wehub-resource-sync
235 lines
7.2 KiB
Python
235 lines
7.2 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest, OpTestTool, convert_float_to_uint16
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from paddle import enable_static
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from paddle.base import core
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from paddle.base.framework import _current_expected_place
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@OpTestTool.skip_if(
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not (isinstance(_current_expected_place(), core.CPUPlace)),
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"GPU is not supported",
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)
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class TestONEDNNElementwiseDivOp(OpTest):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.inputs = {
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'X': OpTest.np_dtype_to_base_dtype(self.x),
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'Y': OpTest.np_dtype_to_base_dtype(self.y),
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}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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self.outputs = {'Out': self.out}
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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self.check_grad(
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['X', 'Y'], 'Out', None, 0.005, False, 0.02, check_pir_onednn=True
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)
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def test_check_grad_ignore_x(self):
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self.check_grad(
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['Y'], 'Out', set("X"), 0.005, False, 0.02, check_pir_onednn=True
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)
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def test_check_grad_ignore_y(self):
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self.check_grad(
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['X'], 'Out', set('Y'), 0.005, False, 0.02, check_pir_onednn=True
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)
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def init_axis(self):
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self.axis = -1
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def init_kernel_type(self):
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self.use_onednn = True
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def init_dtype(self):
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self.dtype = np.float32
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def test_check_output(self):
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self.check_output(check_pir_onednn=True)
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class TestONEDNNElementwiseDivOp2(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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class TestONEDNNElementwiseDivOp3(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [2, 3, 4, 5]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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class TestONEDNNElementwiseDivOp4(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(1, 2, [2, 3, 4, 32]).astype(self.dtype)
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self.y = np.random.uniform(1, 2, [4, 32]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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class TestONEDNNElementwiseDivOp5(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
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self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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class TestONEDNNElementwiseDivOpZeroDim(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.y = np.array(3.0).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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class TestONEDNNElementwiseDivOpZeroDim2(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.array(3.0).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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class TestONEDNNElementwiseDivOpZeroDim3(TestONEDNNElementwiseDivOp):
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def init_input_output(self):
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self.x = np.array(3.0).astype(self.dtype)
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self.y = np.array(3.0).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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@OpTestTool.skip_if_not_cpu_bf16()
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class TestBf16(TestONEDNNElementwiseDivOp):
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def setUp(self):
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self.op_type = "elementwise_div"
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self.init_dtype()
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self.init_input_output()
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self.init_kernel_type()
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self.init_axis()
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self.x_bf16 = convert_float_to_uint16(self.x)
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self.y_bf16 = convert_float_to_uint16(self.y)
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self.inputs = {'X': self.x_bf16, 'Y': self.y_bf16}
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self.attrs = {'axis': self.axis, 'use_onednn': self.use_onednn}
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self.outputs = {'Out': convert_float_to_uint16(self.out)}
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def init_dtype(self):
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self.dtype = np.float32
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self.onednn_data_type = "bfloat16"
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def init_input_output(self):
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self.x = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.y = np.random.uniform(0.1, 1, [100]).astype(self.dtype)
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self.out = np.divide(self.x, self.y)
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def test_check_output(self):
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self.check_output_with_place(core.CPUPlace(), check_pir_onednn=True)
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def test_check_grad_normal(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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["X", "Y"],
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"Out",
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user_defined_grads=[
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np.divide(self.x, self.y),
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np.divide(
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(np.multiply(-self.x, self.x)), np.multiply(self.y, self.y)
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),
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],
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user_defined_grad_outputs=[self.x_bf16],
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)
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def test_check_grad_ignore_x(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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["Y"],
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"Out",
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user_defined_grads=[
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np.divide(
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(np.multiply(-self.x, self.y)), np.multiply(self.y, self.y)
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)
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],
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user_defined_grad_outputs=[self.y_bf16],
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)
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def test_check_grad_ignore_y(self):
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self.check_grad_with_place(
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core.CPUPlace(),
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["X"],
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"Out",
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user_defined_grads=[np.divide(self.x, self.y)],
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user_defined_grad_outputs=[self.x_bf16],
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)
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class TestBf16Broadcasting(TestBf16):
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def init_input_output(self):
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self.x = np.random.uniform(1, 2, [2, 3, 4, 100]).astype(self.dtype)
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self.y = np.random.uniform(1, 2, [100]).astype(self.dtype)
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self.out = np.subtract(self.x, self.y)
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def test_check_grad_normal(self):
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pass
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def test_check_grad_ignore_x(self):
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pass
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if __name__ == '__main__':
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enable_static()
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unittest.main()
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