[CustomOp] Split test and add inference test (#31078)
* split test & add inference test * add timeout config * change to setup install * change to jit compile * add verbose for test * fix load setup name repeat * polish details * resolve conflict * fix code format errorrevert-31068-fix_conv3d_windows
Chen Weihang
4 years ago
committed by
GitHub
parent
d3f09ad702
commit
e60fd1f6a8
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// 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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#include <iostream>
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#include <vector>
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#include "paddle/extension.h"
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template <typename data_t>
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void assign_cpu_kernel(const data_t* x_data,
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data_t* out_data,
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int64_t x_numel) {
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for (int i = 0; i < x_numel; ++i) {
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out_data[i] = x_data[i];
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}
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}
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template <typename data_t>
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void fill_constant_cpu_kernel(data_t* out_data, int64_t x_numel, data_t value) {
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for (int i = 0; i < x_numel; ++i) {
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out_data[i] = value;
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}
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}
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std::vector<paddle::Tensor> MultiOutCPU(const paddle::Tensor& x) {
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auto out = paddle::Tensor(paddle::PlaceType::kCPU);
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out.reshape(x.shape());
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PD_DISPATCH_FLOATING_TYPES(
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x.type(), "assign_cpu_kernel", ([&] {
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assign_cpu_kernel<data_t>(
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x.data<data_t>(), out.mutable_data<data_t>(x.place()), x.size());
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}));
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// fake multi output: Fake_float64 with float64 dtype
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auto fake_float64 = paddle::Tensor(paddle::PlaceType::kCPU);
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fake_float64.reshape(x.shape());
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fill_constant_cpu_kernel<double>(
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fake_float64.mutable_data<double>(x.place()), x.size(), 0.);
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// fake multi output: ZFake_int32 with int32 dtype
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auto zfake_int32 = paddle::Tensor(paddle::PlaceType::kCPU);
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zfake_int32.reshape(x.shape());
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fill_constant_cpu_kernel<int32_t>(
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zfake_int32.mutable_data<int32_t>(x.place()), x.size(), 1);
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return {out, fake_float64, zfake_int32};
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}
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std::vector<std::vector<int64_t>> InferShape(std::vector<int64_t> x_shape) {
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return {x_shape, x_shape, x_shape};
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}
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std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
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return {x_dtype, paddle::DataType::FLOAT64, paddle::DataType::INT32};
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}
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PD_BUILD_OP("multi_out")
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.Inputs({"X"})
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.Outputs({"Out", "Fake_float64", "ZFake_int32"})
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.SetKernelFn(PD_KERNEL(MultiOutCPU))
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.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype));
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@ -0,0 +1,86 @@
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# 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 subprocess
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import unittest
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import paddle
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import numpy as np
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from paddle.utils.cpp_extension import load, get_build_directory
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from paddle.utils.cpp_extension.extension_utils import run_cmd
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from utils import paddle_includes, extra_compile_args
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from test_custom_relu_op_setup import custom_relu_dynamic, custom_relu_static
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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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if os.name == 'nt':
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cmd = 'del {}\\custom_relu_module_jit.pyd'.format(get_build_directory())
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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_relu_op_dup.cc is only used for multi ops test,
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# not a new op, if you want to test only one op, remove this
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# source file
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custom_module = load(
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name='custom_relu_module_jit',
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sources=[
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'custom_relu_op.cc', 'custom_relu_op.cu', 'custom_relu_op_dup.cc'
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],
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extra_include_paths=paddle_includes, # add for Coverage CI
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extra_cflags=extra_compile_args, # add for Coverage CI
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verbose=True)
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class TestJITLoad(unittest.TestCase):
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def setUp(self):
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self.custom_ops = [
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custom_module.custom_relu, custom_module.custom_relu_dup
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]
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self.dtypes = ['float32', 'float64']
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self.devices = ['cpu', 'gpu']
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def test_static(self):
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for device in self.devices:
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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for custom_op in self.custom_ops:
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out = custom_relu_static(custom_op, device, dtype, x)
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pd_out = custom_relu_static(custom_op, device, dtype, x,
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False)
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self.assertTrue(
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np.array_equal(out, pd_out),
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"custom op out: {},\n paddle api out: {}".format(
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out, pd_out))
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def test_dynamic(self):
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for device in self.devices:
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for dtype in self.dtypes:
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x = np.random.uniform(-1, 1, [4, 8]).astype(dtype)
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for custom_op in self.custom_ops:
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out, x_grad = custom_relu_dynamic(custom_op, device, dtype,
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x)
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pd_out, pd_x_grad = custom_relu_dynamic(custom_op, device,
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dtype, x, False)
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self.assertTrue(
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np.array_equal(out, pd_out),
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"custom op out: {},\n paddle api out: {}".format(
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out, pd_out))
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self.assertTrue(
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np.array_equal(x_grad, pd_x_grad),
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"custom op x grad: {},\n paddle api x grad: {}".format(
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x_grad, pd_x_grad))
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if __name__ == '__main__':
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unittest.main()
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