wehub-resource-sync
89 lines
2.7 KiB
Python
89 lines
2.7 KiB
Python
# Copyright (c) 2024 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 tensorrt_test_base import TensorRTBaseTest
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import paddle
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def einsum_wrapper(equation, x):
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if not isinstance(x, list):
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x = [x]
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out = paddle.einsum(equation, *x)
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return out[0]
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class TestEinsumCase1TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = einsum_wrapper
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self.api_args = {
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"equation": "ijk->ij",
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"x": np.random.randn(2, 3, 4).astype("float32"),
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}
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self.program_config = {"feed_list": ["x"]}
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self.min_shape = {"x": [1, 3, 4]}
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self.opt_shape = {"x": [2, 3, 4]}
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self.max_shape = {"x": [4, 3, 4]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestEinsumCase2TRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = einsum_wrapper
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self.api_args = {
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"equation": "abcd,bcd->a",
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"operands": [
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np.random.randn(2, 3, 4, 5).astype("float32"),
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np.random.randn(3, 4, 5).astype("float32"),
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],
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}
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self.program_config = {"feed_list": ["operands"]}
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self.min_shape = {"operands_0": [1, 2, 3, 4], "operands_1": [2, 3, 4]}
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self.opt_shape = {"operands_0": [2, 3, 4, 5], "operands_1": [3, 4, 5]}
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self.max_shape = {"operands_0": [4, 6, 8, 9], "operands_1": [6, 8, 9]}
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def test_trt_result(self):
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self.check_trt_result()
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class TestEinsumCaseTRTPattern(TensorRTBaseTest):
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def setUp(self):
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self.python_api = einsum_wrapper
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self.api_args = {
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"equation": "mij,jk->ki",
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"operands": [
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np.random.randn(2, 3, 4).astype("float16"),
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np.random.randn(4, 3).astype("float16"),
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],
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}
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self.program_config = {"feed_list": ["operands"]}
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self.min_shape = {"operands_0": [1, 3, 4], "operands_1": [1, 3]}
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self.opt_shape = {"operands_0": [2, 3, 4], "operands_1": [4, 3]}
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self.max_shape = {"operands_0": [4, 3, 4], "operands_1": [6, 3]}
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def test_trt_result(self):
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self.check_trt_result(precision_mode="fp16")
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if __name__ == '__main__':
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unittest.main()
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