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#include <torch/csrc/jit/passes/remove_inplace_ops.h>
namespace torch {
namespace jit {
namespace {
static const std::unordered_map<NodeKind, NodeKind> inPlaceToOutOfPlace = {
{aten::add_, aten::add},
{aten::sub_, aten::sub},
{aten::div_, aten::div},
{aten::mul_, aten::mul},
{aten::masked_fill_, aten::masked_fill},
{aten::zero_, aten::zeros_like},
{aten::fill_, aten::full_like}};
// This is a horrible no good awful hack to "fill in" the TensorOptions
// arguments of zeros_like and full_like so that the defaults are filled
// in. Ugh. Would be better to just run the frontend to get the correct
// arity here.
static const std::unordered_map<NodeKind, int> expectedInputCount = {
{aten::zero_, 6},
{aten::fill_, 7}};
bool isInplaceOp(const Node* node) {
return inPlaceToOutOfPlace.count(node->kind()) != 0;
}
// Remove all in-place ops and replace them with out-of-place equivalents.
// e.g.
// %foo = aten::add_(%foo, %n)
// becomes
// %foo.2 = aten::add(%foo, %n)
//
// NOTE: this is NOT SAFE, since it assumes that the LHS is not aliased by
// another value. This is only to avoid breaking ONNX export; when alias
// analysis is done we can emit a warning if someone tries to export.
void RemoveInplaceOps(Block* block) {
auto graph = block->owningGraph();
auto it = block->nodes().begin();
while (it != block->nodes().end()) {
auto node = *it;
++it;
for (auto block : node->blocks()) {
RemoveInplaceOps(block);
}
if (isInplaceOp(node)) {
// create a replacement out of place op
auto newNode = graph->create(inPlaceToOutOfPlace.at(node->kind()));
newNode->insertBefore(node);
newNode->copyMetadata(node);
// copy inputs
for (auto input : node->inputs()) {
newNode->addInput(input);
}
int additionalInputCount = 0;
if (expectedInputCount.find(node->kind()) != expectedInputCount.end()) {
additionalInputCount = expectedInputCount.at(node->kind()) -
static_cast<int>(newNode->inputs().size());
}
for (int i = 0; i < additionalInputCount; ++i) {
auto noneNode = graph->createNone();
noneNode->insertBefore(newNode);
newNode->addInput(noneNode->output());
}
// Create a new output node and replace all uses of self with it
newNode->output()->copyMetadata(node->output());
node->replaceAllUsesWith(newNode);
node->inputs()[0]->replaceAllUsesAfterNodeWith(
newNode, newNode->output());
node->destroy();
}
}
}
} // namespace
// Handles special case of binary inplace ops, where the first input node
// has a lower type precedence than the second input node. When the
// inplace node is converted to a regular op, this information is lost and
// the resulting type is based on type precedence, just like regular ops.
// To avoid this loss of information, we add a cast node before the input
// node with the higher data type precedence, so that both the input types
// are the same.
// An example scenario would be:
// Before:
// graph(%0 : Float),
// %1 : Half):
// # Should result in a Half, but after translation to out-of-place,
// # would become a Float b/c Half+Float -> Float.
// %4 : Float = onnx::Cast[to=1](%1)
// %5 : Float = onnx::Add(%4, %0)
// ...
// After:
// graph(%0 : Float),
// %1 : Half):
// %4 : Half = onnx::Cast[to=10](%0)
// %5 : Half = onnx::Add(%1, %4)
// ...
void ImplicitCastForBinaryInplaceOps(Block* b) {
for (auto it = b->nodes().begin(), end = b->nodes().end(); it != end; ++it) {
for (auto* child_block : it->blocks()) {
ImplicitCastForBinaryInplaceOps(child_block);
}
// Check type if inplace operation is a binary node
if ((it->kind() == aten::add_) || (it->kind() == aten::sub_) ||
(it->kind() == aten::mul_) || (it->kind() == aten::div_)) {
auto originalInputs = it->inputs();
if (originalInputs.at(0) == originalInputs.at(1)) {
continue;
}
auto shape_node = originalInputs.at(0)->node();
if ((shape_node->kind() == prim::NumToTensor) &&
(shape_node->inputs().at(0)->node()->kind() == aten::size)) {
std::cerr
<< "In-place op on output of tensor.shape. See https://pytorch.org/docs/master/onnx.html#"
<< "avoid-inplace-operations-when-using-tensor-shape-in-tracing-mode"
<< std::endl;
}
TensorTypePtr firstInp_tensor =
originalInputs.at(0)->type()->cast<TensorType>();
TensorTypePtr secondInp_tensor =
originalInputs.at(1)->type()->cast<TensorType>();
if (!(firstInp_tensor) || !(secondInp_tensor) ||
!(firstInp_tensor->scalarType().has_value())) {
continue;
}
auto newInputNode = it->owningGraph()->create(aten::type_as, 1);
newInputNode->insertBefore(*it);
newInputNode->addInput(originalInputs.at(1));
newInputNode->addInput(originalInputs.at(0));
it->replaceInput(1, newInputNode->outputs().at(0));
}
}
}
void RemoveInplaceOps(const std::shared_ptr<Graph>& graph) {
ImplicitCastForBinaryInplaceOps(graph->block());
RemoveInplaceOps(graph->block());
}
} // namespace jit
} // namespace torch
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