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!10975 Increase performance of fasttext

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From: @zhouyaqiang0
Reviewed-by: 
Signed-off-by:
pull/10975/MERGE
mindspore-ci-bot 4 years ago committed by Gitee
parent c3dbbd144e 8a0bca7d6b
commit 2f8010f805
8 changed files with 47 additions and 32 deletions
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40
model_zoo/official/nlp/fasttext/eval.py
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@ -71,21 +71,35 @@ class FastTextInferCell(nn.Cell):
return predicted_idx
def load_infer_dataset(batch_size, datafile):
def load_infer_dataset(batch_size, datafile, bucket):
"""data loader for infer"""
data_set = ds.MindDataset(datafile, columns_list=['src_tokens', 'src_tokens_length', 'label_idx'])
type_cast_op = deC.TypeCast(mstype.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens")
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens_length")
data_set = data_set.map(operations=type_cast_op, input_columns="label_idx")
data_set = data_set.batch(batch_size=batch_size, drop_remainder=True)
def batch_per_bucket(bucket_length, input_file):
input_file = input_file + '/test_dataset_bs_' + str(bucket_length) + '.mindrecord'
if not input_file:
raise FileNotFoundError("input file parameter must not be empty.")
data_set = ds.MindDataset(input_file,
columns_list=['src_tokens', 'src_tokens_length', 'label_idx'])
type_cast_op = deC.TypeCast(mstype.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens")
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens_length")
data_set = data_set.map(operations=type_cast_op, input_columns="label_idx")
data_set = data_set.batch(batch_size, drop_remainder=False)
return data_set
for i, _ in enumerate(bucket):
bucket_len = bucket[i]
ds_per = batch_per_bucket(bucket_len, datafile)
if i == 0:
data_set = ds_per
else:
data_set = data_set + ds_per
return data_set
def run_fasttext_infer():
"""run infer with FastText"""
dataset = load_infer_dataset(batch_size=config.batch_size, datafile=args.data_path)
dataset = load_infer_dataset(batch_size=config.batch_size, datafile=args.data_path, bucket=config.test_buckets)
fasttext_model = FastText(config.vocab_size, config.embedding_dims, config.num_class)
parameter_dict = load_checkpoint(args.model_ckpt)
@ -107,7 +121,15 @@ def run_fasttext_infer():
from sklearn.metrics import accuracy_score, classification_report
target_sens = np.array(target_sens).flatten()
merge_target_sens = []
for target_sen in target_sens:
merge_target_sens.extend(target_sen)
target_sens = merge_target_sens
predictions = np.array(predictions).flatten()
merge_predictions = []
for prediction in predictions:
merge_predictions.extend(prediction)
predictions = merge_predictions
acc = accuracy_score(target_sens, predictions)
result_report = classification_report(target_sens, predictions, target_names=target_label1)

4
model_zoo/official/nlp/fasttext/scripts/create_dataset.sh
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@ -60,7 +60,7 @@ then
mkdir ./dbpedia
cd ./dbpedia || exit
echo "start data preprocess for device $DEVICE_ID"
python ../../src/dataset.py --train_file $SOURCE_DATASET_PATH/train.csv --test_file $SOURCE_DATASET_PATH/test.csv --class_num 14 --max_len 3013 --bucket [128,512,3013] --test_bucket [1120]
python ../../src/dataset.py --train_file $SOURCE_DATASET_PATH/train.csv --test_file $SOURCE_DATASET_PATH/test.csv --class_num 14 --max_len 3013 --bucket [64,128,256,512,3013] --test_bucket [64,128,256,512,1120]
cd ..
fi
@ -74,7 +74,7 @@ then
mkdir ./yelp_p
cd ./yelp_p || exit
echo "start data preprocess for device $DEVICE_ID"
python ../../src/dataset.py --train_file $SOURCE_DATASET_PATH/train.csv --test_file $SOURCE_DATASET_PATH/test.csv --class_num 2 --max_len 2955 --bucket [64,128,256,512,2955] --test_bucket [2955]
python ../../src/dataset.py --train_file $SOURCE_DATASET_PATH/train.csv --test_file $SOURCE_DATASET_PATH/test.csv --class_num 2 --max_len 2955 --bucket [64,128,256,512,2955] --test_bucket [64,128,256,512,2955]
cd ..
fi

1
model_zoo/official/nlp/fasttext/scripts/run_eval.sh
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@ -32,7 +32,6 @@ DATASET=$(get_real_path $1)
echo $DATASET
DATANAME=$2
MODEL_CKPT=$(get_real_path $3)
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=5
export RANK_ID=0

1
model_zoo/official/nlp/fasttext/scripts/run_standalone_train.sh
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@ -33,7 +33,6 @@ echo $DATASET
DATANAME=$(basename $DATASET)
echo $DATANAME
ulimit -u unlimited
export DEVICE_NUM=1
export DEVICE_ID=0
export RANK_ID=0

19
model_zoo/official/nlp/fasttext/src/config.py
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@ -20,11 +20,12 @@ from easydict import EasyDict as ed
config_yelpp = ed({
'vocab_size': 6414979,
'buckets': [64, 128, 256, 512, 2955],
'batch_size': 128,
'test_buckets': [64, 128, 256, 512, 2955],
'batch_size': 2048,
'embedding_dims': 16,
'num_class': 2,
'epoch': 5,
'lr': 0.02,
'lr': 0.30,
'min_lr': 1e-6,
'decay_steps': 549,
'warmup_steps': 400000,
@ -37,12 +38,13 @@ config_yelpp = ed({
config_db = ed({
'vocab_size': 6596536,
'buckets': [128, 512, 3013],
'batch_size': 128,
'buckets': [64, 128, 256, 512, 3013],
'test_buckets': [64, 128, 256, 512, 1120],
'batch_size': 4096,
'embedding_dims': 16,
'num_class': 14,
'epoch': 5,
'lr': 0.05,
'lr': 0.8,
'min_lr': 1e-6,
'decay_steps': 549,
'warmup_steps': 400000,
@ -56,15 +58,16 @@ config_db = ed({
config_ag = ed({
'vocab_size': 1383812,
'buckets': [64, 128, 467],
'batch_size': 128,
'test_buckets': [467],
'batch_size': 512,
'embedding_dims': 16,
'num_class': 4,
'epoch': 5,
'lr': 0.05,
'lr': 0.2,
'min_lr': 1e-6,
'decay_steps': 115,
'warmup_steps': 400000,
'poly_lr_scheduler_power': 0.5,
'poly_lr_scheduler_power': 0.001,
'epoch_count': 1,
'pretrain_ckpt_dir': None,
'save_ckpt_steps': 116,

4
model_zoo/official/nlp/fasttext/src/fasttext_model.py
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@ -59,9 +59,7 @@ class FastText(nn.Cell):
src_tokens = self.embeding_func(src_tokens)
embeding = self.reducesum(src_tokens, 1)
length_tiled = self.tile(src_token_length, (1, self.embeding_dims))
embeding = self.realdiv(embeding, length_tiled)
embeding = self.realdiv(embeding, src_token_length)
embeding = self.cast(embeding, mstype.float16)
classifer = self.fc(embeding)

8
model_zoo/official/nlp/fasttext/src/load_dataset.py
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@ -13,9 +13,7 @@
# limitations under the License.
# ============================================================================
"""FastText data loader"""
import mindspore.common.dtype as mstype
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as deC
def load_dataset(dataset_path,
@ -37,14 +35,10 @@ def load_dataset(dataset_path,
shuffle=shuffle,
num_shards=rank_size,
shard_id=rank_id,
num_parallel_workers=8)
num_parallel_workers=4)
ori_dataset_size = data_set.get_dataset_size()
print(f"Dataset size: {ori_dataset_size}")
repeat_count = epoch_count
type_cast_op = deC.TypeCast(mstype.int32)
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens")
data_set = data_set.map(operations=type_cast_op, input_columns="src_tokens_length")
data_set = data_set.map(operations=type_cast_op, input_columns="label_idx")
data_set = data_set.rename(input_columns=['src_tokens', 'src_tokens_length', 'label_idx'],
output_columns=['src_token_text', 'src_tokens_text_length', 'label_idx_tag'])

2
model_zoo/official/nlp/fasttext/train.py
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@ -136,7 +136,7 @@ def _build_training_pipeline(pre_dataset):
loss_monitor = LossCallBack(rank_ids=rank_id)
dataset_size = pre_dataset.get_dataset_size()
time_monitor = TimeMonitor(data_size=dataset_size)
ckpt_config = CheckpointConfig(save_checkpoint_steps=decay_steps,
ckpt_config = CheckpointConfig(save_checkpoint_steps=decay_steps * config.epoch,
keep_checkpoint_max=config.keep_ckpt_max)
callbacks = [time_monitor, loss_monitor]
if rank_size is None or int(rank_size) == 1:

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