import os gpt_neo_series_id = '1.3B_ckpt' os.environ['gpt_neo_series_id'] = gpt_neo_series_id import torch import torch.nn as nn from methods.elasticdnn.api.algs.fm_lora import ElasticDNN_FMLoRAAlg from methods.elasticdnn.api.algs.md_pretraining_wo_fbs import ElasticDNN_MDPretrainingWoFBSAlg from methods.elasticdnn.model.base import ElasticDNNUtil from methods.elasticdnn.pipeline.offline.fm_lora.base import FMLoRA_Util from gpt_neo import getTokenizer, ElasticGPTUtil, FMLoRA_GPT_Util, ElasticDNN_OfflineTextGenFMModel, ElasticDNN_OfflineTextGenMDModel, FM_to_MD_GPT_Util, collate_fn from methods.elasticdnn.pipeline.offline.fm_to_md.base import FM_to_MD_Util from methods.elasticdnn.pipeline.offline.fm_to_md.vit import FM_to_MD_ViT_Util from methods.elasticdnn.model.vit import ElasticViTUtil from methods.elasticdnn.api.algs.md_pretraining_index_v2_train_index_and_md import ElasticDNN_MDPretrainingIndexAlg from utils.dl.common.model import LayerActivation2, get_module, get_parameter from utils.common.exp import save_models_dict_for_init, get_res_save_dir from data import build_gen_scenario import torch.nn.functional as F import os from utils.dl.common.loss import CrossEntropyLossSoft from new_impl.cv.feat_align.main_gpt_neo import OnlineFeatAlignModel, FeatAlignAlg import tqdm from new_impl.cv.feat_align.mmd import mmd_rbf from new_impl.cv.utils.baseline_da import baseline_da from new_impl.cv.elasticdnn.api.online_model_v2 import ElasticDNN_OnlineModel from utils.common.log import logger import nltk from nltk.translate.bleu_score import sentence_bleu, corpus_bleu from nltk.translate.bleu_score import SmoothingFunction import json os.environ['TOKENIZERS_PARALLELISM'] = 'true' os.environ['CUDA_LAUNCH_BLOCKING'] = '1' torch.cuda.set_device(1) device = 'cuda' app_name = 'cls' scenario = build_gen_scenario( source_datasets_name=['No_robots'], target_datasets_order=['Medicine_task', 'Law_task'] * 10, da_mode='close_set', data_dirs={ 'No_robots': '/data/zql/datasets/no_robots', 'Law_task': '/data/zql/datasets/law_task', 'Medicine_task': '/data/zql/datasets/medicine_task', }, ) class TxtgenOnlineFeatAlignModel(OnlineFeatAlignModel): def get_trained_params(self): # TODO: elastic fm only train a part of params #qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters() if 'attention.attention.projection_query' in n or 'attention.attention.projection_key' in n or 'attention.attention.projection_value' in n or 'intermediate.dense' in n or 'output.dense' in n] qkv_and_norm_params = [p for n, p in self.models_dict['main'].named_parameters()] return qkv_and_norm_params def get_feature_hook(self) -> LayerActivation2: return LayerActivation2(get_module(self.models_dict['main'], 'model.lm_head')) def forward_to_get_task_loss(self, x, y): losses = self.infer(x) # print(losses) return losses def get_mmd_loss(self, f1, f2): common_shape = min(f1.shape[0], f2.shape[0]) f1 = f1.view(f1.shape[0], -1) f2 = f2.view(f2.shape[0], -1) f1 = f1[:common_shape,:] f2 = f2[:common_shape,:] return mmd_rbf(f1, f2) def infer(self, x, *args, **kwargs): return self.models_dict['main'](**x) def get_accuracy(self, test_loader, *args, **kwargs): acc = 0 sample_num = 0 tokenizer = getTokenizer() self.to_eval_mode() pred_txt = [] true_txt = [] res = [] with torch.no_grad(): pbar = tqdm.tqdm(enumerate(test_loader), total=len(test_loader), dynamic_ncols=True, leave=False) for batch_index, (x, _) in pbar: if len(x) == 0: continue # if batch_index > 10: # break for k, v in x.items(): if isinstance(v, torch.Tensor): x[k] = v.to(self.device) # input_ids = [] inputlen = x['len'] y = x['labels'] x['labels'] = None outputs = self.models_dict['main'].generate(x, pad_id=tokenizer.pad_token_id) for i, op in enumerate(outputs): op = op.tolist() op = list(filter(lambda x: x != tokenizer.pad_token_id, op)) txt = tokenizer.decode(op) txt = txt.replace(tokenizer.pad_token, "") res.append(txt) txt = txt[inputlen[i]:] pred_txt.append(nltk.word_tokenize(txt)) for tp in y: true_txt.append(nltk.word_tokenize(tokenizer.decode(tp).replace(tokenizer.pad_token, ''))) # pred = F.softmax(output, dim=1).argmax(dim=1) # correct = torch.eq(pred, y).sum().item() # acc += correct sample_num += len(y) # pbar.set_description(f'cur_batch_total: {len(y)}, cur_batch_correct: {correct}, ' # f'cur_batch_acc: {(correct / len(y)):.4f}') json.dump(res, open("./gpt_generation.json", "w")) smooth = SmoothingFunction() score = 0. for pred, true in zip(pred_txt, true_txt): score += sentence_bleu([true], pred, weights=(0.25, 0.25, 0.25, 0.25), smoothing_function=smooth.method1) score /= sample_num return score da_alg = FeatAlignAlg from utils.dl.common.lr_scheduler import get_linear_schedule_with_warmup #from new_impl.cv.model import ClsOnlineFeatAlignModel da_model = TxtgenOnlineFeatAlignModel( app_name, 'new_impl/nlp/gpt-neo/text_generation/results/gen_md_wo_fbs.py/20240113/999999-172009/models/md_best.pt', device ) da_alg_hyp = { 'Medicine_task': { 'train_batch_size': 2, 'val_batch_size': 1, 'num_workers': 2, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 5e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 1000, 'val_freq': 200, 'feat_align_loss_weight': 1.0, }, 'Law_task': { 'train_batch_size': 2, 'val_batch_size': 1, 'num_workers': 2, 'optimizer': 'AdamW', 'optimizer_args': {'lr': 5e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, 'scheduler': '', 'scheduler_args': {}, 'num_iters': 1000, 'val_freq': 200, 'feat_align_loss_weight': 1.0, }, } baseline_da( app_name, scenario, da_alg, da_alg_hyp, da_model, device, __file__, "results", collate_fn=collate_fn )