| |
| |
|
|
| from typing import TYPE_CHECKING |
|
|
| if TYPE_CHECKING: |
| from modules.prompt_parser import SdConditioning |
|
|
| 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 Qwen3TextProcessingEngine: |
| def __init__(self, text_encoder, tokenizer): |
| super().__init__() |
|
|
| self.text_encoder = text_encoder |
| self.tokenizer = tokenizer |
|
|
| self.id_pad = 151643 |
| self.llama_template = "<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" |
| self.intermediate_output = -2 |
| self.layer_norm_hidden_state = False |
|
|
| def tokenize(self, texts): |
| llama_texts = [self.llama_template.format(text) for text in texts] |
| return self.tokenizer(llama_texts)["input_ids"] |
|
|
| def tokenize_line(self, line: str): |
| parsed = parsing.parse_prompt_attention(line, self.emphasis.name) |
| tokenized = self.tokenize([text for text, _ in parsed]) |
|
|
| chunks = [] |
| chunk = PromptChunk() |
|
|
| def next_chunk(): |
| nonlocal chunk |
|
|
| chunks.append(chunk) |
| chunk = PromptChunk() |
|
|
| for tokens, (text, weight) in zip(tokenized, parsed): |
| 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 |
|
|
| def __call__(self, texts: "SdConditioning"): |
| 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 = self.tokenize_line(line) |
| line_z_values = [] |
|
|
| for chunk in chunks: |
| tokens = chunk.tokens |
| multipliers = chunk.multipliers |
|
|
| z = self.process_tokens([tokens], [multipliers])[0] |
| line_z_values.append(z) |
| cache[line] = line_z_values |
|
|
| zs.extend(line_z_values) |
|
|
| return zs |
|
|
| def process_embeds(self, batch_tokens): |
| device = memory_management.text_encoder_device() |
| self.text_encoder.to(device) |
|
|
| embeds_out = [] |
| attention_masks = [] |
| num_tokens = [] |
|
|
| for tokens in batch_tokens: |
| attention_mask = [] |
| tokens_temp = [] |
| eos = False |
| index = 0 |
|
|
| for t in tokens: |
| token = int(t) |
| attention_mask.append(0 if eos else 1) |
| tokens_temp += [token] |
| if not eos and token == self.id_pad: |
| eos = True |
| index += 1 |
|
|
| tokens_embed = torch.tensor([tokens_temp], device=device, dtype=torch.long) |
| tokens_embed = self.text_encoder.get_input_embeddings()(tokens_embed) |
|
|
| index = 0 |
|
|
| embeds_out.append(tokens_embed) |
| attention_masks.append(attention_mask) |
| num_tokens.append(sum(attention_mask)) |
|
|
| return torch.cat(embeds_out), torch.tensor(attention_masks, device=device, dtype=torch.long), num_tokens |
|
|
| def process_tokens(self, batch_tokens, batch_multipliers): |
| embeds, mask, count = self.process_embeds(batch_tokens) |
| _, z = self.text_encoder( |
| None, |
| attention_mask=mask, |
| embeds=embeds, |
| num_tokens=count, |
| intermediate_output=self.intermediate_output, |
| final_layer_norm_intermediate=self.layer_norm_hidden_state, |
| ) |
| return z |
|
|
