# 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. import unittest import numpy as np import paddle import paddle.fluid as fluid import paddle.nn.functional as F paddle.set_device('xpu') fluid.set_flags({"FLAGS_retain_grad_for_all_tensor": True}) unary_api_list = [ paddle.nn.functional.elu, paddle.nn.functional.gelu, paddle.nn.functional.hardsigmoid, paddle.nn.functional.hardswish, paddle.nn.functional.hardshrink, paddle.nn.functional.hardtanh, paddle.nn.functional.leaky_relu, paddle.nn.functional.log_sigmoid, paddle.nn.functional.relu, paddle.nn.functional.relu6, paddle.nn.functional.sigmoid, paddle.nn.functional.softplus, paddle.nn.functional.softshrink, paddle.nn.functional.softsign, paddle.nn.functional.swish, paddle.nn.functional.tanhshrink, paddle.nn.functional.thresholded_relu, paddle.stanh, paddle.nn.functional.celu, paddle.nn.functional.selu, paddle.nn.functional.mish, paddle.nn.functional.silu, paddle.nn.functional.tanh, paddle.nn.functional.dropout, paddle.cosh, paddle.sinh, paddle.abs, paddle.acos, paddle.asin, paddle.atan, paddle.ceil, paddle.cos, paddle.exp, paddle.floor, paddle.log, paddle.log1p, paddle.reciprocal, paddle.round, paddle.sin, paddle.sqrt, paddle.square, paddle.tanh, paddle.acosh, paddle.asinh, paddle.atanh, paddle.expm1, paddle.log10, paddle.log2, paddle.tan, paddle.erf, paddle.erfinv, paddle.rsqrt, paddle.sign, paddle.deg2rad, paddle.rad2deg, paddle.neg, paddle.logit, paddle.trunc, paddle.digamma, paddle.lgamma, paddle.poisson, paddle.bernoulli, ] inplace_api_list = [ paddle.nn.functional.relu_, paddle.nn.functional.tanh_, ] # Use to test zero-dim in unary API. class TestUnaryAPI(unittest.TestCase): def test_dygraph_unary(self): paddle.disable_static() for api in unary_api_list: x = paddle.rand([]) x.stop_gradient = False out = api(x) out.backward() self.assertEqual(x.shape, []) self.assertEqual(out.shape, []) if x.grad is not None: self.assertEqual(x.grad.shape, []) self.assertEqual(out.grad.shape, []) for api in inplace_api_list: x = paddle.rand([]) out = api(x) self.assertEqual(x.shape, []) self.assertEqual(out.shape, []) paddle.enable_static() reduce_api_list = [ paddle.sum, paddle.mean, paddle.nansum, paddle.nanmean, paddle.min, paddle.max, paddle.amin, paddle.amax, paddle.prod, paddle.logsumexp, paddle.all, paddle.any, ] # Use to test zero-dim of reduce API class TestReduceAPI(unittest.TestCase): def test_dygraph(self): paddle.disable_static() for api in reduce_api_list: if api in [paddle.all, paddle.any]: x = paddle.randint(0, 2, []).astype('bool') out = api(x, None) self.assertEqual(x.shape, []) self.assertEqual(out.shape, []) else: x = paddle.rand([]) x.stop_gradient = False out = api(x, None) out.backward() self.assertEqual(x.shape, []) self.assertEqual(x.grad.shape, []) self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) paddle.enable_static() binary_api_list = [ {'func': paddle.add, 'cls_method': '__add__'}, {'func': paddle.subtract, 'cls_method': '__sub__'}, {'func': paddle.multiply, 'cls_method': '__mul__'}, {'func': paddle.divide, 'cls_method': '__div__'}, {'func': paddle.pow, 'cls_method': '__pow__'}, {'func': paddle.equal, 'cls_method': '__eq__'}, {'func': paddle.not_equal, 'cls_method': '__ne__'}, {'func': paddle.greater_equal, 'cls_method': '__ge__'}, {'func': paddle.greater_than, 'cls_method': '__gt__'}, {'func': paddle.less_equal, 'cls_method': '__le__'}, {'func': paddle.less_than, 'cls_method': '__lt__'}, {'func': paddle.remainder, 'cls_method': '__mod__'}, paddle.mod, paddle.floor_mod, paddle.logical_and, paddle.logical_or, paddle.logical_xor, ] binary_int_api_list = [ paddle.bitwise_and, paddle.bitwise_or, paddle.bitwise_xor, ] # Use to test zero-dim of binary API class TestBinaryAPI(unittest.TestCase): def test_dygraph_binary(self): paddle.disable_static() for api in binary_api_list: # 1) x/y is 0D x = paddle.rand([]) y = paddle.rand([]) x.stop_gradient = False y.stop_gradient = False if isinstance(api, dict): out = api['func'](x, y) out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y) np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) else: out = api(x, y) self.assertEqual(out.shape, []) out.backward() if x.grad is not None: self.assertEqual(x.grad.shape, []) self.assertEqual(y.grad.shape, []) self.assertEqual(out.grad.shape, []) # 2) x is not 0D , y is 0D x = paddle.rand([2, 3, 4]) y = paddle.rand([]) x.stop_gradient = False y.stop_gradient = False if isinstance(api, dict): out = api['func'](x, y) out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y) np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) else: out = api(x, y) self.assertEqual(out.shape, [2, 3, 4]) out.backward() if x.grad is not None: self.assertEqual(x.grad.shape, [2, 3, 4]) self.assertEqual(y.grad.shape, []) self.assertEqual(out.grad.shape, [2, 3, 4]) # 3) x is 0D , y is not 0D x = paddle.rand([]) y = paddle.rand([2, 3, 4]) x.stop_gradient = False y.stop_gradient = False if isinstance(api, dict): out = api['func'](x, y) out_cls = getattr(paddle.Tensor, api['cls_method'])(x, y) np.testing.assert_array_equal(out_cls.numpy(), out.numpy()) else: out = api(x, y) self.assertEqual(out.shape, [2, 3, 4]) out.backward() if x.grad is not None: self.assertEqual(x.grad.shape, []) self.assertEqual(y.grad.shape, [2, 3, 4]) self.assertEqual(out.grad.shape, [2, 3, 4]) # 4) x is 0D , y is scalar x = paddle.rand([]) y = 0.5 x.stop_gradient = False if isinstance(api, dict): out = getattr(paddle.Tensor, api['cls_method'])(x, y) self.assertEqual(out.shape, []) for api in binary_int_api_list: # 1) x/y is 0D x = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, []) out = api(x, y) self.assertEqual(out.shape, []) # 2) x is not 0D , y is 0D x = paddle.randint(-10, 10, [3, 5]) y = paddle.randint(-10, 10, []) out = api(x, y) self.assertEqual(out.shape, [3, 5]) # 3) x is 0D , y is not 0D x = paddle.randint(-10, 10, []) y = paddle.randint(-10, 10, [3, 5]) out = api(x, y) self.assertEqual(out.shape, [3, 5]) paddle.enable_static() # Use to test zero-dim of Sundry API, which is unique and can not be classified # with others. It can be implemented here flexibly. class TestSundryAPI(unittest.TestCase): def setUp(self): paddle.disable_static() self.x = paddle.rand([]) def test_linear(self): x = paddle.randn([3, 2]) w = paddle.full(shape=[2, 4], fill_value=0.5) b = paddle.zeros([]) np.testing.assert_array_equal( F.linear(x, w, b).numpy(), F.linear(x, w).numpy() ) def test_is_floating_point(self): self.assertTrue(paddle.is_floating_point(self.x)) def test_is_integer(self): x = paddle.randint(0, 10, []) self.assertTrue(paddle.is_integer(x)) def test_is_tensor(self): self.assertTrue(paddle.is_tensor(self.x)) def test_is_empty(self): x = paddle.rand([3, 0, 5]) self.assertTrue(paddle.is_empty(x)) def test_isfinite(self): out = paddle.isfinite(self.x) np.testing.assert_array_equal(out.numpy(), np.array(True)) def test_isinf(self): x = paddle.to_tensor(np.array(float('-inf'))) out = paddle.isinf(x) np.testing.assert_array_equal(out.numpy(), np.array(True)) def test_isnan(self): x = paddle.to_tensor(np.array(float('nan'))) out = paddle.isnan(x) np.testing.assert_array_equal(out.numpy(), np.array(True)) def test_isclose(self): out = paddle.isclose(self.x, self.x) np.testing.assert_array_equal(out.numpy(), np.array(True)) def test_clone(self): out = paddle.clone(self.x) np.testing.assert_array_equal(out.numpy(), self.x.numpy()) def test_assign(self): out = paddle.assign(self.x) np.testing.assert_array_equal(out.numpy(), self.x.numpy()) def test_item(self): x = paddle.full([], 0.5) self.assertEqual(x.item(), 0.5) def test_tolist(self): x = paddle.full([], 0.5) self.assertEqual(x.tolist(), 0.5) def test_numpy(self): x = paddle.full([], 0.5) np.testing.assert_array_equal(x.numpy(), np.array(0.5)) def test_numel(self): out = paddle.numel(self.x) self.assertEqual(out.shape, []) np.testing.assert_array_equal(out.numpy(), np.array(1)) def test_rank(self): out = paddle.rank(self.x) self.assertEqual(out.shape, []) np.testing.assert_array_equal(out.numpy(), np.array(0)) def test_shape(self): out = paddle.shape(self.x) self.assertEqual(out.shape, [0]) np.testing.assert_array_equal(out.numpy(), np.array([])) def test_pow_factor(self): x = paddle.rand([]) x.stop_gradient = False out = paddle.pow(x, 2.0) out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) self.assertEqual(x.grad.shape, []) def test_cast(self): x = paddle.full([], 1.0, 'float32') x.stop_gradient = False out = paddle.cast(x, 'int32') out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) self.assertEqual(x.grad.shape, []) def test_clip(self): x = paddle.uniform([], None, -10, 10) x.stop_gradient = False out = paddle.clip(x, -5, 5) out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) self.assertEqual(x.grad.shape, []) def test_increment(self): x = paddle.rand([]) x.stop_gradient = False out = paddle.increment(x, 1.0) out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) self.assertEqual(x.grad.shape, []) def test_bitwise_not(self): x = paddle.randint(-1, 1, []) out1 = ~x out2 = paddle.bitwise_not(x) self.assertEqual(out1.shape, []) self.assertEqual(out2.shape, []) def test_logical_not(self): x = paddle.randint(0, 1, []) out = paddle.logical_not(x) self.assertEqual(out.shape, []) def test_searchsorted(self): x = paddle.to_tensor([1, 3, 5, 7, 9]) y = paddle.rand([]) # only has forward kernel out = paddle.searchsorted(x, y) self.assertEqual(out.shape, []) self.assertEqual(out.numpy(), 0) def test_gather_1D(self): x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0], stop_gradient=False) index = paddle.full([], 2, 'int64') out = paddle.gather(x, index) out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.numpy(), 5) self.assertEqual(out.grad.shape, []) def test_gather_xD_axis_0(self): x = paddle.to_tensor( [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], stop_gradient=False ) index = paddle.full([], 1, 'int64') out = paddle.gather(x, index) out.backward() self.assertEqual(out.shape, [3]) for i in range(3): self.assertEqual(out.numpy()[i], x.numpy()[1][i]) self.assertEqual(out.grad.shape, [3]) def test_gather_xD_axis_1(self): x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) index = paddle.full([], 1, 'int64') out = paddle.gather(x, index, axis=1) self.assertEqual(out.shape, [2]) for i in range(2): self.assertEqual(out.numpy()[i], x.numpy()[i][1]) def test_scatter_1D(self): x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0]) index = paddle.full([], 2, 'int64') updates = paddle.full([], 4.0) out = paddle.scatter(x, index, updates) self.assertEqual(out.numpy()[2], 4) def test_scatter_XD(self): x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) index = paddle.full([], 1, 'int64') updates = paddle.to_tensor([1.0, 2.0, 3.0]) out = paddle.scatter(x, index, updates) for i in range(3): self.assertEqual(out.numpy()[1][i], updates.numpy()[i]) def test_diagflat(self): x1 = paddle.rand([]) x2 = paddle.rand([]) x3 = paddle.rand([]) x1.stop_gradient = False x2.stop_gradient = False x3.stop_gradient = False out1 = paddle.diagflat(x1, 1) out2 = paddle.diagflat(x2, -1) out3 = paddle.diagflat(x3, 0) out1.backward() out2.backward() out3.backward() self.assertEqual(out1.shape, [2, 2]) self.assertEqual(out2.shape, [2, 2]) self.assertEqual(out3.shape, [1, 1]) self.assertEqual(out1.grad.shape, [2, 2]) self.assertEqual(out2.grad.shape, [2, 2]) self.assertEqual(out3.grad.shape, [1, 1]) self.assertEqual(x1.grad.shape, []) self.assertEqual(x2.grad.shape, []) self.assertEqual(x3.grad.shape, []) def test_scatter__1D(self): x = paddle.to_tensor([1.0, 3.0, 5.0, 7.0, 9.0]) index = paddle.full([], 2, 'int64') updates = paddle.full([], 4.0) out = paddle.scatter_(x, index, updates) self.assertEqual(out.numpy()[2], 4) def test_scatter__XD(self): x = paddle.to_tensor([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]) index = paddle.full([], 1, 'int64') updates = paddle.to_tensor([1.0, 2.0, 3.0]) out = paddle.scatter_(x, index, updates) for i in range(3): self.assertEqual(out.numpy()[1][i], updates.numpy()[i]) def test_scale(self): x = paddle.rand([]) x.stop_gradient = False out = paddle.scale(x, scale=2.0, bias=1.0) out.backward() self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) self.assertEqual(x.grad.shape, []) def test_floor_divide(self): # 1-d // 0-d x = paddle.to_tensor([1, -2, 3], dtype="int64") y = paddle.full([], 2, dtype='int64') out1_1 = paddle.floor_divide(x, y) out1_2 = paddle.Tensor.__floordiv__(x, y) np.testing.assert_array_equal(out1_1.numpy(), out1_2.numpy()) np.testing.assert_array_equal(out1_1.numpy(), np.asarray([0, -1, 1])) # 0-d // 1-d out2_1 = paddle.floor_divide(y, x) out2_2 = paddle.Tensor.__floordiv__(y, x) np.testing.assert_array_equal(out2_1.numpy(), out2_2.numpy()) np.testing.assert_array_equal(out2_2.numpy(), np.asarray([2, -1, 0])) # 0-d // 0-d x = paddle.full([], 3, dtype='int64') out3_1 = paddle.floor_divide(x, y) out3_2 = paddle.Tensor.__floordiv__(x, y) np.testing.assert_array_equal(out3_1.numpy(), out3_2.numpy()) np.testing.assert_array_equal(out3_2.numpy(), np.asarray(1)) def test_reshape_list(self): x = paddle.rand([]) x.stop_gradient = False out = paddle.reshape(x, []) out.backward() self.assertEqual(x.grad.shape, []) self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) out = paddle.reshape(x, [1]) out.backward() self.assertEqual(x.grad.shape, []) self.assertEqual(out.shape, [1]) self.assertEqual(out.grad.shape, [1]) out = paddle.reshape(x, [-1]) out.backward() self.assertEqual(x.grad.shape, []) self.assertEqual(out.shape, [1]) self.assertEqual(out.grad.shape, [1]) out = paddle.reshape(x, [-1, 1]) out.backward() self.assertEqual(x.grad.shape, []) self.assertEqual(out.shape, [1, 1]) self.assertEqual(out.grad.shape, [1, 1]) def test_reshape_tensor(self): x = paddle.rand([1, 1]) x.stop_gradient = False out = paddle.reshape(x, []) out.backward() self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(out.shape, []) self.assertEqual(out.grad.shape, []) new_shape = paddle.full([], 1, "int32") out = paddle.reshape(x, new_shape) out.backward() self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(out.shape, [1]) self.assertEqual(out.grad.shape, [1]) new_shape = paddle.full([], -1, "int32") out = paddle.reshape(x, new_shape) out.backward() self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(out.shape, [1]) self.assertEqual(out.grad.shape, [1]) new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] out = paddle.reshape(x, new_shape) out.backward() self.assertEqual(x.grad.shape, [1, 1]) self.assertEqual(out.shape, [1, 1]) self.assertEqual(out.grad.shape, [1, 1]) def test_reshape__list(self): x = paddle.rand([]) out = paddle.reshape_(x, []) self.assertEqual(out.shape, []) out = paddle.reshape_(x, [1]) self.assertEqual(out.shape, [1]) out = paddle.reshape_(x, [-1]) self.assertEqual(out.shape, [1]) out = paddle.reshape_(x, [-1, 1]) self.assertEqual(out.shape, [1, 1]) def test_reshape__tensor(self): x = paddle.rand([1, 1]) out = paddle.reshape_(x, []) self.assertEqual(out.shape, []) new_shape = paddle.full([1], 1, "int32") out = paddle.reshape_(x, new_shape) self.assertEqual(out.shape, [1]) new_shape = paddle.full([1], -1, "int32") out = paddle.reshape_(x, new_shape) self.assertEqual(out.shape, [1]) new_shape = [paddle.full([], -1, "int32"), paddle.full([], 1, "int32")] out = paddle.reshape_(x, new_shape) self.assertEqual(out.shape, [1, 1]) # Use to test API whose zero-dim input tensors don't have grad and not need to test backward in OpTest. class TestNoBackwardAPI(unittest.TestCase): def setUp(self): paddle.disable_static() self.shape = [ paddle.full([], 2, 'int32'), paddle.full([], 3, 'int32'), paddle.full([], 4, 'int32'), ] def test_slice(self): starts = [paddle.full([], 1, 'int32'), paddle.full([], 1, 'int32')] ends = [paddle.full([], 3, 'int32'), paddle.full([], 3, 'int32')] x = paddle.rand([5, 3, 3]) out = paddle.slice(x, [1, 2], starts, ends) self.assertEqual(out.shape, [5, 2, 2]) def test_strided_slice(self): starts = [paddle.full([], 0, 'int32'), paddle.full([], 0, 'int32')] ends = [paddle.full([], 4, 'int32'), paddle.full([], 4, 'int32')] strides = [paddle.full([], 2, 'int32'), paddle.full([], 2, 'int32')] x = paddle.rand([5, 5, 5]) out = paddle.strided_slice(x, [1, 2], starts, ends, strides) self.assertEqual(out.shape, [5, 2, 2]) def test_linspace(self): start = paddle.full([], 1.0) stop = paddle.full([], 5.0) num = paddle.full([], 5, 'int32') out = paddle.linspace(start, stop, num) np.testing.assert_array_equal(out.numpy(), [1.0, 2.0, 3.0, 4.0, 5.0]) def test_arange(self): start = paddle.full([], 1.0) stop = paddle.full([], 6.0) step = paddle.full([], 1.0) out = paddle.arange(start, stop, step) np.testing.assert_array_equal(out.numpy(), [1.0, 2.0, 3.0, 4.0, 5.0]) def test_normal(self): mean = paddle.full([], 0.0) std = paddle.full([], 0.0) out = paddle.normal(mean, std) self.assertEqual(out.shape, []) out = paddle.normal(0.0, 1.0, []) self.assertEqual(out.shape, []) out = paddle.normal(0.0, 1.0, self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_rand(self): out = paddle.rand([]) self.assertEqual(out.shape, []) out = paddle.rand(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_randn(self): out = paddle.randn([]) self.assertEqual(out.shape, []) out = paddle.randn(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_randint_and_randint_like(self): out = paddle.randint(-10, 10, []) self.assertEqual(out.shape, []) out = paddle.randint_like(out, -10, 10) self.assertEqual(out.shape, []) out = paddle.randint(-10, 10, self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_standard_normal(self): out = paddle.standard_normal([]) self.assertEqual(out.shape, []) out = paddle.standard_normal(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_uniform(self): out = paddle.uniform([]) self.assertEqual(out.shape, []) out = paddle.uniform(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_empty_and_empty_like(self): out = paddle.empty([]) self.assertEqual(out.shape, []) out = paddle.empty_like(out) self.assertEqual(out.shape, []) out = paddle.empty(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_full_and_full_like(self): out = paddle.full([], 0.5) self.assertEqual(out.shape, []) out = paddle.full_like(out, 0.5) self.assertEqual(out.shape, []) out = paddle.full(self.shape, 0.5) self.assertEqual(out.shape, [2, 3, 4]) def test_ones_and_ones_like(self): out = paddle.ones([]) self.assertEqual(out.shape, []) out = paddle.ones_like(out) self.assertEqual(out.shape, []) out = paddle.ones(self.shape) self.assertEqual(out.shape, [2, 3, 4]) def test_zeros_and_zeros_like(self): out = paddle.zeros([]) self.assertEqual(out.shape, []) out = paddle.zeros_like(out) self.assertEqual(out.shape, []) out = paddle.zeros(self.shape) self.assertEqual(out.shape, [2, 3, 4]) if __name__ == "__main__": unittest.main()