| import torch |
|
|
| from backend import memory_management |
| from backend.text_processing import emphasis, parsing |
| from modules.shared import opts |
|
|
|
|
| class PromptChunk: |
| def __init__(self): |
| self.tokens = [] |
| self.multipliers = [] |
|
|
|
|
| class T5TextProcessingEngine: |
| def __init__(self, text_encoder, tokenizer, min_length: int = 256, min_padding: int = -1): |
| super().__init__() |
|
|
| self.text_encoder = text_encoder.transformer |
| self.tokenizer = tokenizer |
|
|
| self.min_length = min_length |
| self.min_padding = min_padding |
| self.id_end = 1 |
| self.id_pad = 0 |
|
|
| def tokenize(self, texts): |
| tokenized = self.tokenizer(texts, truncation=False, add_special_tokens=False)["input_ids"] |
| return tokenized |
|
|
| def encode_with_transformers(self, tokens): |
| device = memory_management.text_encoder_device() |
| tokens = tokens.to(device) |
| self.text_encoder.shared.to(device=device, dtype=torch.float32) |
|
|
| z = self.text_encoder( |
| input_ids=tokens, |
| ) |
|
|
| return z |
|
|
| def tokenize_line(self, line): |
| parsed = parsing.parse_prompt_attention(line, self.emphasis.name) |
|
|
| tokenized = self.tokenize([text for text, _ in parsed]) |
|
|
| chunks = [] |
| chunk = PromptChunk() |
| token_count = 0 |
|
|
| def next_chunk(): |
| nonlocal token_count |
| nonlocal chunk |
|
|
| chunk.tokens = chunk.tokens + [self.id_end] |
| chunk.multipliers = chunk.multipliers + [1.0] |
|
|
| if self.min_padding > 0: |
| chunk.tokens += [self.id_pad] * self.min_padding |
| chunk.multipliers += [1.0] * self.min_padding |
|
|
| current_chunk_length = len(chunk.tokens) |
|
|
| token_count += current_chunk_length |
| remaining_count = self.min_length - current_chunk_length |
|
|
| if self.min_length > 0 and remaining_count > 0: |
| chunk.tokens += [self.id_pad] * remaining_count |
| chunk.multipliers += [1.0] * remaining_count |
|
|
| chunks.append(chunk) |
| chunk = PromptChunk() |
|
|
| for tokens, (text, weight) in zip(tokenized, parsed): |
| if text == "BREAK" and weight == -1: |
| next_chunk() |
| continue |
|
|
| position = 0 |
| while position < len(tokens): |
| token = tokens[position] |
| chunk.tokens.append(token) |
| chunk.multipliers.append(weight) |
| position += 1 |
|
|
| if chunk.tokens or not chunks: |
| next_chunk() |
|
|
| return chunks, token_count |
|
|
| def __call__(self, texts): |
| zs = [] |
| cache = {} |
|
|
| self.emphasis = emphasis.get_current_option(opts.emphasis)() |
|
|
| for line in texts: |
| if line in cache: |
| line_z_values = cache[line] |
| else: |
| chunks, token_count = self.tokenize_line(line) |
| line_z_values = [] |
|
|
| |
| max_tokens = 0 |
| for chunk in chunks: |
| max_tokens = max(len(chunk.tokens), max_tokens) |
|
|
| for chunk in chunks: |
| tokens = chunk.tokens |
| multipliers = chunk.multipliers |
|
|
| remaining_count = max_tokens - len(tokens) |
| if remaining_count > 0: |
| tokens += [self.id_pad] * remaining_count |
| multipliers += [1.0] * remaining_count |
|
|
| z = self.process_tokens([tokens], [multipliers])[0] |
| line_z_values.append(z) |
| cache[line] = line_z_values |
|
|
| zs.extend(line_z_values) |
|
|
| return torch.stack(zs) |
|
|
| def process_tokens(self, batch_tokens, batch_multipliers): |
| tokens = torch.asarray(batch_tokens) |
|
|
| z = self.encode_with_transformers(tokens) |
|
|
| self.emphasis.tokens = batch_tokens |
| self.emphasis.multipliers = torch.asarray(batch_multipliers).to(z) |
| self.emphasis.z = z |
| self.emphasis.after_transformers() |
| z = self.emphasis.z |
|
|
| return z |
|
|
