[DP] Only use DP padding when cudagraphs are actually used (#34102)

Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com>

tests/v1/cudagraph/test_cudagraph_dispatch.py

@@ -176,10 +176,14 @@ class TestCudagraphDispatcher:
         assert rt_mode == CUDAGraphMode.NONE
         assert key == BatchDescriptor(num_tokens=15)
 
-        # 4. disable_full should have a fall back mode (e.g., cascade attention)
+        # 4. invalid_modes={FULL} should have a fall back mode
+        #    (e.g., cascade attention)
         desc_full_exact = BatchDescriptor(num_tokens=8, uniform=False)
         rt_mode, key = dispatcher.dispatch(
-            num_tokens=8, uniform_decode=False, has_lora=False, disable_full=True
+            num_tokens=8,
+            uniform_decode=False,
+            has_lora=False,
+            invalid_modes={CUDAGraphMode.FULL},
         )
 
         if "PIECEWISE" in cudagraph_mode_str:  # string contains check
@@ -188,6 +192,16 @@ class TestCudagraphDispatcher:
         else:
             assert rt_mode == CUDAGraphMode.NONE
 
+        # 5. valid_modes={NONE} always returns NONE even when keys exist
+        rt_mode, key = dispatcher.dispatch(
+            num_tokens=8,
+            uniform_decode=False,
+            has_lora=False,
+            valid_modes={CUDAGraphMode.NONE},
+        )
+        assert rt_mode == CUDAGraphMode.NONE
+        assert key == BatchDescriptor(num_tokens=8)
+
     @pytest.mark.parametrize(
         "cudagraph_mode_str,compilation_mode,expected_modes",
         [

vllm/config/compilation.py

@@ -87,8 +87,12 @@ class CUDAGraphMode(enum.Enum):
     def separate_routine(self) -> bool:
         return isinstance(self.value, tuple)
 
-    def valid_runtime_modes(self) -> bool:
-        return self in [CUDAGraphMode.NONE, CUDAGraphMode.PIECEWISE, CUDAGraphMode.FULL]
+    @classmethod
+    def valid_runtime_modes(cls) -> frozenset["CUDAGraphMode"]:
+        return frozenset({cls.NONE, cls.PIECEWISE, cls.FULL})
+
+    def is_valid_runtime_mode(self) -> bool:
+        return self in CUDAGraphMode.valid_runtime_modes()
 
     def __str__(self) -> str:
         return self.name

vllm/forward_context.py

@@ -241,7 +241,7 @@ class ForwardContext:
     additional_kwargs: dict[str, Any] = field(default_factory=dict)
 
     def __post_init__(self):
-        assert self.cudagraph_runtime_mode.valid_runtime_modes(), (
+        assert self.cudagraph_runtime_mode.is_valid_runtime_mode(), (
             f"Invalid cudagraph runtime mode: {self.cudagraph_runtime_mode}"
         )
 
@@ -347,7 +347,6 @@ def set_forward_context(
                 num_tokens_unpadded=num_tokens,
                 parallel_config=vllm_config.parallel_config,
                 allow_microbatching=False,
-                allow_dp_padding=False,
             )
             assert num_tokens_across_dp is not None
         dp_metadata = DPMetadata.make(

vllm/v1/cudagraph_dispatcher.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+from collections.abc import Set as AbstractSet
 from dataclasses import replace
 from itertools import product
 
@@ -232,8 +233,9 @@ class CudagraphDispatcher:
         num_tokens: int,
         uniform_decode: bool = False,
         has_lora: bool = False,
-        disable_full: bool = False,
         num_active_loras: int = 0,
+        valid_modes: AbstractSet[CUDAGraphMode] | None = None,
+        invalid_modes: AbstractSet[CUDAGraphMode] | None = None,
     ) -> tuple[CUDAGraphMode, BatchDescriptor]:
         """
         Given conditions(e.g.,batch descriptor and if using piecewise only),
@@ -246,15 +248,29 @@ class CudagraphDispatcher:
             uniform_decode: Whether the batch is uniform decode (i.e. uniform and query
                 length is uniform_decode_query_len).
             has_lora: Whether LoRA is active.
-            disable_full: If True, skip FULL cudagraph checks and
-                return PIECEWISE or NONE only. (can be used for features like
-                cascade attention that are not supported by full cudagraphs)
             num_active_loras: Number of distinct active LoRA adapters.
+            valid_modes: Set of cudagraph modes that are allowed. None means
+                all modes are allowed.
+            invalid_modes: Set of cudagraph modes to exclude. Subtracted from
+                valid_modes to compute allowed modes. (e.g., {FULL} for
+                features like cascade attention not supported by full
+                cudagraphs). None means no modes are excluded.
         """
+        allowed_modes = valid_modes or CUDAGraphMode.valid_runtime_modes()
+
+        if invalid_modes:
+            allowed_modes -= invalid_modes
+
+        assert len(allowed_modes) >= 1, (
+            f"No allowed cudagraph modes: valid_modes={valid_modes}, "
+            f"invalid_modes={invalid_modes}"
+        )
+
         if (
             not self.keys_initialized
             or self.cudagraph_mode == CUDAGraphMode.NONE
             or num_tokens > self.compilation_config.max_cudagraph_capture_size
+            or allowed_modes <= {CUDAGraphMode.NONE}
         ):
             return CUDAGraphMode.NONE, BatchDescriptor(num_tokens)
 
@@ -281,24 +297,26 @@ class CudagraphDispatcher:
             num_tokens, uniform_decode, has_lora, effective_num_active_loras
         )
 
+        if CUDAGraphMode.FULL in allowed_modes:
             # check if key exists for full cudagraph
             # For pure FULL mode, keys are registered with uniform=False.
             batch_desc_to_check = batch_desc
             if self.cudagraph_mode == CUDAGraphMode.FULL:
                 batch_desc_to_check = replace(batch_desc, uniform=False)
-        if (
-            not disable_full
-            and batch_desc_to_check in self.cudagraph_keys[CUDAGraphMode.FULL]
-        ):
+            if batch_desc_to_check in self.cudagraph_keys[CUDAGraphMode.FULL]:
                 return CUDAGraphMode.FULL, batch_desc_to_check
 
+        if CUDAGraphMode.PIECEWISE in allowed_modes:
             # also check if the relaxed key exists for more "general"
             # piecewise cudagraph
             batch_desc_to_check = replace(batch_desc, num_reqs=None, uniform=False)
             if batch_desc_to_check in self.cudagraph_keys[CUDAGraphMode.PIECEWISE]:
                 return CUDAGraphMode.PIECEWISE, batch_desc_to_check
 
-        # finally, just return no cudagraphs and a trivial batch descriptor
+        assert CUDAGraphMode.NONE in allowed_modes, (
+            f"No matching cudagraph found and NONE is not in "
+            f"allowed_modes={allowed_modes}"
+        )
         return CUDAGraphMode.NONE, BatchDescriptor(num_tokens)
 
     def get_capture_descs(self) -> list[tuple[CUDAGraphMode, list[BatchDescriptor]]]:

vllm/v1/spec_decode/eagle.py

@@ -448,17 +448,10 @@ class SpecDecodeBaseProposer:
             assert draft_indexer_metadata is not None
             per_layer_attn_metadata[layer_name] = draft_indexer_metadata
 
-        num_tokens_dp_padded, num_tokens_across_dp = self._pad_batch_across_dp(
-            num_tokens_unpadded=num_tokens, num_tokens_padded=num_tokens
+        cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
+            self._determine_batch_execution_and_padding(num_tokens)
         )
 
-        cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
-            num_tokens_dp_padded
-        )
-        num_input_tokens = batch_desc.num_tokens
-        if num_tokens_across_dp is not None:
-            num_tokens_across_dp[self.dp_rank] = num_input_tokens
-
         if self.supports_mm_inputs:
             mm_embeds, is_mm_embed = mm_embed_inputs or (None, None)
 
@@ -549,17 +542,10 @@ class SpecDecodeBaseProposer:
         # Generate the remaining draft tokens.
         draft_token_ids_list = [draft_token_ids]
 
-        batch_size_dp_padded, batch_size_across_dp = self._pad_batch_across_dp(
-            num_tokens_unpadded=batch_size, num_tokens_padded=batch_size
+        cudagraph_runtime_mode, input_batch_size, batch_size_across_dp = (
+            self._determine_batch_execution_and_padding(batch_size)
         )
 
-        cudagraph_runtime_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
-            batch_size_dp_padded
-        )
-        input_batch_size = batch_desc.num_tokens
-        if batch_size_across_dp is not None:
-            batch_size_across_dp[self.dp_rank] = input_batch_size
-
         common_attn_metadata.num_actual_tokens = batch_size
         common_attn_metadata.max_query_len = 1
         common_attn_metadata.query_start_loc = self.arange[: batch_size + 1]
@@ -1568,19 +1554,11 @@ class SpecDecodeBaseProposer:
             self.num_speculative_tokens if not is_graph_capturing else 1
         ):
             if fwd_idx <= 1:
-                num_tokens_dp_padded, num_tokens_across_dp = self._pad_batch_across_dp(
-                    num_tokens_unpadded=num_tokens, num_tokens_padded=num_tokens
+                cudagraph_runtime_mode, num_input_tokens, num_tokens_across_dp = (
+                    self._determine_batch_execution_and_padding(
+                        num_tokens, use_cudagraphs=use_cudagraphs
                     )
-                if use_cudagraphs:
-                    cudagraph_runtime_mode, batch_desc = (
-                        self.cudagraph_dispatcher.dispatch(num_tokens_dp_padded)
                 )
-                    num_input_tokens = batch_desc.num_tokens
-                else:
-                    cudagraph_runtime_mode = CUDAGraphMode.NONE
-                    num_input_tokens = num_tokens_dp_padded
-                if num_tokens_across_dp is not None:
-                    num_tokens_across_dp[self.dp_rank] = num_input_tokens
 
             # Make sure to use EAGLE's own buffer during cudagraph capture.
             if (
@@ -1680,28 +1658,49 @@ class SpecDecodeBaseProposer:
             == 1
         ), "All drafting layers should belong to the same kv cache group"
 
-    def _pad_batch_across_dp(
+    def _determine_batch_execution_and_padding(
         self,
-        num_tokens_unpadded: int,
-        num_tokens_padded: int,
-    ) -> tuple[int, torch.Tensor]:
+        num_tokens: int,
+        use_cudagraphs: bool = True,
+    ) -> tuple[CUDAGraphMode, int, torch.Tensor | None]:
+        cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
+            num_tokens,
+            valid_modes=({CUDAGraphMode.NONE} if not use_cudagraphs else None),
+        )
+        num_tokens_padded = batch_desc.num_tokens
+
+        # Extra coordination when running data-parallel since we need to
+        # coordinate across ranks
         # TODO(Flechman): support DBO ubatching
-        should_ubatch, num_toks_across_dp, _ = coordinate_batch_across_dp(
-            num_tokens_unpadded=num_tokens_unpadded,
+        should_ubatch, num_tokens_across_dp = False, None
+        if self.vllm_config.parallel_config.data_parallel_size > 1:
+            should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
+                coordinate_batch_across_dp(
+                    num_tokens_unpadded=num_tokens,
                     parallel_config=self.vllm_config.parallel_config,
                     allow_microbatching=False,
-            allow_dp_padding=self.cudagraph_dispatcher.cudagraph_mode
-            != CUDAGraphMode.NONE,
                     num_tokens_padded=num_tokens_padded,
-            uniform_decode=None,
-            num_scheduled_tokens_per_request=None,
+                    cudagraph_mode=cudagraph_mode.value,
+                )
             )
             assert not should_ubatch, "DBO ubatching not implemented for EAGLE"
 
-        num_tokens_dp_padded = num_tokens_padded
-        if num_toks_across_dp is not None:
-            num_tokens_dp_padded = int(num_toks_across_dp[self.dp_rank].item())
-        return num_tokens_dp_padded, num_toks_across_dp
+            # Extract DP-synced values
+            if num_tokens_across_dp is not None:
+                dp_rank = self.dp_rank
+                num_tokens_padded = int(num_tokens_across_dp[dp_rank].item())
+                # Re-dispatch with DP padding so we have the correct
+                # batch_descriptor
+                cudagraph_mode, batch_desc = self.cudagraph_dispatcher.dispatch(
+                    num_tokens_padded,
+                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
+                )
+                # Assert to make sure the agreed upon token count is correct
+                # otherwise num_tokens_across_dp will no-longer be valid
+                assert batch_desc.num_tokens == num_tokens_padded
+                num_tokens_across_dp[dp_rank] = num_tokens_padded
+
+        return cudagraph_mode, num_tokens_padded, num_tokens_across_dp
 
 
 class EagleProposer(SpecDecodeBaseProposer):

vllm/v1/worker/dp_utils.py

@@ -37,7 +37,6 @@ def _get_device_and_group(parallel_config: ParallelConfig):
 
 def _run_ar(
     should_ubatch: bool,
-    should_dp_pad: bool,
     orig_num_tokens_per_ubatch: int,
     padded_num_tokens_per_ubatch: int,
     cudagraph_mode: int,
@@ -46,12 +45,11 @@ def _run_ar(
     dp_size = parallel_config.data_parallel_size
     dp_rank = parallel_config.data_parallel_rank
     device, group = _get_device_and_group(parallel_config)
-    tensor = torch.zeros(5, dp_size, device=device, dtype=torch.int32)
+    tensor = torch.zeros(4, dp_size, device=device, dtype=torch.int32)
     tensor[0][dp_rank] = orig_num_tokens_per_ubatch
     tensor[1][dp_rank] = padded_num_tokens_per_ubatch
     tensor[2][dp_rank] = 1 if should_ubatch else 0
-    tensor[3][dp_rank] = 1 if should_dp_pad else 0
-    tensor[4][dp_rank] = cudagraph_mode
+    tensor[3][dp_rank] = cudagraph_mode
     dist.all_reduce(tensor, group=group)
     return tensor
 
@@ -97,14 +95,13 @@ def _post_process_cudagraph_mode(tensor: torch.Tensor) -> int:
     If any rank has NONE (0), all ranks use NONE.
     This ensures all ranks send consistent values (all padded or all unpadded).
     """
-    return int(tensor[4, :].min().item())
+    return int(tensor[3, :].min().item())
 
 
 def _synchronize_dp_ranks(
     num_tokens_unpadded: int,
     num_tokens_padded: int,
     should_attempt_ubatching: bool,
-    should_attempt_dp_padding: bool,
     cudagraph_mode: int,
     parallel_config: ParallelConfig,
 ) -> tuple[bool, torch.Tensor | None, int]:
@@ -113,8 +110,8 @@ def _synchronize_dp_ranks(
     run with microbatching or none of them do.
 
     2. Determines the total number of tokens that each rank will run.
-    When running microbatched or if should_attempt_dp_padding is True, all
-    ranks will be padded out so that the run with the same number of tokens
+    When running microbatched or if cudagraph is enabled (synced across ranks),
+    all ranks will be padded out so that they run with the same number of tokens.
 
     3. Synchronizes cudagraph_mode across ranks by taking the minimum.
 
@@ -133,29 +130,26 @@ def _synchronize_dp_ranks(
     # will run and if we are using ubatching or not.
     tensor = _run_ar(
         should_ubatch=should_attempt_ubatching,
-        should_dp_pad=should_attempt_dp_padding,
         orig_num_tokens_per_ubatch=num_tokens_unpadded,
         padded_num_tokens_per_ubatch=num_tokens_padded,
         cudagraph_mode=cudagraph_mode,
         parallel_config=parallel_config,
     )
 
-    should_dp_pad = bool(torch.all(tensor[3] == 1).item())
-
-    # DP ranks should all have the same value for should_attempt_dp_padding.
-    assert should_attempt_dp_padding == should_dp_pad
+    # Synchronize cudagraph_mode across ranks first (take min).
+    # This is needed before DP padding decision since we use the synced
+    # cudagraph mode to determine whether DP padding is needed.
+    synced_cudagraph_mode = _post_process_cudagraph_mode(tensor)
 
     # Check conditions for microbatching
     should_ubatch = _post_process_ubatch(tensor, parallel_config.num_ubatches)
 
-    if should_ubatch and not should_dp_pad:
-        logger.debug_once(
-            "Microbatching has been triggered and requires DP padding. "
-            "Enabling DP padding even though it has been explicitly "
-            "disabled.",
-            scope="global",
-        )
-        should_dp_pad = True
+    # DP padding is needed when cudagraph is enabled (synced across ranks)
+    # or when ubatching/DBO is active (ubatching requires uniform batch
+    # sizes across DP ranks currently).
+    # Use the synced runtime cudagraph mode rather than the compilation config
+    # so we can avoid padding when cudagraph is not enabled for this step.
+    should_dp_pad = synced_cudagraph_mode != 0 or should_ubatch
 
     # Pad all DP ranks up to the maximum token count across ranks if
     # should_dp_pad is True
@@ -164,16 +158,12 @@ def _synchronize_dp_ranks(
         should_dp_pad,
     )
 
-    # Synchronize cudagraph_mode across ranks (take min)
-    synced_cudagraph_mode = _post_process_cudagraph_mode(tensor)
-
     return should_ubatch, num_tokens_after_padding, synced_cudagraph_mode
 
 
 def coordinate_batch_across_dp(
     num_tokens_unpadded: int,
     allow_microbatching: bool,
-    allow_dp_padding: bool,
     parallel_config: ParallelConfig,
     num_tokens_padded: int | None = None,
     uniform_decode: bool | None = None,
@@ -187,7 +177,6 @@ def coordinate_batch_across_dp(
     Args:
         num_tokens_unpadded: Number of tokens without accounting for padding
         allow_microbatching: If microbatching should be attempted
-        allow_dp_padding: If all DP ranks should be padded up to the same value
         parallel_config: The parallel config
         num_tokens_padded: Number of tokens including any non-DP padding (CUDA graphs,
             TP, etc)
@@ -195,15 +184,15 @@ def coordinate_batch_across_dp(
             only contains single token decodes
         num_scheduled_tokens_per_request: Only used if allow_microbatching is True. The
             number of tokens per request.
-        cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL)
+        cudagraph_mode: The cudagraph mode for this rank (0=NONE, 1=PIECEWISE, 2=FULL).
+            DP padding is enabled when synced cudagraph mode across ranks is not NONE.
 
     Returns: tuple[
         ubatch_slices: if this is set then all DP ranks have agreed to
         microbatch
         num_tokens_after_padding: A tensor containing the total number of
         tokens per-microbatch for each DP rank including padding. Will be
-        padded up to the max value across all DP ranks when allow_dp_padding
-        is True.
+        padded up to the max value across all DP ranks when cudagraph is enabled.
         synced_cudagraph_mode: The synchronized cudagraph mode (min across ranks)
     ]
 
@@ -231,7 +220,6 @@ def coordinate_batch_across_dp(
             num_tokens_unpadded,
             num_tokens_padded,
             should_attempt_ubatching,
-            allow_dp_padding,
             cudagraph_mode,
             parallel_config,
         )

vllm/v1/worker/gpu_model_runner.py

@@ -2300,7 +2300,7 @@ class GPUModelRunner(
         )
         # Dispatch for the decoder portion of the model.
         _, batch_desc = self.cudagraph_dispatcher.dispatch(
-            num_logits, disable_full=True
+            num_logits, invalid_modes={CUDAGraphMode.FULL}
         )
         num_logits_padded = batch_desc.num_tokens
         logits_indices_padded = self.kv_sharing_fast_prefill_logits_indices[
@@ -3174,20 +3174,19 @@ class GPUModelRunner(
         has_lora = num_active_loras > 0 if force_has_lora is None else force_has_lora
 
         num_tokens_padded = self._pad_for_sequence_parallelism(num_tokens)
-        dispatch_cudagraph = (
-            lambda num_tokens, disable_full: self.cudagraph_dispatcher.dispatch(
+
+        def dispatch_cudagraph(num_tokens, disable_full=False, valid_modes=None):
+            return self.cudagraph_dispatcher.dispatch(
                 num_tokens=num_tokens,
                 has_lora=has_lora,
                 uniform_decode=uniform_decode,
-                disable_full=disable_full,
                 num_active_loras=num_active_loras,
-            )
-            if not force_eager
-            else (CUDAGraphMode.NONE, BatchDescriptor(num_tokens_padded))
+                valid_modes={CUDAGraphMode.NONE} if force_eager else valid_modes,
+                invalid_modes={CUDAGraphMode.FULL} if disable_full else None,
             )
 
         cudagraph_mode, batch_descriptor = dispatch_cudagraph(
-            num_tokens_padded, use_cascade_attn or has_encoder_output
+            num_tokens_padded, disable_full=use_cascade_attn or has_encoder_output
         )
         num_tokens_padded = batch_descriptor.num_tokens
         if self.compilation_config.pass_config.enable_sp:
@@ -3204,20 +3203,11 @@ class GPUModelRunner(
         # across ranks
         should_ubatch, num_tokens_across_dp = False, None
         if self.vllm_config.parallel_config.data_parallel_size > 1:
-            # Disable DP padding when running eager to avoid excessive padding when
-            # running prefills. This lets us set cudagraph_mode="NONE" on the prefiller
-            # in a P/D setup and still use CUDA graphs (enabled by this padding) on the
-            # decoder.
-            allow_dp_padding = (
-                self.compilation_config.cudagraph_mode != CUDAGraphMode.NONE
-            )
-
             should_ubatch, num_tokens_across_dp, synced_cudagraph_mode = (
                 coordinate_batch_across_dp(
                     num_tokens_unpadded=num_tokens,
                     parallel_config=self.parallel_config,
                     allow_microbatching=allow_microbatching,
-                    allow_dp_padding=allow_dp_padding,
                     num_tokens_padded=num_tokens_padded,
                     uniform_decode=uniform_decode,
                     num_scheduled_tokens_per_request=num_scheduled_tokens_np,
@@ -3232,7 +3222,7 @@ class GPUModelRunner(
                 # Re-dispatch with DP padding so we have the correct batch_descriptor
                 cudagraph_mode, batch_descriptor = dispatch_cudagraph(
                     num_tokens_padded,
-                    disable_full=synced_cudagraph_mode <= CUDAGraphMode.PIECEWISE.value,
+                    valid_modes={CUDAGraphMode(synced_cudagraph_mode)},
                 )
                 # Assert to make sure the agreed upon token count is correct otherwise
                 # num_tokens_across_dp will no-longer be valid
@@ -4724,7 +4714,7 @@ class GPUModelRunner(
 
         assert (
             cudagraph_runtime_mode is None
-            or cudagraph_runtime_mode.valid_runtime_modes()
+            or cudagraph_runtime_mode.is_valid_runtime_mode()
         )
 
         # If cudagraph_mode.decode_mode() == FULL and
@@ -5336,7 +5326,7 @@ class GPUModelRunner(
     ):
         assert (
             cudagraph_runtime_mode != CUDAGraphMode.NONE
-            and cudagraph_runtime_mode.valid_runtime_modes()
+            and cudagraph_runtime_mode.is_valid_runtime_mode()
         ), f"Invalid cudagraph runtime mode: {cudagraph_runtime_mode}"
 
         if not batch_descriptors: