88d03a783f
### What this PR does / why we need it? Refactor `vllm_ascend/ops/fused_moe` to replace scattered MoE business `**kwargs` with typed request objects and explicit stage boundaries. - Prepare, dispatch, MLP, and quant stages now have clearer ownership. - Main MoE path no longer depends on business `kwargs.get(...)` lookups. - Comm and dispatcher interfaces are request-only on the main path. - UTs can assert stage-level fields directly instead of inferring behavior indirectly. ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? CI passed. --------- Signed-off-by: linfeng-yuan <1102311262@qq.com>
103 lines
3.4 KiB
Python
103 lines
3.4 KiB
Python
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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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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# This file is a part of the vllm-ascend project.
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import torch
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import torch_npu
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from vllm_ascend.ops.fused_moe.moe_runtime_args import MoEMlpComputeInput
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def quant_apply_mlp(
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hidden_states: torch.Tensor,
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w1: torch.Tensor,
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w1_scale: torch.Tensor,
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w2: torch.Tensor,
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w2_scale: torch.Tensor,
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group_list: torch.Tensor,
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group_list_type: int = 1,
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) -> torch.Tensor:
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if group_list_type == 1:
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# Convert group_list to cumulative sum format if group_list is count format
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group_list = torch.cumsum(group_list, dim=0)
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hidden_states = torch_npu.npu_quant_grouped_matmul_dequant(
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x=hidden_states, quantized_weight=w1, weight_scale=w1_scale, group_list=group_list, quant_mode="pertoken"
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)
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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hidden_states = torch_npu.npu_quant_grouped_matmul_dequant(
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x=hidden_states, quantized_weight=w2, weight_scale=w2_scale, group_list=group_list, quant_mode="pertoken"
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)
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return hidden_states
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def unquant_apply_mlp(
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hidden_states: torch.Tensor, w1: torch.Tensor, w2: torch.Tensor, group_list: torch.Tensor, group_list_type: int = 1
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) -> torch.Tensor:
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gate_up_out = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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)[0]
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act_out = torch_npu.npu_swiglu(gate_up_out)
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[act_out],
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weight=[w2],
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split_item=2,
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group_list_type=group_list_type,
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group_type=0,
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group_list=group_list,
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)[0]
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return hidden_states
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def unified_apply_mlp(*, mlp_compute_input: MoEMlpComputeInput) -> torch.Tensor:
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hidden_states = mlp_compute_input.hidden_states
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w1 = mlp_compute_input.weights.w1
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w2 = mlp_compute_input.weights.w2
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w1_scale = mlp_compute_input.weights.w1_scale
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w2_scale = mlp_compute_input.weights.w2_scale
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group_list = mlp_compute_input.group_list
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group_list_type = mlp_compute_input.group_list_type
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assert isinstance(w1, torch.Tensor)
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assert isinstance(w2, torch.Tensor)
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if mlp_compute_input.quant.is_quant:
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assert isinstance(w1_scale, torch.Tensor)
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assert isinstance(w2_scale, torch.Tensor)
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assert w1_scale is not None and w2_scale is not None
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return quant_apply_mlp(
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hidden_states=hidden_states,
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w1=w1,
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w1_scale=w1_scale,
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w2=w2,
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w2_scale=w2_scale,
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group_list=group_list,
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group_list_type=group_list_type,
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)
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return unquant_apply_mlp(
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hidden_states=hidden_states,
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w1=w1,
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w2=w2,
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group_list=group_list,
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group_list_type=group_list_type,
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)
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