460ea88276
### What this PR does / why we need it?
- Replace the RoPE operator implementation.
- Refactor some leftover implementations of 300I DUO in the main branch.
### Does this PR introduce _any_ user-facing change?
NA
### How was this patch tested?
- vLLM version: v0.14.1
- vLLM main:
dc917cceb8
---------
Signed-off-by: Tflowers-0129 <2906339855@qq.com>
100 lines
3.9 KiB
Python
100 lines
3.9 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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import torch
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import torch_npu
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from vllm_ascend.ops.rotary_embedding import AscendRotaryEmbedding, get_cos_and_sin_slice
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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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is_neox_style: bool,
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offsets: torch.Tensor | None = None,
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) -> tuple[torch.Tensor, torch.Tensor]:
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query_shape, key_shape = query.shape, key.shape
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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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cos, sin = get_cos_and_sin_slice()
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if offsets is not None:
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raise NotImplementedError("Batched rotary embedding is currently not supported on NPU.")
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rotary_mode = "half" if is_neox_style else "interleave"
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if self.head_size == 128 and self.cos_sin_cache.shape[-1] == 128:
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query = query.contiguous().view(1, query.shape[0], -1, self.head_size)
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key = key.contiguous().view(1, key.shape[0], -1, self.head_size)
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query, key = torch_npu.npu_apply_rotary_pos_emb(query, key, cos, sin, rotary_mode=rotary_mode)
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elif self.rotary_dim < self.head_size:
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num_tokens = query.shape[0]
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query = query.view(num_tokens, -1, self.head_size)
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key = key.view(num_tokens, -1, self.head_size)
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q_rot = query[..., : self.rotary_dim]
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q_pass = query[..., self.rotary_dim :]
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k_rot = key[..., : self.rotary_dim]
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k_pass = key[..., self.rotary_dim :]
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if self.rotary_dim == 64:
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q_rot = q_rot.contiguous().view(1, num_tokens, -1, self.rotary_dim)
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k_rot = k_rot.contiguous().view(1, num_tokens, -1, self.rotary_dim)
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q_rot, k_rot = torch_npu.npu_apply_rotary_pos_emb(q_rot, k_rot, cos, sin, rotary_mode=rotary_mode)
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else:
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q_rot = q_rot.contiguous().view(num_tokens, -1)
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k_rot = k_rot.contiguous().view(num_tokens, -1)
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torch_npu._npu_rotary_embedding(
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positions,
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q_rot,
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k_rot,
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self.rotary_dim,
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self.cos_sin_cache,
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is_neox_style,
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)
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q_rot = q_rot.view(num_tokens, -1, self.rotary_dim)
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k_rot = k_rot.view(num_tokens, -1, self.rotary_dim)
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query = torch.cat((q_rot, q_pass), dim=-1).reshape(query_shape)
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key = torch.cat((k_rot, k_pass), dim=-1).reshape(key_shape)
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else:
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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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is_neox_style,
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)
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return query.view(query_shape), key.view(key_shape)
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class AscendRotaryEmbedding310(AscendRotaryEmbedding):
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def 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: torch.Tensor | None = None,
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is_neox_style_override: bool | None = None,
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):
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is_neox_style = self.is_neox_style
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if is_neox_style_override is not None:
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is_neox_style = is_neox_style_override
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return _rope_forward_oot(self, positions, query, key, is_neox_style, offsets)
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