wehub-resource-sync
76 lines
2.9 KiB
Python
76 lines
2.9 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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import paddle
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import paddle.incubate
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from paddle.base import core
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paddle.enable_static()
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np.random.seed(0)
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@unittest.skipIf(
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not paddle.is_compiled_with_cuda()
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or paddle.get_cudnn_version() < 8000
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or paddle.device.cuda.get_device_capability()[0] < 7
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or paddle.device.cuda.get_device_capability()[0] >= 9,
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"only support with cuda and cudnn version is at least 8.0 "
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"and device's compute capability is at least 7.0 and less than 9.0",
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)
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class TestFuseResNetUnit(unittest.TestCase):
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def test_fuse_resnet_unit(self):
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place = paddle.CUDAPlace(0)
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with paddle.pir_utils.OldIrGuard():
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program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with (
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paddle.static.amp.fp16_guard(),
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paddle.static.program_guard(program, startup_program),
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):
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x = paddle.static.data("x", [1, 64, 64, 8], dtype="float16")
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conv2d = paddle.nn.Conv2D(
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8, 32, 1, bias_attr=False, data_format='NHWC'
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)
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batch_norm = paddle.nn.BatchNorm(
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32, act='relu', data_layout='NHWC'
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)
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out = batch_norm(conv2d(x))
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graph = core.Graph(program.desc)
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core.get_pass("fuse_resnet_unit").apply(graph)
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after_program = paddle.base.framework.IrGraph(graph).to_program()
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params = paddle.static.amp.cast_model_to_fp16(program)
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after_params = paddle.static.amp.cast_model_to_fp16(after_program)
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exe = paddle.static.Executor(place)
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exe.run(startup_program)
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paddle.static.amp.cast_parameters_to_fp16(
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place, program, to_fp16_var_names=params
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)
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paddle.static.amp.cast_parameters_to_fp16(
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place, after_program, to_fp16_var_names=after_params
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)
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feed = {"x": np.random.randn(1, 64, 64, 8).astype("float16")}
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before_out = exe.run(program, feed=feed, fetch_list=[out])
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after_out = exe.run(after_program, feed=feed, fetch_list=[out])
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np.testing.assert_allclose(
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before_out[0], after_out[0], rtol=1e-05, atol=0.005
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)
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if __name__ == '__main__':
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unittest.main()
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