Merge branch 'main' into imarkov/eplb_optimizations

.buildkite/test-amd.yaml

@@ -398,7 +398,8 @@ steps:
   timeout_in_minutes: 25
   gpu: h100
   source_file_dependencies:
-    - vllm/
+    - vllm/v1/attention
+    - vllm/model_executor/layers
     - tests/v1/determinism/
   commands:
     - export VLLM_WORKER_MULTIPROC_METHOD=spawn
@@ -440,23 +441,29 @@ steps:
   working_dir: "/vllm-workspace/examples"
   source_file_dependencies:
   - vllm/entrypoints
+  - vllm/multimodal
   - examples/
   commands:
     - pip install tensorizer # for tensorizer test
+    # for basic
+    - python3 offline_inference/basic/chat.py
     - python3 offline_inference/basic/generate.py --model facebook/opt-125m
     - python3 offline_inference/basic/generate.py --model meta-llama/Llama-2-13b-chat-hf --cpu-offload-gb 10
-    - python3 offline_inference/basic/chat.py
-    - python3 offline_inference/prefix_caching.py
-    - python3 offline_inference/llm_engine_example.py
+    - python3 offline_inference/basic/classify.py
+    - python3 offline_inference/basic/embed.py
+    - python3 offline_inference/basic/score.py
+    # for multi-modal models
     - python3 offline_inference/audio_language.py --seed 0
     - python3 offline_inference/vision_language.py --seed 0
     - python3 offline_inference/vision_language_pooling.py --seed 0
     - python3 offline_inference/vision_language_multi_image.py --seed 0
-    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/encoder_decoder_multimodal.py --model-type whisper --seed 0
-    - python3 offline_inference/basic/classify.py
-    - python3 offline_inference/basic/embed.py
-    - python3 offline_inference/basic/score.py
+    # for pooling models
+    - python3 pooling/pooling/vision_language_pooling.py --seed 0
+    # for features demo
+    - python3 offline_inference/prefix_caching.py
+    - python3 offline_inference/llm_engine_example.py
+    - python3 others/tensorize_vllm_model.py --model facebook/opt-125m serialize --serialized-directory /tmp/ --suffix v1 && python3 others/tensorize_vllm_model.py --model facebook/opt-125m deserialize --path-to-tensors /tmp/vllm/facebook/opt-125m/v1/model.tensors
     - python3 offline_inference/spec_decode.py --test --method eagle --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 2048
     # https://github.com/vllm-project/vllm/pull/26682 uses slightly more memory in PyTorch 2.9+ causing this test to OOM in 1xL4 GPU
     - python3 offline_inference/spec_decode.py --test --method eagle3 --num_spec_tokens 3 --dataset-name hf --dataset-path philschmid/mt-bench --num-prompts 80 --temp 0 --top-p 1.0 --top-k -1 --tp 1 --enable-chunked-prefill --max-model-len 1536
@@ -718,6 +725,18 @@ steps:
   - uv pip install --system conch-triton-kernels
   - VLLM_TEST_FORCE_LOAD_FORMAT=auto pytest -v -s quantization/ --ignore quantization/test_blackwell_moe.py
 
+- label: LM Eval Small Models # 53min
+  timeout_in_minutes: 75
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_1
+  # grade: Blocking
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  autorun_on_main: true
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
+
 - label: OpenAI API correctness # 10min
   timeout_in_minutes: 15
   mirror_hardwares: [amdexperimental, amdproduction]
@@ -727,7 +746,7 @@ steps:
   - csrc/
   - vllm/entrypoints/openai/
   - vllm/model_executor/models/whisper.py
-  commands: # LMEval
+  commands: # LMEval+Transcription WER check
   # Transcription WER check is skipped because encoder-decoder models are not supported on ROCm, see https://github.com/vllm-project/vllm/issues/27442
   - pytest -s entrypoints/openai/correctness/
 
@@ -963,6 +982,19 @@ steps:
     - pytest -v -s models/multimodal -m core_model --ignore models/multimodal/generation/test_whisper.py --ignore models/multimodal/processing
     - cd .. && VLLM_WORKER_MULTIPROC_METHOD=spawn pytest -v -s tests/models/multimodal/generation/test_whisper.py -m core_model  # Otherwise, mp_method="spawn" doesn't work
 
+- label: Multi-Modal Accuracy Eval (Small Models) # 150min - 180min
+  timeout_in_minutes: 180
+  mirror_hardwares: [amdexperimental, amdproduction]
+  agent_pool: mi325_1
+  # grade: Blocking
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  source_file_dependencies:
+  - vllm/multimodal/
+  - vllm/inputs/
+  - vllm/v1/core/
+  commands:
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
+
 - label: Multi-Modal Models Test (Extended) 1 # 60min
   timeout_in_minutes: 120
   mirror_hardwares: [amdexperimental]
@@ -1098,7 +1130,6 @@ steps:
   - vllm/model_executor/layers/layernorm.py
   - vllm/model_executor/layers/activation.py
   - vllm/model_executor/layers/quantization/input_quant_fp8.py
-  - vllm/model_executor/layers/fused_moe/layer.py
   - tests/compile/test_fusion_attn.py
   - tests/compile/test_silu_mul_quant_fusion.py
   - tests/compile/distributed/test_fusion_all_reduce.py
@@ -1132,12 +1163,25 @@ steps:
   - vllm/model_executor/layers/activation.py
   - vllm/model_executor/layers/quantization/input_quant_fp8.py
   - tests/compile/distributed/test_fusions_e2e.py
-  - tests/compile/fullgraph/test_full_graph.py
   commands:
     - nvidia-smi
     # Run all e2e fusion tests
     - pytest -v -s tests/compile/distributed/test_fusions_e2e.py
 
+- label: Blackwell GPT-OSS Eval
+  timeout_in_minutes: 60
+  working_dir: "/vllm-workspace/"
+  gpu: b200
+  optional: true # run on nightlies
+  source_file_dependencies:
+  - tests/evals/gpt_oss
+  - vllm/model_executor/models/gpt_oss.py
+  - vllm/model_executor/layers/quantization/mxfp4.py
+  - vllm/v1/attention/backends/flashinfer.py
+  commands:
+    - uv pip install --system 'gpt-oss[eval]==0.0.5'
+    - pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
+
 - label: Blackwell Quantized MoE Test
   timeout_in_minutes: 60
   working_dir: "/vllm-workspace/"
@@ -1155,6 +1199,16 @@ steps:
   commands:
     - pytest -s -v tests/quantization/test_blackwell_moe.py
 
+- label: Blackwell LM Eval Small Models
+  timeout_in_minutes: 120
+  gpu: b200
+  optional: true # run on nightlies
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  commands:
+  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1
+
 #####  1 GPU test  #####
 #####  multi gpus test  #####
 
@@ -1397,6 +1451,39 @@ steps:
   - TARGET_TEST_SUITE=A100 pytest basic_correctness/ -v -s -m 'distributed(num_gpus=2)'
   - pytest -v -s -x lora/test_mixtral.py
 
+
+- label: LM Eval Large Models # optional
+  gpu: a100
+  optional: true
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_4
+  # grade: Blocking
+  num_gpus: 4
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  commands:
+  - export VLLM_WORKER_MULTIPROC_METHOD=spawn
+  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
+
+##### H100 test #####
+- label: LM Eval Large Models (H100) # optional
+  gpu: h100
+  optional: true
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_4
+  # grade: Blocking
+  num_gpus: 4
+  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
+  source_file_dependencies:
+  - csrc/
+  - vllm/model_executor/layers/quantization
+  commands:
+    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
+    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
+
+
 ##### H200 test #####
 - label: Distributed Tests (H200) # optional
   mirror_hardwares: [amdexperimental]
@@ -1440,29 +1527,6 @@ steps:
   commands:
   - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-small.txt --tp-size=1
 
-- label: Blackwell LM Eval Small Models
-  timeout_in_minutes: 120
-  gpu: b200
-  optional: true # run on nightlies
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-  - pytest -s -v evals/gsm8k/test_gsm8k_correctness.py --config-list-file=configs/models-blackwell.txt --tp-size=1
-
-- label: Multi-Modal Accuracy Eval (Small Models) # 10min
-  timeout_in_minutes: 70
-  mirror_hardwares: [amdexperimental, amdproduction]
-  agent_pool: mi325_1
-  # grade: Blocking
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - vllm/multimodal/
-  - vllm/inputs/
-  - vllm/v1/core/
-  commands:
-  - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-mm-small.txt --tp-size=1
-
 - label: LM Eval Large Models (4 Card)
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_4
@@ -1478,21 +1542,6 @@ steps:
   - export VLLM_WORKER_MULTIPROC_METHOD=spawn
   - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large.txt --tp-size=4
 
-- label: LM Eval Large Models (H100) # optional
-  mirror_hardwares: [amdexperimental, amdproduction]
-  agent_pool: mi325_4
-  # grade: Blocking
-  gpu: h100
-  optional: true
-  num_gpus: 4
-  working_dir: "/vllm-workspace/.buildkite/lm-eval-harness"
-  source_file_dependencies:
-  - csrc/
-  - vllm/model_executor/layers/quantization
-  commands:
-    - export VLLM_USE_DEEP_GEMM=0  # We found Triton is faster than DeepGEMM for H100
-    - pytest -s -v test_lm_eval_correctness.py --config-list-file=configs/models-large-hopper.txt --tp-size=4
-
 - label: ROCm LM Eval Large Models (8 Card)
   mirror_hardwares: [amdproduction]
   agent_pool: mi325_8
@@ -1517,6 +1566,20 @@ steps:
     - uv pip install --system 'gpt-oss[eval]==0.0.5'
     - VLLM_ROCM_USE_AITER_MHA=0 VLLM_ROCM_USE_AITER=1 VLLM_USE_AITER_UNIFIED_ATTENTION=1 pytest -s -v tests/evals/gpt_oss/test_gpqa_correctness.py --model openai/gpt-oss-20b --metric 0.58
 
+##### RL Integration Tests #####
+- label: Prime-RL Integration Test # 15min
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_2
+  # grade: Blocking
+  timeout_in_minutes: 30
+  optional: true
+  num_gpus: 2
+  working_dir: "/vllm-workspace"
+  source_file_dependencies:
+  - vllm/
+  - .buildkite/scripts/run-prime-rl-test.sh
+  commands:
+    - bash .buildkite/scripts/run-prime-rl-test.sh
 - label: DeepSeek V2-Lite Accuracy
   mirror_hardwares: [amdexperimental, amdproduction]
   agent_pool: mi325_4
@@ -1550,17 +1613,26 @@ steps:
   commands:
   - bash .buildkite/scripts/scheduled_integration_test/qwen30b_a3b_fp8_block_ep_eplb.sh 0.8 200 8020 2 1
 
-##### RL Integration Tests #####
-- label: Prime-RL Integration Test # 15min
+- label: DeepSeek V2-Lite Async EPLB Accuracy
+  timeout_in_minutes: 60
   mirror_hardwares: [amdexperimental]
-  agent_pool: mi325_2
+  agent_pool: mi325_4
   # grade: Blocking
-  timeout_in_minutes: 30
+  gpu: h100
   optional: true
-  num_gpus: 2
+  num_gpus: 4
   working_dir: "/vllm-workspace"
-  source_file_dependencies:
-  - vllm/
-  - .buildkite/scripts/run-prime-rl-test.sh
   commands:
-    - bash .buildkite/scripts/run-prime-rl-test.sh
+  - bash .buildkite/scripts/scheduled_integration_test/deepseek_v2_lite_ep_async_eplb.sh 0.25 1319 8030
+
+- label: Qwen3-Next-80B-A3B-Instruct MTP Async EPLB Accuracy
+  timeout_in_minutes: 60
+  mirror_hardwares: [amdexperimental]
+  agent_pool: mi325_4
+  # grade: Blocking
+  gpu: h100
+  optional: true
+  num_gpus: 4
+  working_dir: "/vllm-workspace"
+  commands:
+  - bash .buildkite/scripts/scheduled_integration_test/qwen3_next_mtp_async_eplb.sh 0.8 1319 8040

.buildkite/test-pipeline.yaml

@@ -468,7 +468,9 @@ steps:
   # tests covered elsewhere.
   # Use `find` to launch multiple instances of pytest so that
   # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  - "find compile/ -maxdepth 1 -name 'test_*.py' -exec pytest -s -v {} \\\\;"
+  # However, find does not normally propagate error codes, so we combine it with xargs
+  # (using -0 for proper path handling)
+  - "find compile/ -maxdepth 1 -name 'test_*.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph Smoke Test # 15min
   timeout_in_minutes: 30
@@ -482,7 +484,9 @@ steps:
   # as it is a heavy test that is covered in other steps.
   # Use `find` to launch multiple instances of pytest so that
   # they do not suffer from https://github.com/vllm-project/vllm/issues/28965
-  - "find compile/fullgraph/ -name 'test_*.py' -not -name 'test_full_graph.py' -exec pytest -s -v {} \\\\;"
+  # However, find does not normally propagate error codes, so we combine it with xargs
+  # (using -0 for proper path handling)
+  - "find compile/fullgraph -maxdepth 1 -name 'test_*.py' -not -name 'test_full_graph.py' -print0 | xargs -0 -n1 -I{} pytest -s -v '{}'"
 
 - label: PyTorch Fullgraph Test # 27min
   timeout_in_minutes: 40

.github/workflows/cleanup_pr_body.yml

@@ -13,7 +13,7 @@ jobs:
 
     steps:
       - name: Checkout repository
-        uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
+        uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
 
       - name: Set up Python
         uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0

.github/workflows/macos-smoke-test.yml

@@ -12,7 +12,7 @@ jobs:
     timeout-minutes: 30
 
     steps:
-      - uses: actions/checkout@v6
+      - uses: actions/checkout@v6.0.1
 
       - uses: astral-sh/setup-uv@v7
         with:

.github/workflows/pre-commit.yml

@@ -16,7 +16,7 @@ jobs:
   pre-commit:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
+    - uses: actions/checkout@8e8c483db84b4bee98b60c0593521ed34d9990e8 # v6.0.1
     - uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
       with:
         python-version: "3.12"

.github/workflows/stale.yml

@@ -15,7 +15,7 @@ jobs:
       actions: write
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10.1.0
+      - uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
         with:
           # Increasing this value ensures that changes to this workflow
           # propagate to all issues and PRs in days rather than months

benchmarks/auto_tune/auto_tune.sh

@@ -96,8 +96,9 @@ start_server() {
     # This correctly passes each element as a separate argument.
     if [[ -n "$profile_dir" ]]; then
         # Start server with profiling enabled
-        VLLM_SERVER_DEV_MODE=1 VLLM_TORCH_PROFILER_DIR=$profile_dir \
-            vllm serve "${common_args_array[@]}" > "$vllm_log" 2>&1 &
+        local profile_config_json="{\"profiler\": \"torch\", \"torch_profiler_dir\": \"$profile_dir\"}"
+        VLLM_SERVER_DEV_MODE=1 \
+            vllm serve --profiler-config "$profile_config_json" "${common_args_array[@]}" > "$vllm_log" 2>&1 &
     else
         # Start server without profiling
         VLLM_SERVER_DEV_MODE=1 \

benchmarks/benchmark_serving_structured_output.py

@@ -963,8 +963,7 @@ def create_argument_parser():
     parser.add_argument(
         "--profile",
         action="store_true",
-        help="Use Torch Profiler. The endpoint must be launched with "
-        "VLLM_TORCH_PROFILER_DIR to enable profiler.",
+        help="Use vLLM Profiling. --profiler-config must be provided on the server.",
     )
     parser.add_argument(
         "--result-dir",

docs/api/README.md

@@ -15,6 +15,7 @@ API documentation for vLLM's configuration classes.
 - [vllm.config.MultiModalConfig][]
 - [vllm.config.PoolerConfig][]
 - [vllm.config.StructuredOutputsConfig][]
+- [vllm.config.ProfilerConfig][]
 - [vllm.config.ObservabilityConfig][]
 - [vllm.config.KVTransferConfig][]
 - [vllm.config.CompilationConfig][]

docs/contributing/profiling.md

@@ -5,16 +5,15 @@
 
 ## 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/`. Additionally, you can control the profiling content by specifying the following environment variables:
+We support tracing vLLM workers using the `torch.profiler` module. You can enable the torch profiler by setting `--profiler-config`
+when launching the server, and setting the entries `profiler` to `'torch'` and `torch_profiler_dir` to the directory where you want to save the traces. Additionally, you can control the profiling content by specifying the following additional arguments in the config:
 
-- `VLLM_TORCH_PROFILER_RECORD_SHAPES=1` to enable recording Tensor Shapes, off by default
-- `VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1` to record memory, off by default
-- `VLLM_TORCH_PROFILER_WITH_STACK=1` to enable recording stack information, on by default
-- `VLLM_TORCH_PROFILER_WITH_FLOPS=1` to enable recording FLOPs, off by default
-- `VLLM_TORCH_PROFILER_USE_GZIP=0` to disable gzip-compressing profiling files, on by default
-- `VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL=0` to disable dumping and printing the aggregated CUDA self time table, on by default
-
-The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
+- `torch_profiler_record_shapes` to enable recording Tensor Shapes, off by default
+- `torch_profiler_with_memory` to record memory, off by default
+- `torch_profiler_with_stack` to enable recording stack information, on by default
+- `torch_profiler_with_flops` to enable recording FLOPs, off by default
+- `torch_profiler_use_gzip` to control gzip-compressing profiling files, on by default
+- `torch_profiler_dump_cuda_time_total` to control dumping and printing the aggregated CUDA self time table, on by default
 
 When using `vllm bench serve`, you can enable profiling by passing the `--profile` flag.
 
@@ -40,8 +39,7 @@ Refer to [examples/offline_inference/simple_profiling.py](../../examples/offline
 #### OpenAI Server
 
 ```bash
-VLLM_TORCH_PROFILER_DIR=./vllm_profile \
-    vllm serve meta-llama/Llama-3.1-8B-Instruct
+vllm serve meta-llama/Llama-3.1-8B-Instruct --profiler-config '{"profiler": "torch", "torch_profiler_dir": "./vllm_profile"}'
 ```
 
 vllm bench command:
@@ -104,13 +102,12 @@ To profile the server, you will want to prepend your `vllm serve` command with `
 
 ```bash
 # server
-VLLM_TORCH_CUDA_PROFILE=1 \
 nsys profile \
     --trace-fork-before-exec=true \
     --cuda-graph-trace=node \
     --capture-range=cudaProfilerApi \
     --capture-range-end repeat \
-    vllm serve meta-llama/Llama-3.1-8B-Instruct
+    vllm serve meta-llama/Llama-3.1-8B-Instruct --profiler-config.profiler cuda
 
 # client
 vllm bench serve \

docs/features/reasoning_outputs.md

@@ -299,6 +299,9 @@ Additionally, to enable structured output, you'll need to create a new `Reasoner
 
         def is_reasoning_end(self, input_ids: list[int]) -> bool:
             return self.end_token_id in input_ids
+
+        def is_reasoning_end_streaming(self, input_ids: list[int], delta_ids: list[int]) -> bool:
+            return self.end_token_id in delta_token_ids
         ...
     ```
 

examples/offline_inference/simple_profiling.py

@@ -1,14 +1,10 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
-import os
 import time
 
 from vllm import LLM, SamplingParams
 
-# enable torch profiler, can also be set on cmd line
-os.environ["VLLM_TORCH_PROFILER_DIR"] = "./vllm_profile"
-
 # Sample prompts.
 prompts = [
     "Hello, my name is",
@@ -22,7 +18,14 @@ sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
 def main():
     # Create an LLM.
-    llm = LLM(model="facebook/opt-125m", tensor_parallel_size=1)
+    llm = LLM(
+        model="facebook/opt-125m",
+        tensor_parallel_size=1,
+        profiler_config={
+            "profiler": "torch",
+            "torch_profiler_dir": "./vllm_profile",
+        },
+    )
 
     llm.start_profile()
 

tests/compile/fullgraph/test_multimodal_compile.py

@@ -17,7 +17,6 @@ def test_compile():
 # forked needed to workaround https://github.com/vllm-project/vllm/issues/21073
 @pytest.mark.forked
 @pytest.mark.skipif(not current_platform.is_cuda(), reason="Skip if not cuda")
-@pytest.mark.xfail
 def test_qwen2_5_vl_compilation(vllm_runner, monkeypatch):
     """Test that Qwen2.5-VL vision submodules are compiled.
 

tests/compile/test_compile_ranges.py

@@ -80,6 +80,8 @@ def test_compile_ranges(use_fresh_inductor_cache):
     vllm_config = VllmConfig(
         scheduler_config=SchedulerConfig(
             max_num_batched_tokens=8192,
+            max_model_len=8192,
+            is_encoder_decoder=False,
         ),
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
@@ -112,6 +114,8 @@ def test_compile_config_get_compile_ranges():
     VllmConfig(
         scheduler_config=SchedulerConfig(
             max_num_batched_tokens=8192,
+            max_model_len=8192,
+            is_encoder_decoder=False,
         ),
         compilation_config=compilation_config,
     )
@@ -134,6 +138,8 @@ def test_inductor_cache_compile_ranges(monkeypatch, use_fresh_inductor_cache):
     )
     scheduler_config = SchedulerConfig(
         max_num_batched_tokens=8192,
+        max_model_len=8192,
+        is_encoder_decoder=False,
     )
     torch.set_default_device("cuda")
 

tests/compile/test_pass_manager.py

@@ -5,9 +5,14 @@ import copy
 import pytest
 import torch
 
-from vllm.compilation.inductor_pass import CallableInductorPass, InductorPass
+from vllm.compilation.inductor_pass import (
+    CallableInductorPass,
+    InductorPass,
+    pass_context,
+)
 from vllm.compilation.pass_manager import PostGradPassManager
 from vllm.config import ModelConfig, VllmConfig
+from vllm.config.utils import Range
 
 
 # dummy custom pass that doesn't inherit
@@ -42,35 +47,37 @@ class ProperPass(InductorPass):
     ],
 )
 def test_pass_manager_uuid(callable):
-    # Some passes need dtype to be set
-    config = VllmConfig(model_config=ModelConfig(dtype=torch.bfloat16))
+    # Set the pass context as PassManager uuid uses it
+    with pass_context(Range(start=1, end=8)):
+        # Some passes need dtype to be set
+        config = VllmConfig(model_config=ModelConfig(dtype=torch.bfloat16))
 
-    pass_manager = PostGradPassManager()
-    pass_manager.configure(config)
+        pass_manager = PostGradPassManager()
+        pass_manager.configure(config)
 
-    # Check that UUID is different if the same pass is added 2x
-    pass_manager.add(callable)
-    uuid1 = pass_manager.uuid()
-    pass_manager.add(callable)
-    uuid2 = pass_manager.uuid()
-    assert uuid1 != uuid2
+        # Check that UUID is different if the same pass is added 2x
+        pass_manager.add(callable)
+        uuid1 = pass_manager.uuid()
+        pass_manager.add(callable)
+        uuid2 = pass_manager.uuid()
+        assert uuid1 != uuid2
 
-    # UUID should be the same as the original one,
-    # as we constructed in the same way.
-    pass_manager2 = PostGradPassManager()
-    pass_manager2.configure(config)
-    pass_manager2.add(callable)
-    assert uuid1 == pass_manager2.uuid()
+        # UUID should be the same as the original one,
+        # as we constructed in the same way.
+        pass_manager2 = PostGradPassManager()
+        pass_manager2.configure(config)
+        pass_manager2.add(callable)
+        assert uuid1 == pass_manager2.uuid()
 
-    # UUID should be different due to config change
-    config2 = copy.deepcopy(config)
-    config2.compilation_config.pass_config.fuse_norm_quant = (
-        not config2.compilation_config.pass_config.fuse_norm_quant
-    )
-    config2.compilation_config.pass_config.fuse_act_quant = (
-        not config2.compilation_config.pass_config.fuse_act_quant
-    )
-    pass_manager3 = PostGradPassManager()
-    pass_manager3.configure(config2)
-    pass_manager3.add(callable)
-    assert uuid1 != pass_manager3.uuid()
+        # UUID should be different due to config change
+        config2 = copy.deepcopy(config)
+        config2.compilation_config.pass_config.fuse_norm_quant = (
+            not config2.compilation_config.pass_config.fuse_norm_quant
+        )
+        config2.compilation_config.pass_config.fuse_act_quant = (
+            not config2.compilation_config.pass_config.fuse_act_quant
+        )
+        pass_manager3 = PostGradPassManager()
+        pass_manager3.configure(config2)
+        pass_manager3.add(callable)
+        assert uuid1 != pass_manager3.uuid()

tests/kernels/attention/test_mha_attn.py

@@ -26,7 +26,14 @@ def clear_cache():
     _cached_get_attn_backend.cache_clear()
 
 
-@pytest.mark.parametrize("device", ["cpu", "hip", "cuda"])
+devices = ["cpu"]
+if current_platform.is_cuda():
+    devices.append("cuda")
+if current_platform.is_rocm():
+    devices.append("hip")
+
+
+@pytest.mark.parametrize("device", devices)
 def test_mha_attn_platform(device: str):
     """
     Test the attention selector between different platform and device.
@@ -46,7 +53,7 @@ def test_mha_attn_platform(device: str):
             patch("vllm.model_executor.models.vision.current_platform", RocmPlatform()),
         ):
             attn = MultiHeadAttention(16, 64, scale=1)
-            assert attn.attn_backend == AttentionBackendEnum.TORCH_SDPA
+            assert attn.attn_backend == AttentionBackendEnum.FLASH_ATTN
     else:
         # Test CUDA with head_size=64 (divisible by 32)
         # - should use vLLM's FlashAttention

tests/kernels/quant_utils.py

@@ -103,7 +103,7 @@ def ref_dynamic_per_tensor_fp8_quant(
         .clamp(fp8_traits_min, fp8_traits_max)
         .to(FP8_DTYPE)
     )
-    return ref_out, ref_scale.view((1, 1))
+    return ref_out, ref_scale.view(1)
 
 
 def native_w8a8_block_matmul(

tests/reasoning/test_base_thinking_reasoning_parser.py

@@ -132,6 +132,41 @@ class TestBaseThinkingReasoningParserMethods:
             is False
         )
 
+    def test_is_reasoning_end_streaming(self, test_tokenizer):
+        """Test the is_reasoning_end_streaming method."""
+        parser = TestThinkingReasoningParser(test_tokenizer)
+        end_token_id = parser.end_token_id
+        start_token_id = parser.start_token_id
+
+        assert (
+            parser.is_reasoning_end_streaming([1, 2, end_token_id], [end_token_id])
+            is True
+        )
+        assert parser.is_reasoning_end_streaming([1, 2, 3, 4], [4]) is False
+        assert parser.is_reasoning_end_streaming([], []) is False
+        assert (
+            parser.is_reasoning_end_streaming(
+                [1, start_token_id, 2, end_token_id], [end_token_id]
+            )
+            is True
+        )
+        assert (
+            parser.is_reasoning_end_streaming([1, start_token_id, 2, 3], [3]) is False
+        )
+        assert (
+            parser.is_reasoning_end_streaming(
+                [1, start_token_id, 2, end_token_id, 2, start_token_id, 2],
+                [2],
+            )
+            is False
+        )
+        assert (
+            parser.is_reasoning_end_streaming(
+                [1, start_token_id, 2, end_token_id, 2, 2], [2]
+            )
+            is False
+        )
+
     def test_extract_content_ids(self, test_tokenizer):
         """Test the extract_content_ids method."""
         parser = TestThinkingReasoningParser(test_tokenizer)

tests/reasoning/test_deepseekv3_reasoning_parser.py

@@ -40,6 +40,7 @@ def test_identity_reasoning_parser_basic(tokenizer):
     input_tokens = tokenizer.tokenize(input_text)
     input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
     assert parser.is_reasoning_end(input_ids) is True
+    assert parser.is_reasoning_end_streaming(input_ids, input_ids) is True
 
     # Test extract_content_ids returns all input_ids
     assert parser.extract_content_ids(input_ids) == input_ids

tests/tool_use/test_mistral_tool_parser.py

@@ -615,6 +615,7 @@ def test_extract_tool_calls_streaming(
         "single_tool_weather",
         "multiple_tool_calls",
         "content_before_tool",
+        "complex",
     ],
     argnames=["model_output", "expected_tool_calls", "expected_content"],
     argvalues=[
@@ -673,6 +674,21 @@ def test_extract_tool_calls_streaming(
             ],
             "bla",
         ),
+        (
+            # Complex
+            """[TOOL_CALLS]bash{"command": "print(\\"hello world!\\")\\nre.compile(r\'{}\')"}""",  # noqa: E501
+            [
+                ToolCall(
+                    function=FunctionCall(
+                        name="bash",
+                        arguments=json.dumps(
+                            {"command": "print(\"hello world!\")\nre.compile(r'{}')"}
+                        ),
+                    )
+                )
+            ],
+            "",
+        ),
     ],
 )
 def test_extract_tool_calls_streaming_one_chunk(

tests/v1/cudagraph/test_cudagraph_dispatch.py

@@ -161,10 +161,10 @@ class TestCudagraphDispatcher:
         assert rt_mode == CUDAGraphMode.NONE
         assert key == BatchDescriptor(num_tokens=15)
 
-        # 4. Cascade attention should have a fall back mode
+        # 4. disable_full should have a fall back mode (e.g., cascade attention)
         desc_full_exact = BatchDescriptor(num_tokens=8, uniform=False)
         rt_mode, key = dispatcher.dispatch(
-            num_tokens=8, uniform_decode=False, has_lora=False, use_cascade_attn=True
+            num_tokens=8, uniform_decode=False, has_lora=False, disable_full=True
         )
         if "PIECEWISE" in cudagraph_mode_str:  # string contains check
             assert rt_mode == CUDAGraphMode.PIECEWISE

tests/v1/determinism/test_batch_invariance.py

@@ -10,6 +10,7 @@ from utils import (
     BACKENDS,
     _extract_step_logprobs,
     _random_prompt,
+    is_device_capability_below_90,
     resolve_model_name,
     skip_unsupported,
 )
@@ -17,6 +18,8 @@ from utils import (
 import vllm.model_executor.layers.batch_invariant as batch_invariant
 from vllm import LLM, SamplingParams
 
+IS_DEVICE_CAPABILITY_BELOW_90 = is_device_capability_below_90()
+
 
 @skip_unsupported
 @pytest.mark.timeout(1000)
@@ -190,6 +193,7 @@ def test_logprobs_bitwise_batch_invariance_bs1_vs_bsN(
         max_model_len=8192,
         dtype="bfloat16",  # not everything is supported
         gpu_memory_utilization=0.9,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # Use more realistic prompts for better token generation
@@ -393,6 +397,8 @@ def test_simple_generation(backend, monkeypatch: pytest.MonkeyPatch):
         gpu_memory_utilization=0.9,
         max_model_len=2048,
         dtype="bfloat16",
+        enable_prefix_caching=False,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     prompt = "the capital of france is"
@@ -459,6 +465,7 @@ def test_logprobs_without_batch_invariance_should_fail(
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # build ragged prompts to change shapes significantly across BS=1 vs BS=N
@@ -682,6 +689,7 @@ def test_decode_logprobs_match_prefill_logprobs(
         max_num_seqs=32,
         max_model_len=8192,
         dtype="bfloat16",
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
     )
 
     # Use a few test prompts
@@ -925,6 +933,8 @@ def LLM_with_max_seqs(
         max_model_len=max_model_len,
         dtype="bfloat16",
         tensor_parallel_size=int(os.getenv("VLLM_TP_SIZE", "1")),
+        enable_prefix_caching=False,
+        enforce_eager=IS_DEVICE_CAPABILITY_BELOW_90,
         # Enable for MOE models
         # enable_expert_parallel=True,
     )

tests/v1/determinism/utils.py

@@ -11,8 +11,10 @@ from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer
 
 skip_unsupported = pytest.mark.skipif(
-    not (current_platform.is_cuda() and current_platform.has_device_capability(90)),
-    reason="Requires CUDA and >= Hopper (SM90)",
+    not (current_platform.is_cuda() and current_platform.has_device_capability(80)),
+    # Supports testing on Ampere and Ada Lovelace devices.
+    # Note: For devices with SM < 90, batch invariance does not support CUDA Graphs.
+    reason="Requires CUDA and >= Ampere (SM80)",
 )
 
 BACKENDS: list[str] = [
@@ -97,3 +99,7 @@ def _extract_step_logprobs(request_output):
             return t, inner.token_ids
 
     return None, None
+
+
+def is_device_capability_below_90() -> bool:
+    return not current_platform.has_device_capability(90)

tests/v1/e2e/test_async_scheduling.py

@@ -124,6 +124,8 @@ def run_tests(
     with monkeypatch.context() as m:
         # avoid precision errors
         m.setenv("VLLM_ATTENTION_BACKEND", "FLEX_ATTENTION")
+        # lock matmul precision to full FP32
+        m.setenv("VLLM_FLOAT32_MATMUL_PRECISION", "highest")
         # m.setenv("VLLM_BATCH_INVARIANT", "1")
         outputs: list[tuple[str, list, list]] = []
         for n, (

tests/v1/structured_output/test_reasoning_structured_output.py

@@ -70,6 +70,7 @@ class TestReasoningStructuredOutput:
         request.use_structured_output = True
         request.prompt_token_ids = [1, 2, 3, 4, 5]
         request.all_token_ids = [1, 2, 3, 4, 5, 6, 7, 8]
+        request.num_computed_tokens = 5
         return request
 
     def test_should_fill_bitmask_with_enable_in_reasoning(

tests/v1/worker/test_gpu_profiler.py

@@ -2,8 +2,8 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import pytest
 
-import vllm.envs as envs
-from vllm.profiler.gpu_profiler import WorkerProfiler
+from vllm.config import ProfilerConfig
+from vllm.profiler.wrapper import WorkerProfiler
 
 
 class ConcreteWorkerProfiler(WorkerProfiler):
@@ -11,11 +11,11 @@ class ConcreteWorkerProfiler(WorkerProfiler):
     A basic implementation of a worker profiler for testing purposes.
     """
 
-    def __init__(self):
+    def __init__(self, profiler_config: ProfilerConfig):
         self.start_call_count = 0
         self.stop_call_count = 0
         self.should_fail_start = False
-        super().__init__()
+        super().__init__(profiler_config)
 
     def _start(self) -> None:
         if self.should_fail_start:
@@ -26,17 +26,19 @@ class ConcreteWorkerProfiler(WorkerProfiler):
         self.stop_call_count += 1
 
 
-@pytest.fixture(autouse=True)
-def reset_mocks():
-    """Fixture to reset mocks and env variables before each test."""
-    envs.VLLM_PROFILER_DELAY_ITERS = 0
-    envs.VLLM_PROFILER_MAX_ITERS = 0
+@pytest.fixture
+def default_profiler_config():
+    return ProfilerConfig(
+        profiler="torch",
+        torch_profiler_dir="/tmp/mock",
+        delay_iterations=0,
+        max_iterations=0,
+    )
 
 
-def test_immediate_start_stop():
+def test_immediate_start_stop(default_profiler_config):
     """Test standard start without delay."""
-    profiler = ConcreteWorkerProfiler()
-
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
     profiler.start()
     assert profiler._running is True
     assert profiler._active is True
@@ -48,10 +50,10 @@ def test_immediate_start_stop():
     assert profiler.stop_call_count == 1
 
 
-def test_delayed_start():
+def test_delayed_start(default_profiler_config):
     """Test that profiler waits for N steps before actually starting."""
-    envs.VLLM_PROFILER_DELAY_ITERS = 2
-    profiler = ConcreteWorkerProfiler()
+    default_profiler_config.delay_iterations = 2
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     # User requests start
     profiler.start()
@@ -71,10 +73,10 @@ def test_delayed_start():
     assert profiler.start_call_count == 1
 
 
-def test_max_iterations():
+def test_max_iterations(default_profiler_config):
     """Test that profiler stops automatically after max iterations."""
-    envs.VLLM_PROFILER_MAX_ITERS = 2
-    profiler = ConcreteWorkerProfiler()
+    default_profiler_config.max_iterations = 2
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     profiler.start()
     assert profiler._running is True
@@ -95,12 +97,11 @@ def test_max_iterations():
     assert profiler.stop_call_count == 1
 
 
-def test_delayed_start_and_max_iters():
+def test_delayed_start_and_max_iters(default_profiler_config):
     """Test combined delayed start and max iterations."""
-    envs.VLLM_PROFILER_DELAY_ITERS = 2
-    envs.VLLM_PROFILER_MAX_ITERS = 2
-    profiler = ConcreteWorkerProfiler()
-
+    default_profiler_config.delay_iterations = 2
+    default_profiler_config.max_iterations = 2
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
     profiler.start()
 
     # Step 1
@@ -127,9 +128,9 @@ def test_delayed_start_and_max_iters():
     assert profiler.stop_call_count == 1
 
 
-def test_idempotency():
+def test_idempotency(default_profiler_config):
     """Test that calling start/stop multiple times doesn't break logic."""
-    profiler = ConcreteWorkerProfiler()
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     # Double Start
     profiler.start()
@@ -142,10 +143,10 @@ def test_idempotency():
     assert profiler.stop_call_count == 1  # Should only stop once
 
 
-def test_step_inactive():
+def test_step_inactive(default_profiler_config):
     """Test that stepping while inactive does nothing."""
-    envs.VLLM_PROFILER_DELAY_ITERS = 2
-    profiler = ConcreteWorkerProfiler()
+    default_profiler_config.delay_iterations = 2
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     # Not started yet
     profiler.step()
@@ -155,9 +156,9 @@ def test_step_inactive():
     assert profiler.start_call_count == 0
 
 
-def test_start_failure():
+def test_start_failure(default_profiler_config):
     """Test behavior when the underlying _start method raises exception."""
-    profiler = ConcreteWorkerProfiler()
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
     profiler.should_fail_start = True
 
     profiler.start()
@@ -168,9 +169,9 @@ def test_start_failure():
     assert profiler.start_call_count == 0  # Logic failed inside start
 
 
-def test_shutdown():
+def test_shutdown(default_profiler_config):
     """Test that shutdown calls stop only if running."""
-    profiler = ConcreteWorkerProfiler()
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     # Case 1: Not running
     profiler.shutdown()
@@ -182,10 +183,10 @@ def test_shutdown():
     assert profiler.stop_call_count == 1
 
 
-def test_mixed_delay_and_stop():
+def test_mixed_delay_and_stop(default_profiler_config):
     """Test manual stop during the delay period."""
-    envs.VLLM_PROFILER_DELAY_ITERS = 5
-    profiler = ConcreteWorkerProfiler()
+    default_profiler_config.delay_iterations = 5
+    profiler = ConcreteWorkerProfiler(default_profiler_config)
 
     profiler.start()
     profiler.step()

vllm/_aiter_ops.py

@@ -9,6 +9,8 @@ import vllm.envs as envs
 from vllm.platforms import current_platform
 from vllm.utils.torch_utils import direct_register_custom_op, is_torch_equal_or_newer
 
+_FP8_DTYPE = current_platform.fp8_dtype()
+
 
 def is_aiter_found() -> bool:
     from importlib.util import find_spec
@@ -467,6 +469,59 @@ def _rocm_aiter_rmsnorm2d_fwd_with_add_fake(
     return torch.empty_like(x), torch.empty_like(residual)
 
 
+def _rocm_aiter_per_tensor_quant_impl(
+    x: torch.Tensor,
+    quant_dtype: torch.dtype,
+    scale: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    from aiter.ops.quant import per_tensor_quant_hip
+
+    return per_tensor_quant_hip(x, scale, quant_dtype)
+
+
+def _rocm_aiter_per_tensor_quant_fake(
+    x: torch.Tensor,
+    quant_dtype: torch.dtype,
+    scale: torch.Tensor | None = None,
+) -> tuple[torch.Tensor, torch.Tensor]:
+    return torch.empty_like(x, dtype=quant_dtype), torch.empty(
+        1, dtype=torch.float32, device=x.device
+    )
+
+
+def _rocm_aiter_per_token_quant_impl(
+    x: torch.Tensor, quant_dtype: torch.dtype, scale: torch.Tensor | None = None
+) -> tuple[torch.Tensor, torch.Tensor]:
+    from aiter.ops.quant import dynamic_per_token_scaled_quant
+
+    assert quant_dtype in [torch.int8, _FP8_DTYPE]
+
+    out_shape = x.shape
+    out = torch.empty(x.shape, dtype=_FP8_DTYPE, device=x.device)
+    if scale is None:
+        scale = torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device)
+    dynamic_per_token_scaled_quant(
+        out,
+        x,
+        scale,
+        scale_ub=None,
+        shuffle_scale=False,
+        num_rows=None,
+        num_rows_factor=1,
+    )
+    return out, scale
+
+
+def _rocm_aiter_per_token_quant_fake(
+    x: torch.Tensor, quant_dtype: torch.dtype, scale: torch.Tensor | None = None
+) -> tuple[torch.Tensor, torch.Tensor]:
+    out_shape = x.shape
+    return (
+        torch.empty(x.shape, dtype=_FP8_DTYPE, device=x.device),
+        torch.empty((*out_shape[:-1], 1), dtype=torch.float32, device=x.device),
+    )
+
+
 # Global flag to ensure ops are registered only once
 _OPS_REGISTERED = False
 
@@ -665,6 +720,22 @@ class rocm_aiter_ops:
                 dispatch_key=current_platform.dispatch_key,
             )
 
+            direct_register_custom_op(
+                op_name="rocm_aiter_per_tensor_quant",
+                op_func=_rocm_aiter_per_tensor_quant_impl,
+                mutates_args=[],
+                fake_impl=_rocm_aiter_per_tensor_quant_fake,
+                dispatch_key=current_platform.dispatch_key,
+            )
+
+            direct_register_custom_op(
+                op_name="rocm_aiter_per_token_quant",
+                op_func=_rocm_aiter_per_token_quant_impl,
+                mutates_args=["scale"],
+                fake_impl=_rocm_aiter_per_token_quant_fake,
+                dispatch_key=current_platform.dispatch_key,
+            )
+
             _OPS_REGISTERED = True
 
     @staticmethod
@@ -859,6 +930,22 @@ class rocm_aiter_ops:
             kv_scale=kv_scale,
         )
 
+    @staticmethod
+    def per_tensor_quant(
+        x: torch.Tensor,
+        quant_dtype: torch.dtype,
+        scale: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return torch.ops.vllm.rocm_aiter_per_tensor_quant(x, quant_dtype, scale)
+
+    @staticmethod
+    def per_token_quant(
+        x: torch.Tensor,
+        quant_dtype: torch.dtype,
+        scale: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        return torch.ops.vllm.rocm_aiter_per_token_quant(x, quant_dtype, scale)
+
     @staticmethod
     def triton_fp4_gemm_dynamic_qaunt(
         x: torch.Tensor,

vllm/_custom_ops.py

@@ -1726,7 +1726,7 @@ def scaled_fp8_quant(
                 output, input, scale, scale_ub
             )
         else:
-            scale = torch.empty((1, 1), device=input.device, dtype=torch.float32)
+            scale = torch.empty(1, device=input.device, dtype=torch.float32)
             torch.ops._C.dynamic_scaled_fp8_quant(output, input, scale)
     else:
         assert scale.numel() == 1, f"{scale.shape}"

vllm/attention/layer.py

@@ -89,7 +89,10 @@ def maybe_get_vit_flash_attn_backend(
         if attn_backend == AttentionBackendEnum.ROCM_AITER_FA:
             from aiter import flash_attn_varlen_func
         else:
-            from vllm.attention.utils.fa_utils import flash_attn_varlen_func
+            try:
+                from vllm.attention.utils.fa_utils import flash_attn_varlen_func
+            except ImportError:
+                flash_attn_varlen_func = None
     else:
         flash_attn_varlen_func = None
 

vllm/benchmarks/latency.py

@@ -12,7 +12,6 @@ from typing import Any
 import numpy as np
 from tqdm import tqdm
 
-import vllm.envs as envs
 from vllm.benchmarks.lib.utils import convert_to_pytorch_benchmark_format, write_to_json
 from vllm.engine.arg_utils import EngineArgs
 from vllm.inputs import PromptType
@@ -79,12 +78,11 @@ def add_cli_args(parser: argparse.ArgumentParser):
 
 
 def main(args: argparse.Namespace):
-    if args.profile and not envs.VLLM_TORCH_PROFILER_DIR:
-        raise OSError(
-            "The environment variable 'VLLM_TORCH_PROFILER_DIR' is not set. "
-            "Please set it to a valid path to use torch profiler."
-        )
     engine_args = EngineArgs.from_cli_args(args)
+    if args.profile and not engine_args.profiler_config.profiler == "torch":
+        raise ValueError(
+            "The torch profiler is not enabled. Please provide profiler_config."
+        )
 
     # Lazy import to avoid importing LLM when the bench command is not selected.
     from vllm import LLM, SamplingParams
@@ -144,7 +142,7 @@ def main(args: argparse.Namespace):
         run_to_completion(profile_dir=None)
 
     if args.profile:
-        profile_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profile_dir = engine_args.profiler_config.torch_profiler_dir
         print(f"Profiling (results will be saved to '{profile_dir}')...")
         run_to_completion(profile_dir=profile_dir)
         return

vllm/benchmarks/serve.py

@@ -1097,8 +1097,7 @@ def add_cli_args(parser: argparse.ArgumentParser):
     parser.add_argument(
         "--profile",
         action="store_true",
-        help="Use Torch Profiler. The endpoint must be launched with "
-        "VLLM_TORCH_PROFILER_DIR to enable profiler.",
+        help="Use vLLM Profiling. --profiler-config must be provided on the server.",
     )
     parser.add_argument(
         "--save-result",

vllm/benchmarks/throughput.py

@@ -655,8 +655,7 @@ def add_cli_args(parser: argparse.ArgumentParser):
         "--profile",
         action="store_true",
         default=False,
-        help="Use Torch Profiler. The env variable "
-        "VLLM_TORCH_PROFILER_DIR must be set to enable profiler.",
+        help="Use vLLM Profiling. --profiler-config must be provided on the server.",
     )
 
     # prefix repetition dataset

vllm/compilation/inductor_pass.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from __future__ import annotations
+
 import functools
 import hashlib
 import inspect
@@ -8,15 +10,17 @@ import json
 import types
 from collections.abc import Callable
 from contextlib import contextmanager
-from typing import Any
+from typing import TYPE_CHECKING, Any
 
 import torch
 from torch import fx
 from torch._subclasses.fake_tensor import FakeTensorMode, unset_fake_temporarily
 
-from vllm.config.utils import Range
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
+if TYPE_CHECKING:
+    from vllm.config.utils import Range
+
 if is_torch_equal_or_newer("2.6"):
     from torch._inductor.custom_graph_pass import CustomGraphPass
 else:

vllm/compilation/piecewise_backend.py

@@ -53,8 +53,27 @@ class PiecewiseBackend:
         self.is_last_graph = piecewise_compile_index == total_piecewise_compiles - 1
 
         self.is_full_graph = total_piecewise_compiles == 1
+        # TODO: we need to generalize encoder compilation to other models
+        self.is_encoder_compilation = vllm_backend.prefix in [
+            "Qwen2_5_VisionPatchEmbed",
+            "Qwen2_5_VisionPatchMerger",
+            "Qwen2_5_VisionBlock",
+        ]
 
         self.compile_ranges = self.compilation_config.get_compile_ranges()
+        if self.is_encoder_compilation:
+            # For encoder compilation we use the max int32 value
+            # to set the upper bound of the compile ranges
+            max_int32 = 2**31 - 1
+            last_compile_range = self.compile_ranges[-1]
+            assert (
+                last_compile_range.end
+                == vllm_config.scheduler_config.max_num_batched_tokens
+            )
+            self.compile_ranges[-1] = Range(
+                start=last_compile_range.start, end=max_int32
+            )
+
         log_string = f"PiecewiseBackend: compile_ranges: {self.compile_ranges}"
         logger.debug_once(log_string)
 

vllm/config/__init__.py

@@ -24,6 +24,7 @@ from vllm.config.multimodal import MultiModalConfig
 from vllm.config.observability import ObservabilityConfig
 from vllm.config.parallel import EPLBConfig, ParallelConfig
 from vllm.config.pooler import PoolerConfig
+from vllm.config.profiler import ProfilerConfig
 from vllm.config.scheduler import SchedulerConfig
 from vllm.config.speculative import SpeculativeConfig
 from vllm.config.speech_to_text import SpeechToTextConfig
@@ -89,6 +90,8 @@ __all__ = [
     "SpeechToTextConfig",
     # From vllm.config.structured_outputs
     "StructuredOutputsConfig",
+    # From vllm.config.profiler
+    "ProfilerConfig",
     # From vllm.config.utils
     "ConfigType",
     "SupportsMetricsInfo",

vllm/config/profiler.py

@@ -0,0 +1,199 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import os
+from typing import Any, Literal
+
+from pydantic import Field, model_validator
+from pydantic.dataclasses import dataclass
+from typing_extensions import Self
+
+import vllm.envs as envs
+from vllm.config.utils import config
+from vllm.logger import init_logger
+from vllm.utils.hashing import safe_hash
+
+logger = init_logger(__name__)
+
+ProfilerKind = Literal["torch", "cuda"]
+
+
+@config
+@dataclass
+class ProfilerConfig:
+    """Dataclass which contains profiler config for the engine."""
+
+    profiler: ProfilerKind | None = None
+    """Which profiler to use. Defaults to None. Options are:
+
+    - 'torch': Use PyTorch profiler.\n
+    - 'cuda': Use CUDA profiler."""
+
+    torch_profiler_dir: str = ""
+    """Directory to save torch profiler traces. Both AsyncLLM's CPU traces and
+    worker's traces (CPU & GPU) will be saved under this directory. Note that
+    it must be an absolute path."""
+
+    torch_profiler_with_stack: bool = True
+    """If `True`, enables stack tracing in the torch profiler. Enabled by default."""
+
+    torch_profiler_with_flops: bool = False
+    """If `True`, enables FLOPS counting in the torch profiler. Disabled by default."""
+
+    torch_profiler_use_gzip: bool = True
+    """If `True`, saves torch profiler traces in gzip format. Enabled by default"""
+
+    torch_profiler_dump_cuda_time_total: bool = True
+    """If `True`, dumps total CUDA time in torch profiler traces. Enabled by default."""
+
+    torch_profiler_record_shapes: bool = False
+    """If `True`, records tensor shapes in the torch profiler. Disabled by default."""
+
+    torch_profiler_with_memory: bool = False
+    """If `True`, enables memory profiling in the torch profiler.
+    Disabled by default."""
+
+    ignore_frontend: bool = False
+    """If `True`, disables the front-end profiling of AsyncLLM when using the 
+    'torch' profiler. This is needed to reduce overhead when using delay/limit options,
+    since the front-end profiling does not track iterations and will capture the
+    entire range.
+    """
+
+    delay_iterations: int = Field(default=0, ge=0)
+    """Number of engine iterations to skip before starting profiling.
+    Defaults to 0, meaning profiling starts immediately after receiving /start_profile.
+    """
+
+    max_iterations: int = Field(default=0, ge=0)
+    """Maximum number of engine iterations to profile after starting profiling.
+    Defaults to 0, meaning no limit.
+    """
+
+    def compute_hash(self) -> str:
+        """
+        WARNING: Whenever a new field is added to this config,
+        ensure that it is included in the factors list if
+        it affects the computation graph.
+
+        Provide a hash that uniquely identifies all the configs
+        that affect the structure of the computation
+        graph from input ids/embeddings to the final hidden states,
+        excluding anything before input ids/embeddings and after
+        the final hidden states.
+        """
+        # no factors to consider.
+        # this config will not affect the computation graph.
+        factors: list[Any] = []
+        hash_str = safe_hash(str(factors).encode(), usedforsecurity=False).hexdigest()
+        return hash_str
+
+    def _get_from_env_if_set(self, field_name: str, env_var_name: str) -> None:
+        """Get field from env var if set, with deprecation warning."""
+
+        if envs.is_set(env_var_name):
+            value = getattr(envs, env_var_name)
+            logger.warning_once(
+                "Using %s environment variable is deprecated and will be removed in "
+                "v0.14.0 or v1.0.0, whichever is soonest. Please use "
+                "--profiler-config.%s command line argument or "
+                "ProfilerConfig(%s=...) config field instead.",
+                env_var_name,
+                field_name,
+                field_name,
+            )
+            return value
+        return None
+
+    def _set_from_env_if_set(
+        self,
+        field_name: str,
+        env_var_name: str,
+        to_bool: bool = True,
+        to_int: bool = False,
+    ) -> None:
+        """Set field from env var if set, with deprecation warning."""
+        value = self._get_from_env_if_set(field_name, env_var_name)
+        if value is not None:
+            if to_bool:
+                value = value == "1"
+            if to_int:
+                value = int(value)
+            setattr(self, field_name, value)
+
+    @model_validator(mode="after")
+    def _validate_profiler_config(self) -> Self:
+        maybe_use_cuda_profiler = self._get_from_env_if_set(
+            "profiler", "VLLM_TORCH_CUDA_PROFILE"
+        )
+        if maybe_use_cuda_profiler is not None:
+            self.profiler = "cuda" if maybe_use_cuda_profiler == "1" else None
+        else:
+            self._set_from_env_if_set(
+                "torch_profiler_dir", "VLLM_TORCH_PROFILER_DIR", to_bool=False
+            )
+            if self.torch_profiler_dir:
+                self.profiler = "torch"
+                self._set_from_env_if_set(
+                    "torch_profiler_record_shapes",
+                    "VLLM_TORCH_PROFILER_RECORD_SHAPES",
+                )
+                self._set_from_env_if_set(
+                    "torch_profiler_with_memory",
+                    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY",
+                )
+                self._set_from_env_if_set(
+                    "torch_profiler_with_stack",
+                    "VLLM_TORCH_PROFILER_WITH_STACK",
+                )
+                self._set_from_env_if_set(
+                    "torch_profiler_with_flops",
+                    "VLLM_TORCH_PROFILER_WITH_FLOPS",
+                )
+                self._set_from_env_if_set(
+                    "ignore_frontend",
+                    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM",
+                )
+                self._set_from_env_if_set(
+                    "torch_profiler_use_gzip",
+                    "VLLM_TORCH_PROFILER_USE_GZIP",
+                )
+                self._set_from_env_if_set(
+                    "torch_profiler_dump_cuda_time_total",
+                    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL",
+                )
+
+        self._set_from_env_if_set(
+            "delay_iterations", "VLLM_PROFILER_DELAY_ITERS", to_bool=False, to_int=True
+        )
+        self._set_from_env_if_set(
+            "max_iterations", "VLLM_PROFILER_MAX_ITERS", to_bool=False, to_int=True
+        )
+
+        has_delay_or_limit = self.delay_iterations > 0 or self.max_iterations > 0
+        if self.profiler == "torch" and has_delay_or_limit and not self.ignore_frontend:
+            logger.warning_once(
+                "Using 'torch' profiler with delay_iterations or max_iterations "
+                "while ignore_frontend is False may result in high overhead."
+            )
+
+        profiler_dir = self.torch_profiler_dir
+        if profiler_dir and self.profiler != "torch":
+            raise ValueError(
+                "torch_profiler_dir is only applicable when profiler is set to 'torch'"
+            )
+        if self.profiler == "torch" and not profiler_dir:
+            raise ValueError("torch_profiler_dir must be set when profiler is 'torch'")
+
+        if profiler_dir:
+            is_gs_path = (
+                profiler_dir.startswith("gs://")
+                and profiler_dir[5:]
+                and profiler_dir[5] != "/"
+            )
+            if not is_gs_path:
+                self.torch_profiler_dir = os.path.abspath(
+                    os.path.expanduser(profiler_dir)
+                )
+
+        return self

vllm/config/vllm.py

@@ -39,6 +39,7 @@ from .lora import LoRAConfig
 from .model import ModelConfig
 from .observability import ObservabilityConfig
 from .parallel import ParallelConfig
+from .profiler import ProfilerConfig
 from .scheduler import SchedulerConfig
 from .speculative import SpeculativeConfig
 from .structured_outputs import StructuredOutputsConfig
@@ -218,6 +219,8 @@ class VllmConfig:
     You can specify the full compilation config like so:
     `{"mode": 3, "cudagraph_capture_sizes": [1, 2, 4, 8]}`
     """
+    profiler_config: ProfilerConfig = Field(default_factory=ProfilerConfig)
+    """Profiling configuration."""
     kv_transfer_config: KVTransferConfig | None = None
     """The configurations for distributed KV cache transfer."""
     kv_events_config: KVEventsConfig | None = None
@@ -296,6 +299,8 @@ class VllmConfig:
             vllm_factors.append("None")
         if self.structured_outputs_config:
             vllm_factors.append(self.structured_outputs_config.compute_hash())
+        if self.profiler_config:
+            vllm_factors.append(self.profiler_config.compute_hash())
         else:
             vllm_factors.append("None")
         vllm_factors.append(self.observability_config.compute_hash())

vllm/engine/arg_utils.py

@@ -50,6 +50,7 @@ from vllm.config import (
     ObservabilityConfig,
     ParallelConfig,
     PoolerConfig,
+    ProfilerConfig,
     SchedulerConfig,
     SpeculativeConfig,
     StructuredOutputsConfig,
@@ -536,6 +537,8 @@ class EngineArgs:
     worker_cls: str = ParallelConfig.worker_cls
     worker_extension_cls: str = ParallelConfig.worker_extension_cls
 
+    profiler_config: ProfilerConfig = get_field(VllmConfig, "profiler_config")
+
     kv_transfer_config: KVTransferConfig | None = None
     kv_events_config: KVEventsConfig | None = None
 
@@ -1168,7 +1171,7 @@ class EngineArgs:
         vllm_group.add_argument(
             "--structured-outputs-config", **vllm_kwargs["structured_outputs_config"]
         )
-
+        vllm_group.add_argument("--profiler-config", **vllm_kwargs["profiler_config"])
         vllm_group.add_argument(
             "--optimization-level", **vllm_kwargs["optimization_level"]
         )
@@ -1786,6 +1789,7 @@ class EngineArgs:
             kv_transfer_config=self.kv_transfer_config,
             kv_events_config=self.kv_events_config,
             ec_transfer_config=self.ec_transfer_config,
+            profiler_config=self.profiler_config,
             additional_config=self.additional_config,
             optimization_level=self.optimization_level,
         )

vllm/entrypoints/llm.py

@@ -20,6 +20,7 @@ from vllm.beam_search import (
 from vllm.config import (
     CompilationConfig,
     PoolerConfig,
+    ProfilerConfig,
     StructuredOutputsConfig,
     is_init_field,
 )
@@ -211,6 +212,7 @@ class LLM:
         structured_outputs_config: dict[str, Any]
         | StructuredOutputsConfig
         | None = None,
+        profiler_config: dict[str, Any] | ProfilerConfig | None = None,
         kv_cache_memory_bytes: int | None = None,
         compilation_config: int | dict[str, Any] | CompilationConfig | None = None,
         logits_processors: list[str | type[LogitsProcessor]] | None = None,
@@ -282,6 +284,20 @@ class LLM:
         else:
             structured_outputs_instance = StructuredOutputsConfig()
 
+        if profiler_config is not None:
+            if isinstance(profiler_config, dict):
+                profiler_config_instance = ProfilerConfig(
+                    **{
+                        k: v
+                        for k, v in profiler_config.items()
+                        if is_init_field(ProfilerConfig, k)
+                    }
+                )
+            else:
+                profiler_config_instance = profiler_config
+        else:
+            profiler_config_instance = ProfilerConfig()
+
         # warn about single-process data parallel usage.
         _dp_size = int(kwargs.get("data_parallel_size", 1))
         _distributed_executor_backend = kwargs.get("distributed_executor_backend")
@@ -324,6 +340,7 @@ class LLM:
             mm_processor_kwargs=mm_processor_kwargs,
             pooler_config=pooler_config,
             structured_outputs_config=structured_outputs_instance,
+            profiler_config=profiler_config_instance,
             compilation_config=compilation_config_instance,
             logits_processors=logits_processors,
             **kwargs,

vllm/entrypoints/openai/tool_parsers/mistral_tool_parser.py

@@ -99,12 +99,7 @@ class MistralToolParser(ToolParser):
         self.bot_token = "[TOOL_CALLS]"
         self.bot_token_id = self.vocab.get(self.bot_token)
         self.tool_call_regex = re.compile(r"\[{.*}\]", re.DOTALL)
-        if not _is_pre_v11_tokeniser(self.model_tokenizer):
-            self.fn_name_regex = re.compile(
-                r"([a-zA-Z0-9_-]+)(\{[\s\S]*?\}+)", re.DOTALL
-            )
-        else:
-            self.fn_name_regex = None
+        self._is_pre_v11 = _is_pre_v11_tokeniser(self.model_tokenizer)
 
         if self.bot_token_id is None:
             raise RuntimeError(
@@ -148,23 +143,24 @@ class MistralToolParser(ToolParser):
         tool_content = model_output.replace(self.bot_token, "").strip()
 
         try:
-            # we first try to directly load the json as parsing very nested
-            # jsons is difficult
             try:
-                if self.fn_name_regex:
+                if not self._is_pre_v11:
                     function_call_arr = []
                     for single_tool_content in model_output.split(self.bot_token):
-                        matches = self.fn_name_regex.findall(single_tool_content)
+                        if "{" not in single_tool_content:
+                            continue
 
-                        for match in matches:
-                            fn_name = match[0]
-                            args = match[1]
+                        end_name = single_tool_content.find("{")
+                        fn_name, args = (
+                            single_tool_content[:end_name],
+                            single_tool_content[end_name:],
+                        )
 
-                            # fn_name is encoded outside serialized json dump
-                            # only arguments are serialized
-                            function_call_arr.append(
-                                {"name": fn_name, "arguments": json.loads(args)}
-                            )
+                        # fn_name is encoded outside serialized json dump
+                        # only arguments are serialized
+                        function_call_arr.append(
+                            {"name": fn_name, "arguments": json.loads(args)}
+                        )
                 else:
                     function_call_arr = json.loads(tool_content)
             except json.JSONDecodeError:

vllm/entrypoints/serve/profile/api_router.py

@@ -5,7 +5,7 @@
 from fastapi import APIRouter, FastAPI, Request
 from fastapi.responses import Response
 
-import vllm.envs as envs
+from vllm.config import ProfilerConfig
 from vllm.engine.protocol import EngineClient
 from vllm.logger import init_logger
 
@@ -35,15 +35,12 @@ async def stop_profile(raw_request: Request):
 
 
 def attach_router(app: FastAPI):
-    if envs.VLLM_TORCH_PROFILER_DIR:
+    profiler_config = getattr(app.state.args, "profiler_config", None)
+    assert profiler_config is None or isinstance(profiler_config, ProfilerConfig)
+    if profiler_config is not None and profiler_config.profiler is not None:
         logger.warning_once(
-            "Torch Profiler is enabled in the API server. This should ONLY be "
-            "used for local development!"
+            "Profiler with mode '%s' is enabled in the "
+            "API server. This should ONLY be used for local development!",
+            profiler_config.profiler,
         )
-    elif envs.VLLM_TORCH_CUDA_PROFILE:
-        logger.warning_once(
-            "CUDA Profiler is enabled in the API server. This should ONLY be "
-            "used for local development!"
-        )
-    if envs.VLLM_TORCH_PROFILER_DIR or envs.VLLM_TORCH_CUDA_PROFILE:
         app.include_router(router)

vllm/envs.py

@@ -75,6 +75,7 @@ if TYPE_CHECKING:
     VLLM_MM_INPUT_CACHE_GIB: int = 4
     VLLM_TARGET_DEVICE: str = "cuda"
     VLLM_MAIN_CUDA_VERSION: str = "12.9"
+    VLLM_FLOAT32_MATMUL_PRECISION: Literal["highest", "high", "medium"] = "highest"
     MAX_JOBS: str | None = None
     NVCC_THREADS: str | None = None
     VLLM_USE_PRECOMPILED: bool = False
@@ -88,20 +89,23 @@ if TYPE_CHECKING:
     VLLM_HTTP_TIMEOUT_KEEP_ALIVE: int = 5  # seconds
     VLLM_PLUGINS: list[str] | None = None
     VLLM_LORA_RESOLVER_CACHE_DIR: str | None = None
-    VLLM_TORCH_CUDA_PROFILE: bool = False
+    # Deprecated env variables for profiling, kept for backward compatibility
+    # See also vllm/config/profiler.py and `--profiler-config` argument
+    VLLM_TORCH_CUDA_PROFILE: str | None = None
     VLLM_TORCH_PROFILER_DIR: str | None = None
-    VLLM_TORCH_PROFILER_RECORD_SHAPES: bool = False
-    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: bool = False
-    VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: bool = False
+    VLLM_TORCH_PROFILER_RECORD_SHAPES: str | None = None
+    VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY: str | None = None
+    VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM: str | None = None
+    VLLM_TORCH_PROFILER_WITH_STACK: str | None = None
+    VLLM_TORCH_PROFILER_WITH_FLOPS: str | None = None
+    VLLM_TORCH_PROFILER_USE_GZIP: str | None = None
+    VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: str | None = None
+    VLLM_PROFILER_DELAY_ITERS: str | None = None
+    VLLM_PROFILER_MAX_ITERS: str | None = None
+    # End of deprecated env variables for profiling
     VLLM_USE_AOT_COMPILE: bool = False
     VLLM_USE_BYTECODE_HOOK: bool = False
     VLLM_FORCE_AOT_LOAD: bool = False
-    VLLM_TORCH_PROFILER_WITH_STACK: bool = True
-    VLLM_TORCH_PROFILER_WITH_FLOPS: bool = False
-    VLLM_PROFILER_DELAY_ITERS: int = 0
-    VLLM_PROFILER_MAX_ITERS: int = 0
-    VLLM_TORCH_PROFILER_USE_GZIP: bool = True
-    VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: bool = True
     VLLM_USE_TRITON_AWQ: bool = False
     VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
     VLLM_SKIP_P2P_CHECK: bool = False
@@ -453,6 +457,14 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Main CUDA version of vLLM. This follows PyTorch but can be overridden.
     "VLLM_MAIN_CUDA_VERSION": lambda: os.getenv("VLLM_MAIN_CUDA_VERSION", "").lower()
     or "12.9",
+    # Controls PyTorch float32 matmul precision mode within vLLM workers.
+    # Valid options mirror torch.set_float32_matmul_precision
+    "VLLM_FLOAT32_MATMUL_PRECISION": env_with_choices(
+        "VLLM_FLOAT32_MATMUL_PRECISION",
+        "highest",
+        ["highest", "high", "medium"],
+        case_sensitive=False,
+    ),
     # Maximum number of compilation jobs to run in parallel.
     # By default this is the number of CPUs
     "MAX_JOBS": lambda: os.getenv("MAX_JOBS", None),
@@ -842,71 +854,52 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_LORA_RESOLVER_CACHE_DIR": lambda: os.getenv(
         "VLLM_LORA_RESOLVER_CACHE_DIR", None
     ),
-    # Enables torch CUDA profiling if set.
-    # On NVIDIA GPUs, this will start/stop cudaProfilerApi when triggered.
-    "VLLM_TORCH_CUDA_PROFILE": lambda: bool(
-        os.getenv("VLLM_TORCH_CUDA_PROFILE", "0") != "0"
-    ),
+    # Enables torch CUDA profiling if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_CUDA_PROFILE": lambda: os.getenv("VLLM_TORCH_CUDA_PROFILE"),
     # Enables torch profiler if set.
-    # Both AsyncLLM's CPU traces as well as workers'
-    # traces (CPU & GPU) will be saved under this directory.
-    # Note that it must be an absolute path.
-    "VLLM_TORCH_PROFILER_DIR": lambda: (
-        None
-        if (val := os.getenv("VLLM_TORCH_PROFILER_DIR")) is None
-        else (
-            val
-            if val.startswith("gs://") and val[5:] and val[5] != "/"
-            else os.path.abspath(os.path.expanduser(val))
-        )
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_DIR": lambda: os.getenv("VLLM_TORCH_PROFILER_DIR"),
+    # Enable torch profiler to record shapes if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES")
     ),
-    # Enable torch profiler to record shapes if set
-    # VLLM_TORCH_PROFILER_RECORD_SHAPES=1. If not set, torch profiler will
-    # not record shapes.
-    "VLLM_TORCH_PROFILER_RECORD_SHAPES": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_RECORD_SHAPES", "0") != "0"
+    # Enable torch profiler to profile memory if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY")
     ),
-    # Enable torch profiler to profile memory if set
-    # VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1. If not set, torch profiler
-    # will not profile memory.
-    "VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY", "0") != "0"
+    # Enable torch profiler to profile stack if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK")
     ),
-    # Enable torch profiler to profile stack if set
-    # VLLM_TORCH_PROFILER_WITH_STACK=1. If not set, torch profiler WILL
-    # profile stack by default.
-    "VLLM_TORCH_PROFILER_WITH_STACK": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_WITH_STACK", "1") != "0"
+    # Enable torch profiler to profile flops if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS")
     ),
-    # Enable torch profiler to profile flops if set
-    # VLLM_TORCH_PROFILER_WITH_FLOPS=1. If not set, torch profiler will
-    # not profile flops.
-    "VLLM_TORCH_PROFILER_WITH_FLOPS": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_WITH_FLOPS", "0") != "0"
-    ),
-    # Disable torch profiling of the AsyncLLMEngine process.
-    # If set to 1, will not profile the engine process.
-    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM", "0") != "0"
+    # Disable torch profiling of the AsyncLLMEngine process if set to 1.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM")
     ),
     # Delay number of iterations before starting profiling when using
     # the torch/torch CUDA profiler. If set to 0, will start profiling immediately.
-    "VLLM_PROFILER_DELAY_ITERS": lambda: int(
-        os.getenv("VLLM_PROFILER_DELAY_ITERS", "0")
-    ),
+    # Deprecated, see profiler_config.
+    "VLLM_PROFILER_DELAY_ITERS": lambda: (os.getenv("VLLM_PROFILER_DELAY_ITERS")),
     # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
     # If set to 0, will not limit the number of iterations.
-    "VLLM_PROFILER_MAX_ITERS": lambda: int(os.getenv("VLLM_PROFILER_MAX_ITERS", "0")),
+    "VLLM_PROFILER_MAX_ITERS": lambda: os.getenv("VLLM_PROFILER_MAX_ITERS"),
     # Control whether torch profiler gzip-compresses profiling files.
-    # Set VLLM_TORCH_PROFILER_USE_GZIP=0 to disable gzip (enabled by default).
-    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_USE_GZIP", "1") != "0"
-    ),
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: os.getenv("VLLM_TORCH_PROFILER_USE_GZIP"),
     # Control whether torch profiler dumps the self_cuda_time_total table.
-    # Set VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL=0 to disable dumping
-    # (enabled by default).
-    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: bool(
-        os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL", "1") != "0"
+    # Set to 0 to disable dumping the table.
+    # Deprecated, see profiler_config.
+    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: (
+        os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL")
     ),
     # If set, vLLM will use Triton implementations of AWQ.
     "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),

vllm/forward_context.py

@@ -292,7 +292,7 @@ def set_forward_context(
         if num_tokens_across_dp is None:
             assert ubatch_slices is None
             assert num_tokens is not None
-            _, num_tokens_across_dp = coordinate_batch_across_dp(
+            _, num_tokens_across_dp, _ = coordinate_batch_across_dp(
                 num_tokens_unpadded=num_tokens,
                 parallel_config=vllm_config.parallel_config,
                 allow_microbatching=False,

vllm/model_executor/layers/batch_invariant.py

@@ -935,7 +935,11 @@ def enable_batch_invariant_mode():
 
     # Batch invariant matmuls are no longer needed after cublas overrides
     if not is_torch_equal_or_newer("2.10.0.dev"):
-        if current_platform.is_device_capability(100):
+        if (
+            current_platform.is_device_capability(100)
+            or current_platform.is_device_capability(80)
+            or current_platform.is_device_capability(89)
+        ):
             # For PyTorch 2.9, B200 uses GEMV for bs=1
             # Requires https://github.com/pytorch/pytorch/pull/166735
             _batch_invariant_LIB.impl("aten::mm", mm_batch_invariant, "CUDA")

vllm/model_executor/layers/fused_moe/fused_moe.py

@@ -895,6 +895,48 @@ def get_moe_configs(
     return None
 
 
+def _ensure_block_size_k_divisible(
+    size_k: int, block_size_k: int, group_size: int
+) -> int:
+    """Ensure block_size_k is a divisor of size_k and divisible by group_size.
+
+    This ensures BLOCK_SIZE_K compatibility with MoeWNA16 CUDA kernel which
+    requires size_k % BLOCK_SIZE_K == 0 and BLOCK_SIZE_K % group_size == 0.
+
+    Args:
+        size_k: The size_k dimension that must be divisible by result.
+        block_size_k: Preferred block size (will be adjusted if needed).
+        group_size: The result must be divisible by this.
+
+    Returns:
+        A valid BLOCK_SIZE_K that divides size_k and is divisible by group_size.
+    """
+    # Fast path: already valid
+    if size_k % block_size_k == 0 and block_size_k % group_size == 0:
+        return block_size_k
+
+    # Find the largest value that:
+    # 1. Divides size_k (size_k % candidate == 0)
+    # 2. Is divisible by group_size (candidate % group_size == 0)
+    # 3. Is <= block_size_k (prefer smaller values close to block_size_k)
+    #
+    # Strategy: Search from min(block_size_k, size_k) down to group_size,
+    # stepping by group_size to ensure divisibility by group_size
+    max_search = min(block_size_k, size_k)
+    start = (max_search // group_size) * group_size
+    for candidate in range(start, group_size - 1, -group_size):
+        if size_k % candidate == 0:
+            return candidate
+
+    # Fallback: if group_size divides size_k, use it
+    # This should always be true with correct group_size configuration
+    if size_k % group_size == 0:
+        return group_size
+
+    # This should not happen with correct group_size, but ensure divisibility
+    return size_k
+
+
 def get_moe_wna16_block_config(
     config: dict[str, int],
     use_moe_wna16_cuda: bool,
@@ -960,6 +1002,9 @@ def get_moe_wna16_block_config(
             # at the same time.
             block_size_n = 1024
 
+        # Ensure BLOCK_SIZE_K is a divisor of size_k for CUDA kernel compatibility
+        block_size_k = _ensure_block_size_k_divisible(size_k, block_size_k, group_size)
+
         return {"BLOCK_SIZE_N": block_size_n, "BLOCK_SIZE_K": block_size_k}
 
 

vllm/model_executor/layers/quantization/input_quant_fp8.py

@@ -5,6 +5,7 @@ import torch
 import torch.nn.functional as F
 
 from vllm import _custom_ops as ops
+from vllm._aiter_ops import rocm_aiter_ops
 from vllm.model_executor.custom_op import CustomOp
 from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.platforms import current_platform
@@ -45,10 +46,13 @@ class QuantFP8(CustomOp):
         super().__init__()
         self.static = static
         self.group_shape = group_shape
+        self.use_per_token_if_dynamic = group_shape == GroupShape.PER_TOKEN
         self.num_token_padding = num_token_padding
         self.column_major_scales = column_major_scales
         self.use_ue8m0 = use_ue8m0
 
+        self.use_aiter = rocm_aiter_ops.is_linear_fp8_enaled()
+
         self.is_group_quant = group_shape.is_per_group()
         if self.is_group_quant:
             assert not static, "Group quantization only supports dynamic mode"
@@ -92,6 +96,33 @@ class QuantFP8(CustomOp):
             use_per_token_if_dynamic=self.use_per_token_if_dynamic,
         )
 
+    def forward_hip(
+        self,
+        x: torch.Tensor,
+        scale: torch.Tensor | None = None,
+        scale_ub: torch.Tensor | None = None,
+    ) -> tuple[torch.Tensor, torch.Tensor]:
+        use_aiter_quant = (
+            not self.is_group_quant
+            and self.use_aiter
+            and scale_ub is None
+            and x.is_contiguous()
+        )
+        use_aiter_per_tensor_quant = (
+            use_aiter_quant and self.group_shape == GroupShape.PER_TENSOR
+        )
+        use_aiter_per_token_quant = (
+            use_aiter_quant and self.group_shape == GroupShape.PER_TOKEN
+        )
+
+        if use_aiter_per_tensor_quant:
+            return rocm_aiter_ops.per_tensor_quant(x, _FP8_DTYPE, scale)
+        if use_aiter_per_token_quant:
+            return rocm_aiter_ops.per_token_quant(x, _FP8_DTYPE, scale)
+
+        # Fallback to CUDA implementation
+        return self.forward_cuda(x, scale, scale_ub)
+
     def forward_native(
         self,
         x: torch.Tensor,

vllm/model_executor/layers/quantization/moe_wna16.py

@@ -60,7 +60,7 @@ class MoeWNA16Config(QuantizationConfig):
 
         if self.linear_quant_method == "gptq":
             self.use_marlin = GPTQMarlinConfig.is_gptq_marlin_compatible(full_config)
-        elif self.linear_quant_method == "awq":
+        elif self.linear_quant_method in ("awq", "awq_marlin"):
             capability_tuple = current_platform.get_device_capability()
             device_capability = (
                 -1 if capability_tuple is None else capability_tuple.to_int()
@@ -107,7 +107,7 @@ class MoeWNA16Config(QuantizationConfig):
         if linear_quant_method == "gptq":
             has_zp = not cls.get_from_keys(config, ["sym"])
             modules_to_not_convert = []
-        elif linear_quant_method == "awq":
+        elif linear_quant_method in ("awq", "awq_marlin"):
             has_zp = cls.get_from_keys(config, ["zero_point"])
             modules_to_not_convert = cls.get_from_keys_or(
                 config, ["modules_to_not_convert"], None
@@ -184,7 +184,7 @@ class MoeWNA16Config(QuantizationConfig):
                     return GPTQConfig.from_config(self.full_config).get_quant_method(
                         layer, prefix
                     )
-            elif self.linear_quant_method == "awq":
+            elif self.linear_quant_method in ("awq", "awq_marlin"):
                 if self.use_marlin and check_marlin_supports_layer(
                     layer, self.group_size
                 ):
@@ -468,7 +468,8 @@ class MoeWNA16Method(FusedMoEMethodBase):
             shard_size = layer.intermediate_size_per_partition
 
             # convert gptq and awq weight to a standard format
-            if layer.quant_config.linear_quant_method == "awq":
+            # awq_marlin uses the same weight format as awq
+            if layer.quant_config.linear_quant_method in ("awq", "awq_marlin"):
                 assert layer.quant_config.weight_bits == 4
                 if "weight" in weight_name:
                     loaded_weight = convert_awq_tensor(loaded_weight, "qweight")

vllm/model_executor/models/qwen2_moe.py

@@ -367,6 +367,8 @@ class Qwen2MoeModel(nn.Module):
         self.embed_tokens = VocabParallelEmbedding(
             config.vocab_size,
             config.hidden_size,
+            quant_config=quant_config,
+            prefix=f"{prefix}.embed_tokens",
         )
         self.start_layer, self.end_layer, self.layers = make_layers(
             config.num_hidden_layers,
@@ -512,6 +514,12 @@ class Qwen2MoeModel(nn.Module):
                             continue
                         else:
                             name = remapped_kv_scale_name
+                    # GGUF: make sure that shared_expert_gate is a 2D tensor.
+                    if (
+                        "mlp.shared_expert_gate" in name
+                        and len(loaded_weight.shape) == 1
+                    ):
+                        loaded_weight = loaded_weight[None, :]
                     param = params_dict[name]
                     weight_loader = getattr(
                         param, "weight_loader", default_weight_loader

vllm/platforms/rocm.py

@@ -381,6 +381,8 @@ class RocmPlatform(Platform):
         compilation_config = vllm_config.compilation_config
         parallel_config = vllm_config.parallel_config
         is_eager_execution = compilation_config == CUDAGraphMode.NONE
+        use_aiter_rms_norm = rocm_aiter_ops.is_rmsnorm_enabled()
+        use_aiter_fp8_linear = rocm_aiter_ops.is_linear_fp8_enaled()
 
         if compilation_config.cudagraph_mode.has_full_cudagraphs():
             # decode context parallel does not support full cudagraphs
@@ -400,8 +402,6 @@ class RocmPlatform(Platform):
                 )
                 compilation_config.cudagraph_mode = CUDAGraphMode.PIECEWISE
 
-        use_aiter_rms_norm = rocm_aiter_ops.is_rmsnorm_enabled()
-
         if cache_config and cache_config.block_size is None:
             cache_config.block_size = 16
 
@@ -415,6 +415,9 @@ class RocmPlatform(Platform):
         ):
             compilation_config.custom_ops.append("+rms_norm")
 
+        if use_aiter_fp8_linear and "-quant_fp8" not in compilation_config.custom_ops:
+            compilation_config.custom_ops.append("+quant_fp8")
+
     @classmethod
     def verify_model_arch(cls, model_arch: str) -> None:
         if model_arch in _ROCM_UNSUPPORTED_MODELS:

vllm/profiler/wrapper.py

@@ -3,26 +3,27 @@
 
 from abc import ABC, abstractmethod
 from contextlib import nullcontext
+from typing import Literal
 
 import torch
 from typing_extensions import override
 
-import vllm.envs as envs
+from vllm.config import ProfilerConfig
 from vllm.logger import init_logger
 
 logger = init_logger(__name__)
 
 
 class WorkerProfiler(ABC):
-    def __init__(self) -> None:
-        self._delay_iters = envs.VLLM_PROFILER_DELAY_ITERS
+    def __init__(self, profiler_config: ProfilerConfig) -> None:
+        self._delay_iters = profiler_config.delay_iterations
         if self._delay_iters > 0:
             logger.info_once(
                 "GPU profiling will start "
                 f"{self._delay_iters} steps after start_profile."
             )
 
-        self._max_iters = envs.VLLM_PROFILER_MAX_ITERS
+        self._max_iters = profiler_config.max_iterations
         if self._max_iters > 0:
             logger.info_once(
                 "GPU profiling will stop "
@@ -133,12 +134,27 @@ class WorkerProfiler(ABC):
         return nullcontext()
 
 
+TorchProfilerActivity = Literal["CPU", "CUDA", "XPU"]
+TorchProfilerActivityMap = {
+    "CPU": torch.profiler.ProfilerActivity.CPU,
+    "CUDA": torch.profiler.ProfilerActivity.CUDA,
+    "XPU": torch.profiler.ProfilerActivity.XPU,
+}
+
+
 class TorchProfilerWrapper(WorkerProfiler):
-    def __init__(self, worker_name: str, local_rank: int) -> None:
-        super().__init__()
+    def __init__(
+        self,
+        profiler_config: ProfilerConfig,
+        worker_name: str,
+        local_rank: int,
+        activities: list[TorchProfilerActivity],
+    ) -> None:
+        super().__init__(profiler_config)
 
         self.local_rank = local_rank
-        torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+        self.profiler_config = profiler_config
+        torch_profiler_trace_dir = profiler_config.torch_profiler_dir
         if local_rank in (None, 0):
             logger.info(
                 "Torch profiling enabled. Traces will be saved to: %s",
@@ -147,24 +163,23 @@ class TorchProfilerWrapper(WorkerProfiler):
             logger.debug(
                 "Profiler config: record_shapes=%s,"
                 "profile_memory=%s,with_stack=%s,with_flops=%s",
-                envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
-                envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
-                envs.VLLM_TORCH_PROFILER_WITH_STACK,
-                envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+                profiler_config.torch_profiler_record_shapes,
+                profiler_config.torch_profiler_with_memory,
+                profiler_config.torch_profiler_with_stack,
+                profiler_config.torch_profiler_with_flops,
             )
+
+        self.dump_cpu_time_total = "CPU" in activities and len(activities) == 1
         self.profiler = torch.profiler.profile(
-            activities=[
-                torch.profiler.ProfilerActivity.CPU,
-                torch.profiler.ProfilerActivity.CUDA,
-            ],
-            record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
-            profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
-            with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
-            with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
+            activities=[TorchProfilerActivityMap[activity] for activity in activities],
+            record_shapes=profiler_config.torch_profiler_record_shapes,
+            profile_memory=profiler_config.torch_profiler_with_memory,
+            with_stack=profiler_config.torch_profiler_with_stack,
+            with_flops=profiler_config.torch_profiler_with_flops,
             on_trace_ready=torch.profiler.tensorboard_trace_handler(
                 torch_profiler_trace_dir,
                 worker_name=worker_name,
-                use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
+                use_gzip=profiler_config.torch_profiler_use_gzip,
             ),
         )
 
@@ -176,9 +191,10 @@ class TorchProfilerWrapper(WorkerProfiler):
     def _stop(self) -> None:
         self.profiler.stop()
 
-        if envs.VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL:
-            rank = self.local_rank
-            profiler_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profiler_config = self.profiler_config
+        rank = self.local_rank
+        if profiler_config.torch_profiler_dump_cuda_time_total:
+            profiler_dir = profiler_config.torch_profiler_dir
             profiler_out_file = f"{profiler_dir}/profiler_out_{rank}.txt"
             sort_key = "self_cuda_time_total"
             table = self.profiler.key_averages().table(sort_by=sort_key)
@@ -189,6 +205,12 @@ class TorchProfilerWrapper(WorkerProfiler):
             # only print profiler results on rank 0
             if rank == 0:
                 print(table)
+        if self.dump_cpu_time_total and rank == 0:
+            logger.info(
+                self.profiler.key_averages().table(
+                    sort_by="self_cpu_time_total", row_limit=50
+                )
+            )
 
     @override
     def annotate_context_manager(self, name: str):
@@ -196,8 +218,8 @@ class TorchProfilerWrapper(WorkerProfiler):
 
 
 class CudaProfilerWrapper(WorkerProfiler):
-    def __init__(self) -> None:
-        super().__init__()
+    def __init__(self, profiler_config: ProfilerConfig) -> None:
+        super().__init__(profiler_config)
         # Note: lazy import to avoid dependency issues if CUDA is not available.
         import torch.cuda.profiler as cuda_profiler
 

vllm/reasoning/abs_reasoning_parsers.py

@@ -63,6 +63,31 @@ class ReasoningParser:
             True if the reasoning content ends in the input_ids.
         """
 
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        """
+        Check if the reasoning content ends in the input_ids on a
+        decode step.
+
+        It is used in structured engines like `xgrammar` to check if the
+        reasoning content ends in the model output during a decode step.
+        `input_ids` the entire model output and `delta_ids` are the last few
+        computed tokens of the model output (like during a decode step).
+
+        Parameters:
+        input_ids: list[int]
+            The entire model output.
+        delta_ids: list[int]
+            The last few computed tokens of the model output at the current decode step.
+
+        Returns:
+        bool
+            True if the reasoning content ends in the `delta_ids` on a
+            decode step.
+        """
+        return self.is_reasoning_end(input_ids)
+
     @abstractmethod
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         """

vllm/reasoning/basic_parsers.py

@@ -74,6 +74,12 @@ class BaseThinkingReasoningParser(ReasoningParser):
                 return True
         return False
 
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        end_token_id = self.end_token_id
+        return end_token_id in delta_ids
+
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         """
         Extract the content after the end tokens

vllm/reasoning/deepseek_v3_reasoning_parser.py

@@ -35,6 +35,11 @@ class DeepSeekV3ReasoningParser(ReasoningParser):
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         return self._parser.is_reasoning_end(input_ids)
 
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        return self._parser.is_reasoning_end_streaming(input_ids, delta_ids)
+
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         return self._parser.extract_content_ids(input_ids)
 

vllm/reasoning/holo2_reasoning_parser.py

@@ -56,6 +56,11 @@ class Holo2ReasoningParser(ReasoningParser):
     def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
         return self._parser.is_reasoning_end(input_ids)
 
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        return self._parser.is_reasoning_end_streaming(input_ids, delta_ids)
+
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         return self._parser.extract_content_ids(input_ids)
 

vllm/reasoning/identity_reasoning_parser.py

@@ -32,6 +32,11 @@ class IdentityReasoningParser(ReasoningParser):
         # Always return True, since we never treat reasoning specially
         return True
 
+    def is_reasoning_end_streaming(
+        self, input_ids: list[int], delta_ids: list[int]
+    ) -> bool:
+        return True
+
     def extract_content_ids(self, input_ids: list[int]) -> list[int]:
         # Identity: return all tokens as content
         return input_ids

vllm/v1/cudagraph_dispatcher.py

@@ -145,7 +145,7 @@ class CudagraphDispatcher:
         num_tokens: int,
         uniform_decode: bool,
         has_lora: bool,
-        use_cascade_attn: bool = False,
+        disable_full: bool = False,
     ) -> tuple[CUDAGraphMode, BatchDescriptor]:
         """
         Given conditions(e.g.,batch descriptor and if using cascade attention),
@@ -165,7 +165,7 @@ class CudagraphDispatcher:
         )
         relaxed_batch_desc = batch_desc.relax_for_mixed_batch_cudagraphs()
 
-        if not use_cascade_attn:
+        if not disable_full:
             # check if key exists for full cudagraph
             if batch_desc in self.cudagraph_keys[CUDAGraphMode.FULL]:
                 return CUDAGraphMode.FULL, batch_desc

vllm/v1/engine/async_llm.py

@@ -166,32 +166,24 @@ class AsyncLLM(EngineClient):
             pass
 
         if (
-            envs.VLLM_TORCH_PROFILER_DIR
-            and not envs.VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM
+            vllm_config.profiler_config.profiler == "torch"
+            and not vllm_config.profiler_config.ignore_frontend
         ):
+            profiler_dir = vllm_config.profiler_config.torch_profiler_dir
             logger.info(
                 "Torch profiler enabled. AsyncLLM CPU traces will be collected under %s",  # noqa: E501
-                envs.VLLM_TORCH_PROFILER_DIR,
+                profiler_dir,
             )
-            if envs.VLLM_PROFILER_MAX_ITERS > 0 or envs.VLLM_PROFILER_DELAY_ITERS > 0:
-                logger.warning_once(
-                    "Torch profiler received max_iters or delay_iters setting. These "
-                    "are not compatible with the AsyncLLM profiler and will be ignored "
-                    "for the AsyncLLM process. Engine process profiling will still "
-                    "respect these settings. Consider setting "
-                    "VLLM_TORCH_PROFILER_DISABLE_ASYNC_LLM=1 to disable "
-                    "AsyncLLM profiling."
-                )
             worker_name = f"{socket.gethostname()}_{os.getpid()}.async_llm"
             self.profiler = torch.profiler.profile(
                 activities=[
                     torch.profiler.ProfilerActivity.CPU,
                 ],
-                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
+                with_stack=vllm_config.profiler_config.torch_profiler_with_stack,
                 on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    envs.VLLM_TORCH_PROFILER_DIR,
+                    profiler_dir,
                     worker_name=worker_name,
-                    use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
+                    use_gzip=vllm_config.profiler_config.torch_profiler_use_gzip,
                 ),
             )
         else:

vllm/v1/spec_decode/eagle.py

@@ -1258,7 +1258,7 @@ class EagleProposer:
         num_tokens_padded: int,
     ) -> tuple[int, torch.Tensor]:
         # TODO(Flechman): support DBO ubatching
-        should_ubatch, num_toks_across_dp = coordinate_batch_across_dp(
+        should_ubatch, num_toks_across_dp, _ = coordinate_batch_across_dp(
             num_tokens_unpadded=num_tokens_unpadded,
             parallel_config=self.vllm_config.parallel_config,
             allow_microbatching=False,

vllm/v1/structured_output/__init__.py

@@ -339,7 +339,9 @@ class StructuredOutputManager:
             return True
 
         # Check if reasoning ends in *this* step
-        if self.reasoner.is_reasoning_end(request.all_token_ids):
+        if self.reasoner.is_reasoning_end_streaming(
+            request.all_token_ids, request.all_token_ids[request.num_computed_tokens :]
+        ):
             # Reasoning just ended, so we shouldn't advance til
             # next pass
             structured_req.reasoning_ended = True

vllm/v1/worker/cpu_worker.py

@@ -13,6 +13,7 @@ from vllm.logger import init_logger
 from vllm.model_executor.utils import set_random_seed
 from vllm.platforms import CpuArchEnum, current_platform
 from vllm.platforms.cpu import CpuPlatform, LogicalCPUInfo
+from vllm.profiler.wrapper import TorchProfilerWrapper
 from vllm.v1.worker.cpu_model_runner import CPUModelRunner
 from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
 
@@ -38,30 +39,17 @@ class CPUWorker(Worker):
 
         self.parallel_config.disable_custom_all_reduce = True
 
-        # Torch profiler. Enabled and configured through env vars:
-        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        # Torch profiler. Enabled and configured through profiler_config.
         self.profiler: Any | None = None
-        if envs.VLLM_TORCH_PROFILER_DIR:
-            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profiler_config = vllm_config.profiler_config
+        if profiler_config.profiler == "torch":
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
-            logger.info(
-                "Profiling enabled. Traces will be saved to: %s",
-                torch_profiler_trace_dir,
+            self.profiler = TorchProfilerWrapper(
+                profiler_config,
+                worker_name=worker_name,
+                local_rank=self.local_rank,
+                activities=["CPU"],
             )
-            self.profiler = torch.profiler.profile(
-                activities=[
-                    torch.profiler.ProfilerActivity.CPU,
-                ],
-                record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
-                profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
-                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
-                with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
-                on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    torch_profiler_trace_dir, worker_name=worker_name, use_gzip=False
-                ),
-            )
-        else:
-            self.profiler = None
 
     def init_device(self):
         # Setup OpenMP threads affinity.
@@ -202,9 +190,3 @@ class CPUWorker(Worker):
             self.profiler.start()
         else:
             self.profiler.stop()
-            if self.local_rank == 0:
-                logger.info(
-                    self.profiler.key_averages().table(
-                        sort_by="self_cpu_time_total", row_limit=50
-                    )
-                )

vllm/v1/worker/dp_utils.py

@@ -40,16 +40,18 @@ def _run_ar(
     should_dp_pad: bool,
     orig_num_tokens_per_ubatch: int,
     padded_num_tokens_per_ubatch: int,
+    cudagraph_mode: int,
     parallel_config: ParallelConfig,
 ) -> torch.Tensor:
     dp_size = parallel_config.data_parallel_size
     dp_rank = parallel_config.data_parallel_rank
     device, group = _get_device_and_group(parallel_config)
-    tensor = torch.zeros(4, dp_size, device=device, dtype=torch.int32)
+    tensor = torch.zeros(5, dp_size, device=device, dtype=torch.int32)
     tensor[0][dp_rank] = orig_num_tokens_per_ubatch
     tensor[1][dp_rank] = padded_num_tokens_per_ubatch
     tensor[2][dp_rank] = 1 if should_ubatch else 0
     tensor[3][dp_rank] = 1 if should_dp_pad else 0
+    tensor[4][dp_rank] = cudagraph_mode
     dist.all_reduce(tensor, group=group)
     return tensor
 
@@ -89,13 +91,23 @@ def _post_process_dp_padding(tensor: torch.Tensor, should_dp_pad: bool) -> torch
         return num_tokens_across_dp.cpu()
 
 
+def _post_process_cudagraph_mode(tensor: torch.Tensor) -> int:
+    """
+    Synchronize cudagraph_mode across DP ranks by taking the minimum.
+    If any rank has NONE (0), all ranks use NONE.
+    This ensures all ranks send consistent values (all padded or all unpadded).
+    """
+    return int(tensor[4, :].min().item())
+
+
 def _synchronize_dp_ranks(
     num_tokens_unpadded: int,
     num_tokens_padded: int,
     should_attempt_ubatching: bool,
     should_attempt_dp_padding: bool,
+    cudagraph_mode: int,
     parallel_config: ParallelConfig,
-) -> tuple[bool, torch.Tensor | None]:
+) -> tuple[bool, torch.Tensor | None, int]:
     """
     1. Decides if each DP rank is going to microbatch. Either all ranks
     run with microbatching or none of them do.
@@ -104,10 +116,13 @@ def _synchronize_dp_ranks(
     When running microbatched or if should_attempt_dp_padding is True, all
     ranks will be padded out so that the run with the same number of tokens
 
+    3. Synchronizes cudagraph_mode across ranks by taking the minimum.
+
     Returns: tuple[
         should_ubatch: Are all DP ranks going to microbatch
         num_tokens_after_padding: A tensor containing the total number of
         tokens per-microbatch for each DP rank including any DP padding.
+        synced_cudagraph_mode: The synchronized cudagraph mode (min across ranks)
     ]
 
     """
@@ -121,6 +136,7 @@ def _synchronize_dp_ranks(
         should_dp_pad=should_attempt_dp_padding,
         orig_num_tokens_per_ubatch=num_tokens_unpadded,
         padded_num_tokens_per_ubatch=num_tokens_padded,
+        cudagraph_mode=cudagraph_mode,
         parallel_config=parallel_config,
     )
 
@@ -148,7 +164,10 @@ def _synchronize_dp_ranks(
         should_dp_pad,
     )
 
-    return should_ubatch, num_tokens_after_padding
+    # Synchronize cudagraph_mode across ranks (take min)
+    synced_cudagraph_mode = _post_process_cudagraph_mode(tensor)
+
+    return should_ubatch, num_tokens_after_padding, synced_cudagraph_mode
 
 
 def coordinate_batch_across_dp(
@@ -159,7 +178,8 @@ def coordinate_batch_across_dp(
     num_tokens_padded: int | None = None,
     uniform_decode: bool | None = None,
     num_scheduled_tokens_per_request: np.ndarray | None = None,
-) -> tuple[bool, torch.Tensor | None]:
+    cudagraph_mode: int = 0,
+) -> tuple[bool, torch.Tensor | None, int]:
     """
     Coordinates amongst all DP ranks to determine if and how the full batch
     should be split into microbatches.
@@ -175,6 +195,7 @@ def coordinate_batch_across_dp(
             only contains single token decodes
         num_scheduled_tokens_per_request: Only used if allow_microbatching is True. The
             number of tokens per request.
+        cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL)
 
     Returns: tuple[
         ubatch_slices: if this is set then all DP ranks have agreed to
@@ -183,12 +204,13 @@ def coordinate_batch_across_dp(
         tokens per-microbatch for each DP rank including padding. Will be
         padded up to the max value across all DP ranks when allow_dp_padding
         is True.
+        synced_cudagraph_mode: The synchronized cudagraph mode (min across ranks)
     ]
 
     """
     if parallel_config.data_parallel_size == 1:
         # Early exit.
-        return False, None
+        return False, None, cudagraph_mode
 
     # If the caller has explicitly enabled microbatching.
     should_attempt_ubatching = False
@@ -204,12 +226,15 @@ def coordinate_batch_across_dp(
     if num_tokens_padded is None:
         num_tokens_padded = num_tokens_unpadded
 
-    (should_ubatch, num_tokens_after_padding) = _synchronize_dp_ranks(
-        num_tokens_unpadded,
-        num_tokens_padded,
-        should_attempt_ubatching,
-        allow_dp_padding,
-        parallel_config,
+    (should_ubatch, num_tokens_after_padding, synced_cudagraph_mode) = (
+        _synchronize_dp_ranks(
+            num_tokens_unpadded,
+            num_tokens_padded,
+            should_attempt_ubatching,
+            allow_dp_padding,
+            cudagraph_mode,
+            parallel_config,
+        )
     )
 
-    return (should_ubatch, num_tokens_after_padding)
+    return (should_ubatch, num_tokens_after_padding, synced_cudagraph_mode)

vllm/v1/worker/gpu/async_utils.py

@@ -2,14 +2,15 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from contextlib import contextmanager
 
+import numpy as np
 import torch
 
 from vllm.v1.outputs import (
     AsyncModelRunnerOutput,
     LogprobsTensors,
     ModelRunnerOutput,
-    SamplerOutput,
 )
+from vllm.v1.worker.gpu.sample.output import SamplerOutput
 
 
 class AsyncOutput(AsyncModelRunnerOutput):
@@ -34,29 +35,18 @@ class AsyncOutput(AsyncModelRunnerOutput):
         with torch.cuda.stream(self.copy_stream):
             self.copy_stream.wait_stream(default_stream)
 
-            # NOTE(woosuk): We must ensure that CPU tensors are not freed
-            # before the device-to-host copy is fully completed. For instance,
-            # operations like
-            # self.sampled_token_np = ...to("cpu", non_blocking=True).numpy()
-            # are unsafe because the underlying CPU tensor can be prematurely freed and
-            # reused by other tensors before the asynchronous copy finishes, potentially
-            # causing race conditions. To prevent this, we delay freeing by holding
-            # references until the copy event signals completion.
-            # Likewise, we also need to keep the reference to the GPU tensors.
-            # This is done by keeping the reference to sampler_output and
-            # model_runner_output.
-            self.sampled_token_ids = sampler_output.sampled_token_ids.to(
-                "cpu", non_blocking=True
-            )
+            self.sampled_token_ids = async_copy_to_np(sampler_output.sampled_token_ids)
             if sampler_output.logprobs_tensors is not None:
                 self.logprobs_tensors: LogprobsTensors | None = (
                     sampler_output.logprobs_tensors.to_cpu_nonblocking()
                 )
             else:
                 self.logprobs_tensors = None
-            self.num_sampled_tokens_cpu = num_sampled_tokens.to(
-                "cpu", non_blocking=True
-            )
+            if sampler_output.num_nans is not None:
+                self.num_nans = async_copy_to_np(sampler_output.num_nans)
+            else:
+                self.num_nans = None
+            self.num_sampled_tokens_np = async_copy_to_np(num_sampled_tokens)
             self.prompt_logprobs_dict: dict[str, LogprobsTensors | None] = {}
             if self.model_runner_output.prompt_logprobs_dict:
                 for k, v in self.model_runner_output.prompt_logprobs_dict.items():
@@ -68,7 +58,6 @@ class AsyncOutput(AsyncModelRunnerOutput):
 
     def get_output(self) -> ModelRunnerOutput:
         self.copy_event.synchronize()
-        num_sampled_tokens_np = self.num_sampled_tokens_cpu.numpy()
 
         # NOTE(woosuk): The following code is to ensure compatibility with
         # the existing model runner.
@@ -76,10 +65,18 @@ class AsyncOutput(AsyncModelRunnerOutput):
         # rather than Python lists.
         sampled_token_ids: list[list[int]] = self.sampled_token_ids.tolist()
         num_reqs = len(sampled_token_ids)
+        num_sampled_tokens = self.num_sampled_tokens_np.tolist()
         for i in range(num_reqs):
-            del sampled_token_ids[i][num_sampled_tokens_np[i] :]
+            del sampled_token_ids[i][num_sampled_tokens[i] :]
         self.model_runner_output.sampled_token_ids = sampled_token_ids
 
+        if self.num_nans is not None:
+            num_nans = self.num_nans.tolist()
+            self.model_runner_output.num_nans_in_logits = {
+                req_id: num_nans[i]
+                for i, req_id in enumerate(self.model_runner_output.req_ids)
+            }
+
         if self.logprobs_tensors is not None:
             self.model_runner_output.logprobs = self.logprobs_tensors.tolists()
         self.model_runner_output.prompt_logprobs_dict = self.prompt_logprobs_dict
@@ -95,3 +92,7 @@ def async_barrier(event: torch.cuda.Event | None):
     finally:
         if event is not None:
             event.record()
+
+
+def async_copy_to_np(x: torch.Tensor) -> np.ndarray:
+    return x.to("cpu", non_blocking=True).numpy()

vllm/v1/worker/gpu/metrics/logits.py

@@ -0,0 +1,42 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import torch
+from torch._inductor.runtime.triton_helpers import libdevice
+
+from vllm.triton_utils import tl, triton
+
+
+@triton.jit
+def _num_nans_kernel(
+    logits_ptr,
+    logits_stride,
+    num_nans_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+):
+    req_idx = tl.program_id(0)
+    num_nans = 0
+    for i in range(0, vocab_size, BLOCK_SIZE):
+        block = i + tl.arange(0, BLOCK_SIZE)
+        mask = block < vocab_size
+        logits = tl.load(
+            logits_ptr + req_idx * logits_stride + block, mask=mask, other=0
+        )
+        logits = logits.to(tl.float32)
+        is_nan = libdevice.isnan(logits).to(tl.int1)
+        num_nans += tl.sum(is_nan).to(tl.int32)
+    tl.store(num_nans_ptr + req_idx, num_nans)
+
+
+def get_num_nans(logits: torch.Tensor) -> torch.Tensor:
+    num_reqs, vocab_size = logits.shape
+    BLOCK_SIZE = 8192
+    num_nans = torch.empty(num_reqs, dtype=torch.int32, device=logits.device)
+    _num_nans_kernel[(num_reqs,)](
+        logits,
+        logits.stride(0),
+        num_nans,
+        vocab_size,
+        BLOCK_SIZE=BLOCK_SIZE,
+    )
+    return num_nans

vllm/v1/worker/gpu/model_runner.py

@@ -25,7 +25,6 @@ from vllm.v1.outputs import (
     LogprobsTensors,
     ModelRunnerOutput,
 )
-from vllm.v1.sample.sampler import SamplerOutput
 from vllm.v1.worker.gpu.async_utils import AsyncOutput, async_barrier
 from vllm.v1.worker.gpu.attn_utils import (
     build_attn_metadata,
@@ -53,6 +52,7 @@ from vllm.v1.worker.gpu.sample.metadata import (
     SamplingMetadata,
     expand_sampling_metadata,
 )
+from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.sampler import Sampler
 from vllm.v1.worker.gpu.spec_decode import init_speculator
 from vllm.v1.worker.gpu.spec_decode.rejection_sample import rejection_sample

vllm/v1/worker/gpu/sample/min_p.py

@@ -39,9 +39,7 @@ def _min_p_kernel(
         tl.store(logits_ptr + req_idx * logits_stride + block, logits, mask=mask)
 
 
-def apply_min_p(logits: torch.Tensor, min_p: torch.Tensor | None) -> None:
-    if min_p is None:
-        return
+def apply_min_p(logits: torch.Tensor, min_p: torch.Tensor) -> None:
     num_reqs, vocab_size = logits.shape
     BLOCK_SIZE = 1024
     _min_p_kernel[(num_reqs,)](

vllm/v1/worker/gpu/sample/output.py

@@ -0,0 +1,14 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from dataclasses import dataclass
+
+import torch
+
+from vllm.v1.outputs import LogprobsTensors
+
+
+@dataclass
+class SamplerOutput:
+    sampled_token_ids: torch.Tensor
+    logprobs_tensors: LogprobsTensors | None
+    num_nans: torch.Tensor | None

vllm/v1/worker/gpu/sample/penalties.py

@@ -62,6 +62,7 @@ def _penalties_and_temperature_kernel(
                 mask=packed_block < tl.cdiv(vocab_size, 32),
             )
             prompt_bin_mask = (packed_mask[:, None] >> (tl.arange(0, 32)[None, :])) & 1
+            prompt_bin_mask = prompt_bin_mask.to(tl.int1)
             prompt_bin_mask = prompt_bin_mask.reshape(BLOCK_SIZE)
 
             # If token appears in prompt or output, apply, otherwise use 1.0 for no-op.

vllm/v1/worker/gpu/sample/sampler.py

@@ -3,13 +3,15 @@
 
 import torch
 
+import vllm.envs as envs
 from vllm.config.model import LogprobsMode
-from vllm.v1.outputs import SamplerOutput
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
+from vllm.v1.worker.gpu.metrics.logits import get_num_nans
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
 from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
 from vllm.v1.worker.gpu.sample.metadata import SamplingMetadata
 from vllm.v1.worker.gpu.sample.min_p import apply_min_p
+from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.penalties import apply_penalties_and_temperature
 
 
@@ -21,12 +23,16 @@ class Sampler:
         if logprobs_mode not in ["processed_logprobs", "raw_logprobs"]:
             raise NotImplementedError(f"Unsupported logprobs_mode: {logprobs_mode}")
         self.logprobs_mode = logprobs_mode
+        self.compute_nans = envs.VLLM_COMPUTE_NANS_IN_LOGITS  # False by default.
 
     def __call__(
         self,
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
     ) -> SamplerOutput:
+        # NOTE(woosuk): We intentionally compute num_nans before sampling to make clear
+        # that num_nans is computed before applying penalties and temperature.
+        num_nans = get_num_nans(logits) if self.compute_nans else None
         sampled, processed_logits = self.sample(logits, sampling_metadata)
         if sampling_metadata.max_num_logprobs is not None:
             logits = (
@@ -49,6 +55,7 @@ class Sampler:
             # token per request.
             sampled_token_ids=sampled.view(-1, 1),
             logprobs_tensors=logprobs_tensors,
+            num_nans=num_nans,
         )
         return sampler_output
 
@@ -63,7 +70,8 @@ class Sampler:
         # Apply penalties and temperature in place.
         apply_penalties_and_temperature(logits, sampling_metadata)
         # Apply min_p in place.
-        apply_min_p(logits, sampling_metadata.min_p)
+        if sampling_metadata.min_p is not None:
+            apply_min_p(logits, sampling_metadata.min_p)
         # Apply top_k and/or top_p. This might return a new tensor.
         logits = apply_top_k_top_p(
             logits, sampling_metadata.top_k, sampling_metadata.top_p

vllm/v1/worker/gpu_model_runner.py

@@ -2788,17 +2788,19 @@ class GPUModelRunner(
         )
 
         dispatch_cudagraph = (
-            lambda num_tokens: self.cudagraph_dispatcher.dispatch(
+            lambda num_tokens, disable_full: self.cudagraph_dispatcher.dispatch(
                 num_tokens=num_tokens,
                 has_lora=has_lora,
-                use_cascade_attn=use_cascade_attn,
                 uniform_decode=uniform_decode,
+                disable_full=disable_full,
             )
             if not force_eager
             else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
         )
 
-        cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
+        cudagraph_mode, batch_descriptor = dispatch_cudagraph(
+            num_tokens_padded, use_cascade_attn
+        )
         num_tokens_padded = batch_descriptor.num_tokens
 
         # Extra coordination when running data-parallel since we need to coordinate
@@ -2813,23 +2815,28 @@ class GPUModelRunner(
                 self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
             )
 
-            should_ubatch, num_tokens_across_dp = coordinate_batch_across_dp(
-                num_tokens_unpadded=num_tokens,
-                parallel_config=self.parallel_config,
-                allow_microbatching=allow_microbatching,
-                allow_dp_padding=allow_dp_padding,
-                num_tokens_padded=num_tokens_padded,
-                uniform_decode=uniform_decode,
-                num_scheduled_tokens_per_request=num_scheduled_tokens_np,
+            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
+                coordinate_batch_across_dp(
+                    num_tokens_unpadded=num_tokens,
+                    parallel_config=self.parallel_config,
+                    allow_microbatching=allow_microbatching,
+                    allow_dp_padding=allow_dp_padding,
+                    num_tokens_padded=num_tokens_padded,
+                    uniform_decode=uniform_decode,
+                    num_scheduled_tokens_per_request=num_scheduled_tokens_np,
+                    cudagraph_mode=cudagraph_mode.value,
+                )
             )
 
-            # Extract DP padding if there is any
+            # Extract DP-synced values
             if num_tokens_across_dp is not None:
                 dp_rank = self.parallel_config.data_parallel_rank
                 num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
-
-                # Re-dispatch with DP padding
-                cudagraph_mode, batch_descriptor = dispatch_cudagraph(num_tokens_padded)
+                # Re-dispatch with DP padding so we have the correct batch_descriptor
+                cudagraph_mode, batch_descriptor = dispatch_cudagraph(
+                    num_tokens_padded,
+                    disable_full=synced_cudagraph_mode <= CUDAGraphMode.PIECEWISE.value,
+                )
                 # Assert to make sure the agreed upon token count is correct otherwise
                 # num_tokens_across_dp will no-longer be valid
                 assert batch_descriptor.num_tokens == num_tokens_padded
@@ -4161,10 +4168,19 @@ class GPUModelRunner(
 
             if self.speculative_config and self.speculative_config.use_eagle():
                 assert isinstance(self.drafter, EagleProposer)
+                # Eagle currently only supports PIECEWISE cudagraphs.
+                # Therefore only use cudagraphs if the main model uses PIECEWISE
+                # NOTE(lucas): this is a hack, need to clean up.
                 use_cudagraphs = (
-                    cudagraph_runtime_mode.has_mode(CUDAGraphMode.PIECEWISE)
-                    and not self.speculative_config.enforce_eager
-                )
+                    (
+                        is_graph_capturing
+                        and cudagraph_runtime_mode == CUDAGraphMode.PIECEWISE
+                    )
+                    or (
+                        not is_graph_capturing
+                        and cudagraph_runtime_mode != CUDAGraphMode.NONE
+                    )
+                ) and not self.speculative_config.enforce_eager
 
                 # Note(gnovack) - We need to disable cudagraphs for one of the two
                 # lora cases when cudagraph_specialize_lora is enabled. This is a

vllm/v1/worker/gpu_worker.py

@@ -38,7 +38,7 @@ from vllm.model_executor import set_random_seed
 from vllm.model_executor.models.interfaces import is_mixture_of_experts
 from vllm.model_executor.warmup.kernel_warmup import kernel_warmup
 from vllm.platforms import current_platform
-from vllm.profiler.gpu_profiler import CudaProfilerWrapper, TorchProfilerWrapper
+from vllm.profiler.wrapper import CudaProfilerWrapper, TorchProfilerWrapper
 from vllm.sequence import IntermediateTensors
 from vllm.tasks import SupportedTask
 from vllm.utils.mem_constants import GiB_bytes
@@ -79,6 +79,10 @@ class Worker(WorkerBase):
             is_driver_worker=is_driver_worker,
         )
 
+        # configure float32 matmul precision according to vLLM env.
+        precision = envs.VLLM_FLOAT32_MATMUL_PRECISION
+        torch.set_float32_matmul_precision(precision)
+
         if self.model_config.trust_remote_code:
             # note: lazy import to avoid importing torch before initializing
             from vllm.utils.import_utils import init_cached_hf_modules
@@ -88,17 +92,19 @@ class Worker(WorkerBase):
         # Buffers saved before sleep
         self._sleep_saved_buffers: dict[str, torch.Tensor] = {}
 
-        # Torch/CUDA profiler. Enabled and configured through env vars:
-        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
-        # VLLM_TORCH_CUDA_PROFILE=1
+        # Torch/CUDA profiler. Enabled and configured through profiler_config.
         self.profiler: Any | None = None
-        if envs.VLLM_TORCH_PROFILER_DIR:
+        profiler_config = vllm_config.profiler_config
+        if profiler_config.profiler == "torch":
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
             self.profiler = TorchProfilerWrapper(
-                worker_name=worker_name, local_rank=self.local_rank
+                profiler_config,
+                worker_name=worker_name,
+                local_rank=self.local_rank,
+                activities=["CPU", "CUDA"],
             )
-        elif envs.VLLM_TORCH_CUDA_PROFILE:
-            self.profiler = CudaProfilerWrapper()
+        elif profiler_config.profiler == "cuda":
+            self.profiler = CudaProfilerWrapper(profiler_config)
         else:
             self.profiler = None
 

vllm/v1/worker/tpu_worker.py

@@ -98,10 +98,10 @@ class TPUWorker:
         # MP runtime is initialized.
         self.profiler = None
         self.profile_dir = None
-        if envs.VLLM_TORCH_PROFILER_DIR and self.rank < 1:
+        if vllm_config.profiler_config.profiler == "torch" and self.rank < 1:
             # For TPU, we can only have 1 active profiler session for 1 profiler
             # server. So we only profile on rank0.
-            self.profile_dir = envs.VLLM_TORCH_PROFILER_DIR
+            self.profile_dir = vllm_config.profiler_config.torch_profiler_dir
             logger.info(
                 "Profiling enabled. Traces will be saved to: %s", self.profile_dir
             )

vllm/v1/worker/xpu_worker.py

@@ -6,12 +6,12 @@ from typing import Any
 import torch
 import torch.distributed
 
-import vllm.envs as envs
 from vllm.config import VllmConfig
 from vllm.distributed import get_world_group
 from vllm.logger import init_logger
 from vllm.model_executor import set_random_seed
 from vllm.platforms import current_platform
+from vllm.profiler.wrapper import TorchProfilerWrapper
 from vllm.v1.worker.gpu_worker import Worker, init_worker_distributed_environment
 from vllm.v1.worker.xpu_model_runner import XPUModelRunner
 
@@ -36,41 +36,17 @@ class XPUWorker(Worker):
         assert device_config.device_type == "xpu"
         assert current_platform.is_xpu()
 
-        # Torch profiler. Enabled and configured through env vars:
-        # VLLM_TORCH_PROFILER_DIR=/path/to/save/trace
+        # Torch profiler. Enabled and configured through profiler_config.
         self.profiler: Any | None = None
-        if envs.VLLM_TORCH_PROFILER_DIR:
-            torch_profiler_trace_dir = envs.VLLM_TORCH_PROFILER_DIR
+        profiler_config = vllm_config.profiler_config
+        if profiler_config.profiler == "torch":
             worker_name = f"{vllm_config.instance_id}-rank-{self.rank}"
-            logger.info(
-                "Profiling enabled. Traces will be saved to: %s",
-                torch_profiler_trace_dir,
+            self.profiler = TorchProfilerWrapper(
+                profiler_config,
+                worker_name=worker_name,
+                local_rank=self.local_rank,
+                activities=["CPU", "XPU"],
             )
-            logger.debug(
-                "Profiler config: record_shapes=%s,"
-                "profile_memory=%s,with_stack=%s,with_flops=%s",
-                envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
-                envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
-                envs.VLLM_TORCH_PROFILER_WITH_STACK,
-                envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
-            )
-            self.profiler = torch.profiler.profile(
-                activities=[
-                    torch.profiler.ProfilerActivity.CPU,
-                    torch.profiler.ProfilerActivity.XPU,
-                ],
-                record_shapes=envs.VLLM_TORCH_PROFILER_RECORD_SHAPES,
-                profile_memory=envs.VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY,
-                with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
-                with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
-                on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    torch_profiler_trace_dir,
-                    worker_name=worker_name,
-                    use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
-                ),
-            )
-        else:
-            self.profiler = None
 
     # we provide this function due to `torch.xpu.mem_get_info()` doesn't
     # return correct free_gpu_memory on intel client GPU. We need to