wehub-resource-sync
912 lines
30 KiB
Python
912 lines
30 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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extra_nvcc_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_inplace\\custom_inplace.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_inplace = load(
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name='custom_inplace',
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sources=['custom_inplace.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 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=True)
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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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if custom_func:
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out = custom_inplace.custom_add(x, y)
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else:
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out = x.add_(y)
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out.backward()
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return x.numpy(), y.numpy(), out.numpy(), x.grad.numpy(), y.grad.numpy()
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def inplace_static_add(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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y = static.data(name="y", shape=[None, np_y.shape[1]], dtype=dtype)
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x.stop_gradient = False
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y.stop_gradient = False
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out = func(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 = [
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x,
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out,
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ops[-1].result(0),
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ops[-1].result(1),
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ops[-2].result(0),
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]
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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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out.name + "@GRAD",
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]
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x_v, out_v, x_grad_v, y_grad_v, out_grad_v = exe.run(
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static.default_main_program(),
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feed={
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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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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, out_grad_v
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def inplace_dynamic_add_vector(custom_func, device, dtype, np_inputs, np_y):
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paddle.set_device(device)
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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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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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if custom_func:
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out = custom_inplace.custom_add_vec(inputs, y)
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else:
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out = [x.add_(y) for x in inputs]
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mean_out = paddle.mean(paddle.concat(out))
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mean_out.backward()
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return (
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np.concatenate([input.numpy() for input in inputs]),
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y.numpy(),
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np.concatenate([o.numpy() for o in out]),
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np.concatenate([input.grad.numpy() for input in inputs]),
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y.grad.numpy(),
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)
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def inplace_static_add_vector(custom_func, device, dtype, np_inputs, 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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x1 = static.data(
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name="x1", shape=[None, np_inputs[0].shape[1]], dtype=dtype
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)
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x2 = static.data(
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name="x2", shape=[None, np_inputs[1].shape[1]], dtype=dtype
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)
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y = static.data(name="y", shape=[None, np_y.shape[1]], dtype=dtype)
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x1.stop_gradient = False
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x2.stop_gradient = False
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y.stop_gradient = False
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if custom_func:
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out = custom_inplace.custom_add_vec([x1, x2], y)
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else:
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out = [paddle.add(x1, y), paddle.add(x2, y)]
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mean_out = paddle.mean(paddle.concat(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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if custom_func:
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fetch_list = [
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out[0],
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out[1],
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ops[-1].result(0), # x1_grad
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ops[-1].result(1), # x2_grad
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ops[-2].result(1), # y_grad
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ops[-5].result(0), # out0_grad
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ops[-5].result(1),
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] # out1_grad
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else:
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fetch_list = [
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out[0],
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out[1],
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ops[-4].result(0), # x1_grad
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ops[-3].result(0), # x2_grad
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ops[-1].result(0), # y_grad
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ops[-5].result(0), # out0_grad
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ops[-5].result(1),
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] # out1_grad
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else:
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fetch_list = [
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out[0].name,
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out[1].name,
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x1.name + "@GRAD",
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x2.name + "@GRAD",
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y.name + "@GRAD",
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out[0].name + "@GRAD",
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out[1].name + "@GRAD",
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]
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(
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out0_v,
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out1_v,
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x1_grad_v,
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x2_grad_v,
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y_grad_v,
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out0_grad_v,
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out1_grad_v,
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) = exe.run(
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static.default_main_program(),
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feed={
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"x1": np_inputs[0].astype(dtype),
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"x2": np_inputs[1].astype(dtype),
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"y": np_y.astype(dtype),
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},
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fetch_list=fetch_list,
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)
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paddle.disable_static()
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return (
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[out0_v, out1_v],
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[x1_grad_v, x2_grad_v],
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y_grad_v,
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[out0_grad_v, out1_grad_v],
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)
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def inplace_dynamic_relu_net(custom_func, device, dtype, np_x, np_y, np_z):
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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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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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z = paddle.to_tensor(np_z, dtype=dtype, stop_gradient=False)
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out_xy = x + y
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if custom_func:
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out_xy = custom_inplace.custom_relu_inplace(out_xy)
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out_xyz = out_xy + z
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out = custom_inplace.custom_relu_inplace(out_xyz)
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else:
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out_xy = paddle.nn.functional.relu_(out_xy)
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out_xyz = out_xy + z
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out = paddle.nn.functional.relu_(out_xyz)
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out.backward()
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return x.numpy(), y.numpy(), out.numpy(), x.grad.numpy(), y.grad.numpy()
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def inplace_static_relu_net(func, device, dtype, np_x, np_y, np_z):
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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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y = static.data(name="y", shape=[None, np_y.shape[1]], dtype=dtype)
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z = static.data(name="z", shape=[None, np_z.shape[1]], dtype=dtype)
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x.stop_gradient = False
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y.stop_gradient = False
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z.stop_gradient = False
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out_xy = x + y
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out_xy = func(out_xy)
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out_xyz = out_xy + z
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out = func(out_xyz)
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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 = [
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x,
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y,
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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.name,
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y.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, y_v, out_v, x_grad_v, y_grad_v = exe.run(
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static.default_main_program(),
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feed={
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"x": np_x.astype(dtype),
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"y": np_y.astype(dtype),
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"z": np_z.astype(dtype),
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},
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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, y_v, out_v, x_grad_v, y_grad_v
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def dynamic_multi_inplace(
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custom_func,
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device,
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dtype,
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np_x,
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np_y,
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np_a,
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np_b,
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custom_func_with_all_return=False,
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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=True)
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y = paddle.to_tensor(np_y, dtype=dtype, stop_gradient=False)
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a = paddle.to_tensor(np_a, dtype=dtype, stop_gradient=True)
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b = paddle.to_tensor(np_b, dtype=dtype, stop_gradient=False)
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if custom_func and not custom_func_with_all_return:
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out_xy, out_ab = custom_inplace.custom_multi_inplace(x, y, a, b)
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elif custom_func_with_all_return:
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out_xy, out_ab = custom_inplace.custom_multi_inplace_with_all_return(
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x, y, a, b
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)
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else:
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out_xy = x.add_(y)
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out_ab = a.add_(b)
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out = out_xy + out_ab
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out.backward()
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return (
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x.numpy(),
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y.numpy(),
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out_xy.numpy(),
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x.grad.numpy(),
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y.grad.numpy(),
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a.numpy(),
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b.numpy(),
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out_ab.numpy(),
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a.grad.numpy(),
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b.grad.numpy(),
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)
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def static_multi_inplace(
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custom_func,
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device,
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dtype,
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np_x,
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np_y,
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np_a,
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np_b,
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custom_func_with_all_return=False,
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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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y = static.data(name="y", shape=[None, np_y.shape[1]], dtype=dtype)
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a = static.data(name="a", shape=[None, np_x.shape[1]], dtype=dtype)
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b = static.data(name="b", shape=[None, np_y.shape[1]], dtype=dtype)
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x.stop_gradient = False
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y.stop_gradient = False
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a.stop_gradient = False
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b.stop_gradient = False
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if custom_func and not custom_func_with_all_return:
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out_xy, out_ab = custom_inplace.custom_multi_inplace(x, y, a, b)
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elif custom_func_with_all_return:
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out_xy, out_ab = (
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custom_inplace.custom_multi_inplace_with_all_return(x, y, a, b)
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)
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else:
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out_xy = paddle.add(x, y)
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out_ab = paddle.add(a, b)
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mean_out = paddle.mean(paddle.add(out_xy, out_ab))
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static.append_backward(mean_out)
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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 or custom_func_with_all_return:
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fetch_list = [
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x,
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out_xy,
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ops[-1].result(0), # x_grad
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ops[-1].result(1), # y_grad
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ops[-2].result(0), # out_xy_grad
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a,
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out_ab,
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ops[-1].result(2), # a_grad
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ops[-1].result(3), # b_grad
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ops[-2].result(1),
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] # out_ab_grad
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else:
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fetch_list = [
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x,
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out_xy,
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ops[-2].result(0), # x_grad
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ops[-2].result(1), # y_grad
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ops[-3].result(0), # out_xy_grad
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a,
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out_ab,
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ops[-1].result(0), # a_grad
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ops[-1].result(1), # b_grad
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ops[-3].result(1),
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] # out_ab_grad
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else:
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fetch_list = [
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x.name,
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out_xy.name,
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x.name + "@GRAD",
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y.name + "@GRAD",
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out_xy.name + "@GRAD",
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a.name,
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out_ab.name,
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a.name + "@GRAD",
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b.name + "@GRAD",
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out_ab.name + "@GRAD",
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]
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exe = static.Executor()
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exe.run(static.default_startup_program())
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(
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x_v,
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out_xy_v,
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x_grad_v,
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y_grad_v,
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out_xy_grad_v,
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a_v,
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out_ab_v,
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a_grad_v,
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b_grad_v,
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out_ab_grad_v,
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) = exe.run(
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static.default_main_program(),
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feed={
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"x": np_x.astype(dtype),
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"y": np_y.astype(dtype),
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"a": np_a.astype(dtype),
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"b": np_b.astype(dtype),
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},
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fetch_list=fetch_list,
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)
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paddle.disable_static()
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return (
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x_v,
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out_xy_v,
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x_grad_v,
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y_grad_v,
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out_xy_grad_v,
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a_v,
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out_ab_v,
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a_grad_v,
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b_grad_v,
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out_ab_grad_v,
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)
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class TestCustomInplaceJit(unittest.TestCase):
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def setUp(self):
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self.dtypes = ['float32', 'float64']
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self.devices = ['cpu']
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self.np_x = np.random.random((3, 2)).astype("float32")
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self.np_y = np.random.random((3, 2)).astype("float32")
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self.np_z = np.random.random((3, 2)).astype("float32")
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self.np_a = np.random.random((3, 2)).astype("float32")
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self.np_b = np.random.random((3, 2)).astype("float32")
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self.np_inputs = [
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np.random.random((3, 2)).astype("float32"),
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np.random.random((3, 2)).astype("float32"),
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]
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def test_static_add(self):
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for device in self.devices:
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for dtype in self.dtypes:
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(
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pd_x,
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pd_out,
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pd_x_grad,
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pd_y_grad,
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pd_out_grad,
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) = inplace_static_add(
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paddle.add,
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device,
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dtype,
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self.np_x,
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self.np_y,
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)
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(
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custom_x,
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custom_out,
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custom_x_grad,
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custom_y_grad,
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custom_out_grad,
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) = inplace_static_add(
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custom_inplace.custom_add,
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device,
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dtype,
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self.np_x,
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self.np_y,
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)
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check_output(custom_x, custom_out, "inplace_custom_x")
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check_output(
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custom_x_grad, custom_out_grad, "inplace_custom_x_grad"
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)
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check_output(custom_out, pd_out, "out")
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check_output(custom_x_grad, pd_x_grad, "x_grad")
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check_output(custom_y_grad, pd_y_grad, "y_grad")
|
|
check_output(custom_out_grad, pd_out_grad, "out_grad")
|
|
|
|
def test_dynamic_add(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_y,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = inplace_dynamic_add(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_y,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = inplace_dynamic_add(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
)
|
|
|
|
check_output(custom_x, custom_out, "inplace_custom_x")
|
|
check_output(pd_x, pd_out, "inplace_pd_x")
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_y, pd_y, "y")
|
|
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_static_add_vector(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
pd_out_grad,
|
|
) = inplace_static_add_vector(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_inputs,
|
|
self.np_y,
|
|
)
|
|
(
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
custom_out_grad,
|
|
) = inplace_static_add_vector(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_inputs,
|
|
self.np_y,
|
|
)
|
|
|
|
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")
|
|
check_output(custom_out_grad, pd_out_grad, "out_grad")
|
|
|
|
def test_dynamic_add_vector(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_y,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = inplace_dynamic_add_vector(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_inputs,
|
|
self.np_y,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_y,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = inplace_dynamic_add_vector(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_inputs,
|
|
self.np_y,
|
|
)
|
|
|
|
check_output(custom_x, custom_out, "inplace_custom_x")
|
|
check_output(pd_x, pd_out, "inplace_pd_x")
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_y, pd_y, "y")
|
|
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_static_relu_net(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_y,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = inplace_static_relu_net(
|
|
paddle.nn.functional.relu,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_z,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_y,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = inplace_static_relu_net(
|
|
custom_inplace.custom_relu_inplace,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_z,
|
|
)
|
|
check_output_allclose(custom_x, pd_x, "x")
|
|
check_output_allclose(custom_y, pd_y, "y")
|
|
check_output_allclose(custom_out, pd_out, "out")
|
|
check_output_allclose(custom_x_grad, pd_x_grad, "x_grad")
|
|
check_output_allclose(custom_y_grad, pd_y_grad, "y_grad")
|
|
|
|
def test_dynamic_relu_net(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_y,
|
|
pd_out,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
) = inplace_dynamic_relu_net(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_z,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_y,
|
|
custom_out,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
) = inplace_dynamic_relu_net(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_z,
|
|
)
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_y, pd_y, "y")
|
|
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_static_multi_inplace(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_out_xy,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
pd_out_xy_grad,
|
|
pd_a,
|
|
pd_out_ab,
|
|
pd_a_grad,
|
|
pd_b_grad,
|
|
pd_out_ab_grad,
|
|
) = static_multi_inplace(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_out_xy,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
custom_out_xy_grad,
|
|
custom_a,
|
|
custom_out_ab,
|
|
custom_a_grad,
|
|
custom_b_grad,
|
|
custom_out_ab_grad,
|
|
) = static_multi_inplace(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
)
|
|
(
|
|
custom_x_with_all_return,
|
|
custom_out_xy_with_all_return,
|
|
custom_x_grad_with_all_return,
|
|
custom_y_grad_with_all_return,
|
|
custom_out_xy_grad_with_all_return,
|
|
custom_a_with_all_return,
|
|
custom_out_ab_with_all_return,
|
|
custom_a_grad_with_all_return,
|
|
custom_b_grad_with_all_return,
|
|
custom_out_ab_grad_with_all_return,
|
|
) = static_multi_inplace(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
True,
|
|
)
|
|
check_output(custom_x, pd_out_xy, "inplace_custom_x")
|
|
check_output(
|
|
custom_x_grad, custom_out_xy_grad, "inplace_custom_x_grad"
|
|
)
|
|
check_output(custom_a, pd_out_ab, "inplace_custom_a")
|
|
check_output(
|
|
custom_a_grad, custom_out_ab_grad, "inplace_custom_a_grad"
|
|
)
|
|
|
|
check_output(custom_out_xy, pd_out_xy, "outxy")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad, pd_y_grad, "y_grad")
|
|
check_output(custom_out_xy_grad, pd_out_xy_grad, "outxy_grad")
|
|
check_output(custom_out_ab, pd_out_ab, "outab")
|
|
check_output(custom_a_grad, pd_a_grad, "a_grad")
|
|
check_output(custom_b_grad, pd_b_grad, "b_grad")
|
|
check_output(custom_out_ab_grad, pd_out_ab_grad, "outab_grad")
|
|
|
|
check_output(
|
|
custom_x_with_all_return, pd_out_xy, "inplace_custom_x"
|
|
)
|
|
check_output(
|
|
custom_x_grad_with_all_return,
|
|
custom_out_xy_grad,
|
|
"inplace_custom_x_grad",
|
|
)
|
|
check_output(
|
|
custom_a_with_all_return, pd_out_ab, "inplace_custom_a"
|
|
)
|
|
check_output(
|
|
custom_a_grad_with_all_return,
|
|
custom_out_ab_grad,
|
|
"inplace_custom_a_grad",
|
|
)
|
|
|
|
check_output(custom_out_xy_with_all_return, pd_out_xy, "outxy")
|
|
check_output(custom_x_grad_with_all_return, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad_with_all_return, pd_y_grad, "y_grad")
|
|
check_output(
|
|
custom_out_xy_grad_with_all_return,
|
|
pd_out_xy_grad,
|
|
"outxy_grad",
|
|
)
|
|
check_output(custom_out_ab_with_all_return, pd_out_ab, "outab")
|
|
check_output(custom_a_grad_with_all_return, pd_a_grad, "a_grad")
|
|
check_output(custom_b_grad_with_all_return, pd_b_grad, "b_grad")
|
|
check_output(
|
|
custom_out_ab_grad_with_all_return,
|
|
pd_out_ab_grad,
|
|
"outab_grad",
|
|
)
|
|
|
|
def test_dynamic_multi_inplace(self):
|
|
for device in self.devices:
|
|
for dtype in self.dtypes:
|
|
(
|
|
pd_x,
|
|
pd_y,
|
|
pd_out_xy,
|
|
pd_x_grad,
|
|
pd_y_grad,
|
|
pd_a,
|
|
pd_b,
|
|
pd_out_ab,
|
|
pd_a_grad,
|
|
pd_b_grad,
|
|
) = dynamic_multi_inplace(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
)
|
|
(
|
|
custom_x,
|
|
custom_y,
|
|
custom_out_xy,
|
|
custom_x_grad,
|
|
custom_y_grad,
|
|
custom_a,
|
|
custom_b,
|
|
custom_out_ab,
|
|
custom_a_grad,
|
|
custom_b_grad,
|
|
) = dynamic_multi_inplace(
|
|
True,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
)
|
|
(
|
|
custom_x_with_all_return,
|
|
custom_y_with_all_return,
|
|
custom_out_xy_with_all_return,
|
|
custom_x_grad_with_all_return,
|
|
custom_y_grad_with_all_return,
|
|
custom_a_with_all_return,
|
|
custom_b_with_all_return,
|
|
custom_out_ab_with_all_return,
|
|
custom_a_grad_with_all_return,
|
|
custom_b_grad_with_all_return,
|
|
) = dynamic_multi_inplace(
|
|
False,
|
|
device,
|
|
dtype,
|
|
self.np_x,
|
|
self.np_y,
|
|
self.np_a,
|
|
self.np_b,
|
|
True,
|
|
)
|
|
check_output(custom_x, custom_out_xy, "inplace_custom_x")
|
|
check_output(pd_x, pd_out_xy, "inplace_pd_x")
|
|
check_output(custom_a, custom_out_ab, "inplace_custom_a")
|
|
check_output(pd_a, pd_out_ab, "inplace_pd_a")
|
|
|
|
check_output(custom_x, pd_x, "x")
|
|
check_output(custom_y, pd_y, "y")
|
|
check_output(custom_out_xy, pd_out_xy, "outxy")
|
|
check_output(custom_x_grad, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad, pd_y_grad, "y_grad")
|
|
check_output(custom_a, pd_a, "a")
|
|
check_output(custom_b, pd_b, "b")
|
|
check_output(custom_out_ab, pd_out_ab, "outab")
|
|
check_output(custom_a_grad, pd_a_grad, "a_grad")
|
|
check_output(custom_b_grad, pd_b_grad, "b_grad")
|
|
|
|
check_output(
|
|
custom_x_with_all_return,
|
|
custom_out_xy_with_all_return,
|
|
"inplace_custom_x",
|
|
)
|
|
check_output(
|
|
custom_a_with_all_return,
|
|
custom_out_ab_with_all_return,
|
|
"inplace_custom_a",
|
|
)
|
|
|
|
check_output(custom_x_with_all_return, pd_x, "x")
|
|
check_output(custom_y_with_all_return, pd_y, "y")
|
|
check_output(custom_out_xy_with_all_return, pd_out_xy, "outxy")
|
|
check_output(custom_x_grad_with_all_return, pd_x_grad, "x_grad")
|
|
check_output(custom_y_grad_with_all_return, pd_y_grad, "y_grad")
|
|
check_output(custom_a_with_all_return, pd_a, "a")
|
|
check_output(custom_b_with_all_return, pd_b, "b")
|
|
check_output(custom_out_ab_with_all_return, pd_out_ab, "outab")
|
|
check_output(custom_a_grad_with_all_return, pd_a_grad, "a_grad")
|
|
check_output(custom_b_grad_with_all_return, pd_b_grad, "b_grad")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
unittest.main()
|