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- # 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
-
- import paddle
- from paddle.base import core
-
-
- class TestClipOneDNNOp(OpTest):
- def setUp(self):
- self.op_type = "clip"
- self.init_shape()
- self.set_inputs()
- self.set_attrs()
- self.set_additional_inputs()
- self.adjust_op_settings()
-
- self.min = (
- self.attrs['min']
- if 'Min' not in self.inputs
- else self.inputs['Min']
- )
- self.max = (
- self.attrs['max']
- if 'Max' not in self.inputs
- else self.inputs['Max']
- )
-
- self.outputs = {'Out': np.clip(self.x_fp32, self.min, self.max)}
-
- def init_shape(self):
- self.shape = [10, 10]
-
- def set_inputs(self):
- self.inputs = {
- 'X': np.array(np.random.random(self.shape).astype(np.float32) * 25)
- }
- self.x_fp32 = self.inputs['X']
-
- def set_additional_inputs(self):
- pass
-
- def adjust_op_settings(self):
- pass
-
- def set_attrs(self):
- self.attrs = {'min': 7.2, 'max': 9.6, 'use_mkldnn': True}
-
- def test_check_output(self):
- self.check_output(check_dygraph=False, check_pir_onednn=True)
-
- def test_check_grad(self):
- self.check_grad(
- ['X'], 'Out', check_dygraph=False, check_pir_onednn=True
- )
-
-
- class TestClipOneDNNOp_ZeroDim(TestClipOneDNNOp):
- def init_shape(self):
- self.shape = []
-
-
- class TestClipMinAsInputOneDNNOp(TestClipOneDNNOp):
- def set_additional_inputs(self):
- self.inputs['Min'] = np.array([6.8]).astype('float32')
-
-
- class TestClipMaxAsInputOneDNNOp(TestClipOneDNNOp):
- def set_additional_inputs(self):
- self.inputs['Max'] = np.array([9.1]).astype('float32')
-
-
- class TestClipMaxAndMinAsInputsOneDNNOp(TestClipOneDNNOp):
- def set_additional_inputs(self):
- self.inputs['Max'] = np.array([8.5]).astype('float32')
- self.inputs['Min'] = np.array([7.1]).astype('float32')
-
-
- # BF16 TESTS
- def create_bf16_test_class(parent):
- @OpTestTool.skip_if_not_cpu_bf16()
- class TestClipBF16OneDNNOp(parent):
- def set_inputs(self):
- self.x_fp32 = np.random.random((10, 10)).astype(np.float32) * 25
- self.inputs = {'X': convert_float_to_uint16(self.x_fp32)}
-
- def adjust_op_settings(self):
- self.dtype = np.uint16
- self.attrs['mkldnn_data_type'] = "bfloat16"
-
- def calculate_grads(self):
- self.dout = self.outputs['Out']
- self.dx = np.zeros(self.x_fp32.shape).astype("float32")
-
- for i in range(self.dx.shape[0]):
- for j in range(self.dx.shape[1]):
- if (
- self.x_fp32[j][i] > self.min
- and self.x_fp32[j][i] < self.max
- ):
- self.dx[j][i] = self.dout[j][i]
-
- def test_check_output(self):
- self.check_output_with_place(
- core.CPUPlace(), check_dygraph=False, check_pir_onednn=True
- )
-
- def test_check_grad(self):
- self.calculate_grads()
- self.check_grad_with_place(
- core.CPUPlace(),
- ["X"],
- "Out",
- user_defined_grads=[self.dx],
- user_defined_grad_outputs=[convert_float_to_uint16(self.dout)],
- check_dygraph=False,
- check_pir_onednn=True,
- )
-
- cls_name = "{}_{}".format(parent.__name__, "BF16")
- TestClipBF16OneDNNOp.__name__ = cls_name
- globals()[cls_name] = TestClipBF16OneDNNOp
-
-
- create_bf16_test_class(TestClipOneDNNOp)
- create_bf16_test_class(TestClipMinAsInputOneDNNOp)
- create_bf16_test_class(TestClipMaxAsInputOneDNNOp)
- create_bf16_test_class(TestClipMaxAndMinAsInputsOneDNNOp)
-
- if __name__ == "__main__":
- paddle.enable_static()
- unittest.main()
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