Merge pull request #16228 from panyx0718/imperative

graph neural network for imperative mode

paddle/fluid/operators/squeeze_op.cc

@@ -94,6 +94,7 @@ class SqueezeOpInferShape : public framework::InferShapeBase {
   }
 };
 
+// TODO(paddle-dev): Should use OpKernel.
 class SqueezeOp : public framework::OperatorBase {
  public:
   using OperatorBase::OperatorBase;

python/paddle/fluid/framework.py

@@ -430,6 +430,11 @@ class Variable(object):
         Returns:
             str: The debug string.
         """
+        if _in_imperative_mode():
+            # TODO(panyx0718): add more imperative debug info.
+            return 'name %s, dtype: %s shape: %s' % (self.name, self.dtype,
+                                                     self.shape)
+
         assert isinstance(throw_on_error, bool) and isinstance(with_details,
                                                                bool)
         protostr = self.desc.serialize_to_string()

python/paddle/fluid/layers/nn.py

@@ -23,7 +23,7 @@ import os
 import inspect
 from ..layer_helper import LayerHelper
 from ..initializer import Normal, Constant, NumpyArrayInitializer
-from ..framework import Variable, OpProtoHolder
+from ..framework import Variable, OpProtoHolder, _in_imperative_mode
 from ..param_attr import ParamAttr
 from .layer_function_generator import autodoc, templatedoc, _generate_doc_string_
 from .tensor import concat, assign
@@ -4895,7 +4895,8 @@ def matmul(x, y, transpose_x=False, transpose_y=False, alpha=1.0, name=None):
         if transpose_y:
             y_shape[-2], y_shape[-1] = y_shape[-1], y_shape[-2]
         if x_shape[-1] != y_shape[-2]:
-            raise ValueError("Invalid inputs for matmul.")
+            raise ValueError("Invalid inputs for matmul. x: %s, y: %s\n" %
+                             (x_shape, y_shape))
 
         if len(y_shape) > 2 and len(x_shape) > 2:
             for i, dim_x in enumerate(x_shape[:-2]):
@@ -6398,6 +6399,8 @@ def squeeze(input, axes, name=None):
             x = layers.data(name='x', shape=[5, 1, 10])
             y = layers.sequeeze(input=x, axes=[1])
     """
+    assert not _in_imperative_mode(), (
+        "squeeze layer is not supported in imperative mode yet.")
     helper = LayerHelper("squeeze", **locals())
     out = helper.create_variable_for_type_inference(dtype=input.dtype)
     x_shape = helper.create_variable_for_type_inference(dtype=input.dtype)

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

@@ -0,0 +1,144 @@
+# 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.
+
+import contextlib
+import unittest
+import numpy as np
+import six
+import sys
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.imperative.nn import Conv2D, Pool2D, FC
+from test_imperative_base import new_program_scope
+from paddle.fluid.imperative.base import to_variable
+
+
+def gen_data():
+    pass
+
+
+class GraphConv(fluid.imperative.Layer):
+    def __init__(self, name_scope, in_features, out_features):
+        super(GraphConv, self).__init__(name_scope)
+
+        self._in_features = in_features
+        self._out_features = out_features
+        self.weight = self.create_parameter(
+            attr=None,
+            dtype='float32',
+            shape=[self._in_features, self._out_features])
+        self.bias = self.create_parameter(
+            attr=None, dtype='float32', shape=[self._out_features])
+
+    def forward(self, features, adj):
+        support = fluid.layers.matmul(features, self.weight)
+        # TODO(panyx0718): sparse matmul?
+        return fluid.layers.matmul(adj, support) + self.bias
+
+
+class GCN(fluid.imperative.Layer):
+    def __init__(self, name_scope, num_hidden):
+        super(GCN, self).__init__(name_scope)
+        self.gc = GraphConv(self.full_name(), num_hidden, 32)
+        self.gc2 = GraphConv(self.full_name(), 32, 10)
+
+    def forward(self, x, adj):
+        x = fluid.layers.relu(self.gc(x, adj))
+        return self.gc2(x, adj)
+
+
+class TestImperativeGNN(unittest.TestCase):
+    def test_gnn_float32(self):
+        seed = 90
+
+        startup = fluid.Program()
+        startup.random_seed = seed
+        main = fluid.Program()
+        main.random_seed = seed
+
+        scope = fluid.core.Scope()
+        with new_program_scope(main=main, startup=startup, scope=scope):
+            features = fluid.layers.data(
+                name='features',
+                shape=[1, 100, 50],
+                dtype='float32',
+                append_batch_size=False)
+            # Use selected rows when it's supported.
+            adj = fluid.layers.data(
+                name='adj',
+                shape=[1, 100, 100],
+                dtype='float32',
+                append_batch_size=False)
+            labels = fluid.layers.data(
+                name='labels',
+                shape=[100, 1],
+                dtype='int64',
+                append_batch_size=False)
+
+            model = GCN('test_gcn', 50)
+            logits = model(features, adj)
+            logits = fluid.layers.reshape(logits, logits.shape[1:])
+            # In other example, it's nll with log_softmax. However, paddle's
+            # log_loss only supports binary classification now.
+            loss = fluid.layers.softmax_with_cross_entropy(logits, labels)
+            loss = fluid.layers.reduce_sum(loss)
+
+            adam = AdamOptimizer(learning_rate=1e-3)
+            adam.minimize(loss)
+            exe = fluid.Executor(fluid.CPUPlace(
+            ) if not core.is_compiled_with_cuda() else fluid.CUDAPlace(0))
+            exe.run(startup)
+            static_loss = exe.run(feed={
+                'features': np.zeros(
+                    [1, 100, 50], dtype=np.float32),
+                'adj': np.zeros(
+                    [1, 100, 100], dtype=np.float32),
+                'labels': np.zeros(
+                    [100, 1], dtype=np.int64)
+            },
+                                  fetch_list=[loss])[0]
+
+            static_weight = np.array(
+                scope.find_var(model.gc.weight.name).get_tensor())
+
+        with fluid.imperative.guard():
+            fluid.default_startup_program().random_seed = seed
+            fluid.default_main_program().random_seed = seed
+
+            features = np.zeros([1, 100, 50], dtype=np.float32)
+            # Use selected rows when it's supported.
+            adj = np.zeros([1, 100, 100], dtype=np.float32)
+            labels = np.zeros([100, 1], dtype=np.int64)
+
+            model = GCN('test_gcn', 50)
+            logits = model(to_variable(features), to_variable(adj))
+            logits = fluid.layers.reshape(logits, logits.shape[1:])
+            # In other example, it's nll with log_softmax. However, paddle's
+            # log_loss only supports binary classification now.
+            loss = fluid.layers.softmax_with_cross_entropy(logits,
+                                                           to_variable(labels))
+            loss = fluid.layers.reduce_sum(loss)
+            adam = AdamOptimizer(learning_rate=1e-3)
+            adam.minimize(loss)
+            self.assertEqual(static_loss, loss._numpy())
+            self.assertTrue(
+                np.allclose(static_weight, model.gc.weight._numpy()))
+            sys.stderr.write('%s %s\n' % (static_loss, loss._numpy()))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -84,6 +84,27 @@ class TestLayer(LayerTest):
 
         self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
 
+    def test_matmul(self):
+        with self.static_graph():
+            t = layers.data(name='t', shape=[3, 3], dtype='float32')
+            t2 = layers.data(name='t2', shape=[3, 3], dtype='float32')
+            ret = layers.matmul(t, t2)
+            static_ret = self.get_static_graph_result(
+                feed={
+                    't': np.ones(
+                        [3, 3], dtype='float32'),
+                    't2': np.ones(
+                        [3, 3], dtype='float32')
+                },
+                fetch_list=[ret])[0]
+
+        with self.dynamic_graph():
+            t = np.ones([3, 3], dtype='float32')
+            t2 = np.ones([3, 3], dtype='float32')
+            dy_ret = layers.matmul(base.to_variable(t), base.to_variable(t2))
+
+        self.assertTrue(np.allclose(static_ret, dy_ret._numpy()))
+
     def test_conv2d(self):
         with self.static_graph():
             images = layers.data(name='pixel', shape=[3, 5, 5], dtype='float32')