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- # Copyright 2020-2021 Huawei Technologies Co., Ltd
- #
- # 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 numpy as np
- from tests.common.gen_random import random_gaussian
- from akg.utils import kernel_exec as utils
- from akg.utils.result_analysis import gpu_profiling
- from akg.utils.format_transform import to_tvm_nd_array
- from akg.ops.math_gpu.equal import equal
-
- def gen_data(shapes, dtype):
- support_list = {"float16": np.float16, "float32": np.float32}
- inputs = []
- for i in range(len(shapes)):
- shape = shapes[i]
- one_input = random_gaussian(shape, miu=1, sigma=0.1).astype(support_list[dtype])
- inputs.append(one_input)
-
- if len(inputs) != 2:
- raise RuntimeError("inputs num should be 2")
- expect = np.equal(inputs[0], inputs[1])
- output = np.full(expect.shape, 0, bool)
- return inputs, output, expect
-
-
-
- def test_ms_equal(shapes, dtype, poly_sch=False):
- if poly_sch:
- mod = utils.op_build_test(equal, shapes, [dtype, dtype], kernel_name="equal", attrs={"target": "cuda"})
-
- inputs1, output1, expect1 = gen_data(shapes, dtype)
- output1 = utils.mod_launch(mod, (*inputs1, output1), expect=expect1)
-
- if shapes[0] == shapes[1]:
- inputs2 = []
- inputs2.append(inputs1[0])
- inputs2.append(inputs1[0])
- expect2 = np.equal(inputs2[0], inputs2[1])
- output2 = np.full(expect2.shape, 0, bool)
- output2 = utils.mod_launch(mod, (*inputs2, output2), expect=expect1)
-
- res = np.allclose(output1, expect1, rtol=5e-03, atol=1.e-8) and np.allclose(output2, expect2, rtol=5e-03, atol=1.e-8)
- print("Test {}".format("Pass" if res else "Fail"))
- if not res:
- print("Error cuda:========================")
- print(mod.imported_modules[0].get_source())
- raise AssertionError("Test fail")
-
- inputs1 = to_tvm_nd_array(inputs1)
- inputs2 = to_tvm_nd_array(inputs2)
- expect1 = to_tvm_nd_array(expect1)
- expect2 = to_tvm_nd_array(expect2)
- gpu_profiling(mod, *inputs1, expect1, *inputs2, expect2, 400)
- else:
- res = np.allclose(output1, expect1, rtol=5e-03, atol=1.e-8)
- print("Test {}".format("Pass" if res else "Fail"))
- if not res:
- print("Error cuda:========================")
- print(mod.imported_modules[0].get_source())
- raise AssertionError("Test fail")
-
- inputs1 = to_tvm_nd_array(inputs1)
- expect1 = to_tvm_nd_array(expect1)
- gpu_profiling(mod, *inputs1, expect1, 400)
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