Cherry pick 1.4/ptb fix

python/paddle/fluid/dygraph/layer_object_helper.py

@@ -91,6 +91,10 @@ def iter_inputs_and_params(self, inputs_in, param_attr_in=None):
 
         Returns input, param_attr
         """
+        param_attr_in = ParamAttr._to_attr(param_attr_in)
+        if isinstance(param_attr_in, bool):
+            raise ValueError('Param_attr should not be False in {}'.format(
+                self.name))
         inputs = inputs_in if (inputs_in is not None) else []
         inputs = self._multiple_input(inputs)
         param_attrs = self._multiple_param_attr(len(inputs), param_attr_in)

python/paddle/fluid/dygraph/nn.py

@@ -20,7 +20,7 @@
 from .. import core
 from ..layers import utils
 from . import layers
-from ..framework import Variable, OpProtoHolder
+from ..framework import Variable, OpProtoHolder, Parameter
 from ..layers import layer_function_generator
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant, NumpyArrayInitializer
@@ -213,46 +213,69 @@ def __init__(self,
         self._param_attr = param_attr
         self._bias_attr = bias_attr
         self._act = act
+        self.__w = list()
 
-    def _build_once(self, input):
-        input_shape = input.shape
-        param_shape = [
-            reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:], 1)
-        ] + [self._size]
-        self._w = self.create_parameter(
-            attr=self._param_attr,
-            shape=param_shape,
-            dtype=self._dtype,
-            is_bias=False)
+    @property
+    def _w(self, i=0):
+        return self.__w[i]
 
-        if self._bias_attr:
-            size = list([self._size])
-            self._b = self.create_parameter(
-                attr=self._bias_attr,
-                shape=size,
-                dtype=self._dtype,
-                is_bias=True)
-        else:
-            self._b = None
+    @_w.setter
+    def _w(self, value, i=0):
+        assert isinstance(value, Parameter)
+        self.__w[i] = value
 
-    def forward(self, input):
-        tmp = self._helper.create_variable_for_type_inference(self._dtype)
-        self._helper.append_op(
-            type="mul",
-            inputs={"X": input,
-                    "Y": self._w},
-            outputs={"Out": tmp},
-            attrs={
-                "x_num_col_dims": self._num_flatten_dims,
-                "y_num_col_dims": 1
-            })
+    def _build_once(self, input):
+        i = 0
+        for inp, param in self._helper.iter_inputs_and_params(input,
+                                                              self._param_attr):
+            input_shape = inp.shape
+
+            param_shape = [
+                reduce(lambda a, b: a * b, input_shape[self._num_flatten_dims:],
+                       1)
+            ] + [self._size]
+            self.__w.append(
+                self.add_parameter(
+                    '_w%d' % i,
+                    self.create_parameter(
+                        attr=param,
+                        shape=param_shape,
+                        dtype=self._dtype,
+                        is_bias=False)))
+            i += 1
+
+        size = list([self._size])
+        self._b = self.create_parameter(
+            attr=self._bias_attr, shape=size, dtype=self._dtype, is_bias=True)
 
-        pre_bias = self._helper.create_variable_for_type_inference(self._dtype)
-        self._helper.append_op(
-            type="sum",
-            inputs={"X": [tmp]},
-            outputs={"Out": pre_bias},
-            attrs={"use_mkldnn": False})
+    def forward(self, input):
+        mul_results = list()
+        i = 0
+        for inp, param in self._helper.iter_inputs_and_params(input,
+                                                              self._param_attr):
+            tmp = self._helper.create_variable_for_type_inference(self._dtype)
+            self._helper.append_op(
+                type="mul",
+                inputs={"X": inp,
+                        "Y": self.__w[i]},
+                outputs={"Out": tmp},
+                attrs={
+                    "x_num_col_dims": self._num_flatten_dims,
+                    "y_num_col_dims": 1
+                })
+            i += 1
+            mul_results.append(tmp)
+
+        if len(mul_results) == 1:
+            pre_bias = mul_results[0]
+        else:
+            pre_bias = self._helper.create_variable_for_type_inference(
+                self._dtype)
+            self._helper.append_op(
+                type="sum",
+                inputs={"X": mul_results},
+                outputs={"Out": pre_bias},
+                attrs={"use_mkldnn": False})
 
         if self._b:
             pre_activation = self._helper.create_variable_for_type_inference(

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

@@ -200,8 +200,6 @@ def forward(self, input, label, init_hidden, init_cell):
             rnn_out, shape=[-1, self.num_steps, self.hidden_size])
         projection = fluid.layers.matmul(rnn_out, self.softmax_weight)
         projection = fluid.layers.elementwise_add(projection, self.softmax_bias)
-        projection = fluid.layers.reshape(
-            projection, shape=[-1, self.vocab_size])
         projection = fluid.layers.reshape(
             projection, shape=[-1, self.vocab_size])
         loss = fluid.layers.softmax_with_cross_entropy(
@@ -223,6 +221,7 @@ def test_ptb_rnn_cpu_float32(self):
         num_steps = 3
         init_scale = 0.1
         batch_size = 4
+        batch_num = 200
 
         with fluid.dygraph.guard():
             fluid.default_startup_program().random_seed = seed
@@ -242,7 +241,6 @@ def test_ptb_rnn_cpu_float32(self):
             dy_loss = None
             last_hidden = None
             last_cell = None
-            batch_num = 200
 
             for i in range(batch_num):
                 x_data = np.arange(12).reshape(4, 3).astype('int64')
@@ -282,7 +280,8 @@ def test_ptb_rnn_cpu_float32(self):
 
             exe = fluid.Executor(fluid.CPUPlace())
             sgd = SGDOptimizer(learning_rate=1e-3)
-            x = fluid.layers.data(name="x", shape=[-1, 3, 1], dtype='int64')
+            x = fluid.layers.data(
+                name="x", shape=[-1, num_steps, 1], dtype='int64')
             y = fluid.layers.data(name="y", shape=[-1, 1], dtype='float32')
             init_hidden = fluid.layers.data(
                 name="init_hidden", shape=[1], dtype='float32')
@@ -332,21 +331,18 @@ def test_ptb_rnn_cpu_float32(self):
                     for k in range(3, len(out)):
                         static_param_updated[static_param_name_list[k -
                                                                     3]] = out[k]
-
         self.assertTrue(np.allclose(static_loss_value, dy_loss._numpy()))
         self.assertTrue(np.allclose(static_last_cell_value, last_cell._numpy()))
         self.assertTrue(
             np.allclose(static_last_hidden_value, last_hidden._numpy()))
         for key, value in six.iteritems(static_param_init):
             # print("static_init name: {}, value {}".format(key, value))
             # print("dy_init name: {}, value {}".format(key, dy_param_init[key]))
-            self.assertTrue(np.allclose(value, dy_param_init[key], atol=1e-5))
+            self.assertTrue(np.allclose(value, dy_param_init[key]))
         for key, value in six.iteritems(static_param_updated):
             # print("static name: {}, value {}".format(key, value))
             # print("dy name: {}, value {}".format(key, dy_param_updated[key]))
-            self.assertTrue(
-                np.allclose(
-                    value, dy_param_updated[key], atol=1e-5))
+            self.assertTrue(np.allclose(value, dy_param_updated[key]))
 
 
 if __name__ == '__main__':