|
|
|
|
|
|
|
|
|
|
|
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 |
|
|
|
