Increase performance of fasttext

model_zoo/official/nlp/fasttext/eval.py

@@ -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)

model_zoo/official/nlp/fasttext/scripts/create_dataset.sh

@@ -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
 

model_zoo/official/nlp/fasttext/scripts/run_eval.sh

@@ -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

model_zoo/official/nlp/fasttext/scripts/run_standalone_train.sh

@@ -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

model_zoo/official/nlp/fasttext/src/config.py

@@ -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,

model_zoo/official/nlp/fasttext/src/fasttext_model.py

@@ -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)

model_zoo/official/nlp/fasttext/src/load_dataset.py

@@ -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'])

model_zoo/official/nlp/fasttext/train.py

@@ -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: