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import os |
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import warnings |
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import torch |
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from mmengine.dist import master_only |
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from mmengine.hooks import Hook |
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from mmengine.model import is_model_wrapper |
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from mmengine.utils import mkdir_or_exist |
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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 expand2square, load_image |
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from xtuner.model.utils import prepare_inputs_labels_for_multimodal |
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from xtuner.registry import BUILDER |
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from xtuner.utils import (DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX, |
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StopWordStoppingCriteria) |
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class EvaluateChatHook_ST(Hook): |
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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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system='', |
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every_n_iters=None, |
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max_new_tokens=600, |
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stop_word=None, |
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stop_words=[], |
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generation_kwargs={}): |
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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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instruction = '{input}' |
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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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self.stop_criteria = StoppingCriteriaList() |
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default_generation_kwargs = dict( |
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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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default_generation_kwargs.update(generation_kwargs) |
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self.gen_config = GenerationConfig(**default_generation_kwargs) |
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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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@master_only |
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def _save_eval_output(self, runner, eval_outputs): |
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save_path = os.path.join(runner.log_dir, 'vis_data', |
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f'eval_outputs_iter_{runner.iter}.txt') |
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mkdir_or_exist(os.path.dirname(save_path)) |
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with open(save_path, 'w', encoding='utf-8') as f: |
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for i, output in enumerate(eval_outputs): |
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f.write(f'Eval output {i + 1}:\n{output}\n\n') |
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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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model.preparing_for_generation(metainfo={}) |
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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 = sample_image |
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sample_input = DEFAULT_IMAGE_TOKEN + '\n' + sample_input |
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inputs = sample_input |
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generation_output = model.predict_forward(image=image, text=inputs) |
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inputs = generation_output['input_text'] |
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generation_output = generation_output['prediction'] |
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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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def _eval_language(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_input in self.evaluation_inputs: |
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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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input_ids = self.tokenizer.encode(inputs, return_tensors='pt') |
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input_ids = input_ids.to(device) |
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generation_output = model.generate( |
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input_ids=input_ids, |
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max_new_tokens=max_new_tokens, |
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generation_config=self.gen_config, |
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stopping_criteria=self.stop_criteria) |
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generation_output = self.tokenizer.decode(generation_output[0]) |
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runner.logger.info(f'Sample output:\n{generation_output}\n') |
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if save_eval_output: |
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eval_outputs.append(f'{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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def _generate_samples(self, |
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runner, |
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max_new_tokens=None, |
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save_eval_output=False): |
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if max_new_tokens is None: |
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max_new_tokens = self.max_new_tokens |
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model = runner.model |
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if is_model_wrapper(model): |
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model = model.module |
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device = next(iter(model.parameters())).device |
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if self.is_first_run: |
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model.to(device) |
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self.is_first_run = False |
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is_checkpointing = model.llm.is_gradient_checkpointing |
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use_cache = model.llm.config.use_cache |
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model.activation_checkpointing_disable() |
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model.llm.config.use_cache = True |
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model.eval() |
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if self.evaluation_images is not None: |
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self._eval_images(runner, model, device, max_new_tokens, |
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save_eval_output) |
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else: |
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self._eval_language(runner, model, device, max_new_tokens, |
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save_eval_output) |
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if is_checkpointing: |
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model.activation_checkpointing_enable() |
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model.llm.config.use_cache = use_cache |
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model.train() |
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def before_train(self, runner): |
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runner.logger.info('before_train in EvaluateChatHook.') |
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self._generate_samples(runner, max_new_tokens=50) |
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def _is_save_checkpoint(self, runner): |
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hooks = runner.hooks |
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checkpoint_hook = None |
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for hook in hooks: |
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if type(hook).__name__ == 'CheckpointHook': |
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checkpoint_hook = hook |
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break |
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if checkpoint_hook is None or checkpoint_hook.by_epoch: |
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return False |
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if checkpoint_hook.every_n_train_iters( |
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runner, checkpoint_hook.interval, checkpoint_hook.save_begin) or \ |
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(checkpoint_hook.save_last and |
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checkpoint_hook.is_last_train_iter(runner)): |
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return True |
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return False |
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def after_train_iter(self, |
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runner, |
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batch_idx: int, |
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data_batch=None, |
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outputs=None) -> None: |
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if self.every_n_iters is None: |
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return |
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save_eval_output = self._is_save_checkpoint(runner) |
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do_chat = ( |
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save_eval_output |
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or self.every_n_train_iters(runner, self.every_n_iters)) |
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if not do_chat: |
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return |
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runner.logger.info('after_train_iter in EvaluateChatHook.') |
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self._generate_samples(runner, save_eval_output=save_eval_output) |
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def after_train(self, runner): |
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runner.logger.info('after_train in EvaluateChatHook.') |
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self._generate_samples(runner) |
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def after_val(self, runner) -> None: |
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if self.every_n_iters is not None: |
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return |
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runner.logger.info('after_val in EvaluateChatHook.') |
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self._generate_samples(runner) |
