chore: import upstream snapshot with attribution

test/legacy_test/test_fetch_lod_tensor_array.py

@@ -0,0 +1,91 @@
+#   Copyright (c) 2020 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 numpy as np
+from op_test import get_device_place, is_custom_device
+from simple_nets import simple_fc_net, simple_fc_net_with_inputs
+
+import paddle
+from paddle import base
+
+
+class TestFetchDenseTensorArray(unittest.TestCase):
+    def build_program(self, main_program, startup_program):
+        with (
+            base.unique_name.guard(),
+            base.program_guard(main_program, startup_program),
+        ):
+            i = paddle.zeros(shape=[1], dtype='int64')
+            img = paddle.static.data(
+                name='image', shape=[-1, 784], dtype='float32'
+            )
+            label = paddle.static.data(
+                name='label', shape=[-1, 1], dtype='int64'
+            )
+            loss = simple_fc_net_with_inputs(img, label, class_num=10)
+            loss = simple_fc_net()
+            opt = paddle.optimizer.SGD(learning_rate=0.001)
+            opt.minimize(loss)
+
+            array = paddle.tensor.array_write(x=img, i=i)
+            i = paddle.increment(i)
+            paddle.tensor.array_write(x=label, i=i, array=array)
+            i = paddle.increment(i)
+            paddle.tensor.array_write(x=loss, i=i, array=array)
+
+            return loss, array
+
+    def check_network(self, use_cuda=True):
+        main_program = base.Program()
+        startup_program = base.Program()
+
+        loss, array = self.build_program(main_program, startup_program)
+
+        batch_size = 32
+        image = np.random.normal(size=(batch_size, 784)).astype('float32')
+        label = np.random.randint(0, 10, (batch_size, 1), dtype="int64")
+
+        place = get_device_place() if use_cuda else base.CPUPlace()
+        exe = base.Executor(place)
+        exe.run(startup_program)
+        feed_dict = {'image': image, 'label': label}
+
+        if not paddle.base.framework.use_pir_api():
+            build_strategy = base.BuildStrategy()
+            binary = base.CompiledProgram(
+                main_program, build_strategy=build_strategy
+            )
+        else:
+            binary = main_program
+
+        for _ in range(3):
+            loss_v, array_v = exe.run(
+                binary, feed=feed_dict, fetch_list=[loss, array]
+            )
+            self.assertEqual(loss_v.shape, ())
+            self.assertEqual(array_v[0].shape, (batch_size, 784))
+            self.assertEqual(array_v[1].shape, (batch_size, 1))
+            self.assertEqual(array_v[2].shape, ())
+            np.testing.assert_allclose(loss_v, array_v[2], rtol=1e-05)
+
+    def test_fetch_dense_tensor_array(self):
+        if base.core.is_compiled_with_cuda() or is_custom_device():
+            self.check_network(use_cuda=True)
+        self.check_network(use_cuda=False)
+
+
+if __name__ == '__main__':
+    paddle.enable_static()
+    unittest.main()