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run_classifier WIP

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thomwolf 2018-11-01 21:05:04 +01:00
parent 7af7f8173b
commit 4a0b59e980
3 changed files with 46 additions and 68 deletions
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2
modeling_pytorch.py
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@ -237,7 +237,7 @@ class BERTSelfAttention(nn.Module):
context_layer = torch.matmul(attention_probs, value_layer)
context_layer = context_layer.permute(0, 2, 1, 3).contiguous()
new_context_layer_shape = context_layer.size()[:-2] + (self.all_head_size,)
context_layer = context_layer.view(*new_x_shape)
context_layer = context_layer.view(*new_context_layer_shape)
return context_layer

4
optimization_pytorch.py
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@ -22,7 +22,7 @@ SCHEDULES = {
}
class OpenAIAdam(Optimizer):
class BERTAdam(Optimizer):
"""Implements Open AI version of Adam algorithm with weight decay fix.
"""
def __init__(self, params, lr, schedule, warmup, t_total,
@ -43,7 +43,7 @@ class OpenAIAdam(Optimizer):
defaults = dict(lr=lr, schedule=schedule, warmup=warmup, t_total=t_total,
b1=b1, b2=b2, e=e, l2=l2, vector_l2=vector_l2,
max_grad_norm=max_grad_norm)
super(OpenAIAdam, self).__init__(params, defaults)
super(BERTAdam, self).__init__(params, defaults)
def get_lr(self):
lr = []

84
run_classifier_pytorch.py
View File

@ -20,7 +20,8 @@ from __future__ import print_function
import csv
import os
import modeling_pytorch
from modeling_pytorch import BertConfig, BertModel
from optimization_pytorch import BERTAdam
# import optimization
import tokenization_pytorch
import torch
@ -116,10 +117,16 @@ parser.add_argument("--iterations_per_loop",
type = int,
help = "How many steps to make in each estimator call.")
parser.add_argument("--use_gpu",
default = True,
parser.add_argument("--no_cuda",
default = False,
type = bool,
help = "Whether to use GPU")
help = "Whether not to use CUDA when available")
parser.add_argument("--local_rank",
type=int,
default=-1,
help = "local_rank for distributed training on gpus")
### BEGIN - TO DELETE EVENTUALLY --> NO SENSE IN PYTORCH ###
parser.add_argument("--use_tpu",
default = False,
@ -422,18 +429,6 @@ def _truncate_seq_pair(tokens_a, tokens_b, max_length):
tokens_b.pop()
def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
labels, num_labels, use_one_hot_embeddings):
raise NotImplementedError()
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_gpu,
use_one_hot_embeddings):
raise NotImplementedError()
### ATTENTION - I removed the `use_tpu` argument
def input_fn_builder(features, seq_length, is_training, drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator.""" ### ATTENTION - To rewrite ###
@ -478,10 +473,19 @@ def main(_):
"mrpc": MrpcProcessor,
}
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu")
n_gpu = torch.cuda.device_count()
else:
device = torch.device("cuda", args.local_rank)
n_gpu = 1
print("Initializing the distributed backend: NCCL")
print("device", device, "n_gpu", n_gpu)
if not args.do_train and not args.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling_pytorch.BertConfig.from_json_file(args.bert_config_file)
bert_config = BertConfig.from_json_file(args.bert_config_file)
if args.max_seq_length > bert_config.max_position_embeddings:
raise ValueError(
@ -506,49 +510,24 @@ def main(_):
tokenizer = tokenization_pytorch.FullTokenizer(
vocab_file=args.vocab_file, do_lower_case=args.do_lower_case)
# tpu_cluster_resolver = None
# if FLAGS.use_tpu and FLAGS.tpu_name:
# tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
# FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
# is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
# run_config = tf.contrib.tpu.RunConfig(
# cluster=tpu_cluster_resolver,
# master=FLAGS.master,
# model_dir=FLAGS.output_dir,
# save_checkpoints_steps=FLAGS.save_checkpoints_steps,
# tpu_config=tf.contrib.tpu.TPUConfig(
# iterations_per_loop=FLAGS.iterations_per_loop,
# num_shards=FLAGS.num_tpu_cores,
# per_host_input_for_training=is_per_host))
train_examples = None
num_train_steps = None
num_warmup_steps = None
if args.do_train:
train_examples = processor.get_train_examples(args.data_dir)
num_train_steps = int(
len(train_examples) / args.train_batch_size * args.num_train_epochs)
num_warmup_steps = int(num_train_steps * args.warmup_proportion)
model_fn = model_fn_builder(
bert_config=bert_config,
num_labels=len(label_list),
init_checkpoint=args.init_checkpoint,
learning_rate=args.learning_rate,
num_train_steps=num_train_steps,
num_warmup_steps=num_warmup_steps,
use_gpu=args.use_gpu,
use_one_hot_embeddings=args.use_gpu) ### TO DO - to check when model_fn is written)
model = BertModel(bert_config)
if args.init_checkpoint is not None:
model.load_state_dict(torch.load(args.init_checkpoint, map_location='cpu'))
model.to(device)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU. - TO DO
# estimator = tf.contrib.tpu.TPUEstimator(
# use_tpu=args.use_tpu,
# model_fn=model_fn,
# config=run_config,
# train_batch_size=args.train_batch_size,
# eval_batch_size=args.eval_batch_size)
optimizer = BERTAdam([{'params': [p for n, p in model.named_parameters() if n != 'bias'], 'l2': 0.01},
{'params': [p for n, p in model.named_parameters() if n != 'bias']}
],
lr=args.learning_rate, schedule='warmup_linear',
warmup=args.warmup_proportion,
t_total=num_train_steps)
if args.do_train:
train_features = convert_examples_to_features(
@ -606,4 +585,3 @@ def main(_):
if __name__ == "__main__":
main()
return None