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support dilation and feature/batch group count in convolution reverse #181

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merged 3 commits into from
Dec 14, 2024
Merged

support dilation and feature/batch group count in convolution reverse #181

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e82b891
support dilation and feature group count in convolution reverse
Pangoraw Dec 6, 2024
eda95f1
support batch group count
Pangoraw Dec 9, 2024
e8653c9
fix dimensions for post conv transpose (batch gruop count)
Pangoraw Dec 12, 2024
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286 changes: 250 additions & 36 deletions src/enzyme_ad/jax/Implementations/HLODerivatives.td
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Original file line number Diff line number Diff line change
Expand Up @@ -391,6 +391,145 @@ def ConvBatchGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{

// GradData

def GradDataFilterReshape1 : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto featureGroupCount = op.getFeatureGroupCount();
auto batchGroupCount = op.getBatchGroupCount();
assert(featureGroupCount == 1 || batchGroupCount == 1);
auto groupCount = featureGroupCount == 1 ? batchGroupCount : featureGroupCount;

auto rhs = op.getRhs();
auto dimensionNumbers = op.getDimensionNumbers();
auto Ty = cast<RankedTensorType>(rhs.getType());
auto shape = Ty.getShape();

auto odim = dimensionNumbers.getKernelOutputFeatureDimension();

SmallVector<int64_t> newShape;
for (int64_t i = 0, e = shape.size(); i < e; ++i) {
if (i == odim) {
newShape.push_back(groupCount);
newShape.push_back(shape[i] / groupCount);
} else {
newShape.push_back(shape[i]);
}
}

RankedTensorType::get(newShape, Ty.getElementType());
}]>;

def GradDataFilterTranspose : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
SmallVector<int64_t> transposes;
auto dimensionNumbers = op.getDimensionNumbers();
auto idim = dimensionNumbers.getKernelInputFeatureDimension();
auto odim = dimensionNumbers.getKernelOutputFeatureDimension();

if (odim < idim)
idim++;

int64_t i = 0, N = op.getType().getShape().size();
while (i <= N) {
if (i == idim) {
transposes.push_back(odim);
transposes.push_back(idim);
} else if (i != odim) {
transposes.push_back(i);
}
i++;
}

getI64Attr(builder, transposes);
}]>;

def GradDataConvOutputType : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto Ty = op.getLhs().getType();
auto batchGroupCount = op.getBatchGroupCount();

if (batchGroupCount > 1) {
SmallVector<int64_t> shape(Ty.getShape().begin(), Ty.getShape().end());
auto dimensionNumbers = op.getDimensionNumbers();
shape[dimensionNumbers.getInputFeatureDimension()] *= batchGroupCount;
shape[dimensionNumbers.getInputBatchDimension()] /= batchGroupCount;
Ty = Ty.clone(shape);
}

Ty;
}]>;

def GradDataConvBatchGroupCountType : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto Ty = op.getLhs().getType();
auto batchGroupCount = op.getBatchGroupCount();

auto dimensionNumbers = op.getDimensionNumbers();
auto fdim = dimensionNumbers.getInputFeatureDimension();
auto bdim = dimensionNumbers.getInputBatchDimension();

auto shape = Ty.getShape();
SmallVector<int64_t> newShape;
for (int64_t i = 0, e = shape.size(); i < e; ++i) {
if (i == fdim) {
newShape.push_back(batchGroupCount);
newShape.push_back(shape[i]);
} else if (i == bdim) {
newShape.push_back(shape[i] / batchGroupCount);
} else {
newShape.push_back(shape[i]);
}
}

Ty.clone(newShape);
}]>;

def GradDataConvBatchGroupPerm : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
SmallVector<int64_t> transposes;

auto dimensionNumbers = op.getDimensionNumbers();
auto fdim = dimensionNumbers.getInputFeatureDimension();
auto bdim = dimensionNumbers.getInputBatchDimension();

if (fdim < bdim)
bdim++;

int64_t i = 0, N = op.getType().getShape().size();
while (i <= N) {
if (i == bdim) {
transposes.push_back(fdim);
transposes.push_back(bdim);
} else if (i != fdim) {
transposes.push_back(i);
}
i++;
}

getI64Attr(builder, transposes);
}]>;

def GradDataFilterReshape2 : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto featureGroupCount = op.getFeatureGroupCount();
auto batchGroupCount = op.getBatchGroupCount();
auto groupCount = featureGroupCount == 1 ? batchGroupCount : featureGroupCount;

auto rhs = op.getRhs();
auto dimensionNumbers = op.getDimensionNumbers();
auto Ty = cast<RankedTensorType>(rhs.getType());
auto shape = Ty.getShape();

auto odim = dimensionNumbers.getKernelOutputFeatureDimension();
auto idim = dimensionNumbers.getKernelInputFeatureDimension();

SmallVector<int64_t> newShape;
for (int64_t i = 0, e = shape.size(); i < e; ++i) {
if (i == idim) {
newShape.push_back(shape[i] * groupCount);
} else if (i == odim) {
newShape.push_back(shape[i] / groupCount);
} else {
newShape.push_back(shape[i]);
}
}

RankedTensorType::get(newShape, Ty.getElementType());
}]>;

def GradDataConvWindowStrides : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
int64_t N = op.getType().getShape().size() - 2;
llvm::SmallVector<int64_t> windowStrides(N, 1);
Expand All @@ -410,6 +549,15 @@ def GradDataConvPadding : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
initialPadding.value().value_end<int64_t>());
}

auto dilateShape = [](int64_t shape, int64_t dilation) {
if (dilation == 1) return shape;
int64_t dilated = 1 + dilation * (shape - 1);
return dilated < 0 ? 0 : dilated;
};

auto lhsDilations = op.getLhsDilation();
auto rhsDilations = op.getRhsDilation();
auto windowStrides = op.getWindowStrides();
for (int i = 0; i < N; ++i) {
auto weightDim = dimensionNumbers.getKernelSpatialDimensions()[i];
auto dataDim = dimensionNumbers.getInputSpatialDimensions()[i];
Expand All @@ -418,9 +566,19 @@ def GradDataConvPadding : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto padBefore = newPaddingValues[2 * i];
auto padAfter = newPaddingValues[2 * i + 1];

auto rhsShape = op.getRhs().getType().getShape()[weightDim];
auto lhsShape = op.getLhs().getType().getShape()[dataDim];
auto outShape = op.getType().getShape()[outputDim];
auto lhsDilation = lhsDilations.has_value() ?
getI64Value(lhsDilations.value(), i) :
1;
auto rhsDilation = rhsDilations.has_value() ?
getI64Value(rhsDilations.value(), i) :
1;
auto windowStride = windowStrides.has_value() ?
getI64Value(windowStrides.value(), i) :
1;

auto lhsShape = dilateShape(op.getLhs().getType().getShape()[dataDim], lhsDilation);
auto rhsShape = dilateShape(op.getRhs().getType().getShape()[weightDim], rhsDilation);
auto outShape = dilateShape(op.getType().getShape()[outputDim], windowStride);

auto newPadBefore = rhsShape - padBefore - 1;
newPaddingValues[2 * i] = newPadBefore;
Expand Down Expand Up @@ -475,11 +633,14 @@ def GradDataConvDimensionNumbers : GlobalExpr</*needsprimal*/0, /*needsshadow*/0
}]>;

def GradDataConvFeatureGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
op.getFeatureGroupCountAttr();
auto featureGroupCount = op.getFeatureGroupCount();
auto batchGroupCount = op.getBatchGroupCount();
auto groupCount = featureGroupCount == 1 ? batchGroupCount : featureGroupCount;
builder.getI64IntegerAttr(groupCount);
}]>;

def GradDataConvBatchGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
op.getBatchGroupCountAttr();
builder.getI64IntegerAttr(1);
}]>;

// GradFilter
Expand All @@ -500,17 +661,36 @@ def GradFilterConvPadding : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
initialPadding.value().value_end<int64_t>());
}

auto dilateShape = [](int64_t shape, int64_t dilation) {
if (dilation == 1) return shape;
int64_t dilated = 1 + dilation * (shape - 1);
return dilated < 0 ? 0 : dilated;
};

auto lhsDilations = op.getLhsDilation();
auto rhsDilations = op.getRhsDilation();
auto windowStrides = op.getWindowStrides();
for (int i = 0; i < N; ++i) {
auto weightDim = dimensionNumbers.getKernelSpatialDimensions()[i];
auto dataDim = dimensionNumbers.getInputSpatialDimensions()[i];
auto weightDim = dimensionNumbers.getKernelSpatialDimensions()[i];
auto outputDim = dimensionNumbers.getOutputSpatialDimensions()[i];

auto padBefore = newPaddingValues[2 * i];
auto padAfter = newPaddingValues[2 * i + 1];

auto rhsShape = op.getRhs().getType().getShape()[weightDim];
auto lhsShape = op.getLhs().getType().getShape()[dataDim];
auto outShape = op.getType().getShape()[outputDim];
auto lhsDilation = lhsDilations.has_value() ?
getI64Value(lhsDilations.value(), i) :
1;
auto rhsDilation = rhsDilations.has_value() ?
getI64Value(rhsDilations.value(), i) :
1;
auto windowStride = windowStrides.has_value() ?
getI64Value(windowStrides.value(), i) :
1;

auto lhsShape = dilateShape(op.getLhs().getType().getShape()[dataDim], lhsDilation);
auto rhsShape = dilateShape(op.getRhs().getType().getShape()[weightDim], rhsDilation);
auto outShape = dilateShape(op.getType().getShape()[outputDim], windowStride);

newPaddingValues[2 * i] = padBefore;
newPaddingValues[2 * i + 1] = outShape - lhsShape + rhsShape - padBefore - 1;
Expand All @@ -522,6 +702,22 @@ def GradFilterConvPadding : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
newPaddingAttr;
}]>;

def GradFilterConvReverseDims : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
auto windowReversals = op.getWindowReversal();

SmallVector<int64_t> reverseDims;

if (windowReversals.has_value()) {
for (auto it : llvm::enumerate(getBoolIter(windowReversals.value()))) {
if (it.value()) {
reverseDims.push_back(it.index());
}
}
}

getI64Attr(builder, reverseDims);
}]>;

def GradFilterConvLhsDilation : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
op.getLhsDilationAttr();
}]>;
Expand Down Expand Up @@ -552,8 +748,8 @@ def GradFilterConvDimensionNumbers : GlobalExpr</*needsprimal*/0, /*needsshadow*
}]>;

def GradFilterConvFeatureGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
unsigned int newFeatureGroupCount = 1;
newFeatureGroupCount;
auto batchGroupCount = op.getBatchGroupCount();
batchGroupCount;
}]>;

def GradFilterConvBatchGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/0, [{
Expand All @@ -564,33 +760,51 @@ def GradFilterConvBatchGroupCount : GlobalExpr</*needsprimal*/0, /*needsshadow*/

def : HLODerivative<"ConvolutionOp", (Op $lhs, $rhs),
[
(Convolution
(Reshape
(TypeOf $lhs),
(DiffeRet),
$rhs,
(GradDataConvWindowStrides),
(GradDataConvPadding),
(GradDataConvLhsDilation),
(GradDataConvRhsDilation),
(GradDataConvWindowReversal),
(GradDataConvDimensionNumbers),
(GradDataConvFeatureGroupCount),
(GradDataConvBatchGroupCount),
(ResultDotPrec)
(Transpose
(Reshape
(GradDataConvBatchGroupCountType),
(Convolution
(GradDataConvOutputType),
(DiffeRet),
(Reshape
(GradDataFilterReshape2),
(Transpose
(Reshape (GradDataFilterReshape1), $rhs),
(GradDataFilterTranspose)
)
),
(GradDataConvWindowStrides),
(GradDataConvPadding),
(GradDataConvLhsDilation),
(GradDataConvRhsDilation),
(GradDataConvWindowReversal),
(GradDataConvDimensionNumbers),
(GradDataConvFeatureGroupCount),
(GradDataConvBatchGroupCount),
(ResultDotPrec)
)
),
(GradDataConvBatchGroupPerm)
)
),
(Convolution
(TypeOf $rhs),
$lhs,
(DiffeRet),
(GradFilterConvWindowStrides),
(GradFilterConvPadding),
(GradFilterConvLhsDilation),
(GradFilterConvRhsDilation),
(GradFilterConvWindowReversal),
(GradFilterConvDimensionNumbers),
(GradFilterConvFeatureGroupCount),
(GradFilterConvBatchGroupCount),
(ResultDotPrec)
(Reverse
(Convolution
(TypeOf $rhs),
$lhs,
(DiffeRet),
(GradFilterConvWindowStrides),
(GradFilterConvPadding),
(GradFilterConvLhsDilation),
(GradFilterConvRhsDilation),
(GradFilterConvWindowReversal),
(GradFilterConvDimensionNumbers),
(GradFilterConvFeatureGroupCount),
(GradFilterConvBatchGroupCount),
(ResultDotPrec)
),
(GradFilterConvReverseDims)
)
],
(Add
Expand Down
4 changes: 4 additions & 0 deletions src/enzyme_ad/jax/Implementations/MHLOAutoDiffOpInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -37,6 +37,10 @@ static mlir::DenseIntElementsAttr getI64Attr(OpBuilder &builder,
return builder.getI64VectorAttr(vals);
}

static int64_t getI64Value(mlir::DenseIntElementsAttr attr, size_t pos) {
return attr.getValues<int64_t>()[pos];
}

static mlir::DenseElementsAttr getBoolAttr(OpBuilder &builder,
llvm::ArrayRef<bool> vals) {
return builder.getBoolVectorAttr(vals);
Expand Down
4 changes: 4 additions & 0 deletions src/enzyme_ad/jax/Implementations/StableHLOAutoDiffOpInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -38,6 +38,10 @@ static mlir::DenseI64ArrayAttr getI64Attr(OpBuilder &builder,
return builder.getDenseI64ArrayAttr(vals);
}

static int64_t getI64Value(llvm::ArrayRef<int64_t> attr, size_t pos) {
return attr[pos];
}

static mlir::DenseBoolArrayAttr getBoolAttr(OpBuilder &builder,
llvm::ArrayRef<bool> vals) {
return builder.getDenseBoolArrayAttr(vals);
Expand Down
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