Add unittests and fix a few bugs

paddle/fluid/framework/details/data_balance_op_handle.cc

@@ -107,7 +107,6 @@ void DataBalanceOpHandle::RunImpl() {
     auto *tensor_var = local_scope->FindVar(in_var_handles[i]->name_);
     PADDLE_ENFORCE(tensor_var->IsType<LoDTensor>());
     auto *tensor = tensor_var->GetMutable<LoDTensor>();
-    PADDLE_ENFORCE(places_[place_idx] == tensor->place());
     lod_tensors[data_idx].push_back(tensor);
     int ins_size =
         tensor->lod().empty() ? tensor->dims()[0] : tensor->NumElements();

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -67,8 +67,8 @@ void FetchOpHandle::RunImpl() {
 #endif
     } else {
       tensors_[i].ShareDataWith(t);
-      tensors_[i].set_lod(t.lod());
     }
+    tensors_[i].set_lod(t.lod());
   }
 
   this->WaitAndMergeCPUTensors();

paddle/fluid/framework/details/multi_devices_graph_builder.cc

@@ -216,11 +216,13 @@ std::unique_ptr<SSAGraph> MultiDevSSAGraphBuilder::Build(
       } else {
         // This op runs on all devices, and its output may have parameter's
         // gradients.
-        CreateComputationalOps(&result, *op, places_.size());
-
         if (op->Type() == "read") {
+          op->SetAttr("throw_eof_exp", false);
+          CreateComputationalOps(&result, *op, places_.size());
           const auto &data_var_names = op->Output("Out");
           InsertDataBalanceOp(&result, data_var_names);
+        } else {
+          CreateComputationalOps(&result, *op, places_.size());
         }
 
         if (!is_forwarding && places_.size() > 1) {

paddle/fluid/framework/lod_tensor.cc

@@ -393,6 +393,7 @@ void LoDTensor::MergeLoDTensor(
     new_dim[0] += t->dims()[0];
 
     auto &lod = t->lod();
+    PADDLE_ENFORCE_EQ(new_lod.size(), lod.size());
     for (size_t j = 0; j < lod.size(); ++j) {
       auto &sub_lod = new_lod[j];
       auto &offset = sub_lod.back();

paddle/fluid/operators/read_op.cc

@@ -67,12 +67,16 @@ class ReadOp : public framework::OperatorBase {
     std::vector<framework::LoDTensor> ins;
     reader->ReadNext(&ins);
     if (ins.empty()) {
+      if (Attr<bool>("throw_eof_exp")) {
+        PADDLE_THROW("There is no next data.");
+      } else {
         ins.resize(out_arg_names.size());
         for (auto& tensor : ins) {
           // data type is not important for subsequent DataBalanceOpHandle
           tensor.mutable_data<float>(framework::make_ddim({0}), dev_place);
         }
       }
+    }
     PADDLE_ENFORCE_EQ(ins.size(), out_arg_names.size());
     for (size_t i = 0; i < out_arg_names.size(); ++i) {
       auto* out =
@@ -88,6 +92,10 @@ class ReadOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("Reader", "(ReaderHolder) The executed reader.");
     AddOutput("Out", "(LoDTensor) The output data.").AsDuplicable();
+    AddAttr<bool>("throw_eof_exp",
+                  "If set true, an exception will be thrown when the Reader "
+                  "yields empty (which means there is no next data).")
+        .SetDefault(true);
     AddComment(R"DOC(
       Read Operator
 

python/paddle/fluid/tests/unittests/test_data_balance.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.
+
+import unittest
+import paddle.fluid as fluid
+import paddle.v2 as paddle
+import paddle.v2.dataset.mnist as mnist
+import numpy as np
+
+
+class TestDataBalance(unittest.TestCase):
+    def prepare_data(self):
+        def fake_data_generator():
+            for n in xrange(self.total_ins_num):
+                yield np.ones((3, 4)) * n, n
+
+        # Prepare data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            reader = paddle.batch(
+                fake_data_generator, batch_size=self.batch_size)
+            feeder = fluid.DataFeeder(
+                feed_list=[
+                    fluid.layers.data(
+                        name='image', shape=[3, 4], dtype='float32'),
+                    fluid.layers.data(
+                        name='label', shape=[1], dtype='int64'),
+                ],
+                place=fluid.CPUPlace())
+            self.num_batches = fluid.recordio_writer.convert_reader_to_recordio_file(
+                self.data_file_name, reader, feeder)
+
+    def prepare_lod_data(self):
+        def fake_data_generator():
+            for n in xrange(1, self.total_ins_num + 1):
+                d1 = (np.ones((n, 3)) * n).astype('float32')
+                d2 = (np.array(n).reshape((1, 1))).astype('int32')
+                yield d1, d2
+
+        # Prepare lod data
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            with fluid.recordio_writer.create_recordio_writer(
+                    filename=self.lod_data_file_name) as writer:
+                eof = False
+                generator = fake_data_generator()
+                while (not eof):
+                    data_batch = [
+                        np.array([]).reshape((0, 3)), np.array([]).reshape(
+                            (0, 1))
+                    ]
+                    lod = [0]
+                    for _ in xrange(self.batch_size):
+                        try:
+                            ins = generator.next()
+                        except StopIteration:
+                            eof = True
+                            break
+                        for i, d in enumerate(ins):
+                            data_batch[i] = np.concatenate(
+                                (data_batch[i], d), axis=0)
+                        lod.append(lod[-1] + ins[0].shape[0])
+                    if data_batch[0].shape[0] > 0:
+                        for i, d in enumerate(data_batch):
+                            t = fluid.LoDTensor()
+                            t.set(data_batch[i], fluid.CPUPlace())
+                            if i == 0:
+                                t.set_lod([lod])
+                            writer.append_tensor(t)
+                        writer.complete_append_tensor()
+
+    def setUp(self):
+        self.use_cuda = fluid.core.is_compiled_with_cuda()
+        self.data_file_name = './data_balance_test.recordio'
+        self.lod_data_file_name = './data_balance_with_lod_test.recordio'
+        self.total_ins_num = 50
+        self.batch_size = 10
+        self.prepare_data()
+        self.prepare_lod_data()
+
+    def main(self):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader = fluid.layers.io.open_files(
+                filenames=[self.data_file_name],
+                shapes=[[-1, 3, 4], [-1, 1]],
+                lod_levels=[0, 0],
+                dtypes=['float32', 'int64'])
+            if self.use_cuda:
+                data_reader = fluid.layers.double_buffer(data_reader)
+            image, label = fluid.layers.read_file(data_reader)
+
+            place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(startup_prog)
+
+            parallel_exe = fluid.ParallelExecutor(
+                use_cuda=self.use_cuda, main_program=main_prog)
+
+            if (parallel_exe.device_count > self.batch_size):
+                print("WARNING: Unittest TestDataBalance skipped. \
+                    For the result is not correct when device count \
+                    is larger than batch size.")
+                exit(0)
+            fetch_list = [image.name, label.name]
+
+            data_appeared = [False] * self.total_ins_num
+            while (True):
+                try:
+                    image_val, label_val = parallel_exe.run(fetch_list,
+                                                            return_numpy=True)
+                except fluid.core.EnforceNotMet as ex:
+                    self.assertIn("There is no next data.", ex.message)
+                    break
+                ins_num = image_val.shape[0]
+                broadcasted_label = np.ones(
+                    (ins_num, 3, 4)) * label_val.reshape((ins_num, 1, 1))
+                self.assertEqual(image_val.all(), broadcasted_label.all())
+                for l in label_val:
+                    self.assertFalse(data_appeared[l[0]])
+                    data_appeared[l[0]] = True
+            for i in data_appeared:
+                self.assertTrue(i)
+
+    def main_lod(self):
+        main_prog = fluid.Program()
+        startup_prog = fluid.Program()
+        with fluid.program_guard(main_prog, startup_prog):
+            data_reader = fluid.layers.io.open_files(
+                filenames=[self.lod_data_file_name],
+                shapes=[[-1, 3], [-1, 1]],
+                lod_levels=[1, 0],
+                dtypes=['float32', 'int32'],
+                thread_num=1)
+            ins, label = fluid.layers.read_file(data_reader)
+
+            place = fluid.CUDAPlace(0) if self.use_cuda else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+            exe.run(startup_prog)
+
+            parallel_exe = fluid.ParallelExecutor(
+                use_cuda=self.use_cuda, main_program=main_prog)
+
+            if (parallel_exe.device_count > self.batch_size):
+                print("WARNING: Unittest TestDataBalance skipped. \
+                    For the result is not correct when device count \
+                    is larger than batch size.")
+                exit(0)
+            fetch_list = [ins.name, label.name]
+
+            data_appeared = [False] * self.total_ins_num
+            while (True):
+                try:
+                    ins_tensor, label_tensor = parallel_exe.run(
+                        fetch_list, return_numpy=False)
+                except fluid.core.EnforceNotMet as ex:
+                    self.assertIn("There is no next data.", ex.message)
+                    break
+
+                ins_val = np.array(ins_tensor)
+                label_val = np.array(label_tensor)
+                ins_lod = ins_tensor.lod()[0]
+                self.assertEqual(ins_val.shape[1], 3)
+                self.assertEqual(label_val.shape[1], 1)
+                self.assertEqual(len(ins_lod) - 1, label_val.shape[0])
+                for i in range(0, len(ins_lod) - 1):
+                    ins_elem = ins_val[ins_lod[i]:ins_lod[i + 1]][:]
+                    label_elem = label_val[i][0]
+                    self.assertEqual(ins_elem.all(), label_elem.all())
+                    self.assertFalse(data_appeared[int(label_elem - 1)])
+                    data_appeared[int(label_elem - 1)] = True
+
+            for i in data_appeared:
+                self.assertTrue(i)
+
+    def test_all(self):
+        self.main()
+        self.main_lod()