diff options
Diffstat (limited to 'mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp')
| -rw-r--r-- | mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp | 134 |
1 files changed, 59 insertions, 75 deletions
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp index ec55091..0e3de06 100644 --- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp +++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp @@ -22,7 +22,6 @@ #include "mlir/Dialect/Tosa/Utils/ConversionUtils.h" #include "mlir/Dialect/Utils/ReshapeOpsUtils.h" #include "mlir/Dialect/Utils/StaticValueUtils.h" -#include "mlir/IR/ImplicitLocOpBuilder.h" #include "mlir/IR/Matchers.h" #include "mlir/IR/OpDefinition.h" #include "mlir/IR/PatternMatch.h" @@ -570,10 +569,9 @@ static Value createLinalgBodyCalculationForElementwiseOp( // to UIToFP. if (srcTy.isUnsignedInteger() && isa<FloatType>(dstTy)) { auto unrealizedCast = - rewriter - .create<UnrealizedConversionCastOp>( - loc, rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()), - args[0]) + UnrealizedConversionCastOp::create( + rewriter, loc, + rewriter.getIntegerType(srcTy.getIntOrFloatBitWidth()), args[0]) .getResult(0); return arith::UIToFPOp::create(rewriter, loc, resultTypes[0], unrealizedCast); @@ -869,14 +867,13 @@ static Value broadcastDynamicDimension(PatternRewriter &rewriter, Location loc, // Emit 'linalg.generic' op auto resultTensor = - opBuilder - .create<linalg::GenericOp>( - loc, outputTensor.getType(), operand, outputTensor, affineMaps, - getNParallelLoopsAttrs(rank), - [&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) { - // Emit 'linalg.yield' op - linalg::YieldOp::create(opBuilder, loc, blockArgs.front()); - }) + linalg::GenericOp::create( + opBuilder, loc, outputTensor.getType(), operand, outputTensor, + affineMaps, getNParallelLoopsAttrs(rank), + [&](OpBuilder &opBuilder, Location loc, ValueRange blockArgs) { + // Emit 'linalg.yield' op + linalg::YieldOp::create(opBuilder, loc, blockArgs.front()); + }) .getResult(0); // Cast to original operand type if necessary @@ -1156,11 +1153,9 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis, inputs.push_back(input); // First fill the output buffer with the init value. - auto emptyTensor = - rewriter - .create<tensor::EmptyOp>(loc, reduceShape, resultTy.getElementType(), - dynDims) - .getResult(); + auto emptyTensor = tensor::EmptyOp::create(rewriter, loc, reduceShape, + resultTy.getElementType(), dynDims) + .getResult(); auto fillValueAttr = createInitialValueForReduceOp(op, elementTy, rewriter); if (!fillValueAttr) @@ -1168,10 +1163,10 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis, op, "No initial value found for reduction operation"); auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr); - auto filledTensor = rewriter - .create<linalg::FillOp>(loc, ValueRange{fillValue}, - ValueRange{emptyTensor}) - .result(); + auto filledTensor = + linalg::FillOp::create(rewriter, loc, ValueRange{fillValue}, + ValueRange{emptyTensor}) + .result(); outputs.push_back(filledTensor); bool isNanIgnoreMode = false; @@ -1187,14 +1182,12 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis, auto trueAttr = rewriter.getBoolAttr(true); auto trueValue = arith::ConstantOp::create(rewriter, loc, trueAttr); auto emptyBoolTensor = - rewriter - .create<tensor::EmptyOp>(loc, reduceShape, trueValue.getType(), - dynDims) + tensor::EmptyOp::create(rewriter, loc, reduceShape, + trueValue.getType(), dynDims) .getResult(); auto allResultsNaNTensor = - rewriter - .create<linalg::FillOp>(loc, ValueRange{trueValue}, - ValueRange{emptyBoolTensor}) + linalg::FillOp::create(rewriter, loc, ValueRange{trueValue}, + ValueRange{emptyBoolTensor}) .result(); // Note that because the linalg::ReduceOp has two variadic arguments // (inputs and outputs) and it has the SameVariadicOperandSize trait we @@ -1262,22 +1255,19 @@ static LogicalResult reduceMatchAndRewriteHelper(OpTy op, uint64_t axis, APFloat::getNaN(cast<FloatType>(elementTy).getFloatSemantics(), false)); auto nanValue = arith::ConstantOp::create(rewriter, loc, nanValueAttr); auto emptyNanTensor = - rewriter - .create<tensor::EmptyOp>(loc, reduceShape, - resultTy.getElementType(), dynDims) + tensor::EmptyOp::create(rewriter, loc, reduceShape, + resultTy.getElementType(), dynDims) .getResult(); auto nanFilledTensor = - rewriter - .create<linalg::FillOp>(loc, ValueRange{nanValue}, - ValueRange{emptyNanTensor}) + linalg::FillOp::create(rewriter, loc, ValueRange{nanValue}, + ValueRange{emptyNanTensor}) .result(); // Create an empty tensor, non need to fill this since it will be // overwritten by the select. auto finalEmptyTensor = - rewriter - .create<tensor::EmptyOp>(loc, reduceShape, - resultTy.getElementType(), dynDims) + tensor::EmptyOp::create(rewriter, loc, reduceShape, + resultTy.getElementType(), dynDims) .getResult(); // Do a selection between the tensors akin to: @@ -1504,12 +1494,11 @@ public: Value shift = shiftConstant ? shiftConstant : blockArgs[shiftArg]; if (valueTy.isUnsignedInteger()) { - value = nestedBuilder - .create<UnrealizedConversionCastOp>( - nestedLoc, - nestedBuilder.getIntegerType( - valueTy.getIntOrFloatBitWidth()), - value) + value = UnrealizedConversionCastOp::create( + nestedBuilder, nestedLoc, + nestedBuilder.getIntegerType( + valueTy.getIntOrFloatBitWidth()), + value) .getResult(0); } if (valueTy.getIntOrFloatBitWidth() < 32) { @@ -1558,9 +1547,8 @@ public: } if (outIntType.isUnsignedInteger()) { - value = nestedBuilder - .create<UnrealizedConversionCastOp>(nestedLoc, - outIntType, value) + value = UnrealizedConversionCastOp::create(nestedBuilder, nestedLoc, + outIntType, value) .getResult(0); } linalg::YieldOp::create(nestedBuilder, loc, value); @@ -2096,10 +2084,9 @@ public: Value axisDimSize = tensor::DimOp::create(rewriter, loc, input, axis); // First fill the output buffer with the init value. - auto emptyTensor = rewriter - .create<tensor::EmptyOp>(loc, inputTy.getShape(), - inputTy.getElementType(), - ArrayRef<Value>({dynDims})) + auto emptyTensor = tensor::EmptyOp::create( + rewriter, loc, inputTy.getShape(), + inputTy.getElementType(), ArrayRef<Value>({dynDims})) .getResult(); SmallVector<AffineMap, 2> affineMaps = { rewriter.getMultiDimIdentityMap(resultTy.getRank())}; @@ -2242,23 +2229,22 @@ public: } // First fill the output buffer for the index. - auto emptyTensorIdx = rewriter - .create<tensor::EmptyOp>(loc, resultTy.getShape(), - outElementTy, dynDims) - .getResult(); + auto emptyTensorIdx = + tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), + outElementTy, dynDims) + .getResult(); auto fillValueIdx = arith::ConstantOp::create( rewriter, loc, rewriter.getIntegerAttr(outElementTy, 0)); auto filledTensorIdx = - rewriter - .create<linalg::FillOp>(loc, ValueRange{fillValueIdx}, - ValueRange{emptyTensorIdx}) + linalg::FillOp::create(rewriter, loc, ValueRange{fillValueIdx}, + ValueRange{emptyTensorIdx}) .result(); // Second fill the output buffer for the running max. - auto emptyTensorMax = rewriter - .create<tensor::EmptyOp>(loc, resultTy.getShape(), - inElementTy, dynDims) - .getResult(); + auto emptyTensorMax = + tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), inElementTy, + dynDims) + .getResult(); auto fillValueMaxAttr = createInitialValueForReduceOp(argmaxOp, inElementTy, rewriter); @@ -2269,9 +2255,8 @@ public: auto fillValueMax = arith::ConstantOp::create(rewriter, loc, fillValueMaxAttr); auto filledTensorMax = - rewriter - .create<linalg::FillOp>(loc, ValueRange{fillValueMax}, - ValueRange{emptyTensorMax}) + linalg::FillOp::create(rewriter, loc, ValueRange{fillValueMax}, + ValueRange{emptyTensorMax}) .result(); // We need to reduce along the arg-max axis, with parallel operations along @@ -2372,9 +2357,8 @@ public: auto loc = op.getLoc(); auto emptyTensor = - rewriter - .create<tensor::EmptyOp>(loc, resultTy.getShape(), resultElementTy, - dynamicDims) + tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), + resultElementTy, dynamicDims) .getResult(); SmallVector<AffineMap, 2> affineMaps = { @@ -2449,10 +2433,10 @@ public: } } - auto emptyTensor = rewriter - .create<tensor::EmptyOp>(loc, resultTy.getShape(), - resultElementTy, dynDims) - .getResult(); + auto emptyTensor = + tensor::EmptyOp::create(rewriter, loc, resultTy.getShape(), + resultElementTy, dynDims) + .getResult(); SmallVector<AffineMap, 2> affineMaps = { rewriter.getMultiDimIdentityMap(resultTy.getRank()), @@ -2586,10 +2570,10 @@ struct RFFT2dConverter final : public OpRewritePattern<RFFT2dOp> { tensor::EmptyOp::create(rewriter, loc, type, dynamicSizes); auto fillValueAttr = rewriter.getZeroAttr(type.getElementType()); auto fillValue = arith::ConstantOp::create(rewriter, loc, fillValueAttr); - auto filledTensor = rewriter - .create<linalg::FillOp>(loc, ValueRange{fillValue}, - ValueRange{emptyTensor}) - .result(); + auto filledTensor = + linalg::FillOp::create(rewriter, loc, ValueRange{fillValue}, + ValueRange{emptyTensor}) + .result(); return filledTensor; } |