wehub-resource-sync
86 lines
3.0 KiB
C++
86 lines
3.0 KiB
C++
// 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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#include <iostream>
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#include <vector>
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#include "NvInfer.h"
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#include "paddle/extension.h"
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void call_kernel(dim3 gridSize,
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dim3 blockSize,
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size_t share_M,
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const cudaStream_t& stream,
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const float* input,
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float* output,
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const int h,
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const int w);
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std::vector<paddle::Tensor> paddle_gap_forward(
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const paddle::Tensor& x,
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const std::vector<int>& test_attr1,
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const int test_attr2) {
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std::vector<int64_t> dims = x.shape();
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int32_t batch = dims[0];
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int32_t ch = dims[1];
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int32_t h = dims[2];
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int32_t w = dims[3];
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std::vector<int64_t> out_dims{batch, ch, test_attr2, test_attr2};
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auto out = paddle::empty(out_dims, x.dtype(), x.place());
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dim3 blockSize(ch);
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dim3 gridSize(batch);
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if (x.is_gpu() && x.dtype() == paddle::DataType::FLOAT32) {
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const float* input = x.data<float>();
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float* output = out.data<float>();
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PD_DISPATCH_FLOATING_TYPES(
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x.type(), "globalAvgPool", ([&] {
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call_kernel(gridSize, blockSize, 0, x.stream(), input, output, h, w);
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}));
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}
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return {out};
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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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const std::vector<int>& test_attr1,
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const int test_attr2) {
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std::vector<int64_t> out_dims{x_shape[0], x_shape[1], test_attr2, test_attr2};
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return {out_dims};
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}
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std::vector<paddle::DataType> InferDtype(paddle::DataType x_dtype) {
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return {x_dtype};
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}
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nvinfer1::DimsExprs getOutputDimensions(
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std::pair<int32_t, int32_t> outputIndex_nbInputs,
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const nvinfer1::DimsExprs* inputs,
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nvinfer1::IExprBuilder& exprBuilder, // NOLINT
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const std::vector<int>& test_attr1,
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const int test_attr2) noexcept {
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nvinfer1::DimsExprs dimsOutput(inputs[0]);
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dimsOutput.d[dimsOutput.nbDims - 1] = exprBuilder.constant(test_attr2);
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dimsOutput.d[dimsOutput.nbDims - 2] = exprBuilder.constant(test_attr2);
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return dimsOutput;
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}
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PD_BUILD_OP(gap)
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.Inputs({"X"})
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.Outputs({"Out"})
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.SetKernelFn(PD_KERNEL(paddle_gap_forward))
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.Attrs({"test_attr1: std::vector<int>", "test_attr2: int"})
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.SetInferShapeFn(PD_INFER_SHAPE(InferShape))
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.SetInferDtypeFn(PD_INFER_DTYPE(InferDtype))
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.SetTrtInferShapeFn(PD_TRT_INFER_SHAPE(getOutputDimensions))
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.SetTrtSupportsFormatConfig({"float32:LINEAR+float32:LINEAR"});
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