mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
550 lines
16 KiB
Python
550 lines
16 KiB
Python
import torch
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import torch.nn as nn
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from functools import partial
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import clip
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from einops import rearrange, repeat
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from transformers import CLIPTokenizer, CLIPTextModel
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import kornia
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from ldm.dream.devices import choose_torch_device
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from ldm.modules.x_transformer import (
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Encoder,
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TransformerWrapper,
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) # TODO: can we directly rely on lucidrains code and simply add this as a reuirement? --> test
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def _expand_mask(mask, dtype, tgt_len=None):
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"""
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Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
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"""
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bsz, src_len = mask.size()
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tgt_len = tgt_len if tgt_len is not None else src_len
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expanded_mask = (
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mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
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)
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inverted_mask = 1.0 - expanded_mask
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return inverted_mask.masked_fill(
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inverted_mask.to(torch.bool), torch.finfo(dtype).min
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)
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def _build_causal_attention_mask(bsz, seq_len, dtype):
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# lazily create causal attention mask, with full attention between the vision tokens
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# pytorch uses additive attention mask; fill with -inf
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mask = torch.empty(bsz, seq_len, seq_len, dtype=dtype)
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mask.fill_(torch.tensor(torch.finfo(dtype).min))
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mask.triu_(1) # zero out the lower diagonal
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mask = mask.unsqueeze(1) # expand mask
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return mask
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class AbstractEncoder(nn.Module):
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def __init__(self):
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super().__init__()
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def encode(self, *args, **kwargs):
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raise NotImplementedError
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class ClassEmbedder(nn.Module):
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def __init__(self, embed_dim, n_classes=1000, key='class'):
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super().__init__()
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self.key = key
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self.embedding = nn.Embedding(n_classes, embed_dim)
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def forward(self, batch, key=None):
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if key is None:
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key = self.key
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# this is for use in crossattn
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c = batch[key][:, None]
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c = self.embedding(c)
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return c
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class TransformerEmbedder(AbstractEncoder):
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"""Some transformer encoder layers"""
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def __init__(
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self,
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n_embed,
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n_layer,
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vocab_size,
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max_seq_len=77,
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device=choose_torch_device(),
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):
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super().__init__()
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self.device = device
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self.transformer = TransformerWrapper(
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num_tokens=vocab_size,
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max_seq_len=max_seq_len,
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attn_layers=Encoder(dim=n_embed, depth=n_layer),
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)
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def forward(self, tokens):
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tokens = tokens.to(self.device) # meh
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z = self.transformer(tokens, return_embeddings=True)
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return z
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def encode(self, x):
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return self(x)
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class BERTTokenizer(AbstractEncoder):
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"""Uses a pretrained BERT tokenizer by huggingface. Vocab size: 30522 (?)"""
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def __init__(
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self, device=choose_torch_device(), vq_interface=True, max_length=77
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):
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super().__init__()
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from transformers import (
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BertTokenizerFast,
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) # TODO: add to reuquirements
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# Modified to allow to run on non-internet connected compute nodes.
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# Model needs to be loaded into cache from an internet-connected machine
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# by running:
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# from transformers import BertTokenizerFast
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# BertTokenizerFast.from_pretrained("bert-base-uncased")
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try:
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self.tokenizer = BertTokenizerFast.from_pretrained(
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'bert-base-uncased', local_files_only=True
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)
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except OSError:
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raise SystemExit(
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"* Couldn't load Bert tokenizer files. Try running scripts/preload_models.py from an internet-conected machine."
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)
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self.device = device
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self.vq_interface = vq_interface
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self.max_length = max_length
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def forward(self, text):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding='max_length',
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return_tensors='pt',
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)
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tokens = batch_encoding['input_ids'].to(self.device)
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return tokens
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@torch.no_grad()
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def encode(self, text):
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tokens = self(text)
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if not self.vq_interface:
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return tokens
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return None, None, [None, None, tokens]
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def decode(self, text):
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return text
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class BERTEmbedder(AbstractEncoder):
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"""Uses the BERT tokenizr model and add some transformer encoder layers"""
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def __init__(
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self,
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n_embed,
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n_layer,
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vocab_size=30522,
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max_seq_len=77,
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device=choose_torch_device(),
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use_tokenizer=True,
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embedding_dropout=0.0,
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):
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super().__init__()
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self.use_tknz_fn = use_tokenizer
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if self.use_tknz_fn:
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self.tknz_fn = BERTTokenizer(
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vq_interface=False, max_length=max_seq_len
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)
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self.device = device
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self.transformer = TransformerWrapper(
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num_tokens=vocab_size,
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max_seq_len=max_seq_len,
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attn_layers=Encoder(dim=n_embed, depth=n_layer),
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emb_dropout=embedding_dropout,
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)
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def forward(self, text, embedding_manager=None):
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if self.use_tknz_fn:
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tokens = self.tknz_fn(text) # .to(self.device)
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else:
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tokens = text
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z = self.transformer(
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tokens, return_embeddings=True, embedding_manager=embedding_manager
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)
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return z
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def encode(self, text, **kwargs):
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# output of length 77
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return self(text, **kwargs)
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class SpatialRescaler(nn.Module):
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def __init__(
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self,
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n_stages=1,
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method='bilinear',
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multiplier=0.5,
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in_channels=3,
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out_channels=None,
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bias=False,
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):
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super().__init__()
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self.n_stages = n_stages
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assert self.n_stages >= 0
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assert method in [
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'nearest',
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'linear',
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'bilinear',
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'trilinear',
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'bicubic',
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'area',
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]
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self.multiplier = multiplier
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self.interpolator = partial(
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torch.nn.functional.interpolate, mode=method
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)
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self.remap_output = out_channels is not None
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if self.remap_output:
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print(
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f'Spatial Rescaler mapping from {in_channels} to {out_channels} channels after resizing.'
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)
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self.channel_mapper = nn.Conv2d(
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in_channels, out_channels, 1, bias=bias
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)
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def forward(self, x):
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for stage in range(self.n_stages):
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x = self.interpolator(x, scale_factor=self.multiplier)
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if self.remap_output:
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x = self.channel_mapper(x)
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return x
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def encode(self, x):
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return self(x)
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class FrozenCLIPEmbedder(AbstractEncoder):
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"""Uses the CLIP transformer encoder for text (from Hugging Face)"""
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def __init__(
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self,
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version='openai/clip-vit-large-patch14',
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device=choose_torch_device(),
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max_length=77,
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):
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super().__init__()
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self.tokenizer = CLIPTokenizer.from_pretrained(
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version, local_files_only=True
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)
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self.transformer = CLIPTextModel.from_pretrained(
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version, local_files_only=True
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)
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self.device = device
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self.max_length = max_length
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self.freeze()
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def embedding_forward(
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self,
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input_ids=None,
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position_ids=None,
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inputs_embeds=None,
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embedding_manager=None,
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) -> torch.Tensor:
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seq_length = (
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input_ids.shape[-1]
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if input_ids is not None
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else inputs_embeds.shape[-2]
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)
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if position_ids is None:
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position_ids = self.position_ids[:, :seq_length]
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if inputs_embeds is None:
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inputs_embeds = self.token_embedding(input_ids)
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if embedding_manager is not None:
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inputs_embeds = embedding_manager(input_ids, inputs_embeds)
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position_embeddings = self.position_embedding(position_ids)
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embeddings = inputs_embeds + position_embeddings
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return embeddings
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self.transformer.text_model.embeddings.forward = (
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embedding_forward.__get__(self.transformer.text_model.embeddings)
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)
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def encoder_forward(
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self,
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inputs_embeds,
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attention_mask=None,
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causal_attention_mask=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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):
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output_attentions = (
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output_attentions
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if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = (
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return_dict
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if return_dict is not None
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else self.config.use_return_dict
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)
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encoder_states = () if output_hidden_states else None
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all_attentions = () if output_attentions else None
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hidden_states = inputs_embeds
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for idx, encoder_layer in enumerate(self.layers):
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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layer_outputs = encoder_layer(
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hidden_states,
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attention_mask,
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causal_attention_mask,
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output_attentions=output_attentions,
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)
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hidden_states = layer_outputs[0]
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if output_attentions:
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all_attentions = all_attentions + (layer_outputs[1],)
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if output_hidden_states:
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encoder_states = encoder_states + (hidden_states,)
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return hidden_states
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self.transformer.text_model.encoder.forward = encoder_forward.__get__(
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self.transformer.text_model.encoder
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)
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def text_encoder_forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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embedding_manager=None,
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):
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output_attentions = (
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output_attentions
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if output_attentions is not None
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else self.config.output_attentions
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)
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output_hidden_states = (
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output_hidden_states
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if output_hidden_states is not None
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else self.config.output_hidden_states
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)
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return_dict = (
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return_dict
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if return_dict is not None
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else self.config.use_return_dict
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)
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if input_ids is None:
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raise ValueError('You have to specify either input_ids')
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input_shape = input_ids.size()
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input_ids = input_ids.view(-1, input_shape[-1])
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hidden_states = self.embeddings(
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input_ids=input_ids,
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position_ids=position_ids,
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embedding_manager=embedding_manager,
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)
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bsz, seq_len = input_shape
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# CLIP's text model uses causal mask, prepare it here.
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# https://github.com/openai/CLIP/blob/cfcffb90e69f37bf2ff1e988237a0fbe41f33c04/clip/model.py#L324
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causal_attention_mask = _build_causal_attention_mask(
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bsz, seq_len, hidden_states.dtype
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).to(hidden_states.device)
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# expand attention_mask
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if attention_mask is not None:
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# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
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attention_mask = _expand_mask(
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attention_mask, hidden_states.dtype
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)
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last_hidden_state = self.encoder(
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inputs_embeds=hidden_states,
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attention_mask=attention_mask,
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causal_attention_mask=causal_attention_mask,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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)
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last_hidden_state = self.final_layer_norm(last_hidden_state)
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return last_hidden_state
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self.transformer.text_model.forward = text_encoder_forward.__get__(
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self.transformer.text_model
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)
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def transformer_forward(
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self,
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input_ids=None,
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attention_mask=None,
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position_ids=None,
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output_attentions=None,
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output_hidden_states=None,
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return_dict=None,
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embedding_manager=None,
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):
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return self.text_model(
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input_ids=input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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output_attentions=output_attentions,
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output_hidden_states=output_hidden_states,
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return_dict=return_dict,
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embedding_manager=embedding_manager,
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)
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self.transformer.forward = transformer_forward.__get__(
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self.transformer
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)
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def freeze(self):
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self.transformer = self.transformer.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text, **kwargs):
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batch_encoding = self.tokenizer(
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text,
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truncation=True,
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max_length=self.max_length,
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return_length=True,
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return_overflowing_tokens=False,
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padding='max_length',
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return_tensors='pt',
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)
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tokens = batch_encoding['input_ids'].to(self.device)
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z = self.transformer(input_ids=tokens, **kwargs)
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return z
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def encode(self, text, **kwargs):
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return self(text, **kwargs)
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class FrozenCLIPTextEmbedder(nn.Module):
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"""
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Uses the CLIP transformer encoder for text.
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"""
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def __init__(
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self,
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version='ViT-L/14',
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device=choose_torch_device(),
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max_length=77,
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n_repeat=1,
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normalize=True,
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):
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super().__init__()
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self.model, _ = clip.load(version, jit=False, device=device)
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self.device = device
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self.max_length = max_length
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self.n_repeat = n_repeat
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self.normalize = normalize
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def freeze(self):
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self.model = self.model.eval()
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for param in self.parameters():
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param.requires_grad = False
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def forward(self, text):
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tokens = clip.tokenize(text).to(self.device)
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z = self.model.encode_text(tokens)
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if self.normalize:
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z = z / torch.linalg.norm(z, dim=1, keepdim=True)
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return z
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def encode(self, text):
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z = self(text)
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if z.ndim == 2:
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z = z[:, None, :]
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z = repeat(z, 'b 1 d -> b k d', k=self.n_repeat)
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return z
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class FrozenClipImageEmbedder(nn.Module):
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"""
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Uses the CLIP image encoder.
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"""
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def __init__(
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self,
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model,
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jit=False,
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device=choose_torch_device(),
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antialias=False,
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):
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super().__init__()
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self.model, _ = clip.load(name=model, device=device, jit=jit)
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self.antialias = antialias
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self.register_buffer(
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'mean',
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torch.Tensor([0.48145466, 0.4578275, 0.40821073]),
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persistent=False,
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)
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self.register_buffer(
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'std',
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torch.Tensor([0.26862954, 0.26130258, 0.27577711]),
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persistent=False,
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)
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def preprocess(self, x):
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# normalize to [0,1]
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x = kornia.geometry.resize(
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x,
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(224, 224),
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interpolation='bicubic',
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align_corners=True,
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antialias=self.antialias,
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)
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x = (x + 1.0) / 2.0
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# renormalize according to clip
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x = kornia.enhance.normalize(x, self.mean, self.std)
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return x
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def forward(self, x):
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# x is assumed to be in range [-1,1]
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return self.model.encode_image(self.preprocess(x))
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if __name__ == '__main__':
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from ldm.util import count_params
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model = FrozenCLIPEmbedder()
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count_params(model, verbose=True)
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