66a0837963
### What this PR does / why we need it? Adopt custom kernel rotary embedding in actual model inference, customized rotary_embedding will generate contiguous query and key in the cpp side to reduce the overhead of two contiguous and index_select compared with rotary_embedding in torch_npu. For now, rotary_embedding can only support the scenario of `is_neox = true`, non-neox version rope will be updated soon in the future. --------- Signed-off-by: ganyi <pleaplusone.gy@gmail.com>
71 lines
2.3 KiB
Python
71 lines
2.3 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
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import torch
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from vllm.model_executor.layers.rotary_embedding import (
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DeepseekScalingRotaryEmbedding, RotaryEmbedding)
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from vllm_ascend.platform import CUSTOM_OP_ENABLED
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def rope_forward_oot(
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self,
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positions: torch.Tensor,
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query: torch.Tensor,
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key: torch.Tensor,
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offsets: Optional[torch.Tensor] = None,
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) -> Tuple[torch.Tensor, torch.Tensor]:
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import torch_npu
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if self.cos_sin_cache.device != query.device:
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self.cos_sin_cache = self.cos_sin_cache.to(query.device)
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if self.cos_sin_cache.dtype != query.dtype:
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self.cos_sin_cache = self.cos_sin_cache.to(query.dtype)
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# adopt custom kernel path for rotary_embedding
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if CUSTOM_OP_ENABLED and self.is_neox_style:
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return torch.ops._C.rotary_embedding(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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if offsets is not None:
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raise NotImplementedError(
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"Batched rotary embedding is currently not supported on NPU.")
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else:
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# TODO: Remove the contiguous in the future.
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query_shape, key_shape = query.shape, key.shape
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query = query.contiguous().view(query.shape[0], -1)
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key = key.contiguous().view(key.shape[0], -1)
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torch_npu._npu_rotary_embedding(
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positions,
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query,
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key,
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self.head_size,
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self.cos_sin_cache,
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self.is_neox_style,
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)
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return query.view(query_shape), key.view(key_shape)
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RotaryEmbedding.forward_oot = rope_forward_oot
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DeepseekScalingRotaryEmbedding.forward = rope_forward_oot
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