raise textrcnn precision on DynamicRNN

model_zoo/official/nlp/textrcnn/readme.md

@@ -114,13 +114,16 @@ Parameters for both training and evaluation can be set in config.py
   ```python
   'num_epochs': 10, # total training epochs
   'batch_size': 64, # training batch size
-  'cell': 'lstm', # the RNN architecture, can be 'vanilla', 'gru' and 'lstm'.
+  'cell': 'gru', # the RNN architecture, can be 'vanilla', 'gru' and 'lstm'.
   'opt': 'adam', # the optimizer strategy, can be 'adam' or 'momentum'
   'ckpt_folder_path': './ckpt', # the path to save the checkpoints
   'preprocess_path': './preprocess', # the directory to save the processed data
   'preprocess' : 'false', # whethere to preprocess the data
   'data_path': './data/', # the path to store the splited data
   'lr': 1e-3, # the training learning rate
+  'lstm_base_lr': 3e-3, # the training learning rate when using lstm as RNN cell
+  'lstm_decay_rate': 0.9, # lr decay rate when using lstm as RNN cell
+  'lstm_decay_epoch': 1, # lr decay epoch when using lstm as RNN cell
   'emb_path': './word2vec', # the directory to save the embedding file
   'embed_size': 300, # the dimension of the word embedding
   'save_checkpoint_steps': 149, # per step to save the checkpoint
@@ -137,7 +140,7 @@ Parameters for both training and evaluation can be set in config.py
 | Dataset                    | Sentence polarity dataset v1.0                    | Sentence polarity dataset v1.0            |
 | batch_size                 | 64                        | 64                   |
 | Accuracy                   | 0.78                      | 0.78 |
-| Speed                      | 78ms/step                  |  89ms/step                         |
+| Speed                      | 25ms/step                  |  77ms/step                         |
 
 ## [ModelZoo Homepage](#contents)
 

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

@@ -23,13 +23,16 @@ textrcnn_cfg = edict({
     'neg_dir': 'data/rt-polaritydata/rt-polarity.neg',
     'num_epochs': 10,
     'batch_size': 64,
-    'cell': 'lstm',
+    'cell': 'gru',
     'opt': 'adam',
     'ckpt_folder_path': './ckpt',
     'preprocess_path': './preprocess',
     'preprocess': 'false',
     'data_path': './data/',
     'lr': 1e-3,
+    'lstm_base_lr': 3e-3,
+    'lstm_decay_rate': 0.9,
+    'lstm_decay_epoch': 1,
     'emb_path': './word2vec',
     'embed_size': 300,
     'save_checkpoint_steps': 149,

model_zoo/official/nlp/textrcnn/src/textrcnn.py

@@ -45,16 +45,16 @@ class textrcnn(nn.Cell):
             self.lstm = P.DynamicRNN(forget_bias=0.0)
             self.w1_fw = Parameter(
                 np.random.uniform(-k, k, (self.embed_size + self.num_hiddens, 4 * self.num_hiddens)).astype(
-                    np.float16), name="w1_fw")
-            self.b1_fw = Parameter(np.random.uniform(-k, k, (4 * self.num_hiddens)).astype(np.float16),
+                    np.float32), name="w1_fw")
+            self.b1_fw = Parameter(np.random.uniform(-k, k, (4 * self.num_hiddens)).astype(np.float32),
                                    name="b1_fw")
             self.w1_bw = Parameter(
                 np.random.uniform(-k, k, (self.embed_size + self.num_hiddens, 4 * self.num_hiddens)).astype(
-                    np.float16), name="w1_bw")
-            self.b1_bw = Parameter(np.random.uniform(-k, k, (4 * self.num_hiddens)).astype(np.float16),
+                    np.float32), name="w1_bw")
+            self.b1_bw = Parameter(np.random.uniform(-k, k, (4 * self.num_hiddens)).astype(np.float32),
                                    name="b1_bw")
-            self.h1 = Tensor(np.zeros(shape=(1, self.batch_size, self.num_hiddens)).astype(np.float16))
-            self.c1 = Tensor(np.zeros(shape=(1, self.batch_size, self.num_hiddens)).astype(np.float16))
+            self.h1 = Tensor(np.zeros(shape=(1, self.batch_size, self.num_hiddens)).astype(np.float32))
+            self.c1 = Tensor(np.zeros(shape=(1, self.batch_size, self.num_hiddens)).astype(np.float32))
 
         if cell == "vanilla":
             self.rnnW_fw = nn.Dense(self.num_hiddens, self.num_hiddens)

model_zoo/official/nlp/textrcnn/src/utils.py

@@ -0,0 +1,29 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""training utils"""
+from mindspore import dtype as mstype
+from mindspore.nn.dynamic_lr import exponential_decay_lr
+from mindspore import Tensor
+
+
+def get_lr(cfg, dataset_size):
+    if cfg.cell == "lstm":
+        lr = exponential_decay_lr(cfg.lstm_base_lr, cfg.lstm_decay_rate, dataset_size * cfg.num_epochs,
+                                  dataset_size,
+                                  cfg.lstm_decay_epoch)
+        lr_ret = Tensor(lr, mstype.float32)
+    else:
+        lr_ret = cfg.lr
+    return lr_ret

model_zoo/official/nlp/textrcnn/train.py

@@ -29,6 +29,7 @@ from src.config import textrcnn_cfg as cfg
 from src.dataset import create_dataset
 from src.dataset import convert_to_mindrecord
 from src.textrcnn import textrcnn
+from src.utils import get_lr
 
 
 set_seed(1)
@@ -50,25 +51,31 @@ if __name__ == '__main__':
         os.mkdir(cfg.preprocess_path)
         convert_to_mindrecord(cfg.embed_size, cfg.data_path, cfg.preprocess_path, cfg.emb_path)
 
+    if cfg.cell == "vanilla":
+        print("============ Precision is lower than expected when using vanilla RNN architecture ===========")
+
     embedding_table = np.loadtxt(os.path.join(cfg.preprocess_path, "weight.txt")).astype(np.float32)
 
     network = textrcnn(weight=Tensor(embedding_table), vocab_size=embedding_table.shape[0], \
                        cell=cfg.cell, batch_size=cfg.batch_size)
 
+    ds_train = create_dataset(cfg.preprocess_path, cfg.batch_size, cfg.num_epochs, True)
+    step_size = ds_train.get_dataset_size()
+
     loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True)
+    lr = get_lr(cfg, step_size)
+
     if cfg.opt == "adam":
-        opt = nn.Adam(params=network.trainable_params(), learning_rate=cfg.lr)
+        opt = nn.Adam(params=network.trainable_params(), learning_rate=lr)
     elif cfg.opt == "momentum":
-        opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
+        opt = nn.Momentum(network.trainable_params(), lr, cfg.momentum)
 
     loss_cb = LossMonitor()
     model = Model(network, loss, opt, {'acc': Accuracy()}, amp_level="O3")
 
     print("============== Starting Training ==============")
-    ds_train = create_dataset(cfg.preprocess_path, cfg.batch_size, cfg.num_epochs, True)
     config_ck = CheckpointConfig(save_checkpoint_steps=cfg.save_checkpoint_steps, \
                                  keep_checkpoint_max=cfg.keep_checkpoint_max)
     ckpoint_cb = ModelCheckpoint(prefix=cfg.cell, directory=cfg.ckpt_folder_path, config=config_ck)
     model.train(cfg.num_epochs, ds_train, callbacks=[ckpoint_cb, loss_cb])
     print("train success")
-