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提交 c388ae35 编写于 作者: L liuyibing01
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BERT/README.md
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BERT/_ce.py 已删除 100644 → 0
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####this file is only used for continuous evaluation test!
import os
import sys
sys.path.insert(0, os.environ['ceroot'])
#sys.path.append('.')
from kpi import CostKpi, DurationKpi, AccKpi
#### NOTE kpi.py should shared in models in some way!!!!
train_cost_xnli_card1_kpi = CostKpi('train_cost_xnli_card1', 0.002, 0, actived=True)
train_acc_xnli_card1_kpi = AccKpi('train_acc_xnli_card1', 0.002, 0, actived=True)
train_duration_xnli_card1_kpi = DurationKpi(
'train_duration_xnli_card1', 0.01, 0, actived=True)
train_cost_xnli_card4_kpi = CostKpi('train_cost_xnli_card4', 0.002, 0, actived=True)
train_acc_xnli_card4_kpi = AccKpi('train_acc_xnli_card4', 0.02, 0, actived=True)
train_duration_xnli_card4_kpi = DurationKpi(
'train_duration_xnli_card4', 0.03, 0, actived=True)
tracking_kpis = [
train_cost_xnli_card1_kpi,
train_acc_xnli_card1_kpi,
train_duration_xnli_card1_kpi,
train_cost_xnli_card4_kpi,
train_acc_xnli_card4_kpi,
train_duration_xnli_card4_kpi,
]
def parse_log(log):
'''
This method should be implemented by model developers.
The suggestion:
each line in the log should be key, value, for example:
"
train_cost\t1.0
test_cost\t1.0
train_cost\t1.0
train_cost\t1.0
train_acc\t1.2
"
'''
for line in log.split('\n'):
fs = line.strip().split('\t')
print(fs)
if len(fs) == 3 and fs[0] == 'kpis':
print("-----%s" % fs)
kpi_name = fs[1]
kpi_value = float(fs[2])
yield kpi_name, kpi_value
def log_to_ce(log):
kpi_tracker = {}
for kpi in tracking_kpis:
kpi_tracker[kpi.name] = kpi
for (kpi_name, kpi_value) in parse_log(log):
print(kpi_name, kpi_value)
kpi_tracker[kpi_name].add_record(kpi_value)
kpi_tracker[kpi_name].persist()
if __name__ == '__main__':
log = sys.stdin.read()
print("*****")
print(log)
print("****")
log_to_ce(log)
BERT/batching.py 已删除 100644 → 0
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Mask, padding and batching."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def mask(batch_tokens, total_token_num, vocab_size, CLS=1, SEP=2, MASK=3):
"""
Add mask for batch_tokens, return out, mask_label, mask_pos;
Note: mask_pos responding the batch_tokens after padded;
"""
max_len = max([len(sent) for sent in batch_tokens])
mask_label = []
mask_pos = []
prob_mask = np.random.rand(total_token_num)
# Note: the first token is [CLS], so [low=1]
replace_ids = np.random.randint(1, high=vocab_size, size=total_token_num)
pre_sent_len = 0
prob_index = 0
for sent_index, sent in enumerate(batch_tokens):
mask_flag = False
prob_index += pre_sent_len
for token_index, token in enumerate(sent):
prob = prob_mask[prob_index + token_index]
if prob > 0.15:
continue
elif 0.03 < prob <= 0.15:
# mask
if token != SEP and token != CLS:
mask_label.append(sent[token_index])
sent[token_index] = MASK
mask_flag = True
mask_pos.append(sent_index * max_len + token_index)
elif 0.015 < prob <= 0.03:
# random replace
if token != SEP and token != CLS:
mask_label.append(sent[token_index])
sent[token_index] = replace_ids[prob_index + token_index]
mask_flag = True
mask_pos.append(sent_index * max_len + token_index)
else:
# keep the original token
if token != SEP and token != CLS:
mask_label.append(sent[token_index])
mask_pos.append(sent_index * max_len + token_index)
pre_sent_len = len(sent)
# ensure at least mask one word in a sentence
while not mask_flag:
token_index = int(np.random.randint(1, high=len(sent) - 1, size=1))
if sent[token_index] != SEP and sent[token_index] != CLS:
mask_label.append(sent[token_index])
sent[token_index] = MASK
mask_flag = True
mask_pos.append(sent_index * max_len + token_index)
mask_label = np.array(mask_label).astype("int64").reshape([-1, 1])
mask_pos = np.array(mask_pos).astype("int64").reshape([-1, 1])
return batch_tokens, mask_label, mask_pos
def prepare_batch_data(insts,
total_token_num,
voc_size=0,
pad_id=None,
cls_id=None,
sep_id=None,
mask_id=None,
return_input_mask=True,
return_max_len=True,
return_num_token=False):
"""
1. generate Tensor of data
2. generate Tensor of position
3. generate self attention mask, [shape: batch_size * max_len * max_len]
"""
batch_src_ids = [inst[0] for inst in insts]
batch_sent_ids = [inst[1] for inst in insts]
batch_pos_ids = [inst[2] for inst in insts]
labels_list = []
# compatible with squad, whose example includes start/end positions,
# or unique id
for i in range(3, len(insts[0]), 1):
labels = [inst[i] for inst in insts]
labels = np.array(labels).astype("int64").reshape([-1, 1])
labels_list.append(labels)
# First step: do mask without padding
if mask_id >= 0:
out, mask_label, mask_pos = mask(
batch_src_ids,
total_token_num,
vocab_size=voc_size,
CLS=cls_id,
SEP=sep_id,
MASK=mask_id)
else:
out = batch_src_ids
# Second step: padding
src_id, self_input_mask = pad_batch_data(
out, pad_idx=pad_id, return_input_mask=True)
pos_id = pad_batch_data(
batch_pos_ids,
pad_idx=pad_id,
return_pos=False,
return_input_mask=False)
sent_id = pad_batch_data(
batch_sent_ids,
pad_idx=pad_id,
return_pos=False,
return_input_mask=False)
if mask_id >= 0:
return_list = [
src_id, pos_id, sent_id, self_input_mask, mask_label, mask_pos
] + labels_list
else:
return_list = [src_id, pos_id, sent_id, self_input_mask] + labels_list
return return_list if len(return_list) > 1 else return_list[0]
def pad_batch_data(insts,
pad_idx=0,
return_pos=False,
return_input_mask=False,
return_max_len=False,
return_num_token=False):
"""
Pad the instances to the max sequence length in batch, and generate the
corresponding position data and input mask.
"""
return_list = []
max_len = max(len(inst) for inst in insts)
# Any token included in dict can be used to pad, since the paddings' loss
# will be masked out by weights and make no effect on parameter gradients.
inst_data = np.array([
list(inst) + list([pad_idx] * (max_len - len(inst))) for inst in insts
])
return_list += [inst_data.astype("int64").reshape([-1, max_len, 1])]
# position data
if return_pos:
inst_pos = np.array([
list(range(0, len(inst))) + [pad_idx] * (max_len - len(inst))
for inst in insts
])
return_list += [inst_pos.astype("int64").reshape([-1, max_len, 1])]
if return_input_mask:
# This is used to avoid attention on paddings.
input_mask_data = np.array([[1] * len(inst) + [0] *
(max_len - len(inst)) for inst in insts])
input_mask_data = np.expand_dims(input_mask_data, axis=-1)
return_list += [input_mask_data.astype("float32")]
if return_max_len:
return_list += [max_len]
if return_num_token:
num_token = 0
for inst in insts:
num_token += len(inst)
return_list += [num_token]
return return_list if len(return_list) > 1 else return_list[0]
if __name__ == "__main__":
pass
BERT/convert_params.py 已删除 100644 → 0
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Convert Google official BERT models to Fluid parameters."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import argparse
import collections
from utils.args import print_arguments
import tensorflow as tf
import paddle.fluid as fluid
from tensorflow.python import pywrap_tensorflow
def parse_args():
parser = argparse.ArgumentParser(__doc__)
parser.add_argument(
"--init_tf_checkpoint",
type=str,
required=True,
help="Initial TF checkpoint (a pre-trained BERT model).")
parser.add_argument(
"--fluid_params_dir",
type=str,
required=True,
help="The directory to store converted Fluid parameters.")
args = parser.parse_args()
return args
def parse(init_checkpoint):
tf_fluid_param_name_map = collections.OrderedDict()
tf_param_name_shape_map = collections.OrderedDict()
init_vars = tf.train.list_variables(init_checkpoint)
for (var_name, var_shape) in init_vars:
fluid_param_name = ''
if var_name.startswith('bert/'):
key = var_name[5:]
if (key.startswith('embeddings/')):
if (key.endswith('LayerNorm/gamma')):
fluid_param_name = 'pre_encoder_layer_norm_scale'
elif (key.endswith('LayerNorm/beta')):
fluid_param_name = 'pre_encoder_layer_norm_bias'
elif (key.endswith('position_embeddings')):
fluid_param_name = 'pos_embedding'
elif (key.endswith('word_embeddings')):
fluid_param_name = 'word_embedding'
elif (key.endswith('token_type_embeddings')):
fluid_param_name = 'sent_embedding'
else:
print("ignored param: %s" % var_name)
elif (key.startswith('encoder/')):
key = key[8:]
layer_num = int(key[key.find('_') + 1:key.find('/')])
suffix = "encoder_layer_" + str(layer_num)
if key.endswith('attention/output/LayerNorm/beta'):
fluid_param_name = suffix + '_post_att_layer_norm_bias'
elif key.endswith('attention/output/LayerNorm/gamma'):
fluid_param_name = suffix + '_post_att_layer_norm_scale'
elif key.endswith('attention/output/dense/bias'):
fluid_param_name = suffix + '_multi_head_att_output_fc.b_0'
elif key.endswith('attention/output/dense/kernel'):
fluid_param_name = suffix + '_multi_head_att_output_fc.w_0'
elif key.endswith('attention/self/key/bias'):
fluid_param_name = suffix + '_multi_head_att_key_fc.b_0'
elif key.endswith('attention/self/key/kernel'):
fluid_param_name = suffix + '_multi_head_att_key_fc.w_0'
elif key.endswith('attention/self/query/bias'):
fluid_param_name = suffix + '_multi_head_att_query_fc.b_0'
elif key.endswith('attention/self/query/kernel'):
fluid_param_name = suffix + '_multi_head_att_query_fc.w_0'
elif key.endswith('attention/self/value/bias'):
fluid_param_name = suffix + '_multi_head_att_value_fc.b_0'
elif key.endswith('attention/self/value/kernel'):
fluid_param_name = suffix + '_multi_head_att_value_fc.w_0'
elif key.endswith('intermediate/dense/bias'):
fluid_param_name = suffix + '_ffn_fc_0.b_0'
elif key.endswith('intermediate/dense/kernel'):
fluid_param_name = suffix + '_ffn_fc_0.w_0'
elif key.endswith('output/LayerNorm/beta'):
fluid_param_name = suffix + '_post_ffn_layer_norm_bias'
elif key.endswith('output/LayerNorm/gamma'):
fluid_param_name = suffix + '_post_ffn_layer_norm_scale'
elif key.endswith('output/dense/bias'):
fluid_param_name = suffix + '_ffn_fc_1.b_0'
elif key.endswith('output/dense/kernel'):
fluid_param_name = suffix + '_ffn_fc_1.w_0'
else:
print("ignored param: %s" % var_name)
elif (key.startswith('pooler/')):
if key.endswith('dense/bias'):
fluid_param_name = 'pooled_fc.b_0'
elif key.endswith('dense/kernel'):
fluid_param_name = 'pooled_fc.w_0'
else:
print("ignored param: %s" % var_name)
else:
print("ignored param: %s" % var_name)
elif var_name.startswith('cls/'):
if var_name == 'cls/predictions/output_bias':
fluid_param_name = 'mask_lm_out_fc.b_0'
elif var_name == 'cls/predictions/transform/LayerNorm/beta':
fluid_param_name = 'mask_lm_trans_layer_norm_bias'
elif var_name == 'cls/predictions/transform/LayerNorm/gamma':
fluid_param_name = 'mask_lm_trans_layer_norm_scale'
elif var_name == 'cls/predictions/transform/dense/bias':
fluid_param_name = 'mask_lm_trans_fc.b_0'
elif var_name == 'cls/predictions/transform/dense/kernel':
fluid_param_name = 'mask_lm_trans_fc.w_0'
elif var_name == 'cls/seq_relationship/output_bias':
fluid_param_name = 'next_sent_fc.b_0'
elif var_name == 'cls/seq_relationship/output_weights':
fluid_param_name = 'next_sent_fc.w_0'
elif var_name == 'cls/squad/output_weights':
fluid_param_name = 'cls_squad_out_w'
elif var_name == 'cls/squad/output_bias':
fluid_param_name = 'cls_squad_out_b'
else:
print("ignored param: %s" % var_name)
else:
if var_name == 'output_weights':
fluid_param_name = 'cls_out_w'
elif var_name == 'output_bias':
fluid_param_name = 'cls_out_b'
else:
print("ignored param: %s" % var_name)
if fluid_param_name != '':
tf_fluid_param_name_map[var_name] = fluid_param_name
tf_param_name_shape_map[var_name] = var_shape
fluid_param_name = ''
return tf_fluid_param_name_map, tf_param_name_shape_map
def convert(args):
tf_fluid_param_name_map, tf_param_name_shape_map = parse(
args.init_tf_checkpoint)
program = fluid.Program()
global_block = program.global_block()
for param in tf_fluid_param_name_map:
global_block.create_parameter(
name=tf_fluid_param_name_map[param],
shape=tf_param_name_shape_map[param],
dtype='float32',
initializer=fluid.initializer.Constant(value=0.0))
place = fluid.core.CPUPlace()
exe = fluid.Executor(place)
exe.run(program)
print('---------------------- Converted Parameters -----------------------')
print('###### [TF param name] --> [Fluid param name] [param shape] ######')
print('-------------------------------------------------------------------')
reader = pywrap_tensorflow.NewCheckpointReader(args.init_tf_checkpoint)
for param in tf_fluid_param_name_map:
value = reader.get_tensor(param)
if param == 'cls/seq_relationship/output_weights':
value = np.transpose(value)
if param == 'cls/squad/output_weights':
value = np.transpose(value)
if param == 'output_weights':
value = np.transpose(value)
fluid.global_scope().find_var(tf_fluid_param_name_map[
param]).get_tensor().set(value, place)
print(param, ' --> ', tf_fluid_param_name_map[param], ' ', value.shape)
fluid.io.save_params(exe, args.fluid_params_dir, main_program=program)
if __name__ == '__main__':
args = parse_args()
print_arguments(args)
convert(args)
BERT/data/demo_config/bert_config.json 已删除 100644 → 0
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{
"attention_probs_dropout_prob": 0.1,
"directionality": "bidi",
"hidden_act": "gelu",
"hidden_dropout_prob": 0.1,
"hidden_size": 768,
"initializer_range": 0.02,
"intermediate_size": 3072,
"max_position_embeddings": 512,
"num_attention_heads": 12,
"num_hidden_layers": 12,
"pooler_fc_size": 768,
"pooler_num_attention_heads": 12,
"pooler_num_fc_layers": 3,
"pooler_size_per_head": 128,
"pooler_type": "first_token_transform",
"type_vocab_size": 2,
"vocab_size": 21128
}
BERT/data/demo_config/vocab.txt 已删除 100644 → 0
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BERT/data/demo_wiki_tokens.txt 已删除 100644 → 0
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BERT/dist_utils.py 已删除 100644 → 0
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#Licensed under the Apache License, Version 2.0 (the "License");
#you may not use this file except in compliance with the License.
#You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
#Unless required by applicable law or agreed to in writing, software
#distributed under the License is distributed on an "AS IS" BASIS,
#WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#See the License for the specific language governing permissions and
#limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import paddle.fluid as fluid
def nccl2_prepare(trainer_id, startup_prog, main_prog):
config = fluid.DistributeTranspilerConfig()
config.mode = "nccl2"
t = fluid.DistributeTranspiler(config=config)
t.transpile(trainer_id,
trainers=os.environ.get('PADDLE_TRAINER_ENDPOINTS'),
current_endpoint=os.environ.get('PADDLE_CURRENT_ENDPOINT'),
startup_program=startup_prog,
program=main_prog)
def prepare_for_multi_process(exe, build_strategy, train_prog):
# prepare for multi-process
trainer_id = int(os.environ.get('PADDLE_TRAINER_ID', 0))
num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
if num_trainers < 2: return
print("PADDLE_TRAINERS_NUM", num_trainers)
print("PADDLE_TRAINER_ID", trainer_id)
build_strategy.num_trainers = num_trainers
build_strategy.trainer_id = trainer_id
# NOTE(zcd): use multi processes to train the model,
# and each process use one GPU card.
startup_prog = fluid.Program()
nccl2_prepare(trainer_id, startup_prog, train_prog)
# the startup_prog are run two times, but it doesn't matter.
exe.run(startup_prog)
BERT/inference/CMakeLists.txt 已删除 100644 → 0
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CMAKE_MINIMUM_REQUIRED(VERSION 3.2)
PROJECT(inference_demo)
SET(CMAKE_C_COMPILER gcc)
SET(CMAKE_CXX_COMPILER g++)
ADD_COMPILE_OPTIONS(-std=c++11 -D_GLIBCXX_USE_CXX11_ABI=0)
SET(FLUID_INFER_LIB fluid_inference)
SET(FLUID_INC_PATH ${FLUID_INFER_LIB}/paddle/include)
SET(FLUID_LIB_PATH ${FLUID_INFER_LIB}/paddle/lib)
SET(GLOG_INC_PATH ${FLUID_INFER_LIB}/third_party/install/glog/include)
SET(GLOG_LIB_PATH ${FLUID_INFER_LIB}/third_party/install/glog/lib)
SET(GFLAGS_INC_PATH ${FLUID_INFER_LIB}/third_party/install/gflags/include)
SET(GFLAGS_LIB_PATH ${FLUID_INFER_LIB}/third_party/install/gflags/lib)
SET(MKLDNN_LIB_PATH ${FLUID_INFER_LIB}/third_party/install/mkldnn/lib)
SET(MKLML_LIB_PATH ${FLUID_INFER_LIB}/third_party/install/mklml/lib)
INCLUDE_DIRECTORIES(${FLUID_INC_PATH})
INCLUDE_DIRECTORIES(${GLOG_INC_PATH})
INCLUDE_DIRECTORIES(${GFLAGS_INC_PATH})
LINK_DIRECTORIES(${FLUID_LIB_PATH})
LINK_DIRECTORIES(${GLOG_LIB_PATH})
LINK_DIRECTORIES(${GFLAGS_LIB_PATH})
LINK_DIRECTORIES(${MKLML_LIB_PATH})
LINK_DIRECTORIES(${MKLDNN_LIB_PATH})
ADD_EXECUTABLE(inference inference.cc)
TARGET_LINK_LIBRARIES(inference dl paddle_fluid glog gflags pthread)
BERT/inference/README.md 已删除 100644 → 0
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# BERT模型inference demo
## 数据预处理
实际应用场景中，模型部署之后用户还需要编写对应的程序对输入进行处理，然后把得到的数据传给模型进行预测。这里为了演示的需要，用 `gen_demo_data.py` 来进行数据处理，包括 tokenization，batching，numericalization，并且把处理后的数据输出为文本文件。使用方法如下：
``` bash
TASK_NAME="xnli"
DATA_PATH=/path/to/xnli/data/
BERT_BASE_PATH=/path/to/bert/pretrained/model/
python gen_demo_data.py \
--task_name ${TASK_NAME} \
--data_path ${DATA_PATH} \
--vocab_path "${BERT_BASE_PATH}/vocab.txt" \
--batch_size 4096 \
--in_tokens \
> data.txt
```
**生成的数据格式**
生成的数据一行代表一个 `batch`, 包含四个字段
```text
src_id, pos_id, segment_id, input_mask
```
字段之间按照分号(;)分隔，其中各字段内部 `shape` 和 `data` 按照冒号(:)分隔，`shape` 和 `data` 内部按空格分隔，`input_mask` 为 FLOAT32 类型，其余字段为 INT64 类型。
## 编译和运行
为了编译 inference demo，`C++` 编译器需要支持 `C++11` 标准。
首先下载对应的 [PaddlePaddle预测库](http://paddlepaddle.org/documentation/docs/zh/1.3/advanced_usage/deploy/inference/build_and_install_lib_cn.html) , 根据使用的 paddle 的版本和配置状况 (是否使用 avx, mkl, 以及 cuda, cudnn 版本) 选择下载对应的版本，并解压至 `inference` 目录，会得到 `fluid_inference` 子目录。
假设`paddle_infer_lib_path`是刚才解压得到的`fluid_inference`子目录的绝对路径，设置运行相关的环境变量(以 `cpu_avx_mkl` 版本为例)
``` bash
LD_LIBRARY_PATH=${paddle_infer_lib_path}/paddle/lib/:$LD_LIBRARY_PATH
LD_LIBRARY_PATH=${paddle_infer_lib_path}/third_party/install/mklml/lib:$LD_LIBRARY_PATH
LD_LIBRARY_PATH=${paddle_infer_lib_path}/third_party/install/mkldnn/lib:$LD_LIBRARY_PATH
export LD_LIBRARY_PATH
```
编译 demo
``` bash
mkdir build && cd build
cmake .. -DFLUID_INFER_LIB=${paddle_infer_lib_path}
make
```
这会在 `build` 目录下生成运行 `inference` 可执行文件。
运行 demo
```bash
./inference --logtostderr \
--model_dir $INFERENCE_MODEL_PATH \
--data $DATA_PATH \
--repeat $REPEAT_TIMES
--output_prediction \
--use_gpu \
```
参数 `repeat` 设置了执行预测的循环次数，一般在性能测试时可以设置其为大于 1 的某个整数，以观察多次预测的平均时间消耗。 在设置了 `output_prediction` 之后，预测程序会将每个样本的预测结果以概率的形式输出，其格式为：
```
样本id \t 类别0概率 \t 类别1概率 \t 类别2概率 ...
```
最后，在支持 NV GPUs 的环境中可以使能 `use_gpu`，否则就会在 CPU 上执行预测。
BERT/inference/gen_demo_data.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import sys
sys.path.append("..")
from reader import cls
def main():
args = parse_args()
task_name = args.task_name.lower()
processors = {
'xnli': cls.XnliProcessor,
'cola': cls.ColaProcessor,
'mrpc': cls.MrpcProcessor,
'mnli': cls.MnliProcessor,
}
processor = processors[task_name](data_dir=args.data_path,
vocab_path=args.vocab_path,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=args.in_tokens,
random_seed=args.random_seed)
example = processor.get_test_examples(args.data_path)[0]
gen = processor.data_generator(
args.batch_size, phase='test', epoch=1, shuffle=False)()
for i, data in enumerate(gen):
data = data[:4]
sample = []
for field in data:
shape_str = ' '.join(map(str, field.shape))
data_str = ' '.join(map(str, field.reshape(-1).tolist()))
sample.append(shape_str + ':' + data_str)
print(';'.join(sample))
def str2bool(v):
# because argparse does not support to parse "true, False" as python
# boolean directly
return v.lower() in ("true", "t", "1")
def parse_args():
parser = argparse.ArgumentParser(prog="bert data prepare")
parser.add_argument(
"--task_name",
type=str,
default='xnli',
choices=["xnli", "mnli", "cola", "mrpc"],
help="task name, used to specify data preprocessor")
parser.add_argument(
"--batch_size",
type=int,
default=4096,
help="batch size, see also --in_tokens")
parser.add_argument(
"--in_tokens",
action='store_true',
help="if set, batch_size means token number in a batch, otherwise "
"it means example number in a batch")
parser.add_argument(
'--do_lower_case',
type=str2bool,
default=True,
choices=[True, False],
help="Whether to lower case the input text. Should be True for uncased "
"models and False for cased models.")
parser.add_argument("--vocab_path", type=str, help="path of vocabulary")
parser.add_argument("--data_path", type=str, help="path of data to process")
parser.add_argument(
"--max_seq_len", type=int, default=128, help="max sequence length")
parser.add_argument(
"--random_seed", type=int, default=0, help="random seed")
return parser.parse_args()
if __name__ == "__main__":
main()
BERT/inference/inference.cc 已删除 100644 → 0
浏览文件 @ 546ce5e9
// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <paddle_inference_api.h>
#include <chrono>
#include <iostream>
#include <fstream>
#include <numeric>
#include <sstream>
#include <string>
#include <vector>
DEFINE_string(model_dir, "", "Inference model directory.");
DEFINE_string(data, "", "Input data path.");
DEFINE_int32(repeat, 1, "Repeat times.");
DEFINE_int32(num_labels, 3, "Number of labels.");
DEFINE_bool(output_prediction, false, "Whether to output the prediction results.");
DEFINE_bool(use_gpu, false, "Whether to use GPU for prediction.");
template <typename T>
void GetValueFromStream(std::stringstream *ss, T *t) {
(*ss) >> (*t);
}
template <>
void GetValueFromStream<std::string>(std::stringstream *ss, std::string *t) {
*t = ss->str();
}
// Split string to vector
template <typename T>
void Split(const std::string &line, char sep, std::vector<T> *v) {
std::stringstream ss;
T t;
for (auto c : line) {
if (c != sep) {
ss << c;
} else {
GetValueFromStream<T>(&ss, &t);
v->push_back(std::move(t));
ss.str({});
ss.clear();
}
}
if (!ss.str().empty()) {
GetValueFromStream<T>(&ss, &t);
v->push_back(std::move(t));
ss.str({});
ss.clear();
}
}
template <typename T>
constexpr paddle::PaddleDType GetPaddleDType();
template <>
constexpr paddle::PaddleDType GetPaddleDType<int64_t>() {
return paddle::PaddleDType::INT64;
}
template <>
constexpr paddle::PaddleDType GetPaddleDType<float>() {
return paddle::PaddleDType::FLOAT32;
}
// Parse tensor from string
template <typename T>
bool ParseTensor(const std::string &field, paddle::PaddleTensor *tensor) {
std::vector<std::string> data;
Split(field, ':', &data);
if (data.size() < 2) {
LOG(ERROR) << "parse tensor error!";
return false;
}
std::string shape_str = data[0];
std::vector<int> shape;
Split(shape_str, ' ', &shape);
std::string mat_str = data[1];
std::vector<T> mat;
Split(mat_str, ' ', &mat);
tensor->shape = shape;
auto size =
std::accumulate(shape.begin(), shape.end(), 1, std::multiplies<int>()) *
sizeof(T);
tensor->data.Resize(size);
std::copy(mat.begin(), mat.end(), static_cast<T *>(tensor->data.data()));
tensor->dtype = GetPaddleDType<T>();
return true;
}
// Parse input tensors from string
bool ParseLine(const std::string &line,
std::vector<paddle::PaddleTensor> *tensors) {
std::vector<std::string> fields;
Split(line, ';', &fields);
if (fields.size() < 4) return false;
tensors->clear();
tensors->reserve(4);
int i = 0;
// src_id
paddle::PaddleTensor src_id;
ParseTensor<int64_t>(fields[i++], &src_id);
tensors->push_back(src_id);
// pos_id
paddle::PaddleTensor pos_id;
ParseTensor<int64_t>(fields[i++], &pos_id);
tensors->push_back(pos_id);
// segment_id
paddle::PaddleTensor segment_id;
ParseTensor<int64_t>(fields[i++], &segment_id);
tensors->push_back(segment_id);
// input mask
paddle::PaddleTensor input_mask;
ParseTensor<float>(fields[i++], &input_mask);
tensors->push_back(input_mask);
return true;
}
template <typename T>
void PrintTensor(const paddle::PaddleTensor &t) {
std::stringstream ss;
ss.str({});
ss.clear();
ss << "Tensor: shape[";
for (auto i: t.shape) {
ss << i << " ";
}
ss << "], data[";
T *data = static_cast<T *>(t.data.data());
for (int i = 0; i < t.data.length() / sizeof(T); i++) {
ss << data[i] << " ";
}
ss << "]";
LOG(INFO) << ss.str();
}
void PrintInputs(const std::vector<paddle::PaddleTensor> &inputs) {
for (const auto &t : inputs) {
if (t.dtype == paddle::PaddleDType::INT64) {
PrintTensor<int64_t>(t);
} else {
PrintTensor<float>(t);
}
}
}
// Print outputs to log
void PrintOutputs(const std::vector<paddle::PaddleTensor> &outputs, int &cnt) {
for (size_t i = 0; i < outputs.front().data.length() / sizeof(float);
i += FLAGS_num_labels) {
std::cout << cnt << "\t";
for (size_t j = 0; j < FLAGS_num_labels; ++j) {
std::cout << static_cast<float *>(outputs.front().data.data())[i+j] << "\t";
}
std::cout << std::endl;
cnt += 1;
}
}
bool LoadInputData(std::vector<std::vector<paddle::PaddleTensor>> *inputs) {
if (FLAGS_data.empty()) {
LOG(ERROR) << "please set input data path";
return false;
}
std::ifstream fin(FLAGS_data);
std::string line;
int lineno = 0;
while (std::getline(fin, line)) {
std::vector<paddle::PaddleTensor> feed_data;
if (!ParseLine(line, &feed_data)) {
LOG(ERROR) << "Parse line[" << lineno << "] error!";
} else {
inputs->push_back(std::move(feed_data));
}
}
return true;
}
int main(int argc, char *argv[]) {
google::InitGoogleLogging(*argv);
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (FLAGS_model_dir.empty()) {
LOG(ERROR) << "please set model dir";
return -1;
}
paddle::NativeConfig config;
config.model_dir = FLAGS_model_dir;
if (FLAGS_use_gpu) {
config.use_gpu = true;
config.fraction_of_gpu_memory = 0.15;
config.device = 0;
}
auto predictor = CreatePaddlePredictor(config);
std::vector<std::vector<paddle::PaddleTensor>> inputs;
if (!LoadInputData(&inputs)) {
LOG(ERROR) << "load input data error!";
return -1;
}
std::vector<paddle::PaddleTensor> fetch;
int total_time{0};
int num_samples{0};
int out_cnt = 0;
for (int i = 0; i < FLAGS_repeat; i++) {
for (auto feed : inputs) {
fetch.clear();
auto start = std::chrono::system_clock::now();
predictor->Run(feed, &fetch);
if (FLAGS_output_prediction && i == 0) {
PrintOutputs(fetch, out_cnt);
}
auto end = std::chrono::system_clock::now();
if (!fetch.empty()) {
total_time +=
std::chrono::duration_cast<std::chrono::milliseconds>(end - start)
.count();
num_samples += fetch.front().data.length() / FLAGS_num_labels / sizeof(float);
}
}
}
auto per_sample_ms =
static_cast<float>(total_time) / num_samples;
LOG(INFO) << "Run on " << num_samples
<< " samples over "<< FLAGS_repeat << " times, average latency: " << per_sample_ms
<< "ms per sample.";
return 0;
}
BERT/model/bert.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""BERT model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
import json
import numpy as np
import paddle.fluid as fluid
from model.transformer_encoder import encoder, pre_process_layer
class BertConfig(object):
def __init__(self, config_path):
self._config_dict = self._parse(config_path)
def _parse(self, config_path):
try:
with open(config_path) as json_file:
config_dict = json.load(json_file)
except Exception:
raise IOError("Error in parsing bert model config file '%s'" %
config_path)
else:
return config_dict
def __getitem__(self, key):
return self._config_dict[key]
def print_config(self):
for arg, value in sorted(six.iteritems(self._config_dict)):
print('%s: %s' % (arg, value))
print('------------------------------------------------')
class BertModel(object):
def __init__(self,
src_ids,
position_ids,
sentence_ids,
input_mask,
config,
weight_sharing=True,
use_fp16=False):
self._emb_size = config['hidden_size']
self._n_layer = config['num_hidden_layers']
self._n_head = config['num_attention_heads']
self._voc_size = config['vocab_size']
self._max_position_seq_len = config['max_position_embeddings']
self._sent_types = config['type_vocab_size']
self._hidden_act = config['hidden_act']
self._prepostprocess_dropout = config['hidden_dropout_prob']
self._attention_dropout = config['attention_probs_dropout_prob']
self._weight_sharing = weight_sharing
self._word_emb_name = "word_embedding"
self._pos_emb_name = "pos_embedding"
self._sent_emb_name = "sent_embedding"
self._dtype = "float16" if use_fp16 else "float32"
# Initialize all weigths by truncated normal initializer, and all biases
# will be initialized by constant zero by default.
self._param_initializer = fluid.initializer.TruncatedNormal(
scale=config['initializer_range'])
self._build_model(src_ids, position_ids, sentence_ids, input_mask)
def _build_model(self, src_ids, position_ids, sentence_ids, input_mask):
# padding id in vocabulary must be set to 0
emb_out = fluid.layers.embedding(
input=src_ids,
size=[self._voc_size, self._emb_size],
dtype=self._dtype,
param_attr=fluid.ParamAttr(
name=self._word_emb_name, initializer=self._param_initializer),
is_sparse=False)
position_emb_out = fluid.layers.embedding(
input=position_ids,
size=[self._max_position_seq_len, self._emb_size],
dtype=self._dtype,
param_attr=fluid.ParamAttr(
name=self._pos_emb_name, initializer=self._param_initializer))
sent_emb_out = fluid.layers.embedding(
sentence_ids,
size=[self._sent_types, self._emb_size],
dtype=self._dtype,
param_attr=fluid.ParamAttr(
name=self._sent_emb_name, initializer=self._param_initializer))
emb_out = emb_out + position_emb_out
emb_out = emb_out + sent_emb_out
emb_out = pre_process_layer(
emb_out, 'nd', self._prepostprocess_dropout, name='pre_encoder')
if self._dtype == "float16":
input_mask = fluid.layers.cast(x=input_mask, dtype=self._dtype)
self_attn_mask = fluid.layers.matmul(
x=input_mask, y=input_mask, transpose_y=True)
self_attn_mask = fluid.layers.scale(
x=self_attn_mask, scale=10000.0, bias=-1.0, bias_after_scale=False)
n_head_self_attn_mask = fluid.layers.stack(
x=[self_attn_mask] * self._n_head, axis=1)
n_head_self_attn_mask.stop_gradient = True
self._enc_out = encoder(
enc_input=emb_out,
attn_bias=n_head_self_attn_mask,
n_layer=self._n_layer,
n_head=self._n_head,
d_key=self._emb_size // self._n_head,
d_value=self._emb_size // self._n_head,
d_model=self._emb_size,
d_inner_hid=self._emb_size * 4,
prepostprocess_dropout=self._prepostprocess_dropout,
attention_dropout=self._attention_dropout,
relu_dropout=0,
hidden_act=self._hidden_act,
preprocess_cmd="",
postprocess_cmd="dan",
param_initializer=self._param_initializer,
name='encoder')
def get_sequence_output(self):
return self._enc_out
def get_pooled_output(self):
"""Get the first feature of each sequence for classification"""
next_sent_feat = fluid.layers.slice(
input=self._enc_out, axes=[1], starts=[0], ends=[1])
next_sent_feat = fluid.layers.fc(
input=next_sent_feat,
size=self._emb_size,
act="tanh",
param_attr=fluid.ParamAttr(
name="pooled_fc.w_0", initializer=self._param_initializer),
bias_attr="pooled_fc.b_0")
return next_sent_feat
def get_pretraining_output(self, mask_label, mask_pos, labels):
"""Get the loss & accuracy for pretraining"""
mask_pos = fluid.layers.cast(x=mask_pos, dtype='int32')
# extract the first token feature in each sentence
next_sent_feat = self.get_pooled_output()
reshaped_emb_out = fluid.layers.reshape(
x=self._enc_out, shape=[-1, self._emb_size])
# extract masked tokens' feature
mask_feat = fluid.layers.gather(input=reshaped_emb_out, index=mask_pos)
# transform: fc
mask_trans_feat = fluid.layers.fc(
input=mask_feat,
size=self._emb_size,
act=self._hidden_act,
param_attr=fluid.ParamAttr(
name='mask_lm_trans_fc.w_0',
initializer=self._param_initializer),
bias_attr=fluid.ParamAttr(name='mask_lm_trans_fc.b_0'))
# transform: layer norm
mask_trans_feat = pre_process_layer(
mask_trans_feat, 'n', name='mask_lm_trans')
mask_lm_out_bias_attr = fluid.ParamAttr(
name="mask_lm_out_fc.b_0",
initializer=fluid.initializer.Constant(value=0.0))
if self._weight_sharing:
fc_out = fluid.layers.matmul(
x=mask_trans_feat,
y=fluid.default_main_program().global_block().var(
self._word_emb_name),
transpose_y=True)
fc_out += fluid.layers.create_parameter(
shape=[self._voc_size],
dtype=self._dtype,
attr=mask_lm_out_bias_attr,
is_bias=True)
else:
fc_out = fluid.layers.fc(input=mask_trans_feat,
size=self._voc_size,
param_attr=fluid.ParamAttr(
name="mask_lm_out_fc.w_0",
initializer=self._param_initializer),
bias_attr=mask_lm_out_bias_attr)
mask_lm_loss = fluid.layers.softmax_with_cross_entropy(
logits=fc_out, label=mask_label)
mean_mask_lm_loss = fluid.layers.mean(mask_lm_loss)
next_sent_fc_out = fluid.layers.fc(
input=next_sent_feat,
size=2,
param_attr=fluid.ParamAttr(
name="next_sent_fc.w_0", initializer=self._param_initializer),
bias_attr="next_sent_fc.b_0")
next_sent_loss, next_sent_softmax = fluid.layers.softmax_with_cross_entropy(
logits=next_sent_fc_out, label=labels, return_softmax=True)
next_sent_acc = fluid.layers.accuracy(
input=next_sent_softmax, label=labels)
mean_next_sent_loss = fluid.layers.mean(next_sent_loss)
loss = mean_next_sent_loss + mean_mask_lm_loss
return next_sent_acc, mean_mask_lm_loss, loss
BERT/model/classifier.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Model for classifier."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from model.bert import BertModel
def create_model(args,
pyreader_name,
bert_config,
num_labels,
is_prediction=False):
pyreader = fluid.layers.py_reader(
capacity=50,
shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1], [-1, 1]],
dtypes=['int64', 'int64', 'int64', 'float32', 'int64'],
lod_levels=[0, 0, 0, 0, 0],
name=pyreader_name,
use_double_buffer=True)
(src_ids, pos_ids, sent_ids, input_mask,
labels) = fluid.layers.read_file(pyreader)
bert = BertModel(
src_ids=src_ids,
position_ids=pos_ids,
sentence_ids=sent_ids,
input_mask=input_mask,
config=bert_config,
use_fp16=args.use_fp16)
cls_feats = bert.get_pooled_output()
cls_feats = fluid.layers.dropout(
x=cls_feats,
dropout_prob=0.1,
dropout_implementation="upscale_in_train")
logits = fluid.layers.fc(
input=cls_feats,
size=num_labels,
param_attr=fluid.ParamAttr(
name="cls_out_w",
initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
bias_attr=fluid.ParamAttr(
name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
if is_prediction:
probs = fluid.layers.softmax(logits)
feed_targets_name = [
src_ids.name, pos_ids.name, sent_ids.name, input_mask.name
]
return pyreader, probs, feed_targets_name
ce_loss, probs = fluid.layers.softmax_with_cross_entropy(
logits=logits, label=labels, return_softmax=True)
loss = fluid.layers.mean(x=ce_loss)
if args.use_fp16 and args.loss_scaling > 1.0:
loss *= args.loss_scaling
num_seqs = fluid.layers.create_tensor(dtype='int64')
accuracy = fluid.layers.accuracy(input=probs, label=labels, total=num_seqs)
return pyreader, loss, probs, accuracy, num_seqs
BERT/model/transformer_encoder.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Transformer encoder."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from functools import partial
import numpy as np
import paddle.fluid as fluid
import paddle.fluid.layers as layers
def multi_head_attention(queries,
keys,
values,
attn_bias,
d_key,
d_value,
d_model,
n_head=1,
dropout_rate=0.,
cache=None,
param_initializer=None,
name='multi_head_att'):
"""
Multi-Head Attention. Note that attn_bias is added to the logit before
computing softmax activiation to mask certain selected positions so that
they will not considered in attention weights.
"""
keys = queries if keys is None else keys
values = keys if values is None else values
if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3):
raise ValueError(
"Inputs: quries, keys and values should all be 3-D tensors.")
def __compute_qkv(queries, keys, values, n_head, d_key, d_value):
"""
Add linear projection to queries, keys, and values.
"""
q = layers.fc(input=queries,
size=d_key * n_head,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name=name + '_query_fc.w_0',
initializer=param_initializer),
bias_attr=name + '_query_fc.b_0')
k = layers.fc(input=keys,
size=d_key * n_head,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name=name + '_key_fc.w_0',
initializer=param_initializer),
bias_attr=name + '_key_fc.b_0')
v = layers.fc(input=values,
size=d_value * n_head,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name=name + '_value_fc.w_0',
initializer=param_initializer),
bias_attr=name + '_value_fc.b_0')
return q, k, v
def __split_heads(x, n_head):
"""
Reshape the last dimension of inpunt tensor x so that it becomes two
dimensions and then transpose. Specifically, input a tensor with shape
[bs, max_sequence_length, n_head * hidden_dim] then output a tensor
with shape [bs, n_head, max_sequence_length, hidden_dim].
"""
hidden_size = x.shape[-1]
# The value 0 in shape attr means copying the corresponding dimension
# size of the input as the output dimension size.
reshaped = layers.reshape(
x=x, shape=[0, 0, n_head, hidden_size // n_head], inplace=True)
# permuate the dimensions into:
# [batch_size, n_head, max_sequence_len, hidden_size_per_head]
return layers.transpose(x=reshaped, perm=[0, 2, 1, 3])
def __combine_heads(x):
"""
Transpose and then reshape the last two dimensions of inpunt tensor x
so that it becomes one dimension, which is reverse to __split_heads.
"""
if len(x.shape) == 3: return x
if len(x.shape) != 4:
raise ValueError("Input(x) should be a 4-D Tensor.")
trans_x = layers.transpose(x, perm=[0, 2, 1, 3])
# The value 0 in shape attr means copying the corresponding dimension
# size of the input as the output dimension size.
return layers.reshape(
x=trans_x,
shape=[0, 0, trans_x.shape[2] * trans_x.shape[3]],
inplace=True)
def scaled_dot_product_attention(q, k, v, attn_bias, d_key, dropout_rate):
"""
Scaled Dot-Product Attention
"""
scaled_q = layers.scale(x=q, scale=d_key**-0.5)
product = layers.matmul(x=scaled_q, y=k, transpose_y=True)
if attn_bias:
product += attn_bias
weights = layers.softmax(product)
if dropout_rate:
weights = layers.dropout(
weights,
dropout_prob=dropout_rate,
dropout_implementation="upscale_in_train",
is_test=False)
out = layers.matmul(weights, v)
return out
q, k, v = __compute_qkv(queries, keys, values, n_head, d_key, d_value)
if cache is not None: # use cache and concat time steps
# Since the inplace reshape in __split_heads changes the shape of k and
# v, which is the cache input for next time step, reshape the cache
# input from the previous time step first.
k = cache["k"] = layers.concat(
[layers.reshape(
cache["k"], shape=[0, 0, d_model]), k], axis=1)
v = cache["v"] = layers.concat(
[layers.reshape(
cache["v"], shape=[0, 0, d_model]), v], axis=1)
q = __split_heads(q, n_head)
k = __split_heads(k, n_head)
v = __split_heads(v, n_head)
ctx_multiheads = scaled_dot_product_attention(q, k, v, attn_bias, d_key,
dropout_rate)
out = __combine_heads(ctx_multiheads)
# Project back to the model size.
proj_out = layers.fc(input=out,
size=d_model,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name=name + '_output_fc.w_0',
initializer=param_initializer),
bias_attr=name + '_output_fc.b_0')
return proj_out
def positionwise_feed_forward(x,
d_inner_hid,
d_hid,
dropout_rate,
hidden_act,
param_initializer=None,
name='ffn'):
"""
Position-wise Feed-Forward Networks.
This module consists of two linear transformations with a ReLU activation
in between, which is applied to each position separately and identically.
"""
hidden = layers.fc(input=x,
size=d_inner_hid,
num_flatten_dims=2,
act=hidden_act,
param_attr=fluid.ParamAttr(
name=name + '_fc_0.w_0',
initializer=param_initializer),
bias_attr=name + '_fc_0.b_0')
if dropout_rate:
hidden = layers.dropout(
hidden,
dropout_prob=dropout_rate,
dropout_implementation="upscale_in_train",
is_test=False)
out = layers.fc(input=hidden,
size=d_hid,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name=name + '_fc_1.w_0', initializer=param_initializer),
bias_attr=name + '_fc_1.b_0')
return out
def pre_post_process_layer(prev_out, out, process_cmd, dropout_rate=0.,
name=''):
"""
Add residual connection, layer normalization and droput to the out tensor
optionally according to the value of process_cmd.
This will be used before or after multi-head attention and position-wise
feed-forward networks.
"""
for cmd in process_cmd:
if cmd == "a": # add residual connection
out = out + prev_out if prev_out else out
elif cmd == "n": # add layer normalization
out_dtype = out.dtype
if out_dtype == fluid.core.VarDesc.VarType.FP16:
out = layers.cast(x=out, dtype="float32")
out = layers.layer_norm(
out,
begin_norm_axis=len(out.shape) - 1,
param_attr=fluid.ParamAttr(
name=name + '_layer_norm_scale',
initializer=fluid.initializer.Constant(1.)),
bias_attr=fluid.ParamAttr(
name=name + '_layer_norm_bias',
initializer=fluid.initializer.Constant(0.)))
if out_dtype == fluid.core.VarDesc.VarType.FP16:
out = layers.cast(x=out, dtype="float16")
elif cmd == "d": # add dropout
if dropout_rate:
out = layers.dropout(
out,
dropout_prob=dropout_rate,
dropout_implementation="upscale_in_train",
is_test=False)
return out
pre_process_layer = partial(pre_post_process_layer, None)
post_process_layer = pre_post_process_layer
def encoder_layer(enc_input,
attn_bias,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
hidden_act,
preprocess_cmd="n",
postprocess_cmd="da",
param_initializer=None,
name=''):
"""The encoder layers that can be stacked to form a deep encoder.
This module consits of a multi-head (self) attention followed by
position-wise feed-forward networks and both the two components companied
with the post_process_layer to add residual connection, layer normalization
and droput.
"""
attn_output = multi_head_attention(
pre_process_layer(
enc_input,
preprocess_cmd,
prepostprocess_dropout,
name=name + '_pre_att'),
None,
None,
attn_bias,
d_key,
d_value,
d_model,
n_head,
attention_dropout,
param_initializer=param_initializer,
name=name + '_multi_head_att')
attn_output = post_process_layer(
enc_input,
attn_output,
postprocess_cmd,
prepostprocess_dropout,
name=name + '_post_att')
ffd_output = positionwise_feed_forward(
pre_process_layer(
attn_output,
preprocess_cmd,
prepostprocess_dropout,
name=name + '_pre_ffn'),
d_inner_hid,
d_model,
relu_dropout,
hidden_act,
param_initializer=param_initializer,
name=name + '_ffn')
return post_process_layer(
attn_output,
ffd_output,
postprocess_cmd,
prepostprocess_dropout,
name=name + '_post_ffn')
def encoder(enc_input,
attn_bias,
n_layer,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
hidden_act,
preprocess_cmd="n",
postprocess_cmd="da",
param_initializer=None,
name=''):
"""
The encoder is composed of a stack of identical layers returned by calling
encoder_layer.
"""
for i in range(n_layer):
enc_output = encoder_layer(
enc_input,
attn_bias,
n_head,
d_key,
d_value,
d_model,
d_inner_hid,
prepostprocess_dropout,
attention_dropout,
relu_dropout,
hidden_act,
preprocess_cmd,
postprocess_cmd,
param_initializer=param_initializer,
name=name + '_layer_' + str(i))
enc_input = enc_output
enc_output = pre_process_layer(
enc_output, preprocess_cmd, prepostprocess_dropout, name="post_encoder")
return enc_output
BERT/optimization.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Optimization and learning rate scheduling."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import paddle.fluid as fluid
from utils.fp16 import create_master_params_grads, master_param_to_train_param
def linear_warmup_decay(learning_rate, warmup_steps, num_train_steps):
""" Applies linear warmup of learning rate from 0 and decay to 0."""
with fluid.default_main_program()._lr_schedule_guard():
lr = fluid.layers.tensor.create_global_var(
shape=[1],
value=0.0,
dtype='float32',
persistable=True,
name="scheduled_learning_rate")
global_step = fluid.layers.learning_rate_scheduler._decay_step_counter()
with fluid.layers.control_flow.Switch() as switch:
with switch.case(global_step < warmup_steps):
warmup_lr = learning_rate * (global_step / warmup_steps)
fluid.layers.tensor.assign(warmup_lr, lr)
with switch.default():
decayed_lr = fluid.layers.learning_rate_scheduler.polynomial_decay(
learning_rate=learning_rate,
decay_steps=num_train_steps,
end_learning_rate=0.0,
power=1.0,
cycle=False)
fluid.layers.tensor.assign(decayed_lr, lr)
return lr
def optimization(loss,
warmup_steps,
num_train_steps,
learning_rate,
train_program,
startup_prog,
weight_decay,
scheduler='linear_warmup_decay',
use_fp16=False,
loss_scaling=1.0):
if warmup_steps > 0:
if scheduler == 'noam_decay':
scheduled_lr = fluid.layers.learning_rate_scheduler\
.noam_decay(1/(warmup_steps *(learning_rate ** 2)),
warmup_steps)
elif scheduler == 'linear_warmup_decay':
scheduled_lr = linear_warmup_decay(learning_rate, warmup_steps,
num_train_steps)
else:
raise ValueError("Unkown learning rate scheduler, should be "
"'noam_decay' or 'linear_warmup_decay'")
optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
else:
optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
scheduled_lr = learning_rate
clip_norm_thres = 1.0
# When using mixed precision training, scale the gradient clip threshold
# by loss_scaling
if use_fp16 and loss_scaling > 1.0:
clip_norm_thres *= loss_scaling
fluid.clip.set_gradient_clip(
clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres))
def exclude_from_weight_decay(name):
if name.find("layer_norm") > -1:
return True
bias_suffix = ["_bias", "_b", ".b_0"]
for suffix in bias_suffix:
if name.endswith(suffix):
return True
return False
param_list = dict()
if use_fp16:
param_grads = optimizer.backward(loss)
master_param_grads = create_master_params_grads(
param_grads, train_program, startup_prog, loss_scaling)
for param, _ in master_param_grads:
param_list[param.name] = param * 1.0
param_list[param.name].stop_gradient = True
optimizer.apply_gradients(master_param_grads)
if weight_decay > 0:
for param, grad in master_param_grads:
if exclude_from_weight_decay(param.name.rstrip(".master")):
continue
with param.block.program._optimized_guard(
[param, grad]), fluid.framework.name_scope("weight_decay"):
updated_param = param - param_list[
param.name] * weight_decay * scheduled_lr
fluid.layers.assign(output=param, input=updated_param)
master_param_to_train_param(master_param_grads, param_grads,
train_program)
else:
for param in train_program.global_block().all_parameters():
param_list[param.name] = param * 1.0
param_list[param.name].stop_gradient = True
_, param_grads = optimizer.minimize(loss)
if weight_decay > 0:
for param, grad in param_grads:
if exclude_from_weight_decay(param.name):
continue
with param.block.program._optimized_guard(
[param, grad]), fluid.framework.name_scope("weight_decay"):
updated_param = param - param_list[
param.name] * weight_decay * scheduled_lr
fluid.layers.assign(output=param, input=updated_param)
return scheduled_lr
BERT/predict_classifier.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Load classifier's checkpoint to do prediction or save inference model."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import multiprocessing
import paddle.fluid as fluid
import reader.cls as reader
from model.bert import BertConfig
from model.classifier import create_model
from utils.args import ArgumentGroup, print_arguments
from utils.init import init_pretraining_params
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "options to init, resume and save model.")
model_g.add_arg("bert_config_path", str, None, "Path to the json file for bert model config.")
model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
model_g.add_arg("save_inference_model_path", str, None, "If set, save the inference model to this path.")
model_g.add_arg("use_fp16", bool, False, "Whether to resume parameters from fp16 checkpoint.")
data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options.")
data_g.add_arg("data_dir", str, None, "Directory to test data.")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("max_seq_len", int, 128, "Number of words of the longest seqence.")
data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("in_tokens", bool, False,
"If set, the batch size will be the maximum number of tokens in one batch. "
"Otherwise, it will be the maximum number of examples in one batch.")
data_g.add_arg("do_lower_case", bool, True,
"Whether to lower case the input text. Should be True for uncased models and False for cased models.")
run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
run_type_g.add_arg("task_name", str, None,
"The name of task to perform fine-tuning, should be in {'xnli', 'mnli', 'cola', 'mrpc'}.")
run_type_g.add_arg("do_prediction", bool, True, "Whether to do prediction on test set.")
args = parser.parse_args()
# yapf: enable.
def main(args):
bert_config = BertConfig(args.bert_config_path)
bert_config.print_config()
task_name = args.task_name.lower()
processors = {
'xnli': reader.XnliProcessor,
'cola': reader.ColaProcessor,
'mrpc': reader.MrpcProcessor,
'mnli': reader.MnliProcessor,
}
processor = processors[task_name](data_dir=args.data_dir,
vocab_path=args.vocab_path,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=False)
num_labels = len(processor.get_labels())
predict_prog = fluid.Program()
predict_startup = fluid.Program()
with fluid.program_guard(predict_prog, predict_startup):
with fluid.unique_name.guard():
predict_pyreader, probs, feed_target_names = create_model(
args,
pyreader_name='predict_reader',
bert_config=bert_config,
num_labels=num_labels,
is_prediction=True)
predict_prog = predict_prog.clone(for_test=True)
if args.use_cuda:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
place = fluid.CUDAPlace(0) if args.use_cuda == True else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(predict_startup)
if args.init_checkpoint:
init_pretraining_params(exe, args.init_checkpoint, predict_prog)
else:
raise ValueError("args 'init_checkpoint' should be set for prediction!")
# Due to the design that ParallelExecutor would drop small batches (mostly the last batch)
# So using ParallelExecutor may left some data unpredicted
# if prediction of each and every example is needed, please use Executor instead
predict_exe = fluid.ParallelExecutor(
use_cuda=args.use_cuda, main_program=predict_prog)
predict_pyreader.decorate_tensor_provider(
processor.data_generator(
batch_size=args.batch_size, phase='test', epoch=1, shuffle=False))
predict_pyreader.start()
all_results = []
time_begin = time.time()
while True:
try:
results = predict_exe.run(fetch_list=[probs.name])
all_results.extend(results[0])
except fluid.core.EOFException:
predict_pyreader.reset()
break
time_end = time.time()
np.set_printoptions(precision=4, suppress=True)
print("-------------- prediction results --------------")
print("example_id\t" + ' '.join(processor.get_labels()))
for index, result in enumerate(all_results):
print(str(index) + '\t{}'.format(result))
if args.save_inference_model_path:
_, ckpt_dir = os.path.split(args.init_checkpoint.rstrip('/'))
dir_name = ckpt_dir + '_inference_model'
model_path = os.path.join(args.save_inference_model_path, dir_name)
print("save inference model to %s" % model_path)
fluid.io.save_inference_model(
model_path,
feed_target_names, [probs],
exe,
main_program=predict_prog)
if __name__ == '__main__':
print_arguments(args)
main(args)
BERT/reader/cls.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
此差异已折叠。
BERT/reader/pretraining.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
from __future__ import division
import os
import numpy as np
import types
import gzip
import logging
import re
import six
import collections
import tokenization
import paddle
import paddle.fluid as fluid
from batching import prepare_batch_data
class DataReader(object):
def __init__(self,
data_dir,
vocab_path,
batch_size=4096,
in_tokens=True,
max_seq_len=512,
shuffle_files=True,
epoch=100,
voc_size=0,
is_test=False,
generate_neg_sample=False):
self.vocab = self.load_vocab(vocab_path)
self.data_dir = data_dir
self.batch_size = batch_size
self.in_tokens = in_tokens
self.shuffle_files = shuffle_files
self.epoch = epoch
self.current_epoch = 0
self.current_file_index = 0
self.total_file = 0
self.current_file = None
self.voc_size = voc_size
self.max_seq_len = max_seq_len
self.pad_id = self.vocab["[PAD]"]
self.cls_id = self.vocab["[CLS]"]
self.sep_id = self.vocab["[SEP]"]
self.mask_id = self.vocab["[MASK]"]
self.is_test = is_test
self.generate_neg_sample = generate_neg_sample
if self.in_tokens:
assert self.batch_size >= self.max_seq_len, "The number of " \
"tokens in batch should not be smaller than max seq length."
if self.is_test:
self.epoch = 1
self.shuffle_files = False
def get_progress(self):
"""return current progress of traning data
"""
return self.current_epoch, self.current_file_index, self.total_file, self.current_file
def parse_line(self, line, max_seq_len=512):
""" parse one line to token_ids, sentence_ids, pos_ids, label
"""
line = line.strip().decode().split(";")
assert len(line) == 4, "One sample must have 4 fields!"
(token_ids, sent_ids, pos_ids, label) = line
token_ids = [int(token) for token in token_ids.split(" ")]
sent_ids = [int(token) for token in sent_ids.split(" ")]
pos_ids = [int(token) for token in pos_ids.split(" ")]
assert len(token_ids) == len(sent_ids) == len(
pos_ids
), "[Must be true]len(token_ids) == len(sent_ids) == len(pos_ids)"
label = int(label)
if len(token_ids) > max_seq_len:
return None
return [token_ids, sent_ids, pos_ids, label]
def read_file(self, file):
assert file.endswith('.gz'), "[ERROR] %s is not a gzip file" % file
file_path = self.data_dir + "/" + file
with gzip.open(file_path, "rb") as f:
for line in f:
parsed_line = self.parse_line(
line, max_seq_len=self.max_seq_len)
if parsed_line is None:
continue
yield parsed_line
def convert_to_unicode(self, text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(self, vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
fin = open(vocab_file)
for num, line in enumerate(fin):
items = self.convert_to_unicode(line.strip()).split("\t")
if len(items) > 2:
break
token = items[0]
index = items[1] if len(items) == 2 else num
token = token.strip()
vocab[token] = int(index)
return vocab
def random_pair_neg_samples(self, pos_samples):
""" randomly generate negtive samples using pos_samples
Args:
pos_samples: list of positive samples
Returns:
neg_samples: list of negtive samples
"""
np.random.shuffle(pos_samples)
num_sample = len(pos_samples)
neg_samples = []
miss_num = 0
for i in range(num_sample):
pair_index = (i + 1) % num_sample
origin_src_ids = pos_samples[i][0]
origin_sep_index = origin_src_ids.index(2)
pair_src_ids = pos_samples[pair_index][0]
pair_sep_index = pair_src_ids.index(2)
src_ids = origin_src_ids[:origin_sep_index + 1] + pair_src_ids[
pair_sep_index + 1:]
if len(src_ids) >= self.max_seq_len:
miss_num += 1
continue
sent_ids = [0] * len(origin_src_ids[:origin_sep_index + 1]) + [
1
] * len(pair_src_ids[pair_sep_index + 1:])
pos_ids = list(range(len(src_ids)))
neg_sample = [src_ids, sent_ids, pos_ids, 0]
assert len(src_ids) == len(sent_ids) == len(
pos_ids
), "[ERROR]len(src_id) == lne(sent_id) == len(pos_id) must be True"
neg_samples.append(neg_sample)
return neg_samples, miss_num
def mixin_negtive_samples(self, pos_sample_generator, buffer=1000):
""" 1. generate negtive samples by randomly group sentence_1 and sentence_2 of positive samples
2. combine negtive samples and positive samples
Args:
pos_sample_generator: a generator producing a parsed positive sample, which is a list: [token_ids, sent_ids, pos_ids, 1]
Returns:
sample: one sample from shuffled positive samples and negtive samples
"""
pos_samples = []
num_total_miss = 0
pos_sample_num = 0
try:
while True:
while len(pos_samples) < buffer:
pos_sample = next(pos_sample_generator)
label = pos_sample[3]
assert label == 1, "positive sample's label must be 1"
pos_samples.append(pos_sample)
pos_sample_num += 1
neg_samples, miss_num = self.random_pair_neg_samples(
pos_samples)
num_total_miss += miss_num
samples = pos_samples + neg_samples
pos_samples = []
np.random.shuffle(samples)
for sample in samples:
yield sample
except StopIteration:
print("stopiteration: reach end of file")
if len(pos_samples) == 1:
yield pos_samples[0]
elif len(pos_samples) == 0:
yield None
else:
neg_samples, miss_num = self.random_pair_neg_samples(
pos_samples)
num_total_miss += miss_num
samples = pos_samples + neg_samples
pos_samples = []
np.random.shuffle(samples)
for sample in samples:
yield sample
print("miss_num:%d\tideal_total_sample_num:%d\tmiss_rate:%f" %
(num_total_miss, pos_sample_num * 2,
num_total_miss / (pos_sample_num * 2)))
def data_generator(self):
"""
data_generator
"""
files = os.listdir(self.data_dir)
self.total_file = len(files)
assert self.total_file > 0, "[Error] data_dir is empty"
def wrapper():
def reader():
for epoch in range(self.epoch):
self.current_epoch = epoch + 1
if self.shuffle_files:
np.random.shuffle(files)
for index, file in enumerate(files):
self.current_file_index = index + 1
self.current_file = file
sample_generator = self.read_file(file)
if not self.is_test and self.generate_neg_sample:
sample_generator = self.mixin_negtive_samples(
sample_generator)
for sample in sample_generator:
if sample is None:
continue
yield sample
def batch_reader(reader, batch_size, in_tokens):
batch, total_token_num, max_len = [], 0, 0
for parsed_line in reader():
token_ids, sent_ids, pos_ids, label = parsed_line
max_len = max(max_len, len(token_ids))
if in_tokens:
to_append = (len(batch) + 1) * max_len <= batch_size
else:
to_append = len(batch) < batch_size
if to_append:
batch.append(parsed_line)
total_token_num += len(token_ids)
else:
yield batch, total_token_num
batch, total_token_num, max_len = [parsed_line], len(
token_ids), len(token_ids)
if len(batch) > 0:
yield batch, total_token_num
for batch_data, total_token_num in batch_reader(
reader, self.batch_size, self.in_tokens):
yield prepare_batch_data(
batch_data,
total_token_num,
voc_size=self.voc_size,
pad_id=self.pad_id,
cls_id=self.cls_id,
sep_id=self.sep_id,
mask_id=self.mask_id,
return_input_mask=True,
return_max_len=False,
return_num_token=False)
return wrapper
if __name__ == "__main__":
pass
BERT/reader/squad.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
此差异已折叠。
BERT/run_classifier.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on classification tasks."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import argparse
import numpy as np
import subprocess
import multiprocessing
import paddle
import paddle.fluid as fluid
import reader.cls as reader
from model.bert import BertConfig
from model.classifier import create_model
from optimization import optimization
from utils.args import ArgumentGroup, print_arguments, check_cuda
from utils.init import init_pretraining_params, init_checkpoint
from utils.cards import get_cards
import dist_utils
num_trainers = int(os.environ.get('PADDLE_TRAINERS_NUM', 1))
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
model_g.add_arg("bert_config_path", str, None, "Path to the json file for bert model config.")
model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
model_g.add_arg("init_pretraining_params", str, None,
"Init pre-training params which preforms fine-tuning from. If the "
"arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")
train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.")
train_g.add_arg("lr_scheduler", str, "linear_warmup_decay",
"scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
train_g.add_arg("warmup_proportion", float, 0.1,
"Proportion of training steps to perform linear learning rate warmup for.")
train_g.add_arg("save_steps", int, 10000, "The steps interval to save checkpoints.")
train_g.add_arg("validation_steps", int, 1000, "The steps interval to evaluate model performance.")
train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
train_g.add_arg("loss_scaling", float, 1.0,
"Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
log_g = ArgumentGroup(parser, "logging", "logging related.")
log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")
data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
data_g.add_arg("data_dir", str, None, "Path to training data.")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
data_g.add_arg("batch_size", int, 32, "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("in_tokens", bool, False,
"If set, the batch size will be the maximum number of tokens in one batch. "
"Otherwise, it will be the maximum number of examples in one batch.")
data_g.add_arg("do_lower_case", bool, True,
"Whether to lower case the input text. Should be True for uncased models and False for cased models.")
data_g.add_arg("random_seed", int, 0, "Random seed.")
run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).")
run_type_g.add_arg("shuffle", bool, True, "")
run_type_g.add_arg("num_iteration_per_drop_scope", int, 1, "Ihe iteration intervals to clean up temporary variables.")
run_type_g.add_arg("task_name", str, None,
"The name of task to perform fine-tuning, should be in {'xnli', 'mnli', 'cola', 'mrpc'}.")
run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
run_type_g.add_arg("do_val", bool, True, "Whether to perform evaluation on dev data set.")
run_type_g.add_arg("do_test", bool, True, "Whether to perform evaluation on test data set.")
parser.add_argument("--enable_ce", action='store_true', help="The flag indicating whether to run the task for continuous evaluation.")
args = parser.parse_args()
# yapf: enable.
def evaluate(exe, test_program, test_pyreader, fetch_list, eval_phase):
test_pyreader.start()
total_cost, total_acc, total_num_seqs = [], [], []
time_begin = time.time()
while True:
try:
np_loss, np_acc, np_num_seqs = exe.run(program=test_program,
fetch_list=fetch_list)
total_cost.extend(np_loss * np_num_seqs)
total_acc.extend(np_acc * np_num_seqs)
total_num_seqs.extend(np_num_seqs)
except fluid.core.EOFException:
test_pyreader.reset()
break
time_end = time.time()
print("[%s evaluation] ave loss: %f, ave acc: %f, elapsed time: %f s" %
(eval_phase, np.sum(total_cost) / np.sum(total_num_seqs),
np.sum(total_acc) / np.sum(total_num_seqs), time_end - time_begin))
def get_device_num():
# NOTE(zcd): for multi-processe training, each process use one GPU card.
if num_trainers > 1 : return 1
visible_device = os.environ.get('CUDA_VISIBLE_DEVICES', None)
if visible_device:
device_num = len(visible_device.split(','))
else:
device_num = subprocess.check_output(['nvidia-smi','-L']).decode().count('\n')
return device_num
def main(args):
bert_config = BertConfig(args.bert_config_path)
bert_config.print_config()
if args.use_cuda:
place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))
dev_count = get_device_num()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exe = fluid.Executor(place)
task_name = args.task_name.lower()
processors = {
'xnli': reader.XnliProcessor,
'cola': reader.ColaProcessor,
'mrpc': reader.MrpcProcessor,
'mnli': reader.MnliProcessor,
}
processor = processors[task_name](data_dir=args.data_dir,
vocab_path=args.vocab_path,
max_seq_len=args.max_seq_len,
do_lower_case=args.do_lower_case,
in_tokens=args.in_tokens,
random_seed=args.random_seed)
num_labels = len(processor.get_labels())
if not (args.do_train or args.do_val or args.do_test):
raise ValueError("For args `do_train`, `do_val` and `do_test`, at "
"least one of them must be True.")
train_program = fluid.Program()
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
train_program.random_seed = args.random_seed
if args.do_train:
# NOTE: If num_trainers > 1, the shuffle_seed must be set, because
# the order of batch data generated by reader
# must be the same in the respective processes.
shuffle_seed = 1 if num_trainers > 1 else None
train_data_generator = processor.data_generator(
batch_size=args.batch_size,
phase='train',
epoch=args.epoch,
dev_count=dev_count,
shuffle=args.shuffle,
shuffle_seed=shuffle_seed)
num_train_examples = processor.get_num_examples(phase='train')
if args.in_tokens:
max_train_steps = args.epoch * num_train_examples // (
args.batch_size // args.max_seq_len) // dev_count
else:
max_train_steps = args.epoch * num_train_examples // args.batch_size // dev_count
warmup_steps = int(max_train_steps * args.warmup_proportion)
print("Device count: %d" % dev_count)
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, loss, probs, accuracy, num_seqs = create_model(
args,
pyreader_name='train_reader',
bert_config=bert_config,
num_labels=num_labels)
scheduled_lr = optimization(
loss=loss,
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
startup_prog=startup_prog,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16,
loss_scaling=args.loss_scaling)
if args.verbose:
if args.in_tokens:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program,
batch_size=args.batch_size // args.max_seq_len)
else:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program, batch_size=args.batch_size)
print("Theoretical memory usage in training: %.3f - %.3f %s" %
(lower_mem, upper_mem, unit))
if args.do_val or args.do_test:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, loss, probs, accuracy, num_seqs = create_model(
args,
pyreader_name='test_reader',
bert_config=bert_config,
num_labels=num_labels)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
if args.do_train:
if args.init_checkpoint and args.init_pretraining_params:
print(
"WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
"both are set! Only arg 'init_checkpoint' is made valid.")
if args.init_checkpoint:
init_checkpoint(
exe,
args.init_checkpoint,
main_program=startup_prog,
use_fp16=args.use_fp16)
elif args.init_pretraining_params:
init_pretraining_params(
exe,
args.init_pretraining_params,
main_program=startup_prog,
use_fp16=args.use_fp16)
elif args.do_val or args.do_test:
if not args.init_checkpoint:
raise ValueError("args 'init_checkpoint' should be set if"
"only doing validation or testing!")
init_checkpoint(
exe,
args.init_checkpoint,
main_program=startup_prog,
use_fp16=args.use_fp16)
if args.do_train:
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = args.use_fast_executor
exec_strategy.num_threads = dev_count
exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope
build_strategy = fluid.BuildStrategy()
if args.use_cuda and num_trainers > 1:
assert shuffle_seed is not None
dist_utils.prepare_for_multi_process(exe, build_strategy, train_program)
train_data_generator = fluid.contrib.reader.distributed_batch_reader(
train_data_generator)
train_compiled_program = fluid.CompiledProgram(train_program).with_data_parallel(
loss_name=loss.name, build_strategy=build_strategy)
train_pyreader.decorate_tensor_provider(train_data_generator)
if args.do_train:
train_pyreader.start()
steps = 0
total_cost, total_acc, total_num_seqs = [], [], []
time_begin = time.time()
throughput = []
ce_info = []
while True:
try:
# steps += 1
if steps % args.skip_steps == 0:
if warmup_steps <= 0:
fetch_list = [loss.name, accuracy.name, num_seqs.name]
else:
fetch_list = [
loss.name, accuracy.name, scheduled_lr.name,
num_seqs.name
]
else:
fetch_list = []
outputs = exe.run(train_compiled_program, fetch_list=fetch_list)
if steps % args.skip_steps == 0:
if warmup_steps <= 0:
np_loss, np_acc, np_num_seqs = outputs
else:
np_loss, np_acc, np_lr, np_num_seqs = outputs
total_cost.extend(np_loss * np_num_seqs)
total_acc.extend(np_acc * np_num_seqs)
total_num_seqs.extend(np_num_seqs)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
verbose += "learning rate: %f" % (
np_lr[0]
if warmup_steps > 0 else args.learning_rate)
print(verbose)
current_example, current_epoch = processor.get_train_progress(
)
time_end = time.time()
used_time = time_end - time_begin
log_record = "epoch: {}, progress: {}/{}, step: {}, ave loss: {}, ave acc: {}".format(
current_epoch, current_example, num_train_examples,
steps, np.sum(total_cost) / np.sum(total_num_seqs),
np.sum(total_acc) / np.sum(total_num_seqs))
ce_info.append([np.sum(total_cost) / np.sum(total_num_seqs), np.sum(total_acc) / np.sum(total_num_seqs), used_time])
if steps > 0 :
throughput.append( args.skip_steps / used_time)
log_record = log_record + ", speed: %f steps/s" % (args.skip_steps / used_time)
print(log_record)
else:
print(log_record)
total_cost, total_acc, total_num_seqs = [], [], []
time_begin = time.time()
steps += 1
if steps % args.save_steps == 0:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
if steps % args.validation_steps == 0:
print("Average throughtput: %s" % (np.average(throughput)))
throughput = []
# evaluate dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(
processor.data_generator(
batch_size=args.batch_size,
phase='dev',
epoch=1,
dev_count=1,
shuffle=False))
evaluate(exe, test_prog, test_pyreader,
[loss.name, accuracy.name, num_seqs.name],
"dev")
# evaluate test set
if args.do_test:
test_pyreader.decorate_tensor_provider(
processor.data_generator(
batch_size=args.batch_size,
phase='test',
epoch=1,
dev_count=1,
shuffle=False))
evaluate(exe, test_prog, test_pyreader,
[loss.name, accuracy.name, num_seqs.name],
"test")
except fluid.core.EOFException:
save_path = os.path.join(args.checkpoints, "step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
train_pyreader.reset()
break
if args.enable_ce:
card_num = get_cards()
ce_cost = 0
ce_acc = 0
ce_time = 0
try:
ce_cost = ce_info[-2][0]
ce_acc = ce_info[-2][1]
ce_time = ce_info[-2][2]
except:
print("ce info error")
print("kpis\ttrain_duration_%s_card%s\t%s" %
(args.task_name, card_num, ce_time))
print("kpis\ttrain_cost_%s_card%s\t%f" %
(args.task_name, card_num, ce_cost))
print("kpis\ttrain_acc_%s_card%s\t%f" %
(args.task_name, card_num, ce_acc))
# final eval on dev set
if args.do_val:
test_pyreader.decorate_tensor_provider(
processor.data_generator(
batch_size=args.batch_size, phase='dev', epoch=1, dev_count=1,
shuffle=False))
print("Final validation result:")
evaluate(exe, test_prog, test_pyreader,
[loss.name, accuracy.name, num_seqs.name], "dev")
# final eval on test set
if args.do_test:
test_pyreader.decorate_tensor_provider(
processor.data_generator(
batch_size=args.batch_size,
phase='test',
epoch=1,
dev_count=1,
shuffle=False))
print("Final test result:")
evaluate(exe, test_prog, test_pyreader,
[loss.name, accuracy.name, num_seqs.name], "test")
if __name__ == '__main__':
print_arguments(args)
check_cuda(args.use_cuda)
main(args)
BERT/run_squad.py 已删除 100644 → 0
浏览文件 @ 546ce5e9
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Finetuning on SQuAD."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import collections
import multiprocessing
import os
import time
import numpy as np
import paddle
import paddle.fluid as fluid
from reader.squad import DataProcessor, write_predictions
from model.bert import BertConfig, BertModel
from utils.args import ArgumentGroup, print_arguments, check_cuda
from optimization import optimization
from utils.init import init_pretraining_params, init_checkpoint
# yapf: disable
parser = argparse.ArgumentParser(__doc__)
model_g = ArgumentGroup(parser, "model", "model configuration and paths.")
model_g.add_arg("bert_config_path", str, None, "Path to the json file for bert model config.")
model_g.add_arg("init_checkpoint", str, None, "Init checkpoint to resume training from.")
model_g.add_arg("init_pretraining_params", str, None,
"Init pre-training params which preforms fine-tuning from. If the "
"arg 'init_checkpoint' has been set, this argument wouldn't be valid.")
model_g.add_arg("checkpoints", str, "checkpoints", "Path to save checkpoints.")
train_g = ArgumentGroup(parser, "training", "training options.")
train_g.add_arg("epoch", int, 3, "Number of epoches for fine-tuning.")
train_g.add_arg("learning_rate", float, 5e-5, "Learning rate used to train with warmup.")
train_g.add_arg("lr_scheduler", str, "linear_warmup_decay",
"scheduler of learning rate.", choices=['linear_warmup_decay', 'noam_decay'])
train_g.add_arg("weight_decay", float, 0.01, "Weight decay rate for L2 regularizer.")
train_g.add_arg("warmup_proportion", float, 0.1,
"Proportion of training steps to perform linear learning rate warmup for.")
train_g.add_arg("save_steps", int, 1000, "The steps interval to save checkpoints.")
train_g.add_arg("use_fp16", bool, False, "Whether to use fp16 mixed precision training.")
train_g.add_arg("loss_scaling", float, 1.0,
"Loss scaling factor for mixed precision training, only valid when use_fp16 is enabled.")
log_g = ArgumentGroup(parser, "logging", "logging related.")
log_g.add_arg("skip_steps", int, 10, "The steps interval to print loss.")
log_g.add_arg("verbose", bool, False, "Whether to output verbose log.")
data_g = ArgumentGroup(parser, "data", "Data paths, vocab paths and data processing options")
data_g.add_arg("train_file", str, None, "SQuAD json for training. E.g., train-v1.1.json.")
data_g.add_arg("predict_file", str, None, "SQuAD json for predictions. E.g. dev-v1.1.json or test-v1.1.json.")
data_g.add_arg("vocab_path", str, None, "Vocabulary path.")
data_g.add_arg("version_2_with_negative", bool, False,
"If true, the SQuAD examples contain some that do not have an answer. If using squad v2.0, it should be set true.")
data_g.add_arg("max_seq_len", int, 512, "Number of words of the longest seqence.")
data_g.add_arg("max_query_length", int, 64, "Max query length.")
data_g.add_arg("max_answer_length", int, 30, "Max answer length.")
data_g.add_arg("batch_size", int, 12, "Total examples' number in batch for training. see also --in_tokens.")
data_g.add_arg("in_tokens", bool, False,
"If set, the batch size will be the maximum number of tokens in one batch. "
"Otherwise, it will be the maximum number of examples in one batch.")
data_g.add_arg("do_lower_case", bool, True,
"Whether to lower case the input text. Should be True for uncased models and False for cased models.")
data_g.add_arg("doc_stride", int, 128,
"When splitting up a long document into chunks, how much stride to take between chunks.")
data_g.add_arg("n_best_size", int, 20,
"The total number of n-best predictions to generate in the nbest_predictions.json output file.")
data_g.add_arg("null_score_diff_threshold", float, 0.0,
"If null_score - best_non_null is greater than the threshold predict null.")
data_g.add_arg("random_seed", int, 0, "Random seed.")
run_type_g = ArgumentGroup(parser, "run_type", "running type options.")
run_type_g.add_arg("use_cuda", bool, True, "If set, use GPU for training.")
run_type_g.add_arg("use_fast_executor", bool, False, "If set, use fast parallel executor (in experiment).")
run_type_g.add_arg("num_iteration_per_drop_scope", int, 1, "Ihe iteration intervals to clean up temporary variables.")
run_type_g.add_arg("do_train", bool, True, "Whether to perform training.")
run_type_g.add_arg("do_predict", bool, True, "Whether to perform prediction.")
args = parser.parse_args()
# yapf: enable.
def create_model(pyreader_name, bert_config, is_training=False):
if is_training:
pyreader = fluid.layers.py_reader(
capacity=50,
shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, 1], [-1, 1]],
dtypes=[
'int64', 'int64', 'int64', 'float32', 'int64', 'int64'],
lod_levels=[0, 0, 0, 0, 0, 0],
name=pyreader_name,
use_double_buffer=True)
(src_ids, pos_ids, sent_ids, input_mask, start_positions,
end_positions) = fluid.layers.read_file(pyreader)
else:
pyreader = fluid.layers.py_reader(
capacity=50,
shapes=[[-1, args.max_seq_len, 1], [-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1],
[-1, args.max_seq_len, 1], [-1, 1]],
dtypes=['int64', 'int64', 'int64', 'float32', 'int64'],
lod_levels=[0, 0, 0, 0, 0],
name=pyreader_name,
use_double_buffer=True)
(src_ids, pos_ids, sent_ids, input_mask, unique_id) = fluid.layers.read_file(pyreader)
bert = BertModel(
src_ids=src_ids,
position_ids=pos_ids,
sentence_ids=sent_ids,
input_mask=input_mask,
config=bert_config,
use_fp16=args.use_fp16)
enc_out = bert.get_sequence_output()
logits = fluid.layers.fc(
input=enc_out,
size=2,
num_flatten_dims=2,
param_attr=fluid.ParamAttr(
name="cls_squad_out_w",
initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
bias_attr=fluid.ParamAttr(
name="cls_squad_out_b", initializer=fluid.initializer.Constant(0.)))
logits = fluid.layers.transpose(x=logits, perm=[2, 0, 1])
start_logits, end_logits = fluid.layers.unstack(x=logits, axis=0)
batch_ones = fluid.layers.fill_constant_batch_size_like(
input=start_logits, dtype='int64', shape=[1], value=1)
num_seqs = fluid.layers.reduce_sum(input=batch_ones)
if is_training:
def compute_loss(logits, positions):
loss = fluid.layers.softmax_with_cross_entropy(
logits=logits, label=positions)
loss = fluid.layers.mean(x=loss)
return loss
start_loss = compute_loss(start_logits, start_positions)
end_loss = compute_loss(end_logits, end_positions)
total_loss = (start_loss + end_loss) / 2.0
if args.use_fp16 and args.loss_scaling > 1.0:
total_loss = total_loss * args.loss_scaling
return pyreader, total_loss, num_seqs
else:
return pyreader, unique_id, start_logits, end_logits, num_seqs
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_logits", "end_logits"])
def predict(test_exe, test_program, test_pyreader, fetch_list, processor):
if not os.path.exists(args.checkpoints):
os.makedirs(args.checkpoints)
output_prediction_file = os.path.join(args.checkpoints, "predictions.json")
output_nbest_file = os.path.join(args.checkpoints, "nbest_predictions.json")
output_null_log_odds_file = os.path.join(args.checkpoints, "null_odds.json")
test_pyreader.start()
all_results = []
time_begin = time.time()
while True:
try:
np_unique_ids, np_start_logits, np_end_logits, np_num_seqs = test_exe.run(
fetch_list=fetch_list, program=test_program)
for idx in range(np_unique_ids.shape[0]):
if len(all_results) % 1000 == 0:
print("Processing example: %d" % len(all_results))
unique_id = int(np_unique_ids[idx])
start_logits = [float(x) for x in np_start_logits[idx].flat]
end_logits = [float(x) for x in np_end_logits[idx].flat]
all_results.append(
RawResult(
unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
except fluid.core.EOFException:
test_pyreader.reset()
break
time_end = time.time()
features = processor.get_features(
processor.predict_examples, is_training=False)
write_predictions(processor.predict_examples, features, all_results,
args.n_best_size, args.max_answer_length,
args.do_lower_case, output_prediction_file,
output_nbest_file, output_null_log_odds_file,
args.version_2_with_negative,
args.null_score_diff_threshold, args.verbose)
def train(args):
bert_config = BertConfig(args.bert_config_path)
bert_config.print_config()
if not (args.do_train or args.do_predict):
raise ValueError("For args `do_train` and `do_predict`, at "
"least one of them must be True.")
if args.use_cuda:
place = fluid.CUDAPlace(0)
dev_count = fluid.core.get_cuda_device_count()
else:
place = fluid.CPUPlace()
dev_count = int(os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
exe = fluid.Executor(place)
processor = DataProcessor(
vocab_path=args.vocab_path,
do_lower_case=args.do_lower_case,
max_seq_length=args.max_seq_len,
in_tokens=args.in_tokens,
doc_stride=args.doc_stride,
max_query_length=args.max_query_length)
startup_prog = fluid.Program()
if args.random_seed is not None:
startup_prog.random_seed = args.random_seed
if args.do_train:
train_data_generator = processor.data_generator(
data_path=args.train_file,
batch_size=args.batch_size,
phase='train',
shuffle=True,
dev_count=dev_count,
version_2_with_negative=args.version_2_with_negative,
epoch=args.epoch)
num_train_examples = processor.get_num_examples(phase='train')
if args.in_tokens:
max_train_steps = args.epoch * num_train_examples // (
args.batch_size // args.max_seq_len) // dev_count
else:
max_train_steps = args.epoch * num_train_examples // (
args.batch_size) // dev_count
warmup_steps = int(max_train_steps * args.warmup_proportion)
print("Device count: %d" % dev_count)
print("Num train examples: %d" % num_train_examples)
print("Max train steps: %d" % max_train_steps)
print("Num warmup steps: %d" % warmup_steps)
train_program = fluid.Program()
with fluid.program_guard(train_program, startup_prog):
with fluid.unique_name.guard():
train_pyreader, loss, num_seqs = create_model(
pyreader_name='train_reader',
bert_config=bert_config,
is_training=True)
scheduled_lr = optimization(
loss=loss,
warmup_steps=warmup_steps,
num_train_steps=max_train_steps,
learning_rate=args.learning_rate,
train_program=train_program,
startup_prog=startup_prog,
weight_decay=args.weight_decay,
scheduler=args.lr_scheduler,
use_fp16=args.use_fp16,
loss_scaling=args.loss_scaling)
if args.verbose:
if args.in_tokens:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program,
batch_size=args.batch_size // args.max_seq_len)
else:
lower_mem, upper_mem, unit = fluid.contrib.memory_usage(
program=train_program, batch_size=args.batch_size)
print("Theoretical memory usage in training: %.3f - %.3f %s" %
(lower_mem, upper_mem, unit))
if args.do_predict:
test_prog = fluid.Program()
with fluid.program_guard(test_prog, startup_prog):
with fluid.unique_name.guard():
test_pyreader, unique_ids, start_logits, end_logits, num_seqs = create_model(
pyreader_name='test_reader',
bert_config=bert_config,
is_training=False)
test_prog = test_prog.clone(for_test=True)
exe.run(startup_prog)
if args.do_train:
if args.init_checkpoint and args.init_pretraining_params:
print(
"WARNING: args 'init_checkpoint' and 'init_pretraining_params' "
"both are set! Only arg 'init_checkpoint' is made valid.")
if args.init_checkpoint:
init_checkpoint(
exe,
args.init_checkpoint,
main_program=startup_prog,
use_fp16=args.use_fp16)
elif args.init_pretraining_params:
init_pretraining_params(
exe,
args.init_pretraining_params,
main_program=startup_prog,
use_fp16=args.use_fp16)
elif args.do_predict:
if not args.init_checkpoint:
raise ValueError("args 'init_checkpoint' should be set if"
"only doing prediction!")
init_checkpoint(
exe,
args.init_checkpoint,
main_program=startup_prog,
use_fp16=args.use_fp16)
if args.do_train:
exec_strategy = fluid.ExecutionStrategy()
exec_strategy.use_experimental_executor = args.use_fast_executor
exec_strategy.num_threads = dev_count
exec_strategy.num_iteration_per_drop_scope = args.num_iteration_per_drop_scope
train_compiled_program = fluid.CompiledProgram(train_program).with_data_parallel(
loss_name=loss.name, exec_strategy=exec_strategy)
train_pyreader.decorate_tensor_provider(train_data_generator)
train_pyreader.start()
steps = 0
total_cost, total_num_seqs = [], []
time_begin = time.time()
while steps < max_train_steps:
try:
steps += 1
if steps % args.skip_steps == 0:
if warmup_steps <= 0:
fetch_list = [loss.name, num_seqs.name]
else:
fetch_list = [
loss.name, scheduled_lr.name, num_seqs.name
]
else:
fetch_list = []
outputs = exe.run(train_compiled_program, fetch_list=fetch_list)
if steps % args.skip_steps == 0:
if warmup_steps <= 0:
np_loss, np_num_seqs = outputs
else:
np_loss, np_lr, np_num_seqs = outputs
total_cost.extend(np_loss * np_num_seqs)
total_num_seqs.extend(np_num_seqs)
if args.verbose:
verbose = "train pyreader queue size: %d, " % train_pyreader.queue.size(
)
verbose += "learning rate: %f" % (
np_lr[0]
if warmup_steps > 0 else args.learning_rate)
print(verbose)
time_end = time.time()
used_time = time_end - time_begin
current_example, epoch = processor.get_train_progress()
print("epoch: %d, progress: %d/%d, step: %d, loss: %f, "
"speed: %f steps/s" %
(epoch, current_example, num_train_examples, steps,
np.sum(total_cost) / np.sum(total_num_seqs),
args.skip_steps / used_time))
total_cost, total_num_seqs = [], []
time_begin = time.time()
if steps % args.save_steps == 0 or steps == max_train_steps:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps))
fluid.io.save_persistables(exe, save_path, train_program)
except fluid.core.EOFException:
save_path = os.path.join(args.checkpoints,
"step_" + str(steps) + "_final")
fluid.io.save_persistables(exe, save_path, train_program)
train_pyreader.reset()
break
if args.do_predict:
test_pyreader.decorate_tensor_provider(
processor.data_generator(
data_path=args.predict_file,
batch_size=args.batch_size,
phase='predict',
shuffle=False,
dev_count=1,
epoch=1))
predict(exe, test_prog, test_pyreader, [
unique_ids.name, start_logits.name, end_logits.name, num_seqs.name
], processor)
if __name__ == '__main__':
print_arguments(args)
check_cuda(args.use_cuda)
train(args)
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#!/bin/bash
set -xe
# Paddle debug envs
export GLOG_v=1
export GLOG_logtostderr=1
# Unset proxy
unset https_proxy http_proxy
# NCCL debug envs
export NCCL_P2P_DISABLE=1
export NCCL_DEBUG=INFO
# Comment it if your nccl support IB
export NCCL_IB_DISABLE=1
# Add your nodes endpoints here.
export worker_endpoints=127.0.0.1:9184,127.0.0.1:9185
export current_endpoint=127.0.0.1:9184
export CUDA_VISIBLE_DEVICES=0
./train.sh -local n > 0.log 2>&1 &
# Add your nodes endpoints here.
export current_endpoint=127.0.0.1:9185
export CUDA_VISIBLE_DEVICES=1
./train.sh -local n > 1.log 2>&1 &
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
def get_cards():
"""
get gpu cards number
"""
num = 0
cards = os.environ.get('CUDA_VISIBLE_DEVICES', '')
if cards != '':
num = len(cards.split(","))
return num
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