mirror of
https://github.com/saymrwulf/onnxruntime.git
synced 2026-05-16 21:00:14 +00:00
0d204f3f06
Description: This change adds the common part of TVM based codegen library. It includes following parts: * Microsoft TVM Inventory (MTI): a set of TVM ops for neural networks, similar to TOPI * Compiler pass for traversing ONNX graph and generate TVM ops * Compiler pass for traversing generated graph and specify TVM schedule * Compiler pass for handling weight layout * Utils for debugging Motivation and Context: TVM is an open deep learning compiler stack for cpu, gpu and specialized accelerators. To leverage it in ONNX, we built an execution provider named Nuphar. Currently, Nuphar gets good performance on CPUs with AVX2 on quantized LSTM models. This codegen library was part of Nuphar execution provider. It is split out for sharing with other execution providers, as we'd like to reuse TVM in more devices.
62 lines
1.8 KiB
C++
62 lines
1.8 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#pragma once
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#include <cassert>
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#include <iomanip>
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#include <iostream>
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#include <string>
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#include <vector>
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namespace onnxruntime {
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template <typename T1, typename T2>
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void DumpArrayRecursive(const T1* data, int64_t& data_offset, const std::vector<T2>& shape, int idx) {
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int dim = static_cast<int>(shape.size());
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if (dim == 0) {
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std::cout << "[]\n";
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return;
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}
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assert(idx < dim);
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int sz = shape[idx];
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std::cout << "[";
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if (idx < dim - 1) {
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for (auto i = 0; i < sz; ++i) {
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DumpArrayRecursive(data, data_offset, shape, idx + 1);
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if (i < sz - 1) {
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std::cout << ",";
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// print multiple newlines after ',' when necessary
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for (int j = idx + 1; j < dim; j++)
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std::cout << "\n";
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// print leading spaces before "[" when necessary
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for (int j = 0; j < idx + 1; ++j)
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std::cout << " ";
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}
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}
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} else {
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for (auto i = 0; i < sz; ++i) {
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if (std::is_same<T1, int8_t>::value || std::is_same<T1, uint8_t>::value)
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std::cout << std::setw(3) << static_cast<int>(*(data + data_offset));
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else
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std::cout << std::setw(12) << std::setprecision(8) << *(data + data_offset);
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data_offset++;
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if (i < sz - 1)
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std::cout << ",";
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}
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}
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std::cout << "]";
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}
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// A helper function to dump multidimensional arrays in a way similar to numpy
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template <typename T1, typename T2>
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void DumpArray(const std::string& tag, const T1* data, const std::vector<T2>& shape) {
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std::cout << tag << "\n";
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int64_t data_offset = 0;
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DumpArrayRecursive(data, data_offset, shape, 0);
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assert(data_offset == TotalSize(shape));
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std::cout << std::endl;
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}
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} // namespace onnxruntime
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