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Fix compilation errors from MT model 

With the following changes the compilation can continue until
RefineTypes pass:

- Add operators without ODS into `torch_ods_gen.py`
- Add some new optional and list types in `TorchTypes.td`
- Add some folders for aten int type comparator ops
- Modify GlobalizeObjectGraph.cpp. For global slots that's not used,
dont check if an aliased value is stored in more than one of global
slots. This can work around a failure where the same tensor is stored
in multiple "version" slots which are not used.
pull/283/head
Yi Zhang 2021-08-10 21:28:50 -04:00
parent 78fd07da5f
commit 85ff8b692b
12 changed files with 2274 additions and 100 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

128
frontends/pytorch/python/torch_mlir_utils/codegen/torch_ods_gen.py
View File

@ -81,7 +81,7 @@ class JitOperator:
def create_unique_key(self) -> str:
"""Create a unique, human-readable key for this JitOperator.
The key consists of the operator name and its overload name, which
together form a unique identifier. We also redundantly
append a signature to the end, which gives some robustness to changes
@ -217,12 +217,16 @@ OP_INFO_DICT = Dict[str, Union[bool, Tuple[str], SIGLIST_TYPE]]
# Use `get_ods_type` instead of using this directly.
TORCH_TYPE_TO_ODS_TYPE = {
"Tensor": "AnyTorchTensorType",
"Tensor?": "AnyTorchOptionalTensor",
"Tensor?": "AnyTorchOptionalTensorType",
"Tensor?[]": "AnyTorchOptionalTensorListType",
"Tensor[]": "AnyTorchTensorListType",
"Scalar": "AnyTorchScalarType",
"int": "Torch_IntType",
"int[]": "AnyTorchIntListType",
"int[]": "TorchIntListType",
"int?": "TorchOptionalIntType",
"bool": "Torch_BoolType",
"bool[]": "AnyTorchBoolListType",
"bool[]": "TorchBoolListType",
"bool?": "TorchOptionalBoolType",
"float": "Torch_FloatType",
"t[]": "AnyTorchListType",
"t": "AnyTorchType",
@ -230,12 +234,18 @@ TORCH_TYPE_TO_ODS_TYPE = {
"t2": "AnyTorchType",
"Any": "AnyTorchType",
"Device": "Torch_DeviceType",
"Device?": "TorchOptionalDeviceType",
"str": "Torch_StringType",
"str[]": "TorchStringListType",
"Dict": "Torch_DictType",
"__torch__.torch.classes.quantized.LinearPackedParamsBase": "Torch_LinearParamsType",
}
def get_ods_type(type: str):
# TODO: Increase precision on dict type modeling.
if type.startswith("Dict("):
type = "Dict"
ods_type = TORCH_TYPE_TO_ODS_TYPE.get(type)
if ods_type is None:
raise Exception(
@ -364,7 +374,11 @@ def emit_op(operator: JitOperator,
if not operator.is_vararg and not operator.is_varret and all(
"alias_info" not in x
for x in itertools.chain(operator.arguments, operator.returns)):
traits += ["HasValueSemantics"]
# It seems the FunctionSchema of "prim::unchecked_cast : (t) -> (t)" has
# incorrect alias information. The result can alias with other tensors
# but the alias annotation is empty.
if operator.unique_key != "prim::unchecked_cast : (t) -> (t)":
traits += ["HasValueSemantics"]
raw_emit_op(operator,
f,
@ -396,6 +410,7 @@ def emit_prim_ops(torch_ir_dir: str, registry: Registry):
emit("prim::unchecked_cast : (t) -> (t)",
traits=["DeclareOpInterfaceMethods<CastOpInterface>"])
emit("prim::Print : (...) -> ()")
emit("prim::tolist : (...) -> (...)")
def emit_aten_ops(torch_ir_dir: str, registry: Registry):
@ -421,11 +436,27 @@ def emit_aten_ops(torch_ir_dir: str, registry: Registry):
for key in [
"aten::tanh : (Tensor) -> (Tensor)",
"aten::relu : (Tensor) -> (Tensor)",
"aten::sin : (Tensor) -> (Tensor)",
"aten::exp : (Tensor) -> (Tensor)",
"aten::cos : (Tensor) -> (Tensor)",
"aten::neg : (Tensor) -> (Tensor)",
"aten::bitwise_not : (Tensor) -> (Tensor)",
"aten::add.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)",
"aten::sub.Tensor : (Tensor, Tensor, Scalar) -> (Tensor)",
"aten::mul.Tensor : (Tensor, Tensor) -> (Tensor)",
"aten::div.Tensor : (Tensor, Tensor) -> (Tensor)",
"aten::lerp.Tensor : (Tensor, Tensor, Tensor) -> (Tensor)",
"aten::eq.Tensor : (Tensor, Tensor) -> (Tensor)",
"aten::ne.Tensor : (Tensor, Tensor) -> (Tensor)",
"aten::add.Scalar : (Tensor, Scalar, Scalar) -> (Tensor)",
"aten::sub.Scalar : (Tensor, Scalar, Scalar) -> (Tensor)",
"aten::mul.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::div.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::ne.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::eq.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::gt.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::ge.Scalar : (Tensor, Scalar) -> (Tensor)",
"aten::fmod.Scalar : (Tensor, Scalar) -> (Tensor)",
]:
emit_with_mutating_variants(key)
@ -442,22 +473,109 @@ def emit_aten_ops(torch_ir_dir: str, registry: Registry):
"aten::max_pool2d : (Tensor, int[], int[], int[], int[], bool) -> (Tensor)"
)
emit("aten::adaptive_avg_pool2d : (Tensor, int[]) -> (Tensor)")
emit("aten::topk : (Tensor, int, int, bool, bool) -> (Tensor, Tensor)")
emit("aten::transpose.int : (Tensor, int, int) -> (Tensor)")
emit("aten::bmm : (Tensor, Tensor) -> (Tensor)")
emit("aten::cumsum : (Tensor, int, int?) -> (Tensor)")
emit("aten::floor_divide.Scalar : (Tensor, Scalar) -> (Tensor)")
emit("aten::logsumexp : (Tensor, int[], bool) -> (Tensor)")
emit("aten::mean.dim : (Tensor, int[], bool, int?) -> (Tensor)")
emit("aten::__and__.Tensor : (Tensor, Tensor) -> (Tensor)")
# Misc tensor ops.
emit("aten::unsqueeze : (Tensor, int) -> (Tensor)")
emit("aten::flatten.using_ints : (Tensor, int, int) -> (Tensor)")
emit("aten::dim : (Tensor) -> (int)", has_folder=True)
emit("aten::size : (Tensor) -> (int[])", has_canonicalizer=True)
emit("aten::fill_.Scalar : (Tensor, Scalar) -> (Tensor)")
emit("aten::Bool.Tensor : (Tensor) -> (bool)")
emit("aten::ones : (int[], int?, int?, Device?, bool?) -> (Tensor)")
emit("aten::zeros : (int[], int?, int?, Device?, bool?) -> (Tensor)")
emit("aten::tensor : (t[], int?, Device?, bool) -> (Tensor)")
emit("aten::tensor.bool : (bool, int?, Device?, bool) -> (Tensor)")
emit("aten::tensor.int : (int, int?, Device?, bool) -> (Tensor)")
emit("aten::_shape_as_tensor : (Tensor) -> (Tensor)")
emit("aten::all : (Tensor) -> (Tensor)")
emit("aten::any : (Tensor) -> (Tensor)")
emit("aten::any.dim : (Tensor, int, bool) -> (Tensor)")
emit("aten::arange : (Scalar, int?, int?, Device?, bool?) -> (Tensor)")
emit("aten::arange.start : (Scalar, Scalar, int?, int?, Device?, bool?) -> (Tensor)")
emit("aten::contiguous : (Tensor, int) -> (Tensor)")
emit("aten::copy_ : (Tensor, Tensor, bool) -> (Tensor)")
emit("aten::detach : (Tensor) -> (Tensor)")
emit("aten::embedding : (Tensor, Tensor, int, bool, bool) -> (Tensor)")
emit("aten::empty.memory_format : (int[], int?, int?, Device?, bool?, int?) -> (Tensor)")
emit("aten::expand : (Tensor, int[], bool) -> (Tensor)")
emit("aten::index.Tensor : (Tensor, Tensor?[]) -> (Tensor)")
emit("aten::index_put_ : (Tensor, Tensor?[], Tensor, bool) -> (Tensor)")
emit("aten::index_select : (Tensor, int, Tensor) -> (Tensor)")
emit("aten::item : (Tensor) -> (Scalar)")
emit("aten::masked_select : (Tensor, Tensor) -> (Tensor)")
emit("aten::numel : (Tensor) -> (int)")
emit("aten::repeat : (Tensor, int[]) -> (Tensor)")
emit("aten::resize_ : (Tensor, int[], int?) -> (Tensor)")
emit("aten::select.int : (Tensor, int, int) -> (Tensor)")
emit("aten::size.int : (Tensor, int) -> (int)")
emit("aten::stack : (Tensor[], int) -> (Tensor)")
emit("aten::sum.dim_IntList : (Tensor, int[], bool, int?) -> (Tensor)")
emit("aten::to.dtype : (Tensor, int, bool, bool, int?) -> (Tensor)")
emit("aten::to.other : (Tensor, Tensor, bool, bool, int?) -> (Tensor)")
emit("aten::to.prim_Device : (Tensor, Device?, int?, bool, bool) -> (Tensor)")
emit("aten::type_as : (Tensor, Tensor) -> (Tensor)")
emit("aten::view : (Tensor, int[]) -> (Tensor)")
emit("aten::slice.Tensor : (Tensor, int, int?, int?, int) -> (Tensor)")
emit("aten::len.Tensor : (Tensor) -> (int)")
emit("aten::cpu : (Tensor) -> (Tensor)")
emit("aten::gather : (Tensor, int, Tensor, bool) -> (Tensor)")
emit("aten::IntImplicit : (Tensor) -> (int)")
# Dict ops.
emit("aten::__contains__.str : (Dict(str, t), str) -> (bool)")
emit("aten::__getitem__.Dict_str : (Dict(str, t), str) -> (t)")
emit("aten::_set_item.str : (Dict(str, t), str, t) -> ()")
emit("aten::keys.str : (Dict(str, t)) -> (str[])")
emit("aten::get.default_str : (Dict(str, t), str, t) -> (t)")
# List ops.
emit("aten::cat : (Tensor[], int) -> (Tensor)")
emit("aten::append.t : (t[], t) -> (t[])")
emit("aten::add.t : (t[], t[]) -> (t[])")
emit("aten::eq.int_list : (int[], int[]) -> (bool)")
emit("aten::list.t : (t[]) -> (t[])")
emit("aten::slice.t : (t[], int?, int?, int) -> (t[])")
# Str ops.
emit("aten::add.str : (str, str) -> (str)")
emit("aten::str : (t) -> (str)")
emit("aten::format : (...) -> (str)")
emit("aten::join : (str, str[]) -> (str)")
# Type conversion ops.
emit("aten::Float.Scalar : (Scalar) -> (float)")
emit("aten::Float.str : (str) -> (float)")
emit("aten::Int.float : (float) -> (int)")
# Primitive ops
emit("aten::gt.int : (int, int) -> (bool)", has_folder=True)
emit("aten::ge.int : (int, int) -> (bool)", has_folder=True)
emit("aten::lt.int : (int, int) -> (bool)", has_folder=True)
emit("aten::le.int : (int, int) -> (bool)", has_folder=True)
emit("aten::ne.int : (int, int) -> (bool)", has_folder=True)
emit("aten::eq.int : (int, int) -> (bool)", has_folder=True)
emit("aten::floordiv.int : (int, int) -> (int)")
emit("aten::remainder.int : (int, int) -> (int)")
emit("aten::add.int : (int, int) -> (int)")
emit("aten::sub.int : (int, int) -> (int)")
emit("aten::mul.int : (int, int) -> (int)")
emit("aten::log.int : (int) -> (float)")
emit("aten::add.float_int : (float, int) -> (float)")
emit("aten::mul.float : (float, float) -> (float)")
emit("aten::neg.float : (float) -> (float)")
emit("aten::lt.float_int : (float, int) -> (bool)")
emit("aten::__and__.bool : (bool, bool) -> (bool)")
emit("aten::__is__ : (t1, t2) -> (bool)", has_folder=True)
emit("aten::__isnot__ : (t1, t2) -> (bool)", has_folder=True)
emit("aten::__not__ : (bool) -> (bool)", has_folder=True)
emit("aten::len.t : (t[]) -> (int)",
has_folder=True,
has_canonicalizer=True)

5
frontends/pytorch/test/acap_export/test_conv_nllloss_grads.py
View File

@ -3,6 +3,7 @@
# See frontends/pytorch/LICENSE for license information.
# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
# XFAIL: *
import torch
from torch.autograd import Variable
@ -45,7 +46,7 @@ with mb.capture_function("resa", [inputs, target]) as f:
# CHECK: torch.operator "aten.nll_loss2d_backward"
# CHECK: torch.operator "aten._log_softmax_backward_data"
# CHECK: %[[BWD_CONV:.*]]:3 = torch.operator "aten.convolution_backward_overrideable"
# CHECK: %[[BWD_CONV_WEIGHTS:.*]] = torch.operator "aten.copy_"{{.*}}%[[BWD_CONV]]#1
# CHECK: %[[BWD_CONV_BIAS:.*]] = torch.operator "aten.copy_"{{.*}}%[[BWD_CONV]]#2
# CHECK: %[[BWD_CONV_WEIGHTS:.*]] = aten.copy_{{.*}}%[[BWD_CONV]]#1
# CHECK: %[[BWD_CONV_BIAS:.*]] = aten.copy_{{.*}}%[[BWD_CONV]]#2
# CHECK: return %[[FWD]]#0, %[[BWD_CONV_WEIGHTS]], %[[BWD_CONV_BIAS]]
mb.module.operation.print(large_elements_limit=2)

1716
include/npcomp/Dialect/Torch/IR/GeneratedAtenOps.td
View File

File diff suppressed because it is too large Load Diff

20
include/npcomp/Dialect/Torch/IR/GeneratedPrimOps.td
View File

@ -105,7 +105,7 @@ def Torch_PrimMinSelfIntOp : Torch_Op<"prim.min.self_int", [
]> {
let summary = "Generated op for `prim::min.self_int : (int[]) -> (int)`";
let arguments = (ins
AnyTorchIntListType:$self
TorchIntListType:$self
);
let results = (outs
Torch_IntType:$result
@ -134,7 +134,7 @@ def Torch_PrimMaxSelfIntOp : Torch_Op<"prim.max.self_int", [
]> {
let summary = "Generated op for `prim::max.self_int : (int[]) -> (int)`";
let arguments = (ins
AnyTorchIntListType:$self
TorchIntListType:$self
);
let results = (outs
Torch_IntType:$result
@ -185,8 +185,7 @@ def Torch_PrimUninitializedOp : Torch_Op<"prim.Uninitialized", [
def Torch_PrimUncheckedCastOp : Torch_Op<"prim.unchecked_cast", [
DeclareOpInterfaceMethods<CastOpInterface>,
AllowsTypeRefinement,
HasValueSemantics
AllowsTypeRefinement
]> {
let summary = "Generated op for `prim::unchecked_cast : (t) -> (t)`";
let arguments = (ins
@ -210,3 +209,16 @@ def Torch_PrimPrintOp : Torch_Op<"prim.Print", [
let assemblyFormat = "`(` $operands `)` attr-dict `:` type($operands)";
}
def Torch_PrimTolistOp : Torch_Op<"prim.tolist", [
AllowsTypeRefinement
]> {
let summary = "Generated op for `prim::tolist : (...) -> (...)`";
let arguments = (ins
Variadic<AnyTorchType>:$operands
);
let results = (outs
Variadic<AnyTorchType>:$results
);
let assemblyFormat = "`(` $operands `)` attr-dict `:` type($operands) `->` type($results)";
}

26
include/npcomp/Dialect/Torch/IR/TorchOps.h
View File

@ -47,12 +47,36 @@ struct torch_constant_int_op_binder {
};
} // namespace detail
/// Matches the integer stored in a `torch.constant.int`.
/// Matches the integer stored in a `torch.constant.bool`.
inline detail::torch_constant_int_op_binder
m_TorchConstantInt(int64_t *bind_value) {
return detail::torch_constant_int_op_binder(bind_value);
}
namespace detail {
/// Matches the bool stored in a `torch.constant.bool`.
struct torch_constant_bool_op_binder {
bool *bind_value;
/// Creates a matcher instance that binds the value to bv if match succeeds.
torch_constant_bool_op_binder(bool *bv) : bind_value(bv) {}
bool match(Operation *op) {
if (auto constantBool = dyn_cast<Torch::ConstantBoolOp>(op)) {
*bind_value = constantBool.value();
return true;
}
return false;
}
};
} // namespace detail
/// Matches the bool stored in a `torch.constant.bool`.
inline detail::torch_constant_bool_op_binder
m_TorchConstantBool(bool *bind_value) {
return detail::torch_constant_bool_op_binder(bind_value);
}
namespace detail {
/// Matches the constant integers stored in a `torch.ListConstruct`.
struct torch_list_construct_op_binder {

33
include/npcomp/Dialect/Torch/IR/TorchTypes.td
View File

@ -375,27 +375,38 @@ def Torch_DictType : Torch_Type<"Dict", "dict"> {
// Type predicates
//===----------------------------------------------------------------------===//
def AnyTorchOptionalTensor : AnyTypeOf<[
AnyTorchTensorType,
Torch_OptionalType,
Torch_NoneType,
], "optional torch tensor">;
class OptionalOf<Type type, string descr> :
AnyTypeOf<[type, Torch_OptionalType, Torch_NoneType], descr> ;
def AnyTorchOptionalTensorType :
OptionalOf<AnyTorchTensorType, "Optional torch tensor type">;
def TorchOptionalIntType: OptionalOf<Torch_IntType, "Optional torch int type">;
def TorchOptionalBoolType:
OptionalOf<Torch_BoolType, "Optional torch bool type">;
def TorchOptionalDeviceType:
OptionalOf<Torch_DeviceType, "Optional torch device type">;
def IsListTypePred : CPred<"$_self.isa<::mlir::NPCOMP::Torch::ListType>()">;
class ListOf<list<Type> allowedTypes, string descr> :
ContainerType<AnyTypeOf<allowedTypes>, IsListTypePred,
ContainerType<AnyTypeOf<allowedTypes>,
IsListTypePred,
"$_self.cast<::mlir::NPCOMP::Torch::ListType>().getContainedType()",
descr, "::mlir::NPCOMP::Torch::ListType">;
descr, "::mlir::NPCOMP::Torch::ListType">;
def AnyTorchBoolListType : ListOf<[Torch_BoolType], "Any bool list type (bool[])">;
def AnyTorchIntListType : ListOf<[Torch_IntType], "Any int list type (int[])">;
def TorchBoolListType : ListOf<[Torch_BoolType], "Bool list type (bool[])">;
def TorchIntListType : ListOf<[Torch_IntType], "Int list type (int[])">;
def TorchStringListType : ListOf<[Torch_StringType], "Str list type (str[])">;
def AnyTorchTensorListType:
ListOf<[AnyTorchTensorType], "Any int list type (Tensor[])">;
def AnyTorchOptionalTensorListType :
ListOf<[AnyTorchOptionalTensorType],
"Any optional tensor list type (Tensor?[])">;
def AnyTorchScalarType : AnyTypeOf<[
Torch_IntType,
Torch_FloatType,
Torch_BoolType,
Torch_NumberType,
], "Any Python numeric type compatible with being the scalar type of a tensor (`Scalar`)">;
// See function `DictTypePtr create(TypePtr key, TypePtr value)`

136
lib/Dialect/Torch/IR/TorchOps.cpp
View File

@ -184,14 +184,13 @@ static LogicalResult verify(PrimDictConstructOp op) {
};
Type keyType = op.getKeyType();
if (!llvm::all_of(op.keys().getTypes(), isValidSubTypeOf(keyType))) {
if (!llvm::all_of(op.keys().getTypes(), isValidSubTypeOf(keyType)))
return op.emitError() << "keys should be of Dict key type";
}
Type valueType = op.getValueType();
if (!llvm::all_of(op.values().getTypes(), isValidSubTypeOf(valueType))) {
if (!llvm::all_of(op.values().getTypes(), isValidSubTypeOf(valueType)))
return op.emitError() << "values should be of Dict value type";
}
return success();
}
@ -367,21 +366,52 @@ void DerefineOp::getCanonicalizationPatterns(RewritePatternSet &patterns,
});
}
template <typename OpTy>
static OpFoldResult atenIsOrIsNotFoldHelper(OpTy op, bool equalIsTrue) {
Type lhsType = op.self().getType();
Type rhsType = op.obj().getType();
// If either type is a NoneType, make it be the lhsType.
if (rhsType.template isa<Torch::NoneType>())
std::swap(lhsType, rhsType);
// TODO: Implement and use subtype infra for this.
// If neither type is a subtype of the other, then the result is false.
if (lhsType.template isa<Torch::NoneType>() &&
rhsType.template isa<Torch::NoneType>())
return IntegerAttr::get(IntegerType::get(op.getContext(), 1), equalIsTrue);
if (lhsType.template isa<Torch::NoneType>() &&
!rhsType.template isa<Torch::OptionalType>())
return IntegerAttr::get(IntegerType::get(op.getContext(), 1), !equalIsTrue);
return nullptr;
}
//===----------------------------------------------------------------------===//
// Aten__Is__Op
//===----------------------------------------------------------------------===//
OpFoldResult Aten__Is__Op::fold(ArrayRef<Attribute> operands) {
auto lhsType = self().getType();
auto rhsType = obj().getType();
// If either type is a NoneType, make it be the lhsType.
if (rhsType.isa<Torch::NoneType>())
std::swap(lhsType, rhsType);
// TODO: Implement and use subtype infra for this.
// If neither type is a subtype of the other, then the result is false.
if (lhsType.isa<Torch::NoneType>() && !rhsType.isa<Torch::OptionalType>())
return IntegerAttr::get(IntegerType::get(getContext(), 1), 0);
return nullptr;
return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/true);
}
//===----------------------------------------------------------------------===//
// Aten__Isnot__Op
//===----------------------------------------------------------------------===//
OpFoldResult Aten__Isnot__Op::fold(ArrayRef<Attribute> operands) {
return atenIsOrIsNotFoldHelper(*this, /*equalIsTrue=*/false);
}
//===----------------------------------------------------------------------===//
// Aten__Not__Op
//===----------------------------------------------------------------------===//
OpFoldResult Aten__Not__Op::fold(ArrayRef<Attribute> operands) {
bool value;
if (!matchPattern(getOperand(), m_TorchConstantBool(&value)))
return nullptr;
return IntegerAttr::get(IntegerType::get(getContext(), 1), !value);
}
//===----------------------------------------------------------------------===//
@ -465,14 +495,19 @@ static IntegerAttr getI1IntegerAttr(MLIRContext *context, bool value) {
static_cast<int64_t>(value));
}
OpFoldResult AtenGtIntOp::fold(ArrayRef<Attribute> operands) {
auto lhs = operands[0].dyn_cast_or_null<IntegerAttr>();
auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>();
if (lhs && rhs) {
return getI1IntegerAttr(getContext(), lhs.getValue().getSExtValue() >
rhs.getValue().getSExtValue());
}
return nullptr;
using ConstantIntComparator = std::function<bool(int64_t, int64_t)>;
template <typename OpTy>
static OpFoldResult comparatorFoldHelper(OpTy op,
ConstantIntComparator comparator) {
if (op.getOperand(0) == op.getOperand(1))
return getI1IntegerAttr(op.getContext(), comparator(0, 0));
int64_t lhs, rhs;
if (!matchPattern(op.getOperand(0), m_TorchConstantInt(&lhs)) ||
!matchPattern(op.getOperand(1), m_TorchConstantInt(&rhs)))
return nullptr;
return getI1IntegerAttr(op.getContext(), comparator(lhs, rhs));
}
//===----------------------------------------------------------------------===//
@ -480,16 +515,53 @@ OpFoldResult AtenGtIntOp::fold(ArrayRef<Attribute> operands) {
//===----------------------------------------------------------------------===//
OpFoldResult AtenNeIntOp::fold(ArrayRef<Attribute> operands) {
// `torch.aten.ne.int %x, %x` -> `false`
if (getOperand(0) == getOperand(1))
return getI1IntegerAttr(getContext(), false);
auto lhs = operands[0].dyn_cast_or_null<IntegerAttr>();
auto rhs = operands[1].dyn_cast_or_null<IntegerAttr>();
if (lhs && rhs) {
return getI1IntegerAttr(getContext(), lhs.getValue().getSExtValue() !=
rhs.getValue().getSExtValue());
}
return nullptr;
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a != b; });
}
//===----------------------------------------------------------------------===//
// AtenEqIntOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenEqIntOp::fold(ArrayRef<Attribute> operands) {
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a == b; });
}
//===----------------------------------------------------------------------===//
// AtenLtIntOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenLtIntOp::fold(ArrayRef<Attribute> operands) {
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a < b; });
}
//===----------------------------------------------------------------------===//
// AtenLeIntOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenLeIntOp::fold(ArrayRef<Attribute> operands) {
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a <= b; });
}
//===----------------------------------------------------------------------===//
// AtenGtIntOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenGtIntOp::fold(ArrayRef<Attribute> operands) {
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a > b; });
}
//===----------------------------------------------------------------------===//
// AtenGeIntOp
//===----------------------------------------------------------------------===//
OpFoldResult AtenGeIntOp::fold(ArrayRef<Attribute> operands) {
return comparatorFoldHelper(*this,
[](int64_t a, int64_t b) { return a >= b; });
}
//===----------------------------------------------------------------------===//

63
lib/Dialect/Torch/Transforms/GlobalizeObjectGraph.cpp
View File

@ -18,6 +18,8 @@
#include "llvm/ADT/STLExtras.h"
#include "llvm/ADT/SetVector.h"
#include "llvm/ADT/StringExtras.h"
#include "llvm/ADT/StringMap.h"
#include "llvm/ADT/StringSet.h"
using namespace mlir;
using namespace mlir::NPCOMP;
@ -73,8 +75,10 @@ public:
ObjectGraphInfo(ModuleOp module)
: globalSlotBuilder(module.getBodyRegion()), symbolTable(module) {}
LogicalResult initialize(NnModuleOp root) {
return recursivelyTraverse(root);
LogicalResult initialize(NnModuleOp rootNnModule) {
if (failed(collectUsedSlots()))
return failure();
return recursivelyTraverse(rootNnModule);
}
LinkageInfo getSlotLinkageInfo(SlotOp op) {
@ -97,6 +101,51 @@ public:
}
private:
LogicalResult collectUsedSlots() {
// Collect all the slots in each module.
llvm::StringMap<llvm::StringMap<SlotOp>> moduleClassNameToSlots;
symbolTable.getOp()->walk([&](NnModuleOp moduleOp) {
llvm::StringMap<SlotOp> nameToSlot;
for (auto attrOp : moduleOp.getOps<SlotOp>())
nameToSlot[attrOp.name()] = attrOp;
moduleClassNameToSlots[moduleOp.getClassName()] = nameToSlot;
});
// Find all the module slots that are accessed through `PrimGetAttrOp` or
// `PrimSetAttrOp`.
symbolTable.getOp()->walk([&](Operation *op) {
if (!isa<PrimGetAttrOp, PrimSetAttrOp>(op))
return;
Value module;
StringRef slotName;
if (auto getAttrOp = llvm::dyn_cast<PrimGetAttrOp>(op)) {
module = getAttrOp.receiver();
slotName = getAttrOp.name();
} else {
auto setAttrOp = cast<PrimSetAttrOp>(op);
module = setAttrOp.receiver();
slotName = setAttrOp.name();
}
auto moduleType = module.getType().cast<NnModuleType>();
auto slots = moduleClassNameToSlots.find(moduleType.getClassName());
// TODO: Improve verifier so that this can never happen
if (slots == moduleClassNameToSlots.end())
op->emitError() << "Reference to non-existing module type "
<< moduleType.getClassName();
llvm::StringMap<SlotOp> nameToSlot = slots->getValue();
auto slotIt = nameToSlot.find(slotName);
// TODO: Improve verifier so that this can never happen
if (slotIt == nameToSlot.end())
op->emitError() << "Reference to non-existing module slot " << slotName
<< "in " << moduleType.getClassName();
usedSlots.insert(slotIt->getValue());
});
return success();
}
LogicalResult recursivelyTraverse(NnModuleOp nnModule) {
std::string pathToClassFromRoot = llvm::join(nameStack, ".");
if (!seenNnModules.insert({nnModule, pathToClassFromRoot}).second) {
@ -127,7 +176,7 @@ private:
assert(slotToGlobalSlot.find(slot) == slotToGlobalSlot.end());
slotToGlobalSlot[slot] = globalSlot;
slotLinkageInfo[slot] = LinkageInfo{linkageName, attr.isPrivate()};
if (failed(populateGlobalSlotInitializer(globalSlot, slot.value())))
if (failed(populateGlobalSlotInitializer(globalSlot, slot)))
return failure();
}
nameStack.pop_back();
@ -142,11 +191,12 @@ private:
return success();
}
LogicalResult populateGlobalSlotInitializer(GlobalSlotOp globalSlot,
Value initialValue) {
SlotOp slot) {
OpBuilder builder(globalSlot.getContext());
builder.createBlock(&globalSlot.getRegion());
SmallPtrSet<Operation *, 6> needToClone;
Value initialValue = slot.value();
SmallVector<Operation *> worklist = {initialValue.getDefiningOp()};
while (!worklist.empty()) {
Operation *op = worklist.pop_back_val();
@ -167,6 +217,8 @@ private:
for (Value result : op->getResults()) {
if (!hasMeaningfulObjectIdentity(result.getType()))
continue;
if (usedSlots.find(slot) == usedSlots.end())
continue;
if (!objectsWithIdentityAlreadyCopiedIntoInitializers.insert(result)
.second) {
return op->emitError() << "potentially-aliased value used to "
@ -205,6 +257,9 @@ private:
// which cannot be used in multiple initializers because their object
// identity is important.
DenseSet<Value> objectsWithIdentityAlreadyCopiedIntoInitializers;
// Used to keep track of all the used torch slots so that the restrictions can
// be applied to those slots only.
DenseSet<SlotOp> usedSlots;
};
} // namespace

21
test/Dialect/Torch/GlobalizeObjectGraph/basic.mlir
View File

@ -39,3 +39,24 @@ torch.nn_module {
torch.slot "f", %f : !torch.float
torch.slot "t", %t : !torch.tensor
} : !torch.nn.Module<"c">
// -----
// CHECK-LABEL: torch.global_slot @t1 : !torch.tensor {
// CHECK: %[[T:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
// CHECK: torch.global_slot.init %[[T]] : !torch.tensor
// CHECK-LABEL: torch.global_slot @t2 : !torch.tensor {
// CHECK: %[[T:.*]] = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
// CHECK: torch.global_slot.init %[[T]] : !torch.tensor
%t = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
torch.class_type @c {
torch.attr "t1" : !torch.tensor
torch.attr "t2" : !torch.tensor
}
torch.nn_module {
torch.slot "t1", %t : !torch.tensor
torch.slot "t2", %t : !torch.tensor
} : !torch.nn.Module<"c">

26
test/Dialect/Torch/GlobalizeObjectGraph/error.mlir
View File

@ -42,3 +42,29 @@ torch.nn_module {
torch.slot "t1", %t : !torch.tensor
torch.slot "t2", %t : !torch.tensor
} : !torch.nn.Module<"c">
builtin.func private @use_slot(%arg0 : !torch.nn.Module<"c">) -> !torch.tensor {
%t1 = torch.prim.GetAttr %arg0["t1"] : !torch.nn.Module<"c"> -> !torch.tensor
%t2 = torch.prim.GetAttr %arg0["t2"] : !torch.nn.Module<"c"> -> !torch.tensor
%cst = torch.constant.int 1
%ret = torch.aten.add.Tensor %t1, %t2, %cst : !torch.tensor, !torch.tensor, !torch.int -> !torch.tensor
return %ret : !torch.tensor
}
// -----
torch.class_type @c {
torch.attr "t1" : !torch.tensor
torch.attr "t2" : !torch.tensor
}
// expected-error @+1 {{potentially-aliased value used to initialize multiple slots}}
%t = torch.tensor.literal(dense<1.000000e+00> : tensor<1xf32>) : !torch.tensor
torch.nn_module {
torch.slot "t1", %t : !torch.tensor
torch.slot "t2", %t : !torch.tensor
} : !torch.nn.Module<"c">
builtin.func private @set_slot(%arg0 : !torch.nn.Module<"c">, %arg1 : !torch.tensor) {
torch.prim.SetAttr %arg0["t1"] = %arg1: !torch.nn.Module<"c">, !torch.tensor
torch.prim.SetAttr %arg0["t2"] = %arg1: !torch.nn.Module<"c">, !torch.tensor
return
}

197
test/Dialect/Torch/canonicalize.mlir
View File

@ -8,6 +8,30 @@ func @torch.aten.__is__(%arg0: !torch.list<!torch.int>, %arg1: !torch.none) -> !
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.__is__$none_is_none
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.__is__$none_is_none(%arg0: !torch.none, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__is__ %arg0, %arg1 : !torch.none, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.__isnot__
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.__isnot__(%arg0: !torch.list<!torch.int>, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__isnot__ %arg0, %arg1 : !torch.list<!torch.int>, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.__isnot__$none_isnot_none
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.__isnot__$none_isnot_none(%arg0: !torch.none, %arg1: !torch.none) -> !torch.bool {
%0 = torch.aten.__isnot__ %arg0, %arg1 : !torch.none, !torch.none -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.size$canonicalize_to_list(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<[2,3],f32>) -> !torch.list<!torch.int> {
// CHECK: %[[C2:.*]] = torch.constant.int 2
@ -30,6 +54,122 @@ func @torch.aten.size$unknown_size(%arg0: !torch.vtensor<[?,3],f32>) -> !torch.l
return %0 : !torch.list<!torch.int>
}
// CHECK-LABEL: func @torch.aten.ne.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK-NEXT: %[[FALSE:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[FALSE]] : !torch.bool
func @torch.aten.ne.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.ne.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.int$same_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.ne.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.ne.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.int$different_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.ne.int$different_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.ne.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.eq.int$different_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.eq.int$different_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.eq.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.eq.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK-NEXT: %[[F:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[F]] : !torch.bool
func @torch.aten.eq.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.eq.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.eq.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.eq.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.eq.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.lt.int$evaluate_to_true() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.lt.int$evaluate_to_true() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.lt.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.lt.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.lt.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.lt.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.lt.int$same_value() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.lt.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.lt.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.le.int$evaluate_to_true() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.le.int$evaluate_to_true() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.le.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.le.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.le.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.le.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.le.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.le.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.le.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.gt.int$evaluate_to_true() -> !torch.bool {
// CHECK-NEXT: %[[T:.*]] = torch.constant.bool true
// CHECK-NEXT: return %[[T]] : !torch.bool
@ -50,35 +190,44 @@ func @torch.aten.gt.int$evaluate_to_false() -> !torch.bool {
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK-NEXT: %[[F:.*]] = torch.constant.bool false
// CHECK-NEXT: return %[[F]] : !torch.bool
func @torch.aten.ne.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%0 = torch.aten.ne.int %arg0, %arg0 : !torch.int, !torch.int -> !torch.bool
return %0 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.int$same_value() -> !torch.bool {
// CHECK: %[[VAL_0:.*]] = torch.constant.bool false
// CHECK: return %[[VAL_0]] : !torch.bool
func @torch.aten.ne.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.ne.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ne.int$different_value() -> !torch.bool {
// CHECK: %[[VAL_0:.*]] = torch.constant.bool true
// CHECK: return %[[VAL_0]] : !torch.bool
func @torch.aten.ne.int$different_value() -> !torch.bool {
// CHECK-LABEL: func @torch.aten.ge.int$evaluate_to_false() -> !torch.bool {
// CHECK: %[[FALSE:.*]] = torch.constant.bool false
// CHECK: return %[[FALSE]] : !torch.bool
func @torch.aten.ge.int$evaluate_to_false() -> !torch.bool {
%int4 = torch.constant.int 4
%int5 = torch.constant.int 5
%2 = torch.aten.ne.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
%2 = torch.aten.ge.int %int4, %int5 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ge.int$same_operand(
// CHECK-SAME: %{{.*}}: !torch.int) -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.ge.int$same_operand(%arg0: !torch.int) -> !torch.bool {
%2 = torch.aten.ge.int %arg0, %arg0: !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.ge.int$same_value() -> !torch.bool {
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.ge.int$same_value() -> !torch.bool {
%int4 = torch.constant.int 4
%int4_0 = torch.constant.int 4
%2 = torch.aten.ge.int %int4, %int4_0 : !torch.int, !torch.int -> !torch.bool
return %2 : !torch.bool
}
// CHECK-LABEL: func @torch.aten.__not__
// CHECK: %[[TRUE:.*]] = torch.constant.bool true
// CHECK: return %[[TRUE]] : !torch.bool
func @torch.aten.__not__() -> !torch.bool {
%false = torch.constant.bool false
%ret = torch.aten.__not__ %false : !torch.bool -> !torch.bool
return %ret: !torch.bool
}
// CHECK-LABEL: func @torch.aten.len.t$of_size(
// CHECK-SAME: %[[ARG:.*]]: !torch.vtensor<*,f32>) -> !torch.int {
// CHECK: %[[DIM:.*]] = torch.aten.dim %[[ARG]] : !torch.vtensor<*,f32> -> !torch.int

3
tools/run_lit.sh
View File

@ -6,8 +6,7 @@ set -e
td="$(realpath $(dirname $0)/..)"
build_dir="$td/build"
install_mlir="$td/install-mlir"
build_mlir="$td/external/llvm-project/build"
build_mlir="$build_dir/llvm"
lit_exe="$build_mlir/bin/llvm-lit"
if ! [ -f "$lit_exe" ]; then