| /* Copyright 2019 The TensorFlow Authors. All Rights Reserved. |
| |
| Licensed under the Apache License, Version 2.0 (the "License"); |
| you may not use this file except in compliance with the License. |
| You may obtain a copy of the License at |
| |
| http://www.apache.org/licenses/LICENSE-2.0 |
| |
| Unless required by applicable law or agreed to in writing, software |
| distributed under the License is distributed on an "AS IS" BASIS, |
| WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| See the License for the specific language governing permissions and |
| limitations under the License. |
| ==============================================================================*/ |
| |
| // This is the optimization pattern definition file for TensorFlow Lite. |
| |
| include "mlir/IR/OpBase.td" |
| include "mlir/Dialect/StandardOps/IR/Ops.td" |
| include "tensorflow/compiler/mlir/lite/ir/tfl_ops.td" |
| include "tensorflow/compiler/mlir/lite/utils/utils.td" |
| include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.td" |
| |
| // Checks if the param passed is a F32 ElementsAttr. |
| def F32ElementsAttr : ElementsAttrBase< |
| CPred<"$_self.isa<ElementsAttr>() && $_self.cast<ElementsAttr>().getType().getElementType().isF32()">, |
| "32 bit float constant tensor">; |
| |
| // Checks if the param passed is a float ElementsAttr. |
| def FloatElementsAttr : ElementsAttrBase< |
| CPred<"$_self.isa<ElementsAttr>() && $_self.cast<ElementsAttr>().getType().getElementType().isa<FloatType>()">, |
| "float constant tensor">; |
| |
| // Checks if the param passed is of NoneType. |
| def IsNoneType : Constraint<CPred<"$0.getType().isa<NoneType>()">>; |
| |
| def ExtractSingleElementAsFloat : NativeCodeCall< |
| "ExtractSingleElementAsFloat($_self.cast<ElementsAttr>())">; |
| |
| // Checks if the value has rank at most 'n'. |
| class HasRankAtMost<int n> : Constraint< |
| CPred<"$0.getType().cast<ShapedType>().getRank() <= " # n>>; |
| |
| //===----------------------------------------------------------------------===// |
| // Ternary ops patterns. |
| //===----------------------------------------------------------------------===// |
| // Multi-pattern consisting of matching stand-alone convolution op followed by |
| // activation op. |
| multiclass FuseActFnIntoConvOpPat<Op ActFnOp, Attr ActFnAttr> { |
| def FuseActivationFuncWithConv#ActFnOp#ActFnAttr : Pat< |
| (ActFnOp (TFL_Conv2DOp:$conv_out $input, $filter, $bias, $h_factor, |
| $w_factor, TFL_AF_None, $padding, $stride_h, $stride_w)), |
| (TFL_Conv2DOp $input, $filter, $bias, $h_factor, $w_factor, ActFnAttr, |
| $padding, $stride_h, $stride_w), |
| [(HasOneUse $conv_out)]>; |
| def FuseActivationFuncWithDepthwiseConv#ActFnOp#ActFnAttr : Pat< |
| (ActFnOp (TFL_DepthwiseConv2DOp:$conv_out $input, $filter, $bias, $h_factor, |
| $w_factor, TFL_AF_None, $padding, $stride_h, $stride_w, |
| $multiplier)), |
| (TFL_DepthwiseConv2DOp $input, $filter, $bias, $h_factor, $w_factor, |
| ActFnAttr, $padding, $stride_h, $stride_w, $multiplier), |
| [(HasOneUse $conv_out)]>; |
| } |
| |
| multiclass FuseActFnIntoPoolOpPat<Op ActFnOp, Attr ActFnAttr> { |
| def FuseActivationFuncWithAvgPool#ActFnOp#ActFnAttr : Pat< |
| (ActFnOp (TFL_AveragePool2DOp:$pool_out $input, $filter_height, |
| $filter_width, $padding, $stride_h, $stride_w, TFL_AF_None)), |
| (TFL_AveragePool2DOp $input, $filter_height, $filter_width, $padding, |
| $stride_h, $stride_w, ActFnAttr), |
| [(HasOneUse $pool_out)]>; |
| def FuseActivationFuncWithMaxPool#ActFnOp#ActFnAttr : Pat< |
| (ActFnOp (TFL_MaxPool2DOp:$pool_out $input, $padding, $stride_w, $stride_h, |
| $filter_width, $filter_height, TFL_AF_None)), |
| (TFL_MaxPool2DOp $input, $padding, $stride_w, $stride_h, |
| $filter_width, $filter_height, ActFnAttr), |
| [(HasOneUse $pool_out)]>; |
| } |
| |
| // TODO(hinsu): Also fuse ops corresponding to SIGN_BIT fused |
| // activation functions. |
| // Currently we're not fusing tanh, sigmoid, hard_swish and other activations |
| // those cannot be simply translated into clamping. |
| foreach actFnPair = [[TFL_ReluOp, TFL_AF_Relu], |
| [TFL_Relu6Op, TFL_AF_Relu6], |
| [TFL_Relu1Op, TFL_AF_Relu1]] in { |
| defm : FuseActFnIntoConvOpPat<!cast<Op>(actFnPair[0]), !cast<Attr>(actFnPair[1])>; |
| defm : FuseActFnIntoPoolOpPat<!cast<Op>(actFnPair[0]), !cast<Attr>(actFnPair[1])>; |
| } |
| |
| class CanFuseConvOrDepthwiseConv<string is_depthwise> : Constraint< |
| CPred<"TFL::CanFuseConvOrDepthwiseConv($0, $1, " # is_depthwise # ")">>; |
| |
| // If we see a binary op (add, sub) op adding a constant value to a convolution |
| // op with constant bias, we can fuse the binary op into the convolution op by |
| // constant folding the bias and the binary op's constant operand. The following |
| // pattern restricts to float constant values for now. |
| multiclass FuseBinaryOpToPrecedingAffine<Op binaryOp> { |
| def FuseBinaryOpWithConv#binaryOp : Pat< |
| (binaryOp (TFL_Conv2DOp:$output $input, $filter, |
| (ConstantOp FloatElementsAttr:$bias), $h_factor, $w_factor, |
| TFL_AF_None, $padding, $stride_h, $stride_w), |
| (ConstantOp FloatElementsAttr:$value), $act_fn), |
| (TFL_Conv2DOp $input, $filter, |
| (binaryOp (ConstantOp $bias), |
| (ConstantOp $value), TFL_AF_None), |
| $h_factor, $w_factor, $act_fn, $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $filter, $value), |
| (HasOneUse $output)]>; |
| def FuseBinaryOpWithDepthwiseConv#binaryOp : Pat< |
| (binaryOp (TFL_DepthwiseConv2DOp:$output $input, $filter, |
| (ConstantOp FloatElementsAttr:$bias), |
| $h_factor, $w_factor, TFL_AF_None, $padding, $stride_h, |
| $stride_w, $multiplier), |
| (ConstantOp FloatElementsAttr:$value), $act_fn), |
| (TFL_DepthwiseConv2DOp $input, $filter, |
| (binaryOp (ConstantOp $bias), (ConstantOp $value), TFL_AF_None), |
| $h_factor, $w_factor, $act_fn, $padding, $stride_h, $stride_w, |
| $multiplier), |
| [(CanFuseConvOrDepthwiseConv<"true"> $filter, $value), |
| (HasOneUse $output)]>; |
| def FuseBinaryOpWithTransposeConv#binaryOp : Pat< |
| (binaryOp (TFL_TransposeConvOp:$output $output_shape, $weights, $inputs, |
| (ConstantOp FloatElementsAttr:$bias), $padding, |
| $stride_h, $stride_w), |
| (ConstantOp FloatElementsAttr:$value), TFL_AF_None), |
| (TFL_TransposeConvOp $output_shape, $weights, $inputs, |
| (binaryOp (ConstantOp $bias), |
| (ConstantOp $value), TFL_AF_None), |
| $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $weights, $value), |
| (HasOneUse $output)]>; |
| // Fuse for TransposeConv with no bias |
| def FuseBinaryOpWithTransposeConvNoneBias#binaryOp : Pat< |
| (binaryOp (TFL_TransposeConvOp:$output $output_shape, $weights, $inputs, |
| (ConstantOp $bias), $padding, |
| $stride_h, $stride_w), |
| (ConstantOp FloatElementsAttr:$value), TFL_AF_None), |
| (TFL_TransposeConvOp $output_shape, $weights, $inputs, |
| (ConstantOp $value), |
| $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $weights, $value), |
| (IsNoneType $bias), |
| (HasOneUse $output)]>; |
| } |
| foreach binaryOp = [TFL_AddOp, TFL_SubOp]<Op> in |
| defm : FuseBinaryOpToPrecedingAffine<binaryOp>; |
| |
| def ExpandTo4DForConv: NativeCodeCall<"ExpandTo4DForConv($0)">; |
| |
| def ExpandTo4DForDepthwiseConv: NativeCodeCall< |
| "ExpandTo4DForDepthwiseConv($0)">; |
| |
| // If we see a (div or Mul) op (dividing/multiplying) a constant value |
| // to a convolution op with constant filter and bias, we can fuse the div/mul |
| // into the convolution op by constant folding |
| // the filter/bias and the div/mul op's constant operand. |
| // The following pattern restricts to float constant values for now. |
| |
| multiclass FuseMulOrDivWithConv2dOrDepthwiseConv2d<Op BinaryOp> { |
| def FuseMulOrDivWithDepthwiseConv#BinaryOp : Pat< |
| (BinaryOp (TFL_DepthwiseConv2DOp:$output $input, |
| (ConstantOp FloatElementsAttr:$filter), |
| (ConstantOp FloatElementsAttr:$bias), |
| $h_factor, $w_factor, TFL_AF_None, $padding, $stride_h, |
| $stride_w, $multiplier), |
| (ConstantOp FloatElementsAttr:$value), $act_fn), |
| (TFL_DepthwiseConv2DOp $input, |
| (BinaryOp |
| (ConstantOp $filter), |
| (ConstantOp (ExpandTo4DForDepthwiseConv $value)), |
| TFL_AF_None), |
| (BinaryOp |
| (ConstantOp $bias), |
| (ConstantOp $value), |
| TFL_AF_None), |
| $h_factor, $w_factor, $act_fn, $padding, $stride_h, |
| $stride_w, $multiplier), |
| [(CanFuseConvOrDepthwiseConv<"true"> $filter, $value), |
| (HasOneUse $output)]>; |
| def FuseMulOrDivWithConv#BinaryOp : Pat< |
| (BinaryOp (TFL_Conv2DOp:$conv_output $input, |
| (ConstantOp FloatElementsAttr:$filter), |
| (ConstantOp FloatElementsAttr:$bias), |
| $h_factor, $w_factor, TFL_AF_None, |
| $padding, $stride_h, $stride_w), |
| (ConstantOp FloatElementsAttr:$value), $act_fn), |
| (TFL_Conv2DOp $input, |
| (BinaryOp (ConstantOp $filter), |
| (ConstantOp (ExpandTo4DForConv $value)), |
| TFL_AF_None), |
| (BinaryOp (ConstantOp $bias), |
| (ConstantOp $value), |
| TFL_AF_None), |
| $h_factor, $w_factor, $act_fn, $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $filter, $value), |
| (HasOneUse $conv_output)]>; |
| def FuseMulOrDivWithTransposeConv#BinaryOp : Pat< |
| (BinaryOp (TFL_TransposeConvOp:$output $output_shape, |
| (ConstantOp FloatElementsAttr:$weights), $input, |
| (ConstantOp FloatElementsAttr:$bias), |
| $padding, $stride_h, $stride_w), |
| (ConstantOp $value), TFL_AF_None), |
| (TFL_TransposeConvOp $output_shape, |
| (BinaryOp (ConstantOp $weights), |
| (ConstantOp (ExpandTo4DForConv $value)), |
| TFL_AF_None), |
| $input, |
| (BinaryOp (ConstantOp $bias), |
| (ConstantOp $value), |
| TFL_AF_None), |
| $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $weights, $value), |
| (HasOneUse $output)]>; |
| def FuseMulOrDivWithTransposeConvWithNoneBias#BinaryOp : Pat< |
| (BinaryOp (TFL_TransposeConvOp:$output $output_shape, |
| (ConstantOp FloatElementsAttr:$weights), $input, |
| (ConstantOp $bias), |
| $padding, $stride_h, $stride_w), |
| (ConstantOp $value), TFL_AF_None), |
| (TFL_TransposeConvOp $output_shape, |
| (BinaryOp (ConstantOp $weights), |
| (ConstantOp (ExpandTo4DForConv $value)), |
| TFL_AF_None), |
| $input, |
| (ConstantOp $bias), |
| $padding, $stride_h, $stride_w), |
| [(CanFuseConvOrDepthwiseConv<"false"> $weights, $value), |
| (IsNoneType $bias), |
| (HasOneUse $output)]>; |
| } |
| |
| foreach BinaryOp = [TFL_DivOp, TFL_MulOp]<Op> in |
| defm : FuseMulOrDivWithConv2dOrDepthwiseConv2d<BinaryOp>; |
| |
| |
| // This pattern applies when the same quantize/dequantize have been used twice |
| // with the same scale. We want to remove the redundancy. |
| // TODO(fengliuai): move this to the sanity check of pre-quantize pass. |
| def eliminate_dq_q_pairs : Pat< |
| (TFL_QuantizeOp (TFL_DequantizeOp $in), $qt), |
| (replaceWithValue $in), |
| [(NotFromQuantOpOrSameQuantType $in, $qt)]>; |
| |
| |
| |
| |
| // Checks if the operand has rank == n |
| class OperandHasRank<int n> : Constraint< |
| CPred<"$0.getType().cast<ShapedType>().getRank() == " # n>>; |
| |
| // Matching HardSwish |
| def MatchHardSwishPattern1 : Pat< |
| (TFL_MulOp |
| (TFL_MulOp |
| $x, (TFL_AddOp |
| $x, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">), |
| TFL_AF_Relu6), |
| TFL_AF_None), |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">), |
| TFL_AF_None), |
| (TFL_HardSwishOp $x)>; |
| |
| def MatchHardSwishPattern2 : Pat< |
| (TFL_MulOp |
| $x, |
| (TFL_MulOp |
| (TFL_AddOp |
| $x, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">), |
| TFL_AF_Relu6), |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">), |
| TFL_AF_None), |
| TFL_AF_None), |
| (TFL_HardSwishOp $x)>; |
| |
| def MatchHardSwishPattern3 : Pat< |
| (TFL_MulOp |
| (TFL_MulOp |
| $x, |
| (TFL_AddOp |
| $x, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">), |
| TFL_AF_Relu6), |
| TFL_AF_None), |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">), |
| TFL_AF_None), |
| (TFL_HardSwishOp $x)>; |
| |
| def MatchHardSwishPattern4 : Pat< |
| (TFL_MulOp |
| (TFL_MulOp |
| (TFL_AddOp |
| $x, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">), |
| TFL_AF_Relu6), |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">), |
| TFL_AF_None), |
| $x, |
| TFL_AF_None), |
| (TFL_HardSwishOp $x)>; |
| |
| // Matching HardSwish with extra FakeQuant. These FakeQuant ops were due to |
| // incorrect placement in the quantization aware training. |
| def MatchHardSwishQuantized : Pat< |
| (TFL_MulOp (TFL_DequantizeOp (TFL_QuantizeOp |
| (TFL_MulOp |
| $x, (TFL_DequantizeOp (TFL_QuantizeOp (TFL_AddOp |
| $x, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "3.0f">), |
| TFL_AF_Relu6), $qattr2)), |
| TFL_AF_None), $qattr1)), |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.166666666f">), |
| TFL_AF_None), |
| (TFL_HardSwishOp $x)>; |
| |
| // Constraint that the attribute value is less than 'n' |
| class ConstDoubleValueLessThan<string n> : Constraint< |
| CPred<"$0.isa<DenseElementsAttr>() && " |
| "$0.cast<DenseElementsAttr>().getNumElements() == 1 && " |
| "std::abs(*$0.cast<DenseElementsAttr>().getValues<float>().begin()) < " |
| # n>>; |
| |
| def L2NormValidReduceIndex : Constraint<CPred< |
| "L2NormalizeReduceAxis($0, $1.cast<DenseElementsAttr>())">>; |
| |
| // Currently L2Normalization doesn't support activation function |
| // in TFLite. |
| // TODO(karimnosseir): Add constraints that the kernel code assumes. |
| // constraint on axis and depth. |
| multiclass L2NormalizePatterns<Op FirstOp, Op SecondOp> { |
| // This pattern constructs L2NormalizationOp from |
| // Mul->Rsqrt->Sum->Square Or |
| // Div->sqrt->Sum->Square |
| def L2NormalizePattern1#FirstOp#SecondOp : Pat< |
| (FirstOp $x, |
| (SecondOp |
| (TFL_SumOp |
| (TFL_SquareOp:$sq_op $x), |
| (ConstantOp I32ElementsAttr:$axis), |
| $keep_dims)), |
| TFL_AF_None), |
| (TFL_L2NormalizationOp $x, TFL_AF_None), |
| [(L2NormValidReduceIndex $sq_op, $axis)]>; |
| |
| // Below patterns for L2Normalize when there is an Add or Maximum |
| // adding or clamping to a small constant scalar. |
| def L2NormalizePattern2#FirstOp#SecondOp : Pat< |
| (FirstOp $x, |
| (SecondOp |
| (TFL_AddOp |
| (TFL_SumOp |
| (TFL_SquareOp:$sq_op $x), |
| (ConstantOp I32ElementsAttr:$axis), |
| $keep_dims), |
| (ConstantOp $epsilon), TFL_AF_None)), |
| TFL_AF_None), |
| (TFL_L2NormalizationOp $x, TFL_AF_None), |
| [(L2NormValidReduceIndex $sq_op, $axis), |
| (ConstDoubleValueLessThan<"1e-3"> $epsilon)]>; |
| |
| def L2NormalizePattern3#FirstOp#SecondOp : Pat< |
| (FirstOp $x, |
| (SecondOp |
| (TFL_MaximumOp |
| (TFL_SumOp |
| (TFL_SquareOp:$sq_op $x), |
| (ConstantOp I32ElementsAttr:$axis), |
| $keep_dims), |
| (ConstantOp $epsilon))), |
| TFL_AF_None), |
| (TFL_L2NormalizationOp $x, TFL_AF_None), |
| [(L2NormValidReduceIndex $sq_op, $axis), |
| (ConstDoubleValueLessThan<"1e-3"> $epsilon)]>; |
| |
| } |
| |
| foreach L2NormalizePairs = [[TFL_MulOp, TFL_RsqrtOp], [TFL_DivOp, TFL_SqrtOp]] |
| in defm : L2NormalizePatterns<L2NormalizePairs[0], L2NormalizePairs[1]>; |
| |
| //===----------------------------------------------------------------------===// |
| // Binary ops patterns. |
| //===----------------------------------------------------------------------===// |
| def AreBroadcastableTypes : Constraint<CPred< |
| "TFL::IsBroadcastableElementsAttrAndType($0.getType(), $1.getType())">>; |
| |
| def OperandsBroadcastToOutputType : Constraint<CPred< |
| "TFL::OperandsBroadcastToOutputType($0.getType(), $1.getType(), " |
| "$2.getType())">>; |
| |
| def IsTailOfShape : Constraint<CPred< |
| "TFL::IsTailOfShape($0.getType(), $1.getType())">>; |
| |
| def Flatten : NativeCodeCall< |
| "$0.cast<DenseElementsAttr>()" |
| ".reshape(RankedTensorType::get({$0.getType().cast<ShapedType>().getNumElements()}, " |
| "$0.getType().cast<ShapedType>().getElementType()))">; |
| |
| def IsLastDimEqualToNumElements : Constraint<CPred< |
| "$0.getType().cast<ShapedType>().getRank() >= 1 && " |
| "$0.getType().cast<ShapedType>().getDimSize($0.getType().cast<ShapedType>().getRank() - 1) == " |
| "$1.getType().cast<ShapedType>().getNumElements()">>; |
| |
| def IsDefinedByFullyConnectedOp : Constraint<CPred< |
| "$0.getDefiningOp<TFL::FullyConnectedOp>() != nullptr">>; |
| |
| // Pattern for skipping Tile if it is mainly for broadcasting and the |
| // Op is already supporting broadcasting. |
| multiclass FuseTileBroadcastIntoFollowingBinary<Op BinaryOp> { |
| def FuseTileBroadcastToBinaryOp1#BinaryOp : Pat< |
| (BinaryOp:$result (TFL_TileOp $input, (ConstantOp $tile)), |
| $operand, $act_func), |
| (BinaryOp $input, $operand, $act_func), |
| [(OperandsBroadcastToOutputType $input, $operand, $result), |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $operand)]>; |
| |
| def FuseTileBroadcastToBinaryOp2#BinaryOp : Pat< |
| (BinaryOp:$result $operand, |
| (TFL_TileOp $input, (ConstantOp $tile)), $act_func), |
| (BinaryOp $operand, $input, $act_func), |
| [(OperandsBroadcastToOutputType $operand, $input, $result), |
| (HasRankAtMost<4> $operand), |
| (HasRankAtMost<4> $input)]>; |
| } |
| |
| // Multi-pattern consisting of matching stand-alone op or op followed by relu. |
| multiclass FusedBinaryActivationFuncOpPat<Op BinaryOp> { |
| foreach actFnPair = [[TFL_ReluOp, TFL_AF_Relu], |
| [TFL_Relu6Op, TFL_AF_Relu6], |
| [TFL_Relu1Op, TFL_AF_Relu1]] in { |
| def FuseBinaryWithActivation#BinaryOp#actFnPair[0] : Pat< |
| (actFnPair[0] (BinaryOp:$binary_out $lhs, $rhs, TFL_AF_None)), |
| (BinaryOp $lhs, $rhs, actFnPair[1]), |
| [(HasOneUse $binary_out)]>; |
| } |
| } |
| |
| foreach BinaryOp = [TFL_AddOp, TFL_SubOp, TFL_DivOp, TFL_MulOp] in { |
| defm : FuseTileBroadcastIntoFollowingBinary<BinaryOp>; |
| |
| // Instantiated FusedBinary patterns for the from-to pairs of ops. |
| defm : FusedBinaryActivationFuncOpPat<BinaryOp>; |
| |
| // Move binary op before reshape: reshape -> binary => binary -> reshape. |
| // This is valid only when the binary operand is constant and the shape is the |
| // tail of the other operand and the intermediate result isn't used by other |
| // ops. |
| // $rhs is required to be the tail shape of $lhs, so after transformation the |
| // shape of the binary op result is valid. For example, assume the shapes of |
| // $input, $lhs and $rhs are [1600], [1,40,40] and [40x1]. After the |
| // transformation, the shape of the binary op result is [40x1600], which |
| // couldn't be reshaped to [1,40,40]. `IsTailOfShape` constraint is added to |
| // make sure $rhs is the tail shape of $lhs. |
| def MoveBinaryOpConstBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input, (ConstantOp:$shape $s)), |
| (ConstantOp:$rhs $a), $act_fn), |
| (TFL_ReshapeOp (BinaryOp $input, $rhs, $act_fn), $shape), |
| // The broadcasting of "BinaryOp" only happens in the lower |
| // dimensions, and the higher dimensions are same, so we know the |
| // result and input of the "BinaryOp" in the source pattern have |
| // the same shape, which is defined by `shape`. |
| [(IsTailOfShape $rhs, $lhs), |
| (HasOneUse $lhs), |
| // The result of the new "BinaryOp" will have the same shape as |
| // `input`. In other words, the shape of the `Reshape` op are not |
| // changed after the transformation. |
| (IsTailOfShape $rhs, $input), |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $lhs), |
| (HasRankAtMost<4> $rhs)]>; |
| |
| // Move binary op before reshape: |
| // binary(reshape(lhs), reshape(rhs)) => reshape(binary(lhs, rhs)) |
| // This is valid only when both side of the binary operand is reshaped, and |
| // the sizes are the same both before and after the reshape. |
| def MoveBinaryOpBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input1, (ConstantOp:$shape1 $s1)), |
| (TFL_ReshapeOp:$rhs $input2, (ConstantOp:$shape2 $s2)), |
| $act_fn), |
| (TFL_ReshapeOp (BinaryOp $input1, $input2, $act_fn), $shape1), |
| [(IsTailOfShape $rhs, $lhs), |
| (IsTailOfShape $lhs, $rhs), |
| (IsTailOfShape $input1, $input2), |
| (IsTailOfShape $input2, $input1)]>; |
| |
| // Move binary op before reshape: |
| // binary(reshape(lhs), rhs) => reshape(binary(lhs, flatten(rhs))) |
| // This is valid only when the last dimension of lhs is equal to the |
| // number of elements in constant rhs. |
| // Therefore, after transformation broadcast of binary op is always |
| // applied to the last dimension of $input. |
| def MoveBinaryOpFlattenConstBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input, (ConstantOp:$shape $s)), |
| (ConstantOp:$rhs ElementsAttr:$rhs_attr), $act_fn), |
| (TFL_ReshapeOp (BinaryOp $input, (ConstantOp (Flatten $rhs_attr)), |
| $act_fn), |
| $shape), |
| [(AnyStaticShapeTensor $input), |
| (IsTailOfShape $rhs, $lhs), |
| (IsLastDimEqualToNumElements $input, $rhs), |
| (HasOneUse $lhs), |
| // Restrict operands to have at most rank 4 because TFLite binary |
| // kernel supports up to 4D broadcast. |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $lhs), |
| (HasRankAtMost<4> $rhs), |
| (IsDefinedByFullyConnectedOp $input)]>; |
| } |
| |
| foreach BinaryOp = [TFL_FloorDivOp, TFL_FloorModOp, TFL_MinimumOp, |
| TFL_MaximumOp, TFL_LessOp, TFL_LessEqualOp, TFL_GreaterOp, |
| TFL_GreaterEqualOp] in { |
| // Move binary op before reshape: reshape -> binary => binary -> reshape. |
| // This is valid only when the binary operand is constant and the shape is the |
| // tail of the other operand and the intermediate result isn't used by other |
| // ops. |
| // $rhs is required to be the tail shape of $lhs, so after transformation the |
| // shape of the binary op result is valid. For example, assume the shapes of |
| // $input, $lhs and $rhs are [1600], [1,40,40] and [40x1]. After the |
| // transformation, the shape of the binary op result is [40x1600], which |
| // couldn't be reshaped to [1,40,40]. `IsTailOfShape` constraint is added to |
| // make sure $rhs is the tail shape of $lhs. |
| def MoveBinaryOpConstBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input, (ConstantOp:$shape $s)), |
| (ConstantOp:$rhs $a)), |
| (TFL_ReshapeOp (BinaryOp $input, $rhs), $shape), |
| // The broadcasting of "BinaryOp" only happens in the lower |
| // dimensions, and the higher dimensions are same, so we know the |
| // result and input of the "BinaryOp" in the source pattern have |
| // the same shape, which is defined by `shape`. |
| [(IsTailOfShape $rhs, $lhs), |
| (HasOneUse $lhs), |
| // The result of the new "BinaryOp" will have the same shape as |
| // `input`. In other words, the shape of the `Reshape` op are not |
| // changed after the transformation. |
| (IsTailOfShape $rhs, $input), |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $lhs), |
| (HasRankAtMost<4> $rhs)]>; |
| |
| // Move binary op before reshape: |
| // binary(reshape(lhs), reshape(rhs)) => reshape(binary(lhs, rhs)) |
| // This is valid only when both side of the binary operand is reshaped, and |
| // the sizes are the same both before and after the reshape. |
| def MoveBinaryOpBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input1, (ConstantOp:$shape1 $s1)), |
| (TFL_ReshapeOp:$rhs $input2, (ConstantOp:$shape2 $s2))), |
| (TFL_ReshapeOp (BinaryOp $input1, $input2), $shape1), |
| [(IsTailOfShape $rhs, $lhs), |
| (IsTailOfShape $lhs, $rhs), |
| (IsTailOfShape $input1, $input2), |
| (IsTailOfShape $input2, $input1)]>; |
| |
| // Move binary op before reshape: |
| // binary(reshape(lhs), rhs) => reshape(binary(lhs, flatten(rhs))) |
| // This is valid only when the last dimension of lhs is equal to the |
| // number of elements in constant rhs. |
| // Therefore, after transformation broadcast of binary op is always |
| // applied to the last dimension of $input. |
| def MoveBinaryOpFlattenConstBeforeReshape#BinaryOp : Pat< |
| (BinaryOp (TFL_ReshapeOp:$lhs $input, (ConstantOp:$shape $s)), |
| (ConstantOp:$rhs ElementsAttr:$rhs_attr)), |
| (TFL_ReshapeOp (BinaryOp $input, (ConstantOp (Flatten $rhs_attr))), |
| $shape), |
| [(AnyStaticShapeTensor $input), |
| (IsTailOfShape $rhs, $lhs), |
| (IsLastDimEqualToNumElements $input, $rhs), |
| (HasOneUse $lhs), |
| // Restrict operands to have at most rank 4 because TFLite binary |
| // kernel supports up to 4D broadcast. |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $lhs), |
| (HasRankAtMost<4> $rhs), |
| (IsDefinedByFullyConnectedOp $input)]>; |
| } |
| |
| // Reorder the element-wise value operations and the element move operations, |
| // such that the value operation happens before move operation. |
| foreach ValueOp = [TFL_CeilOp, TFL_ExpOp, TFL_FloorOp, TFL_NegOp, |
| TFL_ReluOp, TFL_Relu1Op, TFL_Relu6Op, TFL_RoundOp, |
| TFL_TanhOp, TFL_SqrtOp, TFL_SquareOp, TFL_LogisticOp] in { |
| foreach MoveOp = [TFL_DepthToSpaceOp, TFL_ExpandDimsOp, TFL_SqueezeOp, |
| TFL_ReshapeOp, TFL_TransposeOp] in { |
| def ReorderElementwiseAndMoveOperations#ValueOp#MoveOp : Pat< |
| (ValueOp:$value (MoveOp:$move $input, $move_def)), |
| (MoveOp (ValueOp $input), $move_def), |
| [(HasOneUse $move)]>; |
| } |
| } |
| |
| // Returns shape of a ranked tensor. |
| // if called without a ranked tensor it will fail. |
| def GetShape: NativeCodeCall<"GetShape($0)">; |
| |
| // Returns True if the operand type is RankedTensorType and valid. |
| def HasValidRankedTensor : Constraint<CPred< |
| "$0.getType().isa<RankedTensorType>() && " |
| "$0.getType().cast<RankedTensorType>().getNumDynamicDims() <= 1">>; |
| |
| def ConvertSqueezeToReshape : Pat< |
| (TFL_SqueezeOp:$squeeze_op $input, $squeeze_dims), |
| (TFL_ReshapeOp $input, (ConstantOp (GetShape $squeeze_op))), |
| [(HasValidRankedTensor $squeeze_op)]>; |
| |
| // Convert expand_dims to reshape if possible. |
| def ConvertExpandDimsToReshape : Pat< |
| (TFL_ExpandDimsOp:$expand_dims_op $input, $dim), |
| (TFL_ReshapeOp $input, (ConstantOp (GetShape $expand_dims_op))), |
| [(AnyStaticShapeTensor $expand_dims_op)]>; |
| |
| class FloatValueEquals<string val> : Constraint<CPred< |
| "FloatValueEquals($0, " # val # ")">>; |
| |
| // ReLU patterns |
| def MatchReluPattern : Pat< |
| (TFL_MaximumOp $input, (ConstantOp $Zero)), |
| (TFL_ReluOp $input), |
| [(FloatValueEquals<"0"> $Zero)]>; |
| |
| def MatchRelu1Pattern1 : Pat< |
| (TFL_MinimumOp (TFL_MaximumOp $input, (ConstantOp $NegOne)), |
| (ConstantOp $One)), |
| (TFL_Relu1Op $input), |
| [(FloatValueEquals<"-1"> $NegOne), (FloatValueEquals<"1"> $One)]>; |
| |
| def MatchRelu1Pattern2 : Pat< |
| (TFL_MaximumOp (TFL_MinimumOp $input, (ConstantOp $One)), |
| (ConstantOp $NegOne)), |
| (TFL_Relu1Op $input), |
| [(FloatValueEquals<"-1"> $NegOne), (FloatValueEquals<"1"> $One)]>; |
| |
| def MatchLeakyRelu : Pat< |
| (TFL_MaximumOp |
| (TFL_MulOp:$mul_out $x, |
| (ConstantOp F32ElementsAttr:$alpha), TFL_AF_None), |
| $x), |
| (TFL_LeakyReluOp $x, ExtractSingleElementAsFloat:$alpha), |
| [(ConstDoubleValueLessThan<"1"> $alpha), |
| (HasOneUse $mul_out)]>; |
| |
| // Returns True if all users of this operation are in TF/TFL and don't need |
| // shape exact matching. This prevents from removing cast on return values which |
| // can break the verifier on function type mismatch. |
| def AllUsersInTF : Constraint<CPred<[{ |
| llvm::all_of($0.getUsers(), [&](Operation *user) { |
| auto name = user->getName().getDialectNamespace(); |
| return name == "tf" || name == "tfl"; |
| }) |
| }]>, "all users are TF/TFL operations.">; |
| |
| def RemoveShapeOnlyCast : Pat<(TFL_CastOp:$output $input), |
| (replaceWithValue $input), |
| [(SameElementType $input, $output), |
| (AllUsersInTF $output)]>; |
| |
| |
| // Checks if the operand0's rank is one less than operand1's rank. |
| def PReluAlphaRankCheck : Constraint< |
| CPred<"$0.getType().cast<ShapedType>().getRank() == " |
| "$1.getType().cast<ShapedType>().getRank() - 1">>; |
| |
| // PReLU pattern from Keras: |
| // f(x) = Relu(x) + (-alpha * Relu(-x)) |
| def MatchPRelu : Pat< |
| (TFL_AddOp |
| (TFL_ReluOp:$relu_out $x), |
| (TFL_MulOp:$mul_out |
| (TFL_ReluOp (TFL_NegOp:$input_neg_out $x)), |
| $neg_alpha, |
| TFL_AF_None), |
| TFL_AF_None), |
| (TFL_PReluOp $x, (TFL_NegOp $neg_alpha)), |
| [(PReluAlphaRankCheck $neg_alpha, $x), |
| (HasOneUse $relu_out), |
| (HasOneUse $mul_out), |
| (HasOneUse $input_neg_out)]>; |
| |
| // The constant folding in this pass might produce constant in the tf dialect. |
| // This rule is to legalize these constant to the tfl dialect. |
| def LegalizeConstOp : Pat< |
| (TF_ConstOp ElementsAttr:$value), (TFL_ConstOp $value)>; |
| |
| // Reorders adds to allow constant folding. |
| // Add --> Add $input, $constantA |
| // \--> $constantB |
| // To |
| // Add --> $input |
| // \--> Add ($constantA, $constantB) |
| foreach ActFun = [TFL_AF_Relu, TFL_AF_Relu6, TFL_AF_Relu1, TFL_AF_None] in { |
| def ReorderAddToAllowConstFold_ActFunc_#ActFun : Pat< |
| (TFL_AddOp |
| (TFL_AddOp:$first_output $input, (ConstantOp $a), TFL_AF_None), |
| (ConstantOp $b), ActFun), |
| (TFL_AddOp $input, |
| (TFL_AddOp (ConstantOp $a), (ConstantOp $b), TFL_AF_None), |
| ActFun), |
| [(HasOneUse $first_output), |
| (HasRankAtMost<4> $input), |
| (HasRankAtMost<4> $a), |
| (HasRankAtMost<4> $b)]>; |
| } |
| |
| // We can eliminate Relu from Relu(SquaredDifference(x, y)), |
| // since the result of SquaredDifference is always non-negative. |
| // TFLite interpreter doesn't support Relu+int32 for now. So the test cases |
| // are failing without the following pattern to optimize Relu away fixes |
| // the problem. |
| def OptimizeReluSquaredDifference : Pat< |
| (TFL_ReluOp (TFL_SquaredDifferenceOp $l, $r)), |
| (TFL_SquaredDifferenceOp $l, $r)>; |
| |
| // Optimize X^1 o X |
| def OptimizePow1ToIdentity : Pat< |
| (TFL_PowOp $input, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "1.0f">)), |
| (replaceWithValue $input)>; |
| |
| // Optimize X^2 to X*X |
| def OptimizePow2ToSquare : Pat< |
| (TFL_PowOp $input, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "2.0f">)), |
| (TFL_MulOp $input, $input, TFL_AF_None)>; |
| |
| // Optimize X^(1/2) to √X |
| def OptimizePow2ToSqrt : Pat< |
| (TFL_PowOp $input, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "0.5f">)), |
| (TFL_SqrtOp $input)>; |
| |
| // Optimize X^(-1/2) to 1/√X == rsqrt(x) |
| def OptimizePow2ToRsqrt : Pat< |
| (TFL_PowOp $input, |
| (ConstantOp ConstantAttr<RankedF32ElementsAttr<[]>, "-0.5f">)), |
| (TFL_RsqrtOp $input)>; |
| |
| def CanOptimizeIdentityGatherNdOrScatterNdOp : Constraint<CPred< |
| "TFL::CanOptimizeIdentityGatherNdOrScatterNdOp(" |
| "$0, $1.cast<DenseIntElementsAttr>())">>; |
| |
| def OptimizeIdentityGatherNdOp : Pat< |
| (TFL_GatherNdOp $params, (ConstantOp I32ElementsAttr: $indices)), |
| (replaceWithValue $params), |
| [(CanOptimizeIdentityGatherNdOrScatterNdOp $params, $indices)]>; |
| |
| def OptimizeIdentityScatterNdOp : Pat< |
| (TFL_ScatterNdOp (ConstantOp I32ElementsAttr: $indices), $params, $ignored), |
| (replaceWithValue $params), |
| [(CanOptimizeIdentityGatherNdOrScatterNdOp $params, $indices)]>; |
| |
| def ShapeMatchesReduceWithKeepAxes : Constraint<CPred< |
| "ShapeMatchesReduceWithKeepAxes($0, $1, $2)">>; |
| |
| // Fold reshapes re-inserting reduced dimensions into the results of a reduction |
| // with `keep_dims=false` by changing it to one using `keep_dims=true`. |
| foreach ReduceOp = [TFL_MeanOp, TFL_ReduceMaxOp, TFL_ReduceMinOp, |
| TFL_ReduceProdOp, TFL_SumOp] in { |
| def FoldReshapeTo#ReduceOp : Pat< |
| (TFL_ReshapeOp |
| (ReduceOp:$reduce $input, (ConstantOp I32ElementsAttr: $axes), |
| ConstBoolAttrFalse), |
| (ConstantOp I32ElementsAttr: $shape)), |
| (ReduceOp $input, (ConstantOp $axes), ConstBoolAttrTrue), |
| [(ShapeMatchesReduceWithKeepAxes $input, $axes, $shape), |
| (HasOneUse $reduce)]>; |
| } |
| |
| |
| def IsSame : Constraint<CPred<"$0 == $1">>; |
| def HasTwoUse : Constraint<CPred< |
| "std::distance($0.use_begin(), $0.use_end()) == 2">>; |
| def AxesIsLastDimension : Constraint<CPred< |
| "$0.cast<DenseIntElementsAttr>().getNumElements() == 1 && " |
| "($0.cast<DenseIntElementsAttr>().getValue<APInt>({0}) == " |
| "$1.getType().cast<ShapedType>().getRank() - 1 || $0.cast<DenseIntElementsAttr>().getValue<int32_t>({0}) == -1)">>; |
| |
| // Convert exp(x)/sum(exp(x)) into softmax. |
| def OptimizeToSoftmax : Pat< |
| (TFL_DivOp (TFL_ExpOp:$exp $input), |
| (TFL_SumOp:$sum $sum_input, (ConstantOp I32ElementsAttr: $axes), |
| ConstBoolAttrTrue), TFL_AF_None), |
| (TFL_SoftmaxOp $input, ConstF32Attr<"1.0">), |
| [(IsSame $exp, $sum_input), |
| (AxesIsLastDimension $axes, $sum_input), |
| (HasTwoUse $exp), |
| (HasOneUse $sum)]>; |
| |
| // Convert softmax(x-max(x)) into softmax(x) as the softmax op already deals |
| // with the max normalization. |
| def FoldNormalizationIntoSoftmax : Pat< |
| (TFL_SoftmaxOp |
| (TFL_SubOp:$sub $input, |
| (TFL_ReduceMaxOp:$max $max_input, (ConstantOp I32ElementsAttr: $axes), |
| ConstBoolAttrTrue), |
| TFL_AF_None), |
| $beta), |
| (TFL_SoftmaxOp $input, $beta), |
| [(IsSame $input, $max_input), |
| (AxesIsLastDimension $axes, $max_input), |
| (HasOneUse $sub), |
| (HasOneUse $max)]>; |
| |
| def HaveSameType : Constraint<CPred<"($0.getType() == $1.getType())">>; |
| |
| class AllElementsAreF32<string val> : Constraint<CPred< |
| "($0.isa<DenseElementsAttr>() && " |
| "$0.cast<DenseElementsAttr>().getType().cast<ShapedType>().getElementType().isF32() && " |
| "std::all_of($0.cast<DenseElementsAttr>().getValues<float>().begin(), " |
| "$0.cast<DenseElementsAttr>().getValues<float>().end(), " |
| "[](float v){ return v == " #val# ";}))">>; |
| |
| // Optimize X*1 to X |
| def OptimizeMul1ToIdentity : Pat< |
| (TFL_MulOp:$result $input, |
| (ConstantOp $constant), |
| TFL_AF_None), |
| (replaceWithValue $input), |
| [(HaveSameType $input, $result), |
| (AllElementsAreF32<"1.0f"> $constant)]>; |
| |
| class AllElementsAreBool<string val> : Constraint<CPred< |
| "($0.isa<DenseElementsAttr>() && " |
| "$0.cast<DenseElementsAttr>().getType().cast<ShapedType>().getElementType().isInteger(1) && " |
| "std::all_of($0.cast<DenseElementsAttr>().getValues<bool>().begin(), " |
| "$0.cast<DenseElementsAttr>().getValues<bool>().end(), " |
| "[](bool v){ return v == " #val# ";}))">>; |
| |
| // Remove select operators when the result is known in advance. |
| foreach SelectOp = [TFL_SelectOp, TFL_SelectV2Op] in { |
| // select(true_tensor, A, B) -> A |
| def Optimize#SelectOp#True : Pat< |
| (SelectOp:$result (ConstantOp $constant), |
| $input1, |
| $input2), |
| (replaceWithValue $input1), |
| [(HaveSameType $input1, $result), |
| (AllElementsAreBool<"true"> $constant)]>; |
| // select(false_tensor, A, B) -> B |
| def Optimize#SelectOp#False : Pat< |
| (SelectOp:$result (ConstantOp $constant), |
| $input1, |
| $input2), |
| (replaceWithValue $input2), |
| [(HaveSameType $input2, $result), |
| (AllElementsAreBool<"false"> $constant)]>; |
| } |
| |
| // Remove (log-)softmax before arg-minmax as (log-)softmax is monotonic. |
| foreach ArgMinMaxOp = [TFL_ArgMinOp, TFL_ArgMaxOp] in { |
| def RemoveSoftmaxOpBefore#ArgMinMaxOp : Pat< |
| (ArgMinMaxOp (TFL_SoftmaxOp:$softmax $logits, TFL_FloatNonNegative:$beta), |
| (ConstantOp:$const_axes I32ElementsAttr:$axes)), |
| (ArgMinMaxOp $logits, $const_axes), |
| [(HasOneUse $softmax), |
| (AxesIsLastDimension $axes, $logits)]>; |
| |
| def RemoveLogSoftmaxOpBefore#ArgMinMaxOp : Pat< |
| (ArgMinMaxOp (TFL_LogSoftmaxOp:$log_softmax $logits), |
| (ConstantOp:$const_axes I32ElementsAttr:$axes)), |
| (ArgMinMaxOp $logits, $const_axes), |
| [(HasOneUse $log_softmax), |
| (AxesIsLastDimension $axes, $logits)]>; |
| } |
| |
| def CanOptimizeIdentitySliceOp : Constraint<CPred< |
| "TFL::CanOptimizeIdentitySliceOp($0, $1, $2)">>; |
| |
| // Remove Slice ops slicing the whole input tensor, effectively no-op. |
| def OptimizeSliceOp : Pat< |
| (TFL_SliceOp:$output $input, (ConstantOp $begin), (ConstantOp $size)), |
| (replaceWithValue $input), |
| [(CanOptimizeIdentitySliceOp $input, $begin, $size)]>; |
| |
| def GetNumElementsOrOne: NativeCodeCall<"GetNumElementsOrOne($0)">; |
| |
| def IsLastElementEqualsOne : Constraint<CPred< |
| "TFL::IsLastElementEqualsOne($0)">>; |
| |
| def IsOneHotIndexAttribute : Constraint<CPred< |
| "TFL::IsOneHotIndexAttribute($0)">>; |
| |
| // Replace |
| // Equal(Reshape(X, shape), indices) |
| // With |
| // OneHot(X, N, true, false, -1) |
| // where |
| // - last value in shape is 1 |
| // - indices is a incrementing series from 0 to N-1. (N elements total.) |
| def ReshapeEqualOpToOneHotOp : Pat< |
| (TFL_EqualOp (TFL_ReshapeOp $x, (ConstantOp $shape)), |
| (ConstantOp $series)), |
| (TFL_OneHotOp $x, |
| (ConstantOp (GetNumElementsOrOne $series)), |
| (ConstantOp ConstantAttr<RankedSignlessIntElementsAttr<1, []>, "true">), |
| (ConstantOp ConstantAttr<RankedSignlessIntElementsAttr<1, []>, "false">), |
| ConstantAttr<I32Attr, "-1">), |
| [(IsLastElementEqualsOne $shape), |
| (IsOneHotIndexAttribute $series)]>; |
| |
| def F32ElementsVal : Constraint<CPred< |
| "$0.getType().cast<TensorType>().getElementType().isF32()">, |
| "32 bit float tensor">; |
| |
| def ConvertSingleElementAttrToFloatAttr : |
| NativeCodeCall<"ConvertSingleElementAttrToFloatAttr($0)">; |
| |
| // Replace |
| // (float)OneHot(index, depth, on_val, off_val, axis) |
| // With |
| // OneHot(index, depth, (float)on_val, (float)off_val, axis) |
| def FuseOneHotAndCastToFloat : Pat< |
| (TFL_CastOp:$output (TFL_OneHotOp $indices, |
| $depth, |
| (ConstantOp $on_val), |
| (ConstantOp $off_val), |
| $axis)), |
| (TFL_OneHotOp $indices, |
| $depth, |
| (ConstantOp (ConvertSingleElementAttrToFloatAttr $on_val)), |
| (ConstantOp (ConvertSingleElementAttrToFloatAttr $off_val)), |
| $axis), |
| [(F32ElementsVal $output)]>; |
