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interface.cpp
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interface.cpp
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#include <torch/csrc/jit/codegen/cuda/interface.h>
#include <ATen/core/dispatch/OperatorOptions.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/runtime/register_ops_utils.h>
namespace torch {
namespace jit {
namespace fuser {
namespace cuda {
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
static std::atomic<bool> cuda_fusion_guard_mode{true};
std::atomic<bool>& getCudaFusionGuardMode() {
return cuda_fusion_guard_mode;
}
CudaFuserInterface* getFuserInterface() {
static CudaFuserInterface fuser_interface_;
return &fuser_interface_;
}
void compileFusionGroup(Node* fusion_node) {
TORCH_CHECK(
getFuserInterface()->fn_compile_n_ != nullptr,
"Running the CUDA fuser requires a CUDA build.");
getFuserInterface()->fn_compile_n_(fusion_node);
}
void runFusionGroup(const Node* fusion_node, Stack& stack) {
TORCH_CHECK(
getFuserInterface()->fn_run_n_s_ != nullptr,
"Running the CUDA fuser requires a CUDA build.");
getFuserInterface()->fn_run_n_s_(fusion_node, stack);
}
void fuseGraph(std::shared_ptr<Graph>& graph) {
TORCH_CHECK(
getFuserInterface()->fn_fuse_graph_ != nullptr,
"Running the CUDA fuser requires a CUDA build.");
getFuserInterface()->fn_fuse_graph_(graph);
}
bool canFuseNode(const Node* node) {
return getFuserInterface()->fn_can_fuse_n_ != nullptr &&
getFuserInterface()->fn_can_fuse_n_(node);
}
//! [ Note -- type guard logic in CudaFusionGuard ]
//!
//! CudaFusionGuard is used to Guard input tensor to `CudaFusionGroup` so that
//! we would not feed inputs that violates the graph defined in `GraphCache`.
//!
//! see [ Note -- 2 level cache implementation ] for definition of unique
//! computational graph.
//! see [ Note -- CudaFusionGuard implementation] for details on how guard works
//! in profiling executor
//!
//! Type guard logic is used to query whether a runtime input `tensor` compiles
//! with profiled `guard_tensor_type`. `guard_tensor_type` is the observed
//! tensor type during profiling runs.
//!
//! At this moment, we only do single profiling run, so `guard_tensor_type` has
//! static shape / stride / scalarType. *This might be a little confusing as our
//! implementation is actually more relaxed.
//!
//! Things that we check:
//! a. identical rank & scalar type
//! b. stride check:
//! b.1. identical stride order
//! b.2. identical contiguity
//! note that contiguity here is used for tensor collapsing. So
//! extra attention should be paid to contiguity across size-1
//! dimensions.
//! c. size check:
//! making sure that broadcast semantics are identical. So we want to
//! make sure a given dimension either are both size-1 for `tensor` &
//! `guard_tensor_type`, or are both non-size-1.
//! This is due to the fact that we specialize size-1 dimension as
//! broadcasted dimension while translating PyTorch tensor to Fusion IR.
//!
bool complyWith(
const at::Tensor& tensor,
const c10::TensorTypePtr& guard_tensor_type) {
// guard broadcast semantics, contiguity & stride order;
TORCH_INTERNAL_ASSERT(
guard_tensor_type && guard_tensor_type->dim().has_value());
// check a. if num_dimension check fails or scalar type check fails
if (*guard_tensor_type->dim() != static_cast<size_t>(tensor.ndimension()) ||
(guard_tensor_type->scalarType().has_value() &&
(guard_tensor_type->scalarType().value() != tensor.scalar_type()))) {
return false;
}
// TODO: should we get symbolic_size instead and check for size
// consistency across tensors as well?
const auto& sizes = guard_tensor_type->sizes();
const auto& stride_properties = guard_tensor_type->stride_properties();
const auto& t_sizes = tensor.sizes();
const auto& t_strides = tensor.strides();
int inner_dim = -1;
for (size_t j = 0; j < *guard_tensor_type->dim(); j++) {
// check b. for stride check, we go along dimensions from fastest stride to
// slowest stride
int sorted_index = stride_properties[j]->stride_index_
? static_cast<int>(*stride_properties[j]->stride_index_)
: -1;
// only apply stride check when we have stride_properties
if (sorted_index != -1) {
// check b.1. stride order [current dimension has stride larger
// than its inner dimension(s)], check only applies when both:
// i. already encountered an inner dimension
// ii. not at the fastest dimension
if (j != 0 && inner_dim != -1) {
// we are not looking at dim-j, but dim-sorted_index, which
// is the j-th fastest dim;
// TODO: merge this with above and put a long comment there
if (t_strides[sorted_index] < t_strides[inner_dim]) {
return false;
}
}
// check b.2. contiguity, we only check when it's marked as
// contiguous.
if (stride_properties[j]->contiguous_ &&
*stride_properties[j]->contiguous_) {
if (j != 0) {
// we use contiguity to collapse dimension, if size == 1, it is
// always collapsible
if (t_sizes[sorted_index] != 1) {
TORCH_INTERNAL_ASSERT(
stride_properties[j - 1]->stride_index_.has_value(),
"Counknown index is meaningless");
// TODO: merge this check up
if (t_strides[sorted_index] !=
t_strides[inner_dim] * t_sizes[inner_dim]) {
return false;
}
}
} else {
// TODO: merge this check up
if (t_strides[sorted_index] != 1) {
return false;
}
}
}
// update inner_dim to be current dim. Note that we try to skip update
// when current `t_size[sorted_index] == 1`, because:
// 1. stride comparison on a size-1 dimension is meaningless
// [check b.1]
// 2. contiguity on a size-1 dimension is misleading. For collapsing,
// we should actually look at the next non-size-1 dimension
// [check b.2]
if (inner_dim == -1 || t_sizes[sorted_index] != 1) {
inner_dim = sorted_index;
}
}
// check c, we go along semantic ordered dimensions
// check broadcast / size-1:
bool guard_bcast = sizes[j].has_value() && sizes[j].value() == 1;
if (guard_bcast != (t_sizes[j] == 1)) {
return false;
}
}
return true;
}
} // namespace cuda
} // namespace fuser
namespace {
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
RegisterOperators reg_fusion({
Operator(
prim::CudaFusionGroup,
[](const Node* node) -> Operation {
return [node](Stack* stack) {
fuser::cuda::runFusionGroup(node, *stack);
};
},
aliasAnalysisSpecialCase()),
});
// NOLINTNEXTLINE(cppcoreguidelines-avoid-non-const-global-variables)
RegisterOperators reg_guard({
Operator(
"prim::CudaFusionGuard(...) -> bool",
// prim::CudaFusionGuard returns a fresh Boolean type without aliasing.
// if we would ever return refined tensor, which would change aliasing
// analysis, we should update aliasdb pass.
[](const Node* node) -> Operation {
return [node](Stack* stack) {
// TODO: check latency here!!!!
std::vector<TypePtr> types = node->tys(attr::types);
const auto num_inputs = types.size();
at::ArrayRef<IValue> inputs = last(stack, num_inputs);
drop(stack, num_inputs);
if (!fuser::cuda::getCudaFusionGuardMode()) {
push(stack, IValue(true));
return;
}
for (size_t i = 0; i < num_inputs; i++) {
const c10::TensorTypePtr& guard_tensor_type =
types[i]->cast<TensorType>();
// TODO: maybe we should just push false and fallback
TORCH_INTERNAL_ASSERT(inputs[i].isTensor());
const at::Tensor& tensor = inputs[i].toTensor();
if (!fuser::cuda::complyWith(tensor, guard_tensor_type)) {
push(stack, IValue(false));
return;
}
}
// TODO: check type and return the right flag
// naively return true;
push(stack, IValue(true));
return;
};
},
aliasAnalysisFromSchema()),
});
} // namespace
} // namespace jit
} // namespace torch