIONAL burstiness.
 
     Args:
         input_requests:
-            A list of input requests, each represented as a tuple.
+            A list of input requests, each represented as a SampleRequest.
         request_rate:
             The rate at which requests are generated (requests/s).
         burstiness (optional):
@@ -422,7 +113,7 @@ async def get_request(
             in more bursty requests, while a higher burstiness value
             (burstiness > 1) results in a more uniform arrival of requests.
     """
-    input_requests = iter(input_requests)
+    input_requests: Iterable[SampleRequest] = iter(input_requests)
 
     # Calculate scale parameter theta to maintain the desired request_rate.
     assert burstiness > 0, (
@@ -444,7 +135,7 @@ async def get_request(
 
 
 def calculate_metrics(
-    input_requests: list[tuple[str, int, int]],
+    input_requests: list[SampleRequest],
     outputs: list[RequestFuncOutput],
     dur_s: float,
     tokenizer: PreTrainedTokenizerBase,
@@ -475,7 +166,7 @@ def calculate_metrics(
                     tokenizer(outputs[i].generated_text,
                               add_special_tokens=False).input_ids)
             actual_output_lens.append(output_len)
-            total_input += input_requests[i][1]
+            total_input += input_requests[i].prompt_len
             tpot = 0
             if output_len > 1:
                 latency_minus_ttft = outputs[i].latency - outputs[i].ttft
@@ -558,19 +249,18 @@ async def benchmark(
     model_id: str,
     model_name: str,
     tokenizer: PreTrainedTokenizerBase,
-    input_requests: list[tuple[str, int, int]],
+    input_requests: list[SampleRequest],
     logprobs: Optional[int],
-    best_of: int,
     request_rate: float,
     burstiness: float,
     disable_tqdm: bool,
     profile: bool,
     selected_percentile_metrics: list[str],
-    selected_percentiles: list[str],
+    selected_percentiles: list[float],
     ignore_eos: bool,
     goodput_config_dict: dict[str, float],
     max_concurrency: Optional[int],
-    lora_modules: Optional[list[str]],
+    lora_modules: Optional[Iterable[str]],
 ):
     if backend in ASYNC_REQUEST_FUNCS:
         request_func = ASYNC_REQUEST_FUNCS[backend]
@@ -578,12 +268,16 @@ async def benchmark(
         raise ValueError(f"Unknown backend: {backend}")
 
     print("Starting initial single prompt test run...")
-    test_prompt, test_prompt_len, test_output_len, test_mm_content = (
-        input_requests[0])
+    test_prompt, test_prompt_len, test_output_len, test_mm_content = \
+        input_requests[0].prompt, input_requests[0].prompt_len, \
+        input_requests[0].expected_output_len, \
+            input_requests[0].multi_modal_data
+
     if backend != "openai-chat" and test_mm_content is not None:
         # multi-modal benchmark is only available on OpenAI Chat backend.
         raise ValueError(
             "Multi-modal content is only supported on 'openai-chat' backend.")
+    assert test_mm_content is None or isinstance(test_mm_content, dict)
     test_input = RequestFuncInput(
         model=model_id,
         model_name=model_name,
@@ -592,7 +286,6 @@ async def benchmark(
         prompt_len=test_prompt_len,
         output_len=test_output_len,
         logprobs=logprobs,
-        best_of=best_of,
         multi_modal_content=test_mm_content,
         ignore_eos=ignore_eos,
     )
@@ -608,7 +301,8 @@ async def benchmark(
     if lora_modules:
         # For each input request, choose a LoRA module at random.
         lora_modules = iter(
-            [random.choice(lora_modules) for _ in range(len(input_requests))])
+            [random.choice(lora_modules) \
+                for _ in range(len(input_requests))])
 
     if profile:
         print("Starting profiler...")
@@ -619,7 +313,6 @@ async def benchmark(
                                          prompt_len=test_prompt_len,
                                          output_len=test_output_len,
                                          logprobs=logprobs,
-                                         best_of=best_of,
                                          multi_modal_content=test_mm_content,
                                          ignore_eos=ignore_eos)
         profile_output = await request_func(request_func_input=profile_input)
@@ -655,7 +348,9 @@ async def benchmark(
     benchmark_start_time = time.perf_counter()
     tasks: list[asyncio.Task] = []
     async for request in get_request(input_requests, request_rate, burstiness):
-        prompt, prompt_len, output_len, mm_content = request
+        prompt, prompt_len, output_len, mm_content = request.prompt, \
+            request.prompt_len, request.expected_output_len, \
+                request.multi_modal_data
         req_model_id, req_model_name = model_id, model_name
         if lora_modules:
             req_lora_module = next(lora_modules)
@@ -668,7 +363,6 @@ async def benchmark(
                                               prompt_len=prompt_len,
                                               output_len=output_len,
                                               logprobs=logprobs,
-                                              best_of=best_of,
                                               multi_modal_content=mm_content,
                                               ignore_eos=ignore_eos)
         tasks.append(
@@ -686,7 +380,6 @@ async def benchmark(
             prompt_len=test_prompt_len,
             output_len=test_output_len,
             logprobs=logprobs,
-            best_of=best_of,
         )
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
@@ -872,76 +565,72 @@ def main(args: argparse.Namespace):
             "Please specify '--dataset-name' and the corresponding "
             "'--dataset-path' if required.")
 
-    elif args.dataset_name == "sharegpt":
-        input_requests = sample_sharegpt_requests(
-            dataset_path=args.dataset_path,
-            num_requests=args.num_prompts,
-            tokenizer=tokenizer,
-            fixed_output_len=args.sharegpt_output_len,
-        )
-
-    elif args.dataset_name == "burstgpt":
-        input_requests = sample_burstgpt_requests(
-            dataset_path=args.dataset_path,
-            num_requests=args.num_prompts,
-            random_seed=args.seed,
-            tokenizer=tokenizer,
-        )
-
-    elif args.dataset_name == "sonnet":
-        # Do not format the prompt, pass to message directly
+    if args.dataset_name == "sonnet":
+        dataset = SonnetDataset(dataset_path=args.dataset_path)
+        # For the "sonnet" dataset, formatting depends on the backend.
         if args.backend == "openai-chat":
-            input_requests = sample_sonnet_requests(
-                dataset_path=args.dataset_path,
-                num_requests=args.num_prompts,
+            input_requests = dataset.sample(num_requests=args.num_prompts,
                                             input_len=args.sonnet_input_len,
                                             output_len=args.sonnet_output_len,
                                             prefix_len=args.sonnet_prefix_len,
                                             tokenizer=tokenizer,
-            )
-            input_requests = [(prompt, prompt_len, output_len, None)
-                              for prompt, prompt_formatted, prompt_len,
-                              output_len, _ in input_requests]
+                                            return_prompt_formatted=False)
         else:
-            assert (
-                tokenizer.chat_template or tokenizer.default_chat_template
-            ), "Tokenizer/model must have chat template for sonnet dataset."
-            input_requests = sample_sonnet_requests(
-                dataset_path=args.dataset_path,
-                num_requests=args.num_prompts,
+            assert tokenizer.chat_template or tokenizer.default_chat_template, (
+                "Tokenizer/model must have chat template for sonnet dataset.")
+            input_requests = dataset.sample(num_requests=args.num_prompts,
                                             input_len=args.sonnet_input_len,
                                             output_len=args.sonnet_output_len,
                                             prefix_len=args.sonnet_prefix_len,
                                             tokenizer=tokenizer,
-            )
-            input_requests = [(prompt_formatted, prompt_len, output_len, None)
-                              for prompt, prompt_formatted, prompt_len,
-                              output_len, _ in input_requests]
+                                            return_prompt_formatted=True)
 
     elif args.dataset_name == "hf":
-        input_requests = sample_hf_requests(
+        # Choose between VisionArenaDataset
+        # and HuggingFaceDataset based on provided parameters.
+        dataset_class = (VisionArenaDataset if args.dataset_path
+                         == VisionArenaDataset.VISION_ARENA_DATASET_PATH
+                         and args.hf_subset is None else HuggingFaceDataset)
+        input_requests = dataset_class(
             dataset_path=args.dataset_path,
             dataset_subset=args.hf_subset,
             dataset_split=args.hf_split,
+        ).sample(
             num_requests=args.num_prompts,
             tokenizer=tokenizer,
             random_seed=args.seed,
-            fixed_output_len=args.hf_output_len,
-        )
-
-    elif args.dataset_name == "random":
-        input_requests = sample_random_requests(
-            prefix_len=args.random_prefix_len,
-            input_len=args.random_input_len,
-            output_len=args.random_output_len,
-            num_prompts=args.num_prompts,
-            range_ratio=args.random_range_ratio,
-            tokenizer=tokenizer,
+            output_len=args.hf_output_len,
         )
 
     else:
-        raise ValueError(f"Unknown dataset: {args.dataset_name}")
+        # For datasets that follow a similar structure, use a mapping.
+        dataset_mapping = {
+            "sharegpt":
+            lambda: ShareGPTDataset(random_seed=args.seed,
+                                    dataset_path=args.dataset_path).sample(
+                                        tokenizer=tokenizer,
+                                        num_requests=args.num_prompts,
+                                        output_len=args.sharegpt_output_len,
+                                    ),
+            "burstgpt":
+            lambda: BurstGPTDataset(random_seed=args.seed,
+                                    dataset_path=args.dataset_path).
+            sample(tokenizer=tokenizer, num_requests=args.num_prompts),
+            "random":
+            lambda: RandomDataset(dataset_path=args.dataset_path).sample(
+                tokenizer=tokenizer,
+                num_requests=args.num_prompts,
+                prefix_len=args.random_prefix_len,
+                input_len=args.random_input_len,
+                output_len=args.random_output_len,
+                range_ratio=args.random_range_ratio,
+            )
+        }
 
+        try:
+            input_requests = dataset_mapping[args.dataset_name]()
+        except KeyError as err:
+            raise ValueError(f"Unknown dataset: {args.dataset_name}") from err
     goodput_config_dict = check_goodput_args(args)
 
     # Avoid GC processing "static" data - reduce pause times.
@@ -958,7 +647,6 @@ def main(args: argparse.Namespace):
             tokenizer=tokenizer,
             input_requests=input_requests,
             logprobs=args.logprobs,
-            best_of=args.best_of,
             request_rate=args.request_rate,
             burstiness=args.burstiness,
             disable_tqdm=args.disable_tqdm,
@@ -983,7 +671,6 @@ def main(args: argparse.Namespace):
         result_json["backend"] = backend
         result_json["model_id"] = model_id
         result_json["tokenizer_id"] = tokenizer_id
-        result_json["best_of"] = args.best_of
         result_json["num_prompts"] = args.num_prompts
 
         # Metadata
@@ -997,6 +684,15 @@ def main(args: argparse.Namespace):
                         "Invalid metadata format. Please use KEY=VALUE format."
                     )
 
+        if not args.save_detailed:
+            # Remove fields with too many data points
+            for field in [
+                    "input_lens", "output_lens", "ttfts", "itls",
+                    "generated_texts", "errors"
+            ]:
+                if field in result_json:
+                    del result_json[field]
+
         # Traffic
         result_json["request_rate"] = (args.request_rate if args.request_rate
                                        < float("inf") else "inf")
@@ -1081,13 +777,6 @@ if __name__ == "__main__":
         help=
         "Name or path of the tokenizer, if not using the default tokenizer.",  # noqa: E501
     )
-    parser.add_argument(
-        "--best-of",
-        type=int,
-        default=1,
-        help="Generates `best_of` sequences per prompt and "
-        "returns the best one.",
-    )
     parser.add_argument("--use-beam-search", action="store_true")
     parser.add_argument(
         "--num-prompts",
@@ -1148,6 +837,12 @@ if __name__ == "__main__":
         action="store_true",
         help="Specify to save benchmark results to a json file",
     )
+    parser.add_argument(
+        "--save-detailed",
+        action="store_true",
+        help="When saving the results, whether to include per request "
+        "information such as response, error, ttfs, tpots, etc.",
+    )
     parser.add_argument(
         "--metadata",
         metavar="KEY=VALUE",
@@ -1312,4 +1007,5 @@ if __name__ == "__main__":
                         "script chooses a LoRA module at random.")
 
     args = parser.parse_args()
+
     main(args)

benchmarks/benchmark_serving_structured_output.py

@@ -1,5 +1,5 @@
 # SPDX-License-Identifier: Apache-2.0
-r"""Benchmark online serving throughput with guided decoding.
+r"""Benchmark online serving throughput with structured outputs.
 
 On the server side, run one of the following commands:
     (vLLM OpenAI API server)
@@ -9,12 +9,12 @@ On the server side, run one of the following commands:
     ./launch_tgi_server.sh <your_model> <max_batch_total_tokens>
 
 On the client side, run:
-    python benchmarks/benchmark_serving_guided.py \
+    python benchmarks/benchmark_serving_structured_output.py \
         --backend <backend> \
         --model <your_model> \
         --dataset json \
-        --guided-decoding-ratio 1.0 \
-        --guided-decoding-backend xgrammar \
+        --structured-output-ratio 1.0 \
+        --structured-output-backend xgrammar \
         --request-rate 10 \
         --num-prompts 1000
 
@@ -24,11 +24,13 @@ On the client side, run:
 """
 import argparse
 import asyncio
+import copy
 import dataclasses
 import json
 import os
 import random
 import time
+import uuid
 import warnings
 from collections.abc import AsyncGenerator
 from dataclasses import dataclass
@@ -52,6 +54,9 @@ try:
 except ImportError:
     from argparse import ArgumentParser as FlexibleArgumentParser
 
+from vllm.v1.structured_output.utils import (
+    has_xgrammar_unsupported_json_features)
+
 MILLISECONDS_TO_SECONDS_CONVERSION = 1000
 
 
@@ -106,24 +111,43 @@ class SampleRequest:
 
 def sample_requests(tokenizer: PreTrainedTokenizerBase,
                     args: argparse.Namespace) -> list[SampleRequest]:
-    if args.dataset == 'json':
+    if args.dataset == 'json' or args.dataset == 'json-unique':
         if args.json_schema_path is None:
             dir_path = os.path.dirname(os.path.realpath(__file__))
             args.json_schema_path = os.path.join(dir_path,
                                                  "structured_schemas",
                                                  "structured_schema_1.json")
+        json_schemas = []
         with open(args.json_schema_path) as f:
             schema = json.load(f)
-        prompt = f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
-        input_len = len(tokenizer(prompt).input_ids)
-        print(f"Input length of the prompt: {input_len} tokens")
+
+        if args.dataset == 'json-unique':
+            json_schemas = [
+                copy.deepcopy(schema) for _ in range(args.num_prompts)
+            ]
+            for i in range(len(json_schemas)):
+                json_schemas[i]["properties"][
+                    f"__optional_field_{uuid.uuid4()}"] = {
+                        "type":
+                        "string",
+                        "description":
+                        "An unique optional field to avoid cached schemas"
+                    }
+
+        def gen_prompt(index: int):
+            schema = json_schemas[index % len(json_schemas)]
+            return f"Generate an example of a user profile given the following schema: {json.dumps(schema)}"  # noqa: E501
+
+        def get_schema(index: int):
+            return json_schemas[index % len(json_schemas)]
+
         requests = [
-            SampleRequest(prompt=prompt,
-                          prompt_len=input_len,
+            SampleRequest(prompt=gen_prompt(i),
+                          prompt_len=len(tokenizer(gen_prompt(i)).input_ids),
                           expected_output_len=args.output_len,
-                          schema=schema,
+                          schema=get_schema(i),
                           structure_type=args.structure_type)
-            for _ in range(args.num_prompts)
+            for i in range(args.num_prompts)
         ]
 
     elif args.dataset == "grammar":
@@ -191,7 +215,17 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
         requests: list[SampleRequest] = []
         dataset = datasets.load_dataset("NousResearch/json-mode-eval",
                                         split="train")
-        print(f"dataset has {len(dataset)} entries")
+        full_dataset_len = len(dataset)
+
+        def _filter_func(item):
+            import json
+            schema = json.loads(item["schema"])
+            return not has_xgrammar_unsupported_json_features(schema)
+
+        dataset = dataset.filter(_filter_func)
+        num_filtered_out = full_dataset_len - len(dataset)
+        print(f"dataset has {len(dataset)} entries after filtering "
+              f"out {num_filtered_out} entries with unsupported features")
         len_dataset = len(dataset)
         for data_point_idx in range(args.num_prompts):
             idx = data_point_idx
@@ -378,8 +412,8 @@ async def benchmark(
     selected_percentiles: list[str],
     ignore_eos: bool,
     max_concurrency: Optional[int],
-    guided_decoding_ratio: float,
-    guided_decoding_backend: str,
+    structured_output_ratio: float,
+    structured_output_backend: str,
     goodput_config_dict: Optional[dict[str, float]] = None,
 ):
     if backend in ASYNC_REQUEST_FUNCS:
@@ -391,16 +425,18 @@ async def benchmark(
         extra_body = {}
         # Add the schema to the extra_body
         extra_body[request.structure_type] = request.schema
-        # Add the specific guided_decoding_backend
-        extra_body["guided_decoding_backend"] = guided_decoding_backend
+        # Add the specific structured_output_backend
+        extra_body["guided_decoding_backend"] = structured_output_backend
         return extra_body
 
     print("Starting initial single prompt test run...")
-    guided_decoding_req_idx = random.sample(
+    structured_output_req_idx = random.sample(
         range(len(input_requests)),
-        int(len(input_requests) * guided_decoding_ratio))
+        int(len(input_requests) * structured_output_ratio))
 
     test_request = input_requests[0]
+    test_req_extra_body = (prepare_extra_body(test_request)
+                           if 0 in structured_output_req_idx else None)
     test_input = RequestFuncInput(
         model=model_id,
         prompt=test_request.prompt,
@@ -408,7 +444,7 @@ async def benchmark(
         prompt_len=test_request.prompt_len,
         output_len=test_request.expected_output_len,
         ignore_eos=ignore_eos,
-        extra_body=prepare_extra_body(test_request),
+        extra_body=test_req_extra_body,
     )
     test_output = await request_func(request_func_input=test_input)
     if not test_output.success:
@@ -427,7 +463,7 @@ async def benchmark(
             prompt_len=test_request.prompt_len,
             output_len=test_request.expected_output_len,
             ignore_eos=ignore_eos,
-            extra_body=prepare_extra_body(test_request),
+            extra_body=test_req_extra_body,
         )
         profile_output = await request_func(request_func_input=profile_input)
         if profile_output.success:
@@ -465,7 +501,7 @@ async def benchmark(
     async for i, request in get_request(input_requests, request_rate,
                                         burstiness):
         extra_body = prepare_extra_body(
-            request) if i in guided_decoding_req_idx else None
+            request) if i in structured_output_req_idx else None
         request_func_input = RequestFuncInput(
             model=model_id,
             prompt=request.prompt,
@@ -708,10 +744,10 @@ def main(args: argparse.Namespace):
     else:
         args.structure_type = 'guided_json'
 
-    if args.no_guided_decoding:
-        args.guided_decoding_ratio = 0
+    if args.no_structured_output:
+        args.structured_output_ratio = 0
     if args.save_results:
-        result_file_name = f'{args.guided_decoding_ratio}guided'
+        result_file_name = f'{args.structured_output_ratio}guided'
         result_file_name += f"_{backend}"
         result_file_name += f"_{args.request_rate}qps"
         result_file_name += f"_{args.model.split('/')[-1]}"
@@ -744,8 +780,8 @@ def main(args: argparse.Namespace):
             ],
             ignore_eos=args.ignore_eos,
             max_concurrency=args.max_concurrency,
-            guided_decoding_ratio=args.guided_decoding_ratio,
-            guided_decoding_backend=args.guided_decoding_backend,
+            structured_output_ratio=args.structured_output_ratio,
+            structured_output_backend=args.structured_output_backend,
             goodput_config_dict=goodput_config_dict,
         ))
 
@@ -806,10 +842,12 @@ if __name__ == "__main__":
         default="/v1/completions",
         help="API endpoint.",
     )
-    parser.add_argument(
-        "--dataset",
+    parser.add_argument("--dataset",
                         default='json',
-        choices=['json', 'grammar', 'regex', 'choice', 'xgrammar_bench'])
+                        choices=[
+                            'json', 'json-unique', 'grammar', 'regex',
+                            'choice', 'xgrammar_bench'
+                        ])
     parser.add_argument("--json_schema_path",
                         type=str,
                         default=None,
@@ -943,19 +981,19 @@ if __name__ == "__main__":
         "goodput, refer to DistServe paper: https://arxiv.org/pdf/2401.09670 "
         "and the blog: https://hao-ai-lab.github.io/blogs/distserve")
 
-    parser.add_argument("--no-guided-decoding",
+    parser.add_argument("--no-structured-output",
                         action='store_true',
                         default=False,
                         help="Whether to disable JSON decoding or not.")
-    parser.add_argument("--guided-decoding-ratio",
+    parser.add_argument("--structured-output-ratio",
                         type=float,
                         default=1.0,
-                        help="Ratio of Guided Decoding requests")
-    parser.add_argument("--guided-decoding-backend",
+                        help="Ratio of Structured Outputs requests")
+    parser.add_argument("--structured-output-backend",
                         type=str,
                         choices=["outlines", "lm-format-enforcer", "xgrammar"],
                         default="xgrammar",
-                        help="Backend to use for guided decoding")
+                        help="Backend to use for structured outputs")
 
     args = parser.parse_args()
     main(args)

benchmarks/benchmark_throughput.py

@@ -6,13 +6,15 @@ import json
 import os
 import random
 import time
-from functools import cache
-from typing import Any, Optional
+import warnings
+from typing import Any, Optional, Union
 
 import torch
 import uvloop
+from benchmark_dataset import (BurstGPTDataset, HuggingFaceDataset,
+                               RandomDataset, SampleRequest, ShareGPTDataset,
+                               SonnetDataset, VisionArenaDataset)
 from benchmark_utils import convert_to_pytorch_benchmark_format, write_to_json
-from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
@@ -20,155 +22,19 @@ from transformers import (AutoModelForCausalLM, AutoTokenizer,
 from vllm.engine.arg_utils import AsyncEngineArgs, EngineArgs
 from vllm.entrypoints.openai.api_server import (
     build_async_engine_client_from_engine_args)
-from vllm.inputs import TextPrompt
+from vllm.inputs import TextPrompt, TokensPrompt
 from vllm.lora.request import LoRARequest
-from vllm.lora.utils import get_adapter_absolute_path
-from vllm.multimodal import MultiModalDataDict
+from vllm.outputs import RequestOutput
 from vllm.sampling_params import BeamSearchParams
-from vllm.transformers_utils.tokenizer import AnyTokenizer, get_lora_tokenizer
 from vllm.utils import FlexibleArgumentParser, merge_async_iterators
 
 
-@dataclasses.dataclass
-class SampleRequest:
-    """A class representing a single inference request for benchmarking.
-
-    Attributes:
-        prompt: The input text prompt for the model.
-        prompt_len: The length of the prompt in tokens.
-        expected_output_len: The expected length of the output in tokens.
-        multi_modal_data: Optional dictionary containing multi-modal data (e.g.
-            images).
-        lora_request: Optional LoRARequest specifying the LoRA to use. 
-    """
-    prompt: str
-    prompt_len: int
-    expected_output_len: int
-    multi_modal_data: Optional[MultiModalDataDict] = None
-    lora_request: Optional[LoRARequest] = None
-
-
-def _get_prompt_for_image_model(question: str, *, model: str) -> str:
-    """Prepend and append special tokens around the question to form a prompt.
-
-    Args:
-        question: The input question text to wrap with special tokens
-        model: The name of the model being used, to determine which special
-            tokens to add
-
-    Returns:
-        The formatted prompt string with appropriate special tokens for the
-            model
-
-    Raises:
-        ValueError: If an unsupported model name is provided
-    """
-    model = model.lower()
-    if "pixtral" in model:
-        return f"<s>[INST]{question}\n[IMG][/INST]"
-    raise ValueError(f"Unsupported model {model}")
-
-
-@cache
-def lora_path_on_disk(lora_path: str) -> str:
-    return get_adapter_absolute_path(lora_path)
-
-
-lora_tokenizer_cache: dict[int, AnyTokenizer] = {}
-
-
-def get_random_lora_request(
-        args: argparse.Namespace
-) -> tuple[LoRARequest, Optional[AnyTokenizer]]:
-    global lora_tokenizer_cache
-    lora_id = random.randint(1, args.max_loras)
-    lora_request = LoRARequest(lora_name=str(lora_id),
-                               lora_int_id=lora_id,
-                               lora_path=lora_path_on_disk(args.lora_path))
-    if lora_id not in lora_tokenizer_cache:
-        lora_tokenizer_cache[lora_id] = get_lora_tokenizer(lora_request)
-    return lora_request, lora_tokenizer_cache[lora_id]
-
-
-def sample_requests(tokenizer: PreTrainedTokenizerBase,
-                    args: argparse.Namespace) -> list[SampleRequest]:
-
-    dataset_path: str = args.dataset
-    num_requests: int = args.num_prompts
-    fixed_output_len: Optional[int] = args.output_len
-    model: str = args.model
-    if fixed_output_len is not None and fixed_output_len < 4:
-        raise ValueError("output_len too small")
-
-    # Load the dataset.
-    with open(dataset_path) as f:
-        dataset = json.load(f)
-    # Filter out the conversations with less than 2 turns.
-    dataset = [data for data in dataset if len(data["conversations"]) >= 2]
-    # Shuffle the dataset.
-    random.shuffle(dataset)
-
-    # Filter out sequences that are too long or too short
-    filtered_dataset: list[SampleRequest] = []
-    for data in tqdm(dataset,
-                     total=len(filtered_dataset),
-                     desc="sampling requests"):
-        if len(filtered_dataset) == num_requests:
-            break
-
-        # Only keep the first two turns of each conversation.
-        prompt = data["conversations"][0]["value"]
-        completion = data["conversations"][1]["value"]
-
-        multi_modal_data: Optional[MultiModalDataDict] = None
-        if "image" in data:
-            multi_modal_data = multi_modal_data or {}
-            image_path = data["image"]
-            # TODO(vllm-project/vllm/issues/9778): Support multiple images.
-            assert isinstance(image_path,
-                              str), "Only support single image input"
-            try:
-                multi_modal_data["image"] = Image.open(image_path).convert(
-                    "RGB")
-            except FileNotFoundError:
-                # Ignore datapoint where asset is missing
-                continue
-            prompt = _get_prompt_for_image_model(question=prompt, model=model)
-
-        request_tokenizer = tokenizer
-        lora_request: Optional[LoRARequest] = None
-        if args.enable_lora:
-            lora_request, lora_tokenizer = get_random_lora_request(args)
-            if lora_tokenizer:
-                request_tokenizer = lora_tokenizer
-
-        # Tokenize the prompts and completions.
-        prompt_token_ids = request_tokenizer(prompt).input_ids
-        completion_token_ids = request_tokenizer(completion).input_ids
-        prompt_len = len(prompt_token_ids)
-        output_len = len(completion_token_ids
-                         ) if fixed_output_len is None else fixed_output_len
-        if prompt_len < 4 or output_len < 4:
-            # Prune too short sequences.
-            continue
-        if prompt_len > 1024 or prompt_len + output_len > 2048:
-            # Prune too long sequences.
-            continue
-        filtered_dataset.append(
-            SampleRequest(prompt=prompt,
-                          prompt_len=prompt_len,
-                          expected_output_len=output_len,
-                          multi_modal_data=multi_modal_data,
-                          lora_request=lora_request))
-
-    return filtered_dataset
-
-
 def run_vllm(
     requests: list[SampleRequest],
     n: int,
     engine_args: EngineArgs,
-) -> float:
+    disable_detokenize: bool = False,
+) -> tuple[float, Optional[list[RequestOutput]]]:
     from vllm import LLM, SamplingParams
     llm = LLM(**dataclasses.asdict(engine_args))
     assert all(
@@ -178,10 +44,13 @@ def run_vllm(
             "Please ensure that max_model_len is greater than the sum of"
             " prompt_len and expected_output_len for all requests.")
     # Add the requests to the engine.
-    prompts: list[TextPrompt] = []
+    prompts: list[Union[TextPrompt, TokensPrompt]] = []
     sampling_params: list[SamplingParams] = []
     for request in requests:
         prompts.append(
+            TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                       multi_modal_data=request.multi_modal_data)
+            if "prompt_token_ids" in request.prompt else \
             TextPrompt(prompt=request.prompt,
                        multi_modal_data=request.multi_modal_data))
         sampling_params.append(
@@ -191,6 +60,7 @@ def run_vllm(
                 top_p=1.0,
                 ignore_eos=True,
                 max_tokens=request.expected_output_len,
+                detokenize=not disable_detokenize,
             ))
     lora_requests: Optional[list[LoRARequest]] = None
     if engine_args.enable_lora:
@@ -198,9 +68,10 @@ def run_vllm(
 
     use_beam_search = False
 
+    outputs = None
     if not use_beam_search:
         start = time.perf_counter()
-        llm.generate(prompts,
+        outputs = llm.generate(prompts,
                                sampling_params,
                                lora_request=lora_requests,
                                use_tqdm=True)
@@ -221,7 +92,46 @@ def run_vllm(
                 ignore_eos=True,
             ))
         end = time.perf_counter()
-    return end - start
+    return end - start, outputs
+
+
+def run_vllm_chat(
+        requests: list[SampleRequest],
+        n: int,
+        engine_args: EngineArgs,
+        disable_detokenize: bool = False) -> tuple[float, list[RequestOutput]]:
+    """
+    Run vLLM chat benchmark. This function is recommended ONLY for benchmarking
+    multimodal models as it properly handles multimodal inputs and chat
+    formatting. For non-multimodal models, use run_vllm() instead.
+    """
+    from vllm import LLM, SamplingParams
+    llm = LLM(**dataclasses.asdict(engine_args))
+
+    assert all(
+        llm.llm_engine.model_config.max_model_len >= (
+            request.prompt_len + request.expected_output_len)
+        for request in requests), (
+            "Please ensure that max_model_len is greater than the sum of "
+            "prompt_len and expected_output_len for all requests.")
+
+    prompts = []
+    sampling_params: list[SamplingParams] = []
+    for request in requests:
+        prompts.append(request.prompt)
+        sampling_params.append(
+            SamplingParams(
+                n=n,
+                temperature=1.0,
+                top_p=1.0,
+                ignore_eos=True,
+                max_tokens=request.expected_output_len,
+                detokenize=not disable_detokenize,
+            ))
+    start = time.perf_counter()
+    outputs = llm.chat(prompts, sampling_params, use_tqdm=True)
+    end = time.perf_counter()
+    return end - start, outputs
 
 
 async def run_vllm_async(
@@ -229,6 +139,7 @@ async def run_vllm_async(
     n: int,
     engine_args: AsyncEngineArgs,
     disable_frontend_multiprocessing: bool = False,
+    disable_detokenize: bool = False,
 ) -> float:
     from vllm import SamplingParams
 
@@ -242,11 +153,14 @@ async def run_vllm_async(
                 " prompt_len and expected_output_len for all requests.")
 
         # Add the requests to the engine.
-        prompts: list[TextPrompt] = []
+        prompts: list[Union[TextPrompt, TokensPrompt]] = []
         sampling_params: list[SamplingParams] = []
         lora_requests: list[Optional[LoRARequest]] = []
         for request in requests:
             prompts.append(
+                TokensPrompt(prompt_token_ids=request.prompt["prompt_token_ids"],
+                        multi_modal_data=request.multi_modal_data)
+                if "prompt_token_ids" in request.prompt else \
                 TextPrompt(prompt=request.prompt,
                            multi_modal_data=request.multi_modal_data))
             sampling_params.append(
@@ -256,6 +170,7 @@ async def run_vllm_async(
                     top_p=1.0,
                     ignore_eos=True,
                     max_tokens=request.expected_output_len,
+                    detokenize=not disable_detokenize,
                 ))
             lora_requests.append(request.lora_request)
 
@@ -282,6 +197,7 @@ def run_hf(
     n: int,
     max_batch_size: int,
     trust_remote_code: bool,
+    disable_detokenize: bool = False,
 ) -> float:
     llm = AutoModelForCausalLM.from_pretrained(
         model, torch_dtype=torch.float16, trust_remote_code=trust_remote_code)
@@ -321,6 +237,7 @@ def run_hf(
             use_cache=True,
             max_new_tokens=max_output_len,
         )
+        if not disable_detokenize:
             # Include the decoding time.
             tokenizer.batch_decode(llm_outputs, skip_special_tokens=True)
         pbar.update(len(batch))
@@ -369,58 +286,68 @@ def save_to_pytorch_benchmark_format(args: argparse.Namespace,
         write_to_json(pt_file, pt_records)
 
 
+def get_requests(args, tokenizer):
+    # Common parameters for all dataset types.
+    common_kwargs = {
+        "dataset_path": args.dataset_path,
+        "random_seed": args.seed,
+    }
+    sample_kwargs = {
+        "tokenizer": tokenizer,
+        "lora_path": args.lora_path,
+        "max_loras": args.max_loras,
+        "num_requests": args.num_prompts,
+        "input_len": args.input_len,
+        "output_len": args.output_len,
+    }
+    if args.dataset_path is None or args.dataset_name == "random":
+        sample_kwargs["range_ratio"] = args.random_range_ratio
+        sample_kwargs["prefix_len"] = args.prefix_len
+        dataset_cls = RandomDataset
+    elif args.dataset_name == "sharegpt":
+        dataset_cls = ShareGPTDataset
+        if args.backend == "vllm-chat":
+            sample_kwargs["enable_multimodal_chat"] = True
+    elif args.dataset_name == "sonnet":
+        assert tokenizer.chat_template or tokenizer.default_chat_template, (
+            "Tokenizer/model must have chat template for sonnet dataset.")
+        dataset_cls = SonnetDataset
+        sample_kwargs["prefix_len"] = args.prefix_len
+        sample_kwargs["return_prompt_formatted"] = True
+    elif args.dataset_name == "burstgpt":
+        dataset_cls = BurstGPTDataset
+    elif args.dataset_name == "hf":
+        if args.backend != "vllm-chat":
+            raise ValueError(
+                "hf datasets only are supported by vllm-chat backend")
+        # Choose between VisionArenaDataset and HuggingFaceDataset based on
+        # provided parameters.
+        dataset_cls = (VisionArenaDataset if args.dataset_path
+                       == VisionArenaDataset.VISION_ARENA_DATASET_PATH
+                       and args.hf_subset is None else HuggingFaceDataset)
+        common_kwargs['dataset_subset'] = args.hf_subset
+        common_kwargs['dataset_split'] = args.hf_split
+        sample_kwargs["enable_multimodal_chat"] = True
+
+    else:
+        raise ValueError(f"Unknown dataset name: {args.dataset_name}")
+    # Remove None values
+    sample_kwargs = {k: v for k, v in sample_kwargs.items() if v is not None}
+    return dataset_cls(**common_kwargs).sample(**sample_kwargs)
+
+
 def main(args: argparse.Namespace):
+    if args.seed is None:
+        args.seed = 0
     print(args)
     random.seed(args.seed)
-
     # Sample the requests.
     tokenizer = AutoTokenizer.from_pretrained(
         args.tokenizer, trust_remote_code=args.trust_remote_code)
-    if args.dataset is None:
-        vocab_size = tokenizer.vocab_size
-        requests = []
-        for _ in range(args.num_prompts):
-
-            request_tokenizer = tokenizer
-            lora_request: Optional[LoRARequest] = None
-            if args.enable_lora:
-                lora_request, lora_tokenizer = get_random_lora_request(args)
-                if lora_tokenizer:
-                    request_tokenizer = lora_tokenizer
-
-            # Synthesize a prompt with the given input length.
-            candidate_ids = [
-                random.randint(0, vocab_size - 1)
-                for _ in range(args.input_len)
-            ]
-            # As tokenizer may add additional tokens like BOS, we need to try
-            # different lengths to get the desired input length.
-            for _ in range(5):  # Max attempts to correct
-                candidate_prompt = request_tokenizer.decode(candidate_ids)
-                tokenized_len = len(request_tokenizer.encode(candidate_prompt))
-
-                if tokenized_len == args.input_len:
-                    break
-
-                # Adjust length based on difference
-                diff = args.input_len - tokenized_len
-                if diff > 0:
-                    candidate_ids.extend([
-                        random.randint(100, vocab_size - 100)
-                        for _ in range(diff)
-                    ])
-                else:
-                    candidate_ids = candidate_ids[:diff]
-            requests.append(
-                SampleRequest(prompt=candidate_prompt,
-                              prompt_len=args.input_len,
-                              expected_output_len=args.output_len,
-                              lora_request=lora_request))
-    else:
-        requests = sample_requests(tokenizer, args)
-
+    requests = get_requests(args, tokenizer)
     is_multi_modal = any(request.multi_modal_data is not None
                          for request in requests)
+    request_outputs: Optional[list[RequestOutput]] = None
     if args.backend == "vllm":
         if args.async_engine:
             elapsed_time = uvloop.run(
@@ -429,31 +356,59 @@ def main(args: argparse.Namespace):
                     args.n,
                     AsyncEngineArgs.from_cli_args(args),
                     args.disable_frontend_multiprocessing,
+                    args.disable_detokenize,
                 ))
         else:
-            elapsed_time = run_vllm(requests, args.n,
-                                    EngineArgs.from_cli_args(args))
+            elapsed_time, request_outputs = run_vllm(
+                requests, args.n, EngineArgs.from_cli_args(args),
+                args.disable_detokenize)
     elif args.backend == "hf":
         assert args.tensor_parallel_size == 1
         elapsed_time = run_hf(requests, args.model, tokenizer, args.n,
-                              args.hf_max_batch_size, args.trust_remote_code)
+                              args.hf_max_batch_size, args.trust_remote_code,
+                              args.disable_detokenize)
     elif args.backend == "mii":
         elapsed_time = run_mii(requests, args.model, args.tensor_parallel_size,
                                args.output_len)
+    elif args.backend == "vllm-chat":
+        elapsed_time, request_outputs = run_vllm_chat(
+            requests, args.n, EngineArgs.from_cli_args(args),
+            args.disable_detokenize)
     else:
         raise ValueError(f"Unknown backend: {args.backend}")
-    total_num_tokens = sum(request.prompt_len + request.expected_output_len
-                           for request in requests)
-    total_output_tokens = sum(request.expected_output_len
-                              for request in requests)
-    if is_multi_modal:
-        print("\033[91mWARNING\033[0m: Multi-modal request detected. The "
+
+    if request_outputs:
+        # Note: with the vllm and vllm-chat backends,
+        # we have request_outputs, which we use to count tokens.
+        total_prompt_tokens = 0
+        total_output_tokens = 0
+        for ro in request_outputs:
+            if not isinstance(ro, RequestOutput):
+                continue
+            total_prompt_tokens += len(
+                ro.prompt_token_ids) if ro.prompt_token_ids else 0
+            total_output_tokens += sum(
+                len(o.token_ids) for o in ro.outputs if o)
+        total_num_tokens = total_prompt_tokens + total_output_tokens
+    else:
+        total_num_tokens = sum(r.prompt_len + r.expected_output_len
+                               for r in requests)
+        total_output_tokens = sum(r.expected_output_len for r in requests)
+        total_prompt_tokens = total_num_tokens - total_output_tokens
+
+    if is_multi_modal and args.backend != "vllm-chat":
+        print("\033[91mWARNING\033[0m: Multi-modal request with "
+              f"{args.backend} backend detected. The "
               "following metrics are not accurate because image tokens are not"
               " counted. See vllm-project/vllm/issues/9778 for details.")
-        # TODO(vllm-project/vllm/issues/9778): Count molti-modal token length.
+        # TODO(vllm-project/vllm/issues/9778): Count multi-modal token length.
+        # vllm-chat backend counts the image tokens now
+
     print(f"Throughput: {len(requests) / elapsed_time:.2f} requests/s, "
           f"{total_num_tokens / elapsed_time:.2f} total tokens/s, "
           f"{total_output_tokens / elapsed_time:.2f} output tokens/s")
+    print(f"Total num prompt tokens:  {total_prompt_tokens}")
+    print(f"Total num output tokens:  {total_output_tokens}")
 
     # Output JSON results if specified
     if args.output_json:
@@ -469,18 +424,112 @@ def main(args: argparse.Namespace):
         save_to_pytorch_benchmark_format(args, results)
 
 
+def validate_args(args):
+    """
+    Validate command-line arguments.
+    """
+
+    # === Deprecation and Defaulting ===
+    if args.dataset is not None:
+        warnings.warn(
+            "The '--dataset' argument will be deprecated in the next release. "
+            "Please use '--dataset-name' and '--dataset-path' instead.",
+            stacklevel=2)
+        args.dataset_path = args.dataset
+
+    if not getattr(args, "tokenizer", None):
+        args.tokenizer = args.model
+
+    # === Backend Validation ===
+    valid_backends = {"vllm", "hf", "mii", "vllm-chat"}
+    if args.backend not in valid_backends:
+        raise ValueError(f"Unsupported backend: {args.backend}")
+
+    # === Dataset Configuration ===
+    if not args.dataset and not args.dataset_path:
+        print(
+            "When dataset path is not set, it will default to random dataset")
+        args.dataset_name = 'random'
+        if args.input_len is None:
+            raise ValueError("input_len must be provided for a random dataset")
+
+    # === Dataset Name Specific Checks ===
+    # --hf-subset and --hf-split: only used
+    # when dataset_name is 'hf'
+    if args.dataset_name != "hf" and (
+            getattr(args, "hf_subset", None) is not None
+            or getattr(args, "hf_split", None) is not None):
+        warnings.warn("--hf-subset and --hf-split will be ignored \
+                since --dataset-name is not 'hf'.",
+                      stacklevel=2)
+    elif args.dataset_name == "hf" and args.backend != "vllm-chat":
+        raise ValueError(
+            "When --dataset-name is 'hf', backend must be 'vllm-chat'")
+
+    # --random-range-ratio: only used when dataset_name is 'random'
+    if args.dataset_name != 'random' and args.random_range_ratio is not None:
+        warnings.warn("--random-range-ratio will be ignored since \
+                --dataset-name is not 'random'.",
+                      stacklevel=2)
+
+    # --prefix-len: only used when dataset_name is 'random', 'sonnet', or not
+    # set.
+    if args.dataset_name not in {"random", "sonnet", None
+                                 } and args.prefix_len is not None:
+        warnings.warn("--prefix-len will be ignored since --dataset-name\
+                 is not 'random', 'sonnet', or not set.",
+                      stacklevel=2)
+
+    # === LoRA Settings ===
+    if getattr(args, "enable_lora", False) and args.backend != "vllm":
+        raise ValueError(
+            "LoRA benchmarking is only supported for vLLM backend")
+    if getattr(args, "enable_lora", False) and args.lora_path is None:
+        raise ValueError("LoRA path must be provided when enable_lora is True")
+
+    # === Backend-specific Validations ===
+    if args.backend == "hf" and args.hf_max_batch_size is None:
+        raise ValueError("HF max batch size is required for HF backend")
+    if args.backend != "hf" and args.hf_max_batch_size is not None:
+        raise ValueError("HF max batch size is only for HF backend.")
+
+    if args.backend in {"hf", "mii"} and getattr(args, "quantization",
+                                                 None) is not None:
+        raise ValueError("Quantization is only for vLLM backend.")
+
+    if args.backend == "mii" and args.dtype != "auto":
+        raise ValueError("dtype must be auto for MII backend.")
+    if args.backend == "mii" and args.n != 1:
+        raise ValueError("n must be 1 for MII backend.")
+    if args.backend == "mii" and args.tokenizer != args.model:
+        raise ValueError(
+            "Tokenizer must be the same as the model for MII backend.")
+
+
 if __name__ == "__main__":
     parser = FlexibleArgumentParser(description="Benchmark the throughput.")
     parser.add_argument("--backend",
                         type=str,
-                        choices=["vllm", "hf", "mii"],
+                        choices=["vllm", "hf", "mii", "vllm-chat"],
                         default="vllm")
-    parser.add_argument("--dataset",
+    parser.add_argument(
+        "--dataset-name",
+        type=str,
+        choices=["sharegpt", "random", "sonnet", "burstgpt", "hf"],
+        help="Name of the dataset to benchmark on.",
+        default="sharegpt")
+    parser.add_argument(
+        "--dataset",
         type=str,
         default=None,
-                        help="Path to the dataset. The dataset is expected to "
+        help="Path to the ShareGPT dataset, will be deprecated in\
+            the next release. The dataset is expected to "
         "be a json in form of list[dict[..., conversations: "
         "list[dict[..., value: <prompt_or_response>]]]]")
+    parser.add_argument("--dataset-path",
+                        type=str,
+                        default=None,
+                        help="Path to the dataset")
     parser.add_argument("--input-len",
                         type=int,
                         default=None,
@@ -515,6 +564,11 @@ if __name__ == "__main__":
                         action='store_true',
                         default=False,
                         help="Disable decoupled async engine frontend.")
+    parser.add_argument(
+        "--disable-detokenize",
+        action="store_true",
+        help=("Do not detokenize the response (i.e. do not include "
+              "detokenization time in the measurement)"))
     # LoRA
     parser.add_argument(
         "--lora-path",
@@ -522,43 +576,33 @@ if __name__ == "__main__":
         default=None,
         help="Path to the lora adapters to use. This can be an absolute path, "
         "a relative path, or a Hugging Face model identifier.")
+    parser.add_argument("--prefix-len",
+                        type=int,
+                        default=None,
+                        help="Number of prefix tokens per request."
+                        "This is for the RandomDataset and SonnetDataset")
+    # random dataset
+    parser.add_argument(
+        "--random-range-ratio",
+        type=float,
+        default=None,
+        help="Range of sampled ratio of input/output length, "
+        "used only for RandomDataSet.",
+    )
+
+    # hf dtaset
+    parser.add_argument("--hf-subset",
+                        type=str,
+                        default=None,
+                        help="Subset of the HF dataset.")
+    parser.add_argument("--hf-split",
+                        type=str,
+                        default=None,
+                        help="Split of the HF dataset.")
 
     parser = AsyncEngineArgs.add_cli_args(parser)
     args = parser.parse_args()
     if args.tokenizer is None:
         args.tokenizer = args.model
-    if args.dataset is None:
-        assert args.input_len is not None
-        assert args.output_len is not None
-    else:
-        assert args.input_len is None
-    if args.enable_lora:
-        assert args.lora_path is not None
-
-    if args.backend == "vllm":
-        if args.hf_max_batch_size is not None:
-            raise ValueError("HF max batch size is only for HF backend.")
-    elif args.backend == "hf":
-        if args.hf_max_batch_size is None:
-            raise ValueError("HF max batch size is required for HF backend.")
-        if args.quantization is not None:
-            raise ValueError("Quantization is only for vLLM backend.")
-        if args.enable_lora is not None:
-            raise ValueError("LoRA benchmarking is only supported for vLLM"
-                             " backend")
-    elif args.backend == "mii":
-        if args.dtype != "auto":
-            raise ValueError("dtype must be auto for MII backend.")
-        if args.n != 1:
-            raise ValueError("n must be 1 for MII backend.")
-        if args.quantization is not None:
-            raise ValueError("Quantization is only for vLLM backend.")
-        if args.hf_max_batch_size is not None:
-            raise ValueError("HF max batch size is only for HF backend.")
-        if args.tokenizer != args.model:
-            raise ValueError("Tokenizer must be the same as the model for MII "
-                             "backend.")
-        if args.enable_lora is not None:
-            raise ValueError("LoRA benchmarking is only supported for vLLM"
-                             " backend")
+    validate_args(args)
     main(args)

benchmarks/kernels/benchmark_layernorm.py

@@ -40,7 +40,7 @@ def main(num_tokens: int,
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.

benchmarks/kernels/benchmark_lora.py

@@ -23,6 +23,7 @@ from vllm.lora.ops.triton_ops.bgmv_shrink import bgmv_shrink
 from vllm.lora.ops.triton_ops.sgmv_expand import sgmv_expand
 from vllm.lora.ops.triton_ops.sgmv_shrink import sgmv_shrink
 from vllm.lora.ops.triton_ops.utils import _LORA_A_PTR_DICT, _LORA_B_PTR_DICT
+from vllm.lora.ops.triton_ops.v1 import V1KernelMeta, v1_expand, v1_shrink
 from vllm.utils import FlexibleArgumentParser
 
 DEFAULT_MODELS = list(WEIGHT_SHAPES.keys())
@@ -153,7 +154,6 @@ def ref_group_gemm(ref_out: torch.Tensor, input: torch.Tensor,
         result = torch.nn.functional.linear(x, w)
         result *= scaling
         out_list.append(result)
-    torch.cat(out_list, dim=0)
 
     cat_result = torch.cat(out_list, dim=0)
 
@@ -172,6 +172,8 @@ class OpType(Enum):
     SGMV_EXPAND = auto()
     BGMV_EXPAND = auto()
     BGMV_EXPAND_SLICE = auto()
+    V1_SHRINK = auto()
+    V1_EXPAND = auto()
 
     @staticmethod
     def from_str(s: str) -> "OpType":
@@ -185,28 +187,43 @@ class OpType(Enum):
             return OpType.BGMV_EXPAND
         if s.lower() == "bgmv_expand_slice":
             return OpType.BGMV_EXPAND_SLICE
+        if s.lower() == "v1_shrink":
+            return OpType.V1_SHRINK
+        if s.lower() == "v1_expand":
+            return OpType.V1_EXPAND
         raise ValueError(f"Unrecognized str {s} to convert to OpType")
 
     def is_shrink_fn(self) -> bool:
-        return self in [OpType.SGMV_SHRINK, OpType.BGMV_SHRINK]
+        return self in [
+            OpType.SGMV_SHRINK, OpType.BGMV_SHRINK, OpType.V1_SHRINK
+        ]
 
     def is_expand_fn(self) -> bool:
-        return self in [OpType.SGMV_EXPAND, OpType.BGMV_EXPAND]
+        return self in [
+            OpType.SGMV_EXPAND, OpType.BGMV_EXPAND, OpType.V1_EXPAND
+        ]
 
     def is_prefill_op(self) -> bool:
-        return self in [OpType.SGMV_SHRINK, OpType.SGMV_EXPAND]
+        return self in [
+            OpType.SGMV_SHRINK, OpType.SGMV_EXPAND, OpType.V1_SHRINK,
+            OpType.V1_EXPAND
+        ]
 
     def is_decode_op(self) -> bool:
         return self in [
-            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE
+            OpType.BGMV_SHRINK, OpType.BGMV_EXPAND, OpType.BGMV_EXPAND_SLICE,
+            OpType.V1_SHRINK, OpType.V1_EXPAND
         ]
 
     def is_expand_slice_fn(self) -> bool:
         return self in [OpType.BGMV_EXPAND_SLICE]
 
     def num_slices(self) -> list[int]:
-        if self in [OpType.SGMV_EXPAND, OpType.SGMV_SHRINK]:
-            # SGMV kernels supports slices
+        if self in [
+                OpType.SGMV_EXPAND, OpType.SGMV_SHRINK, OpType.V1_SHRINK,
+                OpType.V1_EXPAND
+        ]:
+            # SGMV kernels and v1 kernels supports slices
             return [1, 2, 3]
         if self in [OpType.BGMV_SHRINK, OpType.BGMV_EXPAND]:
             return [1]
@@ -251,11 +268,13 @@ class OpType(Enum):
         m, k, n = self.mkn(batch_size, seq_length, hidden_size, lora_rank)
 
         b_shape = (num_loras, n, k)  # col-major
-        if self == OpType.SGMV_SHRINK:
-            # SGMV shrink supports num_slices inherently in the kernel
+        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
+            # SGMV shrink and V1 shrink kernels support num_slices inherently
+            # in the kernel.
             return ((m, k), b_shape, (num_slices, m, n))
-        if self == OpType.SGMV_EXPAND:
-            # SGMV expand supports num_slices inherently in the kernel
+        if self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
+            # SGMV expand and V1 expand kernels support num_slices inherently
+            # in the kernel
             return ((num_slices, m, k), b_shape, (m, n * num_slices))
         if self == OpType.BGMV_SHRINK:
             return ((m, k), b_shape, (m, n))
@@ -282,25 +301,30 @@ class OpType(Enum):
             return bgmv_expand
         if self == OpType.BGMV_EXPAND_SLICE:
             return emulate_bgmv_expand_slice
+        if self == OpType.V1_SHRINK:
+            return v1_shrink
+        if self == OpType.V1_EXPAND:
+            return v1_expand
+
         raise ValueError(f"Unrecognized optype {self}")
 
     def run_ref_group_gemm(self, output: torch.Tensor, input: torch.Tensor,
                            lora_weights: list[torch.Tensor],
                            **kwargs) -> Callable:
-        """Each benchmark operation expected the input, lora_weights and outputs
+        """Each benchmark operation expects the input, lora_weights and outputs
            in a slightly different format. Refer to self.matmul_shapes().
            run_ref_group_gemm accounts for those differences in executing a
            reference group gemm for correctness testing.
         """
         w_dtype = lora_weights[0].dtype
         num_slices = len(lora_weights)
-        if self == OpType.SGMV_SHRINK:
+        if self in [OpType.SGMV_SHRINK, OpType.V1_SHRINK]:
             for slice_idx in range(num_slices):
                 ref_group_gemm(ref_out=output[slice_idx, :],
                                input=input,
                                lora_weights=lora_weights[slice_idx],
                                **kwargs)
-        if self == OpType.SGMV_EXPAND:
+        elif self in [OpType.SGMV_EXPAND, OpType.V1_EXPAND]:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -309,19 +333,19 @@ class OpType(Enum):
                     input=input[slice_idx].clone().to(dtype=w_dtype),
                     lora_weights=lora_weights[slice_idx],
                     **kwargs)
-        if self == OpType.BGMV_SHRINK:
+        elif self == OpType.BGMV_SHRINK:
             assert num_slices == 1
             ref_group_gemm(ref_out=output,
                            input=input,
                            lora_weights=lora_weights[0],
                            **kwargs)
-        if self == OpType.BGMV_EXPAND:
+        elif self == OpType.BGMV_EXPAND:
             assert num_slices == 1
             ref_group_gemm(ref_out=output,
                            input=input.clone().to(dtype=w_dtype),
                            lora_weights=lora_weights[0],
                            **kwargs)
-        if self == OpType.BGMV_EXPAND_SLICE:
+        elif self == OpType.BGMV_EXPAND_SLICE:
             hidden_size = lora_weights[0].shape[1]
             for slice_idx in range(num_slices):
                 slice_offset = slice_idx * hidden_size
@@ -330,6 +354,7 @@ class OpType(Enum):
                     input=input[slice_idx].clone().to(dtype=w_dtype),
                     lora_weights=lora_weights[slice_idx],
                     **kwargs)
+        else:
             raise ValueError(f"Unrecognized optype {self}")
 
 
@@ -391,6 +416,8 @@ class BenchmarkTensors:
     seq_start_loc: torch.Tensor
     prompt_lora_mapping: torch.Tensor
     token_lora_mapping: torch.Tensor
+    # v1 kernel metadata
+    v1_kernel_meta: Optional[V1KernelMeta] = None
 
     def io_types(self) -> str:
         return (f"{dtype_to_str(self.input.dtype)}x"
@@ -433,10 +460,19 @@ class BenchmarkTensors:
             total_tokens, ctx.batch_size, prompt_lora_indices_tensor,
             seq_len_tensor, "cpu")
 
+        v1_kernel_meta = None
+        if op_type in [OpType.V1_SHRINK, OpType.V1_EXPAND]:
+            v1_kernel_meta = V1KernelMeta.make(
+                max_loras=ctx.num_loras,
+                max_num_tokens=token_lora_indices_tensor.size(0),
+                device="cpu")
+            v1_kernel_meta.prepare_tensors(
+                token_lora_mapping=token_lora_indices_tensor)
+
         return BenchmarkTensors(input_tensor, lora_weights, output_tensor,
                                 seq_len_tensor, seq_start_loc_tensor,
                                 prompt_lora_indices_tensor,
-                                token_lora_indices_tensor)
+                                token_lora_indices_tensor, v1_kernel_meta)
 
     def sanity_check(self) -> None:
         """
@@ -469,6 +505,13 @@ class BenchmarkTensors:
         for i in range(len(self.lora_weights_lst)):
             self.lora_weights_lst[i] = to_device(self.lora_weights_lst[i])
 
+        # v1 meta
+        if self.v1_kernel_meta:
+            for field_name in V1KernelMeta.__dataclass_fields__:
+                field = getattr(self.v1_kernel_meta, field_name)
+                assert isinstance(field, torch.Tensor)
+                setattr(self.v1_kernel_meta, field_name, to_device(field))
+
     def metadata(self) -> tuple[int, int, int]:
         """
         Return num_seqs, num_tokens and max_seq_len
@@ -668,6 +711,78 @@ class BenchmarkTensors:
             })
         return {'kwargs_list': kwargs_list}
 
+    def as_v1_shrink_kwargs(self) -> dict[str, Any]:
+        assert self.v1_kernel_meta is not None
+        self.sanity_check()
+        self.to_device(self.input.device)
+
+        _, num_tokens, _, num_slices = self.metadata()
+
+        # Sanity check matrix shapes.
+        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
+            0].shape, self.output.shape
+        # Expected input shape [num_tokens, hidden_size]
+        assert len(i_shape) == 2
+        assert i_shape[0] == num_tokens
+        hidden_size = i_shape[1]
+        # Expected lora weight shape [num_loras, lora_rank, hidden_size]
+        assert len(lw_shape) == 3
+        assert lw_shape[2] == hidden_size
+        lora_rank = lw_shape[1]
+        # Expected output shape [num_slices, num_tokens, lora_rank]
+        assert len(o_shape) == 3
+        assert o_shape == (num_slices, num_tokens, lora_rank)
+
+        return {
+            'inputs': self.input,
+            'lora_a_weights': self.lora_weights_lst,
+            'output_tensor': self.output,
+            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_indices_sorted_by_lora_ids':
+            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            'scaling': 1.0,
+        }
+
+    def as_v1_expand_kwargs(self, add_inputs: bool) -> dict[str, Any]:
+        assert self.v1_kernel_meta is not None
+        self.sanity_check()
+        self.to_device(self.input.device)
+
+        _, num_tokens, _, num_slices = self.metadata()
+
+        # Sanity check matrix shapes.
+        i_shape, lw_shape, o_shape = self.input.shape, self.lora_weights_lst[
+            0].shape, self.output.shape
+        # Expected input shape : [num_slices, num_tokens, lora_rank]
+        assert len(i_shape) == 3
+        assert i_shape[0] == num_slices
+        assert i_shape[1] == num_tokens
+        lora_rank = i_shape[2]
+        # Expected lora weight shape : [num_lora, hidden_size, lora_rank]
+        assert len(lw_shape) == 3
+        assert lw_shape[2] == lora_rank
+        hidden_size = lw_shape[1]
+        # Expected output shape : [num_tokens, hidden_size * num_slices]
+        assert len(o_shape) == 2
+        assert o_shape == (num_tokens, hidden_size * num_slices)
+
+        return {
+            'inputs': self.input,
+            'lora_b_weights': self.lora_weights_lst,
+            'output_tensor': self.output,
+            'token_lora_mapping': self.v1_kernel_meta.token_lora_mapping,
+            'token_indices_sorted_by_lora_ids':
+            self.v1_kernel_meta.token_indices_sorted_by_lora_ids,
+            'num_tokens_per_lora': self.v1_kernel_meta.num_tokens_per_lora,
+            'lora_token_start_loc': self.v1_kernel_meta.lora_token_start_loc,
+            'lora_ids': self.v1_kernel_meta.active_lora_ids,
+            'offset_start': 0,
+            'add_inputs': add_inputs,
+        }
+
     def bench_fn_kwargs(self,
                         op_type: OpType,
                         add_inputs: Optional[bool] = None) -> dict[str, Any]:
@@ -686,6 +801,10 @@ class BenchmarkTensors:
             return self.as_bgmv_expand_kwargs(add_inputs)
         if op_type == OpType.BGMV_EXPAND_SLICE:
             return self.as_bgmv_expand_slice_kwargs(add_inputs)
+        if op_type == OpType.V1_SHRINK:
+            return self.as_v1_shrink_kwargs()
+        if op_type == OpType.V1_EXPAND:
+            return self.as_v1_expand_kwargs(add_inputs)
         raise ValueError(f"Unrecognized optype {self}")
 
     def test_correctness(self, op_type: OpType,
@@ -873,12 +992,9 @@ def run(args: argparse.Namespace, bench_ctxs: list[BenchmarkContext]):
     timers = []
     for bench_ctx in bench_ctxs:
         for seq_len in args.seq_lengths:
-            bench_ops: list[OpType] = []
-            if seq_len == 1:
-                # bench all decode ops
-                bench_ops = [op for op in args.op_types if op.is_decode_op()]
-            else:
-                # bench all prefill ops
+            bench_ops: list[OpType] = args.op_types
+            if seq_len > 1:
+                # bench only prefill ops
                 bench_ops = [op for op in args.op_types if op.is_prefill_op()]
 
             seq_len_timers = []

benchmarks/kernels/benchmark_machete.py

@@ -45,7 +45,6 @@ def terse_type_name(dt):
         torch.float16: "fp16",
         torch.int8: "int8",
         torch.float8_e4m3fn: "fp8",
-        torch.bfloat16: "bf16",
         torch.float: "float",
         torch.int: "int",
     }[dt]
@@ -259,7 +258,7 @@ def machete_create_bench_fn(bt: BenchmarkTensors,
 
     return lambda: ops.machete_mm(
         a=bt.a,
-        b_q=bt.w_q,
+        b_q=w_q,
         b_type=bt.wtype,
         b_group_scales=bt.w_g_s,
         b_group_zeros=w_g_zp,

benchmarks/kernels/benchmark_moe.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 
 import argparse
+import json
 import time
 from contextlib import nullcontext
 from datetime import datetime
@@ -17,8 +18,7 @@ from vllm.model_executor.layers.fused_moe.fused_moe import *
 from vllm.platforms import current_platform
 from vllm.utils import FlexibleArgumentParser
 
-FP8_DTYPE = torch.float8_e4m3fnuz if current_platform.is_rocm(
-) else torch.float8_e4m3fn
+FP8_DTYPE = current_platform.fp8_dtype()
 
 
 class BenchmarkConfig(TypedDict):
@@ -365,6 +365,7 @@ class BenchmarkWorker:
         dtype: torch.dtype,
         use_fp8_w8a8: bool,
         use_int8_w8a16: bool,
+        block_quant_shape: List[int] = None,
     ) -> tuple[dict[str, int], float]:
         current_platform.seed_everything(self.seed)
         dtype_str = get_config_dtype_str(dtype,
@@ -385,10 +386,17 @@ class BenchmarkWorker:
         else:
             config = op_config[min(op_config.keys(),
                                    key=lambda x: abs(x - num_tokens))]
-        kernel_time = benchmark_config(config, num_tokens, num_experts,
-                                       shard_intermediate_size, hidden_size,
-                                       topk, dtype, use_fp8_w8a8,
-                                       use_int8_w8a16)
+        kernel_time = benchmark_config(config,
+                                       num_tokens,
+                                       num_experts,
+                                       shard_intermediate_size,
+                                       hidden_size,
+                                       topk,
+                                       dtype,
+                                       use_fp8_w8a8,
+                                       use_int8_w8a16,
+                                       num_iters=100,
+                                       block_quant_shape=block_quant_shape)
         return config, kernel_time
 
     def tune(
@@ -487,6 +495,14 @@ def save_configs(configs: dict[int, BenchmarkConfig], num_experts: int,
         f.write("\n")
 
 
+def get_weight_block_size_safety(config, default_value=None):
+
+    quantization_config = getattr(config, 'quantization_config', {})
+    if isinstance(quantization_config, dict):
+        return quantization_config.get('weight_block_size', default_value)
+    return default_value
+
+
 def main(args: argparse.Namespace):
     print(args)
     block_quant_shape = None
@@ -508,7 +524,12 @@ def main(args: argparse.Namespace):
         topk = config.num_experts_per_tok
         intermediate_size = config.moe_intermediate_size
         shard_intermediate_size = 2 * intermediate_size // args.tp_size
-        block_quant_shape = config.quantization_config['weight_block_size']
+        block_quant_shape = get_weight_block_size_safety(config)
+    elif config.architectures[0] == "Qwen2MoeForCausalLM":
+        E = config.num_experts
+        topk = config.num_experts_per_tok
+        intermediate_size = config.moe_intermediate_size
+        shard_intermediate_size = 2 * intermediate_size // args.tp_size
     else:
         # Default: Mixtral.
         E = config.num_local_experts

benchmarks/kernels/benchmark_paged_attention.py

@@ -176,7 +176,7 @@ def main(
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.

benchmarks/kernels/benchmark_quant.py

@@ -40,7 +40,7 @@ def main(num_tokens: int,
 
         end_time = time.perf_counter()
         if profile:
-            torch.cuda.cudart().cudaProfilerStart()
+            torch.cuda.cudart().cudaProfilerStop()
         return (end_time - start_time) / num_iters
 
     # Warmup.

benchmarks/kernels/benchmark_rmsnorm.py

@@ -139,7 +139,7 @@ def calculate_diff(batch_size, seq_len, hidden_size, use_residual=True):
 
     print(f"Naive output={output_naive}")
     print(f"FlashInfer output={output_flashinfer}")
-    print(f"VLLM output={output_vllm}")
+    print(f"vLLM output={output_vllm}")
 
     if torch.allclose(output_naive, output_flashinfer, atol=1e-2,
                       rtol=1e-2) and torch.allclose(

benchmarks/kernels/deepgemm/README.md

@@ -0,0 +1,129 @@
+# DeepSeek DeepGEMM Kernels Benchmark
+
+This directory includes benchmarks between DeepSeek's DeepGEMM block fp8 kernels against vLLM's existing triton and CUTLASS-based kernels.
+
+Currently this just includes dense GEMMs and only works on Hopper GPUs.
+
+## Setup
+
+You need to install vLLM in your usual fashion, then install DeepGEMM from source in its own directory:
+
+```
+git clone --recursive https://github.com/deepseek-ai/DeepGEMM
+cd DeepGEMM
+python setup.py install
+uv pip install -e .
+```
+
+## Usage
+
+```
+python benchmark_fp8_block_dense_gemm.py
+INFO 02-26 21:55:13 [__init__.py:207] Automatically detected platform cuda.
+===== STARTING FP8 GEMM BENCHMARK =====
+PyTorch version: 2.5.1+cu124
+CUDA version: 12.4
+Triton version: 3.1.0
+Using device: NVIDIA H100 80GB HBM3
+WARNING 02-26 21:55:15 [fp8_utils.py:458] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=4096,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json
+INFO 02-26 21:55:15 [fp8_utils.py:449] Using configuration from /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=7168,K=18432,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json for W8A8 Block FP8 kernel.
+WARNING 02-26 21:55:16 [fp8_utils.py:458] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=18432,K=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json
+WARNING 02-26 21:55:17 [fp8_utils.py:458] Using default W8A8 Block FP8 kernel config. Performance might be sub-optimal! Config file not found at /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=24576,K=1536,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json
+INFO 02-26 21:55:17 [fp8_utils.py:449] Using configuration from /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=32768,K=512,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json for W8A8 Block FP8 kernel.
+INFO 02-26 21:55:17 [fp8_utils.py:449] Using configuration from /home/mgoin/code/vllm/vllm/model_executor/layers/quantization/utils/configs/N=7168,K=16384,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json for W8A8 Block FP8 kernel.
+
+===== PERFORMANCE COMPARISON =====
+
+DeepGEMM Implementation:
++------+-------+-------+-----------+--------+--------+
+| m    | n     | k     | Time (μs) | TFLOPS | GB/s   |
++------+-------+-------+-----------+--------+--------+
+|    8 |  4096 |  7168 | 102.9     | 4.6    | 286.4  |
+|    8 |  7168 | 18432 | 70.8      | 29.8   | 1868.8 |
+|    8 | 18432 |  7168 | 69.3      | 30.5   | 1911.8 |
+|   64 |  4096 |  7168 | 69.1      | 54.4   | 439.0  |
+|   64 |  7168 | 18432 | 69.4      | 243.6  | 1933.6 |
+|   64 | 18432 |  7168 | 70.4      | 240.3  | 1917.2 |
+|   64 | 24576 |  1536 | 70.1      | 68.9   | 584.6  |
+|   64 | 32768 |   512 | 68.4      | 31.4   | 307.1  |
+|   64 |  7168 | 16384 | 69.5      | 216.3  | 1718.5 |
+|  128 |  4096 |  7168 | 141.1     | 53.3   | 222.1  |
+|  128 |  7168 | 18432 | 71.9      | 470.5  | 1896.1 |
+|  128 | 18432 |  7168 | 69.3      | 488.2  | 1988.2 |
+| 1024 |  4096 |  7168 | 89.7      | 670.1  | 502.5  |
+| 1024 | 18432 |  7168 | 279.0     | 969.8  | 635.2  |
+| 2048 |  4096 |  7168 | 175.1     | 687.0  | 347.4  |
+| 4096 |  4096 |  7168 | 335.4     | 717.0  | 275.1  |
++------+-------+-------+-----------+--------+--------+
+
+vLLM Triton Implementation:
++------+-------+-------+-----------+--------+--------+--------------+
+| m    | n     | k     | Time (μs) | TFLOPS | GB/s   | vs DeepGEMM  |
++------+-------+-------+-----------+--------+--------+--------------+
+|    8 |  4096 |  7168 | 74.0      | 6.3    | 398.2  | 1.39x faster |
+|    8 |  7168 | 18432 | 89.6      | 23.6   | 1478.1 | 0.79x slower |
+|    8 | 18432 |  7168 | 113.2     | 18.7   | 1170.4 | 0.61x slower |
+|   64 |  4096 |  7168 | 79.4      | 47.3   | 382.2  | 0.87x slower |
+|   64 |  7168 | 18432 | 98.5      | 171.7  | 1363.0 | 0.70x slower |
+|   64 | 18432 |  7168 | 119.5     | 141.5  | 1129.4 | 0.59x slower |
+|   64 | 24576 |  1536 | 37.6      | 128.4  | 1089.7 | 1.86x faster |
+|   64 | 32768 |   512 | 38.7      | 55.5   | 542.6  | 1.77x faster |
+|   64 |  7168 | 16384 | 86.1      | 174.5  | 1386.4 | 0.81x slower |
+|  128 |  4096 |  7168 | 90.7      | 82.9   | 345.4  | 1.56x faster |
+|  128 |  7168 | 18432 | 144.0     | 234.9  | 946.9  | 0.50x slower |
+|  128 | 18432 |  7168 | 229.5     | 147.4  | 600.1  | 0.30x slower |
+| 1024 |  4096 |  7168 | 242.3     | 248.2  | 186.1  | 0.37x slower |
+| 1024 | 18432 |  7168 | 897.8     | 301.4  | 197.4  | 0.31x slower |
+| 2048 |  4096 |  7168 | 463.0     | 259.7  | 131.4  | 0.38x slower |
+| 4096 |  4096 |  7168 | 901.8     | 266.7  | 102.3  | 0.37x slower |
++------+-------+-------+-----------+--------+--------+--------------+
+
+vLLM CUTLASS Implementation:
++------+-------+-------+-----------+--------+--------+--------------+--------------+
+| m    | n     | k     | Time (μs) | TFLOPS | GB/s   | vs DeepGEMM  | vs Triton    |
++------+-------+-------+-----------+--------+--------+--------------+--------------+
+|    8 |  4096 |  7168 | 34.6      | 13.6   | 852.3  | 2.98x faster | 2.14x faster |
+|    8 |  7168 | 18432 | 78.9      | 26.8   | 1677.3 | 0.90x slower | 1.13x faster |
+|    8 | 18432 |  7168 | 81.2      | 26.0   | 1631.1 | 0.85x slower | 1.39x faster |
+|   64 |  4096 |  7168 | 36.9      | 101.9  | 822.9  | 1.87x faster | 2.15x faster |
+|   64 |  7168 | 18432 | 87.4      | 193.4  | 1535.2 | 0.79x slower | 1.13x faster |
+|   64 | 18432 |  7168 | 85.0      | 199.0  | 1587.6 | 0.83x slower | 1.41x faster |
+|   64 | 24576 |  1536 | 28.0      | 172.8  | 1465.8 | 2.51x faster | 1.35x faster |
+|   64 | 32768 |   512 | 28.8      | 74.5   | 728.5  | 2.37x faster | 1.34x faster |
+|   64 |  7168 | 16384 | 77.9      | 193.0  | 1532.8 | 0.89x slower | 1.11x faster |
+|  128 |  4096 |  7168 | 39.1      | 192.4  | 802.0  | 3.61x faster | 2.32x faster |
+|  128 |  7168 | 18432 | 93.7      | 360.8  | 1454.2 | 0.77x slower | 1.54x faster |
+|  128 | 18432 |  7168 | 85.7      | 394.8  | 1608.0 | 0.81x slower | 2.68x faster |
+| 1024 |  4096 |  7168 | 99.7      | 603.1  | 452.2  | 0.90x slower | 2.43x faster |
+| 1024 | 18432 |  7168 | 331.3     | 816.7  | 534.9  | 0.84x slower | 2.71x faster |
+| 2048 |  4096 |  7168 | 198.3     | 606.6  | 306.7  | 0.88x slower | 2.34x faster |
+| 4096 |  4096 |  7168 | 392.2     | 613.2  | 235.3  | 0.86x slower | 2.30x faster |
++------+-------+-------+-----------+--------+--------+--------------+--------------+
+
+===== AVERAGE PERFORMANCE =====
++----------------+------------+----------+---------------+
+| Implementation | Avg TFLOPS | Avg GB/s | Avg Time (ms) |
++----------------+------------+----------+---------------+
+| DeepGEMM       | 310.98     | 1052.10  | 0.11          |
+| vLLM Triton    | 144.30     | 715.60   | 0.23          |
+| vLLM CUTLASS   | 286.78     | 1076.67  | 0.11          |
++----------------+------------+----------+---------------+
+
+===== AVERAGE SPEEDUPS =====
++-----------------------------+--------------+
+| Comparison                  | Speedup      |
++-----------------------------+--------------+
+| DeepGEMM vs vLLM Triton     | 1.71x faster |
+| DeepGEMM vs vLLM CUTLASS    | 0.94x slower |
+| vLLM CUTLASS vs vLLM Triton | 1.84x faster |
++-----------------------------+--------------+
+
+===== ACCURACY COMPARISON =====
++----------------+-----------------------+
+| Implementation | Avg Diff vs Reference |
++----------------+-----------------------+
+| DeepGEMM       | 0.000684              |
+| vLLM Triton    | 0.000684              |
+| vLLM CUTLASS   | 0.000684              |
++----------------+-----------------------+
+```

benchmarks/kernels/deepgemm/benchmark_fp8_block_dense_gemm.py

@@ -0,0 +1,464 @@
+# SPDX-License-Identifier: Apache-2.0
+# fmt: off
+# ruff: noqa: E501
+import time
+
+# Import DeepGEMM functions
+import deep_gemm
+import torch
+import triton
+from deep_gemm import calc_diff, ceil_div, get_col_major_tma_aligned_tensor
+
+# Import vLLM functions
+from vllm import _custom_ops as ops
+from vllm.model_executor.layers.quantization.utils.fp8_utils import (
+    per_token_group_quant_fp8, w8a8_block_fp8_matmul)
+
+
+# Copied from
+# https://github.com/deepseek-ai/DeepGEMM/blob/78cacf70d41d15d688bd493ebc85845f7f2a3d5d/tests/test_core.py#L9
+def per_token_cast_to_fp8(
+        x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert tensor to FP8 format with per-token scaling."""
+    assert x.dim() == 2 and x.size(1) % 128 == 0
+    m, n = x.shape
+    x_view = x.view(m, -1, 128)
+    x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4)
+    return (x_view * (448.0 / x_amax.unsqueeze(2))).to(
+        torch.float8_e4m3fn).view(m, n), (x_amax / 448.0).view(m, -1)
+
+
+# Copied from
+# https://github.com/deepseek-ai/DeepGEMM/blob/78cacf70d41d15d688bd493ebc85845f7f2a3d5d/tests/test_core.py#L17
+def per_block_cast_to_fp8(
+        x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """Convert tensor to FP8 format with per-block scaling."""
+    assert x.dim() == 2
+    m, n = x.shape
+    x_padded = torch.zeros((ceil_div(m, 128) * 128, ceil_div(n, 128) * 128),
+                           dtype=x.dtype,
+                           device=x.device)
+    x_padded[:m, :n] = x
+    x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128)
+    x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4)
+    x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn)
+    return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (
+        x_amax / 448.0).view(x_view.size(0), x_view.size(2))
+
+
+def benchmark_shape(m: int,
+                    n: int,
+                    k: int,
+                    warmup: int = 100,
+                    repeat: int = 10000,
+                    verbose: bool = False) -> dict:
+    """Benchmark all implementations for a specific (m, n, k) shape."""
+    if verbose:
+        print(f"\n=== Benchmarking shape: m={m}, n={n}, k={k} ===")
+
+    # Create test tensors
+    A = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
+    B = torch.randn((n, k), device='cuda', dtype=torch.bfloat16)
+
+    # Reference result in BF16
+    torch.cuda.synchronize()
+    C_ref = A @ B.t()
+
+    # Pre-quantize B for all implementations
+    # (weights can be pre-quantized offline)
+    B_deepgemm, B_scale_deepgemm = per_block_cast_to_fp8(B)
+    B_vllm, B_scale_vllm = per_block_cast_to_fp8(B)
+
+    # Block size configuration
+    block_size = [128, 128]
+
+    # Pre-quantize A for all implementations
+    A_deepgemm, A_scale_deepgemm = per_token_cast_to_fp8(A)
+    A_scale_deepgemm = get_col_major_tma_aligned_tensor(A_scale_deepgemm)
+    C_deepgemm = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
+    A_vllm, A_scale_vllm = per_token_group_quant_fp8(A, block_size[1])
+    A_vllm_cutlass, A_scale_vllm_cutlass = per_token_group_quant_fp8(
+        A, block_size[1], column_major_scales=True)
+
+    # === DeepGEMM Implementation ===
+    def deepgemm_gemm():
+        # A quantization is inside the loop as it depends on activations
+        # A_deepgemm, A_scale_deepgemm = per_token_cast_to_fp8(A)
+        # A_deepgemm, A_scale_deepgemm = per_token_group_quant_fp8(
+        #     A, block_size[1])
+        # A_scale_aligned = get_col_major_tma_aligned_tensor(A_scale_deepgemm)
+        # C_deepgemm = torch.empty((m, n), device='cuda', dtype=torch.bfloat16)
+        deep_gemm.gemm_fp8_fp8_bf16_nt((A_deepgemm, A_scale_deepgemm),
+                                       (B_deepgemm, B_scale_deepgemm),
+                                       C_deepgemm)
+        return C_deepgemm
+
+    # === vLLM Triton Implementation ===
+    def vllm_triton_gemm():
+        # A quantization is inside the loop as it depends on activations
+        # A_vllm, A_scale_vllm = per_token_group_quant_fp8(A, block_size[1])
+        return w8a8_block_fp8_matmul(A_vllm,
+                                     B_vllm,
+                                     A_scale_vllm,
+                                     B_scale_vllm,
+                                     block_size,
+                                     output_dtype=torch.bfloat16)
+
+    # === vLLM CUTLASS Implementation ===
+    def vllm_cutlass_gemm():
+        # A quantization is inside the loop as it depends on activations
+        # A_vllm_cutlass, A_scale_vllm_cutlass = per_token_group_quant_fp8(
+        #     A, block_size[1], column_major_scales=True)
+        return ops.cutlass_scaled_mm(A_vllm_cutlass,
+                                     B_vllm.T,
+                                     scale_a=A_scale_vllm_cutlass,
+                                     scale_b=B_scale_vllm.T,
+                                     out_dtype=torch.bfloat16)
+
+    # Run correctness check first
+    if verbose:
+        print("Running correctness check...")
+    C_deepgemm = deepgemm_gemm()
+    C_vllm_triton = vllm_triton_gemm()
+    C_vllm_cutlass = vllm_cutlass_gemm()
+
+    deepgemm_diff = calc_diff(C_deepgemm, C_ref)
+    vllm_triton_diff = calc_diff(C_vllm_triton, C_ref)
+    vllm_cutlass_diff = calc_diff(C_vllm_cutlass, C_ref)
+
+    if verbose:
+        print(f"DeepGEMM vs Reference difference: {deepgemm_diff:.6f}")
+        print(f"vLLM Triton vs Reference difference: {vllm_triton_diff:.6f}")
+        print(f"vLLM CUTLASS vs Reference difference: {vllm_cutlass_diff:.6f}")
+        print("vLLM Triton vs DeepGEMM difference: "
+              f"{calc_diff(C_vllm_triton, C_deepgemm):.6f}")
+        print("vLLM CUTLASS vs DeepGEMM difference: "
+              f"{calc_diff(C_vllm_cutlass, C_deepgemm):.6f}")
+
+    # Benchmark implementations
+    implementations = {
+        "DeepGEMM": deepgemm_gemm,
+        "vLLM Triton": vllm_triton_gemm,
+        "vLLM CUTLASS": vllm_cutlass_gemm
+    }
+
+    benchmark_results = {
+        "shape": {
+            "m": m,
+            "n": n,
+            "k": k
+        },
+        "implementations": {}
+    }
+
+    for name, func in implementations.items():
+        # Warmup
+        for _ in range(warmup):
+            func()
+            torch.cuda.synchronize()
+
+        # Timing loop
+        torch.cuda.synchronize()
+        start = time.time()
+        for _ in range(repeat):
+            func()
+        torch.cuda.synchronize()
+        end = time.time()
+
+        # Calculate timing and TFLOPS
+        avg_time_ms = (end - start) / repeat * 1000
+        avg_time_us = avg_time_ms * 1000
+        tflops = 2 * m * n * k / (avg_time_ms * 1e-3) / 1e12
+        gb_s = (m * k + k * n + m * n * 2) / 1e9 / (avg_time_ms * 1e-3)
+
+        benchmark_results["implementations"][name] = {
+            "time_ms": avg_time_ms,
+            "time_us": avg_time_us,
+            "tflops": tflops,
+            "gb_s": gb_s,
+            "diff": {
+                "DeepGEMM":
+                0.0 if name == "DeepGEMM" else calc_diff(func(), C_deepgemm),
+                "Reference":
+                deepgemm_diff if name == "DeepGEMM" else
+                (vllm_triton_diff
+                 if name == "vLLM Triton" else vllm_cutlass_diff)
+            }
+        }
+
+        if verbose:
+            print(
+                f"{name}: {avg_time_ms:.3f} ms, {tflops:.2f} TFLOPS, {gb_s:.2f} GB/s"
+            )
+
+    # Calculate speedups
+    baseline = benchmark_results["implementations"]["DeepGEMM"]["time_ms"]
+    for name, data in benchmark_results["implementations"].items():
+        if name != "DeepGEMM":
+            speedup = baseline / data["time_ms"]
+            benchmark_results["implementations"][name][
+                "speedup_vs_deepgemm"] = speedup
+            if verbose:
+                print(f"DeepGEMM is {1/speedup:.2f}x "
+                      f"{'faster' if 1/speedup > 1 else 'slower'} than {name}")
+
+    vllm_triton_time = benchmark_results["implementations"]["vLLM Triton"][
+        "time_ms"]
+    vllm_cutlass_time = benchmark_results["implementations"]["vLLM CUTLASS"][
+        "time_ms"]
+    cutlass_vs_triton = vllm_triton_time / vllm_cutlass_time
+    benchmark_results["implementations"]["vLLM CUTLASS"][
+        "speedup_vs_triton"] = cutlass_vs_triton
+    if verbose:
+        print(
+            f"vLLM CUTLASS is {cutlass_vs_triton:.2f}x "
+            f"{'faster' if cutlass_vs_triton > 1 else 'slower'} than vLLM Triton"
+        )
+
+    return benchmark_results
+
+
+def format_table_row(values, widths):
+    """Format a row with specified column widths."""
+    return "| " + " | ".join(f"{val:{w}}"
+                             for val, w in zip(values, widths)) + " |"
+
+
+def print_table(headers, rows, title=None):
+    """Print a table with headers and rows."""
+    if title:
+        print(f"\n{title}")
+
+    # Calculate column widths based on headers and data
+    widths = [
+        max(len(str(h)), max(len(str(row[i])) for row in rows))
+        for i, h in enumerate(headers)
+    ]
+
+    # Create separator line
+    separator = "+-" + "-+-".join("-" * w for w in widths) + "-+"
+
+    # Print table
+    print(separator)
+    print(format_table_row(headers, widths))
+    print(separator)
+    for row in rows:
+        print(format_table_row(row, widths))
+    print(separator)
+
+
+def format_speedup(value):
+    """Format speedup value with indicator if it's faster or slower."""
+    return f"{value:.2f}x {'faster' if value > 1.0 else 'slower'}"
+
+
+def run_benchmarks(verbose: bool = False):
+    """Run benchmarks for a set of common shapes."""
+    print("===== STARTING FP8 GEMM BENCHMARK =====")
+
+    # Make sure we're using the GPU
+    if not torch.cuda.is_available():
+        print("CUDA not available! Tests require GPU.")
+        return
+
+    # Print system information
+    print(f"PyTorch version: {torch.__version__}")
+    print(f"CUDA version: {torch.version.cuda}")
+    print(f"Triton version: {triton.__version__}")
+    print(f"Using device: {torch.cuda.get_device_name()}")
+
+    # Enable TF32 for better performance
+    torch.backends.cuda.matmul.allow_tf32 = True
+    torch.backends.cudnn.allow_tf32 = True
+
+    # Set seeds for reproducibility
+    torch.manual_seed(42)
+    torch.cuda.manual_seed(42)
+
+    # Define benchmark shapes (m, n, k)
+    shapes = [
+        (8, 4096, 7168),
+        (8, 7168, 18432),
+        (8, 18432, 7168),
+        (64, 4096, 7168),
+        (64, 7168, 18432),
+        (64, 18432, 7168),
+        (64, 24576, 1536),
+        (64, 32768, 512),
+        (64, 7168, 16384),
+        (128, 4096, 7168),
+        (128, 7168, 18432),
+        (128, 18432, 7168),
+        (1024, 4096, 7168),
+        (1024, 18432, 7168),
+        (2048, 4096, 7168),
+        (4096, 4096, 7168),
+    ]
+    shapes = [
+        # (64, 2112, 7168),
+        (64, 24576, 1536),
+        (64, 32768, 512),
+        (64, 7168, 16384),
+        (64, 4096, 7168),
+        (64, 7168, 2048),
+        # (128, 2112, 7168),
+        (128, 24576, 1536),
+        (128, 32768, 512),
+        (128, 7168, 16384),
+        (128, 4096, 7168),
+        (128, 7168, 2048),
+        # (4096, 2112, 7168),
+        (4096, 24576, 1536),
+        (4096, 32768, 512),
+        (4096, 7168, 16384),
+        (4096, 4096, 7168),
+        (4096, 7168, 2048),
+    ]
+
+    all_results = []
+    for m, n, k in shapes:
+        result = benchmark_shape(m, n, k, verbose=verbose)
+        all_results.append(result)
+
+    # Print results in a nicely formatted table
+    print("\n===== PERFORMANCE COMPARISON =====")
+
+    # Print DeepGEMM table
+    deepgemm_headers = ["m", "n", "k", "Time (μs)", "TFLOPS", "GB/s"]
+    deepgemm_rows = []
+    for result in all_results:
+        shape = result["shape"]
+        impl_data = result["implementations"]["DeepGEMM"]
+        deepgemm_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}"
+        ])
+
+    print_table(deepgemm_headers,
+                deepgemm_rows,
+                title="DeepGEMM Implementation:")
+
+    # Print vLLM Triton table
+    triton_headers = [
+        "m", "n", "k", "Time (μs)", "TFLOPS", "GB/s", "vs DeepGEMM"
+    ]
+    triton_rows = []
+    for result in all_results:
+        shape = result["shape"]
+        impl_data = result["implementations"]["vLLM Triton"]
+        speedup = impl_data.get("speedup_vs_deepgemm", 1.0)
+        triton_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}",
+            format_speedup(speedup)
+        ])
+
+    print_table(triton_headers,
+                triton_rows,
+                title="vLLM Triton Implementation:")
+
+    # Print vLLM CUTLASS table
+    cutlass_headers = [
+        "m", "n", "k", "Time (μs)", "TFLOPS", "GB/s", "vs DeepGEMM",
+        "vs Triton"
+    ]
+    cutlass_rows = []
+    for result in all_results:
+        shape = result["shape"]
+        impl_data = result["implementations"]["vLLM CUTLASS"]
+        vs_deepgemm = impl_data.get("speedup_vs_deepgemm", 1.0)
+        vs_triton = impl_data.get("speedup_vs_triton", 1.0)
+        cutlass_rows.append([
+            shape["m"], shape["n"], shape["k"], f"{impl_data['time_us']:.1f}",
+            f"{impl_data['tflops']:.1f}", f"{impl_data['gb_s']:.1f}",
+            format_speedup(vs_deepgemm),
+            format_speedup(vs_triton)
+        ])
+
+    print_table(cutlass_headers,
+                cutlass_rows,
+                title="vLLM CUTLASS Implementation:")
+
+    # Calculate and print averages
+    print("\n===== AVERAGE PERFORMANCE =====")
+
+    implementations = ["DeepGEMM", "vLLM Triton", "vLLM CUTLASS"]
+    avg_metrics = {
+        impl: {
+            "tflops": 0,
+            "gb_s": 0,
+            "time_ms": 0
+        }
+        for impl in implementations
+    }
+
+    for result in all_results:
+        for impl in implementations:
+            impl_data = result["implementations"][impl]
+            avg_metrics[impl]["tflops"] += impl_data["tflops"]
+            avg_metrics[impl]["gb_s"] += impl_data["gb_s"]
+            avg_metrics[impl]["time_ms"] += impl_data["time_ms"]
+
+    num_shapes = len(all_results)
+    avg_headers = ["Implementation", "Avg TFLOPS", "Avg GB/s", "Avg Time (ms)"]
+    avg_rows = []
+
+    for impl in implementations:
+        avg_tflops = avg_metrics[impl]["tflops"] / num_shapes
+        avg_mem_bw = avg_metrics[impl]["gb_s"] / num_shapes
+        avg_time = avg_metrics[impl]["time_ms"] / num_shapes
+        avg_rows.append([
+            impl, f"{avg_tflops:.2f}", f"{avg_mem_bw:.2f}", f"{avg_time:.2f}"
+        ])
+
+    print_table(avg_headers, avg_rows)
+
+    # Calculate average speedups
+    avg_speedups = {
+        "DeepGEMM vs vLLM Triton": 0,
+        "DeepGEMM vs vLLM CUTLASS": 0,
+        "vLLM CUTLASS vs vLLM Triton": 0
+    }
+
+    for result in all_results:
+        deepgemm_time = result["implementations"]["DeepGEMM"]["time_ms"]
+        vllm_triton_time = result["implementations"]["vLLM Triton"]["time_ms"]
+        vllm_cutlass_time = result["implementations"]["vLLM CUTLASS"][
+            "time_ms"]
+
+        avg_speedups[
+            "DeepGEMM vs vLLM Triton"] += vllm_triton_time / deepgemm_time
+        avg_speedups[
+            "DeepGEMM vs vLLM CUTLASS"] += vllm_cutlass_time / deepgemm_time
+        avg_speedups[
+            "vLLM CUTLASS vs vLLM Triton"] += vllm_triton_time / vllm_cutlass_time
+
+    print("\n===== AVERAGE SPEEDUPS =====")
+    speedup_headers = ["Comparison", "Speedup"]
+    speedup_rows = []
+    for comparison, total in avg_speedups.items():
+        avg_speedup = total / num_shapes
+        status = "faster" if avg_speedup > 1 else "slower"
+        speedup_rows.append([comparison, f"{avg_speedup:.2f}x {status}"])
+
+    print_table(speedup_headers, speedup_rows)
+
+    # Average accuracy comparison
+    print("\n===== ACCURACY COMPARISON =====")
+    avg_diff = {impl: 0 for impl in implementations}
+
+    for result in all_results:
+        for impl in implementations:
+            avg_diff[impl] += result["implementations"][impl]["diff"][
+                "Reference"]
+
+    diff_headers = ["Implementation", "Avg Diff vs Reference"]
+    diff_rows = []
+    for impl in implementations:
+        diff_rows.append([impl, f"{avg_diff[impl] / num_shapes:.6f}"])
+
+    print_table(diff_headers, diff_rows)
+
+
+if __name__ == "__main__":
+    run_benchmarks(verbose=False)

benchmarks/run_structured_output_benchmark.sh

@@ -0,0 +1,64 @@
+#!/bin/bash
+
+# Define the model to use
+MODEL=${1:-"Qwen/Qwen2.5-7B-Instruct"}
+
+# Define the backend to use
+BACKEND=${2:-"vllm"}
+
+# Define the dataset to use
+DATASET=${3:-"xgrammar_bench"}
+
+# Define the guided decoding backend
+GUIDED_BACKEND=${4:-"xgrammar"}
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+OUTPUT_DIR=${5:-"$SCRIPT_DIR/structured_output_benchmark_results"}
+
+GUIDED_RATIO=${6:-0.5}
+
+# Create output directory if it doesn't exist
+mkdir -p "$OUTPUT_DIR"
+
+# Define QPS values to test
+QPS_VALUES=(70 60 50 25 20 15 10)
+
+# Common parameters
+COMMON_PARAMS="--backend $BACKEND \
+               --model $MODEL \
+               --dataset $DATASET \
+               --structured-output-backend $GUIDED_BACKEND \
+               --structured-output-ratio $GUIDED_RATIO \
+               --save-results \
+               --result-dir $OUTPUT_DIR"
+
+echo "Starting structured output benchmark with model: $MODEL"
+echo "Backend: $BACKEND"
+echo "Dataset: $DATASET"
+echo "Structured output backend: $GUIDED_BACKEND"
+echo "Results will be saved to: $OUTPUT_DIR"
+echo "----------------------------------------"
+
+# Run benchmarks with different QPS values
+for qps in "${QPS_VALUES[@]}"; do
+  echo "Running benchmark with QPS: $qps"
+
+  # Get git hash and branch for the filename
+  GIT_HASH=$(git rev-parse --short HEAD 2>/dev/null || echo "unknown")
+  GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD 2>/dev/null || echo "unknown")
+
+  # Construct filename for this run
+  FILENAME="${GUIDED_BACKEND}_${BACKEND}_${qps}qps_$(basename $MODEL)_${DATASET}_${GIT_HASH}.json"
+
+  # Run the benchmark
+  python "$SCRIPT_DIR/benchmark_serving_structured_output.py" $COMMON_PARAMS \
+    --request-rate $qps \
+    --result-filename "$FILENAME" \
+    --port ${PORT:-8000}
+
+  echo "Completed benchmark with QPS: $qps"
+  echo "----------------------------------------"
+done
+
+echo "All benchmarks completed!"
+echo "Results saved to: $OUTPUT_DIR"

benchmarks/structured_schemas/structured_schema_1.json

@@ -1,113 +1,19 @@
 {
-    "$schema":
-    "https://json-schema.org/draft/2020-12/schema",
-    "title":
-    "User Profile",
-    "type":
-    "object",
-    "properties": {
-        "userId": {
-            "type": "string",
-            "description": "Unique identifier for the user."
-        },
-        "personalInfo": {
     "type": "object",
     "properties": {
-                "firstName": {
-                    "type": "string",
-                    "description": "The user's first name."
+      "name": { "type": "string" },
+      "email": { "type": "string" },
+      "street": { "type": "string" },
+      "city": { "type": "string" },
+      "state": { "type": "string" },
+      "zip": { "type": "string" },
+      "phone": { "type": "string" },
+      "website": { "type": "string" },
+      "company": { "type": "string" },
+      "age": { "type": "integer" }
     },
-                "lastName": {
-                    "type": "string",
-                    "description": "The user's last name."
-                },
-                "age": {
-                    "type": "integer",
-                    "minimum": 0,
-                    "description": "The user's age."
-                },
-                "phoneNumbers": {
-                    "type":
-                    "array",
-                    "items": {
-                        "type": "object",
-                        "properties": {
-                            "type": {
-                                "type": "string",
-                                "enum": ["home", "work", "mobile"],
-                                "description": "Type of phone number."
-                            },
-                            "number": {
-                                "type": "string",
-                                "pattern": "^\\+?[1-9]\\d{1,14}$",
-                                "description": "Phone number in E.164 format."
-                            }
-                        },
-                        "required": ["type", "number"]
-                    },
-                    "description":
-                    "List of phone numbers associated with the user."
-                }
-            },
-            "required": ["firstName", "lastName"]
-        },
-        "address": {
-            "type": "object",
-            "properties": {
-                "street": {
-                    "type": "string",
-                    "description": "Street address."
-                },
-                "city": {
-                    "type": "string",
-                    "description": "City name."
-                },
-                "state": {
-                    "type": "string",
-                    "description": "State or province."
-                },
-                "postalCode": {
-                    "type": "string",
-                    "pattern": "^\\d{5}(-\\d{4})?$",
-                    "description": "Postal code."
-                },
-                "country": {
-                    "type": "string",
-                    "description": "Country name."
-                }
-            },
-            "required": ["street", "city", "state", "postalCode", "country"]
-        },
-        "preferences": {
-            "type": "object",
-            "properties": {
-                "newsletterSubscribed": {
-                    "type":
-                    "boolean",
-                    "description":
-                    "Indicates if the user is subscribed to the newsletter."
-                },
-                "favoriteCategories": {
-                    "type": "array",
-                    "items": {
-                        "type": "string"
-                    },
-                    "description": "List of user's favorite categories."
-                }
-            },
-            "required": ["newsletterSubscribed"]
-        },
-        "accountStatus": {
-            "type": "string",
-            "enum": ["active", "inactive", "suspended"],
-            "description": "Current status of the user's account."
-        },
-        "registrationDate": {
-            "type": "string",
-            "format": "date-time",
-            "description": "ISO 8601 formatted date-time of user registration."
-        }
-    },
-    "required":
-    ["userId", "personalInfo", "address", "accountStatus", "registrationDate"]
+    "required": [
+      "name",
+      "email"
+    ]
 }

cmake/cpu_extension.cmake

@@ -81,6 +81,7 @@ else()
     find_isa(${CPUINFO} "POWER9" POWER9_FOUND)
     find_isa(${CPUINFO} "asimd" ASIMD_FOUND) # Check for ARM NEON support
     find_isa(${CPUINFO} "bf16" ARM_BF16_FOUND) # Check for ARM BF16 support
+    find_isa(${CPUINFO} "S390" S390_FOUND)
 endif()
 
 
@@ -129,8 +130,16 @@ elseif (ASIMD_FOUND)
 elseif(APPLE_SILICON_FOUND)
     message(STATUS "Apple Silicon Detected")
     set(ENABLE_NUMA OFF)
+elseif (S390_FOUND)
+    message(STATUS "S390 detected")
+    # Check for S390 VXE support
+    list(APPEND CXX_COMPILE_FLAGS
+        "-mvx"
+        "-mzvector"
+        "-march=native"
+        "-mtune=native")
 else()
-    message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA or ARMv8 support.")
+    message(FATAL_ERROR "vLLM CPU backend requires AVX512, AVX2, Power9+ ISA, S390X ISA or ARMv8 support.")
 endif()
 
 #
@@ -140,7 +149,7 @@ if (AVX512_FOUND AND NOT AVX512_DISABLED)
     FetchContent_Declare(
         oneDNN
         GIT_REPOSITORY https://github.com/oneapi-src/oneDNN.git
-        GIT_TAG  v3.6
+        GIT_TAG  v3.7.1
         GIT_PROGRESS TRUE
         GIT_SHALLOW TRUE
     )

cmake/external_projects/vllm_flash_attn.cmake

@@ -38,7 +38,7 @@ else()
   FetchContent_Declare(
           vllm-flash-attn
           GIT_REPOSITORY https://github.com/vllm-project/flash-attention.git
-          GIT_TAG 720c94869cf2e0ff5a706e9c7f1dce0939686ade
+          GIT_TAG 9bfa9869829d8c593527eb34c5271d0090f7ccc9 
           GIT_PROGRESS TRUE
           # Don't share the vllm-flash-attn build between build types
           BINARY_DIR ${CMAKE_BINARY_DIR}/vllm-flash-attn

csrc/cpu/attention.cpp

@@ -24,8 +24,8 @@ struct KernelVecType<float> {
 
 template <>
 struct KernelVecType<c10::Half> {
-#ifdef __powerpc64__
-  // Power architecture-specific vector types
+#if defined(__powerpc64__) || defined(__s390x__)
+  // Power and s390x architecture-specific vector types
   using q_load_vec_type = vec_op::FP32Vec8;
   using k_load_vec_type = vec_op::FP32Vec16;
   using v_load_vec_type = vec_op::FP32Vec16;

csrc/cpu/cache.cpp

@@ -3,6 +3,12 @@
 
 #include "cpu_types.hpp"
 
+#if defined(__x86_64__)
+  #define DISPATCH_MACRO VLLM_DISPATCH_FLOATING_TYPES_WITH_E5M2
+#else
+  #define DISPATCH_MACRO VLLM_DISPATCH_FLOATING_TYPES
+#endif
+
 namespace {
 template <typename scalar_t>
 void copy_blocks_cpu_impl(std::vector<torch::Tensor> const& key_caches,
@@ -95,8 +101,7 @@ void copy_blocks(std::vector<torch::Tensor> const& key_caches,
   }
 
   const int element_num_per_block = key_caches[0][0].numel();
-  VLLM_DISPATCH_FLOATING_TYPES(
-      key_caches[0].scalar_type(), "copy_blocks_cpu_impl", [&] {
+  DISPATCH_MACRO(key_caches[0].scalar_type(), "copy_blocks_cpu_impl", [&] {
     CPU_KERNEL_GUARD_IN(copy_blocks_cpu_impl)
     copy_blocks_cpu_impl<scalar_t>(key_caches, value_caches, block_mapping,
                                    element_num_per_block, num_layers);
@@ -118,14 +123,13 @@ void reshape_and_cache(torch::Tensor& key, torch::Tensor& value,
   int key_stride = key.stride(0);
   int value_stride = value.stride(0);
 
-  VLLM_DISPATCH_FLOATING_TYPES(
-      key.scalar_type(), "reshape_and_cache_cpu_impl", [&] {
+  DISPATCH_MACRO(key.scalar_type(), "reshape_and_cache_cpu_impl", [&] {
     CPU_KERNEL_GUARD_IN(reshape_and_cache_cpu_impl)
     reshape_and_cache_cpu_impl<scalar_t>(
         key.data_ptr<scalar_t>(), value.data_ptr<scalar_t>(),
         key_cache.data_ptr<scalar_t>(), value_cache.data_ptr<scalar_t>(),
-            slot_mapping.data_ptr<int64_t>(), num_tokens, key_stride,
-            value_stride, num_heads, head_size, block_size, x);
+        slot_mapping.data_ptr<int64_t>(), num_tokens, key_stride, value_stride,
+        num_heads, head_size, block_size, x);
     CPU_KERNEL_GUARD_OUT(reshape_and_cache_cpu_impl)
   });
 }

csrc/cpu/cpu_types.hpp

@@ -7,6 +7,9 @@
 #elif defined(__POWER9_VECTOR__)
   // ppc implementation
   #include "cpu_types_vsx.hpp"
+#elif defined(__s390x__)
+  // s390 implementation
+  #include "cpu_types_vxe.hpp"
 #elif defined(__aarch64__)
   // arm implementation
   #include "cpu_types_arm.hpp"

csrc/cpu/cpu_types_vxe.hpp

@@ -0,0 +1,480 @@
+
+#ifndef CPU_TYPES_VXE_HPP
+#define CPU_TYPES_VXE_HPP
+
+#include <vecintrin.h>
+#include <cmath>
+#include <torch/all.h>
+namespace vec_op {
+
+#define vec_neg(a) (-(a))
+#define vec_add(a, b) ((a) + (b))
+#define vec_sub(a, b) ((a) - (b))
+#define vec_mul(a, b) ((a) * (b))
+#define vec_div(a, b) ((a) / (b))
+#define vec_sr(a, b) ((a) >> (b))  // Vector Shift Right Algebaic
+#define vec_sl(a, b) ((a) << (b))  // Vector Shift Left
+
+// FIXME: FP16 is not fully supported in Torch-CPU
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__)
+
+#define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
+
+#ifndef CPU_OP_GUARD
+  #define CPU_KERNEL_GUARD_IN(NAME)
+  #define CPU_KERNEL_GUARD_OUT(NAME)
+#else
+  #define CPU_KERNEL_GUARD_IN(NAME) \
+    std::cout << #NAME << " invoked." << std::endl;
+  #define CPU_KERNEL_GUARD_OUT(NAME) \
+    std::cout << #NAME << " exit." << std::endl;
+#endif
+
+#define FORCE_INLINE __attribute__((always_inline)) inline
+
+namespace {
+template <typename T, T... indexes, typename F>
+constexpr void unroll_loop_item(std::integer_sequence<T, indexes...>, F&& f) {
+  (f(std::integral_constant<T, indexes>{}), ...);
+}
+};  // namespace
+
+template <typename T, T count, typename F,
+          typename = std::enable_if_t<std::is_invocable_v<F, T>>>
+constexpr void unroll_loop(F&& f) {
+  unroll_loop_item(std::make_integer_sequence<T, count>{}, std::forward<F>(f));
+}
+
+template <typename T>
+struct Vec {
+  constexpr static int get_elem_num() { return T::VEC_ELEM_NUM; }
+};
+
+typedef struct ss16x8x2_t {
+  __vector signed short val[2];
+} ss16x8x2_t;
+
+typedef struct ss16x8x4_t {
+  __vector signed short val[4];
+} ss16x8x4_t;
+
+typedef struct f32x4x2_t {
+  __vector float val[2];
+} f32x4x2_t;
+
+typedef struct f32x4x4_t {
+  __vector float val[4];
+} f32x4x4_t;
+
+struct FP32Vec8;
+struct FP32Vec16;
+
+struct BF16Vec8 : public Vec<BF16Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+
+  __vector signed short reg;
+
+  explicit BF16Vec8(const void* ptr) : reg(*(__vector signed short*)ptr) {}
+  explicit BF16Vec8(const FP32Vec8&);
+
+  void save(void* ptr) const {
+    *reinterpret_cast<__vector signed short*>(ptr) = reg;
+  }
+};
+
+struct BF16Vec16 : public Vec<BF16Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+
+  ss16x8x2_t reg;
+
+  explicit BF16Vec16(const void* ptr) {
+    // Load 256 bits in two parts
+    reg.val[0] = (__vector signed short)vec_xl(0, (signed short*)ptr);
+    reg.val[1] = (__vector signed short)vec_xl(16, (signed short*)ptr);
+  }
+
+  explicit BF16Vec16(const FP32Vec16&);
+
+  void save(void* ptr) const {
+    // Save 256 bits in two parts
+    vec_xst(reg.val[0], 0, (signed short*)ptr);
+    vec_xst(reg.val[1], 16, (signed short*)ptr);
+  }
+};
+
+const static __vector signed short zero = vec_splats((signed short)0);
+
+struct BF16Vec32 : public Vec<BF16Vec32> {
+  constexpr static int VEC_ELEM_NUM = 32;
+
+  ss16x8x4_t reg;
+  explicit BF16Vec32(const void* ptr)
+      : reg(*reinterpret_cast<const ss16x8x4_t*>(ptr)) {}
+
+  explicit BF16Vec32(ss16x8x4_t data) : reg(data) {}
+
+  explicit BF16Vec32(const BF16Vec8& vec8_data)
+      : reg({vec8_data.reg, vec8_data.reg, vec8_data.reg, vec8_data.reg}) {}
+
+  void save(void* ptr) const { *reinterpret_cast<ss16x8x4_t*>(ptr) = reg; }
+};
+
+struct FP32Vec4 : public Vec<FP32Vec4> {
+  constexpr static int VEC_ELEM_NUM = 4;
+  union AliasReg {
+    __vector float reg;
+    float values[VEC_ELEM_NUM];
+  };
+
+  __vector float reg;
+
+  explicit FP32Vec4(float v) : reg(vec_splats(v)) {}
+
+  explicit FP32Vec4() : reg(vec_splats(0.0f)) {}
+
+  explicit FP32Vec4(const float* ptr) : reg(vec_xl(0, ptr)) {}
+
+  explicit FP32Vec4(__vector float data) : reg(data) {}
+
+  explicit FP32Vec4(const FP32Vec4& data) : reg(data.reg) {}
+};
+
+struct FP32Vec8 : public Vec<FP32Vec8> {
+  constexpr static int VEC_ELEM_NUM = 8;
+  union AliasReg {
+    f32x4x2_t reg;
+    float values[VEC_ELEM_NUM];
+  };
+
+  f32x4x2_t reg;
+
+  explicit FP32Vec8(float v) {
+    reg.val[0] = vec_splats(v);
+    reg.val[1] = vec_splats(v);
+  }
+
+  explicit FP32Vec8() {
+    reg.val[0] = vec_splats(0.0f);
+    reg.val[1] = vec_splats(0.0f);
+  }
+
+  explicit FP32Vec8(const float* ptr) {
+    reg.val[0] = vec_xl(0, ptr);
+    reg.val[1] = vec_xl(16, ptr);
+  }
+
+  explicit FP32Vec8(f32x4x2_t data) : reg(data) {}
+
+  explicit FP32Vec8(const FP32Vec8& data) {
+    reg.val[0] = data.reg.val[0];
+    reg.val[1] = data.reg.val[1];
+  }
+
+  explicit FP32Vec8(const BF16Vec8& v) {
+    reg.val[0] = (__vector float)vec_mergeh(zero, v.reg);
+    reg.val[1] = (__vector float)vec_mergel(zero, v.reg);
+  }
+
+  float reduce_sum() const {
+    AliasReg ar;
+    ar.reg = reg;
+    float result = 0;
+    unroll_loop<int, VEC_ELEM_NUM>(
+        [&result, &ar](int i) { result += ar.values[i]; });
+
+    return result;
+  }
+
+  FP32Vec8 exp() const {
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::exp(ar.values[0]);
+    ret.val[0][1] = std::exp(ar.values[1]);
+    ret.val[0][2] = std::exp(ar.values[2]);
+    ret.val[0][3] = std::exp(ar.values[3]);
+    ret.val[1][0] = std::exp(ar.values[4]);
+    ret.val[1][1] = std::exp(ar.values[5]);
+    ret.val[1][2] = std::exp(ar.values[6]);
+    ret.val[1][3] = std::exp(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
+  }
+
+  FP32Vec8 tanh() const {
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::tanh(ar.values[0]);
+    ret.val[0][1] = std::tanh(ar.values[1]);
+    ret.val[0][2] = std::tanh(ar.values[2]);
+    ret.val[0][3] = std::tanh(ar.values[3]);
+    ret.val[1][0] = std::tanh(ar.values[4]);
+    ret.val[1][1] = std::tanh(ar.values[5]);
+    ret.val[1][2] = std::tanh(ar.values[6]);
+    ret.val[1][3] = std::tanh(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
+  }
+
+  FP32Vec8 er() const {
+    // TODO: Vectorize this
+    AliasReg ar;
+    ar.reg = reg;
+    f32x4x4_t ret;
+    ret.val[0][0] = std::erf(ar.values[0]);
+    ret.val[0][1] = std::erf(ar.values[1]);
+    ret.val[0][2] = std::erf(ar.values[2]);
+    ret.val[0][3] = std::erf(ar.values[3]);
+    ret.val[1][0] = std::erf(ar.values[4]);
+    ret.val[1][1] = std::erf(ar.values[5]);
+    ret.val[1][2] = std::erf(ar.values[6]);
+    ret.val[1][3] = std::erf(ar.values[7]);
+    return FP32Vec8(f32x4x2_t({ret.val[0], ret.val[1]}));
+  }
+
+  FP32Vec8 operator*(const FP32Vec8& b) const {
+    return FP32Vec8(
+        {vec_mul(reg.val[0], b.reg.val[0]), vec_mul(reg.val[1], b.reg.val[1])});
+  }
+
+  FP32Vec8 operator+(const FP32Vec8& b) const {
+    return FP32Vec8(
+        {vec_add(reg.val[0], b.reg.val[0]), vec_add(reg.val[1], b.reg.val[1])});
+  }
+
+  FP32Vec8 operator-(const FP32Vec8& b) const {
+    return FP32Vec8(
+        {vec_sub(reg.val[0], b.reg.val[0]), vec_sub(reg.val[1], b.reg.val[1])});
+  }
+
+  FP32Vec8 operator/(const FP32Vec8& b) const {
+    return FP32Vec8(
+        {vec_div(reg.val[0], b.reg.val[0]), vec_div(reg.val[1], b.reg.val[1])});
+  }
+
+  void save(float* ptr) const {
+    vec_xst(reg.val[0], 0, ptr);
+    vec_xst(reg.val[1], 16, ptr);
+  }
+};
+
+struct FP32Vec16 : public Vec<FP32Vec16> {
+  constexpr static int VEC_ELEM_NUM = 16;
+  union AliasReg {
+    f32x4x4_t reg;
+    float values[VEC_ELEM_NUM];
+  };
+
+  f32x4x4_t reg;
+
+  explicit FP32Vec16(float v) {
+    reg.val[0] = vec_splats(v);
+    reg.val[1] = vec_splats(v);
+    reg.val[2] = vec_splats(v);
+    reg.val[3] = vec_splats(v);
+  }
+
+  explicit FP32Vec16() {
+    reg.val[0] = vec_splats(0.0f);
+    reg.val[1] = vec_splats(0.0f);
+    reg.val[2] = vec_splats(0.0f);
+    reg.val[3] = vec_splats(0.0f);
+  }
+
+  explicit FP32Vec16(const float* ptr) {
+    reg.val[0] = vec_xl(0, ptr);
+    reg.val[1] = vec_xl(16, ptr);
+    reg.val[2] = vec_xl(32, ptr);
+    reg.val[3] = vec_xl(48, ptr);
+  }
+
+  explicit FP32Vec16(f32x4x4_t data) : reg(data) {}
+
+  explicit FP32Vec16(const FP32Vec16& data) {
+    reg.val[0] = data.reg.val[0];
+    reg.val[1] = data.reg.val[1];
+    reg.val[2] = data.reg.val[2];
+    reg.val[3] = data.reg.val[3];
+  }
+
+  explicit FP32Vec16(const FP32Vec4& data) {
+    reg.val[0] = data.reg;
+    reg.val[1] = data.reg;
+    reg.val[2] = data.reg;
+    reg.val[3] = data.reg;
+  }
+
+  explicit FP32Vec16(const FP32Vec8& data) {
+    reg.val[0] = data.reg.val[0];
+    reg.val[1] = data.reg.val[1];
+    reg.val[2] = data.reg.val[0];
+    reg.val[3] = data.reg.val[1];
+  }
+
+  explicit FP32Vec16(const BF16Vec16& v) {
+    reg.val[0] = (__vector float)vec_mergeh(zero, v.reg.val[0]);
+    reg.val[1] = (__vector float)vec_mergel(zero, v.reg.val[0]);
+    reg.val[2] = (__vector float)vec_mergeh(zero, v.reg.val[1]);
+    reg.val[3] = (__vector float)vec_mergel(zero, v.reg.val[1]);
+  }
+
+  explicit FP32Vec16(const BF16Vec8& v) : FP32Vec16(FP32Vec8(v)) {}
+
+  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]),
+                                vec_mul(reg.val[2], b.reg.val[2]),
+                                vec_mul(reg.val[3], b.reg.val[3])}));
+  }
+
+  FP32Vec16 operator+(const FP32Vec16& b) const {
+    return FP32Vec16(f32x4x4_t({vec_add(reg.val[0], b.reg.val[0]),
+                                vec_add(reg.val[1], b.reg.val[1]),
+                                vec_add(reg.val[2], b.reg.val[2]),
+                                vec_add(reg.val[3], b.reg.val[3])}));
+  }
+
+  FP32Vec16 operator-(const FP32Vec16& b) const {
+    return FP32Vec16(f32x4x4_t({vec_sub(reg.val[0], b.reg.val[0]),
+                                vec_sub(reg.val[1], b.reg.val[1]),
+                                vec_sub(reg.val[2], b.reg.val[2]),
+                                vec_sub(reg.val[3], b.reg.val[3])}));
+  }
+
+  FP32Vec16 operator/(const FP32Vec16& b) const {
+    return FP32Vec16(f32x4x4_t({vec_div(reg.val[0], b.reg.val[0]),
+                                vec_div(reg.val[1], b.reg.val[1]),
+                                vec_div(reg.val[2], b.reg.val[2]),
+                                vec_div(reg.val[3], b.reg.val[3])}));
+  }
+
+  float reduce_sum() const {
+    AliasReg ar;
+    ar.reg = reg;
+    float result = 0;
+    unroll_loop<int, VEC_ELEM_NUM>(
+        [&result, &ar](int i) { result += ar.values[i]; });
+
+    return result;
+  }
+
+  template <int group_size>
+  float reduce_sub_sum(int idx) {
+    static_assert(VEC_ELEM_NUM % group_size == 0);
+
+    AliasReg ar;
+    ar.reg = reg;
+    float result = 0;
+    const int start = idx * group_size;
+    unroll_loop<int, group_size>(
+        [&result, &start, ar](int i) { result += ar.values[start + i]; });
+
+    return result;
+  }
+
+  void save(float* ptr) const {
+    vec_xst(reg.val[0], 0, ptr);
+    vec_xst(reg.val[1], 16, ptr);
+    vec_xst(reg.val[2], 32, ptr);
+    vec_xst(reg.val[3], 48, ptr);
+  }
+};
+
+template <typename T>
+struct VecType {
+  using vec_type = void;
+};
+
+template <typename T>
+using vec_t = typename VecType<T>::vec_type;
+
+template <>
+struct VecType<float> {
+  using vec_type = FP32Vec8;
+};
+
+template <>
+struct VecType<c10::BFloat16> {
+  using vec_type = BF16Vec8;
+};
+
+template <typename T>
+void storeFP32(float v, T* ptr) {
+  *ptr = v;
+}
+
+inline void fma(FP32Vec16& acc, FP32Vec16& a, FP32Vec16& b) {
+  acc = acc + a * b;
+}
+
+namespace c10 {
+struct BFloat16 {
+  uint16_t value;  // Assume BFloat16 is defined as a struct containing a 16-bit
+                   // value.
+};
+}  // namespace c10
+
+template <>
+inline void storeFP32<c10::BFloat16>(float v, c10::BFloat16* ptr) {
+  c10::BFloat16 __attribute__((__may_alias__))* v_ptr =
+      reinterpret_cast<c10::BFloat16*>(&v);
+  *ptr = *(v_ptr + 1);
+}
+
+#ifndef __VEC_CLASS_FP_NAN
+  #define __VEC_CLASS_FP_NAN (1 << 6)
+#endif
+
+const static __vector unsigned char omask = {2,  3,  6,  7,  10, 11, 14, 15,
+                                             18, 19, 22, 23, 26, 27, 30, 31};
+const static __vector unsigned int bias = {0x00007fff, 0x00007fff, 0x00007fff,
+                                           0x00007fff};
+const static __vector unsigned int nan = {0x7fc00000, 0x7fc00000, 0x7fc00000,
+                                          0x7fc00000};
+const static __vector unsigned int sh16 = {16, 16, 16, 16};
+const static __vector unsigned int one = {1, 1, 1, 1};
+
+inline BF16Vec8::BF16Vec8(const FP32Vec8& v) {
+  __vector unsigned int inp0 = (__vector unsigned int)(v.reg.val[0]);
+  __vector unsigned int inp1 = (__vector unsigned int)(v.reg.val[1]);
+  int cc;
+  __vector __bool int sel0 =
+      vec_fp_test_data_class(v.reg.val[0], __VEC_CLASS_FP_NAN, &cc);
+  __vector __bool int sel1 =
+      vec_fp_test_data_class(v.reg.val[1], __VEC_CLASS_FP_NAN, &cc);
+  inp0 = vec_sel(inp0, nan, sel0) >> sh16;
+  inp1 = vec_sel(inp1, nan, sel1) >> sh16;
+  reg = (__vector signed short)vec_perm(inp0, inp1, omask);
+}
+
+inline BF16Vec16::BF16Vec16(const FP32Vec16& v) {
+  __vector unsigned int inp0 = (__vector unsigned int)(v.reg.val[0]);
+  __vector unsigned int inp1 = (__vector unsigned int)(v.reg.val[1]);
+  __vector unsigned int inp2 = (__vector unsigned int)(v.reg.val[2]);
+  __vector unsigned int inp3 = (__vector unsigned int)(v.reg.val[3]);
+  int cc;
+  __vector __bool int sel0 =
+      vec_fp_test_data_class(v.reg.val[0], __VEC_CLASS_FP_NAN, &cc);
+  __vector __bool int sel1 =
+      vec_fp_test_data_class(v.reg.val[1], __VEC_CLASS_FP_NAN, &cc);
+  __vector __bool int sel2 =
+      vec_fp_test_data_class(v.reg.val[2], __VEC_CLASS_FP_NAN, &cc);
+  __vector __bool int sel3 =
+      vec_fp_test_data_class(v.reg.val[3], __VEC_CLASS_FP_NAN, &cc);
+  inp0 = vec_sel(inp0, nan, sel0) >> sh16;
+  inp1 = vec_sel(inp1, nan, sel1) >> sh16;
+  inp2 = vec_sel(inp2, nan, sel2) >> sh16;
+  inp3 = vec_sel(inp3, nan, sel3) >> sh16;
+  reg.val[0] = (__vector signed short)vec_perm(inp0, inp1, omask);
+  reg.val[1] = (__vector signed short)vec_perm(inp2, inp3, omask);
+}
+
+inline void prefetch(const void* addr) { void __dcbt(const void* addr); }
+
+};  // namespace vec_op
+
+#endif

csrc/cpu/cpu_types_x86.hpp

@@ -16,9 +16,18 @@ namespace vec_op {
   AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
   AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)
 
+#define VLLM_DISPATCH_CASE_FLOATING_TYPES_FP8(...)        \
+  AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__)    \
+  AT_DISPATCH_CASE(at::ScalarType::BFloat16, __VA_ARGS__) \
+  AT_DISPATCH_CASE(at::ScalarType::Float8_e5m2, __VA_ARGS__)
+
 #define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
   AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
 
+#define VLLM_DISPATCH_FLOATING_TYPES_WITH_E5M2(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(TYPE, NAME,                                \
+                     VLLM_DISPATCH_CASE_FLOATING_TYPES_FP8(__VA_ARGS__))
+
 #ifndef CPU_OP_GUARD
   #define CPU_KERNEL_GUARD_IN(NAME)
   #define CPU_KERNEL_GUARD_OUT(NAME)

csrc/cpu/pos_encoding.cpp

@@ -170,7 +170,7 @@ void rotary_embedding_gptj_impl(
 void rotary_embedding(torch::Tensor& positions, torch::Tensor& query,
                       torch::Tensor& key, int64_t head_size,
                       torch::Tensor& cos_sin_cache, bool is_neox) {
-  int num_tokens = query.numel() / query.size(-1);
+  int num_tokens = positions.numel();
   int rot_dim = cos_sin_cache.size(1);
   int num_heads = query.size(-1) / head_size;
   int num_kv_heads = key.size(-1) / head_size;

csrc/cpu/quant.cpp

@@ -25,7 +25,7 @@ struct KernelVecType<c10::BFloat16> {
 
 template <>
 struct KernelVecType<c10::Half> {
-#ifdef __powerpc64__
+#if defined(__powerpc64__) || defined(__s390x__)
   // Power architecture-specific vector type
   using load_vec_type = vec_op::FP32Vec16;
 #else

csrc/cutlass_extensions/gemm/collective/sm90_mma_tma_gmma_ss_warpspecialized_fp8_blockwise_scaling.hpp

@@ -402,7 +402,7 @@ struct CollectiveMma<
 
     // TODO: test `scale_copy_a` with `ScaleMsPerTile` < 128
     TiledCopy scale_copy_a = make_tiled_copy(SmemBlockScalingCopyAtomA{}, 
-      Layout<Shape<_32, _1>>{}, Layout<Shape<_4, _1>>{}); // (1,1,1)
+      Layout<Shape<_32>>{}, Layout<Shape<_1>>{}); // (1,1,1)
     TiledCopy scale_copy_b = make_tiled_copy(SmemBlockScalingCopyAtomB{}, 
       Layout<Shape<_1>>{}, Layout<Shape<_1>>{}); // (1,1,1)
     ThrCopy thr_scale_copy_a = scale_copy_a.get_slice(threadIdx.x);

csrc/dispatch_utils.h

@@ -6,6 +6,11 @@
 
 #include <torch/all.h>
 
+// Need a special dispatch case macro since we will nest the FP8 dispatch.
+// Instead of the usual 'scalar_t', this names the dispatched type 'fp8_t'.
+#define AT_DISPATCH_FP8_CASE(enum_type, ...) \
+  AT_PRIVATE_CASE_TYPE_USING_HINT(enum_type, fp8_t, __VA_ARGS__)
+
 #define VLLM_DISPATCH_CASE_FLOATING_TYPES(...)         \
   AT_DISPATCH_CASE(at::ScalarType::Float, __VA_ARGS__) \
   AT_DISPATCH_CASE(at::ScalarType::Half, __VA_ARGS__)  \
@@ -14,17 +19,32 @@
 #define VLLM_DISPATCH_FLOATING_TYPES(TYPE, NAME, ...) \
   AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FLOATING_TYPES(__VA_ARGS__))
 
-// TODO(luka/varun): use FP8_TYPE macro after refactoring
-#ifndef USE_ROCM
+// ROCm devices might use either fn or fnuz, so set up dispatch table for both.
+// A host-based check at runtime will create a preferred FP8 type for ROCm
+// such that the correct kernel is dispatched.
+#ifdef USE_ROCM
+  #define VLLM_DISPATCH_CASE_FP8_TYPES(...)                          \
+    AT_DISPATCH_FP8_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__) \
+    AT_DISPATCH_FP8_CASE(at::ScalarType::Float8_e4m3fnuz, __VA_ARGS__)
+
+  #define VLLM_DISPATCH_CASE_QUANT_TYPES(...)                      \
+    AT_DISPATCH_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__)   \
+    AT_DISPATCH_CASE(at::ScalarType::Float8_e4m3fnuz, __VA_ARGS__) \
+    AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)
+#else
+  #define VLLM_DISPATCH_CASE_FP8_TYPES(...) \
+    AT_DISPATCH_FP8_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__)
+
   #define VLLM_DISPATCH_CASE_QUANT_TYPES(...)                    \
     AT_DISPATCH_CASE(at::ScalarType::Float8_e4m3fn, __VA_ARGS__) \
     AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)
-#else
-  #define VLLM_DISPATCH_CASE_QUANT_TYPES(...)                      \
-    AT_DISPATCH_CASE(at::ScalarType::Float8_e4m3fnuz, __VA_ARGS__) \
-    AT_DISPATCH_CASE(at::ScalarType::Char, __VA_ARGS__)
 #endif
 
+// When using this dispatch macro, the type is 'fp8_t' not 'scalar_t'.
+// See AT_DISPATCH_FP8_CASE above.
+#define VLLM_DISPATCH_FP8_TYPES(TYPE, NAME, ...) \
+  AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_FP8_TYPES(__VA_ARGS__))
+
 #define VLLM_DISPATCH_QUANT_TYPES(TYPE, NAME, ...) \
   AT_DISPATCH_SWITCH(TYPE, NAME, VLLM_DISPATCH_CASE_QUANT_TYPES(__VA_ARGS__))
 

csrc/layernorm_quant_kernels.cu

@@ -21,9 +21,9 @@
 namespace vllm {
 
 // TODO(woosuk): Further optimize this kernel.
-template <typename scalar_t>
+template <typename scalar_t, typename fp8_type>
 __global__ void rms_norm_static_fp8_quant_kernel(
-    FP8_TYPE* __restrict__ out,           // [..., hidden_size]
+    fp8_type* __restrict__ out,           // [..., hidden_size]
     const scalar_t* __restrict__ input,   // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
     const float* __restrict__ scale,      // [1]
@@ -52,7 +52,7 @@ __global__ void rms_norm_static_fp8_quant_kernel(
     float x = (float)input[blockIdx.x * hidden_size + idx];
     float const out_norm = ((scalar_t)(x * s_variance)) * weight[idx];
     out[blockIdx.x * hidden_size + idx] =
-        scaled_fp8_conversion<true>(out_norm, scale_inv);
+        scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
   }
 }
 
@@ -60,10 +60,10 @@ __global__ void rms_norm_static_fp8_quant_kernel(
    Additional optimizations we can make in this case are
    packed and vectorized operations, which help with the
    memory latency bottleneck. */
-template <typename scalar_t, int width>
+template <typename scalar_t, int width, typename fp8_type>
 __global__ std::enable_if_t<(width > 0) && _typeConvert<scalar_t>::exists>
 fused_add_rms_norm_static_fp8_quant_kernel(
-    FP8_TYPE* __restrict__ out,           // [..., hidden_size]
+    fp8_type* __restrict__ out,           // [..., hidden_size]
     scalar_t* __restrict__ input,         // [..., hidden_size]
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
@@ -114,7 +114,7 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 #pragma unroll
     for (int i = 0; i < width; ++i) {
       out[id * width + i] =
-          scaled_fp8_conversion<true>(float(temp.data[i]), scale_inv);
+          scaled_fp8_conversion<true, fp8_type>(float(temp.data[i]), scale_inv);
     }
   }
 }
@@ -122,10 +122,10 @@ fused_add_rms_norm_static_fp8_quant_kernel(
 /* Generic fused_add_rms_norm_kernel
    The width field is not used here but necessary for other specializations.
  */
-template <typename scalar_t, int width>
+template <typename scalar_t, int width, typename fp8_type>
 __global__ std::enable_if_t<(width == 0) || !_typeConvert<scalar_t>::exists>
 fused_add_rms_norm_static_fp8_quant_kernel(
-    FP8_TYPE* __restrict__ out,           // [..., hidden_size]
+    fp8_type* __restrict__ out,           // [..., hidden_size]
     scalar_t* __restrict__ input,         // [..., hidden_size]
     scalar_t* __restrict__ residual,      // [..., hidden_size]
     const scalar_t* __restrict__ weight,  // [hidden_size]
@@ -158,7 +158,7 @@ fused_add_rms_norm_static_fp8_quant_kernel(
     float x = (float)residual[blockIdx.x * hidden_size + idx];
     float const out_norm = ((scalar_t)(x * s_variance)) * weight[idx];
     out[blockIdx.x * hidden_size + idx] =
-        scaled_fp8_conversion<true>(out_norm, scale_inv);
+        scaled_fp8_conversion<true, fp8_type>(out_norm, scale_inv);
   }
 }
 
@@ -176,25 +176,33 @@ void rms_norm_static_fp8_quant(torch::Tensor& out,     // [..., hidden_size]
   dim3 block(std::min(hidden_size, 1024));
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
-  VLLM_DISPATCH_FLOATING_TYPES(input.scalar_type(), "rms_norm_kernel", [&] {
-    vllm::rms_norm_static_fp8_quant_kernel<scalar_t>
+  VLLM_DISPATCH_FLOATING_TYPES(
+      input.scalar_type(), "rms_norm_kernel_scalar_type", [&] {
+        VLLM_DISPATCH_FP8_TYPES(
+            out.scalar_type(), "rms_norm_kernel_fp8_type", [&] {
+              vllm::rms_norm_static_fp8_quant_kernel<scalar_t, fp8_t>
                   <<<grid, block, 0, stream>>>(
-            out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),
-            weight.data_ptr<scalar_t>(), scale.data_ptr<float>(), epsilon,
-            num_tokens, hidden_size);
+                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
+                      weight.data_ptr<scalar_t>(), scale.data_ptr<float>(),
+                      epsilon, num_tokens, hidden_size);
+            });
       });
 }
 
 #define LAUNCH_FUSED_ADD_RMS_NORM(width)                                     \
   VLLM_DISPATCH_FLOATING_TYPES(                                              \
-      input.scalar_type(), "fused_add_rms_norm_kernel", [&] {               \
-        vllm::fused_add_rms_norm_static_fp8_quant_kernel<scalar_t, width>   \
+      input.scalar_type(), "fused_add_rms_norm_kernel_scalar_type", [&] {    \
+        VLLM_DISPATCH_FP8_TYPES(                                             \
+            out.scalar_type(), "fused_add_rms_norm_kernel_fp8_type", [&] {   \
+              vllm::fused_add_rms_norm_static_fp8_quant_kernel<scalar_t,     \
+                                                               width, fp8_t> \
                   <<<grid, block, 0, stream>>>(                              \
-                out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),       \
-                residual.data_ptr<scalar_t>(), weight.data_ptr<scalar_t>(), \
-                scale.data_ptr<float>(), epsilon, num_tokens, hidden_size); \
+                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),     \
+                      residual.data_ptr<scalar_t>(),                         \
+                      weight.data_ptr<scalar_t>(), scale.data_ptr<float>(),  \
+                      epsilon, num_tokens, hidden_size);                     \
+            });                                                              \
       });
-
 void fused_add_rms_norm_static_fp8_quant(
     torch::Tensor& out,       // [..., hidden_size],
     torch::Tensor& input,     // [..., hidden_size]

csrc/moe/moe_ops.h

@@ -18,3 +18,14 @@ void sgl_moe_align_block_size(torch::Tensor topk_ids, int64_t num_experts,
                               torch::Tensor sorted_token_ids,
                               torch::Tensor experts_ids,
                               torch::Tensor num_tokens_post_pad);
+#ifndef USE_ROCM
+torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
+                             torch::Tensor b_qweight, torch::Tensor b_scales,
+                             std::optional<torch::Tensor> b_qzeros,
+                             std::optional<torch::Tensor> topk_weights,
+                             torch::Tensor sorted_token_ids,
+                             torch::Tensor expert_ids,
+                             torch::Tensor num_tokens_post_pad, int64_t top_k,
+                             int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
+                             int64_t BLOCK_SIZE_K, int64_t bit);
+#endif

csrc/moe/moe_wna16.cu

@@ -0,0 +1,346 @@
+
+#include <torch/all.h>
+#include <c10/cuda/CUDAGuard.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda_runtime.h>
+
+#include <cuda_fp16.h>
+#include <cuda_bf16.h>
+#include "moe_wna16_utils.h"
+
+#define DIVIDE(x, size) (((x) + (size) - 1) / (size))
+
+template <typename scalar_t, int bit, int GROUPS>
+__global__ void moe_wna16_gemm_kernel(
+    const scalar_t* __restrict__ input, scalar_t* __restrict__ output,
+
+    const uint32_t* __restrict__ qweight, const scalar_t* __restrict__ scales,
+    const uint32_t* __restrict__ qzeros,
+
+    const float* __restrict__ topk_weights,
+    const int32_t* __restrict__ sorted_token_ids,
+    const int32_t* __restrict__ expert_ids,
+    const int32_t* __restrict__ num_tokens_post_pad,
+
+    uint16_t num_experts, uint16_t group_size, uint16_t top_k, uint32_t size_m,
+    uint32_t size_n, uint32_t size_k, uint16_t BLOCK_SIZE_M,
+    uint16_t BLOCK_SIZE_N, uint16_t BLOCK_SIZE_K, bool has_zp,
+    bool mul_topk_weight) {
+#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800
+  if constexpr (std::is_same<scalar_t, nv_bfloat16>::value) {
+    return;
+  } else {
+#endif
+
+    using Dtype = ScalarType<scalar_t>;
+    using scalar_t2 = typename ScalarType<scalar_t>::scalar_t2;
+
+    if (blockIdx.x * BLOCK_SIZE_M >= num_tokens_post_pad[0]) return;
+
+    const int32_t offset_n = blockIdx.y * BLOCK_SIZE_N + threadIdx.x;
+    const int32_t offset_k = blockIdx.z * BLOCK_SIZE_K;
+
+    const int32_t expert_id = expert_ids[blockIdx.x];
+
+    int32_t num_valid_tokens = 0;
+    extern __shared__ uint16_t block_input_tmp[];
+    scalar_t* block_input = reinterpret_cast<scalar_t*>(block_input_tmp);
+    scalar_t2* block_input_half2 = reinterpret_cast<scalar_t2*>(block_input);
+
+    // load BLOCK_SIZE_M * BLOCK_SIZE_K into shared memory
+    for (int m = 0; m < BLOCK_SIZE_M; m++) {
+      const int32_t offset_m = blockIdx.x * BLOCK_SIZE_M + m;
+      const int32_t token_index = sorted_token_ids[offset_m];
+      if (token_index / top_k >= size_m) break;
+
+      num_valid_tokens = m + 1;
+      if (blockIdx.z == 0 && offset_n < size_n)
+        output[token_index * size_n + offset_n] = Dtype::int2num(0);
+
+      if (expert_id != -1) {
+        int k_per_thread = DIVIDE(BLOCK_SIZE_K, BLOCK_SIZE_N);
+        for (int i = 0; i < k_per_thread; i++) {
+          int k = BLOCK_SIZE_N * i + threadIdx.x;
+          if (k >= BLOCK_SIZE_K) break;
+          if (offset_k + k >= size_k) break;
+
+          // load input to shared memory
+          // use a special layout to fit the layout of dequanted-weight
+          int origin_k;
+          if constexpr (bit == 4) {
+            // [0, 4, 1, 5, 2, 6, 3, 7]
+            int8_t order = (threadIdx.x % 2) * 4 + ((threadIdx.x % 8) / 2);
+            origin_k = BLOCK_SIZE_N * i + threadIdx.x / 8 * 8 + order;
+          } else {
+            // [0, 2, 1, 3]
+            int8_t order = (threadIdx.x % 2) * 2 + ((threadIdx.x % 4) / 2);
+            origin_k = BLOCK_SIZE_N * i + threadIdx.x / 4 * 4 + order;
+          }
+
+          origin_k += token_index / top_k * size_k + blockIdx.z * BLOCK_SIZE_K;
+          block_input[m * BLOCK_SIZE_K + k] = input[origin_k];
+        }
+      }
+    }
+
+    if (expert_id == -1) return;
+    __syncthreads();
+    if (threadIdx.x >= BLOCK_SIZE_N || offset_n >= size_n) return;
+
+    float res[64];  // assume BLOCK_SIZE_M <= 64
+    scalar_t2 res2;
+    scalar_t2 scale_f2;
+    scalar_t2 qzero_f2;
+
+    // note that (size_n * size_k * expert_id) may greater than 2 ** 31
+    constexpr int8_t pack_factor = 32 / bit;
+    const uint64_t expert_offset = ((uint64_t)size_n) * size_k * expert_id;
+    const uint32_t* expert_qweight = qweight + expert_offset / pack_factor;
+    const scalar_t* expert_scales = scales + expert_offset / group_size;
+    const uint32_t* expert_qzeros =
+        qzeros + expert_offset / group_size / pack_factor;
+
+    // load 4*int32 one time: 4 int32 = 128 bit = 1 float4
+    // weight would be loaded in loop
+    uint32_t expert_qweight_tmp[4];
+    float4* expert_qweight_tmp_float4 =
+        reinterpret_cast<float4*>(expert_qweight_tmp);
+
+    // load all required scales one time
+    scalar_t expert_scales_groups[GROUPS];
+    int scales_offset_tmp =
+        (offset_n * size_k + offset_k) / group_size / GROUPS;
+    if constexpr (GROUPS == 1) {
+      *expert_scales_groups = expert_scales[scales_offset_tmp];
+    } else if constexpr (GROUPS == 2) {
+      float* expert_scales_groups_tmp =
+          reinterpret_cast<float*>(expert_scales_groups);
+      *expert_scales_groups_tmp =
+          reinterpret_cast<const float*>(expert_scales)[scales_offset_tmp];
+    } else if constexpr (GROUPS == 4) {
+      float2* expert_scales_groups_tmp =
+          reinterpret_cast<float2*>(expert_scales_groups);
+      *expert_scales_groups_tmp =
+          reinterpret_cast<const float2*>(expert_scales)[scales_offset_tmp];
+    } else if constexpr (GROUPS == 8) {
+      float4* expert_scales_groups_tmp =
+          reinterpret_cast<float4*>(expert_scales_groups);
+      *expert_scales_groups_tmp =
+          reinterpret_cast<const float4*>(expert_scales)[scales_offset_tmp];
+    }
+
+    // load all required qzeros one time
+    uint8_t expert_qzeros_groups[GROUPS];
+    if (!has_zp) {
+      if constexpr (bit == 4) {
+        qzero_f2 = Dtype::num2num2(Dtype::int2num(8));
+      } else {
+        qzero_f2 = Dtype::num2num2(Dtype::int2num(128));
+      }
+    } else {
+      int qzeros_offset_tmp =
+          (offset_n / (8 / bit)) * (size_k / group_size / GROUPS) +
+          offset_k / group_size / GROUPS;
+      if constexpr (GROUPS == 1) {
+        uint8_t* expert_qzeros_groups_tmp =
+            reinterpret_cast<uint8_t*>(expert_qzeros_groups);
+        *expert_qzeros_groups_tmp =
+            reinterpret_cast<const uint8_t*>(expert_qzeros)[qzeros_offset_tmp];
+      } else if constexpr (GROUPS == 2) {
+        uint16_t* expert_qzeros_groups_tmp =
+            reinterpret_cast<uint16_t*>(expert_qzeros_groups);
+        *expert_qzeros_groups_tmp =
+            reinterpret_cast<const uint16_t*>(expert_qzeros)[qzeros_offset_tmp];
+      } else if constexpr (GROUPS == 4) {
+        uint32_t* expert_qzeros_groups_tmp =
+            reinterpret_cast<uint32_t*>(expert_qzeros_groups);
+        *expert_qzeros_groups_tmp =
+            reinterpret_cast<const uint32_t*>(expert_qzeros)[qzeros_offset_tmp];
+      } else if constexpr (GROUPS == 8) {
+        uint64_t* expert_qzeros_groups_tmp =
+            reinterpret_cast<uint64_t*>(expert_qzeros_groups);
+        *expert_qzeros_groups_tmp =
+            reinterpret_cast<const uint64_t*>(expert_qzeros)[qzeros_offset_tmp];
+      }
+    }
+
+    for (int tmp_k = 0; tmp_k < BLOCK_SIZE_K / pack_factor; tmp_k++) {
+      int k = offset_k + tmp_k * pack_factor;
+      if (k >= size_k) break;
+      const int32_t weight_offset = offset_n * size_k + k;
+
+      if (tmp_k % 4 == 0) {
+        *expert_qweight_tmp_float4 = reinterpret_cast<const float4*>(
+            expert_qweight)[weight_offset / pack_factor / 4];
+      }
+
+      if (tmp_k % (group_size / pack_factor) == 0) {
+        scalar_t scale_f =
+            expert_scales_groups[tmp_k / (group_size / pack_factor)];
+        scale_f2 = Dtype::num2num2(scale_f);
+
+        if (has_zp) {
+          uint8_t qzero =
+              expert_qzeros_groups[tmp_k / (group_size / pack_factor)];
+          if constexpr (bit == 4) {
+            qzero = (qzero >> ((threadIdx.x % 2) * 4)) & 0xF;
+          }
+          qzero_f2 = Dtype::num2num2(Dtype::int2num(qzero));
+        }
+      }
+
+      scalar_t2 weight_half2[16 / bit];
+      dequant<scalar_t2, bit>(expert_qweight_tmp[tmp_k % 4], weight_half2);
+
+      for (int m = 0; m < num_valid_tokens; m++) {
+        res2 = {};
+
+#pragma unroll
+        for (int i = 0; i < 16 / bit; i++) {
+          int32_t offset_input = m * BLOCK_SIZE_K / 2 + tmp_k * (16 / bit) + i;
+          res2 = __hfma2(__hmul2(__hsub2(weight_half2[i], qzero_f2), scale_f2),
+                         block_input_half2[offset_input], res2);
+        }
+
+        if (tmp_k == 0) {
+          res[m] = Dtype::num2float(res2.x) + Dtype::num2float(res2.y);
+        } else {
+          res[m] += Dtype::num2float(res2.x) + Dtype::num2float(res2.y);
+        }
+      }
+    }
+
+    for (int m = 0; m < num_valid_tokens; ++m) {
+      const int32_t token_index =
+          sorted_token_ids[blockIdx.x * BLOCK_SIZE_M + m];
+      if (mul_topk_weight) {
+        res[m] *= topk_weights[token_index];
+      }
+      atomicAdd(&output[token_index * size_n + offset_n],
+                Dtype::float2num(res[m]));
+    }
+
+#if !defined(__CUDA_ARCH__) || __CUDA_ARCH__ < 800
+  }
+#endif
+}
+
+template <typename scalar_t>
+void run_moe_wna16_gemm(const scalar_t* input, scalar_t* output,
+                        const uint32_t* b_qweight, const scalar_t* b_scales,
+                        const uint32_t* b_qzeros, const float* topk_weights,
+                        const int32_t* sorted_token_ids,
+                        const int32_t* expert_ids,
+                        const int32_t* num_tokens_post_pad, int num_experts,
+                        int group_size, int num_token_blocks, int top_k,
+                        int size_m, int size_n, int size_k, int BLOCK_SIZE_M,
+                        int BLOCK_SIZE_N, int BLOCK_SIZE_K, int bit,
+                        bool has_zp, bool mul_topk_weight) {
+  dim3 blockDim, gridDim;
+  blockDim.x = BLOCK_SIZE_N;
+  blockDim.y = 1;
+  blockDim.z = 1;
+  gridDim.x = num_token_blocks;
+  gridDim.y = DIVIDE(size_n, BLOCK_SIZE_N);
+  gridDim.z = DIVIDE(size_k, BLOCK_SIZE_K);
+
+  auto kernel = moe_wna16_gemm_kernel<scalar_t, 4, 1>;
+  if (bit == 4) {
+    if (BLOCK_SIZE_K / group_size == 2) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 4, 2>;
+    } else if (BLOCK_SIZE_K / group_size == 4) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 4, 4>;
+    } else if (BLOCK_SIZE_K / group_size == 8) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 4, 8>;
+    }
+  } else {
+    if (BLOCK_SIZE_K / group_size == 1) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 8, 1>;
+    } else if (BLOCK_SIZE_K / group_size == 2) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 8, 2>;
+    } else if (BLOCK_SIZE_K / group_size == 4) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 8, 4>;
+    } else if (BLOCK_SIZE_K / group_size == 8) {
+      kernel = moe_wna16_gemm_kernel<scalar_t, 8, 8>;
+    }
+  }
+
+  const int shared_mem_size = BLOCK_SIZE_M * BLOCK_SIZE_K * 2;
+  const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
+  kernel<<<gridDim, blockDim, shared_mem_size, stream>>>(
+      input, output, b_qweight, b_scales, b_qzeros, topk_weights,
+      sorted_token_ids, expert_ids, num_tokens_post_pad, num_experts,
+      group_size, top_k, size_m, size_n, size_k, BLOCK_SIZE_M, BLOCK_SIZE_N,
+      BLOCK_SIZE_K, has_zp, mul_topk_weight);
+}
+
+torch::Tensor moe_wna16_gemm(torch::Tensor input, torch::Tensor output,
+                             torch::Tensor b_qweight, torch::Tensor b_scales,
+                             std::optional<torch::Tensor> b_qzeros,
+                             std::optional<torch::Tensor> topk_weights,
+                             torch::Tensor sorted_token_ids,
+                             torch::Tensor expert_ids,
+                             torch::Tensor num_tokens_post_pad, int64_t top_k,
+                             int64_t BLOCK_SIZE_M, int64_t BLOCK_SIZE_N,
+                             int64_t BLOCK_SIZE_K, int64_t bit) {
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
+  auto options =
+      torch::TensorOptions().dtype(input.dtype()).device(input.device());
+
+  const int num_experts = b_qweight.size(0);
+  const int size_m = input.size(0);
+  const int size_n = b_qweight.size(1);
+  const int size_k = input.size(1);
+  const int group_size = size_k / b_scales.size(2);
+
+  int64_t EM = sorted_token_ids.size(0);
+  if (size_m <= BLOCK_SIZE_M) {
+    EM = min(EM, size_m * BLOCK_SIZE_M * top_k);
+  }
+  const int num_token_blocks = (EM + BLOCK_SIZE_M - 1) / BLOCK_SIZE_M;
+
+  const uint32_t* b_qzeros_ptr;
+  if (b_qzeros.has_value())
+    b_qzeros_ptr = (const uint32_t*)b_qzeros.value().data_ptr<uint8_t>();
+  const float* topk_weights_ptr;
+  if (topk_weights.has_value())
+    topk_weights_ptr = (const float*)topk_weights.value().data_ptr();
+
+  int groups_per_block_row = BLOCK_SIZE_K / group_size;
+  TORCH_CHECK(bit == 4 || bit == 8, "bit must be 4 or 8");
+  TORCH_CHECK(size_k % BLOCK_SIZE_K == 0,
+              "size_k must divisible by BLOCK_SIZE_K");
+  TORCH_CHECK(BLOCK_SIZE_K % group_size == 0,
+              "BLOCK_SIZE_K must divisible by group_size");
+  TORCH_CHECK(BLOCK_SIZE_M <= 64, "BLOCK_SIZE_M must less or equal to 64");
+  TORCH_CHECK(groups_per_block_row == 1 || groups_per_block_row == 2 ||
+                  groups_per_block_row == 4 || groups_per_block_row == 8,
+              "BLOCK_SIZE_K // group_size must be one of [1, 2, 4, 8]");
+
+  if (input.scalar_type() == at::ScalarType::Half) {
+    run_moe_wna16_gemm<half>(
+        (const half*)input.data_ptr<at::Half>(),
+        (half*)output.data_ptr<at::Half>(),
+        (const uint32_t*)b_qweight.data_ptr<uint8_t>(),
+        (const half*)b_scales.data_ptr<at::Half>(), b_qzeros_ptr,
+        topk_weights_ptr, sorted_token_ids.data_ptr<int32_t>(),
+        expert_ids.data_ptr<int32_t>(), num_tokens_post_pad.data_ptr<int32_t>(),
+        num_experts, group_size, num_token_blocks, top_k, size_m, size_n,
+        size_k, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, bit,
+        b_qzeros.has_value(), topk_weights.has_value());
+  } else if (input.scalar_type() == at::ScalarType::BFloat16) {
+    run_moe_wna16_gemm<nv_bfloat16>(
+        (const nv_bfloat16*)input.data_ptr<at::BFloat16>(),
+        (nv_bfloat16*)output.data_ptr<at::BFloat16>(),
+        (const uint32_t*)b_qweight.data_ptr<uint8_t>(),
+        (const nv_bfloat16*)b_scales.data_ptr<at::BFloat16>(), b_qzeros_ptr,
+        topk_weights_ptr, sorted_token_ids.data_ptr<int32_t>(),
+        expert_ids.data_ptr<int32_t>(), num_tokens_post_pad.data_ptr<int32_t>(),
+        num_experts, group_size, num_token_blocks, top_k, size_m, size_n,
+        size_k, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, bit,
+        b_qzeros.has_value(), topk_weights.has_value());
+  } else {
+    TORCH_CHECK(false, "moe_wna16_gemm only supports bfloat16 and float16");
+  }
+  return output;
+}

csrc/moe/moe_wna16_utils.h

@@ -0,0 +1,200 @@
+
+#include <cuda_fp16.h>
+#include <cuda_bf16.h>
+
+template <typename scalar_t>
+class ScalarType {};
+
+template <>
+class ScalarType<half> {
+ public:
+  using scalar_t = half;
+  using scalar_t2 = half2;
+
+  static __device__ float inline num2float(const half x) {
+    return __half2float(x);
+  }
+
+  static __device__ half2 inline num2num2(const half x) {
+    return __half2half2(x);
+  }
+
+  static __device__ half2 inline nums2num2(const half x1, const half x2) {
+    return __halves2half2(x1, x2);
+  }
+
+  static __host__ __device__ half inline float2num(const float x) {
+    return __float2half(x);
+  }
+
+  static __host__ __device__ half inline int2num(const float x) {
+    return __int2half_rn(x);
+  }
+
+  static __host__ __device__ float2 inline num22float2(const half2 x) {
+    return __half22float2(x);
+  }
+
+  static __host__ __device__ half2 inline float22num2(const float2 x) {
+    return __float22half2_rn(x);
+  }
+};
+
+template <>
+class ScalarType<nv_bfloat16> {
+ public:
+  using scalar_t = nv_bfloat16;
+  using scalar_t2 = nv_bfloat162;
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+  static __device__ float inline num2float(const nv_bfloat16 x) {
+    return __bfloat162float(x);
+  }
+
+  static __device__ nv_bfloat162 inline num2num2(const nv_bfloat16 x) {
+    return __bfloat162bfloat162(x);
+  }
+
+  static __device__ nv_bfloat162 inline nums2num2(const nv_bfloat16 x1,
+                                                  const nv_bfloat16 x2) {
+    return __halves2bfloat162(x1, x2);
+  }
+
+  static __host__ __device__ nv_bfloat16 inline float2num(const float x) {
+    return __float2bfloat16(x);
+  }
+
+  static __host__ __device__ nv_bfloat16 inline int2num(const float x) {
+    return __int2bfloat16_rn(x);
+  }
+
+  static __host__ __device__ float2 inline num22float2(const nv_bfloat162 x) {
+    return __bfloat1622float2(x);
+  }
+
+  static __host__ __device__ nv_bfloat162 inline float22num2(const float2 x) {
+    return __float22bfloat162_rn(x);
+  }
+#endif
+};
+
+template <int lut>
+__device__ inline int lop3(int a, int b, int c) {
+  int res;
+  asm volatile("lop3.b32 %0, %1, %2, %3, %4;\n"
+               : "=r"(res)
+               : "r"(a), "r"(b), "r"(c), "n"(lut));
+  return res;
+}
+
+template <int start_byte, int mask>
+__device__ inline uint32_t prmt(uint32_t a) {
+  uint32_t res;
+  asm volatile("prmt.b32 %0, %1, %2, %3;\n"
+               : "=r"(res)
+               : "r"(a), "n"(start_byte), "n"(mask));
+  return res;
+}
+
+template <typename scalar_t2, int bit>
+__device__ inline void dequant(int q, scalar_t2* res) {}
+
+template <>
+__device__ inline void dequant<half2, 4>(int q, half2* res) {
+  const int LO = 0x000f000f;
+  const int HI = 0x00f000f0;
+  const int EX = 0x64006400;
+  const int SUB = 0x64006400;
+  const int MUL = 0x2c002c00;
+  const int ADD = 0xd400d400;
+
+  int lo0 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, LO, EX);
+  int hi0 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, HI, EX);
+  q >>= 8;
+  int lo1 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, LO, EX);
+  int hi1 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, HI, EX);
+
+  res[0] = __hsub2(*reinterpret_cast<half2*>(&lo0),
+                   *reinterpret_cast<const half2*>(&SUB));
+  res[1] = __hfma2(*reinterpret_cast<half2*>(&hi0),
+                   *reinterpret_cast<const half2*>(&MUL),
+                   *reinterpret_cast<const half2*>(&ADD));
+  res[2] = __hsub2(*reinterpret_cast<half2*>(&lo1),
+                   *reinterpret_cast<const half2*>(&SUB));
+  res[3] = __hfma2(*reinterpret_cast<half2*>(&hi1),
+                   *reinterpret_cast<const half2*>(&MUL),
+                   *reinterpret_cast<const half2*>(&ADD));
+}
+
+template <>
+__device__ inline void dequant<half2, 8>(int q, half2* res) {
+  static constexpr uint32_t mask_for_elt_01 = 0x5250;
+  static constexpr uint32_t mask_for_elt_23 = 0x5351;
+  static constexpr uint32_t start_byte_for_fp16 = 0x64646464;
+
+  uint32_t lo = prmt<start_byte_for_fp16, mask_for_elt_01>(q);
+  uint32_t hi = prmt<start_byte_for_fp16, mask_for_elt_23>(q);
+
+  static constexpr uint32_t I8s_TO_F16s_MAGIC_NUM = 0x64006400;
+
+  res[0] = __hsub2(*reinterpret_cast<half2*>(&lo),
+                   *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
+  res[1] = __hsub2(*reinterpret_cast<half2*>(&hi),
+                   *reinterpret_cast<const half2*>(&I8s_TO_F16s_MAGIC_NUM));
+}
+
+#if defined(__CUDA_ARCH__) && __CUDA_ARCH__ >= 800
+template <>
+__device__ inline void dequant<nv_bfloat162, 4>(int q, nv_bfloat162* res) {
+  static constexpr uint32_t MASK = 0x000f000f;
+  static constexpr uint32_t EX = 0x43004300;
+
+  int lo0 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX);
+  q >>= 4;
+  int hi0 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX);
+  q >>= 4;
+  int lo1 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX);
+  q >>= 4;
+  int hi1 = lop3 < (0xf0 & 0xcc) | 0xaa > (q, MASK, EX);
+
+  static constexpr uint32_t MUL = 0x3F803F80;
+  static constexpr uint32_t ADD = 0xC300C300;
+
+  res[0] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&lo0),
+                   *reinterpret_cast<const nv_bfloat162*>(&MUL),
+                   *reinterpret_cast<const nv_bfloat162*>(&ADD));
+  res[1] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&hi0),
+                   *reinterpret_cast<const nv_bfloat162*>(&MUL),
+                   *reinterpret_cast<const nv_bfloat162*>(&ADD));
+  res[2] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&lo1),
+                   *reinterpret_cast<const nv_bfloat162*>(&MUL),
+                   *reinterpret_cast<const nv_bfloat162*>(&ADD));
+  res[3] = __hfma2(*reinterpret_cast<nv_bfloat162*>(&hi1),
+                   *reinterpret_cast<const nv_bfloat162*>(&MUL),
+                   *reinterpret_cast<const nv_bfloat162*>(&ADD));
+}
+
+template <>
+__device__ inline void dequant<nv_bfloat162, 8>(int q, nv_bfloat162* res) {
+  float fp32_intermediates[4];
+  uint32_t* fp32_intermediates_casted =
+      reinterpret_cast<uint32_t*>(fp32_intermediates);
+
+  static constexpr uint32_t fp32_base = 0x4B000000;
+  fp32_intermediates_casted[0] = __byte_perm(q, fp32_base, 0x7650);
+  fp32_intermediates_casted[1] = __byte_perm(q, fp32_base, 0x7652);
+  fp32_intermediates_casted[2] = __byte_perm(q, fp32_base, 0x7651);
+  fp32_intermediates_casted[3] = __byte_perm(q, fp32_base, 0x7653);
+
+  fp32_intermediates[0] -= 8388608.f;
+  fp32_intermediates[1] -= 8388608.f;
+  fp32_intermediates[2] -= 8388608.f;
+  fp32_intermediates[3] -= 8388608.f;
+
+  uint32_t* bf16_result_ptr = reinterpret_cast<uint32_t*>(res);
+  bf16_result_ptr[0] = __byte_perm(fp32_intermediates_casted[0],
+                                   fp32_intermediates_casted[1], 0x7632);
+  bf16_result_ptr[1] = __byte_perm(fp32_intermediates_casted[2],
+                                   fp32_intermediates_casted[3], 0x7632);
+}
+#endif

csrc/moe/torch_bindings.cpp

@@ -32,6 +32,16 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
   m.impl("sgl_moe_align_block_size", torch::kCUDA, &sgl_moe_align_block_size);
 
 #ifndef USE_ROCM
+  m.def(
+      "moe_wna16_gemm(Tensor input, Tensor! output, Tensor b_qweight, "
+      "Tensor b_scales, Tensor? b_qzeros, "
+      "Tensor? topk_weights, Tensor sorted_token_ids, "
+      "Tensor expert_ids, Tensor num_tokens_post_pad, "
+      "int top_k, int BLOCK_SIZE_M, int BLOCK_SIZE_N, int BLOCK_SIZE_K, "
+      "int bit) -> Tensor");
+
+  m.impl("moe_wna16_gemm", torch::kCUDA, &moe_wna16_gemm);
+
   m.def(
       "marlin_gemm_moe(Tensor! a, Tensor! b_q_weights, Tensor! sorted_ids, "
       "Tensor! topk_weights, Tensor! topk_ids, Tensor! b_scales, Tensor! "
@@ -42,6 +52,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, m) {
       "int moe_block_size, bool replicate_input, bool apply_weights)"
       " -> Tensor");
   // conditionally compiled so impl registration is in source file
+
 #endif
 }
 

csrc/ops.h

@@ -151,15 +151,25 @@ torch::Tensor ggml_mul_mat_vec_a8(torch::Tensor W, torch::Tensor X,
 torch::Tensor ggml_mul_mat_a8(torch::Tensor W, torch::Tensor X, int64_t type,
                               int64_t row);
 
+torch::Tensor ggml_moe_a8(torch::Tensor X, torch::Tensor W,
+                          torch::Tensor sorted_token_ids,
+                          torch::Tensor expert_ids,
+                          torch::Tensor num_tokens_post_padded, int64_t type,
+                          int64_t row, int64_t top_k, int64_t tokens);
+
+int64_t ggml_moe_get_block_size(int64_t type);
+
 #ifndef USE_ROCM
+
+bool cutlass_scaled_mm_supports_fp4(int64_t cuda_device_capability);
+bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
+bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
+
 void cutlass_scaled_fp4_mm(torch::Tensor& D, torch::Tensor const& A,
                            torch::Tensor const& B, torch::Tensor const& A_sf,
                            torch::Tensor const& B_sf,
                            torch::Tensor const& alpha);
 
-bool cutlass_scaled_mm_supports_fp8(int64_t cuda_device_capability);
-bool cutlass_scaled_mm_supports_block_fp8(int64_t cuda_device_capability);
-
 void cutlass_scaled_mm(torch::Tensor& out, torch::Tensor const& a,
                        torch::Tensor const& b, torch::Tensor const& a_scales,
                        torch::Tensor const& b_scales,

csrc/prepare_inputs/advance_step.cu

@@ -274,7 +274,7 @@ void advance_step_flashinfer(
   cudaDeviceGetAttribute(&blocks, cudaDevAttrMultiProcessorCount, dev);
   cudaDeviceGetAttribute(&threads, cudaDevAttrMaxThreadsPerBlock, dev);
 
-  int block_tables_stride = block_tables.stride(0);
+  [[maybe_unused]] int block_tables_stride = block_tables.stride(0);
   TORCH_CHECK((blocks * threads > num_queries),
               "multi-step: not enough threads to map to num_queries = ",
               num_queries, " block_tables.stride(0) = ", block_tables.stride(0),

csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm100.cu

@@ -0,0 +1,34 @@
+#include <cudaTypedefs.h>
+#include "c3x/scaled_mm_kernels.hpp"
+
+#include "cuda_utils.h"
+
+/*
+   This file defines quantized GEMM operations using the CUTLASS 3.x API, for
+   NVIDIA GPUs with sm100 (Blackwell).
+*/
+
+#if defined ENABLE_SCALED_MM_SM100 && ENABLE_SCALED_MM_SM100
+
+void cutlass_scaled_mm_sm100(torch::Tensor& c, torch::Tensor const& a,
+                             torch::Tensor const& b,
+                             torch::Tensor const& a_scales,
+                             torch::Tensor const& b_scales,
+                             std::optional<torch::Tensor> const& bias) {
+  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
+  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
+
+  int M = a.size(0), N = b.size(1), K = a.size(1);
+  TORCH_CHECK(
+      (a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
+          (b_scales.numel() == 1 || b_scales.numel() == b.size(1)),
+      "Currently, block scaled fp8 gemm is not implemented for Blackwell");
+
+  // Standard per-tensor/per-token/per-channel scaling
+  TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
+  TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn,
+              "Currently, only fp8 gemm is implemented for Blackwell");
+  vllm::cutlass_scaled_mm_sm100_fp8(c, a, b, a_scales, b_scales, bias);
+}
+
+#endif

csrc/quantization/cutlass_w8a8/scaled_mm_c3x_sm90.cu

@@ -5,9 +5,11 @@
 
 /*
    This file defines quantized GEMM operations using the CUTLASS 3.x API, for
-   NVIDIA GPUs with sm90a (Hopper) or later.
+   NVIDIA GPUs with sm90a (Hopper).
 */
 
+#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
+
 void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
@@ -72,27 +74,4 @@ void cutlass_scaled_mm_azp_sm90(torch::Tensor& out, torch::Tensor const& a,
                                         azp, bias);
 }
 
-#if defined CUDA_VERSION && CUDA_VERSION >= 12080
-
-void cutlass_scaled_mm_sm100(torch::Tensor& c, torch::Tensor const& a,
-                             torch::Tensor const& b,
-                             torch::Tensor const& a_scales,
-                             torch::Tensor const& b_scales,
-                             std::optional<torch::Tensor> const& bias) {
-  TORCH_CHECK(a_scales.dtype() == torch::kFloat32);
-  TORCH_CHECK(b_scales.dtype() == torch::kFloat32);
-
-  int M = a.size(0), N = b.size(1), K = a.size(1);
-  TORCH_CHECK(
-      (a_scales.numel() == 1 || a_scales.numel() == a.size(0)) &&
-          (b_scales.numel() == 1 || b_scales.numel() == b.size(1)),
-      "Currently, block scaled fp8 gemm is not implemented for Blackwell");
-
-  // Standard per-tensor/per-token/per-channel scaling
-  TORCH_CHECK(a_scales.is_contiguous() && b_scales.is_contiguous());
-  TORCH_CHECK(a.dtype() == torch::kFloat8_e4m3fn,
-              "Currently, only fp8 gemm is implemented for Blackwell");
-  vllm::cutlass_scaled_mm_sm100_fp8(c, a, b, a_scales, b_scales, bias);
-}
-
 #endif

csrc/quantization/cutlass_w8a8/scaled_mm_entry.cu

@@ -23,12 +23,15 @@ void cutlass_scaled_mm_sm89(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b_scales,
                             std::optional<torch::Tensor> const& bias);
 
-#if defined ENABLE_SCALED_MM_C3X && ENABLE_SCALED_MM_C3X
+#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
 void cutlass_scaled_mm_sm90(torch::Tensor& c, torch::Tensor const& a,
                             torch::Tensor const& b,
                             torch::Tensor const& a_scales,
                             torch::Tensor const& b_scales,
                             std::optional<torch::Tensor> const& bias);
+#endif
+
+#if defined ENABLE_SCALED_MM_SM100 && ENABLE_SCALED_MM_SM100
 void cutlass_scaled_mm_sm100(torch::Tensor& c, torch::Tensor const& a,
                              torch::Tensor const& b,
                              torch::Tensor const& a_scales,
@@ -60,7 +63,7 @@ void cutlass_scaled_mm_azp_sm89(torch::Tensor& c, torch::Tensor const& a,
                                 std::optional<torch::Tensor> const& azp,
                                 std::optional<torch::Tensor> const& bias);
 
-#if defined CUDA_VERSION && CUDA_VERSION >= 12000
+#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
 void cutlass_scaled_mm_azp_sm90(torch::Tensor& c, torch::Tensor const& a,
                                 torch::Tensor const& b,
                                 torch::Tensor const& a_scales,
@@ -121,26 +124,21 @@ void cutlass_scaled_mm(torch::Tensor& c, torch::Tensor const& a,
 
   at::cuda::OptionalCUDAGuard const device_guard(device_of(a));
   int32_t version_num = get_sm_version_num();
-  // Hopper
 
-  // Guard against compilation issues for sm90 kernels
-#if defined ENABLE_SCALED_MM_C3X && ENABLE_SCALED_MM_C3X
-
-  #if defined CUDA_VERSION && CUDA_VERSION < 12080
-  if (version_num >= 90 && version_num < 100) {
-    cutlass_scaled_mm_sm90(c, a, b, a_scales, b_scales, bias);
-    return;
-  }
-  #else
-  if (version_num >= 90 && version_num < 100) {
-    cutlass_scaled_mm_sm90(c, a, b, a_scales, b_scales, bias);
-    return;
-  } else if (version_num >= 100) {
+#if defined ENABLE_SCALED_MM_SM100 && ENABLE_SCALED_MM_SM100
+  if (version_num >= 100) {
     cutlass_scaled_mm_sm100(c, a, b, a_scales, b_scales, bias);
     return;
   }
 #endif
 
+  // Guard against compilation issues for sm90 kernels
+#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
+  if (version_num >= 90 && version_num < 100) {
+    // Hopper
+    cutlass_scaled_mm_sm90(c, a, b, a_scales, b_scales, bias);
+    return;
+  }
 #endif
 
 #if defined ENABLE_SCALED_MM_C2X && ENABLE_SCALED_MM_C2X
@@ -211,7 +209,7 @@ void cutlass_scaled_mm_azp(torch::Tensor& c, torch::Tensor const& a,
 
   int32_t version_num = get_sm_version_num();
 
-#if defined ENABLE_SCALED_MM_C3X && ENABLE_SCALED_MM_C3X
+#if defined ENABLE_SCALED_MM_SM90 && ENABLE_SCALED_MM_SM90
   if (version_num >= 90) {
     cutlass_scaled_mm_azp_sm90(c, a, b, a_scales, b_scales, azp_adj, azp, bias);
     return;

csrc/quantization/fp4/nvfp4_scaled_mm_entry.cu

@@ -36,3 +36,9 @@ void cutlass_scaled_fp4_mm(torch::Tensor& D, torch::Tensor const& A,
                               "be compiled using CUDA 12.8 and target "
                               "compute capability 100 or above.");
 }
+
+bool cutlass_scaled_mm_supports_fp4(int64_t cuda_device_capability) {
+  int runtimeVersion;
+  cudaRuntimeGetVersion(&runtimeVersion);
+  return cuda_device_capability >= 100 && runtimeVersion >= 12080;
+}

csrc/quantization/fp4/nvfp4_scaled_mm_kernels.cu

@@ -201,10 +201,11 @@ void runGemm(at::Tensor& D, at::Tensor const& A, at::Tensor const& B,
 #endif  // defined(CUTLASS_ARCH_MMA_SM100_SUPPORTED)
 
 #define CHECK_TYPE(x, st, m) \
-  TORCH_CHECK(x.scalar_type() == st, "Inconsistency of Tensor type:", m)
-#define CHECK_TH_CUDA(x, m) TORCH_CHECK(x.is_cuda(), m, "must be a CUDA tensor")
+  TORCH_CHECK(x.scalar_type() == st, ": Inconsistency of Tensor type:", m)
+#define CHECK_TH_CUDA(x, m) \
+  TORCH_CHECK(x.is_cuda(), m, ": must be a CUDA tensor")
 #define CHECK_CONTIGUOUS(x, m) \
-  TORCH_CHECK(x.is_contiguous(), m, "must be contiguous")
+  TORCH_CHECK(x.is_contiguous(), m, ": must be contiguous")
 #define CHECK_INPUT(x, st, m) \
   CHECK_TH_CUDA(x, m);        \
   CHECK_CONTIGUOUS(x, m);     \

csrc/quantization/fp8/amd/quant_utils.cuh

@@ -13,6 +13,40 @@ namespace vllm {
 namespace fp8 {
   #ifdef ENABLE_FP8
 
+// Use hardware cvt instruction for fp8 on rocm
+template <typename fp8_type>
+__device__ __forceinline__ fp8_type cvt_c10(float const r) {
+  return {};
+}
+
+// __hip_fp8_e4m3 only exists starting in ROCm 6.3. The macro
+// HIP_FP8_TYPE_OCP comes from the hip_fp8.h header and also makes
+// its first appearance in ROCm 6.3. Since VLLM_DISPATCH_FP8_TYPES
+// on ROCm instantiates both OCP and FNUZ kernels, we need to replace
+// the new HW cvt with something reasonable that doesn't rely on the
+// ROCm 6.3 feature. This allows compiling on ROCm 6.2 or newer.
+template <>
+__device__ __forceinline__ c10::Float8_e4m3fn cvt_c10(float const r) {
+    #if HIP_FP8_TYPE_OCP
+  return c10::Float8_e4m3fn(
+      __hip_cvt_float_to_fp8(r, __hip_fp8_e4m3::__default_saturation,
+                             __hip_fp8_e4m3::__default_interpret),
+      c10::Float8_e4m3fn::from_bits());
+    #else
+  // Cast implemented by pytorch. Uses bit manipulation instead of HW cvt.
+  // HW cvt above is faster when it is available (ROCm 6.3 or newer).
+  return static_cast<c10::Float8_e4m3fn>(r);
+    #endif
+}
+
+template <>
+__device__ __forceinline__ c10::Float8_e4m3fnuz cvt_c10(float const r) {
+  return c10::Float8_e4m3fnuz(
+      __hip_cvt_float_to_fp8(r, __hip_fp8_e4m3_fnuz::__default_saturation,
+                             __hip_fp8_e4m3_fnuz::__default_interpret),
+      c10::Float8_e4m3fnuz::from_bits());
+}
+
 template <typename Tout, typename Tin>
 __inline__ __device__ Tout vec_conversion(const Tin& x) {
   return x;
@@ -412,7 +446,7 @@ scaled_vec_conversion<uint16_t, uint8_t>(const uint8_t& a, float scale) {
 template <>
 __inline__ __device__ uint32_t
 scaled_vec_conversion<uint32_t, uint16_t>(const uint16_t& a, float scale) {
-  __half2_raw h2r =
+  [[maybe_unused]] __half2_raw h2r =
       __hip_cvt_fp8x2_to_halfraw2(a, fp8_type::__default_interpret);
   union {
     __half2_raw h2r;

csrc/quantization/fp8/common.cu

@@ -11,8 +11,8 @@
 
 namespace vllm {
 
-template <typename scalar_t>
-__global__ void scaled_fp8_quant_kernel(FP8_TYPE* __restrict__ out,
+template <typename scalar_t, typename fp8_type>
+__global__ void scaled_fp8_quant_kernel(fp8_type* __restrict__ out,
                                         const scalar_t* __restrict__ input,
                                         const float* __restrict__ scale,
                                         int64_t num_elems) {
@@ -25,12 +25,13 @@ __global__ void scaled_fp8_quant_kernel(FP8_TYPE* __restrict__ out,
       out, input, inverted_scale, num_elems, tid, blockDim.x * gridDim.x);
 }
 
-template <typename scalar_t>
+template <typename scalar_t, typename fp8_type>
 __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
-    FP8_TYPE* __restrict__ out, float* __restrict__ scale,
+    fp8_type* __restrict__ out, float* __restrict__ scale,
     scalar_t const* __restrict__ input, float const* __restrict__ scale_ub,
     const int hidden_size) {
-  float const min_scaling_factor = 1.0f / (FP8_E4M3_MAX * 512.f);
+  float const min_scaling_factor =
+      1.0f / (fp8_e4m3_adjusted_max_v<fp8_type> * 512.f);
 
   int const tid = threadIdx.x;
   int const token_idx = blockIdx.x;
@@ -38,7 +39,7 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
   // Use int64 to avoid overflowing an int32 when calculating this offset
   int64_t offset = static_cast<int64_t>(token_idx) * hidden_size;
   scalar_t const* __restrict__ token_input = &input[offset];
-  FP8_TYPE* __restrict__ token_output = &out[offset];
+  fp8_type* __restrict__ token_output = &out[offset];
 
   // For vectorization, token_input and token_output pointers need to be
   // aligned at 8-byte and 4-byte addresses respectively.
@@ -66,7 +67,8 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
       token_scale = block_absmax_val_maybe;
     }
     // token scale computation
-    token_scale = max(token_scale / FP8_E4M3_MAX, min_scaling_factor);
+    token_scale = max(token_scale / fp8_e4m3_adjusted_max_v<fp8_type>,
+                      min_scaling_factor);
     scale[token_idx] = token_scale;
   }
   __syncthreads();
@@ -77,7 +79,7 @@ __global__ void dynamic_per_token_scaled_fp8_quant_kernel(
         token_output, token_input, token_scale, hidden_size, tid, blockDim.x);
   } else {
     for (int i = tid; i < hidden_size; i += blockDim.x) {
-      token_output[i] = scaled_fp8_conversion<false>(
+      token_output[i] = scaled_fp8_conversion<false, fp8_type>(
           static_cast<float>(token_input[i]), token_scale);
     }
   }
@@ -96,11 +98,15 @@ void static_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(
-      input.scalar_type(), "scaled_fp8_quant_kernel", [&] {
-        vllm::scaled_fp8_quant_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),
+      input.scalar_type(), "scaled_fp8_quant_kernel_scalar_type", [&] {
+        VLLM_DISPATCH_FP8_TYPES(
+            out.scalar_type(), "scaled_fp8_quant_kernel_fp8_type", [&] {
+              vllm::scaled_fp8_quant_kernel<scalar_t, fp8_t>
+                  <<<grid, block, 0, stream>>>(
+                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
                       scale.data_ptr<float>(), num_elems);
             });
+      });
 }
 
 void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
@@ -114,13 +120,19 @@ void dynamic_scaled_fp8_quant(torch::Tensor& out,          // [..., d]
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(
-      input.scalar_type(), "scaled_fp8_quant_kernel", [&] {
-        vllm::segmented_max_reduction<scalar_t><<<grid, block, 0, stream>>>(
-            scale.data_ptr<float>(), input.data_ptr<scalar_t>(), num_elems);
-        vllm::scaled_fp8_quant_kernel<scalar_t><<<grid, block, 0, stream>>>(
-            out.data_ptr<FP8_TYPE>(), input.data_ptr<scalar_t>(),
+      input.scalar_type(), "scaled_fp8_quant_kernel_scalar_type", [&] {
+        VLLM_DISPATCH_FP8_TYPES(
+            out.scalar_type(), "scaled_fp8_quant_kernel_fp8_type", [&] {
+              vllm::segmented_max_reduction<scalar_t, fp8_t>
+                  <<<grid, block, 0, stream>>>(scale.data_ptr<float>(),
+                                               input.data_ptr<scalar_t>(),
+                                               num_elems);
+              vllm::scaled_fp8_quant_kernel<scalar_t, fp8_t>
+                  <<<grid, block, 0, stream>>>(
+                      out.data_ptr<fp8_t>(), input.data_ptr<scalar_t>(),
                       scale.data_ptr<float>(), num_elems);
             });
+      });
 }
 
 void dynamic_per_token_scaled_fp8_quant(
@@ -138,12 +150,18 @@ void dynamic_per_token_scaled_fp8_quant(
   const at::cuda::OptionalCUDAGuard device_guard(device_of(input));
   const cudaStream_t stream = at::cuda::getCurrentCUDAStream();
   VLLM_DISPATCH_FLOATING_TYPES(
-      input.scalar_type(), "dynamic_per_token_scaled_fp8_quant_kernel", [&] {
-        vllm::dynamic_per_token_scaled_fp8_quant_kernel<scalar_t>
+      input.scalar_type(),
+      "dynamic_per_token_scaled_fp8_quant_kernel_scalar_type", [&] {
+        VLLM_DISPATCH_FP8_TYPES(
+            out.scalar_type(),
+            "dynamic_per_token_scaled_fp8_quant_kernel_fp8_type", [&] {
+              vllm::dynamic_per_token_scaled_fp8_quant_kernel<scalar_t, fp8_t>
                   <<<grid, block, 0, stream>>>(
-                out.data_ptr<FP8_TYPE>(), scales.data_ptr<float>(),
+                      out.data_ptr<fp8_t>(), scales.data_ptr<float>(),
                       input.data_ptr<scalar_t>(),
-                scale_ub.has_value() ? scale_ub->data_ptr<float>() : nullptr,
+                      scale_ub.has_value() ? scale_ub->data_ptr<float>()
+                                           : nullptr,
                       hidden_size);
             });
+      });
 }

csrc/quantization/fp8/common.cuh

@@ -7,18 +7,52 @@
 
 #ifndef USE_ROCM
   #include <c10/util/Float8_e4m3fn.h>
-using FP8_TYPE = c10::Float8_e4m3fn;
-C10_HOST_DEVICE constexpr auto FP8_E4M3_MAX =
-    std::numeric_limits<FP8_TYPE>::max();
+  #define MAYBE_HOST_DEVICE C10_HOST_DEVICE
 #else
+  #include <ATen/hip/HIPContext.h>
+  #include <c10/util/Float8_e4m3fn.h>
   #include <c10/util/Float8_e4m3fnuz.h>
   #include "amd/quant_utils.cuh"
-using FP8_TYPE = c10::Float8_e4m3fnuz;
-// Using the default max value from pytorch (240.0) will cause accuracy
-// issue when running dynamic quantization. Here use 224.0f for rocm.
-constexpr auto FP8_E4M3_MAX = 224.0f;
+  // ROCm doesn't seem to need C10_HOST_DEVICE for static constexpr
+  #define MAYBE_HOST_DEVICE
 #endif
-constexpr static auto kFp8Type = c10::CppTypeToScalarType<FP8_TYPE>::value;
+
+// Determines the preferred FP8 type for the current platform.
+// Note that for CUDA this just returns true,
+// but on ROCm it will check device props.
+static bool is_fp8_ocp() {
+#ifndef USE_ROCM
+  return true;
+#else
+  auto dprops = at::cuda::getCurrentDeviceProperties();
+  std::string device_arch = dprops->gcnArchName;
+  size_t substring = device_arch.find("gfx94");
+  return substring == std::string::npos;
+#endif
+}
+
+template <typename T>
+struct fp8_e4m3_adjusted_max;
+
+template <>
+struct fp8_e4m3_adjusted_max<c10::Float8_e4m3fn> {
+  static constexpr c10::Float8_e4m3fn val() {
+    return std::numeric_limits<c10::Float8_e4m3fn>::max();
+  }
+};
+
+// Using the default max value from pytorch (240.0 0x7F) will cause accuracy
+// issues when running dynamic quantization. Here use 224.0 0x7E for rocm.
+template <>
+struct fp8_e4m3_adjusted_max<c10::Float8_e4m3fnuz> {
+  static constexpr c10::Float8_e4m3fnuz val() {
+    return c10::Float8_e4m3fnuz(0x7E, c10::Float8_e4m3fnuz::from_bits());
+  }
+};
+
+template <typename T>
+MAYBE_HOST_DEVICE static constexpr T fp8_e4m3_adjusted_max_v =
+    fp8_e4m3_adjusted_max<T>::val();
 
 namespace vllm {
 
@@ -32,8 +66,8 @@ __device__ __forceinline__ float atomicMaxFloat(float* addr, float value) {
   return old;
 }
 
-template <bool is_scale_inverted>
-__device__ __forceinline__ FP8_TYPE scaled_fp8_conversion(float const val,
+template <bool is_scale_inverted, typename fp8_type>
+__device__ __forceinline__ fp8_type scaled_fp8_conversion(float const val,
                                                           float const scale) {
   float x = 0.0f;
   if constexpr (is_scale_inverted) {
@@ -42,15 +76,13 @@ __device__ __forceinline__ FP8_TYPE scaled_fp8_conversion(float const val,
     x = val / scale;
   }
 
-  float r = fmax(-FP8_E4M3_MAX, fmin(x, FP8_E4M3_MAX));
+  float r = fmax(-fp8_e4m3_adjusted_max_v<fp8_type>,
+                 fmin(x, fp8_e4m3_adjusted_max_v<fp8_type>));
 #ifndef USE_ROCM
-  return static_cast<c10::Float8_e4m3fn>(r);
+  return static_cast<fp8_type>(r);
 #else
   // Use hardware cvt instruction for fp8 on rocm
-  return c10::Float8_e4m3fnuz(
-      __hip_cvt_float_to_fp8(r, fp8::fp8_type::__default_saturation,
-                             fp8::fp8_type::__default_interpret),
-      c10::Float8_e4m3fnuz::from_bits());
+  return fp8::cvt_c10<fp8_type>(r);
 #endif
 }
 
@@ -60,7 +92,7 @@ __device__ __forceinline__ FP8_TYPE scaled_fp8_conversion(float const val,
 // So to get the right answer, *scale needs to be initialized to
 // a value <= 0.0 and we need to wait for all thread blocks to
 // finish before consuming *scale.
-template <typename scalar_t>
+template <typename scalar_t, typename fp8_type>
 __global__ void segmented_max_reduction(float* __restrict__ scale,
                                         const scalar_t* __restrict__ input,
                                         int64_t num_elems) {
@@ -91,7 +123,7 @@ __global__ void segmented_max_reduction(float* __restrict__ scale,
   // Finally, since cache[0] contains the maximum for this thread block,
   // atomically write the max to the target location
   if (threadIdx.x == 0) {
-    atomicMaxFloat(scale, cache[0] / FP8_E4M3_MAX);
+    atomicMaxFloat(scale, cache[0] / fp8_e4m3_adjusted_max_v<fp8_type>);
   }
 }
 
@@ -123,13 +155,13 @@ __device__ float thread_max_vec(scalar_t const* __restrict__ input,
   return absmax_val;
 }
 
-template <typename scalar_t, bool is_scale_inverted>
-__device__ void scaled_fp8_conversion_vec(FP8_TYPE* __restrict__ out,
+template <typename scalar_t, bool is_scale_inverted, typename fp8_type>
+__device__ void scaled_fp8_conversion_vec(fp8_type* __restrict__ out,
                                           scalar_t const* __restrict__ input,
                                           float const scale,
                                           int64_t const num_elems,
                                           int const tid, int const step) {
-  using float8x4_t = q8x4_t<FP8_TYPE>;
+  using float8x4_t = q8x4_t<fp8_type>;
   // Vectorized input/output to better utilize memory bandwidth.
   auto const* vectorized_in = reinterpret_cast<vec4_t<scalar_t> const*>(input);
   auto* vectorized_out = reinterpret_cast<float8x4_t*>(out);
@@ -141,20 +173,20 @@ __device__ void scaled_fp8_conversion_vec(FP8_TYPE* __restrict__ out,
     vec4_t<scalar_t> in_vec = vectorized_in[i];
     float8x4_t out_vec;
 
-    out_vec.x = scaled_fp8_conversion<is_scale_inverted>(
+    out_vec.x = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(in_vec.x), scale);
-    out_vec.y = scaled_fp8_conversion<is_scale_inverted>(
+    out_vec.y = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(in_vec.y), scale);
-    out_vec.z = scaled_fp8_conversion<is_scale_inverted>(
+    out_vec.z = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(in_vec.z), scale);
-    out_vec.w = scaled_fp8_conversion<is_scale_inverted>(
+    out_vec.w = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(in_vec.w), scale);
     vectorized_out[i] = out_vec;
   }
 
   // Handle the remaining elements if num_elems is not divisible by 4
   for (int64_t i = num_vec_elems * 4 + tid; i < num_elems; i += step) {
-    out[i] = scaled_fp8_conversion<is_scale_inverted>(
+    out[i] = scaled_fp8_conversion<is_scale_inverted, fp8_type>(
         static_cast<float>(input[i]), scale);
   }
 }

csrc/quantization/fused_kernels/fused_layernorm_dynamic_per_token_quant.cu

@@ -144,6 +144,9 @@ void rms_norm_dynamic_per_token_quant(
     torch::Tensor& scales,        // [num_tokens]
     double const var_epsilon,     // Variance epsilon used in norm calculation
     std::optional<at::Tensor> scale_ub, std::optional<at::Tensor> residual) {
+  static c10::ScalarType kFp8Type = is_fp8_ocp()
+                                        ? c10::ScalarType::Float8_e4m3fn
+                                        : c10::ScalarType::Float8_e4m3fnuz;
   TORCH_CHECK(out.dtype() == kFp8Type || out.dtype() == torch::kInt8);
   TORCH_CHECK(out.is_contiguous() && input.is_contiguous());
 

csrc/quantization/fused_kernels/quant_conversions.cuh

@@ -31,9 +31,11 @@ static __device__ __forceinline__ int8_t float_to_int8_rn(float const x) {
 #endif
 }
 
-static __device__ __forceinline__ FP8_TYPE float_to_fp8(float const x) {
-  float const r = fmax(-FP8_E4M3_MAX, fmin(x, FP8_E4M3_MAX));
-  return static_cast<FP8_TYPE>(r);
+template <typename fp8_type>
+static __device__ __forceinline__ fp8_type float_to_fp8(float const x) {
+  float const r = fmax(-fp8_e4m3_adjusted_max_v<fp8_type>,
+                       fmin(x, fp8_e4m3_adjusted_max_v<fp8_type>));
+  return static_cast<fp8_type>(r);
 }
 
 template <typename quant_type_t, bool is_scale_inverted, typename enable = void>
@@ -54,15 +56,16 @@ struct ScaledQuant<
 };
 
 template <typename quant_type_t, bool is_scale_inverted>
-struct ScaledQuant<
-    quant_type_t, is_scale_inverted,
-    typename std::enable_if_t<std::is_same_v<quant_type_t, FP8_TYPE>>> {
+struct ScaledQuant<quant_type_t, is_scale_inverted,
+                   typename std::enable_if_t<
+                       std::is_same_v<quant_type_t, c10::Float8_e4m3fn> ||
+                       std::is_same_v<quant_type_t, c10::Float8_e4m3fnuz>>> {
   static __device__ __forceinline__ quant_type_t quant_fn(float const x,
                                                           float const scale) {
     if constexpr (is_scale_inverted) {
-      return float_to_fp8(x * scale);
+      return float_to_fp8<quant_type_t>(x * scale);
     } else {
-      return float_to_fp8(x / scale);
+      return float_to_fp8<quant_type_t>(x / scale);
     }
   }
 };

csrc/quantization/gguf/gguf_kernel.cu

@@ -5,15 +5,18 @@
 #include <c10/cuda/CUDAGuard.h>
 
 #include "cuda_compat.h"
+#include "dispatch_utils.h"
 
 #include "ggml-common.h"
 #include "vecdotq.cuh"
 #include "dequantize.cuh"
 #include "mmvq.cuh"
 #include "mmq.cuh"
+#include "moe.cuh"
 
 // Q8 gemv
-static __global__ void quantize_q8_1(const half* __restrict__ x,
+template <typename scalar_t>
+static __global__ void quantize_q8_1(const scalar_t* __restrict__ x,
                                      void* __restrict__ vy, const int kx,
                                      const int kx_padded) {
   const int ix = blockDim.x * blockIdx.x + threadIdx.x;
@@ -28,7 +31,7 @@ static __global__ void quantize_q8_1(const half* __restrict__ x,
   const int ib = i_padded / QK8_1;   // block index
   const int iqs = i_padded % QK8_1;  // quant index
 
-  const float xi = ix < kx ? __half2float(x[iy * kx + ix]) : 0.0f;
+  const float xi = ix < kx ? static_cast<float>(x[iy * kx + ix]) : 0.0f;
   float amax = fabsf(xi);
   float sum = xi;
 
@@ -51,14 +54,20 @@ static __global__ void quantize_q8_1(const half* __restrict__ x,
   y[ib].ds.y = __float2half(sum);
 }
 
-static void quantize_row_q8_1_cuda(const half* x, void* vy, const int kx,
+template <typename scalar_t>
+static void quantize_row_q8_1_cuda(const scalar_t* x, void* vy, const int kx,
                                    const int ky, cudaStream_t stream) {
   const int64_t kx_padded = (kx + 512 - 1) / 512 * 512;
   const int block_num_x =
       (kx_padded + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE;
-  const dim3 num_blocks(block_num_x, ky, 1);
+  constexpr int MAX_BLOCK_SIZE = 65535;
+  for (int off = 0; off < ky; off += MAX_BLOCK_SIZE) {
+    const int num_blocks_y = std::min(ky, off + MAX_BLOCK_SIZE) - off;
+    const dim3 num_blocks(block_num_x, num_blocks_y, 1);
     const dim3 block_size(CUDA_DEQUANTIZE_BLOCK_SIZE, 1, 1);
-  quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(x, vy, kx, kx_padded);
+    quantize_q8_1<<<num_blocks, block_size, 0, stream>>>(
+        &x[off * kx], (int32_t*)vy + off * (kx_padded / 32 * 9), kx, kx_padded);
+  }
 }
 
 torch::Tensor ggml_dequantize(torch::Tensor W,  // quant weight
@@ -79,101 +88,112 @@ torch::Tensor ggml_mul_mat_vec_a8(torch::Tensor W,  // quant weight
   int col = X.sizes()[1];
   const int padded = (col + 512 - 1) / 512 * 512;
   const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
-  auto options =
-      torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
+  auto options = torch::TensorOptions().dtype(X.dtype()).device(W.device());
   at::Tensor Y = torch::empty({1, row}, options);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
   options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
   at::Tensor quant_X = torch::empty({1, padded / 32 * 9}, options);
-  quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col, 1,
-                         stream);
+  VLLM_DISPATCH_FLOATING_TYPES(X.scalar_type(), "ggml_mul_mat_vec_a8", [&] {
+    quantize_row_q8_1_cuda<scalar_t>((scalar_t*)X.data_ptr(),
+                                     (void*)quant_X.data_ptr(), col, 1, stream);
     switch (type) {
       case 2:
-      mul_mat_vec_q4_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q4_0_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 3:
-      mul_mat_vec_q4_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q4_1_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 6:
-      mul_mat_vec_q5_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q5_0_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 7:
-      mul_mat_vec_q5_1_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q5_1_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 8:
-      mul_mat_vec_q8_0_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q8_0_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 10:
-      mul_mat_vec_q2_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q2_K_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 11:
-      mul_mat_vec_q3_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q3_K_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 12:
-      mul_mat_vec_q4_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q4_K_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 13:
-      mul_mat_vec_q5_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q5_K_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 14:
-      mul_mat_vec_q6_K_q8_1_cuda((void*)W.data_ptr(), (void*)quant_X.data_ptr(),
-                                 (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_q6_K_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 16:
-      mul_mat_vec_iq2_xxs_q8_1_cuda((void*)W.data_ptr(),
-                                    (void*)quant_X.data_ptr(),
-                                    (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq2_xxs_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 17:
-      mul_mat_vec_iq2_xs_q8_1_cuda((void*)W.data_ptr(),
-                                   (void*)quant_X.data_ptr(),
-                                   (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq2_xs_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 18:
-      mul_mat_vec_iq3_xxs_q8_1_cuda((void*)W.data_ptr(),
-                                    (void*)quant_X.data_ptr(),
-                                    (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq3_xxs_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 19:
-      mul_mat_vec_iq1_s_q8_1_cuda((void*)W.data_ptr(),
-                                  (void*)quant_X.data_ptr(),
-                                  (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq1_s_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 20:
-      mul_mat_vec_iq4_nl_q8_1_cuda((void*)W.data_ptr(),
-                                   (void*)quant_X.data_ptr(),
-                                   (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq4_nl_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 21:
-      mul_mat_vec_iq3_s_q8_1_cuda((void*)W.data_ptr(),
-                                  (void*)quant_X.data_ptr(),
-                                  (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq3_s_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 22:
-      mul_mat_vec_iq2_s_q8_1_cuda((void*)W.data_ptr(),
-                                  (void*)quant_X.data_ptr(),
-                                  (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq2_s_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 23:
-      mul_mat_vec_iq4_xs_q8_1_cuda((void*)W.data_ptr(),
-                                   (void*)quant_X.data_ptr(),
-                                   (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq4_xs_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
       case 29:
-      mul_mat_vec_iq1_m_q8_1_cuda((void*)W.data_ptr(),
-                                  (void*)quant_X.data_ptr(),
-                                  (half*)Y.data_ptr(), col, row, stream);
+        mul_mat_vec_iq1_m_q8_1_cuda<scalar_t>(
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, stream);
         break;
     }
+  });
   return Y;
 }
 
@@ -184,66 +204,196 @@ torch::Tensor ggml_mul_mat_a8(torch::Tensor W,  // quant weight
   int padded = (col + 512 - 1) / 512 * 512;
   int batch = X.sizes()[0];
   const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
-  auto options =
-      torch::TensorOptions().dtype(torch::kFloat16).device(W.device());
+  auto options = torch::TensorOptions().dtype(X.dtype()).device(W.device());
   at::Tensor Y = torch::empty({batch, row}, options);
   cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
   options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
   at::Tensor quant_X = torch::empty({batch, padded / 32 * 9}, options);
-  quantize_row_q8_1_cuda((half*)X.data_ptr(), (void*)quant_X.data_ptr(), col,
-                         batch, stream);
+  VLLM_DISPATCH_FLOATING_TYPES(X.scalar_type(), "ggml_mul_mat_a8", [&] {
+    quantize_row_q8_1_cuda((scalar_t*)X.data_ptr(), (void*)quant_X.data_ptr(),
+                           col, batch, stream);
 
     switch (type) {
       case 2:
         ggml_mul_mat_q4_0_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 3:
         ggml_mul_mat_q4_1_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 6:
         ggml_mul_mat_q5_0_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 7:
         ggml_mul_mat_q5_1_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 8:
         ggml_mul_mat_q8_0_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 10:
         ggml_mul_mat_q2_K_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 11:
         ggml_mul_mat_q3_K_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 12:
         ggml_mul_mat_q4_K_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 13:
         ggml_mul_mat_q5_K_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
       case 14:
         ggml_mul_mat_q6_K_q8_1_cuda(
-          (void*)W.data_ptr(), (void*)quant_X.data_ptr(), (half*)Y.data_ptr(),
-          col, row, batch, padded, row, stream);
+            (void*)W.data_ptr(), (void*)quant_X.data_ptr(),
+            (scalar_t*)Y.data_ptr(), col, row, batch, padded, row, stream);
         break;
     }
+  });
   return Y;
 }
+
+torch::Tensor ggml_moe_a8(torch::Tensor X,  // input
+                          torch::Tensor W,  // expert weights
+                          torch::Tensor sorted_token_ids,
+                          torch::Tensor expert_ids,
+                          torch::Tensor num_tokens_post_padded, int64_t type,
+                          int64_t row, int64_t top_k, int64_t tokens) {
+  int col = X.sizes()[1];
+  int padded = (col + 512 - 1) / 512 * 512;
+  const at::cuda::OptionalCUDAGuard device_guard(device_of(X));
+  auto options = torch::TensorOptions().dtype(X.dtype()).device(W.device());
+  at::Tensor Y = torch::empty({tokens * top_k, row}, options);
+  cudaStream_t stream = at::cuda::getCurrentCUDAStream().stream();
+  options = torch::TensorOptions().dtype(torch::kInt32).device(W.device());
+  at::Tensor quant_X = torch::empty({tokens, padded / 32 * 9}, options);
+  VLLM_DISPATCH_FLOATING_TYPES(X.scalar_type(), "ggml_moe_a8", [&] {
+    quantize_row_q8_1_cuda((scalar_t*)X.data_ptr(), (void*)quant_X.data_ptr(),
+                           col, tokens, stream);
+    switch (type) {
+      case 2:
+        ggml_moe_q4_0_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 3:
+        ggml_moe_q4_1_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 6:
+        ggml_moe_q5_0_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 7:
+        ggml_moe_q5_1_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 8:
+        ggml_moe_q8_0_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 10:
+        ggml_moe_q2_K_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 11:
+        ggml_moe_q3_K_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 12:
+        ggml_moe_q4_K_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 13:
+        ggml_moe_q5_K_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+      case 14:
+        ggml_moe_q6_K_q8_1_cuda(
+            (void*)quant_X.data_ptr(), (void*)W.data_ptr(),
+            (scalar_t*)Y.data_ptr(), (int*)sorted_token_ids.data_ptr(),
+            (int*)expert_ids.data_ptr(),
+            (int*)num_tokens_post_padded.data_ptr(), W.stride(0), col, row,
+            tokens, padded, row, top_k, sorted_token_ids.sizes()[0], stream);
+        break;
+    }
+  });
+  return Y;
+}
+
+int64_t ggml_moe_get_block_size(int64_t type) {
+  switch (type) {
+    case 2:
+      return MMQ_X_Q4_0;
+    case 3:
+      return MMQ_X_Q4_1;
+    case 6:
+      return MMQ_X_Q5_0;
+    case 7:
+      return MMQ_X_Q5_1;
+    case 8:
+      return MMQ_X_Q8_0;
+    case 10:
+      return MMQ_X_Q2_K;
+    case 11:
+      return MMQ_X_Q3_K;
+    case 12:
+      return MMQ_X_Q4_K;
+    case 13:
+      return MMQ_X_Q5_K;
+    case 14:
+      return MMQ_X_Q6_K;
+  }
+  return 0;
+}

csrc/quantization/gguf/mmq.cuh

@@ -1,8 +1,8 @@
 // copied from https://github.com/ggerganov/llama.cpp/blob/b2899/ggml-cuda/mmq.cu
-template <int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
+template <typename scalar_t, int qk, int qr, int qi, bool need_sum, typename block_q_t, int mmq_x, int mmq_y, int nwarps,
               allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles, int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
 static __device__ __forceinline__ void mul_mat_q(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
 
     const block_q_t  * x = (const block_q_t  *) vx;
@@ -38,7 +38,7 @@ static __device__ __forceinline__ void mul_mat_q(
                    threadIdx.y, nrows_x-row_x_0-1, threadIdx.x, blocks_per_row_x);
 
 #pragma unroll
-        for (int ir = 0; ir < qr; ++ir) {
+        for (int ir = 0; ir < qr && ib0 + ir * blocks_per_warp/qr < blocks_per_row_x; ++ir) {
             const int kqs = ir*WARP_SIZE_GGUF + threadIdx.x;
             const int kbxd = kqs / QI8_1;
 
@@ -98,7 +98,7 @@ static __device__ __forceinline__ void mul_mat_q(
             if (row_dst >= nrows_dst) {
                 continue;
             }
-            dst[col_dst*nrows_dst + row_dst] = __float2half(sum[i/WARP_SIZE_GGUF][j/nwarps]);
+            dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE_GGUF][j/nwarps];
         }
     }
 }
@@ -113,24 +113,25 @@ static __device__ __forceinline__ void mul_mat_q(
 #define NWARPS_Q4_0 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q4_0, 2)
 #endif
 mul_mat_q4_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q4_0;
     const int mmq_y  =  MMQ_Y_Q4_0;
     const int nwarps = NWARPS_Q4_0;
 
-    mul_mat_q<QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
+    mul_mat_q<scalar_t, QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps, allocate_tiles_q4_0<mmq_y>,
         load_tiles_q4_0<mmq_y, nwarps, need_check>, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q4_0_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
     int mmq_x  =  MMQ_X_Q4_0;
@@ -144,11 +145,11 @@ static void ggml_mul_mat_q4_0_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q4_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -163,24 +164,25 @@ static void ggml_mul_mat_q4_0_q8_1_cuda(
 #define NWARPS_Q4_1 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q4_1, 2)
 #endif
 mul_mat_q4_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q4_1;
     const int mmq_y  =  MMQ_Y_Q4_1;
     const int nwarps = NWARPS_Q4_1;
 
-    mul_mat_q<QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
+    mul_mat_q<scalar_t, QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps, allocate_tiles_q4_1<mmq_y>,
         load_tiles_q4_1<mmq_y, nwarps, need_check>, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q4_1_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
     int mmq_x  =  MMQ_X_Q4_1;
@@ -194,11 +196,11 @@ static void ggml_mul_mat_q4_1_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q4_1<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -213,24 +215,25 @@ static void ggml_mul_mat_q4_1_q8_1_cuda(
 #define NWARPS_Q5_0 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q5_0, 2)
 #endif
 mul_mat_q5_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q5_0;
     const int mmq_y  =  MMQ_Y_Q5_0;
     const int nwarps = NWARPS_Q5_0;
 
-    mul_mat_q<QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
+    mul_mat_q<scalar_t, QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps, allocate_tiles_q5_0<mmq_y>,
         load_tiles_q5_0<mmq_y, nwarps, need_check>, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q5_0_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
     const int mmq_x  =  MMQ_X_Q5_0;
@@ -244,11 +247,11 @@ static void ggml_mul_mat_q5_0_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q5_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -263,24 +266,25 @@ static void ggml_mul_mat_q5_0_q8_1_cuda(
 #define NWARPS_Q5_1 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q5_1, 2)
 #endif
 mul_mat_q5_1(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q5_1;
     const int mmq_y  =  MMQ_Y_Q5_1;
     const int nwarps = NWARPS_Q5_1;
 
-    mul_mat_q<QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
+    mul_mat_q<scalar_t, QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps, allocate_tiles_q5_1<mmq_y>,
         load_tiles_q5_1<mmq_y, nwarps, need_check>, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q5_1_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
     const int mmq_x  =  MMQ_X_Q5_1;
     const int mmq_y  =  MMQ_Y_Q5_1;
@@ -293,11 +297,11 @@ static void ggml_mul_mat_q5_1_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q5_1<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -312,24 +316,25 @@ static void ggml_mul_mat_q5_1_q8_1_cuda(
 #define NWARPS_Q8_0 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q8_0, 2)
 #endif
 mul_mat_q8_0(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q8_0;
     const int mmq_y  =  MMQ_Y_Q8_0;
     const int nwarps = NWARPS_Q8_0;
 
-    mul_mat_q<QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
+    mul_mat_q<scalar_t, QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps, allocate_tiles_q8_0<mmq_y>,
         load_tiles_q8_0<mmq_y, nwarps, need_check>, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q8_0_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
     const int mmq_x  =  MMQ_X_Q8_0;
     const int mmq_y  =  MMQ_Y_Q8_0;
@@ -342,11 +347,11 @@ static void ggml_mul_mat_q8_0_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q8_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q8_0<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q8_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -361,24 +366,25 @@ static void ggml_mul_mat_q8_0_q8_1_cuda(
 #define NWARPS_Q2_K 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q2_K, 2)
 #endif
 mul_mat_q2_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q2_K;
     const int mmq_y  =  MMQ_Y_Q2_K;
     const int nwarps = NWARPS_Q2_K;
 
-    mul_mat_q<QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
+    mul_mat_q<scalar_t, QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps, allocate_tiles_q2_K<mmq_y>,
         load_tiles_q2_K<mmq_y, nwarps, need_check>, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q2_K_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
     const int mmq_x  =  MMQ_X_Q2_K;
     const int mmq_y  =  MMQ_Y_Q2_K;
@@ -391,11 +397,11 @@ static void ggml_mul_mat_q2_K_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q2_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q2_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q2_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -410,25 +416,26 @@ static void ggml_mul_mat_q2_K_q8_1_cuda(
 #define NWARPS_Q3_K 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q3_K, 2)
 #endif
 mul_mat_q3_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
 
     const int mmq_x  =  MMQ_X_Q3_K;
     const int mmq_y  =  MMQ_Y_Q3_K;
     const int nwarps = NWARPS_Q3_K;
 
-    mul_mat_q<QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
+    mul_mat_q<scalar_t, QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps, allocate_tiles_q3_K<mmq_y>,
         load_tiles_q3_K<mmq_y, nwarps, need_check>, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q3_K_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
     const int mmq_x  =  MMQ_X_Q3_K;
@@ -442,11 +449,11 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q3_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q3_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -461,24 +468,25 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
 #define NWARPS_Q4_K 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q4_K, 2)
 #endif
 mul_mat_q4_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q4_K;
     const int mmq_y  =  MMQ_Y_Q4_K;
     const int nwarps = NWARPS_Q4_K;
 
-    mul_mat_q<QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
+    mul_mat_q<scalar_t, QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps, allocate_tiles_q4_K<mmq_y>,
         load_tiles_q4_K<mmq_y, nwarps, need_check>, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q4_K_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
     const int mmq_x  =  MMQ_X_Q4_K;
     const int mmq_y  =  MMQ_Y_Q4_K;
@@ -491,11 +499,11 @@ static void ggml_mul_mat_q4_K_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q4_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q4_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -510,24 +518,25 @@ static void ggml_mul_mat_q4_K_q8_1_cuda(
 #define NWARPS_Q5_K 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q5_K, 2)
 #endif
 mul_mat_q5_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q5_K;
     const int mmq_y  =  MMQ_Y_Q5_K;
     const int nwarps = NWARPS_Q5_K;
 
-    mul_mat_q<QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
+    mul_mat_q<scalar_t, QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps, allocate_tiles_q5_K<mmq_y>,
         load_tiles_q5_K<mmq_y, nwarps, need_check>, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q5_K_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
     const int mmq_x  =  MMQ_X_Q5_K;
@@ -541,11 +550,11 @@ static void ggml_mul_mat_q5_K_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q5_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q5_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }
@@ -560,24 +569,25 @@ static void ggml_mul_mat_q5_K_q8_1_cuda(
 #define NWARPS_Q6_K 4
 #endif
 
-template <bool need_check> static __global__ void
+template<typename scalar_t, bool need_check> static __global__ void
 #if defined(USE_ROCM)
 __launch_bounds__(WARP_SIZE_GGUF*NWARPS_Q6_K, 2)
 #endif
 mul_mat_q6_K(
-    const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst,
+    const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst,
     const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst) {
     const int mmq_x  =  MMQ_X_Q6_K;
     const int mmq_y  =  MMQ_Y_Q6_K;
     const int nwarps = NWARPS_Q6_K;
 
-    mul_mat_q<QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
+    mul_mat_q<scalar_t, QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps, allocate_tiles_q6_K<mmq_y>,
         load_tiles_q6_K<mmq_y, nwarps, need_check>, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>
         (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
 }
 
+template<typename scalar_t>
 static void ggml_mul_mat_q6_K_q8_1_cuda(
-    const void * vx, const void * vy, half * dst, const int ncols_x, const int nrows_x,
+    const void * vx, const void * vy, scalar_t * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
     const int mmq_x  =  MMQ_X_Q6_K;
     const int mmq_y  =  MMQ_Y_Q6_K;
@@ -590,11 +600,11 @@ static void ggml_mul_mat_q6_K_q8_1_cuda(
 
     if (nrows_x % mmq_y == 0) {
         const bool need_check = false;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q6_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     } else {
         const bool need_check = true;
-        mul_mat_q6_K<need_check><<<block_nums, block_dims, 0, stream>>>
+        mul_mat_q6_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
 }

csrc/quantization/gguf/mmvq.cuh

@@ -1,6 +1,6 @@
 // copied and adapted from https://github.com/ggerganov/llama.cpp/blob/b2899/ggml-cuda/mmvq.cu
-template <int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
-static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, half * __restrict__ dst, const int ncols, const int nrows) {
+template <typename scalar_t, int qk, int qi, typename block_q_t, int vdr, vec_dot_q_cuda_t vec_dot_q_cuda>
+static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, scalar_t * __restrict__ dst, const int ncols, const int nrows) {
     const int row = blockIdx.x*blockDim.y + threadIdx.y;
 
     if (row >= nrows) {
@@ -33,158 +33,177 @@ static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void *
     }
 
     if (threadIdx.x == 0) {
-        dst[row] = __float2half(tmp);
+        dst[row] = tmp;
     }
 }
 
-static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
+    mul_mat_vec_q<scalar_t, QK4_0, QI4_0, block_q4_0, VDR_Q4_0_Q8_1_MMVQ, vec_dot_q4_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
+    mul_mat_vec_q<scalar_t, QK4_0, QI4_1, block_q4_1, VDR_Q4_1_Q8_1_MMVQ, vec_dot_q4_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
+    mul_mat_vec_q<scalar_t, QK5_0, QI5_0, block_q5_0, VDR_Q5_0_Q8_1_MMVQ, vec_dot_q5_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
+    mul_mat_vec_q<scalar_t, QK5_1, QI5_1, block_q5_1, VDR_Q5_1_Q8_1_MMVQ, vec_dot_q5_1_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
+    mul_mat_vec_q<scalar_t, QK8_0, QI8_0, block_q8_0, VDR_Q8_0_Q8_1_MMVQ, vec_dot_q8_0_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q2_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q3_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI3_K, block_q3_K, VDR_Q3_K_Q8_1_MMVQ, vec_dot_q3_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q4_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI4_K, block_q4_K, VDR_Q4_K_Q8_1_MMVQ, vec_dot_q4_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q5_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI5_K, block_q5_K, VDR_Q5_K_Q8_1_MMVQ, vec_dot_q5_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_q6_K_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI6_K, block_q6_K, VDR_Q6_K_Q8_1_MMVQ, vec_dot_q6_K_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI2_XXS, block_iq2_xxs, 1, vec_dot_iq2_xxs_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI2_XS, block_iq2_xs, 1, vec_dot_iq2_xs_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq2_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq2_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI2_S, block_iq2_s, 1, vec_dot_iq2_s_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI3_XXS, block_iq3_xxs, 1, vec_dot_iq3_xxs_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq1_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI1_S, block_iq1_s, 1, vec_dot_iq1_s_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq1_m_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq1_m_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI1_M, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI1_M, block_iq1_m, 1, vec_dot_iq1_m_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq4_nl_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
+    mul_mat_vec_q<scalar_t, QK4_NL, QI4_NL, block_iq4_nl, VDR_Q4_0_Q8_1_MMVQ, vec_dot_iq4_nl_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq4_xs_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq4_xs_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI4_XS, block_iq4_xs, 1, vec_dot_iq4_xs_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }
 
-static void mul_mat_vec_iq3_s_q8_1_cuda(const void * vx, const void * vy, half * dst, const int ncols, const int nrows, cudaStream_t stream) {
+template<typename scalar_t>
+static void mul_mat_vec_iq3_s_q8_1_cuda(const void * vx, const void * vy, scalar_t * dst, const int ncols, const int nrows, cudaStream_t stream) {
     const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y;
     const dim3 block_nums(block_num_y, 1, 1);
     const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1);
-    mul_mat_vec_q<QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
+    mul_mat_vec_q<scalar_t, QK_K, QI3_XS, block_iq3_s, 1, vec_dot_iq3_s_q8_1>
         <<<block_nums, block_dims, 0, stream>>>(vx, vy, dst, ncols, nrows);
 }

csrc/quantization/gguf/moe.cuh

@@ -0,0 +1,739 @@
+#include <cstdint>
+
+/* Adapted from ./csrc/quantization/gguf/mmq.cuh
+   based on ./vllm/model_executor/layers/fused_moe/fused_moe.py */
+template <typename scalar_t, int qk, int qr, int qi, bool need_sum,
+          typename block_q_t, int mmq_x, int mmq_y, int nwarps,
+          allocate_tiles_cuda_t allocate_tiles, load_tiles_cuda_t load_tiles,
+          int vdr, vec_dot_q_mul_mat_cuda_t vec_dot>
+static __device__ __forceinline__ void moe_q(
+    const void* __restrict__ vx, const void* __restrict__ vy,
+    scalar_t* __restrict__ dst, const int* __restrict__ sorted_token_ids,
+    const int* __restrict__ expert_ids,
+    const int* __restrict__ num_tokens_post_padded, const int exp_stride,
+    const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y,
+    const int nrows_dst, const int top_k) {
+  const int blocks_per_row_x = ncols_x / qk;
+  const int blocks_per_col_y = nrows_y / QK8_1;
+  const int blocks_per_warp = WARP_SIZE_GGUF / qi;
+
+  const int ncols_dst = ncols_y * top_k;
+
+  const int row_dst_0 = blockIdx.x * mmq_y;
+  const int& row_x_0 = row_dst_0;
+
+  const int col_dst_0 = blockIdx.y * mmq_x;
+
+  int token_offs[mmq_x / nwarps];
+  for (int i = 0; i < mmq_x; i += nwarps) {
+    token_offs[i / nwarps] = sorted_token_ids[col_dst_0 + threadIdx.y + i];
+  }
+
+  const int exp_idx = expert_ids[blockIdx.y];
+  if (exp_idx > 255 || exp_idx < 0) return;
+  if (blockIdx.y * mmq_x > num_tokens_post_padded[0]) return;
+
+  const block_q_t* x = (const block_q_t*)((char*)vx + exp_idx * exp_stride);
+  const block_q8_1* y = (const block_q8_1*)(vy);
+
+  int* tile_x_ql = nullptr;
+  half2* tile_x_dm = nullptr;
+  int* tile_x_qh = nullptr;
+  int* tile_x_sc = nullptr;
+
+  allocate_tiles(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc);
+
+  __shared__ int tile_y_qs[mmq_x * WARP_SIZE_GGUF];
+  __shared__ half2 tile_y_ds[mmq_x * WARP_SIZE_GGUF / QI8_1];
+
+  float sum[mmq_y / WARP_SIZE_GGUF][mmq_x / nwarps] = {{0.0f}};
+
+  for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) {
+    load_tiles(x + row_x_0 * blocks_per_row_x + ib0, tile_x_ql, tile_x_dm,
+               tile_x_qh, tile_x_sc, threadIdx.y, nrows_x - row_x_0 - 1,
+               threadIdx.x, blocks_per_row_x);
+
+    const int n_per_r = ((qk * blocks_per_warp) / qr);
+#pragma unroll
+    for (int ir = 0; ir < qr && ib0 * qk + ir * n_per_r < ncols_x; ++ir) {
+      const int kqs = ir * WARP_SIZE_GGUF + threadIdx.x;
+      const int kbxd = kqs / QI8_1;
+
+#pragma unroll
+      for (int i = 0; i < mmq_x; i += nwarps) {
+        const int col_y_eff = token_offs[i / nwarps] / top_k;
+        const int block_x = ib0 * (qk / QK8_1) + kbxd;
+        if (col_y_eff < ncols_y && block_x < blocks_per_col_y) {
+          const block_q8_1* by0 = &y[col_y_eff * blocks_per_col_y + block_x];
+          const int index_y =
+              (threadIdx.y + i) * WARP_SIZE_GGUF + kqs % WARP_SIZE_GGUF;
+          tile_y_qs[index_y] =
+              get_int_from_int8_aligned(by0->qs, threadIdx.x % QI8_1);
+        }
+      }
+
+      if (threadIdx.x < n_per_r / QK8_1) {
+        const int kby = threadIdx.x % (WARP_SIZE_GGUF / QI8_1);
+        const int col_y_eff = token_offs[threadIdx.y] / top_k;
+        const int block_x =
+            ib0 * (qk / QK8_1) + ir * (WARP_SIZE_GGUF / QI8_1) + kby;
+
+        if (col_y_eff < ncols_y && block_x < blocks_per_col_y) {
+          const half2* dsi_src = &y[col_y_eff * blocks_per_col_y + block_x].ds;
+          half2* dsi_dst =
+              &tile_y_ds[threadIdx.y * (WARP_SIZE_GGUF / QI8_1) + kby];
+
+          if (need_sum) {
+            *dsi_dst = *dsi_src;
+          } else {
+            float* dfi_dst = (float*)dsi_dst;
+            *dfi_dst = __low2float(*dsi_src);
+          }
+        }
+      }
+      __syncthreads();
+
+      // #pragma unroll // unrolling this loop causes too much register pressure
+      for (int k = ir * WARP_SIZE_GGUF / qr; k < (ir + 1) * WARP_SIZE_GGUF / qr;
+           k += vdr) {
+#pragma unroll
+        for (int j = 0; j < mmq_x; j += nwarps) {
+#pragma unroll
+          for (int i = 0; i < mmq_y; i += WARP_SIZE_GGUF) {
+            sum[i / WARP_SIZE_GGUF][j / nwarps] +=
+                vec_dot(tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, tile_y_qs,
+                        tile_y_ds, threadIdx.x + i, threadIdx.y + j, k);
+          }
+        }
+      }
+      __syncthreads();
+    }
+  }
+
+#pragma unroll
+  for (int j = 0; j < mmq_x; j += nwarps) {
+    const int col_dst = token_offs[j / nwarps];
+    if (col_dst >= ncols_dst) {
+      return;
+    }
+
+#pragma unroll
+    for (int i = 0; i < mmq_y; i += WARP_SIZE_GGUF) {
+      const int row_dst = row_dst_0 + threadIdx.x + i;
+      if (row_dst >= nrows_dst) {
+        continue;
+      }
+      dst[col_dst * nrows_dst + row_dst] = sum[i / WARP_SIZE_GGUF][j / nwarps];
+    }
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q4_0 64
+  #define MMQ_Y_Q4_0 128
+  #define NWARPS_Q4_0 8
+#else
+  #define MMQ_X_Q4_0 4
+  #define MMQ_Y_Q4_0 32
+  #define NWARPS_Q4_0 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q4_0, 2)
+#endif
+    moe_q4_0(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q4_0;
+  const int mmq_y = MMQ_Y_Q4_0;
+  const int nwarps = NWARPS_Q4_0;
+
+  moe_q<scalar_t, QK4_0, QR4_0, QI4_0, true, block_q4_0, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q4_0<mmq_y>, load_tiles_q4_0<mmq_y, nwarps, need_check>,
+        VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q4_0_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  int mmq_x = MMQ_X_Q4_0;
+  int mmq_y = MMQ_Y_Q4_0;
+  int nwarps = NWARPS_Q4_0;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q4_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q4_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q4_1 64
+  #define MMQ_Y_Q4_1 128
+  #define NWARPS_Q4_1 8
+#else
+  #define MMQ_X_Q4_1 4
+  #define MMQ_Y_Q4_1 32
+  #define NWARPS_Q4_1 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q4_1, 2)
+#endif
+    moe_q4_1(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q4_1;
+  const int mmq_y = MMQ_Y_Q4_1;
+  const int nwarps = NWARPS_Q4_1;
+
+  moe_q<scalar_t, QK4_1, QR4_1, QI4_1, true, block_q4_1, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q4_1<mmq_y>, load_tiles_q4_1<mmq_y, nwarps, need_check>,
+        VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q4_1_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  int mmq_x = MMQ_X_Q4_1;
+  int mmq_y = MMQ_Y_Q4_1;
+  int nwarps = NWARPS_Q4_1;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q4_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q4_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q5_0 64
+  #define MMQ_Y_Q5_0 128
+  #define NWARPS_Q5_0 8
+#else
+  #define MMQ_X_Q5_0 4
+  #define MMQ_Y_Q5_0 32
+  #define NWARPS_Q5_0 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q5_0, 2)
+#endif
+    moe_q5_0(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q5_0;
+  const int mmq_y = MMQ_Y_Q5_0;
+  const int nwarps = NWARPS_Q5_0;
+
+  moe_q<scalar_t, QK5_0, QR5_0, QI5_0, false, block_q5_0, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q5_0<mmq_y>, load_tiles_q5_0<mmq_y, nwarps, need_check>,
+        VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q5_0_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q5_0;
+  const int mmq_y = MMQ_Y_Q5_0;
+  const int nwarps = NWARPS_Q5_0;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q5_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q5_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q5_1 64
+  #define MMQ_Y_Q5_1 128
+  #define NWARPS_Q5_1 8
+#else
+  #define MMQ_X_Q5_1 4
+  #define MMQ_Y_Q5_1 32
+  #define NWARPS_Q5_1 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q5_1, 2)
+#endif
+    moe_q5_1(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q5_1;
+  const int mmq_y = MMQ_Y_Q5_1;
+  const int nwarps = NWARPS_Q5_1;
+
+  moe_q<scalar_t, QK5_1, QR5_1, QI5_1, true, block_q5_1, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q5_1<mmq_y>, load_tiles_q5_1<mmq_y, nwarps, need_check>,
+        VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q5_1_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q5_1;
+  const int mmq_y = MMQ_Y_Q5_1;
+  const int nwarps = NWARPS_Q5_1;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q5_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q5_1<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q8_0 64
+  #define MMQ_Y_Q8_0 128
+  #define NWARPS_Q8_0 8
+#else
+  #define MMQ_X_Q8_0 4
+  #define MMQ_Y_Q8_0 32
+  #define NWARPS_Q8_0 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q8_0, 2)
+#endif
+    moe_q8_0(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q8_0;
+  const int mmq_y = MMQ_Y_Q8_0;
+  const int nwarps = NWARPS_Q8_0;
+
+  moe_q<scalar_t, QK8_0, QR8_0, QI8_0, false, block_q8_0, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q8_0<mmq_y>, load_tiles_q8_0<mmq_y, nwarps, need_check>,
+        VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q8_0_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q8_0;
+  const int mmq_y = MMQ_Y_Q8_0;
+  const int nwarps = NWARPS_Q8_0;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q8_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q8_0<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q2_K 64
+  #define MMQ_Y_Q2_K 128
+  #define NWARPS_Q2_K 8
+#else
+  #define MMQ_X_Q2_K 4
+  #define MMQ_Y_Q2_K 32
+  #define NWARPS_Q2_K 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q2_K, 2)
+#endif
+    moe_q2_K(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q2_K;
+  const int mmq_y = MMQ_Y_Q2_K;
+  const int nwarps = NWARPS_Q2_K;
+
+  moe_q<scalar_t, QK_K, QR2_K, QI2_K, false, block_q2_K, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q2_K<mmq_y>, load_tiles_q2_K<mmq_y, nwarps, need_check>,
+        VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q2_K_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q2_K;
+  const int mmq_y = MMQ_Y_Q2_K;
+  const int nwarps = NWARPS_Q2_K;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q2_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q2_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q3_K 64
+  #define MMQ_Y_Q3_K 128
+  #define NWARPS_Q3_K 8
+#else
+  #define MMQ_X_Q3_K 4
+  #define MMQ_Y_Q3_K 32
+  #define NWARPS_Q3_K 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q3_K, 2)
+#endif
+    moe_q3_K(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+
+  const int mmq_x = MMQ_X_Q3_K;
+  const int mmq_y = MMQ_Y_Q3_K;
+  const int nwarps = NWARPS_Q3_K;
+
+  moe_q<scalar_t, QK_K, QR3_K, QI3_K, false, block_q3_K, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q3_K<mmq_y>, load_tiles_q3_K<mmq_y, nwarps, need_check>,
+        VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+template <typename scalar_t>
+static void ggml_moe_q3_K_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q3_K;
+  const int mmq_y = MMQ_Y_Q3_K;
+  const int nwarps = NWARPS_Q3_K;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q3_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q3_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q4_K 64
+  #define MMQ_Y_Q4_K 128
+  #define NWARPS_Q4_K 8
+#else
+  #define MMQ_X_Q4_K 4
+  #define MMQ_Y_Q4_K 32
+  #define NWARPS_Q4_K 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q4_K, 2)
+#endif
+    moe_q4_K(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q4_K;
+  const int mmq_y = MMQ_Y_Q4_K;
+  const int nwarps = NWARPS_Q4_K;
+
+  moe_q<scalar_t, QK_K, QR4_K, QI4_K, true, block_q4_K, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q4_K<mmq_y>, load_tiles_q4_K<mmq_y, nwarps, need_check>,
+        VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q4_K_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q4_K;
+  const int mmq_y = MMQ_Y_Q4_K;
+  const int nwarps = NWARPS_Q4_K;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q4_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q4_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q5_K 64
+  #define MMQ_Y_Q5_K 128
+  #define NWARPS_Q5_K 8
+#else
+  #define MMQ_X_Q5_K 4
+  #define MMQ_Y_Q5_K 32
+  #define NWARPS_Q5_K 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q5_K, 2)
+#endif
+    moe_q5_K(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q5_K;
+  const int mmq_y = MMQ_Y_Q5_K;
+  const int nwarps = NWARPS_Q5_K;
+
+  moe_q<scalar_t, QK_K, QR5_K, QI5_K, true, block_q5_K, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q5_K<mmq_y>, load_tiles_q5_K<mmq_y, nwarps, need_check>,
+        VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q5_K_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q5_K;
+  const int mmq_y = MMQ_Y_Q5_K;
+  const int nwarps = NWARPS_Q5_K;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q5_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q5_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}
+
+#if defined(USE_ROCM)
+  #define MMQ_X_Q6_K 64
+  #define MMQ_Y_Q6_K 128
+  #define NWARPS_Q6_K 8
+#else
+  #define MMQ_X_Q6_K 4
+  #define MMQ_Y_Q6_K 32
+  #define NWARPS_Q6_K 4
+#endif
+
+template <typename scalar_t, bool need_check>
+static __global__ void
+#if defined(USE_ROCM)
+__launch_bounds__(WARP_SIZE_GGUF* NWARPS_Q6_K, 2)
+#endif
+    moe_q6_K(const void* __restrict__ vx, const void* __restrict__ vy,
+             scalar_t* __restrict__ dst, const int* sorted_token_ids,
+             const int* expert_ids, const int* num_tokens_post_padded,
+             const int exp_stride, const int ncols_x, const int nrows_x,
+             const int ncols_y, const int nrows_y, const int nrows_dst,
+             const int top_k) {
+  const int mmq_x = MMQ_X_Q6_K;
+  const int mmq_y = MMQ_Y_Q6_K;
+  const int nwarps = NWARPS_Q6_K;
+
+  moe_q<scalar_t, QK_K, QR6_K, QI6_K, false, block_q6_K, mmq_x, mmq_y, nwarps,
+        allocate_tiles_q6_K<mmq_y>, load_tiles_q6_K<mmq_y, nwarps, need_check>,
+        VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat>(
+      vx, vy, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+      exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+}
+
+template <typename scalar_t>
+static void ggml_moe_q6_K_q8_1_cuda(
+    const void* inp, const void* w, scalar_t* dst, const int* sorted_token_ids,
+    const int* expert_ids, const int* num_tokens_post_padded,
+    const int exp_stride, const int ncols_x, const int nrows_x,
+    const int ncols_y, const int nrows_y, const int nrows_dst, const int top_k,
+    const int tokens_post_padded, cudaStream_t stream) {
+  const int mmq_x = MMQ_X_Q6_K;
+  const int mmq_y = MMQ_Y_Q6_K;
+  const int nwarps = NWARPS_Q6_K;
+
+  const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y;
+  const int block_num_y = (tokens_post_padded) / mmq_x;
+  const dim3 block_nums(block_num_x, block_num_y, 1);
+  const dim3 block_dims(WARP_SIZE_GGUF, nwarps, 1);
+
+  if (nrows_x % mmq_y == 0) {
+    constexpr bool need_check = false;
+    moe_q6_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  } else {
+    constexpr bool need_check = true;
+    moe_q6_K<scalar_t, need_check><<<block_nums, block_dims, 0, stream>>>(
+        w, inp, dst, sorted_token_ids, expert_ids, num_tokens_post_padded,
+        exp_stride, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, top_k);
+  }
+}

csrc/quantization/gptq/q_gemm.cu

@@ -206,8 +206,8 @@ __global__ void gemm_half_q_half_gptq_4bit_kernel(
   int offset_m = blockIdx.y * m_count;
   int offset_k = blockIdx.z * BLOCK_KN_SIZE;
 
-  int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
-  int end_m = min(offset_m + m_count, size_m);
+  [[maybe_unused]] int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
+  [[maybe_unused]] int end_m = min(offset_m + m_count, size_m);
   int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
 
   int n = offset_n + t * 4;
@@ -344,8 +344,8 @@ __global__ void gemm_half_q_half_gptq_2bit_kernel(
   int offset_m = blockIdx.y * m_count;
   int offset_k = blockIdx.z * BLOCK_KN_SIZE;
 
-  int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
-  int end_m = min(offset_m + m_count, size_m);
+  [[maybe_unused]] int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
+  [[maybe_unused]] int end_m = min(offset_m + m_count, size_m);
   int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
 
   int n = offset_n + t * 4;
@@ -465,8 +465,8 @@ __global__ void gemm_half_q_half_gptq_3bit_kernel(
   int offset_m = blockIdx.y * m_count;
   int offset_k = blockIdx.z * BLOCK_KN_SIZE;
 
-  int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
-  int end_m = min(offset_m + m_count, size_m);
+  [[maybe_unused]] int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
+  [[maybe_unused]] int end_m = min(offset_m + m_count, size_m);
   int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
 
   int n = offset_n + t * 4;
@@ -593,8 +593,8 @@ __global__ void gemm_half_q_half_gptq_8bit_kernel(
   int offset_m = blockIdx.y * m_count;
   int offset_k = blockIdx.z * BLOCK_KN_SIZE;
 
-  int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
-  int end_m = min(offset_m + m_count, size_m);
+  [[maybe_unused]] int end_n = min(offset_n + BLOCK_KN_SIZE * 4, size_n);
+  [[maybe_unused]] int end_m = min(offset_m + m_count, size_m);
   int end_k = min(offset_k + BLOCK_KN_SIZE, size_k);
 
   int n = offset_n + t * 4;

csrc/quantization/gptq_allspark/allspark_qgemm_w8a16.cu

@@ -437,9 +437,10 @@ struct ComputeTile_W8A16_PerC_MtilexNtilex32_multistage_SM8x_SplitK {
       for (int n_idx = 0; n_idx < WARP_NITER; ++n_idx) {
   #pragma unroll
         for (int k_idx = 0; k_idx < 2; ++k_idx) {
-          FType low16 = static_cast<FType>(C_frag[m_idx][n_idx][k_idx * 2]);
+          FType low16 =
+              ScalarType<FType>::float2num(C_frag[m_idx][n_idx][k_idx * 2]);
           FType high16 =
-              static_cast<FType>(C_frag[m_idx][n_idx][k_idx * 2 + 1]);
+              ScalarType<FType>::float2num(C_frag[m_idx][n_idx][k_idx * 2 + 1]);
           uint32_t tmp = (reinterpret_cast<uint32_t&>(low16) & 0xffff) |
                          (reinterpret_cast<uint32_t&>(high16) << 16);
           int sts_offset =
@@ -793,7 +794,7 @@ __global__ void restore_N32_K16_dequantize_rhs_w8a16_perc_kernel(
   FT scale_reg[4];
   *(reinterpret_cast<uint2*>(scale_reg)) =
       *(reinterpret_cast<const uint2*>(scales + params_nidx));
-  FT zero_reg[4] = {0};
+  FT zero_reg[4];
   if (zeros != nullptr) {
     *(reinterpret_cast<uint2*>(zero_reg)) =
         *(reinterpret_cast<const uint2*>(zeros + params_nidx));
@@ -809,8 +810,10 @@ __global__ void restore_N32_K16_dequantize_rhs_w8a16_perc_kernel(
         reinterpret_cast<typename HalfType<FT>::T2*>(&(fval_reg[ni * 4])));
   #pragma unroll
     for (int ki = 0; ki < 4; ++ki) {
-      fval_reg[ni * 4 + ki] =
-          (fval_reg[ni * 4 + ki] - zero_reg[ni]) * scale_reg[ni];
+      if (zeros != nullptr) {
+        fval_reg[ni * 4 + ki] = __hsub(fval_reg[ni * 4 + ki], zero_reg[ni]);
+      }
+      fval_reg[ni * 4 + ki] = __hmul(fval_reg[ni * 4 + ki], scale_reg[ni]);
       int sts_offset = sts_base_offset + ((ki / 2) * 8 + (ki % 2)) * 32 +
                        ((ni + lane_id % 4) % 4) * 8;
       smem[sts_offset] = fval_reg[ni * 4 + ki];

csrc/quantization/gptq_allspark/allspark_utils.cuh

@@ -7,6 +7,8 @@
 #include <cuda_fp16.h>
 #include <cuda_bf16.h>
 #include <iostream>
+#include "../gptq_marlin/marlin_dtypes.cuh"
+using marlin::ScalarType;
 
 namespace allspark {
 
@@ -66,14 +68,14 @@ __global__ void f16_gemm_splitk_reduce_kernel(const FType* C_split, FType* C,
     return;
   }
 
-  FType sum(0);
+  float sum = 0.f;
 
   int n_mat = N_MATRIX > 0 ? N_MATRIX : (int)n_matrix;
   for (int i = 0; i < n_mat; ++i) {
-    sum += C_split[idx + i * matrix_size];
+    sum += ScalarType<FType>::num2float(C_split[idx + i * matrix_size]);
   }
 
-  C[idx] = sum;
+  C[idx] = ScalarType<FType>::float2num(sum);
 }
 
 template <typename FType>

csrc/quantization/gptq_marlin/gptq_marlin.cu

@@ -538,6 +538,7 @@ __global__ void Marlin(
     int prob_n,           // output dimension n
     int prob_k,           // reduction dimension k
     int* locks,           // extra global storage for barrier synchronization
+    bool use_atomic_add,  // whether to use atomic add to reduce
     bool use_fp32_reduce  // whether to use fp32 global reduce
 ) {
   // Each threadblock processes one "stripe" of the B matrix with (roughly) the
@@ -1542,7 +1543,17 @@ __global__ void Marlin(
          i < div_ceil(16 * thread_m_blocks, threads / (2 * thread_n_blocks));
          i++) {
       if (c_gl_wr < c_gl_wr_end) {
+        if (use_atomic_add && slice_count > 1) {
+          scalar_t2* C_half2 = reinterpret_cast<scalar_t2*>(&C[c_gl_wr]);
+          scalar_t2* sh_red_half2 =
+              reinterpret_cast<scalar_t2*>(&sh_red[c_sh_rd]);
+  #pragma unroll
+          for (int a = 0; a < 4; a++) {
+            atomicAdd(&C_half2[a], sh_red_half2[a]);
+          }
+        } else {
           C[c_gl_wr] = sh_red[c_sh_rd];
+        }
         c_gl_wr += c_gl_wr_delta;
         c_sh_rd += c_sh_rd_delta;
       }
@@ -1644,7 +1655,7 @@ __global__ void Marlin(
           }
           cp_async_fence();
         } else {
-          if (last) {
+          if (last || use_atomic_add) {
             if (s_sh_wr_pred) {
               cp_async4(&sh_s[s_sh_wr], &scales_ptr[s_gl_rd]);
             }
@@ -1664,7 +1675,7 @@ __global__ void Marlin(
           }
 
         } else {
-          if (last) {
+          if (last || use_atomic_add) {
             cp_async_wait<0>();
             __syncthreads();
             if (threadIdx.x / 32 < thread_n_blocks / 4) {
@@ -1703,8 +1714,8 @@ __global__ void Marlin(
         }
       }
 
-      if (slice_count > 1) {  // only globally reduce if there is more than one
-                              // block in a slice
+      if (slice_count > 1 && !use_atomic_add) {
+        // only globally reduce if there is more than one block in a slice
         barrier_acquire(&locks[slice_col], slice_idx);
         if (use_fp32_reduce) {
           global_reduce_fp32(slice_idx == 0, last);
@@ -1713,7 +1724,8 @@ __global__ void Marlin(
         }
         barrier_release(&locks[slice_col], last);
       }
-      if (last)  // only the last block in a slice actually writes the result
+      if (last || use_atomic_add)
+        // only the last block in a slice actuallywrites the result
         write_result();
       slice_row = 0;
       slice_col_par++;
@@ -1768,7 +1780,8 @@ __global__ void Marlin(
                HAS_ZP, GROUP_BLOCKS, IS_ZP_FLOAT>                              \
             <<<blocks, NUM_THREADS, max_shared_mem, stream>>>(                 \
                 A_ptr, B_ptr, C_ptr, C_tmp_ptr, s_ptr, zp_ptr, g_idx_ptr,      \
-                num_groups, prob_m, prob_n, prob_k, locks, use_fp32_reduce);   \
+                num_groups, prob_m, prob_n, prob_k, locks, use_atomic_add,     \
+                use_fp32_reduce);                                              \
       }                                                                        \
     }
 
@@ -2062,7 +2075,8 @@ void marlin_mm(const void* A, const void* B, void* C, void* C_tmp, void* s,
                vllm::ScalarType const& q_type, bool has_act_order,
                bool is_k_full, bool has_zp, int num_groups, int group_size,
                int dev, cudaStream_t stream, int thread_k, int thread_n,
-               int sms, int max_par, bool use_fp32_reduce, bool is_zp_float) {
+               int sms, int max_par, bool use_atomic_add, bool use_fp32_reduce,
+               bool is_zp_float) {
   if (has_zp) {
     TORCH_CHECK(
         q_type == vllm::kU4 || q_type == vllm::kU8,
@@ -2243,7 +2257,7 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
                                torch::Tensor& workspace,
                                vllm::ScalarTypeId const& b_q_type_id,
                                int64_t size_m, int64_t size_n, int64_t size_k,
-                               bool is_k_full, bool has_zp,
+                               bool is_k_full, bool has_zp, bool use_atomic_add,
                                bool use_fp32_reduce, bool is_zp_float) {
   vllm::ScalarType const b_q_type = vllm::ScalarType::from_id(b_q_type_id);
   if (has_zp) {
@@ -2306,19 +2320,34 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
   // Alloc buffers
   const at::cuda::OptionalCUDAGuard device_guard(device_of(a));
   auto options = torch::TensorOptions().dtype(a.dtype()).device(a.device());
-  torch::Tensor c = torch::empty({size_m, size_n}, options);
-  torch::Tensor a_tmp = torch::empty({size_m, size_k}, options);
+  torch::Tensor c;
+  if (use_atomic_add) {
+    c = torch::zeros({size_m, size_n}, options);
+  } else {
+    c = torch::empty({size_m, size_n}, options);
+  }
+
+  torch::Tensor a_tmp;
+  bool has_act_order = g_idx.size(0) != 0;
+  if (has_act_order) {
+    a_tmp = torch::empty({size_m, size_k}, options);
+  } else {
+    a_tmp = torch::empty({0}, options);
+  }
 
   // Alloc C tmp buffer that is going to be used for the global reduce
+  torch::Tensor c_tmp;
   int reduce_max_m = marlin::determine_reduce_max_m(size_m, marlin::max_par);
   int reduce_n = size_n;
   auto options_fp32 =
       torch::TensorOptions().dtype(at::kFloat).device(a.device());
-  if (!use_fp32_reduce) {
+  if (use_fp32_reduce) {
+    c_tmp = torch::empty({reduce_max_m, reduce_n}, options_fp32);
+  } else {
     reduce_max_m = 0;
     reduce_n = 0;
+    c_tmp = torch::empty({0}, options_fp32);
   }
-  torch::Tensor c_tmp = torch::empty({reduce_max_m, reduce_n}, options_fp32);
 
   // thread_k: `k` size of a thread_tile in `weights` (can usually be left as
   // auto -1)
@@ -2339,7 +2368,6 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
   // Detect groupsize and act_order
   int num_groups = -1;
   int group_size = -1;
-  bool has_act_order = g_idx.size(0) != 0;
 
   int rank = b_scales.sizes().size();
   TORCH_CHECK(rank == 2, "b_scales rank = ", rank, " is not 2");
@@ -2407,7 +2435,8 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
         a_tmp.data_ptr<at::Half>(), size_m, size_n, size_k,
         workspace.data_ptr(), b_q_type, has_act_order, is_k_full, has_zp,
         num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
-        thread_k, thread_n, sms, marlin::max_par, use_fp32_reduce, is_zp_float);
+        thread_k, thread_n, sms, marlin::max_par, use_atomic_add,
+        use_fp32_reduce, is_zp_float);
   } else if (a.scalar_type() == at::ScalarType::BFloat16) {
     marlin::marlin_mm<nv_bfloat16>(
         a.data_ptr<at::BFloat16>(), b_q_weight.data_ptr(),
@@ -2416,7 +2445,8 @@ torch::Tensor gptq_marlin_gemm(torch::Tensor& a, torch::Tensor& b_q_weight,
         perm.data_ptr(), a_tmp.data_ptr<at::BFloat16>(), size_m, size_n, size_k,
         workspace.data_ptr(), b_q_type, has_act_order, is_k_full, has_zp,
         num_groups, group_size, dev, at::cuda::getCurrentCUDAStream(dev),
-        thread_k, thread_n, sms, marlin::max_par, use_fp32_reduce, is_zp_float);
+        thread_k, thread_n, sms, marlin::max_par, use_atomic_add,
+        use_fp32_reduce, is_zp_float);
   } else {
     TORCH_CHECK(false, "gpt_marlin_gemm only supports bfloat16 and float16");
   }

csrc/quantization/vectorization.cuh

@@ -4,7 +4,6 @@
  */
 
 // Include both AMD and NVIDIA fp8 types to avoid circular import
-// TODO(luka/varun) use FP8_TYPE instead after refactoring
 #include <c10/util/Float8_e4m3fnuz.h>
 #include <c10/util/Float8_e4m3fn.h>
 

csrc/rocm/attention.cu

@@ -127,7 +127,7 @@ __device__ __forceinline__ T from_float(const float& inp) {
 
 template <typename T>
 __device__ __forceinline__ _B16x4 from_floatx4(const floatx4& inp) {
-  union tmpcvt {
+  [[maybe_unused]] union tmpcvt {
     uint16_t u;
     _Float16 f;
     __hip_bfloat16 b;
@@ -160,7 +160,7 @@ __device__ __forceinline__ _B16x4 from_floatx4(const floatx4& inp) {
 template <typename T>
 __device__ __forceinline__ _B16x4 addx4(const _B16x4& inp1,
                                         const _B16x4& inp2) {
-  union tmpcvt {
+  [[maybe_unused]] union tmpcvt {
     uint16_t u;
     _Float16 f;
     __hip_bfloat16 b;
@@ -308,8 +308,8 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
 
   constexpr int GQA_RATIO4 = DIVIDE_ROUND_UP(GQA_RATIO, 4);
 
-  __shared__ float shared_qk_max[NWARPS][16 + 1];
-  __shared__ float shared_exp_sum[NWARPS][16 + 1];
+  [[maybe_unused]] __shared__ float shared_qk_max[NWARPS][16 + 1];
+  [[maybe_unused]] __shared__ float shared_exp_sum[NWARPS][16 + 1];
   // shared_logits is used for multiple purposes
   __shared__ _B16x4 shared_logits[NWARPS][4][16][4];
 
@@ -426,7 +426,8 @@ __launch_bounds__(NUM_THREADS, 5) void paged_attention_ll4mi_QKV_mfma16_kernel(
     const cache_t* k_ptr2 = k_ptr + kblock_number * kv_block_stride;
     const int klocal_token_idx =
         TOKENS_PER_WARP * warpid + token_depth * 16 + lane16id;
-    const int kglobal_token_idx = partition_start_token_idx + klocal_token_idx;
+    [[maybe_unused]] const int kglobal_token_idx =
+        partition_start_token_idx + klocal_token_idx;
     const int kphysical_block_offset = klocal_token_idx % BLOCK_SIZE;
     const cache_t* k_ptr3 = k_ptr2 + kphysical_block_offset * KX;
 
@@ -1272,9 +1273,9 @@ __launch_bounds__(NUM_THREADS) void paged_attention_ll4mi_reduce_kernel(
   const int seq_idx = blockIdx.y;
   const int context_len = context_lens[seq_idx];
   const int num_partitions = DIVIDE_ROUND_UP(context_len, PARTITION_SIZE);
-  constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
+  [[maybe_unused]] constexpr int NUM_WARPS = NUM_THREADS / WARP_SIZE;
   const int warpid = threadIdx.x / WARP_SIZE;
-  const int laneid = threadIdx.x % WARP_SIZE;
+  [[maybe_unused]] const int laneid = threadIdx.x % WARP_SIZE;
 
   __shared__ float shared_global_exp_sum;
   // max num partitions supported is warp_size * NPAR_LOOPS

csrc/sparse/cutlass/sparse_scaled_mm_entry.cu

@@ -58,7 +58,9 @@ void cutlass_scaled_sparse_mm(torch::Tensor& c, torch::Tensor const& a,
 
   // Guard against compilation issues for sm90 kernels
 #if defined ENABLE_SPARSE_SCALED_MM_C3X && ENABLE_SPARSE_SCALED_MM_C3X
-  if (version_num >= 90) {
+  // We build for 9.0a which is not forward compatible, so restrict this to
+  // Hopper only
+  if (version_num == 90) {
     cutlass_scaled_sparse_mm_sm90(c, a, bt_nzs, bt_meta, a_scales, b_scales,
                                   bias);
     return;
@@ -82,7 +84,9 @@ std::vector<torch::Tensor> cutlass_sparse_compress(torch::Tensor const& a) {
 
   // Guard against compilation issues for sm90 kernels
 #if defined ENABLE_SPARSE_SCALED_MM_C3X && ENABLE_SPARSE_SCALED_MM_C3X
-  if (version_num >= 90) {
+  // We build for 9.0a which is not forward compatible, so restrict this to
+  // Hopper only
+  if (version_num == 90) {
     std::vector<torch::Tensor> result_tensors;
 
     auto [a_meta, a_nzs] = cutlass_sparse_compress_sm90(a);

csrc/torch_bindings.cpp

@@ -4,6 +4,7 @@
 #include "core/registration.h"
 
 #include <torch/library.h>
+#include <torch/version.h>
 
 // Note on op signatures:
 // The X_meta signatures are for the meta functions corresponding to op X.
@@ -17,6 +18,15 @@
 
 TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   // vLLM custom ops
+  //
+
+  // The default behavior in PyTorch 2.6 is "requires_contiguous", so we need
+  // to override this for many GEMMs with the following tag. Otherwise,
+  // torch.compile will force all input tensors to be contiguous(), which
+  // will break many custom ops that require column-major weight matrices.
+  // TODO: remove this for PyTorch 2.8, when the default is planned to switch
+  // to match exact eager-mode strides.
+  at::Tag stride_tag = at::Tag::needs_fixed_stride_order;
 
   ops.def("weak_ref_tensor(Tensor input) -> Tensor");
   ops.impl("weak_ref_tensor", torch::kCUDA, &weak_ref_tensor);
@@ -163,25 +173,29 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "aqlm_gemm(Tensor input, Tensor codes, Tensor codebooks, "
       "Tensor scales, int[] codebook_partition_sizes, Tensor? bias) "
-      "-> Tensor");
+      "-> Tensor",
+      {stride_tag});
   ops.impl("aqlm_gemm", torch::kCUDA, &aqlm_gemm);
 
   // Decompression method for AQLM.
   ops.def(
       "aqlm_dequant(Tensor codes, Tensor codebooks, "
-      "int[] codebook_partition_sizes) -> Tensor");
+      "int[] codebook_partition_sizes) -> Tensor",
+      {stride_tag});
   ops.impl("aqlm_dequant", torch::kCUDA, &aqlm_dequant);
 
   // Quantized GEMM for AWQ.
   ops.def(
       "awq_gemm(Tensor _in_feats, Tensor _kernel, Tensor _scaling_factors, "
-      "Tensor _zeros, SymInt split_k_iters) -> Tensor");
+      "Tensor _zeros, SymInt split_k_iters) -> Tensor",
+      {stride_tag});
   ops.impl("awq_gemm", torch::kCUDA, &awq_gemm);
 
   // Dequantization for AWQ.
   ops.def(
       "awq_dequantize(Tensor _kernel, Tensor _scaling_factors, "
-      "Tensor _zeros, SymInt split_k_iters, int thx, int thy) -> Tensor");
+      "Tensor _zeros, SymInt split_k_iters, int thx, int thy) -> Tensor",
+      {stride_tag});
   ops.impl("awq_dequantize", torch::kCUDA, &awq_dequantize);
 
   // Note about marlin kernel 'workspace' arguments:
@@ -202,7 +216,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "marlin_gemm(Tensor a, Tensor b_q_weight, Tensor b_scales, "
       "Tensor! workspace, SymInt size_m, SymInt size_n, SymInt size_k) -> "
-      "Tensor");
+      "Tensor",
+      {stride_tag});
   // conditionally compiled so impl in source file
 
   // Marlin_24 (Sparse) Optimized Quantized GEMM for GPTQ.
@@ -210,7 +225,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "gptq_marlin_24_gemm(Tensor a, Tensor b_q_weight, Tensor b_meta, "
       "Tensor b_scales, Tensor workspace, "
       "int b_q_type, "
-      "SymInt size_m, SymInt size_n, SymInt size_k) -> Tensor");
+      "SymInt size_m, SymInt size_n, SymInt size_k) -> Tensor",
+      {stride_tag});
   //  conditionally compiled so impl in source file
 
   // Machete (Dense) Optimized Mixed Precision GEMM for Hopper.
@@ -236,7 +252,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "   Tensor? channel_scales,"
       "   Tensor? token_scales,"
       "   str?    schedule"
-      ") -> Tensor");
+      ") -> Tensor",
+      {stride_tag});
   ops.def(
       "machete_prepack_B("
       "   Tensor B,"
@@ -255,7 +272,9 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "Tensor b_zeros, Tensor g_idx, Tensor perm, Tensor workspace, "
       "int b_q_type, "
       "SymInt size_m, SymInt size_n, SymInt size_k, bool is_k_full, "
-      "bool has_zp, bool use_fp32_reduce, bool is_zp_float) -> Tensor");
+      "bool has_zp, bool use_atomic_add, bool use_fp32_reduce, "
+      "bool is_zp_float) -> Tensor",
+      {stride_tag});
   // conditionally compiled so impl registration is in source file
 
   // gptq_marlin repack from GPTQ.
@@ -286,12 +305,23 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "ggml_mul_mat_a8(Tensor W, Tensor X, int type, SymInt row) -> Tensor");
   ops.impl("ggml_mul_mat_a8", torch::kCUDA, &ggml_mul_mat_a8);
 
+  // moe kernel for GGML.
+  ops.def(
+      "ggml_moe_a8(Tensor X, Tensor W, "
+      "Tensor sorted_token_ids, Tensor expert_ids, Tensor "
+      "num_tokens_post_padded, "
+      "int type, SymInt row, SymInt top_k, SymInt tokens) -> Tensor");
+  ops.impl("ggml_moe_a8", torch::kCUDA, &ggml_moe_a8);
+
+  ops.def("ggml_moe_get_block_size", &ggml_moe_get_block_size);
+
 #ifndef USE_ROCM
   // fp8_marlin Optimized Quantized GEMM for FP8 weight-only.
   ops.def(
       "fp8_marlin_gemm(Tensor a, Tensor b_q_weight, Tensor b_scales, "
       "Tensor! workspace, int num_bits, SymInt size_m, SymInt size_n, "
-      "SymInt size_k) -> Tensor");
+      "SymInt size_k) -> Tensor",
+      {stride_tag});
   // conditionally compiled so impl registration is in source file
 
   // marlin_qqq_gemm for QQQ.
@@ -299,14 +329,16 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "marlin_qqq_gemm(Tensor a, Tensor b_q_weight, "
       "Tensor s_tok, Tensor s_ch, Tensor s_group, "
       "Tensor! workspace, SymInt size_m, SymInt size_n, "
-      "SymInt size_k) -> Tensor");
+      "SymInt size_k) -> Tensor",
+      {stride_tag});
   // conditionally compiled so impl registration is in source file
 
   // CUTLASS nvfp4 block scaled GEMM
   ops.def(
       "cutlass_scaled_fp4_mm(Tensor! out, Tensor a, Tensor b,"
       "                      Tensor block_scale_a, Tensor block_scale_b,"
-      "                      Tensor alpha) -> ()");
+      "                      Tensor alpha) -> ()",
+      {stride_tag});
   ops.impl("cutlass_scaled_fp4_mm", torch::kCUDA, &cutlass_scaled_fp4_mm);
 
   // CUTLASS w8a8 GEMM, supporting symmetric per-tensor or per-row/column
@@ -314,7 +346,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "cutlass_scaled_mm(Tensor! out, Tensor a,"
       "                  Tensor b, Tensor a_scales,"
-      "                  Tensor b_scales, Tensor? bias) -> ()");
+      "                  Tensor b_scales, Tensor? bias) -> ()",
+      {stride_tag});
   ops.impl("cutlass_scaled_mm", torch::kCUDA, &cutlass_scaled_mm);
 
   // CUTLASS w8a8 GEMM, supporting asymmetric per-tensor or per-row/column
@@ -323,7 +356,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "cutlass_scaled_mm_azp(Tensor! out, Tensor a,"
       "                  Tensor b, Tensor a_scales,"
       "                  Tensor b_scales, Tensor azp_adj,"
-      "                  Tensor? azp, Tensor? bias) -> ()");
+      "                  Tensor? azp, Tensor? bias) -> ()",
+      {stride_tag});
   ops.impl("cutlass_scaled_mm_azp", torch::kCUDA, &cutlass_scaled_mm_azp);
 
   // Check if cutlass scaled_mm is supported for CUDA devices of the given
@@ -336,7 +370,7 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "cutlass_scaled_mm_supports_block_fp8(int cuda_device_capability) -> "
       "bool");
   ops.impl("cutlass_scaled_mm_supports_block_fp8",
-           &cutlass_scaled_mm_supports_fp8);
+           &cutlass_scaled_mm_supports_block_fp8);
 
   // Check if cutlass sparse scaled_mm is supported for CUDA devices of the
   // given capability
@@ -351,7 +385,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "cutlass_scaled_sparse_mm(Tensor! out, Tensor a,"
       "                         Tensor bt_nzs,"
       "                         Tensor bt_meta, Tensor a_scales,"
-      "                         Tensor b_scales, Tensor? bias) -> ()");
+      "                         Tensor b_scales, Tensor? bias) -> ()",
+      {stride_tag});
   ops.impl("cutlass_scaled_sparse_mm", torch::kCUDA, &cutlass_scaled_sparse_mm);
 
   // CUTLASS sparse matrix compressor
@@ -399,6 +434,10 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
       "                 Tensor! output_scale, Tensor input_scale) -> ()");
   ops.impl("scaled_fp4_quant", torch::kCUDA, &scaled_fp4_quant);
 
+  // Check if cutlass_scaled_mm_fp4 is supported for CUDA devices
+  // of the given capability
+  ops.def("cutlass_scaled_mm_supports_fp4(int cuda_device_capability) -> bool");
+  ops.impl("cutlass_scaled_mm_supports_fp4", &cutlass_scaled_mm_supports_fp4);
 #endif
 
   // Quantized GEMM for GPTQ.
@@ -407,7 +446,8 @@ TORCH_LIBRARY_EXPAND(TORCH_EXTENSION_NAME, ops) {
   ops.def(
       "gptq_gemm(Tensor a, Tensor b_q_weight, Tensor b_gptq_qzeros, "
       "Tensor b_gptq_scales, Tensor b_g_idx, bool use_exllama, int bit) "
-      "-> Tensor");
+      "-> Tensor",
+      {stride_tag});
   ops.impl("gptq_gemm", torch::kCUDA, &gptq_gemm);
 
   // Post processing for GPTQ.

docs/README.md

@@ -4,7 +4,7 @@
 
 ```bash
 # Install dependencies.
-pip install -r requirements-docs.txt
+pip install -r ../requirements/docs.txt
 
 # Build the docs.
 make clean

docs/source/community/meetups.md

@@ -4,6 +4,8 @@
 
 We host regular meetups in San Francisco Bay Area every 2 months. We will share the project updates from the vLLM team and have guest speakers from the industry to share their experience and insights. Please find the materials of our previous meetups below:
 
+- [The East Coast vLLM Meetup](https://lu.ma/7mu4k4xx), March 11th 2025. [[Slides]](https://docs.google.com/presentation/d/1NHiv8EUFF1NLd3fEYODm56nDmL26lEeXCaDgyDlTsRs/edit#slide=id.g31441846c39_0_0)
+- [The ninth vLLM meetup](https://lu.ma/h7g3kuj9), with Meta, February 27th 2025. [[Slides]](https://docs.google.com/presentation/d/1jzC_PZVXrVNSFVCW-V4cFXb6pn7zZ2CyP_Flwo05aqg/edit?usp=sharing)
 - [The eighth vLLM meetup](https://lu.ma/zep56hui), with Google Cloud, January 22nd 2025. [[Slides]](https://docs.google.com/presentation/d/1epVkt4Zu8Jz_S5OhEHPc798emsYh2BwYfRuDDVEF7u4/edit?usp=sharing)
 - [The seventh vLLM meetup](https://lu.ma/h0qvrajz), with Snowflake, November 14th 2024. [[Slides]](https://docs.google.com/presentation/d/1e3CxQBV3JsfGp30SwyvS3eM_tW-ghOhJ9PAJGK6KR54/edit?usp=sharing)
 - [The sixth vLLM meetup](https://lu.ma/87q3nvnh), with NVIDIA, September 9th 2024. [[Slides]](https://docs.google.com/presentation/d/1wrLGwytQfaOTd5wCGSPNhoaW3nq0E-9wqyP7ny93xRs/edit?usp=sharing)

docs/source/contributing/model/multimodal.md

@@ -34,7 +34,8 @@ Further update the model as follows:
             image_features = self.vision_encoder(image_input)
             return self.multi_modal_projector(image_features)
 
-        def get_multimodal_embeddings(self, **kwargs: object) -> Optional[NestedTensors]:
+        def get_multimodal_embeddings(
+                self, **kwargs: object) -> Optional[MultiModalEmbeddings]:
 
             # Validate the multimodal input keyword arguments
             image_input = self._parse_and_validate_image_input(**kwargs)
@@ -61,7 +62,7 @@ Further update the model as follows:
         def get_input_embeddings(
             self,
             input_ids: torch.Tensor,
-            multimodal_embeddings: Optional[NestedTensors] = None,
+            multimodal_embeddings: Optional[MultiModalEmbeddings] = None,
         ) -> torch.Tensor:
 
             # `get_input_embeddings` should already be implemented for the language 

docs/source/contributing/overview.md

@@ -23,7 +23,7 @@ Check out the [building from source](#build-from-source) documentation for detai
 ## Testing
 
 ```bash
-pip install -r requirements-dev.txt
+pip install -r requirements/dev.txt
 
 # Linting, formatting and static type checking
 pre-commit install --hook-type pre-commit --hook-type commit-msg

docs/source/contributing/profiling/profiling_index.md

@@ -4,6 +4,8 @@
 Profiling is only intended for vLLM developers and maintainers to understand the proportion of time spent in different parts of the codebase. **vLLM end-users should never turn on profiling** as it will significantly slow down the inference.
 :::
 
+## Profile with PyTorch Profiler
+
 We support tracing vLLM workers using the `torch.profiler` module. You can enable tracing by setting the `VLLM_TORCH_PROFILER_DIR` environment variable to the directory where you want to save the traces: `VLLM_TORCH_PROFILER_DIR=/mnt/traces/`
 
 The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
@@ -22,13 +24,13 @@ Set the env variable VLLM_RPC_TIMEOUT to a big number before you start the serve
 `export VLLM_RPC_TIMEOUT=1800000`
 :::
 
-## Example commands and usage
+### Example commands and usage
 
-### Offline Inference
+#### Offline Inference
 
 Refer to <gh-file:examples/offline_inference/simple_profiling.py> for an example.
 
-### OpenAI Server
+#### OpenAI Server
 
 ```bash
 VLLM_TORCH_PROFILER_DIR=./vllm_profile python -m vllm.entrypoints.openai.api_server --model meta-llama/Meta-Llama-3-70B
@@ -39,3 +41,86 @@ benchmark_serving.py:
 ```bash
 python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Meta-Llama-3-70B --dataset-name sharegpt --dataset-path sharegpt.json --profile --num-prompts 2
 ```
+
+## Profile with NVIDIA Nsight Systems
+
+Nsight systems is an advanced tool that exposes more profiling details, such as register and shared memory usage, annotated code regions and low-level CUDA APIs and events.
+
+[Install nsight-systems](https://docs.nvidia.com/nsight-systems/InstallationGuide/index.html) using your package manager.
+The following block is an example for Ubuntu.
+
+```bash
+apt update
+apt install -y --no-install-recommends gnupg
+echo "deb http://developer.download.nvidia.com/devtools/repos/ubuntu$(source /etc/lsb-release; echo "$DISTRIB_RELEASE" | tr -d .)/$(dpkg --print-architecture) /" | tee /etc/apt/sources.list.d/nvidia-devtools.list
+apt-key adv --fetch-keys http://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
+apt update
+apt install nsight-systems-cli
+```
+
+### Example commands and usage
+
+#### Offline Inference
+
+For basic usage, you can just append `nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node` before any existing script you would run for offline inference.
+
+The following is an example using the `benchmarks/benchmark_latency.py` script:
+
+```bash
+nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node python benchmarks/benchmark_latency.py --model meta-llama/Llama-3.1-8B-Instruct --num-iters-warmup 5 --num-iters 1 --batch-size 16 --input-len 512 --output-len 8
+```
+
+#### OpenAI Server
+
+To profile the server, you will want to prepend your `vllm serve` command with `nsys profile` just like for offline inference, however you must specify `--delay XX --duration YY` parameters according to the needs of your benchmark. After the duration time has been used up, the server will be killed.
+
+```bash
+# server
+nsys profile -o report.nsys-rep --trace-fork-before-exec=true --cuda-graph-trace=node --delay 30 --duration 60 vllm serve meta-llama/Llama-3.1-8B-Instruct
+
+# client
+python benchmarks/benchmark_serving.py --backend vllm --model meta-llama/Llama-3.1-8B-Instruct --num-prompts 1 --dataset-name random --random-input 1024 --random-output 512
+```
+
+In practice, you should set the `--duration` argument to a large value. Whenever you want the server to stop profiling, run:
+
+```
+nsys sessions list
+```
+
+to get the session id in the form of `profile-XXXXX`, then run:
+
+```
+nsys stop --session=profile-XXXXX
+```
+
+to manually kill the profiler and generate your `nsys-rep` report.
+
+#### Analysis
+
+You can view these profiles either as summaries in the CLI, using `nsys stats [profile-file]`, or in the GUI by installing Nsight [locally following the directions here](https://developer.nvidia.com/nsight-systems/get-started).
+
+CLI example:
+
+```bash
+nsys stats report1.nsys-rep
+...
+ ** CUDA GPU Kernel Summary (cuda_gpu_kern_sum):
+
+ Time (%)  Total Time (ns)  Instances   Avg (ns)     Med (ns)    Min (ns)  Max (ns)   StdDev (ns)                                                  Name                                                
+ --------  ---------------  ---------  -----------  -----------  --------  ---------  -----------  ----------------------------------------------------------------------------------------------------
+     46.3   10,327,352,338     17,505    589,965.9    144,383.0    27,040  3,126,460    944,263.8  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize128x128x64_warpgroupsize1x1x1_execute_segment_k_of…
+     14.8    3,305,114,764      5,152    641,520.7    293,408.0   287,296  2,822,716    867,124.9  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize256x128x64_warpgroupsize2x1x1_execute_segment_k_of…
+     12.1    2,692,284,876     14,280    188,535.4     83,904.0    19,328  2,862,237    497,999.9  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x128x64_warpgroupsize1x1x1_execute_segment_k_off…
+      9.5    2,116,600,578     33,920     62,399.8     21,504.0    15,326  2,532,285    290,954.1  sm90_xmma_gemm_bf16bf16_bf16f32_f32_tn_n_tilesize64x64x64_warpgroupsize1x1x1_execute_segment_k_off_…
+      5.0    1,119,749,165     18,912     59,208.4      9,056.0     6,784  2,578,366    271,581.7  void vllm::act_and_mul_kernel<c10::BFloat16, &vllm::silu_kernel<c10::BFloat16>, (bool)1>(T1 *, cons…
+      4.1      916,662,515     21,312     43,011.6     19,776.0     8,928  2,586,205    199,790.1  void cutlass::device_kernel<flash::enable_sm90_or_later<flash::FlashAttnFwdSm90<flash::CollectiveMa…
+      2.6      587,283,113     37,824     15,526.7      3,008.0     2,719  2,517,756    139,091.1  std::enable_if<T2>(int)0&&vllm::_typeConvert<T1>::exists, void>::type vllm::fused_add_rms_norm_kern…
+      1.9      418,362,605     18,912     22,121.5      3,871.0     3,328  2,523,870    175,248.2  void vllm::rotary_embedding_kernel<c10::BFloat16, (bool)1>(const long *, T1 *, T1 *, const T1 *, in…
+      0.7      167,083,069     18,880      8,849.7      2,240.0     1,471  2,499,996    101,436.1  void vllm::reshape_and_cache_flash_kernel<__nv_bfloat16, __nv_bfloat16, (vllm::Fp8KVCacheDataType)0…
+... 
+```
+
+GUI example:
+
+<img width="1799" alt="Screenshot 2025-03-05 at 11 48 42 AM" src="https://github.com/user-attachments/assets/c7cff1ae-6d6f-477d-a342-bd13c4fc424c" />

docs/source/contributing/vulnerability_management.md

@@ -37,7 +37,7 @@ you may contact the following individuals:
 
 ## Slack Discussion
 
-You may use the `#security` channel in the [VLLM Slack](https://slack.vllm.ai)
+You may use the `#security` channel in the [vLLM Slack](https://slack.vllm.ai)
 to discuss security-related topics. However, please do not disclose any
 vulnerabilities in this channel. If you need to report a vulnerability, please
 use the GitHub security advisory system or contact a VMT member privately.

docs/source/deployment/frameworks/helm.md

@@ -4,9 +4,9 @@
 
 A Helm chart to deploy vLLM for Kubernetes
 
-Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLMm Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variables values.
+Helm is a package manager for Kubernetes. It will help you to deploy vLLM on k8s and automate the deployment of vLLM Kubernetes applications. With Helm, you can deploy the same framework architecture with different configurations to multiple namespaces by overriding variable values.
 
-This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm install and documentation on architecture and values file.
+This guide will walk you through the process of deploying vLLM with Helm, including the necessary prerequisites, steps for helm installation and documentation on architecture and values file.
 
 ## Prerequisites