mirror of https://github.com/llvm/torch-mlir
Fixing implicit double to float casts. (#2476)
MSVC (and other compilers with implicit narrowing warnings) don't like this type mismatch.pull/2480/head snapshot-20230921.968
Ben Vanik
GitHub
parent
023fc90072
commit
b9847b1904
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@ -121,8 +121,8 @@ static bool isInValidRange(bool isFloat, const double &doubleValue, bool isInt,
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return (doubleValue == static_cast<double>(static_cast<T>(doubleValue)));
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} else {
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assert(isInt);
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return (intValue >= std::numeric_limits<T>::min()) &&
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(intValue <= std::numeric_limits<T>::max());
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return (intValue >= static_cast<int64_t>(std::numeric_limits<T>::min())) &&
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(intValue <= static_cast<int64_t>(std::numeric_limits<T>::max()));
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}
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return true;
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}
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@ -145,8 +145,9 @@ LogicalResult torchScalarToTosaTensor(ConversionPatternRewriter &rewriter,
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"Unable to extract the scalar constant");
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if (dtype.isa<mlir::FloatType>()) {
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tosaTensor = tosa::getConstTensor<float>(
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rewriter, op, (isFloat ? doubleValue : intValue), dshape, dtype)
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tosaTensor = tosa::getConstTensor<float>(rewriter, op,
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(isFloat ? doubleValue : intValue),
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dshape, dtype)
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.value();
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} else if (auto intType = dtype.dyn_cast<mlir::IntegerType>()) {
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auto w = intType.getWidth();
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@ -162,7 +163,7 @@ LogicalResult torchScalarToTosaTensor(ConversionPatternRewriter &rewriter,
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"of destination type");
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}
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bool d = isFloat ? static_cast<bool>(doubleValue)
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: static_cast<bool>(intValue);
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: static_cast<bool>(intValue);
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tosaTensor =
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tosa::getConstTensor<bool>(rewriter, op, {d}, dshape).value();
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} else if (w == 32) {
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@ -616,7 +617,9 @@ LogicalResult ConvertAtenOp<AtenLeakyReluOp>::matchAndRewrite(
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op, "Negative slope needs to be a scalar constant for conversion to "
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"TOSA LeakyReLU operation");
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auto zero = tosa::getConstTensor<float>(rewriter, op, 0, {}, selfTy.getElementType()).value();
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auto zero =
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tosa::getConstTensor<float>(rewriter, op, 0, {}, selfTy.getElementType())
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.value();
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auto cond = rewriter.create<tosa::GreaterEqualOp>(
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op->getLoc(),
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RankedTensorType::get(selfTy.getShape(), rewriter.getIntegerType(1)),
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@ -2253,11 +2256,10 @@ LogicalResult ConvertAtenOp<AtenBatchNormOp>::matchAndRewrite(
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if (!matchPattern(op.getEps(), m_TorchConstantFloat(&eps)))
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return rewriter.notifyMatchFailure(op, "eps must be a scalar constant");
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auto epsilonConst =
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tosa::getConstTensor<float>(rewriter, op.getOperation(),
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{static_cast<float>(eps)}, {},
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meanType.getElementType())
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.value();
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auto epsilonConst = tosa::getConstTensor<float>(rewriter, op.getOperation(),
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{static_cast<float>(eps)}, {},
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meanType.getElementType())
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.value();
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auto batchNorm =
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computeBatchNorm(op, rewriter, outType, adaptor.getInput(), varianceVal,
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@ -2571,7 +2573,7 @@ LogicalResult ConvertAtenOp<AtenLog2Op>::matchAndRewrite(
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// Constant value of ln2.
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SmallVector<int64_t> ln2Shape(selfType.getRank(), 1);
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auto ln2Op = tosa::getConstTensor<float>(rewriter, op, {0.69314718056},
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auto ln2Op = tosa::getConstTensor<float>(rewriter, op, {0.69314718056f},
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ln2Shape, selfType.getElementType())
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.value();
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auto rcpOp =
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@ -2802,21 +2804,25 @@ static Value approximateErfOp(ConversionPatternRewriter &rewriter,
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auto zero = tosa::getConstTensor<float>(rewriter, op, 0, {}, dtype).value();
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auto one = tosa::getConstTensor<float>(rewriter, op, 1, {}, dtype).value();
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auto a1 = tosa::getConstTensor<float>(rewriter, op, 0.278393, {}, dtype).value();
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auto a1 =
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tosa::getConstTensor<float>(rewriter, op, 0.278393f, {}, dtype).value();
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auto a1X = rewriter.create<tosa::MulOp>(loc, outType, a1, absX, /*shift=*/0);
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auto sum = rewriter.create<tosa::AddOp>(loc, outType, a1X, one);
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auto a2 = tosa::getConstTensor<float>(rewriter, op, 0.230389, {}, dtype).value();
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auto a2 =
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tosa::getConstTensor<float>(rewriter, op, 0.230389f, {}, dtype).value();
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auto x2 = rewriter.create<tosa::MulOp>(loc, outType, absX, absX, /*shift=*/0);
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auto a2X = rewriter.create<tosa::MulOp>(loc, outType, a2, x2, /*shift=*/0);
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sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a2X);
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auto a3 = tosa::getConstTensor<float>(rewriter, op, 0.000972, {}, dtype).value();
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auto a3 =
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tosa::getConstTensor<float>(rewriter, op, 0.000972f, {}, dtype).value();
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auto x3 = rewriter.create<tosa::MulOp>(loc, outType, x2, absX, /*shift=*/0);
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auto a3X = rewriter.create<tosa::MulOp>(loc, outType, a3, x3, /*shift=*/0);
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sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a3X);
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auto a4 = tosa::getConstTensor<float>(rewriter, op, 0.078108, {}, dtype).value();
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auto a4 =
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tosa::getConstTensor<float>(rewriter, op, 0.078108f, {}, dtype).value();
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auto x4 = rewriter.create<tosa::MulOp>(loc, outType, x3, absX, /*shift=*/0);
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auto a4X = rewriter.create<tosa::MulOp>(loc, outType, a4, x4, /*shift=*/0);
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sum = rewriter.create<tosa::AddOp>(loc, outType, sum, a4X);
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@ -2851,13 +2857,14 @@ static Value buildUnitNormalCdf(ConversionPatternRewriter &rewriter,
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Value xMinusMean = rewriter.create<tosa::SubOp>(loc, outType, x, mean);
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// rsqrt of 2
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Value rsqrt2 =
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tosa::getConstTensor<float>(rewriter, op, 0.70710678, {}, dtype).value();
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tosa::getConstTensor<float>(rewriter, op, 0.70710678f, {}, dtype).value();
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Value erfArg = rewriter.create<tosa::MulOp>(loc, outType, xMinusMean, rsqrt2,
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/*shift=*/0);
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Value erf = approximateErfOp(rewriter, op, erfArg, dtype);
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Value erfPlus1 = rewriter.create<tosa::AddOp>(loc, outType, one, erf);
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Value oneHalf = tosa::getConstTensor<float>(rewriter, op, 0.5, {}, dtype).value();
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Value oneHalf =
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tosa::getConstTensor<float>(rewriter, op, 0.5, {}, dtype).value();
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Value normalCdf = rewriter.create<tosa::MulOp>(loc, outType, oneHalf,
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erfPlus1, /*shift=*/0);
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@ -2891,7 +2898,8 @@ LogicalResult ConvertAtenOp<AtenGeluOp>::matchAndRewrite(
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Value cdf = buildUnitNormalCdf(rewriter, op, adaptor.getSelf(), selfElemTy);
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cdf = rewriter.createOrFold<tosa::CastOp>(
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op->getLoc(), cast<RankedTensorType>(cdf.getType()).cloneWith({}, selfElemTy), cdf);
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op->getLoc(),
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cast<RankedTensorType>(cdf.getType()).cloneWith({}, selfElemTy), cdf);
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rewriter.replaceOpWithNewOp<tosa::MulOp>(
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op, getTypeConverter()->convertType(op.getType()), adaptor.getSelf(), cdf,
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@ -2927,15 +2935,16 @@ LogicalResult ConvertAtenOp<AtenGeluBackwardOp>::matchAndRewrite(
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auto loc = op->getLoc();
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const double cstAlpha0 = 1.12837916709551257390;
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const double cstAlpha1 = 0.70710678118654752440;
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const double oneHalf = 0.5;
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const double kAlpha = cstAlpha0 * cstAlpha1;
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const float cstAlpha0 = 1.12837916709551257390f;
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const float cstAlpha1 = 0.70710678118654752440f;
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const float oneHalf = 0.5f;
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const float kAlpha = cstAlpha0 * cstAlpha1;
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Value kAlphaHalf =
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tosa::getConstTensor<float>(rewriter, op, kAlpha * oneHalf, {}, selfElemTy).value();
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Value kAlphaHalf = tosa::getConstTensor<float>(rewriter, op, kAlpha * oneHalf,
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{}, selfElemTy)
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.value();
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Value negOneHalf =
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tosa::getConstTensor<float>(rewriter, op, -0.5, {}, selfElemTy).value();
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tosa::getConstTensor<float>(rewriter, op, -0.5f, {}, selfElemTy).value();
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Value inputSquared = rewriter.create<tosa::MulOp>(
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loc, selfType, adaptor.getSelf(), adaptor.getSelf(), /*shift=*/0);
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Value negHalfInputSquared = rewriter.create<tosa::MulOp>(
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@ -3006,7 +3015,8 @@ LogicalResult ConvertAtenOp<AtenHardtanhBackwardOp>::matchAndRewrite(
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return rewriter.notifyMatchFailure(op, "Only scalar constant is supported");
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}
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Value replace = tosa::getConstTensor<float>(rewriter, op, 0, {}, selfElemTy).value();
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Value replace =
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tosa::getConstTensor<float>(rewriter, op, 0, {}, selfElemTy).value();
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Type outType = getTypeConverter()->convertType(op.getType());
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Value lesser = rewriter.create<tosa::GreaterOp>(
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@ -3553,7 +3563,7 @@ LogicalResult ConvertAtenOp<Aten_IndexPutImplOp>::matchAndRewrite(
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// convert None to [0,0,0]
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auto indexNext = indexTensors[i + 1];
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auto indexNextTorch = tensorsTorchType[i + 1];
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if (indexNextTorch.getType().isa<Torch::NoneType>()){
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if (indexNextTorch.getType().isa<Torch::NoneType>()) {
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return rewriter.notifyMatchFailure(
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op, "Multiple None index is not support for now.");
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}
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@ -3620,12 +3630,13 @@ LogicalResult ConvertAtenOp<Aten_IndexPutImplOp>::matchAndRewrite(
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indicesTfConcatTensors, lastDim);
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if (!indicesTf) {
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return rewriter.notifyMatchFailure(
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op, "Convert TorchIndex To TfIndices fail.");
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return rewriter.notifyMatchFailure(op,
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"Convert TorchIndex To TfIndices fail.");
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}
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// do the tf scatterNd algorithm with tf style indices as input, algorithm mostly take from convertGatherNdOp.
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// do the tf scatterNd algorithm with tf style indices as input, algorithm
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// mostly take from convertGatherNdOp.
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auto result = tosa::convertScatterNdOp(rewriter, op, outType, input,
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indicesTf.getResult(), fillValues);
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indicesTf.getResult(), fillValues);
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if (!result) {
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return rewriter.notifyMatchFailure(
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