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https://github.com/PaddlePaddle/FastDeploy.git
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3a6058e445
* add stable ci * fix * update * fix * rename tests dir;fix stable ci bug * add timeout limit * update
66 lines
2.0 KiB
Python
66 lines
2.0 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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from itertools import product
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import numpy as np
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import paddle
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from fastdeploy.model_executor.ops.gpu import dequant_int8, gemm_dequant
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class Test(unittest.TestCase):
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def setUp(self):
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"""
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Initialize the test environment,
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including setting random seeds.
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"""
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paddle.seed(2024)
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def testcase1(self):
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"""
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Check if the gemm_dequant function works properly.
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"""
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nks = [[2048, 2048], [2048, 5504], [6144, 2048]]
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m = [2, 8]
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combinations = list(product(m, nks))
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for m, (n, k) in combinations:
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act = paddle.rand([m, k])
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weight = paddle.rand([n, k])
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act_int_tensor = (act * 128).astype("int8")
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weight_int_tensor = (weight * 128).astype("int8")
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scale = paddle.rand([n])
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linear_out = paddle.matmul(act_int_tensor, weight_int_tensor, transpose_y=True)
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result = dequant_int8(linear_out, scale, "bfloat16")
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result_gemm_dequant = gemm_dequant(
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act_int_tensor,
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weight_int_tensor,
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scale,
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out_dtype="bfloat16",
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)
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np.testing.assert_allclose(
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result.numpy(),
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result_gemm_dequant.numpy(),
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rtol=1e-05,
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atol=1e-05,
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)
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if __name__ == "__main__":
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unittest.main()
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