little change

paddle/fluid/inference/utils/CMakeLists.txt

 cc_library(benchmark SRCS benchmark.cc DEPS enforce)
 cc_test(test_benchmark SRCS benchmark_tester.cc DEPS benchmark)
-cc_binary(visualizer SRCS visualizer.cc DEPS analysis
-    paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)
+#cc_binary(visualizer SRCS visualizer.cc DEPS analysis
+#    paddle_pass_builder ir_pass_manager pass graph_viz_pass analysis_passes)

python/paddle/fluid/imperative/nn.py

@@ -295,7 +295,7 @@ class EMBEDDING(layers.Layer):
 
         self._param_attr = param_attr
         self._dtype = dtype
-        self._remote_prefetch = self.is_sparse and (not self.is_distributed)
+        self._remote_prefetch = self._is_sparse and (not self._is_distributed)
         if self._remote_prefetch:
             assert self._is_sparse is True and self._is_distributed is False
 

python/paddle/fluid/tests/unittests/test_imperative_ptb_rnn.py

@@ -18,23 +18,28 @@ import unittest
 import paddle.fluid as fluid
 from paddle.fluid.imperative.nn import EMBEDDING
 import paddle.fluid.framework as framework
-import paddle.fluid.optimizer as optimizer
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.imperative.base import to_variable
+import numpy as np
 from paddle.fluid.backward import append_backward
 
 
 class SimpleLSTMRNN(fluid.imperative.Layer):
-    def __init__(self, hidden_size, num_layers=2, init_scale=0.1, dropout=None):
+    def __init__(self,
+                 hidden_size,
+                 num_steps,
+                 num_layers=2,
+                 init_scale=0.1,
+                 dropout=None):
+        super(SimpleLSTMRNN, self).__init__()
         self._hidden_size = hidden_size
         self._num_layers = num_layers
         self._init_scale = init_scale
         self._dropout = dropout
         self.input = None
+        self.num_steps = num_steps
 
-    def _build_once(self,
-                    input_embedding,
-                    seq_len,
-                    init_hidden=None,
-                    init_cell=None):
+    def _build_once(self, input_embedding, init_hidden=None, init_cell=None):
         self.weight_1_arr = []
         self.weight_2_arr = []
         self.bias_arr = []
@@ -57,7 +62,7 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
                 default_initializer=fluid.initializer.Constant(0.0))
             self.bias_arr.append(bias_1)
 
-            pre_hidden = self.layers.slice(
+            pre_hidden = fluid.layers.slice(
                 init_hidden, axes=[0], starts=[i], ends=[i + 1])
             pre_cell = fluid.layers.slice(
                 init_cell, axes=[0], starts=[i], ends=[i + 1])
@@ -65,22 +70,20 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
                 pre_hidden, shape=[-1, self._hidden_size])
             pre_cell = fluid.layers.reshape(
                 pre_cell, shape=[-1, self._hidden_size])
-            fluid.hidden_array.append(pre_hidden)
-            fluid.cell_array.append(pre_cell)
-
-    def forward(self,
-                input_embedding,
-                seq_len,
-                init_hidden=None,
-                init_cell=None):
+            self.hidden_array.append(pre_hidden)
+            self.cell_array.append(pre_cell)
+
+    def forward(self, input_embedding, init_hidden=None, init_cell=None):
         res = []
-        for index in range(seq_len):
+        for index in range(self.num_steps):
             self.input = fluid.layers.slice(
                 input_embedding, axes=[1], starts=[index], ends=[index + 1])
             self.input = fluid.layers.reshape(
                 self.input, shape=[-1, self._hidden_size])
             for k in range(self._num_layers):
                 pre_hidden = self.hidden_array[k]
+                print("pre_hidden shape is:{}".format(pre_hidden.shape))
+                print("input shape is:{}".format(self.input.shape))
                 pre_cell = self.cell_array[k]
                 weight_1 = self.weight_1_arr[k]
                 bias = self.bias_arr[k]
@@ -89,38 +92,41 @@ class SimpleLSTMRNN(fluid.imperative.Layer):
                 gate_input = fluid.layers.matmul(x=nn, y=weight_1)
 
                 gate_input = fluid.layers.elementwise_add(gate_input, bias)
-                i, j, f, o = fluid.layers.split(
-                    gate_input, num_or_sections=4, dim=-1)
-
-                c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
-                    i) * fluid.layers.tanh(j)
-                m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
-
-                self.hidden_array[k] = m
-                self.cell_array[k] = c
-                self.input = m
-
-                if self.dropout is not None and self.dropout > 0.0:
-                    self.input = fluid.layers.dropout(
-                        self.input,
-                        dropout_prob=self.dropout,
-                        dropout_implementation='upscale_in_train')
-
-            res.append(
-                fluid.layers.reshape(
-                    input, shape=[1, -1, self._hidden_size]))
-        real_res = fluid.layers.concat(res, 0)
-        real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
-        last_hidden = fluid.layers.concat(self.hidden_array, 1)
-        last_hidden = fluid.layers.reshape(
-            last_hidden, shape=[-1, self._num_layers, self._hidden_size])
-        last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
-        last_cell = fluid.layers.concat(self.cell_array, 1)
-        last_cell = fluid.layers.reshape(
-            last_cell, shape=[-1, self._num_layers, self._hidden_size])
-        last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
-
-        return real_res, last_hidden, last_cell
+                print("gate_input shape is: {}".format(gate_input.shape))
+                print("gate_input value is :{}".format(gate_input._numpy()))
+                print("gate_input desc is :{}".format(gate_input))
+                # i, j, f, o = fluid.layers.split(gate_input, num_or_sections=4, dim=-1)
+            #         #
+            #         #         c = pre_cell * fluid.layers.sigmoid(f) + fluid.layers.sigmoid(
+            #         #             i) * fluid.layers.tanh(j)
+            #         #         m = fluid.layers.tanh(c) * fluid.layers.sigmoid(o)
+            #         #
+            #         #         self.hidden_array[k] = m
+            #         #         self.cell_array[k] = c
+            #         #         self.input = m
+            #         #
+            #         #         if self.dropout is not None and self.dropout > 0.0:
+            #         #             self.input = fluid.layers.dropout(
+            #         #                 self.input,
+            #         #                 dropout_prob=self.dropout,
+            #         #                 dropout_implementation='upscale_in_train')
+            #         #
+            #         #     res.append(
+            #         #         fluid.layers.reshape(
+            #         #             input, shape=[1, -1, self._hidden_size]))
+            #         # real_res = fluid.layers.concat(res, 0)
+            #         # real_res = fluid.layers.transpose(x=real_res, perm=[1, 0, 2])
+            #         # last_hidden = fluid.layers.concat(self.hidden_array, 1)
+            #         # last_hidden = fluid.layers.reshape(
+            #         #     last_hidden, shape=[-1, self._num_layers, self._hidden_size])
+            #         # last_hidden = fluid.layers.transpose(x=last_hidden, perm=[1, 0, 2])
+            #         # last_cell = fluid.layers.concat(self.cell_array, 1)
+            #         # last_cell = fluid.layers.reshape(
+            #         #     last_cell, shape=[-1, self._num_layers, self._hidden_size])
+            #         # last_cell = fluid.layers.transpose(x=last_cell, perm=[1, 0, 2])
+            #         #
+            # return real_res, last_hidden, last_cell
+        return [1], [2], [3]
 
 
 class PtbModel(fluid.imperative.Layer):
@@ -137,8 +143,10 @@ class PtbModel(fluid.imperative.Layer):
         self.init_scale = init_scale
         self.num_layers = num_layers
         self.num_steps = num_steps
+        self.dropout = dropout
         self.simple_lstm_rnn = SimpleLSTMRNN(
             hidden_size,
+            num_steps,
             num_layers=num_layers,
             init_scale=init_scale,
             dropout=dropout)
@@ -153,21 +161,23 @@ class PtbModel(fluid.imperative.Layer):
 
     def _build_once(self, input, label, init_hidden, init_cell):
         self.softmax_weight = fluid.layers.create_parameter(
-            [self._hidden_size, self._vocab_size],
+            [self.hidden_size, self.vocab_size],
             dtype="float32",
             name="softmax_weight",
             default_initializer=fluid.initializer.UniformInitializer(
-                low=-self._init_scale, high=self._init_scale))
+                low=-self.init_scale, high=self.init_scale))
         self.softmax_bias = fluid.layers.create_parameter(
-            [self._vocab_size],
+            [self.vocab_size],
             dtype="float32",
             name='softmax_bias',
             default_initializer=fluid.initializer.UniformInitializer(
-                low=-self._init_scale, high=self._init_scale))
+                low=-self.init_scale, high=self.init_scale))
 
     def forward(self, input, label, init_hidden, init_cell):
+
         init_h = fluid.layers.reshape(
             init_hidden, shape=[self.num_layers, -1, self.hidden_size])
+
         init_c = fluid.layers.reshape(
             init_cell, shape=[self.num_layers, -1, self.hidden_size])
 
@@ -179,6 +189,7 @@ class PtbModel(fluid.imperative.Layer):
                 x_emb,
                 dropout_prob=self.drop_out,
                 dropout_implementation='upscale_in_train')
+        print("init_c is {}".format(init_c))
         rnn_out, last_hidden, last_cell = self.simple_lstm_rnn(x_emb, init_h,
                                                                init_c)
         rnn_out = fluid.layers.reshape(
@@ -202,14 +213,53 @@ class PtbModel(fluid.imperative.Layer):
 class TestImperativePtbRnn(unittest.TestCase):
     def test_mnist_cpu_float32(self):
         seed = 90
+        hidden_size = 10
+        vocab_size = 1000
+        num_layers = 1
+        num_steps = 3
+        init_scale = 0.1
+        batch_size = 4
 
         with fluid.imperative.guard():
             fluid.default_startup_program().random_seed = seed
             fluid.default_main_program().random_seed = seed
             # TODO: marsyang1993 Change seed to
             ptb_model = PtbModel(
-                hidden_size=10,
-                vocab_size=1000,
-                num_layers=1,
-                num_steps=3,
-                init_scale=0.1)
+                hidden_size=hidden_size,
+                vocab_size=vocab_size,
+                num_layers=num_layers,
+                num_steps=num_steps,
+                init_scale=init_scale)
+
+            sgd = SGDOptimizer(learning_rate=1e-3)
+            print("q")
+            for i in range(2):
+                x_data = np.arange(12).reshape(4, 3).astype('int64')
+                y_data = np.arange(1, 13).reshape(4, 3).astype('int64')
+                x_data = x_data.reshape((-1, num_steps, 1))
+                y_data = y_data.reshape((-1, 1))
+                init_hidden_data = np.zeros(
+                    (num_layers, batch_size, hidden_size), dtype='float32')
+                init_cell_data = np.zeros(
+                    (num_layers, batch_size, hidden_size), dtype='float32')
+                x = to_variable(x_data)
+                y = to_variable(y_data)
+                init_hidden = to_variable(init_hidden_data)
+                init_cell = to_variable(init_cell_data)
+                dy_loss, last_hidden, last_cell = ptb_model(x, y, init_hidden,
+                                                            init_cell)
+                dy_param_init = dict()
+                if i == 0:
+                    for param in fluid.default_main_program().global_block(
+                    ).all_parameters():
+                        dy_param_init[param.name] = param._numpy()
+                dy_loss._backward()
+                sgd.minimize(dy_loss)
+                dy_param_updated = dict()
+                for param in fluid.default_main_program().global_block(
+                ).all_parameters():
+                    dy_param_updated[param.name] = param._numpy()
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/test_imperative_split.py

+# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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.
+
+from __future__ import print_function
+
+import unittest
+import paddle.fluid as fluid
+from paddle.fluid.imperative.nn import EMBEDDING
+import paddle.fluid.framework as framework
+from paddle.fluid.optimizer import SGDOptimizer
+from paddle.fluid.imperative.base import to_variable
+import numpy as np
+
+
+class Split_test(fluid.imperative.Layer):
+    def __init__(self):
+        super(Split_test, self).__init__()
+
+    def _build_once(self, input):
+        pass
+
+    def forward(self, input):
+        out = fluid.layers.split(input, num_or_sections=4, dim=-1)
+        return out
+
+
+class TestImperativePtbRnn(unittest.TestCase):
+    def test_spilt(self):
+        with fluid.imperative.guard():
+            inp = to_variable(np.arange(160).reshape(4, 40).astype('float32'))
+            st = Split_test()
+            out = st(inp)
+            print(out)
+
+
+if __name__ == '__main__':
+    unittest.main()