# Copyright (c) 2022 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 copy import unittest import numpy as np from get_test_cover_info import ( XPUOpTestWrapper, check_run_big_shape_test, create_test_class, get_xpu_op_support_types, ) from op_test import OpTest, skip_check_grad_ci from op_test_xpu import XPUOpTest import paddle from paddle import base from paddle.base import core paddle.enable_static() class XPUTestElementwiseAddOp(XPUOpTestWrapper): def __init__(self): self.op_name = 'elementwise_add' self.use_dynamic_create_class = False class TestElementwiseAddOp(XPUOpTest): def setUp(self): self.op_type = "elementwise_add" self.init_dtype() self.init_input_output() self.init_axis() self.init_max_relative_error() self.inputs = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y), } self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn} self.outputs = {'Out': self.out} def test_check_output(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_output_with_place(place) def test_check_grad_normal(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['X', 'Y'], 'Out', max_relative_error=self.max_relative_error, ) def test_check_grad_ignore_x(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['Y'], 'Out', no_grad_set=set("X"), max_relative_error=self.max_relative_error, ) def test_check_grad_ignore_y(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['X'], 'Out', no_grad_set=set("Y"), max_relative_error=self.max_relative_error, ) 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 = self.in_type def init_axis(self): self.axis = -1 def init_max_relative_error(self): self.max_relative_error = 0.006 class TestElementwiseAddOp_ZeroDim1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.uniform(-1, 1, []).astype(self.dtype) self.y = np.random.uniform(-1, 1, []).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroDim2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.uniform(-1, 1, []).astype(self.dtype) self.y = np.random.uniform(-1, 1, [13, 17]).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroDim3(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.uniform(-1, 1, [13, 17]).astype(self.dtype) self.y = np.random.uniform(-1, 1, []).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroSize1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(0, 3, 4).astype(self.dtype) self.y = np.random.rand(1).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroSize2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(0, 3, 4).astype(self.dtype) self.y = np.random.rand(0, 3, 4).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroSize3(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(3, 0, 4).astype(self.dtype) self.y = np.random.rand(1, 1, 4).astype(self.dtype) self.out = self.x + self.y class TestElementwiseAddOp_ZeroSize4(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(3, 0, 4).astype(self.dtype) self.y = np.random.rand(1, 0, 4).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 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(1,1) 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 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 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 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 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 TestElementwiseAddOp_broadcast_3(TestElementwiseAddOp): 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, 3, 4).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 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 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 @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 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 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 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, 3, 10, 12).astype(self.dtype) self.out = self.x + self.y def init_axis(self): self.axis = 2 @check_run_big_shape_test() class TestElementwiseAddOpLargeShape1(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(8192, 1920).astype(self.dtype) self.y = np.random.rand(1920).astype(self.dtype) self.out = self.x + self.y @check_run_big_shape_test() class TestElementwiseAddOpLargeShape2(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(1, 8192, 5, 128).astype(self.dtype) self.y = np.random.rand(1, 8192, 5, 128).astype(self.dtype) self.out = self.x + self.y @check_run_big_shape_test() class TestElementwiseAddOpLargeShape3(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(1024, 5120).astype(self.dtype) self.y = np.random.rand(5120).astype(self.dtype) self.out = self.x + self.y @check_run_big_shape_test() class TestElementwiseAddOpLargeShape4(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(8192, 3456).astype(self.dtype) self.y = np.random.rand(3456).astype(self.dtype) self.out = self.x + self.y @check_run_big_shape_test() class TestElementwiseAddOpLargeShape5(TestElementwiseAddOp): def init_input_output(self): self.x = np.random.rand(1, 8192, 31776).astype(self.dtype) self.y = np.random.rand(31776).astype(self.dtype) self.out = self.x + self.y class TestAddOp(unittest.TestCase): def test_name(self): with base.program_guard(base.Program()): x = paddle.static.data(name="x", shape=[2, 3], dtype="float32") y = paddle.static.data(name='y', shape=[2, 3], dtype='float32') y_1 = paddle.add(x, y, name='add_res') if not paddle.framework.use_pir_api(): self.assertEqual(('add_res' in y_1.name), True) def test_declarative(self): with base.program_guard(base.Program()): def gen_data(): return { "x": np.array([2, 3, 4]).astype('float32'), "y": np.array([1, 5, 2]).astype('float32'), } x = paddle.static.data(name="x", shape=[3], dtype='float32') y = paddle.static.data(name="y", shape=[3], dtype='float32') z = paddle.add(x, y) place = base.XPUPlace(0) exe = base.Executor(place) z_value = exe.run(feed=gen_data(), fetch_list=[z]) z_expected = np.array([3.0, 8.0, 6.0]) self.assertEqual((z_value == z_expected).all(), True) def test_dygraph(self): with base.dygraph.guard(): np_x = np.array([2, 3, 4]).astype('float32') np_y = np.array([1, 5, 2]).astype('float32') x = paddle.to_tensor(np_x) y = paddle.to_tensor(np_y) z = paddle.add(x, y) np_z = z.numpy() z_expected = np.array([3.0, 8.0, 6.0]) self.assertEqual((np_z == z_expected).all(), True) support_types = get_xpu_op_support_types('elementwise_add') real_types = [t for t in support_types if t != 'complex64'] for stype in real_types: create_test_class(globals(), XPUTestElementwiseAddOp, stype) if 'complex64' in support_types: class TestElementwiseAddOpComplex(OpTest): def setUp(self): self.op_type = "elementwise_add" self.python_api = paddle.add self.init_dtype() self.init_input_output() self.init_axis() self.inputs = { 'X': OpTest.np_dtype_to_base_dtype(self.x), 'Y': OpTest.np_dtype_to_base_dtype(self.y), } self.outputs = {'Out': self.out} def test_check_output(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_output_with_place(place) def test_check_grad_normal(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['X', 'Y'], 'Out', ) def test_check_grad_ignore_x(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['Y'], 'Out', no_grad_set=set("X"), ) def test_check_grad_ignore_y(self): if paddle.is_compiled_with_xpu(): place = paddle.XPUPlace(0) self.check_grad_with_place( place, ['X'], 'Out', no_grad_set=set("Y"), ) def init_input_output(self): self.x = ( np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4) ).astype(self.dtype) self.y = ( np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4) ).astype(self.dtype) self.out = self.x + self.y def init_dtype(self): self.dtype = np.complex64 def init_axis(self): self.axis = -1 class TestElementwiseAddOpComplex2(TestElementwiseAddOpComplex): def init_input_output(self): self.x = ( np.random.rand(2, 3, 4) + 1j * np.random.rand(2, 3, 4) ).astype(self.dtype) self.y = (np.random.rand(1, 1) + 1j * np.random.rand(1, 1)).astype( self.dtype ) self.out = self.x + self.y class TestElementwiseAddOpComplex3(TestElementwiseAddOpComplex): def init_input_output(self): self.x = ( np.random.rand(10, 2, 3) + 1j * np.random.rand(10, 2, 3) ).astype(self.dtype) self.y = (np.random.rand(1, 1) + 1j * np.random.rand(1, 1)).astype( self.dtype ) self.out = self.x + self.y @unittest.skipIf( core.get_xpu_device_version(0) != core.XPUVersion.XPU3, "only supported on XPU3", ) class TestTensorFloat32Bfloat16OrFloat16Add(unittest.TestCase): def _float32_bfloat16_or_float16_add(self, y_dtype): paddle.disable_static() test_num = 5 val_range = 10000 shapes = [] for i in range(test_num): shape = [ np.random.randint(1, val_range), np.random.randint(1, val_range), ] shapes.append(shape) for i, shape in enumerate(shapes): x = paddle.randn(list(shape), dtype=paddle.float32) x_copy = copy.deepcopy(x) y = paddle.randn(list(shape), dtype=y_dtype) x.add_(y) x_copy.add_(paddle.cast(y, paddle.float32)) np.testing.assert_equal(x.numpy(), x_copy.numpy()) del x, x_copy def test_float32_bfloat16_add(self): self._float32_bfloat16_or_float16_add(y_dtype=paddle.bfloat16) def test_float32_float16_add(self): self._float32_bfloat16_or_float16_add(y_dtype=paddle.float16) if __name__ == '__main__': unittest.main()