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mlir: Add Enzyme ops removal on structured control flow #2200

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merged 10 commits into from
Jan 5, 2025
Merged

mlir: Add Enzyme ops removal on structured control flow #2200

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a2812d8
mlir: Add Enzyme ops removal on structured control flow
Pangoraw Dec 18, 2024
4bf8e8e
format
Pangoraw Dec 18, 2024
ce49423
use AutoDiffTypeInterface for batching
Pangoraw Dec 21, 2024
69bcb8f
remove
Pangoraw Dec 21, 2024
5cb1442
add test with unknown number of iterations
Pangoraw Dec 21, 2024
2ff3f09
don't push same value twice
Pangoraw Dec 25, 2024
150a565
tensor extract/insert
Pangoraw Dec 25, 2024
62a3348
reserve the right size
Pangoraw Jan 5, 2025
80b756a
better batchType
Pangoraw Jan 5, 2025
3320af7
better comment
Pangoraw Jan 5, 2025
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46 changes: 24 additions & 22 deletions enzyme/Enzyme/MLIR/Implementations/BuiltinAutoDiffTypeInterfaceImpl.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,22 @@ using namespace mlir;
using namespace mlir::enzyme;

namespace {

static mlir::Type batchType(mlir::Type type, int64_t width) {
if (width == 1)
return type;

if (auto TT = dyn_cast<mlir::TensorType>(type)) {
SmallVector<int64_t> shape;
shape.reserve(TT.getShape().size() + 1);
shape.push_back(width);
shape.append(TT.getShape().begin(), TT.getShape().end());
return TT.clone(shape);
}

return RankedTensorType::get({width}, type);
}

class FloatTypeInterface
: public AutoDiffTypeInterface::ExternalModel<FloatTypeInterface,
FloatType> {
Expand All @@ -44,12 +60,8 @@ class FloatTypeInterface
return a;
}

Type getShadowType(Type self, unsigned width) const {
if (width > 1) {
return RankedTensorType::get({width}, self);
} else {
return self;
}
Type getShadowType(Type self, int64_t width) const {
return batchType(self, width);
}

bool isMutable(Type self) const { return false; }
Expand Down Expand Up @@ -108,16 +120,8 @@ class TensorTypeInterface
return added;
}

Type getShadowType(Type self, unsigned width) const {
if (width != 1) {
auto tenType = self.cast<TensorType>();
auto shape = tenType.getShape();
SmallVector<int64_t, 4> newShape;
newShape.push_back(width);
newShape.append(shape.begin(), shape.end());
return RankedTensorType::get(newShape, tenType.getElementType());
}
return self;
Type getShadowType(Type self, int64_t width) const {
return batchType(self, width);
}

bool isMutable(Type self) const { return false; }
Expand Down Expand Up @@ -148,9 +152,8 @@ class IntegerTypeInterface
return a;
}

Type getShadowType(Type self, unsigned width) const {
assert(width == 1 && "unsupported width != 1");
return self;
Type getShadowType(Type self, int64_t width) const {
return batchType(self, width);
}

bool isMutable(Type self) const { return false; }
Expand Down Expand Up @@ -182,9 +185,8 @@ class ComplexTypeInterface
return builder.create<complex::ConjOp>(loc, a)->getResult(0);
}

Type getShadowType(Type self, unsigned width) const {
assert(width == 1 && "unsupported width != 1");
return self;
Type getShadowType(Type self, int64_t width) const {
return batchType(self, width);
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in a separate different PR, it may be worthwhile switching getShadowType and the likes to take an ArrayRef<int64_t> indices to batch on (@jumerckx did something similar when adding batched differentiation broadcast earlier)

}

bool isMutable(Type self) const { return false; }
Expand Down
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