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[mlir][tosa] Fix conv op build functions (#126321)

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This patch fixes several issues:
  - buildConvOpWithQuantInfo: 
       call buildConvOpResultTypeInfo to get final output type
  - buildTransConvOpWithQuantInfo:
       add input_zp and weight_zp operands
       remove input_zp/weight_zp attributes
  - createZeroPointTensor:
       add getElementTypeOrSelf to get element type just in case
       remove bad auto-merge lines


Change-Id: Idbf88f500ce57a865da4b7be7b7b8bf2ba194b24

Signed-off-by: Tai Ly <tai.ly@arm.com>
This commit is contained in:
Tai Ly
2025-02-10 10:36:29 -06:00
committed by GitHub
parent 3706dfef66
commit 62ae876b1b
1 changed files with 17 additions and 20 deletions
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37
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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@@ -510,7 +510,13 @@ static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
result.addAttribute("stride", stride);
result.addAttribute("dilation", dilation);
result.addAttribute("acc_type", accType);
result.addTypes(outputType);
Type finalOutputType = outputType;
auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
finalOutputType =
buildConvOpResultTypeInfo(builder, outputType, input, weight);
}
result.addTypes(finalOutputType);
}
/// Handles tosa.transpose_conv2d which has outpad and output shape
@@ -519,25 +525,19 @@ static void buildTransConvOpWithQuantInfo(
OpBuilder &builder, OperationState &result, Type outputType, Value input,
Value weight, Value bias, DenseI64ArrayAttr outpad,
DenseI64ArrayAttr stride, DenseI64ArrayAttr outputShape, TypeAttr accType) {
result.addOperands({input, weight, bias});
auto zps = createZPsAsConst(builder, input, weight);
result.addOperands({input, weight, bias, zps.first, zps.second});
result.addAttribute("out_pad", outpad);
result.addAttribute("stride", stride);
result.addAttribute("out_shape", outputShape);
result.addAttribute("acc_type", accType);
auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
Type finalOutputType = outputType;
auto quantAttr = buildConvOpQuantizationAttr(builder, input, weight);
if (quantAttr) {
result.addAttribute("input_zp",
builder.getI32IntegerAttr(
static_cast<int32_t>(quantAttr.getInputZp())));
result.addAttribute("weight_zp",
builder.getI32IntegerAttr(
static_cast<int32_t>(quantAttr.getWeightZp())));
result.addTypes(
buildConvOpResultTypeInfo(builder, outputType, input, weight));
} else {
result.addTypes(outputType);
finalOutputType =
buildConvOpResultTypeInfo(builder, outputType, input, weight);
}
result.addTypes(finalOutputType);
}
/// The tosa.fully_connected op has its own builder as it does not have
@@ -2492,18 +2492,15 @@ LogicalResult mlir::tosa::getZeroPoint(ElementsAttr zpAttr, int64_t &zp) {
return failure();
}
// Create a rank-0 const tensor for zero point of the source tensor.
// Create a rank-1 const tensor for zero point of the source tensor.
std::optional<Value> mlir::tosa::createZeroPointTensor(OpBuilder &builder,
Location loc,
Type srcElemType,
int64_t zp) {
if (auto quantType =
llvm::dyn_cast<mlir::quant::UniformQuantizedType>(srcElemType))
srcElemType = quantType.getStorageType();
auto zpType = mlir::RankedTensorType::get({1}, srcElemType);
srcElemType = getElementTypeOrSelf(srcElemType);
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(srcElemType))
srcElemType = quantType.getStorageType();
auto zpType = mlir::RankedTensorType::get({1}, srcElemType);
if (llvm::isa<FloatType>(srcElemType)) {
auto zpAttr = DenseElementsAttr::get(
zpType, builder.getFloatAttr(srcElemType, static_cast<double>(zp)));