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#pragma once
#include <c10/util/Optional.h>
#include <functional>
#include <memory>
#include <string>
#include <utility>
#include <ATen/core/symbol.h>
#include <caffe2/serialize/versions.h>
#include <torch/csrc/jit/api/module.h>
#include <torch/csrc/jit/frontend/error_report.h>
#include <torch/csrc/jit/frontend/schema_matching.h>
#include <torch/csrc/jit/frontend/versioned_symbols.h>
#include <torch/csrc/jit/ir/ir.h>
namespace torch {
namespace jit {
using SugaredValuePtr = std::shared_ptr<SugaredValue>;
// The AST can contain nodes like `self`, `self.b` or `python_fn` that
// are not first-class values in the graph representation, but instead
// will be desugared based on how they are used in the AST.
// SugaredValue is used to temporarily represent these values in a way
// that separates their behavior from the AST -> IR converter itself.
// This allows us to keep dependencies on python minimal.
struct TORCH_API SugaredValue
: public std::enable_shared_from_this<SugaredValue> {
// what is this node? for error reporting (e.g. Module, python function)
virtual std::string kind() const = 0;
// what can we do with this thing?
// use it as a value e.g. `this + 4`
virtual Value* asValue(const SourceRange& loc, GraphFunction& m) {
throw ErrorReport(loc) << kind() << " cannot be used as a value";
}
// select an attribute on it, e.g. `this.field`
virtual std::shared_ptr<SugaredValue> attr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) {
throw ErrorReport(loc) << "attribute lookup is not defined on " << kind();
}
virtual bool hasAttr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) {
throw ErrorReport(loc) << "attribute lookup is not defined on " << kind();
}
// assign an attribute on it, e.g. `this.field = newValue`
virtual void setAttr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field,
Value* newValue) {
throw ErrorReport(loc) << "attribute assignment is not defined on "
<< kind();
}
// use it as a vector of values, e.g. a tuple of values as return value from
// a method invocation
virtual std::vector<std::shared_ptr<SugaredValue>> asTuple(
const SourceRange& loc,
GraphFunction& m,
const c10::optional<size_t>& size_hint = {}) {
throw ErrorReport(loc) << kind() << " cannot be used as a tuple";
}
// TODO @wconstab refactor to use ModuleValue::asTuple instead of new API
virtual SugaredValuePtr asTupleValue(
const SourceRange& loc,
GraphFunction& m) {
throw ErrorReport(loc) << kind() << " cannot be used as a tuplevalue";
}
virtual std::vector<std::shared_ptr<SugaredValue>> asType(
const SourceRange& loc,
Method& m) {
throw ErrorReport(loc) << kind() << " cannot be used as a type";
}
// call it like a function, e.g. `outputs = this(inputs)`
virtual std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
// note: names for args will be 'argument 0', 'argument 1', etc..
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) {
// n_binders is always set to the number of variables an expression is
// syntactically bound to:
// a = foo() # 1 binder (note in this case the single binder might be a
// tuple) a, * b = foo() # 1 binder a, b = foo() # 2 binders foo() # 0
// binders
//
// In subexpressions, like bar() in foo(bar()), n_binders is always set to
// 1. n_binders is used as a hint to subexpressions to determine how many
// values they should return when that number is ambiguous statically. In
// particular it is currently used to decide how many tensors a call to a
// python function will return. It is only a hint, functions do not have to
// check that n_binders match the number of things they are returning, the
// assignment logic will do that anyway.
throw ErrorReport(loc) << "cannot call a " << kind();
}
// This function is called when to convert a SugaredValue to its iterator.
// For example, when iterating through a Dict we iterate over its keys
virtual std::shared_ptr<SugaredValue> iter(
const SourceRange& loc,
GraphFunction& m) {
throw ErrorReport(loc) << kind() << " cannot be used as an iterable";
}
// If we are iterating over a Sugared Value and it returns a value from this
// function, then we emit an unrolled loop over the variable. This allows us
// to support containers of Heterogenous types, like Module Containers &
// Tuples
virtual c10::optional<int64_t> staticLen() {
return c10::nullopt;
}
// When iterating over this SugaredValue, should we emit the for loop as an
// unrolled loop.
bool shouldEmitUnrolled() {
return staticLen() != c10::nullopt;
}
// return length of this thing, if not then it can't be iterated.
// If it does not have a statically-determinable length, then it cannot
// be iterated over with a modulelist. If it does it must return a constant
// Value *
virtual Value* len(const SourceRange& loc, GraphFunction& m) {
throw ErrorReport(loc) << "'" << kind() << "'"
<< " object is not iterable";
}
// expression for ith elemement for iterable value
virtual std::shared_ptr<SugaredValue> getitem(
const SourceRange& loc,
GraphFunction& m,
Value* idx,
TypePtr type_hint = nullptr) {
throw ErrorReport(loc) << "'" << kind() << "'"
<< " object is not subscriptable";
}
virtual ~SugaredValue() = default;
};
// most things in the environment are just simple value types
// and not special python syntax sugar types
struct TORCH_API SimpleValue : public SugaredValue {
SimpleValue(Value* value) : value_(value) {}
std::string kind() const override {
std::stringstream ss;
// NOLINTNEXTLINE(clang-analyzer-core.CallAndMessage)
ss << "value of type '" << value_->type()->annotation_str() << "'";
return ss.str();
}
Value* asValue(const SourceRange& range, GraphFunction& m) override {
return value_;
}
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;
bool hasAttr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) override;
void setAttr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field,
Value* newValue) override;
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
// note: names for args will be 'argument 0', 'argument 1', etc..
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override;
std::shared_ptr<SugaredValue> iter(const SourceRange& loc, GraphFunction& m)
override;
Value* getValue() const {
return value_;
}
Value* len(const SourceRange& loc, GraphFunction& m) override;
SugaredValuePtr getitem(
const SourceRange& loc,
GraphFunction& m,
Value* idx,
TypePtr type_hint = nullptr) override;
private:
Value* value_;
};
struct TORCH_API BuiltinFunction : public SugaredValue {
BuiltinFunction(Symbol symbol, c10::optional<NamedValue> self)
: symbol(symbol), self(std::move(self)) {}
// The symbol of the function (e.g. `aten::relu`).
Symbol symbol;
// if this is method, then this is the self argument.
c10::optional<NamedValue> self;
std::string kind() const override {
return "builtin";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override;
// try to create this builtin but if it doesn't exist or the self argument
// cannot possibly match, then return nullptr. Use in situations where it is
// not clear if it is a valid builtin
static std::shared_ptr<BuiltinFunction> tryCreate(
Symbol symbol,
c10::optional<NamedValue> self);
};
struct TORCH_API SugaredTupleValue : public SugaredValue {
explicit SugaredTupleValue(std::vector<std::shared_ptr<SugaredValue>> tup)
: tup_(std::move(tup)){};
std::vector<std::shared_ptr<SugaredValue>> asTuple(
const SourceRange& loc,
GraphFunction& m,
const c10::optional<size_t>& size_hint = {}) override {
return tup_;
};
Value* asValue(const SourceRange& loc, GraphFunction& m) override {
std::vector<Value*> vec;
for (const auto& sv : tup_) {
vec.push_back(sv->asValue(loc, m));
}
Graph& g = *m.graph();
return g.insertNode(g.createTuple(vec))->output();
}
std::string kind() const override {
return "Tuple";
}
SugaredValuePtr getitem(
const SourceRange& loc,
GraphFunction& m,
Value* idx,
TypePtr type_hint = nullptr) override {
if (!(idx->type()->cast<IntType>() && toIValue(idx))) {
throw ErrorReport(loc)
<< "Expected integer literal for index. "
<< "ModuleList/Sequential indexing is only supported with integer literals. "
<< "Enumeration is supported, e.g. 'for index, v in enumerate(self): ...'";
}
auto index = toIValue(idx)->toInt();
int64_t adj_index =
(index < 0) ? index + static_cast<int64_t>(tup_.size()) : index;
if (!(adj_index >= 0 && adj_index < static_cast<int64_t>(tup_.size()))) {
throw ErrorReport(loc)
<< "Index " << index << " out of range of length " << tup_.size();
}
return tup_.at(adj_index);
}
// This function is called when a SugaredValue is used to convert a
// SugaredValue to its iterator. For example, when iterating through a Dict we
// iterate over its keys
std::shared_ptr<SugaredValue> iter(const SourceRange& loc, GraphFunction& m)
override {
return shared_from_this();
};
// Because this is used to contain SugaredValues of Heterogenous types,
// we define staticLen() so that when this is iterated over it is emitted
// as an unrolled loop.
c10::optional<int64_t> staticLen() override {
return static_cast<int64_t>(tup_.size());
}
std::vector<std::shared_ptr<SugaredValue>> tup_;
};
struct TORCH_API BuiltinModule : public SugaredValue {
BuiltinModule(std::string name, c10::optional<int64_t> version = at::nullopt)
: name(std::move(name)), version(version) {}
std::string kind() const override {
return "builtin module";
}
std::shared_ptr<SugaredValue> attr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) override {
if (field == "autograd") {
// When refering torch.autograd, it is also considered to be a
// BuiltinModule and we will dispatch to the aten operators for the
// methods under its module.
return std::make_shared<BuiltinModule>("aten", version);
}
auto sym = Symbol::fromQualString(name + "::" + field);
return std::make_shared<BuiltinFunction>(sym, c10::nullopt);
}
private:
std::string name;
// when we add operator versioning, emit this op as it exising at 'version'
// if not set, use the latest version
c10::optional<int64_t> version;
};
// Represents a class, analagous to `int` or `dict`. Instances of classes,
// like `1` or `{"foo": 5}`, are represented as SimpleValues
struct TORCH_API ClassValue : public SugaredValue {
explicit ClassValue(ClassTypePtr type) : type_(std::move(type)) {}
// Call the type's constructor, as in:
// n = Foo(constructor_arg)
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override;
std::shared_ptr<SugaredValue> attr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) override;
std::string kind() const override {
return type_->str();
}
ClassTypePtr type_;
};
struct TORCH_API NamedTupleConstructor : public SugaredValue {
explicit NamedTupleConstructor(TupleTypePtr type) : type_(std::move(type)) {}
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 {
return type_->str();
}
TupleTypePtr type_;
};
struct FunctionValue : public SugaredValue {
FunctionValue(Function* callee) : callees_({callee}) {}
FunctionValue(const StrongFunctionPtr& p)
: callees_({p.function_}), cu_(p.cu_) {}
FunctionValue(const std::vector<StrongFunctionPtr>& callees) {
for (const StrongFunctionPtr& callee : callees) {
cu_ = cu_ ? cu_ : callee.cu_;
TORCH_INTERNAL_ASSERT(callee.cu_ == cu_);
callees_.push_back(callee.function_);
}
}
std::string kind() const override {
return "function";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& f,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override {
std::vector<const FunctionSchema*> schemas;
for (Function* callee : callees_) {
try {
callee->ensure_defined();
} catch (const RecursiveMethodCallError&) {
throw ErrorReport(loc)
<< " function '" << callee->name() << "' is called recursively. "
<< "Recursive calls are not supported";
}
schemas.push_back(&callee->getSchema());
}
auto match = matchSchemas(schemas, loc, *f.graph(), args, kwargs);
Value* output =
f.graph()->insertFunctionCall(callees_[match.first], match.second);
output->node()->setSourceRange(loc);
return std::make_shared<SimpleValue>(output);
}
const std::vector<Function*>& callees() {
return callees_;
}
private:
std::vector<Function*> callees_;
// TODO holding this thing is creepy
std::shared_ptr<CompilationUnit> cu_;
};
struct TORCH_API ClosureValue : public SugaredValue {
ClosureValue(Value* value) : value_(value) {
TORCH_INTERNAL_ASSERT(value_->node()->kind() == prim::Closure);
}
std::string kind() const override {
return "closure";
}
Value* asValue(const SourceRange& range, GraphFunction& m) override {
return value_;
}
Value* value_;
};
// defines how a method obtained from a module/class/interface behaves in script
struct MethodValue : public SugaredValue {
MethodValue(Value* self, std::vector<std::string> method_names)
: self_(self), method_names_(std::move(method_names)) {}
MethodValue(Value* self, std::string method_name)
: MethodValue(self, std::vector<std::string>({std::move(method_name)})) {}
std::string kind() const override {
return "method";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& f,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override {
std::vector<NamedValue> argsWithSelf = {self_};
argsWithSelf.insert(argsWithSelf.end(), args.begin(), args.end());
std::vector<const FunctionSchema*> schemas;
for (const std::string& method_name : method_names_) {
if (auto class_type = self_->type()->cast<ClassType>()) {
Function& method = class_type->getMethod(method_name);
try {
method.ensure_defined();
} catch (const RecursiveMethodCallError&) {
throw ErrorReport(loc)
<< " method '" << method.name() << "' is called recursively. "
<< "Recursive calls are not supported";
}
schemas.push_back(&method.getSchema());
} else if (auto interface_type = self_->type()->cast<InterfaceType>()) {
schemas.push_back(interface_type->getMethod(method_name));
} else {
TORCH_INTERNAL_ASSERT(
false, "method constructed that is not a class or interface");
}
}
auto match = matchSchemas(schemas, loc, *f.graph(), argsWithSelf, kwargs);
Value* output =
f.graph()->insertMethodCall(method_names_[match.first], match.second);
output->node()->setSourceRange(loc);
return std::make_shared<SimpleValue>(output);
}
private:
Value* self_;
std::vector<std::string> method_names_;
};
struct TORCH_API PrintValue : public SugaredValue {
std::string kind() const override {
return "print";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override;
};
// expressions like int(x)
// these are the same as call prim::Int or equivalent except it
// is a noop when the input is a subtype of 'type'
struct TORCH_API CastValue : public BuiltinFunction {
CastValue(TypePtr type, c10::Symbol method)
: BuiltinFunction(method, c10::nullopt), type_(std::move(type)) {}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override {
if (args.size() == 1 && kwargs.size() == 0) {
auto len_op = std::make_shared<BuiltinFunction>(aten::len, at::nullopt);
auto gt_op = std::make_shared<BuiltinFunction>(aten::gt, at::nullopt);
auto zero = m.graph()->insertConstant(0);
auto v = args[0].value(*m.graph());
if (v->type()->isSubtypeOf(*type_)) {
return std::make_shared<SimpleValue>(v);
} else if (
*type_ == *BoolType::get() &&
(v->type()->isSubtypeOf(*AnyListType::get()) ||
v->type()->isSubtypeOf(*StringType::get()) ||
v->type()->cast<DictType>())) {
auto len = len_op->call(loc, m, {v}, {}, 1);
return gt_op->call(loc, m, {len->asValue(loc, m), zero}, {}, 1);
}
}
return BuiltinFunction::call(loc, m, args, kwargs, n_binders);
}
private:
TypePtr type_;
};
struct TORCH_API TensorCastValue : public SugaredValue {
TensorCastValue(at::ScalarType type, NamedValue self)
: dtype_(type), self_(std::move(self)) {}
std::string kind() const override {
return "Cast";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override {
TORCH_INTERNAL_ASSERT(args.size() == 0 && kwargs.size() == 0);
Value* dtype_const = m.graph()->insertConstant(dtype_, loc);
std::vector<NamedValue> kwargs_{
self_, NamedValue(loc, "dtype", dtype_const)};
Value* casted_val = m.graph()->insert(
/*opname=*/Symbol::fromQualString("aten::to"),
/*args=*/args,
/*kwargs=*/kwargs_,
/*range=*/loc);
return std::make_shared<SimpleValue>(casted_val);
}
at::ScalarType dtype_;
NamedValue self_;
};
// builtins operators and functions that call a method if it exists
// on a class type, like 'len(x)' and 'x + y'
struct TORCH_API MagicMethod : public SugaredValue {
MagicMethod(std::string desugared_name, SugaredValuePtr base)
: base_value_(std::move(base)),
desugared_name_(std::move(desugared_name)) {}
std::string kind() const override {
return desugared_name_;
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> kwargs,
size_t n_binders) override;
private:
SugaredValuePtr base_value_;
std::string desugared_name_;
};
// things that look like function applications, but
// perform non-standard evaluation are represented
// with SpecialFormValues, e.g.
// isinstance(x, int)
// fork(fn)
// annotate(int, 3)
// The implementation of each value is handled by a case inside emitApplyExpr
struct TORCH_API SpecialFormValue : public SugaredValue {
SpecialFormValue(Symbol form) : form_(form) {}
std::string kind() const override {
return form_.toUnqualString();
}
Symbol form() const {
return form_;
}
static std::shared_ptr<SpecialFormValue> create(Symbol form) {
return std::make_shared<SpecialFormValue>(form);
}
private:
Symbol form_;
};
struct TORCH_API LegacyTensorConstructor : public SpecialFormValue {
LegacyTensorConstructor(Symbol form, at::ScalarType dtype, at::Device device)
: SpecialFormValue(form), device_(device), dtype_(dtype) {}
static std::shared_ptr<LegacyTensorConstructor> create(
Symbol form,
at::ScalarType dtype,
at::Device device) {
return std::make_shared<LegacyTensorConstructor>(form, dtype, device);
}
at::ScalarType dtype() const {
return dtype_;
}
private:
at::Device device_;
at::ScalarType dtype_;
};
// matched against for special handling of range expressions
struct TORCH_API RangeValue : SugaredValue {
RangeValue(
const SourceRange& loc,
GraphFunction& m,
std::vector<Value*> input,
c10::optional<int64_t> static_len = c10::nullopt);
std::string kind() const override {
return "range";
}
Value* len(const SourceRange& loc, GraphFunction& m) override;
SugaredValuePtr getitem(
const SourceRange& loc,
GraphFunction& m,
Value* idx,
TypePtr type_hint = nullptr) override;
std::shared_ptr<SugaredValue> iter(const SourceRange& loc, GraphFunction& m)
override;
// When Range is instantiated via enumerate(iterable_with_static_len),
// then it takes the static length of the iterable
c10::optional<int64_t> staticLen() override {
return static_len_;
}
private:
Value* start_;
Value* end_;
Value* step_;
// a flag to determine if it's a simple range() call with only end_ from
// arguments If true, we will not insert length calculation and index
// derivation nodes to simplify the graph and enable more possible
// optimizations
bool has_only_end_;
c10::optional<int64_t> static_len_ = c10::nullopt;
};
// Specialized Tree structure to matched against for special handling
// of builtin functions iterables expressions like zip(), enumerate(), etc.
// zip and enumerate can be modeled as a tree of SimpleValue/RangeValue:
// zip(x, y) -> (x, y) with tuple assignment to each loop target
// enumerate(x) -> (range(0, math.inf, 1), x)
// So a complicated expression like zip(a, enumerate(b), range(0, 100)) will be:
// (a, (range(0, math.inf, 1), b), range(0, 100))
// We use those base iterables to fill in the loop information like
// max_trip_count and set the value table for loop targets
// Iterables can contain lists of SugaredValues like ModuleLists. If it
// does, then we emit it unrolled and require that all values it contains
// have a statically-determinable length.
struct TORCH_API IterableTree : SugaredValue {
IterableTree() = default;
IterableTree(
const SourceRange& range,
GraphFunction& m,
at::ArrayRef<SugaredValuePtr> children) {
for (const auto& child : children) {
addChild(range, m, child);
}
}
std::string kind() const override {
return "iterabletree";
}
std::shared_ptr<SugaredValue> iter(const SourceRange& loc, GraphFunction& m)
override {
return shared_from_this();
}
void addChild(
const SourceRange& range,
GraphFunction& m,
const SugaredValuePtr& iter_value);
std::vector<SugaredValuePtr> get_children() {
return children_;
}
// If this iterable contains a ModuleList or Tuple, then it will have a
// static length, and we will emit it as an unrolled for loop.
c10::optional<int64_t> staticLen() override {
return unroll_length_;
}
// given a IterableTree node, get all the base iterables/leaves under the
// IterableTree node. This enables
// us to get all the basic SugaredValues that contains valid loop information
// with len() and getitem()
std::vector<SugaredValuePtr> get_base_iterables();
Value* len(const SourceRange& loc, GraphFunction& m) override;
SugaredValuePtr getitem(
const SourceRange& loc,
GraphFunction& m,
Value* idx,
TypePtr type_hint = nullptr) override;
private:
c10::optional<int64_t> unroll_length_ = c10::nullopt;
std::vector<SugaredValuePtr> children_;
};
static inline std::vector<Value*> toValues(
Graph& g,
at::ArrayRef<NamedValue> nvs) {
return fmap(nvs, [&](const NamedValue& v) { return v.value(g); });
}
struct SimpleSelf : public Self {
explicit SimpleSelf(ClassTypePtr classType)
: Self(), classType_(std::move(classType)) {}
std::shared_ptr<SugaredValue> makeSugared(Value* v) const override {
v->setType(classType_);
return std::make_shared<SimpleValue>(v);
}
ClassTypePtr getClassType() const override {
return classType_;
}
private:
ClassTypePtr classType_;
};
// This is not a SimpleValue so it can not pass through the code paths that
// expect a SimpleValue as a sugared value.
struct TORCH_API ExceptionMessageValue : public SugaredValue {
explicit ExceptionMessageValue(
Value* value,
Value* qualified_class_name = nullptr)
: value_(value), qualified_class_name_(qualified_class_name) {}
std::string kind() const override {
return "exception message";
}
Value* getValue() {
return value_;
}
// qualified python class name
Value* getQualifiedClassName() {
return qualified_class_name_;
}
private:
Value* value_;
Value* qualified_class_name_;
};
struct TORCH_API ExceptionValue : public SugaredValue {
explicit ExceptionValue(std::string message) : message_(std::move(message)) {}
std::string kind() const override {
return "exception";
}
std::shared_ptr<SugaredValue> call(
const SourceRange& loc,
GraphFunction& m,
at::ArrayRef<NamedValue> args,
at::ArrayRef<NamedValue> /*attributes*/,
size_t /*n_binders*/) override {
auto exception_message = insertConstant(*m.graph(), message_ + ": ", loc);
for (auto& input : args) {
auto input_str = input.value(*m.graph());
if (!input_str->type()->isSubtypeOf(*StringType::get())) {
input_str =
emitBuiltinCall(loc, *m.graph(), aten::str, {input_str}, {});
}
exception_message = emitBuiltinCall(
loc, *m.graph(), aten::add, {exception_message, input_str}, {});
}
return std::make_shared<ExceptionMessageValue>(exception_message);
}
std::string message_;
};
struct TORCH_API SugaredEnumClass : public SugaredValue {
explicit SugaredEnumClass(EnumTypePtr enum_type)
: enum_type_(std::move(enum_type)) {}
std::string kind() const override {
return "EnumClass";
}
SugaredValuePtr attr(
const SourceRange& loc,
GraphFunction& m,
const std::string& field) override;
SugaredValuePtr iter(const SourceRange& loc, GraphFunction& m) override;
private:
EnumTypePtr enum_type_;
};
struct TORCH_API SliceValue : public SugaredValue {
explicit SliceValue(Value* start, Value* stop, Value* step)
: start_(start), stop_(stop), step_(step) {}
std::string kind() const override {
return "Python slice value";
}
Value* start() {
return start_;
};
Value* stop() {
return stop_;
};
Value* step() {
return step_;
};
private:
Value* start_;
Value* stop_;
Value* step_;
};
} // namespace jit
} // namespace torch
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