82544aa0cc
Signed-off-by: xyDong0223 <dongxinyu03@baidu.com> Co-authored-by: xyDong0223 <dongxinyu03@baidu.com>
129 lines
4.0 KiB
Python
129 lines
4.0 KiB
Python
#
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# Copyright (c) 2025 Baidu, Inc. 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, Union
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import torch
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from vllm.model_executor.layers import layernorm
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from vllm.model_executor.layers.layernorm import GemmaRMSNorm as OriGemmaRMSNorm
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from vllm.model_executor.layers.layernorm import RMSNorm
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def vllm_kunlun_forward_cuda(
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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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"""forward_cuda"""
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if not x.is_contiguous():
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# kunlun does not support uncontiguous input and they do not think it is a bug
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# so we must make it contiguous() manually
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x = x.contiguous()
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if self.variance_size_override is not None:
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return self.forward_native(x, residual)
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if residual is not None:
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# residual_output = torch.empty_like(residual)
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torch.ops._C.add_rmsnorm(
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x,
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residual,
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residual_output=residual,
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weight=self.weight.data,
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eps=self.variance_epsilon,
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output=x,
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)
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return x, residual
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out = torch.empty_like(x)
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torch.ops._C.rmsnorm(
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x,
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self.weight.data,
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out,
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self.variance_epsilon,
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)
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return out
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RMSNorm.forward_cuda = vllm_kunlun_forward_cuda
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RMSNorm.forward = vllm_kunlun_forward_cuda
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class KunlunGemmaRMSNorm(OriGemmaRMSNorm):
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@staticmethod
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def forward_xpu(
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weight: torch.Tensor,
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variance_epsilon: float,
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x: torch.Tensor,
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residual: Optional[torch.Tensor],
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) -> Union[torch.Tensor, tuple[torch.Tensor, torch.Tensor]]:
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if not x.is_contiguous():
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# kunlun does not support uncontiguous input and they do not think it is a bug
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# so we must make it contiguous() manually
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x = x.contiguous()
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if x.dim() == 3:
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x_shape = x.shape
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x = x.view(-1, x.size(-1))
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if residual is not None:
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out = torch.empty_like(x)
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out_residual = torch.empty_like(residual)
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torch.ops._C.gemma_add_rmsnorm(
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x,
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residual,
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residual_output=out_residual,
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weight=weight,
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eps=variance_epsilon,
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output=out,
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)
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else:
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out = torch.empty_like(x)
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torch.ops._C.gemma_rmsnorm(
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x,
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weight,
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out,
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variance_epsilon,
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)
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if x.dim() == 3:
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x = x.view(x_shape)
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if out is not None:
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out = out.view(x_shape)
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if residual is not None:
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return out, out_residual
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else:
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return out
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def forward_cuda(
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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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if torch.compiler.is_compiling():
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self.forward_static = self.forward_xpu # only use in cudagraph
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return self.forward_native(x, residual)
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if not getattr(self, "_is_compiled", False):
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self.forward_static = torch.compile( # type: ignore
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self.forward_static, backend="aot_eager"
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)
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self._is_compiled = True
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return self.forward_native(x, residual)
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RMSNorm.forward_cuda = vllm_kunlun_forward_cuda
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RMSNorm.forward = vllm_kunlun_forward_cuda
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layernorm.GemmaRMSNorm = KunlunGemmaRMSNorm
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