[Model Runner V2] support auto resolve cudagraph mode/sizes based on attn backend (#32936)

Signed-off-by: zhuhaoran <[email protected]>

vllm/config/compilation.py

@@ -26,6 +26,8 @@ from vllm.utils.torch_utils import is_torch_equal_or_newer
 
 if TYPE_CHECKING:
     from vllm.config import VllmConfig
+    from vllm.v1.attention.backend import AttentionCGSupport
+    from vllm.v1.kv_cache_interface import KVCacheConfig
 else:
     VllmConfig = object
 
@@ -1241,6 +1243,152 @@ class CompilationConfig:
         assert "none" in self.custom_ops
         return f"+{op}" in self.custom_ops
 
+    def resolve_cudagraph_mode_and_sizes(
+        self,
+        min_cg_support: "AttentionCGSupport",
+        min_cg_attn_backend: str | None,
+        uniform_decode_query_len: int = 1,
+        tensor_parallel_size: int = 1,
+        kv_cache_config: "KVCacheConfig | None" = None,
+        max_num_reqs: int | None = None,
+        is_profiling: bool = False,
+    ) -> CUDAGraphMode:
+        from vllm.v1.attention.backend import AttentionCGSupport
+
+        cudagraph_mode = self.cudagraph_mode
+        if cudagraph_mode is None or cudagraph_mode == CUDAGraphMode.NONE:
+            self.cudagraph_mode = CUDAGraphMode.NONE
+            return CUDAGraphMode.NONE
+
+        # Check cudagraph for mixed batch is supported
+        if (
+            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
+            and min_cg_support != AttentionCGSupport.ALWAYS
+        ):
+            msg = (
+                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
+                f"with {min_cg_attn_backend} backend (support: "
+                f"{min_cg_support})"
+            )
+            if min_cg_support == AttentionCGSupport.NEVER:
+                # if not supported any full cudagraphs, just raise it.
+                msg += (
+                    "; please try cudagraph_mode=PIECEWISE, and "
+                    "make sure compilation mode is VLLM_COMPILE"
+                )
+                raise ValueError(msg)
+
+            # attempt to resolve the full cudagraph related mode
+            if self.splitting_ops_contain_attention():
+                msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE"
+                cudagraph_mode = CUDAGraphMode.FULL_AND_PIECEWISE
+            else:
+                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
+                cudagraph_mode = CUDAGraphMode.FULL_DECODE_ONLY
+            logger.warning(msg)
+
+        # check that if we are doing decode full-cudagraphs it is supported
+        if (
+            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
+            and min_cg_support == AttentionCGSupport.NEVER
+        ):
+            msg = (
+                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
+                f"with {min_cg_attn_backend} backend (support: "
+                f"{min_cg_support})"
+            )
+            if self.mode == CompilationMode.VLLM_COMPILE and (
+                self.splitting_ops_contain_attention()
+                or self.use_inductor_graph_partition
+            ):
+                msg += (
+                    "; setting cudagraph_mode=PIECEWISE because "
+                    "attention is compiled piecewise"
+                )
+                cudagraph_mode = CUDAGraphMode.PIECEWISE
+            else:
+                msg += (
+                    "; setting cudagraph_mode=NONE because "
+                    "attention is not compiled piecewise"
+                )
+                cudagraph_mode = CUDAGraphMode.NONE
+            logger.warning(msg)
+
+        # check that if we are doing spec-decode + decode full-cudagraphs it is
+        # supported
+        if (
+            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
+            and uniform_decode_query_len > 1
+            and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value
+        ):
+            msg = (
+                f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
+                f" with spec-decode for attention backend "
+                f"{min_cg_attn_backend} (support: {min_cg_support})"
+            )
+            if self.splitting_ops_contain_attention():
+                msg += "; setting cudagraph_mode=PIECEWISE"
+                cudagraph_mode = CUDAGraphMode.PIECEWISE
+            else:
+                msg += "; setting cudagraph_mode=NONE"
+                cudagraph_mode = CUDAGraphMode.NONE
+            logger.warning(msg)
+
+        # double check that we can support full cudagraph if they are requested
+        # even after automatic downgrades
+        if (
+            cudagraph_mode.has_full_cudagraphs()
+            and min_cg_support == AttentionCGSupport.NEVER
+        ):
+            raise ValueError(
+                f"CUDAGraphMode.{cudagraph_mode.name} is not "
+                f"supported with {min_cg_attn_backend} backend ("
+                f"support:{min_cg_support}) "
+                "; please try cudagraph_mode=PIECEWISE, "
+                "and make sure compilation mode is VLLM_COMPILE"
+            )
+
+        # Adjust cudagraph sizes to be a multiple of uniform_decode_query_len
+        # to avoid: https://github.com/vllm-project/vllm/issues/28207 and temp-fix:
+        # https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
+        # Will be removed in the near future when we have separate cudagraph capture
+        # sizes for decode and mixed prefill-decode.
+        if (
+            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
+            and uniform_decode_query_len > 1
+        ):
+            self.adjust_cudagraph_sizes_for_spec_decode(
+                uniform_decode_query_len,
+                tensor_parallel_size,
+            )
+
+        # For Mamba models with FULL decode cudagraphs, each decode
+        # sequence needs one Mamba cache block. The decode cudagraph
+        # dispatcher already caps batch sizes at max_num_seqs, so we just
+        # need to verify that enough blocks exist. Raising here instead
+        # of silently capping cudagraph_capture_sizes avoids unintended
+        # restrictions on PIECEWISE (prefill) cudagraphs.
+        # See: https://github.com/vllm-project/vllm/issues/34094
+        if (
+            kv_cache_config is not None
+            and max_num_reqs is not None
+            and cudagraph_mode.has_full_cudagraphs()
+            and not is_profiling
+            and kv_cache_config.has_mamba_layers
+            and max_num_reqs > kv_cache_config.num_blocks
+        ):
+            raise ValueError(
+                f"max_num_seqs ({max_num_reqs}) exceeds available Mamba cache "
+                f"blocks ({kv_cache_config.num_blocks}). Each decode sequence "
+                "requires one Mamba cache block, so CUDA graph capture cannot "
+                "proceed. Please lower max_num_seqs to at most "
+                f"{kv_cache_config.num_blocks} or increase "
+                "gpu_memory_utilization."
+            )
+
+        self.cudagraph_mode = cudagraph_mode
+        return cudagraph_mode
+
     def adjust_cudagraph_sizes_for_spec_decode(
         self, uniform_decode_query_len: int, tensor_parallel_size: int
     ):

vllm/v1/worker/gpu/attn_utils.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from collections.abc import Sequence
+from dataclasses import dataclass
 from typing import Any, cast
 
 import numpy as np
@@ -8,7 +9,11 @@ import torch
 
 from vllm.config import VllmConfig, get_layers_from_vllm_config
 from vllm.model_executor.layers.attention_layer_base import AttentionLayerBase
-from vllm.v1.attention.backend import AttentionBackend, CommonAttentionMetadata
+from vllm.v1.attention.backend import (
+    AttentionBackend,
+    AttentionCGSupport,
+    CommonAttentionMetadata,
+)
 from vllm.v1.kv_cache_interface import (
     AttentionSpec,
     KVCacheConfig,
@@ -18,6 +23,12 @@ from vllm.v1.kv_cache_interface import (
 from vllm.v1.worker.utils import AttentionGroup, bind_kv_cache
 
 
+@dataclass(frozen=True)
+class AttentionCGSupportInfo:
+    min_cg_support: AttentionCGSupport = AttentionCGSupport.ALWAYS
+    min_cg_attn_backend: str | None = None
+
+
 def get_kv_cache_spec(vllm_config: VllmConfig) -> dict[str, KVCacheSpec]:
     kv_cache_spec: dict[str, KVCacheSpec] = {}
     layer_type = cast(type[Any], AttentionLayerBase)
@@ -34,10 +45,17 @@ def init_attn_backend(
     vllm_config: VllmConfig,
     device: torch.device,
     active_layer_names: set[str] | None = None,
-):
+) -> tuple[
+    dict[str, type[AttentionBackend]],
+    list[list[AttentionGroup]],
+    AttentionCGSupportInfo,
+]:
     attn_backends: dict[str, type[AttentionBackend]] = {}
     attn_groups: list[list[AttentionGroup]] = []
     attn_backend_workspace: torch.Tensor | None = None
+    # Find minimum cudagraph support across all attention backends
+    min_cg_support = AttentionCGSupport.ALWAYS
+    min_cg_attn_backend = None
     for kv_cache_group_id, kv_cache_group_spec in enumerate(
         kv_cache_config.kv_cache_groups
     ):
@@ -86,8 +104,24 @@ def init_attn_backend(
             else:
                 if hasattr(builder, "set_workspace_buffer"):
                     builder.set_workspace_buffer(attn_backend_workspace)
+            # Check cudagraph support for the attention backend
+            cg_support = builder.get_cudagraph_support(
+                vllm_config,
+                cast(AttentionSpec, kv_cache_group_spec.kv_cache_spec),
+            )
+            if cg_support.value < min_cg_support.value:
+                min_cg_support = cg_support
+                min_cg_attn_backend = attn_backend.__name__
         attn_groups.append(groups)
-    return attn_backends, attn_groups
+
+    return (
+        attn_backends,
+        attn_groups,
+        AttentionCGSupportInfo(
+            min_cg_support=min_cg_support,
+            min_cg_attn_backend=min_cg_attn_backend,
+        ),
+    )
 
 
 def _allocate_kv_cache(kv_cache_config: KVCacheConfig, device: torch.device):
@@ -110,7 +144,7 @@ def _allocate_kv_cache(kv_cache_config: KVCacheConfig, device: torch.device):
 def _reshape_kv_cache(
     kv_cache_config: KVCacheConfig,
     kv_cache_raw_tensors: dict[str, torch.Tensor],
-    attn_backends: dict[str, AttentionBackend],
+    attn_backends: dict[str, type[AttentionBackend]],
     cache_dtype: str,
 ) -> dict[str, torch.Tensor]:
     kv_caches: dict[str, torch.Tensor] = {}
@@ -158,7 +192,7 @@ def init_kv_cache(
     runner_kv_caches: list[torch.Tensor],
     forward_context: dict[str, Any],
     kv_cache_config: KVCacheConfig,
-    attn_backends: dict[str, AttentionBackend],
+    attn_backends: dict[str, type[AttentionBackend]],
     device: torch.device,
     cache_dtype: str,
 ) -> dict[str, torch.Tensor]:

vllm/v1/worker/gpu/dp_utils.py

@@ -20,7 +20,7 @@ def make_num_tokens_across_dp(dp_size: int, num_tokens: int) -> torch.Tensor | N
 
 
 def sync_cudagraph_and_dp_padding(
-    cudagraph_manager: CudaGraphManager,
+    cudagraph_manager: CudaGraphManager | None,
     desired_batch_desc: BatchExecutionDescriptor,
     num_tokens: int,
     num_reqs: int,
@@ -61,6 +61,10 @@ def sync_cudagraph_and_dp_padding(
             num_reqs=num_reqs,
         ), num_tokens_across_dp
 
+    assert cudagraph_manager is not None, (
+        "cudagraph_manager should only be None during profile run, "
+        "where synced_cg_mode must be NONE across all DP ranks"
+    )
     synced_num_tokens = int(num_tokens_across_dp.max().item())
     synced_uniform_token_count = uniform_token_counts_across_dp[0]
     # If ranks disagree on the uniform token count, or its 0 (means None) set to None
@@ -79,3 +83,41 @@ def sync_cudagraph_and_dp_padding(
     num_tokens_across_dp[:] = synced_desc.num_tokens
 
     return synced_desc, num_tokens_across_dp
+
+
+def dispatch_cg_and_sync_dp(
+    cudagraph_manager: CudaGraphManager | None,
+    num_reqs: int,
+    num_tokens: int,
+    uniform_token_count: int | None,
+    dp_size: int,
+    dp_rank: int,
+    need_eager: bool = False,
+) -> tuple[BatchExecutionDescriptor, torch.Tensor | None]:
+    if need_eager:
+        batch_desc = BatchExecutionDescriptor(
+            cg_mode=CUDAGraphMode.NONE,
+            num_tokens=num_tokens,
+            num_reqs=num_reqs,
+        )
+    else:
+        assert cudagraph_manager is not None, (
+            "cudagraph_manager should only be None during profile run, "
+            "where need_eager must be True"
+        )
+        batch_desc = cudagraph_manager.dispatch(
+            num_reqs, num_tokens, uniform_token_count
+        )
+
+    if dp_size == 1:
+        return batch_desc, None
+
+    return sync_cudagraph_and_dp_padding(
+        cudagraph_manager,
+        batch_desc,
+        num_tokens,
+        num_reqs,
+        uniform_token_count,
+        dp_size,
+        dp_rank,
+    )

vllm/v1/worker/gpu/model_runner.py

@@ -61,7 +61,7 @@ from vllm.v1.worker.gpu.cudagraph_utils import (
     ModelCudaGraphManager,
     get_uniform_token_count,
 )
-from vllm.v1.worker.gpu.dp_utils import sync_cudagraph_and_dp_padding
+from vllm.v1.worker.gpu.dp_utils import dispatch_cg_and_sync_dp
 from vllm.v1.worker.gpu.eplb_utils import EPLBController, step_eplb_after
 from vllm.v1.worker.gpu.input_batch import (
     InputBatch,
@@ -176,6 +176,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
         # Draft tokens propagation - for spec-dec + struct outputs.
         self.draft_tokens_handler = DraftTokensHandler(self.device)
+        self.uniform_decode_query_len = 1 + self.num_speculative_steps
 
         # Pooling models.
         self.is_pooling_model = self.model_config.runner_type == "pooling"
@@ -224,14 +225,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 device=self.device,
             )
 
-        # CUDA graphs.
+        # For CUDA graphs, and will init cudagraph_manager after init_attn_backend.
         self.decode_query_len = self.num_speculative_steps + 1
-        self.cudagraph_manager = ModelCudaGraphManager(
-            self.vllm_config,
-            self.device,
-            self.compilation_config.cudagraph_mode,
-            decode_query_len=self.decode_query_len,
-        )
+        self.cudagraph_manager: ModelCudaGraphManager | None = None
         # LoRA-related workers.
         self.lora_state = LoraState(max_num_reqs=self.max_num_reqs)
         # KV Connector if configured.
@@ -361,9 +357,26 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             cp_interleave=self.cp_interleave,
         )
 
-        self.attn_backends, self.attn_groups = init_attn_backend(
+        self.attn_backends, self.attn_groups, attn_cg_support = init_attn_backend(
             self.kv_cache_config, self.vllm_config, self.device
         )
+        cudagraph_mode = self.compilation_config.resolve_cudagraph_mode_and_sizes(
+            attn_cg_support.min_cg_support,
+            attn_cg_support.min_cg_attn_backend,
+            self.uniform_decode_query_len,
+            self.parallel_config.tensor_parallel_size,
+            self.kv_cache_config,
+            self.max_num_reqs,
+        )
+        self.cudagraph_manager = ModelCudaGraphManager(
+            self.vllm_config,
+            self.device,
+            cudagraph_mode,
+            decode_query_len=self.decode_query_len,
+        )
+        if self.speculator is not None:
+            self.speculator.init_cudagraph_manager(cudagraph_mode)
+
         check_attention_cp_compatibility(self.vllm_config)
         if self.speculator is not None:
             # HACK(woosuk)
@@ -437,6 +450,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             intermediate_tensors=intermediate_tensors,
             dummy_run=True,
             skip_attn_for_dummy_run=skip_attn,
+            is_profile=is_profile,
         )
         self.kv_connector.set_disabled(False)
 
@@ -486,6 +500,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 dummy_run=True,
                 skip_attn_for_dummy_run=skip_attn,
                 mm_inputs=mm_inputs,
+                is_profile=is_profile,
             )
 
         assert hidden_states is not None  # Last PP rank always has hidden_states
@@ -547,6 +562,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
 
     @torch.inference_mode()
     def capture_model(self) -> int:
+        assert self.cudagraph_manager is not None
         if not self.cudagraph_manager.needs_capture():
             logger.warning(
                 "Skipping CUDA graph capture. To turn on CUDA graph capture, "
@@ -915,6 +931,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         intermediate_tensors: IntermediateTensors | None = None,
         dummy_run: bool = False,
         skip_attn_for_dummy_run: bool = False,
+        is_profile: bool = False,
     ) -> ModelRunnerOutput | IntermediateTensors | None:
         if not dummy_run:
             # Update the request states.
@@ -934,34 +951,22 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         max_query_len = max(scheduler_output.num_scheduled_tokens.values())
         uniform_tok_count = get_uniform_token_count(num_reqs, num_toks, max_query_len)
 
-        batch_desc = self.cudagraph_manager.dispatch(
-            num_reqs, num_toks, uniform_tok_count
-        )
-        num_tokens_across_dp = None
-
         skip_compiled = False
         if self.is_encoder_decoder and scheduler_output.scheduled_encoder_inputs:
             # Encoder-decoder models such as Whisper should run eager/non-compiled
             # when encoder inputs are scheduled, because this step updates
             # cross-attention cache with dynamic encoder outputs.
-            # Override batch_desc to NONE.
             skip_compiled = True
-            batch_desc = BatchExecutionDescriptor(
-                cg_mode=CUDAGraphMode.NONE,
-                num_tokens=num_toks,
-                num_reqs=num_reqs,
-            )
 
-        if self.dp_size > 1:
-            batch_desc, num_tokens_across_dp = sync_cudagraph_and_dp_padding(
-                self.cudagraph_manager,
-                batch_desc,
-                num_toks,
-                num_reqs,
-                uniform_tok_count,
-                self.dp_size,
-                self.dp_rank,
-            )
+        batch_desc, num_tokens_across_dp = dispatch_cg_and_sync_dp(
+            self.cudagraph_manager,
+            num_reqs,
+            num_toks,
+            uniform_tok_count,
+            self.dp_size,
+            self.dp_rank,
+            need_eager=is_profile or skip_compiled,
+        )
 
         if batch_desc.num_tokens == 0:
             # All DP ranks have zero tokens to run.
@@ -1059,6 +1064,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
             # Use explicit cudagraph replay for FULL mode.
             # NOTE(woosuk): Here, we don't need to pass the input tensors,
             # because they are already copied to the CUDA graph input buffers.
+            assert self.cudagraph_manager is not None
             self.kv_connector.pre_forward(scheduler_output)
             model_output = self.cudagraph_manager.run_fullgraph(batch_desc)
         else:

vllm/v1/worker/gpu/spec_decode/eagle/speculator.py

@@ -19,10 +19,7 @@ from vllm.v1.worker.gpu.attn_utils import (
     init_attn_backend,
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
-from vllm.v1.worker.gpu.cudagraph_utils import (
-    BatchExecutionDescriptor,
-)
-from vllm.v1.worker.gpu.dp_utils import sync_cudagraph_and_dp_padding
+from vllm.v1.worker.gpu.dp_utils import dispatch_cg_and_sync_dp
 from vllm.v1.worker.gpu.input_batch import InputBatch, InputBuffers
 from vllm.v1.worker.gpu.model_states.interface import ModelState
 from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample
@@ -98,15 +95,19 @@ class EagleSpeculator:
                 device=device,
             )
 
-        # currently we don't  support PIECEWISE for Eagle.
-        cudagraph_mode = vllm_config.compilation_config.cudagraph_mode
+        self.cudagraph_manager: EagleCudaGraphManager | None = None
+
+    def init_cudagraph_manager(self, cudagraph_mode: CUDAGraphMode) -> None:
         if cudagraph_mode.decode_mode() == CUDAGraphMode.FULL:
             cudagraph_mode = CUDAGraphMode.FULL_DECODE_ONLY
         else:
             cudagraph_mode = CUDAGraphMode.NONE
 
         self.cudagraph_manager = EagleCudaGraphManager(
-            vllm_config, device, cudagraph_mode, self.draft_tokens
+            self.vllm_config,
+            self.device,
+            cudagraph_mode,
+            self.draft_tokens,
         )
 
     def load_model(self, target_model: nn.Module) -> None:
@@ -133,7 +134,7 @@ class EagleSpeculator:
     ) -> None:
         self.model_state = model_state
         self.kv_cache_config = kv_cache_config
-        _, self.attn_groups = init_attn_backend(
+        _, self.attn_groups, _ = init_attn_backend(
             kv_cache_config,
             self.vllm_config,
             self.device,
@@ -242,29 +243,6 @@ class EagleSpeculator:
                         idx_mapping, query_start_loc, pos, num_tokens_padded
                     )
 
-    def _dispatch_and_sync_dp(
-        self,
-        cudagraph_manager: EagleCudaGraphManager,
-        num_reqs: int,
-        num_tokens: int,
-        uniform_token_count: int | None,
-    ) -> tuple[BatchExecutionDescriptor, torch.Tensor | None]:
-        batch_desc = cudagraph_manager.dispatch(
-            num_reqs, num_tokens, uniform_token_count
-        )
-        num_tokens_across_dp = None
-        if self.dp_size > 1:
-            batch_desc, num_tokens_across_dp = sync_cudagraph_and_dp_padding(
-                cudagraph_manager,
-                batch_desc,
-                num_tokens,
-                num_reqs,
-                uniform_token_count,
-                self.dp_size,
-                self.dp_rank,
-            )
-        return batch_desc, num_tokens_across_dp
-
     def _build_draft_attn_metadata(
         self,
         num_reqs: int,
@@ -303,8 +281,10 @@ class EagleSpeculator:
         return attn_metadata
 
     def capture_model(self) -> None:
+        assert self.cudagraph_manager is not None
         if self.num_speculative_steps == 1:
             return
+
         logger.info("Capturing model for Eagle speculator...")
         self.cudagraph_manager.capture(
             self.generate_draft,
@@ -342,6 +322,7 @@ class EagleSpeculator:
         dummy_run: bool = False,
         skip_attn_for_dummy_run: bool = False,
         mm_inputs: tuple[list[torch.Tensor], torch.Tensor] | None = None,
+        is_profile: bool = False,
     ) -> torch.Tensor:
         # NOTE(woosuk): To avoid CPU-GPU synchronization without CPU knowing the
         # number of rejected tokens, we maintain the size of eagle's input_ids and
@@ -430,11 +411,14 @@ class EagleSpeculator:
 
         # Each request produces exactly 1 token per draft decode step,
         # enabling FULL cudagraph.
-        decode_batch_desc, num_tokens_across_dp = self._dispatch_and_sync_dp(
+        decode_batch_desc, num_tokens_across_dp = dispatch_cg_and_sync_dp(
             self.cudagraph_manager,
             num_reqs,
             num_reqs,
             uniform_token_count=1,
+            dp_size=self.dp_size,
+            dp_rank=self.dp_rank,
+            need_eager=is_profile,
         )
 
         attn_metadata_updated = None
@@ -461,6 +445,7 @@ class EagleSpeculator:
             )
 
         if decode_batch_desc.cg_mode == CUDAGraphMode.FULL:
+            assert self.cudagraph_manager is not None
             self.cudagraph_manager.run_fullgraph(decode_batch_desc)
         else:
             self.generate_draft(

vllm/v1/worker/gpu_model_runner.py

@@ -6307,7 +6307,7 @@ class GPUModelRunner(
         cudagraph_mode.
         """
         min_cg_support = AttentionCGSupport.ALWAYS
-        min_cg_backend_name = None
+        min_cg_attn_backend = None
 
         for attn_backend_set, kv_cache_group in zip(
             attention_backends, kv_cache_groups
@@ -6320,152 +6320,18 @@ class GPUModelRunner(
                 )
                 if cg_support.value < min_cg_support.value:
                     min_cg_support = cg_support
-                    min_cg_backend_name = attn_backend.__name__
-        # Flexible resolve the cudagraph mode
-        cudagraph_mode = self.compilation_config.cudagraph_mode
-        assert cudagraph_mode is not None
-        # check cudagraph for mixed batch is supported
-        if (
-            cudagraph_mode.mixed_mode() == CUDAGraphMode.FULL
-            and min_cg_support != AttentionCGSupport.ALWAYS
-        ):
-            msg = (
-                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
-                f"with {min_cg_backend_name} backend (support: "
-                f"{min_cg_support})"
-            )
-            if min_cg_support == AttentionCGSupport.NEVER:
-                # if not supported any full cudagraphs, just raise it.
-                msg += (
-                    "; please try cudagraph_mode=PIECEWISE, and "
-                    "make sure compilation mode is VLLM_COMPILE"
-                )
-                raise ValueError(msg)
-
-            # attempt to resolve the full cudagraph related mode
-            if self.compilation_config.splitting_ops_contain_attention():
-                msg += "; setting cudagraph_mode=FULL_AND_PIECEWISE"
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.FULL_AND_PIECEWISE
-                )
-            else:
-                msg += "; setting cudagraph_mode=FULL_DECODE_ONLY"
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.FULL_DECODE_ONLY
-                )
-            logger.warning(msg)
-
-        # check that if we are doing decode full-cudagraphs it is supported
-        if (
-            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
-            and min_cg_support == AttentionCGSupport.NEVER
-        ):
-            msg = (
-                f"CUDAGraphMode.{cudagraph_mode.name} is not supported "
-                f"with {min_cg_backend_name} backend (support: "
-                f"{min_cg_support})"
-            )
-            if self.compilation_config.mode == CompilationMode.VLLM_COMPILE and (
-                self.compilation_config.splitting_ops_contain_attention()
-                or self.compilation_config.use_inductor_graph_partition
-            ):
-                msg += (
-                    "; setting cudagraph_mode=PIECEWISE because "
-                    "attention is compiled piecewise"
-                )
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.PIECEWISE
-                )
-            else:
-                msg += (
-                    "; setting cudagraph_mode=NONE because "
-                    "attention is not compiled piecewise"
-                )
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.NONE
-                )
-            logger.warning(msg)
-
-        # check that if we are doing spec-decode + decode full-cudagraphs it is
-        # supported
-        if (
-            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
-            and self.uniform_decode_query_len > 1
-            and min_cg_support.value < AttentionCGSupport.UNIFORM_BATCH.value
-        ):
-            msg = (
-                f"CUDAGraphMode.{cudagraph_mode.name} is not supported"
-                f" with spec-decode for attention backend "
-                f"{min_cg_backend_name} (support: {min_cg_support})"
-            )
-            if self.compilation_config.splitting_ops_contain_attention():
-                msg += "; setting cudagraph_mode=PIECEWISE"
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.PIECEWISE
-                )
-            else:
-                msg += "; setting cudagraph_mode=NONE"
-                cudagraph_mode = self.compilation_config.cudagraph_mode = (
-                    CUDAGraphMode.NONE
-                )
-            logger.warning(msg)
-
-        # double check that we can support full cudagraph if they are requested
-        # even after automatic downgrades
-        if (
-            cudagraph_mode.has_full_cudagraphs()
-            and min_cg_support == AttentionCGSupport.NEVER
-        ):
-            raise ValueError(
-                f"CUDAGraphMode.{cudagraph_mode.name} is not "
-                f"supported with {min_cg_backend_name} backend ("
-                f"support:{min_cg_support}) "
-                "; please try cudagraph_mode=PIECEWISE, "
-                "and make sure compilation mode is VLLM_COMPILE"
-            )
-
-        # if we have dedicated decode cudagraphs, and spec-decode is enabled,
-        # we need to adjust the cudagraph sizes to be a multiple of the uniform
-        # decode query length to avoid: https://github.com/vllm-project/vllm/issues/28207
-        # temp-fix: https://github.com/vllm-project/vllm/issues/28207#issuecomment-3504004536
-        # Will be removed in the near future when we have separate cudagraph capture
-        # sizes for decode and mixed prefill-decode.
-        if (
-            cudagraph_mode.decode_mode() == CUDAGraphMode.FULL
-            and cudagraph_mode.separate_routine()
-            and self.uniform_decode_query_len > 1
-        ):
-            self.compilation_config.adjust_cudagraph_sizes_for_spec_decode(
-                self.uniform_decode_query_len, self.parallel_config.tensor_parallel_size
-            )
-
-        # For Mamba models with FULL decode cudagraphs, each decode
-        # sequence needs one Mamba cache block. The decode cudagraph
-        # dispatcher already caps batch sizes at max_num_seqs, so we just
-        # need to verify that enough blocks exist. Raising here instead
-        # of silently capping cudagraph_capture_sizes avoids unintended
-        # restrictions on PIECEWISE (prefill) cudagraphs.
-        # See: https://github.com/vllm-project/vllm/issues/34094
-        if cudagraph_mode.has_full_cudagraphs() and not is_profiling:
-            has_mamba = any(
-                isinstance(g.kv_cache_spec, MambaSpec) for g in kv_cache_groups
-            )
-            if has_mamba and self.kv_cache_config is not None:
-                num_blocks = self.kv_cache_config.num_blocks
-                if self.max_num_reqs > num_blocks:
-                    raise ValueError(
-                        f"max_num_seqs ({self.max_num_reqs}) exceeds "
-                        f"available Mamba cache blocks ({num_blocks}). "
-                        f"Each decode sequence requires one Mamba cache "
-                        f"block, so CUDA graph capture cannot proceed. "
-                        f"Please lower max_num_seqs to at most "
-                        f"{num_blocks} or increase "
-                        f"gpu_memory_utilization."
-                    )
-
+                    min_cg_attn_backend = attn_backend.__name__
+        cudagraph_mode = self.compilation_config.resolve_cudagraph_mode_and_sizes(
+            min_cg_support,
+            min_cg_attn_backend,
+            self.uniform_decode_query_len,
+            self.parallel_config.tensor_parallel_size,
+            self.kv_cache_config,
+            self.max_num_reqs,
+            is_profiling=is_profiling,
+        )
         # Trigger cudagraph dispatching keys initialization after
         # resolved cudagraph mode.
-        self.compilation_config.cudagraph_mode = cudagraph_mode
         self.cudagraph_dispatcher.initialize_cudagraph_keys(
             cudagraph_mode, self.uniform_decode_query_len
         )