Merge branch 'v0.9.2-dev' of https://developer.sourcefind.cn/codes/OpenDAS/vllm into v0.9.2-dev

vllm/distributed/communication_op.py

@@ -6,7 +6,7 @@ from typing import Any, Optional, Union
 import torch
 import torch.distributed
 
-from .parallel_state import get_tp_group
+from .parallel_state import get_tp_group, get_ep_group
 
 
 def tensor_model_parallel_all_reduce(input_: torch.Tensor) -> torch.Tensor:
@@ -32,6 +32,17 @@ def tensor_model_parallel_gather(input_: torch.Tensor,
     """Gather the input tensor across model parallel group."""
     return get_tp_group().gather(input_, dst, dim)
 
+def expert_parallel_all_gather(input_: torch.Tensor,
+                                     dim: int = -1) -> torch.Tensor:
+    """All-gather the input tensor across model parallel group."""
+    return get_ep_group().all_gather(input_, dim)
+
+def expert_parallel_gather(input_: torch.Tensor,
+                                 dst: int = 0,
+                                 dim: int = -1) -> Optional[torch.Tensor]:
+    """Gather the input tensor across model parallel group."""
+    return get_ep_group().gather(input_, dst, dim)
+
 
 def broadcast_tensor_dict(tensor_dict: Optional[dict[Any, Union[torch.Tensor,
                                                                 Any]]] = None,

vllm/envs.py

@@ -169,6 +169,7 @@ if TYPE_CHECKING:
     USE_FUSED_RMS_QUANT: bool = False
     VLLM_USE_MERGE_ATTN_STATES_OPT: bool = False
     USE_FUSED_SILU_MUL_QUANT: bool = False
+    VLLM_USE_ALLTOALL_EP: bool = False
 
 def get_default_cache_root():
     return os.getenv(
@@ -1114,6 +1115,10 @@ environment_variables: dict[str, Callable[[], Any]] = {
     "USE_FUSED_SILU_MUL_QUANT": 
     lambda: (os.getenv('USE_FUSED_SILU_MUL_QUANT', '0').lower() in
              ("true", "1")),
+    # vLLM will use all_to_all ep mode
+    "VLLM_USE_ALLTOALL_EP":
+        lambda: (os.environ.get("VLLM_USE_ALLTOALL_EP", "True").lower() in
+                 ("true", "1")),
 }
 
 # --8<-- [end:env-vars-definition]

vllm/model_executor/layers/fused_moe/ep_moe/ep_moe_utlis.py

+import math
+from typing import Callable, List, Optional, Tuple, Union
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.linear import (ColumnParallelLinear,
+                                               MergedColumnParallelLinear,
+                                               ReplicatedLinear,
+                                               RowParallelLinear)
+from vllm.model_executor.layers.activation import SiluAndMul
+from vllm.distributed import (get_dp_group, get_ep_group,
+                              get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size,
+                              tensor_model_parallel_all_reduce)
+
+
+try:
+
+    from transformer_engine.pytorch.permutation import (
+        moe_permute,
+        moe_sort_chunks_by_index,
+        moe_unpermute,
+    )
+
+    fused_permute = moe_permute
+    fused_unpermute = moe_unpermute
+    fused_sort_chunks_by_index = moe_sort_chunks_by_index
+
+    HAVE_TE = True
+
+except ImportError:
+
+    fused_permute = None
+    fused_unpermute = None
+    fused_sort_chunks_by_index = None
+    HAVE_TE = False
+
+
+shared_experts_overlap_stream = torch.cuda.Stream()
+
+@dataclass
+class EpMoeConfig:
+
+    moe_router_topk: int = 2
+    moe_permute_fusion: bool = False
+    moe_shared_expert_overlap: bool = False
+    ep_size: int = 1
+    num_moe_experts: int = 256
+    apply_router_weight_on_input: bool = False
+    routed_scaling_factor: float = 1.0
+
+
+    @staticmethod
+    def make(moe_router_topk: int = 2, 
+             moe_permute_fusion: bool = False,
+             moe_shared_expert_overlap: bool = False,
+             ep_size: int = 1,
+             num_moe_experts: int = 256,
+             routed_scaling_factor: float = 1.0, 
+             apply_router_weight_on_input: bool = False) -> "EpMoeConfig":
+        return EpMoeConfig(moe_router_topk=moe_router_topk,
+                           moe_permute_fusion=moe_permute_fusion,
+                           moe_shared_expert_overlap=moe_shared_expert_overlap,
+                           ep_size=ep_size,
+                           num_moe_experts=num_moe_experts,
+                           routed_scaling_factor=routed_scaling_factor,
+                           apply_router_weight_on_input=apply_router_weight_on_input)
+
+
+class EPSharedExperts(nn.Module):
+
+    def __init__(
+        self,
+        hidden_size: int,
+        intermediate_size: int,
+        hidden_act: str,
+        quant_config: Optional[QuantizationConfig] = None,
+        reduce_results: bool = True,
+        prefix: str = "",
+        moe_shared_expert_overlap: bool = True,
+    ) -> None:
+        super().__init__()
+        self.gate_up_proj = MergedColumnParallelLinear(
+            hidden_size, [intermediate_size] * 2,
+            bias=False,
+            quant_config=quant_config,
+            prefix=f"{prefix}.gate_up_proj")
+        self.down_proj = RowParallelLinear(intermediate_size,
+                                           hidden_size,
+                                           bias=False,
+                                           quant_config=quant_config,
+                                           reduce_results=reduce_results,
+                                           prefix=f"{prefix}.down_proj")
+        if hidden_act != "silu":
+            raise ValueError(f"Unsupported activation: {hidden_act}. "
+                             "Only silu is supported for now.")
+        self.act_fn = SiluAndMul()
+
+        self.moe_shared_expert_overlap = moe_shared_expert_overlap
+        if self.moe_shared_expert_overlap:
+            self.cached_fc1_input = None
+            self.cached_fc2_input = None
+            self.cached_fc2_output = None
+            self.cached_output = None
+            self.gate_score = None
+
+            self.stream = shared_experts_overlap_stream
+
+    def forward(self, x):
+        gate_up, _ = self.gate_up_proj(x)
+        x = self.act_fn(gate_up)
+        x, _ = self.down_proj(x)
+        return x
+
+    def linear_fc1_forward_and_act(self, overlapped_comm_output=None):
+        """
+        Do Linear FC1 and activation function forward.
+        This function is used to overlap shared experts with the dispatcher.
+        It is only useful when --moe-shared-expert-overlap is set and may be changed.
+        """
+        assert self.moe_shared_expert_overlap
+        with torch.cuda.stream(self.stream):
+            # [s, b, 4 * h/p]
+            intermediate_parallel, bias_parallel = self.gate_up_proj(self.cached_fc1_input)
+            self.cached_fc1_input = None
+
+            if bias_parallel is not None:
+                intermediate_parallel = intermediate_parallel + bias_parallel
+            intermediate_parallel = self.act_fn(intermediate_parallel)
+
+            self.cached_fc2_input = intermediate_parallel
+
+    def linear_fc2_forward(self, overlapped_comm_output=None):
+        """
+        Do Linear FC2 forward.
+        This function is used to overlap shared experts with the dispatcher.
+        It is only useful when --moe-shared-expert-overlap is set and may be changed.
+        """
+        assert self.moe_shared_expert_overlap
+        assert self.cached_fc2_input is not None
+        with torch.cuda.stream(self.stream):
+            # [s, b, h]
+            self.cached_fc2_output, _ = self.down_proj(self.cached_fc2_input)
+            self.cached_fc2_input = None
+
+    def pre_forward_comm(self, input):
+        """
+        All Gather for SP before forward.
+        This function is used to overlap shared experts with the dispatcher.
+        It is only useful when --moe-shared-expert-overlap is set and may be changed.
+        """
+        assert self.cached_output is None
+        self.stream.wait_stream(torch.cuda.current_stream())
+        with torch.cuda.stream(self.stream):
+            self.cached_fc1_input = input
+
+    def post_forward_comm(self):
+        """
+        Reduce scatter for SP after forward.
+        This function is used to overlap shared experts with the dispatcher.
+        It is only useful when --moe-shared-expert-overlap is set and may be changed.
+        """
+        assert self.moe_shared_expert_overlap
+        assert self.cached_fc2_output is not None
+        with torch.cuda.stream(self.stream):
+            self.cached_output = tensor_model_parallel_all_reduce(
+                    self.cached_fc2_output
+                )
+            self.cached_fc2_output = None
+
+    def get_output(self):
+        """
+        Gets the module forward output.
+        This function is used to overlap shared experts with the dispatcher.
+        It is only useful when --moe-shared-expert-overlap is set and may be changed.
+        """
+        assert self.moe_shared_expert_overlap
+        assert self.cached_output is not None
+        with torch.cuda.stream(self.stream):
+            output = self.cached_output
+            self.cached_output = None
+        torch.cuda.current_stream().wait_stream(self.stream)
+        return output
+    
+def maybe_move_tensor_to_cpu(tensor, as_numpy=False, record_stream=False):
+    """Move a tensor to CPU if it is on GPU.
+    Args:
+        tensor (torch.Tensor or None): The tensor to move to CPU.
+        as_numpy (bool): Whether to convert the tensor to a numpy array.
+        record_stream (bool): Whether to record the stream of the tensor, to prevent memory leak
+                              when the DtoH data transfer is on a side stream.
+    """
+    if torch.is_tensor(tensor) and tensor.is_cuda:
+        cpu_tensor = tensor.to(torch.device("cpu"), non_blocking=True)
+        if as_numpy:
+            cpu_tensor = cpu_tensor.numpy()
+        if record_stream:
+            tensor.record_stream(torch.cuda.current_stream())
+        tensor = cpu_tensor
+    return tensor
+
+
+def sort_chunks_by_idxs(
+    input: torch.Tensor, split_sizes: torch.Tensor, sorted_idxs: torch.Tensor, fused: bool = False
+):
+    """Split and sort the input tensor based on the split_sizes and sorted indices."""
+    if fused:
+        if not HAVE_TE or fused_sort_chunks_by_index is None:
+            raise ValueError(
+                "fused_sort_chunks_by_index is not available. Please install TE >= 2.1.0."
+            )
+        return fused_sort_chunks_by_index(input, split_sizes, sorted_idxs)
+
+    input = torch.split(input, split_sizes.tolist(), dim=0)
+    output = torch.cat([input[i] for i in sorted_idxs.tolist()], dim=0)
+    return output
+
+def permute(
+    tokens,
+    routing_map,
+    num_out_tokens: Optional[int] = None,
+    fused: bool = False,
+):
+    """Permute the tokens and probs based on the mask.
+    Tokens with the same designated expert will be grouped together.
+    The shape of mask is [tokens, num_experts], it indicates which experts were selected
+    by each token.
+
+    Args:
+        tokens (torch.Tensor): The input token tensor, [num_tokens, hidden].
+        routing_map (torch.Tensor): The sparse token to expert mapping, [num_tokens, num_experts].
+        num_out_tokens (int, optional): The number of output tokens. If None, it's set to
+                                        the number of input tokens.
+        fused (bool, optional): Whether use the fused permute function.
+    """
+    if fused:
+        if not HAVE_TE or fused_permute is None:
+            raise ValueError("fused_permute is not available. Please install TE >= 2.1.0.")
+
+        return fused_permute(tokens, routing_map, num_out_tokens)
+
+    num_tokens, hidden = tokens.shape
+    num_experts = routing_map.shape[1]
+    # mask [num_tokens, num_experts] -> [num_experts, num_tokens]
+    routing_map = routing_map.bool().T.contiguous()
+
+    # Create a dense expert-to-token mapping from the sparse token-to-expert mapping
+    token_indices = (
+        torch.arange(num_tokens, device=routing_map.device).unsqueeze(0).expand(num_experts, -1)
+    )
+    sorted_indices = token_indices.masked_select(routing_map)
+
+    # use the mapping to permute the tokens
+    permuted_input = tokens.index_select(0, sorted_indices)
+
+    return permuted_input, sorted_indices
+
+
+def unpermute(
+    permuted_tokens: torch.Tensor,
+    sorted_indices: torch.Tensor,
+    restore_shape: torch.Size,
+    probs: torch.Tensor = None,
+    routing_map: torch.Tensor = None,
+    fused: bool = False,
+):
+    """
+    Restore the original order of tokens after permutation. If probs are provided, it
+    will also apply them to the tokens before restoring the order.
+
+    This function exploits these features to use ops that support cuda graph.
+
+    Args:
+        permuted_tokens (torch.Tensor): The permuted token tensor.
+        sorted_indices (torch.Tensor): The indices used to sort the tokens.
+        restore_shape (torch.Size): The shape of the unpermuted tensor.
+        probs (torch.Tensor, optional): The unpermuted probs tensor,
+        routing_map (torch.Tensor, optional): Token to expert mapping, shape
+            [num_tokens, num_experts].
+        fused (bool, optional): Whether use the fused unpermute function.
+
+    Returns:
+        torch.Tensor: The tokens restored to their original order.
+    """
+    if fused:
+        if not HAVE_TE or fused_unpermute is None:
+            raise ValueError("fused_unpermute is not available. Please install TE >= 2.1.0.")
+        return fused_unpermute(permuted_tokens, sorted_indices, probs, restore_shape)
+
+    _, hidden = restore_shape
+    input_dtype = permuted_tokens.dtype
+
+    if probs is not None:
+        assert routing_map is not None, "Mask must be provided to permute the probs."
+        permuted_probs = probs.T.contiguous().masked_select(routing_map.T.contiguous())
+        # Here may promote permuted_tokens to higher precision (fp32/fp64) if probs is in
+        # higher precision due to moe_router_dtype being enabled. This can lead to
+        # additional GPU memory usage. Use --moe-permute-fusion flag to avoid this extra memory
+        # allocation.
+        permuted_tokens = permuted_tokens * permuted_probs.unsqueeze(-1)
+
+    # Create an output tensor filled with zeros
+    output_tokens = torch.zeros(
+        restore_shape, dtype=permuted_tokens.dtype, device=permuted_tokens.device
+    )
+    # Scatter add the permuted_input back to the original positions
+    output_tokens.scatter_add_(0, sorted_indices.unsqueeze(1).expand(-1, hidden), permuted_tokens)
+    return output_tokens.to(dtype=input_dtype)
+
+
+def all_to_all(group, input, output_split_sizes, input_split_sizes):
+    world_size = torch.distributed.get_world_size(group=group)
+    # Bypass the function if we are using only 1 GPU.
+    if world_size == 1:
+        return input
+
+    input = input.contiguous()
+    if output_split_sizes is None:
+        # Equal split (all2all)
+        output = torch.empty_like(input)
+    else:
+        # Unequal split (all2all-v)
+        output = input.new_empty(
+            size=[sum(output_split_sizes)] + list(input.size()[1:]),
+            dtype=input.dtype,
+            device=torch.cuda.current_device(),
+        )
+
+    torch.distributed.all_to_all_single(
+        output,
+        input,
+        output_split_sizes=output_split_sizes,
+        input_split_sizes=input_split_sizes,
+        group=group,
+    )
+    return output
+

vllm/model_executor/layers/fused_moe/ep_moe/layer.py

+import os
+import logging
+from typing import Callable, List, Optional, Tuple
+from dataclasses import dataclass
+
+import torch
+import torch.nn.functional as F
+
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.model_executor.custom_op import CustomOp
+from vllm.forward_context import ForwardContext, get_forward_context
+from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
+from vllm.model_executor.layers.quantization.base_config import (
+    QuantizationConfig, QuantizeMethodBase)
+from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.layer import FusedMoEMethodBase, UnquantizedFusedMoEMethod
+from vllm.model_executor.layers.fused_moe.ep_moe.token_dispatcher import MoEAlltoAllTokenDispatcher
+from vllm.model_executor.layers.fused_moe.ep_moe.ep_moe_utlis import EpMoeConfig
+from vllm.utils import direct_register_custom_op
+
+
+logger = init_logger(__name__)
+
+
+@CustomOp.register("unquantized_ep_moe")
+class UnquantizedEPGroupedGemmMethod(UnquantizedFusedMoEMethod):
+    """MoE method without quantization."""
+
+    def __init__(self, moe: FusedMoEConfig):
+        super().__init__(moe)
+        self.topk_indices_dtype = None
+        self.moe = moe
+
+        self.rocm_aiter_moe_enabled = False # is_rocm_aiter_moe_enabled()
+
+    def apply_ep(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        tokens_per_expert: torch.Tensor,
+    ) -> torch.Tensor:
+
+        return self.forward(
+            hidden_states=hidden_states,
+            layer=layer,
+            tokens_per_expert=tokens_per_expert)
+
+    def forward_cuda(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        tokens_per_expert: torch.Tensor,
+    ) -> torch.Tensor:
+
+        # process MoE
+        def custom_forward(layer, hidden_states, tokens_per_expert):
+            tokens_per_expert = tokens_per_expert.cpu().numpy()
+
+            outputs = []
+            start_idx = 0
+            for i, num_tokens in enumerate(tokens_per_expert):
+                end_idx = start_idx + num_tokens
+                if num_tokens == 0:
+                    continue
+                w1 = layer.w13_weight[i]
+                w2 = layer.w2_weight[i]
+                tokens_for_this_expert = hidden_states[start_idx:end_idx]
+
+                gateup_output = torch.matmul(tokens_for_this_expert, w1)
+
+                # Act
+                down_input = torch.zeros(
+                    gateup_output.shape[0],
+                    gateup_output.shape[1] // 2,
+                    device=gateup_output.device,
+                    dtype=hidden_states.dtype
+                )
+                torch.ops._C.silu_and_mul(down_input,
+                                gateup_output.view(-1, w1.shape[1]))
+
+                expert_out = torch.matmul(down_input, w2)
+
+                outputs.append(expert_out)
+                start_idx = end_idx
+
+            if len(outputs) > 0:
+                expert_output = torch.cat(outputs, dim=0)
+            else:
+                assert hidden_states.numel() == 0, f"sorted_tokens: should be empty, but got {hidden_states.shape}"
+                expert_output = hidden_states
+
+            return expert_output
+
+        output = custom_forward(layer, hidden_states, tokens_per_expert)
+
+        return output
+
+    def forward_cpu(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        tokens_per_expert: torch.Tensor,
+        **kwargs,
+    ):
+        raise NotImplementedError
+
+    def forward_hpu(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        tokens_per_expert: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    def forward_tpu(
+        self,
+        layer: torch.nn.Module,
+        hidden_states: torch.Tensor,
+        tokens_per_expert: torch.Tensor,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    if current_platform.is_tpu():
+        forward_native = forward_tpu
+    elif current_platform.is_cpu():
+        forward_native = forward_cpu
+    else:
+        forward_native = forward_cuda
+
+
+class EPMoE(FusedMoE):
+    """
+    dp+ep MoE Expert Parallel Impl
+
+    """
+    def __init__(
+        self,
+        num_experts: int,  # Global number of experts
+        top_k: int,
+        hidden_size: int,
+        intermediate_size: int,
+        params_dtype: Optional[torch.dtype] = None,
+        reduce_results: bool = False,
+        renormalize: bool = True,
+        use_grouped_topk: bool = False,
+        num_expert_group: Optional[int] = None,
+        topk_group: Optional[int] = None,
+        quant_config: Optional[QuantizationConfig] = None,
+        tp_size: Optional[int] = None,
+        ep_size: Optional[int] = None,
+        dp_size: Optional[int] = None,
+        prefix: str = "",
+        custom_routing_function: Optional[Callable] = None,
+        scoring_func: str = "softmax",
+        e_score_correction_bias: Optional[torch.Tensor] = None,
+        apply_router_weight_on_input: bool = False,
+        activation: str = "silu",
+        routed_scaling_factor: Optional[float] = None,
+        enable_eplb: bool = False,
+        num_redundant_experts: int = 0,
+        moe_permute_fusion: bool = True,
+        moe_shared_expert_overlap: bool = False
+    ):
+        super().__init__(num_experts, top_k, hidden_size,
+                         intermediate_size, params_dtype,
+                         reduce_results, renormalize,
+                         use_grouped_topk, num_expert_group,
+                         topk_group, quant_config, tp_size,
+                         ep_size, dp_size, prefix, 
+                         custom_routing_function, scoring_func,
+                         e_score_correction_bias,
+                         apply_router_weight_on_input,
+                         activation, 
+                         routed_scaling_factor=routed_scaling_factor,
+                         enable_eplb=enable_eplb,
+                         num_redundant_experts=num_redundant_experts,
+                         )
+        
+        self.ep_moe_config: EpMoeConfig = EpMoeConfig.make(
+            moe_router_topk=self.top_k,
+            # TODO: support fusion permute
+            moe_permute_fusion=moe_permute_fusion,
+            moe_shared_expert_overlap=moe_shared_expert_overlap,
+            ep_size=self.ep_size,
+            num_moe_experts=self.global_num_experts,
+            routed_scaling_factor=self.routed_scaling_factor,
+            apply_router_weight_on_input=self.apply_router_weight_on_input
+        )
+
+        local_expert_indices_offset = (
+            self.ep_rank * self.local_num_experts
+        )
+        self.local_expert_indices = [
+            local_expert_indices_offset + i for i in range(self.local_num_experts)
+        ]
+
+        self.use_shared_expert = False
+        self.token_dispatcher = MoEAlltoAllTokenDispatcher(
+                self.local_num_experts, self.local_expert_indices, config=self.ep_moe_config
+            )
+        
+        self.shared_expert_overlap = moe_shared_expert_overlap
+        self.shared_experts = None
+
+        self.dpsk_fp16_quick = os.environ.get('DPSK_FP16_QUICK') == '1'
+
+    def set_shared_experts(self, shared_experts: torch.nn.Module):
+        if self.shared_experts is None:
+            self.shared_experts = shared_experts
+            if self.shared_expert_overlap:
+                self.token_dispatcher.set_shared_experts(self.shared_experts)
+
+    def create_quant_method(self, moe, quant_config, prefix):
+        # Note: get_quant_method will look at the layer's local_num_experts
+        # for heuristic purposes, so it must be initialized first.
+        quant_method: Optional[QuantizeMethodBase] = None
+        quant_method = (UnquantizedEPGroupedGemmMethod(moe) if quant_config is None
+                        else quant_config.get_quant_method(self, prefix))
+
+        assert quant_method is not None
+        assert isinstance(quant_method, FusedMoEMethodBase)
+        return quant_method
+    
+    def forward(self, hidden_states: torch.Tensor,
+                router_logits: torch.Tensor):
+        return torch.ops.vllm.ep_moe_forward(hidden_states, router_logits,
+                                              self.layer_name)        
+    
+    def forward_impl(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
+        
+        topk_weights, topk_ids = self.select_experts(
+                hidden_states=hidden_states,
+                router_logits=router_logits,
+                use_grouped_topk=self.use_grouped_topk,
+                top_k=self.top_k,
+                renormalize=self.renormalize,
+                topk_group=self.topk_group,
+                num_expert_group=self.num_expert_group,
+                custom_routing_function=self.custom_routing_function,
+                scoring_func=self.scoring_func,
+                e_score_correction_bias=self.e_score_correction_bias,
+                indices_type=torch.int64,
+                routed_scaling_factor=self.routed_scaling_factor,
+                use_fused_gate=self.use_fused_gate)
+        if not self.ep_moe_config.moe_shared_expert_overlap and self.shared_experts is not None:
+            shared_output = self.shared_experts(hidden_states)
+
+        probs = torch.zeros_like(router_logits, dtype=topk_weights.dtype).scatter(1, topk_ids, topk_weights)
+        routing_map = torch.zeros_like(router_logits).int().scatter(1, topk_ids, 1).bool()
+
+        (dispatched_input, tokens_per_expert) = self.token_dispatcher.token_permutation(
+            hidden_states, probs, routing_map
+        )
+
+        # Matrix multiply.
+        expert_output = self.quant_method.apply_ep(
+            layer=self,
+            hidden_states=dispatched_input,
+            tokens_per_expert=tokens_per_expert
+        )
+
+        final_hidden_states = self.token_dispatcher.token_unpermutation(expert_output)
+        if not self.ep_moe_config.moe_shared_expert_overlap and self.shared_experts is not None:
+            # if shared_expert_overlap is True, the expert calculation happens in
+            # the token_dispatcher to overlap communications and computations
+            shared_output = (
+                    self.maybe_all_reduce_tensor_model_parallel(
+                        shared_output))
+
+            if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
+                final_hidden_states = final_hidden_states + shared_output
+            else:
+                # Fix FP16 overflow
+                # See DeepseekV2DecoderLayer for more details.
+                final_hidden_states = final_hidden_states + shared_output \
+                    * (1. / self.routed_scaling_factor)
+
+        return final_hidden_states
+    
+def ep_moe_forward(hidden_states: torch.Tensor, router_logits: torch.Tensor,
+                layer_name: str) -> torch.Tensor:
+    forward_context: ForwardContext = get_forward_context()
+    self = forward_context.no_compile_layers[layer_name]
+    assert self.quant_method is not None
+
+    return self.forward_impl(hidden_states, router_logits)
+
+
+def ep_moe_forward_fake(hidden_states: torch.Tensor, router_logits: torch.Tensor,
+                     layer_name: str) -> torch.Tensor:
+    return torch.empty_like(hidden_states)
+
+
+direct_register_custom_op(
+    op_name="ep_moe_forward",
+    op_func=ep_moe_forward,
+    mutates_args=["hidden_states"],
+    fake_impl=ep_moe_forward_fake,
+    dispatch_key=current_platform.dispatch_key,
+    tags=(torch.Tag.needs_fixed_stride_order, ),
+)
\ No newline at end of file

vllm/model_executor/layers/fused_moe/ep_moe/token_dispatcher.py

+
+import os
+from abc import ABC, abstractmethod
+from typing import List, Optional, Tuple
+
+import torch
+
+from vllm.distributed.parallel_state import (get_dp_group,
+                                                     get_tp_group,
+                                                     get_ep_group,
+                                                     get_tensor_model_parallel_rank)
+from vllm.model_executor.layers.fused_moe.ep_moe.ep_moe_utlis import (EPSharedExperts, 
+                                                                      maybe_move_tensor_to_cpu,
+                                                                      permute,
+                                                                      sort_chunks_by_idxs,
+                                                                      unpermute,
+                                                                      all_to_all,
+                                                                      EpMoeConfig)
+from vllm.distributed import (tensor_model_parallel_all_gather,
+                              tensor_model_parallel_gather,
+                              expert_parallel_all_gather,
+                              expert_parallel_gather)
+from vllm.platforms import current_platform
+
+
+cuda_dtoh_stream = torch.cuda.Stream()
+cuda_dtoh_sync_event = torch.cuda.Event(enable_timing=False)
+
+class MoETokenDispatcher:
+    """
+    MoE Token Dispatcher
+    """
+
+    def __init__(self, config: EpMoeConfig) -> None:
+        """
+        Initialize the MoE Token Dispatcher.
+        """
+        self.config = config
+
+        self.tp_size = 1
+        self.ep_size = config.ep_size
+
+    @property
+    def ep_group(self):
+        """Get expert model parallel group."""
+        return get_ep_group()
+
+    @property
+    def tp_group(self):
+        """Get expert tensor parallel group."""
+        return get_tp_group()
+
+    @property
+    def tp_rank(self):
+        """Get expert tensor parallel rank."""
+        return 0#get_tensor_model_parallel_rank()
+
+    @property
+    def tp_ep_group(self):
+        """Get expert tensor and model parallel group."""
+        return get_ep_group()
+
+    @abstractmethod
+    def token_permutation(
+        self, tokens: torch.Tensor, probs: torch.Tensor, routing_map: torch.Tensor
+    ):
+        """Dispatch tokens to experts.
+
+        Args:
+            tokens (torch.Tensor): Input tokens.
+            probs (torch.Tensor): The routing probability tensor [num_tokens, num_experts].
+            routing_map (torch.Tensor): Token to expert mapping tensor.
+
+        Returns:
+            torch.Tensor: Tokens tensor.
+        """
+        raise NotImplementedError("Dispatch function not implemented.")
+
+    @abstractmethod
+    def token_unpermutation(self, expert_output: torch.Tensor, bias: torch.Tensor = None):
+        """Restores the expert output to its original ordering.
+
+        Args:
+            expert_output (torch.Tensor): The output tensor from the expert models.
+            bias (torch.Tensor): The bias tensor.
+
+        Returns:
+            (torch.Tensor, torch.Tensor): Unpermuted activation and optional bias.
+        """
+        raise NotImplementedError("Restore function not implemented.")
+
+    def set_shared_experts(self, shared_experts):
+        """Set shared expert to the dispatcher."""
+        assert self.config.moe_shared_expert_overlap
+        self.shared_experts = shared_experts
+
+
+class MoEAlltoAllTokenDispatcher(MoETokenDispatcher):
+    """
+    AlltoAll-based token dispatcher.
+
+    The workflow of AlltoAll token dispatcher is as follows:
+    (1) preprocess(): calculate necessary metadata for communication and permute
+    (2) token_permutation(): permute->A2A(EP)->AG(TP)->sort_chunk(if num_local_experts>1)
+    (3) token_unpermutation(): sort_chunk(if num_local_experts>1)->RS(TP)->A2A(EP)->unpermute
+    """
+
+    def __init__(
+        self, num_local_experts: int, local_expert_indices: List[int], config: EpMoeConfig
+    ) -> None:
+        """
+        Initialize the AlltoAll token dispatcher.
+
+        Args:
+            num_local_experts (int): Number of local experts on the current device.
+            local_expert_indices (List[int]): Indices of local experts on the current device.
+            config (TransformerConfig): Configuration for the transformer model.
+        """
+        super().__init__(config=config)
+        self.num_local_experts = num_local_experts
+        assert config.num_moe_experts is not None
+        self.num_experts = config.num_moe_experts
+        assert self.num_local_experts > 0, "Expected at least one expert"
+        self.local_expert_indices = local_expert_indices
+        assert (
+            len(self.local_expert_indices) == self.num_local_experts
+        ), "Invalid local expert indices"
+        for i in range(len(self.local_expert_indices) - 1):
+            assert (
+                self.local_expert_indices[i] == self.local_expert_indices[i + 1] - 1
+            ), "local_expert_indices must be continous"
+
+        # [ep_size]. Represents the number of tokens sent by the current rank to other
+        # EP ranks.
+        self.input_splits = None
+        # [ep_size]. Represents the number of tokens received by the current rank from
+        # other EP ranks.
+        self.output_splits = None
+        # [tp_size]. Represents the number of tokens received by the current rank from
+        # other TP ranks.
+        #self.output_splits_tp = None
+        self.permute_idx_device = torch.device("cuda") if self.config.moe_permute_fusion else None
+        input_chunk_idxs = torch.arange(
+            self.num_experts * self.tp_size, device=self.permute_idx_device
+        )
+        # [num_local_experts, tp_size * ep_size]. Sort the input chunks by local experts.
+        self.sort_input_by_local_experts = input_chunk_idxs.reshape(
+            -1, self.num_local_experts
+        ).T.ravel()
+        # [tp_size * ep_size, num_local_experts]. Restore the output chunks by local experts.
+        self.restore_output_by_local_experts = input_chunk_idxs.reshape(
+            self.num_local_experts, -1
+        ).T.ravel()
+
+        # A cuda stream synchronization is needed in self.token_permutation() in some cases,
+        # because there are several non-blocking DtoH data transfers called at
+        # `self.cuda_dtoh_point`. The synchronization happens at `self.cuda_sync_point`, which is
+        # decided based on the MoE and parallel settings. Valid points are "before_permutation_1",
+        # "before_ep_alltoall", "before_permutation_2", "before_finish", and "no_sync".
+        self.cuda_sync_point = "no_sync"
+        self.cuda_sync_point_priority = {
+            "before_permutation_1": 0,
+            "before_ep_alltoall": 1,
+            "before_permutation_2": 2,
+            "before_finish": 3,
+            "no_sync": 4,
+        }
+        self.cuda_dtoh_point = "before_permutation_1"
+        #self.cuda_dtoh_stream = torch.cuda.Stream()
+        
+        # Whether to use gather or all-gather to gather the logits.
+        self.use_all_gather = current_platform.use_all_gather()
+
+        self.probs = None
+        self.dpsk_fp16_quick = os.environ.get('DPSK_FP16_QUICK') == '1'
+
+    def preprocess(self, routing_map: torch.Tensor) -> torch.Tensor:
+        """
+        Preprocess token routing map for AlltoAll communication and token permutation.
+
+        This method computes the number of tokens assigned to each expert based on the routing_map.
+        It also initializes the necessary data structures for AlltoAll communication, such as input
+        and output splits, and the mapping between global tokens and local experts. This method
+        should not call any DtoH data copying due to performance consideration. The necessary DtoH
+        copies are made on the `self.cuda_dtoh_stream` at `self.cuda_dtoh_point`.
+
+        Args:
+            routing_map (torch.Tensor): The mapping of tokens to experts, with shape
+                [num_tokens, num_experts].
+
+        Returns:
+            torch.Tensor: Tensor containing the number of tokens assigned to local expert.
+        """
+        # [num_experts], number of tokens assigned to each expert from the current rank's input.
+        num_local_tokens_per_expert = routing_map.sum(dim=0).long()
+
+        self.num_out_tokens = routing_map.size(0) * self.config.moe_router_topk
+
+        if self.ep_size > 1 or self.tp_size > 1:
+            # ===================================================
+            # Calculate input_splits, output_splits for alltoall/allgather in variable size.
+            # ===================================================
+            # [ep_size]. Represents the number of tokens sent by the current rank to other
+            # EP ranks.
+            self.input_splits = num_local_tokens_per_expert.reshape(
+                self.ep_size, self.num_local_experts
+            ).sum(axis=1)
+            # Gather the global distribution of tokens across ranks.
+            # num_global_tokens_per_expert represents the number of tokens sent to each
+            # expert by all ranks.
+            # [tp_size, ep_size, num_experts]
+            if self.use_all_gather:
+                # Gather is not supported for some devices such as TPUs.
+                # Use all-gather instead.
+                num_global_tokens_per_expert = expert_parallel_all_gather(num_local_tokens_per_expert) \
+                    .reshape(self.ep_size, self.tp_size, self.num_experts) \
+                    .transpose(0, 1)
+            else:
+                # None may be returned for rank > 0
+                num_global_tokens_per_expert = expert_parallel_gather(num_local_tokens_per_expert) \
+                    .reshape(self.ep_size, self.tp_size, self.num_experts) \
+                    .transpose(0, 1)
+
+            # [tp_size, ep_size, num_experts] -> [tp_size, ep_size, num_local_experts]
+            num_global_tokens_per_local_expert = num_global_tokens_per_expert[
+                :, :, self.local_expert_indices[0] : self.local_expert_indices[-1] + 1
+            ].contiguous()
+            # [tp_size, ep_size, num_local_experts] -> [tp_size, ep_size]
+            num_global_tokens_per_rank = num_global_tokens_per_local_expert.sum(axis=2)
+            # [tp_size, ep_size] -> [ep_size]
+            # self.output_splits represents the number of tokens received by the current rank
+            # from other EP rank.
+            self.output_splits = num_global_tokens_per_rank[self.tp_rank]
+            # [tp_size, ep_size] -> [tp_size]
+            # self.output_splits_tp represents the number of tokens received by the current
+            # rank from other TP rank.
+            #self.output_splits_tp = num_global_tokens_per_rank.sum(axis=1)
+            # [tp_size, ep_size, num_local_experts] -> [num_local_experts]
+            num_tokens_per_local_expert = num_global_tokens_per_local_expert.sum(dim=(0, 1))
+
+            # A synchronization is needed before expert parallel AlltoAll communication
+            # to get the `input_splits` and `output_splits` CPU values.
+            self._maybe_update_cuda_sync_point("before_ep_alltoall")
+        else:
+            num_global_tokens_per_local_expert = num_local_tokens_per_expert.reshape(
+                self.num_experts
+            )
+            num_tokens_per_local_expert = num_local_tokens_per_expert
+
+            # A synchronization is needed before the returns
+            # to get the `num_tokens_per_local_expert` CPU value.
+            self._maybe_update_cuda_sync_point("before_finish")
+
+        if self.num_local_experts > 1:
+            # [tp_size * ep_size, num_local_experts]. Represents the number of tokens sent
+            # to each local expert by all ranks.
+            self.num_global_tokens_per_local_expert = num_global_tokens_per_local_expert.view(
+                -1, self.num_local_experts
+            )
+            if not self.config.moe_permute_fusion:
+                # A synchronization is needed before permutation 2
+                # to get the `num_global_tokens_per_local_expert` CPU value.
+                self._maybe_update_cuda_sync_point("before_permutation_2")
+
+        assert (
+            self.cuda_sync_point_priority[self.cuda_dtoh_point]
+            <= self.cuda_sync_point_priority[self.cuda_sync_point]
+        ), "cuda_sync_point must be after cuda_dtoh_point."
+        return num_tokens_per_local_expert
+
+    def token_permutation(
+        self, hidden_states: torch.Tensor,
+        probs: torch.Tensor,
+        routing_map: torch.Tensor,
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Dispatch tokens to local experts using AlltoAll communication.
+
+        This method performs the following steps:
+        1. Preprocess the routing map to get metadata for communication and permutation.
+        2. Permute input tokens for AlltoAll communication.
+        3. Perform expert parallel AlltoAll communication.
+        4. Sort tokens by local expert (if multiple local experts exist).
+
+        Args:
+            hidden_states (torch.Tensor): Input token embeddings.
+            probs (torch.Tensor): The probabilities of token to experts assignment.
+            routing_map (torch.Tensor): The mapping of token to experts assignment.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]:
+                - Permuted token embeddings for local experts.
+                - Number of tokens per expert.
+        """
+        # Preprocess: Get the metadata for communication, permutation and computation operations.
+        self.hidden_shape = hidden_states.shape
+        if self.config.apply_router_weight_on_input:
+            self.probs = probs
+        self.routing_map = routing_map
+        assert probs.dim() == 2, "Expected 2D tensor for probs"
+        assert routing_map.dim() == 2, "Expected 2D tensor for token2expert mask"
+        assert routing_map.dtype == torch.bool, "Expected bool tensor for mask"
+        hidden_states = hidden_states.view(-1, self.hidden_shape[-1])
+        tokens_per_expert = self.preprocess(self.routing_map)
+
+        if self.config.moe_shared_expert_overlap and self.shared_experts is not None:
+            self.shared_experts.pre_forward_comm(hidden_states.view(self.hidden_shape))
+
+        # Permutation 1: input to AlltoAll input
+        tokens_per_expert = self._maybe_dtoh_and_synchronize(
+            "before_permutation_1", tokens_per_expert
+        )
+
+        self.hidden_shape_before_permute = hidden_states.shape
+        permutated_local_input_tokens, self.reversed_local_input_permutation_mapping = permute(
+            hidden_states,
+            routing_map,
+            num_out_tokens=self.num_out_tokens,
+            fused=self.config.moe_permute_fusion
+        )
+
+        # Perform expert parallel AlltoAll communication
+        # tokens_per_expert = self._maybe_dtoh_and_synchronize(
+        #     "before_ep_alltoall", tokens_per_expert
+        # )
+
+        ###test##############
+        #cuda_dtoh_stream.synchronize()
+        cuda_dtoh_sync_event.synchronize()
+        ###test##############
+
+        global_input_tokens = all_to_all(
+            self.ep_group.device_group, permutated_local_input_tokens, self.output_splits, self.input_splits
+        )
+
+        if self.config.moe_shared_expert_overlap and self.shared_experts is not None:
+            self.shared_experts.linear_fc1_forward_and_act(global_input_tokens)
+
+        # Permutation 2: Sort tokens by local expert.
+        # tokens_per_expert = self._maybe_dtoh_and_synchronize(
+        #     "before_permutation_2", tokens_per_expert
+        # )
+        if self.num_local_experts > 1:
+            global_input_tokens = sort_chunks_by_idxs(
+                    global_input_tokens,
+                    self.num_global_tokens_per_local_expert.ravel(),
+                    self.sort_input_by_local_experts,
+                    fused=self.config.moe_permute_fusion,
+                )
+
+        #tokens_per_expert = self._maybe_dtoh_and_synchronize("before_finish", tokens_per_expert)
+
+        return global_input_tokens, tokens_per_expert
+
+    def token_unpermutation(
+        self, hidden_states: torch.Tensor,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor]]:
+        """
+        Reverse the token permutation to restore the original order.
+
+        This method performs the following steps:
+        1. Unsort tokens by local expert (if multiple local experts exist).
+        2. Perform expert parallel AlltoAll communication to restore the original order.
+        3. Unpermute tokens to restore the original order.
+
+        Args:
+            hidden_states (torch.Tensor): Output from local experts.
+            bias (torch.Tensor, optional): Bias tensor (not supported).
+
+        Returns:
+            Tuple[torch.Tensor, Optional[torch.Tensor]]:
+                - Unpermuted token embeddings in the original order.
+                - None (bias is not supported).
+        """
+        # Unpermutation 2: Unsort tokens by local expert.
+        if self.num_local_experts > 1:
+            hidden_states = sort_chunks_by_idxs(
+                hidden_states,
+                self.num_global_tokens_per_local_expert.T.ravel(),
+                self.restore_output_by_local_experts,
+                fused=self.config.moe_permute_fusion,
+            )
+
+        # Perform expert parallel AlltoAll communication
+        # hidden_states: [SEQL, H] -> [SEQL, H/TP]
+        permutated_local_input_tokens = all_to_all(
+            self.ep_group.device_group, hidden_states, self.input_splits, self.output_splits
+        )
+
+        if self.config.moe_shared_expert_overlap and self.shared_experts is not None:
+            self.shared_experts.linear_fc2_forward(permutated_local_input_tokens)
+            self.shared_experts.post_forward_comm()
+
+        # Unpermutation 1: AlltoAll output to output
+        output = unpermute(
+            permutated_local_input_tokens,
+            self.reversed_local_input_permutation_mapping,
+            restore_shape=self.hidden_shape_before_permute,
+            probs=self.probs,
+            routing_map=self.routing_map,
+            fused=self.config.moe_permute_fusion,
+        )
+
+        # Reshape the output tensor
+        output = output.view(self.hidden_shape)
+
+        # Add shared experts output
+        if self.config.moe_shared_expert_overlap and self.shared_experts is not None:
+            shared_output = self.shared_experts.get_output()
+
+            if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
+                output = output + shared_output
+            else:
+                # Fix FP16 overflow
+                # See DeepseekV2DecoderLayer for more details.
+                output = output + shared_output \
+                    * (1. / self.config.routed_scaling_factor)
+        return output
+
+    def _maybe_update_cuda_sync_point(self, point: str):
+        """
+        Update the CUDA sync point if the priority of the new point is higher than the current
+        sync point, which means the new point is reached earlier than the current sync point.
+        """
+        if (
+            self.cuda_sync_point_priority[point]
+            < self.cuda_sync_point_priority[self.cuda_sync_point]
+        ):
+            self.cuda_sync_point = point
+
+    def _maybe_dtoh_and_synchronize(
+        self, point: str, tokens_per_expert: torch.Tensor = None
+    ) -> torch.Tensor:
+        """
+        Move all possible GPU tensors to CPU and make a synchronization at the expected point.
+        """
+        if point == self.cuda_dtoh_point:
+            # Move all possible GPU tensors to CPU at self.cuda_dtoh_point.
+            on_side_stream = torch.cuda.current_stream() != cuda_dtoh_stream
+            if on_side_stream:
+                cuda_dtoh_stream.wait_stream(torch.cuda.current_stream())
+            with torch.cuda.stream(cuda_dtoh_stream):
+                # TODO: use MemcpyBatchAsync instead.
+                # tokens_per_expert = maybe_move_tensor_to_cpu(
+                #     tokens_per_expert, record_stream=on_side_stream
+                # )
+                self.input_splits = maybe_move_tensor_to_cpu(
+                    self.input_splits, as_numpy=True, record_stream=on_side_stream
+                )
+                self.output_splits = maybe_move_tensor_to_cpu(
+                    self.output_splits, as_numpy=True, record_stream=on_side_stream
+                )
+                # self.output_splits_tp = maybe_move_tensor_to_cpu(
+                #     self.output_splits_tp, as_numpy=True, record_stream=on_side_stream
+                # )
+                self.num_out_tokens = maybe_move_tensor_to_cpu(
+                    self.num_out_tokens, record_stream=on_side_stream
+                )
+                if self.num_local_experts > 1 and not self.config.moe_permute_fusion:
+                    self.num_global_tokens_per_local_expert = maybe_move_tensor_to_cpu(
+                        self.num_global_tokens_per_local_expert, record_stream=on_side_stream
+                    )
+
+                cuda_dtoh_sync_event.record()
+
+        # if point == self.cuda_sync_point:
+        #     # Synchronize with the dtoh stream at self.cuda_sync_point.
+        #     cuda_dtoh_stream.synchronize()
+
+        return tokens_per_expert
\ No newline at end of file

vllm/model_executor/layers/fused_moe/layer.py

@@ -773,11 +773,7 @@ class FusedMoE(torch.nn.Module):
         self.moe_config = moe
         self.quant_config = quant_config
 
-        # Note: get_quant_method will look at the layer's local_num_experts
-        # for heuristic purposes, so it must be initialized first.
-        quant_method: Optional[QuantizeMethodBase] = None
-        quant_method = (UnquantizedFusedMoEMethod(moe) if quant_config is None
-                        else quant_config.get_quant_method(self, prefix))
+        quant_method = self.create_quant_method(moe, quant_config, prefix)
 
         assert quant_method is not None
         assert isinstance(quant_method, FusedMoEMethodBase)
@@ -852,6 +848,17 @@ class FusedMoE(torch.nn.Module):
                 dtype=moe.in_dtype,
                 device=torch.cuda.current_device())
 
+    def create_quant_method(self, moe, quant_config, prefix):
+        # Note: get_quant_method will look at the layer's local_num_experts
+        # for heuristic purposes, so it must be initialized first.
+        quant_method: Optional[QuantizeMethodBase] = None
+        quant_method = (UnquantizedFusedMoEMethod(moe) if quant_config is None
+                        else quant_config.get_quant_method(self, prefix))
+
+        assert quant_method is not None
+        assert isinstance(quant_method, FusedMoEMethodBase)
+        return quant_method
+
     @property
     def tp_size(self):
         return self.moe_parallel_config.tp_size

vllm/model_executor/layers/quantization/slimquant_w4a8.py

@@ -21,8 +21,14 @@ from vllm.utils import W8a8GetCacheJSON
 
 import os
 from vllm import _custom_ops as ops
+
 from vllm import envs
 
+try:
+    from lmslim.layers.fused_moe.fuse_moe_w4a8 import fused_experts_impl_w4a8_ep
+except Exception:
+    print("INFO: Please install lmslim if you want to infer the quantitative model of moe.\n")
+
 W8A8_TRITONJSON=W8a8GetCacheJSON()
 
 def baseline_scaled_mm(a: torch.Tensor,
@@ -337,65 +343,79 @@ class SlimQuantW4A8Int8MoEMethod:
             layer.w2_weight_scale.data, requires_grad=False
         )
 
-    def apply(
-        self,
-        layer: torch.nn.Module,
-        x: torch.Tensor,
-        router_logits: torch.Tensor,
-        top_k: int,
-        renormalize: bool,
-        use_grouped_topk: bool = False,
-        topk_group: Optional[int] = None,
-        num_expert_group: Optional[int] = None,
-        global_num_experts: int = -1,
-        expert_map: Optional[torch.Tensor] = None,
-        custom_routing_function: Optional[Callable] = None,
-        scoring_func: str = "softmax",
-        e_score_correction_bias: Optional[torch.Tensor] = None,
-        apply_router_weight_on_input: bool = False,
-        activation: str = "silu",
-        enable_eplb: bool = False,
-        use_nn_moe: Optional[bool] = False,
-        routed_scaling_factor: Optional[float] = None,
-        use_fused_gate: Optional[bool] = False,
-        **_  
-    ) -> torch.Tensor:
-        from vllm.model_executor.layers.fused_moe import fused_experts
-        if enable_eplb:
-            raise NotImplementedError(
-                "EPLB not supported for `SlimQuantW4A8Int8MoEMethod` yet.")   
-        # Expert selection
-        topk_weights, topk_ids = FusedMoE.select_experts(
-            hidden_states=x,
-            router_logits=router_logits,
-            use_grouped_topk=use_grouped_topk,
-            top_k=top_k,
-            renormalize=renormalize,
-            topk_group=topk_group,
-            num_expert_group=num_expert_group,
-            custom_routing_function=custom_routing_function,
-            scoring_func=scoring_func,
-            e_score_correction_bias=e_score_correction_bias,
-            routed_scaling_factor=routed_scaling_factor,
-            use_fused_gate=use_fused_gate
-        )
+    def apply_ep(  #dp+ep
+                self,
+                layer: torch.nn.Module,
+                hidden_states: torch.Tensor,
+                tokens_per_expert: torch.Tensor,
+        ) -> torch.Tensor:
+        return fused_experts_impl_w4a8_ep(hidden_states,
+                                          layer.w13_weight,
+                                          layer.w2_weight,
+                                          layer.w13_weight_scale,
+                                          layer.w2_weight_scale,
+                                          tokens_per_expert)
+
+
+    def apply(# tp
+            self,
+            layer: torch.nn.Module,
+            x: torch.Tensor,
+            router_logits: torch.Tensor,
+            top_k: int,
+            renormalize: bool,
+            use_grouped_topk: bool = False,
+            topk_group: Optional[int] = None,
+            num_expert_group: Optional[int] = None,
+            global_num_experts: int = -1,
+            expert_map: Optional[torch.Tensor] = None,
+            custom_routing_function: Optional[Callable] = None,
+            scoring_func: str = "softmax",
+            e_score_correction_bias: Optional[torch.Tensor] = None,
+            apply_router_weight_on_input: bool = False,
+            activation: str = "silu",
+            enable_eplb: bool = False,
+            use_nn_moe: Optional[bool] = False,
+            routed_scaling_factor: Optional[float] = None,
+            use_fused_gate: Optional[bool] = False,
+            **_
+        ) -> torch.Tensor:
+            from vllm.model_executor.layers.fused_moe import fused_experts
+            if enable_eplb:
+                raise NotImplementedError(
+                    "EPLB not supported for `SlimQuantW4A8Int8MoEMethod` yet.")
+            # Expert selection
+            topk_weights, topk_ids = FusedMoE.select_experts(
+                hidden_states=x,
+                router_logits=router_logits,
+                use_grouped_topk=use_grouped_topk,
+                top_k=top_k,
+                renormalize=renormalize,
+                topk_group=topk_group,
+                num_expert_group=num_expert_group,
+                custom_routing_function=custom_routing_function,
+                scoring_func=scoring_func,
+                e_score_correction_bias=e_score_correction_bias,
+                routed_scaling_factor=routed_scaling_factor,
+                use_fused_gate=use_fused_gate
+            )
 
-        return fused_experts(
-            x,
-            layer.w13_weight,
-            layer.w2_weight,
-            topk_weights=topk_weights,
-            topk_ids=topk_ids,
-            inplace=True,
-            use_int4_w4a8=True,
-            per_channel_quant=True,
-            activation=activation,
-            expert_map=expert_map,
-            apply_router_weight_on_input=apply_router_weight_on_input,
-            global_num_experts=global_num_experts,
-            w1_scale=(layer.w13_weight_scale),
-            w2_scale=(layer.w2_weight_scale),
-            a1_scale=layer.w13_input_scale,
-            a2_scale=layer.w2_input_scale,
-            use_nn_moe=use_nn_moe,
-        )
+            return fused_experts(
+                x,
+                layer.w13_weight,
+                layer.w2_weight,
+                topk_weights=topk_weights,
+                topk_ids=topk_ids,
+                inplace=True,
+                use_int4_w4a8=True,
+                per_channel_quant=True,
+                activation=activation,
+                expert_map=expert_map,
+                apply_router_weight_on_input=apply_router_weight_on_input,
+                global_num_experts=global_num_experts,
+                w1_scale=(layer.w13_weight_scale),
+                w2_scale=(layer.w2_weight_scale),
+                a1_scale=layer.w13_input_scale,
+                a2_scale=layer.w2_input_scale,
+                use_nn_moe=use_nn_moe,
+            )

vllm/model_executor/models/deepseek_mtp.py

@@ -11,6 +11,7 @@ import torch
 import torch.nn as nn
 from transformers import PretrainedConfig
 
+import vllm.envs as envs
 from vllm.config import CacheConfig, ModelConfig, VllmConfig
 from vllm.model_executor.layers.fused_moe import FusedMoE
 from vllm.model_executor.layers.layernorm import RMSNorm
@@ -24,6 +25,7 @@ from vllm.sequence import IntermediateTensors
 from vllm.compilation.decorators import support_torch_compile
 from .deepseek_v2 import (DeepseekV2DecoderLayer,
                           get_spec_layer_idx_from_weight_name)
+from vllm.distributed import get_dp_group
 from .interfaces import SupportsPP
 from .utils import maybe_prefix
 from vllm import _custom_ops as ops
@@ -174,6 +176,10 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                                                      prefix, "model"))
         self.use_llama_nn = os.environ.get('LLAMA_NN') == '1'
 
+        parallel_config = vllm_config.parallel_config
+        dp_size = get_dp_group().world_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+
 
     def forward(
         self,
@@ -205,6 +211,10 @@ class DeepSeekMTP(nn.Module, SupportsPP):
             ("gate_up_proj", "up_proj", 1),
         ]
 
+        if self.use_all2all_ep:
+            ep_moe_shared_experts_keys = "mlp.shared_experts"
+            ep_moe_shared_experts_mapping = {ep_moe_shared_experts_keys:"mlp.experts.shared_experts"}
+
         expert_params_mapping = FusedMoE.make_expert_params_mapping(
             ckpt_gate_proj_name="gate_proj",
             ckpt_down_proj_name="down_proj",
@@ -233,6 +243,9 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                 if (("mlp.experts." in name) and name not in params_dict):
                     continue
                 name = name.replace(weight_name, param_name)
+
+                if self.use_all2all_ep:
+                    name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
@@ -248,6 +261,9 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                         continue
                     name = name.replace(weight_name, param_name)
 
+                    if self.use_all2all_ep:
+                        name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
+
                     param = params_dict[name]
                     weight_loader = param.weight_loader
                     weight_loader(param,
@@ -257,6 +273,8 @@ class DeepSeekMTP(nn.Module, SupportsPP):
                                   expert_id=expert_id)
                     break
                 else:
+                    if self.use_all2all_ep:
+                        name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue

vllm/model_executor/models/deepseek_v2.py

@@ -39,10 +39,12 @@ from vllm.attention import Attention
 from vllm.compilation.decorators import support_torch_compile
 from vllm.config import (CacheConfig, ModelConfig, VllmConfig,
                          get_current_vllm_config)
-from vllm.distributed import (get_ep_group, get_pp_group,
+from vllm.distributed import (get_ep_group, get_pp_group, get_dp_group,
                               get_tensor_model_parallel_world_size)
 from vllm.model_executor.layers.activation import SiluAndMul
 from vllm.model_executor.layers.fused_moe import FusedMoE
+from vllm.model_executor.layers.fused_moe.ep_moe.layer import EPMoE
+from vllm.model_executor.layers.fused_moe.ep_moe.ep_moe_utlis import EPSharedExperts
 from vllm.model_executor.layers.layernorm import RMSNorm
 from vllm.model_executor.layers.linear import (ColumnParallelLinear,
                                                MergedColumnParallelLinear,
@@ -165,8 +167,12 @@ class DeepseekV2MoE(nn.Module):
                                       self.n_local_physical_experts)
         self.physical_expert_end = (self.physical_expert_start +
                                     self.n_local_physical_experts)
-
-        self.experts = FusedMoE(
+        
+        dp_size = get_dp_group().world_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+        
+        moe_cls = FusedMoE if not self.use_all2all_ep else EPMoE
+        self.experts = moe_cls(
             num_experts=config.n_routed_experts,
             top_k=config.num_experts_per_tok,
             hidden_size=config.hidden_size,
@@ -187,7 +193,8 @@ class DeepseekV2MoE(nn.Module):
         if config.n_shared_experts is not None:
             intermediate_size = (config.moe_intermediate_size *
                                  config.n_shared_experts)
-            self.shared_experts = DeepseekV2MLP(
+            shared_expert_cls = DeepseekV2MLP if not self.use_all2all_ep else EPSharedExperts
+            self.shared_experts = shared_expert_cls(
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
                 hidden_act=config.hidden_act,
@@ -196,6 +203,9 @@ class DeepseekV2MoE(nn.Module):
                 ),
                 prefix=f"{prefix}.shared_experts",
             )
+            if self.use_all2all_ep:
+                self.experts.set_shared_experts(self.shared_experts)
+
         from vllm.two_batch_overlap.two_batch_overlap import tbo_all_reduce
         self.tbo_all_reduce = tbo_all_reduce
 
@@ -205,44 +215,52 @@ class DeepseekV2MoE(nn.Module):
                 ) -> torch.Tensor:
         num_tokens, hidden_dim = hidden_states.shape
         hidden_states = hidden_states.view(-1, hidden_dim)
-        if self.n_shared_experts is not None:
+        
+        if not self.use_all2all_ep:
             if envs.USE_FUSED_RMS_QUANT:
                 shared_output, new_resi = self.shared_experts(hidden_states, rms_weight, residual, update_hd=True)
             else:
                 shared_output = self.shared_experts(hidden_states)
+
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
-        if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
-            final_hidden_states = self.experts(
-                hidden_states=hidden_states,
-                router_logits=router_logits) * self.routed_scaling_factor
-        else:
-            # Fix FP16 overflow
-            # See DeepseekV2DecoderLayer for more details.
-            final_hidden_states = self.experts(hidden_states=hidden_states,
-                                               router_logits=router_logits)
-        
-        if shared_output is not None:
+        if not self.use_all2all_ep:
             if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
-                final_hidden_states = final_hidden_states + shared_output
+                final_hidden_states = self.experts(
+                    hidden_states=hidden_states,
+                    router_logits=router_logits) * self.routed_scaling_factor
             else:
                 # Fix FP16 overflow
                 # See DeepseekV2DecoderLayer for more details.
-                final_hidden_states = final_hidden_states + shared_output \
-                    * (1. / self.routed_scaling_factor)
+                final_hidden_states = self.experts(hidden_states=hidden_states,
+                                                router_logits=router_logits)
+        else:        
+            final_hidden_states = self.experts(hidden_states=hidden_states,
+                                                router_logits=router_logits)
+        
+        if not self.use_all2all_ep:
+            if shared_output is not None:
+                if hidden_states.dtype != torch.float16 or self.dpsk_fp16_quick:
+                    final_hidden_states = final_hidden_states + shared_output
+                else:
+                    # Fix FP16 overflow
+                    # See DeepseekV2DecoderLayer for more details.
+                    final_hidden_states = final_hidden_states + shared_output \
+                        * (1. / self.routed_scaling_factor)
+
+            if self.tp_size > 1:
+                if envs.VLLM_ENABLE_TBO:
+                    final_hidden_states = self.tbo_all_reduce(final_hidden_states)
+                else:
+                    final_hidden_states = (
+                        self.experts.maybe_all_reduce_tensor_model_parallel(
+                            final_hidden_states))
 
-        if self.tp_size > 1:
-            if envs.VLLM_ENABLE_TBO:
-                final_hidden_states = self.tbo_all_reduce(final_hidden_states)
+            if envs.USE_FUSED_RMS_QUANT:
+                return final_hidden_states.view(num_tokens, hidden_dim), new_resi
             else:
-                final_hidden_states = (
-                    self.experts.maybe_all_reduce_tensor_model_parallel(
-                        final_hidden_states))
-        if envs.USE_FUSED_RMS_QUANT:
-            return final_hidden_states.view(num_tokens, hidden_dim), new_resi
-        else:
-            return final_hidden_states.view(num_tokens, hidden_dim)
+                return final_hidden_states.view(num_tokens, hidden_dim)
 
 
 def yarn_get_mscale(scale: float = 1, mscale: float = 1) -> float:
@@ -901,6 +919,10 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
         self.tritonsingleton.topk = config.num_experts_per_tok
         self.tritonsingleton.quant_method=self.quant_method 
 
+        parallel_config = vllm_config.parallel_config
+        dp_size = get_dp_group().world_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+
     def set_eplb_state(
         self,
         expert_load_view: torch.Tensor,
@@ -982,6 +1004,10 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
             ("gate_up_proj", "up_proj", 1),
         ]
 
+        if self.use_all2all_ep:
+            ep_moe_shared_experts_keys = "mlp.shared_experts"
+            ep_moe_shared_experts_mapping = {ep_moe_shared_experts_keys:"mlp.experts.shared_experts"}
+
         # Params for weights, fp8 weight scales, fp8 activation scales
         # (param_name, weight_name, expert_id, shard_id)
         expert_params_mapping = FusedMoE.make_expert_params_mapping(
@@ -1014,6 +1040,10 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                 if (("mlp.experts." in name) and name not in params_dict):
                     continue
                 name = name.replace(weight_name, param_name)
+
+                if self.use_all2all_ep:
+                    name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
+
                 # Skip loading extra bias for GPTQ models.
                 if name.endswith(".bias") and name not in params_dict:
                     continue
@@ -1040,6 +1070,9 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                     # Instead, create a new variable
                     name_mapped = name.replace(weight_name, param_name)
 
+                    if self.use_all2all_ep:
+                        name_mapped = name_mapped.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
+
                     if is_pp_missing_parameter(name_mapped, self):
                         continue
 
@@ -1064,7 +1097,9 @@ class DeepseekV2ForCausalLM(nn.Module, SupportsPP, MixtureOfExperts):
                         # However it's not mapped locally to this rank
                         # So we simply skip it
                         continue
-
+                    
+                    if self.use_all2all_ep:
+                        name = name.replace(ep_moe_shared_experts_keys, ep_moe_shared_experts_mapping[ep_moe_shared_experts_keys])
                     # Skip loading extra bias for GPTQ models.
                     if name.endswith(".bias") and name not in params_dict:
                         continue

vllm/v1/engine/utils.py

@@ -244,11 +244,18 @@ class CoreEngineActorManager:
         local_engine_count = \
             vllm_config.parallel_config.data_parallel_size_local
 
-        nodes = sorted(list_nodes(),
-                       key=lambda node: node.node_ip != dp_master_ip)
-        assert nodes[0].node_ip == dp_master_ip, (
+        # nodes = sorted(list_nodes(),
+        #                key=lambda node: node.node_ip != dp_master_ip)
+        # assert nodes[0].node_ip == dp_master_ip, (
+        #     "The first node must be the head node")
+        # assert len(nodes) == 1 or nodes[1].node_ip != dp_master_ip, (
+        #     "There can only be one head node")
+        nodes = ray.nodes()
+        nodes = sorted(nodes,
+                       key=lambda node: node["NodeManagerAddress"] != dp_master_ip)
+        assert nodes[0]["NodeManagerAddress"] == dp_master_ip, (
             "The first node must be the head node")
-        assert len(nodes) == 1 or nodes[1].node_ip != dp_master_ip, (
+        assert len(nodes) == 1 or nodes[1]["NodeManagerAddress"] != dp_master_ip, (
             "There can only be one head node")
 
         available_resources = available_resources_per_node()
@@ -257,8 +264,11 @@ class CoreEngineActorManager:
         local_dp_ranks: list[int] = []
 
         for node in nodes:
-            node_ip = node.node_ip
-            node_resources = available_resources[node.node_id]
+            # node_ip = node.node_ip
+            # node_resources = available_resources[node.node_id]
+            node_ip = node["NodeManagerAddress"]
+            node_resources = available_resources[node["NodeID"]]
+
             # For now, each DP rank can only be assigned to one node
             # TODO(rui): support allocating a single DP rank
             # to multiple nodes

vllm/v1/worker/gpu_model_runner.py

 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+import os
 import copy
 import gc
 import time
@@ -319,6 +320,9 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # from the KV cache of `shared_kv_cache_layers[layer_name]`.
         self.shared_kv_cache_layers: dict[str, str] = {}
 
+        dp_size = self.vllm_config.parallel_config.data_parallel_size
+        self.use_all2all_ep = envs.VLLM_USE_ALLTOALL_EP and dp_size > 1 and parallel_config.enable_expert_parallel
+
     def _may_reorder_batch(self, scheduler_output: "SchedulerOutput") -> None:
         """
         Update the order of requests in the batch based on the attention
@@ -1231,7 +1235,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         # TODO(tms) : There are many cases where padding is enabled for
         # prefills, causing unnecessary and excessive padding of activations.
 
-        if dp_size == 1 or self.vllm_config.model_config.enforce_eager:
+        if dp_size == 1 or self.vllm_config.model_config.enforce_eager or self.use_all2all_ep:
             # Early exit.
             return 0, None
 
@@ -2005,7 +2009,10 @@ class GPUModelRunner(LoRAModelRunnerMixin):
         num_reqs = min(num_tokens, max_num_reqs)
         min_tokens_per_req = num_tokens // num_reqs
 
-        if not is_profile and self.speculative_config is not None and self.speculative_config.num_lookahead_slots > 0:
+        if not is_profile and self.speculative_config is not None \
+            and self.speculative_config.num_lookahead_slots > 0 \
+            and num_tokens >= 1 + self.speculative_config.num_lookahead_slots:
+
             min_tokens_per_req = (1 + self.speculative_config.num_lookahead_slots)
             num_reqs = num_tokens // min_tokens_per_req
         num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
@@ -2054,6 +2061,7 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 input_ids = None
                 inputs_embeds = self.inputs_embeds[:num_tokens]
             else:
+                self.input_ids[:num_tokens] = torch.randint(0, self.model_config.get_vocab_size(), (num_tokens,), dtype=torch.int32)
                 input_ids = self.input_ids[:num_tokens]
                 inputs_embeds = None
             if self.uses_mrope: