wehub-resource-sync
799 lines
26 KiB
Python
799 lines
26 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 check_output, 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_optional\\custom_optional.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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# Compile and load custom op Just-In-Time.
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custom_optional = load(
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name='custom_optional',
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sources=['custom_optional.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 cflags
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extra_cuda_cflags=extra_nvcc_args, # test for cflags
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verbose=True,
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)
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def optional_dynamic_add(custom_func, device, dtype, np_x, np_y):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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if np_y is not None:
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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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else:
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y = x
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if custom_func:
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out = custom_optional.custom_add(x, y if np_y is not None else None)
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else:
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out = paddle.add(x, y)
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out.backward()
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return x.numpy(), out.numpy(), x.grad.numpy()
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def optional_static_add(custom_func, device, dtype, np_x, np_y):
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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, np_x.shape[1]], dtype=dtype)
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x.stop_gradient = False
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if np_y is not None:
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y = static.data(name="y", shape=[None, np_x.shape[1]], dtype=dtype)
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y.stop_gradient = False
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feed_dict = {
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"x": np_x.astype(dtype),
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"y": np_y.astype(dtype),
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}
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else:
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y = x
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feed_dict = {
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"x": np_x.astype(dtype),
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}
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if custom_func:
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out = custom_optional.custom_add(x, y if np_y is not None else None)
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else:
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out = paddle.add(x, y)
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mean_out = paddle.mean(out)
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static.append_backward(mean_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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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 = [x, out, ops[-1].result(0)]
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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]
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x_v, out_v, x_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 x_v, out_v, x_grad_v
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'''
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if (y) {
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outX = 2 * x + y;
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outY = x + y;
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} else {
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outX = 2 * x;
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outY = None;
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}
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'''
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def optional_inplace_dynamic_add(custom_func, device, dtype, np_x, np_y):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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if np_y is not None:
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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=True)
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add(x, y)
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else:
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# We need to accumulate y's grad here.
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y.stop_gradient = False
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outx = 2 * x + y
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# Inplace leaf Tensor's stop_gradient should be True
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y.stop_gradient = True
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outy = y.add_(x)
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else:
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y = None
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add(x, y)
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else:
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outx = 2 * x
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outy = None
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assert outy is None, (
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"The output `outy` of optional_inplace_dynamic_add should be None"
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)
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out = outx + outy if outy is not None else outx
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out.backward()
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return (
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x.numpy(),
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outx.numpy(),
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y.numpy() if y is not None else None,
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outy.numpy() if outy is not None else None,
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out.numpy(),
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x.grad.numpy(),
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y.grad.numpy() if y is not None and y.grad is not None else None,
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)
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def optional_inplace_static_add(custom_func, device, dtype, np_x, np_y):
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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, np_x.shape[1]], dtype=dtype)
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x.stop_gradient = False
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if np_y is not None:
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y = static.data(name="y", shape=[None, np_x.shape[1]], dtype=dtype)
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y.stop_gradient = False
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feed_dict = {
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"x": np_x.astype(dtype),
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"y": np_y.astype(dtype),
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}
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add(x, y)
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else:
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outx = 2 * x + y
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outy = x + y
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else:
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feed_dict = {
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"x": np_x.astype(dtype),
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}
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add(
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x, None
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)
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else:
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outx = 2 * x
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outy = None
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out = outx + outy if outy is not None else outx
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mean_out = paddle.mean(out)
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static.append_backward(mean_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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if np_y is not None:
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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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if custom_func:
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fetch_list = [
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x,
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out,
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ops[-1].result(0), # x_grad
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ops[-1].result(1),
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] # y_grad
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else:
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fetch_list = [
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x,
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out,
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ops[-1].result(0), # x_grad
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ops[-3].result(0),
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] # y_grad
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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y.name + "@GRAD",
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]
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x_v, out_v, x_grad_v, y_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 [x_v, out_v, x_grad_v, y_grad_v]
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else:
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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 = [x, out, ops[-1].result(0)]
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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]
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x_v, out_v, x_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 [x_v, out_v, x_grad_v]
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def optional_vector_dynamic_add(custom_func, device, dtype, np_x, np_inputs):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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if np_inputs is not None:
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inputs = [
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paddle.to_tensor(np_input, dtype=dtype, stop_gradient=False)
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for np_input in np_inputs
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]
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if custom_func:
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out = custom_optional.custom_add_vec(x, inputs)
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else:
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out = paddle.add(x, inputs[0])
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for input in inputs[1:]:
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out = paddle.add(out, input)
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else:
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if custom_func:
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out = custom_optional.custom_add_vec(x, None)
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else:
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out = paddle.add(x, x)
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out.backward()
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return x.numpy(), out.numpy(), x.grad.numpy()
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def optional_vector_static_add(custom_func, device, dtype, np_x, np_inputs):
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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, np_x.shape[1]], dtype=dtype)
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x.stop_gradient = False
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feed_dict = {"x": np_x.astype(dtype)}
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if np_inputs is not None:
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y1 = static.data(
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name="y1", shape=[None, np_x.shape[1]], dtype=dtype
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)
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y1.stop_gradient = False
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y2 = static.data(
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name="y2", shape=[None, np_x.shape[1]], dtype=dtype
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)
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y2.stop_gradient = False
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feed_dict.update(
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{
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"y1": np_inputs[0].astype(dtype),
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"y2": np_inputs[1].astype(dtype),
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}
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)
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if custom_func:
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out = custom_optional.custom_add_vec(x, [y1, y2])
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else:
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out = paddle.add(x, y1)
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out = paddle.add(out, y2)
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else:
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if custom_func:
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out = custom_optional.custom_add_vec(x, None)
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else:
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out = paddle.add(x, x)
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mean_out = paddle.mean(out)
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static.append_backward(mean_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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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 = [x, out, ops[-1].result(0)]
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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]
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x_v, out_v, x_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 x_v, out_v, x_grad_v
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'''
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if (y) {
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outX = 2 * x + y[1...n];
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outY[i] = x + y[i];
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} else {
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outX = 2 * x;
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outY = None;
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}
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'''
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def optional_inplace_vector_dynamic_add(
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custom_func, device, dtype, np_x, np_inputs
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):
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paddle.set_device(device)
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x = paddle.to_tensor(np_x, dtype=dtype, stop_gradient=False)
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if np_inputs is not None:
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inputs = [
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paddle.to_tensor(np_input, dtype=dtype, stop_gradient=True)
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for np_input in np_inputs
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]
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add_vec(
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x, inputs
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)
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else:
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outx = 2 * x
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outy = []
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for input in inputs:
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# We need to accumulate y's grad here.
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input.stop_gradient = False
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outx = outx + input
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# Inplace leaf Tensor's stop_gradient should be True
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input.stop_gradient = True
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outy.append(input.add_(x))
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else:
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if custom_func:
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outx, outy = custom_optional.custom_optional_inplace_add_vec(
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x, None
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)
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else:
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outx = 2 * x
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outy = None
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assert outy is None, (
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"The output `outy` of optional_inplace_dynamic_add should be None"
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)
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if outy is not None:
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out = outx
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for tensor in outy:
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out = out + tensor
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else:
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out = outx
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out.backward()
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return (
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x.numpy(),
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outx.numpy(),
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[y.numpy() for y in inputs] if np_inputs is not None else None,
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[t.numpy() for t in outy] if outy is not None else None,
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out.numpy(),
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x.grad.numpy(),
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(
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[y.grad.numpy() for y in inputs]
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if np_inputs is not None and inputs[0].grad is not None
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else None
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),
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)
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def optional_inplace_vector_static_add(
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custom_func, device, dtype, np_x, np_inputs
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):
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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, np_x.shape[1]], dtype=dtype)
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x.stop_gradient = False
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feed_dict = {
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"x": np_x.astype(dtype),
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}
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if np_inputs is not None:
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y1 = static.data(
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name="y1", shape=[None, np_x.shape[1]], dtype=dtype
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)
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y1.stop_gradient = False
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y2 = static.data(
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name="y2", shape=[None, np_x.shape[1]], dtype=dtype
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)
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y2.stop_gradient = False
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feed_dict.update(
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{
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"y1": np_inputs[0].astype(dtype),
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"y2": np_inputs[1].astype(dtype),
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}
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)
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if custom_func:
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(
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outx,
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outy,
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) = custom_optional.custom_optional_inplace_add_vec(x, [y1, y2])
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else:
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outx = paddle.add(paddle.add(paddle.add(x, x), y1), y2)
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# outx = 2 * x + y1 + y2
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outy = [x + y1, x + y2]
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else:
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if custom_func:
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(
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outx,
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outy,
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) = custom_optional.custom_optional_inplace_add_vec(x, None)
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else:
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outx = 2 * x
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outy = None
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if np_inputs is not None:
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out = outx + outy[0] + outy[1]
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else:
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out = outx
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mean_out = paddle.mean(out)
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static.append_backward(mean_out)
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exe = static.Executor()
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exe.run(static.default_startup_program())
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if np_inputs is not None:
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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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if custom_func:
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fetch_list = [
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x,
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out,
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ops[-2].result(0), # x_grad
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ops[-1].result(0), # y1_grad
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ops[-1].result(1),
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] # y2_grad
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else:
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fetch_list = [
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x,
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out,
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ops[-1].result(0), # x_grad
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ops[-3].result(0), # y1_grad
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ops[-6].result(0),
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] # y2_grad
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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y1.name + "@GRAD",
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y2.name + "@GRAD",
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]
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x_v, out_v, x_grad_v, y1_grad_v, y2_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 [x_v, out_v, x_grad_v, y1_grad_v, y2_grad_v]
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else:
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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 = [x, out, ops[-1].result(0)] # y_grad
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else:
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fetch_list = [
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x.name,
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out.name,
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x.name + "@GRAD",
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]
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x_v, out_v, x_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 [x_v, out_v, x_grad_v]
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|
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class TestCustomOptionalJit(unittest.TestCase):
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def setUp(self):
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self.dtypes = ['float32', 'float64']
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self.devices = ['cpu']
|
|
self.np_x = np.random.random((3, 2)).astype("float32")
|
|
self.np_y = np.random.random((3, 2)).astype("float32")
|
|
self.np_inputs = [
|
|
np.random.random((3, 2)).astype("float32"),
|
|
np.random.random((3, 2)).astype("float32"),
|
|
]
|
|
|
|
def test_optional_static_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_y]:
|
|
(
|
|
pd_x,
|
|
pd_out,
|
|
pd_x_grad,
|
|
) = optional_static_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_out,
|
|
custom_x_grad,
|
|
) = optional_static_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
|
|
def test_optional_dynamic_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_y]:
|
|
(
|
|
pd_x,
|
|
pd_out,
|
|
pd_x_grad,
|
|
) = optional_dynamic_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_out,
|
|
custom_x_grad,
|
|
) = optional_dynamic_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
|
|
def test_optional_inplace_static_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_y]:
|
|
pd_tuple = optional_inplace_static_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
custom_tuple = optional_inplace_static_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_tuple[0], pd_tuple[0], "x")
|
|
check_output(custom_tuple[1], pd_tuple[1], "out")
|
|
check_output(custom_tuple[2], pd_tuple[2], "x_grad")
|
|
if len(custom_tuple) > 3:
|
|
check_output(custom_tuple[3], pd_tuple[3], "y_grad")
|
|
|
|
def test_optional_inplace_dynamic_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_y]:
|
|
(
|
|
pd_x,
|
|
pd_outx,
|
|
pd_y,
|
|
pd_outy,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = optional_inplace_dynamic_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_outx,
|
|
custom_y,
|
|
custom_outy,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = optional_inplace_dynamic_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(pd_y, pd_outy, "inplace_pd_y")
|
|
check_output(custom_y, custom_outy, "inplace_custom_y")
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_outx, pd_outx, "outx")
|
|
check_output(custom_y, pd_y, "y")
|
|
check_output(custom_outy, pd_outy, "outy")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad, pd_y_grad, "y_grad")
|
|
|
|
def test_optional_vector_static_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_inputs]:
|
|
(
|
|
custom_x,
|
|
custom_out,
|
|
custom_x_grad,
|
|
) = optional_vector_static_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
pd_x,
|
|
pd_out,
|
|
pd_x_grad,
|
|
) = optional_vector_static_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
|
|
def test_optional_vector_dynamic_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_inputs]:
|
|
(
|
|
custom_x,
|
|
custom_out,
|
|
custom_x_grad,
|
|
) = optional_vector_dynamic_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
pd_x,
|
|
pd_out,
|
|
pd_x_grad,
|
|
) = optional_vector_dynamic_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
|
|
def test_optional_inplace_vector_static_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_inputs]:
|
|
pd_tuple = optional_inplace_vector_static_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
custom_tuple = optional_inplace_vector_static_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(custom_tuple[0], pd_tuple[0], "x")
|
|
check_output(custom_tuple[1], pd_tuple[1], "out")
|
|
check_output(custom_tuple[2], pd_tuple[2], "x_grad")
|
|
if len(custom_tuple) > 3:
|
|
check_output(custom_tuple[3], pd_tuple[3], "y1_grad")
|
|
check_output(custom_tuple[4], pd_tuple[4], "y2_grad")
|
|
|
|
def test_optional_inplace_vector_dynamic_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
for np_y in [None, self.np_inputs]:
|
|
(
|
|
custom_x,
|
|
custom_outx,
|
|
custom_y,
|
|
custom_outy,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = optional_inplace_vector_dynamic_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
(
|
|
pd_x,
|
|
pd_outx,
|
|
pd_y,
|
|
pd_outy,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = optional_inplace_vector_dynamic_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
np_y,
|
|
)
|
|
|
|
check_output(pd_y, pd_outy, "inplace_pd_y")
|
|
check_output(custom_y, custom_outy, "inplace_custom_y")
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_outx, pd_outx, "outx")
|
|
check_output(custom_y, pd_y, "y")
|
|
check_output(custom_outy, pd_outy, "outy")
|
|
check_output(custom_out, pd_out, "out")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad, pd_y_grad, "y_grad")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|