[compile] Enable sequence parallelism matching w/o custom ops enabled (#27126)

Signed-off-by: angelayi <yiangela7@gmail.com>
Signed-off-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
Signed-off-by: ProExpertProg <lgovedic@redhat.com>
Co-authored-by: Luka Govedič <lgovedic@redhat.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
Co-authored-by: Luka Govedič <luka.govedic@gmail.com>
(cherry picked from commit f36292dbee)

.buildkite/test-pipeline.yaml

@@ -441,6 +441,7 @@ steps:
     - vllm/
     - tests/compile
   commands:
+    - pytest -v -s compile/test_config.py
     - pytest -v -s compile/test_pass_manager.py
     - pytest -v -s compile/test_fusion.py
     - pytest -v -s compile/test_fusion_attn.py
@@ -471,10 +472,11 @@ steps:
   - vllm/
   - tests/compile
   commands:
-  - pytest -v -s compile/test_full_graph.py
+    # fp8 kv scales not supported on sm89, tested on Blackwell instead
+  - pytest -v -s compile/test_full_graph.py -k 'not test_fp8_kv_scale_compile'
     # Limit to no custom ops to reduce running time
     # Wrap with quotes to escape yaml and avoid starting -k string with a -
-  - "pytest -v -s compile/test_fusions_e2e.py -k 'TRITON and -quant_fp8'"
+  - "pytest -v -s compile/test_fusions_e2e.py -k 'TRITON and not +quant_fp8 and not Llama-4'"
 
 - label: Cudagraph test
   timeout_in_minutes: 20
@@ -867,12 +869,12 @@ steps:
   optional: true
   commands:
     - pip install --upgrade git+https://github.com/huggingface/transformers
-    - pytest -v -s tests/models/test_initialization.py
+    - pytest -v -s tests/models/test_initialization.py -k 'not (Gemma3 or ModernBert or Qwen2_5_VL or Qwen2_5vl or Qwen2VL or TransformersMultiModalEmbeddingModel or TransformersMultiModalForSequenceClassification or Ultravox or Phi4Multimodal or LlavaNextVideo or MiniCPMO or Lfm2Moe or PaliGemma or RobertaForSequenceClassification or Ovis2_5 or Fuyu or DeepseekOCR or KimiVL)'
     - pytest -v -s tests/models/test_transformers.py
-    - pytest -v -s tests/models/multimodal/processing/
+    # - pytest -v -s tests/models/multimodal/processing/
-    - pytest -v -s tests/models/multimodal/test_mapping.py
+    - pytest -v -s tests/models/multimodal/test_mapping.py -k 'not (Gemma3 or Qwen2VL or Qwen2_5_VL)'
     - python3 examples/offline_inference/basic/chat.py
-    - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
+    # - python3 examples/offline_inference/vision_language.py --model-type qwen2_5_vl
     # Whisper needs spawn method to avoid deadlock
     - VLLM_WORKER_MULTIPROC_METHOD=spawn python3 examples/offline_inference/audio_language.py --model-type whisper
 
@@ -912,7 +914,7 @@ steps:
     - pytest -v -s tests/kernels/moe/test_ocp_mx_moe.py
     - pytest -v -s tests/kernels/moe/test_flashinfer.py
 
-- label: Blackwell Fusion Tests # 30 min
+- label: Blackwell Fusion & Compile Tests # 30 min
   timeout_in_minutes: 40
   working_dir: "/vllm-workspace/"
   gpu: b200
@@ -933,7 +935,9 @@ steps:
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
     # Limit to Inductor partition, no custom ops, and allreduce & attn fusion to reduce running time
     # Wrap with quotes to escape yaml
-    - "pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and Llama-3.1 and -quant_fp8 and -rms_norm'"
+    - "pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm -k 'True and not +quant_fp8 and not +rms_norm'"
+    # test_fp8_kv_scale_compile requires FlashAttention (not supported on default L4/L40)
+    - pytest -v -s tests/compile/test_full_graph.py::test_fp8_kv_scale_compile
 
 - label: Blackwell Fusion E2E Tests # 30 min
   timeout_in_minutes: 40
@@ -951,6 +955,7 @@ steps:
   - vllm/model_executor/layers/activation.py
   - vllm/model_executor/layers/quantization/input_quant_fp8.py
   - tests/compile/test_fusions_e2e.py
+  - tests/compile/test_full_graph.py
   commands:
     - nvidia-smi
     # Run all e2e fusion tests
@@ -1250,7 +1255,8 @@ steps:
     - pytest -v -s tests/compile/test_async_tp.py
     - pytest -v -s tests/compile/test_sequence_parallelism.py
     - pytest -v -s tests/compile/test_fusion_all_reduce.py
-    - pytest -v -s tests/compile/test_fusions_e2e.py::test_tp2_attn_quant_allreduce_rmsnorm
+    - "pytest -v -s tests/compile/test_fusions_e2e.py -k 'not Llama-4'"
+    - pytest -v -s tests/distributed/test_sequence_parallel.py
     - pytest -v -s tests/distributed/test_context_parallel.py
     - CUDA_VISIBLE_DEVICES=1,2 VLLM_ALL2ALL_BACKEND=deepep_high_throughput VLLM_USE_DEEP_GEMM=1 VLLM_LOGGING_LEVEL=DEBUG python3 examples/offline_inference/data_parallel.py --model Qwen/Qwen1.5-MoE-A2.7B --tp-size=1  --dp-size=2 --max-model-len 2048
     - pytest -v -s tests/v1/distributed/test_dbo.py

docs/design/cuda_graphs.md

@@ -218,16 +218,6 @@ outputs = model.generate(
 )
 ```
 
-### Migration from legacy flags
-
-Legacy `use_cudagraph` and `full_cuda_graph` are unified by `cudagraph_mode`:
-
-* `use_cudagraph=False` → `NONE`.
-* `use_cudagraph=True` and `full_cuda_graph=False` → `PIECEWISE`.
-* `full_cuda_graph=True` → directly set `FULL` and rely on the graceful fallback policy.
-
-As they are deprecated and will be removed in the next major or minor release, i.e., v0.11.0 or v1.0.0, we recommend using cudagraph_mode instead.
-
 ### Piecewise compilation and full graph custom passes (attention fusion, sequence parallelism)
 
 Unfortunately, some custom compile passes have to see the whole graph to be effective and hence aren't compatible with piecewise compilation. This includes `AttnFusionPass` and `SequenceParallelismPass`. As a short-term solution, we automatically disable piecewise compilation (by setting `splitting_ops=[]`) when attention fusion is enabled. We use CUDA Graph modes `FULL` or `FULL_DECODE_ONLY` (depending on backend support). However, this leads to another optimization incompatibility and confusing performance tradeoffs.

requirements/cuda.txt

@@ -9,7 +9,6 @@ torch==2.9.0
 torchaudio==2.9.0
 # These must be updated alongside torch
 torchvision==0.24.0 # Required for phi3v processor. See https://github.com/pytorch/vision?tab=readme-ov-file#installation for corresponding version
-# Build from https://github.com/facebookresearch/xformers/releases/tag/v0.0.32.post1
+xformers==0.0.33.post1; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch >= 2.9
-xformers==0.0.33+5d4b92a5.d20251029; platform_system == 'Linux' and platform_machine == 'x86_64'  # Requires PyTorch >= 2.9
 # FlashInfer should be updated together with the Dockerfile
 flashinfer-python==0.5.2

tests/compile/piecewise/test_multiple_graphs.py

@@ -203,7 +203,7 @@ def test_multi_graph_piecewise_compile(use_inductor_graph_partition: bool):
     vllm_config = VllmConfig(
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=True,
+            cudagraph_mode=CUDAGraphMode.PIECEWISE,
             splitting_ops=["silly::attention"],
             cudagraph_capture_sizes=[1, 2],
             use_inductor_graph_partition=use_inductor_graph_partition,
@@ -281,7 +281,7 @@ def test_multi_graph_piecewise_compile(use_inductor_graph_partition: bool):
     vllm_config = VllmConfig(
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=False,
+            cudagraph_mode=CUDAGraphMode.NONE,
             splitting_ops=["silly::attention"],
             use_inductor_graph_partition=use_inductor_graph_partition,
         )

tests/compile/piecewise/test_simple.py

@@ -62,7 +62,6 @@ def _run_simple_model(
     vllm_config = VllmConfig(
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=True,
             use_inductor=use_inductor,
             splitting_ops=splitting_ops,
             use_inductor_graph_partition=use_inductor_graph_partition,

tests/compile/piecewise/test_toy_llama.py

@@ -449,7 +449,6 @@ def benchmark():
         if piecewise:
             compilation_config = CompilationConfig(
                 mode=CompilationMode.VLLM_COMPILE,
-                use_cudagraph=True,
                 splitting_ops=["silly::attention"],
                 cudagraph_capture_sizes=cudagraph_sizes,
             )

tests/compile/test_config.py

@@ -2,8 +2,10 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import copy
 from contextlib import nullcontext
+from unittest.mock import patch
 
 import pytest
+from pydantic import ValidationError
 
 from vllm.compilation.counter import compilation_counter
 from vllm.compilation.fix_functionalization import FixFunctionalizationPass
@@ -11,7 +13,7 @@ from vllm.config import CompilationConfig, CUDAGraphMode, VllmConfig
 from vllm.config.compilation import CompilationMode
 from vllm.engine.arg_utils import EngineArgs
 from vllm.platforms import current_platform
-from vllm.utils.torch_utils import _is_torch_equal_or_newer, is_torch_equal_or_newer
+from vllm.utils.torch_utils import _is_torch_equal_or_newer
 
 
 def test_version():
@@ -23,14 +25,6 @@ def test_version():
     assert not _is_torch_equal_or_newer("2.7.1", "2.8.0.dev")
 
 
-def test_use_cudagraphs_dynamic():
-    vllm_config = VllmConfig()
-    # Default V1 configuration now starts without cudagraphs enabled; the
-    # engine decides when to capture based on runtime settings instead of a
-    # blanket default.
-    assert vllm_config.compilation_config.use_cudagraph
-
-
 def test_copy_pass():
     vllm_config = VllmConfig()
     inductor_pass = FixFunctionalizationPass(vllm_config)
@@ -65,7 +59,7 @@ def test_VLLM_DISABLE_COMPILE_CACHE(vllm_runner, monkeypatch, val):
     monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", val)
 
     compilation_config = {
-        "use_cudagraph": False,  # speed things up a bit
+        "cudagraph_mode": CUDAGraphMode.NONE,  # speed things up a bit
     }
     with (
         compilation_counter.expect(
@@ -83,20 +77,31 @@ def test_VLLM_DISABLE_COMPILE_CACHE(vllm_runner, monkeypatch, val):
 
 # forked needed to workaround https://github.com/vllm-project/vllm/issues/21073
 @pytest.mark.forked
-@pytest.mark.parametrize("enabled", [True, False])
+@pytest.mark.parametrize(
-def test_use_cudagraphs(vllm_runner, monkeypatch, enabled):
+    "cudagraph_mode,num_cudagraph_captured",
+    [
+        (CUDAGraphMode.NONE, 0),
+        (CUDAGraphMode.FULL_DECODE_ONLY, 1),
+        (CUDAGraphMode.PIECEWISE, 13),
+        (CUDAGraphMode.FULL_AND_PIECEWISE, 14),
+    ],
+)
+def test_use_cudagraphs(
+    vllm_runner, monkeypatch, cudagraph_mode, num_cudagraph_captured
+):
     # Disable multiprocessing so that the counter is in the same process
     monkeypatch.setenv("VLLM_ENABLE_V1_MULTIPROCESSING", "0")
 
     compilation_config = {
         "cudagraph_capture_sizes": [100],
-        "use_cudagraph": enabled,
+        "cudagraph_mode": cudagraph_mode,
     }
+    num_gpu_runner_capture_triggers = 1 if cudagraph_mode != CUDAGraphMode.NONE else 0
     with (
         compilation_counter.expect(
             num_graphs_seen=1,
-            num_gpu_runner_capture_triggers=1 if enabled else 0,
+            num_gpu_runner_capture_triggers=num_gpu_runner_capture_triggers,
-            num_cudagraph_captured=13 if enabled else 0,
+            num_cudagraph_captured=num_cudagraph_captured,
         ),
         # loading the model causes compilation (if enabled) to happen
         vllm_runner(
@@ -168,19 +173,18 @@ def test_splitting_ops_dynamic():
     assert not config.compilation_config.splitting_ops_contain_attention()
 
     # When use_inductor_graph_partition=True
-    if is_torch_equal_or_newer("2.9.0.dev"):
+    config = VllmConfig(
-        config = VllmConfig(
+        compilation_config=CompilationConfig(
-            compilation_config=CompilationConfig(
+            mode=CompilationMode.VLLM_COMPILE,
-                mode=CompilationMode.VLLM_COMPILE,
+            use_inductor_graph_partition=True,
-                use_inductor_graph_partition=True,
+            splitting_ops=["vllm::unified_attention"],
-                splitting_ops=["vllm::unified_attention"],
-            )
         )
-        # with inductor partition we use splitting_ops directly for
+    )
-        # partition rules
+    # with inductor partition we use splitting_ops directly for
-        assert config.compilation_config.splitting_ops == ["vllm::unified_attention"]
+    # partition rules
+    assert config.compilation_config.splitting_ops == ["vllm::unified_attention"]
 
-    # When attn_fusion pass enabled, splitting_ops now default to attention ops.
+    # When attn_fusion pass enabled.
     config = VllmConfig(
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
@@ -189,29 +193,41 @@ def test_splitting_ops_dynamic():
             cudagraph_mode=CUDAGraphMode.PIECEWISE,
         )
     )
-    # With the new simplified logic, attention fusion works with splitting_ops
+    assert config.compilation_config.splitting_ops == []
-    assert config.compilation_config.splitting_ops_contain_attention()
+    # cudagraph mode also fall back to FULL
-    # cudagraph mode remains PIECEWISE
+    assert config.compilation_config.cudagraph_mode == CUDAGraphMode.FULL
-    assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
 
-    # When both use_inductor_graph_partition and attn_fusion pass enabled.
+    # splitting_ops can not contain attention ops when attn_fusion
-    if is_torch_equal_or_newer("2.9.0.dev"):
+    # pass enabled.
+    with pytest.raises(ValidationError):
         config = VllmConfig(
             compilation_config=CompilationConfig(
                 mode=CompilationMode.VLLM_COMPILE,
-                use_inductor_graph_partition=True,
                 pass_config={"enable_attn_fusion": True, "enable_noop": True},
                 custom_ops=["+quant_fp8"],
                 cudagraph_mode=CUDAGraphMode.PIECEWISE,
+                # work around for accessing all attntion ops
+                splitting_ops=CompilationConfig()._attention_ops,
             )
         )
-        # With inductor graph partition, attn_fusion and splitting_ops
+
-        # work together. Default splitting_ops include attention ops.
+    # When both use_inductor_graph_partition and attn_fusion pass enabled.
-        assert config.compilation_config.splitting_ops_contain_attention()
+    config = VllmConfig(
-        # enable_attn_fusion is directly supported under
+        compilation_config=CompilationConfig(
-        # use_inductor_graph_partition=True, and cudagraph_mode
+            mode=CompilationMode.VLLM_COMPILE,
-        # is unchanged.
+            use_inductor_graph_partition=True,
-        assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
+            pass_config={"enable_attn_fusion": True, "enable_noop": True},
+            custom_ops=["+quant_fp8"],
+            cudagraph_mode=CUDAGraphMode.PIECEWISE,
+        )
+    )
+    # With inductor graph partition, attn_fusion and splitting_ops
+    # work together. Default splitting_ops include attention ops.
+    assert config.compilation_config.splitting_ops_contain_attention()
+    # enable_attn_fusion is directly supported under
+    # use_inductor_graph_partition=True, and cudagraph_mode
+    # is unchanged.
+    assert config.compilation_config.cudagraph_mode == CUDAGraphMode.PIECEWISE
 
 
 def test_should_split():
@@ -293,25 +309,36 @@ def test_should_split():
         "tp_size",
         "enable_sequence_parallelism",
         "max_num_batched_tokens",
-        "use_cudagraph",
+        "cudagraph_mode",
         "expected_max_size",
     ),
     [
-        (None, None, 1, False, 2048, True, 512),
+        (None, None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
-        ([1, 2, 4], 4, 1, False, 2048, True, 4),
+        ([1, 2, 4], 4, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
-        ([1, 2, 4], 8, 1, False, 2048, True, RuntimeError),
+        (
-        ([1, 256], None, 1, False, 2048, 256),
+            [1, 2, 4],
-        ([], None, 1, False, 2048, False, 0),
+            8,
-        (None, 0, 1, False, 2048, False, 0),
+            1,
+            False,
+            2048,
+            CUDAGraphMode.FULL_AND_PIECEWISE,
+            ValidationError,
+        ),
+        ([1, 256], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
+        ([], None, 1, False, 2048, CUDAGraphMode.NONE, 0),
+        (None, 0, 1, False, 2048, CUDAGraphMode.NONE, 0),
         # truncated to nearest multiple of 8 or 16
-        (None, 257, 1, False, 2048, True, 256),
+        (None, 257, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 256),
-        ([1, 2, 4, 15], None, 1, False, 2048, True, 15),  # max from list
+        # max from list
-        ([1, 2, 4, 15], None, 2, True, 2048, True, 4),  # filtered out 15 due to SP
+        ([1, 2, 4, 15], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 15),
-        ([1, 2, 4, 15], None, 1, False, 8, True, 4),  # limited by the max_tokens
+        # filtered out 15 due to SP
+        ([1, 2, 4, 15], None, 2, True, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
+        # limited by the max_tokens
+        ([1, 2, 4, 15], None, 1, False, 8, CUDAGraphMode.FULL_AND_PIECEWISE, 4),
         # the list should contain at least 1 element when use cudagraph
-        ([], None, 1, False, 2048, True, RuntimeError),
+        ([], None, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, ValidationError),
         # the max capturing size should be >= 1 when use cudagraph
-        (None, 0, 1, False, 2048, True, RuntimeError),
+        (None, 0, 1, False, 2048, CUDAGraphMode.FULL_AND_PIECEWISE, ValidationError),
     ],
 )
 def test_cudagraph_sizes_post_init(
@@ -320,15 +347,17 @@ def test_cudagraph_sizes_post_init(
     tp_size,
     enable_sequence_parallelism,
     max_num_batched_tokens,
-    use_cudagraph,
+    cudagraph_mode,
     expected_max_size,
 ):
     ctx = nullcontext()
-    if isinstance(expected_max_size, Exception):
+    if expected_max_size == ValidationError:
         ctx = pytest.raises(expected_max_size)
 
-    cudagraph_mode = CUDAGraphMode.PIECEWISE if use_cudagraph else CUDAGraphMode.NONE
+    with (
-    with ctx:
+        ctx,
+        patch("vllm.config.parallel.cuda_device_count_stateless", return_value=tp_size),
+    ):
         compilation_config = CompilationConfig(
             cudagraph_capture_sizes=cudagraph_capture_sizes,
             max_cudagraph_capture_size=max_cudagraph_capture_size,
@@ -342,11 +371,13 @@ def test_cudagraph_sizes_post_init(
         engine_args = EngineArgs(
             model="facebook/opt-125m",
             tensor_parallel_size=tp_size,
+            max_num_seqs=min(max_num_batched_tokens, 128),
             max_num_batched_tokens=max_num_batched_tokens,
             compilation_config=compilation_config,
         )
         vllm_config = engine_args.create_engine_config()
 
-    assert (
+        assert (
-        vllm_config.compilation_config.max_cudagraph_capture_size == expected_max_size
+            vllm_config.compilation_config.max_cudagraph_capture_size
-    )
+            == expected_max_size
+        )

tests/compile/test_decorator.py

@@ -80,7 +80,6 @@ def test_ignore_torch_compile_decorator(use_inductor_graph_partition, monkeypatc
     vllm_config = VllmConfig(
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=True,
             splitting_ops=["silly::attention"],
             cudagraph_capture_sizes=[1, 2],
             use_inductor_graph_partition=use_inductor_graph_partition,
@@ -215,7 +214,6 @@ def test_conditional_compile_enable_if(use_inductor_graph_partition, monkeypatch
         ),
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=True,
             splitting_ops=["silly::attention"],
             cudagraph_capture_sizes=[1, 2],
             use_inductor_graph_partition=use_inductor_graph_partition,
@@ -257,7 +255,6 @@ def test_conditional_compile_enable_if(use_inductor_graph_partition, monkeypatch
         ),
         compilation_config=CompilationConfig(
             mode=CompilationMode.VLLM_COMPILE,
-            use_cudagraph=True,
             splitting_ops=["silly::attention"],
             cudagraph_capture_sizes=[1, 2],
             use_inductor_graph_partition=use_inductor_graph_partition,

tests/compile/test_full_graph.py

@@ -183,8 +183,14 @@ def test_custom_compile_config(
     "compilation_mode",
     [CompilationMode.NONE, CompilationMode.VLLM_COMPILE],
 )
-def test_fp8_kv_scale_compile(compilation_mode: int):
+@pytest.mark.parametrize(
-    model = "Qwen/Qwen2-0.5B"
+    "model",
+    [
+        "Qwen/Qwen2-0.5B",  # Standard attention model
+        "deepseek-ai/DeepSeek-V2-Lite",  # MLA (Multi-head Latent Attention) model
+    ],
+)
+def test_fp8_kv_scale_compile(compilation_mode: int, model: str):
     model_kwargs = {
         "quantization": "fp8",
         "kv_cache_dtype": "fp8_e4m3",

tests/compile/test_fusions_e2e.py

@@ -20,13 +20,22 @@ from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 from ..utils import flat_product, multi_gpu_test
 
+is_blackwell = lambda: current_platform.is_device_capability(100)
+"""Are we running on Blackwell, a lot of tests depend on it"""
+
+
+class Matches(NamedTuple):
+    attention_fusion: int = 0
+    allreduce_fusion: int = 0
+    sequence_parallel: int = 0
+    async_tp: int = 0
+
 
 class ModelBackendTestCase(NamedTuple):
     model_name: str
     model_kwargs: dict[str, Any]
-    backend: _Backend
+    backend: AttentionBackendEnum
-    attention_fusions: int
+    matches: Matches
-    allreduce_fusions: int | None = None
 
 
 MODELS_FP8: list[ModelBackendTestCase] = []
@@ -38,17 +47,33 @@ if current_platform.is_cuda():
         ModelBackendTestCase(
             # Use smaller model for L40s in CI
             model_name="RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8",
-            model_kwargs=dict(max_model_len=1024),
+            # TODO while llama4 is broken, use FLASHINFER for llama3 on Blackwell
-            backend=_Backend.TRITON_ATTN,
+            #  so FI attention+fp8_quant is at least tested once
-            attention_fusions=32,
+            model_kwargs=dict(max_model_len=1024, kv_cache_dtype="fp8"),
-            allreduce_fusions=65,
+            backend=AttentionBackendEnum.FLASHINFER
+            if is_blackwell()
+            else AttentionBackendEnum.TRITON_ATTN,
+            matches=Matches(
+                attention_fusion=32,
+                allreduce_fusion=65,
+                sequence_parallel=65,
+                async_tp=128,
+            ),
         ),
         ModelBackendTestCase(
             model_name="nvidia/Llama-4-Scout-17B-16E-Instruct-FP8",
             model_kwargs=dict(max_model_len=1024, kv_cache_dtype="fp8"),
-            backend=_Backend.FLASHINFER,
+            # TODO FlashInfer attn broken on Hopper with kvcache=fp8:
-            attention_fusions=48,
+            # https://github.com/vllm-project/vllm/issues/28568
-            allreduce_fusions=96,
+            # TODO FlashInfer attn broken on Blackwell for llama4:
+            # https://github.com/vllm-project/vllm/issues/28604
+            backend=AttentionBackendEnum.TRITON_ATTN,
+            matches=Matches(
+                attention_fusion=48,
+                allreduce_fusion=96,
+                sequence_parallel=96,
+                async_tp=95,  # mlp is moe, no fusion there
+            ),
         ),
     ]
 
@@ -56,9 +81,13 @@ if current_platform.is_cuda():
         ModelBackendTestCase(
             model_name="nvidia/Llama-3.1-8B-Instruct-FP4",
             model_kwargs=dict(max_model_len=1024, kv_cache_dtype="fp8"),
-            backend=_Backend.FLASHINFER,
+            backend=AttentionBackendEnum.FLASHINFER,
-            attention_fusions=32,
+            matches=Matches(
-            allreduce_fusions=65,
+                attention_fusion=32,
+                allreduce_fusion=65,
+                sequence_parallel=65,
+                async_tp=128,
+            ),
         ),
     ]
 
@@ -67,9 +96,24 @@ if current_platform.is_cuda():
         ModelBackendTestCase(
             model_name="meta-llama/Llama-3.1-8B-Instruct",
             model_kwargs=dict(max_model_len=1024),
-            backend=_Backend.TRITON_ATTN,
+            backend=AttentionBackendEnum.TRITON_ATTN,
-            attention_fusions=0,
+            matches=Matches(
-            allreduce_fusions=65,
+                attention_fusion=0,
+                allreduce_fusion=65,
+                sequence_parallel=65,
+                async_tp=128,
+            ),
+        ),
+        ModelBackendTestCase(
+            model_name="Qwen/Qwen3-30B-A3B",
+            model_kwargs=dict(max_model_len=1024),
+            backend=AttentionBackendEnum.TRITON_ATTN,
+            matches=Matches(
+                attention_fusion=0,
+                allreduce_fusion=97,
+                sequence_parallel=97,
+                async_tp=96,  # MLP is MoE, half the fusions of dense
+            ),
         ),
     ]
 
@@ -78,20 +122,20 @@ elif current_platform.is_rocm():
         ModelBackendTestCase(
             model_name="amd/Llama-3.1-8B-Instruct-FP8-KV",
             model_kwargs=dict(max_model_len=1024),
-            backend=_Backend.TRITON_ATTN,
+            backend=AttentionBackendEnum.TRITON_ATTN,
-            attention_fusions=32,
+            matches=Matches(attention_fusion=32),
         ),
         ModelBackendTestCase(
             model_name="amd/Llama-3.1-8B-Instruct-FP8-KV",
             model_kwargs=dict(max_model_len=1024),
-            backend=_Backend.ROCM_ATTN,
+            backend=AttentionBackendEnum.ROCM_ATTN,
-            attention_fusions=32,
+            matches=Matches(attention_fusion=32),
         ),
         ModelBackendTestCase(
             model_name="amd/Llama-3.1-8B-Instruct-FP8-KV",
             model_kwargs=dict(max_model_len=1024),
-            backend=_Backend.ROCM_AITER_UNIFIED_ATTN,
+            backend=AttentionBackendEnum.ROCM_AITER_UNIFIED_ATTN,
-            attention_fusions=32,
+            matches=Matches(attention_fusion=32),
         ),
     ]
 
@@ -99,8 +143,7 @@ CUSTOM_OPS_FP8 = ["-quant_fp8", "+quant_fp8"]
 
 
 @pytest.mark.parametrize(
-    "model_name, model_kwargs, backend, "
+    "model_name, model_kwargs, backend, matches, custom_ops",
-    "attention_fusions, allreduce_fusions, custom_ops",
     # Test attention+quant_fp8 fusion with custom and torch impls of QuantFP8
     list(flat_product(MODELS_FP8, CUSTOM_OPS_FP8))
     # quant_fp4 only has the custom impl
@@ -110,16 +153,15 @@ CUSTOM_OPS_FP8 = ["-quant_fp8", "+quant_fp8"]
 def test_attn_quant(
     model_name: str,
     model_kwargs: dict[str, Any],
-    backend: _Backend,
+    backend: AttentionBackendEnum,
-    attention_fusions: int,
+    matches: Matches,
-    allreduce_fusions: int,
     custom_ops: str,
     inductor_graph_partition: bool,
     caplog_mp_spawn,
     monkeypatch,
 ):
-    if backend == _Backend.FLASHINFER and (
+    if backend == AttentionBackendEnum.FLASHINFER and (
-        not current_platform.is_device_capability((10, 0)) or not has_flashinfer()
+        not is_blackwell() or not has_flashinfer()
     ):
         pytest.skip("FlashInfer attn fusion requires Blackwell and flashinfer")
     if inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
@@ -162,12 +204,12 @@ def test_attn_quant(
     with caplog_mp_spawn(logging.DEBUG) as log_holder:
         run_model(compilation_config, model_name, **model_kwargs)
 
-    matches = re.findall(
+    log_matches = re.findall(
         r"fusion_attn.py:\d+] Fused quant onto (\d+) attention nodes",
         log_holder.text,
     )
-    assert len(matches) == 1, log_holder.text
+    assert len(log_matches) == 1, log_holder.text
-    assert int(matches[0]) == attention_fusions
+    assert int(log_matches[0]) == matches.attention_fusion
 
 
 CUSTOM_OPS_RMS_NORM = ["-rms_norm", "+rms_norm"]
@@ -180,8 +222,7 @@ def custom_ops_product(*custom_ops_lists: list[str]) -> Iterable[str]:
 
 @multi_gpu_test(num_gpus=2)
 @pytest.mark.parametrize(
-    "model_name, model_kwargs, backend, "
+    "model_name, model_kwargs, backend, matches, custom_ops",
-    "attention_fusions, allreduce_fusions, custom_ops",
     # Toggle RMSNorm and QuantFP8 for FP8 models
     list(
         flat_product(
@@ -201,9 +242,8 @@ def custom_ops_product(*custom_ops_lists: list[str]) -> Iterable[str]:
 def test_tp2_attn_quant_allreduce_rmsnorm(
     model_name: str,
     model_kwargs: dict,
-    backend: _Backend,
+    backend: AttentionBackendEnum,
-    attention_fusions: int,
+    matches: Matches,
-    allreduce_fusions: int,
     custom_ops: str,
     inductor_graph_partition: bool,
     caplog_mp_spawn,
@@ -212,6 +252,13 @@ def test_tp2_attn_quant_allreduce_rmsnorm(
     if inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
         pytest.skip("Inductor graph partition requires torch>=2.9")
 
+    if "fp4" in model_name.lower() and not is_blackwell():
+        pytest.skip("NVFP4 quant requires Blackwell")
+
+    if backend == AttentionBackendEnum.FLASHINFER and not is_blackwell():
+        # FlashInfer attn fusion requires Blackwell
+        matches = matches._replace(attention_fusion=0)
+
     custom_ops_list = custom_ops.split(",") if custom_ops else []
 
     if inductor_graph_partition:
@@ -251,23 +298,135 @@ def test_tp2_attn_quant_allreduce_rmsnorm(
         run_model(
             compilation_config, model_name, tensor_parallel_size=2, **model_kwargs
         )
-    matches = re.findall(
+    log_matches = re.findall(
         r"fusion_attn.py:\d+] Fused quant onto (\d+) attention nodes",
         log_holder.text,
     )
-    assert len(matches) == 2, log_holder.text
+    assert len(log_matches) == 2, log_holder.text
 
-    assert int(matches[0]) == attention_fusions
+    assert int(log_matches[0]) == matches.attention_fusion
-    assert int(matches[1]) == attention_fusions
+    assert int(log_matches[1]) == matches.attention_fusion
 
-    matches = re.findall(
+    log_matches = re.findall(
         r"collective_fusion.py:\d+] Replaced (\d+) patterns",
         log_holder.text,
     )
-    assert len(matches) == 2, log_holder.text
+    assert len(log_matches) == 2, log_holder.text
 
-    assert int(matches[0]) == allreduce_fusions
+    assert int(log_matches[0]) == matches.allreduce_fusion
-    assert int(matches[1]) == allreduce_fusions
+    assert int(log_matches[1]) == matches.allreduce_fusion
+
+
+@multi_gpu_test(num_gpus=2)
+@pytest.mark.parametrize(
+    "model_name, model_kwargs, backend, matches, custom_ops",
+    # Toggle RMSNorm and QuantFP8 for FP8 models
+    list(
+        flat_product(
+            MODELS_FP8, custom_ops_product(CUSTOM_OPS_FP8, CUSTOM_OPS_RMS_NORM)
+        )
+    )
+    # Toggle RMSNorm for FP4 models and unquant models
+    + list(flat_product(MODELS_FP4 + MODELS, CUSTOM_OPS_RMS_NORM)),
+)
+@pytest.mark.parametrize("inductor_graph_partition", [True, False])
+@pytest.mark.skipif(
+    not current_platform.is_cuda(),
+    reason="sequence parallel only tested on CUDA",
+)
+def test_tp2_attn_quant_async_tp(
+    model_name: str,
+    model_kwargs: dict,
+    backend: AttentionBackendEnum,
+    matches: Matches,
+    custom_ops: str,
+    inductor_graph_partition: bool,
+    caplog_mp_spawn,
+    monkeypatch,
+):
+    if is_blackwell():
+        # TODO: https://github.com/vllm-project/vllm/issues/27893
+        pytest.skip("Blackwell is not supported for AsyncTP pass")
+
+    if inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
+        pytest.skip("Inductor graph partition requires torch>=2.9")
+
+    if "fp4" in model_name.lower() and not is_blackwell():
+        pytest.skip("NVFP4 quant requires Blackwell")
+
+    if backend == AttentionBackendEnum.FLASHINFER:
+        if not has_flashinfer():
+            pytest.skip("FlashInfer backend requires flashinfer installed")
+        if not is_blackwell():
+            # FlashInfer attn fusion requires Blackwell
+            matches = matches._replace(attention_fusion=0)
+
+    custom_ops_list = custom_ops.split(",") if custom_ops else []
+
+    if inductor_graph_partition:
+        mode = CUDAGraphMode.FULL_AND_PIECEWISE
+        splitting_ops: list[str] | None = None
+    else:
+        mode = CUDAGraphMode.FULL_DECODE_ONLY
+        splitting_ops = []
+
+    # Disable, compile cache to make sure custom passes run.
+    # Otherwise, we can't verify fusion happened through the logs.
+    monkeypatch.setenv("VLLM_DISABLE_COMPILE_CACHE", "1")
+
+    # To capture subprocess logs, we need to know whether spawn or fork is used.
+    # Force spawn as it is more general.
+    monkeypatch.setenv("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    monkeypatch.setenv("VLLM_ATTENTION_BACKEND", backend.name)
+
+    compilation_config = CompilationConfig(
+        # Testing properties
+        use_inductor_graph_partition=inductor_graph_partition,
+        cudagraph_mode=mode,
+        custom_ops=custom_ops_list,
+        splitting_ops=splitting_ops,
+        # Common
+        level=CompilationMode.VLLM_COMPILE,
+        pass_config=PassConfig(
+            enable_attn_fusion=True,
+            enable_noop=True,
+            enable_sequence_parallelism=True,
+            enable_async_tp=True,
+        ),
+        # Inductor caches custom passes by default as well via uuid
+        inductor_compile_config={"force_disable_caches": True},
+    )
+
+    with caplog_mp_spawn(logging.DEBUG) as log_holder:
+        run_model(
+            compilation_config, model_name, tensor_parallel_size=2, **model_kwargs
+        )
+    log_matches = re.findall(
+        r"fusion_attn.py:\d+] Fused quant onto (\d+) attention nodes",
+        log_holder.text,
+    )
+    assert len(log_matches) == 2, log_holder.text
+
+    assert int(log_matches[0]) == matches.attention_fusion
+    assert int(log_matches[1]) == matches.attention_fusion
+
+    log_matches = re.findall(
+        r"sequence_parallelism.py:\d+] Replaced (\d+) patterns",
+        log_holder.text,
+    )
+    assert len(log_matches) == 2, log_holder.text
+
+    assert int(log_matches[0]) == matches.sequence_parallel
+    assert int(log_matches[1]) == matches.sequence_parallel
+
+    log_matches = re.findall(
+        r"collective_fusion.py:\d+] Replaced (\d+) patterns",
+        log_holder.text,
+    )
+    assert len(log_matches) == 2, log_holder.text
+
+    assert int(log_matches[0]) == matches.async_tp
+    assert int(log_matches[1]) == matches.async_tp
 
 
 def run_model(compile_config: int | CompilationConfig, model: str, **model_kwargs):

tests/compile/test_multimodal_compile.py

@@ -10,8 +10,8 @@ from vllm.platforms import current_platform
 
 def test_compile():
     vllm_config = VllmConfig()
-    # Default configuration compiles mm encoder
+    # Default configuration does not compile mm encoder
-    assert vllm_config.compilation_config.compile_mm_encoder
+    assert not vllm_config.compilation_config.compile_mm_encoder
 
 
 # forked needed to workaround https://github.com/vllm-project/vllm/issues/21073
@@ -39,7 +39,10 @@ def test_qwen2_5_vl_compilation(vllm_runner, monkeypatch):
             "Qwen/Qwen2.5-VL-3B-Instruct",
             max_model_len=2048,
             gpu_memory_utilization=0.8,
-            compilation_config={"mode": CompilationMode.VLLM_COMPILE},
+            compilation_config={
+                "mode": CompilationMode.VLLM_COMPILE,
+                "compile_mm_encoder": True,
+            },
         ) as _,
     ):
         pass

tests/compile/test_sequence_parallelism.py

@@ -5,15 +5,15 @@ import pytest
 import torch
 
 import vllm.envs as envs
-from vllm.compilation.fix_functionalization import FixFunctionalizationPass
 from vllm.compilation.fusion import RMSNormQuantFusionPass
-from vllm.compilation.fx_utils import find_auto_fn, find_auto_fn_maybe, is_func
+from vllm.compilation.fx_utils import find_auto_fn
 from vllm.compilation.noop_elimination import NoOpEliminationPass
 from vllm.compilation.post_cleanup import PostCleanupPass
 from vllm.compilation.sequence_parallelism import SequenceParallelismPass
 from vllm.compilation.vllm_inductor_pass import VllmInductorPass
 from vllm.config import (
     CompilationConfig,
+    CUDAGraphMode,
     DeviceConfig,
     ModelConfig,
     PassConfig,
@@ -27,6 +27,7 @@ from vllm.distributed.parallel_state import (
     initialize_model_parallel,
 )
 from vllm.model_executor.layers.layernorm import RMSNorm
+from vllm.model_executor.layers.quantization.utils.quant_utils import GroupShape
 from vllm.model_executor.layers.quantization.utils.w8a8_utils import Fp8LinearOp
 from vllm.platforms import current_platform
 from vllm.utils.system_utils import update_environment_variables
@@ -43,172 +44,157 @@ prompts = [
 ]
 
 
-class TestModel(torch.nn.Module):
+class TestAllReduceRMSNormModel(torch.nn.Module):
-    def __init__(self, hidden_size=16, intermediate_size=32):
+    def __init__(self, hidden_size=16, eps=1e-6):
         super().__init__()
         self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
+        self.eps = eps
-        self.gate_proj = torch.nn.Parameter(
+        self.norm = [RMSNorm(hidden_size, eps) for i in range(4)]
-            torch.empty((intermediate_size, hidden_size))
+        self.w = [torch.rand(hidden_size, hidden_size) for _ in range(3)]
-        )
-        self.norm = RMSNorm(intermediate_size, 1e-05)
-        # Initialize weights
-        torch.nn.init.normal_(self.gate_proj, std=0.02)
 
-    def forward(self, hidden_states, residual):
+    def forward(self, x):
-        """
+        z = torch.relu(x)
-        Forward pass implementing the operations in the FX graph
+        x = resid = tensor_model_parallel_all_reduce(z)
+        y = self.norm[0](x)
 
-        Args:
+        z2 = torch.mm(y, self.w[0])
-            hidden_states: Input tensor
+        x2 = tensor_model_parallel_all_reduce(z2)
-            residual: Residual tensor from previous layer
 
-        Returns:
+        y2, resid = self.norm[1](x2, resid)
-            Tuple containing the output tensor
-        """
-        # Reshape input
-        view = hidden_states.reshape(-1, self.hidden_size)
 
-        # matrix multiplication
+        z3 = torch.mm(y2, self.w[1])
-        permute = self.gate_proj.permute(1, 0)
+        x3 = tensor_model_parallel_all_reduce(z3)
-        mm = torch.mm(view, permute)
 
-        # Tensor parallel all-reduce
+        y3, resid = self.norm[2](x3, resid)
-        all_reduce = tensor_model_parallel_all_reduce(mm)
 
-        # layer normalization
+        z4 = torch.mm(y3, self.w[2])
-        norm_output, residual_output = self.norm(all_reduce, residual)
+        x4 = tensor_model_parallel_all_reduce(z4)
 
-        return norm_output, residual_output
+        y4, resid = self.norm[3](x4, resid)
+        return y4
 
     def ops_in_model_before(self):
         return [torch.ops.vllm.all_reduce.default]
 
     def ops_in_model_after(self):
         return [
-            torch.ops.vllm.reduce_scatter.default,
             torch.ops.vllm.all_gather.default,
+            torch.ops.vllm.reduce_scatter.default,
         ]
 
     def ops_in_model(self):
-        return [torch.ops._C.fused_add_rms_norm.default]
+        if RMSNorm.enabled():
+            return [
+                torch.ops._C.rms_norm.default,
+                torch.ops._C.fused_add_rms_norm.default,
+            ]
+        else:
+            return []
 
 
-class TestQuantModel(torch.nn.Module):
+class TestAllReduceRMSNormStaticQuantFP8Model(torch.nn.Module):
-    def __init__(self, hidden_size=16, intermediate_size=32):
+    def __init__(self, hidden_size=16, eps=1e-6):
         super().__init__()
-        self.hidden_size = hidden_size
-        self.intermediate_size = intermediate_size
         self.vllm_config = get_current_vllm_config()
-        self.gate_proj = torch.nn.Parameter(
+        self.hidden_size = hidden_size
-            torch.empty((intermediate_size, hidden_size)), requires_grad=False
+        self.eps = eps
-        )
+        self.norm = [RMSNorm(hidden_size, eps) for i in range(4)]
-        self.norm = RMSNorm(intermediate_size, 1e-05)
+        self.wscale = [torch.rand(1, dtype=torch.float32) for _ in range(3)]
-        # Initialize weights
+        self.w = [
-        torch.nn.init.normal_(self.gate_proj, std=0.02)
+            torch.rand(hidden_size, hidden_size)
+            .to(dtype=current_platform.fp8_dtype())
+            .t()
+            for _ in range(3)
+        ]
 
-        self.fp8_linear = Fp8LinearOp(act_quant_static=True)
+        self.fp8_linear = Fp8LinearOp(
-
+            act_quant_static=True,
-        self.scale = torch.rand(1, dtype=torch.float32)
+            act_quant_group_shape=GroupShape.PER_TENSOR,
-        # Create a weight that is compatible with torch._scaled_mm,
-        # which expects a column-major layout.
-        self.w = torch.rand(hidden_size, intermediate_size).to(dtype=FP8_DTYPE).t()
-        self.wscale = torch.rand(1, dtype=torch.float32)
-
-    def forward(self, hidden_states, residual):
-        """
-        Forward pass implementing the operations in the FX graph
-
-        Args:
-            hidden_states: Input tensor
-            residual: Residual tensor from previous layer
-
-        Returns:
-            Tuple containing the output tensor
-        """
-        # Reshape input
-        view = hidden_states.reshape(-1, self.hidden_size)
-
-        # matrix multiplication
-        permute = self.gate_proj.permute(1, 0)
-        mm = torch.mm(view, permute)
-
-        # Tensor parallel all-reduce
-        all_reduce = tensor_model_parallel_all_reduce(mm)
-
-        # layer normalization
-        norm_output, residual_output = self.norm(all_reduce, residual)
-
-        # scaled_mm with static input quantization
-        fp8_linear_result = self.fp8_linear.apply(
-            norm_output,
-            self.w,
-            self.wscale,
-            input_scale=self.scale.to(norm_output.device),
         )
 
-        return fp8_linear_result, residual_output
+        self.scale = [torch.rand(1, dtype=torch.float32) for _ in range(3)]
 
-    def ops_in_model_before(self):
+    def forward(self, hidden_states):
-        ops_to_remove = [torch.ops.vllm.all_reduce.default]  # Always removed by SP
+        # avoid having graph input be an arg to a pattern directly
-        # The following are only removed if fusion happens
+        z = torch.relu(hidden_states)
-        if (
+        x = resid = tensor_model_parallel_all_reduce(z)
-            self.vllm_config
+        y = self.norm[0](x)
-            and self.vllm_config.compilation_config.pass_config.enable_fusion
+
-        ):
+        z2 = self.fp8_linear.apply(
-            ops_to_remove.extend(
+            y, self.w[0], self.wscale[0], input_scale=self.scale[0]
-                [
+        )
-                    torch.ops._C.fused_add_rms_norm.default,
+
-                    torch.ops._C.static_scaled_fp8_quant.default,
+        x2 = tensor_model_parallel_all_reduce(z2)
-                ]
+        y2, resid = self.norm[1](x2, resid)
-            )
+
-        return ops_to_remove
+        z3 = self.fp8_linear.apply(
+            y2, self.w[1], self.wscale[1], input_scale=self.scale[1]
+        )
+
+        x3 = tensor_model_parallel_all_reduce(z3)
+        y3, resid = self.norm[2](x3, resid)  # use resid here
+
+        z4 = self.fp8_linear.apply(
+            y3, self.w[2], self.wscale[2], input_scale=self.scale[2]
+        )
+        x4 = tensor_model_parallel_all_reduce(z4)
+        y4, resid = self.norm[3](x4, resid)  # use resid here
+        return y4
 
     def ops_in_model_after(self):
-        ops_to_add = [
+        return [
-            torch.ops.vllm.reduce_scatter.default,
             torch.ops.vllm.all_gather.default,
+            torch.ops.vllm.reduce_scatter.default,
+        ]
+
+    def ops_in_model_before(self):
+        return [
+            torch.ops.vllm.all_reduce.default,
         ]
-        # The following is only added if fusion happens
-        if (
-            self.vllm_config
-            and self.vllm_config.compilation_config.pass_config.enable_fusion
-        ):
-            ops_to_add.append(torch.ops._C.fused_add_rms_norm_static_fp8_quant.default)
-        return ops_to_add
 
     def ops_in_model(self):
-        if (
+        if self.vllm_config.compilation_config.pass_config.enable_fusion:
-            self.vllm_config
-            and self.vllm_config.compilation_config.pass_config.enable_fusion
-        ):
-            # If fusion happens, the fused op is the one
-            # we check for (de)functionalization
             return [torch.ops._C.fused_add_rms_norm_static_fp8_quant.default]
-        else:
+        elif RMSNorm.enabled():
-            # If no fusion, the original ops are checked
             return [
                 torch.ops._C.fused_add_rms_norm.default,
-                # TODO  functionalization pass does not handle this yet
-                # torch.ops._C.static_scaled_fp8_quant.default,
             ]
+        elif self.fp8_linear.quant_fp8.enabled():
+            return [
+                torch.ops._C.static_scaled_fp8_quant.default,
+            ]
+        else:
+            return []
 
 
 @multi_gpu_test(num_gpus=2)
-@pytest.mark.parametrize("test_model_cls", [TestModel, TestQuantModel])
+@pytest.mark.parametrize(
+    "test_model_cls, custom_ops",
+    [
+        (TestAllReduceRMSNormModel, "+rms_norm"),
+        (TestAllReduceRMSNormModel, "-rms_norm"),
+        (TestAllReduceRMSNormStaticQuantFP8Model, "+rms_norm,+quant_fp8"),
+        (TestAllReduceRMSNormStaticQuantFP8Model, "+rms_norm,-quant_fp8"),
+        (TestAllReduceRMSNormStaticQuantFP8Model, "-rms_norm,+quant_fp8"),
+        (TestAllReduceRMSNormStaticQuantFP8Model, "-rms_norm,-quant_fp8"),
+    ],
+)
 @pytest.mark.parametrize("batch_size", [8])
 @pytest.mark.parametrize("seq_len", [16])
 @pytest.mark.parametrize("hidden_size", [16])
 @pytest.mark.parametrize("dtype", [torch.float16, torch.bfloat16])
 @pytest.mark.parametrize("enable_fusion", [True, False])
+@pytest.mark.parametrize("dynamic", [False, True])
 @pytest.mark.skipif(envs.VLLM_TARGET_DEVICE not in ["cuda"], reason="Only test on CUDA")
 def test_sequence_parallelism_pass(
     test_model_cls: type[torch.nn.Module],
+    custom_ops: str,
     batch_size: int,
     seq_len: int,
     hidden_size: int,
     dtype: torch.dtype,
     enable_fusion: bool,
+    dynamic: bool,
 ):
     num_processes = 2
 
@@ -220,11 +206,13 @@ def test_sequence_parallelism_pass(
             args=(
                 num_processes,
                 test_model_cls,
+                custom_ops,
                 batch_size,
                 seq_len,
                 hidden_size,
                 dtype,
                 enable_fusion,
+                dynamic,
             ),
             nprocs=nprocs,
         )
@@ -236,11 +224,13 @@ def sequence_parallelism_pass_on_test_model(
     local_rank: int,
     world_size: int,
     test_model_cls: type[torch.nn.Module],
+    custom_ops: str,
     batch_size: int,
     seq_len: int,
     hidden_size: int,
     dtype: torch.dtype,
     enable_fusion: bool,
+    dynamic: bool,
 ):
     current_platform.seed_everything(0)
 
@@ -264,12 +254,16 @@ def sequence_parallelism_pass_on_test_model(
     initialize_model_parallel(tensor_model_parallel_size=world_size)
 
     # configure vllm config for SequenceParallelismPass
+    custom_ops_list = custom_ops.split(",") if custom_ops else []
     compilation_config = CompilationConfig(
+        splitting_ops=[],  # avoid automatic rms_norm enablement
+        cudagraph_mode=CUDAGraphMode.NONE,  # avoid piecewise warnings
+        custom_ops=custom_ops_list,
         pass_config=PassConfig(
             enable_sequence_parallelism=True,
             enable_fusion=enable_fusion,
             enable_noop=True,
-        )
+        ),
     )  # NoOp needed for fusion
     device_config = DeviceConfig(device=torch.device("cuda"))
 
@@ -289,7 +283,6 @@ def sequence_parallelism_pass_on_test_model(
     with set_current_vllm_config(vllm_config):
         noop_pass = NoOpEliminationPass(vllm_config)
         sequence_parallelism_pass = SequenceParallelismPass(vllm_config)
-        func_pass = FixFunctionalizationPass(vllm_config)
         cleanup_pass = PostCleanupPass(vllm_config)
         assert (
             sequence_parallelism_pass.compilation_config.splitting_ops
@@ -310,38 +303,29 @@ def sequence_parallelism_pass_on_test_model(
 
         passes_for_backend.append(cleanup_pass)
 
-        backend_no_func = TestBackend(*passes_for_backend)
+        backend = TestBackend(*passes_for_backend)
-        backend_func = TestBackend(*passes_for_backend, func_pass)
 
-        model = test_model_cls(hidden_size, hidden_size * 2)
+        model = test_model_cls(hidden_size)
 
         hidden_states = torch.randn((batch_size * seq_len, hidden_size), dtype=dtype)
-        residual = torch.randn((batch_size * seq_len, hidden_size), dtype=dtype)
 
-        compiled_model_no_func = torch.compile(model, backend=backend_no_func)
+        if dynamic:
-        compiled_model_no_func(hidden_states, residual)
+            torch._dynamo.mark_dynamic(hidden_states, 0)
-        compiled_model_func = torch.compile(model, backend=backend_func)
-        compiled_model_func(hidden_states, residual)
 
-        assert sequence_parallelism_pass.matched_count == 1
+        compiled_model = torch.compile(model, backend=backend)
+        compiled_model(hidden_states)
+
+        assert sequence_parallelism_pass.matched_count == 4
 
         # In pre-nodes, all reduce should be there,
         # reduce scatter and all gather should not
-        backend_no_func.check_before_ops(model.ops_in_model_before())
+        for op in model.ops_in_model_before():
+            assert backend.op_count(op, before=True) == 4
 
         # In post-nodes, reduce scatter and all gather should be there,
         # all reduce should not
-        backend_no_func.check_after_ops(model.ops_in_model_after())
+        for op in model.ops_in_model_after():
+            assert backend.op_count(op, before=False) == 4
 
-        # check if the functionalization pass is applied
         for op in model.ops_in_model():
-            find_auto_fn(backend_no_func.graph_post_pass.nodes, op)
+            find_auto_fn(backend.graph_post_pass.nodes, op)
-            assert find_auto_fn_maybe(backend_func.graph_post_pass.nodes, op) is None
-
-        # make sure the ops were all de-functionalized
-        found = dict()
-        for node in backend_func.graph_post_pass.nodes:
-            for op in model.ops_in_model():
-                if is_func(node, op):
-                    found[op] = True
-        assert all(found[op] for op in model.ops_in_model())

tests/distributed/test_context_parallel.py

@@ -14,6 +14,7 @@ from dataclasses import dataclass
 from typing import Literal, NamedTuple
 
 import pytest
+import torch
 
 from vllm.config.model import RunnerOption
 from vllm.logger import init_logger
@@ -254,6 +255,17 @@ def test_cp_generation(
     test_options: CPTestOptions,
     num_gpus_available,
 ):
+    if (
+        model_id == "deepseek-ai/DeepSeek-V2-Lite-Chat"
+        and torch.cuda.get_device_capability() < (9, 0)
+    ):
+        pytest.skip(reason="MLA+DCP requires compute capability of 9.0 or higher")
+    if (
+        model_id == "bigcode/gpt_bigcode-santacoder"
+        and torch.cuda.get_device_capability() != (9, 0)
+    ):
+        pytest.skip(reason="GQA+DCP currently requires compute capability of 9.0")
+
     _compare_cp_with_tp(
         model_id,
         parallel_setup,

tests/distributed/test_sequence_parallel.py

@@ -18,6 +18,7 @@ import pytest
 from vllm.config.compilation import CompilationMode
 from vllm.config.model import RunnerOption
 from vllm.logger import init_logger
+from vllm.platforms import current_platform
 from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 from ..models.registry import HF_EXAMPLE_MODELS
@@ -161,6 +162,7 @@ def _compare_sp(
     test_options: SPTestOptions,
     num_gpus_available: int,
     use_inductor_graph_partition: bool,
+    enable_async_tp: bool,
     *,
     method: Literal["generate", "encode"],
     is_multimodal: bool,
@@ -244,10 +246,10 @@ def _compare_sp(
 
     compilation_config = {
         "mode": CompilationMode.VLLM_COMPILE,
-        "custom_ops": ["+rms_norm"],
         "compile_sizes": [4, 8],
         "pass_config": {
             "enable_sequence_parallelism": True,
+            "enable_async_tp": enable_async_tp,
             "enable_fusion": enable_fusion,
             "enable_noop": True,
         },
@@ -307,6 +309,7 @@ SP_TEST_MODELS = [
     ],
 )
 @pytest.mark.parametrize("use_inductor_graph_partition", [True, False])
+@pytest.mark.parametrize("enable_async_tp", [False])  # TODO: enable async TP
 @create_new_process_for_each_test()
 def test_tp_sp_generation(
     model_id: str,
@@ -316,10 +319,19 @@ def test_tp_sp_generation(
     test_options: SPTestOptions,
     num_gpus_available,
     use_inductor_graph_partition: bool,
+    enable_async_tp: bool,
 ):
     if use_inductor_graph_partition and not is_torch_equal_or_newer("2.9.0.dev"):
         pytest.skip("inductor graph partition is only available in PyTorch 2.9+")
 
+    # Skip FP8 SP-only test on sm89 (compute capability 8.9)
+    if (
+        "fp8" in model_id.lower()
+        and current_platform.get_device_capability() < (9, 0)
+        and (not enable_async_tp)
+    ):
+        pytest.skip("FP8 reduction support begins with sm90 capable devices.")
+
     _compare_sp(
         model_id,
         parallel_setup,
@@ -328,6 +340,7 @@ def test_tp_sp_generation(
         test_options,
         num_gpus_available,
         use_inductor_graph_partition,
+        enable_async_tp=enable_async_tp,
         method="generate",
         is_multimodal=False,
     )

tests/evals/gsm8k/configs/Qwen1.5-MoE-W4A16-CT.yaml

@@ -3,6 +3,3 @@ accuracy_threshold: 0.45
 num_questions: 1319
 num_fewshot: 5
 max_model_len: 4096
-# Duo stream incompatabilbe with this model: https://github.com/vllm-project/vllm/issues/28220
-env:
-  VLLM_DISABLE_SHARED_EXPERTS_STREAM: "1"

tests/kernels/attention/test_cascade_flash_attn.py

@@ -170,6 +170,7 @@ def test_cascade(
         logits_soft_cap=soft_cap if soft_cap is not None else 0,
         block_table=block_tables,
         common_prefix_len=common_prefix_len,
+        max_num_splits=0,  # no max
         fa_version=fa_version,
     )
 

tests/models/multimodal/generation/test_common.py

@@ -10,11 +10,13 @@ from collections import defaultdict
 from pathlib import PosixPath
 
 import pytest
+from packaging.version import Version
 from transformers import (
     AutoModel,
     AutoModelForImageTextToText,
     AutoModelForTextToWaveform,
 )
+from transformers import __version__ as TRANSFORMERS_VERSION
 
 from vllm.platforms import current_platform
 from vllm.utils.func_utils import identity
@@ -137,6 +139,7 @@ VLM_TEST_SETTINGS = {
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
         img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",
         video_idx_to_prompt=lambda idx: "<|vision_start|><|video_pad|><|vision_end|>",
+        enforce_eager=False,
         max_model_len=4096,
         max_num_seqs=2,
         auto_cls=AutoModelForImageTextToText,
@@ -166,6 +169,7 @@ VLM_TEST_SETTINGS = {
             VLMTestType.MULTI_IMAGE,
             VLMTestType.VIDEO,
         ),
+        enforce_eager=False,
         needs_video_metadata=True,
         prompt_formatter=lambda img_prompt: f"<|im_start|>User\n{img_prompt}<|im_end|>\n<|im_start|>assistant\n",  # noqa: E501
         img_idx_to_prompt=lambda idx: "<|vision_start|><|image_pad|><|vision_end|>",  # noqa: E501
@@ -859,6 +863,12 @@ VLM_TEST_SETTINGS = {
                 limit_mm_per_prompt={"image": 4},
             )
         ],
+        marks=[
+            pytest.mark.skipif(
+                Version(TRANSFORMERS_VERSION) == Version("4.57.1"),
+                reason="This model is broken in Transformers v4.57.1",
+            )
+        ],
     ),
     # regression test for https://github.com/vllm-project/vllm/issues/15122
     "qwen2_5_vl-windows-attention": VLMTestInfo(

tests/models/multimodal/generation/test_qwen2_5_vl.py

@@ -61,10 +61,8 @@ def test_qwen2_5_vl_evs_functionality(
         model,
         runner="generate",
         max_model_len=4000,
-        max_num_seqs=1,
         dtype=dtype,
         limit_mm_per_prompt={"video": 1},
-        tensor_parallel_size=1,
         video_pruning_rate=video_pruning_rate,
     ) as vllm_model:
         # Generate output - this should not crash

tests/v1/worker/test_gpu_model_runner.py

@@ -980,8 +980,10 @@ def test_hybrid_block_table_initialization():
     req_index = 0
     block_table.append_row(kvcache_manager_blocks, req_index)
     # Get expected kernel blocks from the implementation for verification.
-    expected_kernel_blocks = block_table._map_to_kernel_blocks(
+    expected_kernel_blocks = block_table.map_to_kernel_blocks(
-        np.array(kvcache_manager_blocks)
+        np.array(kvcache_manager_blocks),
+        block_table.blocks_per_kv_block,
+        block_table._kernel_block_arange,
     )
     # Verify block table state
     assert block_table.num_blocks_per_row[req_index] == len(expected_kernel_blocks)

vllm/_aiter_ops.py

@@ -938,4 +938,5 @@ class rocm_aiter_ops:
         return tuple(shuffle_weight(tensor, layout=layout) for tensor in tensors)
 
 
-rocm_aiter_ops.register_ops_once()
+if IS_AITER_FOUND:
+    rocm_aiter_ops.register_ops_once()

vllm/attention/layer.py

@@ -745,6 +745,9 @@ class MLAAttention(nn.Module, AttentionLayerBase):
         k_pe: torch.Tensor,
         output_shape: torch.Size | None = None,
     ) -> torch.Tensor:
+        if self.calculate_kv_scales:
+            torch.ops.vllm.maybe_calc_kv_scales(q, kv_c_normed, k_pe, self.layer_name)
+
         if self.use_direct_call:
             forward_context: ForwardContext = get_forward_context()
             attn_metadata = forward_context.attn_metadata
@@ -752,12 +755,6 @@ class MLAAttention(nn.Module, AttentionLayerBase):
                 attn_metadata = attn_metadata[self.layer_name]
             self_kv_cache = self.kv_cache[forward_context.virtual_engine]
 
-            # Mirror Attention.forward scale calculation path
-            if self.calculate_kv_scales and getattr(
-                attn_metadata, "enable_kv_scales_calculation", False
-            ):
-                self.calc_kv_scales(q, kv_c_normed, k_pe)
-
             if self.attn_backend.accept_output_buffer:
                 output = torch.empty(output_shape, dtype=q.dtype, device=q.device)
                 self.impl.forward(
@@ -786,14 +783,6 @@ class MLAAttention(nn.Module, AttentionLayerBase):
                 )
                 return output
             else:
-                # We can still access forward context to check calculation flag
-                if self.calculate_kv_scales:
-                    forward_context = get_forward_context()
-                    attn_metadata = forward_context.attn_metadata
-                    if isinstance(attn_metadata, dict):
-                        attn_metadata = attn_metadata[self.layer_name]
-                    if getattr(attn_metadata, "enable_kv_scales_calculation", False):
-                        self.calc_kv_scales(q, kv_c_normed, k_pe)
                 return torch.ops.vllm.unified_mla_attention(
                     q,
                     kv_c_normed,
@@ -881,17 +870,13 @@ def maybe_calc_kv_scales(
     layer_name: str,
 ) -> None:
     forward_context: ForwardContext = get_forward_context()
-    attn_metadata = forward_context.attn_metadata
+    self = forward_context.no_compile_layers[layer_name]
 
-    if isinstance(attn_metadata, dict):
+    # Only calculate if the layer's calculate_kv_scales flag is True
-        attn_metadata = attn_metadata[layer_name]
+    # This flag gets set to False after the first forward pass
-
+    if not self.calculate_kv_scales:
-    if attn_metadata is None or not getattr(
-        attn_metadata, "enable_kv_scales_calculation", False
-    ):
         return
 
-    self = forward_context.no_compile_layers[layer_name]
     self.calc_kv_scales(query, key, value)
 
 

vllm/attention/ops/common.py

@@ -195,7 +195,6 @@ def cp_lse_ag_out_rs(
     cp_attn_lse = cp_attn_lse.contiguous()
     lses = cp_group.all_gather(cp_attn_lse, dim=0).view_as(lses)
     out, lse = correct_attn_out(cp_attn_out, lses, cp_group.rank_in_group, ctx)
-    assert out.is_contiguous()
     out = cp_group.reduce_scatter(out, dim=1)
 
     if return_lse:

vllm/compilation/sequence_parallelism.py

@@ -1,6 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import functools
+
 import torch
 import torch._inductor.pattern_matcher as pm
 import torch.fx as fx
@@ -10,98 +12,28 @@ from vllm.config import VllmConfig
 from vllm.distributed import get_tp_group, tensor_model_parallel_all_reduce
 from vllm.distributed.parallel_state import get_tensor_model_parallel_world_size
 from vllm.logger import init_logger
+from vllm.model_executor.layers.quantization.utils.quant_utils import (
+    kFp8StaticTensorSym,
+)
 from vllm.platforms import current_platform
 
 from .inductor_pass import enable_fake_mode
+from .matcher_utils import MatcherFusedAddRMSNorm, MatcherQuantFP8, MatcherRMSNorm
+from .noop_elimination import NoOpEliminationPass
 from .vllm_inductor_pass import VllmInductorPass, VllmPatternMatcherPass
 
 logger = init_logger(__name__)
 
 
-class _RMSNormAndQuantOpHelper:
+def get_first_out_wrapper(fn):
-    """Base helper for RMSNorm and RMSNorm + Quantization functionalization."""
+    @functools.wraps(fn)
+    def wrapper(*args):
+        return fn(*args)[0]
 
-    def __init__(
+    return wrapper
-        self,
-        epsilon: float,
-        dtype: torch.dtype,
-        device: str,
-        quant_op: torch._ops.OpOverload | None = None,
-        **kwargs,
-    ):
-        self.epsilon = epsilon
-        self.dtype = dtype
-        self.device = device
-        self.quant_op = quant_op
-
-    def _functional_rmsnorm(self, result_buffer, input_tensor, weight_tensor):
-        return torch.ops.higher_order.auto_functionalized(
-            torch.ops._C.rms_norm.default,
-            result=result_buffer,
-            input=input_tensor,
-            weight=weight_tensor,
-            epsilon=self.epsilon,
-        )
-
-    def _functional_fused_add_rmsnorm(
-        self, input_tensor, residual_tensor, weight_tensor
-    ):
-        return torch.ops.higher_order.auto_functionalized(
-            torch.ops._C.fused_add_rms_norm.default,
-            input=input_tensor,
-            residual=residual_tensor,
-            weight=weight_tensor,
-            epsilon=self.epsilon,
-        )
-
-    def _functional_rmsnorm_then_quant(
-        self,
-        rmsnorm_result_buffer,
-        quant_result_buffer,
-        input_tensor,
-        weight_tensor,
-        scale_tensor,
-    ):
-        if self.quant_op is None:
-            raise RuntimeError(
-                "_RMSNormAndQuantOpHelper was not initialized with a quant_op."
-            )
-        rmsnorm_out_tuple = self._functional_rmsnorm(
-            rmsnorm_result_buffer, input_tensor, weight_tensor
-        )
-        quant_out_tuple = torch.ops.higher_order.auto_functionalized(
-            self.quant_op,
-            result=quant_result_buffer,
-            input=rmsnorm_out_tuple[1],
-            scale=scale_tensor,
-        )
-        return quant_out_tuple
-
-    def _functional_fused_add_rmsnorm_then_quant(
-        self,
-        quant_result_buffer,
-        input_tensor,
-        residual_tensor,
-        weight_tensor,
-        scale_tensor,
-    ):
-        if self.quant_op is None:
-            raise RuntimeError(
-                "_RMSNormAndQuantOpHelper was not initialized with a quant_op."
-            )
-        fused_add_rmsnorm_out_tuple = self._functional_fused_add_rmsnorm(
-            input_tensor, residual_tensor, weight_tensor
-        )
-        quant_out_tuple = torch.ops.higher_order.auto_functionalized(
-            self.quant_op,
-            result=quant_result_buffer,
-            input=fused_add_rmsnorm_out_tuple[1],
-            scale=scale_tensor,
-        )
-        return quant_out_tuple, fused_add_rmsnorm_out_tuple[2]
 
 
-class _SequenceParallelPatternHelper(_RMSNormAndQuantOpHelper):
+class _SequenceParallelPatternHelper:
     """Helper for sequence parallelism patterns."""
 
     def __init__(
@@ -109,10 +41,10 @@ class _SequenceParallelPatternHelper(_RMSNormAndQuantOpHelper):
         epsilon: float,
         dtype: torch.dtype,
         device: str,
-        quant_op: torch._ops.OpOverload | None = None,
-        **kwargs,
     ):
-        super().__init__(epsilon, dtype, device, quant_op=quant_op, **kwargs)
+        self.epsilon = epsilon
+        self.dtype = dtype
+        self.device = device
         self.tp_group = get_tp_group()
         self.tp_size = get_tensor_model_parallel_world_size()
 
@@ -131,36 +63,34 @@ class _SequenceParallelPatternHelper(_RMSNormAndQuantOpHelper):
 
 
 class FirstAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+    def __init__(self, epsilon: float, dtype: torch.dtype, device: str):
+        super().__init__(epsilon, dtype, device)
+        self.rmsnorm_matcher = MatcherRMSNorm(epsilon)
+
     def get_inputs(self):
         input = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
-        permute = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
         arg3_1 = torch.empty([4], device=self.device, dtype=self.dtype)
 
-        return [input, permute, arg3_1]
+        return [input, arg3_1]
 
     def register(self, pm_pass: PatternMatcherPass):
         def pattern(
             input: torch.Tensor,
-            permute: torch.Tensor,
             arg3_1: torch.Tensor,
         ):
             all_reduce = self._all_reduce(input)
-            rmsnorm = self._functional_rmsnorm(permute, all_reduce, arg3_1)
+            rmsnorm = self.rmsnorm_matcher(all_reduce, arg3_1)
 
-            return rmsnorm[1], all_reduce
+            return rmsnorm, all_reduce
 
         def replacement(
             input: torch.Tensor,
-            permute: torch.Tensor,
             arg3_1: torch.Tensor,
         ):
             reduce_scatter = self._reduce_scatter(input)
 
-            rmsnorm_result = torch.empty_like(reduce_scatter)
+            rmsnorm = self.rmsnorm_matcher(reduce_scatter, arg3_1)
-            rmsnorm = self._functional_rmsnorm(rmsnorm_result, reduce_scatter, arg3_1)
+            all_gather = self._all_gather(rmsnorm)
-
-            all_gather = self._all_gather(rmsnorm[1])
-
             return all_gather, reduce_scatter
 
         pm.register_replacement(
@@ -169,6 +99,10 @@ class FirstAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
 
 
 class MiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
+    def __init__(self, epsilon: float, dtype: torch.dtype, device: str):
+        super().__init__(epsilon, dtype, device)
+        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
+
     def get_inputs(self):
         mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)
 
@@ -188,67 +122,34 @@ class MiddleAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
             rms_norm_weights: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             all_reduce = self._all_reduce(mm_1)
-            rmsnorm = self._functional_fused_add_rmsnorm(
+            rmsnorm = self.rmsnorm_matcher(all_reduce, rms_norm_weights, residual)
-                all_reduce, residual, rms_norm_weights
+            return rmsnorm[0], rmsnorm[1]
-            )
-            return rmsnorm[1], rmsnorm[2]
 
         def replacement(
             residual: torch.Tensor,
             mm_1: torch.Tensor,
             rms_norm_weights: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
+            # pattern matcher replaces from top-to-bottom,
+            # so residual is still the full size here.
+            # once the seqpar pattern with the previous rmsnorm is replaced
             reduce_scatter = self._reduce_scatter(mm_1)
-            rmsnorm = self._functional_fused_add_rmsnorm(
+            residual = residual[0 : reduce_scatter.size(0), ...]
-                reduce_scatter, residual, rms_norm_weights
+            rmsnorm = self.rmsnorm_matcher(reduce_scatter, rms_norm_weights, residual)
-            )
+            all_gather = self._all_gather(rmsnorm[0])
-            all_gather = self._all_gather(rmsnorm[1])
+            # shape of residual changes but that's fine,
-            return all_gather, rmsnorm[2]
+            # next node is already slicing it, now becomes a noop
+            return all_gather, rmsnorm[1]
 
         pm.register_replacement(
             pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
         )
-
-
-class LastAllReduceRMSNormPattern(_SequenceParallelPatternHelper):
-    def get_inputs(self):
-        mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-
-        residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-        rms_norm_weights = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-
-        return [
-            residual,
-            mm_1,
-            rms_norm_weights,
-        ]
-
-    def register(self, pm_pass: PatternMatcherPass):
-        def pattern(
-            residual: torch.Tensor,
-            mm_1: torch.Tensor,
-            rms_norm_weights: torch.Tensor,
-        ) -> tuple[torch.Tensor, torch.Tensor]:
-            all_reduce = self._all_reduce(mm_1)
-            rmsnorm = self._functional_fused_add_rmsnorm(
-                all_reduce, residual, rms_norm_weights
-            )
-            return rmsnorm[1]
-
-        def replacement(
-            residual: torch.Tensor,
-            mm_1: torch.Tensor,
-            rms_norm_weights: torch.Tensor,
-        ) -> tuple[torch.Tensor, torch.Tensor]:
-            reduce_scatter = self._reduce_scatter(mm_1)
-            rmsnorm = self._functional_fused_add_rmsnorm(
-                reduce_scatter, residual, rms_norm_weights
-            )
-            normalized = self._all_gather(rmsnorm[1])
-            return normalized
-
         pm.register_replacement(
-            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
+            get_first_out_wrapper(pattern),
+            get_first_out_wrapper(replacement),
+            self.get_inputs(),
+            pm.fwd_only,
+            pm_pass,
         )
 
 
@@ -257,52 +158,41 @@ FP8_DTYPE = current_platform.fp8_dtype()
 
 class FirstAllReduceRMSNormStaticFP8Pattern(_SequenceParallelPatternHelper):
     def __init__(
-        self, epsilon: float, dtype: torch.dtype, device: str, op: torch._ops.OpOverload
+        self,
+        epsilon: float,
+        dtype: torch.dtype,
+        device: str,
     ):
-        super().__init__(epsilon, dtype, device, quant_op=op)
+        super().__init__(epsilon, dtype, device)
+        self.rmsnorm_matcher = MatcherRMSNorm(epsilon)
+        self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)
 
     def get_inputs(self):
         input = torch.zeros([1, 8, 4], device=self.device, dtype=self.dtype)
-        rmsnorm_result = torch.empty([1, 8, 4], device=self.device, dtype=self.dtype)
-        quant_result = torch.empty([1, 8, 4], device=self.device, dtype=FP8_DTYPE)
         weight = torch.empty([4], device=self.device, dtype=self.dtype)
         scale = torch.tensor(1.0, device=self.device, dtype=torch.float32)
-        return [input, rmsnorm_result, quant_result, weight, scale]
+        return [input, weight, scale]
 
     def register(self, pm_pass: PatternMatcherPass):
         def pattern(
             input: torch.Tensor,
-            rmsnorm_result: torch.Tensor,
-            quant_result: torch.Tensor,
             weight: torch.Tensor,
             scale: torch.Tensor,
         ):
             all_reduce = self._all_reduce(input)
-            static_fp8 = self._functional_rmsnorm_then_quant(
+            rms = self.rmsnorm_matcher(all_reduce, weight)
-                rmsnorm_result, quant_result, all_reduce, weight, scale
+            quant, _ = self.quant_matcher(rms, scale)
-            )
+            return quant, all_reduce
-            return static_fp8[1], all_reduce
 
         def replacement(
             input: torch.Tensor,
-            rmsnorm_result: torch.Tensor,
-            quant_result: torch.Tensor,
             weight: torch.Tensor,
             scale: torch.Tensor,
         ):
             reduce_scatter = self._reduce_scatter(input)
-
+            rms = self.rmsnorm_matcher(reduce_scatter, weight)
-            rmsnorm_result = torch.empty_like(
+            quant, _ = self.quant_matcher(rms, scale)
-                reduce_scatter, dtype=rmsnorm_result.dtype
+            all_gather = self._all_gather(quant)
-            )
-            quant_result = torch.empty_like(
-                rmsnorm_result,  # Output of RMSNorm
-                dtype=quant_result.dtype,
-            )
-            static_fp8 = self._functional_rmsnorm_then_quant(
-                rmsnorm_result, quant_result, reduce_scatter, weight, scale
-            )
-            all_gather = self._all_gather(static_fp8[1])
 
             return all_gather, reduce_scatter
 
@@ -312,118 +202,64 @@ class FirstAllReduceRMSNormStaticFP8Pattern(_SequenceParallelPatternHelper):
 
 
 class MiddleAllReduceRMSNormStaticFP8Pattern(_SequenceParallelPatternHelper):
-    def __init__(
+    def __init__(self, epsilon: float, dtype: torch.dtype, device: str):
-        self, epsilon: float, dtype: torch.dtype, device: str, op: torch._ops.OpOverload
+        super().__init__(epsilon, dtype, device)
-    ):
+        self.rmsnorm_matcher = MatcherFusedAddRMSNorm(epsilon)
-        super().__init__(epsilon, dtype, device, quant_op=op)
+        self.quant_matcher = MatcherQuantFP8(kFp8StaticTensorSym)
 
     def get_inputs(self):
         mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-
         residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
         rms_norm_weights = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-        result = torch.empty([4, 4], device=self.device, dtype=FP8_DTYPE)
         scale = torch.empty([1, 1], device=self.device, dtype=torch.float32)
 
-        return [
+        return [residual, mm_1, rms_norm_weights, scale]
-            result,
-            residual,
-            mm_1,
-            rms_norm_weights,
-            scale,
-        ]
 
     def register(self, pm_pass: PatternMatcherPass):
         def pattern(
-            result: torch.Tensor,
             residual: torch.Tensor,
             mm_1: torch.Tensor,
             rms_norm_weights: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
             all_reduce = self._all_reduce(mm_1)
-            static_fp8, rmsnorm_residual_out = (
+            rms, residual_out = self.rmsnorm_matcher(
-                self._functional_fused_add_rmsnorm_then_quant(  # noqa: E501
+                all_reduce, rms_norm_weights, residual
-                    result, all_reduce, residual, rms_norm_weights, scale
-                )
             )
-            return static_fp8[1], rmsnorm_residual_out
+            quant, _ = self.quant_matcher(rms, scale)
+            return quant, residual_out
 
         def replacement(
-            result: torch.Tensor,
             residual: torch.Tensor,
             mm_1: torch.Tensor,
             rms_norm_weights: torch.Tensor,
             scale: torch.Tensor,
         ) -> tuple[torch.Tensor, torch.Tensor]:
+            # pattern matcher replaces from top-to-bottom,
+            # so residual is still the full size here.
+            # add a temporary slice which will become a noop
+            # once the seqpar pattern with the previous rmsnorm is replaced
             reduce_scatter = self._reduce_scatter(mm_1)
-            quant_result_buf = torch.empty_like(reduce_scatter, dtype=result.dtype)
+            residual = residual[0 : reduce_scatter.size(0), ...]
-            static_fp8, rmsnorm_residual_out = (
+            rms, residual_out = self.rmsnorm_matcher(
-                self._functional_fused_add_rmsnorm_then_quant(  # noqa: E501
+                reduce_scatter, rms_norm_weights, residual
-                    quant_result_buf, reduce_scatter, residual, rms_norm_weights, scale
-                )
             )
-            all_gather = self._all_gather(static_fp8[1])
+            quant, _ = self.quant_matcher(rms, scale)
-            return all_gather, rmsnorm_residual_out
+            all_gather = self._all_gather(quant)
+            # shape of residual changes but that's fine,
+            # next node is already slicing it, now becomes a noop
+            return all_gather, residual_out
 
         pm.register_replacement(
             pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
         )
 
-
-class LastAllReduceRMSNormStaticFP8Pattern(_SequenceParallelPatternHelper):
-    def __init__(
-        self, epsilon: float, dtype: torch.dtype, device: str, op: torch._ops.OpOverload
-    ):
-        super().__init__(epsilon, dtype, device, quant_op=op)
-
-    def get_inputs(self):
-        mm_1 = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-
-        residual = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-        rms_norm_weights = torch.empty([4, 4], device=self.device, dtype=self.dtype)
-        result = torch.empty([4, 4], device=self.device, dtype=FP8_DTYPE)
-        scale = torch.empty([1, 1], device=self.device, dtype=torch.float32)
-
-        return [
-            result,
-            residual,
-            mm_1,
-            rms_norm_weights,
-            scale,
-        ]
-
-    def register(self, pm_pass: PatternMatcherPass):
-        def pattern(
-            result: torch.Tensor,
-            residual: torch.Tensor,
-            mm_1: torch.Tensor,
-            rms_norm_weights: torch.Tensor,
-            scale: torch.Tensor,
-        ) -> tuple[torch.Tensor, torch.Tensor]:
-            all_reduce = self._all_reduce(mm_1)
-            static_fp8, _ = self._functional_fused_add_rmsnorm_then_quant(
-                result, all_reduce, residual, rms_norm_weights, scale
-            )
-            return static_fp8[1]
-
-        def replacement(
-            result: torch.Tensor,
-            residual: torch.Tensor,
-            mm_1: torch.Tensor,
-            rms_norm_weights: torch.Tensor,
-            scale: torch.Tensor,
-        ) -> tuple[torch.Tensor, torch.Tensor]:
-            reduce_scatter = self._reduce_scatter(mm_1)
-            quant_result_buf = torch.empty_like(reduce_scatter, dtype=result.dtype)
-            static_fp8, _ = self._functional_fused_add_rmsnorm_then_quant(
-                quant_result_buf, reduce_scatter, residual, rms_norm_weights, scale
-            )
-            normalized = self._all_gather(static_fp8[1])
-            return normalized
-
         pm.register_replacement(
-            pattern, replacement, self.get_inputs(), pm.fwd_only, pm_pass
+            get_first_out_wrapper(pattern),
+            get_first_out_wrapper(replacement),
+            self.get_inputs(),
+            pm.fwd_only,
+            pm_pass,
         )
 
 
@@ -445,27 +281,45 @@ class SequenceParallelismPass(VllmPatternMatcherPass):
     GEMM + ReduceScatter and AllGather + GEMM fusions. These fusions can
     significantly reduce communication overhead and improve overall model
     performance.
+
+
+    This pass splits up the residual tensor across TP ranks and hence divides its size.
+    Because the pattern matcher starts at the end of the graph, the replacement
+    contains a slice that temporarily conforms the input residual to the correct size.
+    After all patterns have been matched, we use a NoOpEliminationPass to clean up
+    what have now become no-op slices.
+
+    Note that an older version of the pass did not need this as it operated only on
+    custom rms_norm and fused_rms_norm_add custom ops which did not complain about
+    mismatched shapes during replacement. So this approach has the same assumption that
+    correctness is only maintained if all rms_norm operations are split across ranks.
+
+    Correctness-wise, this is approach strictly better than before - before,
+    the graph was incorrect semantically and shape-wise during the pass.
+    With this approach there's only semantic incorrectness during the pass.
+    Both approaches restore a correct graph once all patterns are matched.
     """
 
     @enable_fake_mode
     def __init__(self, config: VllmConfig):
         super().__init__(config)
 
+        # Used to cleanup redundant views created temporarily
+        # to circumvent residual shape change issues
+        self.noop_cleanup = NoOpEliminationPass(config)
+        self.noop_cleanup.pass_name = f"{self.pass_name}.{self.noop_cleanup.pass_name}"
+
         self.patterns: PatternMatcherPass = PatternMatcherPass(
             pass_name="sequence_parallelism_pass"
         )
 
         for epsilon in [1e-5, 1e-6]:
             # RMSNorm + Static FP8 quantization patterns
-            fp8_quant_op = torch.ops._C.static_scaled_fp8_quant.default
             FirstAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device, fp8_quant_op
+                epsilon, self.model_dtype, self.device
             ).register(self.patterns)
             MiddleAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device, fp8_quant_op
+                epsilon, self.model_dtype, self.device
-            ).register(self.patterns)
-            LastAllReduceRMSNormStaticFP8Pattern(
-                epsilon, self.model_dtype, self.device, fp8_quant_op
             ).register(self.patterns)
 
             # Normal RMSNorm patterns
@@ -477,9 +331,6 @@ class SequenceParallelismPass(VllmPatternMatcherPass):
                 epsilon, self.model_dtype, self.device
             ).register(self.patterns)
 
-            LastAllReduceRMSNormPattern(
-                epsilon, self.model_dtype, self.device
-            ).register(self.patterns)
         self.dump_patterns(config, self.patterns)
 
     def is_applicable(self, shape: int | None) -> bool:
@@ -508,3 +359,5 @@ class SequenceParallelismPass(VllmPatternMatcherPass):
     def __call__(self, graph: fx.Graph):
         self.matched_count = self.patterns.apply(graph)
         logger.debug("Replaced %s patterns", self.matched_count)
+        # Clean up reshape nodes
+        self.noop_cleanup(graph)

vllm/config/compilation.py

@@ -152,7 +152,6 @@ class CompilationConfig:
         - [`splitting_ops`][vllm.config.CompilationConfig.splitting_ops]
         - [`compile_mm_encoder`][vllm.config.CompilationConfig.compile_mm_encoder]
     - CudaGraph capture:
-        - [`use_cudagraph`][vllm.config.CompilationConfig.use_cudagraph]
         - [`cudagraph_mode`][vllm.config.CompilationConfig.cudagraph_mode]
         - [`cudagraph_capture_sizes`]
         [vllm.config.CompilationConfig.cudagraph_capture_sizes]
@@ -162,7 +161,6 @@ class CompilationConfig:
         [vllm.config.CompilationConfig.cudagraph_num_of_warmups]
         - [`cudagraph_copy_inputs`]
         [vllm.config.CompilationConfig.cudagraph_copy_inputs]
-        - [`full_cuda_graph`][vllm.config.CompilationConfig.full_cuda_graph]
     - Inductor compilation:
         - [`use_inductor`][vllm.config.CompilationConfig.use_inductor]
         - [`compile_sizes`][vllm.config.CompilationConfig.compile_sizes]
@@ -268,9 +266,10 @@ class CompilationConfig:
 
     If None, defaults to attention ops for piecewise cudagraphs.
     If empty list [], no ops are excluded (suitable for full cudagraphs)."""
-    compile_mm_encoder: bool = True
+    compile_mm_encoder: bool = False
     """Whether or not to compile the multimodal encoder.
-    Currently, this only works for `Qwen2_5_vl`."""
+    Currently, this only works for `Qwen2_5_vl` on selected platforms. 
+    Disabled by default until more models are supported/tested to work."""
 
     # Inductor capture
     use_inductor: bool | None = None
@@ -342,18 +341,6 @@ class CompilationConfig:
     Warning: This flag is new and subject to change in addition 
     more modes may be added.
     """
-    use_cudagraph: bool = True
-    """Whether to use cudagraph inside compilation:
-
-    - False: cudagraph inside compilation is not used.\n
-    - True: cudagraph inside compilation is used. It requires
-        that all input buffers have fixed addresses, and all
-        splitting ops write their outputs to input buffers.
-
-    Warning: This flag is deprecated and will be removed in the next major or
-    minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode=FULL_AND
-    _PIECEWISE instead.
-    """
     cudagraph_num_of_warmups: int = 0
     """Number of warmup runs for cudagraph.
     It means the first several runs will be treated as warmup runs.
@@ -371,15 +358,6 @@ class CompilationConfig:
     internally managed buffer. Default is False. 
     Note that this flag is only effective when cudagraph_mode is PIECEWISE.
     """
-    full_cuda_graph: bool | None = False
-    """whether to use a full cuda graph for the entire forward pass rather than
-    splitting certain operations such as attention into subgraphs. Thus this
-    flag cannot be used together with splitting_ops. This may provide
-    performance benefits for smaller models.
-    Warning: This flag is deprecated and will be removed in the next major or
-    minor release, i.e. v0.11.0 or v1.0.0. Please use cudagraph_mode=
-    FULL_AND_PIECEWISE instead.
-    """
     cudagraph_specialize_lora: bool = True
     """Whether to create separate cuda graphs for cases with and without active
     LoRA adapters. When set to False, the LoRA-enabled cuda graph will be used
@@ -528,13 +506,19 @@ class CompilationConfig:
     @field_validator("cudagraph_mode", mode="before")
     @classmethod
     def validate_cudagraph_mode_before(cls, value: Any) -> Any:
-        """
+        """Enable parsing of the `cudagraph_mode` enum type from string."""
-        enable parse the `cudagraph_mode` enum type from string
-        """
         if isinstance(value, str):
             return CUDAGraphMode[value.upper()]
         return value
 
+    @field_validator("pass_config", mode="before")
+    @classmethod
+    def validate_pass_config_before(cls, value: Any) -> Any:
+        """Enable parsing of the `pass_config` field from a dictionary."""
+        if isinstance(value, dict):
+            return PassConfig(**value)
+        return value
+
     @field_validator("compile_cache_save_format")
     @classmethod
     def validate_compile_cache_save_format(cls, value: str) -> str:
@@ -591,8 +575,10 @@ class CompilationConfig:
                 func if isinstance(func, InductorPass) else CallableInductorPass(func)
             )
 
-        if isinstance(self.pass_config, dict):
+        if self.pass_config.enable_qk_norm_rope_fusion:
-            self.pass_config = PassConfig(**self.pass_config)
+            # TODO(zhuhaoran): support rope native forward match and remove this.
+            # Linked issue: https://github.com/vllm-project/vllm/issues/28042
+            self.custom_ops.append("+rotary_embedding")
 
         if (
             is_torch_equal_or_newer("2.9.0.dev")
@@ -604,36 +590,6 @@ class CompilationConfig:
             self.inductor_compile_config["combo_kernels"] = True
             self.inductor_compile_config["benchmark_combo_kernel"] = True
 
-        # migrate the deprecated flags
-        if not self.use_cudagraph:
-            logger.warning(
-                "use_cudagraph is deprecated, use cudagraph_mode=NONE instead."
-            )
-            if (
-                self.cudagraph_mode is not None
-                and self.cudagraph_mode != CUDAGraphMode.NONE
-            ):
-                raise ValueError(
-                    "use_cudagraph and cudagraph_mode are mutually"
-                    " exclusive, prefer cudagraph_mode since "
-                    "use_cudagraph is deprecated."
-                )
-            self.cudagraph_mode = CUDAGraphMode.NONE
-        if self.full_cuda_graph:
-            logger.warning(
-                "full_cuda_graph is deprecated, use cudagraph_mode=FULL instead."
-            )
-            if (
-                self.cudagraph_mode is not None
-                and not self.cudagraph_mode.has_full_cudagraphs()
-            ):
-                raise ValueError(
-                    "full_cuda_graph and cudagraph_mode are "
-                    "mutually exclusive, prefer cudagraph_mode "
-                    "since full_cuda_graph is deprecated."
-                )
-            self.cudagraph_mode = CUDAGraphMode.FULL
-
         if self.use_inductor_graph_partition and not is_torch_equal_or_newer(
             "2.9.0.dev"
         ):
@@ -811,20 +767,19 @@ class CompilationConfig:
 
     def set_splitting_ops_for_attn_fusion(self):
         assert self.pass_config.enable_attn_fusion
-        # For dynamo-partition (non-inductor) attention fusion,
+        if self.splitting_ops is None:
-        # set splitting_ops to empty to avoid splitting at attention ops
+            self.splitting_ops = []
-        self.splitting_ops = []
+            if self.cudagraph_mode.has_piecewise_cudagraphs():
-        if self.cudagraph_mode.has_piecewise_cudagraphs():
+                logger.warning_once(
-            logger.warning_once(
+                    "enable_attn_fusion is incompatible with piecewise "
-                "enable_attn_fusion is incompatible with piecewise "
+                    "cudagraph when use_inductor_graph_partition is off. "
-                "cudagraph when use_inductor_graph_partition is off. "
+                    "In this case, splitting_ops will be set to empty "
-                "In this case, splitting_ops will be set to empty "
+                    "list, and cudagraph_mode will be set to FULL. "
-                "list, and cudagraph_mode will be set to FULL. "
+                    "Please ensure you are using attention backends that "
-                "Please ensure you are using attention backends that "
+                    "support cudagraph or set cudagraph_mode to NONE "
-                "support cudagraph or set cudagraph_mode to NONE "
+                    "explicitly if encountering any problems."
-                "explicitly if encountering any problems."
+                )
-            )
+                self.cudagraph_mode = CUDAGraphMode.FULL
-            self.cudagraph_mode = CUDAGraphMode.FULL
 
         assert not self.splitting_ops_contain_attention(), (
             "attention ops should not be in splitting_ops "

vllm/config/vllm.py

@@ -441,8 +441,6 @@ class VllmConfig:
         # and requires it to be enabled.
         if self.compilation_config.pass_config.enable_async_tp:
             self.compilation_config.pass_config.enable_sequence_parallelism = True
-        if self.compilation_config.pass_config.enable_sequence_parallelism:
-            self.compilation_config.custom_ops.append("+rms_norm")
 
         if current_platform.support_static_graph_mode():
             # if cudagraph_mode is not explicitly set by users, set default
@@ -631,6 +629,32 @@ class VllmConfig:
         if self.compilation_config.mode == CompilationMode.VLLM_COMPILE:
             self.compilation_config.set_splitting_ops_for_v1()
 
+        if self.compilation_config.pass_config.enable_sequence_parallelism:
+            # With pipeline parallelism or dynamo partitioning,
+            # native rms norm tracing errors due to incorrect residual shape.
+            # Use custom rms norm to unblock. In the future,
+            # the pass will operate on higher-level IR to avoid the issue.
+            # TODO: https://github.com/vllm-project/vllm/issues/27894
+            is_fullgraph = (
+                self.compilation_config.use_inductor_graph_partition
+                or len(self.compilation_config.splitting_ops) == 0
+            )
+            if self.parallel_config.pipeline_parallel_size > 1 or not is_fullgraph:
+                if "-rms_norm" not in self.compilation_config.custom_ops:
+                    self.compilation_config.custom_ops.append("+rms_norm")
+                else:
+                    regime = (
+                        "Dynamo partition"
+                        if not is_fullgraph
+                        else "pipeline parallelism"
+                    )
+                    logger.warning_once(
+                        "Sequence parallelism not supported with"
+                        "native rms_norm when using %s, "
+                        "this will likely lead to an error.",
+                        regime,
+                    )
+
         # final check of cudagraph mode after all possible updates
         if current_platform.is_cuda_alike():
             if (
@@ -652,14 +676,6 @@ class VllmConfig:
                     f"cudagraph_mode={self.compilation_config.cudagraph_mode}"
                 )
 
-            # final migrate the deprecated flags
-            self.compilation_config.use_cudagraph = (
-                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
-            )
-            self.compilation_config.full_cuda_graph = (
-                self.compilation_config.cudagraph_mode.has_full_cudagraphs()
-            )
-
         if self.parallel_config.enable_dbo:
             a2a_backend = self.parallel_config.all2all_backend
             assert a2a_backend in ["deepep_low_latency", "deepep_high_throughput"], (
@@ -849,7 +865,9 @@ class VllmConfig:
                 )
                 # de-duplicate the sizes provided by the config
                 dedup_sizes = list(set(self.compilation_config.cudagraph_capture_sizes))
-                cudagraph_capture_sizes = dedup_sizes
+                cudagraph_capture_sizes = [
+                    i for i in dedup_sizes if i <= max_num_tokens
+                ]
                 # sort to make sure the sizes are in ascending order
                 cudagraph_capture_sizes.sort()
             else:

vllm/distributed/device_communicators/shm_object_storage.py

@@ -342,8 +342,8 @@ class MsgpackSerde(ObjectSerde):
         from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
 
         self.encoder = MsgpackEncoder()
-        self.tensor_decoder = MsgpackDecoder(torch.Tensor)
+        self.tensor_decoder = MsgpackDecoder(torch.Tensor, share_mem=False)
-        self.mm_decoder = MsgpackDecoder(MultiModalKwargsItem)
+        self.mm_decoder = MsgpackDecoder(MultiModalKwargsItem, share_mem=False)
         self._mm_kwargs_item_cls = MultiModalKwargsItem
 
     def serialize(self, value: Any) -> tuple[bytes | list[bytes], int, bytes, int]:
@@ -368,7 +368,7 @@ class MsgpackSerde(ObjectSerde):
         # pickle.loads do not read past the end of a pickled object
         # within a large buffer, so we can skip storing the metadata size
         type_name, nbytes, len_arr = pickle.loads(data_view)
-        serialized_data = bytearray(data_view[-nbytes:])
+        serialized_data = data_view[-nbytes:]
 
         if type_name == torch.Tensor.__name__:
             obj = []

vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py

@@ -48,6 +48,7 @@ from vllm.platforms import current_platform
 from vllm.utils.network_utils import make_zmq_path, make_zmq_socket
 from vllm.v1.attention.backends.utils import get_kv_cache_layout
 from vllm.v1.core.sched.output import SchedulerOutput
+from vllm.v1.worker.block_table import BlockTable
 
 if TYPE_CHECKING:
     from vllm.attention.backends.abstract import AttentionMetadata
@@ -110,6 +111,8 @@ class NixlAgentMetadata(KVConnectorHandshakeMetadata):
 @dataclass
 class ReqMeta:
     local_block_ids: list[int]
+    # To be used when logical block size does not match the kernel block size
+    local_physical_block_ids: list[int]
     remote_block_ids: list[int]
     remote_host: str
     remote_port: int
@@ -137,6 +140,7 @@ class NixlConnectorMetadata(KVConnectorMetadata):
         assert load_remote_cache ^ save_to_host
         _req = ReqMeta(
             local_block_ids=local_block_ids,
+            local_physical_block_ids=local_block_ids,
             remote_block_ids=kv_transfer_params["remote_block_ids"],
             remote_engine_id=kv_transfer_params["remote_engine_id"],
             remote_host=kv_transfer_params["remote_host"],
@@ -897,6 +901,8 @@ class NixlConnectorWorker:
             is_mla=self.use_mla,
             total_num_kv_heads=self.model_config.get_total_num_kv_heads(),
         )
+        self._use_pallas = self.kv_topo._use_pallas
+        self._physical_blocks_per_logical_kv_block = 1
 
     def _nixl_handshake(
         self,
@@ -1092,6 +1098,22 @@ class NixlConnectorWorker:
                 if base_addr in seen_base_addresses:
                     continue
 
+                # TODO (NickLucche): Get kernel_block_size in a cleaner way
+                # NHD default "view" for non-MLA cache
+                kernel_block_size = cache.shape[-2] if self.use_mla else cache.shape[-3]
+
+                if self.block_size != kernel_block_size:
+                    logger.info_once(
+                        "User-specified logical block size (%s) does not match"
+                        " physical kernel block size (%s). Using the latter. ",
+                        self.block_size,
+                        kernel_block_size,
+                    )
+                    self._physical_blocks_per_logical_kv_block = (
+                        self.block_size // kernel_block_size
+                    )
+                    self.block_size = kernel_block_size
+
                 seen_base_addresses.append(base_addr)
                 curr_tensor_size_bytes = cache.numel() * cache.element_size()
 
@@ -1438,7 +1460,7 @@ class NixlConnectorWorker:
         assert self.use_host_buffer
         assert self.copy_blocks is not None
 
-        local_block_ids = meta.local_block_ids
+        local_block_ids = meta.local_physical_block_ids
         self.copy_blocks(
             self.host_xfer_buffers,
             self.device_kv_caches,
@@ -1451,7 +1473,7 @@ class NixlConnectorWorker:
                 "synced recved kv of request[%s] to device kv buffer,"
                 "local_block_ids: %s. ",
                 req_id,
-                ",".join(map(str, meta.local_block_ids)),
+                ",".join(map(str, local_block_ids)),
             )
 
     def save_kv_to_host(self, metadata: NixlConnectorMetadata):
@@ -1460,19 +1482,22 @@ class NixlConnectorWorker:
         assert self.copy_blocks is not None
 
         for req_id, meta in metadata.reqs_to_save.items():
+            meta.local_physical_block_ids = self._logical_to_kernel_block_ids(
+                meta.local_block_ids
+            )
             if logger.isEnabledFor(logging.DEBUG):
                 logger.debug(
                     "save_load_kv for request[%s] to host xfer buffer."
                     "local_block_ids: %s. ",
                     req_id,
-                    ",".join(map(str, meta.local_block_ids)),
+                    ",".join(map(str, meta.local_physical_block_ids)),
                 )
             # blocking
             self.copy_blocks(
                 self.device_kv_caches,
                 self.host_xfer_buffers,
-                meta.local_block_ids,
+                meta.local_physical_block_ids,
-                meta.local_block_ids,
+                meta.local_physical_block_ids,
                 "d2h",
             )
 
@@ -1541,7 +1566,7 @@ class NixlConnectorWorker:
             if self.use_host_buffer:
                 self.sync_recved_kv_to_device(req_id, meta)
             if self.enable_permute_local_kv:
-                block_ids_to_permute += meta.local_block_ids
+                block_ids_to_permute += meta.local_physical_block_ids
         if len(block_ids_to_permute) > 0:
             self.permute_device_kv(block_ids_to_permute)
 
@@ -1628,7 +1653,7 @@ class NixlConnectorWorker:
                         req_id,
                         xfer_state,
                     )
-                    # mark all blocks for this request as invalid
+                    # mark all (logical)blocks for this request as invalid
                     if meta := self._recving_metadata.pop(req_id, None):
                         self._invalid_block_ids.update(meta.local_block_ids)
                     self._recving_metadata.pop(req_id, None)
@@ -1645,13 +1670,19 @@ class NixlConnectorWorker:
         We check for these trnxs to complete in each step().
         """
         for req_id, meta in metadata.reqs_to_recv.items():
+            meta.local_physical_block_ids = self._logical_to_kernel_block_ids(
+                meta.local_block_ids
+            )
+            meta.remote_block_ids = self._logical_to_kernel_block_ids(
+                meta.remote_block_ids
+            )
             remote_engine_id = meta.remote_engine_id
             logger.debug(
                 "start_load_kv for request %s from remote engine %s. "
                 "Num local_block_ids: %s. Num remote_block_ids: %s. ",
                 req_id,
                 remote_engine_id,
-                len(meta.local_block_ids),
+                len(meta.local_physical_block_ids),
                 len(meta.remote_block_ids),
             )
             # always store metadata for failure recovery
@@ -1699,7 +1730,7 @@ class NixlConnectorWorker:
         self._read_blocks(
             request_id=req_id,
             dst_engine_id=meta.remote_engine_id,
-            local_block_ids=meta.local_block_ids,
+            local_block_ids=meta.local_physical_block_ids,
             remote_block_ids=meta.remote_block_ids,
         )
 
@@ -1826,7 +1857,7 @@ class NixlConnectorWorker:
                 "Marking blocks as invalid.",
                 request_id,
             )
-            # mark all blocks for this request as invalid
+            # mark all (logical) blocks for this request as invalid
             if meta := self._recving_metadata.get(request_id):
                 self._invalid_block_ids.update(meta.local_block_ids)
             self.xfer_stats.record_failed_transfer()
@@ -1865,6 +1896,23 @@ class NixlConnectorWorker:
         descs_ids = region_ids * num_blocks + block_ids
         return descs_ids.flatten()
 
+    def _logical_to_kernel_block_ids(self, block_ids: list[int]) -> list[int]:
+        """
+        Convert logical block ids to kernel physical block ids.
+        This is required when the logical block size (the one set by the user)
+        does not match the one required by the attn backend.
+        """
+        if self._physical_blocks_per_logical_kv_block == 1:
+            # Noop when physical and logical block sizes are the same
+            return block_ids
+        block_ids_np = np.array(block_ids)
+        block_arange = np.arange(0, self._physical_blocks_per_logical_kv_block).reshape(
+            1, -1
+        )
+        return BlockTable.map_to_kernel_blocks(
+            block_ids_np, self._physical_blocks_per_logical_kv_block, block_arange
+        ).tolist()
+
     def get_backend_aware_kv_block_len(self, layer_idx: int):
         """
         Get the block length for one K/V element (K and V have the same size).

vllm/engine/arg_utils.py

@@ -1625,40 +1625,39 @@ class EngineArgs:
             )
 
         observability_config = ObservabilityConfig(
-            show_hidden_metrics_for_version=(self.show_hidden_metrics_for_version),
+            show_hidden_metrics_for_version=self.show_hidden_metrics_for_version,
             otlp_traces_endpoint=self.otlp_traces_endpoint,
             collect_detailed_traces=self.collect_detailed_traces,
         )
 
         # Compilation config overrides
+        compilation_config = copy.deepcopy(self.compilation_config)
         if self.cuda_graph_sizes is not None:
             logger.warning(
                 "--cuda-graph-sizes is deprecated and will be removed in v0.13.0 or "
                 "v1.0.0, whichever is soonest. Please use --cudagraph-capture-sizes "
                 "instead."
             )
-            if self.compilation_config.cudagraph_capture_sizes is not None:
+            if compilation_config.cudagraph_capture_sizes is not None:
                 raise ValueError(
                     "cuda_graph_sizes and compilation_config."
                     "cudagraph_capture_sizes are mutually exclusive"
                 )
-            self.compilation_config.cudagraph_capture_sizes = self.cuda_graph_sizes
+            compilation_config.cudagraph_capture_sizes = self.cuda_graph_sizes
         if self.cudagraph_capture_sizes is not None:
-            if self.compilation_config.cudagraph_capture_sizes is not None:
+            if compilation_config.cudagraph_capture_sizes is not None:
                 raise ValueError(
                     "cudagraph_capture_sizes and compilation_config."
                     "cudagraph_capture_sizes are mutually exclusive"
                 )
-            self.compilation_config.cudagraph_capture_sizes = (
+            compilation_config.cudagraph_capture_sizes = self.cudagraph_capture_sizes
-                self.cudagraph_capture_sizes
-            )
         if self.max_cudagraph_capture_size is not None:
-            if self.compilation_config.max_cudagraph_capture_size is not None:
+            if compilation_config.max_cudagraph_capture_size is not None:
                 raise ValueError(
                     "max_cudagraph_capture_size and compilation_config."
                     "max_cudagraph_capture_size are mutually exclusive"
                 )
-            self.compilation_config.max_cudagraph_capture_size = (
+            compilation_config.max_cudagraph_capture_size = (
                 self.max_cudagraph_capture_size
             )
 
@@ -1673,7 +1672,7 @@ class EngineArgs:
             load_config=load_config,
             structured_outputs_config=self.structured_outputs_config,
             observability_config=observability_config,
-            compilation_config=self.compilation_config,
+            compilation_config=compilation_config,
             kv_transfer_config=self.kv_transfer_config,
             kv_events_config=self.kv_events_config,
             additional_config=self.additional_config,

vllm/model_executor/layers/fused_moe/layer.py

@@ -2439,28 +2439,6 @@ class FusedMoE(CustomOp):
             staged_hidden_states.copy_(hidden_states, non_blocking=True)
             staged_router_logits.copy_(router_logits, non_blocking=True)
 
-            # If there are shared experts but we are not using a modular kernel,
-            # the shared experts must be called here
-            if has_separate_shared_experts:
-                assert self.shared_experts is not None
-
-                if self.shared_experts_stream is not None:
-                    # For chunked, we start the shared experts stream here
-                    # (Note that no concurrency with the router/gate)
-                    self.shared_experts_stream.wait_stream(current_stream())
-
-                    with torch.cuda.stream(self.shared_experts_stream):
-                        # Note that staged_hidden_states clone() is necessary
-                        # here to avoid conflict with the main stream
-                        shared_output = self.shared_experts(
-                            staged_hidden_states.clone()
-                        )
-                else:
-                    shared_output = self.shared_experts(staged_hidden_states)
-
-            else:
-                shared_output = None
-
             # Matrix multiply.
             final_hidden_states = self.quant_method.apply(
                 layer=self,
@@ -2489,11 +2467,7 @@ class FusedMoE(CustomOp):
             if has_separate_shared_experts:
                 assert not isinstance(final_hidden_states, tuple)
                 assert self.shared_experts is not None
-
+                shared_output = self.shared_experts(staged_hidden_states)
-                # Here we finish the shared experts stream
-                if self.shared_experts_stream is not None:
-                    current_stream().wait_stream(self.shared_experts_stream)
-
                 final_hidden_states = (
                     shared_output,
                     final_hidden_states,
@@ -2602,11 +2576,22 @@ class FusedMoE(CustomOp):
             assert self.shared_experts is not None
 
             if self.shared_experts_stream is not None:
+                # Clone BEFORE switching streams to avoid race condition
+                # where routed_expert kernel may mutate hidden_states.
+                hidden_states_clone = hidden_states.clone()
+                self.shared_experts_stream.wait_stream(current_stream())
+
                 # Run shared experts in parallel on a separate stream
                 with torch.cuda.stream(self.shared_experts_stream):
-                    # Note that hidden_states clone() is necessary here to avoid
+                    shared_output = self.shared_experts(hidden_states_clone)
-                    # conflict with the main stream
+
-                    shared_output = self.shared_experts(hidden_states.clone())
+                # Record that the clone will be used by shared_experts_stream
+                # to avoid gc issue from deallocation of hidden_states_clone
+                # For more details: https://docs.pytorch.org/docs/stable/generated/torch.Tensor.record_stream.html # noqa: E501
+                # NOTE: we dont need shared_output.record_stream(current_stream())
+                # because we synch the streams before using shared_output.
+                hidden_states_clone.record_stream(self.shared_experts_stream)
+
             else:
                 shared_output = self.shared_experts(hidden_states)
         else:

vllm/model_executor/layers/quantization/fp8.py

@@ -43,7 +43,6 @@ from vllm.model_executor.layers.quantization.base_config import (
     QuantizationConfig,
     QuantizeMethodBase,
 )
-from vllm.model_executor.layers.quantization.input_quant_fp8 import QuantFP8
 from vllm.model_executor.layers.quantization.kv_cache import BaseKVCacheMethod
 from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
     FlashinferMoeBackend,
@@ -95,11 +94,9 @@ from vllm.model_executor.utils import set_weight_attrs
 from vllm.platforms import current_platform
 from vllm.scalar_type import scalar_types
 from vllm.utils.deep_gemm import (
-    fp8_gemm_nt,
     get_col_major_tma_aligned_tensor,
     is_deep_gemm_e8m0_used,
     is_deep_gemm_supported,
-    should_use_deepgemm_for_fp8_linear,
 )
 from vllm.utils.flashinfer import has_flashinfer_moe
 from vllm.utils.import_utils import has_deep_gemm
@@ -554,83 +551,19 @@ class Fp8LinearMethod(LinearMethodBase):
         # if batch invariant mode is enabled, prefer DeepGEMM FP8 path
         # we will use BF16 dequant when DeepGEMM is not supported.
         if vllm_is_batch_invariant():
-            # Call is_deep_gemm_supported() ahead of time for torch.compile
-            # dynamo has trouble tracing through
-            if self.block_quant and should_use_deepgemm_for_fp8_linear(
-                torch.bfloat16, layer.weight, self.use_deep_gemm
-            ):
-                # use group quant consistent with block size across K
-                assert self.act_q_group_shape is not None
-                q_input, input_scale = QuantFP8(
-                    False,
-                    self.act_q_group_shape,
-                    column_major_scales=True,
-                )(x)
-
-                output_2d = torch.empty(
-                    (q_input.shape[0], layer.weight.shape[0]),
-                    dtype=torch.bfloat16,
-                    device=q_input.device,
-                )
-                fp8_gemm_nt(
-                    (q_input, input_scale),
-                    (layer.weight, layer.weight_scale),
-                    output_2d,
-                )
-                if bias is not None:
-                    output_2d = output_2d + bias
-                return output_2d
-
-            # Dequantize FP8 weights to BF16
-            weight_fp8 = layer.weight.to(torch.bfloat16)
-            weight_scale = layer.weight_scale.to(torch.bfloat16)
-
-            # Handle different quantization granularities
             if self.block_quant:
-                # Block-wise quantization:
-                # - Weight is NOT transposed, shape is [N, K] (output_size, input_size)
-                # - Scale has shape [num_blocks_k, num_blocks_n] (TRANSPOSED!)
                 assert self.weight_block_size is not None
-                block_n, block_k = self.weight_block_size  # Note: order is [N, K]
+                return self.w8a8_block_fp8_linear.apply(
-
+                    input=x,
-                N, K = weight_fp8.shape
+                    weight=layer.weight,
-
+                    weight_scale=layer.weight_scale,
-                # determine expected number of blocks along N and K
+                    input_scale=layer.input_scale,
-                num_blocks_n = (N + block_n - 1) // block_n
+                    bias=bias,
-                num_blocks_k = (K + block_k - 1) // block_k
+                )
-
-                # scale layout may be [num_blocks_n, num_blocks_k]
-                # or [num_blocks_k, num_blocks_n] depending on backend
-                if weight_scale.dim() != 2:
-                    raise RuntimeError(
-                        f"FP8 block scale must be 2D, got {tuple(weight_scale.shape)}"
-                    )
-
-                scale_rows, scale_cols = weight_scale.shape
-                if (scale_rows, scale_cols) == (num_blocks_k, num_blocks_n):
-                    if num_blocks_n == num_blocks_k:
-                        # ambiguous square case, warn and skip transpose
-                        logger.warning(
-                            "Batch-invariant FP8: square block-scale %dx%d; "
-                            "skipping transpose to avoid misorientation.",
-                            scale_rows,
-                            scale_cols,
-                        )
-                    else:
-                        # clear KN -> transpose to NK
-                        weight_scale = weight_scale.t()
-
-                # Expand scale to match weight dimensions
-                # scale_expanded should have shape [N, K]
-                scale_expanded = weight_scale.repeat_interleave(
-                    block_n, dim=0
-                ).repeat_interleave(block_k, dim=1)
-                # Trim to exact weight size (in case of padding)
-                scale_expanded = scale_expanded[:N, :K]
-                weight_bf16 = weight_fp8 * scale_expanded
             else:
-                # Per-tensor quantization: weight IS transposed to [K, N]
+                # per-tensor/channel: dequant to BF16 and run GEMM
-                # scale should be scalar or [1] or per-output-channel [N]
+                weight_fp8 = layer.weight.to(torch.bfloat16)
+                weight_scale = layer.weight_scale.to(torch.bfloat16)
                 if weight_scale.numel() == 1:
                     # Per-tensor: simple scalar multiplication
                     weight_bf16 = weight_fp8 * weight_scale
@@ -649,16 +582,7 @@ class Fp8LinearMethod(LinearMethodBase):
                     else:
                         # Fallback
                         weight_bf16 = weight_fp8 * weight_scale
-
+                return torch.nn.functional.linear(x, weight_bf16.t(), bias)
-            # For block quant, weight is [N, K], for per-tensor it's [K, N]
-            # F.linear expects weight to be [N, K], so:
-            if self.block_quant:
-                # Already in correct shape [N, K]
-                output = torch.nn.functional.linear(x, weight_bf16, bias)
-            else:
-                # Need to transpose back: [K, N] -> [N, K]
-                output = torch.nn.functional.linear(x, weight_bf16.t(), bias)
-            return output
 
         if self.use_marlin:
             return apply_fp8_marlin_linear(

vllm/model_executor/model_loader/weight_utils.py

@@ -82,7 +82,8 @@ enable_hf_transfer()
 
 class DisabledTqdm(tqdm):
     def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs, disable=True)
+        kwargs["disable"] = True
+        super().__init__(*args, **kwargs)
 
 
 def get_lock(model_name_or_path: str | Path, cache_dir: str | None = None):

vllm/model_executor/models/dots_ocr.py

@@ -779,6 +779,10 @@ class DotsOCRForCausalLM(nn.Module, SupportsMultiModal, SupportsPP, SupportsLoRA
             architectures=["Qwen2ForCausalLM"],
         )
 
+        self.make_empty_intermediate_tensors = (
+            self.language_model.make_empty_intermediate_tensors
+        )
+
     def _parse_and_validate_image_input(
         self, **kwargs: object
     ) -> DotsOCRImageInputs | None:

vllm/model_executor/models/mllama4.py

@@ -35,6 +35,7 @@ from vllm.attention.layer import MultiHeadAttention
 from vllm.config import VllmConfig
 from vllm.config.multimodal import BaseDummyOptions
 from vllm.distributed import get_tensor_model_parallel_world_size
+from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.linear import (
     ColumnParallelLinear,
     QKVParallelLinear,
@@ -45,6 +46,7 @@ from vllm.model_executor.layers.quantization import QuantizationConfig
 from vllm.model_executor.layers.rotary_embedding import get_rope
 from vllm.model_executor.model_loader.utils import initialize_model
 from vllm.model_executor.model_loader.weight_utils import default_weight_loader
+from vllm.model_executor.models.module_mapping import MultiModelKeys
 from vllm.multimodal import MULTIMODAL_REGISTRY
 from vllm.multimodal.inputs import (
     MultiModalDataDict,
@@ -68,11 +70,15 @@ from .interfaces import (
     MixtureOfExperts,
     MultiModalEmbeddings,
     SupportsEagle3,
+    SupportsLoRA,
     SupportsMultiModal,
     SupportsPP,
 )
 from .llama4 import Llama4ForCausalLM
-from .utils import AutoWeightsLoader, maybe_prefix
+from .utils import (
+    AutoWeightsLoader,
+    maybe_prefix,
+)
 from .vision import run_dp_sharded_vision_model
 
 
@@ -724,7 +730,12 @@ class Mllama4DummyInputsBuilder(BaseDummyInputsBuilder[Mllama4ProcessingInfo]):
     dummy_inputs=Mllama4DummyInputsBuilder,
 )
 class Llama4ForConditionalGeneration(
-    nn.Module, SupportsMultiModal, SupportsPP, MixtureOfExperts, SupportsEagle3
+    nn.Module,
+    SupportsMultiModal,
+    SupportsPP,
+    MixtureOfExperts,
+    SupportsEagle3,
+    SupportsLoRA,
 ):
     merge_by_field_config = True
 
@@ -1067,6 +1078,17 @@ class Llama4ForConditionalGeneration(
 
         return updated_params
 
+    def get_expert_mapping(self) -> list[tuple[str, str, int, str]]:
+        # Params for weights, fp8 weight scales, fp8 activation scales
+        # (param_name, weight_name, expert_id, shard_id)
+        return FusedMoE.make_expert_params_mapping(
+            ckpt_gate_proj_name="gate_proj",
+            ckpt_down_proj_name="down_proj",
+            ckpt_up_proj_name="up_proj",
+            num_experts=self.config.text_config.num_local_experts,
+            num_redundant_experts=self.num_redundant_experts,
+        )
+
     def load_weights(self, weights: Iterable[tuple[str, torch.Tensor]]) -> set[str]:
         stacked_params_mapping = [
             # (param_name, shard_name, shard_id)
@@ -1113,3 +1135,13 @@ class Llama4ForConditionalGeneration(
         )
 
         return updated_params
+
+    def get_mm_mapping(self) -> MultiModelKeys:
+        """
+        Get the module prefix in multimodal models
+        """
+        return MultiModelKeys.from_string_field(
+            language_model="language_model",
+            connector="multi_modal_projector.",
+            tower_model="vision_model.",
+        )

vllm/model_executor/models/paddleocr_vl.py

@@ -198,23 +198,18 @@ class PaddleOCRVLProcessingInfo(BaseProcessingInfo):
         if image_processor is None:
             image_processor = self.get_image_processor()
 
-        do_resize = True
         hf_config = self.get_hf_config()
         vision_config = hf_config.vision_config
         patch_size = vision_config.patch_size
         merge_size = vision_config.spatial_merge_size
-
+        resized_height, resized_width = smart_resize(
-        if do_resize:
+            height=image_height,
-            resized_height, resized_width = smart_resize(
+            width=image_width,
-                height=image_height,
+            factor=patch_size * merge_size,
-                width=image_width,
+            min_pixels=image_processor.min_pixels,
-                factor=patch_size * merge_size,
+            max_pixels=image_processor.max_pixels,
-                min_pixels=image_processor.min_pixels,
+        )
-                max_pixels=image_processor.max_pixels,
+        preprocessed_size = ImageSize(width=resized_width, height=resized_height)
-            )
-            preprocessed_size = ImageSize(width=resized_width, height=resized_height)
-        else:
-            preprocessed_size = ImageSize(width=image_width, height=image_height)
 
         grid_t = 1
         grid_h = preprocessed_size.height // patch_size
@@ -227,8 +222,19 @@ class PaddleOCRVLProcessingInfo(BaseProcessingInfo):
 
     def get_image_size_with_most_features(self) -> ImageSize:
         hf_config = self.get_hf_config()
-        image_size = hf_config.vision_config.image_size
+
-        return ImageSize(height=image_size, width=image_size)
+        # See `smart_resize` for the calculation of the image size.
+        merge_size = hf_config.vision_config.spatial_merge_size
+        patch_size = hf_config.vision_config.patch_size
+        factor = merge_size * patch_size
+        max_num_tokens = self.get_image_processor().max_pixels // (factor**2)
+        # Find factors of max_num_tokens close to its square root
+        # to create a dummy image with a reasonable aspect ratio.
+        h_patches = int(math.sqrt(max_num_tokens))
+        while max_num_tokens % h_patches != 0:
+            h_patches -= 1
+        w_patches = max_num_tokens // h_patches
+        return ImageSize(height=h_patches * factor, width=w_patches * factor)
 
 
 class PaddleOCRVLDummyInputsBuilder(BaseDummyInputsBuilder[PaddleOCRVLProcessingInfo]):

vllm/model_executor/models/whisper.py

@@ -13,7 +13,6 @@ from transformers import (
     BatchFeature,
     WhisperConfig,
     WhisperFeatureExtractor,
-    WhisperProcessor,
 )
 from transformers.models.whisper.modeling_whisper import sinusoids
 
@@ -660,16 +659,6 @@ class WhisperProcessingInfo(BaseProcessingInfo):
     def get_hf_config(self) -> WhisperConfig:
         return self.ctx.get_hf_config(WhisperConfig)
 
-    def get_hf_processor(self, **kwargs: object) -> WhisperProcessor:
-        # HACK: Transformers 4.53.2 has issue with whisper tokenizer to
-        # initialize processor. We use a monkeypatch to fix it here.
-        # See: https://github.com/vllm-project/vllm/issues/20224
-        processor_class = WhisperProcessor
-        tokenizer_class = ("WhisperTokenizer", "WhisperTokenizerFast")
-        if processor_class.tokenizer_class != tokenizer_class:
-            processor_class.tokenizer_class = tokenizer_class
-        return self.ctx.get_hf_processor(processor_class, **kwargs)
-
     def get_supported_mm_limits(self) -> Mapping[str, int | None]:
         return {"audio": 1}
 

vllm/v1/attention/backends/flash_attn.py

@@ -675,6 +675,7 @@ class FlashAttentionImpl(AttentionImpl):
             logits_soft_cap=self.logits_soft_cap,
             block_table=attn_metadata.block_table,
             common_prefix_len=attn_metadata.common_prefix_len,
+            max_num_splits=attn_metadata.max_num_splits,
             fa_version=self.vllm_flash_attn_version,
             prefix_scheduler_metadata=attn_metadata.prefix_scheduler_metadata,
             suffix_scheduler_metadata=attn_metadata.scheduler_metadata,
@@ -921,6 +922,7 @@ def cascade_attention(
     logits_soft_cap: float,
     block_table: torch.Tensor,
     common_prefix_len: int,
+    max_num_splits: int,
     fa_version: int,
     prefix_scheduler_metadata: torch.Tensor | None = None,
     suffix_scheduler_metadata: torch.Tensor | None = None,
@@ -965,7 +967,7 @@ def cascade_attention(
         # s_aux is incorporated into prefix_lse inside the GPU kernel,
         # enabling its effect during the final attention merge.
         s_aux=s_aux,
-        num_splits=1 if vllm_is_batch_invariant() else 0,
+        num_splits=1 if vllm_is_batch_invariant() else max_num_splits,
     )
 
     descale_shape = (cu_query_lens.shape[0] - 1, key_cache.shape[-2])
@@ -990,7 +992,7 @@ def cascade_attention(
         q_descale=q_descale.expand(descale_shape) if q_descale is not None else None,
         k_descale=k_descale.expand(descale_shape) if k_descale is not None else None,
         v_descale=v_descale.expand(descale_shape) if v_descale is not None else None,
-        num_splits=1 if vllm_is_batch_invariant() else 0,
+        num_splits=1 if vllm_is_batch_invariant() else max_num_splits,
     )
 
     # Merge prefix and suffix outputs, and store the result in output.

vllm/v1/attention/backends/mamba1_attn.py

@@ -123,7 +123,7 @@ class Mamba1AttentionMetadataBuilder(
         elif (
             num_decodes > 0
             and num_decodes <= self.decode_cudagraph_max_bs
-            and self.compilation_config.full_cuda_graph
+            and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         ):
             padded_decodes = self.vllm_config.pad_for_cudagraph(num_decodes)
             self.state_indices_tensor[:num_decodes].copy_(

vllm/v1/attention/backends/mamba2_attn.py

@@ -302,7 +302,7 @@ class Mamba2AttentionMetadataBuilder(
 
         elif (
             num_decodes <= self.decode_cudagraph_max_bs
-            and self.compilation_config.full_cuda_graph
+            and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         ):
             # Pad state tensor for CUDA graph
             num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)

vllm/v1/attention/backends/mla/common.py

@@ -349,6 +349,7 @@ class MLACommonPrefillMetadata:
         local_context_lens_allranks: list[list[int]] | None = None
         padded_local_cu_seq_lens: torch.Tensor | None = None
         cu_seq_lens_lst: list[list[int]] | None = None
+        chunk_size: int | None = None
 
     block_table: torch.Tensor
     query_start_loc: torch.Tensor
@@ -914,6 +915,7 @@ class MLACommonMetadataBuilder(AttentionMetadataBuilder[M]):
                             device, non_blocking=True
                         ),
                         cu_seq_lens_lst=cu_seq_lens_cpu.tolist(),
+                        chunk_size=padded_local_max_context_chunk_across_ranks,
                     )
                 else:
                     chunked_context_metadata = chunked_context_metadata_cls(
@@ -998,6 +1000,8 @@ def reorg_kvcache(
     local_context_lens_allranks: list[list[int]],
     sum_seq_len: int,
     max_seq_len: int,
+    chunk_size: int,
+    chunk_idx: int,
     toks: int,
 ) -> tuple[torch.Tensor, torch.Tensor]:
     """
@@ -1013,6 +1017,9 @@ def reorg_kvcache(
         local_context_lens_allranks: local context lengths on each CP rank.
         sum_seq_len: the sum of cp_chunk_seq_lens_lst.
         max_seq_len: the max value of cp_chunk_seq_lens_lst.
+        chunk_size: the local padded max context chunk from
+            chunked_context_metadata building.
+        chunk_idx: chunk idx of chunked_prefill.
         toks: the number of tokens for local gather cache.
     """
     kv_c_segments = []
@@ -1024,20 +1031,31 @@ def reorg_kvcache(
     ):
         cur_seq_len = 0
         for rank, local_context_len in enumerate(local_context_lens):
-            if local_context_len != 0:
+            # Note(qcs): We split the context into multiple chunks,
+            # depending on the size of the workspace.
+            # local_context in dcp0:   |-----------------|
+            # local_context in dcp1:   |--------------|
+            # n*padded_local_chunk:    |-----|-----|-----|
+            # local_chunk_len in dcp1: |-----|-----|--|
+            # so we need update the last chunk length in dcp1.
+            local_chunk_len = min(
+                max(0, local_context_len - chunk_idx * chunk_size),
+                padded_local_chunk_seq_len,
+            )
+            if local_chunk_len != 0:
                 kv_c_segment = allgatered_kv_c_normed[
                     rank * toks + src_token_idx : rank * toks
                     + src_token_idx
-                    + local_context_len
+                    + local_chunk_len
                 ]
                 k_pe_segment = allgatered_k_pe[
                     rank * toks + src_token_idx : rank * toks
                     + src_token_idx
-                    + local_context_len
+                    + local_chunk_len
                 ]
                 kv_c_segments.append(kv_c_segment)
                 k_pe_segments.append(k_pe_segment)
-                cur_seq_len += local_context_len
+                cur_seq_len += local_chunk_len
         max_seq_len_check = max(max_seq_len_check, cur_seq_len)
         src_token_idx += padded_local_chunk_seq_len
     reorganized_kv_c_normed = torch.cat(kv_c_segments, dim=0)
@@ -1688,6 +1706,7 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
         assert prefill_metadata.chunked_context.local_context_lens_allranks is not None
         assert prefill_metadata.chunked_context.padded_local_cu_seq_lens is not None
         assert prefill_metadata.chunked_context.cu_seq_lens_lst is not None
+        assert prefill_metadata.chunked_context.chunk_size is not None
 
         output = None
         iters = len(prefill_metadata.chunked_context.seq_tot)
@@ -1737,6 +1756,8 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
                 local_context_lens_allranks=prefill_metadata.chunked_context.local_context_lens_allranks,
                 sum_seq_len=prefill_metadata.chunked_context.cu_seq_lens_lst[i][-1],
                 max_seq_len=prefill_metadata.chunked_context.max_seq_lens[i],
+                chunk_size=prefill_metadata.chunked_context.chunk_size,
+                chunk_idx=i,
                 toks=toks,
             )
 

vllm/v1/attention/backends/short_conv_attn.py

@@ -81,7 +81,7 @@ class ShortConvAttentionMetadataBuilder(
         elif (
             num_decodes > 0
             and num_decodes <= self.decode_cudagraph_max_bs
-            and self.compilation_config.full_cuda_graph
+            and self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         ):
             num_input_tokens = self.vllm_config.pad_for_cudagraph(num_decodes)
             self.state_indices_tensor[:num_decodes].copy_(

vllm/v1/sample/logits_processor/__init__.py

@@ -5,7 +5,7 @@ import inspect
 import itertools
 from abc import abstractmethod
 from collections.abc import Sequence
-from functools import partial
+from functools import lru_cache, partial
 from typing import TYPE_CHECKING
 
 import torch
@@ -216,11 +216,17 @@ def build_logitsprocs(
     )
 
 
+cached_load_custom_logitsprocs = lru_cache(_load_custom_logitsprocs)
+
+
 def validate_logits_processors_parameters(
     logits_processors: Sequence[str | type[LogitsProcessor]] | None,
     sampling_params: SamplingParams,
 ):
-    for logits_procs in _load_custom_logitsprocs(logits_processors):
+    logits_processors = (
+        tuple(logits_processors) if logits_processors is not None else None
+    )
+    for logits_procs in cached_load_custom_logitsprocs(logits_processors):
         logits_procs.validate_params(sampling_params)
 
 

vllm/v1/serial_utils.py

@@ -31,6 +31,7 @@ from vllm.multimodal.inputs import (
     MultiModalSharedField,
     NestedTensors,
 )
+from vllm.utils.platform_utils import is_pin_memory_available
 from vllm.v1.engine import UtilityResult
 from vllm.v1.utils import tensor_data
 
@@ -282,7 +283,9 @@ class MsgpackDecoder:
     not thread-safe when encoding tensors / numpy arrays.
     """
 
-    def __init__(self, t: Any | None = None):
+    def __init__(self, t: Any | None = None, share_mem: bool = True):
+        self.share_mem = share_mem
+        self.pin_tensors = is_pin_memory_available()
         args = () if t is None else (t,)
         self.decoder = msgpack.Decoder(
             *args, ext_hook=self.ext_hook, dec_hook=self.dec_hook
@@ -347,21 +350,30 @@ class MsgpackDecoder:
         # zero-copy decode. We assume the ndarray will not be kept around,
         # as it now locks the whole received message buffer in memory.
         buffer = self.aux_buffers[data] if isinstance(data, int) else data
-        return np.frombuffer(buffer, dtype=dtype).reshape(shape)
+        arr = np.frombuffer(buffer, dtype=dtype)
+        if not self.share_mem:
+            arr = arr.copy()
+        return arr.reshape(shape)
 
     def _decode_tensor(self, arr: Any) -> torch.Tensor:
         dtype, shape, data = arr
-        # Copy from inline representation, to decouple the memory storage
+        is_aux = isinstance(data, int)
-        # of the message from the original buffer. And also make Torch
+        buffer = self.aux_buffers[data] if is_aux else data
-        # not complain about a readonly memoryview.
+        buffer = buffer if isinstance(buffer, memoryview) else memoryview(buffer)
-        buffer = self.aux_buffers[data] if isinstance(data, int) else bytearray(data)
         torch_dtype = getattr(torch, dtype)
         assert isinstance(torch_dtype, torch.dtype)
-        if not buffer:  # torch.frombuffer doesn't like empty buffers
+        if not buffer.nbytes:  # torch.frombuffer doesn't like empty buffers
             assert 0 in shape
             return torch.empty(shape, dtype=torch_dtype)
         # Create uint8 array
         arr = torch.frombuffer(buffer, dtype=torch.uint8)
+        # Clone ensures tensor is backed by pytorch-owned memory for safe
+        # future async CPU->GPU transfer.
+        # Pin larger tensors for more efficient CPU->GPU transfer.
+        if not is_aux:
+            arr = arr.clone()
+        elif not self.share_mem:
+            arr = arr.pin_memory() if self.pin_tensors else arr.clone()
         # Convert back to proper shape & type
         return arr.view(torch_dtype).view(shape)
 

vllm/v1/worker/block_table.py

@@ -98,7 +98,9 @@ class BlockTable:
             return
 
         if self.use_hybrid_blocks:
-            block_ids = self._map_to_kernel_blocks(np.array(block_ids))
+            block_ids = self.map_to_kernel_blocks(
+                np.array(block_ids), self.blocks_per_kv_block, self._kernel_block_arange
+            )
 
         num_blocks = len(block_ids)
         start = self.num_blocks_per_row[row_idx]
@@ -188,7 +190,12 @@ class BlockTable:
         self.block_table.gpu.fill_(0)
         self.block_table.cpu.fill_(0)
 
-    def _map_to_kernel_blocks(self, kv_manager_block_ids: np.ndarray) -> np.ndarray:
+    @staticmethod
+    def map_to_kernel_blocks(
+        kv_manager_block_ids: np.ndarray,
+        blocks_per_kv_block: int,
+        kernel_block_arange: np.ndarray,
+    ) -> np.ndarray:
         """Convert kv_manager_block_id IDs to kernel block IDs.
 
         Example:
@@ -203,12 +210,12 @@ class BlockTable:
             # kv_manager_block_id 1 → kernel block id [2, 3]
             # kv_manager_block_id 2 → kernel block id [4, 5]
         """
-        if not self.use_hybrid_blocks:
+        if blocks_per_kv_block == 1:
             return kv_manager_block_ids
 
         kernel_block_ids = (
-            kv_manager_block_ids.reshape(-1, 1) * self.blocks_per_kv_block
+            kv_manager_block_ids.reshape(-1, 1) * blocks_per_kv_block
-            + self._kernel_block_arange
+            + kernel_block_arange
         )
 
         return kernel_block_ids.reshape(-1)

vllm/v1/worker/gpu_model_runner.py

@@ -279,6 +279,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # This will be overridden in load_model()
         self.is_multimodal_pruning_enabled = False
         self.max_model_len = model_config.max_model_len
+
+        # Always set to false after the first forward pass
+        self.calculate_kv_scales = self.cache_config.calculate_kv_scales
         self.dcp_world_size = self.parallel_config.decode_context_parallel_size
         self.dcp_rank = 0 if self.dcp_world_size <= 1 else get_dcp_group().rank_in_group
         self.max_num_tokens = scheduler_config.max_num_batched_tokens
@@ -2587,27 +2590,28 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     )
                 )
 
-            dp_rank = self.parallel_config.data_parallel_rank
+                dp_rank = self.parallel_config.data_parallel_rank
-            if ubatch_slices:
+                if ubatch_slices:
-                assert num_tokens_across_dp is not None
+                    assert num_tokens_across_dp is not None
-                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
+                    num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
-                self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
+                    self.pad_out_ubatch_slice(ubatch_slices, num_input_tokens)
-            elif num_tokens_across_dp is not None:
+                elif num_tokens_across_dp is not None:
-                num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
+                    num_input_tokens = int(num_tokens_across_dp[dp_rank].item())
-            else:
+                else:
-                num_input_tokens = self._get_num_input_tokens(
+                    num_input_tokens = self._get_num_input_tokens(
-                    scheduler_output.total_num_scheduled_tokens
+                        scheduler_output.total_num_scheduled_tokens
-                )
+                    )
 
-            (
+                (
-                input_ids,
+                    input_ids,
-                inputs_embeds,
+                    inputs_embeds,
-                positions,
+                    positions,
-                intermediate_tensors,
+                    intermediate_tensors,
-                model_kwargs,
+                    model_kwargs,
-            ) = self._preprocess(
+                    ec_connector_output,
-                scheduler_output, num_input_tokens, intermediate_tensors
+                ) = self._preprocess(
-            )
+                    scheduler_output, num_input_tokens, intermediate_tensors
+                )
 
             uniform_decode = (
                 max_num_scheduled_tokens == self.uniform_decode_query_len
@@ -2625,16 +2629,12 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             )
 
         # Set cudagraph mode to none if calc_kv_scales is true.
-        if attn_metadata is not None:
+        # KV scales calculation involves dynamic operations that are incompatible
-            metadata_list = (
+        # with CUDA graph capture.
-                attn_metadata.values()
+        if self.calculate_kv_scales:
-                if isinstance(attn_metadata, dict)
+            cudagraph_runtime_mode = CUDAGraphMode.NONE
-                else [attn_metadata]
+            # Mark KV scales as calculated after the first forward pass
-            )
+            self.calculate_kv_scales = False
-            if any(
-                getattr(m, "enable_kv_scales_calculation", False) for m in metadata_list
-            ):
-                cudagraph_runtime_mode = CUDAGraphMode.NONE
 
         # Run the model.
         # Use persistent buffers for CUDA graphs.