mirror of
https://github.com/zebrajr/pytorch.git
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Summary: **Summary** This commit adds an implementation of `Tensor.tolist()` to the JIT interpreter. **Testing** This commit adds several unit tests that test that this function works correctly for 0D, 1D, 2D and 3D tensors of type `float`, `int` and `bool`. ``` (base) meghanl-mbp:pytorch meghanl$ python test/test_jit.py TestList.test_to_list -v Fail to import hypothesis in common_utils, tests are not derandomized test_to_list (jit.test_list_dict.TestList) Unit tests for Tensor.tolist() function. ... ok ---------------------------------------------------------------------- Ran 1 test in 0.329s OK ``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/33472 Differential Revision: D20109738 Pulled By: SplitInfinity fbshipit-source-id: a6e3fee5e3201d5e1f0c4ca45048488ae2bf5e33
620 lines
21 KiB
C++
620 lines
21 KiB
C++
#include <torch/csrc/jit/frontend/sugared_value.h>
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#include <torch/csrc/jit/ir/ir.h>
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#include <torch/csrc/jit/passes/constant_propagation.h>
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#include <torch/csrc/jit/frontend/schema_matching.h>
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#include <torch/csrc/jit/frontend/tree_views.h>
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namespace torch {
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namespace jit {
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namespace script {
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struct NoneValue : SugaredValue {
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NoneValue() = default;
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std::string kind() const override {
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return "None";
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}
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};
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std::shared_ptr<SugaredValue> PrintValue::call(
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const SourceRange& loc,
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Function& m,
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at::ArrayRef<NamedValue> inputs,
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at::ArrayRef<NamedValue> attributes,
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size_t n_binders) {
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auto& g = *m.graph();
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if (!attributes.empty())
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throw ErrorReport(loc) << "print doesn't accept any keyword arguments";
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std::vector<Value*> lowered_inputs = toValues(*m.graph(), inputs);
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g.insertNode(g.create(prim::Print, lowered_inputs, 0)->setSourceRange(loc));
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return std::make_shared<NoneValue>();
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}
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static const std::unordered_map<std::string, std::string>&
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builtin_cast_methods() {
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static std::unordered_map<std::string, std::string> builtin_cast_methods = {
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{"byte", "_cast_Byte"},
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{"char", "_cast_Char"},
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{"double", "_cast_Double"},
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{"float", "_cast_Float"},
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{"int", "_cast_Int"},
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{"long", "_cast_Long"},
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{"short", "_cast_Short"},
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{"half", "_cast_Half"}};
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return builtin_cast_methods;
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}
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std::shared_ptr<SugaredValue> BuiltinFunction::call(
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const SourceRange& loc,
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Function& m,
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at::ArrayRef<NamedValue> inputs,
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at::ArrayRef<NamedValue> attributes,
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size_t n_binders) {
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return std::make_shared<SimpleValue>(
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emitBuiltinCall(loc, *m.graph(), symbol, inputs, attributes, self));
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}
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// older versions of gcc/clang have a bug where enums can't be used as keys
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// in a map by default
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// https://stackoverflow.com/questions/18837857/cant-use-enum-class-as-unordered-map-key
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struct EnumClassHash {
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template <typename T>
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std::size_t operator()(T t) const {
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return static_cast<std::size_t>(t);
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}
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};
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// support syntax sugar for x.foo(y, z) by allowing x.foo to return a
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// callable value that will resolve to foo(x, y, z) when called.
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std::shared_ptr<SugaredValue> SimpleValue::attr(
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const SourceRange& loc,
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Function& m,
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const std::string& field) {
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// Allow method-style casts on Tensor types. e.g. x.int()
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if (value_->type()->isSubtypeOf(TensorType::get())) {
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if (builtin_cast_methods().count(field)) {
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return std::make_shared<BuiltinFunction>(
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Symbol::aten(builtin_cast_methods().at(field)),
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NamedValue(loc, "self", value_));
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}
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}
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// accessing properties of Tensor and Device that are implemented as
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// prim:: operators
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using PropertiesLookup = std::
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unordered_map<TypeKind, std::unordered_set<std::string>, EnumClassHash>;
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static const PropertiesLookup builtin_properties = {
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{TypeKind::TensorType,
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{
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"dtype",
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"device",
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"grad",
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"data",
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"shape",
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"is_cuda",
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"is_sparse",
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"is_mkldnn",
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"is_quantized",
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"requires_grad",
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"layout",
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}},
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{TypeKind::DeviceObjType, {"type", "index"}}};
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auto kind = value_->type()->kind();
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auto builtin_entry = builtin_properties.find(kind);
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if (builtin_entry != builtin_properties.end()) {
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if (builtin_entry->second.count(field) > 0) {
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auto r =
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m.graph()->insert(Symbol::fromQualString("prim::" + field), {value_});
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return std::make_shared<SimpleValue>(r);
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}
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}
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// accessing fields of named tuples
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if (auto tuple_type = value_->type()->cast<TupleType>()) {
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if (tuple_type->schema()) {
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auto attrs = tuple_type->schema()->arguments();
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for (size_t i = 0; i < attrs.size(); i++) {
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if (attrs[i].name() == field) {
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auto idx = m.graph()->insertConstant(IValue(static_cast<int64_t>(i)));
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auto out_type = tuple_type->elements().at(i);
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auto r = m.graph()
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->insertNode(
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m.graph()->createTupleIndex(value_, idx, out_type))
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->output();
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return std::make_shared<SimpleValue>(r);
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}
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}
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}
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} else if (auto classType = value_->type()->cast<ClassType>()) {
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// This is a class, emit the proper attribute lookup
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if (auto method = classType->getMethod(field)) {
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return std::make_shared<MethodValue>(getValue(), field);
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}
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if (classType->hasAttribute(field)) {
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auto& g = *m.graph();
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auto n = g.insertNode(g.createGetAttr(value_, field));
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return std::make_shared<SimpleValue>(n->output());
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}
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} else if (auto iface = value_->type()->cast<InterfaceType>()) {
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// accessing methods of interfaces
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if (auto schema = iface->getMethod(field)) {
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return std::make_shared<MethodValue>(getValue(), field);
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}
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}
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// none of the more-specific cases worked, so see if this is a builtin method
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if (auto builtin = BuiltinFunction::tryCreate(
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Symbol::aten(field), NamedValue(loc, "self", value_))) {
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return builtin;
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}
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// Handle calling tolist() on a Tensor.
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if (value_->type()->isSubtypeOf(TensorType::get()) && field == "tolist") {
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return SpecialFormValue::create(prim::tolist);
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}
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ErrorReport report(loc);
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report << "Tried to access nonexistent attribute or method '" << field
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<< "' of type '" << value_->type()->python_str() << "'.";
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if (value_->type()->kind() == ClassType::Kind) {
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report << " Did you forget to initialize an attribute in __init__()?";
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}
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throw report;
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}
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std::vector<std::shared_ptr<SugaredValue>> SimpleValue::asTuple(
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const SourceRange& loc,
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Function& m,
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const c10::optional<size_t>& size_hint) {
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static const auto make_simple_value =
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[](Value* v) -> std::shared_ptr<SugaredValue> {
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return std::make_shared<SimpleValue>(v);
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};
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if (value_->type()->kind() == TypeKind::TupleType) {
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auto outputs = createTupleUnpack(value_);
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return fmap(outputs, make_simple_value);
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} else if (value_->type()->kind() == TypeKind::ListType) {
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if (!size_hint) {
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throw ErrorReport(loc)
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<< "cannot statically infer the expected size of a "
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<< "list in this context";
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}
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auto graph = value_->owningGraph();
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Node* unpack =
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graph->insertNode(graph->createListUnpack(value_, *size_hint));
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return fmap(unpack->outputs(), make_simple_value);
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}
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throw ErrorReport(loc) << value_->type()->python_str()
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<< " cannot be used as a tuple";
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}
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static bool isRecursive(const TypePtr& classType, const TypePtr& attrType) {
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if (attrType->isSubtypeOf(classType)) {
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return true;
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}
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// Recursively check contained types. We need to do this because a user may do
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// A -> B -> A.
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for (const auto& type : attrType->containedTypes()) {
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if (isRecursive(classType, type)) {
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return true;
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}
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}
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return false;
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}
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void SimpleValue::setAttr(
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const SourceRange& loc,
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Function& m,
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const std::string& field,
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Value* newValue) {
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const auto classType = value_->type()->cast<ClassType>();
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if (!classType) {
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throw ErrorReport(loc) << "Tried to set an attribute: " << field
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<< " on a non-class: "
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<< value_->type()->python_str();
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}
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auto expectedType = classType->findAttribute(field);
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if (!expectedType) {
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// If we are still compiling the __init__ method for this class, then
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// setting an unknown attribute adds it to the class's definition.
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// We are initializing if:
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const auto isInitializing =
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// 1. The method we're currently inserting into is an init method
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// TODO this can be a qualified name check
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m.name() == "__init__" &&
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// 2. The `self` arg matches this value's type (i.e. we are in the init
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// method for this class, not some other class)
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!m.graph()->inputs().empty() &&
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m.graph()->inputs().at(0)->type() == classType;
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if (isInitializing) {
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if (isRecursive(classType, newValue->type())) {
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throw ErrorReport(loc)
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<< "Assignment to attribute '" << field
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<< "' cannot be of a type that contains class "
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<< "'" << classType->python_str() << "'.\n"
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<< "Classes that recursively contain instances of themselves"
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<< " are not yet supported";
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}
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classType->addAttribute(field, newValue->type());
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expectedType = newValue->type();
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const auto insertPoint = m.graph()->insertPoint();
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const auto topLevelBlock = m.graph()->block();
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if (insertPoint->owningBlock() != topLevelBlock) {
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throw ErrorReport(loc)
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<< "First assignment cannot be in a control-flow block. "
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<< "Initialize the field at the top level first";
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}
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} else {
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throw ErrorReport(loc)
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<< "Tried to set nonexistent attribute: " << field
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<< ". Did you forget to initialize it in __init__()?";
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}
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}
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AT_ASSERT(expectedType);
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// Check type correctness
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const auto newType = newValue->type();
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if (!newType->isSubtypeOf(expectedType)) {
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throw ErrorReport(loc) << "Wrong type for attribute assignment. Expected "
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<< expectedType->python_str() << " but got "
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<< newType->python_str();
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}
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auto& g = *m.graph();
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g.insertNode(g.createSetAttr(value_, field, newValue));
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}
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std::shared_ptr<SugaredValue> SimpleValue::call(
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const SourceRange& loc,
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Function& m,
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at::ArrayRef<NamedValue> inputs,
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at::ArrayRef<NamedValue> attributes,
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size_t n_binders) {
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// allow our 'fake' closures to be called, used for fork serialization
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// at the moment, but can be expanded later
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Node* self = getValue()->node();
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if (self->kind() == prim::TupleConstruct && self->inputs().size() == 2 &&
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self->inputs().at(0)->node()->kind() == prim::Function) {
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std::shared_ptr<Graph> graph =
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self->inputs().at(0)->node()->g(attr::Subgraph);
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Value* context = self->inputs().at(1);
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AT_ASSERT(context->node()->kind() == prim::TupleConstruct);
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// fork nodes are emitted in their own block but we do not simplify
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// tuple construction across blocks. To ensure we clean up the tuple
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// construct create another copy of the tuple construct in the fork block
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Value* close_context =
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m.graph()
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->insertNode(m.graph()->createTuple(context->node()->inputs()))
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->output();
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// TODO this needs to go in `m`s compilation unit
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auto cu = std::make_shared<CompilationUnit>();
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auto fn = cu->create_function(QualifiedName("anon"), graph);
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auto ret = StrongFunctionPtr(std::move(cu), fn);
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std::vector<NamedValue> ctx_inputs = {close_context};
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ctx_inputs.insert(ctx_inputs.end(), inputs.begin(), inputs.end());
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return FunctionValue(ret).call(loc, m, ctx_inputs, attributes, n_binders);
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}
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if (auto class_type = getValue()->type()->cast<ClassType>()) {
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return attr(loc, m, "__call__")
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->call(loc, m, inputs, attributes, n_binders);
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}
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return SugaredValue::call(loc, m, inputs, attributes, n_binders);
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}
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Value* SimpleValue::len(const SourceRange& loc, Function& m) {
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// List, Tuple, Tensor, fill in missing information desugaring
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Value* val = getValue();
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TypePtr val_type = val->type();
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Graph& g = *m.graph();
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if (val_type->cast<ListType>() || val_type->cast<StringType>() ||
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val_type->isSubtypeOf(TensorType::get())) {
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return g.insert(aten::len, {val}, {}, loc);
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} else {
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throw ErrorReport(loc) << "'" << val_type->python_str() << "'"
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<< " object is not iterable";
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}
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}
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SugaredValuePtr SimpleValue::getitem(
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const SourceRange& loc,
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Function& m,
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Value* idx) {
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Value* val = getValue();
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TypePtr val_type = val->type();
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Graph& g = *m.graph();
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// if it's a List/String/Dict, emit a regular __getitem__ op
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if (val_type->cast<ListType>() || val_type->cast<StringType>()) {
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return std::make_shared<SimpleValue>(
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g.insert(aten::__getitem__, {val, idx}, {}, loc));
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} else if (auto dict_type = val_type->cast<DictType>()) {
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return std::make_shared<SimpleValue>(
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g.insert(aten::__getitem__, {val, idx}, {}, loc));
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} else if (val_type->isSubtypeOf(TensorType::get())) {
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return std::make_shared<SimpleValue>(
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g.insert(aten::select, {val, 0, idx}, {}, loc));
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} else if (auto class_type = val_type->cast<ClassType>()) {
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return attr(loc, m, "__getitem__")->call(loc, m, {idx}, {}, 1);
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} else {
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throw ErrorReport(loc) << "'" << val_type->python_str() << "'"
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<< " object is not subscriptable";
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}
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}
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SugaredValuePtr SimpleValue::iter(const SourceRange& loc, Function& m) {
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auto value = getValue();
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auto type = value->type();
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// built-in iterable types
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if (type->cast<ListType>() || type->cast<StringType>() ||
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type->cast<TensorType>()) {
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return std::make_shared<SimpleValue>(value);
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}
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// dicts iterate over keys
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if (type->cast<DictType>()) {
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return std::make_shared<SimpleValue>(
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m.graph()->insert(aten::keys, {value}, {}, loc));
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}
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if (auto tup = type->cast<TupleType>()) {
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auto tup_values = createTupleUnpack(value);
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std::vector<SugaredValuePtr> tup_sugared;
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for (Value* v : tup_values) {
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tup_sugared.push_back(std::make_shared<SimpleValue>(v));
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}
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return std::make_shared<SugaredTupleValue>(tup_sugared);
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} else {
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throw ErrorReport(loc) << "'" << type->python_str() << "'"
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<< " object is not iterable";
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}
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}
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RangeValue::RangeValue(
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const SourceRange& loc,
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Function& m,
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std::vector<Value*> inputs,
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c10::optional<int64_t> static_len) {
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for (size_t i = 0; i < inputs.size(); ++i) {
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auto typ = inputs[i]->type();
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if (!typ->cast<IntType>()) {
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throw ErrorReport(loc)
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<< "all inputs of range must be ints, found " << typ->python_str()
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<< " in argument " << c10::guts::to_string(i);
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}
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}
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Graph& g = *m.graph();
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if (inputs.size() == 0) {
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throw ErrorReport(loc) << "range expected at least 1 arguments, got 0";
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} else if (inputs.size() == 1) {
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end_ = inputs[0];
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start_ = g.insertConstant(0, loc);
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step_ = g.insertConstant(1, loc);
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// range() call only contains end, easier to calculate len() and getitem()
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has_only_end_ = true;
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} else if (inputs.size() <= 3) {
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start_ = inputs[0];
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end_ = inputs[1];
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if (inputs.size() == 3) {
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step_ = inputs[2];
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} else {
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step_ = g.insertConstant(1, loc);
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}
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has_only_end_ = false;
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} else {
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throw ErrorReport(loc) << "range expected at most 3 arguments, got "
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<< inputs.size();
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}
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static_len_ = static_len;
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}
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SugaredValuePtr RangeValue::iter(const SourceRange& loc, Function& m) {
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return shared_from_this();
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};
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Value* RangeValue::len(const SourceRange& loc, Function& m) {
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if (static_len_) {
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return insertConstant(*m.graph(), *static_len_, loc);
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}
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if (has_only_end_) {
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return end_;
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} else {
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Graph& g = *m.graph();
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return g.insert(aten::__range_length, {start_, end_, step_}, {}, loc);
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}
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}
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SugaredValuePtr RangeValue::getitem(
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const SourceRange& loc,
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Function& m,
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Value* idx) {
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if (has_only_end_) {
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return std::make_shared<SimpleValue>(idx);
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} else {
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auto& g = *m.graph();
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return std::make_shared<SimpleValue>(
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g.insert(aten::__derive_index, {idx, start_, step_}, {}, loc));
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}
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}
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std::vector<SugaredValuePtr> IterableTree::get_base_iterables() {
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std::vector<SugaredValuePtr> base_iters{};
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for (SugaredValuePtr sv : children_) {
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if (auto iv = std::dynamic_pointer_cast<IterableTree>(sv)) {
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std::vector<SugaredValuePtr> child_iters = iv->get_base_iterables();
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// merge child iters with the base_iters
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base_iters.insert(
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base_iters.end(),
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std::make_move_iterator(child_iters.begin()),
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std::make_move_iterator(child_iters.end()));
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} else {
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// IterableTree leaves, either SimpleValue or RangeValue
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base_iters.emplace_back(sv);
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}
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}
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|
return base_iters;
|
|
}
|
|
|
|
Value* IterableTree::len(const SourceRange& loc, Function& m) {
|
|
// if it's a iterable tree, we get the base iterables that consists of
|
|
// SimpleValue or RangeValue, and then calculate the minimum length of all the
|
|
// base iterables to be max_trip_count_val
|
|
TORCH_INTERNAL_ASSERT(!unroll_length_);
|
|
Graph& g = *m.graph();
|
|
std::vector<SugaredValuePtr> base_iters = get_base_iterables();
|
|
std::vector<Value*> lengths;
|
|
lengths.reserve(base_iters.size());
|
|
|
|
for (const SugaredValuePtr& base_iter : base_iters) {
|
|
lengths.emplace_back(base_iter->len(loc, m));
|
|
}
|
|
Node* list_node = g.insertNode(g.createList(IntType::get(), lengths));
|
|
return g.insert(prim::min, {list_node->output()}, {}, loc);
|
|
}
|
|
|
|
SugaredValuePtr IterableTree::getitem(
|
|
const SourceRange& loc,
|
|
Function& m,
|
|
Value* idx) {
|
|
std::vector<SugaredValuePtr> child_items;
|
|
for (const SugaredValuePtr& child : children_) {
|
|
child_items.emplace_back(child->getitem(loc, m, idx));
|
|
}
|
|
return std::make_shared<SugaredTupleValue>(child_items);
|
|
}
|
|
|
|
void IterableTree::addChild(
|
|
const SourceRange& range,
|
|
Function& m,
|
|
const SugaredValuePtr iter_value) {
|
|
c10::optional<int64_t> child_len = iter_value->staticLen();
|
|
if (children_.size() == 0) {
|
|
unroll_length_ = child_len;
|
|
} else {
|
|
if ((unroll_length_ && !child_len) || (child_len && !unroll_length_)) {
|
|
throw ErrorReport(range)
|
|
<< "Can not iterate over a module list or tuple with a value "
|
|
"that does not have a statically determinable length\n";
|
|
}
|
|
if (unroll_length_ && child_len) {
|
|
// iterables run for the minimum length of all its leaves
|
|
unroll_length_ = std::min(*child_len, *unroll_length_);
|
|
} else {
|
|
unroll_length_ = c10::nullopt;
|
|
}
|
|
}
|
|
children_.push_back(iter_value);
|
|
}
|
|
|
|
std::shared_ptr<SugaredValue> MagicMethod::call(
|
|
const SourceRange& loc,
|
|
Function& m,
|
|
at::ArrayRef<NamedValue> inputs,
|
|
at::ArrayRef<NamedValue> attributes,
|
|
size_t n_binders) {
|
|
if (inputs.size() > 0) {
|
|
Value* self = inputs[0].value(*m.graph());
|
|
if (auto class_ptr = self->type()->cast<ClassType>()) {
|
|
return SimpleValue(self)
|
|
.attr(loc, m, desugared_name_)
|
|
->call(loc, m, inputs.slice(1), attributes, n_binders);
|
|
}
|
|
}
|
|
TORCH_INTERNAL_ASSERT(base_value_);
|
|
return base_value_->call(loc, m, inputs, attributes, n_binders);
|
|
}
|
|
|
|
std::shared_ptr<SugaredValue> ClassValue::call(
|
|
const SourceRange& loc,
|
|
Function& m,
|
|
// note: names for args will be 'argument 0', 'argument 1', etc..
|
|
at::ArrayRef<NamedValue> inputs,
|
|
at::ArrayRef<NamedValue> attributes,
|
|
size_t n_binders) {
|
|
AT_ASSERT(n_binders <= 1);
|
|
|
|
// Generate a new object of the right type, then call `__init__` on it
|
|
auto& g = *m.graph();
|
|
auto self = g.insertNode(g.createObject(type_))->output();
|
|
if (!type_->getMethod("__init__")) {
|
|
throw ErrorReport(loc) << "Class " << type_->name()->name()
|
|
<< " does not have an __init__ function defined";
|
|
}
|
|
|
|
// Call the init function
|
|
MethodValue(self, "__init__").call(loc, m, inputs, attributes, n_binders);
|
|
|
|
return std::make_shared<SimpleValue>(self);
|
|
}
|
|
|
|
std::shared_ptr<SugaredValue> ClassValue::attr(
|
|
const SourceRange& loc,
|
|
Function& m,
|
|
const std::string& field) {
|
|
if (field != "__new__") {
|
|
throw ErrorReport(loc) << "Tried to lookup unknown attribute on class";
|
|
}
|
|
return SpecialFormValue::create(prim::CreateObject);
|
|
}
|
|
|
|
std::shared_ptr<SugaredValue> NamedTupleConstructor::call(
|
|
const SourceRange& loc,
|
|
Function& m,
|
|
at::ArrayRef<NamedValue> inputs,
|
|
at::ArrayRef<NamedValue> attributes,
|
|
size_t n_binders) {
|
|
auto& g = *m.graph();
|
|
|
|
auto schema = type_->schema();
|
|
TORCH_INTERNAL_ASSERT(schema);
|
|
auto qualname = type_->name();
|
|
auto matched_schema = matchSchema(*schema, loc, g, inputs, attributes);
|
|
|
|
auto self =
|
|
g.insertNode(
|
|
g.createTuple(matched_schema.inputs, type_)->setSourceRange(loc))
|
|
->output();
|
|
self->setType(type_);
|
|
|
|
return std::make_shared<SimpleValue>(self);
|
|
}
|
|
|
|
std::shared_ptr<BuiltinFunction> BuiltinFunction::tryCreate(
|
|
Symbol symbol,
|
|
c10::optional<NamedValue> self) {
|
|
for (const std::shared_ptr<Operator>& op : getAllOperatorsFor(symbol)) {
|
|
if (!self) {
|
|
return std::make_shared<BuiltinFunction>(symbol, nullptr);
|
|
}
|
|
if (auto index = op->schema().argumentIndexWithName("self")) {
|
|
std::unordered_map<std::string, TypePtr> type_env;
|
|
TypePtr formal_type = op->schema().arguments().at(*index).type();
|
|
const MatchTypeReturn matched =
|
|
matchTypeVariables(formal_type, self->type(), type_env);
|
|
if (!matched.success()) {
|
|
continue;
|
|
}
|
|
const auto concrete_type = tryEvalTypeVariables(formal_type, type_env);
|
|
if (!concrete_type || !self->type()->isSubtypeOf(concrete_type)) {
|
|
continue;
|
|
}
|
|
return std::make_shared<BuiltinFunction>(symbol, self);
|
|
}
|
|
}
|
|
return nullptr;
|
|
}
|
|
|
|
} // namespace script
|
|
} // namespace jit
|
|
} // namespace torch
|