aff5189c87
### What this PR does / why we need it?
Fuse `GroupedMatmul`, `Swiglu` and `DynamicQuant` into one fusion
operation `GroupedMatmulSwigluQuant`.
1. extract common functions in `w4a8_dynamic.py` and `w8a8_dynamic.py`
2. if in supported occasion, use fusion operation
`npu_grouped_matmul_swiglu_quant`
### Does this PR introduce _any_ user-facing change?
### How was this patch tested?
Tested on W8A8 quantized Qwen3-235B-A22B model with `bs=16`
1. `tp=8`, `dp=1`, `moe_tp=8`, `moe_ep=1`, TPOP increased 21.54%, Output
Token Throughput increased 27.35%
<img width="3443" height="211" alt="image"
src="https://github.com/user-attachments/assets/a1a9c14d-2310-41be-9a03-36125dabae6e"
/>
3. `tp=8`, `dp=1`, `moe_tp=1`, `moe_ep=8`, TPOP increased 17.38%, Output
Token Throughput increased 6.86%
<img width="3443" height="211" alt="image"
src="https://github.com/user-attachments/assets/1ce92e92-720d-40c0-8b4d-c493e5cb10a6"
/>
- vLLM version: v0.10.1.1
- vLLM main:
6997a25ac6
---------
Signed-off-by: Ruri <33858552+zhoux77899@users.noreply.github.com>
Signed-off-by: zhoux77899 <zhouxiang100@huawei.com>
248 lines
9.4 KiB
Python
248 lines
9.4 KiB
Python
# Copyright (c) 2025 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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from typing import Optional
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import torch
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import torch_npu
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from torch.nn.functional import pad
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from vllm.forward_context import get_forward_context
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from vllm_ascend.ascend_forward_context import FusedMoEState
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from vllm_ascend.utils import dispose_tensor, is_310p
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def cumsum_group_list(group_list: torch.Tensor,
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group_list_type: int,
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active_num: int = 0,
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expert_num: int = 0) -> torch.Tensor:
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if group_list_type not in [0, 1, 2]:
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raise ValueError(
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f"group_list_type should be in [0, 1, 2], but received {group_list_type}"
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)
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if group_list_type == 0:
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return group_list
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if group_list_type == 1:
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return group_list.cumsum(dim=0)
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experts = pad(group_list[:, 0], (1, 0))
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tokens = pad(group_list[:, 1].cumsum(dim=0), (1, 0))
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cumsum_group_list = torch.full(size=(expert_num, ),
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fill_value=active_num,
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dtype=group_list.dtype,
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device=group_list.device)
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for i, (start, end) in enumerate(zip(experts[:-1], experts[1:])):
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if end > start:
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cumsum_group_list[start:end] = tokens[i]
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return cumsum_group_list
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def quant_apply_mlp(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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dynamic_scale: 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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fusion: bool = False) -> torch.Tensor:
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if dynamic_scale is None:
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unquantized_hidden_states = hidden_states
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hidden_states, pertoken_scale = torch_npu.npu_dynamic_quant(
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hidden_states)
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# Dispose the original unquantized hidden states
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# to save npu memory because they're no longer used.
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dispose_tensor(unquantized_hidden_states)
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else:
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pertoken_scale = dynamic_scale
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bias1, bias2 = None, None
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_output_dtype = w2_scale.dtype
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is_mc2 = get_forward_context().fused_moe_state == FusedMoEState.MC2
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if w1_scale_bias is None and is_mc2:
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if w1_scale.dtype != torch.float32:
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w1_scale = w1_scale.to(torch.float32)
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if fusion:
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# gmm1: gate_up_proj & act_fn: swiglu
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hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
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x=hidden_states,
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weight=w1,
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group_list=cumsum_group_list(group_list, group_list_type),
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weight_scale=w1_scale,
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x_scale=pertoken_scale)
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else:
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# gmm1: gate_up_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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split_item=3,
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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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output_dtype=torch.int32)[0]
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# act_fn: swiglu
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hidden_states, swiglu_out_scale = torch_npu.npu_dequant_swiglu_quant(
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x=hidden_states,
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weight_scale=w1_scale,
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activation_scale=pertoken_scale,
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bias=None,
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quant_scale=None,
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quant_offset=None,
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group_index=group_list,
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activate_left=True,
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quant_mode=1,
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)
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# gmm2: down_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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scale=[w2_scale],
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per_token_scale=[swiglu_out_scale],
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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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output_dtype=w2_scale.dtype)[0]
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else:
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if w1_scale_bias is not None:
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if group_list_type == 0:
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group_list = torch.cat(
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[group_list[:1],
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torch.diff(group_list, dim=0)])
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group_list_type = 1
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bias1 = [w1_scale_bias] if not fusion else w1_scale_bias
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bias2 = [w2_scale_bias]
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# TODO w4a8 scene: dynamic acquisition of dtype in the future
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_output_dtype = torch.bfloat16
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if fusion:
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# gmm1: gate_up_proj & act_fn: swiglu
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hidden_states, swiglu_out_scale, _ = torch_npu.npu_grouped_matmul_swiglu_quant(
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x=hidden_states,
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weight=w1,
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bias=bias1,
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group_list=cumsum_group_list(group_list, group_list_type),
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weight_scale=w1_scale,
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x_scale=pertoken_scale)
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else:
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# gmm1: gate_up_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w1],
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scale=[w1_scale.to(w2_scale.dtype)],
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bias=bias1,
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per_token_scale=[pertoken_scale],
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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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output_dtype=_output_dtype)[0]
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# act_fn: swiglu
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hidden_states = torch_npu.npu_swiglu(hidden_states)
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hidden_states, swiglu_out_scale = torch_npu.npu_dynamic_quant(
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hidden_states)
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# gmm2: down_proj
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[hidden_states],
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weight=[w2],
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scale=[w2_scale],
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bias=bias2,
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per_token_scale=[swiglu_out_scale],
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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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output_dtype=_output_dtype)[0]
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return hidden_states
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def unquant_apply_mlp(
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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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group_list: torch.Tensor,
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group_list_type: int = 1,
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topk_scales: Optional[torch.Tensor] = None) -> torch.Tensor:
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w1 = w1.transpose(1, 2)
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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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if is_310p():
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gate_up_out = torch_npu.npu_swiglu(gate_up_out.to(torch.float32)).to(
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torch.float16)
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else:
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gate_up_out = torch_npu.npu_swiglu(gate_up_out)
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if topk_scales is not None:
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gate_up_out *= topk_scales
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w2 = w2.transpose(1, 2)
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hidden_states = torch_npu.npu_grouped_matmul(
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x=[gate_up_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(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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dynamic_scale: torch.Tensor = None,
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group_list_type: int = 1,
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w1_scale_bias: torch.Tensor = None,
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w2_scale_bias: torch.Tensor = None,
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topk_scales: Optional[torch.Tensor] = None,
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with_quant: bool = False,
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fusion: bool = False) -> torch.Tensor:
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if with_quant:
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return quant_apply_mlp(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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dynamic_scale=dynamic_scale,
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group_list_type=group_list_type,
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w1_scale_bias=w1_scale_bias,
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w2_scale_bias=w2_scale_bias,
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fusion=fusion)
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else:
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return unquant_apply_mlp(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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topk_scales=topk_scales)
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