| |
| |
|
|
| 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 QwenTextProcessingEngine: |
| def __init__(self, text_encoder, tokenizer): |
| super().__init__() |
|
|
| self.text_encoder = text_encoder |
| self.tokenizer = tokenizer |
|
|
| self.max_length = 99999999 |
| self.min_length = 1 |
| self.id_pad = 151643 |
| self.id_template = 151644 |
| self.id_image = 151655 |
|
|
| self.llama_template = "<|im_start|>system\nDescribe the image by detailing the color, shape, size, texture, quantity, text, spatial relationships of the objects and background:<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" |
| self.image_template = "<|im_start|>system\nDescribe the key features of the input image (color, shape, size, texture, objects, background), then explain how the user's text instruction should alter or modify the image. Generate a new image that meets the user's requirements while maintaining consistency with the original input where appropriate.<|im_end|>\n<|im_start|>user\n{}<|im_end|>\n<|im_start|>assistant\n" |
|
|
| def tokenize(self, texts, vision=False): |
| llama_texts = [(self.image_template if vision else self.llama_template).format(text) for text in texts] |
| return self.tokenizer(llama_texts)["input_ids"] |
|
|
| def tokenize_line(self, line: str, images=None): |
| parsed = parsing.parse_prompt_attention(line, self.emphasis.name) |
| tokenized = self.tokenize([text for text, _ in parsed], bool(images)) |
|
|
| chunks = [] |
| chunk = PromptChunk() |
|
|
| def next_chunk(): |
| nonlocal chunk |
|
|
| current_chunk_length = len(chunk.tokens) |
| 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): |
| embed_count = 0 |
| position = 0 |
| while position < len(tokens): |
| token = tokens[position] |
|
|
| if token == self.id_image: |
| token = {"type": "image", "data": images[embed_count], "original_type": "image"} |
| embed_count += 1 |
|
|
| chunk.tokens.append(token) |
| chunk.multipliers.append(weight) |
| position += 1 |
|
|
| if chunk.tokens or not chunks: |
| next_chunk() |
|
|
| return chunks |
|
|
| def __call__(self, texts, images=None): |
| 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, images) |
| line_z_values = [] |
|
|
| for chunk in chunks: |
| tokens = chunk.tokens |
| multipliers = chunk.multipliers |
|
|
| z = self.process_tokens([tokens], [multipliers])[0] |
| z = self.strip_template(z, tokens) |
| line_z_values.append(z) |
| cache[line] = line_z_values |
|
|
| zs.extend(line_z_values) |
|
|
| return zs |
|
|
| def strip_template(self, out, tokens): |
| template_end = 0 |
| count_im_start = 0 |
|
|
| for i, v in enumerate(tokens): |
| try: |
| elem = int(v) |
| if elem == self.id_template and count_im_start < 2: |
| template_end = i |
| count_im_start += 1 |
| except TypeError: |
| continue |
|
|
| if out.shape[1] > (template_end + 3): |
| if int(tokens[template_end + 1]) == 872: |
| if int(tokens[template_end + 2]) == 198: |
| template_end += 3 |
|
|
| return out[template_end:] |
|
|
| def process_embeds(self, batch_tokens): |
| device = memory_management.text_encoder_device() |
|
|
| embeds_out = [] |
| attention_masks = [] |
| num_tokens = [] |
|
|
| for tokens in batch_tokens: |
| attention_mask = [] |
| tokens_temp = [] |
| other_embeds = [] |
| eos = False |
| index = 0 |
|
|
| for t in tokens: |
| try: |
| token = int(t) |
| attention_mask.append(0 if eos else 1) |
| tokens_temp += [token] |
| if not eos and token == self.id_pad: |
| eos = True |
| except TypeError: |
| other_embeds.append((index, t)) |
| 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_info = [] |
|
|
| for o in other_embeds: |
| emb, extra = self.text_encoder.preprocess_embed(o[1], device=device) |
| if emb is None: |
| index += -1 |
| continue |
|
|
| ind = index + o[0] |
| emb = emb.view(1, -1, emb.shape[-1]).to(device=device, dtype=torch.float32) |
| emb_shape = emb.shape[1] |
|
|
| assert emb.shape[-1] == tokens_embed.shape[-1] |
| tokens_embed = torch.cat([tokens_embed[:, :ind], emb, tokens_embed[:, ind:]], dim=1) |
| attention_mask = attention_mask[:ind] + [1] * emb_shape + attention_mask[ind:] |
| index += emb_shape - 1 |
| emb_type = o[1].get("type", None) |
| embeds_info.append({"type": emb_type, "index": ind, "size": emb_shape, "extra": extra}) |
|
|
| 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, embeds_info |
|
|
| def process_tokens(self, batch_tokens, batch_multipliers): |
| embeds, mask, count, info = self.process_embeds(batch_tokens) |
| z, _ = self.text_encoder(x=None, embeds=embeds, attention_mask=mask, num_tokens=count, embeds_info=info) |
| return z |
|
|
