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Revert "NFC: Fix some post-review nits for the Tosa dialect." 

* Introduced issue in debug builds.

This reverts commit b5fcd06105.
This commit is contained in:
Stella Laurenzo 2020-11-07 09:35:49 -08:00
parent 784937b9bb
commit 330398052d
8 changed files with 75 additions and 84 deletions
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3
mlir/include/mlir/Dialect/Tosa/IR/TosaInterfaces.td
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@ -17,7 +17,8 @@ include "mlir/IR/OpBase.td"
def TosaOpInterface : OpInterface<"TosaOp"> { def TosaOpInterface : OpInterface<"TosaOp"> {
let description = [{ let description = [{
Implemented by ops that correspond to the Tosa specification. Implements interfaces implemented by ops that correspond to the Tosa
specification.
}]; }];
} }

32
mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
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@ -114,9 +114,9 @@ def Tosa_ConvOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias, (ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias,
"ArrayAttr":$pad, "ArrayAttr":$stride, "ArrayAttr":$dilation), "ArrayAttr":$pad, "ArrayAttr":$stride, "ArrayAttr":$dilation),
[{ [{
buildConvOpWithQuantInfo($_builder, $_state, outputType, ::buildConvOpWithQuantInfo($_builder, $_state, outputType,
input, weight, bias, input, weight, bias,
pad, stride, dilation); pad, stride, dilation);
}]>; }]>;
// Handles tosa.transpose_conv2d which has an outpad and output shape attribute. // Handles tosa.transpose_conv2d which has an outpad and output shape attribute.
@ -125,10 +125,10 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilderDAG<
"ArrayAttr":$outpad, "ArrayAttr":$stride, "ArrayAttr":$dilation, "ArrayAttr":$outpad, "ArrayAttr":$stride, "ArrayAttr":$dilation,
"ArrayAttr":$outputShape), "ArrayAttr":$outputShape),
[{ [{
buildTransConvOpWithQuantInfo($_builder, $_state, outputType, ::buildTransConvOpWithQuantInfo($_builder, $_state, outputType,
input, weight, bias, input, weight, bias,
outpad, stride, dilation, outpad, stride, dilation,
outputShape); outputShape);
}]>; }]>;
// The tosa.fully_connected op has its own builder as it does not have // The tosa.fully_connected op has its own builder as it does not have
@ -136,8 +136,8 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilderDAG<
def Tosa_FCOpQuantInfoBuilder : OpBuilderDAG< def Tosa_FCOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias), (ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias),
[{ [{
buildFCOpWithQuantInfo($_builder, $_state, outputType, ::buildFCOpWithQuantInfo($_builder, $_state, outputType,
input, weight, bias); input, weight, bias);
}]>; }]>;
// The tosa.matmul op is also intended to be generated where a fully_connected // The tosa.matmul op is also intended to be generated where a fully_connected
@ -147,8 +147,8 @@ def Tosa_FCOpQuantInfoBuilder : OpBuilderDAG<
def Tosa_MatMulOpQuantInfoBuilder : OpBuilderDAG< def Tosa_MatMulOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$a, "Value":$b), (ins "Type":$outputType, "Value":$a, "Value":$b),
[{ [{
buildMatMulOpWithQuantInfo($_builder, $_state, outputType, ::buildMatMulOpWithQuantInfo($_builder, $_state, outputType,
a, b); a, b);
}]>; }]>;
// Both the tosa.avg_pool2d and unary ops use the same // Both the tosa.avg_pool2d and unary ops use the same
@ -158,8 +158,8 @@ def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$input, "ArrayAttr":$kernel, (ins "Type":$outputType, "Value":$input, "ArrayAttr":$kernel,
"ArrayAttr":$stride, "ArrayAttr":$pad), "ArrayAttr":$stride, "ArrayAttr":$pad),
[{ [{
buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType, ::buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType,
input, kernel, stride, pad); input, kernel, stride, pad);
}]>; }]>;
// This builder is called on single-parameter unary operators that have a scale // This builder is called on single-parameter unary operators that have a scale
@ -168,7 +168,7 @@ def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilderDAG<
def Tosa_UnaryOpQuantInfoBuilder : OpBuilderDAG< def Tosa_UnaryOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$input), (ins "Type":$outputType, "Value":$input),
[{ [{
buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input); ::buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input);
}]>; }]>;
// This builder is called on the TOSA pad operator that needs to create its own // This builder is called on the TOSA pad operator that needs to create its own
@ -177,8 +177,8 @@ def Tosa_UnaryOpQuantInfoBuilder : OpBuilderDAG<
def Tosa_PadOpQuantInfoBuilder : OpBuilderDAG< def Tosa_PadOpQuantInfoBuilder : OpBuilderDAG<
(ins "Type":$outputType, "Value":$input, "Value":$paddings), (ins "Type":$outputType, "Value":$input, "Value":$paddings),
[{ [{
buildPadOpWithQuantInfo($_builder, $_state, outputType, ::buildPadOpWithQuantInfo($_builder, $_state, outputType,
input, paddings); input, paddings);
}]>; }]>;
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//

8
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
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@ -104,7 +104,7 @@ def Tosa_Conv2DOp : Tosa_Op<"conv2d", [NoSideEffect]> {
let builders = [Tosa_ConvOpQuantInfoBuilder]; let builders = [Tosa_ConvOpQuantInfoBuilder];
let verifier = [{ return verifyConvOp(*this); }]; let verifier = [{ return ::verifyConvOp(*this); }];
} }
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
@ -134,7 +134,7 @@ def Tosa_Conv3DOp : Tosa_Op<"conv3d", [NoSideEffect]> {
let builders = [Tosa_ConvOpQuantInfoBuilder]; let builders = [Tosa_ConvOpQuantInfoBuilder];
let verifier = [{ return verifyConvOp(*this); }]; let verifier = [{ return ::verifyConvOp(*this); }];
} }
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
@ -165,7 +165,7 @@ def Tosa_DepthwiseConv2DOp : Tosa_Op<"depthwise_conv2d", [NoSideEffect]> {
let builders = [Tosa_ConvOpQuantInfoBuilder]; let builders = [Tosa_ConvOpQuantInfoBuilder];
let verifier = [{ return verifyConvOp(*this); }]; let verifier = [{ return ::verifyConvOp(*this); }];
} }
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
@ -191,7 +191,7 @@ def Tosa_FullyConnectedOp : Tosa_Op<"fully_connected", [NoSideEffect]> {
let builders = [Tosa_FCOpQuantInfoBuilder]; let builders = [Tosa_FCOpQuantInfoBuilder];
let verifier = [{ return verifyConvOp(*this); }]; let verifier = [{ return ::verifyConvOp(*this); }];
} }
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//

1
mlir/include/mlir/Dialect/Tosa/Transforms/Passes.h
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@ -16,6 +16,7 @@
#include "mlir/Pass/Pass.h" #include "mlir/Pass/Pass.h"
namespace mlir { namespace mlir {
namespace tosa { namespace tosa {
std::unique_ptr<Pass> createTosaMakeBroadcastablePass(); std::unique_ptr<Pass> createTosaMakeBroadcastablePass();

7
mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h
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@ -19,8 +19,8 @@
#include "mlir/Dialect/Quant/FakeQuantSupport.h" #include "mlir/Dialect/Quant/FakeQuantSupport.h"
#include "mlir/Dialect/Quant/UniformSupport.h" #include "mlir/Dialect/Quant/UniformSupport.h"
namespace mlir { using namespace mlir;
namespace tosa { using namespace mlir::tosa;
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
// Utililty functions to support quantization handling in Tosa. // Utililty functions to support quantization handling in Tosa.
@ -65,7 +65,4 @@ TypeAttr buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDType,
IntegerAttr quantBits, int filterQuantDim, IntegerAttr quantBits, int filterQuantDim,
bool isSigned, BoolAttr narrowRange); bool isSigned, BoolAttr narrowRange);
} // namespace tosa
} // namespace mlir
#endif // DIALECT_TOSA_UTILS_QUANT_UTILS_H #endif // DIALECT_TOSA_UTILS_QUANT_UTILS_H

51
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
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@ -93,8 +93,7 @@ void TosaDialect::initialize() {
// TOSA Operator Verifiers. // TOSA Operator Verifiers.
//===----------------------------------------------------------------------===// //===----------------------------------------------------------------------===//
template <typename T> template <typename T> static LogicalResult verifyConvOp(T op) {
static LogicalResult verifyConvOp(T op) {
// All TOSA conv ops have an input() and weight(). // All TOSA conv ops have an input() and weight().
auto inputType = op.input().getType().template dyn_cast<RankedTensorType>(); auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>(); auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>();
@ -128,10 +127,10 @@ static LogicalResult verifyConvOp(T op) {
/// This builder is called on all convolution operators except TransposeConv, /// This builder is called on all convolution operators except TransposeConv,
/// which has specialized output shape semantics. The builder also defines the /// which has specialized output shape semantics. The builder also defines the
/// bitwidth of the output given the bit width of the input & weight content. /// bitwidth of the output given the bit width of the input & weight content.
static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result, void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight, Type outputType, Value input, Value weight,
Value bias, ArrayAttr pad, Value bias, ArrayAttr pad, ArrayAttr stride,
ArrayAttr stride, ArrayAttr dilation) { ArrayAttr dilation) {
result.addOperands({input, weight, bias}); result.addOperands({input, weight, bias});
result.addAttribute("pad", pad); result.addAttribute("pad", pad);
@ -149,11 +148,11 @@ static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
} }
/// Handles tosa.transpose_conv2d which has outpad and output shape attributes. /// Handles tosa.transpose_conv2d which has outpad and output shape attributes.
static void void buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result, Type outputType, Value input, Value weight,
Type outputType, Value input, Value weight, Value bias, ArrayAttr outpad,
Value bias, ArrayAttr outpad, ArrayAttr stride, ArrayAttr stride, ArrayAttr dilation,
ArrayAttr dilation, ArrayAttr outputShape) { ArrayAttr outputShape) {
result.addOperands({input, weight, bias}); result.addOperands({input, weight, bias});
result.addAttribute("out_pad", outpad); result.addAttribute("out_pad", outpad);
result.addAttribute("stride", stride); result.addAttribute("stride", stride);
@ -172,9 +171,9 @@ buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
/// The tosa.fully_connected op has its own builder as it does not have /// The tosa.fully_connected op has its own builder as it does not have
/// strides/dilation/padding. /// strides/dilation/padding.
static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result, void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Value weight, Type outputType, Value input, Value weight,
Value bias) { Value bias) {
result.addOperands({input, weight, bias}); result.addOperands({input, weight, bias});
auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight); auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
@ -191,9 +190,8 @@ static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
/// op must be constructed where the weight is not a constant. In this case, /// op must be constructed where the weight is not a constant. In this case,
/// the fully_connected op must be expressed using matmul. /// the fully_connected op must be expressed using matmul.
/// TODO: Add link to the leglization document explaining this. /// TODO: Add link to the leglization document explaining this.
static void buildMatMulOpWithQuantInfo(OpBuilder &builder, void buildMatMulOpWithQuantInfo(OpBuilder &builder, OperationState &result,
OperationState &result, Type outputType, Type outputType, Value a, Value b) {
Value a, Value b) {
result.addOperands({a, b}); result.addOperands({a, b});
auto quantAttr = ::buildMatMulOpQuantizationAttr(builder, a, b); auto quantAttr = ::buildMatMulOpQuantizationAttr(builder, a, b);
@ -229,11 +227,10 @@ static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
/// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr /// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr
/// but avg_pool operator has its own builder as it has additional parameters /// but avg_pool operator has its own builder as it has additional parameters
/// not part of the unary ops. /// not part of the unary ops.
static void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, OperationState &result,
OperationState &result, Type outputType, Value input,
Type outputType, Value input, ArrayAttr kernel, ArrayAttr stride,
ArrayAttr kernel, ArrayAttr stride, ArrayAttr pad) {
ArrayAttr pad) {
result.addOperands(input); result.addOperands(input);
result.addAttribute("kernel", kernel); result.addAttribute("kernel", kernel);
result.addAttribute("stride", stride); result.addAttribute("stride", stride);
@ -247,9 +244,8 @@ static void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder,
/// This builder is called on single-parameter unary operators that have scale /// This builder is called on single-parameter unary operators that have scale
/// relationship between their input and output, expressed by the /// relationship between their input and output, expressed by the
/// UnaryOpQuantizationAttr. /// UnaryOpQuantizationAttr.
static void buildUnaryOpWithQuantInfo(OpBuilder &builder, void buildUnaryOpWithQuantInfo(OpBuilder &builder, OperationState &result,
OperationState &result, Type outputType, Type outputType, Value input) {
Value input) {
result.addOperands(input); result.addOperands(input);
auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType); auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
if (quantAttr) if (quantAttr)
@ -260,9 +256,8 @@ static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
/// This builder is called on TOSA pad operator that needs to create its own /// This builder is called on TOSA pad operator that needs to create its own
/// OptionalAttr quantization_attr parameter to scale the padding values /// OptionalAttr quantization_attr parameter to scale the padding values
/// correctly. /// correctly.
static void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result, void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
Type outputType, Value input, Type outputType, Value input, Value paddings) {
Value paddings) {
result.addOperands({input, paddings}); result.addOperands({input, paddings});
auto quantAttr = buildPadOpQuantizationAttr(builder, input); auto quantAttr = buildPadOpQuantizationAttr(builder, input);
if (quantAttr) if (quantAttr)

2
mlir/lib/Dialect/Tosa/Transforms/TosaMakeBroadcastable.cpp
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@ -128,6 +128,8 @@ static int reshapeLowerToHigher(PatternRewriter &rewriter, Location loc,
} }
ArrayRef<int64_t> outputRankShape = outputType.getShape(); ArrayRef<int64_t> outputRankShape = outputType.getShape();
ArrayRef<int64_t> higherRankShape =
higherTensorValue.getType().cast<RankedTensorType>().getShape();
ArrayRef<int64_t> lowerRankShape = ArrayRef<int64_t> lowerRankShape =
lowerTensorValue.getType().cast<RankedTensorType>().getShape(); lowerTensorValue.getType().cast<RankedTensorType>().getShape();

55
mlir/lib/Dialect/Tosa/Utils/QuantUtils.cpp
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@ -19,9 +19,8 @@ using namespace mlir::tosa;
/// From a scale value, generates multiplier and shift values where /// From a scale value, generates multiplier and shift values where
/// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that /// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that
/// multiplier = mantissa*2^shift for 16-bit scaling. /// multiplier = mantissa*2^shift for 16-bit scaling.
static void computeMultiplierAndShiftTosaScale16(double scale, void computeMultiplierAndShiftTosaScale16(double scale, int32_t &multiplier,
int32_t &multiplier, int32_t &shift) {
int32_t &shift) {
const double mantissa = std::frexp(scale, &shift); const double mantissa = std::frexp(scale, &shift);
auto shiftedM = std::round(mantissa * (int64_t(1) << 15)); auto shiftedM = std::round(mantissa * (int64_t(1) << 15));
@ -48,9 +47,8 @@ static void computeMultiplierAndShiftTosaScale16(double scale,
/// From a scale value, generates multiplier and shift values where /// From a scale value, generates multiplier and shift values where
/// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that /// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that
/// multiplier = mantissa*2^shift for 32-bit scaling. /// multiplier = mantissa*2^shift for 32-bit scaling.
static void computeMultiplierAndShiftTosaScale32(double scale, void computeMultiplierAndShiftTosaScale32(double scale, int32_t &multiplier,
int32_t &multiplier, int32_t &shift) {
int32_t &shift) {
const double mantissa = std::frexp(scale, &shift); const double mantissa = std::frexp(scale, &shift);
auto shiftedM = std::round(mantissa * (int64_t(1) << 31)); auto shiftedM = std::round(mantissa * (int64_t(1) << 31));
@ -74,8 +72,8 @@ static void computeMultiplierAndShiftTosaScale32(double scale,
} }
/// Generates a quantized multiplier/shift from double. /// Generates a quantized multiplier/shift from double.
void mlir::tosa::computeMultiplierAndShift(double scale, int32_t &multiplier, void computeMultiplierAndShift(double scale, int32_t &multiplier,
int32_t &shift, int32_t scaleWidth) { int32_t &shift, int32_t scaleWidth) {
switch (scaleWidth) { switch (scaleWidth) {
case 16: case 16:
@ -98,9 +96,8 @@ void mlir::tosa::computeMultiplierAndShift(double scale, int32_t &multiplier,
/// ConvOpQuantInfoBuilder/TransConvOpQuantInfoBuilder: /// ConvOpQuantInfoBuilder/TransConvOpQuantInfoBuilder:
/// input_zp: input zeropoint /// input_zp: input zeropoint
/// weight_zp: weight zeropoint. /// weight_zp: weight zeropoint.
ConvOpQuantizationAttr ConvOpQuantizationAttr buildConvOpQuantizationAttr(OpBuilder &builder,
mlir::tosa::buildConvOpQuantizationAttr(OpBuilder &builder, Value input, Value input, Value weight) {
Value weight) {
auto inputType = input.getType().dyn_cast<RankedTensorType>(); auto inputType = input.getType().dyn_cast<RankedTensorType>();
auto weightType = weight.getType().dyn_cast<RankedTensorType>(); auto weightType = weight.getType().dyn_cast<RankedTensorType>();
@ -147,9 +144,8 @@ mlir::tosa::buildConvOpQuantizationAttr(OpBuilder &builder, Value input,
/// MatMulOpQuantInfoBuilder: /// MatMulOpQuantInfoBuilder:
/// aZp: input a zeropoint /// aZp: input a zeropoint
/// bZp: input b zeropoint. /// bZp: input b zeropoint.
MatMulOpQuantizationAttr MatMulOpQuantizationAttr buildMatMulOpQuantizationAttr(OpBuilder &builder,
mlir::tosa::buildMatMulOpQuantizationAttr(OpBuilder &builder, Value a, Value a, Value b) {
Value b) {
auto aType = a.getType().dyn_cast<RankedTensorType>(); auto aType = a.getType().dyn_cast<RankedTensorType>();
auto bType = b.getType().dyn_cast<RankedTensorType>(); auto bType = b.getType().dyn_cast<RankedTensorType>();
@ -183,9 +179,9 @@ mlir::tosa::buildMatMulOpQuantizationAttr(OpBuilder &builder, Value a,
/// UnaryOpQuantInfoBuilder: /// UnaryOpQuantInfoBuilder:
/// inputZp: input zeropoint /// inputZp: input zeropoint
/// outputZp: output zeropoint. /// outputZp: output zeropoint.
UnaryOpQuantizationAttr UnaryOpQuantizationAttr buildUnaryOpQuantizationAttr(OpBuilder &builder,
mlir::tosa::buildUnaryOpQuantizationAttr(OpBuilder &builder, Value input, Value input,
Type outputRawType) { Type outputRawType) {
auto inputType = input.getType().dyn_cast<RankedTensorType>(); auto inputType = input.getType().dyn_cast<RankedTensorType>();
auto outputType = outputRawType.dyn_cast<RankedTensorType>(); auto outputType = outputRawType.dyn_cast<RankedTensorType>();
@ -217,8 +213,8 @@ mlir::tosa::buildUnaryOpQuantizationAttr(OpBuilder &builder, Value input,
/// Builds PadOpQuantizationAttr, called from PadOpQuantInfoBuilder: /// Builds PadOpQuantizationAttr, called from PadOpQuantInfoBuilder:
/// inputZp: input zeropoint. /// inputZp: input zeropoint.
PadOpQuantizationAttr mlir::tosa::buildPadOpQuantizationAttr(OpBuilder &builder, PadOpQuantizationAttr buildPadOpQuantizationAttr(OpBuilder &builder,
Value input) { Value input) {
auto inputType = input.getType().dyn_cast<RankedTensorType>(); auto inputType = input.getType().dyn_cast<RankedTensorType>();
@ -242,8 +238,8 @@ PadOpQuantizationAttr mlir::tosa::buildPadOpQuantizationAttr(OpBuilder &builder,
/// Builds output type for a quantized ConvOp with the right bitwidth. /// Builds output type for a quantized ConvOp with the right bitwidth.
/// This is called by the builder when dealing with quantized content. /// This is called by the builder when dealing with quantized content.
Type mlir::tosa::buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType, Type buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType, Value input,
Value input, Value weight) { Value weight) {
auto inputType = input.getType().dyn_cast<RankedTensorType>(); auto inputType = input.getType().dyn_cast<RankedTensorType>();
auto weightType = weight.getType().dyn_cast<RankedTensorType>(); auto weightType = weight.getType().dyn_cast<RankedTensorType>();
@ -276,10 +272,10 @@ Type mlir::tosa::buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType,
} }
/// Builds Tosa quantization attributes from min/max values. /// Builds Tosa quantization attributes from min/max values.
Type mlir::tosa::buildQTypeFromMinMax(OpBuilder builder, Type inputDType, Type buildQTypeFromMinMax(OpBuilder builder, Type inputDType, Attribute minAttr,
Attribute minAttr, Attribute maxAttr, Attribute maxAttr, IntegerAttr quantBits,
IntegerAttr quantBits, int filterQuantDim, int filterQuantDim, bool isSigned,
bool isSigned, BoolAttr narrowRange) { BoolAttr narrowRange) {
quant::QuantizedType retType; quant::QuantizedType retType;
@ -343,11 +339,10 @@ Type mlir::tosa::buildQTypeFromMinMax(OpBuilder builder, Type inputDType,
} }
/// Builds Tosa quantization attributes from min/max values. /// Builds Tosa quantization attributes from min/max values.
TypeAttr TypeAttr buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDtype,
mlir::tosa::buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDtype, Attribute minAttr, Attribute maxAttr,
Attribute minAttr, Attribute maxAttr, IntegerAttr quantBits, int filterQuantDim,
IntegerAttr quantBits, int filterQuantDim, bool isSigned, BoolAttr narrowRange) {
bool isSigned, BoolAttr narrowRange) {
return TypeAttr::get(buildQTypeFromMinMax(builder, inputDtype, minAttr, return TypeAttr::get(buildQTypeFromMinMax(builder, inputDtype, minAttr,
maxAttr, quantBits, filterQuantDim, maxAttr, quantBits, filterQuantDim,