Add shape inference for ScalerOp (#228)
* move scalerop to decompose * change clang format * change clang format * add shape inference for scaler op * fixing generated onnxop * generate onnx.md * Add shape inference for scaler op * add benefit for scaler decompose and simplify scaler shape inference
This commit is contained in:
Anh Leu
GitHub
parent
034f98c00c
commit
c9e3ba2d64
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@ -1877,6 +1877,17 @@ LogicalResult ONNXCastOp::inferShapes() {
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return success();
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}
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//===----------------------------------------------------------------------===//
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// Scaler
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//===----------------------------------------------------------------------===//
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LogicalResult ONNXScalerOp::inferShapes() {
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ShapedType inputType = X().getType().dyn_cast<ShapedType>();
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getResult().setType(RankedTensorType::get(
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inputType.getShape(), FloatType::getF32(getContext())));
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return success();
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}
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//===----------------------------------------------------------------------===//
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// Constant
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//===----------------------------------------------------------------------===//
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@ -6103,8 +6103,7 @@ def ONNXSVMRegressorOp:ONNX_Op<"SVMRegressor",
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}
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def ONNXScalerOp:ONNX_Op<"Scaler",
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[NoSideEffect]> {
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let hasCanonicalizer = 1;
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[NoSideEffect, DeclareOpInterfaceMethods<ShapeInferenceOpInterface>]> {
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let summary = "ONNX Scaler operation";
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let description = [{
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"Rescale input data, for example to standardize features by removing the mean and scaling to unit variance."
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@ -24,6 +24,23 @@
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using namespace mlir;
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namespace {
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// Create an DenseElementsAttr of ArrayAttr.
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// This function is used to get Value Type of an EXISTING ArrayAttr for Scaler
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// function.
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DenseElementsAttr createDenseArrayAttr(
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PatternRewriter &rewriter, ArrayAttr origAttrs) {
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mlir::Type elementType = rewriter.getF32Type();
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int nElements = origAttrs.getValue().size();
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SmallVector<float, 4> wrapper(nElements, 0);
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for (int i = 0; i < nElements; ++i) {
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wrapper[i] = origAttrs.getValue()[i].cast<FloatAttr>().getValueAsDouble();
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}
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return DenseElementsAttr::get(
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RankedTensorType::get(wrapper.size(), elementType),
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llvm::makeArrayRef(wrapper));
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}
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/// Include the patterns defined in the Declarative Rewrite framework.
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#include "src/Transform/ONNX/ONNXDecompose.inc"
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@ -47,6 +64,7 @@ void DecomposeONNXToONNXPass::runOnFunction() {
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target.addIllegalOp<ONNXReduceLogSumOp>();
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target.addIllegalOp<ONNXReduceLogSumExpOp>();
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target.addIllegalOp<ONNXReduceSumSquareOp>();
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target.addIllegalOp<ONNXScalerOp>();
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OwningRewritePatternList patterns;
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populateWithGenerated(context, &patterns);
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@ -54,4 +54,68 @@ def ReduceLogSumExpOpPattern: Pat<(ONNXReduceLogSumExpOp $oprd, $axes, $keepdims
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def ReduceSumSquareOpPattern: Pat<(ONNXReduceSumSquareOp $oprd, $axes, $keepdims),
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(ONNXReduceSumOp (ONNXMulOp $oprd, $oprd), $axes, $keepdims)>;
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//===----------------------------------------------------------------------===//
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// ONNXScalerOp %X, %Offest, %Scale
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// x input, a offset, b scale
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//===----------------------------------------------------------------------===//
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// Useful test definitions.
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def AttributeIsNull :
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Constraint<CPred<"! ($_self)">,
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"Attribute is null">;
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def AttributeNotNull :
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Constraint<CPred<" ($_self)">,
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"Attribute exists">;
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def HasFloatType : Constraint<CPred<"(($_self).getType().dyn_cast<ShapedType>().getElementType().isF32())">>;
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def GetNullAttr :
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NativeCodeCall<"Attribute()">;
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// Create a DenseElementsAttr from an ArrayAttr.
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def createDenseArrayAttr:
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NativeCodeCall<"createDenseArrayAttr($_builder, $0)">;
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def ScalerT : NativeCodeCall<"$_builder.getI64IntegerAttr(1)">;
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// No attribute
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def ScalerNullPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(replaceWithValue $x),
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[(HasFloatType:$x), (AttributeIsNull:$a), (AttributeIsNull:$b)],
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(addBenefit 4)>;
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// No attribute, input x not float type
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def ScalerNullPattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXCastOp $x, (ScalerT)),
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[(AttributeIsNull:$a), (AttributeIsNull:$b)],
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(addBenefit 3)>;
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// No scale
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def ScalerNoScalePattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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[(AttributeIsNull:$b)],
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(addBenefit 2)>;
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// No offset
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def ScalerNoOffsetPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[(AttributeIsNull:$a)],
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(addBenefit 2)>;
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// Normal ONNXScalerOp
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def ScalerPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[],
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(addBenefit 1)>;
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#endif // ONNX_DECOMPOSE
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@ -18,23 +18,6 @@ using namespace mlir;
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namespace {
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// Create an DenseElementsAttr of ArrayAttr.
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// This function is used to get Value Type for Scaler function.
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DenseElementsAttr createDenseArrayAttr(
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PatternRewriter &rewriter, ArrayAttr origAttrs) {
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mlir::Type elementType = rewriter.getF32Type();
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int nElements = origAttrs.getValue().size();
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SmallVector<float, 4> wrapper(nElements, 0);
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if (origAttrs) {
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for (int i = 0; i < nElements; ++i) {
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wrapper[i] = origAttrs.getValue()[i].cast<FloatAttr>().getValueAsDouble();
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}
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}
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return DenseElementsAttr::get(
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RankedTensorType::get(wrapper.size(), elementType),
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llvm::makeArrayRef(wrapper));
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}
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// Check whether an ArrayAttr contains non-zero values or not.
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bool hasNonZeroInArrayAttr(ArrayAttr attrs) {
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bool allZeros = true;
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@ -109,13 +92,3 @@ void ONNXConvOp::getCanonicalizationPatterns(
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OwningRewritePatternList &results, MLIRContext *context) {
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results.insert<ConvOpPaddingPattern>(context);
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}
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/// on the ONNXScalerOp.
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void ONNXScalerOp::getCanonicalizationPatterns(
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OwningRewritePatternList &result, MLIRContext *context) {
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result.insert<ScalerNullPattern>(context);
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result.insert<ScalerNullPattern2>(context);
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result.insert<ScalerNoScalePattern>(context);
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result.insert<ScalerNoOffsetPattern>(context);
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result.insert<ScalerPattern>(context);
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}
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@ -100,56 +100,4 @@ def ConvOpPaddingPattern: Pat<
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[(HasNonZeroInArrayAttr:$pads), (IsNotStringAttrOfValue<"VALID"> $auto_pad)]
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>;
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//===----------------------------------------------------------------------===//
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// ONNXScalerOp %X, %Offest, %Scale
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// x input, a offset, b scale
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//===----------------------------------------------------------------------===//
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// Useful test definitions.
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def AttributeIsNull :
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Constraint<CPred<"! ($_self)">,
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"Attribute is null">;
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def HasFloatType : Constraint<CPred<"(($_self).getType().dyn_cast<ShapedType>().getElementType().isF32())">>;
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// Create a DenseElementsAttr from an ArrayAttr.
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def createDenseArrayAttr:
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NativeCodeCall<"createDenseArrayAttr($_builder, $0)">;
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def ScalerT : NativeCodeCall<"$_builder.getI64IntegerAttr(0)">;
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// No attribute
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def ScalerNullPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(replaceWithValue $x),
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[(HasFloatType:$x),(AttributeIsNull:$a), (AttributeIsNull:$b)]>;
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// No attribute, input x not float type
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def ScalerNullPattern2 : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXCastOp $x, (ScalerT)),
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[(AttributeIsNull:$a), (AttributeIsNull:$b)]>;
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// No scale
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def ScalerNoScalePattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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[(AttributeIsNull:$b)]>;
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// No offset
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def ScalerNoOffsetPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b))),
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[(AttributeIsNull:$a)]>;
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// Normal ONNXScalerOp
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def ScalerPattern : Pat<
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(ONNXScalerOp $x, $a, $b),
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(ONNXMulOp
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(ONNXSubOp $x,
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $a))),
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(ONNXConstantOp (GetNullAttr), (createDenseArrayAttr $b)))>;
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#endif // ONNX_REWRITE
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@ -96,86 +96,3 @@ func @test_gemm_add_fusion_rank3(%arg0: tensor<128x128x256xf32>, %arg1: tensor<1
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// CHECK-NEXT: [[GEMM:%.+]] = "onnx.Gemm"(%{{.*}}, %{{.*}}, %{{.*}}) {alpha = 1.000000e+00 : f32, beta = 1.000000e+00 : f32, transA = 0 : i64, transB = 0 : i64} : (tensor<128x128x256xf32>, tensor<128x128x256xf32>, tensor<256xf32>) -> tensor<*xf32>
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// return [[GEMM]] : tensor<*xf32>
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}
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// -----
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// Scaler Pattern test
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// -----
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// null
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// CHECK-LABEL: func @test_scaler_null_float(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_null_float(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: return %arg0 : tensor<3xf32>
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}
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// -----
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// null not float
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// CHECK-LABEL: func @test_scaler_null(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_null(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 0 : i64} : (tensor<3xi32>) -> tensor<3xf32>
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// CHECK-NEXT: return %0 : tensor<3xf32>
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}
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// -----
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// scaler no offset
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// CHECK-LABEL: func @test_scaler_no_offset(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_offset(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// scaler no scale
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// CHECK-LABEL: func @test_scaler_no_scale(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_scale(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// normal scaler
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// CHECK-LABEL: func @test_scaler_normal(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_normal(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32], scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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// -----
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// normal scaler with constant offset and scale
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// CHECK-LABEL: func @test_scaler_constant(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_constant(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32], scale = [3.125000e-02 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
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// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %3 : tensor<3xf32>
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}
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@ -57,3 +57,83 @@ func @test_reducesumsquare(%arg0 : tensor<?x?x?xf32>) -> tensor<*xf32> {
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// CHECK-NEXT: %{{[0-9]+}} = "onnx.ReduceSum"([[SQUARE]]) {axes = [1], keepdims = 0 : i64} : (tensor<*xf32>) -> tensor<*xf32>
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}
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// -----
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// Scaler Pattern test
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// -----
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// null
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// CHECK-LABEL: func @test_scaler_null_float(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_null_float(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: return %arg0 : tensor<3xf32>
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}
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// -----
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// null not float
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// CHECK-LABEL: func @test_scaler_null(%{{.*}}: tensor<3xi32>) -> tensor<3xf32> {
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func @test_scaler_null(%arg0: tensor<3xi32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) : (tensor<3xi32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Cast"(%arg0) {to = 1 : i64} : (tensor<3xi32>) -> tensor<3xf32>
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// CHECK-NEXT: return %0 : tensor<3xf32>
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}
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// -----
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// scaler no offset
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// CHECK-LABEL: func @test_scaler_no_offset(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_offset(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Mul"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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// scaler no scale
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// CHECK-LABEL: func @test_scaler_no_scale(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
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func @test_scaler_no_scale(%arg0: tensor<3xf32>) -> tensor<3xf32> {
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%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
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// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
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// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
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// CHECK-NEXT: return %1 : tensor<3xf32>
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}
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// -----
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||||
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// normal scaler
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// CHECK-LABEL: func @test_scaler_normal(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_normal(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32], scale = [3.125000e-02 : f32, 0.0909090936 : f32, 0.0333333351 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
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return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<[1986.99939, 0.99999988, 0.999999701]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<[3.125000e-02, 0.0909090936, 0.0333333351]> : tensor<3xf32>} : () -> tensor<3xf32>
|
||||
// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<3xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %3 : tensor<3xf32>
|
||||
}
|
||||
|
||||
// -----
|
||||
|
||||
// normal scaler with constant offset and scale
|
||||
// CHECK-LABEL: func @test_scaler_constant(%{{.*}}: tensor<3xf32>) -> tensor<3xf32> {
|
||||
func @test_scaler_constant(%arg0: tensor<3xf32>) -> tensor<3xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32], scale = [3.125000e-02 : f32]} : (tensor<3xf32>) -> tensor<3xf32>
|
||||
return %0 : tensor<3xf32>
|
||||
|
||||
// CHECK-NEXT: %0 = "onnx.Constant"() {value = dense<1986.99939> : tensor<1xf32>} : () -> tensor<1xf32>
|
||||
// CHECK-NEXT: %1 = "onnx.Sub"(%arg0, %0) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: %2 = "onnx.Constant"() {value = dense<3.125000e-02> : tensor<1xf32>} : () -> tensor<1xf32>
|
||||
// CHECK-NEXT: %3 = "onnx.Mul"(%1, %2) : (tensor<3xf32>, tensor<1xf32>) -> tensor<3xf32>
|
||||
// CHECK-NEXT: return %3 : tensor<3xf32>
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1355,3 +1355,15 @@ func @test_slice_all_constant_negative_steps(%arg0 : tensor<2x4xf32>) -> tensor<
|
|||
// CHECK: [[RES:%.+]] = "onnx.Slice"(%arg0, [[STARTS]], [[ENDS]], [[AXES]], [[STEPS]]) : (tensor<2x4xf32>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x2xf32>
|
||||
// CHECK: return [[RES]] : tensor<1x2xf32>
|
||||
}
|
||||
|
||||
//===----------------------------------------------------------------------===//
|
||||
/// Test the shape inferencing for the scaler operation.
|
||||
//===----------------------------------------------------------------------===//
|
||||
func @test_scaler_no_scale_int(%arg0: tensor<3xi32>) -> tensor<*xf32> {
|
||||
%0 = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xi32>) -> tensor<*xf32>
|
||||
"std.return"(%0) : (tensor<*xf32>) -> ()
|
||||
|
||||
// CHECK-LABEL: test_scaler_no_scale_int
|
||||
// CHECK: [[RES_ATTR:%.+]] = "onnx.Scaler"(%arg0) {offset = [1986.99939 : f32, 0.99999988 : f32, 0.999999701 : f32]} : (tensor<3xi32>) -> tensor<3xf32>
|
||||
// CHECK: return [[RES_ATTR]] : tensor<3xf32>
|
||||
}
|
||||
|
|
|
|||
|
|
@ -252,11 +252,11 @@ OpsWithShapeInference = [
|
|||
'Sign', 'Constant', 'AveragePool', 'Abs', 'Conv', 'Concat', 'Neg', 'RNN',
|
||||
'LSTM', 'GRU', 'Split', 'Pad', 'Cast', 'ConvTranspose', 'Flatten',
|
||||
'DynamicQuantizeLinear', 'QuantizeLinear', 'DequantizeLinear', 'ConvInteger',
|
||||
'Squeeze', 'Shape', 'Tile', 'Gather', 'ConstantOfShape', 'Slice'
|
||||
'Squeeze', 'Shape', 'Tile', 'Gather', 'ConstantOfShape', 'Slice', 'Scaler'
|
||||
]
|
||||
|
||||
# Operations supporting canonicalization.
|
||||
OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv', 'Scaler']
|
||||
OpsWithCanonicalizer = ['Add', 'Identity', 'Gemm', 'Conv']
|
||||
|
||||
# Operations who have operands that, if produced by constant operations, should
|
||||
# be promoted to become an attribute (via attribute promotion).
|
||||
|
|
|
|||
Loading…
Reference in New Issue