add lstm api support (#2545)

* add lstm api support; test=develop

* change name from basic_api to basic_lstm; test=develop

* fix format problem; test=develop

PaddleNLP/language_model/args.py

@@ -40,7 +40,7 @@ def parse_args():
         "--rnn_model",
         type=str,
         default="static",
-        help="model_type [static|padding|cudnn]")
+        help="model_type [static|padding|cudnn|basic_lstm]")
     parser.add_argument(
         "--data_path", type=str, help="all the data for train,valid,test")
     parser.add_argument('--para_init', action='store_true')

PaddleNLP/language_model/config.py

@@ -70,7 +70,7 @@ class RNNConfig(object):
         else:
             raise ValueError('Unsupported model_type.')
 
-        if args.rnn_model not in ('static', 'padding', 'cudnn'):
+        if args.rnn_model not in ('static', 'padding', 'cudnn', 'basic_lstm'):
             raise ValueError('Unsupported rnn_model.')
 
         if args.batch_size > 0:

PaddleNLP/language_model/run.sh

@@ -9,4 +9,4 @@ function run_train() {
         --use_gpu True
 }
 
-run_train
\ No newline at end of file
+run_train

PaddleNLP/language_model/train.py

@@ -233,7 +233,7 @@ def main():
                 program=inference_program,
                 feed=input_data_feed,
                 fetch_list=[loss.name, last_hidden.name, last_cell.name],
-                use_program_cache=True)
+                use_program_cache=False)
 
             cost_eval = np.array(fetch_outs[0])
             init_hidden = np.array(fetch_outs[1])
@@ -259,12 +259,9 @@ def main():
 
         total_loss = 0
         iters = 0
+
+        init_hidden, init_cell = generate_init_data()
         for batch_id, batch in enumerate(train_data_iter):
-            if batch_id == 0:
-                init_hidden, init_cell = generate_init_data()
-            else:
-                init_hidden = None
-                init_cell = None
             input_data_feed = prepare_input(
                 batch,
                 init_hidden=init_hidden,
@@ -276,13 +273,16 @@ def main():
             batch_start_time = time.time()
             fetch_outs = exe.run(train_program,
                                  feed=input_data_feed,
-                                 fetch_list=[loss.name, "learning_rate"],
+                                 fetch_list=[loss.name, "learning_rate", \
+                                             last_hidden.name, last_cell.name ],
                                  use_program_cache=True)
             batch_time = time.time() - batch_start_time
             batch_times.append(batch_time)
 
             cost_train = np.array(fetch_outs[0])
             lr = np.array(fetch_outs[1])
+            init_hidden = np.array(fetch_outs[2])
+            init_cell = np.array(fetch_outs[3])
 
             total_loss += cost_train
             iters += config.num_steps
@@ -312,8 +312,6 @@ def main():
                 if batch_id == 0:
                     batch_time = 0
                     batch_start_time = time.time()
-                    data_feeds["init_hidden"] = init_hidden
-                    data_feeds["init_cell"] = init_cell
                 else:
                     batch_time = time.time() - batch_start_time
                     batch_times.append(batch_time)
@@ -321,14 +319,19 @@ def main():
 
                 new_lr = generate_new_lr(epoch_id, device_count)
                 data_feeds['learning_rate'] = new_lr
+                data_feeds["init_hidden"] = init_hidden
+                data_feeds["init_cell"] = init_cell
 
                 fetch_outs = exe.run(train_program,
                                      feed=data_feeds,
-                                     fetch_list=[loss.name, "learning_rate"],
+                                     fetch_list=[loss.name, "learning_rate", \
+                                             last_hidden.name, last_cell.name ],
                                      use_program_cache=True)
 
                 cost_train = np.array(fetch_outs[0])
                 lr = np.array(fetch_outs[1])
+                init_hidden = np.array(fetch_list[2])
+                init_cell = np.array( fetch_list[3] )
 
                 total_loss += cost_train
                 iters += config.num_steps

PaddleNLP/models/language_model/lm_model.py

@@ -20,6 +20,8 @@ import paddle.fluid.layers as layers
 import paddle.fluid as fluid
 from paddle.fluid.layers.control_flow import StaticRNN as PaddingRNN
 import numpy as np
+from paddle.fluid import ParamAttr
+from paddle.fluid.contrib.layers import basic_lstm
 
 
 def lm_model(hidden_size,
@@ -358,6 +360,10 @@ def lm_model(hidden_size,
             default_initializer=fluid.initializer.UniformInitializer(
                 low=-init_scale, high=init_scale))
         rnn_out = layers.transpose(rnn_out, perm=[1, 0, 2])
+    elif rnn_model == "basic_lstm":
+        print("basic api")
+        rnn_out, last_hidden, last_cell = basic_lstm( x_emb, init_hidden, init_cell, hidden_size, num_layers=num_layers, \
+                batch_first=True, dropout_prob=dropout, param_attr = ParamAttr( initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale) ), bias_attr = ParamAttr( initializer = fluid.initializer.Constant(0.0) ))
     else:
         print("type not support")
         return