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[Stablehlo] Improve the lowering of pool op in stablehlo #3259

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merged 4 commits into from
Apr 30, 2024
Merged

[Stablehlo] Improve the lowering of pool op in stablehlo #3259

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4bd68a2
add maxpool1d define
Apr 29, 2024
0049308
fix pool
Apr 29, 2024
4ca986a
fix
Apr 29, 2024
000815a
use pool1d for both max_pool and avg_pool
Apr 30, 2024
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28 changes: 28 additions & 0 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 @@ -6637,6 +6637,34 @@ def Torch_AtenNativeLayerNormOp : Torch_Op<"aten.native_layer_norm", [
}];
}

def Torch_AtenMaxPool1dOp : Torch_Op<"aten.max_pool1d", [
AllowsTypeRefinement,
HasValueSemantics,
ReadOnly
]> {
let summary = "Generated op for `aten::max_pool1d : (Tensor, int[], int[], int[], int[], bool) -> (Tensor)`";
let arguments = (ins
AnyTorchTensorType:$self,
AnyTorchListOfTorchIntType:$kernel_size,
AnyTorchListOfTorchIntType:$stride,
AnyTorchListOfTorchIntType:$padding,
AnyTorchListOfTorchIntType:$dilation,
Torch_BoolType:$ceil_mode
);
let results = (outs
AnyTorchOptionalTensorType:$result
);
let hasCustomAssemblyFormat = 1;
let extraClassDefinition = [{
ParseResult AtenMaxPool1dOp::parse(OpAsmParser &parser, OperationState &result) {
return parseDefaultTorchOp(parser, result, 6, 1);
}
void AtenMaxPool1dOp::print(OpAsmPrinter &printer) {
printDefaultTorchOp(printer, *this, 6, 1);
}
}];
}

def Torch_AtenMaxPool2dOp : Torch_Op<"aten.max_pool2d", [
AllowsTypeRefinement,
HasValueSemantics,
Expand Down
279 changes: 167 additions & 112 deletions lib/Conversion/TorchToStablehlo/Pooling.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ static Value createInitialValueForAtenPoolingOp(Operation *op, Type elementTy,
auto constType = RankedTensorType::get({}, elementTy);
// Avg pooling
if (isa<AtenAvgPool1dOp, AtenAdaptiveAvgPool2dOp, AtenAvgPool2dOp,
AtenCumsumOp>(op)) {
AtenAvgPool3dOp, AtenCumsumOp>(op)) {
if (isa<mlir::FloatType>(elementTy)) {
auto constAttr = DenseElementsAttr::get(
constType, {APFloat::getZero(
Expand All @@ -54,7 +54,8 @@ static Value createInitialValueForAtenPoolingOp(Operation *op, Type elementTy,
}

// Max pooling
if (isa<AtenMaxPool2dOp, AtenMaxPool2dWithIndicesOp>(op)) {
if (isa<AtenMaxPool1dOp, AtenMaxPool2dOp, AtenMaxPool3dOp,
AtenMaxPool2dWithIndicesOp>(op)) {
if (isa<mlir::FloatType>(elementTy)) {
auto constAttr = DenseElementsAttr::get(
constType,
Expand All @@ -75,101 +76,6 @@ static Value createInitialValueForAtenPoolingOp(Operation *op, Type elementTy,
return nullptr;
}

// AtenMaxPool2dOp
template <>
LogicalResult ConvertAtenOp<AtenMaxPool2dOp>::matchAndRewrite(
AtenMaxPool2dOp op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const {
Value input = adaptor.getSelf();
auto inputTy = cast<RankedTensorType>(input.getType());
auto inputElemTy = inputTy.getElementType();

auto inputRank = inputTy.getRank();
auto outTy =
cast<RankedTensorType>(getTypeConverter()->convertType(op.getType()));

if (inputRank <= 2) {
return op.emitError(
"max_pooling2d only supports inputs with rank higher than 2");
}
SmallVector<int64_t, 2> padding, kernelSize, stride, dilation;
bool ceilMode = false;

if (!(matchPattern(op.getKernelSize(),
m_TorchListOfConstantInts(kernelSize)))) {
return rewriter.notifyMatchFailure(
op, "non-const int kernel size unsupported!");
}
if (!(matchPattern(op.getStride(), m_TorchListOfConstantInts(stride)))) {
return rewriter.notifyMatchFailure(op, "non-const int stride unsupported!");
}
if (!(matchPattern(op.getPadding(), m_TorchListOfConstantInts(padding)))) {
return rewriter.notifyMatchFailure(op,
"non-const int padding unsupported!");
}
if (!(matchPattern(op.getDilation(), m_TorchListOfConstantInts(dilation)))) {
return rewriter.notifyMatchFailure(op,
"non-const int dilation unsupported!");
}
if (!(matchPattern(op.getCeilMode(), m_TorchConstantBool(&ceilMode)))) {
return rewriter.notifyMatchFailure(op,
"non-const bool ceil_mode unsupported!");
}

// prepend 1 to kernelSize, stride, dilation until they are of same rank as
// input
SmallVector<int64_t> stablehloStride(inputRank, 1);
SmallVector<int64_t> stablehloDilation(inputRank, 1);
SmallVector<int64_t> stablehloKernelSize(inputRank, 1);
SmallVector<int64_t> stablehloPadding(inputRank * 2, 0);
std::copy(dilation.begin(), dilation.end(),
stablehloDilation.begin() + inputRank - 2);
std::copy(stride.begin(), stride.end(),
stablehloStride.begin() + inputRank - 2);
std::copy(kernelSize.begin(), kernelSize.end(),
stablehloKernelSize.begin() + inputRank - 2);

Value initVal = createInitialValueForAtenPoolingOp(op, inputElemTy, rewriter);

stablehloPadding[stablehloPadding.size() - 4] = padding[0];
stablehloPadding[stablehloPadding.size() - 3] = padding[0];
stablehloPadding[stablehloPadding.size() - 2] = padding[1];
stablehloPadding[stablehloPadding.size() - 1] = padding[1];

auto windowDimensions = rewriter.getDenseI64ArrayAttr(stablehloKernelSize);
auto windowStrides = rewriter.getDenseI64ArrayAttr(stablehloStride);
DenseI64ArrayAttr baseDilations;
auto windowDilations = rewriter.getDenseI64ArrayAttr(stablehloDilation);
DenseIntElementsAttr pad = DenseIntElementsAttr::get(
RankedTensorType::get(
{static_cast<int64_t>(inputRank), static_cast<int64_t>(2)},
rewriter.getI64Type()),
stablehloPadding);
auto reduceWindowOp = rewriter.create<stablehlo::ReduceWindowOp>(
op->getLoc(), outTy, input, initVal, windowDimensions, windowStrides,
baseDilations, windowDilations, pad);

Block &block = reduceWindowOp.getBody().emplaceBlock();

auto blockArgumentTy = RankedTensorType::get({}, inputElemTy);
block.addArgument(blockArgumentTy, op->getLoc());
block.addArgument(blockArgumentTy, op->getLoc());

auto *firstArg = block.args_begin();
auto secondArg = block.args_rbegin();

{
OpBuilder::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(&block);
Value result =
rewriter.create<stablehlo::MaxOp>(op->getLoc(), *firstArg, *secondArg);
rewriter.create<stablehlo::ReturnOp>(op->getLoc(), result);
}

rewriter.replaceOp(op, reduceWindowOp.getResults());
return success();
}

// AtenMaxPool2dWithIndicesOp
template <>
LogicalResult ConvertAtenOp<AtenMaxPool2dWithIndicesOp>::matchAndRewrite(
Expand Down Expand Up @@ -356,6 +262,129 @@ LogicalResult ConvertAtenOp<AtenMaxPool2dWithIndicesOp>::matchAndRewrite(
return success();
}

namespace {
template <typename AtenOpT, int Dim>
class ConvertAtenMaxPoolOp : public ConvertAtenOp<AtenOpT> {
public:
using ConvertAtenOp<AtenOpT>::ConvertAtenOp;
using OpAdaptor = typename AtenOpT::Adaptor;
LogicalResult
matchAndRewrite(AtenOpT op, OpAdaptor adaptor,
ConversionPatternRewriter &rewriter) const override {
Value input = adaptor.getSelf();
auto inputTy = cast<RankedTensorType>(input.getType());
auto inputElemTy = inputTy.getElementType();
auto inputRank = inputTy.getRank();
auto outTy = cast<RankedTensorType>(
ConvertAtenOp<AtenOpT>::getTypeConverter()->convertType(op.getType()));

if (inputRank <= Dim) {
return op.emitError(
"max_pooling1d/2d only supports inputs with rank higher than 1/2");
}
SmallVector<int64_t, Dim> padding, kernelSize, stride, dilation;
bool ceilMode = false;

if (!(matchPattern(op.getKernelSize(),
m_TorchListOfConstantInts(kernelSize)))) {
return rewriter.notifyMatchFailure(
op, "non-const int kernel size unsupported!");
}
if (!(matchPattern(op.getStride(), m_TorchListOfConstantInts(stride)))) {
return rewriter.notifyMatchFailure(op,
"non-const int stride unsupported!");
}
if (!(matchPattern(op.getPadding(), m_TorchListOfConstantInts(padding)))) {
return rewriter.notifyMatchFailure(op,
"non-const int padding unsupported!");
}
if (!(matchPattern(op.getDilation(),
m_TorchListOfConstantInts(dilation)))) {
return rewriter.notifyMatchFailure(op,
"non-const int dilation unsupported!");
}
if (!(matchPattern(op.getCeilMode(), m_TorchConstantBool(&ceilMode)))) {
return rewriter.notifyMatchFailure(
op, "non-const bool ceil_mode unsupported!");
}

if (stride.empty()) {
stride = kernelSize;
}

// prepend 1 to kernelSize, stride, dilation until they are of same rank
// as input
SmallVector<int64_t> stablehloStride(inputRank, 1);
SmallVector<int64_t> stablehloDilation(inputRank, 1);
SmallVector<int64_t> stablehloKernelSize(inputRank, 1);
SmallVector<int64_t> stablehloPadding(inputRank * 2, 0);
std::copy(dilation.begin(), dilation.end(),
stablehloDilation.begin() + inputRank - Dim);
std::copy(stride.begin(), stride.end(),
stablehloStride.begin() + inputRank - Dim);
std::copy(kernelSize.begin(), kernelSize.end(),
stablehloKernelSize.begin() + inputRank - Dim);

Value initVal =
createInitialValueForAtenPoolingOp(op, inputElemTy, rewriter);

if (Dim == 1) {
stablehloPadding[stablehloPadding.size() - 2] = padding[0];
stablehloPadding[stablehloPadding.size() - 1] = padding[0];
} else if (Dim == 2) {
stablehloPadding[stablehloPadding.size() - 4] = padding[0];
stablehloPadding[stablehloPadding.size() - 3] = padding[0];
stablehloPadding[stablehloPadding.size() - 2] = padding[1];
stablehloPadding[stablehloPadding.size() - 1] = padding[1];
} else if (Dim == 3) {
stablehloPadding[stablehloPadding.size() - 6] = padding[0];
stablehloPadding[stablehloPadding.size() - 5] = padding[0];
stablehloPadding[stablehloPadding.size() - 4] = padding[1];
stablehloPadding[stablehloPadding.size() - 3] = padding[1];
stablehloPadding[stablehloPadding.size() - 2] = padding[2];
stablehloPadding[stablehloPadding.size() - 1] = padding[2];
} else {
assert(false && "Unsupported pooling dimension");
}
auto windowDimensions = rewriter.getDenseI64ArrayAttr(stablehloKernelSize);
auto windowStrides = rewriter.getDenseI64ArrayAttr(stablehloStride);
DenseI64ArrayAttr baseDilations;
auto windowDilations = rewriter.getDenseI64ArrayAttr(stablehloDilation);

DenseIntElementsAttr pad = DenseIntElementsAttr::get(
RankedTensorType::get(
{static_cast<int64_t>(inputRank), static_cast<int64_t>(2)},
rewriter.getI64Type()),
stablehloPadding);

auto reduceWindowOp = rewriter.create<stablehlo::ReduceWindowOp>(
op->getLoc(), outTy, input, initVal, windowDimensions, windowStrides,
baseDilations, windowDilations, pad);

Block &block = reduceWindowOp.getBody().emplaceBlock();

// Add bb argument
auto blockArgumentType = RankedTensorType::get({}, inputElemTy);
block.addArgument(blockArgumentType, op->getLoc());
block.addArgument(blockArgumentType, op->getLoc());
auto *firstArg = block.args_begin();
auto secondArg = block.args_rbegin();

{
OpBuilder::InsertionGuard guard(rewriter);
rewriter.setInsertionPointToStart(&block);

Value result = rewriter.create<stablehlo::MaxOp>(op->getLoc(), *firstArg,
*secondArg);
rewriter.create<stablehlo::ReturnOp>(op->getLoc(), result);
}

rewriter.replaceOp(op, reduceWindowOp.getResults());
return success();
}
};
} // namespace

namespace {
template <typename AtenOpT, int Dim>
class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
Expand All @@ -375,8 +404,8 @@ class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
auto outShape = outTy.getShape();

if (inputRank <= Dim) {
return op.emitError(
"avg_pooling1d/2d only supports inputs with rank higher than 1/2");
return op.emitError("avg_pooling1d/2d/3d only supports inputs with rank "
"higher than 1/2/3");
}
SmallVector<int64_t, Dim> padding, kernelSize, stride;
bool ceilMode = false;
Expand Down Expand Up @@ -405,6 +434,10 @@ class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
op, "non-const bool count_include_pad unsupported!");
}

if (stride.empty()) {
stride = kernelSize;
}

if constexpr (std::is_same<AtenOpT, AtenAvgPool2dOp>()) {
if (succeeded(checkNotNone(rewriter, op, op.getDivisorOverride())))
return rewriter.notifyMatchFailure(
Expand All @@ -425,11 +458,20 @@ class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
if (Dim == 1) {
stablehloPadding[stablehloPadding.size() - 2] = padding[0];
stablehloPadding[stablehloPadding.size() - 1] = padding[0];
} else {
} else if (Dim == 2) {
stablehloPadding[stablehloPadding.size() - 4] = padding[0];
stablehloPadding[stablehloPadding.size() - 3] = padding[0];
stablehloPadding[stablehloPadding.size() - 2] = padding[1];
stablehloPadding[stablehloPadding.size() - 1] = padding[1];
} else if (Dim == 3) {
stablehloPadding[stablehloPadding.size() - 6] = padding[0];
stablehloPadding[stablehloPadding.size() - 5] = padding[0];
stablehloPadding[stablehloPadding.size() - 4] = padding[1];
stablehloPadding[stablehloPadding.size() - 3] = padding[1];
stablehloPadding[stablehloPadding.size() - 2] = padding[2];
stablehloPadding[stablehloPadding.size() - 1] = padding[2];
} else {
assert(false && "Unsupported pooling dimension");
}

Value initVal =
Expand Down Expand Up @@ -474,10 +516,17 @@ class ConvertAtenAvgPoolOp : public ConvertAtenOp<AtenOpT> {
divisor =
hlo::getConstTensor<int64_t>(rewriter, op, {kernelSize[0]}, {})
.value();
} else {
} else if (Dim == 2) {
divisor = hlo::getConstTensor<int64_t>(
rewriter, op, {kernelSize[0] * kernelSize[1]}, {})
.value();
} else if (Dim == 3) {
divisor = hlo::getConstTensor<int64_t>(
rewriter, op,
{kernelSize[0] * kernelSize[1] * kernelSize[2]}, {})
.value();
} else {
assert(false && "Unsupported pooling dimension");
}
divisor = hlo::promoteType(rewriter, op.getLoc(), divisor, outTy);
DenseI64ArrayAttr bcastDimensions;
Expand Down Expand Up @@ -611,22 +660,28 @@ void mlir::torch::torch_to_stablehlo::populatePoolingOpPatternsAndLegality(
TypeConverter &typeConverter, RewritePatternSet &patterns,
ConversionTarget &target, const TorchToStablehloOptions &options) {
MLIRContext *context = patterns.getContext();
target.addIllegalOp<AtenAvgPool1dOp>();
patterns.add<ConvertAtenOp<AtenAvgPool1dOp>>(typeConverter, context, options);
target.addIllegalOp<AtenMaxPool2dOp>();
patterns.add<ConvertAtenOp<AtenMaxPool2dOp>>(typeConverter, context, options);
target.addIllegalOp<AtenAvgPool2dOp>();
patterns.add<ConvertAtenOp<AtenAvgPool2dOp>>(typeConverter, context, options);
target.addIllegalOp<AtenMaxPool2dWithIndicesOp>();
patterns.add<ConvertAtenOp<AtenMaxPool2dWithIndicesOp>>(typeConverter,
context, options);
target.addIllegalOp<AtenCumsumOp>();
patterns.add<ConvertAtenOp<AtenCumsumOp>>(typeConverter, context, options);
#define INSERT_ATEN_POOLING_PATTERN(AtenOp) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenOp<AtenOp>>(typeConverter, context, options)
INSERT_ATEN_POOLING_PATTERN(AtenMaxPool2dWithIndicesOp);
INSERT_ATEN_POOLING_PATTERN(AtenCumsumOp);
#undef INSERT_ATEN_POOLING_PATTERN

#define INSERT_ATEN_MAXPOOL_PATTERN(AtenOp, Dim) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenMaxPoolOp<AtenOp, Dim>>(typeConverter, context, \
options)
INSERT_ATEN_MAXPOOL_PATTERN(AtenMaxPool1dOp, 1);
INSERT_ATEN_MAXPOOL_PATTERN(AtenMaxPool2dOp, 2);
INSERT_ATEN_MAXPOOL_PATTERN(AtenMaxPool3dOp, 3);
#undef INSERT_ATEN_MAXPOOL_PATTERN

#define INSERT_ATEN_AVGPOOL_PATTERN(AtenOp, Dim) \
target.addIllegalOp<AtenOp>(); \
patterns.add<ConvertAtenAvgPoolOp<AtenOp, Dim>>(typeConverter, context, \
options)
INSERT_ATEN_AVGPOOL_PATTERN(AtenAvgPool1dOp, 1);
INSERT_ATEN_AVGPOOL_PATTERN(AtenAvgPool2dOp, 2);
INSERT_ATEN_AVGPOOL_PATTERN(AtenAvgPool3dOp, 3);
#undef INSERT_ATEN_AVGPOOL_PATTERN
}
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