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https://github.com/vllm-project/vllm-ascend.git
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4f937f561d
### What this PR does / why we need it? 1. Replace manual memory cleanup with passing parameter. 2. FusedMoEPrepareAndFinalizeWithMC2 inherits All2All avoid duplicated code. ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? e2e & ut - vLLM version: v0.11.0rc3 - vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.0 Signed-off-by: Pr0Wh1teGivee <calvin_zhu0210@outlook.com>
441 lines
19 KiB
Python
441 lines
19 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the vllm-ascend project.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import os.path
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from typing import Any, Callable, Optional
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import torch
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import torch_npu
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from vllm.config import CompilationLevel, get_current_vllm_config
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from vllm.distributed import (get_dp_group, get_ep_group, get_tp_group,
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tensor_model_parallel_all_reduce)
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from vllm.forward_context import get_forward_context
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from vllm.logger import logger
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from vllm.model_executor.layers.fused_moe.config import FusedMoEConfig
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, UnquantizedFusedMoEMethod, determine_expert_map)
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from vllm.model_executor.layers.shared_fused_moe import SharedFusedMoE
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.ascend_forward_context import MoECommType
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.eplb.core.eplb_utils import (determine_default_expert_map,
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determine_default_log2phy_map)
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from vllm_ascend.ops.expert_load_balancer import ExpertLoadBalancer
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from vllm_ascend.ops.moe.experts_selector import select_experts
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from vllm_ascend.ops.moe.moe_comm_method import setup_moe_comm_method
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from vllm_ascend.utils import (ACL_FORMAT_FRACTAL_NZ, is_310p, is_enable_nz,
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npu_stream_switch)
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class AscendUnquantizedFusedMoEMethod(UnquantizedFusedMoEMethod):
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def __init__(self, moe: FusedMoEConfig = None):
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super().__init__(moe=moe)
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# NOTE: Currently, this self.use_aclgraph is only used in
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# UnquantizedFusedMoEMethod.forward_oot to decide whether to use in
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# ops/fused_moe.py:568 to circumvent torch.randint_like not supported issue.
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# Once torch.randint_like is supported or removed, this flag can be removed.
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vllm_config = get_current_vllm_config()
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ascend_config = get_ascend_config()
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self.dynamic_eplb = get_ascend_config().dynamic_eplb
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if ascend_config.torchair_graph_config.enabled:
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self.use_aclgraph = False
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else:
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self.use_aclgraph = (vllm_config.compilation_config.level
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== CompilationLevel.PIECEWISE and
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not vllm_config.model_config.enforce_eager)
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self.transpose = True
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def process_weights_after_loading(self, layer):
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super(UnquantizedFusedMoEMethod,
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self).process_weights_after_loading(layer)
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if self.transpose:
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w13_data = self._maybe_pad_weight(layer.w13_weight.data).transpose(
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1, 2).contiguous()
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layer.w13_weight = torch.nn.Parameter(w13_data,
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requires_grad=False)
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w2_data = self._maybe_pad_weight(layer.w2_weight.data).transpose(
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1, 2).contiguous()
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layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
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self.transpose = False
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else:
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w13_data = self._maybe_pad_weight(layer.w13_weight.data)
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layer.w13_weight = torch.nn.Parameter(w13_data,
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requires_grad=False)
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w2_data = self._maybe_pad_weight(layer.w2_weight.data)
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layer.w2_weight = torch.nn.Parameter(w2_data, requires_grad=False)
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if not is_310p() and is_enable_nz():
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layer.w13_weight.data = torch_npu.npu_format_cast(
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layer.w13_weight.data, ACL_FORMAT_FRACTAL_NZ)
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layer.w2_weight.data = torch_npu.npu_format_cast(
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layer.w2_weight.data, ACL_FORMAT_FRACTAL_NZ)
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def apply(self,
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layer: torch.nn.Module,
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x: torch.Tensor,
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use_grouped_topk: bool,
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top_k: int,
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router_logits: torch.Tensor,
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renormalize: bool,
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topk_group: Optional[int] = None,
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num_expert_group: Optional[int] = None,
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custom_routing_function: Optional[Callable] = None,
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scoring_func: str = "softmax",
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routed_scaling_factor: float = 1.0,
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e_score_correction_bias: Optional[torch.Tensor] = None,
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global_num_experts: int = -1,
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expert_map: Optional[torch.Tensor] = None,
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apply_router_weight_on_input: bool = False,
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enable_force_load_balance: bool = False,
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shared_experts: Optional[Any] = None,
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**kwargs) -> torch.Tensor:
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topk_weights, topk_ids = select_experts(
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hidden_states=x,
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router_logits=router_logits,
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top_k=top_k,
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use_grouped_topk=use_grouped_topk,
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renormalize=renormalize,
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topk_group=topk_group,
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num_expert_group=num_expert_group,
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custom_routing_function=custom_routing_function,
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scoring_func=scoring_func,
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routed_scaling_factor=routed_scaling_factor,
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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts)
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topk_weights = topk_weights.to(x.dtype)
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# this is a naive implementation for experts load balance so as
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# to avoid accumulating too much tokens on a single rank.
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# currently it is only activated when doing profile runs.
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if enable_force_load_balance and not self.use_aclgraph:
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topk_ids = torch.randint_like(topk_ids, 0, global_num_experts)
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moe_comm_method = get_forward_context().moe_comm_method
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return moe_comm_method.fused_experts(
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hidden_states=x,
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w1=layer.w13_weight,
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w2=layer.w2_weight,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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global_num_experts=global_num_experts,
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expert_map=expert_map,
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shared_experts=shared_experts,
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apply_router_weight_on_input=apply_router_weight_on_input,
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dynamic_eplb=self.dynamic_eplb)
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class AscendFusedMoE(FusedMoE):
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moe_counter = -1
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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num_experts = kwargs["num_experts"]
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intermediate_size = kwargs["intermediate_size"]
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AscendFusedMoE.moe_counter += 1
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self.moe_instance_id = AscendFusedMoE.moe_counter
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self.global_num_experts = num_experts
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self.expert_map = None
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self.log2phy = None
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self.global_redundant_expert_num = 0
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if self.quant_config is None:
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self.quant_method = AscendUnquantizedFusedMoEMethod(
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self.moe_config)
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else:
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self.quant_method = self.quant_config.get_quant_method(
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self, self.layer_name)
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assert self.quant_method is not None
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self.moe_config.tp_group = get_tp_group()
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self.moe_config.dp_group = get_dp_group()
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self.moe_config.ep_group = get_ep_group()
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self.moe_config.mc2_group = get_mc2_group()
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ascend_config = get_ascend_config()
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self.dynamic_eplb = ascend_config.dynamic_eplb
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self.expert_map_path = ascend_config.expert_map_path
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self.global_redundant_expert_num = ascend_config.init_redundancy_expert
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self.global_num_experts = num_experts + self.global_redundant_expert_num
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# static eplb initializing with expert_map_path
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if self.expert_map_path and os.path.exists(
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self.expert_map_path) and os.access(self.expert_map_path,
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os.R_OK):
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self.expert_load_balancer = ExpertLoadBalancer(
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self.expert_map_path, self.global_num_experts)
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self.global_redundant_expert_num = (
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self.expert_load_balancer.get_global_redundant_expert_num())
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try:
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self.local_num_experts, self.expert_map = (
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self.expert_load_balancer.get_rank_placement_map(
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self.moe_instance_id, self.ep_rank))
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self.log2phy = self.expert_load_balancer.get_rank_log2phy_map(
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self.moe_instance_id, self.ep_rank).npu()
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except Exception as e:
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logger.warning(
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f"Init expert map of mtp/eagle when using sample.{e}")
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self.local_num_experts, self.expert_map = determine_default_expert_map(
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self.global_num_experts, self.ep_size, self.ep_rank,
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self.global_redundant_expert_num)
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self.log2phy = determine_default_log2phy_map(
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self.global_num_experts, self.ep_size, self.ep_rank,
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self.global_redundant_expert_num).npu()
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else:
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# init moe.
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self.local_num_experts, self.expert_map = determine_expert_map(
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self.ep_size, self.ep_rank, self.global_num_experts)
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# dynamic eplb initializing with not expert_map_path
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if self.dynamic_eplb:
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self.global_redundant_expert_num = ascend_config.init_redundancy_expert
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self.local_num_experts, self.expert_map = determine_default_expert_map(
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self.global_num_experts, self.ep_size, self.ep_rank,
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self.global_redundant_expert_num)
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self.log2phy = determine_default_log2phy_map(
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self.global_num_experts, self.ep_size, self.ep_rank,
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self.global_redundant_expert_num).npu()
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local_num_experts = (torch.sum(
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self.expert_map != -1) if self.expert_map is not None else
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self.global_num_experts)
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if self.dynamic_eplb:
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self.moe_load = torch.zeros(local_num_experts, dtype=torch.int64)
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self.moe_config.num_experts = self.global_num_experts
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self.moe_config.num_local_experts = self.local_num_experts
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self.moe_config.original_num_experts = num_experts
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moe_quant_params = {
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"num_experts": local_num_experts,
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"hidden_size": self.hidden_size,
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"intermediate_size_per_partition":
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self.intermediate_size_per_partition,
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"params_dtype": self.params_dtype,
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"weight_loader": self.weight_loader,
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}
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# need full intermediate size pre-sharding for WNA16 act order
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if (self.quant_method.__class__.__name__
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in ("GPTQMarlinMoEMethod", "CompressedTensorsWNA16MoEMethod")):
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moe_quant_params["intermediate_size_full"] = intermediate_size
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self.quant_method.create_weights(layer=self, **moe_quant_params)
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self.enable_shared_expert_dp = ascend_config.enable_shared_expert_dp
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setup_moe_comm_method(self.moe_config)
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def update_expert_map(self, new_expert_map):
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self.expert_map = new_expert_map
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def get_map(self):
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return self.expert_map
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def get_log2phy_map(self):
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return self.logical_to_physical_map
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def clear_moe_load(self):
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if self.moe_load is not None:
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self.moe_load.zero_()
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def maybe_all_reduce_tensor_model_parallel(
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self, final_hidden_states: torch.Tensor):
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"""NOTE(Yizhou): This is to override the parent class method. In `mc2commimpl`,
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and `alltoallcommimpl`, we do not need to all-reduce the final outputs since
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the outputs are already aggregated across tensor parallel ranks in the
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`finalize` function. In `allgathercommimpl`, we still need to all-reduce the
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outputs since each rank only has partial outputs.
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"""
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return torch.ops.vllm.maybe_all_reduce_tensor_model_parallel(
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final_hidden_states)
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def forward_impl(self, hidden_states: torch.Tensor,
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router_logits: torch.Tensor):
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assert self.quant_method is not None
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# For w8a8 dynamic we can do npu_dynamic_quant and gate in parallel.
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quantized_x_for_share, dynamic_scale_for_share = None, None
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forward_context = get_forward_context()
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# Load balancing for token distribution among experts in dummy_run
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# TODO: The community only considers load balancing when DP > 1.
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# This approach may overlook some extreme scenarios.
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enable_force_load_balance = forward_context.in_profile_run
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forward_context = get_forward_context()
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hidden_states, router_logits, mc2_mask, context_metadata = forward_context.moe_comm_method.prepare(
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hidden_states=hidden_states,
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router_logits=router_logits,
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replace_allreduce=forward_context.sp_enabled,
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enable_shared_expert_dp=self.enable_shared_expert_dp)
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# Matrix multiply.
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final_hidden_states = self.quant_method.apply(
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layer=self,
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x=hidden_states,
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router_logits=router_logits,
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top_k=self.top_k,
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renormalize=self.renormalize,
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use_grouped_topk=self.use_grouped_topk,
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global_num_experts=self.global_num_experts,
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expert_map=self.expert_map,
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topk_group=self.topk_group,
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num_expert_group=self.num_expert_group,
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custom_routing_function=self.custom_routing_function,
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scoring_func=self.scoring_func,
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e_score_correction_bias=self.e_score_correction_bias,
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activation=self.activation,
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apply_router_weight_on_input=self.apply_router_weight_on_input,
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quantized_x_for_share=quantized_x_for_share,
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dynamic_scale_for_share=dynamic_scale_for_share,
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shared_experts=None,
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enable_force_load_balance=enable_force_load_balance,
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log2phy=self.log2phy,
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global_redundant_expert_num=self.global_redundant_expert_num,
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mc2_mask=mc2_mask)
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if isinstance(final_hidden_states, tuple):
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final_hidden_states, group_list_type, expert_tokens = final_hidden_states
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if self.dynamic_eplb:
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self.moe_load += expert_tokens if group_list_type else \
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torch.cat([expert_tokens[:1], expert_tokens[1:] - expert_tokens[:-1]])
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final_hidden_states = forward_context.moe_comm_method.finalize(
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hidden_states=final_hidden_states,
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reduce_results=self.reduce_results,
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context_metadata=context_metadata)
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return final_hidden_states
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def transpose_weight(self, loaded_weight, expert_data, shard_dim):
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# Ensure training and inference weight shapes match during RL weight updates
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if (
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loaded_weight.shape[1] != expert_data.shape[1] and \
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loaded_weight.shape[0] != expert_data.shape[0]
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):
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shard_dim = int(not shard_dim)
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loaded_weight = loaded_weight.transpose(0, 1).contiguous()
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return loaded_weight, shard_dim
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def _load_w13(self,
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expert_data: torch.Tensor,
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shard_dim: int,
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shard_id: str,
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loaded_weight: torch.Tensor,
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tp_rank: int,
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load_full: bool = False):
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# Index the loaded weight for tp sharding.
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# gate_up_proj: "MergedColumnParallel", so tp sharding on output_dim
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loaded_weight, shard_dim = self.transpose_weight(
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loaded_weight, expert_data, shard_dim)
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shard_size = expert_data.shape[shard_dim] // 2
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if not load_full:
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loaded_weight = loaded_weight.narrow(shard_dim,
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shard_size * tp_rank,
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shard_size)
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# Narrow parameter and load.
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# w1, gate_proj: Load into first logical weight of w13.
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if shard_id == "w1":
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expert_data = expert_data.narrow(shard_dim, 0, shard_size)
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# w3, up_proj: Load into second logical weight of w13.
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else:
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assert shard_id == "w3"
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expert_data = expert_data.narrow(shard_dim, shard_size, shard_size)
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expert_data.copy_(loaded_weight)
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def _load_w2(self,
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expert_data: torch.Tensor,
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shard_dim: int,
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loaded_weight: torch.Tensor,
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tp_rank: int,
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load_full: bool = False):
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# Index the loaded weight for tp sharding.
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# down_proj: "RowParallel" so tp sharding on input_dim
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# Narrow parameter and load.
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loaded_weight, shard_dim = self.transpose_weight(
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loaded_weight, expert_data, shard_dim)
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shard_size = expert_data.shape[shard_dim]
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if not load_full:
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loaded_weight = loaded_weight.narrow(shard_dim,
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shard_size * tp_rank,
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shard_size)
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# w2, down_proj: Load into only logical weight of w2.
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expert_data.copy_(loaded_weight)
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class AscendSharedFusedMoE(SharedFusedMoE, AscendFusedMoE):
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def __init__(
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self,
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shared_experts: torch.nn.Module,
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use_overlapped: bool = True,
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**kwargs,
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):
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AscendFusedMoE.__init__(self, **kwargs)
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self._shared_experts = shared_experts
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self.use_overlapped = use_overlapped
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self.shared_expert_stream = None
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ascend_config = get_ascend_config()
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self.multistream_overlap_shared_expert = ascend_config.multistream_overlap_shared_expert
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if self.multistream_overlap_shared_expert:
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self.shared_expert_stream = torch.npu.Stream()
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def forward(
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self,
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hidden_states: torch.Tensor,
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router_logits: torch.Tensor,
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) -> 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:
|
|
self.shared_expert_stream.wait_stream( # type: ignore
|
|
torch.npu.current_stream())
|
|
with npu_stream_switch(self.shared_expert_stream,
|
|
enabled=self.multistream_overlap_shared_expert):
|
|
# Use a separate stream to run shared experts.
|
|
shared_out = self._shared_experts(hidden_states)
|
|
|
|
# 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}:
|
|
shared_out = tensor_model_parallel_all_reduce(shared_out)
|
|
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(self.shared_expert_stream)
|
|
return shared_out, fused_output
|