enable pyreader use pin memory (#14066)

* enable pyreader use pin memory

* add py reader pin memory test test=develop

paddle/fluid/framework/tensor_util.cc

@@ -153,6 +153,12 @@ void TensorCopySync(const Tensor& src, const platform::Place& dst_place,
     auto src_gpu_place = boost::get<platform::CUDAPlace>(src_place);
     auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
     memory::Copy(dst_gpu_place, dst_ptr, src_gpu_place, src_ptr, size, nullptr);
+  } else if (platform::is_cuda_pinned_place(src_place) &&
+             platform::is_gpu_place(dst_place)) {
+    auto src_pinned_place = boost::get<platform::CUDAPinnedPlace>(src_place);
+    auto dst_gpu_place = boost::get<platform::CUDAPlace>(dst_place);
+    memory::Copy(dst_gpu_place, dst_ptr, src_pinned_place, src_ptr, size,
+                 nullptr);
   }
 #endif
 }

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

@@ -0,0 +1,130 @@
+# 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
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+import numpy as np
+from threading import Thread
+
+
+def user_reader(inputs):
+    def _reader():
+        for d in inputs:
+            yield d
+
+    return _reader
+
+
+def batch_feeder(batch_reader, pin_memory=False, img_dtype="float32"):
+    def _feeder():
+        for batch_data in batch_reader():
+            sample_batch = []
+            label_batch = []
+            for sample, label in batch_data:
+                sample_batch.append(sample)
+                label_batch.append([label])
+            tensor = core.LoDTensor()
+            label = core.LoDTensor()
+            place = core.CUDAPinnedPlace() if pin_memory else core.CPUPlace()
+            tensor.set(np.array(sample_batch, dtype=img_dtype), place)
+            label.set(np.array(label_batch, dtype="int64"), place)
+            yield [tensor, label]
+
+    return _feeder
+
+
+class TestPyReader(unittest.TestCase):
+    def setUp(self):
+        self.capacity = 10
+        self.shapes = [(-1, 3, 2, 1), (-1, 1)]
+        self.lod_levels = [0, 0]
+        self.dtypes = ['float32', 'int64']
+
+    def test_pin_memory_pyreader(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            executor = fluid.Executor(place)
+
+            data_file = fluid.layers.py_reader(
+                capacity=self.capacity,
+                dtypes=self.dtypes,
+                lod_levels=self.lod_levels,
+                shapes=self.shapes)
+            # feed_queue = data_file.queue
+            read_out_data = fluid.layers.read_file(data_file)
+
+            self.inputs = []
+            for _ in range(10):
+                sample = np.random.uniform(
+                    low=0, high=1, size=[3, 2, 1]).astype("float32")
+                label = np.random.uniform(
+                    low=0, high=10, size=[1]).astype("int64")
+                self.inputs.append((sample, label))
+
+            self.input_tensors = []
+            for d, l in batch_feeder(
+                    paddle.batch(
+                        user_reader(self.inputs), batch_size=2),
+                    pin_memory=True
+                    if fluid.core.is_compiled_with_cuda() else False)():
+                ta = fluid.LoDTensorArray()
+                ta.append(d)
+                ta.append(l)
+                self.input_tensors.append(ta)
+
+            self.batched_inputs = []
+            for batch in paddle.batch(user_reader(self.inputs), batch_size=2)():
+                feed_d = []
+                feed_l = []
+                for d, l in batch:
+                    feed_d.append(d)
+                    feed_l.append([l])
+                self.batched_inputs.append([feed_d, feed_l])
+
+            data_file.decorate_tensor_provider(
+                batch_feeder(
+                    paddle.batch(
+                        user_reader(self.inputs), batch_size=2),
+                    pin_memory=True
+                    if fluid.core.is_compiled_with_cuda() else False))
+
+            executor.run(fluid.default_startup_program())
+            self.outputs = []
+
+            data_file.start()
+            for _ in self.input_tensors:
+                self.outputs.append(
+                    executor.run(fetch_list=list(read_out_data)))
+            data_file.reset()
+            self.validate()
+
+    def validate(self):
+        self.assertEqual(len(self.batched_inputs), len(self.outputs))
+        for in_data_list, out_data_list in zip(self.batched_inputs,
+                                               self.outputs):
+            self.assertEqual(len(in_data_list), len(out_data_list))
+            in_data_list_np = [
+                np.array(in_lod_tensor) for in_lod_tensor in in_data_list
+            ]
+            for in_data, out_data in zip(in_data_list_np, out_data_list):
+                self.assertTrue((in_data == out_data).all())
+
+
+if __name__ == '__main__':
+    unittest.main()