import os from typing import Any, Dict import torch from model.blip2_opt import Blip2OPT from model.blip2_llama import Blip2Llama from model.blip2_t5 import Blip2T5 import pytorch_lightning as pl from torch import optim from lavis.common.optims import LinearWarmupCosineLRScheduler, LinearWarmupStepLRScheduler import json from model.opt_flash_attention import replace_opt_attn_with_flash_attn, replace_opt_attn_with_original_attn import torch.distributed as dist from peft import LoraConfig, TaskType from model.help_funcs import caption_evaluate, AttrDict from transformers import Adafactor from torch_ema import ExponentialMovingAverage def load_ignore_unexpected(model, state_dict): keys = set(model.state_dict().keys()) state_dict = {k: v for k, v in state_dict.items() if k in keys} ## try to print keys that are not included model.load_state_dict(state_dict, strict=True) # def load_ignore_mismatch(model, state_dict): # keys = set(model.state_dict().keys()) # extra_keys = set() # for key in state_dict: # if key not in keys: # extra_keys.add(key) # missing_keys = set() # for key in keys: # if key not in state_dict: # missing_keys.add(key) # ## try to print keys that are not included # model.load_state_dict(state_dict, strict=False) def get_module_state_dict(state_dict, module_name): module_state_dict = {} for key, value in state_dict.items(): if key.startswith(module_name): key = key[len(module_name) + 1:] if key == '': return value module_state_dict[key] = value return module_state_dict # peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1) class Blip2Model(pl.LightningModule): def on_save_checkpoint(self, checkpoint: Dict[str, Any]) -> None: if self.llm_tune != 'full': to_be_removed = [] for key in checkpoint['state_dict']: if key.startswith('blip2opt.opt_model') or key.startswith('blip2opt.llm_model'): to_be_removed.append(key) for key in to_be_removed: checkpoint['state_dict'].pop(key) if isinstance(self.args.save_every_n_epochs, int) and self.args.save_every_n_epochs > 0: if self.llm_tune == 'lora' and (self.current_epoch + 1) % self.args.save_every_n_epochs == 0: if self.local_rank == 0: # manually fix a bug in peft module if self.args.peft_config: peft_config = LoraConfig(**LoraConfig.from_json_file(self.args.peft_config)) else: peft_config = LoraConfig(task_type=TaskType.CAUSAL_LM, inference_mode=False, r=self.args.lora_r, lora_alpha=self.args.lora_alpha, lora_dropout=self.args.lora_dropout) if hasattr(self.blip2opt, 'opt_model'): self.blip2opt.opt_model.peft_config['default'] = peft_config self.blip2opt.opt_model.save_pretrained(os.path.join(self.logger.save_dir, f'lora_epoch_{self.current_epoch}')) elif hasattr(self.blip2opt, 'llm_model'): self.blip2opt.llm_model.peft_config['default'] = peft_config self.blip2opt.llm_model.save_pretrained(os.path.join(self.logger.save_dir, f'lora_epoch_{self.current_epoch}')) return super().on_save_checkpoint(checkpoint) def __init__(self, args): super().__init__() if isinstance(args, dict): args = AttrDict(**args) self.args = args if not hasattr(args, 'do_sample'): args.do_sample = False self.caption_eval_epoch = args.caption_eval_epoch self.do_sample = args.do_sample self.num_beams = args.num_beams self.max_inference_len = args.max_inference_len self.min_inference_len = args.min_inference_len self.num_generate_captions = args.num_generate_captions self.reaction_weight = args.reaction_weight self.llm_tune = args.llm_tune self.enable_flash = args.enable_flash if args.opt_model.find('galactica') >= 0: self.blip2opt = Blip2OPT(args.bert_name, args.gin_num_layers, args.gin_hidden_dim, args.drop_ratio, args.tune_gnn, not args.not_tune_qformer, args.num_query_token, args.cross_attention_freq, args.llm_tune, args.peft_dir, args.opt_model, args.prompt, args) elif args.opt_model.find('llama') >= 0 or args.opt_model.find('vicuna') >= 0: self.blip2opt = Blip2Llama(args.bert_name, args.gin_num_layers, args.gin_hidden_dim, args.drop_ratio, args.tune_gnn, args.num_query_token, args.cross_attention_freq, args.llm_tune, args.peft_dir, args.opt_model, args.prompt, args) elif args.opt_model.find('t5') >= 0: self.blip2opt = Blip2T5(args.bert_name, args.gin_num_layers, args.gin_hidden_dim, args.drop_ratio, args.tune_gnn, args.num_query_token, args.cross_attention_freq, args.llm_tune, args.peft_dir, args.opt_model, args.prompt, args) else: raise NotImplementedError() self.tokenizer = self.blip2opt.init_tokenizer() self.mode = args.mode self.downstream_task = args.downstream_task self.save_hyperparameters(args) self.save_ema_checkpoint = args.save_ema_checkpoint if self.save_ema_checkpoint: self.ema = ExponentialMovingAverage(self.parameters(), 0.99) self.save_on_steps = args.save_on_steps def load_from_stage1_checkpoint(self, path): ckpt = torch.load(path, map_location='cpu') state_dict = ckpt['state_dict'] graph_encoder_dict = get_module_state_dict(state_dict, 'blip2qformer.graph_encoder') qformer_dict = get_module_state_dict(state_dict, 'blip2qformer.Qformer') ln_graph_dict = get_module_state_dict(state_dict, 'blip2qformer.ln_graph') qs_weight = get_module_state_dict(state_dict, 'blip2qformer.query_tokens') load_ignore_unexpected(self.blip2opt.Qformer, qformer_dict) self.blip2opt.graph_encoder.load_state_dict(graph_encoder_dict) self.blip2opt.ln_graph.load_state_dict(ln_graph_dict) self.blip2opt.query_tokens.data.copy_(qs_weight) return self # def load_from_stage1_checkpoint(self, path): # ckpt = torch.load(path, map_location='cpu') # state_dict = ckpt['state_dict'] # state_dict = {k[13:]: v for k,v in state_dict.items()} # load_ignore_mismatch(self.blip2opt, state_dict) # return self def configure_optimizers(self): if self.args.optimizer == 'adafactor': print('Using adafactor optimizer') optimizer = Adafactor( self.parameters(), lr=1e-3, relative_step=False, scale_parameter=False, warmup_init=False ) self.scheduler = None else: self.trainer.fit_loop.setup_data() # self.trainer.reset_train_dataloader() warmup_steps = min(len(self.trainer.train_dataloader), self.args.warmup_steps) optimizer = optim.AdamW(self.parameters(), lr=self.args.init_lr, weight_decay=self.args.weight_decay) if self.args.scheduler == 'linear_warmup_cosine_lr': self.scheduler = LinearWarmupCosineLRScheduler(optimizer, self.args.max_epochs, self.args.min_lr, self.args.init_lr, warmup_steps, self.args.warmup_lr) elif self.args.scheduler == 'linear_warmup_step_lr': self.scheduler = LinearWarmupStepLRScheduler(optimizer, self.args.max_epochs, self.args.min_lr, self.args.init_lr, self.args.lr_decay_rate, self.args.warmup_lr, warmup_steps) elif self.args.scheduler == 'None': self.scheduler = None else: raise NotImplementedError() return optimizer def test_epoch_end(self, outputs): print('test epoch end') list_ids, list_predictions, list_targets = zip(*outputs) predictions = [i for ii in list_predictions for i in ii] targets = [i for ii in list_targets for i in ii] all_ids = [None for _ in range(self.trainer.world_size)] all_predictions = [None for _ in range(self.trainer.world_size)] all_targets = [None for _ in range(self.trainer.world_size)] dist.all_gather_object(all_ids, list_ids) dist.all_gather_object(all_predictions, predictions) dist.all_gather_object(all_targets, targets) print(len(all_ids), len(all_predictions), len(all_targets)) if self.global_rank == 0: print(f'saveing predictions to {self.logger.log_dir}') all_predictions = [i for ii in all_predictions for i in ii] all_targets = [i for ii in all_targets for i in ii] self.save_predictions(all_ids, all_predictions, all_targets) ## fixme: I am not sure if the max length is the same as previous experiments bleu2, bleu4, rouge_1, rouge_2, rouge_l, meteor_score = \ caption_evaluate(all_predictions, all_targets, self.tokenizer, self.max_inference_len * 2) self.log("bleu2", bleu2, sync_dist=False) self.log("bleu4", bleu4, sync_dist=False) self.log("rouge_1", rouge_1, sync_dist=False) self.log("rouge_2", rouge_2, sync_dist=False) self.log("rouge_l", rouge_l, sync_dist=False) self.log("meteor_score", meteor_score, sync_dist=False) def save_predictions(self, rxn_ids, predictions, targets): assert False assert len(rxn_ids) == len(targets) assert len(predictions) == len(targets) with open(os.path.join(self.logger.log_dir, 'predictions.txt'), 'w', encoding='utf8') as f: for i, p, t in zip(rxn_ids, predictions, targets): line = {'index': i, 'prediction': p, 'target': t} f.write(json.dumps(line, ensure_ascii=False) + '\n') @torch.no_grad() def test_step(self, batch, batch_idx): assert False def gather_dict_results(self, dict_list): list_of_dict_list = [None for _ in range(self.trainer.world_size)] dist.all_gather_object(list_of_dict_list, dict_list) dict_list = [i for ii in list_of_dict_list for i in ii] ## dict list, each dict has values that are lists of predictions, etc. keys = dict_list[0].keys() gathered_dict = {} # each value is a list of predictions, etc. for key in keys: gathered_dict[key] = [i for d in dict_list for i in d[key]] if self.num_generate_captions>1: M = self.num_generate_captions N = len(gathered_dict['index']) assert len(gathered_dict['predictions'])==N*M gathered_dict['predictions'] = [ gathered_dict['predictions'][i * M:(i + 1) * M] for i in range(N) ] dict_list = [] for i in range(len(gathered_dict['predictions'])): d = {k:gathered_dict[k][i] for k in keys} dict_list.append(d) return dict_list def save_results(self, dict_list, log_prefix=""): if log_prefix: name = f'{log_prefix}_predictions.txt' else: name = 'predictions.txt' with open(os.path.join(self.logger.log_dir, name), 'w', encoding='utf8') as f: for i in range(len(dict_list)): f.write(json.dumps(dict_list[i], ensure_ascii=True) + '\n') def on_validation_epoch_start(self): if self.enable_flash: replace_opt_attn_with_original_attn() self.saved_dict_list = [] def on_validation_epoch_end(self): if self.enable_flash: replace_opt_attn_with_flash_attn() if (self.current_epoch+1) % self.caption_eval_epoch != 0: return result_list = self.gather_dict_results(self.saved_dict_list) ## empty cache self.saved_dict_list = [] if self.global_rank == 0: self.save_results(result_list, 'epoch_{}'.format(self.current_epoch)) if self.downstream_task == 'synthesis': return all_predictions = [i['predictions'] for i in result_list] all_targets = [i['targets'] for i in result_list] bleu2, bleu4, rouge_1, rouge_2, rouge_l, meteor_score = \ caption_evaluate(all_predictions, all_targets, self.tokenizer, self.max_inference_len * 2) self.log("bleu2", bleu2, sync_dist=False) self.log("bleu4", bleu4, sync_dist=False) self.log("rouge_1", rouge_1, sync_dist=False) self.log("rouge_2", rouge_2, sync_dist=False) self.log("rouge_l", rouge_l, sync_dist=False) self.log("meteor_score", meteor_score, sync_dist=False) @torch.no_grad() def validation_step(self, batch, batch_idx, dataloader_idx=1): if dataloader_idx == 0: return elif dataloader_idx == 1: if (self.current_epoch+1) % self.caption_eval_epoch != 0: return rxn_ids, graphs, prompt_tokens, texts, inputs = batch ###============== Captioning Results ===================### samples = {'graphs': graphs, 'prompt_tokens': prompt_tokens} if self.mode in {'ft', 'eval', 'pretrain_eval'}: predictions = self.blip2opt.generate( samples, do_sample=self.do_sample, num_beams=self.num_beams, max_length=self.max_inference_len, min_length=self.min_inference_len, num_captions=self.num_generate_captions, use_graph=not self.args.disable_graphs ) else: raise NotImplementedError() self.saved_dict_list.append({ 'index': rxn_ids, 'input': inputs, 'predictions': predictions, 'targets': texts }) else: raise NotImplementedError def on_train_start(self): if hasattr(self, 'ema'): self.ema.to(self.device) def on_before_zero_grad(self, *args, **kwargs): if self.save_ema_checkpoint: if self.trainer.global_step % 100 == 0: self.ema.update(self.parameters()) if self.trainer.global_step in self.save_on_steps: checkpoint_path = os.path.join(f"all_checkpoints/{self.args.filename}/", f'step{self.trainer.global_step}.ckpt') self.trainer.save_checkpoint(checkpoint_path) def on_train_epoch_end(self): save_every_n_epochs = self.args.save_every_n_epochs if self.args.save_every_n_epochs > 0 else self.args.max_epochs if (self.current_epoch + 1) % save_every_n_epochs != 0: return if self.save_ema_checkpoint: with self.ema.average_parameters(): checkpoint_path = os.path.join(f"all_checkpoints/{self.args.filename}/", f'ema_epoch{self.current_epoch}.ckpt') self.trainer.save_checkpoint(checkpoint_path) def training_step(self, batch, batch_idx): if self.scheduler: self.scheduler.step(self.trainer.current_epoch, self.trainer.global_step) batch_size = batch[-1].input_ids.size(0) ###============== Overall Loss ===================### if self.mode == 'ft': loss = self.blip2opt.forward_action(batch, use_gragh=not self.args.disable_graphs) elif self.mode == 'pretrain': loss = self.blip2opt.forward_abstract(batch, use_gragh=not self.args.disable_graphs) else: raise NotImplementedError() self.log("molecule loss", float(loss['loss']), batch_size=batch_size, sync_dist=True, prog_bar=True) self.log("lr", self.trainer.optimizers[0].param_groups[0]['lr'], batch_size=batch_size, sync_dist=True, prog_bar=True) return loss['loss'] @staticmethod def add_model_specific_args(parent_parser): parser = parent_parser.add_argument_group("GINSimclr") # train mode # GIN parser.add_argument('--gin_hidden_dim', type=int, default=300) parser.add_argument('--gin_num_layers', type=int, default=5) parser.add_argument('--drop_ratio', type=float, default=0.0) parser.add_argument('--tune_gnn', action='store_true', default=False) parser.add_argument('--not_tune_qformer', action='store_true', default=False) parser.add_argument('--disable_graphs', action='store_true', default=False) # Bert parser.add_argument('--bert_hidden_dim', type=int, default=2048, help='') parser.add_argument('--bert_name', type=str, default='scibert') parser.add_argument('--cross_attention_freq', type=int, default=2) parser.add_argument('--num_query_token', type=int, default=8) # OPT parser.add_argument('--opt_model', type=str, default="facebook/galactica-1.3b") # parser.add_argument('--prompt', type=str, default='a molecule of ') parser.add_argument('--num_beams', type=int, default=5) parser.add_argument('--do_sample', action='store_true', default=False) parser.add_argument('--max_inference_len', type=int, default=512) parser.add_argument('--min_inference_len', type=int, default=8) parser.add_argument('--llm_tune', type=str, default='freeze') parser.add_argument('--peft_config', type=str, default=None) parser.add_argument('--peft_dir', type=str, default='') parser.add_argument('--save_every_n_epochs', type=int, default=0) ## quantization parser.add_argument('--load_in_8bit', action='store_true', default=False) ## lora config parser.add_argument('--lora_r', type=int, default=8) parser.add_argument('--lora_alpha', type=int, default=32) parser.add_argument('--lora_dropout', type=int, default=0.1) # optimization parser.add_argument('--reaction_weight', type=float, default=1.0) parser.add_argument('--weight_decay', type=float, default=0.05, help='optimizer weight decay') parser.add_argument('--init_lr', type=float, default=1e-4, help='optimizer init learning rate') parser.add_argument('--min_lr', type=float, default=1e-5, help='optimizer min learning rate') parser.add_argument('--warmup_lr', type=float, default=1e-6, help='optimizer warmup learning rate') parser.add_argument('--warmup_steps', type=int, default=1000, help='optimizer warmup steps') parser.add_argument('--lr_decay_rate', type=float, default=0.9, help='optimizer lr decay rate') parser.add_argument('--scheduler', type=str, default='linear_warmup_cosine_lr', help='type of scheduler') # or linear_warmup_step_lr parser.add_argument('--optimizer', type=str, default='adamw', help='type of scheduler') parser.add_argument('--init_checkpoint', type=str, default='') parser.add_argument('--caption_eval_epoch', type=int, default=10) parser.add_argument('--num_generate_captions', type=int, default=1) # OPT Config parser.add_argument('--optconfig_attention_dropout', type=float, default=0.0) parser.add_argument('--optconfig_dropout', type=float, default=0.0) # others parser.add_argument('--save_ema_checkpoint', action='store_true', default=False) parser.add_argument('--save_on_steps', default=[], nargs='+', type=int) return parent_parser