From 241c0282de4f625b499a918d74796a58288225ea Mon Sep 17 00:00:00 2001 From: Meiyim Date: Wed, 27 May 2020 11:14:25 +0800 Subject: [PATCH] Dygraph fix4 (#464) * upgrade version no * seq2seq + length penalty * upgrade to paddle 1.8 * fix readme * update seq2seq to 1.8 * fix seq2seq beam-search * + aisudio tutorial: loading old-styled checkpoint * add mising seq2seq eval file for cnndm * fix seq2seq decode post process * + handlers --- README.en.md | 9 ++- README.zh.md | 3 +- demo/finetune_classifier.py | 1 - demo/finetune_classifier_dygraph.py | 9 +-- ...finetune_classifier_dygraph_distributed.py | 14 +++- demo/finetune_mrc_dygraph.py | 3 +- demo/finetune_ner_dygraph.py | 8 +- demo/finetune_sentiment_analysis_dygraph.py | 3 +- demo/pretrain/pretrain.py | 1 - demo/pretrain/pretrain_dygraph.py | 1 - distill/distill.py | 4 +- experimental/seq2seq/decode.py | 74 +++++++++++++------ .../eval_cnndm/pyrouge_set_rouge_path.py | 18 +++++ .../seq2seq/finetune_seq2seq_dygraph.py | 19 +++-- setup.py | 2 +- 15 files changed, 112 insertions(+), 57 deletions(-) create mode 100755 experimental/seq2seq/eval_cnndm/pyrouge_set_rouge_path.py diff --git a/README.en.md b/README.en.md index 2d8ae732e4daf..9dd3c6e91d942 100644 --- a/README.en.md +++ b/README.en.md @@ -69,10 +69,11 @@ Don't have GPU? try ERNIE in [AIStudio](https://aistudio.baidu.com/aistudio/inde (please choose the latest version and apply for a GPU environment) 1. [ERNIE for beginners](https://aistudio.baidu.com/studio/edu/group/quick/join/314947) -1. [Sementic Analysis](https://aistudio.baidu.com/aistudio/projectdetail/427482) -2. [Cloze Test](https://aistudio.baidu.com/aistudio/projectdetail/433491) -3. [Knowledge Distillation](https://aistudio.baidu.com/aistudio/projectdetail/439460) -4. [Ask Ernie](https://aistudio.baidu.com/aistudio/projectdetail/456443) +1. [Sementic analysis](https://aistudio.baidu.com/aistudio/projectdetail/427482) +2. [Cloze test](https://aistudio.baidu.com/aistudio/projectdetail/433491) +3. [Knowledge distillation](https://aistudio.baidu.com/aistudio/projectdetail/439460) +4. [Ask ERNIE](https://aistudio.baidu.com/aistudio/projectdetail/456443) +5. [Loading old-styled checkpoint](https://aistudio.baidu.com/aistudio/projectdetail/493415) # Setup diff --git a/README.zh.md b/README.zh.md index 866bc436978d9..d9b36551af60f 100644 --- a/README.zh.md +++ b/README.zh.md @@ -70,6 +70,7 @@ print(pooled.numpy()) # convert results to numpy 2. [完形填空](https://aistudio.baidu.com/aistudio/projectdetail/433491) 3. [知识蒸馏](https://aistudio.baidu.com/aistudio/projectdetail/439460) 4. [万事不决问ERNIE](https://aistudio.baidu.com/aistudio/projectdetail/456443) +5. [加载并读取老式checkpoint](https://aistudio.baidu.com/aistudio/projectdetail/493415) # 安装 @@ -230,7 +231,7 @@ sids = np.expand_dims(sids, 0) result = client(ids, sids) ``` -你也可从[此处]((https://ernie.bj.bcebos.com/ernie1.0_zh_inference_model.tar.gz.)下载一个预先制作好的ernie-1.0 base模型的 `inference_model`. +你也可从[此处](https://ernie.bj.bcebos.com/ernie1.0_zh_inference_model.tar.gz.)下载一个预先制作好的ernie-1.0 base模型的 `inference_model`. 该模型没有经过finetune，一般可以用做上层模型结构的 feature-base finetune或者做为一个文本特征抽取器。 因为该模行由老版API 产出，在进行客户端请求时需要在输入tensor后面追加一个维度： diff --git a/demo/finetune_classifier.py b/demo/finetune_classifier.py index b7519197b69fb..3c189050b3348 100644 --- a/demo/finetune_classifier.py +++ b/demo/finetune_classifier.py @@ -43,7 +43,6 @@ log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) def model_fn(features, mode, params, run_config): diff --git a/demo/finetune_classifier_dygraph.py b/demo/finetune_classifier_dygraph.py index 995d2a8860fee..53764aadc2d32 100644 --- a/demo/finetune_classifier_dygraph.py +++ b/demo/finetune_classifier_dygraph.py @@ -34,7 +34,6 @@ import propeller.paddle as propeller log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) @@ -104,12 +103,12 @@ def map_fn(seg_a, seg_b, label): with FD.guard(place): model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='') + g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental if args.use_lr_decay: - opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd) + opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd, grad_clip=g_clip) else: - opt = AdamW(args.lr, parameter_list=model.parameters(), weight_decay=args.wd) + opt = AdamW(args.lr, parameter_list=model.parameters(), weight_decay=args.wd, grad_clip=g_clip) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental for epoch in range(args.epoch): for step, d in enumerate(tqdm(train_ds.start(place), desc='training')): ids, sids, label = d @@ -117,7 +116,7 @@ def map_fn(seg_a, seg_b, label): loss.backward() if step % 10 == 0: log.debug('train loss %.5f lr %.3e' % (loss.numpy(), opt.current_step_lr())) - opt.minimize(loss, grad_clip=g_clip) + opt.minimize(loss) model.clear_gradients() if step % 100 == 0: acc = [] diff --git a/demo/finetune_classifier_dygraph_distributed.py b/demo/finetune_classifier_dygraph_distributed.py index 3290d239a5d1f..fc86f4460b888 100644 --- a/demo/finetune_classifier_dygraph_distributed.py +++ b/demo/finetune_classifier_dygraph_distributed.py @@ -33,7 +33,6 @@ import propeller.paddle as propeller log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) @@ -102,15 +101,22 @@ def map_fn(seg_a, seg_b, label): model = ErnieModelForSequenceClassification.from_pretrained(args.from_pretrained, num_labels=3, name='') model = FD.parallel.DataParallel(model, ctx) - opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental + g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental + opt = AdamW(learning_rate=LinearDecay( + args.lr, + int(args.warmup_proportion * args.max_steps), + args.max_steps), + parameter_list=model.parameters(), + weight_decay=args.wd, + grad_clip=g_clip) + for step, d in enumerate(tqdm(train_ds.start(place), desc='training')): ids, sids, label = d loss, _ = model(ids, sids, labels=label) scaled_loss = model.scale_loss(loss) scaled_loss.backward() model.apply_collective_grads() - opt.minimize(scaled_loss, grad_clip=g_clip) + opt.minimize(scaled_loss) model.clear_gradients() if step % 10 == 0: log.debug('train loss %.5f, lr %.e3' % (loss.numpy(), opt.current_step_lr())) diff --git a/demo/finetune_mrc_dygraph.py b/demo/finetune_mrc_dygraph.py index 84e00247e8a16..de0668d5b33cf 100644 --- a/demo/finetune_mrc_dygraph.py +++ b/demo/finetune_mrc_dygraph.py @@ -48,7 +48,6 @@ from demo.mrc import mrc_metrics log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) @@ -84,7 +83,7 @@ def train(model, train_dataset, dev_dataset, dev_examples, dev_features, tokeniz max_steps = len(train_features) * args.epoch // args.bsz opt = AdamW(learning_rate=args.lr, parameter_list=model.parameters(), weight_decay=args.wd) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental + g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental train_dataset = train_dataset \ .repeat() \ diff --git a/demo/finetune_ner_dygraph.py b/demo/finetune_ner_dygraph.py index 2a2a7e900083c..cf1615a66ba62 100644 --- a/demo/finetune_ner_dygraph.py +++ b/demo/finetune_ner_dygraph.py @@ -39,7 +39,6 @@ import propeller.paddle as propeller log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) from ernie.modeling_ernie import ErnieModel, ErnieModelForSequenceClassification, ErnieModelForTokenClassification @@ -127,13 +126,14 @@ def before(seg, label): test_ds.data_shapes = shapes test_ds.data_types = types - with FD.guard(): + place = F.CUDAPlace(0) + with FD.guard(place): model = ErnieModelForTokenClassification.from_pretrained(args.from_pretrained, num_labels=7, name='') opt = AdamW(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters(), weight_decay=0.01) #opt = F.optimizer.AdamOptimizer(learning_rate=LinearDecay(args.lr, args.warmup_steps, args.max_steps), parameter_list=model.parameters()) for epoch in range(args.epoch): - for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(train_ds.start())): + for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(train_ds.start(place))): loss, _ = model(ids, sids, labels=aligned_label) loss.backward() if step % 10 == 0 : @@ -144,7 +144,7 @@ def before(seg, label): all_pred, all_label = [], [] with FD.base._switch_tracer_mode_guard_(is_train=False): model.eval() - for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(dev_ds.start())): + for step, (ids, sids, aligned_label, label, orig_pos) in enumerate(tqdm(dev_ds.start(place))): loss, logits = model(ids, sids, labels=aligned_label) #print('\n'.join(map(str, logits.numpy().tolist()))) diff --git a/demo/finetune_sentiment_analysis_dygraph.py b/demo/finetune_sentiment_analysis_dygraph.py index 0ed18dbfa7ecf..17ea38483d7c3 100644 --- a/demo/finetune_sentiment_analysis_dygraph.py +++ b/demo/finetune_sentiment_analysis_dygraph.py @@ -34,7 +34,6 @@ import propeller.paddle as propeller log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) log = logging.getLogger() @@ -101,7 +100,7 @@ def map_fn(seg_a, label): int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental + g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental for epoch in range(args.epoch): for step, d in enumerate(tqdm(train_ds.start(place), desc='training')): ids, sids, label = d diff --git a/demo/pretrain/pretrain.py b/demo/pretrain/pretrain.py index d3a3fd90749be..7d03e2aad94ca 100644 --- a/demo/pretrain/pretrain.py +++ b/demo/pretrain/pretrain.py @@ -48,7 +48,6 @@ from propeller import log log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) if six.PY3: diff --git a/demo/pretrain/pretrain_dygraph.py b/demo/pretrain/pretrain_dygraph.py index 2f6d2e680bdc5..f32e37aa4bf30 100644 --- a/demo/pretrain/pretrain_dygraph.py +++ b/demo/pretrain/pretrain_dygraph.py @@ -49,7 +49,6 @@ from propeller import log log.setLevel(logging.DEBUG) -logging.getLogger().addHandler(log.handlers[0]) logging.getLogger().setLevel(logging.DEBUG) if six.PY3: diff --git a/distill/distill.py b/distill/distill.py index e135afe256233..69ad7617cbc99 100644 --- a/distill/distill.py +++ b/distill/distill.py @@ -100,7 +100,7 @@ def evaluate_teacher(model, dataset): teacher_model.train() if not os.path.exists('./teacher_model.pdparams'): opt = AdamW(learning_rate=LinearDecay(LR, 9600*EPOCH*0.1/BATCH, 9600*EPOCH/BATCH), parameter_list=teacher_model.parameters(), weight_decay=0.01) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) + g_clip = F.clip.GradientClipByGlobalNorm(1.0) for epoch in range(EPOCH): for step, (ids_student, ids, sids, labels) in enumerate(train_ds.start(place)): loss, logits = teacher_model(ids, labels=labels) @@ -200,7 +200,7 @@ def KL(pred, target): teacher_model.eval() model = BOW() opt = AdamW(learning_rate=LR, parameter_list=model.parameters(), weight_decay=0.01) -g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) #experimental +g_clip = F.clip.GradientClipByGlobalNorm(1.0) #experimental model.train() for epoch in range(EPOCH): for step, (ids_student, ids, sids, _ ) in enumerate(train_ds.start(place)): diff --git a/experimental/seq2seq/decode.py b/experimental/seq2seq/decode.py index a0485483e5786..49155e9cd9180 100644 --- a/experimental/seq2seq/decode.py +++ b/experimental/seq2seq/decode.py @@ -19,6 +19,7 @@ from __future__ import unicode_literals import sys +import re import argparse import logging import json @@ -81,7 +82,7 @@ def gen_bias(encoder_inputs, decoder_inputs, step): @D.no_grad -def greedy_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_encode_len=640, max_decode_len=100): +def greedy_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_encode_len=640, max_decode_len=100, tgt_type_id=3): model.eval() #log.debug(q_ids.numpy().tolist()) _, logits, info = model(q_ids, q_sids) @@ -104,7 +105,7 @@ def greedy_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_e bias = gen_bias(q_ids, ids, step) pos_ids = D.to_variable(np.tile(np.array([[step, step + 1]], dtype=np.int64), [d_batch, 1])) pos_ids += seqlen - _, logits, info = model(ids, L.ones_like(ids) * 3, pos_ids=pos_ids, attn_bias=bias, past_cache=past_cache) + _, logits, info = model(ids, L.ones_like(ids) * tgt_type_id, pos_ids=pos_ids, attn_bias=bias, past_cache=past_cache) gen_ids = L.argmax(logits, -1) past_cached_k, past_cached_v = past_cache @@ -143,30 +144,30 @@ def mask_prob(p, onehot_eos, finished): return p -def hyp_score(log_probs, length): - factor=1. - lp = L.pow((5.+L.cast(length, 'float32')) / 6., factor) +def hyp_score(log_probs, length, length_penalty): + lp = L.pow((5.+L.cast(length, 'float32')) / 6., length_penalty) return log_probs / lp -def beam_search_step(state, logits, eos_id, beam_width, is_first_step): +def beam_search_step(state, logits, eos_id, beam_width, is_first_step, length_penalty): """logits.shape == [B*W, V]""" _, vocab_size = logits.shape bsz, beam_width = state.log_probs.shape - onehot_eos = L.cast(F.one_hot(L.ones([bsz * beam_width], 'int64') * eos_id, vocab_size), 'int64') #[1, V] + onehot_eos = L.cast(F.one_hot(L.ones([1], 'int64') * eos_id, vocab_size), 'int64') #[1, V] probs = L.log(L.softmax(logits)) #[B*W, V] probs = mask_prob(probs, onehot_eos, state.finished) #[B*W, V] allprobs = L.reshape(state.log_probs, [-1, 1]) + probs #[B*W, V] - length_to_add = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1] - length_to_add = L.cast((length_to_add + 1 - onehot_eos) != 0, 'int64') #[B*W,V] + not_finished = 1 - L.reshape(state.finished, [-1, 1]) #[B*W,1] + not_eos = 1 - onehot_eos + length_to_add = not_finished * not_eos #[B*W,V] alllen = L.reshape(state.lengths, [-1, 1]) + length_to_add allprobs = L.reshape(allprobs, [-1, beam_width * vocab_size]) alllen = L.reshape(alllen, [-1, beam_width * vocab_size]) - allscore = hyp_score(allprobs, alllen) + allscore = hyp_score(allprobs, alllen, length_penalty) if is_first_step: allscore = L.reshape(allscore, [bsz, beam_width, -1])[:,0,:] # first step only consiter beam 0 scores, idx = L.topk(allscore, k=beam_width) #[B, W] @@ -184,6 +185,7 @@ def beam_search_step(state, logits, eos_id, beam_width, is_first_step): #log.debug(next_finished.numpy()) next_finished += L.cast(next_word_id==eos_id, 'int64') + next_finished = L.cast(next_finished > 0, 'int64') #log.debug(next_word_id.numpy()) #log.debug(next_beam_id.numpy()) @@ -194,7 +196,7 @@ def beam_search_step(state, logits, eos_id, beam_width, is_first_step): @D.no_grad -def beam_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_encode_len=640, max_decode_len=100, beam_width=5, tgt_type_id=3): +def beam_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_encode_len=640, max_decode_len=100, beam_width=5, tgt_type_id=3, length_penalty=1.0): model.eval() #log.debug(q_ids.numpy().tolist()) _, __, info = model(q_ids, q_sids) @@ -206,6 +208,12 @@ def beam_search_infilling(model, q_ids, q_sids, sos_id, eos_id, attn_id, max_enc finished=L.zeros([d_batch, beam_width], 'int64')) outputs = [] + def reorder_(t, parent_id): + """reorder cache according to parent beam id""" + gather_idx = L.where(parent_id!=-1)[:, 0] * beam_width + L.reshape(parent_id, [-1]) + t = L.gather(t, gather_idx) + return t + def tile_(t, times): _shapes = list(t.shape[1:]) ret = L.reshape(L.expand(L.unsqueeze(t, [1]), [1, times,] + [1,] * len(_shapes)), [-1,] + _shapes) @@ -230,14 +238,21 @@ def tile_(t, times): pos_ids += seqlen _, logits, info = model(ids, L.ones_like(ids) * tgt_type_id, pos_ids=pos_ids, attn_bias=bias, past_cache=past_cache) + + output, state = beam_search_step(state, logits[:, 1], + eos_id=eos_id, + beam_width=beam_width, + is_first_step=(step==0), + length_penalty=length_penalty) + outputs.append(output) + past_cached_k, past_cached_v = past_cache cached_k, cached_v = info['caches'] - cached_k = [L.concat([pk, k[:, :1, :]], 1) for pk, k in zip(past_cached_k, cached_k)] # concat cached - cached_v = [L.concat([pv, v[:, :1, :]], 1) for pv, v in zip(past_cached_v, cached_v)] + cached_k = [reorder_(L.concat([pk, k[:, :1, :]], 1), output.beam_parent_ids) for pk, k in zip(past_cached_k, cached_k)] # concat cached + cached_v = [reorder_(L.concat([pv, v[:, :1, :]], 1), output.beam_parent_ids) for pv, v in zip(past_cached_v, cached_v)] past_cache = (cached_k, cached_v) - - output, state = beam_search_step(state, logits[:, 1], eos_id=eos_id, beam_width=beam_width, is_first_step=(step==0)) - outputs.append(output) + + pred_ids_flatten = L.reshape(output.predicted_ids, [d_batch * beam_width]) ids = L.stack([pred_ids_flatten, attn_ids], 1) @@ -251,15 +266,19 @@ def tile_(t, times): final_ids = L.gather_tree(final_ids, final_parent_ids)[:,:,0] #pick best beam final_ids = L.transpose(L.reshape(final_ids, [-1, d_batch * 1]), [1, 0]) return final_ids - + +en_patten = re.compile(r'^[a-zA-Z0-9]*$') def post_process(token): if token.startswith('##'): ret = token[2:] else: - ret = ' ' + token + if en_patten.match(token): + ret = ' ' + token + else: + ret = token return ret - + if __name__ == '__main__': parser = argparse.ArgumentParser('seq2seq model with ERNIE') @@ -268,7 +287,9 @@ def post_process(token): parser.add_argument('--max_encode_len', type=int, default=640) parser.add_argument('--max_decode_len', type=int, default=120) parser.add_argument('--tgt_type_id', type=int, default=3) - parser.add_argument('--beam_width', type=int, default=3) + parser.add_argument('--beam_width', type=int, default=5) + parser.add_argument('--attn_token', type=str, default='[ATTN]', help='if [ATTN] not in vocab, you can specified [MAKK] as attn-token') + parser.add_argument('--length_penalty', type=float, default=1.0) parser.add_argument('--save_dir', type=str, required=True, help='model dir to be loaded') args = parser.parse_args() @@ -282,12 +303,14 @@ def post_process(token): rev_dict[tokenizer.pad_id] = '' # replace [PAD] rev_dict[tokenizer.unk_id] = '' # replace [PAD] + sd, _ = D.load_dygraph(args.save_dir) + ernie.set_dict(sd) + def map_fn(src_ids): src_ids = src_ids[: args.max_encode_len] src_ids, src_sids = tokenizer.build_for_ernie(src_ids) return (src_ids, src_sids) - bytes_vocab = {k.encode('utf8'): v for k, v in tokenizer.vocab.items()} feature_column = propeller.data.FeatureColumns([ propeller.data.TextColumn('seg_a', unk_id=tokenizer.unk_id, vocab_dict=tokenizer.vocab, tokenizer=tokenizer.tokenize), ]) @@ -297,17 +320,19 @@ def map_fn(src_ids): #result_ids = greedy_search_infilling(ernie, D.to_variable(encoder_ids), D.to_variable(encoder_sids), # eos_id=tokenizer.sep_id, # sos_id=tokenizer.cls_id, - # attn_id=tokenizer.vocab['[ATTN]'], + # attn_id=tokenizer.vocab[args.attn_id], # max_decode_len=args.max_decode_len, # max_encode_len=args.max_encode_len, - # beam_width=args.beam_width) + # beam_width=args.beam_width, + # tgt_type_id=args.tgt_type_id) result_ids = beam_search_infilling(ernie, D.to_variable(encoder_ids), D.to_variable(encoder_sids), eos_id=tokenizer.sep_id, sos_id=tokenizer.cls_id, - attn_id=tokenizer.vocab['[ATTN]'], + attn_id=tokenizer.vocab[args.attn_token], max_decode_len=args.max_decode_len, max_encode_len=args.max_encode_len, beam_width=args.beam_width, + length_penalty=args.length_penalty, tgt_type_id=args.tgt_type_id) output_str = rev_lookup(result_ids.numpy()) @@ -316,5 +341,6 @@ def map_fn(src_ids): ostr = ostr[: ostr.index('[SEP]')] ostr = ''.join(map(post_process, ostr)) + ostr = ostr.strip() print(ostr) diff --git a/experimental/seq2seq/eval_cnndm/pyrouge_set_rouge_path.py b/experimental/seq2seq/eval_cnndm/pyrouge_set_rouge_path.py new file mode 100755 index 0000000000000..191ed5370c11a --- /dev/null +++ b/experimental/seq2seq/eval_cnndm/pyrouge_set_rouge_path.py @@ -0,0 +1,18 @@ +#!./python3.6/bin/python + +import argparse + +from pyrouge import Rouge155 + +def get_args(): + parser = argparse.ArgumentParser() + parser.add_argument(help="Path of the directory containing ROUGE-1.5.5.pl.", + type=str, action="store", dest="home_dir") + return parser.parse_args() + +def main(): + args = get_args() + Rouge155(args.home_dir) + +if __name__ == "__main__": + main() diff --git a/experimental/seq2seq/finetune_seq2seq_dygraph.py b/experimental/seq2seq/finetune_seq2seq_dygraph.py index b2fa4706a181f..44ee4fd425f83 100644 --- a/experimental/seq2seq/finetune_seq2seq_dygraph.py +++ b/experimental/seq2seq/finetune_seq2seq_dygraph.py @@ -68,6 +68,7 @@ def evaluate(model, datasets, step, args): max_decode_len=args.max_decode_len, max_encode_len=args.max_encode_len, beam_width=args.beam_width, + length_penalty=args.length_penalty, tgt_type_id=args.tgt_type_id,) output_str = rev_lookup(output_ids.numpy()) for eid, ostr in zip(example_id.numpy().tolist(), output_str.tolist()): @@ -228,8 +229,8 @@ def after_padding(example_id, src_ids, src_pids, src_sids, vocab_size, _ = model.word_emb.weight.shape ctx = D.parallel.prepare_context() model = D.parallel.DataParallel(model, ctx) - opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd) - g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0) + g_clip = F.clip.GradientClipByGlobalNorm(1.0) + opt = AdamW(learning_rate=LinearDecay(args.lr, int(args.warmup_proportion * args.max_steps), args.max_steps), parameter_list=model.parameters(), weight_decay=args.wd, grad_clip=g_clip) attn_id = tokenizer.vocab[args.attn_token] for step, data in enumerate(train_ds.start(place)): (example_id, src_ids, src_sids, src_pids, @@ -253,7 +254,7 @@ def after_padding(example_id, src_ids, src_pids, src_sids, scaled_loss = model.scale_loss(loss) scaled_loss.backward() model.apply_collective_grads() - opt.minimize(scaled_loss, grad_clip=g_clip) + opt.minimize(scaled_loss) model.clear_gradients() if step % 10 == 0: loss = loss.numpy() @@ -261,12 +262,13 @@ def after_padding(example_id, src_ids, src_pids, src_sids, log.debug('[step %d]train loss %.5f, ppl %.5f, lr %.3e' % (step, loss, ppl, opt.current_step_lr())) if args.save_dir is not None and step % 1000 == 0 and D.parallel.Env().dev_id == 0: F.save_dygraph(model.state_dict(), args.save_dir) - if args.predict_output_dir is not None and (step + 1) % args.eval_steps == 0: + if args.predict_output_dir is not None and step > args.skip_eval_steps and step % args.eval_steps == 0: assert os.path.exists(args.predict_output_dir), 'predict_output_dir not found: %s' % args.predict_output_dir log.debug('doing predict on gpu %d...' % D.parallel.Env().dev_id) evaluate(model, dev_ds, step, args) if step > args.max_steps: break + evaluate(model, dev_ds, step, args) if args.save_dir is not None: F.save_dygraph(model.state_dict(), args.save_dir) @@ -277,10 +279,10 @@ def after_padding(example_id, src_ids, src_pids, src_sids, parser.add_argument('--from_pretrained', type=str, required=True, help='pretrained model directory or tag') parser.add_argument('--bsz', type=int, default=8, help='batchsize') parser.add_argument('--eval_bsz', type=int, default=20, help='batchsize') - parser.add_argument('--epoch', type=int, default=30, help='epoch') parser.add_argument('--data_dir', type=str, required=True, help='data directory includes train / develop data') parser.add_argument('--max_steps', type=int, required=True, help='max_train_steps, set this to EPOCH * NUM_SAMPLES / BATCH_SIZE') parser.add_argument('--eval_steps', type=int, default=5000, help='evaluation frequency') + parser.add_argument('--skip_eval_steps', type=int, default=1, help='skip evaluate for first n step') parser.add_argument('--max_encode_len', type=int, default=640) parser.add_argument('--max_decode_len', type=int, default=120) parser.add_argument('--tgt_type_id', type=int, default=3) @@ -290,9 +292,11 @@ def after_padding(example_id, src_ids, src_pids, src_sids, parser.add_argument('--use_random_noice', action='store_true', help='if set, replace target tokens with random token from vocabulary, else replace with `[NOISE]`') parser.add_argument('--lr', type=float, default=5e-5, help='learning rate') parser.add_argument('--label_smooth', type=float, default=0.1) + parser.add_argument('--length_penalty', type=float, default=1.0) parser.add_argument('--predict_output_dir', type=str, default=None, help='predict file output directory') parser.add_argument('--attn_token', type=str, default='[ATTN]', help='if [ATTN] not in vocab, you can specified [MAKK] as attn-token') parser.add_argument('--inference_model_dir', type=str, default=None, help='inference model output directory') + parser.add_argument('--init_checkpoint', type=str, default=None) parser.add_argument('--save_dir', type=str, default=None, help='model output directory') parser.add_argument('--wd', type=float, default=0.01, help='weight decay, aka L2 regularizer') @@ -307,4 +311,9 @@ def after_padding(example_id, src_ids, src_pids, src_sids, rev_dict[tokenizer.pad_id] = '' # replace [PAD] rev_dict[tokenizer.unk_id] = '' # replace [PAD] + if args.init_checkpoint is not None: + log.info('loading checkpoint from %s' % args.init_checkpoint) + sd, _ = D.load_dygraph(args.init_checkpoint) + ernie.set_dict(sd) + seq2seq(ernie, tokenizer, args) diff --git a/setup.py b/setup.py index ac97709f4d66d..c3858993f08b8 100644 --- a/setup.py +++ b/setup.py @@ -22,7 +22,7 @@ setuptools.setup( name="paddle-ernie", # Replace with your own username - version="0.0.2dev1", + version="0.0.3dev1", author="Baidu Ernie Team", author_email="ernieernie.team@gmail.com", description="A pretrained NLP model for every NLP tasks",