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src/diffusers/models/transformers/transformer_2d.py
524 строки
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Sayak Paul
[Core] refactor transformers 2d into multiple init variants. (#7491)
03 апр 2024, 10:26
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03 апр 2024, 10:26
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# Copyright 2024 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. from dataclasses import dataclass from typing import Any, Dict, Optional import torch import torch.nn.functional as F from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...utils import BaseOutput, deprecate, is_torch_version, logging from ..attention import BasicTransformerBlock from ..embeddings import ImagePositionalEmbeddings, PatchEmbed, PixArtAlphaTextProjection from ..modeling_utils import ModelMixin from ..normalization import AdaLayerNormSingle logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class Transformer2DModelOutput(BaseOutput): """ The output of [`Transformer2DModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete): The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability distributions for the unnoised latent pixels. """ sample: torch.FloatTensor class Transformer2DModel(ModelMixin, ConfigMixin): """ A 2D Transformer model for image-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. num_vector_embeds (`int`, *optional*): The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**). Includes the class for the masked latent pixel. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. num_embeds_ada_norm ( `int`, *optional*): The number of diffusion steps used during training. Pass if at least one of the norm_layers is `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are added to the hidden states. During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`. attention_bias (`bool`, *optional*): Configure if the `TransformerBlocks` attention should contain a bias parameter. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, 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, sample_size: Optional[int] = None, num_vector_embeds: Optional[int] = None, patch_size: Optional[int] = None, activation_fn: str = "geglu", num_embeds_ada_norm: Optional[int] = None, use_linear_projection: bool = False, only_cross_attention: bool = False, double_self_attention: bool = False, upcast_attention: bool = False, norm_type: str = "layer_norm", # 'layer_norm', 'ada_norm', 'ada_norm_zero', 'ada_norm_single', 'ada_norm_continuous', 'layer_norm_i2vgen' norm_elementwise_affine: bool = True, norm_eps: float = 1e-5, attention_type: str = "default", caption_channels: int = None, interpolation_scale: float = None, ): super().__init__() # Validate inputs. if patch_size is not None: if norm_type not in ["ada_norm", "ada_norm_zero", "ada_norm_single"]: raise NotImplementedError( f"Forward pass is not implemented when `patch_size` is not None and `norm_type` is '{norm_type}'." ) elif norm_type in ["ada_norm", "ada_norm_zero"] and num_embeds_ada_norm is None: raise ValueError( f"When using a `patch_size` and this `norm_type` ({norm_type}), `num_embeds_ada_norm` cannot be None." ) # Set some common variables used across the board. self.use_linear_projection = use_linear_projection self.interpolation_scale = interpolation_scale self.caption_channels = caption_channels self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim self.inner_dim = self.config.num_attention_heads * self.config.attention_head_dim self.in_channels = in_channels self.out_channels = in_channels if out_channels is None else out_channels self.gradient_checkpointing = False # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)` # Define whether input is continuous or discrete depending on configuration self.is_input_continuous = (in_channels is not None) and (patch_size is None) self.is_input_vectorized = num_vector_embeds is not None self.is_input_patches = in_channels is not None and patch_size is not None if norm_type == "layer_norm" and num_embeds_ada_norm is not None: deprecation_message = ( f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or" " incorrectly set to `'layer_norm'`. Make sure to set `norm_type` to `'ada_norm'` in the config." " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect" " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it" " would be very nice if you could open a Pull request for the `transformer/config.json` file" ) deprecate("norm_type!=num_embeds_ada_norm", "1.0.0", deprecation_message, standard_warn=False) norm_type = "ada_norm" if self.is_input_continuous and self.is_input_vectorized: raise ValueError( f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make" " sure that either `in_channels` or `num_vector_embeds` is None." ) elif self.is_input_vectorized and self.is_input_patches: raise ValueError( f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make" " sure that either `num_vector_embeds` or `num_patches` is None." ) elif not self.is_input_continuous and not self.is_input_vectorized and not self.is_input_patches: raise ValueError( f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:" f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None." ) # 2. Initialize the right blocks. # These functions follow a common structure: # a. Initialize the input blocks. b. Initialize the transformer blocks. # c. Initialize the output blocks and other projection blocks when necessary. if self.is_input_continuous: self._init_continuous_input(norm_type=norm_type) elif self.is_input_vectorized: self._init_vectorized_inputs(norm_type=norm_type) elif self.is_input_patches: self._init_patched_inputs(norm_type=norm_type) def _init_continuous_input(self, norm_type): self.norm = torch.nn.GroupNorm( num_groups=self.config.norm_num_groups, num_channels=self.in_channels, eps=1e-6, affine=True ) if self.use_linear_projection: self.proj_in = torch.nn.Linear(self.in_channels, self.inner_dim) else: self.proj_in = torch.nn.Conv2d(self.in_channels, self.inner_dim, kernel_size=1, stride=1, padding=0) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( self.inner_dim, self.config.num_attention_heads, self.config.attention_head_dim, dropout=self.config.dropout, cross_attention_dim=self.config.cross_attention_dim, activation_fn=self.config.activation_fn, num_embeds_ada_norm=self.config.num_embeds_ada_norm, attention_bias=self.config.attention_bias, only_cross_attention=self.config.only_cross_attention, double_self_attention=self.config.double_self_attention, upcast_attention=self.config.upcast_attention, norm_type=norm_type, norm_elementwise_affine=self.config.norm_elementwise_affine, norm_eps=self.config.norm_eps, attention_type=self.config.attention_type, ) for _ in range(self.config.num_layers) ] ) if self.use_linear_projection: self.proj_out = torch.nn.Linear(self.inner_dim, self.out_channels) else: self.proj_out = torch.nn.Conv2d(self.inner_dim, self.out_channels, kernel_size=1, stride=1, padding=0) def _init_vectorized_inputs(self, norm_type): assert self.config.sample_size is not None, "Transformer2DModel over discrete input must provide sample_size" assert ( self.config.num_vector_embeds is not None ), "Transformer2DModel over discrete input must provide num_embed" self.height = self.config.sample_size self.width = self.config.sample_size self.num_latent_pixels = self.height * self.width self.latent_image_embedding = ImagePositionalEmbeddings( num_embed=self.config.num_vector_embeds, embed_dim=self.inner_dim, height=self.height, width=self.width ) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( self.inner_dim, self.config.num_attention_heads, self.config.attention_head_dim, dropout=self.config.dropout, cross_attention_dim=self.config.cross_attention_dim, activation_fn=self.config.activation_fn, num_embeds_ada_norm=self.config.num_embeds_ada_norm, attention_bias=self.config.attention_bias, only_cross_attention=self.config.only_cross_attention, double_self_attention=self.config.double_self_attention, upcast_attention=self.config.upcast_attention, norm_type=norm_type, norm_elementwise_affine=self.config.norm_elementwise_affine, norm_eps=self.config.norm_eps, attention_type=self.config.attention_type, ) for _ in range(self.config.num_layers) ] ) self.norm_out = nn.LayerNorm(self.inner_dim) self.out = nn.Linear(self.inner_dim, self.config.num_vector_embeds - 1) def _init_patched_inputs(self, norm_type): assert self.config.sample_size is not None, "Transformer2DModel over patched input must provide sample_size" self.height = self.config.sample_size self.width = self.config.sample_size self.patch_size = self.config.patch_size interpolation_scale = ( self.config.interpolation_scale if self.config.interpolation_scale is not None else max(self.config.sample_size // 64, 1) ) self.pos_embed = PatchEmbed( height=self.config.sample_size, width=self.config.sample_size, patch_size=self.config.patch_size, in_channels=self.in_channels, embed_dim=self.inner_dim, interpolation_scale=interpolation_scale, ) self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( self.inner_dim, self.config.num_attention_heads, self.config.attention_head_dim, dropout=self.config.dropout, cross_attention_dim=self.config.cross_attention_dim, activation_fn=self.config.activation_fn, num_embeds_ada_norm=self.config.num_embeds_ada_norm, attention_bias=self.config.attention_bias, only_cross_attention=self.config.only_cross_attention, double_self_attention=self.config.double_self_attention, upcast_attention=self.config.upcast_attention, norm_type=norm_type, norm_elementwise_affine=self.config.norm_elementwise_affine, norm_eps=self.config.norm_eps, attention_type=self.config.attention_type, ) for _ in range(self.config.num_layers) ] ) if self.config.norm_type != "ada_norm_single": self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) self.proj_out_1 = nn.Linear(self.inner_dim, 2 * self.inner_dim) self.proj_out_2 = nn.Linear( self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels ) elif self.config.norm_type == "ada_norm_single": self.norm_out = nn.LayerNorm(self.inner_dim, elementwise_affine=False, eps=1e-6) self.scale_shift_table = nn.Parameter(torch.randn(2, self.inner_dim) / self.inner_dim**0.5) self.proj_out = nn.Linear( self.inner_dim, self.config.patch_size * self.config.patch_size * self.out_channels ) # PixArt-Alpha blocks. self.adaln_single = None self.use_additional_conditions = False if self.config.norm_type == "ada_norm_single": self.use_additional_conditions = self.config.sample_size == 128 # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use # additional conditions until we find better name self.adaln_single = AdaLayerNormSingle( self.inner_dim, use_additional_conditions=self.use_additional_conditions ) self.caption_projection = None if self.caption_channels is not None: self.caption_projection = PixArtAlphaTextProjection( in_features=self.caption_channels, hidden_size=self.inner_dim ) def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, added_cond_kwargs: Dict[str, torch.Tensor] = None, class_labels: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, attention_mask: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, ): """ The [`Transformer2DModel`] 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. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. cross_attention_kwargs ( `Dict[str, Any]`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). 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. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ if cross_attention_kwargs is not None: if cross_attention_kwargs.get("scale", None) is not None: logger.warning("Passing `scale` to `cross_attention_kwargs` is deprecated. `scale` will be ignored.") # 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 if self.is_input_continuous: batch, _, height, width = hidden_states.shape residual = hidden_states hidden_states = self.norm(hidden_states) if not self.use_linear_projection: hidden_states = self.proj_in(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) else: inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) elif self.is_input_vectorized: hidden_states = self.latent_image_embedding(hidden_states) elif self.is_input_patches: height, width = hidden_states.shape[-2] // self.patch_size, hidden_states.shape[-1] // self.patch_size hidden_states = self.pos_embed(hidden_states) if self.adaln_single is not None: if self.use_additional_conditions and added_cond_kwargs is None: raise ValueError( "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`." ) batch_size = hidden_states.shape[0] timestep, embedded_timestep = self.adaln_single( timestep, added_cond_kwargs, batch_size=batch_size, hidden_dtype=hidden_states.dtype ) # 2. Blocks if self.is_input_patches and self.caption_projection is not None: batch_size = hidden_states.shape[0] encoder_hidden_states = self.caption_projection(encoder_hidden_states) encoder_hidden_states = encoder_hidden_states.view(batch_size, -1, hidden_states.shape[-1]) for block in self.transformer_blocks: if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(block), hidden_states, attention_mask, encoder_hidden_states, encoder_attention_mask, timestep, cross_attention_kwargs, class_labels, **ckpt_kwargs, ) else: hidden_states = block( hidden_states, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, class_labels=class_labels, ) # 3. Output if self.is_input_continuous: if not self.use_linear_projection: hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() hidden_states = self.proj_out(hidden_states) else: hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() output = hidden_states + residual elif self.is_input_vectorized: hidden_states = self.norm_out(hidden_states) logits = self.out(hidden_states) # (batch, self.num_vector_embeds - 1, self.num_latent_pixels) logits = logits.permute(0, 2, 1) # log(p(x_0)) output = F.log_softmax(logits.double(), dim=1).float() if self.is_input_patches: if self.config.norm_type != "ada_norm_single": conditioning = self.transformer_blocks[0].norm1.emb( timestep, class_labels, hidden_dtype=hidden_states.dtype ) shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None] hidden_states = self.proj_out_2(hidden_states) elif self.config.norm_type == "ada_norm_single": shift, scale = (self.scale_shift_table[None] + embedded_timestep[:, None]).chunk(2, dim=1) hidden_states = self.norm_out(hidden_states) # Modulation hidden_states = hidden_states * (1 + scale) + shift hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.squeeze(1) # unpatchify if self.adaln_single is None: height = width = int(hidden_states.shape[1] ** 0.5) hidden_states = hidden_states.reshape( shape=(-1, height, width, self.patch_size, self.patch_size, self.out_channels) ) hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states) output = hidden_states.reshape( shape=(-1, self.out_channels, height * self.patch_size, width * self.patch_size) ) if not return_dict: return (output,) return Transformer2DModelOutput(sample=output)