wehub-resource-sync
154 lines
4.4 KiB
Python
154 lines
4.4 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 (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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paddle.enable_static()
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class TestTruncOp(OpTest):
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def setUp(self):
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self.op_type = "trunc"
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self.prim_op_type = "prim"
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self.python_api = paddle.trunc
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self.public_python_api = paddle.trunc
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self.init_dtype_type()
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self.init_shape()
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np.random.seed(2021)
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self.inputs = {'X': np.random.random(self.shape).astype(self.dtype)}
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self.outputs = {'Out': (np.trunc(self.inputs['X']))}
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def init_dtype_type(self):
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self.dtype = np.float64
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def init_shape(self):
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self.shape = (20, 20)
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def test_check_output(self):
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self.check_output(
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check_pir=True, check_prim_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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'Out',
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numeric_grad_delta=1e-5,
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check_pir=True,
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check_prim_pir=True,
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)
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class TestFloatTruncOp(TestTruncOp):
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def init_dtype_type(self):
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self.dtype = np.float32
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self.__class__.exist_fp64_check_grad = True
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def test_check_grad(self):
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pass
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class TestIntTruncOp(TestTruncOp):
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def init_dtype_type(self):
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self.dtype = np.int32
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self.__class__.exist_fp64_check_grad = True
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def test_check_grad(self):
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pass
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class TestTruncOp_ZeroSize(TestTruncOp):
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def init_shape(self):
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self.shape = (20, 0)
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class TestTruncAPI(unittest.TestCase):
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def setUp(self):
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self.shape = [20, 20]
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self.x = np.random.random((20, 20)).astype(np.float32)
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self.place = paddle.CPUPlace()
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def test_api_static(self):
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paddle.enable_static()
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', self.shape)
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out = paddle.trunc(x)
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exe = paddle.static.Executor(self.place)
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res = exe.run(feed={'X': self.x}, fetch_list=[out])
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out_ref = np.trunc(self.x)
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for out in res:
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np.testing.assert_allclose(out, out_ref, rtol=1e-08)
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def test_api_dygraph(self):
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paddle.disable_static(self.place)
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x_tensor = paddle.to_tensor(self.x)
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out = paddle.trunc(x_tensor)
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out_ref = np.trunc(self.x)
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np.testing.assert_allclose(out.numpy(), out_ref, rtol=1e-08)
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paddle.enable_static()
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def test_errors(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data('X', [20, 20], 'bool')
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self.assertRaises(TypeError, paddle.trunc, x)
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class TestTruncFP16OP(TestTruncOp):
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def init_dtype_type(self):
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self.dtype = np.float16
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestTruncBF16OP(OpTest):
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def setUp(self):
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self.python_api = paddle.trunc
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self.op_type = "trunc"
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self.dtype = np.uint16
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np.random.seed(2021)
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x = np.random.random((20, 20)).astype("float32")
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out = np.trunc(x)
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self.inputs = {'X': convert_float_to_uint16(x)}
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self.outputs = {'Out': convert_float_to_uint16(out)}
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_pir=True, check_symbol_infer=False
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)
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def test_check_grad(self):
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place = get_device_place()
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self.check_grad_with_place(
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place, ['X'], 'Out', numeric_grad_delta=1e-5, check_pir=True
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)
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if __name__ == "__main__":
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unittest.main()
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