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// 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 <caffe2/torch/csrc/jit/backends/xnnpack/compiler/xnn_compiler.h>
#include <torch/csrc/jit/backends/xnnpack/serialization/schema_generated.h>
#include <ATen/Utils.h>
namespace torch {
namespace jit {
namespace xnnpack {
namespace delegate {
void XNNCompiler::compileModel(
const void* buffer_pointer,
size_t num_bytes,
XNNExecutor* executor) {
auto output_min = -std::numeric_limits<float>::infinity();
auto output_max = std::numeric_limits<float>::infinity();
auto flatbuffer_graph = fb_xnnpack::GetXNNGraph(buffer_pointer);
// initialize xnnpack
xnn_status status = xnn_initialize(/*allocator =*/nullptr);
TORCH_CHECK(xnn_status_success == status, "Failed to initialize xnnpack");
// create xnnpack subgraph
xnn_subgraph_t subgraph_ptr = nullptr;
status = xnn_create_subgraph(
/*external_value_ids=*/flatbuffer_graph->num_externs(),
/*flags=*/0,
&subgraph_ptr);
TORCH_CHECK(xnn_status_success == status, "Failed to create xnn subgraph");
// mapping from old ids to new created value ids
// The old ids that were serialied were generated AoT, since
// we are re-defining tensor values, the defined IDs could be
// different from the ones generated AoT, as a result, we need
// a new mapping from the old ids to the newly created ones
std::unordered_map<uint32_t, uint32_t> remapped_ids;
for (auto value : *flatbuffer_graph->xvalues()) {
switch (value->xvalue_type()) {
case fb_xnnpack::XValueUnion::XNNTensorValue: {
auto tensor_value = value->xvalue_as_XNNTensorValue();
std::vector<size_t> dims_data;
for (auto dim : *tensor_value->dims()) {
dims_data.push_back(static_cast<size_t>(dim));
}
uint32_t id = XNN_INVALID_VALUE_ID;
const auto& constant_buffer = *flatbuffer_graph->constant_buffer();
auto buffer_idx = tensor_value->constant_buffer_idx();
const auto buffer_ptr = buffer_idx == 0
? nullptr
: constant_buffer[buffer_idx]->storage()->data();
status = xnn_define_tensor_value(
/*subgraph=*/subgraph_ptr,
/*datatype=*/xnn_datatype_fp32,
/*num_dims=*/tensor_value->num_dims(),
/*dims=*/dims_data.data(),
/*data=*/buffer_ptr,
/*external_id=*/tensor_value->external_id(),
/*flags=*/tensor_value->flags(),
/*id_out=*/&id);
TORCH_CHECK(
status == xnn_status_success,
"Failed to define tensor values in graph")
// map serialized id to newly generated id
remapped_ids.emplace(std::make_pair(tensor_value->id_out(), id));
break;
}
default: {
TORCH_CHECK(false, "Unhandled value type found in deserialization");
}
}
}
for (auto node : *flatbuffer_graph->xnodes()) {
switch (node->xnode_type()) {
case fb_xnnpack::XNodeUnion::XNNAdd: {
auto graph_node = node->xnode_as_XNNAdd();
status = xnn_define_add2(
subgraph_ptr,
output_min,
output_max,
remapped_ids.at(graph_node->input1_id()),
remapped_ids.at(graph_node->input2_id()),
remapped_ids.at(graph_node->output_id()),
graph_node->flags());
TORCH_CHECK(status == xnn_status_success, "Failed to create add node")
break;
}
default:
TORCH_CHECK(false, "Unhandled node type found in deserialization");
}
}
xnn_runtime_t runtime_ptr = nullptr;
status = xnn_create_runtime_v2(subgraph_ptr, nullptr, 0, &runtime_ptr);
TORCH_CHECK(xnn_status_success == status);
executor->runtime_ =
std::unique_ptr<xnn_runtime, decltype(&xnn_delete_runtime)>(
runtime_ptr, xnn_delete_runtime);
for (auto old_id : *flatbuffer_graph->input_ids()) {
executor->input_ids_.emplace_back(remapped_ids.at(old_id));
}
for (auto old_id : *flatbuffer_graph->output_ids()) {
executor->output_ids_.emplace_back(remapped_ids.at(old_id));
}
};
} // namespace delegate
} // namespace xnnpack
} // namespace jit
} // namespace torch
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