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register_prim_ops.cpp
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register_prim_ops.cpp
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#include <ATen/autocast_mode.h>
#include <ATen/core/Generator.h>
#include <c10/util/irange.h>
#include <torch/csrc/jit/mobile/promoted_prim_ops.h>
#include <torch/csrc/jit/runtime/custom_operator.h>
#include <torch/csrc/jit/runtime/operator.h>
#include <torch/csrc/jit/runtime/register_ops_utils.h>
#include <torch/csrc/jit/runtime/slice_indices_adjust.h>
#include <torch/library.h>
#include <optional>
#include <algorithm>
#include <bitset>
#include <cctype>
#include <cmath>
#include <exception>
#include <fstream>
#include <iostream>
#include <limits>
#include <memory>
#include <mutex>
#include <ostream>
#include <stdexcept>
#include <string>
#include <typeinfo>
#include <unordered_map>
#include <unordered_set>
#include <utility>
#include <vector>
namespace torch::jit {
namespace {
std::string stringSlice(
std::string string,
std::optional<int64_t> start,
std::optional<int64_t> end,
int64_t step) {
int64_t start_val = start.has_value() ? start.value() : INT64_MAX;
int64_t end_val = end.has_value() ? end.value() : INT64_MAX;
const int64_t num_vals =
slice_indices_adjust(string.size(), &start_val, &end_val, step);
int64_t i = start_val;
std::string result = "";
for (const auto j : c10::irange(num_vals)) {
(void)j; // Suppress unused variable warning
result += string[i];
i += step;
}
return result;
}
// consecutive whitespace are regarded as a single separator,
// the result will contain no empty strings at the start or end
// if the string has leading or trailing whitespace.
c10::List<std::string> splitNoneSeparator(const std::string& string) {
c10::List<std::string> splits;
// whitespaces includes tab, space and
// the delimiters defined in the implementation of splitlines
std::string whitespaces =
" \t\n\r\r\n\v\x0b\f\x0c\x1c\x1d\x1e\x85\u2028\u2029";
std::string::size_type prev_pos = 0;
std::string::size_type pos = 0;
while ((pos = string.find_first_of(whitespaces, pos)) != std::string::npos) {
auto substr = string.substr(prev_pos, pos - prev_pos);
// skip the whitespaces as the Python split() method
if (!substr.empty()) {
splits.emplace_back(substr);
}
pos++;
prev_pos = pos;
}
if (prev_pos != string.size()) {
splits.emplace_back(string.substr(prev_pos));
}
return splits;
}
bool isSortableTupleType(
const TupleTypePtr& tuple_type,
std::stringstream& why_not) {
for (const TypePtr& ele_type : tuple_type->containedTypes()) {
switch (ele_type->kind()) {
case TypeKind::IntType:
case TypeKind::BoolType:
case TypeKind::FloatType:
case TypeKind::StringType:
case TypeKind::TensorType:
continue;
case TypeKind::TupleType:
if (!isSortableTupleType(ele_type->expect<TupleType>(), why_not)) {
return false;
}
continue;
case TypeKind::ClassType:
if (!c10::checkObjectSortSchema(
ele_type->expect<ClassType>(), why_not)) {
return false;
}
continue;
default:
why_not << "Contained elements in " << *tuple_type
<< " are not sortable. Only Int, Bool, Float, String, Tensor, "
<< "a User Defined Class with __lt__ method defined or Tuples "
<< "of aforementionted types can be sorted.";
return false;
}
}
return true;
}
bool isSortableListOfObjectsOrTuples(
c10::List<IValue>& ivalues,
std::stringstream& why_not) {
if (ivalues.empty()) {
return true;
}
auto type = ivalues.get(0).type();
// We assume lists have homogenous types, use first element to determine
// best sorting methods. If in the future we need to support heterogenous
// types inside list, then sorting needs to have runtime sortable checks.
const size_t n = ivalues.size();
for (const auto i : c10::irange(n)) {
const IValue& v = ivalues.get(i);
auto curr_type = v.type();
if (*curr_type != *type) {
why_not << "Only values of same type can be compared. "
<< "Found " << type->repr_str() << " and "
<< curr_type->repr_str();
return false;
}
}
if (auto tuple_type = type->cast<TupleType>()) {
return isSortableTupleType(tuple_type, why_not);
}
if (auto class_type = type->cast<ClassType>()) {
return c10::checkObjectSortSchema(class_type, why_not) != nullptr;
}
// Basic types like tensors/ints/floats/bools/strs are not checked in this
// method because they should have been schema matched to specialized
// aten::sort kernels using listSort<T>.
why_not << "Only list of Tensors, ints, floats, bools, strs, "
<< "a User Defined Class that defines the __lt__ compare method "
<< "or Tuples of aforementioned types can be sorted, got list of "
<< type->repr_str() << "\n";
return false;
}
template <bool has_reverse_arg, bool copy_return_list>
void sort_op(Stack& stack) {
bool reverse = has_reverse_arg ? pop(stack).toBool() : false;
auto g_list = pop(stack).toList();
if (copy_return_list) {
g_list = g_list.copy();
}
if (!g_list.empty()) {
std::stringstream error_str;
if (!isSortableListOfObjectsOrTuples(g_list, error_str)) {
throw std::runtime_error(error_str.str());
}
c10::IValueComparator comparator;
if (reverse) {
comparator = c10::getGreaterThanComparator(g_list.get(0));
} else {
comparator = c10::getLessThanComparator(g_list.get(0));
}
std::sort(g_list.begin(), g_list.end(), comparator);
}
if (copy_return_list) {
push(stack, g_list);
}
}
template <typename T, typename U>
auto powWrapper(T a, U b) {
TORCH_CHECK(
!(a == 0.0 && b < 0.0), "0.0 cannot be raised to a negative power")
return pow(a, b);
}
static const std::vector<OperatorGeneratorArgs> opGenArgs{
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::str(t elem) -> str"),
[](Stack& stack) {
std::stringstream ss;
ss << pop(stack);
push(stack, ss.str());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::list(str t) -> str[]"),
[](Stack& stack) {
auto str = pop(stack).toStringRef();
c10::List<std::string> chars;
chars.reserve(str.size());
for (auto c : str) {
chars.push_back(std::string(1, c));
}
push(stack, std::move(chars));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::cpu(Tensor(a) self) -> Tensor(a|b)"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.cpu());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::numpy_T.a(Tensor(a) self) -> Tensor(a)"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.numpy_T());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::matrix_H.a(Tensor(a) self) -> Tensor(a)"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.matrix_H());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::mT.a(Tensor(a) self) -> Tensor(a)"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.mT());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::mH.a(Tensor(a) self) -> Tensor(a)"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.mH());
},
aliasAnalysisFromSchema()),
// only used internally in range() translation
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::__range_length(int lo, int hi, int step) -> int"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t lo, hi, step;
pop(stack, lo, hi, step);
// error handling when step_val = 0 during runtime
if (step == 0) {
throw std::runtime_error("range() arg 3 must not be zero");
}
if (step > 0 && lo < hi) {
push(stack, 1 + (hi - 1 - lo) / step);
} else if (step < 0 && lo > hi) {
push(stack, 1 + (lo - 1 - hi) / (0 - step));
} else {
push(stack, 0);
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::__derive_index(int index, int start, int step) -> int"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t index, start, step;
pop(stack, index, start, step);
push(stack, start + index * step);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::TupleUnpack(Any tup) -> ..."),
[](Stack& stack) { tupleUnpack(stack); },
aliasAnalysisSpecialCase()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::unchecked_cast(t x) -> t"),
noop,
aliasAnalysisSpecialCase()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::IntImplicit(Tensor a) -> int"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ true);
push(stack, a.item<int64_t>());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::ComplexImplicit(Tensor a) -> complex"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item<c10::complex<double>>());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::FloatImplicit(Tensor a) -> float"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item<double>());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::ScalarImplicit(Tensor a) -> Scalar"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
checkImplicitTensorToNum(a, /*to int*/ false);
push(stack, a.item());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Bool.Tensor(Tensor a) -> bool"),
boolTensor,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Bool.int(int a) -> bool"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t i;
pop(stack, i);
push(stack, (bool)i);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Bool.float(float a) -> bool"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
double d;
pop(stack, d);
push(stack, (bool)d);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Int.Tensor(Tensor a) -> int"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.item<int64_t>());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Int.bool(bool a) -> int"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
bool b;
pop(stack, b);
push(stack, static_cast<int64_t>(b));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Int.float(float a) -> int"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
double d;
pop(stack, d);
push(stack, static_cast<int64_t>(d));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Int.Scalar(Scalar a) -> int"),
[](Stack& stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isInt()) {
push(stack, std::move(scalar));
} else {
// toScalar() needed to avoid strict type check in IValue::toInt.
push(stack, static_cast<int64_t>(scalar.toScalar().toInt()));
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Int.str(str a) -> int"),
[](Stack& stack) {
auto s = pop(stack).toString();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
std::string::size_type sz;
int64_t val = static_cast<int64_t>(std::stoll(s->string(), &sz));
if (sz == s->string().size()) {
push(stack, val);
} else {
std::stringstream error_str;
error_str << "invalid literal for int() "
<< "with base 10: '" << s->string() << "'";
throw std::runtime_error(error_str.str());
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Float.Tensor(Tensor a) -> float"),
[](Stack& stack) {
at::Tensor a;
pop(stack, a);
push(stack, a.item<double>());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Float.Scalar(Scalar a) -> float"),
[](Stack& stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isDouble()) {
push(stack, std::move(scalar));
} else if (scalar.isComplexDouble()) {
push(stack, scalar.toComplexDouble().real());
} else {
push(stack, static_cast<double>(scalar.toInt()));
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Float.int(int a) -> float"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
int64_t i;
pop(stack, i);
push(stack, (float)i);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Float.bool(bool a) -> float"),
[](Stack& stack) {
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
bool b;
pop(stack, b);
push(stack, (float)b);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Float.str(str a) -> float"),
[](Stack& stack) {
auto s = pop(stack).toString();
// NOLINTNEXTLINE(cppcoreguidelines-init-variables)
std::string::size_type sz;
double b = std::stod(s->string(), &sz);
if (sz == s->string().size()) {
push(stack, b);
} else {
std::stringstream error_str;
error_str << "could not convert string "
<< "to float: '" << s->string() << "'";
throw std::runtime_error(error_str.str());
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Complex.Scalar(Scalar a) -> complex"),
[](Stack& stack) {
IValue scalar;
pop(stack, scalar);
if (scalar.isComplexDouble()) {
push(stack, std::move(scalar));
} else if (scalar.isDouble()) {
push(stack, c10::complex<double>(scalar.toDouble(), 0));
} else {
push(stack, c10::complex<double>(scalar.toInt(), 0));
}
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::Complex.Tensor_Tensor(Tensor a, Tensor b) -> complex"),
[](Stack& stack) {
at::Tensor a, b;
pop(stack, a, b);
push(stack, c10::complex<double>(a.item<double>(), b.item<double>()));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::format(str self, ...) -> str"),
[](Stack& stack) { aten_format(stack); },
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::einsum.sublist(Tensor a, ...) -> Tensor"),
[](Stack& stack) {
size_t num_inputs = pop(stack).toInt();
einsum(stack, num_inputs);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::NumToTensor.Scalar(Scalar a) -> Tensor"),
numToTensorScalar,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"prim::RaiseException(str msg, str? cls=None) -> ()"),
raiseException,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Size(int[] sizes) -> int[]"),
[](Stack& stack) {},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::size(Tensor self) -> int[]"),
size,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::sym_size(Tensor self) -> SymInt[]"),
sym_size,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::stride(Tensor self) -> int[]"),
[](Stack& stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.strides());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::sym_stride(Tensor self) -> SymInt[]"),
sym_stride,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::EnumName(AnyEnumType enum) -> str"),
[](Stack& stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->name());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::EnumValue.int(AnyEnumType enum) -> int"),
[](Stack& stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"prim::EnumValue.float(AnyEnumType enum) -> float"),
[](Stack& stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::EnumValue.str(AnyEnumType enum) -> str"),
[](Stack& stack) {
IValue e = pop(stack);
push(stack, e.toEnumHolder()->value());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
// note the compiler knows to type TupleIndex more accurately than it
// is listed here.
TORCH_SELECTIVE_SCHEMA("prim::TupleIndex(Any tup, int i) -> Any"),
tupleIndex,
aliasAnalysisSpecialCase()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::ne.int_list(int[] a, int[] b) -> bool"),
listNe<int64_t>,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"prim::unchecked_unwrap_optional(t(a)? optional) -> t(a)"),
noop,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::device(Tensor a) -> Device"),
device,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::dtype(Tensor a) -> int"),
dtype,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::layout(Tensor a) -> Layout"),
layout,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::__not__(bool self) -> bool"),
_not,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::__is__(t1 self, t2 obj) -> bool"),
is,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::__isnot__(t1 self, t2 obj) -> bool"),
isNot,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::element_size(Tensor self) -> int"),
[](Stack& stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.element_size());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::numel(Tensor self) -> int"),
[](Stack& stack) {
at::Tensor arg = pop(stack).toTensor();
push(stack, arg.numel());
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::dim(Tensor self) -> int"),
dim,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::get_device(Tensor self) -> int"),
[](Stack& stack) {
RECORD_FUNCTION("get_device", c10::ArrayRef<const c10::IValue>{});
auto result =
at::get_device((std::move(peek(stack, 0, 1))).toTensor());
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::storage_offset(Tensor self) -> int"),
[](Stack& stack) {
RECORD_FUNCTION("storage_offset", c10::ArrayRef<const c10::IValue>{});
auto result =
((std::move(peek(stack, 0, 1))).toTensor()).storage_offset();
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::is_contiguous(Tensor self) -> bool"),
[](Stack& stack) {
RECORD_FUNCTION("is_contiguous", c10::ArrayRef<const c10::IValue>{});
auto result =
((std::move(peek(stack, 0, 1))).toTensor()).is_contiguous();
drop(stack, 1);
pack(stack, result);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::is_contiguous.memory_format(Tensor self, MemoryFormat memory_format) -> bool"),
[](Stack& stack) {
auto memory_format = pop(stack).toMemoryFormat();
auto t = pop(stack).toTensor();
push(stack, t.is_contiguous(memory_format));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
// NB: intentionally suffixed with extra _format to prevent tests for
// "_like" suffix from triggering on this
TORCH_SELECTIVE_SCHEMA(
"aten::is_strides_like_format(Tensor self, MemoryFormat memory_format) -> bool"),
[](Stack& stack) {
auto memory_format = pop(stack).toMemoryFormat();
auto t = pop(stack).toTensor();
push(stack, t.unsafeGetTensorImpl()->is_strides_like(memory_format));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::is_non_overlapping_and_dense(Tensor self) -> bool"),
[](Stack& stack) {
auto t = pop(stack).toTensor();
push(stack, t.unsafeGetTensorImpl()->is_non_overlapping_and_dense());
},
aliasAnalysisFromSchema()),
// these ops are generic over the list element type.
// CREATING GENERIC_LIST_OPS
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::select.t(t[](a) list, int idx) -> t(*)"),
listSelect,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::__getitem__.t(t[](a) list, int idx) -> t(*)"),
listSelect,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::append.t(t[](a!) self, t(c -> *) el) -> t[](a!)"),
listAppend,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::reverse.t(t[](a!) self) -> ()"),
listReverse,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::extend.t(t[](a!) self, t[] other) -> ()"),
listExtend,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::copy.t(t[](a) self) -> t[]"),
listCopy,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::_set_item.t(t [](a!) l, int idx, t(b -> *) el) -> t[](a!)"),
listSetItem,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::clear.t(t[](a!) self) -> ()"),
listClear,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::Delete.t(t[](a!) self, int idx) -> ()"),
listDelete,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::insert.t(t[](a!) self, int idx, t(b -> *) el) -> ()"),
listInsert,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::pop.t(t[](a!) self, int idx=-1) -> t(*)"),
listPop,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::add.t(t[] a, t[] b) -> t[]"),
listAdd,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::add_.t(t[](a!) self, t[] b) -> t[]"),
listInplaceAdd,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::slice.t(t[] l, int? start=None, int? end=None, int step=1) -> t[]"),
listSlice,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::list.t(t[] l) -> t[]"),
listList,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::mul.left_t(t[] l, int n) -> t[]"),
listMulIntLeft,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::mul.right_(int n, t[] l) -> t[]"),
listMulIntRight,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::mul_.t(t[](a!) l, int n) -> t[](a!)"),
listMulIntLeftInPlace,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::len.t(t[] a) -> int"),
listLen,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::eq.int_list(int[] a, int[] b) -> bool"),
listEq<int64_t>,
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::eq.device(Device a, Device b) -> bool"),
[](Stack& stack) {
auto a = pop(stack).toDevice();
auto b = pop(stack).toDevice();
push(stack, a == b);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::ne.device(Device a, Device b) -> bool"),
[](Stack& stack) {
auto a = pop(stack).toDevice();
auto b = pop(stack).toDevice();
push(stack, a != b);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::eq.bool(bool a, bool b) -> bool"),
[](Stack& stack) {
auto a = pop(stack);
auto b = pop(stack);
push(stack, a == b);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::ne.bool(bool a, bool b) -> bool"),
[](Stack& stack) {
auto a = pop(stack);
auto b = pop(stack);
push(stack, a != b);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::is_autocast_enabled() -> bool"),
[](Stack& stack) {
#if defined BUILD_LITE_INTERPRETER || defined C10_MOBILE
bool enabled = false;
#else
bool enabled = at::autocast::is_autocast_enabled(at::kCUDA);
#endif
push(stack, enabled);
},
aliasAnalysisConservative()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::is_autocast_cpu_enabled() -> bool"),
[](Stack& stack) {
#if defined BUILD_LITE_INTERPRETER || defined C10_MOBILE
bool enabled = false;
#else
bool enabled = at::autocast::is_autocast_enabled(at::kCPU);
#endif
push(stack, enabled);
},
aliasAnalysisConservative()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::get_autocast_dtype(str device_type) -> ScalarType"),
[](Stack& stack) {
#if defined BUILD_LITE_INTERPRETER || defined C10_MOBILE
// autocast is not supported.
at::ScalarType dtype = at::ScalarType::Undefined;
#else
at::DeviceType device_type =
at::Device(pop(stack).toStringRef()).type();
at::ScalarType dtype = at::autocast::get_autocast_dtype(device_type);
#endif
push(stack, dtype);
},
aliasAnalysisConservative()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::Uninitialized() -> Any"),
unInitialized,
aliasAnalysisSpecialCase()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::Print(...) -> ()"),
[](Stack& stack) {
auto num_inputs = pop(stack).toInt();
std::stringstream ss;
bool first = true;
for (const IValue& i : last(stack, num_inputs)) {
if (!first)
ss << " ";
first = false;
ss << i;
}
drop(stack, num_inputs);
ss << std::endl;
auto* handler = getPrintHandler();
TORCH_INTERNAL_ASSERT(handler);
handler(ss.str());
},
aliasAnalysisSpecialCase()),
// This is an alternative to aten::cat op that takes variable number of
// parameters as input.
// Format:
// prim::VarConcat(Tensors..., dim) -> Tensor
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::VarConcat(...) -> Tensor"),
[](Stack& stack) {
auto num_inputs = pop(stack).toInt();
auto dim = pop(stack).toInt();
std::vector<at::Tensor> inputs(num_inputs - 1);
for (int i = 0; i < num_inputs - 1; ++i) {
inputs[num_inputs - 2 - i] = pop(stack).toTensor();
}
push(stack, at::cat(inputs, dim));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("prim::VarStack(...) -> Tensor"),
[](Stack& stack) {
auto num_inputs = pop(stack).toInt();
auto dim = pop(stack).toInt();
std::vector<at::Tensor> inputs(num_inputs - 1);
for (int i = 0; i < num_inputs - 1; ++i) {
inputs[num_inputs - 2 - i] = pop(stack).toTensor();
}
push(stack, at::stack(inputs, dim));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"prim::IfThenElse(bool cond, Any(a) x, Any(b) y) -> Any(a|b)"),
[](Stack& stack) {
const auto cond = stack[stack.size() - 3].toBool();
stack[stack.size() - 3] =
std::move(stack[stack.size() - (cond ? 2 : 1)]);
stack.pop_back();
stack.pop_back();
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::eq.enum(AnyEnumType a, AnyEnumType b) -> bool"),
[](Stack& stack) {
IValue x = pop(stack);
IValue y = pop(stack);
push(stack, x == y);
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::ne.enum(AnyEnumType a, AnyEnumType b) -> bool"),
[](Stack& stack) {
IValue x = pop(stack);
IValue y = pop(stack);
push(stack, x != y);
},
aliasAnalysisFromSchema()),
// We define aten::dequantize in both native_functions.yaml and here,
// however, aten::dequantize.any defined here overrides
// aten::dequantize.tensors in native_functions.yaml. The variants here
// are only for graph mode quantization, and they should be removed once
// we deprecate graph mode quantization, and use the variants in
// native_functions.yaml.
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::dequantize.tensor(Tensor qtensor) -> Tensor"),
[](Stack& stack) {
at::Tensor qtensor;
pop(stack, qtensor);
push(stack, at::dequantize(qtensor));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA(
"aten::dequantize.list(Tensor[] qtensors) -> Tensor[]"),
[](Stack& stack) {
auto qtensors = pop(stack).toTensorVector();
push(stack, at::dequantize(qtensors));
},
aliasAnalysisFromSchema()),
OperatorGeneratorArgs(
TORCH_SELECTIVE_SCHEMA("aten::dequantize.any(Any tensors) -> Any"),
[](Stack& stack) { dequantize(stack); },
aliasAnalysisFromSchema()),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::log, std::log(a), float, float),
DEFINE_STRING_OP(aten::add, a + b, str),
DEFINE_COMPARISON_OP_WITH_COMPLEX(aten::eq, a == b),
DEFINE_COMPARISON_OP_WITH_COMPLEX(aten::ne, a != b),
DEFINE_GENERIC_OP(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
c10::polar(static_cast<double>(a), static_cast<double>(b)),
complex,
complex),
DEFINE_INT_FLOAT_OP(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
complex),
DEFINE_SCALAR_BINARY_OP_AVOID_COLLISION(
aten::polar,
c10::polar(static_cast<double>(a), static_cast<double>(b)),
c10::polar(static_cast<double>(a), static_cast<double>(b)),
Scalar),
DEFINE_COMPARISON_OP(aten::lt, a < b),
DEFINE_COMPARISON_OP(aten::gt, a > b),
DEFINE_COMPARISON_OP(aten::le, a <= b),
DEFINE_COMPARISON_OP(aten::ge, a >= b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::add, a + b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::sub, a - b),
DEFINE_BINARY_OP_WITH_COMPLEX(aten::mul, a* b),
DEFINE_BOOL_OP(aten::__and__, a&& b),
DEFINE_BOOL_OP(aten::__or__, a || b),
DEFINE_BOOL_OP(aten::__xor__, a != b),
DEFINE_UNARY_OP(aten::round, round_to_even(a), float, float),
DEFINE_UNARY_OP(aten::floor, floor(a), int, int),
DEFINE_UNARY_OP(aten::ceil, ceil(a), int, int),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::neg, -a, int, float),
DEFINE_UNARY_OP_WITH_COMPLEX(aten::exp, std::exp(a), float, float),
// Pass in two ops for handling int and float separately as % in C++ only
// works for int The modulus calculation is different between C++ and
// Python (on negative), we preserve the python behavior as it's more
// common and match python syntax, hence the conversion.
DEFINE_GENERIC_OP(
aten::remainder,
(b + (a % b)) % b,
fmod((b + fmod(a, b)), b),
int,
float),
DEFINE_INT_FLOAT_OP(aten::remainder, fmod((b + fmod(a, b)), b), float),
DEFINE_SCALAR_BINARY_OP(
aten::remainder,
(b + (a % b)) % b,
fmod((b + fmod(a, b)), b),
Scalar),
// NB: This is the python truediv operation
DEFINE_GENERIC_OP_WITH_COMPLEX(
aten::div,
static_cast<double>(a) / static_cast<double>(b),
a / b,
a / b,
float,
float,
complex),
DEFINE_SCALAR_BINARY_OP(
aten::div,