[MISC] cudagraph_capture_sizes related improvements (#26016)

Signed-off-by: fhl <2410591650@qq.com>
Signed-off-by: fhl2000 <63384265+fhl2000@users.noreply.github.com>
Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
Co-authored-by: Luka Govedič <ProExpertProg@users.noreply.github.com>
Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>

tests/compile/test_config.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 import copy
+from contextlib import nullcontext
 
 import pytest
 
@@ -8,6 +9,8 @@ from vllm.compilation.counter import compilation_counter
 from vllm.compilation.fix_functionalization import FixFunctionalizationPass
 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
 
 
@@ -233,3 +236,73 @@ def test_resolve_operator_overload():
     assert len(resolved) == 2  # Only 2 valid ops
     assert resolved[0] is torch.ops.aten.mm.default
     assert resolved[1] is torch.ops.aten.addmm.default
+
+
+@pytest.mark.skipif(
+    not current_platform.support_static_graph_mode(),
+    reason="Skip if not cudagraph mode supported",
+)
+@pytest.mark.parametrize(
+    (
+        "cudagraph_capture_sizes",
+        "max_cudagraph_capture_size",
+        "tp_size",
+        "enable_sequence_parallelism",
+        "max_num_batched_tokens",
+        "use_cudagraph",
+        "expected_max_size",
+    ),
+    [
+        (None, None, 1, False, 2048, True, 512),
+        ([1, 2, 4], 4, 1, False, 2048, True, 4),
+        ([1, 2, 4], 8, 1, False, 2048, True, RuntimeError),
+        ([1, 256], None, 1, False, 2048, 256),
+        ([], None, 1, False, 2048, False, 0),
+        (None, 0, 1, False, 2048, False, 0),
+        # truncated to nearest multiple of 8 or 16
+        (None, 257, 1, False, 2048, True, 256),
+        ([1, 2, 4, 15], None, 1, False, 2048, True, 15),  # 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, 8, True, 4),  # limited by the max_tokens
+        # the list should contain at least 1 element when use cudagraph
+        ([], None, 1, False, 2048, True, RuntimeError),
+        # the max capturing size should be >= 1 when use cudagraph
+        (None, 0, 1, False, 2048, True, RuntimeError),
+    ],
+)
+def test_cudagraph_sizes_post_init(
+    cudagraph_capture_sizes,
+    max_cudagraph_capture_size,
+    tp_size,
+    enable_sequence_parallelism,
+    max_num_batched_tokens,
+    use_cudagraph,
+    expected_max_size,
+):
+    ctx = nullcontext()
+    if isinstance(expected_max_size, Exception):
+        ctx = pytest.raises(expected_max_size)
+
+    cudagraph_mode = CUDAGraphMode.PIECEWISE if use_cudagraph else CUDAGraphMode.NONE
+    with ctx:
+        compilation_config = CompilationConfig(
+            cudagraph_capture_sizes=cudagraph_capture_sizes,
+            max_cudagraph_capture_size=max_cudagraph_capture_size,
+            pass_config={
+                "enable_sequence_parallelism": enable_sequence_parallelism,
+                "enable_fusion": True,
+                "enable_noop": True,
+            },
+            cudagraph_mode=cudagraph_mode,
+        )
+        engine_args = EngineArgs(
+            model="facebook/opt-125m",
+            tensor_parallel_size=tp_size,
+            max_num_batched_tokens=max_num_batched_tokens,
+            compilation_config=compilation_config,
+        )
+        vllm_config = engine_args.create_engine_config()
+
+    assert (
+        vllm_config.compilation_config.max_cudagraph_capture_size == expected_max_size
+    )

vllm/config/compilation.py

@@ -154,6 +154,8 @@ class CompilationConfig:
         - [`cudagraph_mode`][vllm.config.CompilationConfig.cudagraph_mode]
         - [`cudagraph_capture_sizes`]
         [vllm.config.CompilationConfig.cudagraph_capture_sizes]
+        - [`max_cudagraph_capture_size`]
+        [vllm.config.CompilationConfig.max_cudagraph_capture_size]
         - [`cudagraph_num_of_warmups`]
         [vllm.config.CompilationConfig.cudagraph_num_of_warmups]
         - [`cudagraph_copy_inputs`]
@@ -327,18 +329,16 @@ class CompilationConfig:
     more modes may be added.
     """
     use_cudagraph: bool = True
-    """Whether to use cudagraph inside compilation.
-    - False: cudagraph inside compilation is not used.
+    """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.
-    In the vLLM V1 Engine, this flag only applies for
-    CompilationMode.VLLM_COMPILE (aka -O3).
-    Note that this is orthogonal to the cudagraph capture logic
-    outside of compilation.
+
     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=PIECEWISE
-    instead.
+    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.
@@ -398,8 +398,22 @@ class CompilationConfig:
     pass_config: PassConfig = field(default_factory=PassConfig)
     """Custom inductor passes, see PassConfig for more details"""
 
-    max_capture_size: int = field(default=None, init=False)  # type: ignore
-    """not configurable, computed after init"""
+    max_cudagraph_capture_size: int | None = field(default=None)
+    """The maximum cudagraph capture size.
+    
+    If cudagraph_capture_sizes is specified, this will be set to the largest 
+    size in that list (or checked for consistency if specified). If
+    cudagraph_capture_sizes is not specified, the list of sizes is generated
+    automatically following the pattern:
+
+        [1, 2, 4] + list(range(8, 256, 8)) + list(
+        range(256, max_cudagraph_capture_size + 1, 16))
+
+    If not specified, max_cudagraph_capture_size is set to min(max_num_seqs*2,
+    512) by default. This voids OOM in tight memory scenarios with small 
+    max_num_seqs, and prevents capture of many large graphs (>512) that would
+    greatly increase startup time with limited performance benefit.
+    """
     local_cache_dir: str = field(default=None, init=False)  # type: ignore
     """local cache dir for each rank"""
     bs_to_padded_graph_size: list[int] = field(
@@ -408,7 +422,7 @@ class CompilationConfig:
     )
     """optimization:
     Intuitively, bs_to_padded_graph_size should be dict[int, int].
-    since we know all keys are in a range [0, max_capture_size],
+    since we know all keys are in a range [0, max_cudagraph_capture_size],
     we can optimize it to list[int] for better lookup performance."""
 
     # keep track of enabled and disabled custom ops
@@ -672,25 +686,12 @@ class CompilationConfig:
 
         return VllmBackend(vllm_config)
 
-    def init_with_cudagraph_sizes(self, cudagraph_capture_sizes: list[int]) -> None:
-        """To complete the initialization of config,
-        we need to know the cudagraph sizes."""
-
-        if self.cudagraph_capture_sizes is None:
-            self.cudagraph_capture_sizes = cudagraph_capture_sizes
-        else:
-            # de-duplicate the sizes provided by the config
-            dedup_sizes = list(set(self.cudagraph_capture_sizes))
-            if len(dedup_sizes) < len(self.cudagraph_capture_sizes):
-                logger.info(
-                    (
-                        "cudagraph sizes specified by model runner"
-                        " %s is overridden by config %s"
-                    ),
-                    cudagraph_capture_sizes,
-                    dedup_sizes,
-                )
-            self.cudagraph_capture_sizes = dedup_sizes
+    def post_init_cudagraph_sizes(self) -> None:
+        """To complete the initialization after cudagraph related
+        configs are set. This includes:
+        - initialize compile_sizes
+        - pre-compute the mapping bs_to_padded_graph_size
+        """
 
         computed_compile_sizes = []
         if self.compile_sizes is not None:
@@ -708,23 +709,24 @@ class CompilationConfig:
                     computed_compile_sizes.append(x)
         self.compile_sizes = computed_compile_sizes  # type: ignore
 
-        # sort to make sure cudagraph capture sizes are in descending order
-        self.cudagraph_capture_sizes.sort(reverse=True)
-        self.max_capture_size = (
-            self.cudagraph_capture_sizes[0] if self.cudagraph_capture_sizes else 0
-        )
+        # make sure the sizes are in ascending order
+        self.cudagraph_capture_sizes.sort()
+        if self.cudagraph_capture_sizes:
+            assert self.cudagraph_capture_sizes[-1] == self.max_cudagraph_capture_size
 
         # pre-compute the mapping from batch size to padded graph size
-        self.bs_to_padded_graph_size = [0 for i in range(self.max_capture_size + 1)]
+        self.bs_to_padded_graph_size = [
+            0 for i in range(self.max_cudagraph_capture_size + 1)
+        ]
         for end, start in zip(
-            self.cudagraph_capture_sizes, self.cudagraph_capture_sizes[1:] + [0]
+            self.cudagraph_capture_sizes + [self.max_cudagraph_capture_size + 1],
+            [0] + self.cudagraph_capture_sizes,
         ):
             for bs in range(start, end):
                 if bs == start:
                     self.bs_to_padded_graph_size[bs] = start
                 else:
                     self.bs_to_padded_graph_size[bs] = end
-        self.bs_to_padded_graph_size[self.max_capture_size] = self.max_capture_size
 
     def set_splitting_ops_for_v1(self):
         # NOTE: this function needs to be called only when mode is

vllm/config/scheduler.py

@@ -71,14 +71,6 @@ class SchedulerConfig:
     NOTE: This will be replaced by speculative config in the future; it is
     present to enable correctness tests until then."""
 
-    cuda_graph_sizes: list[int] = field(default_factory=list)
-    """Cuda graph capture sizes
-    1. if none provided, then default set to [min(max_num_seqs * 2, 512)]
-    2. if one value is provided, then the capture list would follow the
-    pattern: [1, 2, 4] + [i for i in range(8, cuda_graph_sizes + 1, 8)]
-    3. more than one value (e.g. 1 2 128) is provided, then the capture list
-    will follow the provided list."""
-
     enable_chunked_prefill: SkipValidation[bool] = None  # type: ignore
     """If True, prefill requests can be chunked based
     on the remaining max_num_batched_tokens."""
@@ -235,13 +227,6 @@ class SchedulerConfig:
                 self.long_prefill_token_threshold,
             )
 
-        # NOTE: Default set cuda_graph_sizes to [min(max_num_seqs * 2, 512)].
-        # This avoids OOM in tight memory scenarios with small max_num_seqs,
-        # and prevents capture of many large graphs (>512) that would greatly
-        # increase startup time with limited performance benefit.
-        if not self.cuda_graph_sizes:
-            self.cuda_graph_sizes = [min(self.max_num_seqs * 2, 512)]
-
         if self.async_scheduling:
             self.scheduler_cls = "vllm.v1.core.sched.async_scheduler.AsyncScheduler"
 

vllm/config/vllm.py

@@ -197,10 +197,10 @@ class VllmConfig:
         return hash_str
 
     def pad_for_cudagraph(self, batch_size: int) -> int:
-        # if batch_size > self.compilation_config.max_capture_size,
+        # if batch_size > self.compilation_config.max_cudagraph_capture_size,
         # it should raise an IndexError.
         # the caller should make sure the batch_size is within the range,
-        # i.e., batch_size <= self.compilation_config.max_capture_size
+        # i.e., batch_size <= self.compilation_config.max_cudagraph_capture_size
         return self.compilation_config.bs_to_padded_graph_size[batch_size]
 
     @staticmethod
@@ -396,6 +396,9 @@ class VllmConfig:
             if self.model_config is not None and self.model_config.enforce_eager:
                 logger.info("Cudagraph is disabled under eager mode")
                 self.compilation_config.cudagraph_mode = CUDAGraphMode.NONE
+                # override related settings when enforce eager
+                self.compilation_config.max_cudagraph_capture_size = 0
+                self.compilation_config.cudagraph_capture_sizes = []
             elif envs.VLLM_USE_V1:
                 self.compilation_config.cudagraph_num_of_warmups = 1
 
@@ -654,11 +657,13 @@ class VllmConfig:
 
         ```python
         max_graph_size = min(max_num_seqs * 2, 512)
-        # 1, 2, 4, then multiples of 8 up to max_graph_size
-        cuda_graph_sizes = [1, 2, 4, 8, 16, 24, 32, 40, ..., max_graph_size]
+        # 1, 2, 4, then multiples of 8 up to 256 and then multiples of 16
+        # up to max_graph_size
+        cuda_graph_sizes = [1, 2, 4] + list(range(8, 256, 8)) + list(
+            range(256, max_graph_size + 1, 16))
 
         In the end, `vllm_config.compilation_config.cudagraph_capture_sizes`
-        will be the final sizes to capture cudagraph (in descending order).
+        will be the final sizes to capture cudagraph (in ascending order).
 
         These sizes are used to capture and reuse CUDA graphs for
         performance-critical paths (e.g., decoding). Capturing enables
@@ -685,35 +690,111 @@ class VllmConfig:
             not be used.
         """
 
-        # calculate the default `batch_size_capture_list`
-        batch_size_capture_list = []
-        if self.model_config is not None and not self.model_config.enforce_eager:
-            cuda_graph_sizes = self.scheduler_config.cuda_graph_sizes
-            if len(cuda_graph_sizes) == 1:
-                max_graph_size = cuda_graph_sizes[0]
-                assert max_graph_size >= 1, (
-                    "Maximum cudagraph size should be greater than or equal to 1."
+        if (
+            self.model_config is not None
+            and not self.model_config.enforce_eager
+            and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
+        ):
+            # determine the initial max_cudagraph_capture_size
+            max_cudagraph_capture_size = (
+                self.compilation_config.max_cudagraph_capture_size
+            )
+            if max_cudagraph_capture_size is None:
+                max_cudagraph_capture_size = min(
+                    self.scheduler_config.max_num_seqs * 2, 512
                 )
-                batch_size_capture_list = [
-                    i for i in [1, 2, 4] if i <= max_graph_size
-                ] + list(range(8, max_graph_size + 1, 8))
-            elif len(cuda_graph_sizes) > 1:
-                batch_size_capture_list = sorted(cuda_graph_sizes)
+            max_num_tokens = self.scheduler_config.max_num_batched_tokens
+            max_cudagraph_capture_size = min(max_num_tokens, max_cudagraph_capture_size)
+
+            assert max_cudagraph_capture_size >= 1, (
+                "Maximum cudagraph size should be greater than or equal to 1 "
+                "when using cuda graph."
+            )
+
+            # determine the cudagraph_capture_sizes
+            if self.compilation_config.cudagraph_capture_sizes is not None:
+                assert len(self.compilation_config.cudagraph_capture_sizes) > 0, (
+                    "cudagraph_capture_sizes should contain at least one element "
+                    "when using cuda graph."
+                )
+                # de-duplicate the sizes provided by the config
+                dedup_sizes = list(set(self.compilation_config.cudagraph_capture_sizes))
+                cudagraph_capture_sizes = dedup_sizes
+                # sort to make sure the sizes are in ascending order
+                cudagraph_capture_sizes.sort()
             else:
-                raise TypeError(f"Invalid value for {cuda_graph_sizes=}.")
+                cudagraph_capture_sizes = [
+                    i for i in [1, 2, 4] if i <= max_cudagraph_capture_size
+                ]
+                if max_cudagraph_capture_size >= 8:
+                    # Step size 8 for small batch sizes, up to 256(not included)
+                    cudagraph_capture_sizes += list(
+                        range(8, min(max_cudagraph_capture_size + 1, 256), 8)
+                    )
+                if max_cudagraph_capture_size >= 256:
+                    # Step size 16 for larger batch sizes
+                    cudagraph_capture_sizes += list(
+                        range(256, max_cudagraph_capture_size + 1, 16)
+                    )
+
             if (
                 self.parallel_config.tensor_parallel_size > 1
                 and self.compilation_config.pass_config.enable_sequence_parallelism
             ):
-                batch_size_capture_list = self.update_sizes_for_sequence_parallelism(
-                    batch_size_capture_list
+                cudagraph_capture_sizes = self.update_sizes_for_sequence_parallelism(
+                    cudagraph_capture_sizes
                 )
-            max_num_tokens = self.scheduler_config.max_num_batched_tokens
-            batch_size_capture_list = [
-                size for size in batch_size_capture_list if size <= max_num_tokens
-            ]
 
-        self.compilation_config.init_with_cudagraph_sizes(batch_size_capture_list)
+            # user-specific compilation_config.max_cudagraph_capture_size get
+            # truncated to valid_max_size when they are inconsistent.
+            valid_max_size = (
+                cudagraph_capture_sizes[-1] if cudagraph_capture_sizes else 0
+            )
+            if (
+                self.compilation_config.max_cudagraph_capture_size is not None
+                and self.compilation_config.max_cudagraph_capture_size != valid_max_size
+            ):
+                # raise error only when both two flags are user-specified
+                # and they are inconsistent with each other
+                if self.compilation_config.cudagraph_capture_sizes is not None:
+                    raise ValueError(
+                        "customized max_cudagraph_capture_size"
+                        f"(={self.compilation_config.max_cudagraph_capture_size}) "
+                        "should be consistent with the max value of "
+                        f"cudagraph_capture_sizes(={valid_max_size})"
+                    )
+
+                logger.warning(
+                    "Truncating max_cudagraph_capture_size to %d",
+                    valid_max_size,
+                )
+            # always set the final max_cudagraph_capture_size
+            self.compilation_config.max_cudagraph_capture_size = valid_max_size
+
+            if self.compilation_config.cudagraph_capture_sizes is not None and len(
+                cudagraph_capture_sizes
+            ) < len(self.compilation_config.cudagraph_capture_sizes):
+                # If users have specified capture sizes, we only need to
+                # compare the lens before and after modification since the modified
+                # list is only the subset of the original list.
+                logger.warning(
+                    (
+                        "cudagraph_capture_sizes specified in compilation_config"
+                        " %s is overridden by config %s"
+                    ),
+                    self.compilation_config.cudagraph_capture_sizes,
+                    cudagraph_capture_sizes,
+                )
+            # always write back the final sizes
+            self.compilation_config.cudagraph_capture_sizes = cudagraph_capture_sizes
+
+        else:
+            # no cudagraph in use
+            self.compilation_config.max_cudagraph_capture_size = 0
+            self.compilation_config.cudagraph_capture_sizes = []
+
+        # complete the remaining process.
+        self.compilation_config.post_init_cudagraph_sizes()
 
     def recalculate_max_model_len(self, max_model_len: int):
         # Can only be called in try_verify_and_update_config

vllm/engine/arg_utils.py

@@ -364,7 +364,13 @@ class EngineArgs:
     kv_cache_dtype: CacheDType = CacheConfig.cache_dtype
     seed: int | None = ModelConfig.seed
     max_model_len: int | None = ModelConfig.max_model_len
-    cuda_graph_sizes: list[int] = get_field(SchedulerConfig, "cuda_graph_sizes")
+    cuda_graph_sizes: list[int] | None = CompilationConfig.cudagraph_capture_sizes
+    cudagraph_capture_sizes: list[int] | None = (
+        CompilationConfig.cudagraph_capture_sizes
+    )
+    max_cudagraph_capture_size: int | None = get_field(
+        CompilationConfig, "max_cudagraph_capture_size"
+    )
     # Note: Specifying a custom executor backend by passing a class
     # is intended for expert use only. The API may change without
     # notice.
@@ -1007,9 +1013,6 @@ class EngineArgs:
             "--max-long-partial-prefills",
             **scheduler_kwargs["max_long_partial_prefills"],
         )
-        scheduler_group.add_argument(
-            "--cuda-graph-sizes", **scheduler_kwargs["cuda_graph_sizes"]
-        )
         scheduler_group.add_argument(
             "--long-prefill-token-threshold",
             **scheduler_kwargs["long_prefill_token_threshold"],
@@ -1039,6 +1042,29 @@ class EngineArgs:
             "--async-scheduling", **scheduler_kwargs["async_scheduling"]
         )
 
+        # Compilation arguments
+        compilation_kwargs = get_kwargs(CompilationConfig)
+        compilation_group = parser.add_argument_group(
+            title="CompilationConfig",
+            description=CompilationConfig.__doc__,
+        )
+        compilation_group.add_argument(
+            "--cudagraph-capture-sizes", **compilation_kwargs["cudagraph_capture_sizes"]
+        )
+        compilation_kwargs["cudagraph_capture_sizes"]["help"] = (
+            "--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."
+        )
+        compilation_group.add_argument(
+            "--cuda-graph-sizes",
+            **compilation_kwargs["cudagraph_capture_sizes"],
+            deprecated=True,
+        )
+        compilation_group.add_argument(
+            "--max-cudagraph-capture-size",
+            **compilation_kwargs["max_cudagraph_capture_size"],
+        )
+
         # vLLM arguments
         vllm_kwargs = get_kwargs(VllmConfig)
         vllm_group = parser.add_argument_group(
@@ -1548,7 +1574,6 @@ class EngineArgs:
             max_num_batched_tokens=self.max_num_batched_tokens,
             max_num_seqs=self.max_num_seqs,
             max_model_len=model_config.max_model_len,
-            cuda_graph_sizes=self.cuda_graph_sizes,
             num_lookahead_slots=num_lookahead_slots,
             enable_chunked_prefill=self.enable_chunked_prefill,
             disable_chunked_mm_input=self.disable_chunked_mm_input,
@@ -1616,6 +1641,38 @@ class EngineArgs:
             collect_detailed_traces=self.collect_detailed_traces,
         )
 
+        # Compilation config overrides
+        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:
+                raise ValueError(
+                    "cuda_graph_sizes and compilation_config."
+                    "cudagraph_capture_sizes are mutually exclusive"
+                )
+            self.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:
+                raise ValueError(
+                    "cudagraph_capture_sizes and compilation_config."
+                    "cudagraph_capture_sizes are mutually exclusive"
+                )
+            self.compilation_config.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:
+                raise ValueError(
+                    "max_cudagraph_capture_size and compilation_config."
+                    "max_cudagraph_capture_size are mutually exclusive"
+                )
+            self.compilation_config.max_cudagraph_capture_size = (
+                self.max_cudagraph_capture_size
+            )
+
         config = VllmConfig(
             model_config=model_config,
             cache_config=cache_config,

vllm/model_executor/layers/quantization/mxfp4.py

@@ -185,7 +185,7 @@ class Mxfp4MoEMethod(FusedMoEMethodBase):
         self.moe = moe
         self.mxfp4_backend = get_mxfp4_backend()
         self.max_capture_size = (
-            get_current_vllm_config().compilation_config.max_capture_size
+            get_current_vllm_config().compilation_config.max_cudagraph_capture_size
         )
 
         assert self.mxfp4_backend != Mxfp4Backend.NONE, (

vllm/model_executor/models/config.py

@@ -259,21 +259,19 @@ class GptOssForCausalLMConfig(VerifyAndUpdateConfig):
         # Increase the max capture size from 512 to 992 for performance.
         # NOTE(woosuk): This will increase the number of CUDA graphs
         # from 67 to 81.
-        scheduler_config = vllm_config.scheduler_config
-        if len(scheduler_config.cuda_graph_sizes) == 1:
-            max_capture_size = scheduler_config.cuda_graph_sizes[0]
+        compilation_config = vllm_config.compilation_config
+        # Only override when the user has not set either of
+        # cudagraph_capture_sizes or max_cudagraph_capture_size.
+        if (
+            compilation_config.cudagraph_capture_sizes is None
+            and compilation_config.max_cudagraph_capture_size is None
+        ):
             # FIXME(woosuk): When using full cuda graph with FA3, the max
             # supported size is 992.
-            if max_capture_size < 992:
-                cuda_graph_sizes = [1, 2, 4]
-                # Step size 8 for small batch sizes
-                cuda_graph_sizes += [i for i in range(8, 256, 8)]
-                # Step size 16 for larger batch sizes
-                cuda_graph_sizes += [i for i in range(256, 993, 16)]
-                scheduler_config.cuda_graph_sizes = cuda_graph_sizes
-                logger.info(
-                    "Overriding max cuda graph capture size to %d for performance.", 992
-                )
+            compilation_config.max_cudagraph_capture_size = 992
+            logger.info(
+                "Overriding max cuda graph capture size to %d for performance.", 992
+            )
 
 
 class MambaModelConfig(VerifyAndUpdateConfig):

vllm/v1/attention/backends/flash_attn.py

@@ -236,7 +236,7 @@ class FlashAttentionMetadataBuilder(AttentionMetadataBuilder[FlashAttentionMetad
         self.use_full_cuda_graph = (
             self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         )
-        self.max_cudagraph_size = self.compilation_config.max_capture_size
+        self.max_cudagraph_size = self.compilation_config.max_cudagraph_capture_size
 
         if self.use_full_cuda_graph and self.aot_schedule:
             if self.max_cudagraph_size > 992:

vllm/v1/attention/backends/flashinfer.py

@@ -324,7 +324,7 @@ class FlashInferMetadataBuilder(AttentionMetadataBuilder[FlashInferMetadata]):
             ] = {}
             self._decode_cudagraph_max_bs = min(
                 (1 + num_spec_tokens) * max_num_reqs,
-                self.compilation_config.max_capture_size,
+                self.compilation_config.max_cudagraph_capture_size,
             )
 
         self.num_qo_heads = self.model_config.get_num_attention_heads(

vllm/v1/attention/backends/gdn_attn.py

@@ -87,7 +87,7 @@ class GDNAttentionMetadataBuilder(AttentionMetadataBuilder[GDNAttentionMetadata]
         )
         self.decode_cudagraph_max_bs = min(
             self.vllm_config.scheduler_config.max_num_seqs * (self.num_spec + 1),
-            self.compilation_config.max_capture_size,
+            self.compilation_config.max_cudagraph_capture_size,
         )
 
         self.spec_state_indices_tensor = torch.empty(

vllm/v1/attention/backends/mamba_attn.py

@@ -36,7 +36,7 @@ class BaseMambaAttentionMetadataBuilder(AttentionMetadataBuilder[M], abc.ABC):
         self.compilation_config = vllm_config.compilation_config
         self.decode_cudagraph_max_bs = min(
             self.vllm_config.scheduler_config.max_num_seqs,
-            self.compilation_config.max_capture_size,
+            self.compilation_config.max_cudagraph_capture_size,
         )
         self.state_indices_tensor = torch.empty(
             (self.decode_cudagraph_max_bs,),

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

@@ -89,7 +89,7 @@ class FlashAttnMLAMetadataBuilder(MLACommonMetadataBuilder[FlashAttnMLAMetadata]
         self.use_full_cuda_graph = (
             self.compilation_config.cudagraph_mode.has_full_cudagraphs()
         )
-        self.max_cudagraph_size = self.compilation_config.max_capture_size
+        self.max_cudagraph_size = self.compilation_config.max_cudagraph_capture_size
 
         if self.use_full_cuda_graph and self.fa_aot_schedule:
             if self.max_cudagraph_size > 992:

vllm/v1/spec_decode/eagle.py

@@ -104,7 +104,7 @@ class EagleProposer:
             )
 
         self.cudagraph_batch_sizes = (
-            list(reversed(self.vllm_config.compilation_config.cudagraph_capture_sizes))
+            (sorted(self.vllm_config.compilation_config.cudagraph_capture_sizes))
             if self.use_cuda_graph
             else []
         )

vllm/v1/worker/gpu_model_runner.py

@@ -379,16 +379,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             self.async_output_copy_stream = torch.cuda.Stream()
             self.prepare_inputs_event = torch.cuda.Event()
 
-        # TODO(woosuk): Provide an option to tune the max cudagraph batch size.
-        # The convention is different.
         # self.cudagraph_batch_sizes sorts in ascending order.
-        # The batch sizes in the config are in descending order.
         if (
             self.compilation_config.cudagraph_capture_sizes
             and self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
         ):
-            self.cudagraph_batch_sizes = list(
-                reversed(self.compilation_config.cudagraph_capture_sizes)
+            self.cudagraph_batch_sizes = sorted(
+                self.compilation_config.cudagraph_capture_sizes
             )
 
         # Cache the device properties.
@@ -3791,7 +3788,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
 
             if cudagraph_mode.mixed_mode() != CUDAGraphMode.NONE:
                 cudagraph_runtime_mode = cudagraph_mode.mixed_mode()
-
+                # make sure we capture the largest batch size first
                 compilation_cases = list(
                     product(reversed(self.cudagraph_batch_sizes), lora_cases)
                 )