# # Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # This file is a part of the vllm-ascend project. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from typing import Any, Callable, Dict, Optional, Tuple, Union import torch import torch.distributed as dist import torch_npu from vllm.distributed import GroupCoordinator import vllm_ascend.envs as envs from vllm_ascend.ascend_config import get_ascend_config from vllm_ascend.distributed.parallel_state import get_ep_group from vllm_ascend.ops.fused_moe import select_experts from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, FusedMoEState, dispose_tensor, get_fused_moe_state, npu_stream_switch, npu_wait_tensor) def apply_mlp(hidden_states: torch.Tensor, w1: torch.Tensor, w1_scale: torch.Tensor, w2: torch.Tensor, w2_scale: torch.Tensor, group_list: torch.Tensor, dynamic_scale: torch.Tensor = None, group_list_type: int = 1) -> torch.Tensor: """ apply MLP: gate_up_proj -> swiglu -> down_proj Args: hidden_states: input hidden states with shape (num_tokens, hidden_size). w1: expert weights1 with shape (num_experts, hidden_size, intermediate_size * 2) w1_scale: weights1 scale with shape (num_experts, intermediate_size * 2) w2: expert weights2 with shape (num_experts, intermediate_size, hidden_size) w2_scale: weights2 scale with shape (num_experts, hidden_size) group_list: number of tokens for each expert, follow cumsum mode, and with shape (num_experts). transpose_weight: w1: (num_experts, intermediate_size * 2, hidden_size) -> (num_experts, hidden_size, intermediate_size * 2) w2: (num_experts, hidden_size, intermediate_size) -> (num_experts, intermediate_size, hidden_size) Returns: hidden_states: output hidden states after MLP. """ if dynamic_scale is None: unquantized_hidden_states = hidden_states hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant( hidden_states) # Dispose the original unquantized hidden states # to save npu memory because they're no longer used. dispose_tensor(unquantized_hidden_states) else: pertoken_scale = dynamic_scale # gmm1: gate_up_proj hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w1], scale=[w1_scale], per_token_scale=[pertoken_scale], split_item=2, group_list_type=group_list_type, group_type=0, group_list=group_list, output_dtype=w2_scale.dtype)[0] # act_fn: swiglu hidden_states = torch_npu.npu_swiglu(hidden_states) hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant( hidden_states) # gmm2: down_proj hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w2], scale=[w2_scale], per_token_scale=[swiglu_out_scale], split_item=2, group_list_type=group_list_type, group_type=0, group_list=group_list, output_dtype=w2_scale.dtype)[0] return hidden_states def fused_experts_with_mc2( hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, w1_scale: torch.Tensor, w2_scale: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None, moe_all_to_all_group_name: str = "", log2phy: torch.Tensor = None, global_redundant_expert_num: int = 0, shared_experts: Optional[Any] = None, ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]: if log2phy is not None: topk_ids = log2phy[topk_ids] global_bs = 0 moe_expert_num = len(expert_map) + global_redundant_expert_num # hidden_states = hidden_states.bfloat16() kwargs_mc2 = { "x": hidden_states, "expert_ids": topk_ids, "expert_shard_type": 0, "shared_expert_rank_num": 0, "moe_expert_num": moe_expert_num, "global_bs": global_bs, "expert_scales": topk_weights.to(torch.float32), } rank = torch.distributed.get_rank() quant_mode = 2 ep_group = get_ep_group().device_group local_rank = torch.distributed.get_rank(group=ep_group) all_to_all_group_size = torch.distributed.get_world_size(ep_group) world_size = torch.distributed.get_world_size() tp_size = world_size // all_to_all_group_size tp_rank = rank % tp_size stage1_kwargs = { "scales": None, "quant_mode": quant_mode, "group_ep": moe_all_to_all_group_name, "ep_world_size": all_to_all_group_size, "ep_rank_id": local_rank, # "group_tp": self.moe_rs_group_name, "group_tp": moe_all_to_all_group_name, "tp_world_size": tp_size, "tp_rank_id": tp_rank, } kwargs_mc2.update(stage1_kwargs) output = torch_npu.npu_moe_distribute_dispatch(**kwargs_mc2) # comm_stream.wait_stream(torch.npu.current_stream()) expand_x, dynamic_scale, expand_idx, expert_token_nums, ep_recv_counts, _, expand_scales = output[ 0:7] if shared_experts is not None: with npu_stream_switch("moe_secondary", 0): npu_wait_tensor(hidden_states, topk_weights) shared_gate_up, _ = shared_experts.gate_up_proj(hidden_states) npu_wait_tensor(shared_gate_up[0], expand_x) shared_act = shared_experts.act_fn(shared_gate_up) # `expand_x` will be disposed in the `apply_mlp` function down_out_list = apply_mlp(expand_x, w1, w1_scale, w2, w2_scale, expert_token_nums, dynamic_scale=dynamic_scale) # moeCombine kwargs_mc2 = { "expand_x": down_out_list, "expert_ids": topk_ids, "expand_idx": expand_idx, "expert_scales": topk_weights.to(torch.float32), "expert_shard_type": 0, "shared_expert_rank_num": 0, "moe_expert_num": moe_expert_num, "global_bs": 0, "expand_scales": expand_scales, } tp_recv_counts = torch.empty(1, dtype=torch.int32, device=hidden_states.device) stage3_kwargs = { "ep_send_counts": ep_recv_counts, "group_ep": moe_all_to_all_group_name, "ep_world_size": all_to_all_group_size, "ep_rank_id": local_rank, "tp_send_counts": tp_recv_counts, # "group_tp": self.moe_rs_group_name, "group_tp": moe_all_to_all_group_name, "tp_world_size": tp_size, "tp_rank_id": tp_rank, } kwargs_mc2.update(stage3_kwargs) hidden_states = torch_npu.npu_moe_distribute_combine(**kwargs_mc2) if shared_experts is None: return hidden_states else: with npu_stream_switch("moe_secondary", 0): npu_wait_tensor(shared_act[0], down_out_list) shared_output, _ = shared_experts.down_proj(shared_act) return hidden_states, shared_output # currently expert parallelism implemented with all2all # is under-optimized. def fused_experts_with_all2all( hidden_states: torch.Tensor, w1: torch.Tensor, w1_scale: torch.Tensor, w2: torch.Tensor, w2_scale: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None, ep_group: GroupCoordinator = None, log2phy: torch.Tensor = None, global_redundant_expert_num: int = 0, ): if log2phy is not None: topk_ids = log2phy[topk_ids] original_shape = hidden_states.shape if len(original_shape) == 3: hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) num_tokens, _ = hidden_states.shape num_experts = w1.shape[0] device = hidden_states.device if expert_map is not None: global_num_experts = len(expert_map) + global_redundant_expert_num local_num_experts = global_num_experts // ep_group.world_size row_idx_len = num_tokens * top_k row_idx = (torch.arange(0, row_idx_len, dtype=torch.int32, device=device).view(top_k, -1).permute( 1, 0).contiguous()) hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing( hidden_states, row_idx=row_idx, expert_idx=topk_ids, active_num=num_tokens) global_expert_tokens = torch.bincount(expanded_expert_idx, minlength=global_num_experts) scatter_sizes = global_expert_tokens.view(ep_group.world_size, -1).sum(-1) gather_sizes = torch.empty_like(scatter_sizes) dist.all_to_all_single(gather_sizes, scatter_sizes, group=ep_group.device_group) scatter_size_list = scatter_sizes.cpu().tolist() gather_size_list = gather_sizes.cpu().tolist() expanded_expert_idx = expanded_expert_idx % local_num_experts hidden_states = ep_group.all_to_all(hidden_states, 0, 0, scatter_size_list, gather_size_list) local_expert_idx = ep_group.all_to_all(expanded_expert_idx, 0, 0, scatter_size_list, gather_size_list) sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx) expert_tokens = torch_npu.npu_moe_compute_expert_tokens( sorted_local_expert_idx, local_num_experts).to(torch.int64) hidden_states = hidden_states[sorted_idx] group_list_type = 0 else: row_idx_len = num_tokens * top_k row_idx = torch.arange(0, row_idx_len, dtype=torch.int32, device=topk_weights.device).view( top_k, -1).permute(1, 0).contiguous() hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing( hidden_states, row_idx=row_idx, expert_idx=topk_ids, active_num=num_tokens) expert_tokens = torch_npu.npu_moe_compute_expert_tokens( expanded_expert_idx, num_experts) expert_tokens = expert_tokens.to(torch.int64) group_list_type = 0 # `hidden_states` will be disposed in the `apply_mlp` function hidden_states = apply_mlp( hidden_states, w1, w1_scale, #17 w2, w2_scale, expert_tokens, #16 group_list_type=group_list_type) if expert_map is not None: resorted_idx = torch.argsort(sorted_idx) hidden_states = hidden_states[resorted_idx] hidden_states = ep_group.all_to_all(hidden_states, 0, 0, gather_size_list, scatter_size_list) final_hidden_states = torch_npu.npu_moe_finalize_routing( hidden_states, skip1=None, skip2=None, bias=None, scales=topk_weights, expanded_src_to_dst_row=expanded_row_idx, export_for_source_row=topk_ids, ) else: # TODO: Reorder device memory 2 times here, replace the current # implementation here when suitable operators become available. final_hidden_states = torch_npu.npu_moe_finalize_routing( hidden_states, skip1=None, skip2=None, bias=None, scales=topk_weights, expanded_src_to_dst_row=expanded_row_idx, export_for_source_row=topk_ids, ) if len(original_shape) == 3: final_hidden_states = final_hidden_states.view(original_shape) return final_hidden_states def fused_experts_with_allgather(hidden_states: torch.Tensor, w1: torch.Tensor, w1_scale: torch.Tensor, w2: torch.Tensor, w2_scale: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None): original_shape = hidden_states.shape if len(original_shape) == 3: hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) num_tokens = hidden_states.shape[0] batch_size, hidden_size = hidden_states.shape ep_group = get_ep_group().device_group ep_rank = torch.distributed.get_rank(group=ep_group) ep_size = torch.distributed.get_world_size(ep_group) global_num_experts = len(expert_map) local_num_experts = global_num_experts // ep_size hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(hidden_states) hidden_states, expanded_x_idx, expert_tokens, pertoken_scale = torch_npu.npu_moe_init_routing_v2( hidden_states, topk_ids, scale=pertoken_scale, offset=None, active_num=num_tokens * top_k, expert_num=global_num_experts, expert_tokens_num_type=1, expert_tokens_num_flag=True, active_expert_range=[ ep_rank * local_num_experts, (ep_rank + 1) * local_num_experts ], quant_mode=-1, row_idx_type=0) group_list_type = 1 hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w1], split_item=3, group_list_type=group_list_type, group_type=0, group_list=expert_tokens, output_dtype=torch.int32)[0] # act_fn: swiglu hidden_states, pertoken_scale = torch_npu.npu_dequant_swiglu_quant( x=hidden_states, weight_scale=w1_scale.to(torch.float32), activation_scale=pertoken_scale, bias=None, quant_scale=None, quant_offset=None, group_index=expert_tokens, activate_left=True, quant_mode=1, ) hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w2], scale=[w2_scale.to(torch.bfloat16)], per_token_scale=[pertoken_scale.view(-1)], split_item=3, group_list_type=group_list_type, group_type=0, group_list=expert_tokens, output_dtype=torch.bfloat16)[0] final_hidden_states = torch_npu.npu_moe_finalize_routing( expanded_permuted_rows=hidden_states.unsqueeze(1), skip1=None, skip2=None, bias=None, scales=topk_weights.to(torch.bfloat16), expanded_src_to_dst_row=expanded_x_idx.to(torch.int32), export_for_source_row=topk_ids, drop_pad_mode=3 ).to(torch.bfloat16) if len(original_shape) == 3: final_hidden_states = final_hidden_states.view(original_shape) return final_hidden_states def fused_experts(hidden_states: torch.Tensor, w1: torch.Tensor, w1_scale: torch.Tensor, w2: torch.Tensor, w2_scale: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None): original_shape = hidden_states.shape if len(original_shape) == 3: hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) num_tokens, _ = hidden_states.shape num_experts = w1.shape[0] dtype = hidden_states.dtype device = hidden_states.device if expert_map is not None: # Generate token indices and flatten token_indices = (torch.arange(num_tokens, device=device, dtype=torch.int64).unsqueeze(1).expand( -1, top_k).reshape(-1)) # Flatten token-to-expert mappings and map to local experts weights_flat = topk_weights.view(-1) experts_flat = topk_ids.view(-1) local_experts_flat = expert_map[experts_flat] # Filter valid token-expert pairs mask = local_experts_flat != -1 filtered_weights = torch.where( mask, weights_flat, torch.zeros_like(weights_flat)).to(dtype) filtered_experts = torch.where( mask, local_experts_flat, torch.full_like(local_experts_flat, num_experts)).to(topk_ids.dtype) # Sort by local expert IDs sort_indices = torch.argsort(filtered_experts) sorted_token_indices = token_indices[sort_indices] sorted_weights = filtered_weights[sort_indices] # Compute token counts with minlength of num_experts # This is equivalent to but faster than: # >>> token_counts = torch.bincount(filtered_experts, minlength=num_experts)[:-1] token_counts = torch.zeros(num_experts + 1, device=device, dtype=torch.int64) ones = torch.ones_like(filtered_experts, dtype=torch.int64) token_counts.scatter_add_(0, filtered_experts.to(torch.int64), ones) expert_tokens = token_counts[:num_experts] # Rearrange hidden_states hidden_states = hidden_states[sorted_token_indices] group_list_type = 1 else: row_idx_len = num_tokens * top_k row_idx = torch.arange(0, row_idx_len, dtype=torch.int32, device=topk_weights.device).view( top_k, -1).permute(1, 0).contiguous() hidden_states, expanded_row_idx, expanded_expert_idx = torch_npu.npu_moe_init_routing( hidden_states, row_idx=row_idx, expert_idx=topk_ids, active_num=num_tokens) expert_tokens = torch_npu.npu_moe_compute_expert_tokens( expanded_expert_idx, num_experts) expert_tokens = expert_tokens.to(torch.int64) group_list_type = 0 # `hidden_states` will be disposed in the `apply_mlp` function hidden_states = apply_mlp(hidden_states, w1, w1_scale, w2, w2_scale, expert_tokens, group_list_type=group_list_type) if expert_map is not None: hidden_states.mul_(sorted_weights.unsqueeze(1)) final_hidden_states = torch.zeros(*original_shape, device=device, dtype=dtype) num_valid_tokens = mask.sum() valid_token_mask = torch.arange( 0, sorted_token_indices.shape[0], device=device).unsqueeze(1) < num_valid_tokens hidden_states = hidden_states.masked_fill_(~valid_token_mask, 0).to(dtype) final_hidden_states.index_add_(0, sorted_token_indices, hidden_states) else: # TODO: Reorder device memory 2 times here, replace the current # implementation here when suitable operators become available. final_hidden_states = torch_npu.npu_moe_finalize_routing( hidden_states, skip1=None, skip2=None, bias=None, scales=topk_weights, expanded_src_to_dst_row=expanded_row_idx, export_for_source_row=topk_ids, ) if len(original_shape) == 3: final_hidden_states = final_hidden_states.view(original_shape) return final_hidden_states class AscendW8A8DynamicLinearMethod: """Linear method for Ascend W8A8_DYNAMIC. """ def __init__(self): self.transpose_weight = True @staticmethod def get_weight(input_size: int, output_size: int, params_dtype: torch.dtype) -> Dict[str, Any]: params_dict = { "weight": torch.empty(output_size, input_size, dtype=torch.int8) } return params_dict @staticmethod def get_pertensor_param(params_dtype: torch.dtype) -> Dict[str, Any]: return {} @staticmethod def get_perchannel_param( output_size: int, params_dtype: torch.dtype, ) -> Dict[str, Any]: params_dict = {} params_dict["weight_scale"] = torch.empty(output_size, 1, dtype=params_dtype) params_dict["weight_offset"] = torch.empty(output_size, 1, dtype=params_dtype) return params_dict @staticmethod def apply( layer: torch.nn.Module, x: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]], bias: Optional[torch.Tensor] = None, tp_rank: Optional[int] = 0, ) -> torch.Tensor: config = getattr(layer, "_ascend_quant_config", {}) if not isinstance(x, tuple): output_dtype = config.get("output_dtype", x.dtype) quantized_x, dynamic_scale = torch_npu.npu_dynamic_quant(x) else: assert "output_dtype" in config.keys(), ( f"DynamicLinearMethod needs explicitly specified `output_dtype`" f"for pre-quantized input, got config [{config}]") output_dtype = config["output_dtype"] quantized_x, dynamic_scale = x pertoken_scale = (dynamic_scale if config.get("pertoken_scale", True) else None) output = torch_npu.npu_quant_matmul( quantized_x, layer.weight, layer.weight_scale, pertoken_scale=pertoken_scale, bias=bias, output_dtype=output_dtype, ) return ((output, dynamic_scale) if config.get("return_scale", False) else output) def process_weights_after_loading(self, layer): if self.transpose_weight: layer.weight.data = layer.weight.data.transpose(0, 1).contiguous() # cast quantized weight tensors in NZ format (29) for higher inference speed layer.weight.data = torch_npu.npu_format_cast(layer.weight.data, 29) layer.weight_scale.data = layer.weight_scale.data.flatten() layer.weight_scale_fp32 = layer.weight_scale.data.to(torch.float32) layer.weight_offset.data = layer.weight_offset.data.flatten() class AscendW8A8DynamicFusedMoEMethod: """FusedMoe method for Ascend W8A8_DYNAMIC. """ def __init__(self): self.transpose_weight = True self.ep_group = get_ep_group() ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled try: device_group = self.ep_group.device_group # TODO: Try local_rank = ep_group.rank_in_group local_rank = torch.distributed.get_rank(group=device_group) backend = device_group._get_backend(torch.device("npu")) self.moe_all_to_all_group_name = backend.get_hccl_comm_name( local_rank) except AttributeError: self.moe_all_to_all_group_name = "" @staticmethod def get_weight(num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype) -> Dict[str, Any]: param_dict = {} param_dict["w13_weight"] = torch.empty(num_experts, 2 * intermediate_size_per_partition, hidden_sizes, dtype=torch.int8) param_dict["w2_weight"] = torch.empty(num_experts, hidden_sizes, intermediate_size_per_partition, dtype=torch.int8) return param_dict @staticmethod def get_dynamic_quant_param(num_experts: int, intermediate_size_per_partition: int, hidden_sizes: int, params_dtype: torch.dtype) -> Dict[str, Any]: param_dict = {} param_dict["w13_weight_scale"] = torch.empty( num_experts, 2 * intermediate_size_per_partition, 1, dtype=params_dtype) param_dict["w13_weight_offset"] = torch.empty( num_experts, 2 * intermediate_size_per_partition, 1, dtype=params_dtype) param_dict["w2_weight_scale"] = torch.empty(num_experts, hidden_sizes, 1, dtype=params_dtype) param_dict["w2_weight_offset"] = torch.empty(num_experts, hidden_sizes, 1, dtype=params_dtype) return param_dict def apply( self, layer: torch.nn.Module, x: torch.Tensor, router_logits: torch.Tensor, top_k: int, renormalize: bool, use_grouped_topk: bool = False, global_num_experts: int = -1, expert_map: Optional[torch.Tensor] = None, topk_group: Optional[int] = None, num_expert_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, scoring_func: str = "softmax", e_score_correction_bias: Optional[torch.Tensor] = None, is_prefill: bool = True, enable_force_load_balance: bool = True, log2phy: torch.Tensor = None, global_redundant_expert_num: int = 0, shared_experts: Optional[Any] = None, **kwargs, ) -> torch.Tensor: assert router_logits.shape[ 1] == global_num_experts, "Number of global experts mismatch" is_deepseek_v3_r1 = global_num_experts == 256 use_grouped_topk = (topk_group > 1 or num_expert_group > 1) # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern if use_grouped_topk and is_deepseek_v3_r1: topk_weights, topk_ids, _ = torch_npu.npu_moe_gating_top_k( router_logits, k=top_k, # topk当前写8 bias=e_score_correction_bias, k_group=topk_group, # fix: 4 group_count=num_expert_group, # fix 8 group_select_mode=1, # 0: group中的最大; 1: topk2.sum(fix) renorm=0, # 0: softmax->topk(fix); 1: topk->softmax norm_type=1, # 0: softmax; 1: sigmoid(fix) # out_flag=False, # todo new api; 第三个输出是否输出 # y2_flag=False, # old api; 第三个输出是否输出 routed_scaling_factor=1, eps=float(1e-20)) else: topk_weights, topk_ids = select_experts( hidden_states=x, router_logits=router_logits, top_k=top_k, use_grouped_topk=use_grouped_topk, 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, ) # this is a naive implementation for experts load balance so as # to avoid accumulating too much tokens on a single rank. # currently it is only activated when doing profile runs. if enable_force_load_balance: topk_ids = torch.randint_like(topk_ids, 0, global_num_experts) topk_weights = topk_weights.to(x.dtype) fused_moe_state = get_fused_moe_state(self.ep_group.world_size, is_prefill, is_deepseek_v3_r1) if fused_moe_state == FusedMoEState.AllGatherEP: return fused_experts_with_allgather( hidden_states=x, w1=layer.w13_weight, w1_scale=layer.w13_weight_scale, w2=layer.w2_weight, w2_scale=layer.w2_weight_scale, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map) elif fused_moe_state == FusedMoEState.MC2: return fused_experts_with_mc2( hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, w1_scale=layer.w13_weight_scale, w2_scale=layer.w2_weight_scale, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map, moe_all_to_all_group_name=self.moe_all_to_all_group_name, log2phy=log2phy, global_redundant_expert_num=global_redundant_expert_num, shared_experts=shared_experts) elif fused_moe_state in [ FusedMoEState.AllGather, FusedMoEState.NaiveMulticast ]: return fused_experts(hidden_states=x, w1=layer.w13_weight, w1_scale=layer.w13_weight_scale, w2=layer.w2_weight, w2_scale=layer.w2_weight_scale, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map) else: # The current implementation of deepseek moe splits hidden_states # according to tp_size before they are feed into fused_moe module. # Therefore, all2all is needed no matter how dp/tp is set so as to # dispatch/combine tokens. return fused_experts_with_all2all( hidden_states=x, w1=layer.w13_weight, w1_scale=layer.w13_weight_scale, w2=layer.w2_weight, w2_scale=layer.w2_weight_scale, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map, ep_group=self.ep_group, log2phy=log2phy, global_redundant_expert_num=global_redundant_expert_num, ) def process_weights_after_loading(self, layer): if self.transpose_weight: layer.w13_weight.data = layer.w13_weight.data.transpose( 1, 2).contiguous() layer.w2_weight.data = layer.w2_weight.data.transpose( 1, 2).contiguous() layer.w13_weight_scale.data = layer.w13_weight_scale.data.view( layer.w13_weight_scale.data.shape[0], -1) layer.w13_weight_offset.data = layer.w13_weight_offset.data.view( layer.w13_weight_offset.data.shape[0], -1) layer.w2_weight_scale.data = layer.w2_weight_scale.data.view( layer.w2_weight_scale.data.shape[0], -1) layer.w2_weight_offset.data = layer.w2_weight_offset.data.view( layer.w2_weight_offset.data.shape[0], -1)