Batched autodiff #2181
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Batched autodiff #2181
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wsmoses
reviewed
Nov 28, 2024
|
I haven't yet fully made the changes in enzyme-tblgen.cpp, and either way this just works for the simple test case. mlir::Value itmp = ({
// Computing MulFOp
auto fwdarg_0 = dif;
auto fwdarg_1 = gutils->getNewFromOriginal(op->getOperand(1));
if (gutils->width != 1)
{
fwdarg_1 = builder.create<tensor::SplatOp>(
op.getLoc(),
mlir::RankedTensorType::get({gutils->width},
fwdarg_1.getType()),
fwdarg_1);
}
builder.create<arith::MulFOp>(op.getLoc(), fwdarg_0, fwdarg_1);
});But this is the MLIR code that is generated for this simple test: func.func private @fwddiffe2square(%arg0: f64, %arg1: tensor<2xf64>) -> tensor<2xf64> {
%splat = tensor.splat %arg0 : tensor<2xf64>
%0 = arith.mulf %arg1, %splat : tensor<2xf64>
%splat_0 = tensor.splat %arg0 : tensor<2xf64>
%1 = arith.mulf %arg1, %splat_0 : tensor<2xf64>
%2 = arith.addf %0, %1 : tensor<2xf64>
%3 = arith.mulf %arg0, %arg0 : f64
return %2 : tensor<2xf64>
} |
This still requires changes in the tblgenerated derivative files. For example, createForwardModeTangent in MulFOpFwdDerivative could be altered like this:
```
LogicalResult createForwardModeTangent(Operation *op0, OpBuilder &builder, MGradientUtils *gutils) const
{
auto op = cast<arith::MulFOp>(op0);
if (gutils->width != 1) {
auto newop = gutils->getNewFromOriginal(op0);
for (auto res : newop->getResults()) {
res.setType(mlir::RankedTensorType::get({gutils->width}, res.getType()));
}
}
gutils->eraseIfUnused(op);
if (gutils->isConstantInstruction(op))
return success();
mlir::Value res = nullptr;
if (!gutils->isConstantValue(op->getOperand(0)))
{
auto dif = gutils->invertPointerM(op->getOperand(0), builder);
{
mlir::Value itmp = ({
// Computing MulFOp
auto fwdarg_0 = dif;
dif.dump();
// TODO: gutils->makeBatched(...)
auto fwdarg_1 = gutils->getNewFromOriginal(op->getOperand(1));
builder.create<arith::MulFOp>(op.getLoc(), fwdarg_0, fwdarg_1);
});
itmp.dump();
if (!res)
res = itmp;
else
{
auto operandType = cast<AutoDiffTypeInterface>(res.getType());
res = operandType.createAddOp(builder, op.getLoc(), res, itmp);
}
}
}
if (!gutils->isConstantValue(op->getOperand(1)))
{
auto dif = gutils->invertPointerM(op->getOperand(1), builder);
{
mlir::Value itmp = ({
// Computing MulFOp
auto fwdarg_0 = dif;
dif.dump();
auto fwdarg_1 = gutils->getNewFromOriginal(op->getOperand(0));
builder.create<arith::MulFOp>(op.getLoc(), fwdarg_0, fwdarg_1);
});
if (!res)
res = itmp;
else
{
auto operandType = cast<AutoDiffTypeInterface>(res.getType());
res = operandType.createAddOp(builder, op.getLoc(), res, itmp);
}
}
}
assert(res);
gutils->setDiffe(op->getResult(0), res, builder);
return success();
}
```
This reverts commit c06ed01.
wsmoses
reviewed
Dec 20, 2024
wsmoses
approved these changes
Dec 20, 2024
wsmoses
reviewed
Dec 22, 2024
wsmoses
reviewed
Dec 22, 2024
wsmoses
approved these changes
Dec 22, 2024
|
fix the format/etc then I think this is good to go! |
Co-authored-by: Billy Moses <wmoses@google.com>
|
@wsmoses I started looking into control flow ( |
|
That’s totally fine/go for it, but also feel free to merge whenever it’s in a good state |
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Added some type conversions to tensor types if
width != 1. The simple test case seems correct now.Corresponding Enzyme-JAX PR: EnzymeAD/Enzyme-JAX#197