Merge pull request #1493 from phlrain/add_cudnn_lm

add cudnn lm

fluid/PaddleNLP/language_model/lstm/.run_ce.sh

@@ -7,5 +7,6 @@ python  train.py \
     --data_path data/simple-examples/data/ \
     --model_type small \
     --use_gpu True \
+    --rnn_model static \
     --enable_ce | python _ce.py
 

fluid/PaddleNLP/language_model/lstm/args.py

@@ -26,7 +26,12 @@ def parse_args():
         "--model_type",
         type=str,
         default="small",
-        help="model_type [test|small|med|big]")
+        help="model_type [test|small|medium|large]")
+    parser.add_argument(
+        "--rnn_model",
+        type=str,
+        default="static",
+        help="model_type [static|padding|cudnn]")
     parser.add_argument(
         "--data_path", type=str, help="all the data for train,valid,test")
     parser.add_argument('--para_init', action='store_true')

fluid/PaddleNLP/language_model/lstm/lm_model.py

@@ -28,7 +28,8 @@ def lm_model(hidden_size,
              num_layers=2,
              num_steps=20,
              init_scale=0.1,
-             dropout=None):
+             dropout=None, 
+             rnn_model='static'):
     def padding_rnn(input_embedding, len=3, init_hidden=None, init_cell=None):
         weight_1_arr = []
         weight_2_arr = []
@@ -243,7 +244,7 @@ def lm_model(hidden_size,
         input=x,
         size=[vocab_size, hidden_size],
         dtype='float32',
-        is_sparse=True,
+        is_sparse=False,
         param_attr=fluid.ParamAttr(
             name='embedding_para',
             initializer=fluid.initializer.UniformInitializer(
@@ -256,8 +257,21 @@ def lm_model(hidden_size,
             dropout_prob=dropout,
             dropout_implementation='upscale_in_train')
     
+    if rnn_model == "padding":
         rnn_out, last_hidden, last_cell = padding_rnn(
             x_emb, len=num_steps, init_hidden=init_hidden, init_cell=init_cell)
+    elif rnn_model == "static":
+        rnn_out, last_hidden, last_cell = encoder_static(
+            x_emb, len=num_steps, init_hidden=init_hidden, init_cell=init_cell)
+    elif rnn_model == "cudnn":
+        x_emb = layers.transpose( x_emb, perm=[1, 0, 2])
+        rnn_out, last_hidden, last_cell = layers.lstm( x_emb, init_hidden, init_cell,  num_steps, hidden_size, num_layers, \
+                is_bidirec=False, \
+                default_initializer=fluid.initializer.UniformInitializer(low=-init_scale, high=init_scale) )
+        rnn_out = layers.transpose( rnn_out, perm=[1, 0, 2])
+    else:
+        print( "type not support")
+        return
     rnn_out = layers.reshape(rnn_out, shape=[-1, num_steps, hidden_size])
 
 

fluid/PaddleNLP/language_model/lstm/train.py

@@ -77,6 +77,7 @@ def save_para_npz(train_prog, train_exe):
 def train():
     args = parse_args()
     model_type = args.model_type
+    rnn_model = args.rnn_model
     logger = logging.getLogger("lm")
     logger.setLevel(logging.INFO)
     formatter = logging.Formatter(
@@ -157,7 +158,8 @@ def train():
         num_layers=num_layers,
         num_steps=num_steps,
         init_scale=init_scale,
-        dropout=dropout)
+        dropout=dropout, 
+        rnn_model=rnn_model)
     # clone from default main program and use it as the validation program
     main_program = fluid.default_main_program()
     inference_program = fluid.default_main_program().clone(for_test=True)
@@ -206,18 +208,19 @@ def train():
 
     def eval(data):
         # when eval the batch_size set to 1
-        eval_data_iter = reader.get_data_iter(data, 1, num_steps)
+        eval_data_iter = reader.get_data_iter(data, batch_size, num_steps)
         total_loss = 0.0
         iters = 0
-        init_hidden = np.zeros((num_layers, 1, hidden_size), dtype='float32')
-        init_cell = np.zeros((num_layers, 1, hidden_size), dtype='float32')
+        init_hidden = np.zeros((num_layers, batch_size, hidden_size), dtype='float32')
+        init_cell = np.zeros((num_layers, batch_size, hidden_size), dtype='float32')
         for batch_id, batch in enumerate(eval_data_iter):
             input_data_feed = prepare_input(
                 batch, init_hidden, init_cell, epoch_id, with_lr=False)
             fetch_outs = exe.run(
                 inference_program,
                 feed=input_data_feed,
-                fetch_list=[loss.name, last_hidden.name, last_cell.name])
+                fetch_list=[loss.name, last_hidden.name, last_cell.name],
+                use_program_cache=True)
 
             cost_train = np.array(fetch_outs[0])
             init_hidden = np.array(fetch_outs[1])