wehub-resource-sync
421 lines
14 KiB
Python
421 lines
14 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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check_output,
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check_output_allclose,
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extra_cc_args,
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paddle_includes,
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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_tensor_operator\\custom_tensor_operator.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_tensor_operator',
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sources=['custom_tensor_operator.cc'],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_cxx_cflags=extra_cc_args, # test for cc flags
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verbose=True,
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)
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def test_custom_add_dynamic(func, device, dtype, np_x, use_func=True):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x + 1
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out.stop_gradient = False
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out.backward()
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if x.grad is None:
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return out.numpy(), x.grad
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else:
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return out.numpy(), x.grad.numpy()
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def test_custom_add_static(func, device, dtype, np_x, use_func=True):
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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=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x + 1
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static.append_backward(out)
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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 = exe.run(
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static.default_main_program(),
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feed={'X': np_x},
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fetch_list=[out],
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)
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paddle.disable_static()
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return out_v
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def test_custom_subtract_dynamic(func, device, dtype, np_x, use_func=True):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x - 1
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out.stop_gradient = False
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out.backward()
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if x.grad is None:
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return out.numpy(), x.grad
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else:
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return out.numpy(), x.grad.numpy()
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def test_custom_subtract_static(func, device, dtype, np_x, use_func=True):
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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=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x - 1
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static.append_backward(out)
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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 = exe.run(
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static.default_main_program(),
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feed={'X': np_x},
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fetch_list=[out],
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)
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paddle.disable_static()
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return out_v
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def test_custom_multiply_dynamic(func, device, dtype, np_x, use_func=True):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x * 5
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out.stop_gradient = False
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out.backward()
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if x.grad is None:
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return out.numpy(), x.grad
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else:
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return out.numpy(), x.grad.numpy()
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def test_custom_multiply_static(func, device, dtype, np_x, use_func=True):
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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=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = x * 5
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static.append_backward(out)
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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 = exe.run(
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static.default_main_program(),
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feed={'X': np_x},
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fetch_list=[out],
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)
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paddle.disable_static()
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return out_v
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def test_custom_divide_dynamic(func, device, dtype, np_x, use_func=True):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = paddle.reciprocal(x)
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out.stop_gradient = False
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out.backward()
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if x.grad is None:
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return out.numpy(), x.grad
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else:
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return out.numpy(), x.grad.numpy()
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def test_custom_divide_static(func, device, dtype, np_x, use_func=True):
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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=[4, 8], dtype=dtype)
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x.stop_gradient = False
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if use_func:
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out = func(x)
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else:
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out = paddle.reciprocal(x)
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static.append_backward(out)
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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 = exe.run(
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static.default_main_program(),
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feed={'X': np_x},
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fetch_list=[out],
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)
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paddle.disable_static()
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return out_v
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class TestJITLoad(unittest.TestCase):
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def setUp(self):
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self.custom_module = custom_module
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self.devices = ['cpu']
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self.dtypes = ['float32', 'float64']
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if paddle.is_compiled_with_cuda():
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self.devices.append('gpu')
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self.dtypes.append('float16')
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def test_dynamic(self):
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self.add = self.custom_module.custom_add
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self.subtract = self.custom_module.custom_subtract
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self.multiply = self.custom_module.custom_multiply
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self.divide = self.custom_module.custom_divide
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self._test_dynamic()
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self.add = self.custom_module.custom_scalar_add
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self.subtract = self.custom_module.custom_scalar_subtract
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self.multiply = self.custom_module.custom_scalar_multiply
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self.divide = self.custom_module.custom_scalar_divide
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self._test_dynamic()
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self.add = self.custom_module.custom_left_scalar_add
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self.subtract = self.custom_module.custom_left_scalar_subtract
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self.multiply = self.custom_module.custom_left_scalar_multiply
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self.divide = self.custom_module.custom_left_scalar_divide
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self._test_dynamic()
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self._test_logical_operants()
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self._test_compare_operants()
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def test_static(self):
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self.add = self.custom_module.custom_add
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self.subtract = self.custom_module.custom_subtract
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self.multiply = self.custom_module.custom_multiply
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self.divide = self.custom_module.custom_divide
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self._test_static()
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self.add = self.custom_module.custom_scalar_add
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self.subtract = self.custom_module.custom_scalar_subtract
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self.multiply = self.custom_module.custom_scalar_multiply
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self.divide = self.custom_module.custom_scalar_divide
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self._test_static()
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self.add = self.custom_module.custom_left_scalar_add
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self.subtract = self.custom_module.custom_left_scalar_subtract
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self.multiply = self.custom_module.custom_left_scalar_multiply
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self.divide = self.custom_module.custom_left_scalar_divide
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self._test_static()
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def _test_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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if device == 'cpu' and dtype == 'float16':
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continue
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out = test_custom_add_static(self.add, device, dtype, x)
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pd_out = test_custom_add_static(
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self.add, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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out = test_custom_subtract_static(
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self.subtract, device, dtype, x
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)
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pd_out = test_custom_subtract_static(
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self.subtract, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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out = test_custom_multiply_static(
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self.multiply, device, dtype, x
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)
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pd_out = test_custom_multiply_static(
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self.multiply, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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out = test_custom_divide_static(self.divide, device, dtype, x)
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pd_out = test_custom_divide_static(
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self.divide, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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def _test_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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if device == 'cpu' and dtype == 'float16':
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continue
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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out, x_grad = test_custom_add_dynamic(
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self.add, device, dtype, x
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)
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pd_out, pd_x_grad = test_custom_add_dynamic(
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self.add, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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check_output_allclose(
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x_grad, pd_x_grad, "x_grad", rtol=1e-5, atol=1e-8
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)
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out, x_grad = test_custom_subtract_dynamic(
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self.subtract, device, dtype, x
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)
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pd_out, pd_x_grad = test_custom_subtract_dynamic(
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self.subtract, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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check_output_allclose(
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x_grad, pd_x_grad, "x_grad", rtol=1e-5, atol=1e-8
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)
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out, x_grad = test_custom_multiply_dynamic(
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self.multiply, device, dtype, x
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)
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pd_out, pd_x_grad = test_custom_multiply_dynamic(
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self.multiply, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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check_output_allclose(
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x_grad, pd_x_grad, "x_grad", rtol=1e-5, atol=1e-8
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)
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out, x_grad = test_custom_divide_dynamic(
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self.divide, device, dtype, x
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)
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pd_out, pd_x_grad = test_custom_divide_dynamic(
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self.divide, device, dtype, x, False
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)
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check_output_allclose(out, pd_out, "out", rtol=1e-5, atol=1e-8)
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def _test_logical_operants(self):
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for device in self.devices:
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paddle.set_device(device)
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np_x = paddle.randint(0, 2, [4, 8])
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x = paddle.to_tensor(np_x, dtype="int32")
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np_y = paddle.randint(0, 2, [4, 8])
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y = paddle.to_tensor(np_y, dtype="int32")
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out = self.custom_module.custom_logical_and(x, y)
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pd_out = paddle.bitwise_and(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_logical_or(x, y)
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pd_out = paddle.bitwise_or(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_logical_xor(x, y)
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pd_out = paddle.bitwise_xor(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_logical_not(x)
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pd_out = paddle.bitwise_not(x)
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check_output(out.numpy(), pd_out.numpy(), "out")
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def _test_compare_operants(self):
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for device in self.devices:
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paddle.set_device(device)
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np_x = paddle.randint(0, 2, [4, 8])
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x = paddle.to_tensor(np_x, dtype="int32")
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np_y = paddle.randint(0, 2, [4, 8])
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y = paddle.to_tensor(np_y, dtype="int32")
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out = self.custom_module.custom_less_than(x, y)
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pd_out = paddle.less_than(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_less_equal(x, y)
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pd_out = paddle.less_equal(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_equal(x, y)
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pd_out = paddle.equal(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_not_equal(x, y)
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pd_out = paddle.not_equal(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_greater_than(x, y)
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pd_out = paddle.greater_than(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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out = self.custom_module.custom_greater_equal(x, y)
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pd_out = paddle.greater_equal(x, y)
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check_output(out.numpy(), pd_out.numpy(), "out")
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if __name__ == '__main__':
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unittest.main()
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