# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved. # Copyright 2023 The vLLM team. # # 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. # This file is a part of the vllm-ascend project. # Adapted from vllm/tests/kernels/test_moe.py import os from typing import Callable, List, Optional import torch import torch.distributed as dist import torch_npu from vllm.config import get_current_vllm_config from vllm.distributed import (GroupCoordinator, get_tensor_model_parallel_world_size, tensor_model_parallel_all_reduce) from vllm.distributed.parallel_state import get_dp_group from vllm.model_executor.layers.fused_moe.layer import ( FusedMoE, FusedMoEParallelConfig, MoEConfig, UnquantizedFusedMoEMethod, determine_expert_map) from vllm.model_executor.layers.quantization.base_config import \ QuantizationConfig import vllm_ascend.envs as envs_ascend from vllm_ascend.ascend_config import get_ascend_config from vllm_ascend.distributed.parallel_state import get_ep_group, get_etp_group from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer VLLM_ENABLE_MC2: bool = envs_ascend.VLLM_ENABLE_MC2 USING_LCCL_COM: bool = envs_ascend.USING_LCCL_COM MOE_ALL2ALL_BUFFER: bool = envs_ascend.MOE_ALL2ALL_BUFFER def process_topk_ids(topk_ids: torch.Tensor, expert_num: int, ep_size: int, max_row_per_ep_rank: int, num_tokens: int, top_k: int) -> tuple[torch.Tensor, torch.Tensor]: original_total_elements = num_tokens * top_k device = topk_ids.device original_dtype = topk_ids.dtype if original_total_elements == 0: output_len = ep_size * max_row_per_ep_rank topk_ids_pad = torch.full((output_len, ), expert_num, dtype=original_dtype, device=device) unpad_indices = torch.full((original_total_elements, ), -1, dtype=torch.long, device=device) return topk_ids_pad, unpad_indices experts_per_ep_rank_val = expert_num // ep_size if experts_per_ep_rank_val == 0: raise ValueError( "expert_num // ep_size is 0, which leads to division by zero in ep_rank calculation. " "Ensure expert_num >= ep_size.") assigned_ep_rank = (topk_ids.float() / experts_per_ep_rank_val).to(original_dtype) indices_arange = torch.arange(topk_ids.shape[0], device=device) is_new_segment = torch.cat((torch.tensor([True], device=device), assigned_ep_rank[1:] != assigned_ep_rank[:-1])) temp_start_markers = torch.full_like(indices_arange, -1, dtype=indices_arange.dtype) temp_start_markers[is_new_segment] = indices_arange[is_new_segment] start_offset_for_each_token = torch.cummax(temp_start_markers, dim=0)[0] token_intra_ep_rank_idx = indices_arange - start_offset_for_each_token is_kept_mask = token_intra_ep_rank_idx < max_row_per_ep_rank cumsum_kept = torch.cumsum(is_kept_mask.float(), dim=0).to(torch.long) indices_in_rec_cond_list_for_all = cumsum_kept - 1 unpad_indices = torch.where( is_kept_mask, indices_in_rec_cond_list_for_all, torch.tensor(-1, device=device, dtype=torch.long)) output_len = ep_size * max_row_per_ep_rank topk_ids_pad = torch.full((output_len, ), expert_num, dtype=original_dtype, device=device) if topk_ids.shape[0] > 0: all_destination_indices = assigned_ep_rank * max_row_per_ep_rank + token_intra_ep_rank_idx temp_pad_buffer = torch.full((output_len + 1, ), expert_num, dtype=original_dtype, device=device) output_len_tensor = torch.tensor(output_len, dtype=torch.long, device=device) scatter_indices = torch.where(is_kept_mask, all_destination_indices, output_len_tensor) temp_pad_buffer.scatter_(0, scatter_indices, topk_ids) topk_ids_pad = temp_pad_buffer[:output_len] return topk_ids_pad, unpad_indices def fused_experts_with_mc2(hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None, moe_all_to_all_group_name: Optional[str] = None, **kwargs) -> torch.Tensor: global_bs = 0 moe_expert_num = len(expert_map) 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, } rank = torch.distributed.get_rank() quant_mode = 0 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) tp_size = get_etp_group().world_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 = output[ 0:5] w1 = w1.transpose(1, 2) group_list = expert_token_nums.to(torch.int64) gate_up_out_list = torch_npu.npu_grouped_matmul( x=[expand_x], weight=[w1], split_item=2, # 1 means count mode, to avoid cumulative operation of the group list group_list_type=1, group_type=0, group_list=group_list, ) # TODO: Remove this in the future. gate_up_out = torch.cat(gate_up_out_list, dim=0) gate_up_out = torch_npu.npu_swiglu(gate_up_out) w2 = w2.transpose(1, 2) down_out_list = torch_npu.npu_grouped_matmul( x=[gate_up_out], weight=[w2], split_item=2, group_list_type=1, group_type=0, group_list=group_list, ) down_out_list = torch.cat(down_out_list, dim=0) # 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, } tp_recv_counts = output[5] 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) return hidden_states def apply_mlp(hidden_states_wrapper: List[torch.Tensor], w1: torch.Tensor, w2: torch.Tensor, group_list: torch.Tensor, group_list_type: int = 1) -> torch.Tensor: """ apply MLP: gate_up_proj -> swiglu -> down_proj Args: hidden_states_wrapper: wrapper of input hidden states with shape (num_tokens, hidden_size). w1: expert weights1 with shape (num_experts, hidden_size, intermediate_size * 2) w2: expert weights2 with shape (num_experts, intermediate_size, 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. """ assert len(hidden_states_wrapper) == 1 hidden_states = hidden_states_wrapper.pop() w1 = w1.transpose(1, 2) hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w1], split_item=2, group_list_type=group_list_type, group_type=0, group_list=group_list, ) hidden_states = torch.cat(hidden_states, dim=0) hidden_states = torch_npu.npu_swiglu(hidden_states) w2 = w2.transpose(1, 2) hidden_states = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w2], split_item=2, group_list_type=group_list_type, group_type=0, group_list=group_list, ) hidden_states = torch.cat(hidden_states, dim=0) return hidden_states def fused_experts_with_all2all( hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None, ep_group: GroupCoordinator = 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] device = hidden_states.device if expert_map is not None: global_num_experts = len(expert_map) 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] 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) w1 = w1.transpose(1, 2) gate_up_out_list = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w1], split_item=2, group_list_type=0, group_type=0, group_list=expert_tokens, ) # TODO: Remove this in the future. hidden_states = torch.cat(gate_up_out_list, dim=0) hidden_states = torch_npu.npu_swiglu(hidden_states) w2 = w2.transpose(1, 2) down_out_list = torch_npu.npu_grouped_matmul( x=[hidden_states], weight=[w2], split_item=2, group_list_type=0, group_type=0, group_list=expert_tokens, ) hidden_states = torch.cat(down_out_list, dim=0) 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 # currently expert parallelism implemented with all2all # is under-optimized. def fused_experts_with_all2all_buffer( hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, max_model_len: int, global_batch_size: int, expert_map: torch.Tensor = None, ep_group: GroupCoordinator = 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 device = hidden_states.device global_num_experts = len(expert_map) 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) max_row_per_ep_rank = (-(-global_batch_size // ep_group.world_size) * max_model_len // ep_group.world_size + 1) * top_k * 2 expert_idx_buffer_scatter, unpad_indices = process_topk_ids( expanded_expert_idx, global_num_experts, ep_group.world_size, max_row_per_ep_rank, num_tokens, top_k) hidden_states_pad_idx = torch.zeros( expert_idx_buffer_scatter.shape, dtype=expert_idx_buffer_scatter.dtype, device=expert_idx_buffer_scatter.device) non_pad_len = torch.sum( (expert_idx_buffer_scatter != global_num_experts).to(torch.int32)) hidden_states_pad_idx[ expert_idx_buffer_scatter != global_num_experts] = torch.arange( non_pad_len, dtype=expert_idx_buffer_scatter.dtype, device=hidden_states.device) hidden_states_buffer_scatter = hidden_states[hidden_states_pad_idx] expert_idx_buffer_gather = torch.empty_like( expert_idx_buffer_scatter, dtype=expert_idx_buffer_scatter.dtype, device=expert_idx_buffer_scatter.device) hidden_states_buffer_gather = torch.empty_like( hidden_states_buffer_scatter, dtype=hidden_states_buffer_scatter.dtype, device=hidden_states_buffer_scatter.device) dist.all_to_all_single(expert_idx_buffer_gather, expert_idx_buffer_scatter, group=ep_group.device_group) dist.all_to_all_single(hidden_states_buffer_gather, hidden_states_buffer_scatter, group=ep_group.device_group) mask = expert_idx_buffer_gather != global_num_experts local_expert_idx = expert_idx_buffer_gather[mask] - ep_group.rank * ( global_num_experts // ep_group.world_size) hidden_states = hidden_states_buffer_gather[mask] idx_type = local_expert_idx.dtype sorted_local_expert_idx, sorted_idx = torch.sort(local_expert_idx.float()) sorted_local_expert_idx = sorted_local_expert_idx.to(idx_type) 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 hidden_states_wrapper = [hidden_states] del hidden_states hidden_states = apply_mlp(hidden_states_wrapper, w1, w2, expert_tokens, group_list_type=group_list_type) resorted_idx = torch.argsort(sorted_idx.float()).to(sorted_idx.dtype) hidden_states = hidden_states[resorted_idx] hidden_states_scatter = torch.zeros( (mask.shape[0], hidden_states.shape[1]), dtype=hidden_states.dtype, device=hidden_states.device) hidden_states_scatter[mask] = hidden_states hidden_states_gatter = torch.empty_like( hidden_states_scatter, dtype=hidden_states_scatter.dtype, device=hidden_states_scatter.device) dist.all_to_all_single(hidden_states_gatter, hidden_states_scatter, group=ep_group.device_group) hidden_states_gatter = hidden_states_gatter[ expert_idx_buffer_scatter != global_num_experts] if hidden_states_gatter.shape[0] != row_idx_len: hidden_states = torch.zeros((row_idx_len, hidden_states.shape[1]), dtype=hidden_states.dtype, device=hidden_states.device) hidden_states[unpad_indices != -1] = hidden_states_gatter else: # TODO: Reorder device memory 2 times here, replace the current hidden_states = hidden_states_gatter 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( hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, topk_weights: torch.Tensor, topk_ids: torch.Tensor, top_k: int, expert_map: torch.Tensor = None, apply_router_weight_on_input: bool = False, ) -> torch.Tensor: """ Fused experts with top-k routing. Args: hidden_states: Hidden states of shape (num_tokens, hidden_size). w1: Expert weights1 of shape (num_experts, intermediate_size * 2, hidden_size). w2: Expert weights2 of shape (num_experts, hidden_size, intermediate_size). topk_weights: Routing weights of shape (num_tokens, top_k). topk_ids: Selected expert IDs of shape (num_tokens, top_k). top_k: Number of experts to select. expert_map: Expert mapping of shape (num_experts,). Returns: hidden_states: Hidden states after routing. """ """ # Check constraints. assert hidden_states.shape[1] == w1.shape[2], "Hidden size mismatch" assert topk_weights.shape == topk_ids.shape, "topk shape mismatch" assert hidden_states.is_contiguous(), "Hidden_states must be contiguous" assert w1.is_contiguous(), "Expert weights1 must be contiguous" assert w2.is_contiguous(), "Expert weights2 must be contiguous" """ # if torch.distributed.get_rank() == 0: # print(w1.shape) # print(hidden_states.shape) original_shape = hidden_states.shape # assert len(original_shape) == 2 num_tokens = hidden_states.shape[:-1].numel() num_experts = w1.shape[0] dtype = hidden_states.dtype device = hidden_states.device # assert dtype in [torch.float32, torch.float16, torch.bfloat16 # ], "Only float32, float16, and bfloat16 are supported" if apply_router_weight_on_input: assert (topk_weights.dim() == 2 ), "`topk_weights` should be in shape (num_tokens, topk)" _, topk = topk_weights.shape assert ( topk == 1 ), "Only support topk=1 when `apply_router_weight_on_input` is True" hidden_states = hidden_states * topk_weights.to(hidden_states.dtype) 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.view(torch.float32)) 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) token_counts = token_counts[:num_experts] expert_tokens = torch.cumsum(token_counts, dim=0, dtype=torch.int64) # Rearrange hidden_states sorted_hidden_states = hidden_states[sorted_token_indices] else: 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()) sorted_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) w1 = w1.transpose(1, 2) gate_up_out_list = torch_npu.npu_grouped_matmul( x=[sorted_hidden_states], weight=[w1], split_item=2, group_list_type=0, group_type=0, group_list=expert_tokens, ) # TODO: Remove this in the future. gate_up_out = torch.cat(gate_up_out_list, dim=0) gate_up_out = torch_npu.npu_swiglu(gate_up_out) w2 = w2.transpose(1, 2) down_out_list = torch_npu.npu_grouped_matmul( x=[gate_up_out], weight=[w2], split_item=2, group_list_type=0, group_type=0, group_list=expert_tokens, ) down_out_list = torch.cat(down_out_list, dim=0) if expert_map is not None: weighted_down_out = down_out_list * sorted_weights.unsqueeze(1) final_hidden_states = torch.zeros(*original_shape, device=hidden_states.device, dtype=dtype) # TODO: npu_grouped_matmul output random values at [num_valid_tokens:, ...] # This created multiple NaN and index_add_ will mix them up which harms accuracy # remove this mask and filter after it being fixed num_valid_tokens = mask.sum() valid_token_mask = torch.arange( 0, sorted_token_indices.shape[0], device=device).unsqueeze(1) < num_valid_tokens valid_output = torch.where( valid_token_mask, weighted_down_out, torch.zeros_like(weighted_down_out)).to(dtype) final_hidden_states.index_add_(0, sorted_token_indices, valid_output) else: scales = torch.ones_like( topk_weights) if apply_router_weight_on_input else topk_weights # 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( down_out_list, skip1=None, skip2=None, bias=None, scales=scales, expanded_src_to_dst_row=expanded_row_idx, export_for_source_row=topk_ids, ) return final_hidden_states def native_grouped_topk( topk_weights: torch.Tensor, num_expert_group: Optional[int], topk_group: Optional[int], ): topk_group = 0 if topk_group is None else topk_group num_expert_group = 0 if num_expert_group is None else num_expert_group num_token = topk_weights.shape[0] grouped_weights = topk_weights.view(num_token, num_expert_group, -1).max(dim=-1).values topk_group_indices = torch.topk(grouped_weights.to(torch.float32), k=topk_group, dim=-1, sorted=False)[1] topk_group_mask = torch.zeros_like(grouped_weights) topk_group_mask.scatter_(1, topk_group_indices, 1) topk_weight_mask = (topk_group_mask.unsqueeze(-1).expand( num_token, num_expert_group, topk_weights.shape[-1] // num_expert_group).reshape(num_token, -1)) topk_weights = topk_weights.masked_fill(~topk_weight_mask.bool(), 0.0) return topk_weights def select_experts( hidden_states: torch.Tensor, router_logits: torch.Tensor, top_k: int, use_grouped_topk: bool, renormalize: bool, 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, ) -> tuple[torch.Tensor, torch.Tensor]: """ Select top-k experts based on router logits. Args: hidden_states: Hidden states of shape (num_tokens, hidden_size). router_logits: Router logits of shape (num_tokens, num_experts). top_k: Number of experts to select. use_grouped_topk: Whether to group experts before selecting top-k. renormalize: Whether to renormalize the routing weights. topk_group: Number of expert groups to select from. num_expert_group: Number of experts in each group. custom_routing_function: Custom routing function. scoring_func: Scoring function to use. e_score_correction_bias: Correction bias to apply to expert scores. Returns: topk_weights: Routing weights of shape (num_tokens, top_k). topk_ids: Selected expert IDs of shape (num_tokens, top_k). Raises: ValueError: If an unsupported scoring function is provided. """ if scoring_func == "softmax": # NOTE: vLLM use dtype=torch.float here topk_weights = router_logits.softmax(dim=-1) elif scoring_func == "sigmoid": topk_weights = router_logits.sigmoid() else: raise ValueError(f"Unsupported scoring function: {scoring_func}") if use_grouped_topk: assert topk_group is not None assert num_expert_group is not None if e_score_correction_bias is not None: # Store original scores before applying correction bias. We use biased # scores for expert selection but original scores for routing weights original_weights = topk_weights topk_weights = topk_weights + e_score_correction_bias.unsqueeze(0) # TODO: Change to npu_group_topk when the latest CANN and NNAL is available # >>> torch_npu._npu_group_topk(topk_weights, group_num=num_expert_group, k=topk_group) topk_weights = native_grouped_topk(topk_weights, num_expert_group, topk_group) # TODO bfloat16 is not supported in torch.topk with ge graph. if e_score_correction_bias is not None: topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False)[1] # Use original unbiased scores for the routing weights topk_weights = original_weights.gather(1, topk_ids) else: topk_weights, topk_ids = torch.topk(topk_weights.to(torch.float32), k=top_k, dim=-1, sorted=False) elif custom_routing_function is None: topk_weights, topk_ids = topk_weights.topk(top_k, dim=-1) topk_weights = topk_weights.to(hidden_states.dtype) else: topk_weights, topk_ids = custom_routing_function( hidden_states=hidden_states, gating_output=router_logits, topk=top_k, renormalize=renormalize) # Required by npu_moe_init_routing topk_ids = topk_ids.to(torch.int32) return topk_weights, topk_ids # Required by npu_moe_init_routing topk_ids = topk_ids.to(torch.int32) if renormalize: topk_weights = topk_weights / topk_weights.sum(dim=-1, keepdim=True) return topk_weights, topk_ids class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod): def __init__(self, moe: MoEConfig = None): super().__init__(moe=moe) vllm_config = get_current_vllm_config() ep_group = get_ep_group() self.ep_size = ep_group.world_size self.global_batch_size = vllm_config.scheduler_config.max_num_seqs self.local_batch_size = self.global_batch_size // self.ep_size self.max_model_len = vllm_config.model_config.max_model_len ascend_config = get_ascend_config() self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled try: device_group = 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 = None def process_weights_after_loading(self, layer): super(UnquantizedFusedMoEMethod, self).process_weights_after_loading(layer) layer.w13_weight = torch.nn.Parameter(self._maybe_pad_weight( layer.w13_weight.data), requires_grad=False) layer.w2_weight = torch.nn.Parameter(self._maybe_pad_weight( layer.w2_weight.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, 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 = False, enable_force_load_balance: bool = False, **kwargs, ) -> torch.Tensor: # NOTE: now npu_moe_gating_top_k can only support `group_count=256` pattern if global_num_experts == 256: 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, ) topk_weights = topk_weights.to(x.dtype) # 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) if VLLM_ENABLE_MC2 and not is_prefill: return fused_experts_with_mc2( hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, 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, **kwargs) elif self.torchair_graph_enabled or get_ep_group().world_size == 1: return fused_experts(hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map) elif MOE_ALL2ALL_BUFFER: return fused_experts_with_all2all_buffer( hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, max_model_len=self.max_model_len, global_batch_size=self.global_batch_size, expert_map=expert_map, ep_group=get_ep_group()) else: return fused_experts_with_all2all(hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, top_k=top_k, expert_map=expert_map, ep_group=get_ep_group()) class AscendFusedMoE(FusedMoE): # The moe_counter parameter is required during the initialization of EPLB # to identify the current layer index within the MOE model. moe_counter = -1 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, activation: str = "silu", apply_router_weight_on_input: bool = False, ): # TODO: This could not initialize FusedMoE baseclass, # fixme and make __init__() of AscendFusedMoE more clear super(FusedMoE, self).__init__() AscendFusedMoE.moe_counter += 1 self.moe_instance_id = AscendFusedMoE.moe_counter if params_dtype is None: params_dtype = torch.get_default_dtype() vllm_config = get_current_vllm_config() self.moe_parallel_config: FusedMoEParallelConfig = ( FusedMoEParallelConfig.make( tp_size_=(tp_size if tp_size is not None else get_tensor_model_parallel_world_size()), dp_size_=(dp_size if dp_size is not None else get_dp_group().world_size), vllm_parallel_config=vllm_config.parallel_config)) self.moe_parallel_config.ep_size = get_ep_group().world_size self.moe_parallel_config.tp_size = get_etp_group().world_size self.top_k = top_k self.num_experts = num_experts self.global_num_experts = num_experts assert intermediate_size % self.tp_size == 0 self.intermediate_size_per_partition = intermediate_size // self.tp_size self.reduce_results = reduce_results self.renormalize = renormalize self.use_grouped_topk = use_grouped_topk if self.use_grouped_topk: assert num_expert_group is not None and topk_group is not None self.num_expert_group = num_expert_group self.topk_group = topk_group self.custom_routing_function = custom_routing_function self.scoring_func = scoring_func self.e_score_correction_bias = e_score_correction_bias self.expert_map = None self.activation = activation self.log2phy = None self.global_redundant_expert_num = 0 ascend_config = get_ascend_config() expert_map_path = ascend_config.expert_map_path if expert_map_path and os.path.exists(expert_map_path): # moe expert load balance expert_load_balancer = ExpertLoadBalancer(expert_map_path, self.global_num_experts) self.local_num_experts, self.expert_map = \ expert_load_balancer.get_rank_placement_map( self.moe_instance_id, get_ep_group().rank_in_group) self.log2phy = expert_load_balancer.get_rank_log2phy_map( self.moe_instance_id, get_ep_group().rank_in_group) self.global_redundant_expert_num = \ expert_load_balancer.get_global_redundant_expert_num() else: # Create a tensor of size num_experts filled with -1 self.local_num_experts, self.expert_map = determine_expert_map( self.ep_size, get_ep_group().rank_in_group, self.global_num_experts) self.moe_parallel_config.tp_rank = get_etp_group().rank_in_group self.moe_parallel_config.ep_rank = get_ep_group().rank_in_group self.torchair_graph_enabled = ascend_config.torchair_graph_config.enabled # NOTE: multistream only effective when `VLLM_ENABLE_MC2` is on self.enable_multistream_shared_expert = \ ascend_config.torchair_graph_config.enable_multistream_shared_expert and VLLM_ENABLE_MC2 if self.scoring_func != "softmax" and not self.use_grouped_topk: raise ValueError("Only softmax scoring function is supported for " "non-grouped topk.") moe = MoEConfig( num_experts=self.global_num_experts, experts_per_token=top_k, hidden_dim=hidden_size, num_local_experts=self.local_num_experts, moe_parallel_config=self.moe_parallel_config, # TODO (bnell): this needs to be fixed for quantized types. in_dtype=params_dtype, ) if quant_config is None: self.quant_method = AscendUnquantizedFusedMoEMethod(moe) else: self.quant_method = quant_config.get_quant_method(self, prefix) assert self.quant_method is not None local_num_experts = torch.sum(self.expert_map != -1) \ if self.expert_map is not None else num_experts moe_quant_params = { "num_experts": local_num_experts, "hidden_size": hidden_size, "intermediate_size_per_partition": self.intermediate_size_per_partition, "params_dtype": params_dtype, "weight_loader": self.weight_loader, } # need full intermediate size pre-sharding for WNA16 act order if (self.quant_method.__class__.__name__ in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")): moe_quant_params["intermediate_size_full"] = intermediate_size self.ep_group = get_ep_group() self.quant_method.create_weights(layer=self, **moe_quant_params) def forward(self, hidden_states: torch.Tensor, router_logits: torch.Tensor, is_prefill: bool, enable_force_load_balance: bool = False, top_k=None, **kwargs): assert self.quant_method is not None if top_k: real_top_k = top_k else: real_top_k = self.top_k # MC2 ag/rs broadcast/all_reduce # prefill_req x x √ # decode_req √ x √ # graph_mode √ √ x if self.dp_size > 1: if VLLM_ENABLE_MC2 and not is_prefill: ... elif self.torchair_graph_enabled: if USING_LCCL_COM: # type: ignore hidden_states = get_dp_group().all_gather( hidden_states, 0, False) router_logits = get_dp_group().all_gather( router_logits, 0, False) elif self.torchair_graph_enabled and not is_prefill: hidden_states = get_dp_group().all_gather(hidden_states, 0) router_logits = get_dp_group().all_gather(router_logits, 0) else: hidden_states, router_logits = get_ep_group().dispatch( hidden_states, router_logits) # Matrix multiply. hidden_states = self.quant_method.apply( layer=self, x=hidden_states, router_logits=router_logits, top_k=real_top_k, renormalize=self.renormalize, use_grouped_topk=self.use_grouped_topk, global_num_experts=self.global_num_experts, expert_map=self.expert_map, 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, is_prefill=is_prefill, enable_force_load_balance=enable_force_load_balance, log2phy=self.log2phy, global_redundant_expert_num=self.global_redundant_expert_num, **kwargs) if self.enable_multistream_shared_expert and not is_prefill: hidden_states, shared_output = hidden_states if self.dp_size > 1: if VLLM_ENABLE_MC2 and not is_prefill: ... elif self.torchair_graph_enabled: if USING_LCCL_COM: # type: ignore hidden_states = dist._functional_collectives.reduce_scatter_tensor( hidden_states, "sum", scatter_dim=0, group=get_dp_group().device_group) elif self.torchair_graph_enabled and not is_prefill: hidden_states = dist._functional_collectives.reduce_scatter_tensor( hidden_states, "sum", scatter_dim=0, group=get_dp_group().device_group) else: hidden_states = get_ep_group().combine(hidden_states) if self.reduce_results and (self.tp_size > 1 or self.ep_size > 1): hidden_states = tensor_model_parallel_all_reduce(hidden_states) if self.enable_multistream_shared_expert and not is_prefill: return hidden_states, shared_output return hidden_states # ----------------------------------------- TBO-related -------------------------------------------- def _forward_ms_fused_moe_comp( self, hidden_states: torch.Tensor, router_logits: torch.Tensor, is_prefill: bool, real_top_k, enable_force_load_balance: bool = False, ): hidden_states = self.quant_method.apply( layer=self, x=hidden_states, router_logits=router_logits, top_k=real_top_k, renormalize=self.renormalize, use_grouped_topk=self.use_grouped_topk, global_num_experts=self.global_num_experts, expert_map=self.expert_map, 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, is_prefill=is_prefill, enable_force_load_balance=enable_force_load_balance) return hidden_states