wehub-resource-sync
195 lines
6.6 KiB
Python
195 lines
6.6 KiB
Python
# Copyright (c) 2025 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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import paddle
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from paddle.base import core
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@unittest.skipIf(
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not core.is_compiled_with_cuda(), "core is not compiled with CUDA"
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)
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class TestReduceOp_Stride(unittest.TestCase):
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def setUp(self):
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self.python_api = paddle.max
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self.numpy_api = np.max
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def init_dtype(self):
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self.dtype = np.float64
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def init_place(self):
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self.place = core.CUDAPlace(0)
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def init_input_output(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [13, 17]).astype(self.dtype)
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self.out = self.numpy_api(self.x)
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self.perm = [1, 0]
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self.x_trans = np.transpose(self.x, self.perm)
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def test_dynamic_api(self):
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self.init_dtype()
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self.init_place()
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self.init_input_output()
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paddle.disable_static()
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self.pd_x_trans = paddle.to_tensor(self.x_trans, place=self.place)
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if self.strided_input_type == "transpose":
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x_trans_tmp = paddle.transpose(self.pd_x_trans, self.perm)
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elif self.strided_input_type == "as_stride":
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x_trans_tmp = paddle.as_strided(
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self.pd_x_trans, self.shape_param, self.stride_param
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)
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else:
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raise TypeError(f"Unsupported test type {self.strided_input_type}.")
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res = self.python_api(x_trans_tmp)
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res = res.cpu().numpy()
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np.testing.assert_allclose(res, self.out, rtol=1e-05)
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def create_test_act_stride_class(base_class, api_name, paddle_api, numpy_api):
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class TestStride1(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(
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self.dtype
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)
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self.out = self.numpy_api(self.x)
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self.perm = [0, 1, 3, 2]
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self.x_trans = np.transpose(self.x, self.perm)
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cls_name = "{}_{}_{}".format(base_class.__name__, api_name, "Stride1")
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TestStride1.__name__ = cls_name
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globals()[cls_name] = TestStride1
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class TestStride2(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(
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self.dtype
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)
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self.out = self.numpy_api(self.x)
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self.perm = [0, 2, 1, 3]
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self.x_trans = np.transpose(self.x, self.perm)
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cls_name = "{}_{}_{}".format(base_class.__name__, api_name, "Stride2")
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TestStride2.__name__ = cls_name
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globals()[cls_name] = TestStride2
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class TestStride3(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [20, 2, 13, 17]).astype(
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self.dtype
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)
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self.out = self.numpy_api(self.x)
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self.perm = [0, 1, 3, 2]
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self.x_trans = np.transpose(self.x, self.perm)
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cls_name = "{}_{}_{}".format(base_class.__name__, api_name, "Stride3")
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TestStride3.__name__ = cls_name
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globals()[cls_name] = TestStride3
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class TestStride4(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "transpose"
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self.x = np.random.uniform(0.1, 1, [1, 2, 13, 17]).astype(
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self.dtype
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)
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self.out = self.numpy_api(self.x)
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self.perm = [1, 0, 2, 3]
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self.x_trans = np.transpose(self.x, self.perm)
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cls_name = "{}_{}_{}".format(base_class.__name__, api_name, "Stride4")
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TestStride4.__name__ = cls_name
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globals()[cls_name] = TestStride4
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class TestStride5(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "as_stride"
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self.x = np.random.uniform(0.1, 1, [23, 2, 13, 20]).astype(
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self.dtype
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)
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self.x_trans = self.x
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self.x = self.x[:, 0:1, :, 0:1]
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self.out = self.numpy_api(self.x)
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self.shape_param = [23, 1, 13, 1]
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self.stride_param = [520, 260, 20, 1]
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cls_name = "{}_{}_{}".format(base_class.__name__, api_name, "Stride5")
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TestStride5.__name__ = cls_name
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globals()[cls_name] = TestStride5
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class TestStrideZeroSize1(base_class):
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def setUp(self):
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self.python_api = paddle_api
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self.numpy_api = numpy_api
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def init_input(self):
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self.strided_input_type = "transpose"
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self.x = np.random.rand(1, 0, 2).astype('float32')
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self.out = self.numpy_api(self.x)
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self.perm = [2, 1, 0]
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self.x_trans = np.transpose(self.x, self.perm)
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cls_name = "{}_{}_{}".format(
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base_class.__name__, api_name, "StrideZeroSize1"
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)
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TestStrideZeroSize1.__name__ = cls_name
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globals()[cls_name] = TestStrideZeroSize1
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create_test_act_stride_class(TestReduceOp_Stride, "Max", paddle.max, np.max)
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create_test_act_stride_class(TestReduceOp_Stride, "Min", paddle.min, np.min)
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create_test_act_stride_class(TestReduceOp_Stride, "Amax", paddle.amax, np.amax)
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create_test_act_stride_class(TestReduceOp_Stride, "Amin", paddle.amin, np.amin)
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create_test_act_stride_class(TestReduceOp_Stride, "Sum", paddle.sum, np.sum)
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create_test_act_stride_class(TestReduceOp_Stride, "Mean", paddle.mean, np.mean)
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create_test_act_stride_class(TestReduceOp_Stride, "Prod", paddle.prod, np.prod)
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create_test_act_stride_class(TestReduceOp_Stride, "All", paddle.all, np.all)
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create_test_act_stride_class(TestReduceOp_Stride, "Any", paddle.any, np.any)
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if __name__ == '__main__':
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unittest.main()
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