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import os |
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import warnings |
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import torch |
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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.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(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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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=600, |
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stop_word=None, |
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stop_words=[]): |
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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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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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with open(save_path, 'w') 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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for sample_image, sample_input in zip(self.evaluation_images, |
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self.evaluation_inputs): |
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image = expand2square( |
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sample_image, |
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tuple(int(x * 255) for x in self.image_processor.image_mean)) |
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image = self.image_processor.preprocess( |
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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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inputs = (self.system + self.instruction).format( |
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input=sample_input, round=1, **runner.cfg) |
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chunk_encode = [] |
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for idx, chunk in enumerate(inputs.split(DEFAULT_IMAGE_TOKEN)): |
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if idx == 0: |
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cur_encode = self.tokenizer.encode(chunk) |
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else: |
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cur_encode = self.tokenizer.encode( |
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chunk, add_special_tokens=False) |
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chunk_encode.append(cur_encode) |
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assert len(chunk_encode) == 2 |
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input_ids = [] |
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for idx, cur_chunk_encode in enumerate(chunk_encode): |
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input_ids.extend(cur_chunk_encode) |
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if idx != len(chunk_encode) - 1: |
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input_ids.append(IMAGE_TOKEN_INDEX) |
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input_ids = torch.tensor(input_ids).to(device) |
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siglip_out = model.siglip( |
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image.unsqueeze(0), output_hidden_states=True).hidden_states[model.visual_select_layer] |
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dino_out = model.dino( |
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image.unsqueeze(0), output_hidden_states=True).hidden_states[-1][:, 1:] |
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visual_out = torch.cat((siglip_out, dino_out), dim=-1) |
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pixel_values = model.projector(visual_out) |
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mm_inputs = prepare_inputs_labels_for_multimodal( |
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llm=model.llm, |
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input_ids=input_ids.unsqueeze(0), |
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pixel_values=pixel_values) |
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generation_output = model.generate( |
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**mm_inputs, |
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max_new_tokens=max_new_tokens, |
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generation_config=self.gen_config, |
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bos_token_id=self.tokenizer.bos_token_id, |
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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' |
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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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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 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 = False |
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try: |
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save_ckpt_freq = runner.cfg.default_hooks.checkpoint.interval |
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save_eval_output = self.every_n_train_iters(runner, save_ckpt_freq) |
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except KeyError: |
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pass |
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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) |
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