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# Copyright 2023 The HuggingFace Team. 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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#
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# --------
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#
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# Modified 2024 by the Tripo AI and Stability AI Team.
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#
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# Copyright (c) 2024 Tripo AI & Stability AI
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
|
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
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# copies of the Software, and to permit persons to whom the Software is
|
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# furnished to do so, subject to the following conditions:
|
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#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from torch import nn
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class Attention(nn.Module):
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r"""
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A cross attention layer.
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Parameters:
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query_dim (`int`):
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The number of channels in the query.
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cross_attention_dim (`int`, *optional*):
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The number of channels in the encoder_hidden_states. If not given, defaults to `query_dim`.
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heads (`int`, *optional*, defaults to 8):
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The number of heads to use for multi-head attention.
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dim_head (`int`, *optional*, defaults to 64):
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The number of channels in each head.
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dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability to use.
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bias (`bool`, *optional*, defaults to False):
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Set to `True` for the query, key, and value linear layers to contain a bias parameter.
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upcast_attention (`bool`, *optional*, defaults to False):
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Set to `True` to upcast the attention computation to `float32`.
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upcast_softmax (`bool`, *optional*, defaults to False):
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Set to `True` to upcast the softmax computation to `float32`.
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cross_attention_norm (`str`, *optional*, defaults to `None`):
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The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`.
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cross_attention_norm_num_groups (`int`, *optional*, defaults to 32):
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The number of groups to use for the group norm in the cross attention.
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added_kv_proj_dim (`int`, *optional*, defaults to `None`):
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The number of channels to use for the added key and value projections. If `None`, no projection is used.
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norm_num_groups (`int`, *optional*, defaults to `None`):
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The number of groups to use for the group norm in the attention.
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spatial_norm_dim (`int`, *optional*, defaults to `None`):
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The number of channels to use for the spatial normalization.
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out_bias (`bool`, *optional*, defaults to `True`):
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Set to `True` to use a bias in the output linear layer.
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scale_qk (`bool`, *optional*, defaults to `True`):
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Set to `True` to scale the query and key by `1 / sqrt(dim_head)`.
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only_cross_attention (`bool`, *optional*, defaults to `False`):
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Set to `True` to only use cross attention and not added_kv_proj_dim. Can only be set to `True` if
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`added_kv_proj_dim` is not `None`.
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eps (`float`, *optional*, defaults to 1e-5):
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An additional value added to the denominator in group normalization that is used for numerical stability.
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rescale_output_factor (`float`, *optional*, defaults to 1.0):
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A factor to rescale the output by dividing it with this value.
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residual_connection (`bool`, *optional*, defaults to `False`):
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Set to `True` to add the residual connection to the output.
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_from_deprecated_attn_block (`bool`, *optional*, defaults to `False`):
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Set to `True` if the attention block is loaded from a deprecated state dict.
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processor (`AttnProcessor`, *optional*, defaults to `None`):
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The attention processor to use. If `None`, defaults to `AttnProcessor2_0` if `torch 2.x` is used and
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`AttnProcessor` otherwise.
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"""
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def __init__(
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self,
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query_dim: int,
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cross_attention_dim: Optional[int] = None,
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heads: int = 8,
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dim_head: int = 64,
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dropout: float = 0.0,
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bias: bool = False,
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upcast_attention: bool = False,
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upcast_softmax: bool = False,
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cross_attention_norm: Optional[str] = None,
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cross_attention_norm_num_groups: int = 32,
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added_kv_proj_dim: Optional[int] = None,
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norm_num_groups: Optional[int] = None,
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out_bias: bool = True,
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scale_qk: bool = True,
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only_cross_attention: bool = False,
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eps: float = 1e-5,
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rescale_output_factor: float = 1.0,
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residual_connection: bool = False,
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_from_deprecated_attn_block: bool = False,
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processor: Optional["AttnProcessor"] = None,
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out_dim: int = None,
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):
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super().__init__()
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self.inner_dim = out_dim if out_dim is not None else dim_head * heads
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self.query_dim = query_dim
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self.cross_attention_dim = (
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cross_attention_dim if cross_attention_dim is not None else query_dim
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)
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self.upcast_attention = upcast_attention
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self.upcast_softmax = upcast_softmax
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self.rescale_output_factor = rescale_output_factor
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self.residual_connection = residual_connection
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self.dropout = dropout
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self.fused_projections = False
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self.out_dim = out_dim if out_dim is not None else query_dim
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# we make use of this private variable to know whether this class is loaded
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# with an deprecated state dict so that we can convert it on the fly
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self._from_deprecated_attn_block = _from_deprecated_attn_block
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self.scale_qk = scale_qk
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self.scale = dim_head**-0.5 if self.scale_qk else 1.0
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self.heads = out_dim // dim_head if out_dim is not None else heads
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# for slice_size > 0 the attention score computation
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# is split across the batch axis to save memory
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# You can set slice_size with `set_attention_slice`
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self.sliceable_head_dim = heads
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self.added_kv_proj_dim = added_kv_proj_dim
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self.only_cross_attention = only_cross_attention
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if self.added_kv_proj_dim is None and self.only_cross_attention:
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raise ValueError(
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"`only_cross_attention` can only be set to True if `added_kv_proj_dim` is not None. Make sure to set either `only_cross_attention=False` or define `added_kv_proj_dim`."
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)
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if norm_num_groups is not None:
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self.group_norm = nn.GroupNorm(
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num_channels=query_dim, num_groups=norm_num_groups, eps=eps, affine=True
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)
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else:
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self.group_norm = None
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self.spatial_norm = None
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if cross_attention_norm is None:
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self.norm_cross = None
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elif cross_attention_norm == "layer_norm":
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self.norm_cross = nn.LayerNorm(self.cross_attention_dim)
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elif cross_attention_norm == "group_norm":
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if self.added_kv_proj_dim is not None:
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# The given `encoder_hidden_states` are initially of shape
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# (batch_size, seq_len, added_kv_proj_dim) before being projected
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# to (batch_size, seq_len, cross_attention_dim). The norm is applied
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# before the projection, so we need to use `added_kv_proj_dim` as
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# the number of channels for the group norm.
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norm_cross_num_channels = added_kv_proj_dim
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else:
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norm_cross_num_channels = self.cross_attention_dim
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self.norm_cross = nn.GroupNorm(
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num_channels=norm_cross_num_channels,
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num_groups=cross_attention_norm_num_groups,
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eps=1e-5,
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affine=True,
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)
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else:
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raise ValueError(
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f"unknown cross_attention_norm: {cross_attention_norm}. Should be None, 'layer_norm' or 'group_norm'"
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)
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linear_cls = nn.Linear
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self.linear_cls = linear_cls
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self.to_q = linear_cls(query_dim, self.inner_dim, bias=bias)
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if not self.only_cross_attention:
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# only relevant for the `AddedKVProcessor` classes
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self.to_k = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
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self.to_v = linear_cls(self.cross_attention_dim, self.inner_dim, bias=bias)
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else:
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self.to_k = None
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self.to_v = None
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if self.added_kv_proj_dim is not None:
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self.add_k_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
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self.add_v_proj = linear_cls(added_kv_proj_dim, self.inner_dim)
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self.to_out = nn.ModuleList([])
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self.to_out.append(linear_cls(self.inner_dim, self.out_dim, bias=out_bias))
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self.to_out.append(nn.Dropout(dropout))
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# set attention processor
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# We use the AttnProcessor2_0 by default when torch 2.x is used which uses
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# torch.nn.functional.scaled_dot_product_attention for native Flash/memory_efficient_attention
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# but only if it has the default `scale` argument. TODO remove scale_qk check when we move to torch 2.1
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if processor is None:
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processor = (
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AttnProcessor2_0()
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if hasattr(F, "scaled_dot_product_attention") and self.scale_qk
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else AttnProcessor()
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)
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self.set_processor(processor)
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def set_processor(self, processor: "AttnProcessor") -> None:
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self.processor = processor
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def forward(
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self,
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hidden_states: torch.FloatTensor,
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encoder_hidden_states: Optional[torch.FloatTensor] = None,
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attention_mask: Optional[torch.FloatTensor] = None,
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**cross_attention_kwargs,
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) -> torch.Tensor:
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r"""
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The forward method of the `Attention` class.
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Args:
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hidden_states (`torch.Tensor`):
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The hidden states of the query.
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encoder_hidden_states (`torch.Tensor`, *optional*):
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The hidden states of the encoder.
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attention_mask (`torch.Tensor`, *optional*):
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The attention mask to use. If `None`, no mask is applied.
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**cross_attention_kwargs:
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Additional keyword arguments to pass along to the cross attention.
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Returns:
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`torch.Tensor`: The output of the attention layer.
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"""
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# The `Attention` class can call different attention processors / attention functions
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# here we simply pass along all tensors to the selected processor class
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# For standard processors that are defined here, `**cross_attention_kwargs` is empty
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return self.processor(
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self,
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hidden_states,
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encoder_hidden_states=encoder_hidden_states,
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attention_mask=attention_mask,
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**cross_attention_kwargs,
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)
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def batch_to_head_dim(self, tensor: torch.Tensor) -> torch.Tensor:
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r"""
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Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size // heads, seq_len, dim * heads]`. `heads`
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is the number of heads initialized while constructing the `Attention` class.
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|
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Args:
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tensor (`torch.Tensor`): The tensor to reshape.
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Returns:
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`torch.Tensor`: The reshaped tensor.
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"""
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head_size = self.heads
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batch_size, seq_len, dim = tensor.shape
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tensor = tensor.reshape(batch_size // head_size, head_size, seq_len, dim)
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tensor = tensor.permute(0, 2, 1, 3).reshape(
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batch_size // head_size, seq_len, dim * head_size
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)
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return tensor
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def head_to_batch_dim(self, tensor: torch.Tensor, out_dim: int = 3) -> torch.Tensor:
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r"""
|
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Reshape the tensor from `[batch_size, seq_len, dim]` to `[batch_size, seq_len, heads, dim // heads]` `heads` is
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the number of heads initialized while constructing the `Attention` class.
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|
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Args:
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tensor (`torch.Tensor`): The tensor to reshape.
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out_dim (`int`, *optional*, defaults to `3`): The output dimension of the tensor. If `3`, the tensor is
|
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reshaped to `[batch_size * heads, seq_len, dim // heads]`.
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Returns:
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`torch.Tensor`: The reshaped tensor.
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"""
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head_size = self.heads
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batch_size, seq_len, dim = tensor.shape
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tensor = tensor.reshape(batch_size, seq_len, head_size, dim // head_size)
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tensor = tensor.permute(0, 2, 1, 3)
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if out_dim == 3:
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tensor = tensor.reshape(batch_size * head_size, seq_len, dim // head_size)
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return tensor
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def get_attention_scores(
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||||
self,
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query: torch.Tensor,
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key: torch.Tensor,
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attention_mask: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
r"""
|
||||
Compute the attention scores.
|
||||
|
||||
Args:
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||||
query (`torch.Tensor`): The query tensor.
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key (`torch.Tensor`): The key tensor.
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attention_mask (`torch.Tensor`, *optional*): The attention mask to use. If `None`, no mask is applied.
|
||||
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Returns:
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`torch.Tensor`: The attention probabilities/scores.
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"""
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dtype = query.dtype
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if self.upcast_attention:
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query = query.float()
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key = key.float()
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|
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if attention_mask is None:
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baddbmm_input = torch.empty(
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query.shape[0],
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query.shape[1],
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key.shape[1],
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dtype=query.dtype,
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device=query.device,
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||||
)
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beta = 0
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else:
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baddbmm_input = attention_mask
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beta = 1
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attention_scores = torch.baddbmm(
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baddbmm_input,
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query,
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key.transpose(-1, -2),
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beta=beta,
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||||
alpha=self.scale,
|
||||
)
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||||
del baddbmm_input
|
||||
|
||||
if self.upcast_softmax:
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||||
attention_scores = attention_scores.float()
|
||||
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attention_probs = attention_scores.softmax(dim=-1)
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del attention_scores
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||||
|
||||
attention_probs = attention_probs.to(dtype)
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||||
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||||
return attention_probs
|
||||
|
||||
def prepare_attention_mask(
|
||||
self,
|
||||
attention_mask: torch.Tensor,
|
||||
target_length: int,
|
||||
batch_size: int,
|
||||
out_dim: int = 3,
|
||||
) -> torch.Tensor:
|
||||
r"""
|
||||
Prepare the attention mask for the attention computation.
|
||||
|
||||
Args:
|
||||
attention_mask (`torch.Tensor`):
|
||||
The attention mask to prepare.
|
||||
target_length (`int`):
|
||||
The target length of the attention mask. This is the length of the attention mask after padding.
|
||||
batch_size (`int`):
|
||||
The batch size, which is used to repeat the attention mask.
|
||||
out_dim (`int`, *optional*, defaults to `3`):
|
||||
The output dimension of the attention mask. Can be either `3` or `4`.
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`: The prepared attention mask.
|
||||
"""
|
||||
head_size = self.heads
|
||||
if attention_mask is None:
|
||||
return attention_mask
|
||||
|
||||
current_length: int = attention_mask.shape[-1]
|
||||
if current_length != target_length:
|
||||
if attention_mask.device.type == "mps":
|
||||
# HACK: MPS: Does not support padding by greater than dimension of input tensor.
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||||
# Instead, we can manually construct the padding tensor.
|
||||
padding_shape = (
|
||||
attention_mask.shape[0],
|
||||
attention_mask.shape[1],
|
||||
target_length,
|
||||
)
|
||||
padding = torch.zeros(
|
||||
padding_shape,
|
||||
dtype=attention_mask.dtype,
|
||||
device=attention_mask.device,
|
||||
)
|
||||
attention_mask = torch.cat([attention_mask, padding], dim=2)
|
||||
else:
|
||||
# TODO: for pipelines such as stable-diffusion, padding cross-attn mask:
|
||||
# we want to instead pad by (0, remaining_length), where remaining_length is:
|
||||
# remaining_length: int = target_length - current_length
|
||||
# TODO: re-enable tests/models/test_models_unet_2d_condition.py#test_model_xattn_padding
|
||||
attention_mask = F.pad(attention_mask, (0, target_length), value=0.0)
|
||||
|
||||
if out_dim == 3:
|
||||
if attention_mask.shape[0] < batch_size * head_size:
|
||||
attention_mask = attention_mask.repeat_interleave(head_size, dim=0)
|
||||
elif out_dim == 4:
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
attention_mask = attention_mask.repeat_interleave(head_size, dim=1)
|
||||
|
||||
return attention_mask
|
||||
|
||||
def norm_encoder_hidden_states(
|
||||
self, encoder_hidden_states: torch.Tensor
|
||||
) -> torch.Tensor:
|
||||
r"""
|
||||
Normalize the encoder hidden states. Requires `self.norm_cross` to be specified when constructing the
|
||||
`Attention` class.
|
||||
|
||||
Args:
|
||||
encoder_hidden_states (`torch.Tensor`): Hidden states of the encoder.
|
||||
|
||||
Returns:
|
||||
`torch.Tensor`: The normalized encoder hidden states.
|
||||
"""
|
||||
assert (
|
||||
self.norm_cross is not None
|
||||
), "self.norm_cross must be defined to call self.norm_encoder_hidden_states"
|
||||
|
||||
if isinstance(self.norm_cross, nn.LayerNorm):
|
||||
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
|
||||
elif isinstance(self.norm_cross, nn.GroupNorm):
|
||||
# Group norm norms along the channels dimension and expects
|
||||
# input to be in the shape of (N, C, *). In this case, we want
|
||||
# to norm along the hidden dimension, so we need to move
|
||||
# (batch_size, sequence_length, hidden_size) ->
|
||||
# (batch_size, hidden_size, sequence_length)
|
||||
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
|
||||
encoder_hidden_states = self.norm_cross(encoder_hidden_states)
|
||||
encoder_hidden_states = encoder_hidden_states.transpose(1, 2)
|
||||
else:
|
||||
assert False
|
||||
|
||||
return encoder_hidden_states
|
||||
|
||||
@torch.no_grad()
|
||||
def fuse_projections(self, fuse=True):
|
||||
is_cross_attention = self.cross_attention_dim != self.query_dim
|
||||
device = self.to_q.weight.data.device
|
||||
dtype = self.to_q.weight.data.dtype
|
||||
|
||||
if not is_cross_attention:
|
||||
# fetch weight matrices.
|
||||
concatenated_weights = torch.cat(
|
||||
[self.to_q.weight.data, self.to_k.weight.data, self.to_v.weight.data]
|
||||
)
|
||||
in_features = concatenated_weights.shape[1]
|
||||
out_features = concatenated_weights.shape[0]
|
||||
|
||||
# create a new single projection layer and copy over the weights.
|
||||
self.to_qkv = self.linear_cls(
|
||||
in_features, out_features, bias=False, device=device, dtype=dtype
|
||||
)
|
||||
self.to_qkv.weight.copy_(concatenated_weights)
|
||||
|
||||
else:
|
||||
concatenated_weights = torch.cat(
|
||||
[self.to_k.weight.data, self.to_v.weight.data]
|
||||
)
|
||||
in_features = concatenated_weights.shape[1]
|
||||
out_features = concatenated_weights.shape[0]
|
||||
|
||||
self.to_kv = self.linear_cls(
|
||||
in_features, out_features, bias=False, device=device, dtype=dtype
|
||||
)
|
||||
self.to_kv.weight.copy_(concatenated_weights)
|
||||
|
||||
self.fused_projections = fuse
|
||||
|
||||
|
||||
class AttnProcessor:
|
||||
r"""
|
||||
Default processor for performing attention-related computations.
|
||||
"""
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
attn: Attention,
|
||||
hidden_states: torch.FloatTensor,
|
||||
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
) -> torch.Tensor:
|
||||
residual = hidden_states
|
||||
|
||||
input_ndim = hidden_states.ndim
|
||||
|
||||
if input_ndim == 4:
|
||||
batch_size, channel, height, width = hidden_states.shape
|
||||
hidden_states = hidden_states.view(
|
||||
batch_size, channel, height * width
|
||||
).transpose(1, 2)
|
||||
|
||||
batch_size, sequence_length, _ = (
|
||||
hidden_states.shape
|
||||
if encoder_hidden_states is None
|
||||
else encoder_hidden_states.shape
|
||||
)
|
||||
attention_mask = attn.prepare_attention_mask(
|
||||
attention_mask, sequence_length, batch_size
|
||||
)
|
||||
|
||||
if attn.group_norm is not None:
|
||||
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
|
||||
1, 2
|
||||
)
|
||||
|
||||
query = attn.to_q(hidden_states)
|
||||
|
||||
if encoder_hidden_states is None:
|
||||
encoder_hidden_states = hidden_states
|
||||
elif attn.norm_cross:
|
||||
encoder_hidden_states = attn.norm_encoder_hidden_states(
|
||||
encoder_hidden_states
|
||||
)
|
||||
|
||||
key = attn.to_k(encoder_hidden_states)
|
||||
value = attn.to_v(encoder_hidden_states)
|
||||
|
||||
query = attn.head_to_batch_dim(query)
|
||||
key = attn.head_to_batch_dim(key)
|
||||
value = attn.head_to_batch_dim(value)
|
||||
|
||||
attention_probs = attn.get_attention_scores(query, key, attention_mask)
|
||||
hidden_states = torch.bmm(attention_probs, value)
|
||||
hidden_states = attn.batch_to_head_dim(hidden_states)
|
||||
|
||||
# linear proj
|
||||
hidden_states = attn.to_out[0](hidden_states)
|
||||
# dropout
|
||||
hidden_states = attn.to_out[1](hidden_states)
|
||||
|
||||
if input_ndim == 4:
|
||||
hidden_states = hidden_states.transpose(-1, -2).reshape(
|
||||
batch_size, channel, height, width
|
||||
)
|
||||
|
||||
if attn.residual_connection:
|
||||
hidden_states = hidden_states + residual
|
||||
|
||||
hidden_states = hidden_states / attn.rescale_output_factor
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class AttnProcessor2_0:
|
||||
r"""
|
||||
Processor for implementing scaled dot-product attention (enabled by default if you're using PyTorch 2.0).
|
||||
"""
|
||||
|
||||
def __init__(self):
|
||||
if not hasattr(F, "scaled_dot_product_attention"):
|
||||
raise ImportError(
|
||||
"AttnProcessor2_0 requires PyTorch 2.0, to use it, please upgrade PyTorch to 2.0."
|
||||
)
|
||||
|
||||
def __call__(
|
||||
self,
|
||||
attn: Attention,
|
||||
hidden_states: torch.FloatTensor,
|
||||
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
) -> torch.FloatTensor:
|
||||
residual = hidden_states
|
||||
|
||||
input_ndim = hidden_states.ndim
|
||||
|
||||
if input_ndim == 4:
|
||||
batch_size, channel, height, width = hidden_states.shape
|
||||
hidden_states = hidden_states.view(
|
||||
batch_size, channel, height * width
|
||||
).transpose(1, 2)
|
||||
|
||||
batch_size, sequence_length, _ = (
|
||||
hidden_states.shape
|
||||
if encoder_hidden_states is None
|
||||
else encoder_hidden_states.shape
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
attention_mask = attn.prepare_attention_mask(
|
||||
attention_mask, sequence_length, batch_size
|
||||
)
|
||||
# scaled_dot_product_attention expects attention_mask shape to be
|
||||
# (batch, heads, source_length, target_length)
|
||||
attention_mask = attention_mask.view(
|
||||
batch_size, attn.heads, -1, attention_mask.shape[-1]
|
||||
)
|
||||
|
||||
if attn.group_norm is not None:
|
||||
hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(
|
||||
1, 2
|
||||
)
|
||||
|
||||
query = attn.to_q(hidden_states)
|
||||
|
||||
if encoder_hidden_states is None:
|
||||
encoder_hidden_states = hidden_states
|
||||
elif attn.norm_cross:
|
||||
encoder_hidden_states = attn.norm_encoder_hidden_states(
|
||||
encoder_hidden_states
|
||||
)
|
||||
|
||||
key = attn.to_k(encoder_hidden_states)
|
||||
value = attn.to_v(encoder_hidden_states)
|
||||
|
||||
inner_dim = key.shape[-1]
|
||||
head_dim = inner_dim // attn.heads
|
||||
|
||||
query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
|
||||
|
||||
# the output of sdp = (batch, num_heads, seq_len, head_dim)
|
||||
# TODO: add support for attn.scale when we move to Torch 2.1
|
||||
hidden_states = F.scaled_dot_product_attention(
|
||||
query, key, value, attn_mask=attention_mask, dropout_p=0.0, is_causal=False
|
||||
)
|
||||
|
||||
hidden_states = hidden_states.transpose(1, 2).reshape(
|
||||
batch_size, -1, attn.heads * head_dim
|
||||
)
|
||||
hidden_states = hidden_states.to(query.dtype)
|
||||
|
||||
# linear proj
|
||||
hidden_states = attn.to_out[0](hidden_states)
|
||||
# dropout
|
||||
hidden_states = attn.to_out[1](hidden_states)
|
||||
|
||||
if input_ndim == 4:
|
||||
hidden_states = hidden_states.transpose(-1, -2).reshape(
|
||||
batch_size, channel, height, width
|
||||
)
|
||||
|
||||
if attn.residual_connection:
|
||||
hidden_states = hidden_states + residual
|
||||
|
||||
hidden_states = hidden_states / attn.rescale_output_factor
|
||||
|
||||
return hidden_states
|
||||
@@ -0,0 +1,334 @@
|
||||
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
# --------
|
||||
#
|
||||
# Modified 2024 by the Tripo AI and Stability AI Team.
|
||||
#
|
||||
# Copyright (c) 2024 Tripo AI & Stability AI
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from .attention import Attention
|
||||
|
||||
|
||||
class BasicTransformerBlock(nn.Module):
|
||||
r"""
|
||||
A basic Transformer block.
|
||||
|
||||
Parameters:
|
||||
dim (`int`): The number of channels in the input and output.
|
||||
num_attention_heads (`int`): The number of heads to use for multi-head attention.
|
||||
attention_head_dim (`int`): The number of channels in each head.
|
||||
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
||||
cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention.
|
||||
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
||||
attention_bias (:
|
||||
obj: `bool`, *optional*, defaults to `False`): Configure if the attentions should contain a bias parameter.
|
||||
only_cross_attention (`bool`, *optional*):
|
||||
Whether to use only cross-attention layers. In this case two cross attention layers are used.
|
||||
double_self_attention (`bool`, *optional*):
|
||||
Whether to use two self-attention layers. In this case no cross attention layers are used.
|
||||
upcast_attention (`bool`, *optional*):
|
||||
Whether to upcast the attention computation to float32. This is useful for mixed precision training.
|
||||
norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
|
||||
Whether to use learnable elementwise affine parameters for normalization.
|
||||
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
|
||||
The normalization layer to use. Can be `"layer_norm"`, `"ada_norm"` or `"ada_norm_zero"`.
|
||||
final_dropout (`bool` *optional*, defaults to False):
|
||||
Whether to apply a final dropout after the last feed-forward layer.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
num_attention_heads: int,
|
||||
attention_head_dim: int,
|
||||
dropout=0.0,
|
||||
cross_attention_dim: Optional[int] = None,
|
||||
activation_fn: str = "geglu",
|
||||
attention_bias: bool = False,
|
||||
only_cross_attention: bool = False,
|
||||
double_self_attention: bool = False,
|
||||
upcast_attention: bool = False,
|
||||
norm_elementwise_affine: bool = True,
|
||||
norm_type: str = "layer_norm",
|
||||
final_dropout: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
self.only_cross_attention = only_cross_attention
|
||||
|
||||
assert norm_type == "layer_norm"
|
||||
|
||||
# Define 3 blocks. Each block has its own normalization layer.
|
||||
# 1. Self-Attn
|
||||
self.norm1 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
self.attn1 = Attention(
|
||||
query_dim=dim,
|
||||
heads=num_attention_heads,
|
||||
dim_head=attention_head_dim,
|
||||
dropout=dropout,
|
||||
bias=attention_bias,
|
||||
cross_attention_dim=cross_attention_dim if only_cross_attention else None,
|
||||
upcast_attention=upcast_attention,
|
||||
)
|
||||
|
||||
# 2. Cross-Attn
|
||||
if cross_attention_dim is not None or double_self_attention:
|
||||
# We currently only use AdaLayerNormZero for self attention where there will only be one attention block.
|
||||
# I.e. the number of returned modulation chunks from AdaLayerZero would not make sense if returned during
|
||||
# the second cross attention block.
|
||||
self.norm2 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
|
||||
self.attn2 = Attention(
|
||||
query_dim=dim,
|
||||
cross_attention_dim=(
|
||||
cross_attention_dim if not double_self_attention else None
|
||||
),
|
||||
heads=num_attention_heads,
|
||||
dim_head=attention_head_dim,
|
||||
dropout=dropout,
|
||||
bias=attention_bias,
|
||||
upcast_attention=upcast_attention,
|
||||
) # is self-attn if encoder_hidden_states is none
|
||||
else:
|
||||
self.norm2 = None
|
||||
self.attn2 = None
|
||||
|
||||
# 3. Feed-forward
|
||||
self.norm3 = nn.LayerNorm(dim, elementwise_affine=norm_elementwise_affine)
|
||||
self.ff = FeedForward(
|
||||
dim,
|
||||
dropout=dropout,
|
||||
activation_fn=activation_fn,
|
||||
final_dropout=final_dropout,
|
||||
)
|
||||
|
||||
# let chunk size default to None
|
||||
self._chunk_size = None
|
||||
self._chunk_dim = 0
|
||||
|
||||
def set_chunk_feed_forward(self, chunk_size: Optional[int], dim: int):
|
||||
# Sets chunk feed-forward
|
||||
self._chunk_size = chunk_size
|
||||
self._chunk_dim = dim
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.FloatTensor,
|
||||
attention_mask: Optional[torch.FloatTensor] = None,
|
||||
encoder_hidden_states: Optional[torch.FloatTensor] = None,
|
||||
encoder_attention_mask: Optional[torch.FloatTensor] = None,
|
||||
) -> torch.FloatTensor:
|
||||
# Notice that normalization is always applied before the real computation in the following blocks.
|
||||
# 0. Self-Attention
|
||||
norm_hidden_states = self.norm1(hidden_states)
|
||||
|
||||
attn_output = self.attn1(
|
||||
norm_hidden_states,
|
||||
encoder_hidden_states=(
|
||||
encoder_hidden_states if self.only_cross_attention else None
|
||||
),
|
||||
attention_mask=attention_mask,
|
||||
)
|
||||
|
||||
hidden_states = attn_output + hidden_states
|
||||
|
||||
# 3. Cross-Attention
|
||||
if self.attn2 is not None:
|
||||
norm_hidden_states = self.norm2(hidden_states)
|
||||
|
||||
attn_output = self.attn2(
|
||||
norm_hidden_states,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
attention_mask=encoder_attention_mask,
|
||||
)
|
||||
hidden_states = attn_output + hidden_states
|
||||
|
||||
# 4. Feed-forward
|
||||
norm_hidden_states = self.norm3(hidden_states)
|
||||
|
||||
if self._chunk_size is not None:
|
||||
# "feed_forward_chunk_size" can be used to save memory
|
||||
if norm_hidden_states.shape[self._chunk_dim] % self._chunk_size != 0:
|
||||
raise ValueError(
|
||||
f"`hidden_states` dimension to be chunked: {norm_hidden_states.shape[self._chunk_dim]} has to be divisible by chunk size: {self._chunk_size}. Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`."
|
||||
)
|
||||
|
||||
num_chunks = norm_hidden_states.shape[self._chunk_dim] // self._chunk_size
|
||||
ff_output = torch.cat(
|
||||
[
|
||||
self.ff(hid_slice)
|
||||
for hid_slice in norm_hidden_states.chunk(
|
||||
num_chunks, dim=self._chunk_dim
|
||||
)
|
||||
],
|
||||
dim=self._chunk_dim,
|
||||
)
|
||||
else:
|
||||
ff_output = self.ff(norm_hidden_states)
|
||||
|
||||
hidden_states = ff_output + hidden_states
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class FeedForward(nn.Module):
|
||||
r"""
|
||||
A feed-forward layer.
|
||||
|
||||
Parameters:
|
||||
dim (`int`): The number of channels in the input.
|
||||
dim_out (`int`, *optional*): The number of channels in the output. If not given, defaults to `dim`.
|
||||
mult (`int`, *optional*, defaults to 4): The multiplier to use for the hidden dimension.
|
||||
dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
|
||||
activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to be used in feed-forward.
|
||||
final_dropout (`bool` *optional*, defaults to False): Apply a final dropout.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
dim: int,
|
||||
dim_out: Optional[int] = None,
|
||||
mult: int = 4,
|
||||
dropout: float = 0.0,
|
||||
activation_fn: str = "geglu",
|
||||
final_dropout: bool = False,
|
||||
):
|
||||
super().__init__()
|
||||
inner_dim = int(dim * mult)
|
||||
dim_out = dim_out if dim_out is not None else dim
|
||||
linear_cls = nn.Linear
|
||||
|
||||
if activation_fn == "gelu":
|
||||
act_fn = GELU(dim, inner_dim)
|
||||
if activation_fn == "gelu-approximate":
|
||||
act_fn = GELU(dim, inner_dim, approximate="tanh")
|
||||
elif activation_fn == "geglu":
|
||||
act_fn = GEGLU(dim, inner_dim)
|
||||
elif activation_fn == "geglu-approximate":
|
||||
act_fn = ApproximateGELU(dim, inner_dim)
|
||||
|
||||
self.net = nn.ModuleList([])
|
||||
# project in
|
||||
self.net.append(act_fn)
|
||||
# project dropout
|
||||
self.net.append(nn.Dropout(dropout))
|
||||
# project out
|
||||
self.net.append(linear_cls(inner_dim, dim_out))
|
||||
# FF as used in Vision Transformer, MLP-Mixer, etc. have a final dropout
|
||||
if final_dropout:
|
||||
self.net.append(nn.Dropout(dropout))
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
for module in self.net:
|
||||
hidden_states = module(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class GELU(nn.Module):
|
||||
r"""
|
||||
GELU activation function with tanh approximation support with `approximate="tanh"`.
|
||||
|
||||
Parameters:
|
||||
dim_in (`int`): The number of channels in the input.
|
||||
dim_out (`int`): The number of channels in the output.
|
||||
approximate (`str`, *optional*, defaults to `"none"`): If `"tanh"`, use tanh approximation.
|
||||
"""
|
||||
|
||||
def __init__(self, dim_in: int, dim_out: int, approximate: str = "none"):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(dim_in, dim_out)
|
||||
self.approximate = approximate
|
||||
|
||||
def gelu(self, gate: torch.Tensor) -> torch.Tensor:
|
||||
if gate.device.type != "mps":
|
||||
return F.gelu(gate, approximate=self.approximate)
|
||||
# mps: gelu is not implemented for float16
|
||||
return F.gelu(gate.to(dtype=torch.float32), approximate=self.approximate).to(
|
||||
dtype=gate.dtype
|
||||
)
|
||||
|
||||
def forward(self, hidden_states):
|
||||
hidden_states = self.proj(hidden_states)
|
||||
hidden_states = self.gelu(hidden_states)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class GEGLU(nn.Module):
|
||||
r"""
|
||||
A variant of the gated linear unit activation function from https://arxiv.org/abs/2002.05202.
|
||||
|
||||
Parameters:
|
||||
dim_in (`int`): The number of channels in the input.
|
||||
dim_out (`int`): The number of channels in the output.
|
||||
"""
|
||||
|
||||
def __init__(self, dim_in: int, dim_out: int):
|
||||
super().__init__()
|
||||
linear_cls = nn.Linear
|
||||
|
||||
self.proj = linear_cls(dim_in, dim_out * 2)
|
||||
|
||||
def gelu(self, gate: torch.Tensor) -> torch.Tensor:
|
||||
if gate.device.type != "mps":
|
||||
return F.gelu(gate)
|
||||
# mps: gelu is not implemented for float16
|
||||
return F.gelu(gate.to(dtype=torch.float32)).to(dtype=gate.dtype)
|
||||
|
||||
def forward(self, hidden_states, scale: float = 1.0):
|
||||
args = ()
|
||||
hidden_states, gate = self.proj(hidden_states, *args).chunk(2, dim=-1)
|
||||
return hidden_states * self.gelu(gate)
|
||||
|
||||
|
||||
class ApproximateGELU(nn.Module):
|
||||
r"""
|
||||
The approximate form of Gaussian Error Linear Unit (GELU). For more details, see section 2:
|
||||
https://arxiv.org/abs/1606.08415.
|
||||
|
||||
Parameters:
|
||||
dim_in (`int`): The number of channels in the input.
|
||||
dim_out (`int`): The number of channels in the output.
|
||||
"""
|
||||
|
||||
def __init__(self, dim_in: int, dim_out: int):
|
||||
super().__init__()
|
||||
self.proj = nn.Linear(dim_in, dim_out)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.proj(x)
|
||||
return x * torch.sigmoid(1.702 * x)
|
||||
@@ -0,0 +1,219 @@
|
||||
# Copyright 2023 The HuggingFace Team. All rights reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
# --------
|
||||
#
|
||||
# Modified 2024 by the Tripo AI and Stability AI Team.
|
||||
#
|
||||
# Copyright (c) 2024 Tripo AI & Stability AI
|
||||
#
|
||||
# Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
# of this software and associated documentation files (the "Software"), to deal
|
||||
# in the Software without restriction, including without limitation the rights
|
||||
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
# copies of the Software, and to permit persons to whom the Software is
|
||||
# furnished to do so, subject to the following conditions:
|
||||
#
|
||||
# The above copyright notice and this permission notice shall be included in all
|
||||
# copies or substantial portions of the Software.
|
||||
#
|
||||
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
# SOFTWARE.
|
||||
|
||||
from dataclasses import dataclass
|
||||
from typing import Optional
|
||||
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
from torch import nn
|
||||
|
||||
from ...utils import BaseModule
|
||||
from .basic_transformer_block import BasicTransformerBlock
|
||||
|
||||
|
||||
class Transformer1D(BaseModule):
|
||||
@dataclass
|
||||
class Config(BaseModule.Config):
|
||||
num_attention_heads: int = 16
|
||||
attention_head_dim: int = 88
|
||||
in_channels: Optional[int] = None
|
||||
out_channels: Optional[int] = None
|
||||
num_layers: int = 1
|
||||
dropout: float = 0.0
|
||||
norm_num_groups: int = 32
|
||||
cross_attention_dim: Optional[int] = None
|
||||
attention_bias: bool = False
|
||||
activation_fn: str = "geglu"
|
||||
only_cross_attention: bool = False
|
||||
double_self_attention: bool = False
|
||||
upcast_attention: bool = False
|
||||
norm_type: str = "layer_norm"
|
||||
norm_elementwise_affine: bool = True
|
||||
gradient_checkpointing: bool = False
|
||||
|
||||
cfg: Config
|
||||
|
||||
def configure(self) -> None:
|
||||
self.num_attention_heads = self.cfg.num_attention_heads
|
||||
self.attention_head_dim = self.cfg.attention_head_dim
|
||||
inner_dim = self.num_attention_heads * self.attention_head_dim
|
||||
|
||||
linear_cls = nn.Linear
|
||||
|
||||
# 2. Define input layers
|
||||
self.in_channels = self.cfg.in_channels
|
||||
|
||||
self.norm = torch.nn.GroupNorm(
|
||||
num_groups=self.cfg.norm_num_groups,
|
||||
num_channels=self.cfg.in_channels,
|
||||
eps=1e-6,
|
||||
affine=True,
|
||||
)
|
||||
self.proj_in = linear_cls(self.cfg.in_channels, inner_dim)
|
||||
|
||||
# 3. Define transformers blocks
|
||||
self.transformer_blocks = nn.ModuleList(
|
||||
[
|
||||
BasicTransformerBlock(
|
||||
inner_dim,
|
||||
self.num_attention_heads,
|
||||
self.attention_head_dim,
|
||||
dropout=self.cfg.dropout,
|
||||
cross_attention_dim=self.cfg.cross_attention_dim,
|
||||
activation_fn=self.cfg.activation_fn,
|
||||
attention_bias=self.cfg.attention_bias,
|
||||
only_cross_attention=self.cfg.only_cross_attention,
|
||||
double_self_attention=self.cfg.double_self_attention,
|
||||
upcast_attention=self.cfg.upcast_attention,
|
||||
norm_type=self.cfg.norm_type,
|
||||
norm_elementwise_affine=self.cfg.norm_elementwise_affine,
|
||||
)
|
||||
for d in range(self.cfg.num_layers)
|
||||
]
|
||||
)
|
||||
|
||||
# 4. Define output layers
|
||||
self.out_channels = (
|
||||
self.cfg.in_channels
|
||||
if self.cfg.out_channels is None
|
||||
else self.cfg.out_channels
|
||||
)
|
||||
|
||||
self.proj_out = linear_cls(inner_dim, self.cfg.in_channels)
|
||||
|
||||
self.gradient_checkpointing = self.cfg.gradient_checkpointing
|
||||
|
||||
def forward(
|
||||
self,
|
||||
hidden_states: torch.Tensor,
|
||||
encoder_hidden_states: Optional[torch.Tensor] = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
encoder_attention_mask: Optional[torch.Tensor] = None,
|
||||
):
|
||||
"""
|
||||
The [`Transformer1DModel`] forward method.
|
||||
|
||||
Args:
|
||||
hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
|
||||
Input `hidden_states`.
|
||||
encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
|
||||
Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
|
||||
self-attention.
|
||||
attention_mask ( `torch.Tensor`, *optional*):
|
||||
An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
|
||||
is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
|
||||
negative values to the attention scores corresponding to "discard" tokens.
|
||||
encoder_attention_mask ( `torch.Tensor`, *optional*):
|
||||
Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:
|
||||
|
||||
* Mask `(batch, sequence_length)` True = keep, False = discard.
|
||||
* Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.
|
||||
|
||||
If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
|
||||
above. This bias will be added to the cross-attention scores.
|
||||
|
||||
Returns:
|
||||
torch.FloatTensor
|
||||
"""
|
||||
# ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
|
||||
# we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
|
||||
# we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
|
||||
# expects mask of shape:
|
||||
# [batch, key_tokens]
|
||||
# adds singleton query_tokens dimension:
|
||||
# [batch, 1, key_tokens]
|
||||
# this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
|
||||
# [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn)
|
||||
# [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
|
||||
if attention_mask is not None and attention_mask.ndim == 2:
|
||||
# assume that mask is expressed as:
|
||||
# (1 = keep, 0 = discard)
|
||||
# convert mask into a bias that can be added to attention scores:
|
||||
# (keep = +0, discard = -10000.0)
|
||||
attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
|
||||
attention_mask = attention_mask.unsqueeze(1)
|
||||
|
||||
# convert encoder_attention_mask to a bias the same way we do for attention_mask
|
||||
if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
|
||||
encoder_attention_mask = (
|
||||
1 - encoder_attention_mask.to(hidden_states.dtype)
|
||||
) * -10000.0
|
||||
encoder_attention_mask = encoder_attention_mask.unsqueeze(1)
|
||||
|
||||
# 1. Input
|
||||
batch, _, seq_len = hidden_states.shape
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
inner_dim = hidden_states.shape[1]
|
||||
hidden_states = hidden_states.permute(0, 2, 1).reshape(
|
||||
batch, seq_len, inner_dim
|
||||
)
|
||||
hidden_states = self.proj_in(hidden_states)
|
||||
|
||||
# 2. Blocks
|
||||
for block in self.transformer_blocks:
|
||||
if self.training and self.gradient_checkpointing:
|
||||
hidden_states = torch.utils.checkpoint.checkpoint(
|
||||
block,
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
encoder_hidden_states,
|
||||
encoder_attention_mask,
|
||||
use_reentrant=False,
|
||||
)
|
||||
else:
|
||||
hidden_states = block(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
encoder_hidden_states=encoder_hidden_states,
|
||||
encoder_attention_mask=encoder_attention_mask,
|
||||
)
|
||||
|
||||
# 3. Output
|
||||
hidden_states = self.proj_out(hidden_states)
|
||||
hidden_states = (
|
||||
hidden_states.reshape(batch, seq_len, inner_dim)
|
||||
.permute(0, 2, 1)
|
||||
.contiguous()
|
||||
)
|
||||
|
||||
output = hidden_states + residual
|
||||
|
||||
return output
|
||||
Reference in New Issue
Block a user