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from xtuner.utils import (DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX) |
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import warnings |
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from mmengine.utils.misc import get_object_from_string |
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from transformers import GenerationConfig, StoppingCriteriaList |
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from xtuner.dataset.utils import load_image |
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from xtuner.registry import BUILDER |
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from xtuner.utils import StopWordStoppingCriteria |
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from xtuner.engine.hooks import EvaluateChatHook |
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class EvaluateChatHook_solo(EvaluateChatHook): |
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priority = 'LOW' |
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def __init__(self, |
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tokenizer, |
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evaluation_inputs, |
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evaluation_images=None, |
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image_processor=None, |
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system='', |
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prompt_template=None, |
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every_n_iters=None, |
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max_new_tokens=50, |
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stop_word=None, |
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stop_words=[], |
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): |
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self.evaluation_inputs = evaluation_inputs |
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if isinstance(self.evaluation_inputs, str): |
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self.evaluation_inputs = [self.evaluation_inputs] |
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self.evaluation_images = evaluation_images |
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if isinstance(self.evaluation_images, str): |
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self.evaluation_images = [self.evaluation_images] |
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if self.evaluation_images is not None: |
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assert len( |
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self.evaluation_images) in [1, len(self.evaluation_inputs)] |
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if len(self.evaluation_images) == 1: |
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self.evaluation_images = [self.evaluation_images[0]] * len( |
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self.evaluation_inputs) |
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self.evaluation_images = [ |
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load_image(img) for img in self.evaluation_images |
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] |
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if prompt_template is None: |
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instruction = '{input}' |
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else: |
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if isinstance(prompt_template, str): |
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prompt_template = get_object_from_string(prompt_template) |
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instruction = prompt_template.get('INSTRUCTION', '{input}') |
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if system != '': |
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system = prompt_template.get( |
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'SYSTEM', '{system}\n').format(system=system) |
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stop_words += prompt_template.get('STOP_WORDS', []) |
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if stop_word is not None: |
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warnings.warn( |
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('The `stop_word` argument is deprecated and will be removed ' |
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'in v0.3.0, use `stop_words` instead.'), DeprecationWarning) |
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stop_words.append(stop_word) |
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self.instruction = instruction |
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self.system = system |
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self.every_n_iters = every_n_iters |
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self.max_new_tokens = max_new_tokens |
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self.tokenizer = BUILDER.build(tokenizer) |
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if image_processor is not None: |
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self.image_processor = BUILDER.build(image_processor) |
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self.stop_criteria = StoppingCriteriaList() |
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self.gen_config = GenerationConfig( |
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max_new_tokens=max_new_tokens, |
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do_sample=True, |
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temperature=0.1, |
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top_p=0.75, |
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top_k=40, |
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eos_token_id=self.tokenizer.eos_token_id, |
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pad_token_id=self.tokenizer.pad_token_id |
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if self.tokenizer.pad_token_id is not None else |
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self.tokenizer.eos_token_id, |
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) |
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self.stop_criteria = StoppingCriteriaList() |
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for word in stop_words: |
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self.stop_criteria.append( |
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StopWordStoppingCriteria(self.tokenizer, word)) |
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self.is_first_run = True |
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self.metainfo = { |
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'template': prompt_template, |
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} |
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def _eval_images(self, |
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runner, |
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model, |
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device, |
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max_new_tokens=None, |
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save_eval_output=False): |
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if save_eval_output: |
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eval_outputs = [] |
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for sample_image, sample_input in zip(self.evaluation_images, |
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self.evaluation_inputs): |
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image = self.image_processor.preprocess( |
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sample_image, return_tensors='pt')['pixel_values'][0] |
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image = image.to(device) |
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sample_input = DEFAULT_IMAGE_TOKEN + '\n' + sample_input |
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model.preparing_for_generation(self.metainfo) |
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generation_output = model.predict_forward( |
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image, sample_input |
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)['prediction'] |
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inputs = (self.system + self.instruction).format( |
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input=sample_input, round=1, **runner.cfg) |
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runner.logger.info(f'Sample output:\n' |
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f'{inputs + generation_output}\n') |
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if save_eval_output: |
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eval_outputs.append(f'{inputs + generation_output}\n') |
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if save_eval_output: |
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self._save_eval_output(runner, eval_outputs) |
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