[Mlir-commits] [mlir] 5685def - [mlir][tosa] Convert RESCALE op multiplier and shift from attributes to inputs (#129720)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Fri Mar 7 07:59:34 PST 2025
Author: Peng Sun
Date: 2025-03-07T15:59:29Z
New Revision: 5685def507ed7c95bf93572e5cf8c30efbc7d99b
URL: https://github.com/llvm/llvm-project/commit/5685def507ed7c95bf93572e5cf8c30efbc7d99b
DIFF: https://github.com/llvm/llvm-project/commit/5685def507ed7c95bf93572e5cf8c30efbc7d99b.diff
LOG: [mlir][tosa] Convert RESCALE op multiplier and shift from attributes to inputs (#129720)
Added:
Modified:
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h
mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
mlir/test/Dialect/Tosa/availability.mlir
mlir/test/Dialect/Tosa/canonicalize.mlir
mlir/test/Dialect/Tosa/invalid.mlir
mlir/test/Dialect/Tosa/level_check.mlir
mlir/test/Dialect/Tosa/ops.mlir
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
Removed:
################################################################################
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 8782d0bb55878..5340ce52d73fc 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -2279,9 +2279,7 @@ def Tosa_CastOp: Tosa_Op<"cast", [Pure,
//===----------------------------------------------------------------------===//
// Operator: rescale
//===----------------------------------------------------------------------===//
-def Tosa_RescaleOp: Tosa_Op<"rescale", [Pure,
- DeclareOpInterfaceMethods<InferShapedTypeOpInterface,
- ["inferReturnTypeComponents"]>]> {
+def Tosa_RescaleOp : Tosa_InferShapedTypeOp<"rescale"> {
let summary = "Tosa rescale operator";
let description = [{
@@ -2307,10 +2305,10 @@ def Tosa_RescaleOp: Tosa_Op<"rescale", [Pure,
let arguments = (ins
Tosa_Tensor:$input,
+ Tosa_1DInt16Or32Tensor:$multiplier,
+ Tosa_1DInt8Tensor:$shift,
I32Attr:$input_zp,
I32Attr:$output_zp,
- DenseI32ArrayAttr:$multiplier,
- DenseI8ArrayAttr:$shift,
BoolAttr:$scale32,
BoolAttr:$double_round,
BoolAttr:$per_channel,
@@ -2327,6 +2325,8 @@ def Tosa_RescaleOp: Tosa_Op<"rescale", [Pure,
Extension<[Tosa_EXT_INT16]>,
];
+ let hasVerifier = 1;
+
let assemblyFormat = "operands attr-dict `:` functional-type(operands, results)";
}
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
index 7a8357ecfa430..08c0e02139b0c 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -43,6 +43,7 @@ class Tosa_QuantizedType<string n, list<int> params, bit signed>
def Tosa_Int4 : I<4>;
def Tosa_Int8 : I<8>;
+def Tosa_Int16 : I<16>;
def Tosa_Int32 : I<32>;
def Tosa_Int64 : I<64>;
@@ -54,7 +55,10 @@ def Tosa_Int : AnyTypeOf<[AnyUnsignedInteger,
AnySignlessInteger]>;
def Tosa_Int32Or64 : AnyTypeOf<[Tosa_Int32,
- Tosa_Int64]>;
+ Tosa_Int64]>;
+
+def Tosa_Int16Or32 : AnyTypeOf<[Tosa_Int16,
+ Tosa_Int32]>;
//===----------------------------------------------------------------------===//
// Quantized Integer Types.
@@ -163,6 +167,10 @@ def Tosa_Tensor3D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNu
def Tosa_Tensor4D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [4]>], "4-d tosa-conformant tensor", "::mlir::TensorType">;
def Tosa_Tensor5D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [5]>], "5-d tosa-conformant tensor", "::mlir::TensorType">;
+// 1D tensor of specific types
+def Tosa_1DInt8Tensor : 1DTensorOf<[Tosa_Int8]>;
+def Tosa_1DInt16Or32Tensor : 1DTensorOf<[Tosa_Int16Or32]>;
+
// Ranked tensors up to given rank.
def Tosa_Tensor1Dto4D : AnyTypeOf<[
Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4]>]>;
diff --git a/mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h b/mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h
index cb0df7aff35f7..bdd8713037eea 100644
--- a/mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h
+++ b/mlir/include/mlir/Dialect/Tosa/Utils/QuantUtils.h
@@ -31,6 +31,26 @@ namespace tosa {
bool computeMultiplierAndShift(double scale, int32_t &multiplier,
int32_t &shift, int32_t scaleWidth);
+// Return a const value for array of IntType vec
+template <typename IntType>
+Value getConstTensorInt(OpBuilder &builder, Location loc,
+ ArrayRef<IntType> vec) {
+ static_assert(
+ std::is_same<IntType, int8_t>::value ||
+ std::is_same<IntType, int16_t>::value ||
+ std::is_same<IntType, int32_t>::value,
+ "getConstTensorInt only supports int8_t, int16_t, and int32_t types.");
+
+ int64_t count = vec.size();
+ assert(count > 0 && "Vector must not be empty");
+ auto element_type = builder.getIntegerType(sizeof(IntType) * 8);
+ mlir::RankedTensorType const_type =
+ RankedTensorType::get({count}, element_type);
+ mlir::DenseElementsAttr const_attr = DenseElementsAttr::get(const_type, vec);
+ auto const_op = builder.create<tosa::ConstOp>(loc, const_type, const_attr);
+ return const_op.getResult();
+}
+
//// Builds ConvOpQuantizationAttr from input and weight.
ConvOpQuantizationAttr buildConvOpQuantizationAttr(OpBuilder &builder,
Value input, Value weight);
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index a99cf293b9eac..f7dd33c7e8b53 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -138,7 +138,7 @@ static Value createLinalgBodyCalculationForElementwiseOp(
// tosa::MulOp
if (isa<tosa::MulOp>(op)) {
auto shift_val = cast<tosa::MulOp>(op).getShift();
- ElementsAttr shift_elem;
+ DenseElementsAttr shift_elem;
if (!shift_val.getImpl() ||
!matchPattern(shift_val, m_Constant(&shift_elem))) {
(void)rewriter.notifyMatchFailure(op, "shift value of mul not found");
@@ -1389,8 +1389,24 @@ class RescaleConverter : public OpRewritePattern<tosa::RescaleOp> {
}
// The shift and multiplier values.
- SmallVector<int32_t> multiplierValues(op.getMultiplier());
- SmallVector<int8_t> shiftValues(op.getShift());
+ DenseElementsAttr shiftElems;
+ if (!matchPattern(op.getShift(), m_Constant(&shiftElems)))
+ return rewriter.notifyMatchFailure(
+ op, "tosa.rescale requires constant shift input values");
+
+ DenseElementsAttr multiplierElems;
+ if (!matchPattern(op.getMultiplier(), m_Constant(&multiplierElems)))
+ return rewriter.notifyMatchFailure(
+ op, "tosa.rescale requires constant multiplier input values");
+
+ llvm::SmallVector<int8_t> shiftValues =
+ llvm::to_vector(shiftElems.getValues<int8_t>());
+ // explicit cast is required here
+ llvm::SmallVector<int32_t> multiplierValues = llvm::to_vector(
+ llvm::map_range(multiplierElems.getValues<IntegerAttr>(),
+ [](IntegerAttr attr) -> int32_t {
+ return static_cast<int32_t>(attr.getInt());
+ }));
// If we shift by more than the bitwidth, this just sets to 0.
for (int i = 0, s = multiplierValues.size(); i < s; i++) {
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index 5fdf5f4b5cb6a..854196250bb0c 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -2226,7 +2226,6 @@ NARY_SHAPE_INFER(tosa::MinimumOp)
NARY_SHAPE_INFER(tosa::NegateOp)
NARY_SHAPE_INFER(tosa::PowOp)
NARY_SHAPE_INFER(tosa::ReciprocalOp)
-NARY_SHAPE_INFER(tosa::RescaleOp)
NARY_SHAPE_INFER(tosa::ReverseOp)
NARY_SHAPE_INFER(tosa::RsqrtOp)
NARY_SHAPE_INFER(tosa::SinOp)
@@ -2676,6 +2675,147 @@ LogicalResult TransposeConv2DOp::verify() {
return success();
}
+LogicalResult RescaleOp::verify() {
+ auto inputType = llvm::dyn_cast<ShapedType>(getInput().getType());
+ if (!inputType) {
+ emitOpError("expect shaped tensor for input, got ") << getInput().getType();
+ return failure();
+ }
+
+ auto inputElementType =
+ getStorageElementTypeOrSelf(inputType.getElementType());
+ if (!mlir::isa<IntegerType>(inputElementType)) {
+ emitOpError("expect input to have integer element type, got ")
+ << inputElementType;
+ return failure();
+ }
+
+ auto outputType = llvm::dyn_cast<ShapedType>(getOutput().getType());
+ if (!outputType) {
+ emitOpError("expect shaped tensor for output, got ")
+ << getOutput().getType();
+ return failure();
+ }
+
+ auto outputElementType =
+ getStorageElementTypeOrSelf(outputType.getElementType());
+ if (!mlir::isa<IntegerType>(outputElementType)) {
+ emitOpError("expect output to have integer element type, got ")
+ << outputElementType;
+ return failure();
+ }
+
+ auto input_zp = getInputZpAttr().getInt();
+ if (input_zp != 0) {
+ // only int8/uint8 and uint16 input can have non-zero input_zp
+ if (!inputElementType.isInteger(8) &&
+ !(inputElementType.isInteger(16) && getInputUnsigned())) {
+ emitOpError("expect input_zp of 0, got ") << input_zp;
+ return failure();
+ }
+ // input_zp must be either 0 or 32768 for uint16 input
+ if (inputElementType.isInteger(16) && getInputUnsigned() &&
+ input_zp != 32768) {
+ emitOpError(
+ "expect input_zp of 0 or 32768 for unsigned int16 input, got ")
+ << input_zp;
+ return failure();
+ }
+ }
+
+ auto output_zp = getOutputZpAttr().getInt();
+ if (output_zp != 0) {
+ // only int8/uint8 and uint16 output can have non-zero output_zp
+ if (!outputElementType.isInteger(8) &&
+ !(outputElementType.isInteger(16) && getOutputUnsigned())) {
+ emitOpError("expect output_zp of 0, got ") << output_zp;
+ return failure();
+ }
+ // output_zp must be either 0 or 32768 for uint16 output
+ if (outputElementType.isInteger(16) && getOutputUnsigned() &&
+ output_zp != 32768) {
+ emitOpError(
+ "expect output_zp of 0 or 32768 for unsigned int16 output, got ")
+ << output_zp;
+ return failure();
+ }
+ }
+
+ auto multiplierType = llvm::dyn_cast<ShapedType>(getMultiplier().getType());
+ if (!multiplierType) {
+ emitOpError("expect shaped tensor for multiplier, got ")
+ << getMultiplier().getType();
+ return failure();
+ }
+
+ auto shiftType = llvm::dyn_cast<ShapedType>(getShift().getType());
+ if (!shiftType) {
+ emitOpError("expect shaped tensor for shift, got ") << getShift().getType();
+ return failure();
+ }
+
+ // multiplier element type must be i32 for scale32 = true
+ if (getScale32() && !multiplierType.getElementType().isInteger(32)) {
+ emitOpError("expect i32 element type for multiplier for scale32=true, got ")
+ << multiplierType.getElementType();
+ return failure();
+ }
+
+ // multiplier element type must be i16 for scale32 = false
+ if (!getScale32() && !multiplierType.getElementType().isInteger(16)) {
+ emitOpError(
+ "expect i16 element type for multiplier for scale32=false, got ")
+ << multiplierType.getElementType();
+ return failure();
+ }
+
+ if (!inputType.hasRank())
+ return success();
+
+ // multiplier/shift must have shape = {numChannels},
+ // where numChannel is 1 if per_channel = false
+ // otherwise numChannel is dimension in input shape's last axis
+ int64_t numChannels = 1;
+ if (getPerChannel()) {
+ numChannels = inputType.getDimSize(inputType.getRank() - 1);
+ }
+
+ if (!multiplierType.hasRank())
+ return success();
+
+ ArrayRef<int64_t> multiplierShape = multiplierType.getShape();
+ // multiplier input has rank 1 by dialect definition
+ if (multiplierShape[0] != ShapedType::kDynamic &&
+ multiplierShape[0] != numChannels) {
+ emitOpError("expect shape of { ")
+ << numChannels << " } for multiplier input, got { "
+ << multiplierShape[0] << " }";
+ return failure();
+ }
+
+ if (!shiftType.hasRank())
+ return success();
+
+ ArrayRef<int64_t> shiftShape = shiftType.getShape();
+ // shift input has rank 1 by dialect definition
+ if (shiftShape[0] != ShapedType::kDynamic && shiftShape[0] != numChannels) {
+ emitOpError("expect shape of { ")
+ << numChannels << " } for shift input, got { " << shiftShape[0] << " }";
+ return failure();
+ }
+
+ return success();
+}
+
+LogicalResult RescaleOp::inferReturnTypeComponents(
+ MLIRContext *context, ::std::optional<Location> location,
+ RescaleOp::Adaptor adaptor,
+ SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
+ ShapeAdaptor inputShape(adaptor.getInput().getType());
+ inferredReturnShapes.push_back(ShapedTypeComponents(inputShape));
+ return success();
+}
+
LogicalResult IfOp::inferReturnTypeComponents(
MLIRContext *context, ::std::optional<Location> location,
IfOp::Adaptor adaptor,
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
index cc737e6c94b48..77687b83e5e3c 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-invalid.mlir
@@ -33,8 +33,10 @@ func.func @unranked_add(%arg0 : tensor<10x10xf32> , %arg1 : tensor<10x10xf32>, %
// CHECK-LABEL: @rescale_unsupported_type
func.func @rescale_unsupported_type(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error at +1 {{failed to legalize operation 'tosa.rescale'}}
- %0 = tosa.rescale %arg0 {double_round = false, input_zp = 127 : i32, multiplier = array<i32: 1073741824>, output_zp = -1 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 127 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
}
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 9ba9965315fd3..a3ed8c2805282 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -1149,7 +1149,9 @@ func.func @rescale_i8(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK-DAG: linalg.yield [[TRUNC]]
- %0 = tosa.rescale %arg0 {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
+ %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 17 : i32, output_zp = 22 : i32, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: return
return
@@ -1178,7 +1180,9 @@ func.func @rescale_i8_unsigned_output(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: linalg.yield [[TRUNC]]
- %1 = tosa.rescale %arg0 {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
+ %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 17 : i32, output_zp = 22 : i32, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<2xi8>
// CHECK: return
return
@@ -1191,17 +1195,19 @@ func.func @rescale_i8_unsigned_output(%arg0 : tensor<2xi8>) -> () {
// CHECK-LABEL: @rescale_i8_dyn_batch
// CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]:
func.func @rescale_i8_dyn_batch(%arg0 : tensor<?x2xi8>) -> () {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
+ %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x2xi8>
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x2xi8>) outs(%[[INIT]] : tensor<?x2xi8>)
- %0 = tosa.rescale %arg0 {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<?x2xi8>) -> tensor<?x2xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 17 : i32, output_zp = 22 : i32, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<?x2xi8>
// CHECK: %[[C0:.+]] = arith.constant 0
// CHECK: %[[BATCH:.+]] = tensor.dim %[[ARG0]], %[[C0]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[BATCH]]) : tensor<?x2xi8>
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel", "parallel"]} ins(%[[ARG0]] : tensor<?x2xi8>) outs(%[[INIT]] : tensor<?x2xi8>)
- %1 = tosa.rescale %arg0 {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<?x2xi8>) -> tensor<?x2xi8>
+ %1 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 17 : i32, output_zp = 22 : i32, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = true} : (tensor<?x2xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<?x2xi8>
return
}
@@ -1219,7 +1225,9 @@ func.func @rescale_dyn(%arg0 : tensor<1x?x?x32xi32>) -> () {
// CHECK: %[[DIM2:.+]] = tensor.dim %[[ARG0]], %[[C2]]
// CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM2]])
// CHECK: [[GENERIC:%.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP1]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%[[ARG0]] : tensor<1x?x?x32xi32>) outs(%[[INIT]] : tensor<1x?x?x32xi8>)
- %0 = tosa.rescale %arg0 {double_round = true, input_zp = 0 : i32, multiplier = array<i32: 1376784203>, output_zp = 0 : i32, per_channel = false, scale32 = true, shift = array<i8: 38>, input_unsigned = false, output_unsigned = false} : (tensor<1x?x?x32xi32>) -> tensor<1x?x?x32xi8>
+ %multiplier = "tosa.const"() {values = dense<1376784203> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<38> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = true, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x?x?x32xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x?x?x32xi8>
return
}
@@ -1247,7 +1255,9 @@ func.func @rescale_i8_unsigned_input(%arg0 : tensor<2xi8>) -> () {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK: linalg.yield [[TRUNC]]
- %0 = tosa.rescale %arg0 {input_zp = 17 : i32, output_zp = 22 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
+ %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 17 : i32, output_zp = 22 : i32, scale32 = false, double_round = false, per_channel = false, input_unsigned = true, output_unsigned = false} : (tensor<2xi8>, tensor<1xi16>, tensor<1xi8>) -> tensor<2xi8>
return
}
@@ -1277,7 +1287,9 @@ func.func @rescale_per_channel(%arg0 : tensor<3xi8>) -> (tensor<3xi8>) {
// CHECK-DAG: [[BOUNDED:%.+]] = arith.minsi [[CMAX]], [[LOWER]]
// CHECK-DAG: [[TRUNC:%.+]] = arith.trunci [[BOUNDED]]
// CHECK-DAG: linalg.yield [[TRUNC]]
- %0 = tosa.rescale %arg0 {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = array<i32: 42, 43, 44>, shift = array<i8: 14, 15, 64>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<3xi8>) -> tensor<3xi8>
+ %multiplier = "tosa.const"() {values = dense<[42, 43, 44]> : tensor<3xi16> } : () -> tensor<3xi16>
+ %shift = "tosa.const"() {values = dense<[14, 15, 64]> : tensor<3xi8> } : () -> tensor<3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = array<i32: 42, 43, 44>, shift = array<i8: 14, 15, 64>, scale32 = false, double_round = false, per_channel = true, input_unsigned = false, output_unsigned = false} : (tensor<3xi8>, tensor<3xi16>, tensor<3xi8>) -> tensor<3xi8>
// CHECK: return [[GENERIC]]
return %0 : tensor<3xi8>
@@ -1290,7 +1302,9 @@ func.func @rescaleDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>) {
// CHECK: linalg.generic
// CHECK: tosa.apply_scale
// CHECK-SAME: {double_round = true}
- %0 = tosa.rescale %arg0 {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = array<i32: 19689>, shift = array<i8: 33>, scale32 = true, double_round = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<33> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 243 : i32, output_zp = 252 : i32, scale32 = true, double_round = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
@@ -1299,7 +1313,9 @@ func.func @rescaleUnnecessaryDoubleRound(%arg0 : tensor<2xi8>) -> (tensor<2xi8>)
// CHECK: linalg.generic
// CHECK: tosa.apply_scale
// CHECK-SAME: {double_round = false}
- %0 = tosa.rescale %arg0 {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = array<i32: 19689>, shift = array<i8: 15>, scale32 = true, double_round = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<15> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {input_zp = 243 : i32, output_zp = 252 : i32, scale32 = true, double_round = true, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<2xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<2xi8>
return %0 : tensor<2xi8>
}
diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
index e90f0af1f9c7d..1952ad79392c7 100644
--- a/mlir/test/Dialect/Tosa/availability.mlir
+++ b/mlir/test/Dialect/Tosa/availability.mlir
@@ -614,7 +614,7 @@ func.func @test_cast1(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
func.func @test_rescale(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, %multiplier : tensor<1xi32>, %shift : tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>> {
// CHECK: profiles: [ [pro_int] ]
// CHECK: extensions: [ [int16] ]
- %0 = tosa.rescale %arg0 {double_round = false, input_zp = 127 : i32, multiplier = array<i32: 1073741824>, output_zp = -1 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 127 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
}
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index a78479cc0bead..4242f68609634 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -903,7 +903,9 @@ func.func @reshape_quant_nofold() -> tensor<1x1x1x1xi32> {
%0 = "tosa.const"() {values = dense<127> : tensor<i8>} : () -> tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
%cst0 = "tosa.const_shape"() {values = dense<[1, 1, 1, 1]> : tensor<4xindex>} : () -> !tosa.shape<4>
%1 = tosa.reshape %0, %cst0 : (tensor<!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, !tosa.shape<4>) -> tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>
- %2 = tosa.rescale %1 {double_round = true, input_zp = -128 : i32, multiplier = array<i32: 1073741824>, output_zp = 0 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>) -> tensor<1x1x1x1xi32>
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %2 = tosa.rescale %1, %multiplier, %shift {double_round = true, input_zp = -128 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x!quant.uniform<i8:f32, 3.0757404601899907E-5:-128>>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x1x1x1xi32>
return %2 : tensor<1x1x1x1xi32>
}
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index f2956d84c9650..05700ca3765e4 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -1431,3 +1431,184 @@ func.func @test_argmax_invalid_output_shape(%arg0: tensor<1x2x3xf32>) -> tensor<
%0 = tosa.argmax %arg0 {axis = 0 : i32}: (tensor<1x2x3xf32>) -> tensor<1x2x3xi32>
return %0 : tensor<1x2x3xi32>
}
+
+// -----
+
+func.func @test_rescale_invalid_input_type(%arg0: tensor<13x21x3xf32>) -> tensor<13x21x3xi32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect input to have integer element type, got 'f32'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xf32>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi32>
+ return %0 : tensor<13x21x3xi32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_output_type(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect output to have integer element type, got 'f32'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xf32>
+ return %0 : tensor<13x21x3xf32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_multiplier_type(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi48> } : () -> tensor<1xi48>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi16> } : () -> tensor<1xi16>
+ // expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1xi48>'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi48>, tensor<1xi16>) -> tensor<13x21x3xf32>
+ return %0 : tensor<13x21x3xf32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_shift_type(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xf32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi16> } : () -> tensor<1xi16>
+ // expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tensor of 8-bit signless integer values, but got 'tensor<1xi16>'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi16>) -> tensor<13x21x3xf32>
+ return %0 : tensor<13x21x3xf32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_input_zp_i32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect input_zp of 0, got 1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 1 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi32>
+ return %0 : tensor<13x21x3xi32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_input_zp_s16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect input_zp of 0, got 1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 1 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_input_zp_u16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect input_zp of 0 or 32768 for unsigned int16 input, got 1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 1 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+
+// -----
+
+func.func @test_rescale_invalid_output_zp_i32(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3xi32> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect output_zp of 0, got -1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi32>
+ return %0 : tensor<13x21x3xi32>
+}
+
+// -----
+
+func.func @test_rescale_invalid_output_zp_s16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect output_zp of 0, got -1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = true, output_unsigned = false} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_output_zp_u16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect output_zp of 0 or 32768 for unsigned int16 output, got -1}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_multiplier_i16(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi16> } : () -> tensor<1xi16>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect i32 element type for multiplier for scale32=true, got 'i16'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi16>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_multiplier_i32(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect i16 element type for multiplier for scale32=false, got 'i32'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = false, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_multiplier_rank(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1x1xi32> } : () -> tensor<1x1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1x1xi32>'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1x1xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_shift_rank(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1x1xi8> } : () -> tensor<1x1xi8>
+ // expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1x1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_perchannel_multiplier_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<3xi8> } : () -> tensor<3xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect shape of { 3 } for multiplier input, got { 1 }}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = true, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_non_perchannel_multiplier_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<3xi32> } : () -> tensor<3xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect shape of { 1 } for multiplier input, got { 3 }}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_perchannel_shift_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<3xi32> } : () -> tensor<3xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect shape of { 3 } for shift input, got { 1 }}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = true, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<3xi32>, tensor<1xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
+
+// -----
+
+func.func @test_rescale_invalid_non_perchannel_shift_shape(%arg0: tensor<13x21x3xi16>) -> tensor<13x21x3xi16> {
+ %multiplier = "tosa.const"() {values = dense<19689> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<3xi8> } : () -> tensor<3xi8>
+ // expected-error at +1 {{'tosa.rescale' op expect shape of { 1 } for shift input, got { 3 }}}
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 0 : i32, output_zp = 0 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<3xi8>) -> tensor<13x21x3xi16>
+ return %0 : tensor<13x21x3xi16>
+}
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 8f64f217514de..bc13b614e3f9d 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -432,8 +432,10 @@ func.func @test_cast_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi32>) -> tensor<
// -----
func.func @test_rescale_rank_invalid(%arg0: tensor<1x1x1x1x13x21x3xi8>) -> tensor<1x1x1x1x13x21x3xi8> {
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
// expected-error at +1 {{'tosa.rescale' op failed level check: operand rank(shape) <= MAX_RANK}}
- %0 = tosa.rescale %arg0 {double_round = false, input_zp = 127 : i32, output_zp = -1 : i32, multiplier = array<i32: 1073741824>, shift = array<i8: 30>, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x13x21x3xi8>) -> tensor<1x1x1x1x13x21x3xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 127 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x1x13x21x3xi8>, tensor<1xi32>, tensor<1xi8>) -> tensor<1x1x1x1x13x21x3xi8>
return %0 : tensor<1x1x1x1x13x21x3xi8>
}
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index f9cd342ccf1d4..e1ac7d5f51d0e 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -94,7 +94,9 @@ func.func @test_conv2d_q8xi4(%arg0: tensor<1x11x11x3xi8>) -> tensor<1x1x1x3xi8>
%izp = "tosa.const"() {values = dense<0> : tensor<1xi8>} : () -> tensor<1xi8>
%wzp = "tosa.const"() {values = dense<0> : tensor<1xi4>} : () -> tensor<1xi4>
%2 = "tosa.conv2d"(%arg0, %0, %1, %izp, %wzp) {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<1x11x11x3xi8>, tensor<3x11x11x3xi4>, tensor<3xi32>, tensor<1xi8>, tensor<1xi4>) -> tensor<1x1x1x3xi32>
- %3 = "tosa.rescale"(%2) {double_round = true, input_zp = 0 : i32, multiplier = array<i32: 2026291432, 1079222024, 1693132724>, output_zp = 27 : i32, per_channel = true, scale32 = true, shift = array<i8: 37, 36, 37>, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x3xi32>) -> tensor<1x1x1x3xi8>
+ %multiplier = "tosa.const"() {values = dense<[2026291432, 1079222024, 1693132724]> : tensor<3xi32>} : () -> tensor<3xi32>
+ %shift = "tosa.const"() {values = dense<[37, 36, 37]> : tensor<3xi8>} : () -> tensor<3xi8>
+ %3 = tosa.rescale %2, %multiplier, %shift {double_round = true, input_zp = 0 : i32, output_zp = 27 : i32, per_channel = true, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<1x1x1x3xi32>, tensor<3xi32>, tensor<3xi8>) -> tensor<1x1x1x3xi8>
return %3 : tensor<1x1x1x3xi8>
}
@@ -718,7 +720,9 @@ func.func @test_cast3(%arg0: tensor<13x21x3xi32>) -> tensor<13x21x3x!quant.unifo
// -----
// CHECK-LABEL: rescale
func.func @test_rescale(%arg0: tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>> {
- %0 = tosa.rescale %arg0 {double_round = false, input_zp = 127 : i32, multiplier = array<i32: 1073741824>, output_zp = -1 : i32, per_channel = false, scale32 = true, shift = array<i8: 30>, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
+ %multiplier = "tosa.const"() {values = dense<1073741824> : tensor<1xi32> } : () -> tensor<1xi32>
+ %shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
+ %0 = tosa.rescale %arg0, %multiplier, %shift {double_round = false, input_zp = 127 : i32, output_zp = -1 : i32, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3x!quant.uniform<u8:f32, 0.015655439347028732:127>>, tensor<1xi32>, tensor<1xi8>) -> tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
return %0 : tensor<13x21x3x!quant.uniform<i8:f32, 0.015655439347028732:-1>>
}
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index 6d480b841441a..55c5c3f6bdfb6 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -74,7 +74,7 @@ func.func @test_unary_f32(%arg0 : tensor<4xf32>) -> () {
// -----
// CHECK-LABEL: @test_unary_i32
-func.func @test_unary_i32(%arg0 : tensor<4xi32>) -> () {
+func.func @test_unary_i32(%arg0 : tensor<4xi32>, %arg1 : tensor<2xi8>) -> () {
// CHECK: tosa.abs %arg0 : (tensor<4xi32>) -> tensor<4xi32>
%0 = tosa.abs %arg0 : (tensor<4xi32>) -> tensor<*xi32>
@@ -93,8 +93,12 @@ func.func @test_unary_i32(%arg0 : tensor<4xi32>) -> () {
// CHECK: tosa.reverse %arg0 {axis = 0 : i32} : (tensor<4xi32>) -> tensor<4xi32>
%5 = tosa.reverse %arg0 { axis = 0 : i32 } : (tensor<4xi32>) -> tensor<?xi32>
- // CHECK: tosa.rescale %arg0 {{.+}} : (tensor<4xi32>) -> tensor<4xi16>
- %6 = tosa.rescale %arg0 {input_zp = 243 : i32, output_zp = 252 : i32, multiplier = array<i32: 42, 43>, shift = array<i8: 14, 15>, scale32 = false, double_round = false, per_channel = false, input_unsigned = false, output_unsigned = false} : (tensor<4xi32>) -> tensor<*xi16>
+ // CHECK-DAG: %[[MULT:.+]] = "tosa.const"() <{values = dense<[42, 43]> : tensor<2xi16>}> : () -> tensor<2xi16>
+ // CHECK-DAG: %[[SHIFT:.+]] = "tosa.const"() <{values = dense<[14, 15]> : tensor<2xi8>}> : () -> tensor<2xi8>
+ // CHECK: tosa.rescale %arg1, %[[MULT]], %[[SHIFT]] {{.+}} : (tensor<2xi8>, tensor<2xi16>, tensor<2xi8>) -> tensor<2xi8>
+ %multiplier = "tosa.const"() {values = dense<[42, 43]> : tensor<2xi16>} : () -> tensor<2xi16>
+ %shift = "tosa.const"() {values = dense<[14, 15]> : tensor<2xi8>} : () -> tensor<2xi8>
+ %6 = tosa.rescale %arg1, %multiplier, %shift {input_zp = 243 : i32, output_zp = 252 : i32, scale32 = false, double_round = false, per_channel = true, input_unsigned = true, output_unsigned = true} : (tensor<2xi8>, tensor<2xi16>, tensor<2xi8>) -> tensor<*xi8>
// CHECK: tosa.identity %arg0 : (tensor<4xi32>) -> tensor<4xi32>
%7 = tosa.identity %arg0 : (tensor<4xi32>) -> tensor<?xi32>
diff --git a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
index 1297596ef193a..a5d1cf863bbf8 100644
--- a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
+++ b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
@@ -166,18 +166,22 @@ ConvertTosaConv2DOp::matchAndRewrite(Operation *op,
if (!computeMultiplierAndShift(opTensorScale, multiplier, shift, 32))
return failure();
- bool input_unsigned =
+ bool inputUnsigned =
newTosaConv2DOp.getResult().getType().isUnsignedInteger();
- bool output_unsigned = outputType.isUnsignedInteger();
+ bool outputUnsigned = outputType.isUnsignedInteger();
auto newTosaRescaleOp = rewriter.create<tosa::RescaleOp>(
op->getLoc(), outputType, newTosaConv2DOp.getResult(),
- rewriter.getI32IntegerAttr(0), rewriter.getI32IntegerAttr(outputZp),
- rewriter.getDenseI32ArrayAttr({multiplier}),
- rewriter.getDenseI8ArrayAttr({static_cast<int8_t>(shift)}),
- rewriter.getBoolAttr(true), rewriter.getBoolAttr(true),
- rewriter.getBoolAttr(false), rewriter.getBoolAttr(input_unsigned),
- rewriter.getBoolAttr(output_unsigned));
+ getConstTensorInt<int32_t>(rewriter, op->getLoc(), {multiplier}),
+ getConstTensorInt<int8_t>(rewriter, op->getLoc(),
+ {static_cast<int8_t>(shift)}),
+ /* input_zp = */ rewriter.getI32IntegerAttr(0),
+ /* output_zp = */ rewriter.getI32IntegerAttr(outputZp),
+ /* scale32 = */ rewriter.getBoolAttr(true),
+ /* double_round = */ rewriter.getBoolAttr(true),
+ /* per_channel = */ rewriter.getBoolAttr(false),
+ rewriter.getBoolAttr(inputUnsigned),
+ rewriter.getBoolAttr(outputUnsigned));
rewriter.replaceOp(op, {newTosaRescaleOp.getResult()});
return success();
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