6a4ec186e7
### What this PR does / why we need it?
Integrate the arange operator to reduce the time spent and improve
performance
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
- vLLM version: v0.10.1.1
- vLLM main:
56dcf4e7e9
---------
Signed-off-by: s30076806 <songjiayang2@h-partners.com>
283 lines
10 KiB
Python
283 lines
10 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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from typing import Any, Callable, Optional
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import torch
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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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from vllm.forward_context import get_forward_context
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from vllm.model_executor.layers.fused_moe.layer import (
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FusedMoE, UnquantizedFusedMoEMethod)
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from vllm_ascend.ascend_config import get_ascend_config
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from vllm_ascend.distributed.moe_comm_method import (AllGatherCommImpl,
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DummyCommImpl,
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MC2CommImpl,
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MoECommMethod)
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from vllm_ascend.distributed.parallel_state import get_mc2_group
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from vllm_ascend.ops.fused_moe import apply_mlp, fused_experts_moge
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from vllm_ascend.ops.layers.experts_selector import select_experts
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from vllm_ascend.utils import is_310p
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original_unquantized_fused_moe_init_func = UnquantizedFusedMoEMethod.__init__
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def fused_experts(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w2: torch.Tensor,
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topk_weights: torch.Tensor,
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topk_ids: torch.Tensor,
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activation: str = "silu",
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apply_router_weight_on_input: bool = False,
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use_int8_w8a8: bool = False,
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use_int4_w4a8: bool = False,
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global_num_experts: Optional[int] = None,
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expert_map: Optional[torch.Tensor] = None,
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w1_scale: Optional[torch.Tensor] = None,
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w2_scale: Optional[torch.Tensor] = None,
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w1_scale_bias: torch.Tensor = None,
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w2_scale_bias: torch.Tensor = None,
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moe_comm_method: Optional[MoECommMethod] = None,
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# For TorchAir graph
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is_torchair: bool = False,
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# For Cube/Vector parallel
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shared_experts: Optional[Any] = None,
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quantized_x_for_share: Optional[Any] = None,
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dynamic_scale_for_share: Optional[Any] = None,
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# For load balance
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log2phy: torch.Tensor = None,
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global_redundant_expert_num: int = 0,
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) -> torch.Tensor:
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# Check constraints
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assert hidden_states.shape[1] == w1.shape[2], (
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f"Hidden size mismatch {hidden_states.shape[1]} != {w1.shape[2]}")
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assert topk_weights.shape == topk_ids.shape, "topk shape mismatch"
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assert hidden_states.is_contiguous(), "Hidden_states must be contiguous"
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assert w1.stride(-1) == 1, "Stride of last dimension must be 1"
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assert w2.stride(-1) == 1, "Stride of last dimension must be 1"
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assert hidden_states.dtype in [
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torch.float32, torch.float16, torch.bfloat16
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]
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assert moe_comm_method is not None, "Missing communication context"
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num_experts = w1.shape[0]
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permuted_hidden_states, expert_tokens, group_list_type = moe_comm_method.permute(
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hidden_states, topk_ids, topk_weights, expert_map, num_experts)
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mlp_output = apply_mlp(
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permuted_hidden_states,
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w1,
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w2,
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expert_tokens,
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group_list_type=group_list_type,
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)
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moe_comm_method.unpermute(mlp_output, hidden_states)
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return hidden_states
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def unquantized_fused_moe_init_func(self, *args, **kwargs):
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original_unquantized_fused_moe_init_func(self, *args, **kwargs)
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vllm_config = get_current_vllm_config()
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self.max_num_batched_tokens = vllm_config.scheduler_config.max_num_batched_tokens
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ascend_config = get_ascend_config()
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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
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and not vllm_config.model_config.enforce_eager)
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def forward_oot(
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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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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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activation: str = "silu",
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enable_eplb: bool = False,
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expert_load_view: Optional[torch.Tensor] = None,
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logical_to_physical_map: Optional[torch.Tensor] = None,
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logical_replica_count: Optional[torch.Tensor] = None) -> 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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e_score_correction_bias=e_score_correction_bias,
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global_num_experts=global_num_experts)
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if topk_ids.shape[1] < top_k or is_310p():
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assert global_num_experts is not None
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return fused_experts_moge(
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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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moe_parallel_config=self.moe.moe_parallel_config,
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topk_weights=topk_weights,
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topk_ids=topk_ids,
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top_k=top_k,
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global_num_experts=global_num_experts,
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expert_map=expert_map,
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apply_router_weight_on_input=apply_router_weight_on_input)
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moe_comm_method = get_forward_context().moe_comm_method
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return 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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moe_comm_method=moe_comm_method,
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)
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class AscendFusedMoE(FusedMoE):
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def __init__(
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self,
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num_experts,
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top_k,
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hidden_size,
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intermediate_size,
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params_dtype=None,
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reduce_results=False,
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renormalize=True,
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use_grouped_topk=False,
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num_expert_group=None,
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topk_group=None,
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quant_config=None,
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tp_size=None,
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ep_size=None,
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dp_size=None,
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prefix="",
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custom_routing_function=None,
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scoring_func="softmax",
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e_score_correction_bias=None,
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apply_router_weight_on_input=False,
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activation="silu",
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enable_eplb=False,
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num_redundant_experts=0,
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has_bias=False,
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):
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super().__init__(
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num_experts,
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top_k,
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hidden_size,
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intermediate_size,
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params_dtype,
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reduce_results,
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renormalize,
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use_grouped_topk,
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num_expert_group,
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topk_group,
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quant_config,
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tp_size,
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ep_size,
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dp_size,
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prefix,
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custom_routing_function,
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scoring_func,
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e_score_correction_bias,
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apply_router_weight_on_input,
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activation,
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enable_eplb,
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num_redundant_experts,
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has_bias,
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)
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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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for method in {AllGatherCommImpl, DummyCommImpl, MC2CommImpl}:
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setattr(
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self, method.__name__.lower(),
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method(moe_config=self.moe_config)) # type: ignore[abstract]
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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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forward_context = get_forward_context()
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moe_comm_method_name = forward_context.moe_comm_method_name
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if not self.moe_config.use_ep and moe_comm_method_name != "dummycommimpl":
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moe_comm_method_name = "allgathercommimpl"
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forward_context.moe_comm_method = getattr(self, moe_comm_method_name)
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hidden_states, router_logits = forward_context.moe_comm_method.prepare(
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hidden_states=hidden_states, router_logits=router_logits)
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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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enable_eplb=self.enable_eplb,
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expert_load_view=self.expert_load_view,
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logical_to_physical_map=self.logical_to_physical_map,
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logical_replica_count=self.logical_replica_count,
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)
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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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return final_hidden_states
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UnquantizedFusedMoEMethod.__init__ = unquantized_fused_moe_init_func
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UnquantizedFusedMoEMethod.forward_oot = forward_oot
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