[mlir][tosa] Change zero points of convolution ops to required inputs #127679
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@llvm/pr-subscribers-mlir-tosa @llvm/pr-subscribers-mlir Author: Tai Ly (Tai78641) ChangesThis patch changes the input_zp and weight_zp for convolution operators to be required inputs Convolution operators affected are: Change-Id: I7aa6e05580c83c617394c3f7fd18fba8c3f3b0ec Patch is 151.48 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/127679.diff 15 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index 069073bc2d164..c18b46c9474fc 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -161,112 +161,6 @@ namespace tosa {
std::optional<Value> createZeroPointTensor(OpBuilder &builder, Location loc,
Type srcElemType, int64_t zp = 0);
-// Get zero point value from the attribute argument.
-LogicalResult getZeroPoint(ElementsAttr zpAttr, int64_t &zp);
-
-// Verify if zero point falls into valid range.
-template <typename T>
-LogicalResult verifyZeroPoint(Type zpElemType, int64_t zp) {
- if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
- !std::is_same_v<T, DepthwiseConv2DOp> &&
- !std::is_same_v<T, TransposeConv2DOp>) {
- return failure();
- }
-
- if (!zpElemType.isIntOrFloat())
- return failure();
-
- if (!zpElemType.isInteger(8) && zp != 0)
- return failure();
-
- if (zpElemType.isSignedInteger(8) && (zp < -128 || zp > 127))
- return failure();
-
- if (zpElemType.isUnsignedInteger(8) && (zp < 0 || zp > 255))
- return failure();
-
- return success();
-}
-
-// Helper type trait to determine if an operation is a tosa convolution.
-template <typename Op>
-struct IsTosaConv : std::false_type {};
-
-template <>
-struct IsTosaConv<tosa::Conv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::DepthwiseConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::TransposeConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::Conv3DOp> : std::true_type {};
-
-template <typename Op>
-constexpr bool is_tosa_conv_v = IsTosaConv<Op>::value;
-
-// Helper struct to hold the zero points of a TOSA convolution operation as
-// named 64-bit integer fields.
-struct ConvZpPair {
- ConvZpPair(std::int64_t inputZp, std::int64_t weightZp)
- : inputZp(inputZp), weightZp(weightZp) {}
- std::int64_t inputZp;
- std::int64_t weightZp;
-};
-
-// Helper function which attempts to extract the zero points from a TOSA
-// convolution by matching them against defining ops which should be tosa.const
-// operations.
-//
-// There are three possible results:
-// 1. Failed to extract the zero-points i.e. they should exist and don't or they
-// do exist but are invalid.
-// 2. Succeeded in extracting zero-points.
-// 3. Zero points are "empty" and meaningless for this op i.e. non-quantized
-// convolution.
-using FailOrMaybeZP = llvm::FailureOr<std::optional<ConvZpPair>>;
-template <typename TosaConvOp>
-std::enable_if_t<is_tosa_conv_v<TosaConvOp>, FailOrMaybeZP>
-extractConvZpPair(TosaConvOp op, PatternRewriter &rewriter) {
- // Strictly speaking the base TOSA spec requires that for non int8 types
- // zero points must be zero. However, in the dialect these operands are
- // optional and only required for int8. They have no semantic meaning for
- // non-quantized types and can therefore be safely ignored. This is case 3.
- if (auto opElementTY =
- cast<ShapedType>(op->getOperand(0).getType()).getElementType();
- !opElementTY.isInteger(8))
- return FailOrMaybeZP(std::nullopt);
-
- // Now we know we should have a zero point check it is valid.
- if (!op.getInputZp())
- return rewriter.notifyMatchFailure(op, "missing input zero point");
-
- // Helper to extract the zero point by matching its definition against a
- // constant.
- auto extractZeroPoint = [](Value zpValue) -> std::optional<int64_t> {
- ElementsAttr zpAttr;
- if (!matchPattern(zpValue, m_Constant(&zpAttr)))
- return std::nullopt;
-
- int64_t zp;
- if (tosa::getZeroPoint(zpAttr, zp).failed())
- return std::nullopt;
-
- return std::make_optional(zp);
- };
-
- auto maybeInputZp = extractZeroPoint(op.getInputZp());
- if (!maybeInputZp)
- return rewriter.notifyMatchFailure(op, "unable to extract input zp");
-
- if (!op.getWeightZp())
- return rewriter.notifyMatchFailure(op, "missing weight zero point");
-
- auto maybeWeightZp = extractZeroPoint(op.getWeightZp());
- if (!maybeWeightZp)
- return rewriter.notifyMatchFailure(op, "unable to extract weight zp");
-
- return std::make_optional<ConvZpPair>(*maybeInputZp, *maybeWeightZp);
-}
} // namespace tosa
} // namespace mlir
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index d11ba65a13736..8ac6a81dc38af 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -105,8 +105,9 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -118,6 +119,13 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -136,8 +144,9 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$input,
TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr6:$pad,
Tosa_IntArrayAttr3:$stride,
Tosa_IntArrayAttr3:$dilation,
@@ -149,6 +158,13 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -168,8 +184,9 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -181,6 +198,13 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -330,8 +354,9 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$out_pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$out_shape,
@@ -343,6 +368,13 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_TransConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index a8fd536dd2548..bb7d5a23d9365 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -259,11 +259,21 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
DenseI64ArrayAttr strideTosaAttr = op.getStrideAttr();
DenseI64ArrayAttr dilationTosaAttr = op.getDilationAttr();
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
+
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
- auto maybeZps = failureOrMaybeZps.value();
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
if (!weightTy.hasStaticShape() || !biasTy.hasStaticShape())
return rewriter.notifyMatchFailure(
@@ -289,7 +299,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -297,11 +307,11 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.conv op quantization has zp outside of input range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -314,8 +324,8 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// For 2D convolutions, we need to check if the target convolution op
// wants a HWCF kernel layout.
bool wantHwcf =
- maybeZps ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
- : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
+ hasZp ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
+ : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
if (wantHwcf) {
// Transpose the kernel to match dimension ordering of the linalg
// convolution operation.
@@ -376,9 +386,9 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
Value broadcastBias =
linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
- if (maybeZps) {
- auto iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- auto kZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ if (hasZp) {
+ auto iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ auto kZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, kZp);
@@ -441,18 +451,27 @@ class DepthwiseConvConverter
/*inputSizeDims=*/{1, 2},
/*kernelSizeDims=*/{0, 1}, rewriter);
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
- auto maybeZps = failureOrMaybeZps.value();
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
auto weightShape = weightTy.getShape();
auto resultShape = resultTy.getShape();
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -460,12 +479,12 @@ class DepthwiseConvConverter
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.depthwise_conv op quantization has zp outside of input "
"range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -505,7 +524,7 @@ class DepthwiseConvConverter
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
- if (!maybeZps) {
+ if (!hasZp) {
Value conv = rewriter
.create<linalg::DepthwiseConv2DNhwcHwcmOp>(
loc, linalgConvTy, ValueRange{input, weight},
@@ -532,8 +551,8 @@ class DepthwiseConvConverter
.getResult(0);
rewriter.replaceOp(op, result);
} else {
- IntegerAttr iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- IntegerAttr wZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ IntegerAttr iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ IntegerAttr wZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, wZp);
Value conv =
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 67021d6c07401..ad8077d3d47a6 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -211,6 +211,18 @@ void mlir::tosa::printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
}
}
+//===----------------------------------------------------------------------===//
+// Tosa utilities.
+//===----------------------------------------------------------------------===//
+
+static Type getStorageElementTypeOrSelf(Type type) {
+ auto elementType = getElementTypeOrSelf(type);
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(elementType))
+ elementType = quantType.getStorageType();
+
+ return elementType;
+}
+
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
@@ -243,6 +255,9 @@ static LogicalResult verifyConvOp(T op) {
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
inputEType = quantType.getStorageType();
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(weightEType))
+ weightEType = quantType.getStorageType();
+
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(biasEType))
biasEType = quantType.getStorageType();
@@ -279,36 +294,32 @@ static LogicalResult verifyConvOp(T op) {
return failure();
}
- // We require an explicit input zero point and weight zero point for i8
- // convolution.
- if (!op.getInputZp() && !op.getWeightZp())
- return inputEType.isInteger(8) ? failure() : success();
+ auto inputZpEType = getStorageElementTypeOrSelf(op.getInputZp().getType());
+ if (inputEType != inputZpEType) {
+ return op.emitOpError("expect both input and its zero point are the same "
+ "element type, got ")
+ << inputEType << " and " << inputZpEType;
+ }
- ElementsAttr inputZpAttr;
- ElementsAttr weightZpAttr;
- if (!matchPattern(op.getInputZp(), m_Constant(&inputZpAttr)) ||
- !matchPattern(op.getWeightZp(), m_Constant(&weightZpAttr))) {
- op.emitOpError(
- "bail out if the actual value of zero points cannot be determined");
- return failure();
+ auto weightZpEType = getStorageElementTypeOrSelf(op.getWeightZp().getType());
+ if (weightEType != weightZpEType) {
+ return op.emitOpError("expect both weight and its zero point are the same "
+ "element type, got ")
+ << weightEType << " and " << weightZpEType;
}
- // Get and verify explicit zero points.
int64_t inputZpVal;
- int64_t weightZpVal;
-
- if (tosa::getZeroPoint(inputZpAttr, inputZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(inputZpAttr), inputZpVal)
- .failed()) {
- op.emitOpError("input zero point must be zero for non-int8 integer types");
- return failure();
+ if (op.getInputZeroPoint(inputZpVal).succeeded()) {
+ if (op.verifyInputZeroPoint(inputZpVal).failed())
+ return op.emitOpError(
+ "input zero point must be zero for non-int8 integer types");
}
- if (tosa::getZeroPoint(weightZpAttr, weightZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(weightZpAttr), weightZpVal)
- .failed()) {
- op.emitOpError("weight zero point must be zero for non-int8 integer types");
- return failure();
+ int64_t weightZpVal;
+ if (op.getWeightZeroPoint(weightZpVal).succeeded()) {
+ if (op.verifyWeightZeroPoint(weightZpVal).failed())
+ return op.emitOpError(
+ "weight zero point must be zero for non-int8 integer types");
}
return success();
@@ -1371,6 +1382,79 @@ llvm::LogicalResult tosa::ReshapeOp::verify() {
return mlir::success();
}
+template <typename T>
+static LogicalResult getZeroPoint(T op, Value val, int64_t &zp) {
+ ElementsAttr zpAttr;
+ if (!matchPattern(val, m_Constant(&zpAttr))) {
+ return failure();
+ }
+
+ Type zpElemType = zpAttr.getElementType();
+ if (auto quantType =
+ llvm::dyn_cast<mlir::quant::UniformQuantizedType>(zpElemType)) {
+ zp = quantType.getZeroPoint();
+ return success();
+ }
+
+ if (llvm::isa<FloatType>(zpElemType)) {
+ if (!zpAttr.getValues<APFloat>()[0].isZero())
+ return op.emitOpError(
+ "non-zero zero point is not allowed for float types");
+ zp = 0;
+ return success();
+ }
+
+ if (llvm::isa<IntegerType>(zpElemType)) {
+ zp = zpAttr.getValues<APInt>()[0].getSExtValue();
+ return success();
+ }
+
+ return op.emitOpError("zero point is not allowed for unsupported types");
+}
+
+template <typename T>
+static LogicalResult verifyZeroPoint(T op, Value val, int64_t &zp) {
+ // TODO clean it up when the entire zero point (attribute -> input tensor
+ // type) change is done. Remaining Matmul, Rescale, Negate, and AvgPool2D.
+ if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
+ !std::is_same_v<T, DepthwiseConv2DOp> &&
+ !std::is_same_v<T, TransposeConv2DOp>)
+ return failure();
+
+ Type zpElemType = getElementTypeOrSelf(val);
+
+ if (!zpElemType.isIntOrFloat())
+ return op.emitOpError("zero point is not integer or float typss");
+
+ if (!zpElemType.isInteger(8) && zp != 0)
+ return op.emitOpError("zero point must be zero for non-int8 integer types");
+
+ if (zp < -128 || zp > 127)
+ return failure();
+
+ return success();
+}
+
+#define ZERO_POINT_HELPER(OP) \
+ LogicalResult tosa::OP::getInputZeroPoint(int64_t &zp) { \
+ return getZeroPoint(*this, getInputZp(), zp); \
+ } ...
[truncated]
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@llvm/pr-subscribers-mlir-linalg Author: Tai Ly (Tai78641) ChangesThis patch changes the input_zp and weight_zp for convolution operators to be required inputs Convolution operators affected are: Change-Id: I7aa6e05580c83c617394c3f7fd18fba8c3f3b0ec Patch is 151.48 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/127679.diff 15 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index 069073bc2d164..c18b46c9474fc 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -161,112 +161,6 @@ namespace tosa {
std::optional<Value> createZeroPointTensor(OpBuilder &builder, Location loc,
Type srcElemType, int64_t zp = 0);
-// Get zero point value from the attribute argument.
-LogicalResult getZeroPoint(ElementsAttr zpAttr, int64_t &zp);
-
-// Verify if zero point falls into valid range.
-template <typename T>
-LogicalResult verifyZeroPoint(Type zpElemType, int64_t zp) {
- if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
- !std::is_same_v<T, DepthwiseConv2DOp> &&
- !std::is_same_v<T, TransposeConv2DOp>) {
- return failure();
- }
-
- if (!zpElemType.isIntOrFloat())
- return failure();
-
- if (!zpElemType.isInteger(8) && zp != 0)
- return failure();
-
- if (zpElemType.isSignedInteger(8) && (zp < -128 || zp > 127))
- return failure();
-
- if (zpElemType.isUnsignedInteger(8) && (zp < 0 || zp > 255))
- return failure();
-
- return success();
-}
-
-// Helper type trait to determine if an operation is a tosa convolution.
-template <typename Op>
-struct IsTosaConv : std::false_type {};
-
-template <>
-struct IsTosaConv<tosa::Conv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::DepthwiseConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::TransposeConv2DOp> : std::true_type {};
-template <>
-struct IsTosaConv<tosa::Conv3DOp> : std::true_type {};
-
-template <typename Op>
-constexpr bool is_tosa_conv_v = IsTosaConv<Op>::value;
-
-// Helper struct to hold the zero points of a TOSA convolution operation as
-// named 64-bit integer fields.
-struct ConvZpPair {
- ConvZpPair(std::int64_t inputZp, std::int64_t weightZp)
- : inputZp(inputZp), weightZp(weightZp) {}
- std::int64_t inputZp;
- std::int64_t weightZp;
-};
-
-// Helper function which attempts to extract the zero points from a TOSA
-// convolution by matching them against defining ops which should be tosa.const
-// operations.
-//
-// There are three possible results:
-// 1. Failed to extract the zero-points i.e. they should exist and don't or they
-// do exist but are invalid.
-// 2. Succeeded in extracting zero-points.
-// 3. Zero points are "empty" and meaningless for this op i.e. non-quantized
-// convolution.
-using FailOrMaybeZP = llvm::FailureOr<std::optional<ConvZpPair>>;
-template <typename TosaConvOp>
-std::enable_if_t<is_tosa_conv_v<TosaConvOp>, FailOrMaybeZP>
-extractConvZpPair(TosaConvOp op, PatternRewriter &rewriter) {
- // Strictly speaking the base TOSA spec requires that for non int8 types
- // zero points must be zero. However, in the dialect these operands are
- // optional and only required for int8. They have no semantic meaning for
- // non-quantized types and can therefore be safely ignored. This is case 3.
- if (auto opElementTY =
- cast<ShapedType>(op->getOperand(0).getType()).getElementType();
- !opElementTY.isInteger(8))
- return FailOrMaybeZP(std::nullopt);
-
- // Now we know we should have a zero point check it is valid.
- if (!op.getInputZp())
- return rewriter.notifyMatchFailure(op, "missing input zero point");
-
- // Helper to extract the zero point by matching its definition against a
- // constant.
- auto extractZeroPoint = [](Value zpValue) -> std::optional<int64_t> {
- ElementsAttr zpAttr;
- if (!matchPattern(zpValue, m_Constant(&zpAttr)))
- return std::nullopt;
-
- int64_t zp;
- if (tosa::getZeroPoint(zpAttr, zp).failed())
- return std::nullopt;
-
- return std::make_optional(zp);
- };
-
- auto maybeInputZp = extractZeroPoint(op.getInputZp());
- if (!maybeInputZp)
- return rewriter.notifyMatchFailure(op, "unable to extract input zp");
-
- if (!op.getWeightZp())
- return rewriter.notifyMatchFailure(op, "missing weight zero point");
-
- auto maybeWeightZp = extractZeroPoint(op.getWeightZp());
- if (!maybeWeightZp)
- return rewriter.notifyMatchFailure(op, "unable to extract weight zp");
-
- return std::make_optional<ConvZpPair>(*maybeInputZp, *maybeWeightZp);
-}
} // namespace tosa
} // namespace mlir
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index d11ba65a13736..8ac6a81dc38af 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -105,8 +105,9 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -118,6 +119,13 @@ def Tosa_Conv2DOp : Tosa_ConvOp<"conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -136,8 +144,9 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$input,
TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr6:$pad,
Tosa_IntArrayAttr3:$stride,
Tosa_IntArrayAttr3:$dilation,
@@ -149,6 +158,13 @@ def Tosa_Conv3DOp : Tosa_ConvOp<"conv3d"> {
Tosa_Tensor5D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -168,8 +184,9 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -181,6 +198,13 @@ def Tosa_DepthwiseConv2DOp : Tosa_ConvOp<"depthwise_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_ConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
@@ -330,8 +354,9 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$input,
TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
- Optional<Tosa_ScalarTensor>:$input_zp,
- Optional<Tosa_ScalarTensor>:$weight_zp,
+ Tosa_ScalarTensor:$input_zp,
+ Tosa_ScalarTensor:$weight_zp,
+
Tosa_IntArrayAttr4:$out_pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$out_shape,
@@ -343,6 +368,13 @@ def Tosa_TransposeConv2DOp : Tosa_ConvOp<"transpose_conv2d"> {
Tosa_Tensor4D:$output
);
+ let extraClassDeclaration = [{
+ LogicalResult getInputZeroPoint(int64_t &zp);
+ LogicalResult getWeightZeroPoint(int64_t &zp);
+ LogicalResult verifyInputZeroPoint(int64_t zp);
+ LogicalResult verifyWeightZeroPoint(int64_t zp);
+ }];
+
let builders = [Tosa_TransConvOpQuantInfoBuilder];
let hasVerifier = 1;
}
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index a8fd536dd2548..bb7d5a23d9365 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -259,11 +259,21 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
DenseI64ArrayAttr strideTosaAttr = op.getStrideAttr();
DenseI64ArrayAttr dilationTosaAttr = op.getDilationAttr();
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
+
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
- auto maybeZps = failureOrMaybeZps.value();
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
if (!weightTy.hasStaticShape() || !biasTy.hasStaticShape())
return rewriter.notifyMatchFailure(
@@ -289,7 +299,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -297,11 +307,11 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.conv op quantization has zp outside of input range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -314,8 +324,8 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// For 2D convolutions, we need to check if the target convolution op
// wants a HWCF kernel layout.
bool wantHwcf =
- maybeZps ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
- : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
+ hasZp ? std::is_same_v<LinalgConvQOp, linalg::Conv2DNhwcHwcfQOp>
+ : std::is_same_v<LinalgConvOp, linalg::Conv2DNhwcHwcfOp>;
if (wantHwcf) {
// Transpose the kernel to match dimension ordering of the linalg
// convolution operation.
@@ -376,9 +386,9 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
Value broadcastBias =
linalgBroadcastAndMaybeExtSI(rewriter, loc, bias, biasEmptyTensor);
- if (maybeZps) {
- auto iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- auto kZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ if (hasZp) {
+ auto iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ auto kZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, kZp);
@@ -441,18 +451,27 @@ class DepthwiseConvConverter
/*inputSizeDims=*/{1, 2},
/*kernelSizeDims=*/{0, 1}, rewriter);
- auto failureOrMaybeZps = extractConvZpPair(op, rewriter);
- if (llvm::failed(failureOrMaybeZps))
- return failure();
+ // Get and verify zero points.
+ int64_t inputZpVal;
+ int64_t weightZpVal;
+
+ if (op.getInputZeroPoint(inputZpVal).failed() ||
+ op.getWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "bail out if zero points cannot statically be determined");
- auto maybeZps = failureOrMaybeZps.value();
+ if (op.verifyInputZeroPoint(inputZpVal).failed() ||
+ op.verifyWeightZeroPoint(weightZpVal).failed())
+ return rewriter.notifyMatchFailure(
+ op, "zero point must be zero for non-int8 integer types");
+ bool hasZp = (inputZpVal != 0) || (weightZpVal != 0);
auto weightShape = weightTy.getShape();
auto resultShape = resultTy.getShape();
// Apply padding as necessary.
TypedAttr zeroAttr = rewriter.getZeroAttr(inputETy);
- if (maybeZps) {
+ if (hasZp) {
int64_t intMin =
APInt::getSignedMinValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
@@ -460,12 +479,12 @@ class DepthwiseConvConverter
APInt::getSignedMaxValue(inputETy.getIntOrFloatBitWidth())
.getSExtValue();
- if (maybeZps->inputZp < intMin || maybeZps->inputZp > intMax)
+ if (inputZpVal < intMin || inputZpVal > intMax)
return rewriter.notifyMatchFailure(
op, "tosa.depthwise_conv op quantization has zp outside of input "
"range");
- zeroAttr = rewriter.getIntegerAttr(inputETy, maybeZps->inputZp);
+ zeroAttr = rewriter.getIntegerAttr(inputETy, inputZpVal);
}
llvm::SmallVector<int64_t> pad;
@@ -505,7 +524,7 @@ class DepthwiseConvConverter
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
indexingMaps.push_back(rewriter.getMultiDimIdentityMap(resultRank));
- if (!maybeZps) {
+ if (!hasZp) {
Value conv = rewriter
.create<linalg::DepthwiseConv2DNhwcHwcmOp>(
loc, linalgConvTy, ValueRange{input, weight},
@@ -532,8 +551,8 @@ class DepthwiseConvConverter
.getResult(0);
rewriter.replaceOp(op, result);
} else {
- IntegerAttr iZp = rewriter.getI32IntegerAttr(maybeZps->inputZp);
- IntegerAttr wZp = rewriter.getI32IntegerAttr(maybeZps->weightZp);
+ IntegerAttr iZp = rewriter.getI32IntegerAttr(inputZpVal);
+ IntegerAttr wZp = rewriter.getI32IntegerAttr(weightZpVal);
auto iZpVal = rewriter.create<arith::ConstantOp>(loc, iZp);
auto kZpVal = rewriter.create<arith::ConstantOp>(loc, wZp);
Value conv =
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 67021d6c07401..ad8077d3d47a6 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -211,6 +211,18 @@ void mlir::tosa::printTypeOrAttr(OpAsmPrinter &p, Operation *op, TypeAttr type,
}
}
+//===----------------------------------------------------------------------===//
+// Tosa utilities.
+//===----------------------------------------------------------------------===//
+
+static Type getStorageElementTypeOrSelf(Type type) {
+ auto elementType = getElementTypeOrSelf(type);
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(elementType))
+ elementType = quantType.getStorageType();
+
+ return elementType;
+}
+
//===----------------------------------------------------------------------===//
// TOSA Operator Verifiers.
//===----------------------------------------------------------------------===//
@@ -243,6 +255,9 @@ static LogicalResult verifyConvOp(T op) {
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(inputEType))
inputEType = quantType.getStorageType();
+ if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(weightEType))
+ weightEType = quantType.getStorageType();
+
if (auto quantType = llvm::dyn_cast<mlir::quant::QuantizedType>(biasEType))
biasEType = quantType.getStorageType();
@@ -279,36 +294,32 @@ static LogicalResult verifyConvOp(T op) {
return failure();
}
- // We require an explicit input zero point and weight zero point for i8
- // convolution.
- if (!op.getInputZp() && !op.getWeightZp())
- return inputEType.isInteger(8) ? failure() : success();
+ auto inputZpEType = getStorageElementTypeOrSelf(op.getInputZp().getType());
+ if (inputEType != inputZpEType) {
+ return op.emitOpError("expect both input and its zero point are the same "
+ "element type, got ")
+ << inputEType << " and " << inputZpEType;
+ }
- ElementsAttr inputZpAttr;
- ElementsAttr weightZpAttr;
- if (!matchPattern(op.getInputZp(), m_Constant(&inputZpAttr)) ||
- !matchPattern(op.getWeightZp(), m_Constant(&weightZpAttr))) {
- op.emitOpError(
- "bail out if the actual value of zero points cannot be determined");
- return failure();
+ auto weightZpEType = getStorageElementTypeOrSelf(op.getWeightZp().getType());
+ if (weightEType != weightZpEType) {
+ return op.emitOpError("expect both weight and its zero point are the same "
+ "element type, got ")
+ << weightEType << " and " << weightZpEType;
}
- // Get and verify explicit zero points.
int64_t inputZpVal;
- int64_t weightZpVal;
-
- if (tosa::getZeroPoint(inputZpAttr, inputZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(inputZpAttr), inputZpVal)
- .failed()) {
- op.emitOpError("input zero point must be zero for non-int8 integer types");
- return failure();
+ if (op.getInputZeroPoint(inputZpVal).succeeded()) {
+ if (op.verifyInputZeroPoint(inputZpVal).failed())
+ return op.emitOpError(
+ "input zero point must be zero for non-int8 integer types");
}
- if (tosa::getZeroPoint(weightZpAttr, weightZpVal).failed() ||
- tosa::verifyZeroPoint<T>(getElementTypeOrSelf(weightZpAttr), weightZpVal)
- .failed()) {
- op.emitOpError("weight zero point must be zero for non-int8 integer types");
- return failure();
+ int64_t weightZpVal;
+ if (op.getWeightZeroPoint(weightZpVal).succeeded()) {
+ if (op.verifyWeightZeroPoint(weightZpVal).failed())
+ return op.emitOpError(
+ "weight zero point must be zero for non-int8 integer types");
}
return success();
@@ -1371,6 +1382,79 @@ llvm::LogicalResult tosa::ReshapeOp::verify() {
return mlir::success();
}
+template <typename T>
+static LogicalResult getZeroPoint(T op, Value val, int64_t &zp) {
+ ElementsAttr zpAttr;
+ if (!matchPattern(val, m_Constant(&zpAttr))) {
+ return failure();
+ }
+
+ Type zpElemType = zpAttr.getElementType();
+ if (auto quantType =
+ llvm::dyn_cast<mlir::quant::UniformQuantizedType>(zpElemType)) {
+ zp = quantType.getZeroPoint();
+ return success();
+ }
+
+ if (llvm::isa<FloatType>(zpElemType)) {
+ if (!zpAttr.getValues<APFloat>()[0].isZero())
+ return op.emitOpError(
+ "non-zero zero point is not allowed for float types");
+ zp = 0;
+ return success();
+ }
+
+ if (llvm::isa<IntegerType>(zpElemType)) {
+ zp = zpAttr.getValues<APInt>()[0].getSExtValue();
+ return success();
+ }
+
+ return op.emitOpError("zero point is not allowed for unsupported types");
+}
+
+template <typename T>
+static LogicalResult verifyZeroPoint(T op, Value val, int64_t &zp) {
+ // TODO clean it up when the entire zero point (attribute -> input tensor
+ // type) change is done. Remaining Matmul, Rescale, Negate, and AvgPool2D.
+ if constexpr (!std::is_same_v<T, Conv2DOp> && !std::is_same_v<T, Conv3DOp> &&
+ !std::is_same_v<T, DepthwiseConv2DOp> &&
+ !std::is_same_v<T, TransposeConv2DOp>)
+ return failure();
+
+ Type zpElemType = getElementTypeOrSelf(val);
+
+ if (!zpElemType.isIntOrFloat())
+ return op.emitOpError("zero point is not integer or float typss");
+
+ if (!zpElemType.isInteger(8) && zp != 0)
+ return op.emitOpError("zero point must be zero for non-int8 integer types");
+
+ if (zp < -128 || zp > 127)
+ return failure();
+
+ return success();
+}
+
+#define ZERO_POINT_HELPER(OP) \
+ LogicalResult tosa::OP::getInputZeroPoint(int64_t &zp) { \
+ return getZeroPoint(*this, getInputZp(), zp); \
+ } ...
[truncated]
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This patch changes the input_zp and weight_zp for convolution operators to be required inputs. Convolution operators affected are: CONV2D, CONV3D, DEPTHWISE_CONV2D, and TRANSPOSE_CONV2D. Signed-off-by: Tai Ly <[email protected]> Change-Id: I7aa6e05580c83c617394c3f7fd18fba8c3f3b0ec
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This patch changes the input_zp and weight_zp for convolution operators to be required inputs
in order to align with the TOSA Spec 1.0.
Convolution operators affected are:
CONV2D, CONV3D, DEPTHWISE_CONV2D, and TRANSPOSE_CONV2D.
Change-Id: I7aa6e05580c83c617394c3f7fd18fba8c3f3b0ec