| |
| import os |
| import warnings |
|
|
| import torch |
| from mmengine.hooks import Hook |
| from mmengine.model import is_model_wrapper |
| from mmengine.utils.misc import get_object_from_string |
| from transformers import GenerationConfig, StoppingCriteriaList |
|
|
| from xtuner.dataset.utils import expand2square, load_image |
| from xtuner.model.utils import prepare_inputs_labels_for_multimodal |
| from xtuner.registry import BUILDER |
| from xtuner.utils import (DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX, |
| StopWordStoppingCriteria) |
|
|
|
|
| class EvaluateChatHook(Hook): |
|
|
| priority = 'LOW' |
|
|
| def __init__(self, |
| tokenizer, |
| evaluation_inputs, |
| evaluation_images=None, |
| image_processor=None, |
| system='', |
| prompt_template=None, |
| every_n_iters=None, |
| max_new_tokens=600, |
| stop_word=None, |
| stop_words=[]): |
| self.evaluation_inputs = evaluation_inputs |
| if isinstance(self.evaluation_inputs, str): |
| self.evaluation_inputs = [self.evaluation_inputs] |
| self.evaluation_images = evaluation_images |
| if isinstance(self.evaluation_images, str): |
| self.evaluation_images = [self.evaluation_images] |
| if self.evaluation_images is not None: |
| assert len( |
| self.evaluation_images) in [1, len(self.evaluation_inputs)] |
| if len(self.evaluation_images) == 1: |
| self.evaluation_images = [self.evaluation_images[0]] * len( |
| self.evaluation_inputs) |
| self.evaluation_images = [ |
| load_image(img) for img in self.evaluation_images |
| ] |
| if prompt_template is None: |
| instruction = '{input}' |
| else: |
| if isinstance(prompt_template, str): |
| prompt_template = get_object_from_string(prompt_template) |
| instruction = prompt_template.get('INSTRUCTION', '{input}') |
| if system != '': |
| system = prompt_template.get( |
| 'SYSTEM', '{system}\n').format(system=system) |
| stop_words += prompt_template.get('STOP_WORDS', []) |
| if stop_word is not None: |
| |
| warnings.warn( |
| ('The `stop_word` argument is deprecated and will be removed ' |
| 'in v0.3.0, use `stop_words` instead.'), DeprecationWarning) |
| stop_words.append(stop_word) |
| self.instruction = instruction |
| self.system = system |
| self.every_n_iters = every_n_iters |
| self.max_new_tokens = max_new_tokens |
| self.tokenizer = BUILDER.build(tokenizer) |
| if image_processor is not None: |
| self.image_processor = BUILDER.build(image_processor) |
| self.stop_criteria = StoppingCriteriaList() |
| |
| self.gen_config = GenerationConfig( |
| max_new_tokens=max_new_tokens, |
| do_sample=True, |
| temperature=0.1, |
| top_p=0.75, |
| top_k=40, |
| eos_token_id=self.tokenizer.eos_token_id, |
| pad_token_id=self.tokenizer.pad_token_id |
| if self.tokenizer.pad_token_id is not None else |
| self.tokenizer.eos_token_id, |
| ) |
| self.stop_criteria = StoppingCriteriaList() |
| for word in stop_words: |
| self.stop_criteria.append( |
| StopWordStoppingCriteria(self.tokenizer, word)) |
|
|
| def _save_eval_output(self, runner, eval_outputs): |
| save_path = os.path.join(runner.log_dir, 'vis_data', |
| f'eval_outputs_iter_{runner.iter}.txt') |
| with open(save_path, 'w') as f: |
| for i, output in enumerate(eval_outputs): |
| f.write(f'Eval output {i + 1}:\n{output}\n\n') |
|
|
| def _eval_images(self, |
| runner, |
| model, |
| device, |
| max_new_tokens=None, |
| save_eval_output=False): |
| if save_eval_output: |
| eval_outputs = [] |
|
|
| for sample_image, sample_input in zip(self.evaluation_images, |
| self.evaluation_inputs): |
| image = expand2square( |
| sample_image, |
| tuple(int(x * 255) for x in self.image_processor.image_mean)) |
| image = self.image_processor.preprocess( |
| image, return_tensors='pt')['pixel_values'][0] |
| image = image.to(device) |
| sample_input = DEFAULT_IMAGE_TOKEN + '\n' + sample_input |
| inputs = (self.system + self.instruction).format( |
| input=sample_input, round=1, **runner.cfg) |
| chunk_encode = [] |
| for idx, chunk in enumerate(inputs.split(DEFAULT_IMAGE_TOKEN)): |
| if idx == 0: |
| cur_encode = self.tokenizer.encode(chunk) |
| else: |
| cur_encode = self.tokenizer.encode( |
| chunk, add_special_tokens=False) |
| chunk_encode.append(cur_encode) |
| assert len(chunk_encode) == 2 |
| input_ids = [] |
| for idx, cur_chunk_encode in enumerate(chunk_encode): |
| input_ids.extend(cur_chunk_encode) |
| if idx != len(chunk_encode) - 1: |
| input_ids.append(IMAGE_TOKEN_INDEX) |
| input_ids = torch.tensor(input_ids).to(device) |
| siglip_out = model.siglip( |
| image.unsqueeze(0), output_hidden_states=True).hidden_states[model.visual_select_layer] |
| dino_out = model.dino( |
| image.unsqueeze(0), output_hidden_states=True).hidden_states[-1][:, 1:] |
| visual_out = torch.cat((siglip_out, dino_out), dim=-1) |
| pixel_values = model.projector(visual_out) |
|
|
| mm_inputs = prepare_inputs_labels_for_multimodal( |
| llm=model.llm, |
| input_ids=input_ids.unsqueeze(0), |
| pixel_values=pixel_values) |
|
|
| generation_output = model.generate( |
| **mm_inputs, |
| max_new_tokens=max_new_tokens, |
| generation_config=self.gen_config, |
| bos_token_id=self.tokenizer.bos_token_id, |
| stopping_criteria=self.stop_criteria) |
| generation_output = self.tokenizer.decode(generation_output[0]) |
| runner.logger.info(f'Sample output:\n' |
| f'{inputs + generation_output}\n') |
| if save_eval_output: |
| eval_outputs.append(f'{inputs + generation_output}\n') |
|
|
| if save_eval_output: |
| self._save_eval_output(runner, eval_outputs) |
|
|
| def _eval_language(self, |
| runner, |
| model, |
| device, |
| max_new_tokens=None, |
| save_eval_output=False): |
| if save_eval_output: |
| eval_outputs = [] |
|
|
| for sample_input in self.evaluation_inputs: |
| inputs = (self.system + self.instruction).format( |
| input=sample_input, round=1, **runner.cfg) |
| input_ids = self.tokenizer.encode(inputs, return_tensors='pt') |
| input_ids = input_ids.to(device) |
| generation_output = model.generate( |
| input_ids=input_ids, |
| max_new_tokens=max_new_tokens, |
| generation_config=self.gen_config, |
| stopping_criteria=self.stop_criteria) |
| generation_output = self.tokenizer.decode(generation_output[0]) |
| runner.logger.info(f'Sample output:\n{generation_output}\n') |
| if save_eval_output: |
| eval_outputs.append(f'{generation_output}\n') |
|
|
| if save_eval_output: |
| self._save_eval_output(runner, eval_outputs) |
|
|
| def _generate_samples(self, |
| runner, |
| max_new_tokens=None, |
| save_eval_output=False): |
| if max_new_tokens is None: |
| max_new_tokens = self.max_new_tokens |
| model = runner.model |
| if is_model_wrapper(model): |
| model = model.module |
|
|
| device = next(iter(model.parameters())).device |
| is_checkpointing = model.llm.is_gradient_checkpointing |
| use_cache = model.llm.config.use_cache |
|
|
| |
| model.activation_checkpointing_disable() |
| model.llm.config.use_cache = True |
| model.eval() |
| if self.evaluation_images is not None: |
| self._eval_images(runner, model, device, max_new_tokens, |
| save_eval_output) |
| else: |
| self._eval_language(runner, model, device, max_new_tokens, |
| save_eval_output) |
|
|
| |
| if is_checkpointing: |
| model.activation_checkpointing_enable() |
| model.llm.config.use_cache = use_cache |
| model.train() |
|
|
| def before_train(self, runner): |
| runner.logger.info('before_train in EvaluateChatHook.') |
| self._generate_samples(runner, max_new_tokens=50) |
|
|
| def after_train_iter(self, |
| runner, |
| batch_idx: int, |
| data_batch=None, |
| outputs=None) -> None: |
| if self.every_n_iters is None: |
| return |
|
|
| save_eval_output = False |
| try: |
| save_ckpt_freq = runner.cfg.default_hooks.checkpoint.interval |
| save_eval_output = self.every_n_train_iters(runner, save_ckpt_freq) |
| except KeyError: |
| pass |
|
|
| do_chat = ( |
| save_eval_output |
| or self.every_n_train_iters(runner, self.every_n_iters)) |
| if not do_chat: |
| return |
|
|
| runner.logger.info('after_train_iter in EvaluateChatHook.') |
| self._generate_samples(runner, save_eval_output=save_eval_output) |
|
|
| def after_train(self, runner): |
| runner.logger.info('after_train in EvaluateChatHook.') |
| self._generate_samples(runner) |
|
|
| def after_val(self, runner) -> None: |
| if self.every_n_iters is not None: |
| return |
| runner.logger.info('after_val in EvaluateChatHook.') |
| self._generate_samples(runner) |
|
|
