# Copyright (c) 2021 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 from op_test import OpTest, OpTestTool, convert_float_to_uint16 from paddle import enable_static from paddle.base import core from paddle.base.framework import _current_expected_place @OpTestTool.skip_if( not (isinstance(_current_expected_place(), core.CPUPlace)), "GPU is not supported", ) class TestONEDNNElementwiseDivOp(OpTest): def setUp(self): self.op_type = "elementwise_div" self.init_dtype() self.init_input_output() self.init_kernel_type() self.init_axis() 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 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.divide(self.x, self.y) def test_check_grad_normal(self): self.check_grad( ['X', 'Y'], 'Out', None, 0.005, False, 0.02, check_pir_onednn=True ) def test_check_grad_ignore_x(self): self.check_grad( ['Y'], 'Out', set("X"), 0.005, False, 0.02, check_pir_onednn=True ) def test_check_grad_ignore_y(self): self.check_grad( ['X'], 'Out', set('Y'), 0.005, False, 0.02, check_pir_onednn=True ) def init_axis(self): self.axis = -1 def init_kernel_type(self): self.use_onednn = True def init_dtype(self): self.dtype = np.float32 def test_check_output(self): self.check_output(check_pir_onednn=True) class TestONEDNNElementwiseDivOp2(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.out = np.divide(self.x, self.y) class TestONEDNNElementwiseDivOp3(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype) self.out = np.divide(self.x, self.y) class TestONEDNNElementwiseDivOp4(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.random.uniform(1, 2, [2, 3, 4, 32]).astype(self.dtype) self.y = np.random.uniform(1, 2, [4, 32]).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass class TestONEDNNElementwiseDivOp5(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype) self.y = np.random.uniform(1, 2, [100]).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass class TestONEDNNElementwiseDivOpZeroDim(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.y = np.array(3.0).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass class TestONEDNNElementwiseDivOpZeroDim2(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.array(3.0).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass class TestONEDNNElementwiseDivOpZeroDim3(TestONEDNNElementwiseDivOp): def init_input_output(self): self.x = np.array(3.0).astype(self.dtype) self.y = np.array(3.0).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass @OpTestTool.skip_if_not_cpu_bf16() class TestBf16(TestONEDNNElementwiseDivOp): def setUp(self): self.op_type = "elementwise_div" self.init_dtype() self.init_input_output() self.init_kernel_type() self.init_axis() self.x_bf16 = convert_float_to_uint16(self.x) self.y_bf16 = convert_float_to_uint16(self.y) self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16} self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn} self.outputs = {'Out': convert_float_to_uint16(self.out)} def init_dtype(self): self.dtype = np.float32 self.onednn_data_type = "bfloat16" def init_input_output(self): self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype) self.out = np.divide(self.x, self.y) def test_check_output(self): self.check_output_with_place(core.CPUPlace(), check_pir_onednn=True) def test_check_grad_normal(self): self.check_grad_with_place( core.CPUPlace(), ["X", "Y"], "Out", user_defined_grads=[ np.divide(self.x, self.y), np.divide( (np.multiply(-self.x, self.x)), np.multiply(self.y, self.y) ), ], user_defined_grad_outputs=[self.x_bf16], ) def test_check_grad_ignore_x(self): self.check_grad_with_place( core.CPUPlace(), ["Y"], "Out", user_defined_grads=[ np.divide( (np.multiply(-self.x, self.y)), np.multiply(self.y, self.y) ) ], user_defined_grad_outputs=[self.y_bf16], ) def test_check_grad_ignore_y(self): self.check_grad_with_place( core.CPUPlace(), ["X"], "Out", user_defined_grads=[np.divide(self.x, self.y)], user_defined_grad_outputs=[self.x_bf16], ) class TestBf16Broadcasting(TestBf16): def init_input_output(self): self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype) self.y = np.random.uniform(1, 2, [100]).astype(self.dtype) self.out = np.subtract(self.x, self.y) def test_check_grad_normal(self): pass def test_check_grad_ignore_x(self): pass if __name__ == '__main__': enable_static() unittest.main()