177 lines
7.4 KiB
Python
177 lines
7.4 KiB
Python
import os
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import warnings
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from typing import Optional
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import torch
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from ftfy import fix_text
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from loguru import logger
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from transformers import AutoConfig, AutoModel, AutoModelForCausalLM, AutoTokenizer, PretrainedConfig, PreTrainedModel
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from transformers import VisionEncoderDecoderConfig, VisionEncoderDecoderModel
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from transformers.models.vision_encoder_decoder.modeling_vision_encoder_decoder import logger as base_model_logger
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from .unimer_swin import UnimerSwinConfig, UnimerSwinModel, UnimerSwinImageProcessor
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from .unimer_mbart import UnimerMBartConfig, UnimerMBartForCausalLM
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from ...utils import latex_rm_whitespace
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AutoConfig.register(UnimerSwinConfig.model_type, UnimerSwinConfig)
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AutoConfig.register(UnimerMBartConfig.model_type, UnimerMBartConfig)
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AutoModel.register(UnimerSwinConfig, UnimerSwinModel)
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AutoModelForCausalLM.register(UnimerMBartConfig, UnimerMBartForCausalLM)
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# TODO: rewrite tokenizer
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class TokenizerWrapper:
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def __init__(self, tokenizer):
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self.tokenizer = tokenizer
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self.pad_token_id = self.tokenizer.pad_token_id
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self.bos_token_id = self.tokenizer.bos_token_id
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self.eos_token_id = self.tokenizer.eos_token_id
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def __len__(self):
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return len(self.tokenizer)
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def tokenize(self, text, **kwargs):
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return self.tokenizer(
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text,
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return_token_type_ids=False,
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return_tensors="pt",
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padding="longest",
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truncation=True,
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**kwargs,
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)
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def token2str(self, tokens) -> list:
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generated_text = self.tokenizer.batch_decode(tokens, skip_special_tokens=True)
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generated_text = [fix_text(text) for text in generated_text]
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return generated_text
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def detokenize(self, tokens):
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toks = [self.tokenizer.convert_ids_to_tokens(tok) for tok in tokens]
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for b in range(len(toks)):
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for i in reversed(range(len(toks[b]))):
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if toks[b][i] is None:
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toks[b][i] = ''
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toks[b][i] = toks[b][i].replace('Ġ', ' ').strip()
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if toks[b][i] in ([self.tokenizer.bos_token, self.tokenizer.eos_token, self.tokenizer.pad_token]):
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del toks[b][i]
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return toks
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class UnimernetModel(VisionEncoderDecoderModel):
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def __init__(
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self,
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config: Optional[PretrainedConfig] = None,
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encoder: Optional[PreTrainedModel] = None,
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decoder: Optional[PreTrainedModel] = None,
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):
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# VisionEncoderDecoderModel's checking log has bug, disable for temp.
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base_model_logger.disabled = True
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try:
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super().__init__(config, encoder, decoder)
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finally:
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base_model_logger.disabled = False
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if not config or not hasattr(config, "_name_or_path"):
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raise RuntimeError("config._name_or_path is required by UnimernetModel.")
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model_path = config._name_or_path
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self.transform = UnimerSwinImageProcessor()
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self.tokenizer = TokenizerWrapper(AutoTokenizer.from_pretrained(model_path))
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self._post_check()
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def _post_check(self):
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tokenizer = self.tokenizer
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if tokenizer.tokenizer.model_max_length != self.config.decoder.max_position_embeddings:
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warnings.warn(
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f"decoder.max_position_embeddings={self.config.decoder.max_position_embeddings}," +
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f" but tokenizer.model_max_length={tokenizer.tokenizer.model_max_length}, will set" +
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f" tokenizer.model_max_length to {self.config.decoder.max_position_embeddings}.")
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tokenizer.tokenizer.model_max_length = self.config.decoder.max_position_embeddings
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assert self.config.decoder.vocab_size == len(tokenizer)
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assert self.config.decoder_start_token_id == tokenizer.bos_token_id
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assert self.config.pad_token_id == tokenizer.pad_token_id
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@classmethod
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def from_checkpoint(cls, model_path: str, model_filename: str = "pytorch_model.pth", state_dict_strip_prefix="model.model."):
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config = VisionEncoderDecoderConfig.from_pretrained(model_path)
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config._name_or_path = model_path
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config.encoder = UnimerSwinConfig(**vars(config.encoder))
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config.decoder = UnimerMBartConfig(**vars(config.decoder))
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encoder = UnimerSwinModel(config.encoder)
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decoder = UnimerMBartForCausalLM(config.decoder)
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model = cls(config, encoder, decoder)
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# load model weights
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model_file_path = os.path.join(model_path, model_filename)
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checkpoint = torch.load(model_file_path, map_location="cpu", weights_only=True)
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state_dict = checkpoint["model"] if "model" in checkpoint else checkpoint
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if not state_dict:
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raise RuntimeError("state_dict is empty.")
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if state_dict_strip_prefix:
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state_dict = {
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k[len(state_dict_strip_prefix):] if k.startswith(state_dict_strip_prefix) else k: v
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for k, v in state_dict.items()
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}
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missing_keys, unexpected_keys = model.load_state_dict(state_dict, strict=False)
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if len(unexpected_keys) > 0:
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warnings.warn("Unexpected key(s) in state_dict: {}.".format(", ".join(f'"{k}"' for k in unexpected_keys)))
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if len(missing_keys) > 0:
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raise RuntimeError("Missing key(s) in state_dict: {}.".format(", ".join(f'"{k}"' for k in missing_keys)))
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return model
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def forward_bak(self, samples):
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pixel_values, text = samples["image"], samples["text_input"]
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text_inputs = self.tokenizer.tokenize(text).to(pixel_values.device)
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decoder_input_ids, decoder_attention_mask = text_inputs["input_ids"], text_inputs["attention_mask"]
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num_channels = pixel_values.shape[1]
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if num_channels == 1:
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pixel_values = pixel_values.repeat(1, 3, 1, 1)
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labels = decoder_input_ids * 1
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labels = labels.masked_fill(labels == self.tokenizer.pad_token_id, -100)
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loss = self.model(
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pixel_values=pixel_values,
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decoder_input_ids=decoder_input_ids[:, :-1],
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decoder_attention_mask=decoder_attention_mask[:, :-1],
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labels=labels[:, 1:],
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).loss
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return {"loss": loss}
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def generate(self, samples, do_sample: bool = False, temperature: float = 0.2, top_p: float = 0.95, batch_size=64):
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pixel_values = samples["image"]
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num_channels = pixel_values.shape[1]
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if num_channels == 1:
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pixel_values = pixel_values.repeat(1, 3, 1, 1)
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kwargs = {}
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if do_sample:
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kwargs["temperature"] = temperature
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kwargs["top_p"] = top_p
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if self.tokenizer.tokenizer.model_max_length > 1152:
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if batch_size <= 32:
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self.tokenizer.tokenizer.model_max_length = 1152 # 6g
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else:
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self.tokenizer.tokenizer.model_max_length = 1344 # 8g
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outputs = super().generate(
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pixel_values=pixel_values,
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max_new_tokens=self.tokenizer.tokenizer.model_max_length, # required
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decoder_start_token_id=self.tokenizer.tokenizer.bos_token_id,
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do_sample=do_sample,
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**kwargs,
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)
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outputs = outputs[:, 1:].cpu().numpy()
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pred_tokens = self.tokenizer.detokenize(outputs)
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pred_str = self.tokenizer.token2str(outputs)
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fixed_str = [latex_rm_whitespace(s) for s in pred_str]
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return {"pred_ids": outputs, "pred_tokens": pred_tokens, "pred_str": pred_str, "fixed_str": fixed_str}
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