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#include <c10/core/ScalarType.h>
#include <torch/csrc/lazy/ts_backend/ts_backend_impl.h>
#include <torch/csrc/lazy/ts_backend/ts_lowering_context.h>
#include <torch/csrc/lazy/ts_backend/ts_node.h>
namespace torch {
namespace lazy {
TSLoweringContext::TSLoweringContext(
const std::string& name,
BackendDevice device)
: torch::lazy::LoweringContext(name, device),
graph_(std::make_shared<torch::jit::Graph>()),
function_(
std::make_shared<torch::jit::GraphFunction>(name, graph_, nullptr)) {}
TSLoweringContext::TSLoweringContext(
const std::string& name,
BackendDevice device,
c10::ArrayRef<Node*> post_order,
Util::EmissionMap emit_status)
: torch::lazy::LoweringContext(name, device, post_order, emit_status),
graph_(std::make_shared<torch::jit::Graph>()),
function_(
std::make_shared<torch::jit::GraphFunction>(name, graph_, nullptr)) {
for (auto node : post_order) {
Lower(node);
}
}
void TSLoweringContext::Lower(const Node* node) {
if (auto* tsnode = dynamic_cast<const torch::lazy::TsNode*>(node)) {
// First, we call the node lowering function, which exists for newly
// codegenned or refactored nodes
TSOpVector ops = tsnode->Lower(function_, this);
CHECK(!ops.empty()) << "Failed to lower: " << *node;
TORCH_CHECK_EQ(node->num_outputs(), ops.size());
for (size_t i = 0; i < ops.size(); ++i) {
AssignOutputOp(torch::lazy::Output(node, i), ops[i]);
}
} else {
throw std::runtime_error(
"Expected torch::lazy::TsNode but could not dynamic cast");
}
}
void TSLoweringContext::AssignOutputOp(
const Output& output,
torch::jit::Value* op) {
const TsNode* ts_node = static_cast<const TsNode*>(output.node);
std::string stack_trace = ts_node->getPythonStacktrace();
if (!stack_trace.empty()) {
op->node()->s_(c10::Symbol::attr("source"), stack_trace);
}
emitted_outputs_[output] = op;
}
torch::jit::Value* TSLoweringContext::GetParameter(BackendDataPtr data) {
const auto ts_data = std::static_pointer_cast<TSData>(data);
BackendData::Handle handle = ts_data->GetHandle();
auto it = parameters_map_.find(handle);
if (it == parameters_map_.end()) {
torch::jit::Value* param =
graph_->addInput(c10::str("p", parameters_.size()));
if (ts_data->scalar.has_value()) {
auto scalarType = ts_data->scalar.value().type();
if (isFloatingType(scalarType)) {
param->setType(c10::FloatType::get());
} else if (isIntegralType(scalarType, /*includeBool=*/true)) {
param->setType(c10::IntType::get());
} else {
TORCH_CHECK(
false, "Unhandled scalar type: ", c10::toString(scalarType));
}
}
it = parameters_map_.emplace(handle, Parameter{param, parameters_.size()})
.first;
parameters_.push_back(ts_data);
}
parameter_sequence_.push_back(it->second.index);
return it->second.param;
}
} // namespace lazy
} // namespace torch
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