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batchNorm and softmax #3

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batchNorm and softmax #3

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2 changes: 1 addition & 1 deletion lib/Conversion/TorchOnnxToTorch/DefaultDomainAtoF.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -342,7 +342,7 @@ void mlir::torch::onnx_c::populateDefaultDomainAtoF(
return success();
});
patterns.onOp(
"BatchNormalization", 15,
"BatchNormalization", 7,
[](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input, weight, bias, inputMean, inputVar;
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29 changes: 29 additions & 0 deletions lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -1273,6 +1273,35 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
rewriter.replaceOp(binder.op, result);
return success();
});
patterns.onOp(
"Softmax", 1, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Value input;
int64_t axis;
if (binder.tensorOperand(input) ||
binder.s64IntegerAttr(axis, "axis", -1) ||
binder.tensorResultType(resultType))
return failure();

auto inputType = cast<Torch::ValueTensorType>(input.getType());
if (!inputType.hasSizes()) {
return rewriter.notifyMatchFailure(
binder.op, "unimplemented: expected input to have sizes");
}

int64_t inputRank = inputType.getSizes().size();
// Handle negative axis
if (axis < 0)
axis = inputRank + axis;

Value constAxis = rewriter.create<Torch::ConstantIntOp>(
binder.getLoc(), rewriter.getI64IntegerAttr(axis));
Value dtypeNone = rewriter.create<Torch::ConstantNoneOp>(binder.getLoc());

rewriter.replaceOpWithNewOp<Torch::AtenSoftmaxIntOp>(
binder.op, resultType, input, constAxis, dtypeNone);
return success();
});
patterns.onOp(
"Softmax", 13, [](OpBinder binder, ConversionPatternRewriter &rewriter) {
Torch::ValueTensorType resultType;
Expand Down