wehub-resource-sync
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
# Copyright (c) 2023 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 os
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import unittest
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import numpy as np
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from utils import (
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extra_cc_args,
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extra_nvcc_args,
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paddle_includes,
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paddle_libraries,
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)
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import paddle
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from paddle import static
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from paddle.utils.cpp_extension import get_build_directory, load
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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# Because Windows don't use docker, the shared lib already exists in the
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# cache dir, it will not be compiled again unless the shared lib is removed.
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file = f'{get_build_directory()}\\custom_cast_module_jit\\custom_cast_module_jit.pyd'
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if os.name == 'nt' and os.path.isfile(file):
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cmd = f'del {file}'
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run_cmd(cmd, True)
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custom_module = load(
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name='custom_cast_module_jit',
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sources=['custom_cast_op.cc'],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_library_paths=paddle_libraries,
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extra_cxx_cflags=extra_cc_args, # test for cc flags
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extra_cuda_cflags=extra_nvcc_args, # test for nvcc flags
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verbose=True,
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)
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def custom_cast_dynamic(device, dtype, np_x):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype="float32")
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x.stop_gradient = False
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out = custom_module.custom_cast(x, dtype)
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out.stop_gradient = False
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out.backward()
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assert str(out.dtype).split(".")[-1] == dtype
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assert str(x.grad.dtype).split(".")[-1] == dtype
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def custom_cast_static(device, dtype, np_x):
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paddle.enable_static()
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paddle.set_device(device)
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with (
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static.scope_guard(static.Scope()),
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static.program_guard(static.Program()),
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):
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x = static.data(name='X', shape=[None, 8], dtype="float32")
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x.stop_gradient = False
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out = custom_module.custom_cast(x, dtype)
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static.append_backward(out)
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if paddle.framework.in_pir_mode():
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fetch_list = [
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out,
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static.default_main_program().global_block().ops[-1].result(0),
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]
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else:
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fetch_list = [out, x.name + "@GRAD"]
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exe = static.Executor()
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exe.run(static.default_startup_program())
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# in static graph mode, x data has been covered by out
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out_v, x_grad_v = exe.run(
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static.default_main_program(),
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feed={'X': np_x},
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fetch_list=fetch_list,
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)
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assert x_grad_v[0].dtype == dtype
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assert out_v[0].dtype == dtype
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paddle.disable_static()
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return out_v
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class TestCustomCastOp(unittest.TestCase):
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def setUp(self):
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self.dtypes = ['float32', 'float64']
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def test_static(self):
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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custom_cast_static('cpu', dtype, x)
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def test_dynamic(self):
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype("float32")
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custom_cast_dynamic('cpu', dtype, x)
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if __name__ == '__main__':
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unittest.main()
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