fix split

paddle/fluid/framework/operator.cc

@@ -555,18 +555,17 @@ Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const {
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const {
-  auto names = op().Outputs(name);
+  auto it = ctx_.outputs.find(name);
+  if (it == ctx_.outputs.end()) {
+    return {};
+  }
+  const std::vector<Variable*>& vars = it->second;
   std::vector<Tensor*> res;
-  res.reserve(names.size());
-  std::transform(names.begin(), names.end(), std::back_inserter(res),
-                 [&](const std::string& sub_name) -> Tensor* {
-                   auto var = scope_.FindVar(sub_name);
-                   if (var == nullptr) return nullptr;
-                   PADDLE_ENFORCE(
-                       var->IsType<LoDTensor>(),
-                       "%s should be LoDTensor, but the received type is %s",
-                       sub_name, ToTypeName(var->Type()));
-                   return var->GetMutable<LoDTensor>();
+  res.reserve(vars.size());
+  std::transform(vars.begin(), vars.end(), std::back_inserter(res),
+                 [&](Variable* var) -> Tensor* {
+                   return var == nullptr ? nullptr
+                                         : var->GetMutable<LoDTensor>();
                  });
   return res;
 }

python/paddle/fluid/imperative/nn.py

@@ -22,13 +22,7 @@ from . import layers
 from ..framework import Variable, OpProtoHolder
 from ..param_attr import ParamAttr
 from ..initializer import Normal, Constant
-__all__ = [
-    'Conv2D',
-    'Pool2D',
-    'FC',
-    'BatchNorm',
-    'EMBEDDING'
-]
+__all__ = ['Conv2D', 'Pool2D', 'FC', 'BatchNorm', 'EMBEDDING']
 
 
 class Conv2D(layers.Layer):
@@ -419,8 +413,6 @@ class BatchNorm(layers.Layer):
 
         # Currently, we don't support inplace in imperative mode
         return self._helper.append_activation(batch_norm_out)
-        outputs={'Out': [bias_out]},
-
 
 
 class EMBEDDING(layers.Layer):

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

@@ -338,7 +338,6 @@ class TestImperative(unittest.TestCase):
             dy_grad_i2h = simple_rnn._cell._i2h_w._gradient()
 
         with new_program_scope():
-            print("im here")
             inp = fluid.layers.data(
                 name="inp", shape=[1, 4, 3], append_batch_size=False)
             simple_rnn = SimpleRNN()

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

+#   Copyright (c) 2018 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
+from paddle.fluid.backward import append_backward
+
+
+class SimpleLSTMRNN(fluid.imperative.Layer):
+    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, init_hidden=None, init_cell=None):
+        self.weight_1_arr = []
+        self.weight_2_arr = []
+        self.bias_arr = []
+        self.hidden_array = []
+        self.cell_array = []
+        self.mask_array = []
+
+        for i in range(self._num_layers):
+            weight_1 = fluid.layers.create_parameter(
+                shape=[self._hidden_size * 2, self._hidden_size * 4],
+                dtype="float32",
+                name="fc_weight1_" + str(i),
+                default_initializer=fluid.initializer.UniformInitializer(
+                    low=-self._init_scale, high=self._init_scale))
+            self.weight_1_arr.append(weight_1)
+            bias_1 = fluid.layers.create_parameter(
+                [self._hidden_size * 4],
+                dtype="float32",
+                name="fc_bias1_" + str(i),
+                default_initializer=fluid.initializer.Constant(0.0))
+            self.bias_arr.append(bias_1)
+
+            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])
+            pre_hidden = fluid.layers.reshape(
+                pre_hidden, shape=[-1, self._hidden_size])
+            pre_cell = fluid.layers.reshape(
+                pre_cell, shape=[-1, self._hidden_size])
+            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(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]
+
+                nn = fluid.layers.concat([self.input, pre_hidden], 1)
+                gate_input = fluid.layers.matmul(x=nn, y=weight_1)
+
+                gate_input = fluid.layers.elementwise_add(gate_input, bias)
+                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):
+    def __init__(self,
+                 hidden_size,
+                 vocab_size,
+                 num_layers=2,
+                 num_steps=20,
+                 init_scale=0.1,
+                 dropout=None):
+        super(PtbModel, self).__init__()
+        self.hidden_size = hidden_size
+        self.vocab_size = vocab_size
+        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)
+        self.embedding = EMBEDDING(
+            size=[vocab_size, hidden_size],
+            dtype='float32',
+            is_sparse=False,
+            param_attr=fluid.ParamAttr(
+                name='embedding_para',
+                initializer=fluid.initializer.UniformInitializer(
+                    low=-init_scale, high=init_scale)))
+
+    def _build_once(self, input, label, init_hidden, init_cell):
+        self.softmax_weight = fluid.layers.create_parameter(
+            [self.hidden_size, self.vocab_size],
+            dtype="float32",
+            name="softmax_weight",
+            default_initializer=fluid.initializer.UniformInitializer(
+                low=-self.init_scale, high=self.init_scale))
+        self.softmax_bias = fluid.layers.create_parameter(
+            [self.vocab_size],
+            dtype="float32",
+            name='softmax_bias',
+            default_initializer=fluid.initializer.UniformInitializer(
+                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])
+
+        x_emb = self.embedding(input)
+        x_emb = fluid.layers.reshape(
+            x_emb, shape=[-1, self.num_steps, self.hidden_size])
+        if self.dropout is not None and self.dropout > 0.0:
+            x_emb = fluid.layers.dropout(
+                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(
+            rnn_out, shape=[-1, self.num_steps, self.hidden_size])
+        projection = fluid.layers.reshape(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(
+            logits=projection, label=label, soft_label=False)
+        loss = fluid.layers.reshape(loss, shape=[-1, self.num_steps])
+        loss = fluid.layers.reduce_mean(loss, dim=[0])
+        loss = fluid.layers.reduce_sum(loss)
+        loss.permissions = True
+
+        return loss, last_hidden, last_cell
+
+
+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=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.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()