acc3578f58
### What this PR does / why we need it?
1. In addition to
[#4168](https://github.com/vllm-project/vllm-ascend/pull/4168),
[#5011](https://github.com/vllm-project/vllm-ascend/pull/5011)， this PR
adds two more pattern for AddRmsnormQuant with SP enabled. The key
difference is to insert an additional `maybe_all_gather_and_maybe_unpad`
between `addrmsnorm` and `quantize`.
2. This PR also introduce another api `torch.ops.vllm.quantize`, so that
we pass `input_scale` and `input_scale_reciprocal` at the same time.
This is because `npu_add_rms_norm_quant` and `npu_quantize` requires
different `div_mode`. To avoid introducing additional reciprocal
calculation in runtime, we have to pass both of them to quantize api.
3. Removes redundant `AscendQuantRmsnorm`.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: Angazenn <supperccell@163.com>
98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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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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#
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from typing import Optional, Tuple, Union
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import torch
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from vllm.config import get_current_vllm_config
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm, RMSNorm
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class AscendRMSNorm(RMSNorm):
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def __init__(
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self,
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hidden_size: int,
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eps: float = 1e-6,
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var_hidden_size: Optional[int] = None,
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has_weight: bool = True,
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dtype: Optional[torch.dtype] = None,
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) -> None:
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super().__init__(hidden_size, eps, var_hidden_size, has_weight, dtype)
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vllm_config = get_current_vllm_config()
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self.bias = None
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# quantization with anti_method m4 will generate none-zero norm bias
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if vllm_config.quant_config is not None and \
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any("norm.bias" in name for name in vllm_config.quant_config.quant_description.keys()):
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self.bias = torch.nn.Parameter(torch.zeros(hidden_size),
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requires_grad=False)
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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import torch_npu
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from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
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if residual is not None:
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if get_ascend_device_type() == AscendDeviceType._310P:
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orig_dtype = residual.dtype
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x = x + residual.to(x.dtype)
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residual = x.to(orig_dtype)
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x, _ = torch_npu.npu_rms_norm(x, self.weight,
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self.variance_epsilon)
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else:
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x, _, residual = torch_npu.npu_add_rms_norm(
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x, residual, self.weight, self.variance_epsilon)
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if self.bias is not None:
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x.add_(self.bias)
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return x, residual
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x, residual = torch_npu.npu_rms_norm(x, self.weight,
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self.variance_epsilon)
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if self.bias is not None:
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x.add_(self.bias)
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return x
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class AscendGemmaRMSNorm(GemmaRMSNorm):
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def forward_oot(
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self,
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x: torch.Tensor,
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residual: Optional[torch.Tensor] = None,
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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import torch_npu
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from vllm_ascend.utils import AscendDeviceType, get_ascend_device_type
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if residual is not None:
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if get_ascend_device_type() == AscendDeviceType._310P:
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orig_dtype = residual.dtype
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x = x + residual.to(x.dtype)
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residual = x.to(orig_dtype)
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x, _ = torch_npu.npu_rms_norm(x, 1.0 + self.weight,
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self.variance_epsilon)
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else:
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x, _, residual = torch_npu.npu_add_rms_norm(
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x, residual, 1.0 + self.weight, self.variance_epsilon)
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return x, residual
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x, _ = torch_npu.npu_rms_norm(x, 1.0 + self.weight,
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self.variance_epsilon)
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return x
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