# # 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. # import os.path from typing import Any, Callable, Optional import torch import torch_npu from vllm.config import get_current_vllm_config from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group, tensor_model_parallel_all_reduce) from vllm.forward_context import get_forward_context from vllm.logger import logger from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig from vllm.model_executor.layers.fused_moe.layer import ( FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map, get_compressed_expert_map) from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE from vllm_npu.ascend_config import get_ascend_config from vllm_npu.ascend_forward_context import MoECommType from vllm_npu.distributed.parallel_state import get_mc2_group from vllm_npu.eplb.core.eplb_utils import determine_default_log2phy_map from vllm_npu.ops.expert_load_balancer import ExpertLoadBalancer from vllm_npu.ops.moe.experts_selector import select_experts from vllm_npu.ops.moe.moe_comm_method import setup_moe_comm_method from vllm_npu.quantization.w8a8_dynamic import \ AscendW8A8DynamicFusedMoEMethod from vllm_npu.utils import (ACL_FORMAT_FRACTAL_NZ, enable_sp, is_310p, is_enable_nz, npu_stream_switch, shared_expert_dp_enabled, shared_experts_compute_stream) class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod): def __init__(self, moe: FusedMoEConfig = None): super().__init__(moe=moe) self.dynamic_eplb = get_ascend_config().dynamic_eplb self.transpose = True def process_weights_after_loading(self, layer): super(UnquantizedFusedMoEMethod, self).process_weights_after_loading(layer) if self.transpose: w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose( 1, 2).contiguous() layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False) w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose( 1, 2).contiguous() layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False) self.transpose = False else: w13_data = self._maybe_pad_weight(layer.w13_weight.data) layer.w13_weight = torch.nn.Parameter(w13_data, requires_grad=False) w2_data = self._maybe_pad_weight(layer.w2_weight.data) layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False) if not is_310p() and is_enable_nz(layer.w13_weight.data.dtype): layer.w13_weight.data = torch_npu.npu_format_cast( layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ) layer.w2_weight.data = torch_npu.npu_format_cast( layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ) def apply(self, layer: torch.nn.Module, x: torch.Tensor, use_grouped_topk: bool, top_k: int, router_logits: torch.Tensor, renormalize: bool, topk_group: Optional[int] = None, num_expert_group: Optional[int] = None, custom_routing_function: Optional[Callable] = None, scoring_func: str = "softmax", routed_scaling_factor: float = 1.0, e_score_correction_bias: Optional[torch.Tensor] = None, global_num_experts: int = -1, expert_map: Optional[torch.Tensor] = None, apply_router_weight_on_input: bool = False, enable_force_load_balance: bool = False, shared_experts: Optional[Any] = None, **kwargs) -> torch.Tensor: 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, routed_scaling_factor=routed_scaling_factor, e_score_correction_bias=e_score_correction_bias, global_num_experts=global_num_experts) 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) moe_comm_method = get_forward_context().moe_comm_method return moe_comm_method.fused_experts( hidden_states=x, w1=layer.w13_weight, w2=layer.w2_weight, topk_weights=topk_weights, topk_ids=topk_ids, global_num_experts=global_num_experts, expert_map=expert_map, shared_experts=shared_experts, apply_router_weight_on_input=apply_router_weight_on_input, dynamic_eplb=self.dynamic_eplb) class AscendFusedMoE(FusedMoE): moe_counter = -1 def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) num_experts = kwargs["num_experts"] intermediate_size = kwargs["intermediate_size"] AscendFusedMoE.moe_counter += 1 self.moe_instance_id = AscendFusedMoE.moe_counter self.expert_map = None self.log2phy = None if self.quant_config is None: self.quant_method = AscendUnquantizedFusedMoEMethod( self.moe_config) else: self.quant_method = self.quant_config.get_quant_method( self, self.layer_name) assert self.quant_method is not None self.moe_config.tp_group = get_tp_group() self.moe_config.dp_group = get_dp_group() self.moe_config.ep_group = get_ep_group() self.moe_config.mc2_group = get_mc2_group() ascend_config = get_ascend_config() self.dynamic_eplb = ascend_config.dynamic_eplb or ascend_config.expert_map_record_path self.expert_map_path = ascend_config.expert_map_path self.global_redundant_expert_num = ascend_config.init_redundancy_expert self.global_num_experts = num_experts + self.global_redundant_expert_num # TODO: Flag for static expert placement. This is a temporary workaround # to allow dynamic EPLB with float weights by skipping quantization checks. self.static_eplb_enabled = False if self.custom_routing_function is None and self.e_score_correction_bias is not None: vllm_config = get_current_vllm_config() self.e_score_correction_bias.data = self.e_score_correction_bias.data.to( dtype=vllm_config.model_config.dtype) # static eplb initializing with expert_map_path init_eplb_enable = False if self.expert_map_path and os.path.exists( self.expert_map_path) and os.access(self.expert_map_path, os.R_OK): self.expert_load_balancer = ExpertLoadBalancer( self.expert_map_path, num_experts) self.expert_load_balancer.check_expert_map_tensor() self.global_redundant_expert_num = ( self.expert_load_balancer.get_global_redundant_expert_num()) self.global_num_experts = num_experts + self.global_redundant_expert_num try: self.local_num_experts, self.expert_map = ( self.expert_load_balancer.get_rank_placement_map( self.moe_instance_id, self.ep_rank)) self.log2phy = self.expert_load_balancer.get_rank_log2phy_map( self.moe_instance_id, self.ep_rank).npu() init_eplb_enable = True except Exception as e: logger.warning( f"Init expert map of mtp/eagle when using sample.{e}") self.local_num_experts, self.expert_map = determine_expert_map( self.ep_size, self.ep_rank, self.global_num_experts) self.log2phy = determine_default_log2phy_map( self.global_num_experts, self.ep_size, self.ep_rank).npu() else: # init moe. self.local_num_experts, self.expert_map = determine_expert_map( self.ep_size, self.ep_rank, self.global_num_experts) # dynamic eplb initializing with not expert_map_path if self.dynamic_eplb: self.log2phy = determine_default_log2phy_map( self.global_num_experts, self.ep_size, self.ep_rank).npu() if self.expert_map is not None and isinstance(self.expert_map, torch.Tensor): logger.info_once( "[EP Rank %s/%s] Expert parallelism is enabled. Local/global" " number of experts: %s/%s. Experts local to global index map:" " %s.", self.ep_rank, self.ep_size, self.local_num_experts, self.global_num_experts, get_compressed_expert_map(self.expert_map)) local_num_experts = (torch.sum( self.expert_map != -1) if self.expert_map is not None else self.global_num_experts) if self.dynamic_eplb: self.moe_load = torch.zeros(local_num_experts, dtype=torch.int64).npu() if init_eplb_enable and ( not hasattr(self.quant_method, "quant_method") or not isinstance(self.quant_method.quant_method, AscendW8A8DynamicFusedMoEMethod)): raise ValueError("Eplb supports only w8a8_dynamic quantization.") self.moe_config.num_experts = self.global_num_experts self.moe_config.num_local_experts = self.local_num_experts self.moe_config.original_num_experts = num_experts moe_quant_params = { "num_experts": local_num_experts, "hidden_size": self.hidden_size, "intermediate_size_per_partition": self.intermediate_size_per_partition, "params_dtype": self.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.quant_method.create_weights(layer=self, **moe_quant_params) self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp setup_moe_comm_method(self.moe_config) def update_expert_map(self, new_expert_map): self.expert_map = new_expert_map def get_map(self): return self.expert_map def get_log2phy_map(self): return self.log2phy def clear_moe_load(self): if self.moe_load is not None: self.moe_load.zero_() def maybe_all_reduce_tensor_model_parallel( self, final_hidden_states: torch.Tensor): """NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`, and `alltoallcommimpl`, we do not need to all-reduce the final outputs since the outputs are already aggregated across tensor parallel ranks in the `finalize` function. In `allgathercommimpl`, we still need to all-reduce the outputs since each rank only has partial outputs. """ return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel( final_hidden_states) def forward_impl(self, hidden_states: torch.Tensor, router_logits: torch.Tensor): assert self.quant_method is not None # For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel. quantized_x_for_share, dynamic_scale_for_share = None, None forward_context = get_forward_context() # Load balancing for token distribution among experts in dummy_run # TODO: The community only considers load balancing when DP > 1. # This approach may overlook some extreme scenarios. enable_force_load_balance = forward_context.in_profile_run hidden_states, router_logits = forward_context.moe_comm_method.prepare( hidden_states=hidden_states, router_logits=router_logits, replace_allreduce=forward_context.sp_enabled, enable_shared_expert_dp=self.enable_shared_expert_dp) # Matrix multiply. final_hidden_states = self.quant_method.apply( layer=self, x=hidden_states, router_logits=router_logits, top_k=self.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, activation=self.activation, apply_router_weight_on_input=self.apply_router_weight_on_input, quantized_x_for_share=quantized_x_for_share, dynamic_scale_for_share=dynamic_scale_for_share, shared_experts=None, enable_force_load_balance=enable_force_load_balance, log2phy=self.log2phy, global_redundant_expert_num=self.global_redundant_expert_num) if isinstance(final_hidden_states, tuple): final_hidden_states, group_list_type, expert_tokens = final_hidden_states if self.dynamic_eplb: self.moe_load += expert_tokens if group_list_type == 1 else \ torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]]) final_hidden_states = forward_context.moe_comm_method.finalize( hidden_states=final_hidden_states, reduce_results=self.reduce_results) return final_hidden_states def transpose_weight(self, loaded_weight, expert_data, shard_dim): # Ensure training and inference weight shapes match during RL weight updates if ( loaded_weight.shape[1] != expert_data.shape[1] and \ loaded_weight.shape[0] != expert_data.shape[0] ): shard_dim = int(not shard_dim) loaded_weight = loaded_weight.transpose(0, 1).contiguous() return loaded_weight, shard_dim def _load_w13(self, expert_data: torch.Tensor, shard_dim: int, shard_id: str, loaded_weight: torch.Tensor, tp_rank: int, load_full: bool = False): # Index the loaded weight for tp sharding. # gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim loaded_weight, shard_dim = self.transpose_weight( loaded_weight, expert_data, shard_dim) shard_size = expert_data.shape[shard_dim] // 2 if not load_full: loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank, shard_size) # Narrow parameter and load. # w1, gate_proj: Load into first logical weight of w13. if shard_id == "w1": expert_data = expert_data.narrow(shard_dim, 0, shard_size) # w3, up_proj: Load into second logical weight of w13. else: assert shard_id == "w3" expert_data = expert_data.narrow(shard_dim, shard_size, shard_size) expert_data.copy_(loaded_weight) def _load_w2(self, expert_data: torch.Tensor, shard_dim: int, loaded_weight: torch.Tensor, tp_rank: int, load_full: bool = False): # Index the loaded weight for tp sharding. # down_proj: "RowParallel" so tp sharding on input_dim # Narrow parameter and load. loaded_weight, shard_dim = self.transpose_weight( loaded_weight, expert_data, shard_dim) shard_size = expert_data.shape[shard_dim] if not load_full: loaded_weight = loaded_weight.narrow(shard_dim, shard_size * tp_rank, shard_size) # w2, down_proj: Load into only logical weight of w2. expert_data.copy_(loaded_weight) class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE): def __init__( self, shared_experts: torch.nn.Module, use_overlapped: bool = True, **kwargs, ): AscendFusedMoE.__init__(self, **kwargs) self._shared_experts = shared_experts self.use_overlapped = use_overlapped self.shared_expert_stream = None ascend_config = get_ascend_config() self.multistream_overlap_shared_expert = ascend_config.multistream_overlap_shared_expert if enable_sp(): logger.info_once( "Sequence parallelism is enabled, shared experts are replicated for best performance." ) def forward( self, hidden_states: torch.Tensor, router_logits: torch.Tensor, ) -> tuple[torch.Tensor, torch.Tensor]: shared_out, fused_out = AscendFusedMoE.forward( self, hidden_states=hidden_states, router_logits=router_logits, ) return shared_out, fused_out def forward_impl(self, hidden_states: torch.Tensor, router_logits: torch.Tensor): # Make sure the shared experts stream begins after hidden_states are ready. if self.multistream_overlap_shared_expert: shared_experts_compute_stream().wait_stream( # type: ignore torch.npu.current_stream()) with npu_stream_switch(shared_experts_compute_stream(), enabled=self.multistream_overlap_shared_expert): # Use a separate stream to run shared experts. # Note that currently we only support calculations in separate streams with aclgraph. # Communication operations in another stream might cause unknown errors. shared_out = self._shared_experts(hidden_states) fused_output = AscendFusedMoE.forward_impl( self, hidden_states=hidden_states, router_logits=router_logits, ) # Make sure the default stream waits for the shared experts stream to finish. if self.multistream_overlap_shared_expert: torch.npu.current_stream().wait_stream( shared_experts_compute_stream()) # NOTE: This is exactly the opposite of `maybe_all_reduce_tensor_model_parallel` forward_context = get_forward_context() moe_comm_type = forward_context.moe_comm_type if moe_comm_type in {MoECommType.ALLTOALL, MoECommType.MC2} \ and not shared_expert_dp_enabled(): shared_out = tensor_model_parallel_all_reduce(shared_out) return shared_out, fused_output