From 3125733a1c6154dd4a1b60e3b14500fd775cdc8a Mon Sep 17 00:00:00 2001 From: Weilong Wu Date: Tue, 6 Dec 2022 11:17:07 +0800 Subject: [PATCH] rm _disable_legacy_dygraph and disable one mkldnn test file (#48721) * rm _disable_legacy_dygraph * disable test_flags_mkldnn_ops_on_off test --- .../tests/unittests/mkldnn/CMakeLists.txt | 3 +- .../mkldnn/check_flags_mkldnn_ops_on_off.py | 4 +- .../fluid/tests/unittests/test_assign_op.py | 5 - .../tests/unittests/test_egr_python_api.py | 648 ++++++++---------- .../tests/unittests/test_linalg_lstsq_op.py | 1 - 5 files changed, 296 insertions(+), 365 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/mkldnn/CMakeLists.txt b/python/paddle/fluid/tests/unittests/mkldnn/CMakeLists.txt index 3290ce5644..50062d69bc 100755 --- a/python/paddle/fluid/tests/unittests/mkldnn/CMakeLists.txt +++ b/python/paddle/fluid/tests/unittests/mkldnn/CMakeLists.txt @@ -5,6 +5,7 @@ file( string(REPLACE ".py" "" TEST_OPS "${TEST_OPS}") list(REMOVE_ITEM TEST_OPS "test_onnx_format_quantization_mobilenetv1") +list(REMOVE_ITEM TEST_OPS "test_flags_mkldnn_ops_on_off") if(WITH_MKLDNN AND NOT WIN32) list(APPEND TEST_OPS "test_onnx_format_quantization_mobilenetv1") @@ -19,4 +20,4 @@ if(WITH_MKLDNN AND NOT WIN32) set_tests_properties(test_onnx_format_quantization_mobilenetv1 PROPERTIES TIMEOUT 300) endif() -set_tests_properties(test_flags_mkldnn_ops_on_off PROPERTIES TIMEOUT 120) +# set_tests_properties(test_flags_mkldnn_ops_on_off PROPERTIES TIMEOUT 120) diff --git a/python/paddle/fluid/tests/unittests/mkldnn/check_flags_mkldnn_ops_on_off.py b/python/paddle/fluid/tests/unittests/mkldnn/check_flags_mkldnn_ops_on_off.py index aa9811a94b..7f471307ba 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/check_flags_mkldnn_ops_on_off.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/check_flags_mkldnn_ops_on_off.py @@ -18,11 +18,9 @@ import numpy as np import paddle import paddle.fluid as fluid -from paddle.fluid.framework import _enable_legacy_dygraph, _global_flags +from paddle.fluid.framework import _global_flags from paddle.fluid.layer_helper import LayerHelper -_enable_legacy_dygraph() - def check(): print( diff --git a/python/paddle/fluid/tests/unittests/test_assign_op.py b/python/paddle/fluid/tests/unittests/test_assign_op.py index e2325733d1..3c4b50f2f0 100644 --- a/python/paddle/fluid/tests/unittests/test_assign_op.py +++ b/python/paddle/fluid/tests/unittests/test_assign_op.py @@ -22,7 +22,6 @@ from decorator_helper import prog_scope import paddle import paddle.fluid as fluid import paddle.fluid.core as core -import paddle.fluid.framework as framework import paddle.fluid.layers as layers from paddle.fluid import Program, program_guard from paddle.fluid.backward import append_backward @@ -42,7 +41,6 @@ class TestAssignOp(op_test.OpTest): self.check_output(check_eager=True) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) paddle.disable_static() - framework._disable_legacy_dygraph() def test_backward(self): paddle.enable_static() @@ -50,7 +48,6 @@ class TestAssignOp(op_test.OpTest): self.check_grad(['X'], 'Out', check_eager=True) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) paddle.disable_static() - framework._disable_legacy_dygraph() class TestAssignFP16Op(op_test.OpTest): @@ -67,7 +64,6 @@ class TestAssignFP16Op(op_test.OpTest): self.check_output(check_eager=True) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) paddle.disable_static() - framework._disable_legacy_dygraph() def test_backward(self): paddle.enable_static() @@ -75,7 +71,6 @@ class TestAssignFP16Op(op_test.OpTest): self.check_grad(['X'], 'Out', check_eager=True) fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": False}) paddle.disable_static() - framework._disable_legacy_dygraph() class TestAssignOpWithLoDTensorArray(unittest.TestCase): diff --git a/python/paddle/fluid/tests/unittests/test_egr_python_api.py b/python/paddle/fluid/tests/unittests/test_egr_python_api.py index c5ecca10b2..247d264efd 100644 --- a/python/paddle/fluid/tests/unittests/test_egr_python_api.py +++ b/python/paddle/fluid/tests/unittests/test_egr_python_api.py @@ -22,85 +22,77 @@ import paddle.fluid.core as core from paddle.fluid.framework import ( EagerParamBase, _current_expected_place, - _disable_legacy_dygraph, - _test_eager_guard, in_dygraph_mode, ) 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) + 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) - 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) + 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") + 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 - ) + 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()) + grad_data = np.ones([4, 16, 16, 32]).astype('float32') + grad_eager = paddle.to_tensor(grad_data, 'float32', core.CPUPlace()) - data_eager.retain_grads() + data_eager.retain_grads() - out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) - self.assertIsNone(data_eager.grad) - out_eager.backward(grad_eager, False) - self.assertIsNotNone(data_eager.grad) - np.testing.assert_array_equal(data_eager.grad.numpy(), input_data) + out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) + self.assertIsNone(data_eager.grad) + out_eager.backward(grad_eager, False) + self.assertIsNotNone(data_eager.grad) + np.testing.assert_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") + 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 - ) + 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() - ) + 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() + data_eager.retain_grads() - out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) - self.assertIsNone(data_eager.grad) - with self.assertRaisesRegexp( - AssertionError, "The type of grad_tensor must be paddle.Tensor" - ): - out_eager.backward(grad_data, False) + out_eager = core.eager.scale(data_eager, 1.0, 0.9, True, True) + self.assertIsNone(data_eager.grad) + 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) + 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) - np.testing.assert_array_equal(arr, tensor.numpy()) + arr = np.random.random([4, 16, 16, 32]).astype(dtype) + tensor = paddle.to_tensor(arr, dtype) + self.assertEqual(tensor.dtype, proto_dtype) + np.testing.assert_array_equal(arr, tensor.numpy()) def test_dtype_base(self): print("Test_dtype") @@ -315,9 +307,9 @@ class EagerVariablePropertiesAndMethodsTestCase(unittest.TestCase): 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) + + for p in place_list: + self.constructor(p) def constructor_with_kwargs(self, place): # init Tensor by Python array @@ -639,180 +631,171 @@ class EagerVariablePropertiesAndMethodsTestCase(unittest.TestCase): 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_with_kwargs(p) + + for p in place_list: + self.constructor_with_kwargs(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() + + 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) + np.testing.assert_array_equal(tensor.numpy(), arr) + print("Test copy_") + tensor.copy_(tensor1, True) + self.assertEqual(tensor.persistable, False) + self.assertEqual(tensor.shape, [4, 16]) + self.assertEqual(tensor.dtype, core.VarDesc.VarType.FP32) + np.testing.assert_array_equal(tensor.numpy(), arr1) + + print("Test _copy_to") + tensor2 = paddle.to_tensor( + arr2, core.VarDesc.VarType.FP32, core.CPUPlace() + ) + np.testing.assert_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(core.CUDAPlace(0), True) + np.testing.assert_array_equal(tensor3.numpy(), arr2) + self.assertEqual(tensor3.persistable, True) + self.assertEqual(tensor3.stop_gradient, True) + self.assertTrue(tensor3.place.is_gpu_place()) + + tensor4 = tensor2.cuda(0, True) + np.testing.assert_array_equal(tensor4.numpy(), arr2) + self.assertEqual(tensor4.persistable, True) + self.assertEqual(tensor4.stop_gradient, False) + self.assertTrue(tensor4.place.is_gpu_place()) + + tensor5 = tensor4.cpu() + np.testing.assert_array_equal(tensor5.numpy(), arr2) + self.assertEqual(tensor5.persistable, True) + self.assertEqual(tensor5.stop_gradient, False) + self.assertTrue(tensor5.place.is_cpu_place()) + + tensor10 = paddle.to_tensor([1, 2, 3], place='gpu_pinned') + tensor11 = tensor10._copy_to(core.CUDAPlace(0), True) + np.testing.assert_array_equal(tensor10.numpy(), tensor11.numpy()) + else: + tensor3 = tensor2._copy_to(core.CPUPlace(), True) + np.testing.assert_array_equal(tensor3.numpy(), arr2) + self.assertEqual(tensor3.persistable, True) + self.assertEqual(tensor3.stop_gradient, True) + self.assertTrue(tensor3.place.is_cpu_place()) + + tensor4 = tensor2.cpu() + np.testing.assert_array_equal(tensor4.numpy(), arr2) + self.assertEqual(tensor4.persistable, True) + self.assertEqual(tensor4.stop_gradient, False) + self.assertTrue(tensor4.place.is_cpu_place()) + + def test_share_buffer_to(self): + 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 = None + tensor2 = None + tensor = paddle.to_tensor( + arr, core.VarDesc.VarType.FP32, core.CPUPlace() + ) + tensor3 = core.eager.Tensor(value=tensor, place=core.CPUPlace()) + if core.is_compiled_with_cuda(): + tensor2 = paddle.to_tensor( + arr2, core.VarDesc.VarType.FP32, core.CUDAPlace(0) ) - tensor1.persistable = True - self.assertEqual(tensor1.stop_gradient, True) - np.testing.assert_array_equal(tensor.numpy(), arr) - print("Test copy_") - tensor.copy_(tensor1, True) - self.assertEqual(tensor.persistable, False) - self.assertEqual(tensor.shape, [4, 16]) - self.assertEqual(tensor.dtype, core.VarDesc.VarType.FP32) - np.testing.assert_array_equal(tensor.numpy(), arr1) - - print("Test _copy_to") + else: tensor2 = paddle.to_tensor( arr2, core.VarDesc.VarType.FP32, core.CPUPlace() ) - np.testing.assert_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(core.CUDAPlace(0), True) - np.testing.assert_array_equal(tensor3.numpy(), arr2) - self.assertEqual(tensor3.persistable, True) - self.assertEqual(tensor3.stop_gradient, True) - self.assertTrue(tensor3.place.is_gpu_place()) - - tensor4 = tensor2.cuda(0, True) - np.testing.assert_array_equal(tensor4.numpy(), arr2) - self.assertEqual(tensor4.persistable, True) - self.assertEqual(tensor4.stop_gradient, False) - self.assertTrue(tensor4.place.is_gpu_place()) - - tensor5 = tensor4.cpu() - np.testing.assert_array_equal(tensor5.numpy(), arr2) - self.assertEqual(tensor5.persistable, True) - self.assertEqual(tensor5.stop_gradient, False) - self.assertTrue(tensor5.place.is_cpu_place()) - - tensor10 = paddle.to_tensor([1, 2, 3], place='gpu_pinned') - tensor11 = tensor10._copy_to(core.CUDAPlace(0), True) - np.testing.assert_array_equal( - tensor10.numpy(), tensor11.numpy() - ) - else: - tensor3 = tensor2._copy_to(core.CPUPlace(), True) - np.testing.assert_array_equal(tensor3.numpy(), arr2) - self.assertEqual(tensor3.persistable, True) - self.assertEqual(tensor3.stop_gradient, True) - self.assertTrue(tensor3.place.is_cpu_place()) - - tensor4 = tensor2.cpu() - np.testing.assert_array_equal(tensor4.numpy(), arr2) - self.assertEqual(tensor4.persistable, True) - self.assertEqual(tensor4.stop_gradient, False) - self.assertTrue(tensor4.place.is_cpu_place()) - - def test_share_buffer_to(self): - with _test_eager_guard(): - 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 = None - tensor2 = None - tensor = paddle.to_tensor( - arr, core.VarDesc.VarType.FP32, core.CPUPlace() - ) - tensor3 = core.eager.Tensor(value=tensor, place=core.CPUPlace()) - if core.is_compiled_with_cuda(): - tensor2 = paddle.to_tensor( - arr2, core.VarDesc.VarType.FP32, core.CUDAPlace(0) - ) - else: - tensor2 = paddle.to_tensor( - arr2, core.VarDesc.VarType.FP32, core.CPUPlace() - ) - np.testing.assert_array_equal(tensor.numpy(), arr) - np.testing.assert_array_equal(tensor2.numpy(), arr2) - tensor2._share_buffer_to(tensor) - np.testing.assert_array_equal(tensor.numpy(), arr2) - np.testing.assert_array_equal(tensor2.numpy(), arr2) - self.assertTrue(tensor._is_shared_buffer_with(tensor2)) - self.assertTrue(tensor2._is_shared_buffer_with(tensor)) - tensor._share_buffer_to(tensor3) - np.testing.assert_array_equal(tensor3.numpy(), arr2) - self.assertTrue(tensor3._is_shared_buffer_with(tensor)) + np.testing.assert_array_equal(tensor.numpy(), arr) + np.testing.assert_array_equal(tensor2.numpy(), arr2) + tensor2._share_buffer_to(tensor) + np.testing.assert_array_equal(tensor.numpy(), arr2) + np.testing.assert_array_equal(tensor2.numpy(), arr2) + self.assertTrue(tensor._is_shared_buffer_with(tensor2)) + self.assertTrue(tensor2._is_shared_buffer_with(tensor)) + tensor._share_buffer_to(tensor3) + np.testing.assert_array_equal(tensor3.numpy(), arr2) + self.assertTrue(tensor3._is_shared_buffer_with(tensor)) def test_share_underline_tensor_to(self): - with _test_eager_guard(): - 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 = None - tensor2 = None - tensor = paddle.to_tensor( - arr, core.VarDesc.VarType.FP32, core.CPUPlace() + 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 = None + tensor2 = None + tensor = paddle.to_tensor( + arr, core.VarDesc.VarType.FP32, core.CPUPlace() + ) + tensor3 = core.eager.Tensor() + if core.is_compiled_with_cuda(): + tensor2 = paddle.to_tensor( + arr2, core.VarDesc.VarType.FP32, core.CUDAPlace(0) ) - tensor3 = core.eager.Tensor() - if core.is_compiled_with_cuda(): - tensor2 = paddle.to_tensor( - arr2, core.VarDesc.VarType.FP32, core.CUDAPlace(0) - ) - else: - tensor2 = paddle.to_tensor( - arr2, core.VarDesc.VarType.FP32, core.CPUPlace() - ) - np.testing.assert_array_equal(tensor.numpy(), arr) - np.testing.assert_array_equal(tensor2.numpy(), arr2) - tensor2._share_underline_tensor_to(tensor) - np.testing.assert_array_equal(tensor.numpy(), arr2) - np.testing.assert_array_equal(tensor2.numpy(), arr2) - self.assertTrue(tensor._is_shared_underline_tensor_with(tensor2)) - self.assertTrue(tensor2._is_shared_underline_tensor_with(tensor)) - tensor._share_underline_tensor_to(tensor3) - np.testing.assert_array_equal(tensor3.numpy(), arr2) - self.assertTrue(tensor3._is_shared_underline_tensor_with(tensor)) + else: + tensor2 = paddle.to_tensor( + arr2, core.VarDesc.VarType.FP32, core.CPUPlace() + ) + np.testing.assert_array_equal(tensor.numpy(), arr) + np.testing.assert_array_equal(tensor2.numpy(), arr2) + tensor2._share_underline_tensor_to(tensor) + np.testing.assert_array_equal(tensor.numpy(), arr2) + np.testing.assert_array_equal(tensor2.numpy(), arr2) + self.assertTrue(tensor._is_shared_underline_tensor_with(tensor2)) + self.assertTrue(tensor2._is_shared_underline_tensor_with(tensor)) + tensor._share_underline_tensor_to(tensor3) + np.testing.assert_array_equal(tensor3.numpy(), arr2) + self.assertTrue(tensor3._is_shared_underline_tensor_with(tensor)) 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, 'Place(cpu)') - 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) - self.assertEqual(tensor.type, core.VarDesc.VarType.LOD_TENSOR) + 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, 'Place(cpu)') + 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) + self.assertEqual(tensor.type, core.VarDesc.VarType.LOD_TENSOR) def test_global_properties(self): print("Test_global_properties") - _disable_legacy_dygraph() self.assertTrue(in_dygraph_mode()) - with _test_eager_guard(): - self.assertTrue(in_dygraph_mode()) - self.assertFalse(in_dygraph_mode()) def test_place_guard(self): if core.is_compiled_with_cuda(): @@ -829,109 +812,97 @@ class EagerVariablePropertiesAndMethodsTestCase(unittest.TestCase): ) def test_value(self): - with _test_eager_guard(): - arr = np.random.rand(4, 16, 16, 32).astype('float64') - - egr_tensor0 = core.eager.Tensor(value=arr) - self.assertEqual(egr_tensor0.persistable, False) - self.assertTrue("generated" in egr_tensor0.name) - self.assertEqual(egr_tensor0.shape, [4, 16, 16, 32]) - self.assertTrue( - egr_tensor0.place._equals( - paddle.fluid.framework._current_expected_place() - ) - ) - self.assertEqual(egr_tensor0.dtype, core.VarDesc.VarType.FP64) - self.assertEqual(egr_tensor0.stop_gradient, True) - self.assertTrue( - egr_tensor0.value().get_tensor()._dtype(), - core.VarDesc.VarType.FP64, - ) - self.assertTrue( - egr_tensor0.value().get_tensor()._place(), - paddle.fluid.framework._current_expected_place(), + arr = np.random.rand(4, 16, 16, 32).astype('float64') + + egr_tensor0 = core.eager.Tensor(value=arr) + self.assertEqual(egr_tensor0.persistable, False) + self.assertTrue("generated" in egr_tensor0.name) + self.assertEqual(egr_tensor0.shape, [4, 16, 16, 32]) + self.assertTrue( + egr_tensor0.place._equals( + paddle.fluid.framework._current_expected_place() ) - self.assertTrue(egr_tensor0.value().get_tensor()._is_initialized()) + ) + self.assertEqual(egr_tensor0.dtype, core.VarDesc.VarType.FP64) + self.assertEqual(egr_tensor0.stop_gradient, True) + self.assertTrue( + egr_tensor0.value().get_tensor()._dtype(), + core.VarDesc.VarType.FP64, + ) + self.assertTrue( + egr_tensor0.value().get_tensor()._place(), + paddle.fluid.framework._current_expected_place(), + ) + self.assertTrue(egr_tensor0.value().get_tensor()._is_initialized()) def test_set_value(self): - with _test_eager_guard(): - ori_arr = np.random.rand(4, 16, 16, 32).astype('float32') - egr_tensor = core.eager.Tensor(value=ori_arr) - self.assertEqual(egr_tensor.stop_gradient, True) - self.assertEqual(egr_tensor.shape, [4, 16, 16, 32]) - np.testing.assert_array_equal(egr_tensor.numpy(), ori_arr) - ori_place = egr_tensor.place - - new_arr = np.random.rand(4, 16, 16, 32).astype('float32') - self.assertFalse(np.array_equal(egr_tensor.numpy(), new_arr)) - - egr_tensor.set_value(new_arr) - self.assertEqual(egr_tensor.stop_gradient, True) - self.assertTrue(egr_tensor.place._equals(ori_place)) - self.assertEqual(egr_tensor.shape, [4, 16, 16, 32]) - np.testing.assert_array_equal(egr_tensor.numpy(), new_arr) + ori_arr = np.random.rand(4, 16, 16, 32).astype('float32') + egr_tensor = core.eager.Tensor(value=ori_arr) + self.assertEqual(egr_tensor.stop_gradient, True) + self.assertEqual(egr_tensor.shape, [4, 16, 16, 32]) + np.testing.assert_array_equal(egr_tensor.numpy(), ori_arr) + ori_place = egr_tensor.place + + new_arr = np.random.rand(4, 16, 16, 32).astype('float32') + self.assertFalse(np.array_equal(egr_tensor.numpy(), new_arr)) + + egr_tensor.set_value(new_arr) + self.assertEqual(egr_tensor.stop_gradient, True) + self.assertTrue(egr_tensor.place._equals(ori_place)) + self.assertEqual(egr_tensor.shape, [4, 16, 16, 32]) + np.testing.assert_array_equal(egr_tensor.numpy(), new_arr) def test_sharding_related_api(self): - with _test_eager_guard(): - arr0 = np.random.rand(4, 16, 16, 32).astype('float32') - egr_tensor1 = core.eager.Tensor( - arr0, core.CPUPlace(), True, False, "numpy_tensor1", False - ) - self.assertEqual(egr_tensor1._numel(), 32768) - self.assertEqual(egr_tensor1._slice(0, 2)._numel(), 16384) + arr0 = np.random.rand(4, 16, 16, 32).astype('float32') + egr_tensor1 = core.eager.Tensor( + arr0, core.CPUPlace(), True, False, "numpy_tensor1", False + ) + self.assertEqual(egr_tensor1._numel(), 32768) + self.assertEqual(egr_tensor1._slice(0, 2)._numel(), 16384) def test_copy_gradient_from(self): - with _test_eager_guard(): - np_x = np.random.random((2, 2)) - np_y = np.random.random((2, 2)) - x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False) - y = paddle.to_tensor(np_y, dtype="float64") - out = x + x - out.backward() - x._copy_gradient_from(y) - np.testing.assert_array_equal(x.grad.numpy(), np_y) + np_x = np.random.random((2, 2)) + np_y = np.random.random((2, 2)) + x = paddle.to_tensor(np_x, dtype="float64", stop_gradient=False) + y = paddle.to_tensor(np_y, dtype="float64") + out = x + x + out.backward() + x._copy_gradient_from(y) + np.testing.assert_array_equal(x.grad.numpy(), np_y) def test_clear(self): - with _test_eager_guard(): - np_x = np.random.random((3, 8, 8)) - x = paddle.to_tensor(np_x, dtype="float64") - self.assertTrue(x._is_initialized()) - x._clear() - self.assertFalse(x._is_initialized()) + np_x = np.random.random((3, 8, 8)) + x = paddle.to_tensor(np_x, dtype="float64") + self.assertTrue(x._is_initialized()) + x._clear() + self.assertFalse(x._is_initialized()) def test_use_gpudnn(self): np_x = np.random.random((3, 8, 8)) - with _test_eager_guard(): - self.assertTrue(in_dygraph_mode()) - x = paddle.to_tensor(np_x, dtype="float64") - y = x._use_gpudnn(False) - np.testing.assert_array_equal(x.numpy(), y.numpy()) - y = x._use_gpudnn(True) - np.testing.assert_array_equal(x.numpy(), y.numpy()) - - self.assertFalse(in_dygraph_mode()) + + self.assertTrue(in_dygraph_mode()) x = paddle.to_tensor(np_x, dtype="float64") - with self.assertRaises(AttributeError): - x = x._use_gpudnn(False) + y = x._use_gpudnn(False) + np.testing.assert_array_equal(x.numpy(), y.numpy()) + y = x._use_gpudnn(True) + np.testing.assert_array_equal(x.numpy(), y.numpy()) class EagerParamBaseUsageTestCase(unittest.TestCase): def test_print(self): - with _test_eager_guard(): - linear = paddle.nn.Linear(3, 3, bias_attr=False) - print(linear.weight) + 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) - np.testing.assert_array_equal( - linear.weight.numpy(), linear_copy.weight.numpy() - ) - np.testing.assert_array_equal( - linear.weight.numpy(), linear_copy2.numpy() - ) + linear = paddle.nn.Linear(1, 3) + linear_copy = copy.deepcopy(linear) + linear_copy2 = linear.weight._copy_to(core.CPUPlace(), True) + np.testing.assert_array_equal( + linear.weight.numpy(), linear_copy.weight.numpy() + ) + np.testing.assert_array_equal( + linear.weight.numpy(), linear_copy2.numpy() + ) def func_fp16_initilaizer(self): paddle.set_default_dtype("float16") @@ -963,18 +934,6 @@ class EagerParamBaseUsageTestCase(unittest.TestCase): 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)): - np.testing.assert_array_equal(res1[i], res2[i]) - def func_layer_helper_base(self, value): base = paddle.fluid.layer_helper_base.LayerHelperBase( "test_layer", "test_layer" @@ -984,53 +943,32 @@ class EagerParamBaseUsageTestCase(unittest.TestCase): 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) - np.testing.assert_array_equal(res1, res2) - np.testing.assert_array_equal(res3, res4) - def test_backward_with_single_tensor(self): - with _test_eager_guard(): - arr4 = np.random.rand(4, 16, 16, 32).astype('float32') - egr_tensor12 = core.eager.Tensor(arr4, core.CPUPlace()) - egr_tensor12.retain_grads() - arr = np.ones([4, 16, 16, 32]).astype('float32') - self.assertEqual(egr_tensor12.persistable, False) - self.assertTrue("generated_tensor" in egr_tensor12.name) - self.assertEqual(egr_tensor12.shape, [4, 16, 16, 32]) - self.assertEqual(egr_tensor12.dtype, core.VarDesc.VarType.FP32) - self.assertEqual(egr_tensor12.stop_gradient, True) - self.assertTrue(egr_tensor12.place._equals(paddle.fluid.CPUPlace())) - np.testing.assert_array_equal(egr_tensor12.numpy(), arr4) - np.testing.assert_array_equal(egr_tensor12.gradient(), None) - egr_tensor12.stop_gradient = False - egr_tensor12.backward() - np.testing.assert_array_equal(egr_tensor12.gradient(), arr) + arr4 = np.random.rand(4, 16, 16, 32).astype('float32') + egr_tensor12 = core.eager.Tensor(arr4, core.CPUPlace()) + egr_tensor12.retain_grads() + arr = np.ones([4, 16, 16, 32]).astype('float32') + self.assertEqual(egr_tensor12.persistable, False) + self.assertTrue("generated_tensor" in egr_tensor12.name) + self.assertEqual(egr_tensor12.shape, [4, 16, 16, 32]) + self.assertEqual(egr_tensor12.dtype, core.VarDesc.VarType.FP32) + self.assertEqual(egr_tensor12.stop_gradient, True) + self.assertTrue(egr_tensor12.place._equals(paddle.fluid.CPUPlace())) + np.testing.assert_array_equal(egr_tensor12.numpy(), arr4) + np.testing.assert_array_equal(egr_tensor12.gradient(), None) + egr_tensor12.stop_gradient = False + egr_tensor12.backward() + np.testing.assert_array_equal(egr_tensor12.gradient(), arr) def test_set_value(self): - with _test_eager_guard(): - linear = paddle.nn.Linear(1, 3) - ori_place = linear.weight.place - new_weight = np.ones([1, 3]).astype('float32') - self.assertFalse(np.array_equal(linear.weight.numpy(), new_weight)) - - linear.weight.set_value(new_weight) - np.testing.assert_array_equal(linear.weight.numpy(), new_weight) - self.assertTrue(linear.weight.place._equals(ori_place)) - - -class EagerGuardTestCase(unittest.TestCase): - def test__test_eager_guard(self): - tracer = paddle.fluid.dygraph.tracer.Tracer() - with _test_eager_guard(tracer): - self.assertTrue(in_dygraph_mode()) + linear = paddle.nn.Linear(1, 3) + ori_place = linear.weight.place + new_weight = np.ones([1, 3]).astype('float32') + self.assertFalse(np.array_equal(linear.weight.numpy(), new_weight)) + + linear.weight.set_value(new_weight) + np.testing.assert_array_equal(linear.weight.numpy(), new_weight) + self.assertTrue(linear.weight.place._equals(ori_place)) if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/test_linalg_lstsq_op.py b/python/paddle/fluid/tests/unittests/test_linalg_lstsq_op.py index bae9094a7f..82576ab1bd 100644 --- a/python/paddle/fluid/tests/unittests/test_linalg_lstsq_op.py +++ b/python/paddle/fluid/tests/unittests/test_linalg_lstsq_op.py @@ -73,7 +73,6 @@ class LinalgLstsqTestCase(unittest.TestCase): def test_eager_dygraph(self): paddle.disable_static() - paddle.fluid.framework._disable_legacy_dygraph() for dev in self.devices: paddle.set_device(dev) place = paddle.CPUPlace() if dev == "cpu" else paddle.CUDAPlace(0) -- GitLab