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79 lines
2.7 KiB
Python
79 lines
2.7 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 RMSNorm
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_original_re_init = RMSNorm.__init__
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def torchair_rmsnorm_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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_original_re_init(self, hidden_size, eps, var_hidden_size, has_weight,
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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 torchair_rmsnorm_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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"""AscendRMSNorm forward in torchair mode.
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The key difference from the original implementation is the removal of operators
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from the torch.ops.vllm class, as these operators only function in non-torchair
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modes. Adding them back would cause the graph compilation to fail.
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"""
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import torch_npu
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from vllm_npu.utils import is_310p
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if residual is not None:
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if is_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, 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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