# Copyright (c) 2021 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 paddle.fluid.core as core import paddle.fluid.eager.eager_tensor_patch_methods as eager_tensor_patch_methods import paddle import numpy as np from paddle.fluid.framework import _test_eager_guard, EagerParamBase from paddle.fluid.data_feeder import convert_dtype import unittest import copy class EagerScaleTestCase(unittest.TestCase): def test_scale_base(self): with _test_eager_guard(): paddle.set_device("cpu") arr = np.ones([4, 16, 16, 32]).astype('float32') tensor = paddle.to_tensor(arr, 'float32', core.CPUPlace()) print(tensor) tensor = core.eager.scale(tensor, 2.0, 0.9, True, False) for i in range(0, 100): tensor = core.eager.scale(tensor, 2.0, 0.9, True, False) print(tensor) self.assertEqual(tensor.shape, [4, 16, 16, 32]) self.assertEqual(tensor.stop_gradient, True) def test_retain_grad_and_run_backward(self): with _test_eager_guard(): paddle.set_device("cpu") input_data = np.ones([4, 16, 16, 32]).astype('float32') data_eager = paddle.to_tensor(input_data, 'float32', core.CPUPlace(), False) grad_data = np.ones([4, 16, 16, 32]).astype('float32') grad_eager = paddle.to_tensor(grad_data, 'float32', core.CPUPlace()) data_eager.retain_grads() out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) self.assertFalse(data_eager.grad._is_initialized()) out_eager.backward(grad_eager, False) self.assertTrue(data_eager.grad._is_initialized()) self.assertTrue(np.array_equal(data_eager.grad.numpy(), input_data)) def test_retain_grad_and_run_backward_raises(self): with _test_eager_guard(): paddle.set_device("cpu") input_data = np.ones([4, 16, 16, 32]).astype('float32') data_eager = paddle.to_tensor(input_data, 'float32', core.CPUPlace(), False) grad_data = np.ones([4, 16, 16, 32]).astype('float32') grad_data2 = np.ones([4, 16]).astype('float32') grad_eager = paddle.to_tensor(grad_data, 'float32', core.CPUPlace()) grad_eager2 = paddle.to_tensor(grad_data2, 'float32', core.CPUPlace()) data_eager.retain_grads() out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) self.assertFalse(data_eager.grad._is_initialized()) with self.assertRaisesRegexp( AssertionError, "The type of grad_tensor must be paddle.Tensor"): out_eager.backward(grad_data, False) with self.assertRaisesRegexp( AssertionError, "Tensor shape not match, Tensor of grad_tensor /*"): out_eager.backward(grad_eager2, False) class EagerDtypeTestCase(unittest.TestCase): def check_to_tesnsor_and_numpy(self, dtype, proto_dtype): with _test_eager_guard(): arr = np.random.random([4, 16, 16, 32]).astype(dtype) tensor = paddle.to_tensor(arr, dtype) self.assertEqual(tensor.dtype, proto_dtype) self.assertTrue(np.array_equal(arr, tensor.numpy())) def test_dtype_base(self): print("Test_dtype") self.check_to_tesnsor_and_numpy('bool', core.VarDesc.VarType.BOOL) self.check_to_tesnsor_and_numpy('int8', core.VarDesc.VarType.INT8) self.check_to_tesnsor_and_numpy('uint8', core.VarDesc.VarType.UINT8) self.check_to_tesnsor_and_numpy('int16', core.VarDesc.VarType.INT16) self.check_to_tesnsor_and_numpy('int32', core.VarDesc.VarType.INT32) self.check_to_tesnsor_and_numpy('int64', core.VarDesc.VarType.INT64) self.check_to_tesnsor_and_numpy('float16', core.VarDesc.VarType.FP16) self.check_to_tesnsor_and_numpy('float32', core.VarDesc.VarType.FP32) self.check_to_tesnsor_and_numpy('float64', core.VarDesc.VarType.FP64) self.check_to_tesnsor_and_numpy('complex64', core.VarDesc.VarType.COMPLEX64) self.check_to_tesnsor_and_numpy('complex128', core.VarDesc.VarType.COMPLEX128) class EagerTensorPropertiesTestCase(unittest.TestCase): def constructor(self, place): egr_tensor = core.eager.EagerTensor() self.assertEqual(egr_tensor.persistable, False) self.assertTrue("generated" in egr_tensor.name) self.assertEqual(egr_tensor.shape, []) self.assertEqual(egr_tensor.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor.stop_gradient, True) egr_tensor0 = core.eager.EagerTensor( core.VarDesc.VarType.FP32, [4, 16, 16, 32], "test_eager_tensor", core.VarDesc.VarType.LOD_TENSOR, True) self.assertEqual(egr_tensor0.persistable, True) self.assertEqual(egr_tensor0.name, "test_eager_tensor") self.assertEqual(egr_tensor0.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor0.dtype, core.VarDesc.VarType.FP32) arr0 = np.random.rand(4, 16, 16, 32).astype('float32') egr_tensor1 = core.eager.EagerTensor(arr0, place, True, False, "numpy_tensor1", False) self.assertEqual(egr_tensor1.persistable, True) self.assertEqual(egr_tensor1.name, "numpy_tensor1") self.assertEqual(egr_tensor1.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor1.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor1.stop_gradient, False) self.assertTrue(egr_tensor1.place._equals(place)) self.assertTrue(np.array_equal(egr_tensor1.numpy(), arr0)) arr1 = np.random.randint(100, size=(4, 16, 16, 32), dtype=np.int64) egr_tensor2 = core.eager.EagerTensor(arr1, place, False, True, "numpy_tensor2", True) self.assertEqual(egr_tensor2.persistable, False) self.assertEqual(egr_tensor2.name, "numpy_tensor2") self.assertEqual(egr_tensor2.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor2.dtype, core.VarDesc.VarType.INT64) self.assertEqual(egr_tensor2.stop_gradient, True) self.assertTrue(egr_tensor2.place._equals(place)) self.assertTrue(np.array_equal(egr_tensor2.numpy(), arr1)) arr2 = np.random.rand(4, 16, 16, 32, 64).astype('float32') egr_tensor3 = core.eager.EagerTensor(arr2) self.assertEqual(egr_tensor3.persistable, False) self.assertTrue("generated_tensor" in egr_tensor3.name) self.assertEqual(egr_tensor3.shape, [4, 16, 16, 32, 64]) self.assertEqual(egr_tensor3.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor3.stop_gradient, True) self.assertTrue( egr_tensor3.place._equals( paddle.fluid.framework._current_expected_place())) self.assertTrue(np.array_equal(egr_tensor3.numpy(), arr2)) egr_tensor3.stop_gradient = False egr_tensor4 = core.eager.EagerTensor(egr_tensor3) self.assertEqual(egr_tensor4.persistable, False) self.assertTrue("generated_tensor" in egr_tensor4.name) self.assertEqual(egr_tensor4.shape, egr_tensor3.shape) self.assertEqual(egr_tensor4.dtype, egr_tensor3.dtype) self.assertEqual(egr_tensor4.stop_gradient, True) self.assertTrue( egr_tensor4.place._equals( paddle.fluid.framework._current_expected_place())) self.assertTrue( np.array_equal(egr_tensor4.numpy(), egr_tensor3.numpy())) arr4 = np.random.rand(4, 16, 16, 32).astype('float32') egr_tensor5 = core.eager.EagerTensor(arr4, place) self.assertEqual(egr_tensor5.persistable, False) self.assertTrue("generated_tensor" in egr_tensor5.name) self.assertEqual(egr_tensor5.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor5.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor5.stop_gradient, True) self.assertTrue(egr_tensor5.place._equals(place)) self.assertTrue(np.array_equal(egr_tensor5.numpy(), arr4)) egr_tensor6 = core.eager.EagerTensor(egr_tensor5, core.CPUPlace()) self.assertEqual(egr_tensor6.persistable, False) self.assertTrue("generated_tensor" in egr_tensor6.name) self.assertEqual(egr_tensor6.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor6.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor6.stop_gradient, True) self.assertEqual(egr_tensor6.place.is_cpu_place(), True) self.assertTrue( np.array_equal(egr_tensor6.numpy(), egr_tensor5.numpy())) egr_tensor7 = core.eager.EagerTensor(arr4, place, True) self.assertEqual(egr_tensor7.persistable, True) self.assertTrue("generated_tensor" in egr_tensor7.name) self.assertEqual(egr_tensor7.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor7.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor7.stop_gradient, True) self.assertTrue(egr_tensor7.place._equals(place)) self.assertTrue(np.array_equal(egr_tensor7.numpy(), arr4)) egr_tensor8 = core.eager.EagerTensor(egr_tensor6, place, "egr_tensor8") self.assertEqual(egr_tensor8.persistable, False) self.assertEqual(egr_tensor8.name, "egr_tensor8") self.assertEqual(egr_tensor8.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor8.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor8.stop_gradient, True) self.assertTrue(egr_tensor8.place._equals(place)) self.assertTrue( np.array_equal(egr_tensor8.numpy(), egr_tensor5.numpy())) egr_tensor9 = core.eager.EagerTensor(arr4, place, True, True) self.assertEqual(egr_tensor9.persistable, True) self.assertTrue("generated_tensor" in egr_tensor9.name) self.assertEqual(egr_tensor9.shape, [4, 16, 16, 32]) self.assertEqual(egr_tensor9.dtype, core.VarDesc.VarType.FP32) self.assertEqual(egr_tensor9.stop_gradient, True) self.assertTrue(egr_tensor9.place._equals(place)) self.assertTrue(np.array_equal(egr_tensor9.numpy(), arr4)) with self.assertRaisesRegexp( ValueError, "The shape of Parameter should not be None"): eager_param = EagerParamBase(shape=None, dtype="float32") with self.assertRaisesRegexp( ValueError, "The dtype of Parameter should not be None"): eager_param = EagerParamBase(shape=[1, 1], dtype=None) with self.assertRaisesRegexp( ValueError, "The dimensions of shape for Parameter must be greater than 0"): eager_param = EagerParamBase(shape=[], dtype="float32") with self.assertRaisesRegexp( ValueError, "Each dimension of shape for Parameter must be greater than 0, but received /*" ): eager_param = EagerParamBase(shape=[-1], dtype="float32") eager_param = EagerParamBase(shape=[1, 1], dtype="float32") self.assertTrue(eager_param.trainable) eager_param.trainable = False self.assertFalse(eager_param.trainable) with self.assertRaisesRegexp( ValueError, "The type of trainable MUST be bool, but the type is /*"): eager_param.trainable = "False" def test_constructor(self): print("Test_constructor") paddle.set_device("cpu") place_list = [core.CPUPlace()] if core.is_compiled_with_cuda(): place_list.append(core.CUDAPlace(0)) with _test_eager_guard(): for p in place_list: self.constructor(p) def test_copy_and_copy_to(self): print("Test_copy_and_copy_to") with _test_eager_guard(): paddle.set_device("cpu") arr = np.ones([4, 16, 16, 32]).astype('float32') arr1 = np.zeros([4, 16]).astype('float32') arr2 = np.ones([4, 16, 16, 32]).astype('float32') + np.ones( [4, 16, 16, 32]).astype('float32') tensor = paddle.to_tensor(arr, core.VarDesc.VarType.FP32, core.CPUPlace()) self.assertEqual(tensor.stop_gradient, True) tensor.stop_gradient = False print("Set persistable") tensor.persistable = False tensor1 = paddle.to_tensor(arr1, core.VarDesc.VarType.FP32, core.CPUPlace()) tensor1.persistable = True self.assertEqual(tensor1.stop_gradient, True) self.assertTrue(np.array_equal(tensor.numpy(), arr)) print("Test copy_") tensor.copy_(tensor1, True) self.assertEqual(tensor.persistable, True) self.assertEqual(tensor.shape, [4, 16]) self.assertEqual(tensor.dtype, core.VarDesc.VarType.FP32) self.assertTrue(np.array_equal(tensor.numpy(), arr1)) print("Test _copy_to") tensor2 = paddle.to_tensor(arr2, core.VarDesc.VarType.FP32, core.CPUPlace()) self.assertTrue(np.array_equal(tensor2.numpy(), arr2)) self.assertTrue(tensor2.place.is_cpu_place()) tensor2.persistable = True tensor2.stop_gradient = False if core.is_compiled_with_cuda(): tensor3 = tensor2._copy_to(True, core.CUDAPlace(0)) self.assertTrue(np.array_equal(tensor3.numpy(), arr2)) self.assertTrue(tensor3.persistable, True) self.assertTrue(tensor3.stop_gradient, True) self.assertTrue(tensor3.place.is_gpu_place()) else: tensor3 = tensor2._copy_to(True, core.CPUPlace()) self.assertTrue(np.array_equal(tensor3.numpy(), arr2)) self.assertTrue(tensor3.persistable, True) self.assertTrue(tensor3.stop_gradient, True) self.assertTrue(tensor3.place.is_cpu_place()) def test_properties(self): print("Test_properties") with _test_eager_guard(): paddle.set_device("cpu") arr = np.ones([4, 16, 16, 32]).astype('float32') tensor = paddle.to_tensor(arr, core.VarDesc.VarType.FP32, core.CPUPlace()) self.assertEqual(tensor.shape, [4, 16, 16, 32]) tensor.name = 'tensor_name_test' self.assertEqual(tensor.name, 'tensor_name_test') self.assertEqual(tensor.persistable, False) tensor.persistable = True self.assertEqual(tensor.persistable, True) tensor.persistable = False self.assertEqual(tensor.persistable, False) self.assertTrue(tensor.place.is_cpu_place()) self.assertEqual(tensor._place_str, 'CPUPlace') self.assertEqual(tensor.stop_gradient, True) tensor.stop_gradient = False self.assertEqual(tensor.stop_gradient, False) tensor.stop_gradient = True self.assertEqual(tensor.stop_gradient, True) def test_global_properties(self): print("Test_global_properties") self.assertFalse(core._in_eager_mode()) with _test_eager_guard(): self.assertTrue(core._in_eager_mode()) self.assertFalse(core._in_eager_mode()) def test_place_guard(self): core._enable_eager_mode() if core.is_compiled_with_cuda(): paddle.set_device("gpu:0") with paddle.fluid.framework._dygraph_place_guard(core.CPUPlace()): self.assertTrue(core.eager._get_expected_place().is_cpu_place()) else: paddle.set_device("cpu") with paddle.fluid.framework._dygraph_place_guard(core.CPUPlace()): self.assertTrue(core.eager._get_expected_place().is_cpu_place()) core._disable_eager_mode() class EagerParamBaseUsageTestCase(unittest.TestCase): def test_print(self): with _test_eager_guard(): linear = paddle.nn.Linear(3, 3, bias_attr=False) print(linear.weight) def test_copy(self): with _test_eager_guard(): linear = paddle.nn.Linear(1, 3) linear_copy = copy.deepcopy(linear) linear_copy2 = linear.weight._copy_to(core.CPUPlace(), True) self.assertTrue( np.array_equal(linear.weight.numpy(), linear_copy.weight.numpy())) self.assertTrue( np.array_equal(linear.weight.numpy(), linear_copy2.numpy())) def func_fp16_initilaizer(self): paddle.set_default_dtype("float16") linear1 = paddle.nn.Linear(1, 3, bias_attr=False) linear2 = paddle.nn.Linear( 1, 3, bias_attr=False, weight_attr=paddle.fluid.initializer.Uniform()) linear3 = paddle.nn.Linear( 1, 3, bias_attr=False, weight_attr=paddle.fluid.initializer.TruncatedNormalInitializer()) linear4 = paddle.nn.Linear( 1, 3, bias_attr=False, weight_attr=paddle.fluid.initializer.MSRAInitializer()) res = [ linear1.weight.numpy(), linear2.weight.numpy(), linear3.weight.numpy(), linear4.weight.numpy() ] paddle.set_default_dtype("float32") return res def test_fp16_initializer(self): res1 = list() res2 = list() paddle.seed(102) paddle.framework.random._manual_program_seed(102) with _test_eager_guard(): res1 = self.func_fp16_initilaizer() res2 = self.func_fp16_initilaizer() for i in range(len(res1)): self.assertTrue(np.array_equal(res1[i], res2[i])) def func_layer_helper_base(self, value): base = paddle.fluid.layer_helper_base.LayerHelperBase("test_layer", "test_layer") return base.to_variable(value).numpy() def func_base_to_variable(self, value): paddle.fluid.dygraph.base.to_variable(value) def test_to_variable(self): value = np.random.rand(4, 16, 16, 32).astype('float32') res1 = None res3 = None with _test_eager_guard(): res1 = self.func_layer_helper_base(value) res3 = self.func_base_to_variable(value) res2 = self.func_layer_helper_base(value) res4 = self.func_base_to_variable(value) self.assertTrue(np.array_equal(res1, res2)) self.assertTrue(np.array_equal(res3, res4)) if __name__ == "__main__": unittest.main()