mirror of
https://github.com/pytorch/pytorch.git
synced 2025-10-20 21:14:14 +08:00
fb18c29486
s/Facebook, Inc./Meta Platforms, Inc/ s/Confidential and proprietary./This source code is licensed under the BSD-style license/ Per https://www.internalfb.com/intern/wiki/Open_Source/Licenses/Straight_BSD/ Also, add linter that prevents adding those in the future Fixes https://github.com/pytorch/pytorch/issues/90187 Pull Request resolved: https://github.com/pytorch/pytorch/pull/90805 Approved by: https://github.com/zpao
90 lines
2.7 KiB
C++
90 lines
2.7 KiB
C++
// Copyright (c) Meta Platforms, Inc. and affiliates.
|
|
//
|
|
// This source code is licensed under the BSD-style license found in the
|
|
// LICENSE file in the root directory of this source tree.
|
|
|
|
#include <torch/csrc/jit/backends/xnnpack/serialization/schema_generated.h>
|
|
#include <cstddef>
|
|
#include <cstdint>
|
|
#include <string>
|
|
#include <vector>
|
|
|
|
namespace torch {
|
|
namespace jit {
|
|
namespace xnnpack {
|
|
namespace delegate {
|
|
|
|
using namespace fb_xnnpack; // Specified in the schema
|
|
|
|
class XNNSerializer {
|
|
public:
|
|
// Constructors
|
|
// initial buffersize of 1024 which will grow
|
|
// automatically, constant buffer and buffer sizes initialized with dummy
|
|
// values as 0 index is reserved for non-constant tensors
|
|
XNNSerializer() : XNNSerializer(1024) {}
|
|
|
|
explicit XNNSerializer(size_t bufferSize)
|
|
: _builder(bufferSize),
|
|
_nodes(),
|
|
_values(),
|
|
_constantBuffer({CreateBuffer(
|
|
_builder,
|
|
{})}), // index 0 is reserved for non-const data
|
|
_bufferSizes({0}) {}
|
|
|
|
// Serializing Nodes
|
|
|
|
// Serialize add node, we are serializing the argument needed to call
|
|
// xnn_define_add2. Serializing these values, and at run time we build
|
|
// teh graph by re running xnn_define_add2
|
|
void serializeAddNode(
|
|
uint32_t input1_id,
|
|
uint32_t input2_id,
|
|
uint32_t output_id,
|
|
uint32_t flags);
|
|
|
|
// Serializing Values
|
|
void serializeTensorValue(
|
|
uint32_t xnn_datatype,
|
|
size_t num_dims,
|
|
std::vector<size_t> dims,
|
|
size_t buffer_data_idx,
|
|
uint32_t external_id,
|
|
uint32_t flags,
|
|
uint32_t id_out);
|
|
|
|
// finish and serialize xnngraph returning serialized data
|
|
std::string finishAndSerialize(
|
|
std::vector<uint32_t> input_ids,
|
|
std::vector<uint32_t> output_ids,
|
|
size_t num_extern_ids);
|
|
|
|
// decoupled data serialization with tensor values. This way constant tensor
|
|
// data can be referenced by multiple intermediate tensors. This call
|
|
// serializes the num_bytes of the data_ptr and returns the index it was
|
|
// placed in.
|
|
size_t serializeData(const uint8_t* data_ptr, size_t num_bytes);
|
|
|
|
private:
|
|
// xnnpack version we are serializing
|
|
const char* _version_sha1 = "ae108ef49aa5623b896fc93d4298c49d1750d9ba";
|
|
|
|
// flatbuffer objects we will create and serialize together to create xnngraph
|
|
flatbuffers_fbsource::FlatBufferBuilder _builder;
|
|
|
|
// Vector of the serialized xnnpack nodes
|
|
std::vector<flatbuffers_fbsource::Offset<XNode>> _nodes;
|
|
|
|
// Vector of the serialized xnnpack values
|
|
std::vector<flatbuffers_fbsource::Offset<XValue>> _values;
|
|
|
|
std::vector<flatbuffers_fbsource::Offset<Buffer>> _constantBuffer;
|
|
std::vector<uint32_t> _bufferSizes;
|
|
};
|
|
|
|
} // namespace delegate
|
|
} // namespace xnnpack
|
|
} // namespace jit
|
|
} // namespace torch
|