46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
# Copyright (c) 2025 Bytedance Ltd. and/or its affiliates. All rights reserved.
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# Copyright (c) 2024 Black Forest Labs and The XLabs-AI Team. All rights reserved.
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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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# http://www.apache.org/licenses/LICENSE-2.0
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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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import torch
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from einops import rearrange
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from torch import Tensor
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def attention(q: Tensor, k: Tensor, v: Tensor, pe: Tensor) -> Tensor:
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q, k = apply_rope(q, k, pe)
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x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
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x = rearrange(x, "B H L D -> B L (H D)")
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return x
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def rope(pos: Tensor, dim: int, theta: int) -> Tensor:
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assert dim % 2 == 0
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scale = torch.arange(0, dim, 2, dtype=torch.float64, device=pos.device) / dim
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omega = 1.0 / (theta**scale)
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out = torch.einsum("...n,d->...nd", pos, omega)
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out = torch.stack([torch.cos(out), -torch.sin(out), torch.sin(out), torch.cos(out)], dim=-1)
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out = rearrange(out, "b n d (i j) -> b n d i j", i=2, j=2)
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return out.float()
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def apply_rope(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> tuple[Tensor, Tensor]:
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xq_ = xq.float().reshape(*xq.shape[:-1], -1, 1, 2)
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xk_ = xk.float().reshape(*xk.shape[:-1], -1, 1, 2)
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xq_out = freqs_cis[..., 0] * xq_[..., 0] + freqs_cis[..., 1] * xq_[..., 1]
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xk_out = freqs_cis[..., 0] * xk_[..., 0] + freqs_cis[..., 1] * xk_[..., 1]
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return xq_out.reshape(*xq.shape).type_as(xq), xk_out.reshape(*xk.shape).type_as(xk)
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