临时上传大ep代码

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/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
+
+
+@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
+
+    @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) -> "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)
+
+
+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 = torch.cuda.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,
+    drop_and_pad: 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.
+
+    When drop_and_pad=True, in routing_map, the number of non-zeros in each column equals to
+    expert capacity. This function exploits this feature to use ops that support cuda graph.
+
+    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.
+        drop_and_pad (bool, optional): Whether or not the token dispatcher uses token-drop
+                                       and pads the number of tokens to the expert capacity.
+                                       If set to true, routing_map has a fixed number of non-zeros
+                                       in each column.
+    """
+    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]
+    if drop_and_pad and not (num_out_tokens is None):
+        capacity = num_out_tokens // num_experts
+        assert not routing_map.requires_grad
+        # mask [num_tokens, num_experts] -> [num_experts, num_tokens]
+        routing_map = routing_map.to(dtype=torch.int8).T.contiguous()
+        # use argsort to put indices of all non-zeros in the beginning of list
+        # and keep the first `capacity` number of indices
+        sorted_indices = routing_map.argsort(dim=-1, descending=True, stable=True)[
+            :, :capacity
+        ].contiguous()
+        # flatten from [num_experts, capacity] to 1D
+        sorted_indices = sorted_indices.view(-1)
+    else:
+        # 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,
+    drop_and_pad: 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.
+        drop_and_pad (bool, optional): Whether or not the token dispatcher uses token-drop
+                                       and pads the number of tokens to the expert capacity.
+
+    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."
+        if drop_and_pad:
+            num_experts = routing_map.size(1)
+            num_permuted_tokens = sorted_indices.size(0)
+            capacity = num_permuted_tokens // num_experts
+            num_unpermuted_tokens = probs.size(0)
+
+            # [num_unpermuted_tokens, num_experts] -> num_experts * num_unpermuted_tokens
+            probs_T_1D = probs.T.contiguous().view(-1)
+
+            # get 1D indices of the probs selected by routing_map
+            indices_dim0 = torch.arange(num_experts, device=routing_map.device).unsqueeze(-1)
+            indices_dim1 = sorted_indices.view(num_experts, capacity)
+            indices_1D = (indices_dim0 * num_unpermuted_tokens + indices_dim1).view(-1)
+
+            # get probs from indices
+            permuted_probs = probs_T_1D.index_select(0, indices_1D)
+        else:
+            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):
+
+    # torch.cuda.synchronize()
+    # import sys
+    # sys.stderr.write(f"############all_to_all input_split_sizes:{input_split_sizes}\n output_split_sizes:{output_split_sizes}")
+    # sys.stderr.flush()
+    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 logging
+from typing import Callable, List, Optional, Tuple
+from dataclasses import dataclass
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+from vllm.logger import init_logger
+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.ep_moe.token_dispatcher import MoEAlltoAllTokenDispatcher
+from vllm.model_executor.layers.fused_moe.ep_moe.ep_moe_utlis import EPSharedExperts, EpMoeConfig
+from vllm.model_executor.layers.fused_moe.ep_moe.kernels import grouped_gemm_triton
+
+
+logger = init_logger(__name__)
+
+
+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,
+        moe_permute_fusion: bool = False,
+        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
+                         )
+        
+        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
+        )
+
+        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.shared_experts = None
+        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.seg_indptr = None
+
+        if quant_config is None:
+            self.use_fp8_w8a8 = False
+            self.use_block_quant = False
+            self.block_shape = None
+            self.activation_scheme = None
+            self.w13_weight_scale = None
+            self.w2_weight_scale = None
+        else:
+            self.use_fp8_w8a8 = True
+            self.use_block_quant = getattr(self.quant_method, "block_quant", False)
+            self.block_shape = (
+                self.quant_method.quant_config.weight_block_size
+                if self.use_block_quant
+                else None
+            )
+            self.fp8_dtype = torch.float8_e4m3fn
+            self.activation_scheme = quant_config.activation_scheme
+
+    def set_shared_experts(self, shared_experts):
+        self.shared_experts = shared_experts
+        self.use_shared_expert = shared_experts is not None
+        if self.shared_expert_overlap:
+            self.token_dispatcher.set_shared_experts(shared_experts)
+
+    def triton_grouped_gemm_impl(self, hidden_states, tokens_per_expert, use_nn_moe):
+        torch.cumsum(tokens_per_expert,
+                     dim=0,
+                     out=self.seg_indptr[1:])
+        _, N, _ = self.w13_weight.shape
+        gateup_input = hidden_states
+        weight_indices_cur_rank = torch.arange(
+            0,
+            self.local_num_experts,
+            device=hidden_states.device,
+            dtype=torch.int64,
+        )
+        # GroupGemm-0
+        gateup_output = torch.empty(
+            gateup_input.shape[0],
+            self.w13_weight.shape[1],
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+
+        gateup_output = grouped_gemm_triton(
+            a=gateup_input,
+            b=self.w13_weight,
+            c=gateup_output,
+            batch_size=self.local_num_experts,
+            weight_column_major=True,
+            seg_indptr=self.seg_indptr,
+            weight_indices=weight_indices_cur_rank,
+            use_fp8_w8a8=self.use_fp8_w8a8,
+            scale_a=self.w13_input_scale if self.quant_config is not None else None,
+            scale_b=(
+                self.w13_weight_scale_inv
+                if self.use_block_quant
+                else self.w13_weight_scale
+            ) if self.quant_config is not None else None,
+            block_shape=self.block_shape,
+        )
+
+        # Act
+        down_input = torch.empty(
+            gateup_output.shape[0],
+            gateup_output.shape[1] // 2,
+            device=gateup_output.device,
+            dtype=(
+                self.fp8_dtype
+                if (self.use_fp8_w8a8 and not self.use_block_quant)
+                else hidden_states.dtype
+            ),
+        )
+        if self.quant_config is not None and self.w2_input_scale is None and not self.use_block_quant:
+            self.w2_input_scale = torch.ones(
+                self.local_num_experts,
+                dtype=torch.float32,
+                device=hidden_states.device,
+            )
+
+        if self.activation == "silu":
+            torch.ops._C.silu_and_mul(down_input,
+                                      gateup_output.view(-1, N))
+        elif self.activation == "gelu":
+            torch.ops._C.gelu_and_mul(down_input,
+                                      gateup_output.view(-1, N))
+        else:
+            raise ValueError(f"Unsupported FusedMoe activation: {self.activation}")
+
+        # GroupGemm-1
+        down_output = torch.empty(
+            down_input.shape[0],
+            self.w2_weight.shape[1],
+            device=hidden_states.device,
+            dtype=hidden_states.dtype,
+        )
+        down_output = grouped_gemm_triton(
+            a=down_input,
+            b=self.w2_weight,
+            c=down_output,
+            batch_size=self.local_num_experts,
+            weight_column_major=True,
+            seg_indptr=self.seg_indptr,
+            weight_indices=weight_indices_cur_rank,
+            use_fp8_w8a8=self.use_fp8_w8a8,
+            scale_a=self.w2_input_scale if self.quant_config is not None else None,
+            scale_b=(
+                self.w2_weight_scale_inv
+                if self.use_block_quant
+                else self.w2_weight_scale
+            ) if self.quant_config is not None else None,
+            block_shape=self.block_shape,
+        )
+        return down_output
+
+        
+    
+    def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor):
+        if (
+            self.training
+            and self.config.tensor_model_parallel_size > 1
+            and not self.config.sequence_parallel
+        ):
+            raise ValueError(
+                "During training, performance may degrade if MoE and tensor parallelism"
+                "are enabled without also enabling sequence parallelism."
+            )
+        
+        if self.seg_indptr is None:
+            self.seg_indptr = torch.zeros(self.local_num_experts+1, device=hidden_states.   device, dtype=torch.int64)
+
+        # process MoE
+        def custom_forward(hidden_states, router_logits):
+            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)
+            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
+            )
+            expert_output = self.triton_grouped_gemm_impl(dispatched_input, tokens_per_expert, self.use_nn_moe)
+            output = self.token_dispatcher.token_unpermutation(expert_output)
+            if self.use_shared_expert and not self.shared_expert_overlap:
+                # if shared_expert_overlap is True, the expert calculation happens in
+                # the token_dispatcher to overlap communications and computations
+                output = output + self.shared_experts(hidden_states)
+            return output
+
+        output = custom_forward(hidden_states, router_logits)
+
+        return output
\ No newline at end of file

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,
@@ -151,45 +153,71 @@ 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(
-            num_experts=config.n_routed_experts,
-            top_k=config.num_experts_per_tok,
-            hidden_size=config.hidden_size,
-            intermediate_size=config.moe_intermediate_size,
-            reduce_results=False,
-            renormalize=config.norm_topk_prob,
-            quant_config=quant_config,
-            use_grouped_topk=True,
-            num_expert_group=config.n_group,
-            topk_group=config.topk_group,
-            prefix=f"{prefix}.experts",
-            scoring_func=config.scoring_func,
-            e_score_correction_bias=self.gate.e_score_correction_bias,
-            enable_eplb=self.enable_eplb,
-            num_redundant_experts=self.n_redundant_experts,
-            routed_scaling_factor=self.routed_scaling_factor)
+        
+        dp_size = get_dp_group().world_size
+        self.use_ep_opt = dp_size > 1 and parallel_config.enable_expert_parallel
+        self.shared_experts = None
 
         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_ep_opt else EPSharedExperts
+            self.shared_experts = shared_expert_cls(
                 hidden_size=config.hidden_size,
                 intermediate_size=intermediate_size,
                 hidden_act=config.hidden_act,
                 quant_config=quant_config,
-                reduce_results=self.experts.must_reduce_shared_expert_outputs(
-                ),
+                reduce_results=False,
                 prefix=f"{prefix}.shared_experts",
             )
+
+        if not self.use_ep_opt:
+            self.experts = FusedMoE(
+                num_experts=config.n_routed_experts,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size,
+                reduce_results=False,
+                renormalize=config.norm_topk_prob,
+                quant_config=quant_config,
+                use_grouped_topk=True,
+                num_expert_group=config.n_group,
+                topk_group=config.topk_group,
+                prefix=f"{prefix}.experts",
+                scoring_func=config.scoring_func,
+                e_score_correction_bias=self.gate.e_score_correction_bias,
+                enable_eplb=self.enable_eplb,
+                num_redundant_experts=self.n_redundant_experts,
+                routed_scaling_factor=self.routed_scaling_factor)
+        else:
+            self.experts = EPMoE(
+                num_experts=config.n_routed_experts,
+                top_k=config.num_experts_per_tok,
+                hidden_size=config.hidden_size,
+                intermediate_size=config.moe_intermediate_size,
+                reduce_results=False,
+                renormalize=config.norm_topk_prob,
+                quant_config=quant_config,
+                use_grouped_topk=True,
+                num_expert_group=config.n_group,
+                topk_group=config.topk_group,
+                prefix=f"{prefix}.experts",
+                scoring_func=config.scoring_func,
+                e_score_correction_bias=self.gate.e_score_correction_bias,
+                routed_scaling_factor=self.routed_scaling_factor)
+
+        
+        if self.use_ep_opt:
+            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
 
     def forward(self, hidden_states: torch.Tensor) -> 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:
-            shared_output = self.shared_experts(hidden_states)
+        if not self.use_ep_opt:
+            if self.n_shared_experts is not None:
+                shared_output = self.shared_experts(hidden_states)
         # router_logits: (num_tokens, n_experts)
         router_logits, _ = self.gate(hidden_states)
 
@@ -203,22 +231,23 @@ class DeepseekV2MoE(nn.Module):
             final_hidden_states = self.experts(hidden_states=hidden_states,
                                                router_logits=router_logits)
         
-        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 not self.use_ep_opt:
+            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))
 
         return final_hidden_states.view(num_tokens, hidden_dim)
 
@@ -619,6 +648,8 @@ class DeepseekV2DecoderLayer(nn.Module):
             hidden_states=hidden_states,
         )
 
+        #ops.print_tensor(hidden_states)
+
         if hidden_states.dtype == torch.float16 and not self.dpsk_fp16_quick:
             # Fix FP16 overflow
             # We scale both hidden_states and residual before
@@ -714,7 +745,9 @@ class DeepseekV2Model(nn.Module):
             residual = intermediate_tensors["residual"]
 
         for layer in self.layers[self.start_layer:self.end_layer]:
-            hidden_states, residual = layer(positions, hidden_states, residual)
+            hidden_states, residual = layer(positions, hidden_states, residual)\
+        
+        #ops.print_tensor(hidden_states)
 
         if not get_pp_group().is_last_rank:
             return IntermediateTensors({

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
@@ -428,6 +438,9 @@ def launch_core_engines(
         else:
             local_engine_manager = None
 
+        import torch
+        torch.cuda.synchronize()
+        logger.info(("launch_core_engines end==============================="))
         yield local_engine_manager, coordinator, addresses
 
         # Now wait for engines to start.
@@ -440,6 +453,8 @@ def launch_core_engines(
             local_engine_manager,
             coordinator.proc if coordinator else None,
         )
+        torch.cuda.synchronize()
+        logger.info(("engine startup==============================="))
 
 
 def wait_for_engine_startup(

vllm/v1/worker/gpu_model_runner.py

@@ -2051,6 +2051,8 @@ class GPUModelRunner(LoRAModelRunnerMixin):
                 input_ids = None
                 inputs_embeds = self.inputs_embeds[:num_tokens]
             else:
+                #self.input_ids[:num_tokens] = torch.randint(0, 120000, (num_tokens,), dtype=torch.int32)
+                self.input_ids[:num_tokens] = torch.arange(num_tokens, dtype=torch.int32, device=self.input_ids.device)
                 input_ids = self.input_ids[:num_tokens]
                 inputs_embeds = None
             if self.uses_mrope: