# 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 paddle import paddle.fluid.core as core from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci, convert_float_to_uint16 import paddle.fluid as fluid from paddle.fluid import compiler, Program, program_guard class TestElementwiseAddOp(OpTest): def init_kernel_type(self): self.use_mkldnn = False def setUp(self): self.op_type = "elementwise_add" self.python_api = paddle.add self.init_dtype() self.init_input_output() self.init_kernel_type() self.init_axis() self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(self.x), 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) } self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn} self.outputs = {'Out': self.out} def check_eager(self): return (self.use_mkldnn == False and self.axis == -1) def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode self.check_output(check_dygraph=(self.use_mkldnn == False), check_eager=self.check_eager()) def test_check_grad_normal(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return self.check_grad(['X', 'Y'], 'Out', check_dygraph=(self.use_mkldnn == False), check_eager=self.check_eager()) def test_check_grad_ingore_x(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return self.check_grad(['Y'], 'Out', no_grad_set=set("X"), check_dygraph=(self.use_mkldnn == False), check_eager=self.check_eager()) def test_check_grad_ingore_y(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if self.dtype == np.float16: return self.check_grad(['X'], 'Out', no_grad_set=set('Y'), check_dygraph=(self.use_mkldnn == False), check_eager=self.check_eager()) def init_input_output(self): self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype) self.out = np.add(self.x, self.y) def init_dtype(self): self.dtype = np.float64 def init_axis(self): self.axis = -1 @unittest.skipIf(not core.is_compiled_with_cuda(), "core is not compiled with CUDA") class TestFP16ElementwiseAddOp(TestElementwiseAddOp): def init_dtype(self): self.dtype = np.float16 def test_check_output(self): # TODO(wangzhongpu): support mkldnn op in dygraph mode if core.is_compiled_with_cuda(): place = core.CUDAPlace(0) if core.is_float16_supported(place): self.check_output_with_place( place, atol=1e-3, check_dygraph=(self.use_mkldnn == False)) @unittest.skipIf( not core.is_compiled_with_cuda() or core.cudnn_version() < 8100, "core is not compiled with CUDA and cudnn version need larger than 8.1.0") class TestBF16ElementwiseAddOp(OpTest): def setUp(self): self.op_type = "elementwise_add" self.dtype = np.uint16 self.x = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) self.y = np.random.uniform(0.1, 1, [13, 17]).astype(np.float32) self.out = np.add(self.x, self.y) self.axis = -1 self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(convert_float_to_uint16(self.x)), 'Y': OpTest.np_dtype_to_fluid_dtype(convert_float_to_uint16(self.y)) } self.attrs = {'axis': self.axis, 'use_mkldnn': False} self.outputs = {'Out': convert_float_to_uint16(self.out)} def test_check_output(self): place = core.CUDAPlace(0) self.check_output_with_place(place, check_eager=False) def test_check_grad_normal(self): place = core.CUDAPlace(0) self.check_grad_with_place(place, ['X', 'Y'], 'Out', check_eager=False) def test_check_grad_ingore_x(self): place = core.CUDAPlace(0) self.check_grad_with_place(place, ['Y'], 'Out', no_grad_set=set("X"), check_eager=False) def test_check_grad_ingore_y(self): place = core.CUDAPlace(0) self.check_grad_with_place(place, ['X'], 'Out', no_grad_set=set('Y'), check_eager=False) @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestElementwiseAddOp_scalar(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 4).astype(self.dtype) self.y = np.random.rand(1).astype(self.dtype) self.out = self.x + self.y @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestFP16ElementwiseAddOp_scalar(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 4).astype(self.dtype) self.y = np.random.rand(1).astype(self.dtype) self.out = self.x + self.y @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1,1) to test broadcast.") class TestElementwiseAddOp_scalar2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 4).astype(self.dtype) self.y = np.random.rand(1, 1).astype(self.dtype) self.out = self.x + self.y @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1,1) to test broadcast.") class TestFP16ElementwiseAddOp_scalar2(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 4).astype(self.dtype) self.y = np.random.rand(1, 1).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_Vector(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.random((100, )).astype(self.dtype) self.y = np.random.random((100, )).astype(self.dtype) self.out = np.add(self.x, self.y) class TestFP16ElementwiseAddOp_Vector(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.random((100, )).astype(self.dtype) self.y = np.random.random((100, )).astype(self.dtype) self.out = np.add(self.x, self.y) class TestElementwiseAddOp_broadcast_0(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 3).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(100, 1, 1) def init_axis(self): self.axis = 0 class TestFP16ElementwiseAddOp_broadcast_0(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 3).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(100, 1, 1) def init_axis(self): self.axis = 0 class TestElementwiseAddOp_broadcast_1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 100, 3).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(1, 100, 1) def init_axis(self): self.axis = 1 class TestFP16ElementwiseAddOp_broadcast_1(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 100, 3).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(1, 100, 1) def init_axis(self): self.axis = 1 class TestElementwiseAddOp_broadcast_2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 100).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(1, 1, 100) class TestFP16ElementwiseAddOp_broadcast_2(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 100).astype(self.dtype) self.y = np.random.rand(100).astype(self.dtype) self.out = self.x + self.y.reshape(1, 1, 100) class TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 10, 12, 1).astype(self.dtype) self.y = np.random.rand(10, 12).astype(self.dtype) self.out = self.x + self.y.reshape(1, 10, 12, 1) def init_axis(self): self.axis = 1 class TestFP16ElementwiseAddOp_broadcast_3(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 10, 12, 3).astype(self.dtype) self.y = np.random.rand(10, 12).astype(self.dtype) self.out = self.x + self.y.reshape(1, 10, 12, 1) def init_axis(self): self.axis = 1 class TestElementwiseAddOp_broadcast_4(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype) self.y = np.random.rand(100, 1).astype(self.dtype) self.out = self.x + self.y.reshape(100, 1, 1, 1) def init_axis(self): self.axis = 0 class TestFP16ElementwiseAddOp_broadcast_4(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 1, 2).astype(self.dtype) self.y = np.random.rand(100, 1).astype(self.dtype) self.out = self.x + self.y.reshape(100, 1, 1, 1) def init_axis(self): self.axis = 0 class TestElementwiseAddOp_broadcast_5(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(10, 3, 12).astype(self.dtype) self.y = np.random.rand(10, 1, 12).astype(self.dtype) self.out = self.x + self.y class TestFP16ElementwiseAddOp_broadcast_5(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(10, 3, 12).astype(self.dtype) self.y = np.random.rand(10, 1, 12).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_broadcast_6(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype) self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_broadcast_7(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(1, 1, 20, 5).astype(self.dtype) self.y = np.random.rand(20, 5, 1, 1).astype(self.dtype) self.out = self.x + self.y class TestFP16ElementwiseAddOp_broadcast_6(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 12, 3, 5).astype(self.dtype) self.y = np.random.rand(2, 12, 1, 5).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_rowwise_add_0(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 10, 12).astype(self.dtype) self.y = np.random.rand(10, 12).astype(self.dtype) self.out = self.x + self.y.reshape(1, 10, 12) def init_axis(self): self.axis = 1 class TestFP16ElementwiseAddOp_rowwise_add_0(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 10, 12).astype(self.dtype) self.y = np.random.rand(10, 12).astype(self.dtype) self.out = self.x + self.y.reshape(1, 10, 12) def init_axis(self): self.axis = 1 @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestElementwiseAddOp_rowwise_add_1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 1).astype(self.dtype) self.y = np.random.rand(1).astype(self.dtype) self.out = self.x + self.y.reshape(1, 1) def init_axis(self): self.axis = 1 @skip_check_grad_ci( reason="[skip shape check] Use y_shape(1) to test broadcast.") class TestFP16ElementwiseAddOp_rowwise_add_1(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 1).astype(self.dtype) self.y = np.random.rand(1).astype(self.dtype) self.out = self.x + self.y.reshape(1, 1) def init_axis(self): self.axis = 1 class TestElementwiseAddOp_channelwise_add(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 3).astype(self.dtype) self.y = np.random.rand(100, 1, 1).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = -1 class TestFP16ElementwiseAddOp_channelwise_add(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(100, 2, 3).astype(self.dtype) self.y = np.random.rand(100, 1, 1).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = -1 class TestElementwiseAddOp_commonuse_add1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 100).astype(self.dtype) self.y = np.random.rand(1, 1, 100).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = -1 class TestElementwiseFP16AddOp_commonuse_add1(TestFP16ElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(2, 3, 100).astype(self.dtype) self.y = np.random.rand(1, 1, 100).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = -1 class TestElementwiseAddOp_commonuse_add2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(10, 3, 1, 4).astype(self.dtype) self.y = np.random.rand(10, 1, 12, 1).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = -1 class TestElementwiseAddOp_xsize_lessthan_ysize_add(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(10, 12).astype(self.dtype) self.y = np.random.rand(2, 2, 10, 12).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = 2 class TestElementwiseAddOp_same_shape_ysize_large(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(10, 1, 12).astype(self.dtype) self.y = np.random.rand(10, 2, 12).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = 0 class TestElementwiseAddOpError(unittest.TestCase): def test_errors(self): with program_guard(Program(), Program()): # the input of elementwise_add must be Variable. x1 = fluid.create_lod_tensor(np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()) y1 = fluid.create_lod_tensor(np.array([-1, 3, 5, 5]), [[1, 1, 1, 1]], fluid.CPUPlace()) self.assertRaises(TypeError, fluid.layers.elementwise_add, x1, y1) # the input dtype of elementwise_add must be float16 or float32 or float64 or int32 or int64 # float16 only can be set on GPU place x2 = fluid.layers.data(name='x2', shape=[3, 4, 5, 6], dtype="uint8") y2 = fluid.layers.data(name='y2', shape=[3, 4, 5, 6], dtype="uint8") self.assertRaises(TypeError, fluid.layers.elementwise_add, x2, y2) class TestAddApi(unittest.TestCase): def _executed_api(self, x, y, name=None): return paddle.add(x, y, name) def test_name(self): with fluid.program_guard(fluid.Program()): x = fluid.data(name="x", shape=[2, 3], dtype="float32") y = fluid.data(name='y', shape=[2, 3], dtype='float32') y_1 = self._executed_api(x, y, name='add_res') self.assertEqual(('add_res' in y_1.name), True) def test_declarative(self): with fluid.program_guard(fluid.Program()): def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32') } x = fluid.data(name="x", shape=[3], dtype='float32') y = fluid.data(name="y", shape=[3], dtype='float32') z = self._executed_api(x, y) place = fluid.CPUPlace() exe = fluid.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z.name]) z_expected = np.array([3., 8., 6.]) self.assertEqual((z_value == z_expected).all(), True) def test_dygraph(self): with fluid.dygraph.guard(): np_x = np.array([2, 3, 4]).astype('float64') np_y = np.array([1, 5, 2]).astype('float64') x = fluid.dygraph.to_variable(np_x) y = fluid.dygraph.to_variable(np_y) z = self._executed_api(x, y) np_z = z.numpy() z_expected = np.array([3., 8., 6.]) self.assertEqual((np_z == z_expected).all(), True) class TestAddInplaceApi(TestAddApi): def _executed_api(self, x, y, name=None): return x.add_(y, name) class TestAddInplaceBroadcastSuccess(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(2, 3, 4).astype('float') self.y_numpy = np.random.rand(3, 4).astype('float') def test_broadcast_success(self): paddle.disable_static() self.init_data() x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) inplace_result = x.add_(y) numpy_result = self.x_numpy + self.y_numpy self.assertEqual((inplace_result.numpy() == numpy_result).all(), True) paddle.enable_static() class TestAddInplaceBroadcastSuccess2(TestAddInplaceBroadcastSuccess): def init_data(self): self.x_numpy = np.random.rand(1, 2, 3, 1).astype('float') self.y_numpy = np.random.rand(3, 1).astype('float') class TestAddInplaceBroadcastSuccess3(TestAddInplaceBroadcastSuccess): def init_data(self): self.x_numpy = np.random.rand(2, 3, 1, 5).astype('float') self.y_numpy = np.random.rand(1, 3, 1, 5).astype('float') class TestAddInplaceBroadcastError(unittest.TestCase): def init_data(self): self.x_numpy = np.random.rand(3, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') def test_broadcast_errors(self): paddle.disable_static() self.init_data() x = paddle.to_tensor(self.x_numpy) y = paddle.to_tensor(self.y_numpy) def broadcast_shape_error(): x.add_(y) self.assertRaises(ValueError, broadcast_shape_error) paddle.enable_static() class TestAddInplaceBroadcastError2(TestAddInplaceBroadcastError): def init_data(self): self.x_numpy = np.random.rand(2, 1, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') class TestAddInplaceBroadcastError3(TestAddInplaceBroadcastError): def init_data(self): self.x_numpy = np.random.rand(5, 2, 1, 4).astype('float') self.y_numpy = np.random.rand(2, 3, 4).astype('float') class TestComplexElementwiseAddOp(OpTest): def setUp(self): self.op_type = "elementwise_add" self.dtype = np.float64 self.shape = (2, 3, 4, 5) self.init_input_output() self.init_grad_input_output() self.inputs = { 'X': OpTest.np_dtype_to_fluid_dtype(self.x), 'Y': OpTest.np_dtype_to_fluid_dtype(self.y) } self.attrs = {'axis': -1, 'use_mkldnn': False} self.outputs = {'Out': self.out} def init_base_dtype(self): self.dtype = np.float64 def init_input_output(self): self.x = np.random.random(self.shape).astype( self.dtype) + 1J * np.random.random(self.shape).astype(self.dtype) self.y = np.random.random(self.shape).astype( self.dtype) + 1J * np.random.random(self.shape).astype(self.dtype) self.out = self.x + self.y def init_grad_input_output(self): self.grad_out = np.ones( self.shape, self.dtype) + 1J * np.ones(self.shape, self.dtype) self.grad_x = self.grad_out self.grad_y = self.grad_out def test_check_output(self): self.check_output(check_eager=False) def test_check_grad_normal(self): self.check_grad(['X', 'Y'], 'Out', user_defined_grads=[self.grad_x, self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_x(self): self.check_grad(['Y'], 'Out', no_grad_set=set("X"), user_defined_grads=[self.grad_y], user_defined_grad_outputs=[self.grad_out]) def test_check_grad_ingore_y(self): self.check_grad(['X'], 'Out', no_grad_set=set('Y'), user_defined_grads=[self.grad_x], user_defined_grad_outputs=[self.grad_out]) class TestRealComplexElementwiseAddOp(TestComplexElementwiseAddOp): def init_input_output(self): self.x = np.random.random(self.shape).astype(self.dtype) self.y = np.random.random(self.shape).astype( self.dtype) + 1J * np.random.random(self.shape).astype(self.dtype) self.out = self.x + self.y def init_grad_input_output(self): self.grad_out = np.ones( self.shape, self.dtype) + 1J * np.ones(self.shape, self.dtype) self.grad_x = np.real(self.grad_out) self.grad_y = self.grad_out class TestBoolAddFloatElementwiseAddop(unittest.TestCase): def test_static_add(self): paddle.enable_static() a = 1.5 b = paddle.full([4, 5, 6], True, dtype='bool') c = a + b self.assertTrue(c.dtype == core.VarDesc.VarType.FP32) paddle.enable_static() def test_dygraph_add(self): paddle.disable_static() a = 1.5 b = paddle.full([4, 5, 6], True, dtype='bool') c = a + b self.assertTrue(c.dtype == core.VarDesc.VarType.FP32) if __name__ == '__main__': paddle.enable_static() unittest.main()