wehub-resource-sync
200 lines
6.8 KiB
Python
200 lines
6.8 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 os
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import unittest
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import numpy as np
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from utils import extra_cc_args, extra_nvcc_args, paddle_includes
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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_concat\\custom_concat.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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if os.name == 'nt':
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test_include = "..\\python\\paddle\\base\\tests\\custom_op"
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else:
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test_include = "../python/paddle/base/tests/custom_op"
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paddle_includes.append(test_include)
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custom_ops = load(
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name='custom_concat_jit',
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sources=['custom_concat_op.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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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 concat_dynamic(func, dtype, np_inputs, axis_v, with_attr=False):
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paddle.set_device("cpu")
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inputs = [
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paddle.to_tensor(x, dtype=dtype, stop_gradient=False) for x in np_inputs
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]
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if with_attr:
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axis = axis_v
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else:
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axis = paddle.full(shape=[1], dtype='int64', fill_value=axis_v)
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out = func(inputs, axis)
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out.stop_gradient = False
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out.backward()
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grad_inputs = [x.grad.numpy() for x in inputs]
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return out.numpy(), grad_inputs
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def concat_static(func, dtype, np_inputs, axis_v, with_attr=False):
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paddle.enable_static()
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paddle.set_device("cpu")
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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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x1 = static.data(name="x1", shape=[2, 3], dtype=dtype)
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x2 = static.data(name="x2", shape=[2, 3], dtype=dtype)
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if with_attr:
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axis = axis_v
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else:
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axis = paddle.full(shape=[1], dtype='int64', fill_value=axis_v)
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x1.stop_gradient = False
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x2.stop_gradient = False
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out = func([x1, x2], axis)
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# mean only support float, so here use sum
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sum_out = paddle.sum(out)
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static.append_backward(sum_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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if with_attr:
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feed_dict = {
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"x1": np_inputs[0].astype(dtype),
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"x2": np_inputs[1].astype(dtype),
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}
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else:
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feed_dict = {
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"x1": np_inputs[0].astype(dtype),
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"x2": np_inputs[1].astype(dtype),
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"axis": axis,
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}
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if paddle.framework.in_pir_mode():
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ops = static.default_main_program().global_block().ops
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fetch_list = [
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out,
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ops[-1].result(0), # x1_grad
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ops[-1].result(1),
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] # x2_grad
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else:
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fetch_list = [out.name, x1.name + "@GRAD", x2.name + "@GRAD"]
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out_v, x1_grad_v, x2_grad_v = exe.run(
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static.default_main_program(),
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feed=feed_dict,
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fetch_list=fetch_list,
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)
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paddle.disable_static()
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return out_v, x1_grad_v, x2_grad_v
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class TestCustomConcatDynamicAxisJit(unittest.TestCase):
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def setUp(self):
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self.dtypes = ['float32', 'float64', 'int32', 'int64']
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self.np_inputs = [
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np.array([[1, 2, 3], [4, 5, 6]]),
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np.array([[11, 12, 13], [14, 15, 16]]),
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]
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self.axes = [0, 1]
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def check_output(self, out, pd_out, name):
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np.testing.assert_array_equal(
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out,
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pd_out,
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err_msg=f'custom op {name}: {out},\n paddle api {name}: {pd_out}',
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)
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def test_dynamic(self):
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for dtype in self.dtypes:
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for axis in self.axes:
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out, grad_inputs = concat_dynamic(
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custom_ops.custom_concat, dtype, self.np_inputs, axis
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)
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pd_out, pd_grad_inputs = concat_dynamic(
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paddle.concat, dtype, self.np_inputs, axis
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)
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self.check_output(out, pd_out, "out")
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for x_grad, pd_x_grad in zip(grad_inputs, pd_grad_inputs):
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self.check_output(x_grad, pd_x_grad, "x_grad")
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def test_static(self):
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for dtype in self.dtypes:
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for axis in self.axes:
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out, x1_grad, x2_grad = concat_static(
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custom_ops.custom_concat, dtype, self.np_inputs, axis
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)
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pd_out, pd_x1_grad, pd_x2_grad = concat_static(
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paddle.concat, dtype, self.np_inputs, axis
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)
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self.check_output(out, pd_out, "out")
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self.check_output(x1_grad, pd_x1_grad, "x1_grad")
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self.check_output(x2_grad, pd_x2_grad, "x2_grad")
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def test_dynamic_with_attr(self):
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for dtype in self.dtypes:
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for axis in self.axes:
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out, grad_inputs = concat_dynamic(
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custom_ops.custom_concat_with_attr,
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dtype,
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self.np_inputs,
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axis,
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True,
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)
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pd_out, pd_grad_inputs = concat_dynamic(
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paddle.concat, dtype, self.np_inputs, axis, True
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)
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self.check_output(out, pd_out, "out")
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for x_grad, pd_x_grad in zip(grad_inputs, pd_grad_inputs):
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self.check_output(x_grad, pd_x_grad, "x_grad")
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def test_static_with_attr(self):
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for dtype in self.dtypes:
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for axis in self.axes:
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out, x1_grad, x2_grad = concat_static(
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custom_ops.custom_concat_with_attr,
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dtype,
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self.np_inputs,
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axis,
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True,
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)
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pd_out, pd_x1_grad, pd_x2_grad = concat_static(
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paddle.concat, dtype, self.np_inputs, axis, True
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)
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self.check_output(out, pd_out, "out")
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self.check_output(x1_grad, pd_x1_grad, "x1_grad")
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self.check_output(x2_grad, pd_x2_grad, "x2_grad")
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if __name__ == "__main__":
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unittest.main()
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