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| 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.elasticfm_da import init_online_model, elasticfm_da | |
| from methods.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' | |
| device = 'cuda:1' | |
| app_name = 'cls' | |
| sd_sparsity = 0.8 | |
| settings = { | |
| 'involve_fm': True | |
| } | |
| torch.cuda.set_device(1) | |
| 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 ElasticDNN_TxtgenOnlineModel(ElasticDNN_OnlineModel): | |
| 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 | |
| def get_elastic_dnn_util(self) -> ElasticDNNUtil: | |
| return ElasticGPTUtil() | |
| def get_fm_matched_param_of_md_param(self, md_param_name): | |
| # only between qkv.weight, norm.weight/bias | |
| self_param_name = md_param_name | |
| fm = self.models_dict['fm'] | |
| # if any([k in self_param_name for k in ['fbs', 'cls_token', 'pos_embed']]): | |
| # return None | |
| # p = get_parameter(self.models_dict['md'], self_param_name) | |
| # if p.dim() == 0: | |
| # return None | |
| # elif p.dim() == 1 and 'norm' in self_param_name and 'weight' in self_param_name: | |
| # return get_parameter(fm, self_param_name) | |
| if any([k in self_param_name for k in ['fbs', 'wte', 'wpe']]): | |
| return None | |
| p = get_parameter(self.models_dict['md'], self_param_name) | |
| if p.dim() == 0: | |
| return None | |
| # elif p.dim() == 1 and 'layernorm' in self_param_name and 'weight' in self_param_name: | |
| # return get_parameter(fm, self_param_name) | |
| # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| # if 'qkv.weight' in self_param_name: | |
| # ss = self_param_name.split('.') | |
| # fm_qkv_name = '.'.join(ss[0: -1]) + '.qkv' | |
| # fm_qkv = get_module(fm, fm_qkv_name) | |
| # fm_abs_name = '.'.join(ss[0: -1]) + '.abs' | |
| # fm_abs = get_module(fm, fm_abs_name) | |
| # # NOTE: unrecoverable operation! multiply LoRA parameters to allow it being updated in update_fm_param() | |
| # # TODO: if fm will be used for inference, _mul_lora_weight will not be applied! | |
| # if not hasattr(fm_abs, '_mul_lora_weight'): | |
| # logger.debug(f'set _mul_lora_weight in {fm_abs_name}') | |
| # setattr(fm_abs, '_mul_lora_weight', | |
| # nn.Parameter(torch.cat([(_abs[0].weight.T @ _abs[1].weight.T).T for _abs in fm_abs], dim=0))) | |
| # return torch.cat([ | |
| # fm_qkv.weight.data, # task-agnositc params | |
| # fm_abs._mul_lora_weight.data # task-specific params (LoRA) | |
| # ], dim=0) | |
| # # elif 'to_qkv.bias' in self_param_name: | |
| # # ss = self_param_name.split('.') | |
| # # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias' | |
| # # return get_parameter(fm, fm_qkv_name) | |
| # elif 'mlp.fc1' in self_param_name and 'weight' in self_param_name: | |
| # fm_param_name = self_param_name.replace('.linear', '') | |
| # return get_parameter(fm, fm_param_name) | |
| # elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name: | |
| # fm_param_name = self_param_name | |
| # return get_parameter(fm, fm_param_name) | |
| # else: | |
| # # return get_parameter(fm, self_param_name) | |
| # return None | |
| if ('q_proj' in self_param_name or 'k_proj' in self_param_name or \ | |
| 'v_proj' in self_param_name) and ('weight' in self_param_name): | |
| ss = self_param_name.split('.') | |
| fm_qkv_name = '.'.join(ss[0: -1]) + '.fc' | |
| fm_qkv = get_module(fm, fm_qkv_name) | |
| fm_abs_name = '.'.join(ss[0: -1]) + '.ab' | |
| fm_abs = get_module(fm, fm_abs_name) | |
| if not hasattr(fm_abs, '_mul_lora_weight'): | |
| logger.debug(f'set _mul_lora_weight in {fm_abs_name}') | |
| setattr(fm_abs, '_mul_lora_weight', | |
| nn.Parameter(fm_abs[1].weight @ fm_abs[0].weight)) | |
| return torch.cat([ | |
| fm_qkv.weight.data, # task-agnositc params | |
| fm_abs._mul_lora_weight.data # task-specific params (LoRA) | |
| ], dim=0) | |
| elif ('q_proj' in self_param_name or 'k_proj' in self_param_name or \ | |
| 'v_proj' in self_param_name) and ('bias' in self_param_name): | |
| ss = self_param_name.split('.') | |
| fm_qkv_name = '.'.join(ss[0: -1]) + '.fc.bias' | |
| return get_parameter(fm, fm_qkv_name) | |
| elif 'mlp.c_fc' in self_param_name and 'weight' in self_param_name: | |
| fm_param_name = self_param_name.replace('.linear', '') | |
| return get_parameter(fm, fm_param_name) | |
| elif 'mlp.c_fc' in self_param_name and 'bias' in self_param_name: | |
| fm_param_name = self_param_name.replace('.linear', '') | |
| return get_parameter(fm, fm_param_name) | |
| # elif 'mlp.fc2' in self_param_name and 'weight' in self_param_name: | |
| # fm_param_name = self_param_name.replace('.linear', '') | |
| # return get_parameter(fm, fm_param_name) | |
| else: | |
| #return get_parameter(fm, self_param_name) | |
| return None | |
| def update_fm_param(self, md_param_name, cal_new_fm_param_by_md_param): | |
| if not (('q_proj' in md_param_name or 'k_proj' in md_param_name or \ | |
| 'v_proj' in md_param_name) and ('weight' in md_param_name)): | |
| matched_fm_param_ref = self.get_fm_matched_param_of_md_param(md_param_name) | |
| matched_fm_param_ref.copy_(cal_new_fm_param_by_md_param) | |
| else: | |
| new_fm_attn_weight, new_fm_lora_weight = torch.chunk(cal_new_fm_param_by_md_param, 2, 0) | |
| ss = md_param_name.split('.') | |
| fm = self.models_dict['fm'] | |
| # update task-agnostic parameters | |
| fm_qkv_name = '.'.join(ss[0: -1]) + '.fc' | |
| fm_qkv = get_module(fm, fm_qkv_name) | |
| fm_qkv.weight.data.copy_(new_fm_attn_weight) | |
| # update task-specific parameters | |
| fm_abs_name = '.'.join(ss[0: -1]) + '.ab' | |
| fm_abs = get_module(fm, fm_abs_name) | |
| fm_abs._mul_lora_weight.data.copy_(new_fm_lora_weight) # TODO: this will not be applied in inference! | |
| def get_md_matched_param_of_fm_param(self, fm_param_name): | |
| return super().get_md_matched_param_of_fm_param(fm_param_name) | |
| def get_md_matched_param_of_sd_param(self, sd_param_name): | |
| # raise NotImplementedError | |
| # only between qkv.weight, norm.weight/bias | |
| self_param_name = sd_param_name | |
| md = self.models_dict['md'] | |
| if any([k in self_param_name for k in ['fbs', 'wte', 'wpe']]): | |
| return None | |
| p = get_parameter(self.models_dict['sd'], self_param_name) | |
| if p.dim() == 0: | |
| return None | |
| elif p.dim() == 1 and ('LayerNorm' in self_param_name or 'ln' in self_param_name) and 'weight' in self_param_name: | |
| return get_parameter(md, self_param_name) | |
| # 1. xx.qkv.to_qkv.yy to xx.qkv.qkv.aa and xx.qkv.abs.zz | |
| if ('q_proj' in self_param_name or 'k_proj' in self_param_name or \ | |
| 'v_proj' in self_param_name) and ('weight' in self_param_name): | |
| return get_parameter(md, self_param_name) # NOTE: no fbs in qkv! | |
| # elif 'to_qkv.bias' in self_param_name: | |
| # ss = self_param_name.split('.') | |
| # fm_qkv_name = '.'.join(ss[0: -2]) + '.qkv.bias' | |
| # return get_parameter(fm, fm_qkv_name) | |
| elif 'mlp.c_fc.0.weight' in self_param_name: | |
| fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.weight' | |
| return get_parameter(md, fm_param_name) | |
| elif 'mlp.c_fc.0.bias' in self_param_name: | |
| fm_param_name = '.'.join(self_param_name.split('.')[0: -2]) + '.linear.bias' | |
| return get_parameter(md, fm_param_name) | |
| elif 'mlp.c_proj' in self_param_name and 'weight' in self_param_name: | |
| fm_param_name = self_param_name | |
| return get_parameter(md, fm_param_name) | |
| elif 'static_channel_attention' not in self_param_name: | |
| return get_parameter(md, self_param_name) | |
| # return None | |
| def get_task_head_params(self): | |
| head = get_module(self.models_dict['sd'], 'classifier') | |
| return list(head.parameters()) | |
| 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 | |
| #from new_impl.cv.model import ElasticDNN_ClsOnlineModel | |
| elasticfm_model = ElasticDNN_TxtgenOnlineModel('gen', init_online_model( | |
| 'new_impl/nlp/gpt-neo/text_generation/results/gen_md_w_fbs_index.py/20231222/999995-003118-results/models/fm_best.pt', | |
| 'new_impl/nlp/gpt-neo/text_generation/results/gen_md_w_fbs_index.py/20231222/999995-003118-results/models/md_best.pt', | |
| 'gen', __file__ | |
| ), device, { | |
| 'md_to_fm_alpha': 0.01, | |
| 'fm_to_md_alpha': 0.1 | |
| }) | |
| 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 | |
| da_alg_hyp = { | |
| 'Medicine_task': { | |
| 'train_batch_size': 2, | |
| 'val_batch_size': 1, | |
| 'num_workers': 2, | |
| 'optimizer': 'AdamW', | |
| 'optimizer_args': {'lr': 1e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, | |
| 'scheduler': '', | |
| 'scheduler_args': {}, | |
| 'num_iters': 1000, | |
| 'val_freq': 200, | |
| 'sd_sparsity':0.3, | |
| 'feat_align_loss_weight': 1.0, | |
| }, | |
| 'Law_task': { | |
| 'train_batch_size': 2, | |
| 'val_batch_size': 1, | |
| 'num_workers': 2, | |
| 'optimizer': 'AdamW', | |
| 'optimizer_args': {'lr': 1e-6, 'betas': [0.9, 0.999], 'weight_decay': 0.01}, | |
| 'scheduler': '', | |
| 'scheduler_args': {}, | |
| 'num_iters': 1000, | |
| 'val_freq': 200, | |
| 'sd_sparsity':0.3, | |
| 'feat_align_loss_weight': 1.0, | |
| }, | |
| } | |
| elasticfm_da( | |
| [app_name], | |
| [scenario], | |
| [elasticfm_model], | |
| [da_alg], | |
| [da_alg_hyp], | |
| [da_model], | |
| device, | |
| settings, | |
| __file__, | |
| "results", | |
| collate_fn=collate_fn | |
| ) |