updating examples

examples/run_glue.py

@@ -18,46 +18,37 @@
 from __future__ import absolute_import, division, print_function
 
 import argparse
+import glob
 import logging
 import os
 import random
-from tqdm import tqdm, trange
 
 import numpy as np
-
 import torch
+from tensorboardX import SummaryWriter
 from torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,
                               TensorDataset)
 from torch.utils.data.distributed import DistributedSampler
+from tqdm import tqdm, trange
 
-from tensorboardX import SummaryWriter
-
-from pytorch_transformers import (BertForSequenceClassification, XLNetForSequenceClassification,
-                                  XLMForSequenceClassification, BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-                                  XLNET_PRETRAINED_MODEL_ARCHIVE_MAP, XLM_PRETRAINED_MODEL_ARCHIVE_MAP)
-from pytorch_transformers import (BertTokenizer, XLNetTokenizer,
-                                  XLMTokenizer)
+from pytorch_transformers import WEIGHTS_NAME
+from pytorch_transformers import (BertConfig, BertForSequenceClassification,
+                                  BertTokenizer, XLMConfig,
+                                  XLMForSequenceClassification, XLMTokenizer,
+                                  XLNetConfig, XLNetForSequenceClassification,
+                                  XLNetTokenizer)
 from pytorch_transformers.optimization import BertAdam
-
-from utils_glue import processors, output_modes, convert_examples_to_features, compute_metrics
-
+from utils_glue import (compute_metrics, convert_examples_to_features,
+                        output_modes, processors)
 
 logger = logging.getLogger(__name__)
 
-ALL_MODELS = sum((tuple(m.keys()) for m in (BERT_PRETRAINED_MODEL_ARCHIVE_MAP,
-                                            XLNET_PRETRAINED_MODEL_ARCHIVE_MAP,
-                                            XLM_PRETRAINED_MODEL_ARCHIVE_MAP)), ())
+ALL_MODELS = sum((tuple(conf.pretrained_config_archive_map.keys()) for conf in (BertConfig, XLNetConfig, XLMConfig)), ())
 
 MODEL_CLASSES = {
-    'bert': BertForSequenceClassification,
-    'xlnet': XLNetForSequenceClassification,
-    'xlm': XLMForSequenceClassification,
-}
-
-TOKENIZER_CLASSES = {
-    'bert': BertTokenizer,
-    'xlnet': XLNetTokenizer,
-    'xlm': XLMTokenizer,
+    'bert': (BertConfig, BertForSequenceClassification, BertTokenizer),
+    'xlnet': (XLNetConfig, XLNetForSequenceClassification, XLNetTokenizer),
+    'xlm': (XLMConfig, XLMForSequenceClassification, XLMTokenizer),
 }
 
 def train(args, train_dataset, model, tokenizer):
@@ -130,14 +121,26 @@ def train(args, train_dataset, model, tokenizer):
                 optimizer.step()
                 optimizer.zero_grad()
                 global_step += 1
+
                 if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:
+                    # Log metrics
                     if args.local_rank == -1:  # Only evaluate on single GPU otherwise metrics may not average well
-                        results = evaluate(args, model, tokenizer)
+                        results = evaluate(args, model, tokenizer, prefix=global_step)
                         for key, value in results.items():
                             tb_writer.add_scalar('eval_{}'.format(key), value, global_step)
                     tb_writer.add_scalar('lr', optimizer.get_lr()[0], global_step)
                     tb_writer.add_scalar('loss', (tr_loss - logging_loss)/args.logging_steps, global_step)
                     logging_loss = tr_loss
+
+                if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:
+                    # Save model checkpoint
+                    output_dir = os.path.join(args.output_dir, 'checkpoint-{}'.format(global_step))
+                    if not os.path.exists(output_dir):
+                        os.makedirs(output_dir)
+                    model_to_save = model.module if hasattr(model, 'module') else model  # Take care of distributed/parallel training
+                    model_to_save.save_pretrained(output_dir)
+                    torch.save(args, os.path.join(output_dir, 'training_args.bin'))
+
             if args.max_steps > 0 and global_step > args.max_steps:
                 break
         if args.max_steps > 0 and global_step > args.max_steps:
@@ -146,7 +149,7 @@ def train(args, train_dataset, model, tokenizer):
     return global_step, tr_loss / global_step
 
 
-def evaluate(args, model, tokenizer):
+def evaluate(args, model, tokenizer, prefix=""):
     # Loop to handle MNLI double evaluation (matched, mis-matched)
     eval_task_names = ("mnli", "mnli-mm") if args.task_name == "mnli" else (args.task_name,)
     eval_outputs_dirs = (args.output_dir, args.output_dir + '-MM') if args.task_name == "mnli" else (args.output_dir,)
@@ -202,7 +205,7 @@ def evaluate(args, model, tokenizer):
 
         output_eval_file = os.path.join(eval_output_dir, "eval_results.txt")
         with open(output_eval_file, "w") as writer:
-            logger.info("***** Eval results *****")
+            logger.info("***** Eval results {} *****".format(prefix))
             for key in sorted(result.keys()):
                 logger.info("  %s = %s", key, str(result[key]))
                 writer.write("%s = %s\n" % (key, str(result[key])))
@@ -264,6 +267,10 @@ def main():
                         help="The output directory where the model predictions and checkpoints will be written.")
 
     ## Other parameters
+    parser.add_argument("--config_name", default="", type=str,
+                        help="Pretrained config name or path if not the same as model_name")
+    parser.add_argument("--tokenizer_name", default="", type=str,
+                        help="Pretrained tokenizer name or path if not the same as model_name")
     parser.add_argument("--cache_dir", default="", type=str,
                         help="Where do you want to store the pre-trained models downloaded from s3")
     parser.add_argument("--max_seq_length", default=128, type=int,
@@ -293,8 +300,12 @@ def main():
     parser.add_argument("--warmup_proportion", default=0.1, type=float,
                         help="Proportion of training with linear learning rate warmup (0.1 = 10%% of training).")
 
-    parser.add_argument('--logging_steps', type=int, default=100,
+    parser.add_argument('--logging_steps', type=int, default=50,
                         help="Log every X updates steps.")
+    parser.add_argument('--save_steps', type=int, default=50,
+                        help="Save checkpoint every X updates steps.")
+    parser.add_argument("--eval_all_checkpoints", action='store_true',
+                        help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number")
     parser.add_argument("--no_cuda", action='store_true',
                         help="Avoid using CUDA when available")
     parser.add_argument('--overwrite_output_dir', action='store_true',
@@ -363,11 +374,15 @@ def main():
         # Make sure only the first process in distributed training will download model & vocab
         torch.distributed.barrier()
 
-    args.model_type = args.model_name.lower().split('-')[0]
-    tokenizer_class = TOKENIZER_CLASSES[args.model_type]
-    model_class = MODEL_CLASSES[args.model_type]
-    tokenizer = tokenizer_class.from_pretrained(args.model_name, do_lower_case=args.do_lower_case)
-    model = model_class.from_pretrained(args.model_name, num_labels=num_labels)
+    args.model_type = ""
+    for key in MODEL_CLASSES:
+        if key in args.model_name.lower():
+            args.model_type = key  # take the first match in model types
+            break
+    config_class, model_class, tokenizer_class = MODEL_CLASSES[args.model_type]
+    config = config_class.from_pretrained(args.config_name if args.config_name else args.model_name, num_labels=num_labels, finetuning_task=args.task_name)
+    tokenizer = tokenizer_class.from_pretrained(args.tokenizer_name if args.tokenizer_name else args.model_name, do_lower_case=args.do_lower_case)
+    model = model_class.from_pretrained(args.model_name, from_tf=bool('.ckpt' in args.model_name), config=config)
 
     if args.local_rank == 0:
         torch.distributed.barrier()
@@ -410,8 +425,17 @@ def main():
 
     # Evaluation
     if args.do_eval and args.local_rank in [-1, 0]:
-        results = evaluate(args, model, tokenizer)
-
+        checkpoints = [args.output_dir + './' + WEIGHTS_NAME]
+        if args.eval_all_checkpoints:
+            checkpoints = list(os.path.dirname(c) for c in glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME, recursive=True))
+        logger.info("Evaluate the following checkpoints: %s", checkpoints)
+        results = {}
+        for checkpoint in checkpoints:
+            global_step = int(checkpoints.split('-')[-1])
+            model = model_class.from_pretrained(checkpoints)
+            model.to(args.device)
+            result = evaluate(args, model, tokenizer, prefix=global_step)
+            result = dict(n + '_{}'.format())
         return results
 
 

examples/utils_glue.py

@@ -21,6 +21,7 @@ import csv
 import logging
 import os
 import sys
+from io import open
 
 from scipy.stats import pearsonr, spearmanr
 from sklearn.metrics import matthews_corrcoef, f1_score

pytorch_transformers/modeling_bert.py

@@ -73,17 +73,17 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
         import numpy as np
         import tensorflow as tf
     except ImportError:
-        print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
             "https://www.tensorflow.org/install/ for installation instructions.")
         raise
     tf_path = os.path.abspath(tf_checkpoint_path)
-    print("Converting TensorFlow checkpoint from {}".format(tf_path))
+    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
     # Load weights from TF model
     init_vars = tf.train.list_variables(tf_path)
     names = []
     arrays = []
     for name, shape in init_vars:
-        print("Loading TF weight {} with shape {}".format(name, shape))
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
         array = tf.train.load_variable(tf_path, name)
         names.append(name)
         arrays.append(array)
@@ -93,7 +93,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
         # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
         # which are not required for using pretrained model
         if any(n in ["adam_v", "adam_m", "global_step"] for n in name):
-            print("Skipping {}".format("/".join(name)))
+            logger.info("Skipping {}".format("/".join(name)))
             continue
         pointer = model
         for m_name in name:
@@ -113,7 +113,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
                 try:
                     pointer = getattr(pointer, l[0])
                 except AttributeError:
-                    print("Skipping {}".format("/".join(name)))
+                    logger.info("Skipping {}".format("/".join(name)))
                     continue
             if len(l) >= 2:
                 num = int(l[1])
@@ -127,7 +127,7 @@ def load_tf_weights_in_bert(model, config, tf_checkpoint_path):
         except AssertionError as e:
             e.args += (pointer.shape, array.shape)
             raise
-        print("Initialize PyTorch weight {}".format(name))
+        logger.info("Initialize PyTorch weight {}".format(name))
         pointer.data = torch.from_numpy(array)
     return model
 

pytorch_transformers/modeling_gpt2.py

@@ -49,17 +49,17 @@ def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
         import numpy as np
         import tensorflow as tf
     except ImportError:
-        print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
             "https://www.tensorflow.org/install/ for installation instructions.")
         raise
     tf_path = os.path.abspath(gpt2_checkpoint_path)
-    print("Converting TensorFlow checkpoint from {}".format(tf_path))
+    logger.info("Converting TensorFlow checkpoint from {}".format(tf_path))
     # Load weights from TF model
     init_vars = tf.train.list_variables(tf_path)
     names = []
     arrays = []
     for name, shape in init_vars:
-        print("Loading TF weight {} with shape {}".format(name, shape))
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
         array = tf.train.load_variable(tf_path, name)
         names.append(name)
         arrays.append(array.squeeze())
@@ -90,7 +90,7 @@ def load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path):
         except AssertionError as e:
             e.args += (pointer.shape, array.shape)
             raise
-        print("Initialize PyTorch weight {}".format(name))
+        logger.info("Initialize PyTorch weight {}".format(name))
         pointer.data = torch.from_numpy(array)
     return model
 

pytorch_transformers/modeling_openai.py

@@ -110,7 +110,7 @@ def load_tf_weights_in_openai_gpt(model, config, openai_checkpoint_folder_path):
         except AssertionError as e:
             e.args += (pointer.shape, array.shape)
             raise
-        print("Initialize PyTorch weight {}".format(name))
+        logger.info("Initialize PyTorch weight {}".format(name))
         pointer.data = torch.from_numpy(array)
     return model
 

pytorch_transformers/modeling_transfo_xl.py

@@ -126,7 +126,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
         import numpy as np
         import tensorflow as tf
     except ImportError:
-        print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
             "https://www.tensorflow.org/install/ for installation instructions.")
         raise
     # Build TF to PyTorch weights loading map
@@ -136,7 +136,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
     init_vars = tf.train.list_variables(tf_path)
     tf_weights = {}
     for name, shape in init_vars:
-        print("Loading TF weight {} with shape {}".format(name, shape))
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
         array = tf.train.load_variable(tf_path, name)
         tf_weights[name] = array
 
@@ -157,7 +157,7 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
                 except AssertionError as e:
                     e.args += (p_i.shape, arr_i.shape)
                     raise
-                print("Initialize PyTorch weight {} for layer {}".format(name, i))
+                logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
                 p_i.data = torch.from_numpy(arr_i)
         else:
             try:
@@ -165,13 +165,13 @@ def load_tf_weights_in_transfo_xl(model, config, tf_path):
             except AssertionError as e:
                 e.args += (pointer.shape, array.shape)
                 raise
-            print("Initialize PyTorch weight {}".format(name))
+            logger.info("Initialize PyTorch weight {}".format(name))
             pointer.data = torch.from_numpy(array)
         tf_weights.pop(name, None)
         tf_weights.pop(name + '/Adam', None)
         tf_weights.pop(name + '/Adam_1', None)
 
-    print("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
+    logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
     return model
 
 

pytorch_transformers/modeling_transfo_xl_utilities.py

@@ -272,7 +272,6 @@ class LogUniformSampler(object):
             self.range_max = range_max
             log_indices = torch.arange(1., range_max+2., 1.).log_()
             self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
-            # print('P', self.dist.numpy().tolist()[-30:])
 
             self.log_q = (- (-self.dist.double().log1p_() * 2 * n_sample).expm1_()).log_().float()
 
@@ -331,72 +330,3 @@ def sample_logits(embedding, bias, labels, inputs, sampler):
     logits = torch.cat([true_logits[:, :, None], sample_logits], -1)
 
     return logits
-
-
-# class LogUniformSampler(object):
-#     def __init__(self, range_max, unique=False):
-#         """
-#         Reference : https://github.com/tensorflow/tensorflow/blob/r1.10/tensorflow/python/ops/candidate_sampling_ops.py
-#             `P(class) = (log(class + 2) - log(class + 1)) / log(range_max + 1)`
-#         """
-#         self.range_max = range_max
-#         log_indices = torch.arange(1., range_max+2., 1.).log_()
-#         self.dist = (log_indices[1:] - log_indices[:-1]) / log_indices[-1]
-
-#         self.unique = unique
-
-#         if self.unique:
-#             self.exclude_mask = torch.ByteTensor(range_max).fill_(0)
-
-#     def sample(self, n_sample, labels):
-#         pos_sample, new_labels = labels.unique(return_inverse=True)
-#         n_pos_sample = pos_sample.size(0)
-#         n_neg_sample = n_sample - n_pos_sample
-
-#         if self.unique:
-#             self.exclude_mask.index_fill_(0, pos_sample, 1)
-#             sample_dist = self.dist.clone().masked_fill_(self.exclude_mask, 0)
-#             self.exclude_mask.index_fill_(0, pos_sample, 0)
-#         else:
-#             sample_dist = self.dist
-
-#         neg_sample = torch.multinomial(sample_dist, n_neg_sample)
-
-#         sample = torch.cat([pos_sample, neg_sample])
-#         sample_prob = self.dist[sample]
-
-#         return new_labels, sample, sample_prob
-
-
-if __name__ == '__main__':
-    S, B = 3, 4
-    n_vocab = 10000
-    n_sample = 5
-    H = 32
-
-    labels = torch.LongTensor(S, B).random_(0, n_vocab)
-
-    # sampler = LogUniformSampler(n_vocab, unique=False)
-    # new_labels, sample, sample_prob = sampler.sample(n_sample, labels)
-
-    sampler = LogUniformSampler(n_vocab, n_sample)#, unique=True)
-    # true_probs, samp_probs, neg_samples = sampler.sample(n_sample, labels)
-
-    # print('true_probs', true_probs.numpy().tolist())
-    # print('samp_probs', samp_probs.numpy().tolist())
-    # print('neg_samples', neg_samples.numpy().tolist())
-
-    # print('sum', torch.sum(sampler.dist).item())
-
-    # assert torch.all(torch.sort(sample.unique())[0].eq(torch.sort(sample)[0])).item()
-
-    embedding = nn.Embedding(n_vocab, H)
-    bias = torch.zeros(n_vocab)
-    inputs = torch.Tensor(S, B, H).normal_()
-
-    logits, out_labels = sample_logits(embedding, bias, labels, inputs, sampler, n_sample)
-    print('logits', logits.detach().numpy().tolist())
-    print('logits shape', logits.size())
-    print('out_labels', out_labels.detach().numpy().tolist())
-    print('out_labels shape', out_labels.size())
-

pytorch_transformers/modeling_utils.py

@@ -57,16 +57,18 @@ class PretrainedConfig(object):
             pretrained_model_name_or_path: either:
                 - a str with the name of a pre-trained model to load selected in the list of:
                     . `xlnet-large-cased`
-                - a path or url to a pretrained model archive containing:
-                    . `config.json` a configuration file for the model
+                - a path or url to a directory containing a configuration file `config.json` for the model,
+                - a path or url to a configuration file for the model.
             cache_dir: an optional path to a folder in which the pre-trained model configuration will be cached.
         """
         cache_dir = kwargs.pop('cache_dir', None)
 
         if pretrained_model_name_or_path in cls.pretrained_config_archive_map:
             config_file = cls.pretrained_config_archive_map[pretrained_model_name_or_path]
-        else:
+        elif os.path.isdir(pretrained_model_name_or_path):
             config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
+        else:
+            config_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
             resolved_config_file = cached_path(config_file, cache_dir=cache_dir)
@@ -200,6 +202,7 @@ class PreTrainedModel(nn.Module):
                 - a path or url to a tensorflow pretrained model checkpoint containing:
                     . `config.json` a configuration file for the model
                     . `model.chkpt` a TensorFlow checkpoint
+            config: an optional configuration for the model
             from_tf: should we load the weights from a locally saved TensorFlow checkpoint
             cache_dir: an optional path to a folder in which the pre-trained models will be cached.
             state_dict: an optional state dictionnary (collections.OrderedDict object) to use
@@ -207,23 +210,31 @@ class PreTrainedModel(nn.Module):
             *inputs, **kwargs: additional input for the specific XLNet class
                 (ex: num_labels for XLNetForSequenceClassification)
         """
+        config = kwargs.pop('config', None)
         state_dict = kwargs.pop('state_dict', None)
         cache_dir = kwargs.pop('cache_dir', None)
         from_tf = kwargs.pop('from_tf', False)
         output_loading_info = kwargs.pop('output_loading_info', False)
 
         # Load config
+        if config is None:
             config = cls.config_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
 
         # Load model
         if pretrained_model_name_or_path in cls.pretrained_model_archive_map:
             archive_file = cls.pretrained_model_archive_map[pretrained_model_name_or_path]
-        else:
+        elif os.path.isdir(pretrained_model_name_or_path):
             if from_tf:
                 # Directly load from a TensorFlow checkpoint
                 archive_file = os.path.join(pretrained_model_name_or_path, TF_WEIGHTS_NAME + ".index")
             else:
                 archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
+        else:
+            if from_tf:
+                # Directly load from a TensorFlow checkpoint
+                archive_file = pretrained_model_name_or_path + ".index"
+            else:
+                archive_file = pretrained_model_name_or_path
         # redirect to the cache, if necessary
         try:
             resolved_archive_file = cached_path(archive_file, cache_dir=cache_dir)

pytorch_transformers/modeling_xlnet.py

@@ -122,14 +122,14 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
         import numpy as np
         import tensorflow as tf
     except ImportError:
-        print("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
+        logger.error("Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
             "https://www.tensorflow.org/install/ for installation instructions.")
         raise
     # Load weights from TF model
     init_vars = tf.train.list_variables(tf_path)
     tf_weights = {}
     for name, shape in init_vars:
-        print("Loading TF weight {} with shape {}".format(name, shape))
+        logger.info("Loading TF weight {} with shape {}".format(name, shape))
         array = tf.train.load_variable(tf_path, name)
         tf_weights[name] = array
 
@@ -137,15 +137,15 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
     tf_to_pt_map = build_tf_xlnet_to_pytorch_map(model, config, tf_weights)
 
     for name, pointer in tf_to_pt_map.items():
-        print("Importing {}".format(name))
+        logger.info("Importing {}".format(name))
         if name not in tf_weights:
-            print("{} not in tf pre-trained weights, skipping".format(name))
+            logger.info("{} not in tf pre-trained weights, skipping".format(name))
             continue
         array = tf_weights[name]
         # adam_v and adam_m are variables used in AdamWeightDecayOptimizer to calculated m and v
         # which are not required for using pretrained model
         if 'kernel' in name and ('ff' in name or 'summary' in name or 'logit' in name):
-            print("Transposing")
+            logger.info("Transposing")
             array = np.transpose(array)
         if isinstance(pointer, list):
             # Here we will split the TF weigths
@@ -157,7 +157,7 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
                 except AssertionError as e:
                     e.args += (p_i.shape, arr_i.shape)
                     raise
-                print("Initialize PyTorch weight {} for layer {}".format(name, i))
+                logger.info("Initialize PyTorch weight {} for layer {}".format(name, i))
                 p_i.data = torch.from_numpy(arr_i)
         else:
             try:
@@ -165,13 +165,13 @@ def load_tf_weights_in_xlnet(model, config, tf_path):
             except AssertionError as e:
                 e.args += (pointer.shape, array.shape)
                 raise
-            print("Initialize PyTorch weight {}".format(name))
+            logger.info("Initialize PyTorch weight {}".format(name))
             pointer.data = torch.from_numpy(array)
         tf_weights.pop(name, None)
         tf_weights.pop(name + '/Adam', None)
         tf_weights.pop(name + '/Adam_1', None)
 
-    print("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
+    logger.info("Weights not copied to PyTorch model: {}".format(', '.join(tf_weights.keys())))
     return model
 
 

pytorch_transformers/tokenization_transfo_xl.py

@@ -98,14 +98,14 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
             self.build_vocab()
 
     def count_file(self, path, verbose=False, add_eos=False):
-        if verbose: print('counting file {} ...'.format(path))
+        if verbose: logger.info('counting file {} ...'.format(path))
         assert os.path.exists(path)
 
         sents = []
         with open(path, 'r', encoding='utf-8') as f:
             for idx, line in enumerate(f):
                 if verbose and idx > 0 and idx % 500000 == 0:
-                    print('    line {}'.format(idx))
+                    logger.info('    line {}'.format(idx))
                 symbols = self.tokenize(line, add_eos=add_eos)
                 self.counter.update(symbols)
                 sents.append(symbols)
@@ -116,10 +116,10 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         """
             sents : a list of sentences, each a list of tokenized symbols
         """
-        if verbose: print('counting {} sents ...'.format(len(sents)))
+        if verbose: logger.info('counting {} sents ...'.format(len(sents)))
         for idx, symbols in enumerate(sents):
             if verbose and idx > 0 and idx % 500000 == 0:
-                print('    line {}'.format(idx))
+                logger.info('    line {}'.format(idx))
             self.counter.update(symbols)
 
     def _build_from_file(self, vocab_file):
@@ -147,11 +147,11 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
 
     def build_vocab(self):
         if self.vocab_file:
-            print('building vocab from {}'.format(self.vocab_file))
+            logger.info('building vocab from {}'.format(self.vocab_file))
             self._build_from_file(self.vocab_file)
-            print('final vocab size {}'.format(len(self)))
+            logger.info('final vocab size {}'.format(len(self)))
         else:
-            print('building vocab with min_freq={}, max_size={}'.format(
+            logger.info('building vocab with min_freq={}, max_size={}'.format(
                 self.min_freq, self.max_size))
             self.idx2sym = []
             self.sym2idx = OrderedDict()
@@ -163,18 +163,18 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
                 if cnt < self.min_freq: break
                 self.add_symbol(sym)
 
-            print('final vocab size {} from {} unique tokens'.format(
+            logger.info('final vocab size {} from {} unique tokens'.format(
                 len(self), len(self.counter)))
 
     def encode_file(self, path, ordered=False, verbose=False, add_eos=True,
             add_double_eos=False):
-        if verbose: print('encoding file {} ...'.format(path))
+        if verbose: logger.info('encoding file {} ...'.format(path))
         assert os.path.exists(path)
         encoded = []
         with open(path, 'r', encoding='utf-8') as f:
             for idx, line in enumerate(f):
                 if verbose and idx > 0 and idx % 500000 == 0:
-                    print('    line {}'.format(idx))
+                    logger.info('    line {}'.format(idx))
                 symbols = self.tokenize(line, add_eos=add_eos,
                     add_double_eos=add_double_eos)
                 encoded.append(self.convert_to_tensor(symbols))
@@ -185,11 +185,11 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         return encoded
 
     def encode_sents(self, sents, ordered=False, verbose=False):
-        if verbose: print('encoding {} sents ...'.format(len(sents)))
+        if verbose: logger.info('encoding {} sents ...'.format(len(sents)))
         encoded = []
         for idx, symbols in enumerate(sents):
             if verbose and idx > 0 and idx % 500000 == 0:
-                print('    line {}'.format(idx))
+                logger.info('    line {}'.format(idx))
             encoded.append(self.convert_to_tensor(symbols))
 
         if ordered:
@@ -218,7 +218,7 @@ class TransfoXLTokenizer(PreTrainedTokenizer):
         if sym in self.sym2idx:
             return self.sym2idx[sym]
         else:
-            # print('encounter unk {}'.format(sym))
+            # logger.info('encounter unk {}'.format(sym))
             # assert '<eos>' not in sym
             if hasattr(self, 'unk_idx'):
                 return self.sym2idx.get(sym, self.unk_idx)
@@ -544,14 +544,14 @@ def get_lm_corpus(datadir, dataset):
     fn = os.path.join(datadir, 'cache.pt')
     fn_pickle = os.path.join(datadir, 'cache.pkl')
     if os.path.exists(fn):
-        print('Loading cached dataset...')
+        logger.info('Loading cached dataset...')
         corpus = torch.load(fn_pickle)
     elif os.path.exists(fn):
-        print('Loading cached dataset from pickle...')
+        logger.info('Loading cached dataset from pickle...')
         with open(fn, "rb") as fp:
             corpus = pickle.load(fp)
     else:
-        print('Producing dataset {}...'.format(dataset))
+        logger.info('Producing dataset {}...'.format(dataset))
         kwargs = {}
         if dataset in ['wt103', 'wt2']:
             kwargs['special'] = ['<eos>']