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#include <torch/csrc/jit/codegen/cuda/shape_inference.h>
#include <c10/core/ScalarType.h>
#include <torch/csrc/jit/codegen/cuda/instrumentation.h>
#include <torch/csrc/jit/ir/constants.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <ATen/ExpandUtils.h>
#include <ATen/core/jit_type.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
namespace {
bool hasTypeDeviceAndDim(const TensorTypePtr& op) {
return op->sizes().size().has_value() && op->scalarType().has_value() &&
op->device().has_value();
}
/* NaiveTypePropagator
* Populate type/device tag on tensor, this is a transition module to
* cover the absence of type inference in codegen cuda fuser.
*
* We only cover operations supported in codegen. We focus on propagate concrete
* types.
* It does NOT handle aliases (not supported in codegen anyway); Type promotion
* is not guaranteed to be consistent with PyTorch (we need to serve the need of
* codegen instead).
*/
class NaiveTypePropagator {
public:
NaiveTypePropagator(std::shared_ptr<Graph> graph)
: graph_(std::move(graph)) {}
void PropagateOnBlock(Block* block) {
for (Node* node : block->nodes()) {
PropagateOnNode(node);
}
}
void PropagateOnNode(Node* node) {
switch (node->kind()) {
// Constant:
case prim::Constant: {
if (node->output()->type()->isSubtypeOf(TensorType::get())) {
node->output()->inferTypeFrom(node->t(attr::value));
}
break;
}
// unary operations that forward meta info:
case aten::neg:
case aten::abs:
case aten::log:
case aten::log10:
case aten::log1p:
case aten::log2:
case aten::lgamma:
case aten::exp:
case aten::expm1:
case aten::erf:
case aten::erfc:
case aten::cos:
case aten::acos:
case aten::cosh:
case aten::sin:
case aten::asin:
case aten::sinh:
case aten::tan:
case aten::atan:
case aten::sqrt:
case aten::rsqrt:
case aten::ceil:
case aten::floor:
case aten::round:
case aten::trunc:
case aten::frac:
case aten::reciprocal:
case aten::relu:
case aten::sigmoid:
case aten::threshold:
case aten::clamp:
case aten::gelu:
case aten::tanh: {
TORCH_CHECK(
hasTypeDeviceAndDim(node->input(0)->type()->cast<TensorType>()),
"Type, device, and dimensionality propagation has failed, or was not provided enough information.");
node->output()->setType(node->input(0)->type()->cast<TensorType>());
break;
}
// TODO: rand_like should support cast.
case aten::rand_like: {
TORCH_CHECK(
hasTypeDeviceAndDim(node->input(0)->type()->cast<TensorType>()),
"Type, device, and dimensionality propagation has failed, or was not provided enough information.");
node->output()->setType(node->input(0)->type()->cast<TensorType>());
break;
}
// binary operations that forward meta info and broadcast shape:
case aten::mul:
case aten::div:
case aten::atan2:
// TODO: double check type casting logic for min/max/pow
case aten::min:
case aten::max:
case aten::pow:
case aten::remainder:
case aten::fmod:
case aten::lerp:
// add/sub could be ternary op and the third argument does not contribute
// to neither type promoteion nor shape.
case aten::add:
case aten::sub: {
const auto promoted_type = binary_broadcast_type(
node->input(0)->type()->cast<TensorType>(),
node->input(1)->type()->cast<TensorType>());
node->output()->setType(promoted_type);
break;
}
// TODO: double check type casting logic for operations commented out.
case aten::lt:
case aten::le:
case aten::gt:
case aten::ge:
case aten::ne:
case aten::eq: {
const auto promoted_type = binary_broadcast_type(
node->input(0)->type()->cast<TensorType>(),
node->input(1)->type()->cast<TensorType>(),
at::ScalarType::Bool);
node->output()->setType(promoted_type);
break;
}
case aten::where: {
const auto promoted_type = binary_broadcast_type(
node->input(1)->type()->cast<TensorType>(),
node->input(2)->type()->cast<TensorType>());
node->output()->setType(promoted_type);
break;
}
case aten::addcmul: {
auto promoted_type = binary_broadcast_type(
node->input(1)->type()->cast<TensorType>(),
node->input(2)->type()->cast<TensorType>());
promoted_type = binary_broadcast_type(
promoted_type, node->input(0)->type()->cast<TensorType>());
node->output()->setType(promoted_type);
break;
}
case aten::sum: {
auto out_type = node->input(0)->type()->cast<TensorType>();
// accept dtype input to `aten::sum` node
if (!node->input(3)->type()->isSubtypeOf(
static_cast<c10::TypePtr>(NoneType::get()))) {
if (auto opt_ivalue = toIValue(node->input(3))) {
out_type = out_type->withScalarType(opt_ivalue->toScalarType());
}
}
const auto dims = constant_as<c10::List<int64_t>>(node->input(1));
const auto keepdim = constant_as<bool>(node->input(2));
TORCH_CHECK(
dims.has_value() && keepdim.has_value() && !keepdim.value(),
"Shape inference cannot handle options.");
node->output()->setType(
unary_reduce_type(out_type, dims->vec(), keepdim.value()));
break;
}
default:
TORCH_CHECK(
false,
"type inference failed, unrecognized operation encountered.");
// TODO: generate a proper error log, as this probably means something
// went unexpected.
break;
}
}
void run() {
PropagateOnBlock(graph_->block());
}
protected:
TensorTypePtr unary_reduce_type(
const TensorTypePtr& op,
const std::vector<int64_t>& dims,
bool keepdim) {
TORCH_CHECK(hasTypeDeviceAndDim(op), "requires complete shape on input");
auto input_size = op->sizes();
int64_t ndims = keepdim ? input_size.size().value() : 0;
if (!keepdim) {
for (size_t i = 0; i < input_size.size(); i++) {
if (std::find(dims.begin(), dims.end(), i) == dims.end()) {
ndims++;
}
}
}
return TensorType::create(
*op->scalarType(), *op->device(), ndims, c10::nullopt);
}
// TODO: we should comply to codegen type promotion.
TensorTypePtr binary_broadcast_type(
TensorTypePtr const& op0,
TensorTypePtr const& op1,
c10::optional<at::ScalarType> scalar_type = c10::nullopt) {
TORCH_CHECK(
op0 != nullptr || op1 != nullptr,
"Scalar operations on binary broadcast type, not supported yet.");
if (op0 != nullptr && op1 != nullptr) {
TORCH_CHECK(
op0->sizes().size().has_value() && op1->sizes().size().has_value(),
"Cannot process input tensor without concrete number of dimensions.");
int64_t ndims = *op0->sizes().size() > *op1->sizes().size()
? *op0->sizes().size()
: *op1->sizes().size();
auto promoted_scalar_type = scalar_type.has_value()
? *scalar_type
: c10::promoteTypes(*op0->scalarType(), *op1->scalarType());
return TensorType::create(
promoted_scalar_type, *op0->device(), ndims, c10::nullopt);
} else {
auto ptr = (op0 != nullptr) ? op0 : op1;
TORCH_CHECK(
hasTypeDeviceAndDim(ptr),
"Type, device, and dimensionality propagation has failed, or was not provided enough information.");
return TensorType::create(
scalar_type.has_value() ? *scalar_type : *ptr->scalarType(),
*ptr->device(),
*ptr->sizes().size(),
c10::nullopt);
}
}
private:
std::shared_ptr<Graph> graph_;
};
} // namespace
void TypePropagate(std::shared_ptr<Graph>& graph) {
FUSER_PERF_SCOPE("TypePropagate");
NaiveTypePropagator(graph).run();
}
} // namespace cuda
} // namespace fuser
} // namespace jit
} // namespace torch
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