[MoE Refactor] Remove MoE DP chunking (#39107)

Signed-off-by: Bill Nell <bnell@redhat.com>
Co-authored-by: Robert Shaw <114415538+robertgshaw2-redhat@users.noreply.github.com>

.buildkite/test_areas/kernels.yaml

@@ -200,7 +200,14 @@ steps:
   timeout_in_minutes: 90
   device: h100
   num_devices: 2
-  optional: true
+  source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/distributed/device_communicators/
+  - vllm/config
   commands:
     - pytest -v -s kernels/moe/test_moe_layer.py
 
@@ -209,6 +216,13 @@ steps:
   timeout_in_minutes: 90
   device: b200
   num_devices: 2
-  optional: true
+  source_file_dependencies:
+  - csrc/quantization/cutlass_w8a8/moe/
+  - csrc/moe/
+  - tests/kernels/moe
+  - vllm/model_executor/layers/fused_moe/
+  - vllm/model_executor/layers/quantization/
+  - vllm/distributed/device_communicators/
+  - vllm/config
   commands:
     - pytest -v -s kernels/moe/test_moe_layer.py

tests/kernels/moe/modular_kernel_tools/common.py

@@ -46,6 +46,7 @@ from vllm.utils.import_utils import (
     has_deep_gemm,
     has_mori,
 )
+from vllm.utils.math_utils import next_power_of_2
 
 from .mk_objects import (
     TestMoEQuantConfig,
@@ -604,13 +605,6 @@ def make_modular_kernel(
     vllm_config: VllmConfig,
     quant_config: FusedMoEQuantConfig,
 ) -> mk.FusedMoEKernel:
-    def next_power_of_2(x):
-        import math
-
-        if x == 0:
-            return 1
-        return 2 ** math.ceil(math.log2(x))
-
     # make moe config
     moe_parallel_config: FusedMoEParallelConfig = FusedMoEParallelConfig.make(
         tp_size_=get_tensor_model_parallel_world_size(),

tests/kernels/moe/modular_kernel_tools/parallel_utils.py

@@ -126,7 +126,7 @@ def parallel_launch_with_config(
     world_size: int,
     worker: Callable[Concatenate[ProcessGroupInfo, VllmConfig, Any, P], None],
     vllm_config: VllmConfig,
-    env_dict: dict[Any, Any],
+    env_dict: dict[Any, Any] | None,
     *args: P.args,
     **kwargs: P.kwargs,
 ) -> None:

tests/kernels/moe/test_deepep_deepgemm_moe.py

@@ -29,6 +29,7 @@ from vllm.utils.deep_gemm import (
     is_deep_gemm_supported,
 )
 from vllm.utils.import_utils import has_deep_ep, has_deep_gemm
+from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.worker.workspace import init_workspace_manager
 
@@ -84,14 +85,6 @@ def with_dp_metadata(M: int, world_size: int):
         yield
 
 
-def next_power_of_2(x):
-    import math
-
-    if x == 0:
-        return 1
-    return 2 ** math.ceil(math.log2(x))
-
-
 def make_block_quant_fp8_weights(
     e: int,
     n: int,

tests/kernels/moe/test_flashinfer.py

@@ -32,6 +32,7 @@ from vllm.model_executor.layers.quantization.utils.flashinfer_utils import (
 from vllm.model_executor.layers.quantization.utils.fp8_utils import input_to_float8
 from vllm.model_executor.models.llama4 import Llama4MoE
 from vllm.platforms import current_platform
+from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
 
 try:
@@ -174,6 +175,7 @@ class TestData:
             routing_method=layer.routing_method_type,
             activation=activation,
             device=w13_quantized.device,
+            max_num_tokens=next_power_of_2(m),
         )
 
         return TestData(
@@ -348,6 +350,7 @@ def test_flashinfer_cutlass_moe_fp8_no_graph(
             in_dtype=torch.bfloat16,
             is_act_and_mul=activation.is_gated,
             routing_method=RoutingMethodType.TopK,
+            max_num_tokens=next_power_of_2(m),
         )
 
         kernel = mk.FusedMoEKernel(

tests/kernels/moe/test_flashinfer_moe.py

@@ -29,6 +29,7 @@ from vllm.model_executor.layers.fused_moe.flashinfer_cutlass_moe import (
 from vllm.model_executor.layers.fused_moe.modular_kernel import FusedMoEKernel
 from vllm.platforms import current_platform
 from vllm.utils.flashinfer import has_flashinfer_cutlass_fused_moe
+from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
 
 if not has_flashinfer_cutlass_fused_moe() or not current_platform.has_device_capability(
@@ -105,6 +106,7 @@ def test_flashinfer_fp4_moe_no_graph(
             in_dtype=dtype,
             is_act_and_mul=is_gated_act,
             routing_method=RoutingMethodType.TopK,
+            max_num_tokens=next_power_of_2(m),
         )
 
         flashinfer_experts = FusedMoEKernel(

tests/kernels/moe/test_moe.py

@@ -59,6 +59,7 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import quantize_w
 from vllm.model_executor.models.mixtral import MixtralMoE
 from vllm.platforms import current_platform
 from vllm.scalar_type import ScalarType, scalar_types
+from vllm.utils.math_utils import next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.worker.workspace import init_workspace_manager
 
@@ -1676,7 +1677,7 @@ def test_unquantized_bf16_flashinfer_trtllm_backend(
         in_dtype=dtype,
         is_act_and_mul=True,
         routing_method=RoutingMethodType.Renormalize,
-        max_num_tokens=m,
+        max_num_tokens=next_power_of_2(m),
     )
 
     with set_current_vllm_config(vllm_config):

tests/kernels/moe/test_moe_layer.py

@@ -26,6 +26,7 @@ from tests.kernels.moe.utils import TestMLP, make_test_weights, moe_quantize_wei
 from vllm.config import (
     CompilationConfig,
     ParallelConfig,
+    SchedulerConfig,
     VllmConfig,
     set_current_vllm_config,
 )
@@ -53,7 +54,7 @@ from vllm.utils.flashinfer import (
     has_flashinfer_nvlink_two_sided,
 )
 from vllm.utils.import_utils import has_deep_ep, has_mori, has_nixl_ep
-from vllm.utils.math_utils import cdiv
+from vllm.utils.math_utils import cdiv, next_power_of_2
 from vllm.utils.torch_utils import set_random_seed
 from vllm.v1.worker.workspace import (
     init_workspace_manager,
@@ -65,8 +66,9 @@ fp8_dtype = torch.float8_e4m3fn  # current_platform.fp8_dtype
 SHAPE_COMBOS = [
     (1, 128, 256),
     (32, 1024, 512),
-    (222, 2048, 2048),  # should be big enough to exercise DP chunking
+    (222, 2048, 2048),
 ]
+MAX_M = max([x[0] for x in SHAPE_COMBOS])
 
 NUM_EXPERTS = [8, 64]
 TOP_KS = [2, 6]
@@ -112,7 +114,7 @@ BACKEND_SUPPORTED_QUANTS: dict[str, set[str | None]] = {
     "mori":                        {None, "fp8", "modelopt_fp8"},
     "flashinfer_nvlink_two_sided": {None,        "modelopt_fp8", "modelopt_fp4"},
     "flashinfer_nvlink_one_sided": {None,        "modelopt_fp8", "modelopt_fp4"},
-    "deepep_low_latency":          {None, "fp8", "modelopt_fp8", "modelopt_fp4"},
+    "deepep_low_latency":          {None,        "modelopt_fp8", "modelopt_fp4"},
     "deepep_high_throughput":      {None, "fp8", "modelopt_fp8", "modelopt_fp4"},
     "nixl_ep":                     {None, "fp8", "modelopt_fp8"},
 }
@@ -363,9 +365,9 @@ def is_valid_config(config: MoETestConfig) -> tuple[bool, str | None]:
         )
 
     # routed_input_transform + quantization + high hidden dimensions
-    # TODO: Disable >= 2048 w/fp8 + deepep LL for now due to insane errors.
+    # TODO: Disable >= 2048 for now due to insane errors.
     if (
-        (config.use_routed_input_transform or config.backend == "deepep_low_latency")
+        config.use_routed_input_transform
         and config.quantization is not None
         and config.k >= 2048
     ):
@@ -1663,9 +1665,6 @@ def test_moe_layer(
 
     verbosity = pytestconfig.getoption("verbose")
 
-    test_env = dict()
-    test_env["VLLM_MOE_DP_CHUNK_SIZE"] = "128"
-    monkeypatch.setenv("VLLM_MOE_DP_CHUNK_SIZE", "128")
     if os.environ.get("VLLM_LOGGING_LEVEL") is None:
         monkeypatch.setenv("VLLM_LOGGING_LEVEL", "ERROR")
 
@@ -1690,7 +1689,11 @@ def test_moe_layer(
     compilation_config.pass_config.fuse_allreduce_rms = False  # for now
 
     vllm_config = VllmConfig(
-        parallel_config=parallel_config, compilation_config=compilation_config
+        parallel_config=parallel_config,
+        compilation_config=compilation_config,
+        scheduler_config=SchedulerConfig.default_factory(
+            max_num_batched_tokens=next_power_of_2(MAX_M)
+        ),
     )
 
     test_configs = generate_valid_test_configs(
@@ -1718,7 +1721,7 @@ def test_moe_layer(
             world_size,
             _parallel_worker,
             vllm_config,
-            test_env,
+            None,
             test_configs,
             verbosity,
         )

tests/kernels/moe/utils.py

@@ -69,6 +69,7 @@ def make_dummy_moe_config(
         in_dtype=in_dtype,
         device="cuda",
         routing_method=RoutingMethodType.TopK,
+        max_num_tokens=512,
     )
 
 

vllm/config/parallel.py

@@ -622,6 +622,18 @@ class ParallelConfig:
             and self.data_parallel_size > 1
         )
 
+    @property
+    def use_batched_dp_moe(self) -> bool:
+        return (
+            self.all2all_backend
+            in (
+                "deepep_low_latency",
+                "nixl_ep",
+            )
+            and self.enable_expert_parallel
+            and self.data_parallel_size > 1
+        )
+
     @property
     def node_rank_within_dp(self) -> int:
         return self.node_rank % self.nnodes_within_dp

vllm/config/scheduler.py

@@ -40,6 +40,7 @@ class SchedulerConfig:
     """
 
     DEFAULT_MAX_NUM_BATCHED_TOKENS: ClassVar[int] = 2048
+    DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP: ClassVar[int] = 256
     DEFAULT_MAX_NUM_SEQS: ClassVar[int] = 128
 
     runner_type: RunnerType = "generate"

vllm/engine/arg_utils.py

@@ -1588,9 +1588,6 @@ class EngineArgs:
 
         self._check_feature_supported()
         self._set_default_chunked_prefill_and_prefix_caching_args(model_config)
-        self._set_default_max_num_seqs_and_batched_tokens_args(
-            usage_context, model_config
-        )
         self._set_default_reasoning_config_args()
         sliding_window: int | None = None
         if not is_interleaved(model_config.hf_text_config):
@@ -1846,6 +1843,12 @@ class EngineArgs:
             target_parallel_config=parallel_config,
         )
 
+        self._set_default_max_num_seqs_and_batched_tokens_args(
+            usage_context,
+            model_config,
+            parallel_config,
+        )
+
         assert self.max_num_batched_tokens is not None, (
             "max_num_batched_tokens must be set by this point"
         )
@@ -2244,6 +2247,7 @@ class EngineArgs:
         self,
         usage_context: UsageContext | None,
         model_config: ModelConfig,
+        parallel_config: ParallelConfig,
     ):
         world_size = self.pipeline_parallel_size * self.tensor_parallel_size
         (
@@ -2255,10 +2259,15 @@ class EngineArgs:
         orig_max_num_seqs = self.max_num_seqs
 
         if self.max_num_batched_tokens is None:
-            self.max_num_batched_tokens = default_max_num_batched_tokens.get(
-                usage_context,
-                SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS,
-            )
+            if parallel_config.use_batched_dp_moe:
+                self.max_num_batched_tokens = (
+                    SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
+                )
+            else:
+                self.max_num_batched_tokens = default_max_num_batched_tokens.get(
+                    usage_context,
+                    SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS,
+                )
 
         if self.max_num_seqs is None:
             self.max_num_seqs = default_max_num_seqs.get(

vllm/envs.py

@@ -146,8 +146,6 @@ if TYPE_CHECKING:
     VLLM_ENABLE_PREGRAD_PASSES: bool = False
     VLLM_DP_MASTER_IP: str = ""
     VLLM_DP_MASTER_PORT: int = 0
-    VLLM_MOE_DP_CHUNK_SIZE: int = 256
-    VLLM_ENABLE_MOE_DP_CHUNK: bool = True
     VLLM_RANDOMIZE_DP_DUMMY_INPUTS: bool = False
     VLLM_RAY_DP_PACK_STRATEGY: Literal["strict", "fill", "span"] = "strict"
     VLLM_RAY_EXTRA_ENV_VAR_PREFIXES_TO_COPY: str = ""
@@ -1140,15 +1138,6 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "VLLM_DP_MASTER_IP": lambda: os.getenv("VLLM_DP_MASTER_IP", "127.0.0.1"),
     # Port of the master node in the data parallel setting
     "VLLM_DP_MASTER_PORT": lambda: int(os.getenv("VLLM_DP_MASTER_PORT", "0")),
-    # In the context of executing MoE models with Data-Parallel, Expert-Parallel
-    # and Batched All-to-All dispatch/combine kernels, VLLM_MOE_DP_CHUNK_SIZE
-    # dictates the quantum of tokens that can be dispatched from a DP
-    # rank. All DP ranks process the activations in VLLM_MOE_DP_CHUNK_SIZE
-    # units.
-    "VLLM_MOE_DP_CHUNK_SIZE": lambda: int(os.getenv("VLLM_MOE_DP_CHUNK_SIZE", "256")),
-    "VLLM_ENABLE_MOE_DP_CHUNK": lambda: bool(
-        int(os.getenv("VLLM_ENABLE_MOE_DP_CHUNK", "1"))
-    ),
     # Randomize inputs during dummy runs when using Data Parallel
     "VLLM_RANDOMIZE_DP_DUMMY_INPUTS": lambda: (
         os.environ.get("VLLM_RANDOMIZE_DP_DUMMY_INPUTS", "0") == "1"

vllm/forward_context.py

@@ -70,27 +70,8 @@ def _compute_sp_num_tokens(
     return sp_tokens.tolist()
 
 
-def _compute_chunked_local_num_tokens(
-    num_tokens_across_dp_cpu: torch.Tensor,
-    sequence_parallel_size: int,
-    max_num_tokens: int,
-    chunk_idx: int,
-) -> list[int]:
-    sp_tokens = _compute_sp_num_tokens(num_tokens_across_dp_cpu, sequence_parallel_size)
-    sp_size = len(sp_tokens)
-
-    local_size = [-1] * sp_size
-    for i in range(sp_size):
-        # Take into account sharding if MoE activation is sequence parallel.
-        local_size[i] = min(max_num_tokens, sp_tokens[i] - (max_num_tokens * chunk_idx))
-        if local_size[i] <= 0:
-            local_size[i] = 1  # ensure lockstep even if done
-    return local_size
-
-
 @dataclass
 class DPMetadata:
-    max_tokens_across_dp_cpu: torch.Tensor
     num_tokens_across_dp_cpu: torch.Tensor
 
     # NOTE: local_sizes should only be set by the chunked_sizes context manager
@@ -113,47 +94,7 @@ class DPMetadata:
         assert num_tokens_across_dp_cpu[dp_rank] == batchsize, (
             f"{num_tokens_across_dp_cpu[dp_rank]} {batchsize}"
         )
-        max_tokens_across_dp_cpu = torch.max(num_tokens_across_dp_cpu)
-        return DPMetadata(max_tokens_across_dp_cpu, num_tokens_across_dp_cpu)
-
-    @contextmanager
-    def chunked_sizes(
-        self, sequence_parallel_size: int, max_chunk_size_per_rank: int, chunk_idx: int
-    ):
-        """
-        Context manager to compute and temporarily set the per-rank local token
-        sizes for a specific chunk during chunked forward execution.
-
-        This is necessary to ensure each DP (data parallel) rank processes its
-        designated portion of tokens in lockstep with others, even when the
-        token counts are uneven or some ranks have completed their input early.
-
-        For chunked execution, we break up the total tokens on each rank into
-        multiple chunks (of at most `max_chunk_size_per_rank`), and for a given
-        `chunk_idx`, this context manager sets `self.local_sizes` to the number
-        of tokens to process in that chunk on each rank.
-
-        `self.local_sizes` is only valid inside the context.
-
-        Args:
-            sequence_parallel_size: When Attn is TP and MoE layers are EP,
-                                    we use SP between the layers to avoid
-                                    redundant ops. We need this value to
-                                    compute the chunked sizes.
-            max_chunk_size_per_rank: The max number of tokens each rank is
-                                     allowed to process in this chunk.
-            chunk_idx: The index of the chunk to compute sizes for.
-        """
-        self.local_sizes = _compute_chunked_local_num_tokens(
-            self.num_tokens_across_dp_cpu,
-            sequence_parallel_size,
-            max_chunk_size_per_rank,
-            chunk_idx,
-        )
-        try:
-            yield self.local_sizes
-        finally:
-            self.local_sizes = None
+        return DPMetadata(num_tokens_across_dp_cpu)
 
     @contextmanager
     def sp_local_sizes(self, sequence_parallel_size: int):

vllm/model_executor/layers/fused_moe/config.py

@@ -6,8 +6,7 @@ from typing import Union
 
 import torch
 
-import vllm.envs as envs
-from vllm.config import ParallelConfig
+from vllm.config import ParallelConfig, SchedulerConfig
 from vllm.distributed import get_dp_group, get_pcp_group, get_tensor_model_parallel_rank
 from vllm.logger import init_logger
 from vllm.model_executor.layers.fused_moe.activation import MoEActivation
@@ -937,15 +936,6 @@ class FusedMoEParallelConfig:
     all2all_backend: str  # all2all backend for MoE communication
     enable_eplb: bool  # whether to enable expert load balancing
 
-    @property
-    def use_dp_chunking(self) -> bool:
-        return (
-            self.use_deepep_ll_kernels
-            or self.use_mori_kernels
-            or self.use_fi_nvl_two_sided_kernels
-            or self.use_nixl_ep_kernels
-        ) and envs.VLLM_ENABLE_MOE_DP_CHUNK
-
     @property
     def is_sequence_parallel(self) -> bool:
         return self.sp_size > 1
@@ -1184,7 +1174,7 @@ class FusedMoEConfig:
     intermediate_size_per_partition_unpadded: int | None = None
 
     moe_backend: str = "auto"
-    max_num_tokens: int = envs.VLLM_MOE_DP_CHUNK_SIZE
+    max_num_tokens: int = SchedulerConfig.DEFAULT_MAX_NUM_BATCHED_TOKENS_FOR_BATCHED_DP
     has_bias: bool = False
     is_act_and_mul: bool = True
     is_lora_enabled: bool = False

vllm/model_executor/layers/fused_moe/layer.py

@@ -8,7 +8,6 @@ from typing import Literal, cast, get_args, overload
 import torch
 from torch.nn.parameter import UninitializedParameter
 
-import vllm.envs as envs
 from vllm._aiter_ops import rocm_aiter_ops
 from vllm.config import VllmConfig, get_current_vllm_config
 from vllm.config.parallel import ExpertPlacementStrategy
@@ -479,7 +478,7 @@ class FusedMoE(CustomOp):
             in_dtype=moe_in_dtype,
             moe_backend=vllm_config.kernel_config.moe_backend,
             router_logits_dtype=router_logits_dtype,
-            max_num_tokens=envs.VLLM_MOE_DP_CHUNK_SIZE,
+            max_num_tokens=vllm_config.scheduler_config.max_num_batched_tokens,
             has_bias=has_bias,
             is_act_and_mul=is_act_and_mul,
             is_lora_enabled=vllm_config.lora_config is not None,

vllm/model_executor/layers/fused_moe/runner/chunking_moe_runner.py

-# SPDX-License-Identifier: Apache-2.0
-# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
-
-import torch
-
-from vllm.forward_context import (
-    get_forward_context,
-)
-from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
-    FusedMoEMethodBase,
-)
-from vllm.model_executor.layers.fused_moe.runner.moe_runner_base import MoERunnerBase
-from vllm.model_executor.layers.fused_moe.runner.shared_experts import (
-    SharedExperts,
-)
-from vllm.utils.math_utils import cdiv
-from vllm.v1.worker.ubatching import dbo_current_ubatch_id
-from vllm.v1.worker.workspace import current_workspace_manager
-
-
-class ChunkingMoERunner(MoERunnerBase):
-    """
-    MoE runner wrapper that adds chunked processing to any MoERunnerBase.
-
-    This runner wraps an inner MoERunnerBase and overrides _forward_impl to
-    process large batches by breaking them into smaller chunks. Each chunk
-    is delegated to the inner runner's _forward_impl, making chunking
-    composable with any runner implementation.
-
-    All MoERunnerBase state (moe_config, router, quant_method, etc.) is
-    transparently delegated to the inner runner via __getattr__.
-    ChunkingMoERunner only owns chunking-specific state: the pre-allocated
-    workspace buffers and the reduce_results override.
-
-    Key behaviors:
-    - Pre-allocates workspace tensors for CUDA graph compatibility
-    - Processes chunks via inner._forward_impl per chunk
-    - Never reduces results (reduce_results always returns False)
-    """
-
-    def __init__(self, inner: MoERunnerBase):
-        # Assert that _maybe_dispatch/_maybe_combine will be nops.
-        assert inner.moe_config.pcp_size == 1
-
-        # Skip MoERunnerBase.__init__ — all state is delegated to inner
-        # via __getattr__. Only chunking-specific state lives here.
-        self._inner = inner
-
-        # Pre-allocated staging buffers. These need to exist ahead of time
-        # due to CUDA graph construction needing fixed buffer addresses.
-        self.batched_hidden_states, self.batched_router_logits = (
-            self._init_dp_chunking()
-        )
-
-    def __getattr__(self, name):
-        # Delegate attribute access to the inner runner. This is only
-        # called when normal lookup (instance __dict__, class MRO) fails,
-        # so ChunkingMoERunner's own attributes and methods take priority.
-        return getattr(self._inner, name)
-
-    @property
-    def shared_experts(self) -> SharedExperts | None:
-        return self._inner.shared_experts
-
-    # TODO(bnell): temporary hack, do not call this method.
-    def _replace_quant_method(self, quant_method: FusedMoEMethodBase):
-        self._inner._replace_quant_method(quant_method)
-        self.quant_method = quant_method
-
-    def is_internal_router(self) -> bool:
-        return self._inner.gate is not None
-
-    # Reducing results when chunking is handled by the MK finalize operations
-    # when DP chunking is enabled..
-    # This will be removed by #35949
-    @property
-    def reduce_results(self) -> bool:
-        return False
-
-    def _init_dp_chunking(self) -> list[torch.Tensor]:
-        states_shape: tuple[int, ...]
-        logits_shape: tuple[int, ...]
-
-        moe = self.moe_config
-
-        if self.enable_dbo:
-            states_shape = (2, moe.max_num_tokens, self.moe_config.hidden_dim)
-            logits_shape = (2, moe.max_num_tokens, self.moe_config.num_logical_experts)
-        else:
-            states_shape = (moe.max_num_tokens, self.moe_config.hidden_dim)
-            logits_shape = (moe.max_num_tokens, self.moe_config.num_logical_experts)
-
-        # Does this need some kind of profiling run check like modular_kernel.py?
-        return current_workspace_manager().get_simultaneous(
-            (states_shape, moe.in_dtype),
-            (logits_shape, moe.router_logits_dtype),
-        )
-
-    def _allocate_dp_chunking_outputs(
-        self,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> tuple[torch.Tensor | None, torch.Tensor]:
-        # Assert the inputs are of the proper type and shape.
-        assert self.batched_hidden_states is not None
-        assert self.batched_router_logits is not None
-
-        assert self.batched_hidden_states.dtype == hidden_states.dtype, (
-            f"{self.batched_hidden_states.dtype} == {hidden_states.dtype}"
-        )
-        assert self.batched_router_logits.dtype == router_logits.dtype, (
-            f"{self.batched_router_logits.dtype} == {router_logits.dtype}"
-        )
-
-        # Check size compatibility.
-        assert self.batched_hidden_states.size(-1) == hidden_states.size(-1)
-        assert self.batched_router_logits.size(-1) == router_logits.size(-1)
-
-        final_fused_hidden_states = torch.empty_like(hidden_states)
-        if self.shared_experts is not None:
-            if shared_experts_input is not None:
-                final_shared_hidden_states = torch.empty_like(shared_experts_input)
-            else:
-                final_shared_hidden_states = torch.empty_like(hidden_states)
-        else:
-            final_shared_hidden_states = None
-
-        return final_shared_hidden_states, final_fused_hidden_states
-
-    def _slice_and_copy_input(
-        self,
-        out_slice: torch.Tensor,
-        orig: torch.Tensor | None,
-        start: int,
-        end: int,
-    ) -> torch.Tensor:
-        assert orig is not None
-        slice_size = end - start
-        orig_slice = orig[start:end, :]
-        if self.enable_dbo:
-            assert out_slice.dim() == 3
-            batch_buffer_idx = dbo_current_ubatch_id()
-            out_slice = out_slice[batch_buffer_idx, :]
-
-        assert out_slice.size(0) >= slice_size
-        out_slice = out_slice[:slice_size, :]
-        out_slice.copy_(orig_slice, non_blocking=True)
-        return out_slice
-
-    def _forward_impl(
-        self,
-        layer: torch.nn.Module,
-        hidden_states: torch.Tensor,
-        router_logits: torch.Tensor,
-        shared_experts_input: torch.Tensor | None,
-    ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
-        final_shared_hidden_states, final_fused_hidden_states = (
-            self._allocate_dp_chunking_outputs(
-                hidden_states, router_logits, shared_experts_input
-            )
-        )
-
-        ctx = get_forward_context()
-        # flashinfer_cutlass_kernels can handle: optional DP + TP/EP
-        max_tokens_across_dispatchers = ctx.dp_metadata.max_tokens_across_dp_cpu
-        moe_dp_chunk_size_per_rank = self.moe_config.max_num_tokens
-
-        # If the input to the MoE is sequence parallel then divide by sp_size
-        # to find the maximum number of tokens for any individual dispatcher.
-        if self.moe_config.is_sequence_parallel:
-            max_tokens_across_dispatchers = cdiv(
-                max_tokens_across_dispatchers, self.moe_config.sp_size
-            )
-
-        num_tokens = hidden_states.size(0)
-        for chunk_idx, chunk_start_ in enumerate(
-            range(0, max_tokens_across_dispatchers, moe_dp_chunk_size_per_rank)
-        ):
-            chunk_start = chunk_start_
-            chunk_end = min(
-                chunk_start + moe_dp_chunk_size_per_rank, max_tokens_across_dispatchers
-            )
-            # clamp start and end
-            chunk_start = min(chunk_start, num_tokens - 1)
-            chunk_end = min(chunk_end, num_tokens)
-            chunk_sizes = ctx.dp_metadata.chunked_sizes(
-                self.moe_config.sp_size, moe_dp_chunk_size_per_rank, chunk_idx
-            )
-            with chunk_sizes:
-                hidden_states_chunk = self._slice_and_copy_input(
-                    self.batched_hidden_states,
-                    hidden_states,
-                    chunk_start,
-                    chunk_end,
-                )
-
-                router_logits_chunk = self._slice_and_copy_input(
-                    self.batched_router_logits,
-                    router_logits,
-                    chunk_start,
-                    chunk_end,
-                )
-
-                shared_experts_input_chunk = (
-                    shared_experts_input[chunk_start:chunk_end, :]
-                    if shared_experts_input is not None
-                    else None
-                )
-
-                # Delegate per-chunk computation to the inner runner.
-                chunk_result = self._inner._forward_impl(
-                    layer=layer,
-                    hidden_states=hidden_states_chunk,
-                    router_logits=router_logits_chunk,
-                    shared_experts_input=shared_experts_input_chunk,
-                )
-
-                # Store outputs
-                # TODO(bnell): document when chunk_start >= num_tokens
-                if chunk_start < num_tokens:
-                    if self.shared_experts is not None:
-                        assert isinstance(chunk_result, tuple)
-                        shared_output_chunk, hidden_states_chunk = chunk_result
-                        final_fused_hidden_states[chunk_start:chunk_end, :].copy_(
-                            hidden_states_chunk, non_blocking=True
-                        )
-                        assert shared_output_chunk is not None
-                        assert final_shared_hidden_states is not None
-                        final_shared_hidden_states[chunk_start:chunk_end, :].copy_(
-                            shared_output_chunk, non_blocking=True
-                        )
-                    else:
-                        assert isinstance(chunk_result, torch.Tensor)
-                        final_fused_hidden_states[chunk_start:chunk_end, :].copy_(
-                            chunk_result, non_blocking=True
-                        )
-
-        if self.shared_experts is None:
-            return final_fused_hidden_states
-        else:
-            assert final_shared_hidden_states is not None
-            return (final_shared_hidden_states, final_fused_hidden_states)

vllm/model_executor/layers/fused_moe/runner/moe_runner_factory.py

@@ -12,9 +12,6 @@ from vllm.model_executor.layers.fused_moe.fused_moe_method_base import (
 from vllm.model_executor.layers.fused_moe.router.fused_moe_router import (
     FusedMoERouter,
 )
-from vllm.model_executor.layers.fused_moe.runner.chunking_moe_runner import (
-    ChunkingMoERunner,
-)
 from vllm.model_executor.layers.fused_moe.runner.default_moe_runner import (
     DefaultMoERunner,
 )
@@ -35,7 +32,7 @@ def create_moe_runner(
     reduce_results: bool,
     enable_dbo: bool,
 ) -> MoERunner:
-    runner = DefaultMoERunner(
+    return DefaultMoERunner(
         layer_name,
         moe_config,
         router,
@@ -46,6 +43,3 @@ def create_moe_runner(
         reduce_results,
         enable_dbo,
     )
-    if moe_config.moe_parallel_config.use_dp_chunking:
-        return ChunkingMoERunner(runner)
-    return runner

vllm/model_executor/layers/fused_moe/runner/shared_experts.py

@@ -69,7 +69,6 @@ class SharedExperts:
         self._moe_config = moe_config
         self._quant_method = quant_method
         self._reduce_results = reduce_results
-        self._use_dp_chunking = moe_config.moe_parallel_config.use_dp_chunking
 
         # Allow disabling of the separate shared experts stream for
         # debug purposes.
@@ -87,20 +86,6 @@ class SharedExperts:
                     "Enabled separate cuda stream for MoE shared_experts", scope="local"
                 )
 
-    @property
-    def _use_external_experts(self) -> bool:
-        if self._use_dp_chunking:
-            return False
-
-        # Disable shared expert overlap if:
-        #   - we are using eplb with non-default backend, because of correctness issues
-        #   - we are using flashinfer with DP, since there nothing to gain
-        backend = self._moe_config.moe_parallel_config.all2all_backend
-        return (
-            self._moe_config.moe_parallel_config.enable_eplb
-            and backend != "allgather_reducescatter"
-        ) or self._moe_config.moe_parallel_config.use_fi_nvl_two_sided_kernels
-
     def _determine_shared_experts_order(
         self,
         hidden_states: torch.Tensor,
@@ -110,7 +95,6 @@ class SharedExperts:
 
         should_run_shared_in_aux_stream = (
             current_platform.is_cuda()
-            and not self._use_dp_chunking
             and self._stream is not None
             and hidden_states.shape[0]
             <= envs.VLLM_SHARED_EXPERTS_STREAM_TOKEN_THRESHOLD

vllm/model_executor/layers/quantization/quark/quark_moe.py

@@ -1502,9 +1502,9 @@ class QuarkOCP_MX_MoEMethod_OSS(QuarkOCP_MX_MoEMethod):
         layer.w2_bias = torch.nn.Parameter(w2_bias, requires_grad=False)
 
         # FIXME warp need to be adjusted based on batch size
-        # only apply to  batched mode
+        # only apply to batched mode
         if self.moe.use_ep:
-            num_warps = 4 if envs.VLLM_MOE_DP_CHUNK_SIZE <= 512 else 8
+            num_warps = 4 if self.moe.max_num_tokens <= 512 else 8
         else:
             num_warps = 8