import math from contextlib import nullcontext from functools import partial from typing import Dict, List, Optional, Tuple, Union import kornia import numpy as np import torch import torch.nn as nn from einops import rearrange, repeat from omegaconf import ListConfig from torch.utils.checkpoint import checkpoint from transformers import ( T5EncoderModel, T5Tokenizer, ) from ...util import ( append_dims, autocast, count_params, default, disabled_train, expand_dims_like, instantiate_from_config, ) class AbstractEmbModel(nn.Module): def __init__(self): super().__init__() self._is_trainable = None self._ucg_rate = None self._input_key = None @property def is_trainable(self) -> bool: return self._is_trainable @property def ucg_rate(self) -> Union[float, torch.Tensor]: return self._ucg_rate @property def input_key(self) -> str: return self._input_key @is_trainable.setter def is_trainable(self, value: bool): self._is_trainable = value @ucg_rate.setter def ucg_rate(self, value: Union[float, torch.Tensor]): self._ucg_rate = value @input_key.setter def input_key(self, value: str): self._input_key = value @is_trainable.deleter def is_trainable(self): del self._is_trainable @ucg_rate.deleter def ucg_rate(self): del self._ucg_rate @input_key.deleter def input_key(self): del self._input_key class GeneralConditioner(nn.Module): OUTPUT_DIM2KEYS = {2: "vector", 3: "crossattn", 4: "concat", 5: "concat"} KEY2CATDIM = {"vector": 1, "crossattn": 2, "concat": 1} def __init__(self, emb_models: Union[List, ListConfig], cor_embs=[], cor_p=[]): super().__init__() embedders = [] for n, embconfig in enumerate(emb_models): embedder = instantiate_from_config(embconfig) assert isinstance( embedder, AbstractEmbModel ), f"embedder model {embedder.__class__.__name__} has to inherit from AbstractEmbModel" embedder.is_trainable = embconfig.get("is_trainable", False) embedder.ucg_rate = embconfig.get("ucg_rate", 0.0) if not embedder.is_trainable: embedder.train = disabled_train for param in embedder.parameters(): param.requires_grad = False embedder.eval() print( f"Initialized embedder #{n}: {embedder.__class__.__name__} " f"with {count_params(embedder, False)} params. Trainable: {embedder.is_trainable}" ) if "input_key" in embconfig: embedder.input_key = embconfig["input_key"] elif "input_keys" in embconfig: embedder.input_keys = embconfig["input_keys"] else: raise KeyError(f"need either 'input_key' or 'input_keys' for embedder {embedder.__class__.__name__}") embedder.legacy_ucg_val = embconfig.get("legacy_ucg_value", None) if embedder.legacy_ucg_val is not None: embedder.ucg_prng = np.random.RandomState() embedders.append(embedder) self.embedders = nn.ModuleList(embedders) if len(cor_embs) > 0: assert len(cor_p) == 2 ** len(cor_embs) self.cor_embs = cor_embs self.cor_p = cor_p def possibly_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict) -> Dict: assert embedder.legacy_ucg_val is not None p = embedder.ucg_rate val = embedder.legacy_ucg_val for i in range(len(batch[embedder.input_key])): if embedder.ucg_prng.choice(2, p=[1 - p, p]): batch[embedder.input_key][i] = val return batch def surely_get_ucg_val(self, embedder: AbstractEmbModel, batch: Dict, cond_or_not) -> Dict: assert embedder.legacy_ucg_val is not None val = embedder.legacy_ucg_val for i in range(len(batch[embedder.input_key])): if cond_or_not[i]: batch[embedder.input_key][i] = val return batch def get_single_embedding( self, embedder, batch, output, cond_or_not: Optional[np.ndarray] = None, force_zero_embeddings: Optional[List] = None, ): embedding_context = nullcontext if embedder.is_trainable else torch.no_grad with embedding_context(): if hasattr(embedder, "input_key") and (embedder.input_key is not None): if embedder.legacy_ucg_val is not None: if cond_or_not is None: batch = self.possibly_get_ucg_val(embedder, batch) else: batch = self.surely_get_ucg_val(embedder, batch, cond_or_not) emb_out = embedder(batch[embedder.input_key]) elif hasattr(embedder, "input_keys"): emb_out = embedder(*[batch[k] for k in embedder.input_keys]) assert isinstance( emb_out, (torch.Tensor, list, tuple) ), f"encoder outputs must be tensors or a sequence, but got {type(emb_out)}" if not isinstance(emb_out, (list, tuple)): emb_out = [emb_out] for emb in emb_out: out_key = self.OUTPUT_DIM2KEYS[emb.dim()] if embedder.ucg_rate > 0.0 and embedder.legacy_ucg_val is None: if cond_or_not is None: emb = ( expand_dims_like( torch.bernoulli((1.0 - embedder.ucg_rate) * torch.ones(emb.shape[0], device=emb.device)), emb, ) * emb ) else: emb = ( expand_dims_like( torch.tensor(1 - cond_or_not, dtype=emb.dtype, device=emb.device), emb, ) * emb ) if hasattr(embedder, "input_key") and embedder.input_key in force_zero_embeddings: emb = torch.zeros_like(emb) if out_key in output: output[out_key] = torch.cat((output[out_key], emb), self.KEY2CATDIM[out_key]) else: output[out_key] = emb return output def forward(self, batch: Dict, force_zero_embeddings: Optional[List] = None) -> Dict: output = dict() if force_zero_embeddings is None: force_zero_embeddings = [] if len(self.cor_embs) > 0: batch_size = len(batch[list(batch.keys())[0]]) rand_idx = np.random.choice(len(self.cor_p), size=(batch_size,), p=self.cor_p) for emb_idx in self.cor_embs: cond_or_not = rand_idx % 2 rand_idx //= 2 output = self.get_single_embedding( self.embedders[emb_idx], batch, output=output, cond_or_not=cond_or_not, force_zero_embeddings=force_zero_embeddings, ) for i, embedder in enumerate(self.embedders): if i in self.cor_embs: continue output = self.get_single_embedding( embedder, batch, output=output, force_zero_embeddings=force_zero_embeddings ) return output def get_unconditional_conditioning(self, batch_c, batch_uc=None, force_uc_zero_embeddings=None): if force_uc_zero_embeddings is None: force_uc_zero_embeddings = [] ucg_rates = list() for embedder in self.embedders: ucg_rates.append(embedder.ucg_rate) embedder.ucg_rate = 0.0 cor_embs = self.cor_embs cor_p = self.cor_p self.cor_embs = [] self.cor_p = [] c = self(batch_c) uc = self(batch_c if batch_uc is None else batch_uc, force_uc_zero_embeddings) for embedder, rate in zip(self.embedders, ucg_rates): embedder.ucg_rate = rate self.cor_embs = cor_embs self.cor_p = cor_p return c, uc class FrozenT5Embedder(AbstractEmbModel): """Uses the T5 transformer encoder for text""" def __init__( self, model_dir="google/t5-v1_1-xxl", device="cuda", max_length=77, freeze=True, cache_dir=None, ): super().__init__() if model_dir is not "google/t5-v1_1-xxl": self.tokenizer = T5Tokenizer.from_pretrained(model_dir) self.transformer = T5EncoderModel.from_pretrained(model_dir) else: self.tokenizer = T5Tokenizer.from_pretrained(model_dir, cache_dir=cache_dir) self.transformer = T5EncoderModel.from_pretrained(model_dir, cache_dir=cache_dir) self.device = device self.max_length = max_length if freeze: self.freeze() def freeze(self): self.transformer = self.transformer.eval() for param in self.parameters(): param.requires_grad = False # @autocast def forward(self, text): batch_encoding = self.tokenizer( text, truncation=True, max_length=self.max_length, return_length=True, return_overflowing_tokens=False, padding="max_length", return_tensors="pt", ) tokens = batch_encoding["input_ids"].to(self.device) with torch.autocast("cuda", enabled=False): outputs = self.transformer(input_ids=tokens) z = outputs.last_hidden_state return z def encode(self, text): return self(text)