# 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 numpy as np import six import copy import paddle import paddle.fluid as fluid import paddle.fluid.core as core from paddle.fluid.framework import _test_eager_guard, _in_legacy_dygraph class TestVarBase(unittest.TestCase): def setUp(self): self.shape = [512, 1234] self.dtype = np.float32 self.array = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) def func_test_to_tensor(self): def _test_place(place): with fluid.dygraph.guard(): paddle.set_default_dtype('float32') # set_default_dtype should not take effect on int x = paddle.to_tensor(1, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1])) self.assertNotEqual(x.dtype, core.VarDesc.VarType.FP32) y = paddle.to_tensor(2, place=x.place) self.assertEqual(str(x.place), str(y.place)) # set_default_dtype should not take effect on numpy x = paddle.to_tensor( np.array([1.2]).astype('float16'), place=place, stop_gradient=False) self.assertTrue( np.array_equal(x.numpy(), np.array([1.2], 'float16'))) self.assertEqual(x.dtype, core.VarDesc.VarType.FP16) # set_default_dtype take effect on int x = paddle.to_tensor(1, place=place) self.assertTrue(x.dtype, core.VarDesc.VarType.INT64) # set_default_dtype take effect on float x = paddle.to_tensor(1.2, place=place, stop_gradient=False) self.assertTrue( np.array_equal(x.numpy(), np.array([1.2]).astype( 'float32'))) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) clone_x = x.clone() self.assertTrue( np.array_equal(clone_x.numpy(), np.array([1.2]).astype('float32'))) self.assertEqual(clone_x.dtype, core.VarDesc.VarType.FP32) y = clone_x**2 y.backward() self.assertTrue( np.array_equal(x.grad.numpy(), np.array([2.4]).astype('float32'))) y = x.cpu() self.assertEqual(y.place.__repr__(), "Place(cpu)") if core.is_compiled_with_cuda(): y = x.pin_memory() self.assertEqual(y.place.__repr__(), "Place(gpu_pinned)") y = x.cuda() y = x.cuda(None) self.assertEqual(y.place.__repr__(), "Place(gpu:0)") y = x.cuda(device_id=0) self.assertEqual(y.place.__repr__(), "Place(gpu:0)") y = x.cuda(blocking=False) self.assertEqual(y.place.__repr__(), "Place(gpu:0)") y = x.cuda(blocking=True) self.assertEqual(y.place.__repr__(), "Place(gpu:0)") with self.assertRaises(ValueError): y = x.cuda("test") # support 'dtype' is core.VarType x = paddle.rand((2, 2)) y = paddle.to_tensor([2, 2], dtype=x.dtype) self.assertEqual(y.dtype, core.VarDesc.VarType.FP32) # set_default_dtype take effect on complex x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j])) self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX64) paddle.set_default_dtype('float64') x = paddle.to_tensor(1.2, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1.2])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP64) x = paddle.to_tensor(1 + 2j, place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j])) self.assertEqual(x.dtype, core.VarDesc.VarType.COMPLEX128) x = paddle.to_tensor( 1, dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1.])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.shape, [1]) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) x = paddle.to_tensor( (1, 2), dtype='float32', place=place, stop_gradient=False) x = paddle.to_tensor( [1, 2], dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), [1., 2.])) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.grad, None) self.assertEqual(x.shape, [2]) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) x = paddle.to_tensor( self.array, dtype='float32', place=place, stop_gradient=False) self.assertTrue(np.array_equal(x.numpy(), self.array)) self.assertEqual(x.dtype, core.VarDesc.VarType.FP32) self.assertEqual(x.shape, self.shape) self.assertEqual(x.stop_gradient, False) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) y = paddle.to_tensor(x) y = paddle.to_tensor(y, dtype='float64', place=place) self.assertTrue(np.array_equal(y.numpy(), self.array)) self.assertEqual(y.dtype, core.VarDesc.VarType.FP64) self.assertEqual(y.shape, self.shape) self.assertEqual(y.stop_gradient, True) self.assertEqual(y.type, core.VarDesc.VarType.LOD_TENSOR) z = x + y self.assertTrue(np.array_equal(z.numpy(), 2 * self.array)) x = paddle.to_tensor( [1 + 2j, 1 - 2j], dtype='complex64', place=place) y = paddle.to_tensor(x) self.assertTrue(np.array_equal(x.numpy(), [1 + 2j, 1 - 2j])) self.assertEqual(y.dtype, core.VarDesc.VarType.COMPLEX64) self.assertEqual(y.shape, [2]) paddle.set_default_dtype('float32') x = paddle.randn([3, 4]) x_array = np.array(x) self.assertEqual(x_array.shape, x.numpy().shape) self.assertEqual(x_array.dtype, x.numpy().dtype) self.assertTrue(np.array_equal(x_array, x.numpy())) x = paddle.to_tensor(1.0) self.assertEqual(x.item(), 1.0) self.assertTrue(isinstance(x.item(), float)) x = paddle.randn([3, 2, 2]) self.assertTrue(isinstance(x.item(5), float)) self.assertTrue(isinstance(x.item(1, 0, 1), float)) self.assertEqual(x.item(5), x.item(1, 0, 1)) self.assertTrue( np.array_equal(x.item(1, 0, 1), x.numpy().item(1, 0, 1))) x = paddle.to_tensor([[1.111111, 2.222222, 3.333333]]) self.assertEqual(x.item(0, 2), x.item(2)) self.assertAlmostEqual(x.item(2), 3.333333) self.assertTrue(isinstance(x.item(0, 2), float)) x = paddle.to_tensor(1.0, dtype='float64') self.assertEqual(x.item(), 1.0) self.assertTrue(isinstance(x.item(), float)) x = paddle.to_tensor(1.0, dtype='float16') self.assertEqual(x.item(), 1.0) self.assertTrue(isinstance(x.item(), float)) x = paddle.to_tensor(1, dtype='uint8') self.assertEqual(x.item(), 1) self.assertTrue(isinstance(x.item(), int)) x = paddle.to_tensor(1, dtype='int8') self.assertEqual(x.item(), 1) self.assertTrue(isinstance(x.item(), int)) x = paddle.to_tensor(1, dtype='int16') self.assertEqual(x.item(), 1) self.assertTrue(isinstance(x.item(), int)) x = paddle.to_tensor(1, dtype='int32') self.assertEqual(x.item(), 1) self.assertTrue(isinstance(x.item(), int)) x = paddle.to_tensor(1, dtype='int64') self.assertEqual(x.item(), 1) self.assertTrue(isinstance(x.item(), int)) x = paddle.to_tensor(True) self.assertEqual(x.item(), True) self.assertTrue(isinstance(x.item(), bool)) x = paddle.to_tensor(1 + 1j) self.assertEqual(x.item(), 1 + 1j) self.assertTrue(isinstance(x.item(), complex)) # empty tensor x = paddle.to_tensor([]) self.assertEqual(x.shape, [0]) expected_result = np.array([], dtype='float32') self.assertEqual(x.numpy().shape, expected_result.shape) self.assertTrue(np.array_equal(x.numpy(), expected_result)) numpy_array = np.random.randn(3, 4) # covert core.LoDTensor to paddle.Tensor lod_tensor = paddle.fluid.core.LoDTensor() place = paddle.fluid.framework._current_expected_place() lod_tensor.set(numpy_array, place) x = paddle.to_tensor(lod_tensor) self.assertTrue(np.array_equal(x.numpy(), numpy_array)) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) self.assertEqual(str(x.place), str(place)) # covert core.Tensor to paddle.Tensor x = paddle.to_tensor(numpy_array) dlpack = x.value().get_tensor()._to_dlpack() tensor_from_dlpack = paddle.fluid.core.from_dlpack(dlpack) x = paddle.to_tensor(tensor_from_dlpack) self.assertTrue(np.array_equal(x.numpy(), numpy_array)) self.assertEqual(x.type, core.VarDesc.VarType.LOD_TENSOR) with self.assertRaises(ValueError): paddle.randn([3, 2, 2]).item() with self.assertRaises(ValueError): paddle.randn([3, 2, 2]).item(18) with self.assertRaises(ValueError): paddle.randn([3, 2, 2]).item(1, 2) with self.assertRaises(ValueError): paddle.randn([3, 2, 2]).item(2, 1, 2) with self.assertRaises(TypeError): paddle.to_tensor('test') with self.assertRaises(TypeError): paddle.to_tensor(1, dtype='test') with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]]) with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]], place='test') with self.assertRaises(ValueError): paddle.to_tensor([[1], [2, 3]], place=1) _test_place(core.CPUPlace()) _test_place("cpu") if core.is_compiled_with_cuda(): _test_place(core.CUDAPinnedPlace()) _test_place("gpu_pinned") _test_place(core.CUDAPlace(0)) _test_place("gpu:0") if core.is_compiled_with_npu(): _test_place(core.NPUPlace(0)) _test_place("npu:0") def test_to_tensor(self): with _test_eager_guard(): self.func_test_to_tensor() self.func_test_to_tensor() def func_test_to_tensor_not_change_input_stop_gradient(self): with paddle.fluid.dygraph.guard(core.CPUPlace()): a = paddle.zeros([1024]) a.stop_gradient = False b = paddle.to_tensor(a) self.assertEqual(a.stop_gradient, False) self.assertEqual(b.stop_gradient, True) def test_to_tensor_not_change_input_stop_gradient(self): with _test_eager_guard(): self.func_test_to_tensor_not_change_input_stop_gradient() self.func_test_to_tensor_not_change_input_stop_gradient() def func_test_to_tensor_change_place(self): if core.is_compiled_with_cuda(): a_np = np.random.rand(1024, 1024) with paddle.fluid.dygraph.guard(core.CPUPlace()): a = paddle.to_tensor(a_np, place=paddle.CUDAPinnedPlace()) a = paddle.to_tensor(a) self.assertEqual(a.place.__repr__(), "Place(cpu)") with paddle.fluid.dygraph.guard(core.CUDAPlace(0)): a = paddle.to_tensor(a_np, place=paddle.CUDAPinnedPlace()) a = paddle.to_tensor(a) self.assertEqual(a.place.__repr__(), "Place(gpu:0)") with paddle.fluid.dygraph.guard(core.CUDAPlace(0)): a = paddle.to_tensor(a_np, place=paddle.CPUPlace()) a = paddle.to_tensor(a, place=paddle.CUDAPinnedPlace()) self.assertEqual(a.place.__repr__(), "Place(gpu_pinned)") def test_to_tensor_change_place(self): with _test_eager_guard(): self.func_test_to_tensor_change_place() self.func_test_to_tensor_change_place() def func_test_to_tensor_with_lodtensor(self): if core.is_compiled_with_cuda(): a_np = np.random.rand(1024, 1024) with paddle.fluid.dygraph.guard(core.CPUPlace()): lod_tensor = core.LoDTensor() lod_tensor.set(a_np, core.CPUPlace()) a = paddle.to_tensor(lod_tensor) self.assertTrue(np.array_equal(a_np, a.numpy())) with paddle.fluid.dygraph.guard(core.CUDAPlace(0)): lod_tensor = core.LoDTensor() lod_tensor.set(a_np, core.CUDAPlace(0)) a = paddle.to_tensor(lod_tensor, place=core.CPUPlace()) self.assertTrue(np.array_equal(a_np, a.numpy())) self.assertTrue(a.place.__repr__(), "Place(cpu)") def test_to_tensor_with_lodtensor(self): with _test_eager_guard(): self.func_test_to_tensor_with_lodtensor() self.func_test_to_tensor_with_lodtensor() def func_test_to_variable(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array, name="abc") self.assertTrue(np.array_equal(var.numpy(), self.array)) self.assertEqual(var.name, 'abc') # default value self.assertEqual(var.persistable, False) self.assertEqual(var.stop_gradient, True) self.assertEqual(var.shape, self.shape) self.assertEqual(var.dtype, core.VarDesc.VarType.FP32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) # The type of input must be 'ndarray' or 'Variable', it will raise TypeError with self.assertRaises(TypeError): var = fluid.dygraph.to_variable("test", name="abc") # test to_variable of LayerObjectHelper(LayerHelperBase) with self.assertRaises(TypeError): linear = fluid.dygraph.Linear(32, 64) var = linear._helper.to_variable("test", name="abc") def test_to_variable(self): with _test_eager_guard(): self.func_test_to_variable() self.func_test_to_variable() def func_test_list_to_variable(self): with fluid.dygraph.guard(): array = [[[1, 2], [1, 2], [1.0, 2]], [[1, 2], [1, 2], [1, 2]]] var = fluid.dygraph.to_variable(array, dtype='int32') self.assertTrue(np.array_equal(var.numpy(), array)) self.assertEqual(var.shape, [2, 3, 2]) self.assertEqual(var.dtype, core.VarDesc.VarType.INT32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) def test_list_to_variable(self): with _test_eager_guard(): self.func_test_list_to_variable() self.func_test_list_to_variable() def func_test_tuple_to_variable(self): with fluid.dygraph.guard(): array = (((1, 2), (1, 2), (1, 2)), ((1, 2), (1, 2), (1, 2))) var = fluid.dygraph.to_variable(array, dtype='float32') self.assertTrue(np.array_equal(var.numpy(), array)) self.assertEqual(var.shape, [2, 3, 2]) self.assertEqual(var.dtype, core.VarDesc.VarType.FP32) self.assertEqual(var.type, core.VarDesc.VarType.LOD_TENSOR) def test_tuple_to_variable(self): with _test_eager_guard(): self.func_test_tuple_to_variable() self.func_test_tuple_to_variable() def func_test_tensor_to_variable(self): with fluid.dygraph.guard(): t = fluid.Tensor() t.set(np.random.random((1024, 1024)), fluid.CPUPlace()) var = fluid.dygraph.to_variable(t) self.assertTrue(np.array_equal(t, var.numpy())) def test_tensor_to_variable(self): with _test_eager_guard(): self.func_test_tensor_to_variable() self.func_test_tensor_to_variable() def func_test_leaf_tensor(self): with fluid.dygraph.guard(): x = paddle.to_tensor(np.random.uniform(-1, 1, size=[10, 10])) self.assertTrue(x.is_leaf) y = x + 1 self.assertTrue(y.is_leaf) x = paddle.to_tensor( np.random.uniform( -1, 1, size=[10, 10]), stop_gradient=False) self.assertTrue(x.is_leaf) y = x + 1 self.assertFalse(y.is_leaf) linear = paddle.nn.Linear(10, 10) input = paddle.to_tensor( np.random.uniform( -1, 1, size=[10, 10]).astype('float32'), stop_gradient=False) self.assertTrue(input.is_leaf) out = linear(input) self.assertTrue(linear.weight.is_leaf) self.assertTrue(linear.bias.is_leaf) self.assertFalse(out.is_leaf) def test_leaf_tensor(self): with _test_eager_guard(): self.func_test_leaf_tensor() self.func_test_leaf_tensor() def func_test_detach(self): with fluid.dygraph.guard(): x = paddle.to_tensor(1.0, dtype="float64", stop_gradient=False) detach_x = x.detach() self.assertTrue(detach_x.stop_gradient, True) cmp_float = np.allclose if core.is_compiled_with_rocm( ) else np.array_equal detach_x[:] = 10.0 self.assertTrue(cmp_float(x.numpy(), [10.0])) y = x**2 y.backward() self.assertTrue(cmp_float(x.grad.numpy(), [20.0])) self.assertEqual(detach_x.grad, None) detach_x.stop_gradient = False # Set stop_gradient to be False, supported auto-grad z = 3 * detach_x**2 z.backward() self.assertTrue(cmp_float(x.grad.numpy(), [20.0])) self.assertTrue(cmp_float(detach_x.grad.numpy(), [60.0])) with self.assertRaises(ValueError): detach_x[:] = 5.0 detach_x.stop_gradient = True # Due to sharing of data with origin Tensor, There are some unsafe operations: with self.assertRaises(RuntimeError): y = 2**x detach_x[:] = 5.0 y.backward() def test_detach(self): with _test_eager_guard(): self.func_test_detach() self.func_test_detach() def func_test_write_property(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertEqual(var.name, 'generated_tensor_0') var.name = 'test' self.assertEqual(var.name, 'test') self.assertEqual(var.persistable, False) var.persistable = True self.assertEqual(var.persistable, True) self.assertEqual(var.stop_gradient, True) var.stop_gradient = False self.assertEqual(var.stop_gradient, False) def test_write_property(self): with _test_eager_guard(): self.func_test_write_property() self.func_test_write_property() def func_test_deep_copy(self): with fluid.dygraph.guard(): if _in_legacy_dygraph(): empty_var = core.VarBase() else: empty_var = core.eager.Tensor() empty_var_copy = copy.deepcopy(empty_var) self.assertEqual(empty_var.stop_gradient, empty_var_copy.stop_gradient) self.assertEqual(empty_var.persistable, empty_var_copy.persistable) self.assertEqual(empty_var.type, empty_var_copy.type) self.assertEqual(empty_var.dtype, empty_var_copy.dtype) x = paddle.to_tensor([2.], stop_gradient=False) y = paddle.to_tensor([3.], stop_gradient=False) z = x * y memo = {} x_copy = copy.deepcopy(x, memo) y_copy = copy.deepcopy(y, memo) self.assertEqual(x_copy.stop_gradient, y_copy.stop_gradient) self.assertEqual(x_copy.persistable, y_copy.persistable) self.assertEqual(x_copy.type, y_copy.type) self.assertEqual(x_copy.dtype, y_copy.dtype) self.assertTrue(np.array_equal(x.numpy(), x_copy.numpy())) self.assertTrue(np.array_equal(y.numpy(), y_copy.numpy())) self.assertNotEqual(id(x), id(x_copy)) self.assertTrue(np.array_equal(x.numpy(), [2.])) with self.assertRaises(ValueError): x_copy[:] = 5. with self.assertRaises(RuntimeError): copy.deepcopy(z) x_copy2 = copy.deepcopy(x, memo) y_copy2 = copy.deepcopy(y, memo) self.assertEqual(id(x_copy), id(x_copy2)) self.assertEqual(id(y_copy), id(y_copy2)) # test copy selected rows if _in_legacy_dygraph(): x = core.VarBase(core.VarDesc.VarType.FP32, [3, 100], "selected_rows", core.VarDesc.VarType.SELECTED_ROWS, True) else: x = core.eager.Tensor(core.VarDesc.VarType.FP32, [3, 100], "selected_rows", core.VarDesc.VarType.SELECTED_ROWS, True) selected_rows = x.value().get_selected_rows() selected_rows.get_tensor().set( np.random.rand(3, 100), core.CPUPlace()) selected_rows.set_height(10) selected_rows.set_rows([3, 5, 7]) x_copy = copy.deepcopy(x) self.assertEqual(x_copy.stop_gradient, x.stop_gradient) self.assertEqual(x_copy.persistable, x.persistable) self.assertEqual(x_copy.type, x.type) self.assertEqual(x_copy.dtype, x.dtype) copy_selected_rows = x_copy.value().get_selected_rows() self.assertEqual(copy_selected_rows.height(), selected_rows.height()) self.assertEqual(copy_selected_rows.rows(), selected_rows.rows()) self.assertTrue( np.array_equal( np.array(copy_selected_rows.get_tensor()), np.array(selected_rows.get_tensor()))) def test_deep_copy(self): with _test_eager_guard(): self.func_test_deep_copy() self.func_test_deep_copy() # test some patched methods def func_test_set_value(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) tmp1 = np.random.uniform(0.1, 1, [2, 2, 3]).astype(self.dtype) self.assertRaises(AssertionError, var.set_value, tmp1) tmp2 = np.random.uniform(0.1, 1, self.shape).astype(self.dtype) var.set_value(tmp2) self.assertTrue(np.array_equal(var.numpy(), tmp2)) def test_set_value(self): with _test_eager_guard(): self.func_test_set_value() self.func_test_set_value() def func_test_to_string(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertTrue(isinstance(str(var), str)) def test_to_string(self): with _test_eager_guard(): self.func_test_to_string() self.func_test_to_string() def func_test_element_size(self): with fluid.dygraph.guard(): x = paddle.to_tensor(1, dtype='bool') self.assertEqual(x.element_size(), 1) x = paddle.to_tensor(1, dtype='float16') self.assertEqual(x.element_size(), 2) x = paddle.to_tensor(1, dtype='float32') self.assertEqual(x.element_size(), 4) x = paddle.to_tensor(1, dtype='float64') self.assertEqual(x.element_size(), 8) x = paddle.to_tensor(1, dtype='int8') self.assertEqual(x.element_size(), 1) x = paddle.to_tensor(1, dtype='int16') self.assertEqual(x.element_size(), 2) x = paddle.to_tensor(1, dtype='int32') self.assertEqual(x.element_size(), 4) x = paddle.to_tensor(1, dtype='int64') self.assertEqual(x.element_size(), 8) x = paddle.to_tensor(1, dtype='uint8') self.assertEqual(x.element_size(), 1) x = paddle.to_tensor(1, dtype='complex64') self.assertEqual(x.element_size(), 8) x = paddle.to_tensor(1, dtype='complex128') self.assertEqual(x.element_size(), 16) def test_element_size(self): with _test_eager_guard(): self.func_test_element_size() self.func_test_element_size() def func_test_backward(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) var.stop_gradient = False loss = fluid.layers.relu(var) loss.backward() grad_var = var._grad_ivar() self.assertEqual(grad_var.shape, self.shape) def test_backward(self): with _test_eager_guard(): self.func_test_backward() self.func_test_backward() def func_test_gradient(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) var.stop_gradient = False loss = fluid.layers.relu(var) loss.backward() grad_var = var.gradient() self.assertEqual(grad_var.shape, self.array.shape) def test_gradient(self): with _test_eager_guard(): self.func_test_gradient() self.func_test_gradient() def func_test_block(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertEqual(var.block, fluid.default_main_program().global_block()) def test_block(self): with _test_eager_guard(): self.func_test_block() self.func_test_block() def _test_slice(self): w = fluid.dygraph.to_variable( np.random.random((784, 100, 100)).astype('float64')) for i in range(3): nw = w[i] self.assertEqual((100, 100), tuple(nw.shape)) nw = w[:] self.assertEqual((784, 100, 100), tuple(nw.shape)) nw = w[:, :] self.assertEqual((784, 100, 100), tuple(nw.shape)) nw = w[:, :, -1] self.assertEqual((784, 100), tuple(nw.shape)) nw = w[1, 1, 1] self.assertEqual(len(nw.shape), 1) self.assertEqual(nw.shape[0], 1) nw = w[:, :, :-1] self.assertEqual((784, 100, 99), tuple(nw.shape)) tensor_array = np.array( [[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]).astype('float32') var = fluid.dygraph.to_variable(tensor_array) var1 = var[0, 1, 1] var2 = var[1:] var3 = var[0:1] var4 = var[::-1] var5 = var[1, 1:, 1:] var_reshape = fluid.layers.reshape(var, [3, -1, 3]) var6 = var_reshape[:, :, -1] var7 = var[:, :, :-1] var8 = var[:1, :1, :1] var9 = var[:-1, :-1, :-1] var10 = var[::-1, :1, :-1] var11 = var[:-1, ::-1, -1:] var12 = var[1:2, 2:, ::-1] var13 = var[2:10, 2:, -2:-1] var14 = var[1:-1, 0:2, ::-1] var15 = var[::-1, ::-1, ::-1] var16 = var[-4:4] var17 = var[:, 0, 0:0] var18 = var[:, 1:1:2] vars = [ var, var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16, var17, var18 ] local_out = [var.numpy() for var in vars] self.assertTrue(np.array_equal(local_out[1], tensor_array[0, 1, 1:2])) self.assertTrue(np.array_equal(local_out[2], tensor_array[1:])) self.assertTrue(np.array_equal(local_out[3], tensor_array[0:1])) self.assertTrue(np.array_equal(local_out[4], tensor_array[::-1])) self.assertTrue(np.array_equal(local_out[5], tensor_array[1, 1:, 1:])) self.assertTrue( np.array_equal(local_out[6], tensor_array.reshape((3, -1, 3))[:, :, -1])) self.assertTrue(np.array_equal(local_out[7], tensor_array[:, :, :-1])) self.assertTrue(np.array_equal(local_out[8], tensor_array[:1, :1, :1])) self.assertTrue( np.array_equal(local_out[9], tensor_array[:-1, :-1, :-1])) self.assertTrue( np.array_equal(local_out[10], tensor_array[::-1, :1, :-1])) self.assertTrue( np.array_equal(local_out[11], tensor_array[:-1, ::-1, -1:])) self.assertTrue( np.array_equal(local_out[12], tensor_array[1:2, 2:, ::-1])) self.assertTrue( np.array_equal(local_out[13], tensor_array[2:10, 2:, -2:-1])) self.assertTrue( np.array_equal(local_out[14], tensor_array[1:-1, 0:2, ::-1])) self.assertTrue( np.array_equal(local_out[15], tensor_array[::-1, ::-1, ::-1])) self.assertTrue(np.array_equal(local_out[16], tensor_array[-4:4])) self.assertTrue(np.array_equal(local_out[17], tensor_array[:, 0, 0:0])) self.assertTrue(np.array_equal(local_out[18], tensor_array[:, 1:1:2])) def _test_slice_for_tensor_attr(self): tensor_array = np.array( [[[1, 2, 3], [4, 5, 6], [7, 8, 9]], [[10, 11, 12], [13, 14, 15], [16, 17, 18]], [[19, 20, 21], [22, 23, 24], [25, 26, 27]]]).astype('float32') var = paddle.to_tensor(tensor_array) one = paddle.ones(shape=[1], dtype="int32") two = paddle.full(shape=[1], fill_value=2, dtype="int32") negative_one = paddle.full(shape=[1], fill_value=-1, dtype="int32") four = paddle.full(shape=[1], fill_value=4, dtype="int32") var = fluid.dygraph.to_variable(tensor_array) var1 = var[0, one, one] var2 = var[one:] var3 = var[0:one] var4 = var[::negative_one] var5 = var[one, one:, one:] var_reshape = fluid.layers.reshape(var, [3, negative_one, 3]) var6 = var_reshape[:, :, negative_one] var7 = var[:, :, :negative_one] var8 = var[:one, :one, :1] var9 = var[:-1, :negative_one, :negative_one] var10 = var[::negative_one, :one, :negative_one] var11 = var[:negative_one, ::-1, negative_one:] var12 = var[one:2, 2:, ::negative_one] var13 = var[two:10, 2:, -2:negative_one] var14 = var[1:negative_one, 0:2, ::negative_one] var15 = var[::negative_one, ::-1, ::negative_one] var16 = var[-4:4] vars = [ var, var1, var2, var3, var4, var5, var6, var7, var8, var9, var10, var11, var12, var13, var14, var15, var16 ] local_out = [var.numpy() for var in vars] self.assertTrue(np.array_equal(local_out[1], tensor_array[0, 1, 1:2])) self.assertTrue(np.array_equal(local_out[2], tensor_array[1:])) self.assertTrue(np.array_equal(local_out[3], tensor_array[0:1])) self.assertTrue(np.array_equal(local_out[4], tensor_array[::-1])) self.assertTrue(np.array_equal(local_out[5], tensor_array[1, 1:, 1:])) self.assertTrue( np.array_equal(local_out[6], tensor_array.reshape((3, -1, 3))[:, :, -1])) self.assertTrue(np.array_equal(local_out[7], tensor_array[:, :, :-1])) self.assertTrue(np.array_equal(local_out[8], tensor_array[:1, :1, :1])) self.assertTrue( np.array_equal(local_out[9], tensor_array[:-1, :-1, :-1])) self.assertTrue( np.array_equal(local_out[10], tensor_array[::-1, :1, :-1])) self.assertTrue( np.array_equal(local_out[11], tensor_array[:-1, ::-1, -1:])) self.assertTrue( np.array_equal(local_out[12], tensor_array[1:2, 2:, ::-1])) self.assertTrue( np.array_equal(local_out[13], tensor_array[2:10, 2:, -2:-1])) self.assertTrue( np.array_equal(local_out[14], tensor_array[1:-1, 0:2, ::-1])) self.assertTrue( np.array_equal(local_out[15], tensor_array[::-1, ::-1, ::-1])) self.assertTrue(np.array_equal(local_out[16], tensor_array[-4:4])) def _test_for_getitem_ellipsis_index(self): shape = (64, 3, 5, 256) np_fp32_value = np.random.random(shape).astype('float32') np_int_value = np.random.randint(1, 100, shape) var_fp32 = paddle.to_tensor(np_fp32_value) var_int = paddle.to_tensor(np_int_value) def assert_getitem_ellipsis_index(var_tensor, var_np): var = [ var_tensor[..., 0].numpy(), var_tensor[..., 1, 0].numpy(), var_tensor[0, ..., 1, 0].numpy(), var_tensor[1, ..., 1].numpy(), var_tensor[2, ...].numpy(), var_tensor[2, 0, ...].numpy(), var_tensor[2, 0, 1, ...].numpy(), var_tensor[...].numpy(), var_tensor[:, ..., 100].numpy(), ] self.assertTrue(np.array_equal(var[0], var_np[..., 0])) self.assertTrue(np.array_equal(var[1], var_np[..., 1, 0])) self.assertTrue(np.array_equal(var[2], var_np[0, ..., 1, 0])) self.assertTrue(np.array_equal(var[3], var_np[1, ..., 1])) self.assertTrue(np.array_equal(var[4], var_np[2, ...])) self.assertTrue(np.array_equal(var[5], var_np[2, 0, ...])) self.assertTrue(np.array_equal(var[6], var_np[2, 0, 1, ...])) self.assertTrue(np.array_equal(var[7], var_np[...])) self.assertTrue(np.array_equal(var[8], var_np[:, ..., 100])) var_fp32 = paddle.to_tensor(np_fp32_value) var_int = paddle.to_tensor(np_int_value) assert_getitem_ellipsis_index(var_fp32, np_fp32_value) assert_getitem_ellipsis_index(var_int, np_int_value) # test 1 dim tensor var_one_dim = paddle.to_tensor([1, 2, 3, 4]) self.assertTrue( np.array_equal(var_one_dim[..., 0].numpy(), np.array([1]))) def _test_none_index(self): shape = (8, 64, 5, 256) np_value = np.random.random(shape).astype('float32') var_tensor = paddle.to_tensor(np_value) var = [ var_tensor[1, 0, None].numpy(), var_tensor[None, ..., 1, 0].numpy(), var_tensor[:, :, :, None].numpy(), var_tensor[1, ..., 1, None].numpy(), var_tensor[2, ..., None, None].numpy(), var_tensor[None, 2, 0, ...].numpy(), var_tensor[None, 2, None, 1].numpy(), var_tensor[None].numpy(), var_tensor[0, 0, None, 0, 0, None].numpy(), var_tensor[None, None, 0, ..., None].numpy(), var_tensor[..., None, :, None].numpy(), var_tensor[0, 1:10:2, None, None, ...].numpy(), ] self.assertTrue(np.array_equal(var[0], np_value[1, 0, None])) self.assertTrue(np.array_equal(var[1], np_value[None, ..., 1, 0])) self.assertTrue(np.array_equal(var[2], np_value[:, :, :, None])) self.assertTrue(np.array_equal(var[3], np_value[1, ..., 1, None])) self.assertTrue(np.array_equal(var[4], np_value[2, ..., None, None])) self.assertTrue(np.array_equal(var[5], np_value[None, 2, 0, ...])) self.assertTrue(np.array_equal(var[6], np_value[None, 2, None, 1])) self.assertTrue(np.array_equal(var[7], np_value[None])) self.assertTrue( np.array_equal(var[8], np_value[0, 0, None, 0, 0, None])) self.assertTrue( np.array_equal(var[9], np_value[None, None, 0, ..., None])) self.assertTrue(np.array_equal(var[10], np_value[..., None, :, None])) # TODO(zyfncg) there is a bug of dimensions when slice step > 1 and # indexs has int type # self.assertTrue( # np.array_equal(var[11], np_value[0, 1:10:2, None, None, ...])) def _test_bool_index(self): shape = (4, 2, 5, 64) np_value = np.random.random(shape).astype('float32') var_tensor = paddle.to_tensor(np_value) index = [[True, True, True, True], [True, False, True, True], [True, False, False, True], [False, 0, 1, True, True], [False, False, False, False]] index2d = np.array([[True, True], [False, False], [True, False], [True, True]]) tensor_index = paddle.to_tensor(index2d) var = [ var_tensor[index[0]].numpy(), var_tensor[index[1]].numpy(), var_tensor[index[2]].numpy(), var_tensor[index[3]].numpy(), var_tensor[paddle.to_tensor(index[0])].numpy(), var_tensor[tensor_index].numpy(), var_tensor[paddle.to_tensor(index[4])].numpy() ] self.assertTrue(np.array_equal(var[0], np_value[index[0]])) self.assertTrue(np.array_equal(var[1], np_value[index[1]])) self.assertTrue(np.array_equal(var[2], np_value[index[2]])) self.assertTrue(np.array_equal(var[3], np_value[index[3]])) self.assertTrue(np.array_equal(var[4], np_value[index[0]])) self.assertTrue(np.array_equal(var[5], np_value[index2d])) self.assertTrue(np.array_equal(var[6], np_value[index[4]])) self.assertTrue( np.array_equal(var_tensor[var_tensor > 0.67], np_value[np_value > 0.67])) self.assertTrue( np.array_equal(var_tensor[var_tensor < 0.55], np_value[np_value < 0.55])) with self.assertRaises(ValueError): var_tensor[[False, False, False, False]] with self.assertRaises(ValueError): var_tensor[[True, False]] with self.assertRaises(ValueError): var_tensor[[True, False, False, False, False]] with self.assertRaises(IndexError): var_tensor[paddle.to_tensor([[True, False, False, False]])] def _test_scalar_bool_index(self): shape = (1, 2, 5, 64) np_value = np.random.random(shape).astype('float32') var_tensor = paddle.to_tensor(np_value) index = [True] tensor_index = paddle.to_tensor(index) var = [var_tensor[tensor_index].numpy(), ] self.assertTrue(np.array_equal(var[0], np_value[index])) def _test_for_var(self): np_value = np.random.random((30, 100, 100)).astype('float32') w = fluid.dygraph.to_variable(np_value) for i, e in enumerate(w): self.assertTrue(np.array_equal(e.numpy(), np_value[i])) def _test_numpy_index(self): array = np.arange(120).reshape([4, 5, 6]) t = paddle.to_tensor(array) self.assertTrue(np.array_equal(t[np.longlong(0)].numpy(), array[0])) self.assertTrue( np.array_equal(t[np.longlong(0):np.longlong(4):np.longlong(2)] .numpy(), array[0:4:2])) self.assertTrue(np.array_equal(t[np.int64(0)].numpy(), array[0])) self.assertTrue( np.array_equal(t[np.int32(1):np.int32(4):np.int32(2)].numpy(), array[1:4:2])) self.assertTrue( np.array_equal(t[np.int16(0):np.int16(4):np.int16(2)].numpy(), array[0:4:2])) def _test_list_index(self): # case1: array = np.arange(120).reshape([6, 5, 4]) x = paddle.to_tensor(array) py_idx = [[0, 2, 0, 1, 3], [0, 0, 1, 2, 0]] idx = [paddle.to_tensor(py_idx[0]), paddle.to_tensor(py_idx[1])] self.assertTrue(np.array_equal(x[idx].numpy(), array[py_idx])) self.assertTrue(np.array_equal(x[py_idx].numpy(), array[py_idx])) # case2: tensor_x = paddle.to_tensor( np.zeros(12).reshape(2, 6).astype(np.float32)) tensor_y1 = paddle.zeros([1], dtype='int32') + 2 tensor_y2 = paddle.zeros([1], dtype='int32') + 5 tensor_x[:, tensor_y1:tensor_y2] = 42 res = tensor_x.numpy() exp = np.array([[0., 0., 42., 42., 42., 0.], [0., 0., 42., 42., 42., 0.]]) self.assertTrue(np.array_equal(res, exp)) # case3: row = np.array([0, 1, 2]) col = np.array([2, 1, 3]) self.assertTrue(np.array_equal(array[row, col], x[row, col].numpy())) def func_test_slice(self): with fluid.dygraph.guard(): self._test_slice() self._test_slice_for_tensor_attr() self._test_for_var() self._test_for_getitem_ellipsis_index() self._test_none_index() self._test_bool_index() self._test_scalar_bool_index() self._test_numpy_index() self._test_list_index() var = fluid.dygraph.to_variable(self.array) self.assertTrue(np.array_equal(var[1, :].numpy(), self.array[1, :])) self.assertTrue(np.array_equal(var[::-1].numpy(), self.array[::-1])) with self.assertRaises(IndexError): y = var[self.shape[0]] with self.assertRaises(IndexError): y = var[0 - self.shape[0] - 1] with self.assertRaises(IndexError): mask = np.array([1, 0, 1, 0], dtype=bool) var[paddle.to_tensor([0, 1]), mask] def test_slice(self): with _test_eager_guard(): self.func_test_slice() self.func_test_slice() def func_test_var_base_to_np(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertTrue( np.array_equal(var.numpy(), fluid.framework._var_base_to_np(var))) def test_var_base_to_np(self): with _test_eager_guard(): self.func_test_var_base_to_np() self.func_test_var_base_to_np() def func_test_var_base_as_np(self): with fluid.dygraph.guard(): var = fluid.dygraph.to_variable(self.array) self.assertTrue(np.array_equal(var.numpy(), np.array(var))) self.assertTrue( np.array_equal( var.numpy(), np.array( var, dtype=np.float32))) def test_var_base_as_np(self): with _test_eager_guard(): self.func_test_var_base_as_np() self.func_test_var_base_as_np() def func_test_if(self): with fluid.dygraph.guard(): var1 = fluid.dygraph.to_variable(np.array([[[0]]])) var2 = fluid.dygraph.to_variable(np.array([[[1]]])) var1_bool = False var2_bool = False if var1: var1_bool = True if var2: var2_bool = True assert var1_bool == False, "if var1 should be false" assert var2_bool == True, "if var2 should be true" assert bool(var1) == False, "bool(var1) is False" assert bool(var2) == True, "bool(var2) is True" def test_if(self): with _test_eager_guard(): self.func_test_if() self.func_test_if() def func_test_to_static_var(self): with fluid.dygraph.guard(): # Convert VarBase into Variable or Parameter var_base = fluid.dygraph.to_variable(self.array, name="var_base_1") static_var = var_base._to_static_var() self._assert_to_static(var_base, static_var) var_base = fluid.dygraph.to_variable(self.array, name="var_base_2") static_param = var_base._to_static_var(to_parameter=True) self._assert_to_static(var_base, static_param, True) # Convert ParamBase into Parameter fc = fluid.dygraph.Linear( 10, 20, param_attr=fluid.ParamAttr( learning_rate=0.001, do_model_average=True, regularizer=fluid.regularizer.L1Decay())) weight = fc.parameters()[0] static_param = weight._to_static_var() self._assert_to_static(weight, static_param, True) def test_to_static_var(self): with _test_eager_guard(): self.func_test_to_static_var() self.func_test_to_static_var() def _assert_to_static(self, var_base, static_var, is_param=False): if is_param: self.assertTrue(isinstance(static_var, fluid.framework.Parameter)) self.assertTrue(static_var.persistable, True) if isinstance(var_base, fluid.framework.ParamBase): for attr in ['trainable', 'is_distributed', 'do_model_average']: self.assertEqual( getattr(var_base, attr), getattr(static_var, attr)) self.assertEqual(static_var.optimize_attr['learning_rate'], 0.001) self.assertTrue( isinstance(static_var.regularizer, fluid.regularizer.L1Decay)) else: self.assertTrue(isinstance(static_var, fluid.framework.Variable)) attr_keys = ['block', 'dtype', 'type', 'name'] for attr in attr_keys: self.assertEqual(getattr(var_base, attr), getattr(static_var, attr)) self.assertListEqual(list(var_base.shape), list(static_var.shape)) def func_test_tensor_str(self): paddle.enable_static() paddle.disable_static(paddle.CPUPlace()) paddle.seed(10) a = paddle.rand([10, 20]) paddle.set_printoptions(4, 100, 3) a_str = str(a) expected = '''Tensor(shape=[10, 20], dtype=float32, place=Place(cpu), stop_gradient=True, [[0.2727, 0.5489, 0.8655, ..., 0.2916, 0.8525, 0.9000], [0.3806, 0.8996, 0.0928, ..., 0.9535, 0.8378, 0.6409], [0.1484, 0.4038, 0.8294, ..., 0.0148, 0.6520, 0.4250], ..., [0.3426, 0.1909, 0.7240, ..., 0.4218, 0.2676, 0.5679], [0.5561, 0.2081, 0.0676, ..., 0.9778, 0.3302, 0.9559], [0.2665, 0.8483, 0.5389, ..., 0.4956, 0.6862, 0.9178]])''' self.assertEqual(a_str, expected) def test_tensor_str(self): with _test_eager_guard(): self.func_test_tensor_str() self.func_test_tensor_str() def func_test_tensor_str2(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor([[1.5111111, 1.0], [0, 0]]) a_str = str(a) expected = '''Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[1.5111, 1. ], [0. , 0. ]])''' self.assertEqual(a_str, expected) def test_tensor_str2(self): with _test_eager_guard(): self.func_test_tensor_str2() self.func_test_tensor_str2() def func_test_tensor_str3(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor([[-1.5111111, 1.0], [0, -0.5]]) a_str = str(a) expected = '''Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, [[-1.5111, 1. ], [ 0. , -0.5000]])''' self.assertEqual(a_str, expected) def test_tensor_str3(self): with _test_eager_guard(): self.func_test_tensor_str3() self.func_test_tensor_str3() def func_test_tensor_str_scaler(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor(np.array(False)) a_str = str(a) expected = '''Tensor(shape=[], dtype=bool, place=Place(cpu), stop_gradient=True, False)''' self.assertEqual(a_str, expected) def test_tensor_str_scaler(self): with _test_eager_guard(): self.func_test_tensor_str_scaler() self.func_test_tensor_str_scaler() def func_test_tensor_str_shape_with_zero(self): paddle.disable_static(paddle.CPUPlace()) x = paddle.ones((10, 10)) y = paddle.fluid.layers.where(x == 0) a_str = str(y) expected = '''Tensor(shape=[0, 2], dtype=int64, place=Place(cpu), stop_gradient=True, [])''' self.assertEqual(a_str, expected) def test_tensor_str_shape_with_zero(self): with _test_eager_guard(): self.func_test_tensor_str_shape_with_zero() self.func_test_tensor_str_shape_with_zero() def func_test_tensor_str_linewidth(self): paddle.disable_static(paddle.CPUPlace()) paddle.seed(2021) x = paddle.rand([128]) paddle.set_printoptions( precision=4, threshold=1000, edgeitems=3, linewidth=80) a_str = str(x) expected = '''Tensor(shape=[128], dtype=float32, place=Place(cpu), stop_gradient=True, [0.3759, 0.0278, 0.2489, 0.3110, 0.9105, 0.7381, 0.1905, 0.4726, 0.2435, 0.9142, 0.3367, 0.7243, 0.7664, 0.9915, 0.2921, 0.1363, 0.8096, 0.2915, 0.9564, 0.9972, 0.2573, 0.2597, 0.3429, 0.2484, 0.9579, 0.7003, 0.4126, 0.4274, 0.0074, 0.9686, 0.9910, 0.0144, 0.6564, 0.2932, 0.7114, 0.9301, 0.6421, 0.0538, 0.1273, 0.5771, 0.9336, 0.6416, 0.1832, 0.9311, 0.7702, 0.7474, 0.4479, 0.3382, 0.5579, 0.0444, 0.9802, 0.9874, 0.3038, 0.5640, 0.2408, 0.5489, 0.8866, 0.1006, 0.5881, 0.7560, 0.7928, 0.8604, 0.4670, 0.9285, 0.1482, 0.4541, 0.1307, 0.6221, 0.4902, 0.1147, 0.4415, 0.2987, 0.7276, 0.2077, 0.7551, 0.9652, 0.4369, 0.2282, 0.0047, 0.2934, 0.4308, 0.4190, 0.1442, 0.3650, 0.3056, 0.6535, 0.1211, 0.8721, 0.7408, 0.4220, 0.5937, 0.3123, 0.9198, 0.0275, 0.5338, 0.4622, 0.7521, 0.3609, 0.4703, 0.1736, 0.8976, 0.7616, 0.3756, 0.2416, 0.2907, 0.3246, 0.4305, 0.5717, 0.0735, 0.0361, 0.5534, 0.4399, 0.9260, 0.6525, 0.3064, 0.4573, 0.9210, 0.8269, 0.2424, 0.7494, 0.8945, 0.7098, 0.8078, 0.4707, 0.5715, 0.7232, 0.4678, 0.5047])''' self.assertEqual(a_str, expected) def test_tensor_str_linewidth(self): with _test_eager_guard(): self.func_test_tensor_str_linewidth() self.func_test_tensor_str_linewidth() def func_test_tensor_str_linewidth2(self): paddle.disable_static(paddle.CPUPlace()) paddle.seed(2021) x = paddle.rand([128]) paddle.set_printoptions(precision=4, linewidth=160, sci_mode=True) a_str = str(x) expected = '''Tensor(shape=[128], dtype=float32, place=Place(cpu), stop_gradient=True, [3.7587e-01, 2.7798e-02, 2.4891e-01, 3.1097e-01, 9.1053e-01, 7.3811e-01, 1.9045e-01, 4.7258e-01, 2.4354e-01, 9.1415e-01, 3.3666e-01, 7.2428e-01, 7.6640e-01, 9.9146e-01, 2.9215e-01, 1.3625e-01, 8.0957e-01, 2.9153e-01, 9.5642e-01, 9.9718e-01, 2.5732e-01, 2.5973e-01, 3.4292e-01, 2.4841e-01, 9.5794e-01, 7.0029e-01, 4.1260e-01, 4.2737e-01, 7.3788e-03, 9.6863e-01, 9.9102e-01, 1.4416e-02, 6.5640e-01, 2.9318e-01, 7.1136e-01, 9.3008e-01, 6.4209e-01, 5.3849e-02, 1.2730e-01, 5.7712e-01, 9.3359e-01, 6.4155e-01, 1.8320e-01, 9.3110e-01, 7.7021e-01, 7.4736e-01, 4.4793e-01, 3.3817e-01, 5.5794e-01, 4.4412e-02, 9.8023e-01, 9.8735e-01, 3.0376e-01, 5.6397e-01, 2.4082e-01, 5.4893e-01, 8.8659e-01, 1.0065e-01, 5.8812e-01, 7.5600e-01, 7.9280e-01, 8.6041e-01, 4.6701e-01, 9.2852e-01, 1.4821e-01, 4.5410e-01, 1.3074e-01, 6.2210e-01, 4.9024e-01, 1.1466e-01, 4.4154e-01, 2.9868e-01, 7.2758e-01, 2.0766e-01, 7.5508e-01, 9.6522e-01, 4.3688e-01, 2.2823e-01, 4.7394e-03, 2.9342e-01, 4.3083e-01, 4.1902e-01, 1.4416e-01, 3.6500e-01, 3.0560e-01, 6.5350e-01, 1.2115e-01, 8.7206e-01, 7.4081e-01, 4.2203e-01, 5.9372e-01, 3.1230e-01, 9.1979e-01, 2.7486e-02, 5.3383e-01, 4.6224e-01, 7.5211e-01, 3.6094e-01, 4.7034e-01, 1.7355e-01, 8.9763e-01, 7.6165e-01, 3.7557e-01, 2.4157e-01, 2.9074e-01, 3.2458e-01, 4.3049e-01, 5.7171e-01, 7.3509e-02, 3.6087e-02, 5.5341e-01, 4.3993e-01, 9.2601e-01, 6.5248e-01, 3.0640e-01, 4.5727e-01, 9.2104e-01, 8.2688e-01, 2.4243e-01, 7.4937e-01, 8.9448e-01, 7.0981e-01, 8.0783e-01, 4.7065e-01, 5.7154e-01, 7.2319e-01, 4.6777e-01, 5.0465e-01])''' self.assertEqual(a_str, expected) def test_tensor_str_linewidth2(self): with _test_eager_guard(): self.func_test_tensor_str_linewidth2() self.func_test_tensor_str_linewidth2() def func_tensor_str_bf16(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.to_tensor([[1.5, 1.0], [0, 0]]) a = paddle.cast(a, dtype=core.VarDesc.VarType.BF16) paddle.set_printoptions(precision=4) a_str = str(a) expected = '''Tensor(shape=[2, 2], dtype=bfloat16, place=Place(cpu), stop_gradient=True, [[1.5000, 1. ], [0. , 0. ]])''' self.assertEqual(a_str, expected) def test_tensor_str_bf16(self): with _test_eager_guard(): self.func_tensor_str_bf16() self.func_tensor_str_bf16() def test_tensor_str_bf16(self): with _test_eager_guard(): self.func_tensor_str_bf16() self.func_tensor_str_bf16() def func_test_print_tensor_dtype(self): paddle.disable_static(paddle.CPUPlace()) a = paddle.rand([1]) a_str = str(a.dtype) expected = 'paddle.float32' self.assertEqual(a_str, expected) def test_print_tensor_dtype(self): with _test_eager_guard(): self.func_test_print_tensor_dtype() self.func_test_print_tensor_dtype() class TestVarBaseSetitem(unittest.TestCase): def func_setUp(self): self.set_dtype() self.tensor_x = paddle.to_tensor(np.ones((4, 2, 3)).astype(self.dtype)) self.np_value = np.random.random((2, 3)).astype(self.dtype) self.tensor_value = paddle.to_tensor(self.np_value) def set_dtype(self): self.dtype = "int32" def _test(self, value): if _in_legacy_dygraph(): self.assertEqual(self.tensor_x.inplace_version, 0) id_origin = id(self.tensor_x) self.tensor_x[0] = value if _in_legacy_dygraph(): self.assertEqual(self.tensor_x.inplace_version, 1) if isinstance(value, (six.integer_types, float)): result = np.zeros((2, 3)).astype(self.dtype) + value else: result = self.np_value self.assertTrue(np.array_equal(self.tensor_x[0].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) self.tensor_x[1:2] = value if _in_legacy_dygraph(): self.assertEqual(self.tensor_x.inplace_version, 2) self.assertTrue(np.array_equal(self.tensor_x[1].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) self.tensor_x[...] = value if _in_legacy_dygraph(): self.assertEqual(self.tensor_x.inplace_version, 3) self.assertTrue(np.array_equal(self.tensor_x[3].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) def func_test_value_tensor(self): self._test(self.tensor_value) def test_value_tensor(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_tensor() self.func_setUp() self.func_test_value_tensor() def func_test_value_numpy(self): self._test(self.np_value) def test_value_numpy(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_numpy() self.func_setUp() self.func_test_value_numpy() def func_test_value_int(self): self._test(10) def test_value_int(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_int() self.func_setUp() self.func_test_value_int() class TestVarBaseSetitemInt64(TestVarBaseSetitem): def set_dtype(self): self.dtype = "int64" class TestVarBaseSetitemFp32(TestVarBaseSetitem): def set_dtype(self): self.dtype = "float32" def func_test_value_float(self): paddle.disable_static() self._test(3.3) def test_value_float(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_float() self.func_setUp() self.func_test_value_float() class TestVarBaseSetitemFp64(TestVarBaseSetitem): def set_dtype(self): self.dtype = "float64" class TestVarBaseSetitemBoolIndex(unittest.TestCase): def func_setUp(self): paddle.disable_static() self.set_dtype() self.set_input() def set_input(self): self.tensor_x = paddle.to_tensor(np.ones((4, 2, 3)).astype(self.dtype)) self.np_value = np.random.random((2, 3)).astype(self.dtype) self.tensor_value = paddle.to_tensor(self.np_value) def set_dtype(self): self.dtype = "int32" def _test(self, value): paddle.disable_static() self.assertEqual(self.tensor_x.inplace_version, 0) id_origin = id(self.tensor_x) index_1 = paddle.to_tensor(np.array([True, False, False, False])) self.tensor_x[index_1] = value self.assertEqual(self.tensor_x.inplace_version, 1) if isinstance(value, (six.integer_types, float)): result = np.zeros((2, 3)).astype(self.dtype) + value else: result = self.np_value self.assertTrue(np.array_equal(self.tensor_x[0].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) index_2 = paddle.to_tensor(np.array([False, True, False, False])) self.tensor_x[index_2] = value self.assertEqual(self.tensor_x.inplace_version, 2) self.assertTrue(np.array_equal(self.tensor_x[1].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) index_3 = paddle.to_tensor(np.array([True, True, True, True])) self.tensor_x[index_3] = value self.assertEqual(self.tensor_x.inplace_version, 3) self.assertTrue(np.array_equal(self.tensor_x[3].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) def func_test_value_tensor(self): paddle.disable_static() self._test(self.tensor_value) def test_value_tensor(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_tensor() self.func_setUp() self.func_test_value_tensor() def func_test_value_numpy(self): paddle.disable_static() self._test(self.np_value) def test_value_numpy(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_numpy() self.func_setUp() self.func_test_value_numpy() def func_test_value_int(self): paddle.disable_static() self._test(10) def test_value_int(self): with _test_eager_guard(): self.func_setUp() self.func_test_value_int() self.func_setUp() self.func_test_value_int() class TestVarBaseSetitemBoolScalarIndex(unittest.TestCase): def set_input(self): self.tensor_x = paddle.to_tensor(np.ones((1, 2, 3)).astype(self.dtype)) self.np_value = np.random.random((2, 3)).astype(self.dtype) self.tensor_value = paddle.to_tensor(self.np_value) def _test(self, value): paddle.disable_static() self.assertEqual(self.tensor_x.inplace_version, 0) id_origin = id(self.tensor_x) index = paddle.to_tensor(np.array([True])) self.tensor_x[index] = value self.assertEqual(self.tensor_x.inplace_version, 1) if isinstance(value, (six.integer_types, float)): result = np.zeros((2, 3)).astype(self.dtype) + value else: result = self.np_value self.assertTrue(np.array_equal(self.tensor_x[0].numpy(), result)) self.assertEqual(id_origin, id(self.tensor_x)) class TestVarBaseInplaceVersion(unittest.TestCase): def func_test_setitem(self): paddle.disable_static() var = paddle.ones(shape=[4, 2, 3], dtype="float32") self.assertEqual(var.inplace_version, 0) var[1] = 1 self.assertEqual(var.inplace_version, 1) var[1:2] = 1 self.assertEqual(var.inplace_version, 2) def test_setitem(self): with _test_eager_guard(): self.func_test_setitem() self.func_test_setitem() def func_test_bump_inplace_version(self): paddle.disable_static() var = paddle.ones(shape=[4, 2, 3], dtype="float32") self.assertEqual(var.inplace_version, 0) var._bump_inplace_version() self.assertEqual(var.inplace_version, 1) var._bump_inplace_version() self.assertEqual(var.inplace_version, 2) def test_bump_inplace_version(self): with _test_eager_guard(): self.func_test_bump_inplace_version() self.func_test_bump_inplace_version() class TestVarBaseSlice(unittest.TestCase): def func_test_slice(self): paddle.disable_static() np_x = np.random.random((3, 8, 8)) x = paddle.to_tensor(np_x, dtype="float64") actual_x = x._slice(0, 1) actual_x = paddle.to_tensor(actual_x) self.assertEqual(actual_x.numpy().all(), np_x[0:1].all()) def test_slice(self): with _test_eager_guard(): self.func_test_slice() self.func_test_slice() class TestVarBaseClear(unittest.TestCase): def func_test_clear(self): paddle.disable_static() np_x = np.random.random((3, 8, 8)) x = paddle.to_tensor(np_x, dtype="float64") x._clear() self.assertEqual(str(x), "Tensor(Not initialized)") def test_clear(self): with _test_eager_guard(): self.func_test_clear() self.func_test_clear() class TestVarBaseOffset(unittest.TestCase): def func_offset(self): paddle.disable_static() np_x = np.random.random((3, 8, 8)) x = paddle.to_tensor(np_x, dtype="float64") expected_offset = 0 actual_x = x._slice(expected_offset, 1) actual_x = paddle.to_tensor(actual_x) self.assertEqual(actual_x._offset(), expected_offset) def test_offset(self): with _test_eager_guard(): self.func_offset() self.func_offset() class TestVarBaseShareBufferTo(unittest.TestCase): def func_test_share_buffer_To(self): paddle.disable_static() np_src = np.random.random((3, 8, 8)) src = paddle.to_tensor(np_src, dtype="float64") # empty_var if _in_legacy_dygraph(): dst = core.VarBase() else: dst = core.eager.Tensor() src._share_buffer_to(dst) self.assertEqual(src._is_shared_buffer_with(dst), True) def test_share_buffer_To(self): with _test_eager_guard(): self.func_test_share_buffer_To() self.func_test_share_buffer_To() class TestVarBaseTo(unittest.TestCase): def func_setUp(self): paddle.disable_static() self.np_x = np.random.random((3, 8, 8)) self.x = paddle.to_tensor(self.np_x, dtype="float32") def func_test_to_api(self): x_double = self.x._to(dtype='double') self.assertEqual(x_double.dtype, paddle.fluid.core.VarDesc.VarType.FP64) self.assertTrue(np.allclose(self.np_x, x_double)) x_ = self.x._to() self.assertEqual(self.x.dtype, paddle.fluid.core.VarDesc.VarType.FP64) self.assertTrue(np.allclose(self.np_x, x_)) if paddle.fluid.is_compiled_with_cuda(): x_gpu = self.x._to(device=paddle.CUDAPlace(0)) self.assertTrue(x_gpu.place.is_gpu_place()) self.assertEqual(x_gpu.place.gpu_device_id(), 0) x_gpu0 = self.x._to(device='gpu:0') self.assertTrue(x_gpu0.place.is_gpu_place()) self.assertEqual(x_gpu0.place.gpu_device_id(), 0) x_gpu1 = self.x._to(device='gpu:0', dtype="float64") self.assertTrue(x_gpu1.place.is_gpu_place()) self.assertEqual(x_gpu1.place.gpu_device_id(), 0) self.assertEqual(x_gpu1.dtype, paddle.fluid.core.VarDesc.VarType.FP64) x_gpu2 = self.x._to(device='gpu:0', dtype="float16") self.assertTrue(x_gpu2.place.is_gpu_place()) self.assertEqual(x_gpu2.place.gpu_device_id(), 0) self.assertEqual(x_gpu2.dtype, paddle.fluid.core.VarDesc.VarType.FP16) x_cpu = self.x._to(device=paddle.CPUPlace()) self.assertTrue(x_cpu.place.is_cpu_place()) x_cpu0 = self.x._to(device='cpu') self.assertTrue(x_cpu0.place.is_cpu_place()) x_cpu1 = self.x._to(device=paddle.CPUPlace(), dtype="float64") self.assertTrue(x_cpu1.place.is_cpu_place()) self.assertEqual(x_cpu1.dtype, paddle.fluid.core.VarDesc.VarType.FP64) x_cpu2 = self.x._to(device='cpu', dtype="float16") self.assertTrue(x_cpu2.place.is_cpu_place()) self.assertEqual(x_cpu2.dtype, paddle.fluid.core.VarDesc.VarType.FP16) self.assertRaises(ValueError, self.x._to, device=1) self.assertRaises(AssertionError, self.x._to, blocking=1) def test_to_api(self): with _test_eager_guard(): self.func_setUp() self.func_test_to_api() self.func_setUp() self.func_test_to_api() class TestVarBaseInitVarBaseFromTensorWithDevice(unittest.TestCase): def func_test_varbase_init(self): paddle.disable_static() t = fluid.Tensor() np_x = np.random.random((3, 8, 8)) t.set(np_x, fluid.CPUPlace()) if paddle.fluid.is_compiled_with_cuda(): device = paddle.CUDAPlace(0) if _in_legacy_dygraph(): tmp = fluid.core.VarBase(t, device) else: tmp = fluid.core.eager.Tensor(t, device) self.assertTrue(tmp.place.is_gpu_place()) self.assertEqual(tmp.numpy().all(), np_x.all()) device = paddle.CPUPlace() if _in_legacy_dygraph(): tmp = fluid.core.VarBase(t, device) else: tmp = fluid.core.eager.Tensor(t, device) self.assertEqual(tmp.numpy().all(), np_x.all()) def test_varbase_init(self): with _test_eager_guard(): self.func_test_varbase_init() self.func_test_varbase_init() class TestVarBaseNumel(unittest.TestCase): def func_test_numel_normal(self): paddle.disable_static() np_x = np.random.random((3, 8, 8)) x = paddle.to_tensor(np_x, dtype="float64") x_actual_numel = x._numel() x_expected_numel = np.product((3, 8, 8)) self.assertEqual(x_actual_numel, x_expected_numel) def test_numel_normal(self): with _test_eager_guard(): self.func_test_numel_normal() self.func_test_numel_normal() def func_test_numel_without_holder(self): paddle.disable_static() if _in_legacy_dygraph(): x_without_holder = core.VarBase() else: x_without_holder = core.eager.Tensor() x_actual_numel = x_without_holder._numel() self.assertEqual(x_actual_numel, 0) def ttest_numel_without_holder(self): with _test_eager_guard(): self.func_test_numel_without_holder() self.func_test_numel_without_holder() class TestVarBaseCopyGradientFrom(unittest.TestCase): def func_test_copy_gradient_from(self): paddle.disable_static() 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) self.assertEqual(x.grad.numpy().all(), np_y.all()) def test_copy_gradient_from(self): with _test_eager_guard(): self.func_test_copy_gradient_from() self.func_test_copy_gradient_from() if __name__ == '__main__': unittest.main()