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BladeDISC patch 20230623
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* Rewrite mhlo with stablehlo after rebase.
* Fix BAZEL building error of multiple definition.
* Fix float width
* Fix divide_floor & export promoteTypes api (#9)
* To comply with the old pytorch versions
* Add native_dropout_backward & native_layer_norm_backward decomposition (#15)
* Add native_dropout and related ops pattern (llvm#1211)
* [MHLO] fix dot general contract
* Fix batch_norm, div.Tensor_mode and folder (#21)
* Reimplement linear lowering
* Reimplement 2-D rhs for mutmul
* Add torchdynamo
* Decompose torch.slice_scatter (llvm#1622)
* Fix i64 torch.tensor dtype
* Add more mhlo basic converters
* Alleviate softmax datatype check (#24)
* Fix decompose native_batch_norm (#27)
* Support group_norm lowering (#25)
* Decompose torch.ones/zeros (#28)
* Fix softmax output type
* Fix gather
* Fix some decompose patterns
* Not check assert at runtime (#31)
* Fix bool tensor attr conversion bug (#32)
* Fix mlirDenseElementsAttrBoolGet
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@JamesTheZ
JamesTheZ committed Jul 25, 2023
1 parent 0caaf8d commit 0ca0698
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Showing 26 changed files with 1,117 additions and 138 deletions.
3 changes: 2 additions & 1 deletion include/torch-mlir/Conversion/TorchToStablehlo/StablehloLegalizeUtils.h
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,8 @@ Value getSplatConstTensor(ConversionPatternRewriter &rewriter, Operation *op,
Value scalarToStablehloTensor(ConversionPatternRewriter &rewriter,
Operation *op, Value scalarValue, Type dtype);

Value promoteType(PatternRewriter &rewriter, Value input, TensorType outType);
Value promoteType(PatternRewriter &rewriter, Location loc, Value input,
TensorType outType);

Value promoteAndBroadcast(ConversionPatternRewriter &rewriter, Value input,
TensorType outType);
Expand Down
96 changes: 78 additions & 18 deletions include/torch-mlir/Dialect/Torch/IR/GeneratedTorchOps.td
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Original file line number Diff line number Diff line change
Expand Up @@ -4929,6 +4929,65 @@ def Torch_AtenNativeLayerNormOp : Torch_Op<"aten.native_layer_norm", [
}];
}

def Torch_AtenInstanceNormOp : Torch_Op<"aten.instance_norm", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::instance_norm : (Tensor, Tensor?, Tensor?, Tensor?, Tensor?, bool, float, float, bool) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$input,
AnyTorchOptionalTensorType:$weight,
AnyTorchOptionalTensorType:$bias,
AnyTorchOptionalTensorType:$running_mean,
AnyTorchOptionalTensorType:$running_var,
Torch_BoolType:$use_input_stats,
Torch_FloatType:$momentum,
Torch_FloatType:$eps,
Torch_BoolType:$cudnn_enabled
);
let results = (outs
AnyTorchTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenInstanceNormOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 9, 1);
}
void AtenInstanceNormOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 9, 1);
}
}];
}

def Torch_AtenGroupNormOp : Torch_Op<"aten.group_norm", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::group_norm : (Tensor, int, Tensor?, Tensor?, float, bool) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$input,
Torch_IntType:$num_groups,
AnyTorchOptionalTensorType:$weight,
AnyTorchOptionalTensorType:$bias,
Torch_FloatType:$eps,
Torch_BoolType:$cudnn_enabled
);
let results = (outs
AnyTorchTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenGroupNormOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 6, 1);
}
void AtenGroupNormOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 6, 1);
}
}];
}

def Torch_AtenMaxPool2dOp : Torch_Op<"aten.max_pool2d", [
AllowsTypeRefinement,
HasValueSemantics,
Expand Down Expand Up @@ -7233,9 +7292,10 @@ def Torch_AtenOnesLikeOp : Torch_Op<"aten.ones_like", [
}

def Torch_AtenEmptyMemoryFormatOp : Torch_Op<"aten.empty.memory_format", [
Pure,
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
ReadOnly,
]> {
let summary = "Generated op for `aten::empty.memory_format : (int[], int?, int?, Device?, bool?, int?) -> (Tensor)`";
let arguments = (ins
Expand Down Expand Up @@ -7742,53 +7802,53 @@ def Torch_AtenMaxOp : Torch_Op<"aten.max", [
}];
}

def Torch_AtenMaxDimOp : Torch_Op<"aten.max.dim", [
def Torch_AtenAmaxOp : Torch_Op<"aten.amax", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::max.dim : (Tensor, int, bool) -> (Tensor, Tensor)`";
let summary = "Generated op for `aten::amax : (Tensor, int[]?, bool) -> Tensor`";
let arguments = (ins
AnyTorchTensorType:$self,
Torch_IntType:$dim,
AnyTorchOptionalListOfTorchIntType:$dim,
Torch_BoolType:$keepdim
);
let results = (outs
AnyTorchTensorType:$values,
AnyTorchTensorType:$indices
AnyTorchTensorType:$results
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenMaxDimOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 2);
ParseResult AtenAmaxOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 1);
}
void AtenMaxDimOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 2);
void AtenAmaxOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 1);
}
}];
}

def Torch_AtenAmaxOp : Torch_Op<"aten.amax", [
def Torch_AtenMaxDimOp : Torch_Op<"aten.max.dim", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::amax : (Tensor, int[], bool) -> (Tensor)`";
let summary = "Generated op for `aten::max.dim : (Tensor, int, bool) -> (Tensor, Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
AnyTorchListOfTorchIntType:$dim,
Torch_IntType:$dim,
Torch_BoolType:$keepdim
);
let results = (outs
AnyTorchTensorType:$result
AnyTorchTensorType:$values,
AnyTorchTensorType:$indices
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenAmaxOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 1);
ParseResult AtenMaxDimOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 3, 2);
}
void AtenAmaxOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 1);
void AtenMaxDimOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 3, 2);
}
}];
}
Expand Down
1 change: 1 addition & 0 deletions include/torch-mlir/Dialect/Torch/Utils/TorchUpstream.h
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Original file line number Diff line number Diff line change
Expand Up @@ -170,6 +170,7 @@ enum ReductionType { MAX, MEAN, MIN, SUM, PROD };

ReductionType get_reduction_enum(const llvm::StringRef &reduce);

ScalarType promoteTypes(ScalarType a, ScalarType b);
} // namespace torch_upstream
} // namespace torch
} // namespace mlir
Expand Down
4 changes: 4 additions & 0 deletions include/torch-mlir/Dialect/TorchConversion/IR/TorchConversionOps.td
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Original file line number Diff line number Diff line change
Expand Up @@ -42,6 +42,7 @@ def TorchConversion_ToBuiltinTensorOp : TorchConversion_Op<"to_builtin_tensor",
let assemblyFormat = [{
$operand attr-dict `:` qualified(type($operand)) `->` qualified(type($result))
}];
let hasCanonicalizer = 1;
let hasVerifier = 1;
}

Expand All @@ -61,6 +62,7 @@ def TorchConversion_FromBuiltinTensorOp : TorchConversion_Op<"from_builtin_tenso
let assemblyFormat = [{
$operand attr-dict `:` qualified(type($operand)) `->` qualified(type($result))
}];
let hasCanonicalizer = 1;
let hasVerifier = 1;
}

Expand All @@ -80,6 +82,7 @@ def TorchConversion_ToI1Op : TorchConversion_Op<"to_i1", [
let assemblyFormat = [{
$operand attr-dict
}];
let hasFolder = 1;
}

def TorchConversion_FromI1Op : TorchConversion_Op<"from_i1", [
Expand All @@ -98,6 +101,7 @@ def TorchConversion_FromI1Op : TorchConversion_Op<"from_i1", [
let assemblyFormat = [{
$operand attr-dict
}];
let hasFolder = 1;
}

def TorchConversion_ToI64Op : TorchConversion_Op<"to_i64", [
Expand Down
9 changes: 8 additions & 1 deletion lib/Conversion/TorchToArith/TorchToArith.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -429,13 +429,20 @@ class ConvertTorchToArith : public ConvertTorchToArithBase<ConvertTorchToArith>
target.addIllegalOp<AtenAddOp>();
patterns.add<ConvertAtenAddOp>(typeConverter, context);

target.addIllegalOp<AtenAddIntOp, AtenSubIntOp, AtenMulIntOp>();
// target.addIllegalOp<AtenAddIntOp, AtenSubIntOp, AtenMulIntOp>();
// patterns.add<ConvertTorchConstantOp<Torch::ConstantIntOp>>(typeConverter,
// context);
target.addIllegalOp<AtenAddIntOp, AtenSubIntOp, AtenMulIntOp,
AtenRemainderIntOp>();
patterns.add<ConvertAtenBinaryOp<AtenAddIntOp, arith::AddIOp>>(
typeConverter, context);
patterns.add<ConvertAtenBinaryOp<AtenSubIntOp, arith::SubIOp>>(
typeConverter, context);
patterns.add<ConvertAtenBinaryOp<AtenMulIntOp, arith::MulIOp>>(
typeConverter, context);
patterns.add<ConvertAtenBinaryOp<AtenRemainderIntOp, arith::RemSIOp>>(
typeConverter, context);

target.addIllegalOp<AtenSubFloatOp>();
patterns.add<ConvertAtenBinaryOp<AtenSubFloatOp, arith::SubFOp>>(
typeConverter, context);
Expand Down
48 changes: 24 additions & 24 deletions lib/Conversion/TorchToStablehlo/Basic.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -32,6 +32,7 @@ using namespace mlir;
using namespace mlir::torch;
using namespace mlir::torch::Torch;
using namespace mlir::torch::torch_to_stablehlo;
using namespace mlir::torch::TorchConversion;

LogicalResult broadcastRanks(PatternRewriter &rewriter, Operation *op,
mlir::Value &self, mlir::Value &other,
Expand Down Expand Up @@ -148,7 +149,7 @@ class ConvertAtenUnaryOp : public OpConversionPattern<AtenOpT> {
auto outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<TensorType>();
self = hlo::promoteType(rewriter, self, outType);
self = hlo::promoteType(rewriter, op.getLoc(), self, outType);
rewriter.replaceOpWithNewOp<StablehloOpT>(op, outType, self);
return success();
}
Expand Down Expand Up @@ -253,8 +254,8 @@ class ConvertAtenBinaryBroadcastOp : public OpConversionPattern<AtenOpT> {
->convertType(op.getType())
.template cast<TensorType>();

lhs = hlo::promoteType(rewriter, lhs, outTy);
rhs = hlo::promoteType(rewriter, rhs, outTy);
lhs = hlo::promoteType(rewriter, op.getLoc(), lhs, outTy);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, outTy);

rewriter.replaceOpWithNewOp<ChloOpT>(op, outTy, lhs, rhs,
/*broadcast_attr*/ nullptr);
Expand Down Expand Up @@ -300,8 +301,8 @@ class ConvertAtenAddSubOp : public OpConversionPattern<AtenOpT> {
}
}

lhs = hlo::promoteType(rewriter, lhs, outType);
rhs = hlo::promoteType(rewriter, rhs, outType);
lhs = hlo::promoteType(rewriter, op.getLoc(), lhs, outType);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, outType);

if (!skipMultiplyAlpha(op.getAlpha())) {
Value alpha = hlo::scalarToStablehloTensor(rewriter, op,
Expand Down Expand Up @@ -354,8 +355,8 @@ class ConvertAtenMulDivOp : public OpConversionPattern<AtenOpT> {
outElemTy);
}
DenseIntElementsAttr bcastDimensions;
lhs = hlo::promoteType(rewriter, lhs, outType);
rhs = hlo::promoteType(rewriter, rhs, outType);
lhs = hlo::promoteType(rewriter, op.getLoc(), lhs, outType);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, outType);
auto loc = op.getLoc();
Value result =
rewriter.create<ChloOpT>(loc, outType, lhs, rhs, bcastDimensions);
Expand Down Expand Up @@ -427,7 +428,7 @@ class ConvertAtenCompareOp : public OpConversionPattern<AtenOpT> {
}

// TODO: what is the PyTorch default type promotion?
rhs = hlo::promoteType(rewriter, rhs, lhsTy);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, lhsTy);

chlo::ComparisonTypeAttr compareTypeAttr;
chlo::ComparisonDirectionAttr compareDirectionAttr;
Expand Down Expand Up @@ -494,8 +495,10 @@ class ConvertAtenLogicalBinaryOp : public OpConversionPattern<AtenOpT> {
TensorType outType = OpConversionPattern<AtenOpT>::getTypeConverter()
->convertType(op.getType())
.template cast<TensorType>();
Value lhs = hlo::promoteType(rewriter, adaptor.getSelf(), outType);
Value rhs = hlo::promoteType(rewriter, adaptor.getOther(), outType);
Value lhs =
hlo::promoteType(rewriter, op.getLoc(), adaptor.getSelf(), outType);
Value rhs =
hlo::promoteType(rewriter, op.getLoc(), adaptor.getOther(), outType);

DenseIntElementsAttr bcastDimensions;
rewriter.replaceOpWithNewOp<ChloOpT>(op, outType, lhs, rhs,
Expand Down Expand Up @@ -610,8 +613,8 @@ LogicalResult ConvertAtenOp<AtenWhereSelfOp>::matchAndRewrite(
auto outType =
getTypeConverter()->convertType(op.getType()).cast<RankedTensorType>();
// promote self and other types
self = hlo::promoteType(rewriter, self, outType);
other = hlo::promoteType(rewriter, other, outType);
self = hlo::promoteType(rewriter, op.getLoc(), self, outType);
other = hlo::promoteType(rewriter, op.getLoc(), other, outType);

if (failed(
broadcastRanks(rewriter, op, self, cond, options.dimSizeIndexBits)))
Expand Down Expand Up @@ -807,8 +810,8 @@ LogicalResult ConvertAtenOp<AtenPowTensorScalarOp>::matchAndRewrite(
rhs = hlo::scalarToStablehloTensor(rewriter, op, rhs, outElemTy);
}
DenseIntElementsAttr bcastDimensions;
lhs = hlo::promoteType(rewriter, lhs, outType);
rhs = hlo::promoteType(rewriter, rhs, outType);
lhs = hlo::promoteType(rewriter, op.getLoc(), lhs, outType);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, outType);
auto loc = op.getLoc();
Value result = rewriter.create<chlo::BroadcastPowOp>(loc, outType, lhs, rhs,
bcastDimensions);
Expand Down Expand Up @@ -1212,7 +1215,7 @@ LogicalResult ConvertAtenOp<AtenCatOp>::matchAndRewrite(

// Promote type
for (auto &v : builtinTensors) {
v = hlo::promoteType(rewriter, v, outType);
v = hlo::promoteType(rewriter, op.getLoc(), v, outType);
}

rewriter.replaceOpWithNewOp<stablehlo::ConcatenateOp>(
Expand Down Expand Up @@ -1404,8 +1407,8 @@ LogicalResult ConvertAtenOp<AtenPowTensorTensorOp>::matchAndRewrite(
auto outTy =
this->getTypeConverter()->convertType(op.getType()).cast<TensorType>();

lhs = hlo::promoteType(rewriter, lhs, outTy);
rhs = hlo::promoteType(rewriter, rhs, outTy);
lhs = hlo::promoteType(rewriter, op.getLoc(), lhs, outTy);
rhs = hlo::promoteType(rewriter, op.getLoc(), rhs, outTy);

rewriter.replaceOpWithNewOp<chlo::BroadcastPowOp>(op, outTy, lhs, rhs,
/*broadcast_attr*/ nullptr);
Expand Down Expand Up @@ -1547,12 +1550,10 @@ class ConvertRuntimeAssertOp : public OpConversionPattern<RuntimeAssertOp> {
matchAndRewrite(RuntimeAssertOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
bool condition;
if (!matchPattern(op.getCondition(), m_TorchConstantBool(&condition))) {
return rewriter.notifyMatchFailure(
op, "unimplemented: condition must be a constant");
}
if (!condition) {
return op->emitError("condition must be true");
if (matchPattern(op.getCondition(), m_TorchConstantBool(&condition))) {
if (!condition) {
return op->emitError("condition must be true");
}
}
rewriter.eraseOp(op);
return success();
Expand Down Expand Up @@ -1679,7 +1680,6 @@ void mlir::torch::torch_to_stablehlo::populateBasicOpPatternsAndLegality(
INSERT_ATENOP_PATTERN(AtenReciprocalOp);
INSERT_ATENOP_PATTERN(AtenPowTensorScalarOp);
INSERT_ATENOP_PATTERN(PrimNumToTensorScalarOp);
INSERT_ATENOP_PATTERN(AtenContiguousOp);

INSERT_ATENOP_PATTERN(AtenReluOp);
INSERT_ATENOP_PATTERN(AtenGeluOp);
Expand Down
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