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#pragma once
#include <ATen/core/symbol.h>
#include <functional>
#include <memory>
#include <set>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
#include <c10/core/ScalarType.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/Flags.h>
#include <torch/csrc/lazy/core/hash.h>
#include <torch/csrc/lazy/core/ir_metadata.h>
#include <torch/csrc/lazy/core/shape.h>
C10_DECLARE_bool(ltc_enable_dynamic_shapes);
namespace torch {
namespace lazy {
static const hash_t kHashSeed(static_cast<uint32_t>(0x5a2d296e9));
class Node;
struct Output;
struct Value;
using NodePtr = std::shared_ptr<Node>;
// The Kind of operation a Node can be associated to.
struct TORCH_API OpKind {
OpKind() = default;
explicit OpKind(c10::Symbol op) : op(op) {}
bool operator==(const OpKind& rhs) const {
return op == rhs.op;
}
bool operator!=(const OpKind& rhs) const {
return !operator==(rhs);
}
bool operator<(const OpKind& rhs) const {
return c10::unique_t(op) < c10::unique_t(rhs.op);
}
hash_t hash() const;
std::string ToString() const {
return op.toQualString();
}
// Retrieves an existing operation object, or creates a new one. Operations
// that are specific to lazy tensors, should live within the 'lazy_tensors::'
// namespace.
static OpKind Get(const std::string& name);
c10::Symbol op;
};
inline std::ostream& operator<<(std::ostream& stream, const OpKind& op) {
stream << op.ToString();
return stream;
}
using OpList = c10::ArrayRef<Value>;
hash_t OperandHashes(
const OpList& operands,
const hash_t& seed,
bool bakeInSizes);
// A node in the graph. Nodes for operations which require extra data to be
// stored for lowering should inherit from this class and add an operation
// specific member there. For example, a constant might create a new
// NodeConstant class (inheriting from Node) with an extra lazy_tensors::Literal
// field, or a tensor value might create a new NodeTensor with a computation
// client data handle in it.
class TORCH_API Node {
public:
static bool enableDynamicShape();
// Creates a new node with the given op name. The op is a unique identifier
// for the operation. The num_outputs tells how many outputs a given operation
// generates.
//
// None leaf node's node_hash does not contains shape information always.
// So we pass in the hash value rather than a function.
Node(OpKind op, size_t num_outputs);
// Construct node with operands and shapes
Node(
OpKind op,
OpList operands,
std::vector<Shape>&& shapes,
size_t num_outputs = 1);
// Construct node with operands and shape generated from a function
Node(
OpKind op,
OpList operands,
const std::function<Shape()>& shape_fn,
size_t num_outputs = 1);
// Construct node with operands and no shape
Node(OpKind op, OpList operands, size_t num_outputs = 1);
// Construct node with shape and no operands
Node(OpKind op, Shape shape, size_t num_outputs = 1);
virtual ~Node();
const OpKind& op() const {
return op_;
}
size_t num_outputs() const {
return num_outputs_;
}
// Retrieves the full shape of the IR Node.
virtual c10::ArrayRef<Shape> shapes() const;
virtual const Shape& shape(size_t output_index = 0) const;
// Add the shape computed by the shape_fn
void addComputedShape(const std::function<Shape()>& shape_fn);
// Compute the shape using the provided shape_fn if not previously cached
Shape computeShape(const std::function<Shape()>& shape_fn);
virtual const std::vector<Output>& operands() const;
virtual const Output& operand(size_t i) const;
// Gets operand at index i if index is valid, or kNullOutput otherwise.
virtual const Output& nullable_operand(size_t i) const;
// Returns the hash of the dag used to look up the compiled graph
virtual hash_t hash() const = 0;
// Returns the hash of the dag used to for shape caching
virtual hash_t shapeHash() const = 0;
const MetaData& metadata() const {
return metadata_;
}
UserMetaData* user_metadata() const {
return user_metadata_.get();
}
std::shared_ptr<UserMetaData> SetUserMetadata(
std::shared_ptr<UserMetaData> user_meta) {
std::swap(user_metadata_, user_meta);
return user_meta;
}
virtual std::string ToString() const;
private:
// The ID of the operation captured by this node.
OpKind op_;
size_t num_outputs_ = 1;
// The IR specific metadata attached to the IR node.
MetaData metadata_;
// The IR framework user can attach a user defined metadata object deriving
// from UserMetaData.
std::shared_ptr<UserMetaData> user_metadata_;
protected:
// Adds node's index output number as operand.
void AddOperand(NodePtr node, size_t index = 0);
std::vector<Shape> shapes_;
// A node holds a real reference to its operands.
std::vector<NodePtr> operands_;
// Outputs do not hold references on the nodes, and neither do the uses, since
// otherwise we get into circular reference counting.
std::vector<Output> operands_as_outputs_;
};
inline std::ostream& operator<<(std::ostream& stream, const Node& node) {
stream << node.ToString();
return stream;
}
// Note: Keep this version of NodeCast for smooth PyTorch/XLA migration, and
// clean up once the migration is done.
template <typename T>
const T* NodeCast(const Node* node, OpKind op) {
if (op != node->op()) {
return nullptr;
}
#ifdef NDEBUG
return static_cast<const T*>(node);
#else
return &dynamic_cast<const T&>(*node);
#endif
}
template <typename T>
const T* NodeCast(const Node* node) {
if (T::ClassOpKind() != node->op()) {
return nullptr;
}
// TODO: Some IR classes share the same opkind, such as Mean and MeanDim, so
// static_cast is not safe here. Unless we have opkind unique for each class,
// we have to use dynamic_cast here.
return dynamic_cast<const T*>(node);
}
// Represents a specific output produced by a node. Since the output of a node
// can be composed by multiple outputs, the node+index coordinates fully qualify
// each single output.
struct TORCH_API Output {
struct Hasher {
size_t operator()(const Output& output) const;
};
Output() = default;
explicit Output(const Node* node, size_t index = 0)
: node(node), index(index) {}
hash_t hash() const;
hash_t shapeHash() const;
bool operator==(const Output& rhs) const {
return node == rhs.node && index == rhs.index;
}
// To compare the operands of to-be-constructed node and to-be-reused node
bool operator==(const Value& rhs) const;
bool operator!=(const Output& rhs) const {
return !operator==(rhs);
}
const Shape& shape() const {
return node->shape(index);
}
std::string ToString() const;
// The node providing the output.
const Node* node{nullptr};
// The index in the node's output this output refers to.
size_t index{0};
};
inline std::ostream& operator<<(std::ostream& stream, const Output& output) {
stream << output.ToString();
return stream;
}
template <typename T>
using OutputMap = std::unordered_map<Output, T, Output::Hasher>;
// Represents an input/operand for a Node object.
struct TORCH_API Value {
Value() = default;
/* implicit */ Value(NodePtr&& node, size_t index = 0)
: node(std::move(node)), index(index) {}
/* implicit */ Value(const NodePtr& node, size_t index = 0)
: node(node), index(index) {}
hash_t hash() const;
hash_t shapeHash() const;
operator bool() const {
return node != nullptr;
}
operator Output() const {
return Output(node.get(), index);
}
const Shape& shape() const {
return node->shape(index);
}
Node* operator->() const {
return node.get();
}
NodePtr node;
size_t index = 0;
};
} // namespace lazy
} // namespace torch
namespace c10 {
// Explicit template instantiation to make ArrayRef<Value> work
template class at::ArrayRef<torch::lazy::Value>;
} // namespace c10
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