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env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/alias_analysis.h

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+#pragma once
+
+#include <ATen/core/alias_info.h>
+#include <c10/util/flat_hash_map.h>
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/jit/ir/type_hashing.h>
+#include <torch/csrc/jit/passes/create_functional_graphs.h>
+#include <torch/csrc/jit/passes/utils/memory_dag.h>
+
+namespace torch {
+namespace jit {
+
+/**
+ * Alias analysis pass.
+ *
+ * This pass produces an AliasDb that contains aliasing and mutation
+ * information about the graph. Users can use this information to determine
+ * whether mutations to the graph are safe, i.e. they don't reorder/change
+ * nodes in a way that affects output.
+ *
+ * Every value with a mutable type (Tensors, Lists, Tuples, etc.) will be
+ * associated with one or more "alias sets". If two values share an alias set,
+ * that means they may alias, implying that a mutation to one value cannot be
+ * reordered past a use of the other. Only reordering two reads of an alias set
+ * is considered safe.
+ *
+ * There is a special alias set called the "wildcard set", which indicates that
+ * we're not sure what this value may alias. To be conservative, we consider the
+ * wildcard alias set as potentially aliasing any other wildcard value within
+ * the same type class. Whenever a value becomes contained by another value,
+ * such as when a Tensor is appended to a List[Tensor], the contained element
+ * becomes part of the wildcard set.
+ *
+ * Values that contain other mutable types, such as List[Tensor], are
+ * initialized as containing the Wildcard set for all contained mutable types.
+ *
+ * The AliasDb API references the idea of "mutable" vs "immutable"
+ * types. "Mutable" means that the object's value can change, while
+ * "immutable" means that the value is fixed. (For example, `List` is
+ * mutable, so you can add and delete elements from it. On the other
+ * hand, you can't modify a Tuple once you create it, making `Tuple` an
+ * immutable container.)
+ *
+ * `isFrozen` - if the Module is frozen then consider attributes as freshly
+ * created objects. Freezing API invokes alias analysis to check if they are
+ * mutated internally.
+ *
+ * `descendFunctionCalls` - recursively analyze function and method calls
+ * instead of conservative analysis. Generally analysis should be done after
+ * inlining so the implmentation for recursive analysis is unoptimized.
+ */
+class AliasDb {
+ public:
+  TORCH_API explicit AliasDb(
+      std::shared_ptr<Graph> graphi,
+      bool isFrozen = false,
+      bool descendFunctionCalls = false);
+  TORCH_API ~AliasDb();
+
+  // There are limitations to what effects the alias analysis can track. Two
+  // kinds of nodes may have untracked effects:
+  // 1. Nodes that write to a value that may alias the graph inputs (since
+  //    the inputs can be used outside the graph).
+  // 2. Nodes that write to something in the wildcard set.
+  //
+  // These nodes are considered not safe to eliminate or mutate under any
+  // circumstances.
+  bool writesToWildcard(Node* n) const;
+
+  // Does `n` write to an alias of one of the values in `vs`?
+  // if `recurseBlocks` is true, consider writes on the nodes in `n`s sub-blocks
+  TORCH_API bool writesToAlias(Node* n, const ValueSet& vs) const;
+
+  // Does `a` and `b` potentially share a memory location or do either
+  // hold in memory any element that exists in the other
+  TORCH_API bool mayContainAlias(Value* a, Value* b) const;
+
+  TORCH_API bool mayContainAlias(Value* a, const at::ArrayRef<Value*> b) const;
+
+  // Do any values in group `a` share a memory location or hold in memory
+  // any element that exists in group `b`
+  TORCH_API bool mayContainAlias(
+      const at::ArrayRef<Value*> a,
+      const at::ArrayRef<Value*> b) const;
+
+  // Do `a` and `b` potentially share a memory location?
+  TORCH_API bool mayAlias(const Value* a, const Value* b) const;
+  // Do any values in group `a` potentially share a memory location with any
+  // value in group `b`? i.e. may they overlap?
+  TORCH_API bool mayAlias(const ValueSet& a, const ValueSet& b) const;
+
+  // Do any nodes write to an alias set input to `n`?
+  TORCH_API bool hasInputWriters(const Node* n) const;
+
+  // Do any nodes write to an alias set output by `n`?
+  TORCH_API bool hasOutputWriters(const Node* n) const;
+
+  // Do any nodes write to an alias set inputed/outputed by `n`?
+  TORCH_API bool hasWriters(const Node* n) const;
+
+  // Do any nodes write to `v`s memory location?
+  TORCH_API bool hasWriters(const Value* v) const;
+
+  // Is the operation in-place? i.e. doesn't write anywhere but locations it
+  // reads from.
+  TORCH_API bool isMutable(Node* n) const;
+
+  TORCH_API bool escapesScope(const at::ArrayRef<Value*>& vs) const;
+
+  // Is it safe to change whether `a` and `b` alias each other ?
+  TORCH_API bool safeToChangeAliasingRelationship(
+      const at::ArrayRef<Value*>& a,
+      const at::ArrayRef<Value*>& b) const;
+
+  // Move `n` (already in the graph) after `movePoint` in the topological order.
+  //
+  // Tries to preserve value dependencies, so other nodes might be moved. We
+  // make two guarantees about the postcondition of the node list:
+  //   - `n` is directly after `movePoint`.
+  //   - only nodes between `n` and `movePoint` have been moved.
+  //
+  // Returns `false` if it's impossible to move `n` after `MovePoint` without
+  // violating dependencies, otherwise executes the move and returns `true`
+  TORCH_API bool moveAfterTopologicallyValid(Node* n, Node* movePoint);
+  TORCH_API bool moveBeforeTopologicallyValid(Node* n, Node* movePoint);
+
+  bool couldMoveAfterTopologically(Node* n, Node* movePoint);
+  bool couldMoveBeforeTopologically(Node* n, Node* movePoint);
+
+  // For debugging: print alias db state to stdout
+  TORCH_API void dump() const;
+  TORCH_API std::string toString() const;
+
+  // Generates a DOT (www.graphviz.org) graph representation
+  //
+  // Returns `true` if the output file was successfully generated
+  //
+  // WARNING: The output dot file path can't include shell specific notations,
+  //  for example you can't use "~/temp/aliasdb.dot"
+  //  (instead, use "/home/user/temp/aliasdb.dot")
+  //
+  TORCH_API bool dumpToGraphvizFile(const char* filename) const;
+  TORCH_API std::string toGraphviz() const;
+
+  // Returns `true` if the given element is mutable or if it is a
+  // container type with an internal mutable element (e.g.
+  // `Tuple[int, Tensor]` has an internal mutable type `Tensor`, so
+  // it would be considered a "mutable type" in AliasDb)
+  static bool isMutableType(const Value* v);
+  static bool isMutableType(const TypePtr& type);
+
+  /**
+   * Mutation API
+   *
+   * These methods allow you to update AliasDb in-place if you are performing
+   * graph mutation.
+   *
+   * WARNING: These methods should be considered INTERNAL. They do not perform
+   * very many correctness checks, the user is responsible for making sure they
+   * are updating AliasDb correctly. `Lint()`ing the AliasDb can help with
+   * this.
+   */
+  // Copy `existing`s aliasing info to `new_value`, and remove `existing`.
+  TORCH_API void replaceWithNewValue(Value* existing, Value* new_value);
+  // Copy `from`s aliasing info to `to`.
+  TORCH_API void copyValue(Value* from, Value* to);
+  // Create a new `value` that does not alias anything else.
+  TORCH_API void createValue(const Value* value);
+
+  // Enable more precise treatment of prim::TupleConstruct.
+  void enablePreciseTupleContainerAnalysis();
+
+  friend struct MutationRemover;
+
+ private:
+  // Helper for topologically-safe node moves.
+  class WorkingSet;
+  enum class MoveSide { BEFORE, AFTER };
+  bool tryMove(Node* toMove, Node* movePoint, MoveSide moveSide, bool dryRun);
+  void move(Node* toMove, Node* movePoint, MoveSide moveSide);
+  bool isBeforeOrAfter(const Node* n, MoveSide moveSide) const;
+
+  bool isMutableTypeInternal(const Value* v) const;
+  bool isMutableTypeInternal(const TypePtr& type) const;
+
+  /**
+   * Write and read internal API
+   */
+  // Get all the values that `n` writes to.
+  // NOTE: this only returns values directly written to, not aliases thereof
+  //
+  // if `recurseBlocks` is true, gather writes on the nodes in `n`s sub-blocks
+  MemoryLocations getWrites(Node* n) const;
+  void getWritesImpl(Node* n, MemoryLocations& ret) const;
+  // Register the fact that `n` writes to `v`.
+  void registerWrite(const Value* v, Node* n, bool writeToContained = false);
+  // Get all the values that `n` reads from.
+  // if `recurseBlocks` is true, gather reads on the nodes in `n`s sub-blocks
+  MemoryLocations getReads(Node* n) const;
+  void getReadsImpl(Node* n, MemoryLocations& ret) const;
+
+  /**
+   * Wildcard methods
+   */
+  // Register `v` as a wildcard value.
+  c10::optional<Element*> setWildcard(const Value* v);
+
+  // Is this a value which will not alias?
+  bool nonAliasingValue(const Value* elem) const;
+
+  /**
+   * Special analysis methods
+   */
+  void analyze(const std::shared_ptr<Graph>& graph);
+  void analyze(Block* block);
+  void analyze(Node* node);
+  void analyzeImpl(Node* node);
+  void analyzeIf(Node* node);
+  void analyzeLoop(Node* node);
+  void analyzeSubgraph(Node* node, std::shared_ptr<Graph> subgraph);
+  void analyzeSubgraph(Node* node);
+  void analyzeCreator(Node* node);
+  void analyzeExtractor(Node* node);
+  void analyzeChunk(Node* node);
+  void analyzeBroadcastingChunk(Node* node);
+  void analyzeFork(Node* node);
+  void analyzeWait(Node* node);
+  void analyzeAwaitable(Node* node);
+  void analyzeAwaitableWait(Node* node);
+  void analyzeRpcAsync(Node* node);
+  void analyzeBatchNorm(Node* node);
+  void analyzeInstanceNorm(Node* node);
+  void analyzeGradOf(Node* node);
+  void analyzeSetAttr(Node* node);
+  void analyzeConservative(Node* node);
+  void analyzeContainerConstruct(Node* node);
+  bool tryRegisteredAnalysis(Node* node);
+
+  /**
+   * Alias manipulation methods
+   */
+  void makeAllAlias(const std::vector<Value*>& values);
+  void makePointerTo(const Value* value, const Value* to);
+  TORCH_API void addToContainedElements(
+      const Value* element,
+      const Value* container);
+  void mapAliases(at::ArrayRef<Value*> to, at::ArrayRef<Value*> from);
+  void giveFreshAlias(
+      const Value* value,
+      bool add_wildcard_to_contained_elems = true);
+  Element* getOrCreateElement(const Value* value);
+
+  const AliasTypeSet* mapTypeToAliasTypeSetPtr(const TypePtr& type) const;
+  bool functionalNonEscapingListUse(const Use& use) const;
+  bool functionalNonEscapingTupleUse(const Use& use) const;
+
+  std::shared_ptr<Graph> graph_;
+
+  // If the Module is frozen then consider attributes as freshly created
+  // objects. Freezing API invokes alias analysis to check if they are mutated
+  // internally.
+  bool isFrozen_;
+
+  bool descend_function_calls_;
+  std::unordered_map<Graph*, std::vector<std::shared_ptr<Graph>>>
+      function_call_copies_;
+
+  // The points-to graph that stores aliasing relationships
+  std::unique_ptr<MemoryDAGBuilder> memoryDAGBuilder_;
+  std::unique_ptr<MemoryDAG> memoryDAG_;
+
+  // Mapping of values to MemoryDAG elements
+  ska::flat_hash_map<const Value*, Element*> elementMap_;
+  // All wildcard Elements (one for each unique mutable type)
+  ska::flat_hash_map<TypePtr, Element*, HashType, EqualType> wildcardIndex_;
+  Element* getWildcard(const TypePtr& type) const;
+  c10::optional<Element*> tryGetOrCreateWildcard(const TypePtr& type);
+  void addContainedTypesToFreshElement(
+      Element* container_elem,
+      const AliasTypeSet& mut_types);
+  void pointUnionTypeElementToAllContainedTypes(
+      Element* container_elem,
+      const AliasTypeSet& mut_types);
+
+  std::vector<Element*> getElements(at::ArrayRef<Value*> vs) const;
+  bool mayAliasWildcard(const Value* v) const;
+  bool mayAliasWildcard(const at::ArrayRef<Value*> vs) const;
+  bool hasWriters(const at::ArrayRef<Value*>& values) const;
+
+  // Cached mapping of type ptrs to their mutable types
+  mutable ska::flat_hash_map<TypePtr, AliasTypeSet> mapped_mutable_types_;
+
+  /**
+   * State for tracking write info.
+   */
+  // Write registry where the analysis can record the writes as it sees them.
+  // This information is later denormalized into various caches to improve query
+  // efficiency.
+  struct WriteRegistry;
+  std::unique_ptr<WriteRegistry> writeRegistry_;
+
+  // Map of nodes to the memory locations that they write to
+  using TWriteIndex = ska::flat_hash_map<Node*, MemoryLocations>;
+  c10::optional<TWriteIndex> writeIndex_;
+  // Collection of all memory locations that are written to.
+  c10::optional<MemoryLocations> writtenToLocationsIndex_;
+  void buildWrittenToLocationsIndex();
+
+  std::unordered_set<const Value*> wildcards_;
+
+  std::string getElementName(const Element* e) const;
+
+  friend void Lint(const AliasDb* db);
+};
+
+// Helper check that invariants over AliasDb are maintained.
+// Useful if you are using the AliasDb mutation API and want to check you did
+// the right thing.
+TORCH_API void Lint(const AliasDb* db);
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/attributes.h

@@ -0,0 +1,184 @@
+#pragma once
+#include <ATen/core/Tensor.h>
+#include <string>
+#include <vector>
+
+#include <ATen/core/jit_type_base.h>
+#include <ATen/core/symbol.h>
+
+#include <torch/csrc/Export.h>
+
+namespace torch {
+namespace jit {
+
+using ::c10::Symbol;
+
+constexpr int max_tensor_display_size = 10;
+
+enum class AttributeKind {
+  f,
+  fs,
+  c,
+  cs,
+  i,
+  is,
+  s,
+  ss,
+  t,
+  ts,
+  g,
+  gs,
+  ty,
+  tys,
+  ival
+};
+static inline const char* toString(AttributeKind kind) {
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+  static const char* names[] = {
+      "f",
+      "c",
+      "cs",
+      "fs",
+      "i",
+      "is",
+      "s",
+      "ss",
+      "t",
+      "ts",
+      "g",
+      "gs",
+      "ty",
+      "tys",
+      "ival"};
+  AT_ASSERT(size_t(kind) < sizeof(names) / sizeof(*names));
+  return names[int(kind)];
+}
+
+struct AttributeValue {
+  AttributeValue(Symbol name) : name(name) {}
+  using Ptr = std::unique_ptr<AttributeValue>;
+  Symbol name;
+  virtual AttributeKind kind() const = 0;
+  virtual Ptr clone() const = 0;
+  virtual ~AttributeValue() = default;
+};
+
+template <typename T, AttributeKind Kind>
+struct ScalarAttributeValue : public AttributeValue {
+  using ConstructorType = T;
+  using ValueType = T;
+  ScalarAttributeValue(Symbol name, ConstructorType value_)
+      : AttributeValue(name), value_(std::move(value_)) {}
+  ValueType& value() {
+    return value_;
+  }
+  Ptr clone() const override {
+    return Ptr(new ScalarAttributeValue(name, value_));
+  }
+  AttributeKind kind() const override {
+    return Kind;
+  }
+
+ private:
+  ValueType value_;
+};
+
+template <typename T, AttributeKind Kind>
+struct VectorAttributeValue : public AttributeValue {
+  using ConstructorType = std::vector<T>;
+  using ValueType = std::vector<T>;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VectorAttributeValue(Symbol name, ConstructorType value_)
+      : AttributeValue(name), value_(std::move(value_)) {}
+  ValueType& value() {
+    return value_;
+  }
+  AttributeKind kind() const override {
+    return Kind;
+  }
+  std::unique_ptr<AttributeValue> clone() const override {
+    auto copy = value_;
+    return Ptr(new VectorAttributeValue(name, std::move(copy)));
+  }
+
+ private:
+  ValueType value_;
+};
+
+using ComplexAttr =
+    ScalarAttributeValue<c10::complex<double>, AttributeKind::c>;
+using ComplexValsAttr =
+    VectorAttributeValue<c10::complex<double>, AttributeKind::cs>;
+using FloatAttr = ScalarAttributeValue<double, AttributeKind::f>;
+using FloatsAttr = VectorAttributeValue<double, AttributeKind::fs>;
+using IntAttr = ScalarAttributeValue<int64_t, AttributeKind::i>;
+using IntsAttr = VectorAttributeValue<int64_t, AttributeKind::is>;
+using StringAttr = ScalarAttributeValue<std::string, AttributeKind::s>;
+using StringsAttr = VectorAttributeValue<std::string, AttributeKind::ss>;
+using TensorAttr = ScalarAttributeValue<at::Tensor, AttributeKind::t>;
+using TensorsAttr = VectorAttributeValue<at::Tensor, AttributeKind::ts>;
+using TypeAttr = ScalarAttributeValue<c10::TypePtr, AttributeKind::ty>;
+using TypesAttr = VectorAttributeValue<c10::TypePtr, AttributeKind::tys>;
+using IValueAttr = ScalarAttributeValue<at::IValue, AttributeKind::ival>;
+
+struct Graph;
+
+// We special case Graph attributes like this because we want to ensure that
+// Graph::copy() is called when we clone() these attributes.
+struct TORCH_API GraphAttr : public AttributeValue {
+  using ConstructorType = std::shared_ptr<Graph>;
+  using ValueType = std::shared_ptr<Graph>;
+  GraphAttr(Symbol name, ConstructorType value_)
+      : AttributeValue(name), value_(std::move(value_)) {}
+  ValueType& value() {
+    return value_;
+  }
+  Ptr clone() const override;
+  AttributeKind kind() const override {
+    return AttributeKind::g;
+  }
+
+ private:
+  std::shared_ptr<Graph> value_;
+};
+
+struct TORCH_API GraphsAttr : public AttributeValue {
+  using ConstructorType = std::vector<std::shared_ptr<Graph>>;
+  using ValueType = std::vector<std::shared_ptr<Graph>>;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  GraphsAttr(Symbol name, ConstructorType value_)
+      : AttributeValue(name), value_(std::move(value_)) {}
+  ValueType& value() {
+    return value_;
+  }
+  AttributeKind kind() const override {
+    return AttributeKind::gs;
+  }
+  std::unique_ptr<AttributeValue> clone() const override;
+
+ private:
+  ValueType value_;
+};
+
+struct IRAttributeError : public std::exception {
+  IRAttributeError(Symbol name, bool defined) {
+    std::stringstream ss;
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    if (!defined) {
+      ss << "required keyword attribute '" << name.toUnqualString()
+         << "' is undefined";
+    } else {
+      ss << "required keyword attribute '" << name.toUnqualString()
+         << "' has the wrong type";
+    }
+    msg = ss.str();
+  }
+  const char* what() const noexcept override {
+    return msg.c_str();
+  }
+
+ private:
+  std::string msg;
+};
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/constants.h

@@ -0,0 +1,61 @@
+#pragma once
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/frontend/source_range.h>
+#include <torch/csrc/jit/ir/scope.h>
+
+// helpers for handling constants in the IR
+// - create constant nodes from ints, floats, complex, intlist, Tensors, and
+// other types
+// - implement primitive constant ops.
+namespace torch {
+namespace jit {
+
+using ::c10::IValue;
+
+struct Graph;
+struct Value;
+
+// thrown when insertConstant cannot encode the IValue into a graph
+struct TORCH_API constant_not_supported_error : public std::runtime_error {
+  using runtime_error::runtime_error;
+};
+
+TORCH_API Value* insertConstant(
+    Graph& g,
+    const IValue& val,
+    c10::optional<SourceRange> loc = c10::nullopt,
+    c10::optional<ScopePtr> scope = c10::nullopt);
+
+// note: prefer g.insertConsant(val, loc) which does exactly the same thing
+// this function is only declared/defined here because its implementation is
+// closely related to the implementation of prim::Constant that is also in
+// constants.cpp.
+//
+// returns a c10::nullopt if the IValue kind cannot be inserted as a constant
+TORCH_API c10::optional<Value*> tryInsertConstant(
+    Graph& g,
+    const IValue& val,
+    c10::optional<SourceRange> loc = c10::nullopt,
+    c10::optional<ScopePtr> scope = c10::nullopt);
+
+////////////////////////////////////////////////////////////////////////////////
+// Helper for retrieving constants
+////////////////////////////////////////////////////////////////////////////////
+
+// attempt to convert a (possibly constant) Value* into an interpreter value
+// (IValue). returns c10::nullopt if the Value* was not constant
+TORCH_API c10::optional<IValue> toIValue(const Value* v);
+
+// if a value is a constant then try to turn into type T using the
+// same rules as the interpreter
+template <typename T>
+c10::optional<T> constant_as(const Value* v) {
+  if (auto ivalue = toIValue(v)) {
+    return ivalue->to<T>();
+  }
+  return c10::nullopt;
+}
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/graph_node_list.h

@@ -0,0 +1,201 @@
+#pragma once
+
+#include <c10/util/Exception.h>
+
+namespace torch {
+namespace jit {
+
+// Intrusive doubly linked lists with sane reverse iterators.
+// The header file is named generic_graph_node_list.h because it is ONLY
+// used for Graph's Node lists, and if you want to use it for other
+// things, you will have to do some refactoring.
+//
+// At the moment, the templated type T must support a few operations:
+//
+//  - It must have a field: T* next_in_graph[2] = { nullptr, nullptr };
+//    which are used for the intrusive linked list pointers.
+//
+//  - It must have a method 'destroy()', which removes T from the
+//    list and frees a T.
+//
+// In practice, we are only using it with Node and const Node.  'destroy()'
+// needs to be renegotiated if you want to use this somewhere else.
+//
+// Regardless of the iteration direction, iterators always physically point
+// to the element they logically point to, rather than
+// the off-by-one behavior for all standard library reverse iterators like
+// std::list.
+
+// The list is includes two sentinel nodes, one at the beginning and one at the
+// end with a circular link between them. It is an error to insert nodes after
+// the end sentinel node but before the beginning node:
+
+// Visualization showing only the next() links:
+//  HEAD -> first -> second  -> ... -> last -> TAIL
+//   ^------------------------------------------
+
+// Visualization showing only the prev() links:
+//  HEAD <- first <- second  <- ... <- last <- TAIL
+//   ------------------------------------------^
+
+static constexpr int kNextDirection = 0;
+static constexpr int kPrevDirection = 1;
+
+template <typename T>
+struct generic_graph_node_list;
+
+template <typename T>
+struct generic_graph_node_list_iterator;
+
+struct Node;
+using graph_node_list = generic_graph_node_list<Node>;
+using const_graph_node_list = generic_graph_node_list<const Node>;
+using graph_node_list_iterator = generic_graph_node_list_iterator<Node>;
+using const_graph_node_list_iterator =
+    generic_graph_node_list_iterator<const Node>;
+
+template <typename T>
+struct generic_graph_node_list_iterator {
+  generic_graph_node_list_iterator() : cur(nullptr), d(kNextDirection) {}
+  generic_graph_node_list_iterator(T* cur, int d) : cur(cur), d(d) {}
+  generic_graph_node_list_iterator(
+      const generic_graph_node_list_iterator& rhs) = default;
+  generic_graph_node_list_iterator(
+      generic_graph_node_list_iterator&& rhs) noexcept = default;
+  generic_graph_node_list_iterator& operator=(
+      const generic_graph_node_list_iterator& rhs) = default;
+  generic_graph_node_list_iterator& operator=(
+      generic_graph_node_list_iterator&& rhs) noexcept = default;
+  T* operator*() const {
+    return cur;
+  }
+  T* operator->() const {
+    return cur;
+  }
+  generic_graph_node_list_iterator& operator++() {
+    AT_ASSERT(cur);
+    cur = cur->next_in_graph[d];
+    return *this;
+  }
+  generic_graph_node_list_iterator operator++(int) {
+    generic_graph_node_list_iterator old = *this;
+    ++(*this);
+    return old;
+  }
+  generic_graph_node_list_iterator& operator--() {
+    AT_ASSERT(cur);
+    cur = cur->next_in_graph[reverseDir()];
+    return *this;
+  }
+  generic_graph_node_list_iterator operator--(int) {
+    generic_graph_node_list_iterator old = *this;
+    --(*this);
+    return old;
+  }
+
+  // erase cur without invalidating this iterator
+  // named differently from destroy so that ->/. bugs do not
+  // silently cause the wrong one to be called.
+  // iterator will point to the previous entry after call
+  void destroyCurrent() {
+    T* n = cur;
+    cur = cur->next_in_graph[reverseDir()];
+    n->destroy();
+  }
+  generic_graph_node_list_iterator reverse() {
+    return generic_graph_node_list_iterator(cur, reverseDir());
+  }
+
+ private:
+  int reverseDir() {
+    return d == kNextDirection ? kPrevDirection : kNextDirection;
+  }
+  T* cur;
+  int d; // direction 0 is forward 1 is reverse, see next_in_graph
+};
+
+template <typename T>
+struct generic_graph_node_list {
+  using iterator = generic_graph_node_list_iterator<T>;
+  using const_iterator = generic_graph_node_list_iterator<const T>;
+  generic_graph_node_list_iterator<T> begin() {
+    return generic_graph_node_list_iterator<T>(head->next_in_graph[d], d);
+  }
+  generic_graph_node_list_iterator<const T> begin() const {
+    return generic_graph_node_list_iterator<const T>(head->next_in_graph[d], d);
+  }
+  generic_graph_node_list_iterator<T> end() {
+    return generic_graph_node_list_iterator<T>(head->next_in_graph[!d], d);
+  }
+  generic_graph_node_list_iterator<const T> end() const {
+    return generic_graph_node_list_iterator<const T>(
+        head->next_in_graph[!d], d);
+  }
+  generic_graph_node_list_iterator<T> rbegin() {
+    return reverse().begin();
+  }
+  generic_graph_node_list_iterator<const T> rbegin() const {
+    return reverse().begin();
+  }
+  generic_graph_node_list_iterator<T> rend() {
+    return reverse().end();
+  }
+  generic_graph_node_list_iterator<const T> rend() const {
+    return reverse().end();
+  }
+  generic_graph_node_list reverse() {
+    return generic_graph_node_list(head->next_in_graph[!d], !d);
+  }
+  const generic_graph_node_list reverse() const {
+    return generic_graph_node_list(head->next_in_graph[!d], !d);
+  }
+  T* front() {
+    return head->next_in_graph[d];
+  }
+  const T* front() const {
+    return head->next_in_graph[d];
+  }
+  T* back() {
+    return head->next_in_graph[!d];
+  }
+  const T* back() const {
+    return head->next_in_graph[!d];
+  }
+  generic_graph_node_list(T* head, int d) : head(head), d(d) {}
+
+ private:
+  T* head; // both head and tail are sentinel nodes
+           // the first real node is head->next_in_graph[d]
+           // the tail sentinel is head->next_in_graph[!d]
+  int d;
+};
+
+template <typename T>
+static inline bool operator==(
+    generic_graph_node_list_iterator<T> a,
+    generic_graph_node_list_iterator<T> b) {
+  return *a == *b;
+}
+
+template <typename T>
+static inline bool operator!=(
+    generic_graph_node_list_iterator<T> a,
+    generic_graph_node_list_iterator<T> b) {
+  return *a != *b;
+}
+
+} // namespace jit
+} // namespace torch
+
+namespace std {
+
+template <typename T>
+struct iterator_traits<torch::jit::generic_graph_node_list_iterator<T>> {
+  using difference_type = int64_t;
+  using value_type = T*;
+  using pointer = T**;
+  using reference = T*&;
+  using iterator_category = bidirectional_iterator_tag;
+};
+
+} // namespace std

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/graph_utils.h

@@ -0,0 +1,25 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+#include <vector>
+
+namespace torch {
+namespace jit {
+
+TORCH_API TypePtr getTensorType(const at::Tensor& t, bool complete);
+
+TORCH_API TypePtr inferShapeAndTypeForInput(
+    TypePtr input_type,
+    Stack::const_iterator& s_iter,
+    const Stack::const_iterator& s_iter_end,
+    bool complete);
+
+TORCH_API void setInputTensorTypes(
+    Graph& g,
+    const Stack& stack,
+    bool complete,
+    const std::vector<int>& param_count_list = {});
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/ir.h

@@ -0,0 +1,1841 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/attributes.h>
+#include <torch/csrc/jit/ir/graph_node_list.h>
+#include <torch/csrc/jit/ir/named_value.h>
+#include <torch/csrc/jit/ir/scope.h>
+#include <torch/csrc/jit/runtime/operator.h>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/utils/python_stub.h>
+#include <torch/csrc/utils/schema_info.h>
+
+#include <ATen/Utils.h>
+#include <ATen/core/Tensor.h>
+#include <ATen/core/dynamic_type.h>
+#include <ATen/core/enum_type.h>
+#include <ATen/core/functional.h>
+#include <ATen/core/interned_strings.h>
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <c10/util/ArrayRef.h>
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+
+#include <functional>
+#include <iosfwd>
+#include <unordered_set>
+#include <vector>
+
+// Forward declare, the real meat is in python_ir.cpp
+template <class T>
+class THPPointer;
+using THPObjectPtr = THPPointer<PyObject>;
+using pyobj_list = std::vector<THPObjectPtr>;
+
+namespace torch {
+namespace jit {
+namespace utils {
+TORCH_API std::string getNodesModuleHierarchy(const Node& n);
+} // namespace utils
+class AliasDb;
+
+using ::c10::Argument;
+using ::c10::FunctionSchema;
+using ::c10::Symbol;
+
+using ::c10::ivalue::Shared;
+
+using ::c10::IValue;
+using ::c10::ivalue::Future;
+
+using ::c10::ivalue::ConstantString;
+
+#define C10_USING(T) using ::c10::T;
+C10_FORALL_TYPES(C10_USING)
+#undef C10_USING
+
+#define C10_USING(T) using ::c10::T##Ptr;
+C10_FORALL_TYPES(C10_USING)
+#undef C10_USING
+
+using ::c10::Type;
+using ::c10::TypeEnv;
+using ::c10::TypePtr;
+
+using ::c10::getTypePtr;
+using ::c10::MatchTypeReturn;
+using ::c10::TypeKind;
+
+using ::c10::fmap;
+
+namespace prim {
+using namespace ::c10::prim;
+}
+namespace attr {
+using namespace ::c10::attr;
+}
+namespace aten {
+using namespace ::c10::aten;
+}
+namespace cuda {
+#if !defined(USE_ROCM)
+using namespace ::c10::cuda;
+#endif
+} // namespace cuda
+
+struct Function;
+struct GraphFunction;
+struct MatchedSchema;
+
+// A Graph represents one "function" of computation.
+// It uses a simple ownership model where the graph owns all the nodes inside
+// it. All references inside the graph are raw pointers. Destroying the Graph
+// will invalidate any pointers to nodes in the graph.
+struct Graph;
+
+// Node is the base class of the IR graph. It represents one computation
+// and dependencies on a list of Values. The "prim-ops", so to speak.
+struct Node;
+
+// A Value represents an input or output to node that is either a
+// Tensor or an opaque Handle object, as determined by type().
+struct Value;
+
+TORCH_API std::ostream& operator<<(std::ostream& out, const Graph& g);
+TORCH_API std::ostream& operator<<(std::ostream& out, const Node& n);
+
+// A list of nodes, with inputs and outputs
+struct Block;
+
+// Each use is represented by this type, see 'Node::uses()'
+// 'user' is the consumer of the value, 'offset' is the index into
+// 'user's input this where the producers will be found.
+struct Use {
+  Use(Node* user, size_t offset) : user(user), offset(offset) {}
+  Node* user;
+  size_t offset;
+
+  bool operator==(const Use& b) {
+    return user == b.user && offset == b.offset;
+  }
+};
+
+// Note [User node does not uniquely identify use]
+// ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+// A while back, we wrote some code manipulating uses that looked like this:
+//
+//    for (auto& use : used_val->uses_) {
+//      if (use.user == this_node) {
+//        use.offset += 1;
+//        break;
+//      }
+//    }
+//
+// This code is trying to find a particular use (our node's use) to update it.
+// However, it's wrong: there may be *multiple* uses of a value %x in a node,
+// as might be the case in this IR:
+//
+//    %y = Add %x %x
+//
+// In this case, there are two uses of %x whose user is the node 'Add %x %x'.
+// So, "use induced by this node" is not a well-formed concept.
+//
+// If you are looking for "use induced by an input", it's best to use
+// findUseForInput() to get it.
+
+// the list types are intentionally simple, but we type-def
+// them here so if we need to change them, refactoring will be easier
+using node_list = std::vector<Node*>;
+using value_list = std::vector<Value*>;
+using use_list = std::vector<Use>;
+template <typename T>
+using ArrayRef = at::ArrayRef<T>;
+using NodeKind = Symbol;
+using topo_position_t = int64_t;
+using ValueSet = std::unordered_set<const Value*>;
+
+struct OperatorSet;
+template <typename T>
+struct OperatorMap;
+
+// This is a wrapper to allow invalidating the Python object
+// safely when the C++ object for a Node/Value/Block is deleted
+// like much of graph, it isn't safe for different threads to
+// access the same graph
+template <typename T>
+struct Wrap {
+  explicit Wrap(T* p) : elem(p), clear_cb(nullptr) {}
+  void clear() {
+    if (clear_cb) {
+      clear_cb(elem);
+    }
+    elem = nullptr;
+  }
+  T* elem;
+  void (*clear_cb)(void*);
+};
+
+struct Value {
+  AT_DISALLOW_COPY_AND_ASSIGN(Value);
+  Value(Node* node_, size_t offset_);
+
+ private:
+  friend struct Node;
+  friend struct Graph;
+  Node* node_;
+  size_t offset_;
+  size_t unique_ = 0; // unique id
+  use_list uses_;
+  std::string unique_name_;
+  TypePtr type_;
+  // a managing wrapper for Python to allow invalidation
+  std::shared_ptr<Wrap<Value>> wrap_;
+
+ public:
+  Value* setType(TypePtr type);
+  TORCH_API void inferTypeFrom(const at::Tensor& output);
+  TORCH_API void inferTypeFrom(
+      const c10::intrusive_ptr<c10::ivalue::Object>& output);
+  const TypePtr& type() const {
+    AT_ASSERT(type_ != nullptr);
+    return type_;
+  }
+  bool requires_grad() const {
+    return type()->requires_grad();
+  }
+  bool isCompleteTensor() const {
+    if (auto pt = type()->cast<TensorType>()) {
+      return pt->isComplete();
+    }
+    return false;
+  }
+  TORCH_API bool mustBeNone() const;
+  TORCH_API bool mustNotBeNone() const;
+  size_t unique() const {
+    return unique_;
+  }
+  bool hasDebugName() const {
+    return !unique_name_.empty();
+  }
+  static bool isValidName(const std::string& name);
+  TORCH_API Value* setDebugName(const std::string& name);
+  std::string debugName() const {
+    if (hasDebugName()) {
+      return unique_name_;
+    }
+    return c10::to_string(unique());
+  }
+  TORCH_API std::string debugNameBase() const;
+  Node* node() {
+    return node_;
+  }
+  size_t offset() const {
+    return offset_;
+  }
+  void setOffset(size_t offset) {
+    offset_ = offset;
+  }
+  const Node* node() const {
+    return node_;
+  }
+
+  /**
+   * @warning NEVER pass raw pointer of smart pointer managed Graph to Python.
+   * Check #87343 for details.
+   */
+  Graph* owningGraph();
+  const Graph* owningGraph() const;
+  // TODO: make this more const correct
+  const use_list& uses() const {
+    return uses_;
+  }
+
+  bool hasUses() const {
+    return !uses().empty();
+  }
+
+  TORCH_API void replaceFirstUseWith(Value* newValue);
+
+  // Replaces all uses of this value with 'newValue'.
+  //
+  // Given:   %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = h(%3, %3)
+  // Execute: %3.replaceAllUsesWith(%6)
+  // Result:  %3 = f(%1, %2)
+  //          %4 = g(%6)
+  //          %5 = h(%6, %6)
+  TORCH_API void replaceAllUsesWith(Value* newValue);
+
+  // Replaces all uses of this value with 'newValue' after 'node'.
+  // Given:   %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = inplace_(%3)
+  //          %6 = h(%3, %3)
+  // Execute: %3.replaceAllUsesAfterNodeWith(%5.node(), %5)
+  // Result:  %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = inplace_(%3)
+  //          %6 = h(%5, %5)
+  // XXX: does not check scoping legality, consider using
+  // replaceAllUsesDominatedByNodeWith
+  TORCH_API void replaceAllUsesAfterNodeWith(const Node* node, Value* newValue);
+
+  // Replaces all uses of this value with 'newValue' that are dominated by
+  // 'node'. Given:
+  // x = op(...).
+  // if cond:
+  //    z = foo(..)
+  //    bar(x)
+  // else:
+  //    print(x)
+  // x.replaceAllUsesDominatedByNodeWith(foo, z) would replace bar(x)
+  // but not print(x) because print is not dominated by foo.
+  // replaceAllUsesAfterNode does not check domination, so in this example
+  // it would produce invalid IR.
+  TORCH_API void replaceAllUsesDominatedByNodeWith(
+      const Node* node,
+      Value* newValue);
+
+  TORCH_API Value* copyMetadata(Value* from);
+
+  TORCH_API std::shared_ptr<Wrap<Value>> wrap() {
+    if (!wrap_) {
+      wrap_ = std::make_shared<Wrap<Value>>(this);
+    }
+    return wrap_;
+  }
+
+  virtual ~Value() {
+    if (wrap_) {
+      wrap_->clear();
+    }
+  }
+};
+
+struct TORCH_API Node {
+  AT_DISALLOW_COPY_AND_ASSIGN(Node);
+  friend struct Graph;
+  friend struct Block;
+  friend struct Value;
+  friend graph_node_list;
+  friend const_graph_node_list;
+  friend graph_node_list_iterator;
+  friend const_graph_node_list_iterator;
+
+ private:
+  const NodeKind kind_;
+  std::vector<Value*> inputs_;
+  std::vector<Value*> outputs_;
+  // subblocks
+  std::vector<Block*> blocks_;
+  Graph* graph_;
+  Block* owning_block_;
+  c10::optional<SourceRange> source_range_;
+  ScopePtr scope_;
+  c10::optional<InlinedCallStackPtr> callstack_;
+  // Assumes FunctionSchemas are persistent, so we don't manage their lifetime.
+  // This field is effective a cache that's populated on attribute lookups and
+  // invalidated every time we perform an operation that could potentially
+  // change the schema. note: mutable because schema_ is effectively a cache
+  mutable const Operator* op_;
+  topo_position_t topo_position_ = 0;
+  // a managing wrapper for Python to allow invalidation
+  std::shared_ptr<Wrap<Node>> wrap_;
+  // Stores the full schema name, if the operator is historic
+  // When the operator is deprecated or the name of the operator
+  // is changed, we need to rely on this name
+  // to retrieve old schemas to successfully apply upgraders
+  // for this operator.
+  c10::optional<std::string> historic_schema_name_ = c10::nullopt;
+
+ protected:
+  Node(Graph* graph_, NodeKind kind_); // defined after graph
+ public:
+  // Each Node but Return/Param Nodes are associated with exactly one
+  // place in the Node list of the Graph. The Graph itself is a circular
+  // doubly-linked list. The Return Node is used as the sentinel for the
+  // "beginning"/"end" of the list. This means that you can tell when
+  // you've traversed the entire list without means worrying about null
+  // pointers. `next_in_graph[0]` is the pointer to the next Node, while
+  // `next_in_graph[1]` is the pointer to the previous Node. The
+  // linked list is implemented as an array to allow the same iterator
+  // class for forward and reversed Node lists. Taken together, this
+  // list also represents a topological sort of the Nodes in the Graph.
+  // NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,cppcoreguidelines-non-private-member-variables-in-classes,modernize-avoid-c-arrays)
+  Node* next_in_graph[2] = {nullptr, nullptr};
+
+  std::shared_ptr<Wrap<Node>> wrap() {
+    if (!wrap_) {
+      wrap_ = std::make_shared<Wrap<Node>>(this);
+    }
+    return wrap_;
+  }
+
+  const c10::optional<std::string> getHistoricSchemaName() {
+    return historic_schema_name_;
+  }
+
+  void setHistoricSchemaName(const std::string& name) {
+    historic_schema_name_ = name;
+  }
+
+  Node*& next() {
+    return next_in_graph[kNextDirection];
+  }
+  Node*& prev() {
+    return next_in_graph[kPrevDirection];
+  }
+  Node* const& next() const {
+    return next_in_graph[kNextDirection];
+  }
+  Node* const& prev() const {
+    return next_in_graph[kPrevDirection];
+  }
+
+  NodeKind kind() const {
+    return kind_;
+  }
+  Node* setSourceRange(SourceRange r) {
+    source_range_ = std::move(r);
+    return this;
+  }
+  SourceRange sourceRange() const;
+
+  /**
+   * @warning NEVER pass raw pointer of smart pointer managed Graph to Python.
+   * Check #87343 for details.
+   */
+  Graph* owningGraph() {
+    return graph_;
+  }
+  const Graph* owningGraph() const {
+    return graph_;
+  }
+  Block* owningBlock() {
+    return owning_block_;
+  }
+  const Block* owningBlock() const {
+    return owning_block_;
+  }
+  ScopePtr scope() {
+    return scope_;
+  }
+  void setScope(ScopePtr scope) {
+    scope_ = std::move(scope);
+  }
+  std::string scopeName() const {
+    if (!scope_) {
+      return "";
+    }
+    return scope_->namesFromRoot();
+  }
+
+  // Copies the source range, scope and callstack from another node.
+  Node* copyMetadata(Node* from) {
+    this->setSourceRange(from->sourceRange());
+    this->setScope(from->scope());
+    if (auto cs = from->callstack()) {
+      this->setCallStack(*cs);
+    }
+    return this;
+  }
+
+  c10::optional<InlinedCallStackPtr> callstack() const {
+    return callstack_;
+  }
+  void setCallStack(InlinedCallStackPtr cs) {
+    callstack_ = std::move(cs);
+  }
+
+  // NB: This returns an ArrayRef; that means that it will
+  // get invalidated if you resize inputs (e.g., using addInput)
+  // We can't return a std::vector<Node*>& because there's no
+  // way to soundly cast to std::vector<const Node*> (an insane
+  // implementation of std::vector could make this representationally
+  // different.)
+  at::ArrayRef<Value*> inputs() {
+    return inputs_;
+  }
+  at::ArrayRef<const Value*> inputs() const {
+    // Vectors are not convertible in const-ness of elements, but
+    // raw pointers are.
+    return {inputs_.data(), inputs_.size()};
+  }
+  // NB: This returns an ArrayRef; that means that it will
+  // get invalidated if you resize inputs (e.g., using addInput)
+  // We can't return a std::vector<Node*>& because there's no
+  // way to soundly cast to std::vector<const Node*> (an insane
+  // implementation of std::vector could make this representationally
+  // different.)
+  at::ArrayRef<Value*> outputs() {
+    return outputs_;
+  }
+  at::ArrayRef<const Value*> outputs() const {
+    // Vectors are not convertible in const-ness of elements, but
+    // raw pointers are.
+    return {outputs_.data(), outputs_.size()};
+  }
+  Value* output(size_t i) const {
+    return outputs_.at(i);
+  }
+  bool hasUses() const {
+    for (auto o : outputs()) {
+      if (!o->uses().empty()) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  void replaceAllUsesWith(Node* n);
+
+  // replaces `this` with a new node with the same inputs and outputs
+  // but a new node symbol. does not destroy `this`
+  Node* replaceWithNewSymbol(Symbol new_symbol);
+
+  // Checks if this node is dominated by `dominator` which means that
+  // `dominator` will always be executed before `this` and `dominator`
+  // is in scope of `this.
+  bool isDominatedBy(const Node* dominator) const;
+
+  // lots of things like chunk have a single input or single output, so we have
+  // a helper to make accessing it easier
+  Value* input() {
+    AT_ASSERT(inputs_.size() == 1);
+    return inputs_.at(0);
+  }
+  Value* output() {
+    AT_ASSERT(outputs_.size() == 1);
+    return outputs_.at(0);
+  }
+  const Value* output() const {
+    AT_ASSERT(outputs_.size() == 1);
+    return outputs_.at(0);
+  }
+  const Value* input() const {
+    AT_ASSERT(inputs_.size() == 1);
+    return inputs_.at(0);
+  }
+  // Access a particular input.  This is a checked index.
+  Value* input(size_t i) const {
+    return inputs_.at(i);
+  }
+
+  bool hasNamedInput(const std::string& unqualName) const;
+  Value* namedInput(const std::string& unqualName) const;
+  Value* namedInput(Symbol name) const;
+
+  c10::optional<IValue> get(Symbol name) const;
+
+  template <typename T>
+  c10::optional<T> get(Symbol name) const {
+    if (auto v = get(name)) {
+      return v->template to<T>();
+    }
+    return c10::nullopt;
+  }
+
+  // Returns true if the value of input name is statically known
+  bool is_constant(Symbol name) const {
+    return static_cast<bool>(get(name));
+  }
+  bool mustBeNone() const;
+
+  bool isNondeterministic() const;
+  bool hasSideEffects() const;
+
+  // instructions lowered by the interpreter and not run in the optimized graph
+  bool notExecutedOp() const {
+    return kind_ == prim::Constant || kind_ == prim::profile ||
+        kind_ == prim::profile_ivalue;
+  }
+
+  // Graphs
+
+  // Note [Topological invariant]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+  // We always maintain an up-to-date topological ordering of all nodes via
+  // the next()/prev() links.  All transformations to graphs must preserve
+  // this topological ordering: for example, it is only valid to 'addInput'
+  // with an input which is topologically before the current node.
+  //
+  // Usually, it is obvious whether or not topological order is maintained;
+  // for example, if you are adding nodes to the end of the topsort, it's
+  // impossible for them to refer to inputs that are not in the topsort.
+  // If it is not obvious, please comment accordingly.
+
+  // Add 'node' as an input to 'this' at the end of existing
+  // arguments.  Returns the added node for ease of chaining.
+  //
+  // Given:   %3 = f(%1, %2)
+  // Execute: %3.addInput(%4)
+  // Result:  %3 = f(%1, %2, %4)
+  Value* addInput(Value* value);
+
+  // Add 'value' as an input to 'this' at the specified position in the
+  // arguments. Returns the added value for ease of chaining.
+  Value* insertInput(size_t i, Value* value);
+
+  // Replace the input of 'this' at position 'i' with
+  // 'newValue', returning the old node.
+  //
+  // Given:   %3 = f(%1, %2)
+  // Execute: %3.replaceInput(1, %4)
+  // Result:  %3 = f(%1, %4)
+  Value* replaceInput(size_t i, Value* newValue);
+
+  // Replace all occurrences of 'from' in the inputs of this
+  // node with 'to'. Corresponds to llvm's replaceUsesOfWith.
+  //
+  // Given:   %3 = f(%1, %2, %1)
+  // Execute: %3.replaceInputWith(%1, %4)
+  // Result:  %3 = f(%4, %2, %4)
+  void replaceInputWith(Value* from, Value* to);
+
+  Value* addOutput();
+
+  Value* insertOutput(size_t i);
+
+  void eraseOutput(size_t i);
+
+  Block* addBlock();
+  void eraseBlock(size_t i);
+
+  // Each Node can have a list of subblocks. These are used to define structured
+  // nested control flow operators such as If and Loop.
+  // The meaning of a block is specific to the kind of node it is in, but
+  // all blocks share these semantics:
+  // * Nested lexical scoping: If a node 'Parent' has a subblock which contains
+  //   a node 'Child', Child can use any value that was in scope for the Parent
+  //   node in addition to any values defined before 'Child' in the subblock.
+  // * The list of inputs to the block are in scope for the duration of the
+  //   block
+  // * the outputs of the Parent node are not in scope for the subblocks
+  // Typically the inputs to a block that represents control flow act as
+  // as the equivalents phi-nodes in standard SSA form,
+  // defining a new Value to represent any term that has multiple
+  // definitions depending on how control flowed. Outputs of the node containing
+  // control flow serve a similiar purpose defining new values for variables
+  // that would have different definitions depending on which way control
+  // flowed.
+
+  at::ArrayRef<Block*> blocks() {
+    return blocks_;
+  }
+  at::ArrayRef<const Block*> blocks() const {
+    // Vectors are not convertible in const-ness of elements, but
+    // raw pointers are.
+    return {blocks_.data(), blocks_.size()};
+  }
+
+  // Is 'this' before 'n' in the topological order?
+  bool isBefore(const Node* n) const;
+
+  // Is 'this' after 'n' in the topological order?
+  bool isAfter(const Node* n) const;
+
+  // Insert unattached 'this' node before 'n' in the topological order.
+  // Returns this (for chaining).
+  //
+  // Given:   %3 = f(%1, %2)
+  //          %4 = g(%3)
+  // and unattached: %5 = h(%1)
+  // Execute: %5.insertBefore(%4)
+  // Result:  %3 = f(%1, %2)
+  //          %5 = h(%1)
+  //          %4 = g(%3)
+  Node* insertBefore(Node* n);
+
+  // Insert unattached 'this' node after 'n' in the topological order.
+  // Returns this (for chaining).
+  //
+  // Given: %3 = f(%1, %2)
+  //        %4 = g(%3)
+  // and unattached: %5 = h(%1)
+  // Execute: %5.insertAfter(%4)
+  // Result:  %3 = f(%1, %2)
+  //          %4 = g(%3)
+  //          %5 = h(%1)
+  Node* insertAfter(Node* n);
+
+  // Move 'this' (already in the graph) after 'n' in the topological order.
+  //
+  // NOTE: Does not check that value dependencies are preserved, see
+  //   AliasDb::moveAfterTopologicallyValid
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %2.moveAfter(%3)
+  // Result: %3 = g(%1)
+  //         %2 = f(%1)
+  //
+  void moveAfter(Node* n);
+
+  // Move a node 'n' (already in the graph) before 'this' in the topological
+  // order.
+  //
+  // NOTE: Does not check that value dependencies are preserved, see
+  //   AliasDb::moveBeforeTopologicallyValid
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %3.moveBefore(%2)
+  // Result: %3 = g(%1)
+  //         %2 = f(%1)
+  void moveBefore(Node* n);
+
+  // Remove the input at 'i' from this node.
+  //
+  // WARNING: This is O(n) in the number of inputs, so avoid repeatedly calling
+  // removeInput.
+  //
+  // Given: %3 = f(%1, %2)
+  // Execute: %3.removeInput(1)
+  // Result: %3 = f(%1)
+  void removeInput(size_t i);
+
+  // Remove all inputs from a node.
+  //
+  // Given: %3 = f(%1, %2)
+  // Execute: %3.removeAllInputs()
+  // Result: %3 = f()
+  void removeAllInputs();
+
+  // Remove all outputs from a node.
+  //
+  // Given: %1, %2 = f()
+  // Execute:removeAllInputs()
+  // Result: = f()
+  void removeAllOutputs();
+
+  // Rearrange the ordering of inputs or outputs of a node
+  // Given: %3 = f(%1, %2)
+  // Execute: %3.permuteInputs({1, 0})
+  // Result: %3 = f(%2, %1)
+  // Each index must appear exactly once
+  void permuteInputs(const std::vector<size_t>& new_inputs);
+  void permuteOutputs(const std::vector<size_t>& new_inputs);
+
+  // iterators of the node list starting at this node
+  // useful for resuming a search starting at this node
+  inline graph_node_list_iterator iterator() {
+    return {this, 0};
+  }
+  inline graph_node_list_iterator reverseIterator() {
+    return iterator().reverse();
+  }
+  inline const_graph_node_list_iterator iterator() const {
+    return {this, 0};
+  }
+  inline const_graph_node_list_iterator reverseIterator() const {
+    return iterator().reverse();
+  }
+
+  // Remove 'this' from the instruction list and deallocate it.
+  //
+  // Invariant: no outputs of 'this' may have any uses.
+  //
+  // Given: %2 = f(%1)
+  //        %3 = g(%1)
+  // Execute: %2.destroy()
+  // Result: %3 = g(%1)
+  void destroy();
+
+  // Dynamically cast this node to the subclass indicated by the
+  // template variable, returning nullptr if the cast is invalid..
+  //
+  // Example usage: if(auto s = n.cast<Select>()) { ... }
+  template <typename T>
+  T* cast() {
+    if (T::Kind == kind()) {
+      return static_cast<T*>(this);
+    }
+    return nullptr;
+  }
+  template <typename T>
+  const T* cast() const {
+    if (T::Kind == kind()) {
+      return static_cast<const T*>(this);
+    }
+    return nullptr;
+  }
+
+  template <typename T>
+  T* expect() {
+    TORCH_CHECK(
+        T::Kind == kind(),
+        "expected a ",
+        T::Kind.toDisplayString(),
+        " but found a ",
+        kind().toDisplayString());
+    return static_cast<T*>(this);
+  }
+
+  bool matches(const FunctionSchema& schema) const;
+
+  // XXX: this function is meant to be used with string literals only!
+  bool matches(
+      const char* signature_literal,
+      at::ArrayRef<Symbol> const_inputs = {}) const;
+
+  bool isMemberOf(const OperatorSet& os) const;
+  template <typename T>
+  bool isMemberOf(const OperatorMap<T>& om) const {
+    auto it = om.map.find(kind());
+    if (it == om.map.end()) {
+      return false;
+    }
+    for (auto& op : it->second) {
+      if (matches(op.first->schema())) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  const FunctionSchema& schema() const;
+  const FunctionSchema* maybeSchema() const;
+  const Operator& getOperator() const;
+  Operation getOperation() const;
+
+  const Operator* maybeOperator() const;
+
+  void dump() const;
+
+  std::ostream& print(
+      std::ostream& out,
+      size_t level,
+      std::vector<const Node*>* groups,
+      bool print_source_locations = true,
+      bool print_attributes = true,
+      bool print_scopes = true,
+      bool print_body = true) const;
+
+  virtual ~Node() {
+    if (wrap_) {
+      wrap_->clear();
+    }
+  }
+
+  // Methods for accessing attributes
+  Node* copyAttributes(const Node& rhs) {
+    values_.clear();
+    for (const AVPtr& i : rhs.values_) {
+      values_.push_back(i->clone());
+    }
+    return this;
+  }
+  bool hasAttribute(Symbol name) const {
+    AT_ASSERT(name.is_attr());
+    return findAttr(name, false) != values_.end();
+  }
+  bool hasAttributeS(const std::string& name) const {
+    return hasAttribute(Symbol::attr(name));
+  }
+  AttributeKind kindOf(Symbol name) const {
+    AT_ASSERT(name.is_attr());
+    return (*findAttr(name, true))->kind();
+  }
+  AttributeKind kindOfS(const std::string& name) const {
+    return kindOf(Symbol::attr(name));
+  }
+  Node* removeAttribute(Symbol name) {
+    AT_ASSERT(name.is_attr());
+    values_.erase(findAttr(name, true));
+    return this;
+  }
+  Node* removeAttributeS(const std::string& name) {
+    return removeAttribute(Symbol::attr(name));
+  }
+  bool hasAttributes() const {
+    return !values_.empty();
+  }
+  size_t numAttributes() const {
+    return values_.size();
+  }
+  // The names are returned in order, since name actually is the index.
+  std::vector<Symbol> attributeNames() const {
+    std::vector<Symbol> names;
+    names.reserve(values_.size());
+    for (const AVPtr& a : values_) {
+      names.push_back(a->name);
+    }
+    return names;
+  }
+  std::vector<const char*> attributeNamesS() const {
+    std::vector<const char*> names;
+    names.reserve(values_.size());
+    for (const AVPtr& a : values_) {
+      names.push_back(a->name.toUnqualString());
+    }
+    return names;
+  }
+
+#define CREATE_ACCESSOR(Kind, method)                           \
+  Node* method##_(Symbol name, Kind##Attr::ConstructorType v) { \
+    return setAttr<Kind##Attr>(                                 \
+        name, std::forward<Kind##Attr::ConstructorType>(v));    \
+  }                                                             \
+  const Kind##Attr::ValueType& method(Symbol name) const {      \
+    return getAttr<Kind##Attr>(name);                           \
+  }
+
+  CREATE_ACCESSOR(Float, f)
+  CREATE_ACCESSOR(Complex, c)
+  CREATE_ACCESSOR(Floats, fs)
+  CREATE_ACCESSOR(ComplexVals, cs)
+  CREATE_ACCESSOR(String, s)
+  CREATE_ACCESSOR(Strings, ss)
+  CREATE_ACCESSOR(Int, i)
+  CREATE_ACCESSOR(Ints, is)
+  CREATE_ACCESSOR(Graph, g)
+  CREATE_ACCESSOR(Graphs, gs)
+  CREATE_ACCESSOR(Type, ty)
+  CREATE_ACCESSOR(Types, tys)
+  CREATE_ACCESSOR(IValue, ival)
+
+#undef CREATE_ACCESSOR
+
+  // Our Graphs are not very const-correct, so we need to allow returning
+  // non-const references too
+  GraphAttr::ValueType& g(Symbol name) {
+    return getAttr<GraphAttr>(name);
+  }
+
+  // does not use CREATE_ACCESSOR because we need additional asserts
+  Node* t_(Symbol name, TensorAttr::ConstructorType v) {
+    return setAttr<TensorAttr>(
+        name, std::forward<TensorAttr::ConstructorType>(v));
+  }
+  const TensorAttr::ValueType& t(Symbol name) const {
+    return getAttr<TensorAttr>(name);
+  }
+
+  Node* ts_(Symbol name, TensorsAttr::ConstructorType v) {
+    return setAttr<TensorsAttr>(
+        name, std::forward<TensorsAttr::ConstructorType>(v));
+  }
+  const TensorsAttr::ValueType& ts(Symbol name) const {
+    return getAttr<TensorsAttr>(name);
+  }
+
+  Block* findCommonAncestorBlockWith(Node* n);
+
+  size_t blocksFromGraphBlock();
+
+ private:
+  void printAttrValue(std::ostream& out, const Symbol& name) const;
+  void printAttributes(std::ostream& out, bool ignore_subgraph) const;
+
+  template <typename T>
+  Node* setAttr(Symbol name, typename T::ConstructorType v) {
+    AT_ASSERT(name.is_attr());
+    auto it = findAttr(name, false);
+    auto nv = AVPtr(new T(name, std::forward<typename T::ConstructorType>(v)));
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    if (it == values_.end()) {
+      values_.push_back(std::move(nv));
+    } else {
+      *it = std::move(nv);
+    }
+    return this;
+  }
+  template <typename T>
+  typename T::ValueType& getAttr(Symbol name) const {
+    AT_ASSERT(name.is_attr());
+    auto it = findAttr(name, true);
+    auto* child = dynamic_cast<T*>(it->get());
+    if (child == nullptr) {
+      throw IRAttributeError(name, true);
+    }
+    return child->value();
+  }
+  using AVPtr = AttributeValue::Ptr;
+  // NB: For determinism, we use a vector rather than a hash map.  This does
+  // mean that lookups are O(n), so you shouldn't use Attributes to store
+  // a big pile of messages.
+  std::vector<AVPtr> values_;
+  std::vector<AVPtr>::iterator findAttr(Symbol name, bool required) {
+    AT_ASSERT(name.is_attr());
+    auto it = std::find_if(values_.begin(), values_.end(), [&](const AVPtr& v) {
+      return v->name == name;
+    });
+    if (required && it == values_.end()) {
+      throw IRAttributeError(name, false);
+    }
+    AT_ASSERT(!required || it != values_.end());
+    return it;
+  }
+  std::vector<AVPtr>::const_iterator findAttr(Symbol name, bool required)
+      const {
+    AT_ASSERT(name.is_attr());
+    auto it = std::find_if(values_.begin(), values_.end(), [&](const AVPtr& v) {
+      return v->name == name;
+    });
+    if (required && it == values_.end()) {
+      throw IRAttributeError(name, false);
+    }
+    AT_ASSERT(!required || it != values_.end());
+    return it;
+  }
+
+  enum class MoveSide { BEFORE, AFTER };
+  bool isBeforeOrAfter(const Node* n, MoveSide moveSide) const;
+
+  std::pair<Value*, const Argument&> findInput(Symbol name);
+  // Lookup iterator in use list of _input i_ that corresponds to its use of
+  // _this_
+  use_list::iterator findUseForInput(size_t i);
+
+  // remove the use of input i, this sets input i to nullptr, but
+  // is only used internally to Node before setting it to a new value
+  // or erasing the entry from the list.
+  Value* dropInput(size_t i);
+
+  bool inBlockList() const {
+    if (next() == nullptr) {
+      AT_ASSERT(prev() == nullptr);
+    }
+    return next() != nullptr;
+  }
+
+  void removeFromList();
+  void lint() const;
+
+  void assignTopoPosition();
+
+ protected:
+  // subclasses must override
+  // this function is used by createClone to initialize a new version
+  // of a node in another graph. It should allocate a new instance of the same
+  // concrete type as 'this', but in graph 'g' which might be different
+  // than graph_
+  virtual Node* allocNewInstance(Graph* g) {
+    return new Node(g, kind());
+  }
+  // create a copy of all properties of Node s into this.
+  // subclasses should extend if they have additional information to copy.
+  // 'this' will be allocated with s->allocNewInstance(g) so it should have
+  // the same concrete type as 's'
+  virtual void cloneFrom(Node* s);
+};
+
+struct Block {
+  friend struct Node;
+  friend struct Graph;
+
+  AT_DISALLOW_COPY_AND_ASSIGN(Block);
+  TORCH_API Block(Graph* graph_, Node* node_);
+
+  at::ArrayRef<Value*> inputs() {
+    return input_->outputs();
+  }
+  at::ArrayRef<const Value*> inputs() const {
+    const auto& inputs = input_->outputs();
+    return {inputs.data(), inputs.size()};
+  }
+  at::ArrayRef<Value*> outputs() {
+    return output_->inputs();
+  }
+  at::ArrayRef<const Value*> outputs() const {
+    return static_cast<const Node*>(output_)->inputs();
+  }
+  graph_node_list nodes() {
+    return {input_, kNextDirection};
+  }
+  const_graph_node_list nodes() const {
+    return {input_, kNextDirection};
+  }
+  Node* return_node() {
+    return output_;
+  }
+  const Node* return_node() const {
+    return output_;
+  }
+  Node* param_node() {
+    return input_;
+  }
+  const Node* param_node() const {
+    return input_;
+  }
+  /**
+   * @warning NEVER pass raw pointer of smart pointer managed Graph to Python.
+   * Check #87343 for details.
+   */
+  Graph* owningGraph() {
+    return graph_;
+  }
+  const Graph* owningGraph() const {
+    return graph_;
+  }
+  Node* owningNode() {
+    return owning_node_;
+  }
+  const Node* owningNode() const {
+    return owning_node_;
+  }
+
+  Value* addInput(const std::string& name = "") {
+    Value* v = input_->addOutput();
+    v->setDebugName(name);
+    return v;
+  }
+  Value* insertInput(size_t i, const std::string& name = "") {
+    Value* v = input_->insertOutput(i);
+    v->setDebugName(name);
+    return v;
+  }
+  void eraseInput(size_t i) {
+    input_->eraseOutput(i);
+  }
+  void removeAllInputs() {
+    input_->removeAllOutputs();
+  }
+  size_t registerOutput(Value* v) {
+    output_->addInput(v);
+    return outputs().size() - 1;
+  }
+  size_t insertOutput(size_t i, Value* n) {
+    output_->insertInput(i, n);
+    return i;
+  }
+  void eraseOutput(size_t i) {
+    output_->removeInput(i);
+  }
+  void removeAllOutputs() {
+    output_->removeAllInputs();
+  }
+
+  void replaceOutput(size_t i, Value* n) {
+    output_->replaceInput(i, n);
+  }
+  void permuteOutputs(const std::vector<size_t>& new_inputs) {
+    output_->permuteInputs(new_inputs);
+  }
+  void permuteInputs(const std::vector<size_t>& new_inputs) {
+    input_->permuteOutputs(new_inputs);
+  }
+
+  Node* appendNode(Node* n) {
+    AT_ASSERT(n->graph_ == graph_ && !n->inBlockList());
+    n->insertBefore(output_);
+    return n;
+  }
+  Node* prependNode(Node* n) {
+    AT_ASSERT(n->graph_ == graph_ && !n->inBlockList());
+    n->insertAfter(input_);
+    return n;
+  }
+
+  // clone all inputs, nodes, and outputs from src and append them
+  // to the inputs, nodes, and outputs of this block
+  // value_map is used whenever a node in src references a free variable
+  // in src to look up its corresponding value
+  TORCH_API void cloneFrom(Block* src, std::function<Value*(Value*)> value_map);
+  TORCH_API void remapTypes(const std::function<TypePtr(TypePtr)>& type_map);
+
+  TORCH_API std::shared_ptr<Wrap<Block>> wrap() {
+    if (!wrap_) {
+      wrap_ = std::make_shared<Wrap<Block>>(this);
+    }
+    return wrap_;
+  }
+
+  virtual ~Block() {
+    if (wrap_) {
+      wrap_->clear();
+    }
+  }
+
+  void clear() {
+    removeAllOutputs();
+    for (auto it = nodes().rbegin(); it != nodes().rend(); it++) {
+      it.destroyCurrent();
+    }
+    removeAllInputs();
+  }
+
+ private:
+  void reIndexTopology();
+
+  // get rid of all nodes
+  // destroys in reverse order so that uses internal to this block
+  // do not have to be removed before you can destroy the block
+  void destroy();
+
+  Graph* const graph_;
+  // holds outputs in a way that can be reflected
+  // as a Use object
+  // also used as the beginning/end of the circular node list to avoid
+  // having corner cases where the list is empty.
+  Node* const output_;
+  Node* const input_;
+  Node* const
+      owning_node_; // either the node that has this block or nullptr for root
+  // a managing wrapper for Python to allow invalidation
+  std::shared_ptr<Wrap<Block>> wrap_;
+};
+
+struct Graph : std::enable_shared_from_this<Graph> {
+  AT_DISALLOW_COPY_AND_ASSIGN(Graph);
+  friend struct Node;
+  friend struct Value;
+  friend struct Block;
+
+ private:
+  // only used to keep track of allocated nodes
+  // actual representation of Graph is done with
+  // inputs, outputs, nodes
+
+  std::unordered_set<const Node*> all_nodes;
+  std::unordered_set<const Value*> all_values;
+  std::unordered_set<const Block*> all_blocks;
+  size_t next_unique_;
+
+  std::unordered_map<std::string, Value*> unique_names_;
+  // name_base_suffix tracks largest suffix currently used by all names sharing
+  // same name_base. Key of this map is name_base, value is largest suffix
+  // numeric value.
+  std::unordered_map<std::string, size_t> name_base_suffix_;
+
+  ScopePtr current_scope_;
+
+  Block* const block_;
+  // when insertNode() is called, the node is inserted before this node
+  // by default this is set to append to the top level block
+  Node* insert_before_;
+  int64_t predicted_insert_count_ = 0;
+
+  c10::optional<size_t> op_version_;
+
+ public:
+  Graph(ScopePtr scope_root = c10::make_intrusive<Scope>())
+      : next_unique_(0),
+        current_scope_(std::move(scope_root)),
+        block_(new Block(this, nullptr)),
+        insert_before_(return_node()) {}
+
+  at::ArrayRef<Value*> inputs() {
+    return block_->inputs();
+  }
+  at::ArrayRef<const Value*> inputs() const {
+    const Block& block = *block_;
+    return block.inputs();
+  }
+  at::ArrayRef<Value*> outputs() {
+    return block_->outputs();
+  }
+  at::ArrayRef<const Value*> outputs() const {
+    const Block& block = *block_;
+    return block.outputs();
+  }
+  graph_node_list nodes() {
+    return block_->nodes();
+  }
+  const_graph_node_list nodes() const {
+    const Block& block = *block_;
+    return block.nodes();
+  }
+  Node* param_node() {
+    return block_->param_node();
+  }
+  const Node* param_node() const {
+    return block_->param_node();
+  }
+  Node* return_node() {
+    return block_->return_node();
+  }
+  const Node* return_node() const {
+    return block_->return_node();
+  }
+  const std::unordered_map<std::string, Value*>& debugNames() const {
+    return unique_names_;
+  }
+
+  TORCH_API void push_scope(const std::string& scope_name);
+  TORCH_API void pop_scope();
+
+  ScopePtr current_scope() {
+    return current_scope_;
+  }
+
+  void set_op_version(c10::optional<size_t> version) {
+    op_version_ = version;
+  }
+
+  c10::optional<size_t> get_op_version() {
+    return op_version_;
+  }
+
+  void set_current_scope(ScopePtr scope) {
+    current_scope_ = std::move(scope);
+  }
+
+  Value* addInput(const std::string& name = "") {
+    return block_->addInput(name);
+  }
+  Value* insertInput(size_t i, const std::string& name = "") {
+    return block_->insertInput(i, name);
+  }
+  void eraseInput(size_t i) {
+    block_->eraseInput(i);
+  }
+  size_t registerOutput(Value* n) {
+    return block_->registerOutput(n);
+  }
+  void eraseOutput(size_t i) {
+    block_->eraseOutput(i);
+  }
+
+  TORCH_API Node* create(NodeKind kind, size_t num_outputs = 1);
+  TORCH_API Node* create(
+      NodeKind kind,
+      ArrayRef<Value*> inputs,
+      size_t num_outputs = 1);
+
+  TORCH_API Node* createNone();
+  TORCH_API Node* createAutogradZero();
+  TORCH_API Node* createUninitialized(TypePtr typ);
+  TORCH_API Node* createWithSubgraph(Symbol kind);
+  TORCH_API Node* createDifferentiableSubgraph();
+  TORCH_API Node* createTuple(
+      at::ArrayRef<Value*> values,
+      TupleTypePtr optional_named_tuple = nullptr);
+  TORCH_API Node* createTupleUnpack(Value* v);
+  TORCH_API Node* createTupleIndex(
+      Value* tup,
+      Value* idx,
+      const TypePtr& output_type);
+  TORCH_API Node* createTupleSlice(
+      Value* tup,
+      int64_t beg,
+      int64_t step_size,
+      int64_t num_values);
+  TORCH_API Node* createEnumName(Value* e);
+  TORCH_API Node* createEnumValue(Value* e);
+  TORCH_API Node* createList(
+      const TypePtr& contained_type,
+      at::ArrayRef<Value*> values);
+  TORCH_API Node* createListUnpack(Value* v, size_t size);
+  TORCH_API Node* createDict(
+      const TypePtr& key_type,
+      const TypePtr& value_type,
+      at::ArrayRef<Value*> keys,
+      at::ArrayRef<Value*> values);
+  TORCH_API Node* createNumToTensor(Value* value);
+  TORCH_API Node* createObject(const ClassTypePtr& type);
+  TORCH_API Node* createSetAttr(
+      Value* obj,
+      const std::string& field,
+      Value* newValue);
+  TORCH_API Node* createGetAttr(Value* obj, const std::string& field);
+  Value* insertGetAttr(Value* obj, const std::string& field) {
+    return insertNode(createGetAttr(obj, field))->output();
+  }
+  TORCH_API Node* createStore(const std::string& name, Value* v);
+  TORCH_API Node* createLoad(const std::string& name, const TypePtr& type);
+  TORCH_API Node* createIsInstance(Value* v, at::ArrayRef<TypePtr> types);
+
+  TORCH_API Value* insertUncheckedCast(Value* v, TypePtr type);
+
+  // Insert a ToList operator with argument \p v and output type \p type.
+  // \returns the output of the operation.
+  TORCH_API Value* insertToList(Value* v, TypePtr type);
+
+  TORCH_API Value* insertFunctionCall(
+      Function* callee,
+      const MatchedSchema& matched);
+  TORCH_API Value* insertMethodCall(
+      std::string method_name,
+      const MatchedSchema& matched);
+
+  // Note: defined in python_ir.cpp and can be used only in python extension
+  Node* createPythonOp(
+      THPObjectPtr&& pyobj,
+      const std::string& cconv,
+      pyobj_list&& scalar_args);
+  // clone n, making a new node in _this_ graph.
+  // use value_map to translate inputs of n to inputs of the cloned node
+  // if copy_blocks is false, it will not recursively clone the nested blocks
+  // this node contains.
+  TORCH_API Node* createClone(
+      Node* n,
+      const std::function<Value*(Value*)>& value_map,
+      bool copy_blocks = true);
+
+  // Insert constant IValue into the graph.
+  TORCH_API Value* insertConstant(
+      const IValue& val,
+      c10::optional<SourceRange> loc = c10::nullopt,
+      c10::optional<ScopePtr> scope = c10::nullopt);
+
+  // Schema-driven insert:
+  // This inserts a node into the graph with inputs determined from args and
+  // kwargs using Python argument matching rules, and checks that the op matches
+  // a known schema.
+  //
+  // If this node successfully completes, it guarentees the node
+  // is a correctly-formed invocation of opname
+  TORCH_API Value* insert(
+      Symbol opname,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs = {},
+      const c10::optional<SourceRange>& range = {});
+
+  Node* appendNode(Node* n) {
+    return block_->appendNode(n);
+  }
+
+  Node* prependNode(Node* n) {
+    return block_->prependNode(n);
+  }
+
+  // insert before insert_before_ node
+  // initialized to insert at the end of the top level block
+  // can be changed with setInsertPoint()
+  Node* insertNode(Node* n) {
+    AT_ASSERT(
+        insert_before_->inBlockList() &&
+        "insert point node is no longer in a block list");
+    return n->insertBefore(insert_before_);
+  }
+  // set where nodes are inserted to append to the end of this block
+  void setInsertPoint(Block* b) {
+    AT_ASSERT(b->owningGraph() == this);
+    setInsertPoint(b->return_node());
+  }
+  // set where nodes are inserted to insert _before_ this node
+  // for implementation simplicity we only support inserting before a node for
+  // now
+  void setInsertPoint(Node* n) {
+    AT_ASSERT(n->owningGraph() == this && n->inBlockList());
+    insert_before_ = n;
+    predicted_insert_count_ = 0;
+  }
+  Node* insertPoint() {
+    return insert_before_;
+  }
+
+  // the top level block
+  Block* block() {
+    return block_;
+  }
+  const Block* block() const {
+    return block_;
+  }
+
+  // Checks well-formedness and invariants of graph
+  TORCH_API void lint() const;
+  // for use in debugger
+  TORCH_API void dump() const;
+
+  TORCH_API ~Graph();
+
+  TORCH_API std::string toString(bool print_source_locations = true) const;
+
+  TORCH_API std::ostream& print(
+      std::ostream& out,
+      bool print_source_locations = true) const;
+
+  friend TORCH_API std::ostream& operator<<(std::ostream& out, const Graph& g);
+
+  TORCH_API std::shared_ptr<Graph> copy();
+  TORCH_API std::unique_ptr<Graph> copyUnique();
+  TORCH_API void remapTypes(const std::function<TypePtr(TypePtr)>& type_map);
+
+ private:
+  friend TORCH_API void Lint(const AliasDb* db);
+  TORCH_API void freeNode(Node* n);
+  TORCH_API void freeValue(Value* v);
+  TORCH_API void freeBlock(Block* b);
+  void cloneFrom(Graph& src);
+};
+
+/** \brief An utility class for setting temporary insertion points.
+ *
+ * When an object of this class is created, it stores the current insertion
+ * point, sets the new one, and restores the original insertion point when the
+ * object is destroyed.
+ */
+struct WithInsertPoint {
+  WithInsertPoint(Node* n) : prev_(n->owningGraph()->insertPoint()) {
+    n->owningGraph()->setInsertPoint(n);
+  }
+  WithInsertPoint(Block* b) : WithInsertPoint(b->return_node()) {}
+
+  ~WithInsertPoint() {
+    prev_->owningGraph()->setInsertPoint(prev_);
+  }
+
+ private:
+  Node* prev_;
+};
+
+/** \brief An utility class for setting temporary scopes.
+ *
+ * When an object of this class is created, it stores the current scope, sets
+ * the new one, and restores the original scope when the object is destroyed.
+ */
+struct WithCurrentScope {
+  WithCurrentScope(Graph& g, ScopePtr scope)
+      : graph_(&g), prev_scope_(g.current_scope()) {
+    g.set_current_scope(std::move(scope));
+  }
+  ~WithCurrentScope() {
+    graph_->set_current_scope(prev_scope_);
+  }
+
+ private:
+  Graph* graph_;
+  ScopePtr prev_scope_;
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+inline Value::Value(Node* node_, size_t offset_)
+    : node_(node_),
+      offset_(offset_),
+      unique_(node_->graph_->next_unique_++),
+      type_(TensorType::get()) {
+  node_->graph_->all_values.emplace(this);
+}
+
+inline Value* Value::setType(TypePtr type) {
+  AT_ASSERT(type);
+  if (auto dyn = type->castRaw<c10::DynamicType>()) {
+    type = dyn->fallback();
+  }
+  type_ = std::move(type);
+  for (Use& use : uses_) {
+    use.user->op_ = nullptr;
+  }
+  return this;
+}
+
+inline Graph* Value::owningGraph() {
+  return node()->owningGraph();
+}
+
+inline const Graph* Value::owningGraph() const {
+  return node()->owningGraph();
+}
+
+/************* All nodes not required to be defined before Graph **************/
+struct ProfileOp : public Node {
+  static const Symbol Kind;
+  ProfileOp(Graph* graph, std::function<void(std::vector<IValue>&)> callback)
+      : Node(graph, ::c10::prim::profile), callback_(std::move(callback)) {}
+
+  void cloneFrom(Node* other_) override;
+  Node* allocNewInstance(Graph* g) override;
+
+  const std::function<void(std::vector<IValue>&)>& getCallback() const {
+    return callback_;
+  }
+
+  void setCallback(std::function<void(std::vector<IValue>&)> callback) {
+    callback_ = std::move(callback);
+  }
+
+  bool hasSeenTensor() const {
+    return has_seen_tensor_;
+  }
+
+  void setHasSeenTensor(bool has_seen_tensor) {
+    has_seen_tensor_ = has_seen_tensor;
+  }
+
+ private:
+  std::function<void(std::vector<IValue>&)> callback_;
+  bool has_seen_tensor_ = false;
+};
+
+struct TORCH_API ProfileIValueOp : public Node {
+  static const Symbol Kind;
+  ProfileIValueOp(
+      Graph* graph,
+      std::function<void(std::vector<IValue>&)> callback)
+      : Node(graph, ::c10::prim::profile_ivalue),
+        callback_(std::move(callback)) {}
+
+  void cloneFrom(Node* other_) override;
+  Node* allocNewInstance(Graph* g) override;
+
+  const std::function<void(std::vector<IValue>&)>& getCallback() const {
+    return callback_;
+  }
+
+  void setCallback(std::function<void(std::vector<IValue>&)> callback) {
+    callback_ = std::move(callback);
+  }
+
+ private:
+  std::function<void(std::vector<IValue>&)> callback_;
+};
+
+// execute a Python function, used for Ops we can't optimize but that we want to
+// optimize around
+//
+// Note: actual implementation (ConcretePythonOp) is defined in python_ir.cpp
+// which is not included in libtorch.so. We still include some bits and pieces
+// of PythonOp here to enable writing simple passes generically. In general,
+// python-aware bits need to be moved to the descendant classes.
+struct TORCH_API PythonOp : public Node {
+  using Node::Node;
+
+  virtual std::string name() const = 0;
+  virtual void writeScalars(std::ostream& out) const = 0;
+  void cloneFrom(Node* other_) override = 0;
+  Node* allocNewInstance(Graph* g) override = 0;
+  // recover the autograd.Function instance, if this PythonOp's function
+  // was originally SomeFunction.apply
+  // used in ONNX for discovering symbolics
+  virtual c10::optional<THPObjectPtr> autogradFunction() const = 0;
+
+  virtual void lint_python() const = 0;
+};
+
+TORCH_API void LintGraph(const std::shared_ptr<Graph>& graph);
+
+TORCH_API at::ArrayRef<Value*> createTupleUnpack(Value* v);
+
+/** Insert graph \p CALLEE into graph \p G using \p INPUTS as input values.
+ * The insertion happens at the current insertion point.
+ * Optionally, one can also pass \p VALUE_MAP to get a map between \p CALLEE
+ * values and their cloned copies in \p G.
+ */
+TORCH_API std::vector<Value*> insertGraph(
+    Graph& g,
+    Graph& callee,
+    ArrayRef<Value*> inputs);
+TORCH_API std::vector<Value*> insertGraph(
+    Graph& g,
+    Graph& callee,
+    ArrayRef<Value*> inputs,
+    std::unordered_map<Value*, Value*>& value_map);
+
+/** Insert function \p CALLEE after node \p TO_REPLACE, remove the node and
+ * replace all its uses with corresponding outputs of the inserted function.
+ * This asserts that the number of outputs of the original node and the
+ * graph are the same.
+ */
+TORCH_API std::vector<Value*> inlineCallTo(
+    Node* to_replace,
+    GraphFunction* callee,
+    bool use_graph = true);
+
+TORCH_API std::vector<Value*> inlineCallTo(
+    Node* to_replace,
+    GraphFunction* callee,
+    Graph* callee_graph);
+
+/** If there is only one value in \p OUTPUTS and its kind is Tuple, insert a
+ * tuple unpack node and return the resulting values.
+ */
+TORCH_API std::vector<Value*> unpackOutputs(const std::vector<Value*>& outputs);
+
+TORCH_API std::vector<Node*> findAllNodes(Graph& g, Symbol kind, bool recurse);
+TORCH_API std::vector<Node*> findAllNodes(Block& b, Symbol kind, bool recurse);
+TORCH_API std::vector<Node*> findAllNodes(
+    at::ArrayRef<Block*> a,
+    Symbol kind,
+    bool recurse);
+
+struct TORCH_API OperatorSet {
+  OperatorSet(std::initializer_list<const char*> sig_literals);
+  std::vector<std::shared_ptr<Operator>> getOps() const;
+  void insert(std::initializer_list<const char*> sig_literals);
+
+ private:
+  friend struct Node;
+  std::unordered_map<Symbol, std::vector<std::shared_ptr<Operator>>> ops;
+};
+
+template <typename T>
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct OperatorMap {
+  // Type aliasing
+  using OpMapType = typename std::pair<std::shared_ptr<Operator>, T>;
+  using ValueType = std::vector<OpMapType>;
+  using MapType = std::unordered_map<Symbol, ValueType>;
+
+  OperatorMap() = default;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit OperatorMap(
+      std::initializer_list<std::pair<std::shared_ptr<Operator>, T>> init) {
+    insert(init);
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit OperatorMap(std::initializer_list<std::pair<const char*, T>> init) {
+    insert(init);
+  }
+
+  void insert(const std::shared_ptr<Operator>& op, T val) {
+    // Remove if exists before insert
+    erase(op);
+    map[Symbol::fromQualString(op->schema().name())].emplace_back(
+        std::make_pair(op, val));
+  }
+
+  void insert(const OperatorSet& op_set, T val) {
+    for (auto& op : op_set.getOps()) {
+      insert(op, val);
+    }
+  }
+
+  void insert(
+      std::initializer_list<std::pair<std::shared_ptr<Operator>, T>> v) {
+    for (auto& el : v) {
+      insert(el.first, el.second);
+    }
+  }
+
+  void insert(std::initializer_list<std::pair<const char*, T>> v) {
+    for (auto& el : v) {
+      insert(getOperatorForLiteral(el.first), el.second);
+    }
+  }
+
+  void erase(const std::shared_ptr<Operator>& op) {
+    auto it = map.find(Symbol::fromQualString(op->schema().name()));
+    if (it == map.end()) {
+      return;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first->schema() == op->schema()) {
+        it->second.erase(vit);
+        break;
+      }
+    }
+    if (it->second.size() == 0) {
+      map.erase(Symbol::fromQualString(op->schema().name()));
+    }
+  }
+
+  bool contains(const Operator& op) const {
+    const auto it = map.find(Symbol::fromQualString(op.schema().name()));
+    if (it == map.end()) {
+      return false;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first->schema() == op.schema()) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  bool contains(const Node* n) const {
+    return n->maybeOperator() && contains(n->getOperator());
+  }
+
+  c10::optional<T> find(const Operator& op) {
+    const auto it = map.find(Symbol::fromQualString(op.schema().name()));
+    if (it == map.end()) {
+      return c10::nullopt;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first->schema() == op.schema()) {
+        return vit->second;
+      }
+    }
+    return c10::nullopt;
+  }
+
+  // TODO: return iterator
+  std::vector<OpMapType> getAllKeysAndValues() const {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<OpMapType> keys_values;
+    for (auto& symbol_mapping : map) {
+      auto& vec = symbol_mapping.second;
+      for (auto& pair : vec) {
+        keys_values.push_back(pair);
+      }
+    }
+    return keys_values;
+  }
+
+ private:
+  friend struct Node;
+  MapType map;
+};
+
+template <typename T>
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct FunctionSchemaMap {
+  // Type aliasing
+  using FuncSchemaMapType = typename std::pair<FunctionSchema, T>;
+  using ValueType = std::vector<FuncSchemaMapType>;
+  using MapType = std::unordered_map<Symbol, ValueType>;
+
+  FunctionSchemaMap() = default;
+  void insert(const FunctionSchema& schema, T val) {
+    // Remove if exists before insert
+    erase(schema);
+    map[Symbol::fromQualString(schema.name())].emplace_back(
+        std::make_pair(schema, val));
+  }
+
+  void erase(const FunctionSchema& schema) {
+    auto it = map.find(Symbol::fromQualString(schema.name()));
+    if (it == map.end()) {
+      return;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first == schema) {
+        it->second.erase(vit);
+        break;
+      }
+    }
+    if (it->second.size() == 0) {
+      map.erase(Symbol::fromQualString(schema.name()));
+    }
+  }
+
+  bool contains(const FunctionSchema& schema) const {
+    const auto it = map.find(Symbol::fromQualString(schema.name()));
+    if (it == map.end()) {
+      return false;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first->schema() == schema) {
+        return true;
+      }
+    }
+    return false;
+  }
+
+  c10::optional<T> find(const FunctionSchema& schema) const {
+    const auto it = map.find(Symbol::fromQualString(schema.name()));
+    if (it == map.end()) {
+      return c10::nullopt;
+    }
+    for (auto vit = it->second.begin(); vit != it->second.end(); ++vit) {
+      if (vit->first == schema) {
+        return vit->second;
+      }
+    }
+    return c10::nullopt;
+  }
+
+  // TODO: return iterator
+  std::vector<FuncSchemaMapType> getAllKeysAndValues() const {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<FuncSchemaMapType> keys_values;
+    for (auto& symbol_mapping : map) {
+      auto& vec = symbol_mapping.second;
+      for (auto& pair : vec) {
+        keys_values.push_back(pair);
+      }
+    }
+    return keys_values;
+  }
+
+ private:
+  friend struct Node;
+  MapType map;
+};
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/ir_views.h

@@ -0,0 +1,164 @@
+#pragma once
+
+#include <c10/util/irange.h>
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+struct IfView {
+  explicit IfView(Node* node) : node_(node) {
+    AT_ASSERT(node->kind() == ::c10::prim::If);
+  }
+  Value* cond() const {
+    return node_->input(0);
+  }
+  Block* thenBlock() const {
+    return node_->blocks().at(0);
+  }
+  Block* elseBlock() const {
+    return node_->blocks().at(1);
+  }
+  ArrayRef<Value*> thenOutputs() const {
+    return thenBlock()->outputs();
+  }
+  ArrayRef<Value*> elseOutputs() const {
+    return elseBlock()->outputs();
+  }
+  ArrayRef<Value*> outputs() const {
+    return node_->outputs();
+  }
+  Node* node() const {
+    return node_;
+  }
+  operator Node*() const {
+    return node_;
+  }
+
+  void permuteOutputs(const std::vector<size_t>& new_output_order) {
+    node_->permuteOutputs(new_output_order);
+    thenBlock()->permuteOutputs(new_output_order);
+    elseBlock()->permuteOutputs(new_output_order);
+  }
+
+ private:
+  Node* node_;
+};
+
+struct LoopView {
+  explicit LoopView(Node* node) : node_(node) {
+    AT_ASSERT(
+        node->kind() == ::c10::prim::Loop || node->kind() == ::c10::onnx::Loop);
+  }
+  Block* bodyBlock() const {
+    return node_->blocks().at(0);
+  }
+  Value* cond() const {
+    return node_->input(0);
+  }
+  Value* maxTripCount() const {
+    return node_->input(0);
+  }
+  Value* inputCond() const {
+    return node_->input(1);
+  }
+  Value* nextCond() const {
+    return bodyBlock()->outputs().at(0);
+  }
+  Value* currentTripCount() const {
+    return bodyBlock()->inputs().at(0);
+  }
+  ArrayRef<Value*> carriedInputs() const {
+    // skip trip count and cond
+    return node_->inputs().slice(2);
+  }
+  ArrayRef<Value*> carriedInputsWithCond() const {
+    // skip trip count and cond
+    return node_->inputs().slice(1);
+  }
+  ArrayRef<Value*> carriedOutputs() const {
+    return node_->outputs();
+  }
+  ArrayRef<Value*> bodyCarriedInputs() const {
+    // skip trip count and cond
+    return bodyBlock()->inputs().slice(1);
+  }
+  ArrayRef<Value*> bodyCarriedOutputs() const {
+    return bodyBlock()->outputs().slice(1);
+  }
+  Node* node() const {
+    return node_;
+  }
+  operator Node*() const {
+    return node_;
+  }
+
+  void permuteLoopCarried(const std::vector<size_t>& new_output_order) {
+    node_->permuteOutputs(new_output_order);
+    // skip trip count and cond
+    node_->permuteInputs(adjustIndices(2, new_output_order));
+    auto adjusted_block_order = adjustIndices(1, new_output_order);
+    bodyBlock()->permuteOutputs(adjusted_block_order);
+    bodyBlock()->permuteInputs(adjusted_block_order);
+  }
+
+  void replaceMaxTripCount(Value* new_max_trip_count) {
+    node_->replaceInput(0, new_max_trip_count);
+  }
+  void replaceInputCondition(Value* new_input_condition) {
+    node_->replaceInput(1, new_input_condition);
+  }
+
+  // our way of encoding loops makes them difficult to turn back into python
+  // syntax. we have to check properties of the condition and trip count inputs
+  // to figure out which one it initially was. ModifiedLoops are not directly
+  // mappable to either For or While
+  enum LoopType { While, For, ModifiedLoop };
+
+  LoopType loopType() {
+    auto trip_count = toIValue(maxTripCount());
+    auto cond_input = toIValue(inputCond());
+    auto cond_next = toIValue(nextCond());
+
+    bool condition_is_always_true =
+        cond_input && cond_input->toBool() && cond_next && cond_next->toBool();
+    bool trip_count_is_specified = !trip_count || // trip is not a constant
+        trip_count->toInt() !=
+            std::numeric_limits<int64_t>::max() || // it is a constant but not
+                                                   // the default one
+        !currentTripCount()
+             ->uses()
+             .empty(); // it is actually being used in the body.
+
+    if (condition_is_always_true) {
+      // if the trip count was not specified this was a user-written while True:
+      return trip_count_is_specified ? For : While;
+    } else {
+      if (trip_count_is_specified) {
+        return ModifiedLoop;
+      }
+      return While;
+    }
+  }
+
+ private:
+  Node* node_;
+
+  // adjust index_ordering by adding indices 0 - thorugh adjust, and
+  // incrementing all existing inputs by adjust
+  static std::vector<size_t> adjustIndices(
+      size_t adjust,
+      const std::vector<size_t>& index_ordering) {
+    std::vector<size_t> adjusted;
+    adjusted.reserve(adjust + index_ordering.size());
+    for (const auto i : c10::irange(adjust)) {
+      adjusted.push_back(i);
+    }
+    for (auto index : index_ordering) {
+      adjusted.push_back(index + adjust);
+    }
+    return adjusted;
+  }
+};
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/irparser.h

@@ -0,0 +1,40 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <string>
+#include <unordered_map>
+
+#include <c10/util/Optional.h>
+#include <torch/csrc/Export.h>
+
+namespace torch {
+namespace jit {
+
+struct Graph;
+struct Value;
+
+// \brief Parse IR from \p STR constructing the corresponding IR in\ GRAPH.
+// if parse_tensor_constants is true will construct empty tensors
+// for Tensor constants with random or unitialized contents, otherwise will
+// throw
+TORCH_API void parseIR(
+    const std::string& str,
+    torch::jit::Graph* graph,
+    bool parse_tensor_constants = false);
+
+/** \brief Parse IR from \p STR constructing the corresponding IR in\ GRAPH.
+ *
+ * \p VMAP is filled with String to Value pairs allowing to index Values in the
+ * newly created graph by their name in the original IR string.
+ * if parse_tensor_constants is true will construct empty tensors
+ * for Tensor constants with random or unitialized contents, otherwise will
+ * throw
+ */
+TORCH_API void parseIR(
+    const std::string& str,
+    torch::jit::Graph* graph,
+    std::unordered_map<std::string, Value*>& vmap,
+    bool parse_tensor_constants = false);
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/named_value.h

@@ -0,0 +1,84 @@
+#pragma once
+#include <ATen/core/ivalue.h>
+#include <torch/csrc/jit/frontend/source_range.h>
+#include <torch/csrc/jit/ir/constants.h>
+#include <torch/csrc/utils/variadic.h>
+
+namespace torch {
+namespace jit {
+
+struct Value;
+
+/**
+ * A value with optional extra name and location information. Used during
+ * schema matching to provide extra error information and resolve kwargs.
+ */
+struct NamedValue {
+  NamedValue(const SourceRange& loc, const std::string& name, Value* value)
+      : loc_(loc), name_(name), value_(value) {}
+  NamedValue(const SourceRange& loc, Value* value) : loc_(loc), value_(value) {}
+
+  /* implicit */ NamedValue(Value* value) : value_(value) {}
+  NamedValue(const std::string& name, Value* value)
+      : name_(name), value_(value) {}
+
+  /* implicit */ NamedValue(IValue value)
+      : value_(nullptr), ivalue_(std::move(value)) {}
+
+  NamedValue(const std::string& name, IValue value)
+      : name_(name), ivalue_(std::move(value)) {}
+
+  template <
+      typename T,
+      typename = enable_if_t<
+          (!std::is_same<decay_t<T>, NamedValue>::value &&
+           !std::is_same<decay_t<T>, Value*>::value &&
+           !std::is_same<decay_t<T>, IValue>::value)>>
+  // NOLINTNEXTLINE(bugprone-forwarding-reference-overload)
+  NamedValue(T&& t) : NamedValue(IValue(std::forward<T>(t))) {}
+
+  template <
+      typename T,
+      typename = enable_if_t<
+          (!std::is_same<decay_t<T>, Value*>::value &&
+           !std::is_same<decay_t<T>, IValue>::value)>>
+  NamedValue(const std::string& name, T&& t)
+      : NamedValue(name, IValue(std::forward<T>(t))) {}
+
+  SourceRange locOr(const SourceRange& backup_location) const {
+    if (!loc_)
+      return backup_location;
+    return loc();
+  }
+
+  // note: this will insert a constant node into the graph at the current
+  // insert point if this NamedValue is actually a constant
+  Value* value(Graph& g) const {
+    if (!value_)
+      return insertConstant(
+          g, ivalue_); // use insertConstant to remove need to include ir.h here
+    return value_;
+  }
+
+  const std::string& name() const {
+    AT_ASSERT(name_);
+    return *name_;
+  }
+
+  const SourceRange& loc() const {
+    AT_ASSERT(loc_);
+    return *loc_;
+  }
+
+  at::TypePtr type() const;
+
+ private:
+  c10::optional<SourceRange> loc_;
+  c10::optional<std::string> name_;
+  Value* value_{nullptr};
+  // only valid if value_ == nullptr;
+  IValue ivalue_;
+};
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/node_hashing.h

@@ -0,0 +1,17 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+struct TORCH_API HashNode {
+  size_t operator()(const Node* k) const;
+};
+
+struct TORCH_API EqualNode {
+  bool operator()(const Node* lhs, const Node* rhs) const;
+};
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/scope.h

@@ -0,0 +1,220 @@
+#pragma once
+#include <ATen/core/jit_type.h>
+#include <ATen/core/symbol.h>
+#include <c10/util/Optional.h>
+#include <c10/util/intrusive_ptr.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/frontend/source_range.h>
+#include <unordered_map>
+
+namespace torch {
+namespace jit {
+struct ModuleInstanceInfo;
+constexpr size_t kModuleInstanceInfo = 2;
+
+namespace utils {
+std::string get_module_info(const ModuleInstanceInfo& module_instance_info);
+} // namespace utils
+
+// Scope is a node of a trie that represents the tree of nested scopes.
+// Individual scopes are pushed and popped from Graph, which holds a
+// pointer to the current scope. Each Node in Graph holds a pointer
+// to the scope that was current when the node was created.
+// The trie never needs to shrink, it only grows until it is disposed
+// of when Graph is deallocated. Hence, pointers to scopes held by nodes
+// will always be valid as long as Graph is alive.
+struct Scope;
+using ScopePtr = c10::intrusive_ptr<Scope>;
+using c10::Symbol;
+
+struct TORCH_API Scope : public c10::intrusive_ptr_target {
+ private:
+  ScopePtr parent_;
+  Symbol name_;
+  ScopePtr intrusive_from_this();
+
+ public:
+  Scope();
+
+  Scope(ScopePtr parent, Symbol name);
+
+  ScopePtr push(Symbol name);
+
+  ScopePtr parent();
+
+  bool isRoot() const;
+
+  bool isBlank() const;
+
+  ScopePtr getRoot();
+
+  size_t getDepth();
+
+  Symbol name() const;
+
+  std::string namesFromRoot(const std::string& separator = "/") const;
+};
+
+struct Function;
+struct InlinedCallStack;
+
+/**
+ * ModuleInstanceInfo is a structure to include the module type and instance
+ * name. It also provide public methods to get the pointer to module type and
+ * instance name.
+ *
+ * This structure is mainly used as a private member in InlinedCallStack, such
+ * that one can follow the callstack to find the relevant module hierarchy.
+ */
+struct ModuleInstanceInfo {
+ private:
+  c10::ClassTypePtr module_type_{nullptr};
+  std::string instance_name_;
+
+ public:
+  ModuleInstanceInfo() = default;
+  ModuleInstanceInfo(c10::ClassTypePtr module_type, std::string instance_name);
+  c10::ClassTypePtr class_type() {
+    return module_type_;
+  }
+  c10::ClassTypePtr class_type() const {
+    return module_type_;
+  }
+  std::string instance_name() const {
+    return instance_name_;
+  }
+
+  bool operator==(const ModuleInstanceInfo& rhs) const {
+    return (class_type() == rhs.class_type()) &&
+        (instance_name() == rhs.instance_name());
+  }
+};
+
+/**
+ * InlinedCallStack is an element in a list representing callstack of functions
+ * that have been inlined.
+ *
+ * Each such element holds info about the current callsite (Function and
+ * SourceRange) and a pointer to the next element in the list. The last element
+ * in the list represents the innermost function that was inlined.
+ *
+ * For instance, if a node has a callstack
+ *    [foo, source_range1] -> [bar, source_range2]
+ * it means that this node was originally from function 'bar' that was called
+ * at 'source_range2' in function 'foo' that was called in the current function
+ * at 'source_range1'.
+ *
+ * If a node did not come from any inlined function, its callstack will be
+ * empty.
+ *
+ * The callstack lists only grow, we never remove elements from them, which
+ * allows us to reuse same elements in different lists. For instance, if we
+ * inline function 'bar' to 'foo' and then inline 'foo' to two functions 'ham'
+ * and 'baz', the callstacks would look like:
+ *
+ *  [baz, source_range3]  --
+ *                           \
+ *                             --> [foo, source_range1] -> [bar, source_range2]
+ *                           /
+ *  [ham, source_range4]  --
+ */
+using InlinedCallStackPtr = c10::intrusive_ptr<InlinedCallStack>;
+using InlinedCallStackEntry =
+    std::tuple<Function*, SourceRange, c10::optional<ModuleInstanceInfo>>;
+
+struct TORCH_API InlinedCallStack : public c10::intrusive_ptr_target {
+ private:
+  c10::optional<InlinedCallStackPtr> callee_;
+  Function* fn_;
+  // Reason for fn_name_ even though we have fn_
+  // Serialized callstack is used in circustmances where InlinedCallstack
+  // cannot be constructed during runtime, e.g. mobile runtime or
+  // delegated backends.
+  // Since in those cases we do not have Function* we store function name
+  // fn_name does not give you access to the same information that Function*
+  // does, however in mobile/delegated backend runtime we use InlindedCallStack
+  // for exception stack and for that purpose fn_name_ suffices.
+  const std::string fn_name_;
+  SourceRange source_range_;
+  InlinedCallStackPtr intrusive_from_this();
+  c10::optional<ModuleInstanceInfo> module_instance_info_;
+
+ public:
+  // Constructor for a leaf callstack node.
+  InlinedCallStack(Function* fn, SourceRange source_range);
+
+  // Constructor for a leaf callstack node.
+  InlinedCallStack(
+      Function* fn,
+      SourceRange source_range,
+      c10::optional<ModuleInstanceInfo> module_instance_info);
+
+  // Constructor for a leaf callstack node.
+  InlinedCallStack(
+      Function* fn,
+      SourceRange source_range,
+      c10::optional<ModuleInstanceInfo> module_instance_info,
+      std::string& function_name);
+
+  // Constructor for an inner callstack node.
+  InlinedCallStack(
+      InlinedCallStackPtr callee,
+      Function* fn,
+      SourceRange source_range);
+
+  InlinedCallStack(
+      InlinedCallStackPtr callee,
+      Function* fn,
+      SourceRange source_range,
+      c10::optional<ModuleInstanceInfo> module_instance_info);
+
+  InlinedCallStack(
+      InlinedCallStackPtr callee,
+      Function* fn,
+      SourceRange source_range,
+      c10::optional<ModuleInstanceInfo> module_instance_info,
+      std::string& function_name);
+
+  // Return next element in the callstack list.
+  c10::optional<InlinedCallStackPtr> callee() const;
+
+  // Return module instance associated with the current element.
+  c10::optional<ModuleInstanceInfo> module_instance() const;
+
+  // Returns the source range of the node
+  SourceRange source_range() const;
+
+  Function* function() const;
+
+  const std::string& function_name() const;
+
+  // Return callstack as a vector of [Function, SourceRange] pairs.
+  std::vector<InlinedCallStackEntry> vec();
+
+  void setCallee(c10::optional<InlinedCallStackPtr>);
+
+  bool operator==(const InlinedCallStack& rhs) const {
+    // No need to compare fn_, since source_range equivalence check
+    // should suffice.
+    return (module_instance().has_value() ==
+            rhs.module_instance().has_value()) &&
+        (module_instance().has_value() &&
+         module_instance().value() == rhs.module_instance().value()) &&
+        callee() == rhs.callee() && source_range() == rhs.source_range();
+  }
+
+  bool operator!=(const InlinedCallStack& rhs) const {
+    return !(*this == rhs);
+  }
+};
+
+// {source range, node name, InlinedCallStack}
+// We store node name because same debug infor will be used for
+// profiling as well, so we need to know op names as well.
+using DebugInfoTuple =
+    std::tuple<SourceRange, std::string, InlinedCallStackPtr>;
+constexpr size_t kDebugInfoTupleSourceRangeIndex{0};
+constexpr size_t kDebugInfoTupleNodeNameIndex{1};
+constexpr size_t kDebugInfoTupleInlinedCSIndex{2};
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/subgraph_matcher.h

@@ -0,0 +1,74 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+#include <unordered_map>
+#include <vector>
+
+namespace torch {
+namespace jit {
+
+/**
+ * \brief A structure describing a match of a pattern in a graph.
+ *
+ * The structure contains an anchor node, from which the match was found, and
+ * match-maps for nodes and values. A match-map specifies the correspondance
+ * between nodes in the pattern graph (match-map keys) with nodes in the actual
+ * graph (match-map values). We keep such maps for both nodes and values.
+ */
+struct Match {
+  Node* anchor;
+  std::unordered_map<const Node*, Node*> nodes_map;
+  std::unordered_map<const Value*, Value*> values_map;
+};
+
+/**
+ * \brief Find all matches of a \p PATTERN in a \p GRAPH.
+ *
+ * The function returns a vector of match-descriptors (see description of
+ * `struct Match`).
+ *
+ * Matching rules:
+ *  - Pattern graph must contain a single block.
+ *  - Matched subgraphs do not span across different blocks.
+ *  - No uses outside the match are allowed, except for Param and Return nodes.
+ *  Basically, we're matching hammocks, not arbitrary subgraphs.
+ *  - The pattern graph must return only one value (i.e. it must have a single
+ *  node leading to return).
+ *  - Nodes that are not used in computation of the return value in the pattern
+ * graph are ignored during matching (IOW, we're essentially performing DCE on
+ * the pattern).
+ *  - Pattern graph nodes cannot alias. TODO: the check not implemented yet.
+ *  - Aliasing nodes in the graph cannot consitute a match (i.e. through all
+ * found matches, no nodes in the subgraph alias with each other). TODO: check
+ * not implemented yet.
+ *  - The matcher will not mutate either the pattern graph or the matched graph.
+ * The matched graph is taken as non-const so that Match may contain non-const
+ * pointers.  This enables clients of this API to use Match to drive mutations.
+ *
+ * Note [Multi-output Patterns]
+ * ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+ * Subgraph matcher provides limited support for multi-output patterns. With a
+ * single output pattern, a single scan through the graph is sufficient to
+ * find all the matches: given a starting node (an "anchor"), we can
+ * deterministically check whether a pattern matches a subgraph corresponding to
+ * this anchor node. For a general case of multi-output patterns, we would have
+ * N anchors, which would result in M^N comparisons (M is the size of the
+ * graph). Clearly this is computationally prohibitive.
+ *
+ * To overcome this, we impose some constraints on the multi-output patterns
+ * that we accept. We require that checking whether the pattern matches a
+ * subgraph would still be fully determined by a single node in the graph. To
+ * achieve this, we designate the first output in the pattern as the "main"
+ * output and assume that we can traverse up from this node to match the
+ * entire pattern.
+ *
+ * Corrolary 1: the order of outputs in the pattern matters!
+ * Corollary 2: patterns cannot contain any nodes not participating in the main
+ * output computation.
+ */
+std::vector<Match> TORCH_API
+findPatternMatches(const Graph& pattern, Graph& graph);
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/ir/type_hashing.h

@@ -0,0 +1,20 @@
+#pragma once
+
+#include <ATen/core/jit_type.h>
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+
+struct HashType {
+  size_t operator()(const TypePtr& type) const;
+  size_t operator()(const c10::ConstTypePtr& type) const;
+};
+
+struct EqualType {
+  bool operator()(const TypePtr& a, const TypePtr& b) const;
+  bool operator()(const c10::ConstTypePtr& a, const c10::ConstTypePtr& b) const;
+};
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/debug_info.h

@@ -0,0 +1,57 @@
+#pragma once
+#include <c10/util/flat_hash_map.h>
+#include <caffe2/serialize/inline_container.h>
+#include <torch/csrc/jit/api/compilation_unit.h>
+#include <torch/csrc/jit/ir/scope.h>
+#include <torch/csrc/jit/serialization/source_range_serialization.h>
+
+namespace torch {
+namespace jit {
+/*
+ * MobileDebugTable:
+ * Deserializes debug_pkl and callstack_map records from PT model's zip archive
+ * and stores them in a map of debug handles to DebugInfoPair. Debug handles are
+ * unique per model and runtime, be in lite interpreter or delegate, an
+ * exception of BackendRuntimeException should raised using debug handles.
+ * getSourceDebugString method is responsible for translating debug
+ * handles to correspond debug information.
+ * This debug informatin includes stack trace of model level source code and
+ * module hierarchy where the exception occurred.
+ */
+class MobileDebugTable {
+ public:
+  MobileDebugTable() = default;
+  MobileDebugTable(
+      std::unique_ptr<caffe2::serialize::PyTorchStreamReader>& reader,
+      const std::shared_ptr<CompilationUnit>& cu);
+
+  template <typename It>
+  MobileDebugTable(It begin, It end) : callstack_ptr_map_(begin, end) {}
+
+  std::string getSourceDebugString(
+      const int64_t debug_handle,
+      const std::string& top_module_type_name = "ModuleTypeUnknown") const;
+  std::string getSourceDebugString(
+      const std::vector<int64_t>& debug_handles,
+      const std::string& top_module_type_name = "ModuleTypeUnknown") const;
+  std::string getModuleHierarchyInfo(
+      const int64_t debug_handle,
+      const std::string& top_module_type_name = "ModuleTypeUnknown") const;
+  std::string getModuleHierarchyInfo(
+      const std::vector<int64_t>& debug_handles,
+      const std::string& top_module_type_name = "ModuleTypeUnknown") const;
+
+  const ska::flat_hash_map<int64_t, DebugInfoTuple>& getCallStackPtrMap()
+      const {
+    return callstack_ptr_map_;
+  }
+
+ private:
+  std::pair<std::string, std::string> getSourceDebugModuleHierarchyInfo(
+      const std::vector<int64_t>& debug_handles,
+      const std::string& top_module_type_name = "ModuleTypeUnknown") const;
+  ska::flat_hash_map<int64_t, DebugInfoTuple> callstack_ptr_map_;
+};
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/file_format.h

@@ -0,0 +1,196 @@
+#pragma once
+
+#include <array>
+#include <cerrno>
+#include <cstddef>
+#include <cstring>
+#include <fstream>
+#include <istream>
+#include <memory>
+
+#include <c10/core/CPUAllocator.h>
+#include <c10/core/impl/alloc_cpu.h>
+#include <caffe2/serialize/read_adapter_interface.h>
+
+#if defined(HAVE_MMAP)
+#include <fcntl.h>
+#include <sys/mman.h>
+#include <sys/stat.h>
+#include <sys/types.h>
+#include <unistd.h>
+#endif
+
+/**
+ * @file
+ *
+ * Helpers for identifying file formats when reading serialized data.
+ *
+ * Note that these functions are declared inline because they will typically
+ * only be called from one or two locations per binary.
+ */
+
+namespace torch {
+namespace jit {
+
+/**
+ * The format of a file or data stream.
+ */
+enum class FileFormat {
+  UnknownFileFormat = 0,
+  FlatbufferFileFormat,
+  ZipFileFormat,
+};
+
+/// The size of the buffer to pass to #getFileFormat(), in bytes.
+constexpr size_t kFileFormatHeaderSize = 8;
+constexpr size_t kMaxAlignment = 16;
+
+/**
+ * Returns the likely file format based on the magic header bytes in @p header,
+ * which should contain the first bytes of a file or data stream.
+ */
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline FileFormat getFileFormat(const char* data) {
+  // The size of magic strings to look for in the buffer.
+  static constexpr size_t kMagicSize = 4;
+
+  // Bytes 4..7 of a Flatbuffer-encoded file produced by
+  // `flatbuffer_serializer.h`. (The first four bytes contain an offset to the
+  // actual Flatbuffer data.)
+  static constexpr std::array<char, kMagicSize> kFlatbufferMagicString = {
+      'P', 'T', 'M', 'F'};
+  static constexpr size_t kFlatbufferMagicOffset = 4;
+
+  // The first four bytes of a ZIP file.
+  static constexpr std::array<char, kMagicSize> kZipMagicString = {
+      'P', 'K', '\x03', '\x04'};
+
+  // Note that we check for Flatbuffer magic first. Since the first four bytes
+  // of flatbuffer data contain an offset to the root struct, it's theoretically
+  // possible to construct a file whose offset looks like the ZIP magic. On the
+  // other hand, bytes 4-7 of ZIP files are constrained to a small set of values
+  // that do not typically cross into the printable ASCII range, so a ZIP file
+  // should never have a header that looks like a Flatbuffer file.
+  if (std::memcmp(
+          data + kFlatbufferMagicOffset,
+          kFlatbufferMagicString.data(),
+          kMagicSize) == 0) {
+    // Magic header for a binary file containing a Flatbuffer-serialized mobile
+    // Module.
+    return FileFormat::FlatbufferFileFormat;
+  } else if (std::memcmp(data, kZipMagicString.data(), kMagicSize) == 0) {
+    // Magic header for a zip file, which we use to store pickled sub-files.
+    return FileFormat::ZipFileFormat;
+  }
+  return FileFormat::UnknownFileFormat;
+}
+
+/**
+ * Returns the likely file format based on the magic header bytes of @p data.
+ * If the stream position changes while inspecting the data, this function will
+ * restore the stream position to its original offset before returning.
+ */
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline FileFormat getFileFormat(std::istream& data) {
+  FileFormat format = FileFormat::UnknownFileFormat;
+  std::streampos orig_pos = data.tellg();
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  std::array<char, kFileFormatHeaderSize> header;
+  data.read(header.data(), header.size());
+  if (data.good()) {
+    format = getFileFormat(header.data());
+  }
+  data.seekg(orig_pos, data.beg);
+  return format;
+}
+
+/**
+ * Returns the likely file format based on the magic header bytes of the file
+ * named @p filename.
+ */
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline FileFormat getFileFormat(const std::string& filename) {
+  std::ifstream data(filename, std::ifstream::binary);
+  return getFileFormat(data);
+}
+
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static void file_not_found_error() {
+  std::stringstream message;
+  message << "Error while opening file: ";
+  if (errno == ENOENT) {
+    message << "no such file or directory" << std::endl;
+  } else {
+    message << "error no is: " << errno << std::endl;
+  }
+  TORCH_CHECK(false, message.str());
+}
+
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline std::tuple<std::shared_ptr<char>, size_t> get_file_content(
+    const char* filename) {
+#if defined(HAVE_MMAP)
+  int fd = open(filename, O_RDONLY);
+  if (fd < 0) {
+    // failed to open file, chances are it's no such file or directory.
+    file_not_found_error();
+  }
+  struct stat statbuf {};
+  fstat(fd, &statbuf);
+  size_t size = statbuf.st_size;
+  void* ptr = mmap(nullptr, statbuf.st_size, PROT_READ, MAP_PRIVATE, fd, 0);
+  close(fd);
+  auto deleter = [statbuf](char* ptr) { munmap(ptr, statbuf.st_size); };
+  std::shared_ptr<char> data(reinterpret_cast<char*>(ptr), deleter);
+#else
+  FILE* f = fopen(filename, "rb");
+  if (f == nullptr) {
+    file_not_found_error();
+  }
+  fseek(f, 0, SEEK_END);
+  size_t size = ftell(f);
+  fseek(f, 0, SEEK_SET);
+  // make sure buffer size is multiple of alignment
+  size_t buffer_size = (size / kMaxAlignment + 1) * kMaxAlignment;
+  std::shared_ptr<char> data(
+      static_cast<char*>(c10::alloc_cpu(buffer_size)), c10::free_cpu);
+  fread(data.get(), size, 1, f);
+  fclose(f);
+#endif
+  return std::make_tuple(data, size);
+}
+
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline std::tuple<std::shared_ptr<char>, size_t> get_stream_content(
+    std::istream& in) {
+  // get size of the stream and reset to orig
+  std::streampos orig_pos = in.tellg();
+  in.seekg(orig_pos, std::ios::end);
+  const long size = in.tellg();
+  in.seekg(orig_pos, in.beg);
+
+  // read stream
+  // NOLINT make sure buffer size is multiple of alignment
+  size_t buffer_size = (size / kMaxAlignment + 1) * kMaxAlignment;
+  std::shared_ptr<char> data(
+      static_cast<char*>(c10::alloc_cpu(buffer_size)), c10::free_cpu);
+  in.read(data.get(), size);
+
+  // reset stream to original position
+  in.seekg(orig_pos, in.beg);
+  return std::make_tuple(data, size);
+}
+
+// NOLINTNEXTLINE(facebook-hte-NamespaceScopedStaticDeclaration)
+static inline std::tuple<std::shared_ptr<char>, size_t> get_rai_content(
+    caffe2::serialize::ReadAdapterInterface* rai) {
+  size_t buffer_size = (rai->size() / kMaxAlignment + 1) * kMaxAlignment;
+  std::shared_ptr<char> data(
+      static_cast<char*>(c10::alloc_cpu(buffer_size)), c10::free_cpu);
+  rai->read(
+      0, data.get(), rai->size(), "Loading ReadAdapterInterface to bytes");
+  return std::make_tuple(data, buffer_size);
+}
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/flatbuffer_loader.h

@@ -0,0 +1,136 @@
+#pragma once
+
+#include <istream>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+#include <ATen/core/ivalue.h>
+#include <c10/core/Device.h>
+#include <c10/macros/Macros.h>
+#include <c10/util/Optional.h>
+#include <torch/csrc/jit/mobile/module.h>
+
+/**
+ * Defines the public API for loading flatbuffer-serialized mobile modules.
+ * Note that this header must not include or depend on flatbuffer-defined
+ * types, to avoid leaking those details to PyTorch clients.
+ */
+
+namespace torch {
+namespace jit {
+
+/// All non-copied data pointers provided to `parse_and_initialize_*` functions
+/// must be aligned to this boundary. Since the Module will point directly into
+/// the data, this alignment is necessary to ensure that certain types/structs
+/// are properly aligned.
+constexpr size_t kFlatbufferDataAlignmentBytes = 16;
+
+/// Maps file names to file contents.
+using ExtraFilesMap = std::unordered_map<std::string, std::string>;
+
+// On high level, to produce a Module from a file on disk, we need to go
+// through the follow steps:
+// 1. Read: Read the file from disk -> memory
+// 2. Deserialize: Parse the bytes to produce some in memory manipulable
+//    structure
+// 3. Module initialization: Produce mobile::Module out of the structure
+//    produced in 2.
+// Under this context, the structure described in 2. is the flatbuffer-defined
+// type mobile::serialization::Module. However, this step/type is not visible in
+// the public API.
+
+// Parse a mobile::Module from raw bytes.
+//
+// This function does steps 2+3 described above.
+//
+// Does not take ownership of `data`; if you want it to take ownership, see the
+// shared_ptr overload of this function.
+//
+// If should_copy_tensor_memory is true, then the returned module will NOT have
+// refences to `data`, so `data` can be freed immediately.
+//
+// If should_copy_tensor_memory is false, then returned module will have tensors
+// that points inside of `data`; the caller will need to make sure that `data`
+// outlives the returned Module. Also, `data` must be aligned to
+// kFlatbufferDataAlignmentBytes.
+TORCH_API mobile::Module parse_and_initialize_mobile_module(
+    void* data,
+    size_t size, // of `data`, in bytes.
+    c10::optional<at::Device> device = c10::nullopt,
+    ExtraFilesMap* extra_files = nullptr,
+    bool should_copy_tensor_memory = false);
+
+// Parse a mobile::Module from raw bytes.
+//
+// This function does steps 2+3 described above.
+//
+// The returned Module holds a reference to `data`, which must be aligned to
+// kFlatbufferDataAlignmentBytes.
+//
+// If you do not want the Module to hold a reference to `data`, see the raw
+// pointer overload of this function.
+TORCH_API mobile::Module parse_and_initialize_mobile_module(
+    std::shared_ptr<char> data,
+    size_t size, // of `data`, in bytes.
+    c10::optional<at::Device> device = c10::nullopt,
+    ExtraFilesMap* extra_files = nullptr);
+
+// Parse a mobile::Module from raw bytes, also returning JIT-related metadata.
+//
+// This is the same as parse_and_initialize_mobile_module() except that it also
+// extracts JIT source files and constants. Can be used to construct a
+// jit::Module.
+TORCH_API mobile::Module parse_and_initialize_mobile_module_for_jit(
+    void* data,
+    size_t size, // of `data`, in bytes.
+    ExtraFilesMap& jit_sources,
+    std::vector<IValue>& jit_constants,
+    c10::optional<at::Device> device = c10::nullopt,
+    ExtraFilesMap* extra_files = nullptr);
+
+// Load a mobile::Module from a filepath.
+//
+// This function does steps 1+2+3 described above.
+//
+// We need to have this as a convienience because Python API will need to wrap
+// this. C++ clients should use one of the versions of
+// parse_and_initialize_mobile_module() so they can manage the raw data more
+// directly.
+TORCH_API mobile::Module load_mobile_module_from_file(
+    const std::string& filename,
+    c10::optional<at::Device> device = c10::nullopt,
+    ExtraFilesMap* extra_files = nullptr);
+
+TORCH_API uint64_t get_bytecode_version(std::istream& in);
+TORCH_API uint64_t get_bytecode_version(const std::string& filename);
+TORCH_API uint64_t get_bytecode_version_from_bytes(char* flatbuffer_content);
+
+TORCH_API mobile::ModuleInfo get_module_info_from_flatbuffer(
+    char* flatbuffer_content);
+
+// The methods below are less efficient because it need to read the stream in
+// its entirity to a buffer
+TORCH_API mobile::Module load_mobile_module_from_stream_with_copy(
+    std::istream& in,
+    c10::optional<at::Device> device = c10::nullopt,
+    ExtraFilesMap* extra_files = nullptr);
+
+TORCH_API mobile::Module parse_flatbuffer_no_object(
+    std::shared_ptr<char> data,
+    size_t size,
+    c10::optional<at::Device> device);
+
+TORCH_API mobile::Module parse_and_initialize_mobile_module(
+    void* data,
+    size_t,
+    c10::optional<at::Device>,
+    ExtraFilesMap* extra_files,
+    bool should_copy_tensor_memory);
+
+// no op, TODO(qihan) delete
+TORCH_API bool register_flatbuffer_loader();
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/frame.h

@@ -0,0 +1,53 @@
+#pragma once
+
+#include <cstddef>
+
+#include <c10/util/Optional.h>
+#include <torch/csrc/jit/mobile/code.h>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+
+class Frame {
+ public:
+  explicit Frame(const Code& code) : code_(code) {}
+  const Code& getCode() const {
+    return code_;
+  }
+
+  void step() {
+    pc_++;
+  }
+
+  void jump(size_t n) {
+    pc_ += n;
+  }
+
+  size_t getPC() const {
+    return pc_;
+  }
+
+  const Instruction& getInstruction() const {
+    return code_.instructions_.at(pc_);
+  }
+
+  c10::optional<int64_t> getDebugHandle() const {
+    return getDebugHandle(pc_);
+  }
+
+  c10::optional<int64_t> getDebugHandle(size_t pc) const {
+    if (pc >= code_.debug_handles_.size()) {
+      return {};
+    }
+    return code_.debug_handles_[pc];
+  }
+
+ private:
+  const Code& code_;
+  size_t pc_{0};
+};
+
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/function.h

@@ -0,0 +1,86 @@
+#pragma once
+
+#include <vector>
+
+#include <ATen/core/function.h>
+#include <ATen/core/function_schema.h>
+#include <ATen/core/ivalue.h>
+#include <torch/csrc/jit/mobile/code.h>
+
+namespace torch {
+namespace jit {
+enum OpCode : uint8_t;
+struct Instruction;
+struct OperatorString;
+
+namespace mobile {
+
+class TORCH_API Function : public torch::jit::Function {
+ public:
+  explicit Function(c10::QualifiedName name);
+  Function(
+      c10::QualifiedName name,
+      Code code,
+      at::optional<c10::FunctionSchema> schema);
+  void run(Stack& stack) override;
+  at::IValue operator()(Stack& stack);
+  void ensure_defined() override {}
+  size_t num_inputs() const override;
+  const c10::QualifiedName& qualname() const override;
+  bool call(Stack&, c10::function_ref<void(const mobile::Code&)>) override;
+
+  // NOTE: the APIs below is dangerous: if you call append_instruction with
+  // dbg_handle and then call it without; then the dbg_handle will become
+  // misaligned. Therefore only use ONE variant at time.
+  void append_instruction(OpCode op, int X, int N, int64_t dbg_handle);
+  void append_instruction(OpCode op, int X, int N);
+  void append_operator(
+      const std::string& name,
+      const std::string& overload_name,
+      const c10::optional<int>& num_specified_args);
+  void append_constant(const c10::IValue& constant);
+  void append_type(const c10::TypePtr& type);
+  void append_function(mobile::Function& func);
+
+  void set_register_size(size_t size);
+
+  int64_t get_debug_handle(size_t pc) const;
+  const Code& get_code() const;
+  Code& get_code();
+
+  torch::jit::Function& setSchema(c10::FunctionSchema schema) override;
+  bool hasSchema() const;
+  const c10::FunctionSchema& getSchema() const override;
+
+  // Returns the debug handle corresponding to where the execution
+  // is halted due to exception.
+  // If no corresponding debug handle is found then -1 is returned.
+  const std::vector<int64_t>& getExceptionDebugHandles() const;
+  static Function& registerFunc(
+      const std::string& qualified_name,
+      const std::vector<Instruction>& instructions,
+      const std::vector<c10::IValue>& constants,
+      const std::vector<c10::TypePtr>& types,
+      const size_t register_size);
+
+  // if not initialize, initialize by loading operators.
+  // return true of all op loaded, return false if some op is not found
+  // in the current runtime. Then, the ops that did not found will be filled
+  // in unsupported_op_names
+  bool initialize_operators(bool should_check_operators);
+
+ private:
+  c10::QualifiedName name_;
+  Code code_;
+  at::optional<c10::FunctionSchema> schema_; // (byte-code version 4+)
+};
+
+c10::optional<std::function<void(Stack&)>> makeOperatorFunction(
+    c10::OperatorName opname,
+    c10::optional<int> num_specified_args);
+
+TORCH_API std::string operator_str(const c10::OperatorName& opname);
+
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/import.h

@@ -0,0 +1,112 @@
+#pragma once
+#include <torch/csrc/jit/mobile/module.h>
+#include <torch/csrc/jit/mobile/parse_operators.h>
+
+#include <istream>
+#include <memory>
+
+#include <caffe2/serialize/file_adapter.h>
+
+namespace torch {
+namespace jit {
+using caffe2::serialize::FileAdapter;
+using caffe2::serialize::IStreamAdapter;
+using caffe2::serialize::ReadAdapterInterface;
+using ExtraFilesMap = std::unordered_map<std::string, std::string>;
+
+constexpr const char* kArchiveNameBytecode = "bytecode";
+constexpr const char* kArchiveNameConstants = "constants";
+constexpr const char* kArchiveNameVersion = "version";
+
+// The family of methods below load a serialized Mobile Module
+// into a mobile::Module object.
+TORCH_API mobile::Module _load_for_mobile(
+    std::istream& in,
+    c10::optional<at::Device> device,
+    ExtraFilesMap& extra_file,
+    uint64_t module_load_options = kDefaultMobileLoadOptions);
+
+TORCH_API mobile::Module _load_for_mobile(
+    const std::string& filename,
+    c10::optional<at::Device> device,
+    ExtraFilesMap& extra_files);
+
+TORCH_API mobile::Module _load_for_mobile(
+    std::unique_ptr<ReadAdapterInterface> rai,
+    c10::optional<c10::Device> device,
+    ExtraFilesMap& extra_files,
+    uint64_t module_load_options = kDefaultMobileLoadOptions);
+
+TORCH_API mobile::Module _load_for_mobile(
+    const std::string& filename,
+    c10::optional<at::Device> device,
+    ExtraFilesMap& extra_files,
+    uint64_t module_load_options);
+
+TORCH_API mobile::Module _load_for_mobile(
+    std::istream& in,
+    c10::optional<at::Device> device = c10::nullopt);
+
+TORCH_API mobile::Module _load_for_mobile(
+    const std::string& filename,
+    c10::optional<at::Device> device = c10::nullopt);
+
+TORCH_API mobile::Module _load_for_mobile(
+    std::unique_ptr<ReadAdapterInterface> rai,
+    c10::optional<c10::Device> device = c10::nullopt);
+
+/**
+ * Load only the contents of the "extra/" files whose names are
+ * passed in the map (extra_files). Populate the corresponding values
+ * with the contents of those files. Do not attempt to load the entire
+ * model, and stop once the extra files have been extracted.
+ *
+ * This API is needed to be able to load GPU models on linux CPU
+ * machines and extract only the extra files so that we can inspect
+ * the metadata that was added to the .ptl archive when it was
+ * generated.
+ *
+ */
+void _load_extra_only_for_mobile(
+    const std::string& filename,
+    c10::optional<at::Device> device,
+    ExtraFilesMap& extra_files);
+
+// Currently used by both mobile/import.cpp and model_compatibility.cpp.
+// Should be removed after model_compatibility.cpp start using simplified
+// version type_resolver and obj_loader.
+at::TypePtr resolveTypeNameMobile(
+    const c10::QualifiedName& qn,
+    std::shared_ptr<CompilationUnit> compilation_unit);
+c10::StrongTypePtr typeResolverMobile(
+    const c10::QualifiedName& qn,
+    const std::shared_ptr<CompilationUnit>& compilation_unit);
+c10::intrusive_ptr<c10::ivalue::Object> objLoaderMobile(
+    const at::StrongTypePtr& type,
+    const at::IValue& input,
+    mobile::CompilationUnit& mobile_compilation_unit);
+
+// Given a reader, which has access to a model file,
+// return true if there exists tensors in `bytecode` archive
+bool isTensorInBytecodeArchive(
+    caffe2::serialize::PyTorchStreamReader& stream_reader);
+
+namespace mobile {
+
+/**
+ * Given a torch::jit::mobile::Module, return a set of operator names
+ * (with overload name) that are used by any method in this mobile
+ * Mobile. This method runs through the bytecode for all methods
+ * in the specified model (module), and extracts all the root
+ * operator names. Root operators are operators that are called
+ * directly by the model (as opposed to non-root operators, which
+ * may be called transitively by the root operators).
+ *
+ */
+TORCH_API std::set<std::string> _export_operator_list(
+    torch::jit::mobile::Module& module);
+
+} // namespace mobile
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/import_export_common.h

@@ -0,0 +1,23 @@
+#pragma once
+
+/**
+ * @file
+ * Declarations shared between import_data.cpp and export_data.cpp
+ */
+
+namespace torch {
+namespace jit {
+namespace mobile {
+
+namespace internal {
+/**
+ * The name of the mobile::Module attribute which contains saved parameters, as
+ * a Dict of names to Tensors. Only used for Flatbuffer serialization.
+ */
+// NOLINTNEXTLINE(cppcoreguidelines-avoid-c-arrays,modernize-avoid-c-arrays)
+constexpr char kSavedParametersAttributeName[] = "data";
+} // namespace internal
+
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/interpreter.h

@@ -0,0 +1,30 @@
+#pragma once
+
+#include <vector>
+
+#include <torch/csrc/jit/mobile/code.h>
+#include <torch/csrc/jit/mobile/frame.h>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+
+struct InterpreterState {
+  TORCH_API explicit InterpreterState(const Code& code);
+  TORCH_API bool run(Stack& stack);
+
+ private:
+  void enterFrame(const Code&);
+  void leaveFrame();
+  void saveExceptionDebugHandles();
+  void callFunction(torch::jit::Function& f, Stack& stack);
+
+  c10::IValue& reg(size_t reg);
+  std::vector<c10::IValue> registers_;
+  std::vector<Frame> frames_;
+};
+
+const std::vector<DebugHandle>& getInterpretersExceptionDebugHandles();
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/parse_bytecode.h

@@ -0,0 +1,25 @@
+#pragma once
+#include <torch/csrc/jit/mobile/function.h>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+using c10::IValue;
+TORCH_API void parseInstructions(
+    const std::string& function_name,
+    c10::ivalue::TupleElements&& ins_list,
+    c10::ivalue::TupleElements& debug_handles_m_tuple,
+    mobile::Function* function);
+TORCH_API void parseConstants(
+    const c10::ivalue::TupleElements& consts_list,
+    mobile::Function* function);
+TORCH_API void parseTypes(
+    const c10::ivalue::TupleElements& types_list,
+    mobile::Function* function);
+TORCH_API void parseRegisterSize(size_t rsize, mobile::Function* function);
+TORCH_API void applyUpgrader(
+    mobile::Function* function,
+    uint64_t operator_version);
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/prim_ops_registery.h

@@ -0,0 +1,32 @@
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <functional>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+
+using Stack = std::vector<c10::IValue>;
+
+void registerPrimOpsFunction(
+    const std::string& name,
+    const std::function<void(Stack&)>& fn);
+
+bool hasPrimOpsFn(const std::string& name);
+
+std::function<void(Stack&)>& getPrimOpsFn(const std::string& name);
+
+class prim_op_fn_register {
+ public:
+  prim_op_fn_register(
+      const std::string& name,
+      const std::function<void(Stack&)>& fn) {
+    registerPrimOpsFunction(name, fn);
+  }
+};
+
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/promoted_prim_ops.h

@@ -0,0 +1,63 @@
+#pragma once
+#include <torch/csrc/jit/mobile/prim_ops_registery.h>
+#include <torch/csrc/jit/mobile/register_ops_common_utils.h>
+
+namespace torch {
+namespace jit {
+
+void tupleIndex(Stack& stack);
+
+void raiseException(Stack& stack);
+
+void is(Stack& stack);
+
+void unInitialized(Stack& stack);
+
+void isNot(Stack& stack);
+
+void aten_format(Stack& stack);
+
+void size(Stack& stack);
+
+void sym_size(Stack& stack);
+
+void sym_size_int(Stack& stack);
+
+void sym_stride_int(Stack& stack);
+
+void sym_numel(Stack& stack);
+
+void sym_storage_offset(Stack& stack);
+
+void sym_stride(Stack& stack);
+
+void device(Stack& stack);
+
+void device_with_index(Stack& stack);
+
+void dtype(Stack& stack);
+
+void layout(Stack& stack);
+
+void toPrimDType(Stack& stack);
+
+void dim(Stack& stack);
+
+void _not(Stack& stack);
+
+void boolTensor(Stack& stack);
+
+void toList(Stack& stack);
+
+void numToTensorScalar(Stack& stack);
+
+void isCuda(Stack& stack);
+
+void numToTensorBool(Stack& stack);
+
+void dictIndex(Stack& stack);
+
+void raiseExceptionWithMessage(Stack& stack);
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/quantization.h

@@ -0,0 +1,38 @@
+#pragma once
+
+#include <c10/macros/Export.h>
+#include <string>
+
+namespace torch {
+namespace jit {
+namespace mobile {
+class Module;
+namespace quantization {
+/*
+ * Device side PTQ API.
+ * Once the model has been prepared for quantization on server side, such model
+ * is sent to device. On device side the model is further trained. At the end of
+ * the training, before the model is readied for inference, we need to quantize
+ * the model.
+ * Usage of this API is as follows.
+ * PTQQuanizationHelper ptq_helper;
+ * ptq_helper.quantize_dynamic(m, "forward");
+ * Args:
+ * m: Captured by reference, an instance of mobile::Module. This module will be
+ * mutated in place to replace its <method_name> method with quantized
+ * equivalent. method:name: Name of the method to be quantized. AOT preparation
+ * for quantization must also have been done for this method. Returns: In place
+ * mutated `m` whose size should be smaller due to weight quantization and whose
+ * <method_name> method should use quantized ops
+ */
+class TORCH_API PTQQuanizationHelper {
+ public:
+  PTQQuanizationHelper() = default;
+  void quantize_dynamic(
+      torch::jit::mobile::Module& m,
+      const std::string& method_name);
+};
+} // namespace quantization
+} // namespace mobile
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/register_ops_common_utils.h

@@ -0,0 +1,55 @@
+#pragma once
+
+#include <ATen/Context.h>
+#include <ATen/NativeFunctions.h>
+#include <ATen/core/ivalue.h>
+#include <ATen/core/stack.h>
+#include <torch/csrc/jit/runtime/jit_exception.h>
+#include <torch/csrc/jit/runtime/vararg_functions.h>
+
+namespace torch {
+namespace jit {
+
+inline void noop(Stack& n) {}
+
+int64_t normalizeIndex(int64_t idx, int64_t list_size);
+
+// reference function THPVariable_to in python_variable_methods.cpp
+static C10_UNUSED at::Tensor to_dispatch(
+    at::Tensor self,
+    c10::optional<at::Device> device,
+    c10::optional<at::ScalarType> scalarType,
+    bool non_blocking,
+    bool copy) {
+  if (device && device->is_cuda()) {
+    at::globalContext().lazyInitCUDA();
+  }
+  if (!device && !scalarType && !copy) {
+    return self;
+  } else if (!device) {
+    return self.to(*scalarType, non_blocking, copy);
+  } else if (!scalarType) {
+    return self.to(*device, non_blocking, copy);
+  } else {
+    return self.to(*device, *scalarType, non_blocking, copy);
+  }
+}
+
+// Convert the tensor pointed to by \p data to a nested list. \p dim is the
+// number of dimensions in the tensor and \p cur_dim is the dimension being
+// processed by the current invocation. \p ty is the expected output IR type of
+// the operation. \p is the scalar type of \p data. \p sizes and \p strides are
+// the sizes and strides of the tensor operand and \p element_size is the size
+// in bytes of one tensor element.
+IValue tensorToListRecursive(
+    char* data,
+    int64_t cur_dim,
+    int64_t num_tensor_dims,
+    at::TypePtr ty,
+    at::ScalarType scalar_ty,
+    at::IntArrayRef sizes,
+    at::IntArrayRef strides,
+    size_t element_size);
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/type_parser.h

@@ -0,0 +1,54 @@
+#pragma once
+
+#include <ATen/core/dynamic_type.h>
+#include <ATen/core/jit_type.h>
+#include <unordered_set>
+
+namespace c10 {
+
+class TORCH_API TypeParser {
+ public:
+  explicit TypeParser(std::string pythonStr);
+  explicit TypeParser(std::vector<std::string>& pythonStrs);
+
+  TypePtr parse();
+  std::vector<TypePtr> parseList();
+  static const std::unordered_set<std::string>& getNonSimpleType();
+  static const std::unordered_set<std::string>& getCustomType();
+  std::unordered_set<std::string> getContainedTypes();
+
+ private:
+  TypePtr parseNamedTuple(const std::string& qualified_name);
+  TypePtr parseCustomType();
+  TypePtr parseTorchbindClassType();
+  TypePtr parseNonSimple(const std::string& token);
+
+  void expect(const char* s);
+  void expectChar(char c);
+  template <typename T>
+  TypePtr parseSingleElementType();
+
+  void lex();
+
+  std::string next();
+  c10::string_view nextView();
+  void advance();
+  C10_NODISCARD c10::string_view cur() const;
+
+  std::string pythonStr_;
+  size_t start_;
+  c10::string_view next_token_;
+
+  // Used for parsing string list
+  std::vector<std::string> pythonStrs_;
+  std::unordered_map<std::string, c10::TypePtr> str_type_ptr_map_;
+
+  // Store all contained types when parsing a string
+  std::unordered_set<std::string> contained_types_;
+};
+
+TORCH_API TypePtr parseType(const std::string& pythonStr);
+
+TORCH_API std::vector<TypePtr> parseType(std::vector<std::string>& pythonStr);
+
+} // namespace c10

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/mobile/upgrader_mobile.h

@@ -0,0 +1,43 @@
+#pragma once
+
+// #include <ATen/core/ivalue.h>
+#include <ATen/core/ivalue_inl.h>
+
+#include <torch/csrc/jit/mobile/code.h>
+#include <torch/csrc/jit/mobile/function.h>
+#include <torch/csrc/jit/serialization/import_export_functions.h>
+#include <memory>
+#include <string>
+#include <unordered_map>
+#include <vector>
+
+namespace torch {
+namespace jit {
+struct Instruction;
+struct Upgrader {
+  int min_version;
+  int max_version;
+  std::string upgrader_name;
+  int index;
+};
+
+// From operator_versions.yaml
+TORCH_API const std::unordered_map<std::string, std::vector<Upgrader>>
+getOperatorVersionMapForMobile();
+
+struct OperatorString {
+  const std::string name;
+  const std::string overload_name;
+  const c10::optional<int> num_specified_args;
+};
+
+struct ByteCodeFunctionWithOperator {
+  mobile::Function& function;
+  std::vector<OperatorString> operators;
+};
+
+TORCH_API const std::vector<ByteCodeFunctionWithOperator>&
+getUpgraderBytecodeList();
+
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/module_python.h

@@ -0,0 +1,35 @@
+#pragma once
+#include <pybind11/pybind11.h>
+#include <pybind11/stl.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace py = pybind11;
+
+namespace torch::jit {
+
+inline c10::optional<Module> as_module(py::handle obj) {
+  static py::handle ScriptModule =
+      py::module::import("torch.jit").attr("ScriptModule");
+  if (py::isinstance(obj, ScriptModule)) {
+    return py::cast<Module>(obj.attr("_c"));
+  }
+  return c10::nullopt;
+}
+
+inline c10::optional<Object> as_object(py::handle obj) {
+  static py::handle ScriptObject =
+      py::module::import("torch").attr("ScriptObject");
+  if (py::isinstance(obj, ScriptObject)) {
+    return py::cast<Object>(obj);
+  }
+
+  static py::handle RecursiveScriptClass =
+      py::module::import("torch.jit").attr("RecursiveScriptClass");
+  if (py::isinstance(obj, RecursiveScriptClass)) {
+    return py::cast<Object>(obj.attr("_c"));
+  }
+  return c10::nullopt;
+}
+
+} // namespace torch::jit

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/pybind_utils.h

@@ -0,0 +1,1115 @@
+#pragma once
+
+#include <ATen/core/ivalue.h>
+#include <ATen/core/jit_type.h>
+#include <ATen/core/qualified_name.h>
+#include <ATen/core/stack.h>
+#include <pybind11/complex.h>
+#include <pybind11/pybind11.h>
+#include <pybind11/pytypes.h>
+#include <torch/csrc/Device.h>
+#include <torch/csrc/Dtype.h>
+#include <torch/csrc/Export.h>
+#include <torch/csrc/Layout.h>
+#include <torch/csrc/QScheme.h>
+#include <torch/csrc/Stream.h>
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/frontend/schema_matching.h>
+#include <torch/csrc/jit/frontend/tracer.h>
+#include <torch/csrc/jit/python/module_python.h>
+#include <torch/csrc/jit/python/python_custom_class.h>
+#include <torch/csrc/jit/python/python_tracer.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/runtime/operator.h>
+#include <torch/csrc/utils/pybind.h>
+#include <torch/csrc/utils/python_arg_parser.h>
+#include <torch/csrc/utils/six.h>
+#ifdef USE_DISTRIBUTED
+#include <torch/csrc/distributed/rpc/py_rref.h>
+#include <torch/csrc/distributed/rpc/rref_impl.h>
+#endif
+
+#include <ATen/core/function_schema.h>
+#include <c10/core/Stream.h>
+#ifdef USE_C10D_NCCL
+#include <c10/cuda/CUDACachingAllocator.h>
+#include <c10/cuda/CUDAStream.h>
+#endif
+#include <c10/util/Exception.h>
+#include <c10/util/Optional.h>
+#include <c10/util/irange.h>
+
+#include <algorithm>
+#include <cstddef>
+#include <string>
+#include <utility>
+#include <vector>
+
+// The visibility attribute is to avoid a warning about storing a field in the
+// struct that has a different visibility (from pybind) than the struct.
+#ifdef _WIN32
+#define VISIBILITY_HIDDEN
+#else
+#define VISIBILITY_HIDDEN __attribute__((visibility("hidden")))
+#endif
+
+namespace torch::jit {
+
+using ResolutionCallback = std::function<py::object(std::string)>;
+
+void clear_registered_instances(void* ptr);
+
+TORCH_PYTHON_API IValue toIValue(
+    py::handle obj,
+    const TypePtr& type,
+    c10::optional<int32_t> N = c10::nullopt);
+
+TORCH_PYTHON_API py::object toPyObject(IValue ivalue);
+
+// Hack to overload the behavior of toIValue to accept Python
+// numbers in places where a Tensor is expected
+// See also torch::should_allow_numbers_as_tensors
+class ToIValueAllowNumbersAsTensors {
+  bool old_;
+
+ public:
+  ToIValueAllowNumbersAsTensors(bool enable);
+  ~ToIValueAllowNumbersAsTensors();
+};
+
+// Wrap Python function to guard deref
+// NB: Need VISIBILITY_HIDDEN for silencing compiler error,
+// 'torch::jit::PythonFunctionGuard' declared with greater visibility than the
+// type of its field 'torch::jit::PythonFunctionGuard::func_'
+struct VISIBILITY_HIDDEN PythonFunctionGuard {
+  explicit PythonFunctionGuard(py::function func) : func_(std::move(func)) {}
+
+  ~PythonFunctionGuard() {
+    pybind11::gil_scoped_acquire ag;
+    func_.dec_ref();
+    // explicitly setting PyObject* to nullptr to prevent py::object's dtor to
+    // decref on the PyObject again.
+    // See Note [Destructing py::object] in python_ivalue.h
+    func_.ptr() = nullptr;
+  }
+
+  py::function func_;
+};
+
+// The PythonFutureWrapper for ivalue::Future
+//
+// NB: VISIBILITY_HIDDEN is for silencing compiling error,
+// "error: 'torch::jit::PythonFutureWrapper' declared with greater visibility
+// than the type of its field 'torch::jit::PythonFutureWrapper::unwrap_func'
+// [-Werror=attributes]"
+//
+// NB: inherit from enable_shared_from_this because then(py::function) needs to
+//     get a shared_ptr from this pointer.
+struct VISIBILITY_HIDDEN PythonFutureWrapper
+    : std::enable_shared_from_this<PythonFutureWrapper> {
+  using UnwrapFunc = std::function<void(py::object)>;
+
+  explicit PythonFutureWrapper(
+      c10::intrusive_ptr<c10::ivalue::Future> fut,
+      c10::optional<UnwrapFunc> unwrap_func = c10::nullopt)
+      : fut(std::move(fut)), unwrap_func(std::move(unwrap_func)) {}
+
+  explicit PythonFutureWrapper(const PythonFutureWrapper&) = delete;
+  PythonFutureWrapper& operator=(const PythonFutureWrapper&) = delete;
+
+  bool done() {
+    return fut->completed();
+  }
+
+  py::object value() {
+    // acquiring GIL as toPyObject creates new py::object
+    // without grabbing the GIL.
+    py::gil_scoped_acquire acquire;
+    py::object py_obj = toPyObject(fut->value());
+    // unwrap_func is a general compositional function that takes in a
+    // py::object and executes some python function. It is currently mostly used
+    // to throw python exceptions.
+    if (unwrap_func) {
+      (*unwrap_func)(py_obj);
+    }
+    return py_obj;
+  }
+
+  py::object wait() {
+    fut->wait();
+    if (jit::tracer::isTracing()) {
+      auto graph = jit::tracer::getTracingState()->graph;
+
+      Value* fut_val = jit::tracer::getValueTrace(fut);
+      auto output = graph->insert(aten::wait, {fut_val});
+      jit::tracer::setValueTrace(fut->value(), output);
+    }
+    return value();
+  }
+
+  // The py::function cb arg must take a std::shared_ptr<PythonFutureWrapper>
+  // (i.e., torch._C.Future) as the only argument. If the type mismatches, an
+  // error will be thrown when waiting for the value of this returned Future.
+  std::shared_ptr<PythonFutureWrapper> then(py::function cb) {
+    // We need this an additional layer of wrapper here to guard the
+    // destruction of the py::function object. Because, the
+    // Future owns a reference to the py::function in its callback
+    // vector, but Future does not acquire GIL on destruction.
+    auto pf = std::make_shared<PythonFunctionGuard>(std::move(cb));
+
+    return std::make_shared<jit::PythonFutureWrapper>(fut->then(
+        // Capture a copy of the ivalue::Future instead of the `this` pointer
+        // because the PythonFutureWrapper object could have been deleted
+        // when the callbacks are fired. For example, RPC only captures the
+        // ivalue::Future instead of PythonFutureWrapper in JitFuture's
+        // callback functions. Hence, if user code does not hold a reference to
+        // this PythonFutureWrapper object, there is no guarantee that the
+        // PythonFutureWrapper is still valid when running the callback.
+        [pyFut(this->getPtr()),
+         pf(std::move(pf))](c10::ivalue::Future& /* unused */) -> IValue {
+          try {
+            pybind11::gil_scoped_acquire ag;
+            return toIValue(pf->func_(pyFut), PyObjectType::get());
+          } catch (py::error_already_set& e) {
+            auto err = std::runtime_error(c10::str(
+                "Got the following error when running the callback: ",
+                e.what()));
+            {
+              pybind11::gil_scoped_acquire ag;
+              // Release ownership on py::objects and also restore Python
+              // Error Indicator.
+              e.restore();
+              // Clear the Python Error Indicator as we has recorded the
+              // exception in the response message.
+              PyErr_Clear();
+            }
+
+            throw err;
+          }
+        },
+        PyObjectType::get()));
+  }
+
+  void add_done_callback(py::function cb) {
+    auto pf = std::make_shared<PythonFunctionGuard>(std::move(cb));
+    // NOLINTNEXTLINE(modernize-avoid-bind)
+    fut->addCallback(std::bind(
+        [pyFut(this->getPtr())](std::shared_ptr<PythonFunctionGuard> pf) {
+          try {
+            pybind11::gil_scoped_acquire ag;
+            pf->func_(pyFut);
+          } catch (py::error_already_set& e) {
+            {
+              pybind11::gil_scoped_acquire ag;
+              // Release ownership on py::objects and also restore Python
+              // Error Indicator.
+              e.restore();
+              // Clear the Python Error Indicator as we has recorded the
+              // exception in the response message.
+              PyErr_Clear();
+            }
+            // Log and ignore exceptions raised through the callback
+            LOG(ERROR) << "Got the following error when running the callback: "
+                       << e.what();
+
+          } catch (const std::exception& e) {
+            // Log and ignore exceptions raised through the callback
+            LOG(ERROR) << "Got the following error when running the callback: "
+                       << e.what();
+          }
+        },
+        std::move(pf)));
+  }
+
+  void markCompleted(const py::object& pyValue) {
+    DCHECK(PyGILState_Check());
+    IValue value = toIValue(pyValue, PyObjectType::get());
+
+    py::gil_scoped_release release;
+    fut->markCompleted(std::move(value));
+  }
+
+  c10::intrusive_ptr<c10::ivalue::Future> fut;
+  // unwrap_func works like a callback for the value returned by
+  // PythonFutureWrapper::wait().
+  c10::optional<UnwrapFunc> unwrap_func;
+
+ private:
+  std::shared_ptr<PythonFutureWrapper> getPtr() {
+    return shared_from_this();
+  }
+};
+
+// The PythonAwaitWrapper for ivalue::Await
+//
+// Expresses delayed function execution with Lazy semantic.
+// i.e. Await[W] in eager mode can be used as W.
+// When the attribute of W type is requested, Await[W] will return the
+// attribute of W, transparently calling wait() beforehand.
+// No Lazy semantic for script, explicit wait(Await[W]) -> W must be called to
+// convert to type W.
+//
+// The Await object takes shared ownership of specified function and the
+// arguments. After first call for wait() it owns the result. Deliberately no
+// type inference for eager mode.
+struct VISIBILITY_HIDDEN PythonAwaitWrapper
+    : std::enable_shared_from_this<PythonAwaitWrapper> {
+  explicit PythonAwaitWrapper(c10::intrusive_ptr<c10::ivalue::Await> aw)
+      : aw_(std::move(aw)) {}
+  explicit PythonAwaitWrapper(py::handle input) {
+    args_ = py::tuple(1u);
+    args_[0] = input;
+    auto type = PyObjectType::get();
+    aw_ = c10::make_intrusive<c10::ivalue::Await>(type);
+    aw_->markCompleted(toIValue(input, type));
+  }
+
+  explicit PythonAwaitWrapper(py::function pf, py::tuple args) {
+    pyfg_ = std::make_shared<torch::jit::PythonFunctionGuard>(std::move(pf));
+    args_ = std::move(args);
+    std::function<IValue()> f = [fg(pyfg_), &args(args_)]() {
+      pybind11::gil_scoped_acquire ag;
+      return toIValue(fg->func_(*args), PyObjectType::get());
+    };
+    aw_ = c10::make_intrusive<c10::ivalue::Await>(
+        PyObjectType::get(), std::move(f));
+  }
+
+  explicit PythonAwaitWrapper(const PythonAwaitWrapper&) = delete;
+  PythonAwaitWrapper& operator=(const PythonAwaitWrapper&) = delete;
+
+  py::object wait() {
+    py::gil_scoped_acquire acquire;
+    return toPyObject(aw_->wait());
+  }
+
+  // Nowait semantic means trivial case when Await is constructed from the
+  // result
+  bool is_nowait() {
+    return pyfg_ == nullptr;
+  }
+
+  const py::function fn() {
+    TORCH_CHECK(
+        pyfg_, "Await constructed as awaitable_nowait does not have fn");
+    return pyfg_->func_;
+  }
+
+  const py::tuple args() {
+    return args_;
+  }
+
+  TypePtr type() {
+    return aw_->type();
+  }
+
+  c10::intrusive_ptr<c10::ivalue::Await> aw_;
+  std::shared_ptr<torch::jit::PythonFunctionGuard> pyfg_;
+  py::tuple args_;
+
+ private:
+  std::shared_ptr<PythonAwaitWrapper> getPtr() {
+    return shared_from_this();
+  }
+};
+
+// error reporting: when reporting user-caused errors, these functions should
+// not use AT_ERROR macros, since these macros add stack trace information
+// that is confusing to display to the end user since it always reports
+// locations in libtorch code rather than user code.
+
+inline std::shared_ptr<CompilationUnit> get_python_cu() {
+  return py::module::import("torch.jit._state")
+      .attr("_python_cu")
+      .cast<std::shared_ptr<CompilationUnit>>();
+}
+
+struct TypedIValue : public std::pair<IValue, TypePtr> {
+  using pair::pair;
+
+  IValue& ivalue() {
+    return this->first;
+  }
+  TypePtr& type() {
+    return this->second;
+  }
+};
+
+inline TypedIValue toDictKeyIValue(py::handle key) {
+  if (py::isinstance<py::str>(key)) {
+    return TypedIValue(
+        ConstantString::create(py::cast<std::string>(key)), StringType::get());
+  } else if (py::isinstance<py::int_>(key)) {
+    return TypedIValue(py::cast<int64_t>(key), IntType::get());
+  } else if (py::isinstance<py::float_>(key)) {
+    return TypedIValue(py::cast<double>(key), FloatType::get());
+  } else {
+    AT_ERROR("Dictionary inputs may only have string, int, or float keys");
+  }
+}
+
+inline c10::optional<TypePtr> unifyOrInitializeType(
+    const TypePtr& accum,
+    const TypePtr& unify) {
+  if (!accum) {
+    return unify;
+  }
+  return unifyTypes(accum, unify);
+}
+
+using InferredType = c10::InferredType;
+
+InferredType tryToInferContainerType(py::handle input);
+
+// Try to infer the type of a Python object
+// The type cannot be inferred if:
+//   input is an empty container (list, dict)
+//   input is an list with element types that cannot be unified
+//   input is an dict with key or value types that cannot be unified
+inline InferredType tryToInferType(py::handle input) {
+  // Try tensor types
+  if (THPVariable_Check(input.ptr())) {
+    return InferredType(TensorType::get());
+  }
+
+  if (input.is_none()) {
+    return InferredType(NoneType::get());
+  }
+
+  if (py::isinstance<StrongFunctionPtr>(input)) {
+    auto fn = py::cast<StrongFunctionPtr>(input).function_;
+    return InferredType(FunctionType::create(fn));
+  }
+
+  // Try basic types first
+  if (py::isinstance<py::bool_>(input)) {
+    return InferredType(BoolType::get());
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+  } else if (py::isinstance<py::int_>(input)) {
+    return InferredType(IntType::get());
+  } else if (py::isinstance<py::float_>(input)) {
+    return InferredType(FloatType::get());
+  } else if (PyComplex_CheckExact(input.ptr())) {
+    return InferredType(ComplexType::get());
+  } else if (py::isinstance<py::str>(input)) {
+    return InferredType(StringType::get());
+  } else if (THPLayout_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPDevice_Check(input.ptr())) {
+    return InferredType(DeviceObjType::get());
+  } else if (THPGenerator_Check(input.ptr())) {
+    return InferredType(GeneratorType::get());
+  } else if (THPStream_Check(input.ptr())) {
+    return InferredType(StreamObjType::get());
+  } else if (THPDtype_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPQScheme_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  } else if (THPLayout_Check(input.ptr())) {
+    return InferredType(IntType::get());
+  }
+
+  auto enum_type = py::module::import("enum").attr("Enum");
+  py::bool_ isEnumValue = py::isinstance(input, enum_type);
+  if (py::cast<bool>(isEnumValue)) {
+    auto enum_class = input.attr("__class__");
+    auto enum_type = py::cast<TypePtr>(
+        py::module::import("torch.jit.annotations")
+            .attr("try_ann_to_type")(enum_class, SourceRange()));
+    return InferredType(std::move(enum_type));
+  }
+
+  py::bool_ isClass =
+      py::module::import("inspect").attr("isclass")(input.get_type());
+  if (py::cast<bool>(isClass)) {
+    // Assume that the class is compiled already or will compile. Invalidate
+    // this later if needed.
+    bool class_compiled = true;
+
+    // Check if the type is already compiled.
+    py::object existing_ty = py::module::import("torch.jit._state")
+                                 .attr("_get_script_class")(input.get_type());
+
+    if (existing_ty.is_none()) {
+      // If not, try to compile it.
+      py::bool_ can_compile = py::module::import("torch._jit_internal")
+                                  .attr("can_compile_class")(input.get_type());
+
+      if (py::cast<bool>(can_compile)) {
+        // Try to compile the class. This is wrapped in a try-catch because
+        // compilation of class types can raise an Exception and in that case,
+        // we want to defer to other attempts at type inference below rather
+        // than fail compilation altogether.
+        try {
+          py::module::import("torch.jit._script")
+              .attr("_recursive_compile_class")(
+                  input.get_type(), SourceRange());
+        } catch (...) {
+          // Invalidate the assumption that the class compiled so that we don't
+          // look up and return its JIT type as the type for the input.
+          class_compiled = false;
+        }
+      }
+    }
+
+    // If the class compiled successfully, look up the existing JIT type by
+    // qualified name and return it.
+    if (class_compiled) {
+      auto script_class = py::module::import("torch.jit._state")
+                              .attr("_get_script_class")(input.get_type());
+
+      if (!script_class.is_none()) {
+        auto class_type = py::cast<ClassTypePtr>(script_class);
+
+        if (class_type && !class_type->is_module()) {
+          return InferredType(std::move(class_type));
+        }
+      }
+    }
+  }
+
+  if (py::isinstance<Object>(input)) {
+    auto object = py::cast<Object>(input);
+    return InferredType(object.type());
+#ifdef USE_RPC
+  } else if (py::isinstance<torch::distributed::rpc::PyRRef>(input)) {
+    auto rref_ivalue = input.cast<torch::distributed::rpc::PyRRef>().toIValue();
+    return InferredType(rref_ivalue.type());
+#endif
+  }
+
+  auto await_type = py::module::import("torch._awaits").attr("_Await");
+  py::bool_ is_await = py::isinstance(input, await_type);
+  if (py::cast<bool>(is_await)) {
+    auto awptr = input.cast<std::shared_ptr<PythonAwaitWrapper>>();
+    return InferredType(AwaitType::create(awptr->aw_->elementType()));
+  }
+
+  if (as_module(py::cast<py::object>(input))) {
+    return InferredType("Cannot infer type of ScriptModule");
+  }
+
+  auto module_type = py::module::import("torch.nn").attr("Module");
+  py::bool_ is_module = py::isinstance(input, module_type);
+  if (py::cast<bool>(is_module)) {
+    return InferredType("Cannot infer concrete type of torch.nn.Module");
+  }
+
+  // Try container types
+  return tryToInferContainerType(input);
+}
+
+inline InferredType tryToInferContainerType(py::handle input) {
+  if (six::isTuple(input)) {
+    py::tuple tuple = py::cast<py::tuple>(input);
+    std::vector<TypePtr> element_types;
+    element_types.reserve(tuple.size());
+
+    for (py::handle elem : tuple) {
+      auto type_match = tryToInferType(elem);
+      if (type_match.success()) {
+        element_types.push_back(type_match.type());
+      } else {
+        // Forward error message along
+        return type_match.reason();
+      }
+    }
+    return InferredType(TupleType::create(std::move(element_types)));
+  } else if (PyDict_Check(input.ptr())) {
+    // Check to make sure we can generate useful input/output types
+    auto dict = py::cast<py::dict>(input);
+    size_t len = py::len(dict);
+    if (!len) {
+      return InferredType("Dictionary inputs must have entries");
+    }
+
+    TypePtr key_type = nullptr;
+    TypePtr value_type = nullptr;
+
+    for (auto entry : dict) {
+      // Try to infer the key type and unify it with the existing one
+      auto entry_key_type_match = tryToInferType(entry.first);
+      if (!entry_key_type_match.success()) {
+        return entry_key_type_match.reason();
+      }
+      auto unified_key =
+          unifyOrInitializeType(key_type, entry_key_type_match.type());
+      if (!unified_key) {
+        return InferredType(c10::str(
+            "Dictionary inputs to traced functions must have consistent type. Found ",
+            key_type->repr_str(),
+            " and ",
+            (entry_key_type_match.type())->repr_str()));
+      }
+
+      // Try to infer the value type and unify it with the existing one
+      auto entry_value_type_match = tryToInferType(entry.second);
+      if (!entry_value_type_match.success()) {
+        return entry_value_type_match.reason();
+      }
+      auto unified_value =
+          unifyOrInitializeType(value_type, entry_value_type_match.type());
+      if (!unified_value) {
+        return InferredType(c10::str(
+            "Dictionary inputs to traced functions must have consistent type. Found ",
+            value_type->repr_str(),
+            " and ",
+            (entry_value_type_match.type())->repr_str()));
+      }
+
+      key_type = *unified_key;
+      value_type = *unified_value;
+    }
+    return InferredType(
+        DictType::create(std::move(key_type), std::move(value_type)));
+  } else if (PyList_Check(input.ptr())) {
+    auto list = py::cast<py::list>(input);
+    size_t len = py::len(list);
+    if (!len) {
+      return InferredType("List trace inputs must have elements");
+    }
+
+    TypePtr element_type = nullptr;
+    for (auto elem : list) {
+      auto element_type_match = tryToInferType(elem);
+      if (!element_type_match.success()) {
+        return InferredType(c10::str(
+            "Could not infer type of list element: ",
+            element_type_match.reason()));
+      }
+      auto unified_type =
+          unifyOrInitializeType(element_type, element_type_match.type());
+      if (!unified_type) {
+        return InferredType(c10::str(
+            "List inputs to traced functions must have consistent element type. Found ",
+            element_type->repr_str(),
+            " and ",
+            (element_type_match.type())->repr_str()));
+      }
+      element_type = *unified_type;
+    }
+    return InferredType(ListType::create(element_type));
+  } else {
+    // TODO: this message is not correct anymore, since this InferredType is
+    // used from a bunch of circumstances unrelated to tracing. We can re-use
+    // this instead of the attribute_failure stuff in concreteType
+    return InferredType(c10::str(
+        "Only tensors and (possibly nested) tuples of tensors, lists, or dicts",
+        "are supported ",
+        "as inputs or outputs of traced functions",
+        ", but instead got value of type ",
+        py::str(input.get_type().attr("__name__")),
+        "."));
+  }
+}
+
+inline bool isTraceableType(const TypePtr& type) {
+  if (type->isSubtypeOf(*TensorType::get())) {
+    return true;
+  }
+
+  if (auto list_type = type->cast<ListType>()) {
+    return isTraceableType(list_type->getElementType());
+  }
+
+  if (auto tuple_type = type->cast<TupleType>()) {
+    return std::all_of(
+        tuple_type->elements().begin(),
+        tuple_type->elements().end(),
+        [](const TypePtr& element_type) {
+          return isTraceableType(element_type);
+        });
+  }
+
+  if (auto dict_type = type->cast<DictType>()) {
+    return isTraceableType(dict_type->getValueType());
+  }
+
+  return false;
+}
+
+inline IValue toTypeInferredIValue(py::handle input) {
+  auto match = tryToInferType(input);
+  if (!match.success()) {
+    auto object = py::cast<py::object>(input);
+    if (auto mod = as_module(object)) {
+      // if obj is already a ScriptModule, just return its ivalue
+      auto ptr = mod.value()._ivalue();
+      // explict copy semantics for strong ownership of the resource.
+      return c10::intrusive_ptr<c10::ivalue::Object>::reclaim_copy(
+          ptr.release());
+    }
+
+    // Check if the obj is a ScriptObject.
+    if (auto script_obj = as_object(object)) {
+      auto ptr = script_obj.value()._ivalue();
+      return c10::intrusive_ptr<c10::ivalue::Object>::reclaim_copy(
+          ptr.release());
+    }
+    AT_ERROR(
+        "Tracer cannot infer type of ", py::str(input), "\n:", match.reason());
+  }
+  return toIValue(input, match.type());
+}
+
+inline Stack toTraceableStack(const py::tuple& inputs) {
+  auto info = toTypeInferredIValue(inputs);
+  TORCH_CHECK(
+      isTraceableType(info.type()),
+      "Type '",
+      info.type()->repr_str(),
+      "' cannot be traced. Only Tensors and (possibly nested) Lists, Dicts, and"
+      " Tuples of Tensors can be traced");
+  return info.toTupleRef().elements().vec();
+}
+
+// Serialize the python dictionary into a traceable stack.
+inline Stack toTraceableStack(const py::dict& inputs) {
+  Stack res;
+  for (auto it = inputs.begin(); it != inputs.end(); it++) {
+    if (THPVariable_Check(it->second.ptr())) {
+      res.push_back(toIValue(it->second, tryToInferType(it->second).type()));
+    }
+  }
+  return res;
+}
+
+inline IValue createGenericList(py::handle obj, const TypePtr& elem_type) {
+  auto elems = c10::impl::GenericList(elem_type);
+  for (auto elem : obj) {
+    elems.push_back(toIValue(elem, elem_type));
+  }
+  return IValue(elems);
+}
+
+inline IValue createGenericDict(
+    const py::dict& obj,
+    const TypePtr& key_type,
+    const TypePtr& value_type) {
+  c10::impl::GenericDict elems(key_type, value_type);
+  elems.reserve(py::len(obj));
+  for (auto& entry : obj) {
+    elems.insert(
+        toIValue(entry.first, key_type), toIValue(entry.second, value_type));
+  }
+  return IValue(elems);
+}
+
+template <class T>
+inline void guardAgainstNamedTensor(const T& var) {
+  TORCH_CHECK(
+      !var.has_names(),
+      "NYI: Named tensors are currently unsupported in TorchScript. As a  "
+      "workaround please drop names via `tensor = tensor.rename(None)`.");
+}
+
+// Extract custom class registered with torchbind
+template <typename T>
+c10::intrusive_ptr<T> toCustomClass(py::handle obj) {
+  static_assert(
+      std::is_base_of<CustomClassHolder, T>::value, "T is not a CustomClass");
+  const auto& type = c10::getCustomClassType<c10::intrusive_ptr<T>>();
+  c10::IValue ivalue = toIValue(obj, type);
+  return std::move(ivalue).toCustomClass<T>();
+}
+
+// Small wrapper around getting the type name string from Python to make
+// types easier to interpret, e.g. give the structural type for a NamedTuple
+inline std::string friendlyTypeName(py::handle obj) {
+  if (py::isinstance<py::tuple>(obj) && py::hasattr(obj, "_fields")) {
+    auto field_names =
+        py::cast<std::vector<std::string>>(py::getattr(obj, "_fields"));
+    std::stringstream ss;
+    ss << py::str(obj.get_type().attr("__name__"));
+    ss << " (aka NamedTuple(";
+    bool first = true;
+    for (auto& field_name : field_names) {
+      if (!first) {
+        ss << ", ";
+      }
+      ss << field_name;
+      first = false;
+    }
+    ss << "))";
+    return ss.str();
+  } else {
+    return py::str(obj.get_type().attr("__name__"));
+  }
+}
+
+// Thrown when trying to create a schema for a list of python
+// arguments that cannot be converted.
+// Can be caught by the caller to attempt to use other schema
+// when there is an overloaded operator.
+struct schema_match_error : public std::runtime_error {
+  using std::runtime_error::runtime_error;
+};
+
+inline IValue argumentToIValue(
+    const FunctionSchema& schema,
+    size_t argumentPosition,
+    py::handle object) {
+  const auto& argument = schema.arguments().at(argumentPosition);
+  try {
+    return toIValue(object, argument.real_type(), argument.N());
+  } catch (const py::cast_error& error) {
+    throw schema_match_error(c10::str(
+        schema.formatTypeMismatchMsg(
+            argument,
+            friendlyTypeName(object),
+            argumentPosition,
+            py::repr(object)),
+        "\nCast error details: ",
+        error.what()));
+  } catch (const py::error_already_set& error) {
+    throw schema_match_error(c10::str(
+        schema.formatTypeMismatchMsg(
+            argument,
+            friendlyTypeName(object),
+            argumentPosition,
+            py::repr(object)),
+        "\n Python error details: ",
+        error.what()));
+  }
+}
+
+inline IValue returnToIValue(const TypePtr& type, py::handle object) {
+  try {
+    return toIValue(object, type);
+  } catch (const py::cast_error& error) {
+    throw std::runtime_error(c10::str(
+        " expected value of type ",
+        type->str(),
+        " for return value but instead got value of type ",
+        py::str(object.get_type().attr("__name__")),
+        ".",
+        "\nValue: ",
+        py::repr(object),
+        "\nCast error details: ",
+        error.what()));
+  }
+}
+
+inline py::object getScriptedClassOrError(const c10::NamedTypePtr& classType) {
+  auto py_class =
+      py::module::import("torch.jit._state")
+          .attr("_get_python_class")(classType->name()->qualifiedName());
+  if (py_class.is_none()) {
+    std::stringstream err;
+    err << "Unknown reference to ScriptClass ";
+    err << classType->name()->qualifiedName();
+    err << ". (Did you forget to import it?)";
+    throw std::runtime_error(err.str());
+  }
+  return py_class;
+}
+
+struct VISIBILITY_HIDDEN tuple_slice {
+  /*implicit*/ tuple_slice(py::tuple tup_)
+      : tup(std::move(tup_)), b(0), e(tup.size()) {}
+  tuple_slice(py::tuple tup_, int64_t b_)
+      : tup(std::move(tup_)), b(b_), e(tup.size()) {}
+  tuple_slice(py::tuple tup_, int64_t b_, int64_t e_)
+      : tup(std::move(tup_)), b(b_), e(e_) {}
+  py::detail::tuple_iterator begin() const {
+    return {tup, static_cast<pybind11::ssize_t>(b)};
+  }
+  py::detail::tuple_iterator end() const {
+    return {tup, static_cast<pybind11::ssize_t>(e)};
+  }
+  size_t size() const {
+    return e - b;
+  }
+  py::detail::tuple_accessor operator[](size_t index) const {
+    return {tup, static_cast<size_t>(b + index)};
+  }
+
+ private:
+  py::tuple tup;
+  int64_t b;
+  int64_t e;
+};
+
+inline Stack createStackForSchema(
+    const FunctionSchema& schema,
+    const tuple_slice& args,
+    const py::kwargs& kwargs,
+    c10::optional<IValue> self) {
+  size_t all_arguments = (self ? 1 : 0) + args.size() + kwargs.size();
+  if (all_arguments > schema.arguments().size()) {
+    throw schema_match_error(c10::str(
+        schema.name(),
+        "() expected at most ",
+        schema.arguments().size(),
+        " argument(s) but received ",
+        all_arguments,
+        " argument(s). Declaration: ",
+        schema));
+  }
+  Stack stack;
+  stack.reserve(schema.arguments().size());
+
+  int64_t arg_idx = 0;
+  if (self) {
+    push(stack, std::move(*self));
+    arg_idx++;
+  }
+  // First push all positional args.
+  for (const auto& arg : args) {
+    // ...but refuse to do it if the schema says that this was supposed
+    // to be keyword only
+    if (schema.arguments()[arg_idx].kwarg_only()) {
+      throw schema_match_error(c10::str(
+          schema.name(),
+          "() takes ",
+          arg_idx,
+          " positional argument(s) but ",
+          self ? 1 + args.size() : args.size(),
+          " was/were given.  Declaration: ",
+          schema));
+    }
+    // Use the type information from the schema to convert the PyObject.
+    push(stack, argumentToIValue(schema, stack.size(), arg));
+    arg_idx++;
+  }
+
+  // Now for every remaining non-positional argument in the schema, look for it
+  // in the kwargs dict and push it if found, or use its default value if it
+  // has one.
+  size_t consumed_kwargs = 0;
+  for (size_t i = stack.size(); i < schema.arguments().size(); ++i) {
+    const auto& arg = schema.arguments()[i];
+    if (kwargs.contains(arg.name().c_str())) {
+      push(stack, argumentToIValue(schema, i, kwargs[arg.name().c_str()]));
+      consumed_kwargs += 1;
+    } else if (arg.default_value()) {
+      push(stack, *arg.default_value());
+    } else {
+      throw schema_match_error(c10::str(
+          schema.name(),
+          "() is missing value for argument '",
+          arg.name(),
+          "'. Declaration: ",
+          schema));
+    }
+  }
+
+  if (consumed_kwargs != kwargs.size()) {
+    std::vector<std::string> names;
+    for (const auto& kwarg : kwargs) {
+      names.emplace_back(py::cast<std::string>(kwarg.first));
+    }
+    throw schema_match_error(schema.findErrorInKwargs(names));
+  }
+
+  return stack;
+}
+
+inline py::object createPyObjectForStack(Stack&& stack) {
+  if (stack.empty()) {
+    return py::none();
+  }
+
+  // Return a simple value and not a single-element tuple if there is only one
+  // return value.
+  if (stack.size() == 1) {
+    return toPyObject(std::move(stack[0]));
+  }
+
+  // If there is more than one return value, pop them into a py::tuple.
+  py::tuple return_values(stack.size());
+  for (const auto ret : c10::irange(return_values.size())) {
+    return_values[ret] = toPyObject(std::move(stack[ret]));
+  }
+
+  return std::move(return_values);
+}
+
+// TODO: Remove once we clean up the GraphExecutor usage.
+inline Stack evilDeprecatedBadCreateStackDoNotUse(
+    const py::tuple& tuple,
+    at::ArrayRef<Value*> inputs,
+    size_t reserve_extra_space = 0) {
+  if (tuple.size() != inputs.size()) {
+    AT_ERROR(
+        "expected " + std::to_string(inputs.size()) + " inputs, but got " +
+        std::to_string(tuple.size()));
+  }
+  Stack result;
+  result.reserve(tuple.size() + reserve_extra_space);
+  for (const auto i : c10::irange(inputs.size())) {
+    result.push_back(toIValue(std::move(tuple[i]), inputs[i]->type()));
+  }
+  return result;
+}
+
+// Run `callee`, potentially inserting a CallFunction/CallMethod node into the
+// tracing graph.
+inline py::object runAndInsertCall(
+    Function& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs,
+    c10::optional<IValue> self,
+    // Lambda that tells this function how to insert `callee` into the graph if
+    // we're tracing.
+    const std::function<Value*(Graph&, const MatchedSchema& match)>&
+        callInserter) {
+  auto stack =
+      createStackForSchema(callee.getSchema(), args, kwargs, std::move(self));
+  const auto& tracing_state = tracer::getTracingState();
+  if (!tracing_state) {
+    pybind11::gil_scoped_release no_gil_guard;
+    // If we're not tracing, just run the callee as normal.
+    callee.run(stack);
+  } else {
+    // If we are tracing, insert the appropriate CallFunction or CallMethod node
+    // and then run the callee with tracing disabled.
+
+    // Get the graph `Value`s that represent the input IValues
+    auto inputs = last(stack, callee.num_inputs());
+    auto input_values =
+        fmap(inputs, [](const IValue& v) { return tracer::getValueTrace(v); });
+    TORCH_INTERNAL_ASSERT(callee.getSchema().returns().size() == 1)
+    auto return_type = callee.getSchema().returns().at(0).type();
+    auto graph = tracing_state->graph;
+    std::vector<NamedValue> named_values;
+    named_values.reserve(input_values.size());
+    for (Value* v : input_values) {
+      named_values.emplace_back(v);
+    }
+
+    // Add a call node.
+    MatchedSchema match = matchSchema(
+        callee.getSchema(),
+        tracer::getPythonInterpreterSourceRange(),
+        *graph,
+        named_values,
+        {});
+    auto output_value = callInserter(*graph, match);
+
+    // Actually run the callee. Pause the tracer so that we don't double-add the
+    // callee nodes.
+    {
+      pybind11::gil_scoped_release no_gil_guard;
+      ResourceGuard guard(tracer::pauseTracing());
+      callee.run(stack);
+    }
+
+    // Associate the output IValues with the output `Value`s in the graph
+    tracer::setValueTrace(stack.back(), output_value);
+  }
+
+  TORCH_CHECK(
+      !stack.empty(),
+      "Expected values in the stack after execution but found none");
+  return toPyObject(std::move(stack.back()));
+}
+
+inline c10::optional<py::object> maybeTorchFunctionDispatch(
+    const py::object& callee,
+    const tuple_slice& args_no_self,
+    const py::kwargs& kwargs,
+    const c10::QualifiedName qualname) {
+  std::vector<py::handle> args_vec;
+  for (const auto& arg : args_no_self) {
+    args_vec.push_back(arg);
+  }
+  py::tuple args = py::cast(args_vec);
+
+  // Handle __torch_function__ dispatch
+  std::vector<PyObject*> overloaded_args;
+  size_t total_arg_num = args.size() + kwargs.size();
+  for (const auto& arg : args) {
+    is_tensor_and_append_overloaded(arg.ptr(), &overloaded_args);
+    is_tensor_list_and_append_overloaded(
+        arg.ptr(),
+        &overloaded_args,
+        static_cast<int>(total_arg_num),
+        false /* throw_error */);
+  }
+  // NB: for kwargs, we cannot guarantee the order of appending
+  // is the same as the argument order in operator's schema.
+  // This is suboptimal, but should be fine. Later when we have
+  // better schema matching and argument parsing, we could
+  // match the operator in `operations` first, then the order will
+  // be guaranteed.
+  for (auto item : kwargs) {
+    is_tensor_and_append_overloaded(item.second.ptr(), &overloaded_args);
+    is_tensor_list_and_append_overloaded(
+        item.second.ptr(),
+        &overloaded_args,
+        total_arg_num,
+        false /* throw_error */);
+  }
+  if (!overloaded_args.empty()) {
+    return pybind11::reinterpret_steal<py::object>(
+        handle_torch_function_no_python_arg_parser(
+            /*overloaded_args=*/overloaded_args,
+            /*args=*/args.ptr(),
+            /*kwargs=*/kwargs.ptr(),
+            /*func_name=*/qualname.name().c_str(),
+            /*torch_api_function=*/callee.ptr(),
+            /*module_name=*/qualname.prefix().c_str()));
+  }
+
+  return c10::nullopt;
+}
+
+inline py::object invokeScriptFunctionFromPython(
+    Function& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs) {
+  // TODO: we could add __torch_function__ dispatch here but I don't know
+  // the implications of doing so
+
+  return runAndInsertCall(
+      callee,
+      args,
+      kwargs,
+      /*self=*/c10::nullopt,
+      [&](Graph& graph, const MatchedSchema& match) {
+        return graph.insertFunctionCall(&callee, match);
+      });
+}
+
+inline py::object invokeScriptMethodFromPython(
+    Method& callee,
+    const tuple_slice& args,
+    const py::kwargs& kwargs) {
+  auto self = callee.owner()._ivalue();
+
+  if (auto torch_fn_result = maybeTorchFunctionDispatch(
+          py::cast(callee), args, kwargs, callee.name())) {
+    return *torch_fn_result;
+  }
+
+  return runAndInsertCall(
+      callee.function(),
+      args,
+      kwargs,
+      self,
+      [&](Graph& graph, const MatchedSchema& match) {
+        return graph.insertMethodCall(callee.name(), match);
+      });
+}
+
+TORCH_PYTHON_API std::pair<std::shared_ptr<Operator>, Stack> getOpWithStack(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    py::args args,
+    const py::kwargs& kwargs);
+
+TORCH_PYTHON_API py::object invokeOperatorFromPython(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    py::args args,
+    const py::kwargs& kwargs,
+    c10::optional<c10::DispatchKey> dk = c10::nullopt);
+
+TORCH_PYTHON_API py::object _get_operation_for_overload_or_packet(
+    const std::vector<std::shared_ptr<Operator>>& operations,
+    Symbol symbol,
+    py::args args,
+    const py::kwargs& kwargs,
+    bool is_overload,
+    c10::optional<c10::DispatchKey> dk = c10::nullopt);
+
+} // namespace torch::jit

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_arg_flatten.h

@@ -0,0 +1,119 @@
+#pragma once
+
+#include <c10/util/hash.h>
+#include <c10/util/irange.h>
+#include <torch/csrc/autograd/variable.h>
+#include <torch/csrc/jit/python/pybind.h>
+
+#include <ATen/ATen.h>
+#include <functional>
+#include <tuple>
+#include <vector>
+
+namespace torch::jit::python {
+
+struct IODescriptor {
+  struct VariableMetadata {
+    VariableMetadata(const autograd::Variable& var)
+        : sizes(var.sizes().vec()),
+          type(var.scalar_type()),
+          device(var.device()),
+          requires_grad(var.requires_grad()) {}
+
+    bool operator==(const VariableMetadata& o) const {
+      return std::tie(device, requires_grad, type, sizes) ==
+          std::tie(o.device, o.requires_grad, o.type, o.sizes);
+    }
+
+    static size_t hash(const VariableMetadata& m) {
+      return c10::get_hash(m.sizes, m.device, m.requires_grad, m.type);
+    }
+
+    std::vector<int64_t> sizes;
+    at::ScalarType type;
+    at::Device device;
+    bool requires_grad;
+  };
+
+  bool operator==(const IODescriptor& o) const {
+    return std::tie(structure, metadata, grad_enabled) ==
+        std::tie(o.structure, o.metadata, o.grad_enabled);
+  }
+
+  static size_t hash(const IODescriptor& o) {
+    return c10::get_hash(o.structure, o.metadata, o.grad_enabled);
+  }
+
+  void extend(const autograd::variable_list& list) {
+    metadata.reserve(metadata.size() + list.size());
+    for (auto& var : list)
+      metadata.emplace_back(var);
+  }
+
+  // Description of argument structure. Variables are replaced with
+  // different characters, depending on their flags, beginnings and
+  // ends of tuples and lists are denoted by a pair of parenthesis
+  // of their corresponding kind. They should always be paired.
+  // Example desc: (vv[v(v)v])
+  // NOTE: if extend() was ever called then metadata.size() can be
+  // different than the number of 'v's in structure.
+  std::string structure;
+  std::vector<std::string> strings;
+  std::vector<VariableMetadata> metadata;
+  bool grad_enabled = false;
+};
+
+static inline std::ostream& operator<<(
+    std::ostream& out,
+    const IODescriptor::VariableMetadata& meta) {
+  at::Device meta_device = meta.device;
+  auto& t = at::getDeprecatedTypeProperties(
+      meta_device.is_cpu() ? at::Backend::CPU : at::Backend::CUDA, meta.type);
+  out << t << "(requires_grad=" << meta.requires_grad;
+  if (meta_device.is_cuda()) {
+    out << ", device=" << meta_device.index();
+  }
+  out << ") {";
+  for (const auto i : c10::irange(meta.sizes.size())) {
+    if (i > 0)
+      out << ", ";
+    out << meta.sizes[i];
+  }
+  out << "}";
+  return out;
+}
+
+static inline std::ostream& operator<<(
+    std::ostream& out,
+    const IODescriptor& desc) {
+  out << desc.structure << "\n";
+  out << "  with grad_enabled=" << desc.grad_enabled << "\n";
+  for (const auto i : c10::irange(desc.metadata.size())) {
+    out << "  with v" << i << " having type " << desc.metadata[i] << "\n";
+  }
+  return out;
+}
+
+struct ParsedArgs {
+  // Flat vector of Variables found in arguments
+  autograd::variable_list vars;
+  // Metadata describing nesting of objects received from Python and
+  // metadata of vars and whether grad is enabled.
+  IODescriptor desc;
+
+  void extend(const autograd::variable_list& list) {
+    if (list.empty())
+      return;
+    vars.reserve(vars.size() + list.size());
+    for (auto& var : list)
+      vars.emplace_back(var);
+    desc.extend(list);
+  }
+};
+
+ParsedArgs flatten(py::handle obj);
+PyObject* unflatten(
+    at::ArrayRef<autograd::Variable> vars,
+    const IODescriptor& structure);
+
+} // namespace torch::jit::python

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_ir.h

@@ -0,0 +1,50 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+#include <torch/csrc/utils/object_ptr.h>
+
+namespace torch::jit {
+
+void initPythonIRBindings(PyObject* module);
+
+// execute a Python function, used for Ops we can't optimize but that we want to
+// optimize around
+struct ConcretePythonOp : public PythonOp {
+  static Symbol Kind;
+
+  ConcretePythonOp(Graph* graph) : PythonOp(graph, ::c10::prim::PythonOp) {}
+  ConcretePythonOp* init(
+      THPObjectPtr&& pyobj,
+      const std::string& cconv,
+      pyobj_list&& scalar_args) {
+    this->pyobj = std::move(pyobj);
+    this->scalar_args = std::move(scalar_args);
+    this->cconv = cconv;
+    return this;
+  }
+  // The Python object which contains the implementation of this function.
+  // This is either a class (non-legacy) or an object (legacy).  See
+  // TraceInterpreterState for execution semantics.
+  THPObjectPtr pyobj;
+  // The calling convention for the Python function.
+  // 'c' -- constant argument
+  // 'd' -- dynamic argument
+  std::string cconv;
+  // Scalar arguments to the Python function.  Not necessarily passed to
+  // the function in this order; see cconv for the correct order.
+  std::vector<THPObjectPtr> scalar_args;
+
+  std::string name() const override;
+  void cloneFrom(Node* other_) override;
+  Node* allocNewInstance(Graph* g) override {
+    return new ConcretePythonOp(g);
+  }
+  // recover the autograd.Function instance, if this PythonOp's function
+  // was originally SomeFunction.apply
+  // used in ONNX for discovering symbolics
+  c10::optional<THPObjectPtr> autogradFunction() const override;
+  void writeScalars(std::ostream& out) const override;
+  void lint_python() const override;
+};
+
+} // namespace torch::jit

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_ivalue.h

@@ -0,0 +1,97 @@
+#pragma once
+#include <ATen/core/ivalue.h>
+#include <pybind11/pybind11.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <torch/csrc/python_headers.h>
+#include <torch/csrc/utils/pybind.h>
+
+namespace py = pybind11;
+
+namespace c10::ivalue {
+
+// concrete ivalue Holder that hold a py::object
+struct C10_EXPORT ConcretePyObjectHolder final : PyObjectHolder {
+ public:
+  static c10::intrusive_ptr<PyObjectHolder> create(py::object py_obj) {
+    return c10::make_intrusive<ConcretePyObjectHolder>(std::move(py_obj));
+  }
+
+  static c10::intrusive_ptr<PyObjectHolder> create(const py::handle& handle) {
+    py::gil_scoped_acquire ag;
+    return c10::make_intrusive<ConcretePyObjectHolder>(
+        handle.cast<py::object>());
+  }
+
+  PyObject* getPyObject() override {
+    return py_obj_.ptr();
+  }
+
+  InferredType tryToInferType() override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::tryToInferType(py_obj_);
+  }
+
+  IValue toIValue(const TypePtr& type, c10::optional<int32_t> N = c10::nullopt)
+      override {
+    pybind11::gil_scoped_acquire ag;
+    return torch::jit::toIValue(py_obj_, type, N);
+  }
+
+  std::string toStr() override {
+    pybind11::gil_scoped_acquire ag;
+    return py::str(py_obj_);
+  }
+
+  std::vector<at::Tensor> extractTensors() override {
+    // We could implement this entirely in C++ via pybind11 but it turns out to
+    // be substantially slower. Namely, the total time taken by markCompleted on
+    // a CUDAFuture is 21.5us with this implementation, but goes up to 58.7us
+    // when using C++. The reason is unclear.
+    try {
+      pybind11::gil_scoped_acquire ag;
+      static py::object& extractorFn = *new py::object(
+          py::module::import("torch._jit_internal").attr("_extract_tensors"));
+      return extractorFn(py_obj_).cast<std::vector<at::Tensor>>();
+    } catch (py::error_already_set& e) {
+      auto err = std::runtime_error(
+          c10::str("Cannot extract tensors from value: ", e.what()));
+      {
+        pybind11::gil_scoped_acquire ag;
+        e.restore();
+        PyErr_Clear();
+      }
+      throw err;
+    }
+  }
+
+  // Note [Destructing py::object]
+  // ~~~~~~~~~~~~~~~~~~~~~~~~~~
+  //
+  // (1) Why py_obj_ = py::none(); does not work. Because we also need to
+  // acquire GIL when destructing py::object of None that de-references None.
+  // https://docs.python.org/3/c-api/none.html#c.Py_RETURN_NONE
+  //
+  // https://stackoverflow.com/questions/15287590/why-should-py-increfpy-none-be-required-before-returning-py-none-in-c
+  //
+  // (2) Why we need to call dec_ref() explicitly. Because py::object of
+  // nullptr, on destruction, effectively does nothing because of it calls
+  // Py_XDECREF(NULL) underlying.
+  // https://docs.python.org/3/c-api/refcounting.html#c.Py_XDECREF
+  ~ConcretePyObjectHolder() override {
+    pybind11::gil_scoped_acquire ag;
+    py_obj_.dec_ref();
+    // explicitly setting PyObject* to nullptr to prevent py::object's dtor to
+    // decref on the PyObject again.
+    py_obj_.ptr() = nullptr;
+  }
+
+  // explicit construction to avoid errornous implicit conversion and
+  // copy-initialization
+  explicit ConcretePyObjectHolder(py::object py_obj)
+      : py_obj_(std::move(py_obj)) {}
+
+ private:
+  py::object py_obj_;
+};
+
+} // namespace c10::ivalue

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_sugared_value.h

@@ -0,0 +1,376 @@
+#pragma once
+
+#include <torch/csrc/jit/api/module.h>
+#include <torch/csrc/jit/frontend/concrete_module_type.h>
+#include <torch/csrc/jit/frontend/sugared_value.h>
+#include <torch/csrc/jit/python/pybind_utils.h>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+namespace torch::jit {
+
+std::string typeString(py::handle h);
+
+inline std::shared_ptr<SugaredValue> toSimple(Value* v) {
+  return std::make_shared<SimpleValue>(v);
+}
+
+// NB: This should be the single entry-point for instantiating a SugaredValue
+// from a Python object. If you are adding support for converting a new Python
+// type, *add it in this function's implementation*.
+std::shared_ptr<SugaredValue> toSugaredValue(
+    py::object obj,
+    GraphFunction& m,
+    const SourceRange& loc,
+    bool is_constant = false);
+
+c10::optional<StrongFunctionPtr> as_function(const py::object& obj);
+
+struct VISIBILITY_HIDDEN PythonValue : public SugaredValue {
+  PythonValue(
+      py::object the_self,
+      c10::optional<py::object> rcb = c10::nullopt,
+      Value* module_self = nullptr)
+      : self(std::move(the_self)),
+        rcb(std::move(rcb)),
+        moduleSelf_(module_self) {}
+
+  FunctionSchema getSchema(
+      const size_t n_args,
+      const size_t n_binders,
+      const SourceRange& loc);
+
+  // call it like a function, e.g. `outputs = this(inputs)`
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& m,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override;
+
+  std::string kind() const override;
+
+  std::vector<std::shared_ptr<SugaredValue>> asTuple(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const c10::optional<size_t>& size_hint = {}) override;
+
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+  Value* asValue(const SourceRange& loc, GraphFunction& m) override {
+    throw ErrorReport(loc)
+        << kind() << " cannot be used as a value. "
+        << "Perhaps it is a closed over global variable? If so, please "
+        << "consider passing it in as an argument or use a local varible "
+        << "instead.";
+  }
+
+ protected:
+  py::object getattr(const SourceRange& loc, const std::string& name);
+
+  void checkForAddToConstantsError(std::stringstream& ss);
+
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  py::object self;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  c10::optional<py::object> rcb;
+  // NOLINTNEXTLINE(cppcoreguidelines-non-private-member-variables-in-classes)
+  Value* moduleSelf_ = nullptr;
+};
+
+struct VISIBILITY_HIDDEN PythonModuleValue : public PythonValue {
+  explicit PythonModuleValue(py::object mod) : PythonValue(std::move(mod)) {}
+
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+};
+
+// Used for desugaring uses of the torch.cuda module. All the CUDA APIs with
+// torch.cuda.* are resolved using CUDAPythonModuleValue.
+struct VISIBILITY_HIDDEN CUDAPythonModuleValue : public PythonValue {
+  explicit CUDAPythonModuleValue(py::object mod)
+      : PythonValue(std::move(mod)) {}
+
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+};
+
+// Represents all the parameters of a module as a List[Tensor]
+struct VISIBILITY_HIDDEN ConstantParameterList : public SugaredValue {
+  ConstantParameterList(Value* the_list) : the_list_(the_list) {}
+  std::string kind() const override {
+    return "constant parameter list";
+  }
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& caller,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override {
+    return toSimple(the_list_);
+  }
+
+ private:
+  Value* the_list_;
+};
+
+struct VISIBILITY_HIDDEN ModuleDictMethod : public SugaredValue {
+  explicit ModuleDictMethod(SugaredValuePtr iterable, std::string name)
+      : iterable_(std::move(iterable)), name_(std::move(name)){};
+
+  std::string kind() const override {
+    return name_;
+  }
+
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& f,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override {
+    if (!args.empty() || !kwargs.empty()) {
+      throw ErrorReport(loc)
+          << name_ << " method does not accept any arguments";
+    }
+    return iterable_;
+  }
+
+  SugaredValuePtr iterable_;
+  const std::string name_;
+};
+
+struct SugaredDict;
+
+// defines how modules/methods behave inside the script subset.
+// for now this does not have any interaction with python.
+// in the future, we will add the ability to resolve `self.foo` to python
+// {functions, modules, constants} so this SugaredValue is defined here
+// anticipating we will eventually need to replace Module with a py::object
+// holding the actual nn.Module class.
+
+struct VISIBILITY_HIDDEN ModuleValue : public SugaredValue {
+  ModuleValue(Value* self, std::shared_ptr<ConcreteModuleType> concreteType)
+      : self_(self), concreteType_(std::move(concreteType)) {}
+
+  std::string kind() const override {
+    return "module";
+  }
+
+  Value* asValue(const SourceRange& loc, GraphFunction& m) override;
+
+  SugaredValuePtr asTupleValue(const SourceRange& loc, GraphFunction& m)
+      override;
+
+  // select an attribute on it, e.g. `this.field`
+  std::shared_ptr<SugaredValue> tryGetAttr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field);
+
+  // select an attribute on it, e.g. `this.field`
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+  // select an attribute on it, e.g. `this.field`
+  bool hasAttr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+  // call module.forward with pre_hooks and hooks
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& caller,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override;
+
+  std::shared_ptr<SugaredDict> getSugaredDict(
+      const SourceRange& loc,
+      GraphFunction& m);
+
+  std::shared_ptr<SugaredDict> getSugaredNamedBufferDict(
+      const SourceRange& loc,
+      GraphFunction& m);
+
+  std::shared_ptr<SugaredDict> getSugaredNamedParameterList(
+      const SourceRange& loc,
+      GraphFunction& m);
+
+  std::shared_ptr<SugaredDict> getSugaredNamedParameterDict(
+      const SourceRange& loc,
+      GraphFunction& m);
+
+  void setAttr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field,
+      Value* newValue) override;
+
+  SugaredValuePtr iter(const SourceRange& loc, GraphFunction& m) override;
+
+  std::shared_ptr<SugaredValue> getitem(
+      const SourceRange& loc,
+      GraphFunction& m,
+      Value* idx,
+      TypePtr type_hint) override;
+
+ private:
+  // Check that the type of all submodules is a subtype of ty. If the function
+  // returns false, more information about why it returns false (e.g. which
+  // submodule's type is not a subtype of ty) is printed it why_not if it is not
+  // null.
+  bool areAllSubmodulesSubtypeOf(
+      const TypePtr& ty,
+      std::ostream* why_not = nullptr) const;
+
+  Value* self_;
+  std::shared_ptr<ConcreteModuleType> concreteType_;
+};
+
+bool isNamedTupleClass(const py::object& obj);
+TypePtr registerNamedTuple(
+    const py::object& obj,
+    const SourceRange& loc,
+    const ResolutionCallback& rcb);
+
+void recurseThroughNestedModules(
+    const SourceRange& loc,
+    GraphFunction& m,
+    std::vector<SugaredValuePtr>& keys,
+    std::vector<SugaredValuePtr>& values,
+    std::shared_ptr<ModuleValue>& self,
+    const std::string& prefix,
+    const std::string& field);
+
+// Used to support named_modules()
+struct VISIBILITY_HIDDEN SugaredDict : public SugaredValue {
+  explicit SugaredDict(
+      std::shared_ptr<ModuleValue> self,
+      std::shared_ptr<SugaredTupleValue> keys,
+      std::shared_ptr<SugaredTupleValue> modules)
+      : self_(std::move(self)),
+        keys_(std::move(keys)),
+        modules_(std::move(modules)) {}
+
+  std::string kind() const override {
+    return "ModuleDict";
+  }
+
+  std::shared_ptr<SugaredTupleValue> getKeys() {
+    return keys_;
+  }
+
+  std::shared_ptr<SugaredTupleValue> getModules() {
+    return modules_;
+  }
+
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+  SugaredValuePtr iter(const SourceRange& loc, GraphFunction& m) override {
+    return keys_;
+  };
+
+  std::shared_ptr<ModuleValue> self_;
+  std::shared_ptr<SugaredTupleValue> keys_;
+  std::shared_ptr<SugaredTupleValue> modules_;
+};
+
+struct VISIBILITY_HIDDEN BooleanDispatchValue : public SugaredValue {
+  BooleanDispatchValue(py::dict dispatched_fn)
+      : dispatched_fn_(std::move(dispatched_fn)) {}
+
+  std::string kind() const override {
+    return "boolean dispatch";
+  }
+
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& caller,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override;
+
+ private:
+  py::dict dispatched_fn_;
+};
+
+struct VISIBILITY_HIDDEN PythonClassValue : public ClassValue {
+  PythonClassValue(ClassTypePtr type, py::object py_type)
+      : ClassValue(std::move(type)), py_type_(std::move(py_type)) {}
+
+  std::string kind() const override {
+    return "Python type";
+  }
+
+  std::shared_ptr<SugaredValue> attr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+  bool hasAttr(
+      const SourceRange& loc,
+      GraphFunction& m,
+      const std::string& field) override;
+
+ private:
+  py::object py_type_;
+};
+
+struct VISIBILITY_HIDDEN PythonExceptionValue : public ExceptionValue {
+  explicit PythonExceptionValue(const py::object& exception_class)
+      : ExceptionValue(
+            py::str(py::getattr(exception_class, "__name__", py::str("")))),
+        exception_class_qualified_name_(
+            py::str(py::module::import("torch._jit_internal")
+                        .attr("_qualified_name")(
+                            exception_class,
+                            /*mangle_name=*/false))) {}
+
+  std::string kind() const override {
+    return "Python exception";
+  }
+
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& caller,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override;
+
+ private:
+  std::string exception_class_qualified_name_;
+};
+
+// Python Slice class.
+struct VISIBILITY_HIDDEN PythonSliceClass : public SugaredValue {
+  explicit PythonSliceClass() = default;
+
+  std::string kind() const override {
+    return "Python slice class";
+  }
+
+  std::shared_ptr<SugaredValue> call(
+      const SourceRange& loc,
+      GraphFunction& caller,
+      at::ArrayRef<NamedValue> args,
+      at::ArrayRef<NamedValue> kwargs,
+      size_t n_binders) override;
+};
+
+} // namespace torch::jit

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/python/python_tree_views.h

@@ -0,0 +1,9 @@
+#pragma once
+
+#include <torch/csrc/python_headers.h>
+
+namespace torch::jit {
+
+void initTreeViewBindings(PyObject* module);
+
+} // namespace torch::jit

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/block_codegen.h

@@ -0,0 +1,150 @@
+#pragma once
+
+#include <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <utility>
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/resource_guard.h>
+#include <torch/csrc/jit/tensorexpr/analysis.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/ir_visitor.h>
+#include <torch/csrc/jit/tensorexpr/unique_name_manager.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// A class that analyzes the given program relevant for Block backend.
+class BlockAnalysis : public IRVisitor {
+ public:
+  bool is_buf_store_target(BufPtr buf) const {
+    return store_targets_.count(buf) > 0;
+  }
+
+  const std::unordered_set<BufPtr>& loads() const {
+    return loads_;
+  }
+
+  const std::unordered_set<BufPtr>& stores() const {
+    return store_targets_;
+  }
+
+  int block_size() const {
+    return block_size_;
+  }
+
+  bool areBufsInMap(const std::unordered_set<BufPtr>& bufs) const;
+
+  BufPtr getMultiDimBuf(BufPtr buf) const;
+
+  std::string getInputName(BufPtr buf) const;
+
+  std::string getFlatInputName(BufPtr buf) const {
+    return getInputName(std::move(buf)) + "_flat";
+  }
+
+  std::unordered_map<std::string, BufPtr> getBufferMap() const {
+    return map_input_to_tensor_bufs_;
+  }
+
+ private:
+  void visit(StorePtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(ForPtr v) override;
+
+  std::unordered_map<std::string, BufPtr> map_input_to_tensor_bufs_;
+  std::unordered_set<BufPtr> store_targets_;
+  std::unordered_set<BufPtr> loads_;
+  int block_size_ = 32;
+};
+
+// A class that overrides the underlying IRPrinter to produce Block.
+class BlockPrinter : public IRPrinter {
+ public:
+  BlockPrinter(std::ostream* os, BlockAnalysis* block_analysis)
+      : IRPrinter(*os), block_analysis_(block_analysis) {}
+
+  using IRPrinter::name_manager;
+  using IRPrinter::visit;
+
+ private:
+  BlockAnalysis* block_analysis_;
+  std::unordered_map<std::string, int> dim_values_map;
+  std::vector<std::string> dim_names = {"N", "H", "W", "C"};
+  std::vector<std::string> flat_dim_names = {"N", "NH", "NHW", "NHWC"};
+  void PrintTensorInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintArguments(const std::unordered_set<BufPtr>& bufs);
+  void PrintBufferInfo(const std::unordered_set<BufPtr>& bufs);
+  void PrintDistribution(const std::unordered_set<BufPtr>& bufs);
+  void PrintLoop(const std::unordered_set<BufPtr>& bufs, bool block_idx = true);
+  void PrintReshapeInfo(
+      const std::unordered_set<BufPtr>& bufs,
+      bool reverse = false);
+  void PrintDMAs(const std::unordered_set<BufPtr>& bufs);
+  void PrintAdjustBuffers(const std::unordered_set<BufPtr>& bufs);
+
+  void visit(ForPtr v) override;
+  void visit(LoadPtr v) override;
+  void visit(StorePtr v) override;
+  void visit(BlockPtr v) override;
+  void visit(AddPtr v) override;
+  void visit(MulPtr v) override;
+};
+
+class TORCH_API BlockCodeGen : public CodeGen {
+ public:
+  template <typename... Ts>
+  /* implicit */
+  BlockCodeGen(StmtPtr stmt, Ts... ts)
+      : CodeGen(
+            stmt,
+            std::vector<BufferArg>({BufferArg(ts)...}),
+            at::Device(at::kCPU)) {
+    Initialize();
+  }
+
+  BlockCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::Device(at::kCPU),
+      const std::string& kernel_func_name = "func")
+      : CodeGen(stmt, buffer_args, device, kernel_func_name) {
+    Initialize();
+  }
+
+  ~BlockCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  void Initialize();
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  UniqueNameManager* name_manager() {
+    if (!printer_) {
+      throw std::runtime_error("Null IRPrinter is not expected");
+    }
+    return printer_->name_manager();
+  }
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<BlockPrinter> printer_;
+  std::unique_ptr<BlockAnalysis> block_analysis_;
+
+  std::string GetUniqueFuncName(const std::string& func_prefix);
+};
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_inference.h

@@ -0,0 +1,80 @@
+#pragma once
+
+#include <map>
+#include <unordered_map>
+#include <vector>
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/mem_dependency_checker.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class Expr;
+class Buf;
+class Stmt;
+
+enum C10_API_ENUM TensorAccessKind { kLoad, kStore, kMutate };
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+struct TORCH_API TensorAccessBoundsInfo {
+  TensorAccessKind kind;
+  std::vector<ExprPtr> start;
+  std::vector<ExprPtr> stop;
+};
+
+using BoundsInfo =
+    std::unordered_map<BufPtr, std::vector<TensorAccessBoundsInfo>>;
+
+TORCH_API BoundsInfo inferBounds(StmtPtr s, bool distinctAccessKinds = true);
+
+// Bounds inference caching the analysis. The MemDependencyChecker must already
+// have been run.
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr s,
+    bool distinctAccessKinds = true);
+TORCH_API BoundsInfo getInferredBounds(
+    analysis::MemDependencyChecker& analyzer,
+    ExprPtr e,
+    bool distinctAccessKinds = true);
+
+TORCH_API void printBoundsInfo(const BoundsInfo& v);
+
+TORCH_API std::vector<ExprPtr> getBoundExtents(
+    const std::vector<TensorAccessBoundsInfo>& infos);
+
+// The kind of dependency found, in increasing order of exclusivity.
+enum class HazardKind {
+  ReadAfterWrite,
+  WriteAfterRead,
+  WriteAfterWrite,
+  NoDependency,
+};
+TORCH_API HazardKind getPotentialHazards(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+
+// Returns true if there is a conflicting overlap between accesses in
+// statements A and B. A conflicting overlap is an overlap in buffer accesses
+// where at least one of the accesses is a Store.
+TORCH_API bool hasConflictingOverlap(
+    analysis::MemDependencyChecker& analyzer,
+    StmtPtr A,
+    StmtPtr B);
+// Same as above, between accesses in stores S1 and S2.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S1,
+    StorePtr S2);
+// Same as above, between accesses in store S and load L.
+TORCH_API bool isOverlapping(
+    analysis::MemDependencyChecker& analyzer,
+    StorePtr S,
+    LoadPtr L);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/bounds_overlap.h

@@ -0,0 +1,128 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+
+#include <deque>
+#include <utility>
+#include <vector>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+namespace analysis {
+
+// A simple class containing the start and end of a range in a single dimension.
+struct TORCH_API Bound {
+  ExprPtr start{nullptr};
+  ExprPtr end{nullptr};
+
+  // This stores whether or not the start and end of this Bound have previously
+  // been swapped. This occurs when the bound is in a loop with a negative
+  // stride.
+  bool swapped{false};
+
+  Bound() = default;
+  Bound(ExprPtr s, ExprPtr e) : start(std::move(s)), end(std::move(e)) {}
+
+  void print() const;
+  bool equals(const Bound& other) const;
+
+  // The comparison operators are conservative. If the compare operator returns
+  // true, it means that all the elements satisfy the logical expression. But
+  // the false does not mean the opposite comparison is satisfied. It could be
+  // but not always.
+  bool operator==(const Bound& other) const;
+  bool operator!=(const Bound& other) const;
+  bool operator<(const Bound& other) const;
+  bool operator<=(const Bound& other) const;
+  bool operator>(const Bound& other) const;
+  bool operator>=(const Bound& other) const;
+
+  void swap() {
+    std::swap(start, end);
+    swapped = !swapped;
+  }
+};
+
+struct BoundHash {
+  size_t operator()(const Bound& b) const {
+    return std::hash<ExprPtr>()(b.start) ^ std::hash<ExprPtr>()(b.end);
+  }
+};
+
+// The type of overlap found. Each condition is true only if none of the
+// previous conditions hold.
+//     ContainedOrEqual: All elements in the Bound A are in the Bound B (this
+//                       includes the case where the bounds are equal).
+//     Contains: All elements in the Bound B are in the Bound B.
+//     PartialOverlap: Any elements in the Bound B are in the Bound A.
+//     NoOverlap: No elements in the Bound A are in the bound B.
+enum class OverlapKind {
+  ContainedOrEqual,
+  Contains,
+  PartialOverlap,
+  NoOverlap
+};
+
+// The Bound comparison result.
+//     True: Every Bound element always satisfies the given comparison operator
+//     False: Every Bound element always does NOT satisfy the given comparison
+//     operator
+//     NotDetermined: Some elements satisfy the given comparison operator and
+//     some elements not
+enum class CmpEvalResult { True, False, NotDetermined };
+
+// Returns the kind of overlap between Bound A and Bound A in a single
+// dimension.
+OverlapKind TORCH_API boundOverlap(Bound A, Bound B);
+
+// The comparison is conservative and the compare result is deterministic.
+// It means that every element of the Bound to be compared needs to satisfy
+// the given comparison operator.
+CmpEvalResult TORCH_API compareBound(
+    const Bound& a,
+    const Bound& b,
+    const CompareSelectOperation& cmp_op);
+
+// A multi dimensional bound representing the bound of a set of indices.
+using IndexBounds = std::vector<Bound>;
+
+// Returns true if two IndexBounds are equivalent.
+bool TORCH_API indexBoundsEquals(const IndexBounds& A, const IndexBounds& B);
+
+// Flattens a multi dimensional bound to a single dimension. The IndexBounds "a"
+// *must* encapsulate the entire range of the buffer.
+Bound TORCH_API flattenBounds(const IndexBounds& a);
+
+// Determines the kind of overlap in X dimensions.
+OverlapKind TORCH_API overlaps(const IndexBounds& a, const IndexBounds& b);
+
+// Returns the Bound slices created by subtracing bound B from bound A.
+// Multiple Bounds can be returned in the case where B slices A into two
+// distinct regions with no overlap.
+//
+// For example:
+//    subtractBound((0, 10), (2, 4)) => [(0, 1), (5, 10)]
+//       bound A: (0, 10)
+//       bound B: (2, 4)
+//       If we remove slice (2, 4) from the slice (0, 10), we will be left
+//       with 2 slices, one at the start (0, 1), and one at the end (5, 10).
+//       So, the result of this subtraction is [(0, 1), (5, 10)].
+//
+// Note: this doesn't use IndexBounds because the Bounds returned do not
+// represent multiple different dimensions.
+std::vector<Bound> TORCH_API subtractBound(Bound a, Bound b);
+
+// Returns the bound slices created by subtracting the IndexBounds B from A.
+std::vector<IndexBounds> TORCH_API subtractIndicesBounds(
+    const IndexBounds& A,
+    const IndexBounds& B,
+    OverlapKind overlap);
+std::vector<IndexBounds> TORCH_API
+subtractIndicesBounds(const IndexBounds& A, const IndexBounds& B);
+
+} // namespace analysis
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/codegen.h

@@ -0,0 +1,283 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+
+#include <utility>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename T>
+class PaddedBuffer;
+
+class TORCH_API CodeGen {
+ public:
+  class BufferArg;
+  class CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(StmtPtr stmt, Ts... ts)
+      : stmt_(std::move(stmt)), buffer_args_({BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CodeGen(
+      StmtPtr stmt,
+      std::vector<BufferArg> buffer_args,
+      at::Device device = at::kCPU,
+      std::string kernel_func_name = "func");
+
+  virtual ~CodeGen() = default;
+
+  StmtPtr stmt() const {
+    return stmt_;
+  }
+
+  void set_stmt(StmtPtr s) {
+    stmt_ = s;
+  }
+
+  void apply_mutator(IRMutator* mutator) {
+    stmt_ = stmt_->accept_mutator(mutator);
+  }
+
+  void apply_visitor(IRVisitor* visitor) {
+    stmt_->accept(visitor);
+  }
+
+  std::vector<BufferArg>& buffer_args() {
+    return buffer_args_;
+  }
+
+  const std::vector<BufferArg>& buffer_args() const {
+    return buffer_args_;
+  }
+
+  at::Device device() {
+    return device_;
+  }
+
+  // This function returns the generated code as
+  // a string.
+  virtual std::string getCodeText(const std::string& attr = "") {
+    return ("");
+  }
+
+  // TODO: Figure out how to unify these call interfaces.
+
+  /// Call a function with a vector of CallArgs, which are tagged
+  /// unions that properly type the arguments.
+  virtual void call(const std::vector<CallArg>& args) = 0;
+
+  /// Call a function faster than a regular `call` by assuming that
+  /// the generated kernel already knows the type of the arguments, so
+  /// they can be type-punned with `void*`s.
+  virtual void call_raw(const std::vector<void*>& args) = 0;
+
+  /// Call a function even faster than a regular call, by assuming
+  /// that the number of thread blocks can be derived from `numel` via
+  /// a simple division, rather than evaluating an expression.
+  virtual void call_with_numel(void** args, int64_t numel);
+
+  virtual at::Tensor empty_strided(
+      c10::IntArrayRef size,
+      c10::IntArrayRef stride,
+      c10::optional<c10::ScalarType> dtype_opt,
+      c10::optional<c10::Layout> layout_opt,
+      c10::optional<c10::Device> device_opt,
+      c10::optional<bool> pin_memory_opt) {
+    return at::empty_strided(
+        size, stride, dtype_opt, layout_opt, device_opt, pin_memory_opt);
+  }
+
+  const std::string& kernel_func_name() const {
+    return kernel_func_name_;
+  }
+
+  void allocIntermediateBufs();
+
+ protected:
+  static void* argToPtr(const BufferArg& bufferArg, const CallArg& callArg);
+
+ private:
+  StmtPtr stmt_;
+  std::vector<BufferArg> buffer_args_;
+  at::Device device_ = at::kCPU;
+  std::string kernel_func_name_ = "func";
+};
+
+class TORCH_API ExtCallMemoryReuse : public IRMutator {
+  static std::unordered_map<std::string, std::string> makeExtCallFuncNameMap();
+  static const std::unordered_map<std::string, std::string> extCallFuncNameMap_;
+
+ public:
+  explicit ExtCallMemoryReuse(
+      const std::vector<CodeGen::BufferArg>& bufferArgs);
+  ~ExtCallMemoryReuse() override = default;
+  StmtPtr mutate(ExternalCallPtr v) override;
+
+ private:
+  std::unordered_set<BufPtr> bufferArgs_;
+};
+
+class CodeGen::BufferArg {
+ public:
+  BufferArg(const Tensor& tensor) : buf_(tensor.buf()) {}
+  BufferArg(const VarHandle& var) : var_(var.node()), isVar_(true) {}
+  BufferArg(const BufHandle& buf) : buf_(buf.node()) {}
+  BufferArg(BufPtr buf) : buf_(std::move(buf)) {}
+
+  VarPtr var() const {
+    return isVar_ ? var_ : buf_->base_handle();
+  }
+
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  bool isVar() const {
+    return isVar_;
+  }
+
+  Dtype dtype() const {
+    return isVar_ ? var_->dtype() : buf_->dtype();
+  }
+
+ private:
+  VarPtr var_ = nullptr;
+  BufPtr buf_ = nullptr;
+  bool isVar_ = false;
+};
+
+class CodeGen::CallArg {
+ public:
+  template <typename T>
+  CallArg(const PaddedBuffer<T>& buffer);
+
+  template <typename T>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init,cppcoreguidelines-pro-type-const-cast)
+  CallArg(const std::vector<T>& buffer)
+      // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+      : data_(const_cast<T*>(buffer.data())) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CallArg(void* ptr) : data_(ptr) {}
+
+#define ARG_TYPE_CTOR(Type, Name)      \
+  CallArg(Type v) {                    \
+    memcpy(buffer_, &v, sizeof(Type)); \
+    data_ = (void*)buffer_;            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_TYPE_CTOR);
+#undef ARG_TYPE_CTOR
+
+  void* data() const {
+    return data_;
+  }
+
+  CallArg(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+  }
+
+  CallArg& operator=(const CallArg& rhs) {
+    if (rhs.data_ == rhs.buffer_) {
+      memcpy(this->buffer_, rhs.buffer_, sizeof(rhs.buffer_));
+      this->data_ = (void*)(this->buffer_);
+    } else {
+      this->data_ = rhs.data_;
+    }
+    return *this;
+  }
+
+#define ARG_PTR_DEFINE(Type, Name)                  \
+  Type* Name##Ptr() const {                         \
+    TORCH_INTERNAL_ASSERT(data_ == (void*)buffer_); \
+    return (Type*)data_;                            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-const-cast)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, ARG_PTR_DEFINE);
+#undef ARG_PTR_DEFINE
+
+ private:
+  void* data_;
+  // Regarding a scalar value, CallArg uses void**=&data_ to store it. But the
+  // bit width of a pointer is 32bit on a 32bit platform. It cannot store the
+  // scalar if the bit width of the scalar is larger than 32bit, such as double
+  // and long. Hence, we add 8 bytes buffer dedicated to storing the scalar
+  // value regardless its bit width is less or greater than 32bits.
+  char buffer_[8] = {0}; // 64bits
+};
+
+// NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+class RegisterCodeGenList {
+ public:
+  TORCH_API static RegisterCodeGenList& GetInstance() {
+    static RegisterCodeGenList codegen_list;
+    return codegen_list;
+  }
+
+  using StmtFactoryMethod = std::function<std::unique_ptr<CodeGen>(
+      StmtPtr stmt,
+      const std::vector<CodeGen::BufferArg>&,
+      at::Device device,
+      const std::string& kernel_func_name)>;
+
+  TORCH_API StmtFactoryMethod FindStmtFactoryMethod(const std::string& name);
+  RegisterCodeGenList(const RegisterCodeGenList&) = delete;
+  RegisterCodeGenList& operator=(const RegisterCodeGenList&) = delete;
+
+ private:
+  template <class CodeGenType>
+  friend class RegisterCodeGen;
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  RegisterCodeGenList() = default;
+  TORCH_API void AddStmtFactoryMethod(
+      const std::string& name,
+      const StmtFactoryMethod& stmt_factory_method);
+
+  std::unordered_map<std::string, StmtFactoryMethod> stmt_factory_methods_;
+};
+
+template <class CodeGenType>
+class RegisterCodeGen {
+ public:
+  explicit RegisterCodeGen(const std::string& name) {
+    RegisterCodeGenList& codegen_list = RegisterCodeGenList::GetInstance();
+    codegen_list.AddStmtFactoryMethod(
+        name,
+        [](StmtPtr stmt,
+           const std::vector<CodeGen::BufferArg>& params,
+           at::Device device,
+           const std::string& kernel_func_name) {
+          // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+          std::unique_ptr<CodeGen> method(
+              new CodeGenType(stmt, params, device, kernel_func_name));
+          return method;
+        });
+  }
+};
+
+TORCH_API std::unique_ptr<CodeGen> CreateCodeGen(
+    const std::string& name,
+    StmtPtr stmt,
+    const std::vector<CodeGen::BufferArg>& params,
+    at::Device device = at::kCPU,
+    const std::string& kernel_func_name = "func");
+
+class TORCH_API GenericIntrinsicsExpander : public IRMutator {
+ protected:
+  ExprPtr mutate(IntrinsicsPtr v) override;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_codegen.h

@@ -0,0 +1,102 @@
+#pragma once
+
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class CppVarNameRewriter;
+
+// Generates C++ code from the IR.
+//
+// Vector operations are unrolled.
+// For example:
+// C[Ramp(0, 1, 3)] = A[Ramp(0, 2, 3)] + B[Ramp(0, 3, 3)];
+// is unrolled into:
+// C[0] = A[0] + B[0];
+// C[1] = A[2] + B[3];
+// C[2] = A[4] + B[6];
+class TORCH_API CppPrinter : public IRPrinter {
+ public:
+  explicit CppPrinter(std::ostream* os);
+  ~CppPrinter() override;
+
+  void printPrologue();
+
+  using IRPrinter::visit;
+
+  // Binary expressions.
+  void visit(ModPtr) override;
+  void visit(MaxPtr) override;
+  void visit(MinPtr) override;
+
+  // Conditional expressions.
+  void visit(CompareSelectPtr) override;
+  void visit(IfThenElsePtr) override;
+
+  // Tensor operations.
+  void visit(AllocatePtr) override;
+  void visit(FreePtr) override;
+  void visit(LoadPtr) override;
+  void visit(StorePtr) override;
+
+  // Casts.
+  void visit(CastPtr) override;
+  void visit(BitCastPtr) override;
+
+  // Calls.
+  void visit(IntrinsicsPtr) override;
+  void visit(ExternalCallPtr) override;
+
+  // Vars.
+  void visit(LetPtr) override;
+  void visit(VarPtr) override;
+
+  // Vector data types.
+  void visit(RampPtr) override;
+  void visit(BroadcastPtr) override;
+
+ private:
+  int lane_;
+  std::unordered_map<VarPtr, ExprPtr> vector_vars_;
+};
+
+class TORCH_API CppCodeGen : public CodeGen {
+ public:
+  CppCodeGen(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~CppCodeGen() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    call(std::vector<CallArg>({CallArg(ts)...}));
+  }
+
+  std::string getCodeText(const std::string& attr = "") override {
+    return oss_.str();
+  }
+
+ private:
+  void init();
+
+  std::ostream& os() {
+    return printer_->os();
+  }
+
+  std::ostringstream oss_;
+  std::unique_ptr<CppPrinter> printer_;
+  std::unique_ptr<CppVarNameRewriter> var_name_rewriter_;
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cpp_intrinsics.h

@@ -0,0 +1,36 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto cpp_intrinsics_definition = R"(
+namespace std {
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T rsqrt(T v) {
+  return 1.0f / std::sqrt(v);
+}
+
+template <typename T,
+          typename std::enable_if<std::is_floating_point<T>::value, int>::type = 0>
+T frac(T v) {
+  T intpart;
+  return std::modf(v, &intpart);
+}
+
+template <typename From, typename To>
+To bitcast(const From& v) {
+  assert(sizeof(To) == sizeof(From));
+  To res;
+  std::memcpy(&res, &v, sizeof(From));
+  return res;
+}
+
+} // namespace std
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/cuda_random.h

@@ -0,0 +1,104 @@
+#pragma once
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+constexpr auto philox_random_string = R"(
+
+class Philox {
+public:
+  __device__ inline Philox(unsigned long long seed,
+                           unsigned long long subsequence,
+                           unsigned long long offset) {
+    key.x = (unsigned int)seed;
+    key.y = (unsigned int)(seed >> 32);
+    counter = make_uint4(0, 0, 0, 0);
+    counter.z = (unsigned int)(subsequence);
+    counter.w = (unsigned int)(subsequence >> 32);
+    STATE = 0;
+    incr_n(offset / 4);
+  }
+
+  __device__ inline unsigned long operator()() {
+    if(STATE == 0) {
+      uint4 counter_ = counter;
+      uint2 key_ = key;
+      for(int i = 0; i < 9; i++) {
+        counter_ = single_round(counter_, key_);
+        key_.x += (kPhilox10A); key_.y += (kPhilox10B);
+      }
+      output = single_round(counter_, key_);
+      incr();
+    }
+    unsigned long ret;
+    switch(STATE) {
+      case 0: ret = output.x; break;
+      case 1: ret = output.y; break;
+      case 2: ret = output.z; break;
+      case 3: ret = output.w; break;
+    }
+    STATE = (STATE + 1) % 4;
+    return ret;
+  }
+
+private:
+  uint4 counter;
+  uint4 output;
+  uint2 key;
+  unsigned int STATE;
+  __device__ inline void incr_n(unsigned long long n) {
+    unsigned int nlo = (unsigned int)(n);
+    unsigned int nhi = (unsigned int)(n >> 32);
+    counter.x += nlo;
+    if (counter.x < nlo)
+      nhi++;
+    counter.y += nhi;
+    if (nhi <= counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ inline void incr() {
+    if (++counter.x)
+      return;
+    if (++counter.y)
+      return;
+    if (++counter.z)
+      return;
+    ++counter.w;
+  }
+  __device__ unsigned int mulhilo32(unsigned int a, unsigned int b,
+                                    unsigned int *result_high) {
+    *result_high = __umulhi(a, b);
+    return a*b;
+  }
+
+  __device__ inline uint4 single_round(uint4 ctr, uint2 key) {
+    unsigned int hi0;
+    unsigned int hi1;
+    unsigned int lo0 = mulhilo32(kPhiloxSA, ctr.x, &hi0);
+    unsigned int lo1 = mulhilo32(kPhiloxSB, ctr.z, &hi1);
+
+    uint4 ret = {hi1 ^ ctr.y ^ key.x, lo1, hi0 ^ ctr.w ^ key.y, lo0};
+    return ret;
+  }
+
+  static const unsigned long kPhilox10A = 0x9E3779B9;
+  static const unsigned long kPhilox10B = 0xBB67AE85;
+  static const unsigned long kPhiloxSA = 0xD2511F53;
+  static const unsigned long kPhiloxSB = 0xCD9E8D57;
+};
+
+// Inverse of 2^32.
+#define M_RAN_INVM32 2.3283064e-10f
+__device__  __inline__ float Uint32ToFloat(unsigned int x) {
+  return x * M_RAN_INVM32;
+}
+
+)";
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/eval.h

@@ -0,0 +1,347 @@
+#pragma once
+
+#include <cmath>
+#include <cstring>
+#include <type_traits>
+#include <unordered_map>
+#include <utility>
+#include <vector>
+
+#include <c10/macros/Macros.h>
+#include <c10/util/Logging.h>
+#include <c10/util/math_compat.h>
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/codegen.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/ir.h>
+#include <torch/csrc/jit/tensorexpr/ir_printer.h>
+#include <torch/csrc/jit/tensorexpr/tensor.h>
+#include <torch/csrc/jit/tensorexpr/types.h>
+#include <torch/csrc/jit/tensorexpr/var_substitutor.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class InterpValue {
+ public:
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  InterpValue() : dtype_(kInt) {
+    Intvalues.push_back(0);
+  }
+
+  template <typename T>
+  InterpValue(Dtype dtype, T v) : dtype_(dtype) {
+#define TYPE_CASE(Type, Name)  \
+  if (dtype == k##Name) {      \
+    Name##values.push_back(v); \
+    return;                    \
+  }
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    throw unsupported_dtype();
+  }
+
+#define VALUE_CTOR(Type, Name)            \
+  InterpValue(Type v) : dtype_(k##Name) { \
+    Name##values.push_back(v);            \
+  }
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_CTOR);
+#undef VALUE_CTOR
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::quint8 v) : dtype_(kQUInt8) {
+    QUInt8values.emplace_back(v.val_);
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  explicit InterpValue(c10::qint8 v) : dtype_(kQInt8) {
+    QInt8values.emplace_back(v.val_);
+  }
+
+#define VALUE_VEC_CTOR(Type, Name)        \
+  InterpValue(const std::vector<Type>& v) \
+      : dtype_(Dtype(k##Name, v.size())), Name##values(v) {}
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_VEC_CTOR);
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::quint8, QUInt8)
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  VALUE_VEC_CTOR(c10::qint8, QInt8)
+#undef VALUE_VEC_CTOR
+
+  template <typename T>
+  T as() const;
+
+  template <typename T>
+  const std::vector<T>& as_vec() const;
+
+  int64_t intValue() const;
+
+  Dtype dtype() const {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+
+#define VALUE_STORAGE(Type, Name) std::vector<Type> Name##values;
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_STORAGE);
+  VALUE_STORAGE(c10::qint8, QInt8);
+  VALUE_STORAGE(c10::quint8, QUInt8);
+#undef VALUE_STORAGE
+  void* ptr;
+};
+
+#define VALUE_AS_DISPATCH(Type, Name)         \
+  template <>                                 \
+  inline Type InterpValue::as<Type>() const { \
+    if (dtype_ != k##Name) {                  \
+      throw unsupported_dtype();              \
+    }                                         \
+    return Name##values[0];                   \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_DISPATCH);
+VALUE_AS_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_DISPATCH
+
+#define VALUE_AS_VEC_DISPATCH(Type, Name)                             \
+  template <>                                                         \
+  inline const std::vector<Type>& InterpValue::as_vec<Type>() const { \
+    if (dtype_.scalar_type() != ScalarType::Name) {                   \
+      throw unsupported_dtype();                                      \
+    }                                                                 \
+    return Name##values;                                              \
+  }
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, VALUE_AS_VEC_DISPATCH);
+VALUE_AS_VEC_DISPATCH(c10::quint8, QUInt8);
+VALUE_AS_VEC_DISPATCH(c10::qint8, QInt8);
+#undef VALUE_AS_VEC_DISPATCH
+
+template <typename Type>
+auto underlyingValue(Type x) {
+  return x;
+}
+
+template <>
+inline auto underlyingValue<c10::quint8>(c10::quint8 x) {
+  return x.val_;
+}
+
+template <>
+inline auto underlyingValue<c10::qint8>(c10::qint8 x) {
+  return x.val_;
+}
+
+template <typename To, typename From>
+To raw_bitcast(const From& src) {
+  TORCH_CHECK(sizeof(To) == sizeof(From), "Invalid bitcast invocation");
+  To storage;
+  std::memcpy(&storage, &src, sizeof(To));
+  return reinterpret_cast<To&>(storage);
+}
+
+class SimpleIREvaluatorImpl;
+class TORCH_API SimpleIREvaluator : public CodeGen {
+ public:
+  SimpleIREvaluator(
+      StmtPtr stmt,
+      const std::vector<BufferArg>& buffer_args,
+      at::Device device = at::kCPU,
+      const std::string& kernel_func_name = "func");
+
+  ~SimpleIREvaluator() override;
+
+  void call(const std::vector<CallArg>& args) override;
+  void call_raw(const std::vector<void*>& args) override;
+
+  template <typename... Ts>
+  void operator()(const Ts&... ts) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> args({CallArg(ts)...});
+    call(args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e);
+  InterpValue value() const;
+
+ private:
+  void bindArg(const BufferArg& buf, void* data);
+  void expand_intrinsics() {
+    GenericIntrinsicsExpander intrinsics_expander;
+    apply_mutator(&intrinsics_expander);
+  }
+
+  std::unique_ptr<SimpleIREvaluatorImpl> impl_;
+};
+
+template <class CodeGenType>
+class ExprEval {
+ public:
+  using BufferArg = CodeGen::BufferArg;
+  using CallArg = CodeGen::CallArg;
+
+  template <typename... Ts>
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, Ts... ts)
+      : ExprEval(expr, {BufferArg(ts)...}) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  ExprEval(const ExprHandle& expr, const std::vector<BufferArg>& buffer_args)
+      : dtype_(expr.dtype()) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<BufferArg> buffer_args_extended = buffer_args;
+    BufHandle ret_buf("ret_val", {1}, dtype_);
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprHandle> indices;
+    ExprHandle zero = IntImm::make(0);
+    for (size_t i = 0; i < ret_buf.ndim(); i++) {
+      indices.push_back(zero);
+    }
+    StmtPtr store_stmt = Store::make(ret_buf, indices, expr);
+    buffer_args_extended.emplace_back(ret_buf);
+    codegen_.reset(new CodeGenType(store_stmt, buffer_args_extended));
+  }
+
+  template <typename... Ts>
+  void operator()(Ts... ts) {
+    call(ts...);
+  }
+
+  void operator()(const std::vector<CallArg>& call_args) {
+    call(call_args);
+  }
+
+  void bindVar(VarPtr v, ExprPtr e) {
+    codegen_->bindVar(v, e);
+  }
+
+  void bindVar(const VarHandle& v, const ExprHandle& e) {
+    codegen_->bindVar(v.node(), e.node());
+  }
+
+  template <typename... Ts>
+  void call(Ts... ts) {
+    call({CallArg(ts)...});
+  }
+
+  void call(const std::vector<CallArg>& call_args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<CallArg> call_args_extended = call_args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                           \
+  case ScalarType::Name: {                              \
+    std::vector<Type> ret_val_arg(1);                   \
+    call_args_extended.push_back(CallArg(ret_val_arg)); \
+    codegen_->call(call_args_extended);                 \
+    ret_value_ = InterpValue(ret_val_arg[0]);           \
+  } break;
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::quint8, QUInt8);
+      // NOLINTNEXTLINE(modernize-use-emplace)
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        call_args_extended.emplace_back(ret_val_arg.data());
+        codegen_->call(call_args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  void call_raw(const std::vector<void*>& args) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<void*> args_extended = args;
+    switch (dtype_.scalar_type()) {
+#define TYPE_CASE(Type, Name)                    \
+  case ScalarType::Name: {                       \
+    std::vector<Type> ret_val_arg(1);            \
+    args_extended.push_back(ret_val_arg.data()); \
+    codegen_->call_raw(args_extended);           \
+    ret_value_ = InterpValue(ret_val_arg[0]);    \
+  } break;
+      AT_FORALL_SCALAR_TYPES_AND2(Half, BFloat16, TYPE_CASE);
+      TYPE_CASE(c10::quint8, QUInt8);
+      TYPE_CASE(c10::qint8, QInt8);
+#undef TYPE_CASE
+      case ScalarType::Bool: {
+        // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+        std::vector<unsigned char> ret_val_arg(1);
+        args_extended.push_back(ret_val_arg.data());
+        codegen_->call_raw(args_extended);
+        ret_value_ = InterpValue((bool)ret_val_arg[0]);
+      } break;
+      default:
+        throw unsupported_dtype();
+    }
+  }
+
+  template <typename T>
+  T value(const std::vector<void*>& args) {
+    call_raw(args);
+    return ret_value_.as<T>();
+  }
+
+  template <typename T, typename... Ts>
+  T value(Ts... ts) {
+    call(std::forward<Ts>(ts)...);
+    return ret_value_.as<T>();
+  }
+
+  Dtype dtype() {
+    return dtype_;
+  }
+
+ private:
+  Dtype dtype_;
+  std::unique_ptr<CodeGenType> codegen_;
+  InterpValue ret_value_;
+};
+
+// Evaluates the given expression and returns an int64_t value if the result of
+// the given expression is int64_t.
+c10::optional<int64_t> evalInt(ExprPtr e);
+
+// Substitutes the given vars with their corresponding expressions in the input
+// expression.
+inline ExprPtr Substitute(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return expr->accept_mutator(&var_sub);
+}
+
+// Substitutes the given vars with their corresponding expressions in the input
+// statement.
+inline StmtPtr Substitute(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return stmt->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input expression and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline ExprPtr SubstituteInClone(ExprPtr expr, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Expr::clone(std::move(expr))->accept_mutator(&var_sub);
+}
+
+// Creates a clone of the input statement and substitutes the given vars with
+// their corresponding expressions in the clone.
+// NOTE: This works because cloning reuses variables and does not create new
+// ones, and `VarMapping` input has variables as the key.
+inline StmtPtr SubstituteInClone(StmtPtr stmt, const VarMapping& var_mapping) {
+  VarSubMutator var_sub(var_mapping);
+  return Stmt::clone(std::move(stmt))->accept_mutator(&var_sub);
+}
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/exceptions.h

@@ -0,0 +1,91 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+
+#include <sstream>
+#include <stdexcept>
+
+// Forward declarations of types
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+class Expr;
+class Stmt;
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+
+// Forward declarations of functions
+namespace std {
+TORCH_API std::string to_string(const torch::jit::tensorexpr::ExprPtr);
+TORCH_API std::string to_string(const torch::jit::tensorexpr::StmtPtr);
+} // namespace std
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+class unsupported_dtype : public std::runtime_error {
+ public:
+  explicit unsupported_dtype() : std::runtime_error("UNSUPPORTED DTYPE") {}
+  explicit unsupported_dtype(const std::string& err)
+      : std::runtime_error("UNSUPPORTED DTYPE: " + err) {}
+};
+
+class out_of_range_index : public std::runtime_error {
+ public:
+  explicit out_of_range_index() : std::runtime_error("OUT OF RANGE INDEX") {}
+  explicit out_of_range_index(const std::string& err)
+      : std::runtime_error("OUT OF RANGE INDEX: " + err) {}
+};
+
+class unimplemented_lowering : public std::runtime_error {
+ public:
+  explicit unimplemented_lowering()
+      : std::runtime_error("UNIMPLEMENTED LOWERING") {}
+  explicit unimplemented_lowering(ExprPtr expr)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(expr)) {}
+  explicit unimplemented_lowering(StmtPtr stmt)
+      : std::runtime_error("UNIMPLEMENTED LOWERING: " + std::to_string(stmt)) {}
+};
+
+class malformed_input : public std::runtime_error {
+ public:
+  explicit malformed_input() : std::runtime_error("MALFORMED INPUT") {}
+  explicit malformed_input(const std::string& err)
+      : std::runtime_error("MALFORMED INPUT: " + err) {}
+  explicit malformed_input(ExprPtr expr)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(expr)) {}
+  explicit malformed_input(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_input(StmtPtr stmt)
+      : std::runtime_error("MALFORMED INPUT: " + std::to_string(stmt)) {}
+  explicit malformed_input(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED INPUT: " + err + " - " + std::to_string(stmt)) {}
+};
+
+class malformed_ir : public std::runtime_error {
+ public:
+  explicit malformed_ir() : std::runtime_error("MALFORMED IR") {}
+  explicit malformed_ir(const std::string& err)
+      : std::runtime_error("MALFORMED IR: " + err) {}
+  explicit malformed_ir(ExprPtr expr)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(expr)) {}
+  explicit malformed_ir(const std::string& err, ExprPtr expr)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(expr)) {}
+  explicit malformed_ir(StmtPtr stmt)
+      : std::runtime_error("MALFORMED IR: " + std::to_string(stmt)) {}
+  explicit malformed_ir(const std::string& err, StmtPtr stmt)
+      : std::runtime_error(
+            "MALFORMED IR: " + err + " - " + std::to_string(stmt)) {}
+};
+
+TORCH_API std::string buildErrorMessage(const std::string& s = "");
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_core.h

@@ -0,0 +1,29 @@
+#pragma once
+
+#include <ATen/ATen.h>
+#include <ATen/Parallel.h>
+#include <torch/csrc/Export.h>
+#include <cstdint>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+#ifdef C10_MOBILE
+extern "C" {
+#endif
+void DispatchParallel(
+    int8_t* func,
+    int64_t start,
+    int64_t stop,
+    int8_t* packed_data) noexcept;
+
+TORCH_API void nnc_aten_free(int64_t bufs_num, void** ptrs) noexcept;
+
+#ifdef C10_MOBILE
+} // extern "C"
+#endif
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/external_functions_registry.h

@@ -0,0 +1,61 @@
+#pragma once
+
+#include <torch/csrc/Export.h>
+#include <cstdint>
+#include <string>
+#include <unordered_map>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// The external functions that could be called from NNC must have the same
+// signature defined by `NNCExternalFunction`.
+//
+// Why this signature?
+// It was picked for two reasons: 1) it should be generic enough to represent
+// most of the ops we might want to call, 2) it should be possible to generate a
+// code for this call in LLVM codegen.
+// The first 5 parameters allow to pass any number of contiguous CPU tensors in
+// case we need to run aten ops (TODO: support different devices). The first
+// buffer in the array is assumed to be the output buffer. We couldn't use
+// `at::Tensor` (or `c10::IValue`) type there directly as it would mean that
+// we'd need to declare it in LLVM codegen in LLVM IR form, which would be very
+// cumbersome and hard to maintain. Note that the dimensions of all tensors are
+// concatenated into a single array buf_dims. We do not need to pass its length,
+// since it can be deduced from total number of buffers and their ranks.
+//
+// The last 2 arguments allow to pass any non-tensor arguments encoded as an
+// array of int64_t values. The way they are encoded is not specified and could
+// be arbitrary - whatever the most convenient for the specific bridge function
+// is.
+//
+// The bridge functions must not throw exceptions - properly propagating them
+// from the generated code is too cumbersome, and thus all calls to functions
+// that could throw must be wrapped with try-catch blocks.
+using NNCExternalFunction = void (*)(
+    int64_t bufs_num,
+    void** buf_data,
+    int64_t* buf_ranks,
+    int64_t* buf_dims,
+    int64_t* buf_strides,
+    int8_t* buf_dtypes,
+    int64_t args_num,
+    int64_t* extra_args);
+
+// Return a global map "function-name" -> "function-pointer" for all registered
+// in NNC external functions
+TORCH_API std::unordered_map<std::string, NNCExternalFunction>&
+getNNCFunctionRegistry();
+
+// To register a new external function in NNC one needs to create an instance of
+// this struct
+struct RegisterNNCExternalFunction {
+  RegisterNNCExternalFunction(const std::string& name, NNCExternalFunction fn) {
+    getNNCFunctionRegistry()[name] = fn;
+  }
+};
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/fwd_decls.h

@@ -0,0 +1,129 @@
+#pragma once
+#include <c10/core/ScalarType.h>
+#include <memory>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+template <typename Node>
+using NodePtr = std::shared_ptr<Node>;
+
+template <typename To, typename From>
+NodePtr<To> to(NodePtr<From> x) {
+  return std::dynamic_pointer_cast<To>(x);
+}
+
+template <typename To, typename From>
+NodePtr<To> static_to(NodePtr<From> x) {
+  return std::static_pointer_cast<To>(x);
+}
+
+template <typename Node, typename... Args>
+NodePtr<Node> alloc(Args&&... args) {
+  return std::make_shared<Node>(std::forward<Args>(args)...);
+}
+
+class Buf;
+class Expr;
+class Stmt;
+class Var;
+
+using BufPtr = NodePtr<Buf>;
+using ExprPtr = NodePtr<Expr>;
+using StmtPtr = NodePtr<Stmt>;
+using VarPtr = NodePtr<Var>;
+
+class ExprHandle;
+class VarHandle;
+class BufHandle;
+
+class Add;
+class And;
+class BitCast;
+class Broadcast;
+class Cast;
+class CompareSelect;
+class Div;
+class IfThenElse;
+class Intrinsics;
+class Let;
+class Load;
+class Lshift;
+class Max;
+class MaxTerm;
+class Min;
+class MinTerm;
+class Mod;
+class Mul;
+class Or;
+class Polynomial;
+class Ramp;
+class ReduceOp;
+class RoundOff;
+class Rshift;
+class Store;
+class Sub;
+class Term;
+class Xor;
+using AddPtr = NodePtr<Add>;
+using AndPtr = NodePtr<And>;
+using BitCastPtr = NodePtr<BitCast>;
+using BroadcastPtr = NodePtr<Broadcast>;
+using CastPtr = NodePtr<Cast>;
+using CompareSelectPtr = NodePtr<CompareSelect>;
+using DivPtr = NodePtr<Div>;
+using IfThenElsePtr = NodePtr<IfThenElse>;
+using IntrinsicsPtr = NodePtr<Intrinsics>;
+using LetPtr = NodePtr<Let>;
+using LoadPtr = NodePtr<Load>;
+using LshiftPtr = NodePtr<Lshift>;
+using MaxPtr = NodePtr<Max>;
+using MaxTermPtr = NodePtr<MaxTerm>;
+using MinPtr = NodePtr<Min>;
+using MinTermPtr = NodePtr<MinTerm>;
+using ModPtr = NodePtr<Mod>;
+using MulPtr = NodePtr<Mul>;
+using OrPtr = NodePtr<Or>;
+using PolynomialPtr = NodePtr<Polynomial>;
+using RampPtr = NodePtr<Ramp>;
+using ReduceOpPtr = NodePtr<ReduceOp>;
+using RoundOffPtr = NodePtr<RoundOff>;
+using RshiftPtr = NodePtr<Rshift>;
+using StorePtr = NodePtr<Store>;
+using SubPtr = NodePtr<Sub>;
+using TermPtr = NodePtr<Term>;
+using XorPtr = NodePtr<Xor>;
+
+class Allocate;
+class AtomicAdd;
+class Block;
+class Cond;
+class ExternalCall;
+class ExternalCallWithAlloc;
+class For;
+class Free;
+class FreeExt;
+class PlacementAllocate;
+class SyncThreads;
+using AllocatePtr = NodePtr<Allocate>;
+using AtomicAddPtr = NodePtr<AtomicAdd>;
+using BlockPtr = NodePtr<Block>;
+using CondPtr = NodePtr<Cond>;
+using ExternalCallPtr = NodePtr<ExternalCall>;
+using ExternalCallWithAllocPtr = NodePtr<ExternalCallWithAlloc>;
+using ForPtr = NodePtr<For>;
+using FreePtr = NodePtr<Free>;
+using FreeExtPtr = NodePtr<FreeExt>;
+using PlacementAllocatePtr = NodePtr<PlacementAllocate>;
+using SyncThreadsPtr = NodePtr<SyncThreads>;
+
+#define IMM_DECLARE(Type, Name) \
+  class Name##Imm;              \
+  using Name##ImmPtr = NodePtr<Name##Imm>;
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/graph_opt.h

@@ -0,0 +1,115 @@
+#pragma once
+
+#include <torch/csrc/jit/ir/ir.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+// Optimize aten::cat ops in the given subgraph.
+//
+// Moving users of cat to its inputs.
+//    Cat ops get lowered into multiple loops, one per input. When the result
+//    of cat is used by some other op, it results in a situation where inlining
+//    of cat does not happen. This in turn results in intermediate buffers
+//    being created for the result of cat, since it is not inlined.
+//
+//    For example, consider the following graph:
+//       graph(%x : Float(10, strides=[1], device=cpu),
+//             %y : Float(20, strides=[1], device=cpu)):
+//         %dim : int = prim::Constant[value=0]()
+//         %xy_list : Tensor[] = prim::ListConstruct(%x, %y)
+//         %cat : Float(60, strides=[1], device=cpu) = aten::cat(%xy_list, %dim)
+//         %5 : Float(60, strides=[1], device=cpu) = aten::log(%cat)
+//         return (%5))IR";
+//
+//     This will get lowered into:
+//         Allocate(aten_cat);
+//         for (...)
+//           aten_cat[...] = x[...]
+//         for (...)
+//           aten_cat[...] = y[...]
+//         for (...)
+//           aten_log[...] = log(aten_cat[...])
+//         Free(aten_cat);
+//     Note that aten_cat is not inlined into aten_log and it results in
+//     an intermediate buffer allocation as well.
+//
+//     Optimization:
+//        We move the ops that use the result of `cat` into its inputs whenever
+//     possible.
+//
+//     The graph above will be transformed to:
+//        graph(%x : Float(10, strides=[1], device=cpu),
+//              %y : Float(20, strides=[1], device=cpu)):
+//          %3 : int = prim::Constant[value=0]()
+//          %7 : Float(10, strides=[1], device=cpu) = aten::log(%x)
+//          %8 : Float(20, strides=[1], device=cpu) = aten::log(%y)
+//          %9 : Tensor[] = prim::ListConstruct(%7, %8)
+//          %10 : Float(60, strides=[1], device=cpu) = aten::cat(%9, %3)
+//          return (%10)
+//
+//     This will get lowered into:
+//         for (...)
+//           aten_cat[...] = log(x[...])
+//         for (...)
+//           aten_cat[...] = log(y[...])
+//     aten_cat is the output buffer here.
+
+bool OptimizeCat(const std::shared_ptr<Graph>& graph);
+
+TORCH_API void annotateInputShapes(
+    const std::shared_ptr<Graph>& graph,
+    const std::vector<c10::optional<at::Tensor>>& example_inputs);
+TORCH_API std::shared_ptr<Graph> removeUnusedSelfArgument(
+    const std::shared_ptr<Graph>& graph);
+TORCH_API std::shared_ptr<Graph> removeGraphOutput(
+    const std::shared_ptr<Graph>& graph,
+    size_t idx);
+TORCH_API std::shared_ptr<Graph> replaceListOutputWithTuple(
+    const std::shared_ptr<Graph>& graph);
+
+// Perform \p ITERS rounds of "trimming" for the given \p GRAPH.
+//
+// Trimming means that we try to remove a small portion of the graph while
+// keeping it valid. This is useful for debugging when we try to find a minimal
+// example reproducing the issue at hand. When ITERS is 0, the graph remains
+// unchanged, when ITERS is a big number, the graph usually becomes empty.
+TORCH_API std::shared_ptr<Graph> trimGraph(
+    const std::shared_ptr<Graph>& graph,
+    int64_t iters);
+
+// Scan all values in the given graph and replace each dimension with a size Xi
+// present in \p SIZES with a symbolic shape Yi. Return a vector of symbol
+// values [Y0, Y1, .., Yn].
+//
+// For example:
+// Input:
+// graph(%x : Float(10, 20, 30, 40)):
+//   %y : Float(10, 20, 30, 40) = aten::relu(%x)
+//   return %y
+//
+// If we run makeShapesSymbolic(graph, {20, 40}), then we'll get:
+//
+// graph(%x : Float(10, SS(-3), 30, SS(-5))):
+//   %y : Float(10, SS(-3), 30, SS(-5)) = aten::relu(%x)
+//   return %y
+//
+// and get {-3, -5} as the return value.
+TORCH_API std::vector<int64_t> makeShapesSymbolic(
+    std::shared_ptr<Graph>& graph,
+    const std::vector<int64_t>& sizes);
+
+// Inspect the graph and report whether it can be converted to TE IR.
+// TODO: add error reporting for graphs that can't be converted.
+TORCH_API bool isGraphCompilable(const std::shared_ptr<Graph>& graph);
+
+// Examine the graph and (hackily) fill in missing tensor type info, such as
+// scalar type, device, and strides. Ideally, this should be done by a proper
+// dtype/device/shape propagation passes, but until they are ready we can use
+// this, not always correct, workaround pass.
+TORCH_API void fixupMissingShapeInfo(const std::shared_ptr<Graph>& graph);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/intrinsic_symbols.h

@@ -0,0 +1,22 @@
+#pragma once
+
+#ifdef TORCH_ENABLE_LLVM
+#include <c10/util/ArrayRef.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+struct SymbolAddress {
+  const char* symbol;
+  void* address;
+
+  SymbolAddress(const char* sym, void* addr) : symbol(sym), address(addr) {}
+};
+
+c10::ArrayRef<SymbolAddress> getIntrinsicSymbols();
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch
+#endif // TORCH_ENABLE_LLVM

env-llmeval/lib/python3.10/site-packages/torch/include/torch/csrc/jit/tensorexpr/ir.h

@@ -0,0 +1,934 @@
+#pragma once
+
+#include <string>
+#include <utility>
+#include <vector>
+
+#include <c10/util/string_utils.h>
+#include <torch/csrc/jit/tensorexpr/exceptions.h>
+#include <torch/csrc/jit/tensorexpr/expr.h>
+#include <torch/csrc/jit/tensorexpr/fwd_decls.h>
+#include <torch/csrc/jit/tensorexpr/stmt.h>
+
+#include <ATen/core/ivalue.h>
+
+namespace torch {
+namespace jit {
+namespace tensorexpr {
+
+enum CompareSelectOperation {
+  kEQ = 0,
+  kGT,
+  kGE,
+  kLT,
+  kLE,
+  kNE,
+};
+
+enum CompareSelectBias {
+  kUnbiased,
+  kLikely,
+  kUnlikely,
+};
+
+inline int getPrecedence(IRNodeType ty) {
+  // Match C++ operator precedence rules, since some pretty-print expressions to
+  // C++. SEE: https://en.cppreference.com/w/cpp/language/operator_precedence
+  switch (ty) {
+    case kPrimitive:
+      return 0;
+    case kCast:
+    case kBitCast:
+      return 2;
+    case kAdd:
+    case kSub:
+      return 6;
+    case kMul:
+    case kDiv:
+    case kMod:
+      return 5;
+    case kMax:
+    case kMin:
+      return 99;
+    case kAnd:
+      return 11;
+    case kOr:
+      return 13;
+    case kLshift:
+    case kRshift:
+      return 7;
+    case kXor:
+      return 12;
+    case kCompareSelect:
+      return 16;
+    default:
+      return 99;
+  }
+}
+
+class TORCH_API Cast : public ExprNode<Cast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<Cast>(dtype, src_value.node()));
+  }
+  Cast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kCast), src_value_(std::move(src_value)) {}
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle cast(const ExprHandle& src_value) {
+  return Cast::make(Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// This is a bitwise cast, akin to bitcast in LLVM
+class TORCH_API BitCast : public ExprNode<BitCast> {
+ public:
+  ExprPtr src_value() const {
+    return src_value_;
+  }
+
+  void set_src_value(ExprPtr src_value) {
+    src_value_ = std::move(src_value);
+  }
+
+  static ExprHandle make(Dtype dtype, const ExprHandle& src_value) {
+    return ExprHandle(alloc<BitCast>(dtype, src_value.node()));
+  }
+  BitCast(Dtype dtype, ExprPtr src_value)
+      : ExprNodeBase(dtype, kBitCast), src_value_(std::move(src_value)) {
+    TORCH_CHECK(src_value_->dtype().byte_size() == dtype.byte_size());
+  }
+
+  bool isConstant() const override {
+    return src_value_->isConstant();
+  }
+
+ private:
+  ExprPtr src_value_;
+};
+
+template <typename T>
+ExprHandle bitcast(const ExprHandle& src_value) {
+  return BitCast::make(
+      Dtype(ToDtype<T>(), src_value.dtype().lanes()), src_value);
+}
+
+// Represent the expression node for binary operators.
+// A CRTP pattern to share common code among the operators.
+template <typename Op>
+class BinaryOpNode : public ExprNode<Op> {
+ public:
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    return ExprHandle(alloc<Op>(lhs.node(), rhs.node()));
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  BinaryOpNode(
+      ExprPtr lhs_v,
+      ExprPtr rhs_v,
+      IRNodeType expr_type,
+      ScalarType ret_type = ScalarType::Undefined)
+      : ExprNode<Op>(
+            // NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
+            BinaryOpDtype(lhs_v->dtype(), rhs_v->dtype(), ret_type),
+            expr_type),
+        lhs_(CastIfNeeded(std::move(lhs_v), ExprNode<Op>::dtype())),
+        rhs_(CastIfNeeded(std::move(rhs_v), ExprNode<Op>::dtype())) {}
+
+ private:
+  static ExprPtr CastIfNeeded(ExprPtr expr, Dtype dst_dtype) {
+    if (expr->dtype() == dst_dtype) {
+      return expr;
+    }
+    return Cast::make(dst_dtype, ExprHandle(std::move(expr))).node();
+  }
+
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+};
+
+namespace detail {
+template <typename T>
+void bin_op_deducer(BinaryOpNode<T>);
+bool bin_op_deducer(...);
+} // namespace detail
+
+class TORCH_API Add : public BinaryOpNode<Add> {
+ public:
+  Add(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAdd) {}
+};
+
+class TORCH_API Sub : public BinaryOpNode<Sub> {
+ public:
+  Sub(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kSub) {}
+};
+
+class TORCH_API Mul : public BinaryOpNode<Mul> {
+ public:
+  Mul(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMul) {}
+};
+
+class TORCH_API Div : public BinaryOpNode<Div> {
+ public:
+  Div(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kDiv) {}
+};
+
+class TORCH_API Mod : public BinaryOpNode<Mod> {
+ public:
+  Mod(ExprPtr lhs, ExprPtr rhs)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMod) {}
+};
+
+template <typename Op>
+class BitwiseOpNode : public BinaryOpNode<Op> {
+ public:
+  BitwiseOpNode(ExprPtr lhs, ExprPtr rhs, IRNodeType type)
+      : BinaryOpNode<Op>(std::move(lhs), std::move(rhs), type) {}
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) {
+    if (!lhs.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("lhs/rhs dtype mismatch");
+    }
+    return BinaryOpNode<Op>::make(lhs, rhs);
+  }
+};
+
+class TORCH_API And : public BitwiseOpNode<And> {
+ public:
+  And(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kAnd) {}
+};
+
+class TORCH_API Or : public BitwiseOpNode<Or> {
+ public:
+  Or(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kOr) {}
+};
+
+class TORCH_API Xor : public BitwiseOpNode<Xor> {
+ public:
+  Xor(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kXor) {}
+};
+
+class TORCH_API Lshift : public BitwiseOpNode<Lshift> {
+ public:
+  Lshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kLshift) {}
+};
+
+class TORCH_API Rshift : public BitwiseOpNode<Rshift> {
+ public:
+  Rshift(ExprPtr lhs, ExprPtr rhs)
+      : BitwiseOpNode(std::move(lhs), std::move(rhs), IRNodeType::kRshift) {}
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Max : public BinaryOpNode<Max> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Max(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMax),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Max>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// TODO: add TORCH_API
+// Currently adding it results in a compilation error on Windows
+class Min : public BinaryOpNode<Min> {
+ private:
+  bool propagate_nans_;
+
+ public:
+  Min(ExprPtr lhs, ExprPtr rhs, bool propagate_nans)
+      : BinaryOpNode(std::move(lhs), std::move(rhs), IRNodeType::kMin),
+        propagate_nans_(propagate_nans) {}
+
+  bool propagate_nans() const {
+    return propagate_nans_;
+  }
+
+  static ExprHandle make(const ExprHandle& lhs, const ExprHandle& rhs) = delete;
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      bool propagate_nans) {
+    return ExprHandle(alloc<Min>(lhs.node(), rhs.node(), propagate_nans));
+  }
+};
+
+// Encode typed immediate values e.g. IntImm, FloatImm.
+#define IMM_DECLARE(Type, Name)                               \
+  class TORCH_API Name##Imm : public ExprNode<Name##Imm> {    \
+   public:                                                    \
+    Name##Imm(Type value)                                     \
+        : ExprNodeBase(k##Name, kPrimitive), value_(value) {} \
+    bool isConstant() const override {                        \
+      return true;                                            \
+    }                                                         \
+    Type value() const {                                      \
+      return value_;                                          \
+    }                                                         \
+    static ExprHandle make(Type value) {                      \
+      return ExprHandle(alloc<Name##Imm>(value));             \
+    }                                                         \
+                                                              \
+   private:                                                   \
+    Type value_;                                              \
+  };
+AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, IMM_DECLARE);
+#undef IMM_DECLARE
+
+// Get immediate by ScalarType.
+template <typename T>
+ExprPtr getImmediateByType(ScalarType immType, T initialVal) {
+  switch (immType) {
+#define TYPE_CASE(Type, Name) \
+  case ScalarType::Name:      \
+    return alloc<Name##Imm>(Type(initialVal));
+    // NOLINTNEXTLINE(bugprone-branch-clone)
+    AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+    default:
+      throw unsupported_dtype();
+  }
+  return nullptr;
+}
+
+template <typename T>
+ExprPtr getImmediateByType(Dtype dtype, T initialVal) {
+  return getImmediateByType<T>(dtype.scalar_type(), initialVal);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprPtr& e, T v) {
+  return getImmediateByType<T>(e->dtype(), v);
+}
+
+template <typename T>
+ExprPtr immLike(const ExprHandle& e, T v) {
+  return immLike(e.node(), v);
+}
+
+inline c10::optional<int64_t> intValue(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)      \
+  if (auto v = to<Name##Imm>(e)) { \
+    return v->value();             \
+  }
+  AT_FORALL_INT_TYPES(TYPE_CASE);
+#undef TYPE_CASE
+  return c10::nullopt;
+}
+
+inline c10::optional<int64_t> intValue(const ExprHandle& e) {
+  return intValue(e.node());
+}
+
+template <typename T>
+T immediateAs(const ExprPtr& e) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value();                     \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return 0;
+}
+
+template <typename T>
+T immediateAs(const ExprHandle& e) {
+  return immediateAs<T>(e.node());
+}
+
+template <typename T>
+bool immediateEquals(const ExprPtr& e, T val) {
+#define TYPE_CASE(Type, Name)                \
+  if (Name##ImmPtr imm = to<Name##Imm>(e)) { \
+    return imm->value() == val;              \
+  }
+  AT_FORALL_SCALAR_TYPES_AND3(Bool, Half, BFloat16, TYPE_CASE);
+#undef TYPE_CASE
+  throw unsupported_dtype();
+  return false;
+}
+
+TORCH_API bool immediateIsNegative(const ExprPtr& e);
+
+TORCH_API bool immediateIsPositive(const ExprPtr& e);
+
+TORCH_API bool immediateIsZero(const ExprPtr& e);
+
+// Represents a ramp vector node:
+//     [base, base + 1 * stride, ... , base + (lanes - 1) * stride]
+class TORCH_API Ramp : public ExprNode<Ramp> {
+ public:
+  ExprPtr base() const {
+    return base_;
+  }
+  ExprPtr stride() const {
+    return stride_;
+  }
+
+  void set_base(ExprPtr base) {
+    base_ = std::move(base);
+  }
+
+  void set_stride(ExprPtr stride) {
+    stride_ = std::move(stride);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& base,
+      const ExprHandle& stride,
+      int lanes) {
+    if (stride.dtype() != base.dtype()) {
+      throw malformed_input("Bad stride in Ramp");
+    }
+    return ExprHandle(alloc<Ramp>(base.node(), stride.node(), lanes));
+  }
+  int lanes() const {
+    return lanes_;
+  }
+
+  Ramp(ExprPtr base, ExprPtr stride, int lanes)
+      : ExprNodeBase(Dtype(base->dtype(), lanes)),
+        base_(std::move(base)),
+        stride_(std::move(stride)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr base_;
+  ExprPtr stride_;
+  int lanes_;
+};
+
+class TORCH_API Load : public ExprNode<Load> {
+ public:
+  VarPtr base_handle() const {
+    return buf_->base_handle();
+  }
+  std::vector<ExprPtr> indices() const {
+    return indices_;
+  }
+  ExprPtr flat_index() const {
+    TORCH_CHECK(indices_.size() == 1, "Indices haven't been flattened.");
+    return indices_[0];
+  }
+  BufPtr buf() const {
+    return buf_;
+  }
+
+  void set_buf(BufPtr buf) {
+    buf_ = std::move(buf);
+  }
+
+  void set_indices(std::vector<ExprPtr> indices) {
+    indices_ = std::move(indices);
+  }
+
+  static ExprHandle make(
+      Dtype dtype,
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+  static ExprHandle make(
+      const BufHandle& buf,
+      const std::vector<ExprHandle>& indices);
+
+  Load(Dtype dtype, BufPtr base_handle, std::vector<ExprPtr> indices);
+  Load(BufPtr base_handle, const std::vector<ExprPtr>& indices);
+
+ private:
+  BufPtr buf_;
+  std::vector<ExprPtr> indices_;
+};
+
+class TORCH_API Broadcast : public ExprNode<Broadcast> {
+ public:
+  ExprPtr value() const {
+    return value_;
+  }
+
+  void set_value(ExprPtr value) {
+    value_ = std::move(value);
+  }
+
+  int lanes() const {
+    return lanes_;
+  }
+  static ExprHandle make(const ExprHandle& value, int lanes) {
+    return ExprHandle(alloc<Broadcast>(value.node(), lanes));
+  }
+  Broadcast(ExprPtr value, int lanes)
+      : ExprNodeBase(Dtype(value->dtype(), lanes)),
+        value_(std::move(value)),
+        lanes_(lanes) {}
+
+ private:
+  ExprPtr value_;
+  int lanes_;
+};
+
+class TORCH_API IfThenElse : public ExprNode<IfThenElse> {
+ public:
+  ExprPtr condition() const {
+    return condition_;
+  }
+
+  // Lazily evaluated only if condition is true
+  ExprPtr true_value() const {
+    return true_;
+  }
+
+  // Lazily evaluated only if condition is false
+  ExprPtr false_value() const {
+    return false_;
+  }
+
+  void set_condition(ExprPtr condition) {
+    condition_ = std::move(condition);
+  }
+
+  void set_true_value(ExprPtr true_value) {
+    true_ = std::move(true_value);
+  }
+
+  void set_false_value(ExprPtr false_value) {
+    false_ = std::move(false_value);
+  }
+
+  static ExprHandle make(
+      const ExprHandle& c,
+      const ExprHandle& t,
+      const ExprHandle& f) {
+    if (!c.dtype().is_integral()) {
+      throw unsupported_dtype();
+    }
+    if (c.dtype().lanes() != 1) {
+      throw unsupported_dtype();
+    }
+    if (t.dtype() != f.dtype()) {
+      throw malformed_input("Bad dtype in IfThenElse");
+    }
+    return ExprHandle(alloc<IfThenElse>(c.node(), t.node(), f.node()));
+  }
+
+  IfThenElse(ExprPtr c, ExprPtr t, ExprPtr f)
+      : ExprNodeBase(t->dtype()),
+        condition_(std::move(c)),
+        true_(std::move(t)),
+        false_(std::move(f)) {}
+
+ private:
+  ExprPtr condition_;
+  ExprPtr true_;
+  ExprPtr false_;
+};
+
+class TORCH_API CompareSelect : public ExprNode<CompareSelect> {
+ public:
+  CompareSelectOperation compare_select_op() const {
+    return compare_op_;
+  }
+  ExprPtr lhs() const {
+    return this->lhs_;
+  }
+  ExprPtr rhs() const {
+    return this->rhs_;
+  }
+  ExprPtr ret_val1() const {
+    return this->ret_val1_;
+  }
+  ExprPtr ret_val2() const {
+    return this->ret_val2_;
+  }
+
+  void set_lhs(ExprPtr lhs) {
+    lhs_ = std::move(lhs);
+  }
+
+  void set_rhs(ExprPtr rhs) {
+    rhs_ = std::move(rhs);
+  }
+
+  void set_ret_val1(ExprPtr ret_val1) {
+    ret_val1_ = std::move(ret_val1);
+  }
+
+  void set_ret_val2(ExprPtr ret_val2) {
+    ret_val2_ = std::move(ret_val2);
+  }
+
+  CompareSelectBias bias() const {
+    return bias_;
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        IntImm::make(1).node(),
+        IntImm::make(0).node(),
+        cmp_op,
+        bias));
+  }
+
+  static ExprHandle make(
+      const ExprHandle& lhs,
+      const ExprHandle& rhs,
+      const ExprHandle& ret_val1,
+      const ExprHandle& ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased) {
+    if (lhs.dtype() != rhs.dtype() || ret_val1.dtype() != ret_val2.dtype()) {
+      throw malformed_input("bad dtype in CompareSelect");
+    }
+    return ExprHandle(alloc<CompareSelect>(
+        lhs.node(),
+        rhs.node(),
+        ret_val1.node(),
+        ret_val2.node(),
+        cmp_op,
+        bias));
+  }
+
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      ExprPtr ret_val1,
+      ExprPtr ret_val2,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(ret_val1->dtype()),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(std::move(ret_val1)),
+        ret_val2_(std::move(ret_val2)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  CompareSelect(
+      ExprPtr lhs,
+      ExprPtr rhs,
+      CompareSelectOperation cmp_op,
+      CompareSelectBias bias = kUnbiased)
+      : ExprNodeBase(kInt),
+        lhs_(std::move(lhs)),
+        rhs_(std::move(rhs)),
+        ret_val1_(alloc<IntImm>(1)),
+        ret_val2_(alloc<IntImm>(0)),
+        compare_op_(cmp_op),
+        bias_(bias) {}
+
+ private:
+  ExprPtr lhs_;
+  ExprPtr rhs_;
+  ExprPtr ret_val1_;
+  ExprPtr ret_val2_;
+  CompareSelectOperation compare_op_;
+  CompareSelectBias bias_;
+};
+
+enum IntrinsicsOp {
+  kSin,
+  kCos,
+  kTan,
+  kAsin,
+  kAcos,
+  kAtan,
+  kAtan2,
+  kSinh,
+  kCosh,
+  kTanh,
+  kSigmoid,
+  kExp,
+  kExpm1,
+  kAbs,
+  kLog,
+  kLog2,
+  kLog10,
+  kLog1p,
+  kErf,
+  kErfc,
+  kSqrt,
+  kRsqrt,
+  kPow,
+  kCeil,
+  kFloor,
+  kRound,
+  kTrunc,
+  kFmod,
+  kRemainder,
+  kLgamma,
+  kFrac,
+  kIsNan,
+  kRand, // We need more discussions on this. Should we consider stateful?
+  kMaxIntrinsicsOp,
+};
+
+class TORCH_API Intrinsics : public ExprNode<Intrinsics> {
+ public:
+  static ExprHandle make(IntrinsicsOp op_type, const ExprHandle& v1) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const ExprHandle& v1,
+      const ExprHandle& v2) {
+    return ExprHandle(alloc<Intrinsics>(op_type, v1.node(), v2.node()));
+  }
+
+  static ExprHandle make(
+      IntrinsicsOp op_type,
+      const std::vector<ExprHandle>& params) {
+    // NOLINTNEXTLINE(cppcoreguidelines-init-variables)
+    std::vector<ExprPtr> params_nodes(params.size());
+    for (size_t i = 0; i < params.size(); i++) {
+      params_nodes[i] = params[i].node();
+    }
+    return ExprHandle(alloc<Intrinsics>(op_type, params_nodes));
+  }
+
+  static ExprHandle make(IntrinsicsOp op_type, Dtype dtype) {
+    return ExprHandle(alloc<Intrinsics>(op_type, dtype));
+  }
+
+  IntrinsicsOp op_type() const {
+    return op_type_;
+  }
+
+  std::string func_name() const {
+    switch (op_type()) {
+      case kSin:
+        return "sin";
+      case kCos:
+        return "cos";
+      case kTan:
+        return "tan";
+      case kAsin:
+        return "asin";
+      case kAcos:
+        return "acos";
+      case kAtan:
+        return "atan";
+      case kAtan2:
+        return "atan2";
+      case kSinh:
+        return "sinh";
+      case kCosh:
+        return "cosh";
+      case kTanh:
+        return "tanh";
+      case kSigmoid:
+        return "sigmoid";
+      case kExp:
+        return "exp";
+      case kAbs:
+        return "abs";
+      case kLog:
+        return "log";
+      case kLog2:
+        return "log2";
+      case kLog10:
+        return "log10";
+      case kLog1p:
+        return "log1p";
+      case kErf:
+        return "erf";
+      case kSqrt:
+        return "sqrt";
+      case kRsqrt:
+        return "rsqrt";
+      case kPow:
+        return "pow";
+      case kCeil:
+        return "ceil";
+      case kFloor:
+        return "floor";
+      case kRound:
+        return "round";
+      case kTrunc:
+        return "trunc";
+      case kRand:
+        return "rand";
+      case kFmod:
+        return "fmod";
+      case kRemainder:
+        return "remainder";
+      case kLgamma:
+        return "lgamma";
+      case kExpm1:
+        return "expm1";
+      case kErfc:
+        return "erfc";
+      case kFrac:
+        return "frac";
+      case kIsNan:
+        return "isnan";
+      default:
+        throw std::runtime_error(
+            "invalid op_type: " + c10::to_string(op_type()));
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, Dtype dtype)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_({}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 0) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype())),
+        params_({std::move(v1)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 1) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, ExprPtr v1, ExprPtr v2)
+      : ExprNodeBase(IntrinsicsDtype(op_type, v1->dtype(), v2->dtype())),
+        params_({std::move(v1), std::move(v2)}),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != 2) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(IntrinsicsOp op_type, const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, params)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
+  Intrinsics(
+      IntrinsicsOp op_type,
+      Dtype dtype,
+      const std::vector<ExprPtr>& params)
+      : ExprNodeBase(IntrinsicsDtype(op_type, dtype)),
+        params_(params),
+        op_type_(op_type) {
+    if (OpArgCount(op_type) != nparams()) {
+      throw malformed_input("bad arg count in Intrinsics");
+    }
+  }
+
+  bool isPure() const {
+    return op_type_ != kRand;
+  }
+
+  int nparams() const {
+    return params_.size();
+  }
+
+  ExprPtr param(int index) const {
+    return params_[index];
+  }
+  const std::vector<ExprPtr>& params() const {
+    return params_;
+  }
+
+  void set_params(std::vector<ExprPtr> params) {
+    params_ = std::move(params);
+  }
+
+  static int OpArgCount(IntrinsicsOp op_type);
+
+ private:
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1);
+  static Dtype IntrinsicsDtype(IntrinsicsOp op_type, Dtype dt1, Dtype dt2);
+  static Dtype IntrinsicsDtype(
+      IntrinsicsOp op_type,
+      const std::vector<ExprPtr>& params);
+
+  std::vector<ExprPtr> params_;
+  IntrinsicsOp op_type_;
+};
+
+TORCH_API std::vector<ExprPtr> ExprHandleVectorToExprVector(
+    const std::vector<ExprHandle>&);
+TORCH_API std::vector<ExprHandle> ExprVectorToExprHandleVector(
+    const std::vector<ExprPtr>&);
+TORCH_API std::vector<VarPtr> VarHandleVectorToVarVector(
+    const std::vector<VarHandle>&);
+TORCH_API std::vector<VarHandle> VarVectorToVarHandleVector(
+    const std::vector<VarPtr>&);
+TORCH_API ExprPtr flatten_index(
+    const std::vector<ExprPtr>& dims,
+    const std::vector<ExprPtr>& indices,
+    const std::vector<ExprPtr>& strides);
+
+} // namespace tensorexpr
+} // namespace jit
+} // namespace torch