forked from OSchip/llvm-project
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:
parent
784937b9bb
commit
330398052d
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@ -17,7 +17,8 @@ include "mlir/IR/OpBase.td"
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def TosaOpInterface : OpInterface<"TosaOp"> {
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let description = [{
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Implemented by ops that correspond to the Tosa specification.
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Implements interfaces implemented by ops that correspond to the Tosa
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specification.
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}];
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}
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@ -114,9 +114,9 @@ def Tosa_ConvOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias,
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"ArrayAttr":$pad, "ArrayAttr":$stride, "ArrayAttr":$dilation),
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[{
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buildConvOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias,
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pad, stride, dilation);
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::buildConvOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias,
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pad, stride, dilation);
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}]>;
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// Handles tosa.transpose_conv2d which has an outpad and output shape attribute.
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@ -125,10 +125,10 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilderDAG<
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"ArrayAttr":$outpad, "ArrayAttr":$stride, "ArrayAttr":$dilation,
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"ArrayAttr":$outputShape),
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[{
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buildTransConvOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias,
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outpad, stride, dilation,
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outputShape);
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::buildTransConvOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias,
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outpad, stride, dilation,
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outputShape);
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}]>;
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// The tosa.fully_connected op has its own builder as it does not have
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@ -136,8 +136,8 @@ def Tosa_TransConvOpQuantInfoBuilder : OpBuilderDAG<
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def Tosa_FCOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias),
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[{
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buildFCOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias);
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::buildFCOpWithQuantInfo($_builder, $_state, outputType,
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input, weight, bias);
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}]>;
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// The tosa.matmul op is also intended to be generated where a fully_connected
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@ -147,8 +147,8 @@ def Tosa_FCOpQuantInfoBuilder : OpBuilderDAG<
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def Tosa_MatMulOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$a, "Value":$b),
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[{
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buildMatMulOpWithQuantInfo($_builder, $_state, outputType,
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a, b);
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::buildMatMulOpWithQuantInfo($_builder, $_state, outputType,
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a, b);
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}]>;
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// Both the tosa.avg_pool2d and unary ops use the same
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@ -158,8 +158,8 @@ def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$input, "ArrayAttr":$kernel,
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"ArrayAttr":$stride, "ArrayAttr":$pad),
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[{
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buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType,
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input, kernel, stride, pad);
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::buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType,
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input, kernel, stride, pad);
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}]>;
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// This builder is called on single-parameter unary operators that have a scale
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@ -168,7 +168,7 @@ def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilderDAG<
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def Tosa_UnaryOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$input),
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[{
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buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input);
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::buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input);
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}]>;
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// This builder is called on the TOSA pad operator that needs to create its own
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@ -177,8 +177,8 @@ def Tosa_UnaryOpQuantInfoBuilder : OpBuilderDAG<
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def Tosa_PadOpQuantInfoBuilder : OpBuilderDAG<
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(ins "Type":$outputType, "Value":$input, "Value":$paddings),
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[{
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buildPadOpWithQuantInfo($_builder, $_state, outputType,
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input, paddings);
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::buildPadOpWithQuantInfo($_builder, $_state, outputType,
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input, paddings);
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}]>;
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//===----------------------------------------------------------------------===//
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@ -104,7 +104,7 @@ def Tosa_Conv2DOp : Tosa_Op<"conv2d", [NoSideEffect]> {
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let builders = [Tosa_ConvOpQuantInfoBuilder];
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let verifier = [{ return verifyConvOp(*this); }];
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let verifier = [{ return ::verifyConvOp(*this); }];
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}
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//===----------------------------------------------------------------------===//
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@ -134,7 +134,7 @@ def Tosa_Conv3DOp : Tosa_Op<"conv3d", [NoSideEffect]> {
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let builders = [Tosa_ConvOpQuantInfoBuilder];
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let verifier = [{ return verifyConvOp(*this); }];
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let verifier = [{ return ::verifyConvOp(*this); }];
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}
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//===----------------------------------------------------------------------===//
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@ -165,7 +165,7 @@ def Tosa_DepthwiseConv2DOp : Tosa_Op<"depthwise_conv2d", [NoSideEffect]> {
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let builders = [Tosa_ConvOpQuantInfoBuilder];
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let verifier = [{ return verifyConvOp(*this); }];
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let verifier = [{ return ::verifyConvOp(*this); }];
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}
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//===----------------------------------------------------------------------===//
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@ -191,7 +191,7 @@ def Tosa_FullyConnectedOp : Tosa_Op<"fully_connected", [NoSideEffect]> {
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let builders = [Tosa_FCOpQuantInfoBuilder];
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let verifier = [{ return verifyConvOp(*this); }];
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let verifier = [{ return ::verifyConvOp(*this); }];
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}
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//===----------------------------------------------------------------------===//
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@ -16,6 +16,7 @@
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#include "mlir/Pass/Pass.h"
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namespace mlir {
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namespace tosa {
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std::unique_ptr<Pass> createTosaMakeBroadcastablePass();
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@ -19,8 +19,8 @@
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#include "mlir/Dialect/Quant/FakeQuantSupport.h"
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#include "mlir/Dialect/Quant/UniformSupport.h"
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namespace mlir {
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namespace tosa {
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using namespace mlir;
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using namespace mlir::tosa;
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//===----------------------------------------------------------------------===//
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// Utililty functions to support quantization handling in Tosa.
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@ -65,7 +65,4 @@ TypeAttr buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDType,
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IntegerAttr quantBits, int filterQuantDim,
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bool isSigned, BoolAttr narrowRange);
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} // namespace tosa
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} // namespace mlir
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#endif // DIALECT_TOSA_UTILS_QUANT_UTILS_H
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@ -93,8 +93,7 @@ void TosaDialect::initialize() {
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// TOSA Operator Verifiers.
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//===----------------------------------------------------------------------===//
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template <typename T>
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static LogicalResult verifyConvOp(T op) {
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template <typename T> static LogicalResult verifyConvOp(T op) {
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// All TOSA conv ops have an input() and weight().
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auto inputType = op.input().getType().template dyn_cast<RankedTensorType>();
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auto weightType = op.weight().getType().template dyn_cast<RankedTensorType>();
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@ -128,10 +127,10 @@ static LogicalResult verifyConvOp(T op) {
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/// This builder is called on all convolution operators except TransposeConv,
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/// which has specialized output shape semantics. The builder also defines the
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/// bitwidth of the output given the bit width of the input & weight content.
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static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias, ArrayAttr pad,
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ArrayAttr stride, ArrayAttr dilation) {
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void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias, ArrayAttr pad, ArrayAttr stride,
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ArrayAttr dilation) {
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result.addOperands({input, weight, bias});
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result.addAttribute("pad", pad);
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@ -149,11 +148,11 @@ static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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}
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/// Handles tosa.transpose_conv2d which has outpad and output shape attributes.
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static void
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buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias, ArrayAttr outpad, ArrayAttr stride,
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ArrayAttr dilation, ArrayAttr outputShape) {
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void buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias, ArrayAttr outpad,
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ArrayAttr stride, ArrayAttr dilation,
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ArrayAttr outputShape) {
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result.addOperands({input, weight, bias});
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result.addAttribute("out_pad", outpad);
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result.addAttribute("stride", stride);
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@ -172,9 +171,9 @@ buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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/// The tosa.fully_connected op has its own builder as it does not have
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/// strides/dilation/padding.
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static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias) {
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void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value weight,
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Value bias) {
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result.addOperands({input, weight, bias});
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auto quantAttr = ::buildConvOpQuantizationAttr(builder, input, weight);
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@ -191,9 +190,8 @@ static void buildFCOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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/// op must be constructed where the weight is not a constant. In this case,
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/// the fully_connected op must be expressed using matmul.
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/// TODO: Add link to the leglization document explaining this.
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static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
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OperationState &result, Type outputType,
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Value a, Value b) {
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void buildMatMulOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value a, Value b) {
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result.addOperands({a, b});
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auto quantAttr = ::buildMatMulOpQuantizationAttr(builder, a, b);
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@ -229,11 +227,10 @@ static void buildMatMulOpWithQuantInfo(OpBuilder &builder,
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/// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr
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/// but avg_pool operator has its own builder as it has additional parameters
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/// not part of the unary ops.
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static void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder,
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OperationState &result,
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Type outputType, Value input,
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ArrayAttr kernel, ArrayAttr stride,
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ArrayAttr pad) {
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void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input,
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ArrayAttr kernel, ArrayAttr stride,
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ArrayAttr pad) {
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result.addOperands(input);
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result.addAttribute("kernel", kernel);
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result.addAttribute("stride", stride);
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/// This builder is called on single-parameter unary operators that have scale
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/// relationship between their input and output, expressed by the
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/// UnaryOpQuantizationAttr.
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static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
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OperationState &result, Type outputType,
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Value input) {
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void buildUnaryOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input) {
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result.addOperands(input);
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auto quantAttr = buildUnaryOpQuantizationAttr(builder, input, outputType);
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if (quantAttr)
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@ -260,9 +256,8 @@ static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
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/// This builder is called on TOSA pad operator that needs to create its own
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/// OptionalAttr quantization_attr parameter to scale the padding values
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/// correctly.
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static void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input,
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Value paddings) {
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void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
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Type outputType, Value input, Value paddings) {
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result.addOperands({input, paddings});
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auto quantAttr = buildPadOpQuantizationAttr(builder, input);
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if (quantAttr)
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@ -128,6 +128,8 @@ static int reshapeLowerToHigher(PatternRewriter &rewriter, Location loc,
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}
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ArrayRef<int64_t> outputRankShape = outputType.getShape();
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ArrayRef<int64_t> higherRankShape =
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higherTensorValue.getType().cast<RankedTensorType>().getShape();
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ArrayRef<int64_t> lowerRankShape =
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lowerTensorValue.getType().cast<RankedTensorType>().getShape();
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@ -19,9 +19,8 @@ using namespace mlir::tosa;
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/// From a scale value, generates multiplier and shift values where
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/// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that
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/// multiplier = mantissa*2^shift for 16-bit scaling.
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static void computeMultiplierAndShiftTosaScale16(double scale,
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int32_t &multiplier,
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int32_t &shift) {
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void computeMultiplierAndShiftTosaScale16(double scale, int32_t &multiplier,
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int32_t &shift) {
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const double mantissa = std::frexp(scale, &shift);
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auto shiftedM = std::round(mantissa * (int64_t(1) << 15));
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/// From a scale value, generates multiplier and shift values where
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/// mantissa is in [-1.0,-0.5] or [0.5, 1.0] such that
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/// multiplier = mantissa*2^shift for 32-bit scaling.
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static void computeMultiplierAndShiftTosaScale32(double scale,
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int32_t &multiplier,
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int32_t &shift) {
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void computeMultiplierAndShiftTosaScale32(double scale, int32_t &multiplier,
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int32_t &shift) {
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const double mantissa = std::frexp(scale, &shift);
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auto shiftedM = std::round(mantissa * (int64_t(1) << 31));
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@ -74,8 +72,8 @@ static void computeMultiplierAndShiftTosaScale32(double scale,
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}
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/// Generates a quantized multiplier/shift from double.
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void mlir::tosa::computeMultiplierAndShift(double scale, int32_t &multiplier,
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int32_t &shift, int32_t scaleWidth) {
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void computeMultiplierAndShift(double scale, int32_t &multiplier,
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int32_t &shift, int32_t scaleWidth) {
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switch (scaleWidth) {
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case 16:
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@ -98,9 +96,8 @@ void mlir::tosa::computeMultiplierAndShift(double scale, int32_t &multiplier,
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/// ConvOpQuantInfoBuilder/TransConvOpQuantInfoBuilder:
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/// input_zp: input zeropoint
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/// weight_zp: weight zeropoint.
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ConvOpQuantizationAttr
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mlir::tosa::buildConvOpQuantizationAttr(OpBuilder &builder, Value input,
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Value weight) {
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ConvOpQuantizationAttr buildConvOpQuantizationAttr(OpBuilder &builder,
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Value input, Value weight) {
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auto inputType = input.getType().dyn_cast<RankedTensorType>();
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auto weightType = weight.getType().dyn_cast<RankedTensorType>();
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/// MatMulOpQuantInfoBuilder:
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/// aZp: input a zeropoint
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/// bZp: input b zeropoint.
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MatMulOpQuantizationAttr
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mlir::tosa::buildMatMulOpQuantizationAttr(OpBuilder &builder, Value a,
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Value b) {
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MatMulOpQuantizationAttr buildMatMulOpQuantizationAttr(OpBuilder &builder,
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Value a, Value b) {
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auto aType = a.getType().dyn_cast<RankedTensorType>();
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auto bType = b.getType().dyn_cast<RankedTensorType>();
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@ -183,9 +179,9 @@ mlir::tosa::buildMatMulOpQuantizationAttr(OpBuilder &builder, Value a,
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/// UnaryOpQuantInfoBuilder:
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/// inputZp: input zeropoint
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/// outputZp: output zeropoint.
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UnaryOpQuantizationAttr
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mlir::tosa::buildUnaryOpQuantizationAttr(OpBuilder &builder, Value input,
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Type outputRawType) {
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UnaryOpQuantizationAttr buildUnaryOpQuantizationAttr(OpBuilder &builder,
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Value input,
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Type outputRawType) {
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auto inputType = input.getType().dyn_cast<RankedTensorType>();
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auto outputType = outputRawType.dyn_cast<RankedTensorType>();
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@ -217,8 +213,8 @@ mlir::tosa::buildUnaryOpQuantizationAttr(OpBuilder &builder, Value input,
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/// Builds PadOpQuantizationAttr, called from PadOpQuantInfoBuilder:
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/// inputZp: input zeropoint.
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PadOpQuantizationAttr mlir::tosa::buildPadOpQuantizationAttr(OpBuilder &builder,
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Value input) {
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PadOpQuantizationAttr buildPadOpQuantizationAttr(OpBuilder &builder,
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Value input) {
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auto inputType = input.getType().dyn_cast<RankedTensorType>();
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@ -242,8 +238,8 @@ PadOpQuantizationAttr mlir::tosa::buildPadOpQuantizationAttr(OpBuilder &builder,
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/// Builds output type for a quantized ConvOp with the right bitwidth.
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/// This is called by the builder when dealing with quantized content.
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Type mlir::tosa::buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType,
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Value input, Value weight) {
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Type buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType, Value input,
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Value weight) {
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auto inputType = input.getType().dyn_cast<RankedTensorType>();
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auto weightType = weight.getType().dyn_cast<RankedTensorType>();
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@ -276,10 +272,10 @@ Type mlir::tosa::buildConvOpResultTypeInfo(OpBuilder &builder, Type outputType,
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}
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/// Builds Tosa quantization attributes from min/max values.
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Type mlir::tosa::buildQTypeFromMinMax(OpBuilder builder, Type inputDType,
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Attribute minAttr, Attribute maxAttr,
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IntegerAttr quantBits, int filterQuantDim,
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bool isSigned, BoolAttr narrowRange) {
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Type buildQTypeFromMinMax(OpBuilder builder, Type inputDType, Attribute minAttr,
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Attribute maxAttr, IntegerAttr quantBits,
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int filterQuantDim, bool isSigned,
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BoolAttr narrowRange) {
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quant::QuantizedType retType;
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@ -343,11 +339,10 @@ Type mlir::tosa::buildQTypeFromMinMax(OpBuilder builder, Type inputDType,
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}
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/// Builds Tosa quantization attributes from min/max values.
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TypeAttr
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mlir::tosa::buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDtype,
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Attribute minAttr, Attribute maxAttr,
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IntegerAttr quantBits, int filterQuantDim,
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bool isSigned, BoolAttr narrowRange) {
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TypeAttr buildQTypeAttrFromMinMax(OpBuilder builder, Type inputDtype,
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Attribute minAttr, Attribute maxAttr,
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IntegerAttr quantBits, int filterQuantDim,
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bool isSigned, BoolAttr narrowRange) {
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return TypeAttr::get(buildQTypeFromMinMax(builder, inputDtype, minAttr,
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maxAttr, quantBits, filterQuantDim,
|
||||
|
|
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Reference in New Issue