[MLIR][TOSA] add additional verification to TOSA #108133
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---------- Motivation: ---------- Spec conformance. Allows assumptions to be made in TOSA code. ------------ Changes Made: ------------ Add full permutation tensor verification to tosa.TRANSPOSE. Priorly would not verify that permuted values were between 0 - (rank - 1). Update tosa.TRANSPOSE perms data type to be strictly i32. Verify input/output shapes for tosa.TRANSPOSE. Add verifier to tosa.CONST, with consideration for quantization. Fix TOSA conformance of tensor type to disallow dimensions with size 0 for ranked tensors, per spec. This is not the same as rank 0 tensors. Here is an example of a disallowed tensor: tensor<3x0xi32>. Naturally, this means that the number of elements in a TOSA tensor will always be greater than 0. Signed-off-by: Arteen Abrishami <arteen.abrishami@arm.com>
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@llvm/pr-subscribers-mlir-linalg @llvm/pr-subscribers-mlir-tosa Author: Arteen Abrishami (arteen1000) ChangesMotivation:Spec conformance. Allows assumptions to be made in TOSA code. Changes Made:Add full permutation tensor verification to tosa.TRANSPOSE. Priorly would not verify that permuted values were between 0 - (rank - 1). Update tosa.TRANSPOSE perms data type to be strictly i32. Verify input/output shapes for tosa.TRANSPOSE. Add verifier to tosa.CONST, with consideration for quantization. Fix TOSA conformance of tensor type to disallow dimensions with size 0 for ranked tensors, per spec. Patch is 51.58 KiB, truncated to 20.00 KiB below, full version: /~https://github.com/llvm/llvm-project/pull/108133.diff 12 Files Affected:
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/CMakeLists.txt b/mlir/include/mlir/Dialect/Tosa/IR/CMakeLists.txt
index 12b4fc402c390f..1ee105f0ceb98b 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/CMakeLists.txt
+++ b/mlir/include/mlir/Dialect/Tosa/IR/CMakeLists.txt
@@ -3,8 +3,8 @@ add_mlir_doc(TosaOps TosaOps Dialects/ -gen-op-doc)
add_mlir_interface(TosaInterfaces)
set(LLVM_TARGET_DEFINITIONS TosaOps.td)
-mlir_tablegen(TosaAttributes.h.inc -gen-attrdef-decls)
-mlir_tablegen(TosaAttributes.cpp.inc -gen-attrdef-defs)
+mlir_tablegen(TosaAttributes.h.inc -gen-attrdef-decls -attrdefs-dialect=tosa)
+mlir_tablegen(TosaAttributes.cpp.inc -gen-attrdef-defs -attrdefs-dialect=tosa)
add_public_tablegen_target(MLIRTosaAttributesIncGen)
set(LLVM_TARGET_DEFINITIONS TosaDialectBytecode.td)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index ab6daa39708d13..63572f287b7dde 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -73,7 +73,6 @@ def Tosa_AvgPool2dOp : Tosa_InferShapedTypeOp<"avg_pool2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
-
Tosa_IntArrayAttr2:$kernel,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr4:$pad,
@@ -102,9 +101,8 @@ def Tosa_Conv2DOp : Tosa_InferShapedTypeOp<"conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- 4DTensorOf<[Tosa_Weight]>:$weight,
+ TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
-
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -132,9 +130,8 @@ def Tosa_Conv3DOp : Tosa_InferShapedTypeOp<"conv3d"> {
let arguments = (ins
Tosa_Tensor5D:$input,
- TensorRankOf<[Tosa_Weight], [5]>:$weight,
+ TosaTensorRankOf<[Tosa_Weight], [5]>:$weight,
Tosa_Tensor1D:$bias,
-
Tosa_IntArrayAttr6:$pad,
Tosa_IntArrayAttr3:$stride,
Tosa_IntArrayAttr3:$dilation,
@@ -163,9 +160,8 @@ def Tosa_DepthwiseConv2DOp : Tosa_InferShapedTypeOp<"depthwise_conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- 4DTensorOf<[Tosa_Weight]>:$weight,
+ TosaTensorRankOf<[Tosa_Weight], [4]>:$weight,
Tosa_Tensor1D:$bias,
-
Tosa_IntArrayAttr4:$pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttr2:$dilation,
@@ -232,7 +228,7 @@ def Tosa_FullyConnectedOp : Tosa_InferShapedTypeOp<"fully_connected"> {
let arguments = (ins
Tosa_Tensor2D:$input,
- 2DTensorOf<[Tosa_Weight]>:$weight,
+ TosaTensorRankOf<[Tosa_Weight], [2]>:$weight,
Tosa_Tensor1D:$bias,
OptionalAttr<Tosa_ConvOpQuantizationAttr>:$quantization_info
);
@@ -347,9 +343,8 @@ def Tosa_TransposeConv2DOp : Tosa_InferShapedTypeOp<"transpose_conv2d"> {
let arguments = (ins
Tosa_Tensor4D:$input,
- 4DTensorOf<[Tosa_Weight]>:$filter,
+ TosaTensorRankOf<[Tosa_Weight], [4]>:$filter,
Tosa_Tensor1D:$bias,
-
Tosa_IntArrayAttr4:$out_pad,
Tosa_IntArrayAttr2:$stride,
Tosa_IntArrayAttrUpto4:$out_shape,
@@ -641,12 +636,12 @@ def Tosa_LogicalAndOp : Tosa_ElementwiseOp<"logical_and", [
}];
let arguments = (ins
- I1Tensor:$input1,
- I1Tensor:$input2
+ Tosa_I1Tensor:$input1,
+ Tosa_I1Tensor:$input2
);
let results = (outs
- I1Tensor:$z
+ Tosa_I1Tensor:$z
);
}
@@ -708,12 +703,12 @@ def Tosa_LogicalOrOp : Tosa_ElementwiseOp<"logical_or", [
}];
let arguments = (ins
- I1Tensor:$input1,
- I1Tensor:$input2
+ Tosa_I1Tensor:$input1,
+ Tosa_I1Tensor:$input2
);
let results = (outs
- I1Tensor:$z
+ Tosa_I1Tensor:$z
);
}
@@ -731,12 +726,12 @@ def Tosa_LogicalXorOp : Tosa_ElementwiseOp<"logical_xor", [
}];
let arguments = (ins
- I1Tensor:$input1,
- I1Tensor:$input2
+ Tosa_I1Tensor:$input1,
+ Tosa_I1Tensor:$input2
);
let results = (outs
- I1Tensor:$z
+ Tosa_I1Tensor:$z
);
}
@@ -1085,11 +1080,11 @@ def Tosa_LogicalNotOp : Tosa_ElementwiseOp<"logical_not",
}];
let arguments = (ins
- I1Tensor:$input1
+ Tosa_I1Tensor:$input1
);
let results = (outs
- I1Tensor:$output
+ Tosa_I1Tensor:$output
);
}
@@ -1208,7 +1203,7 @@ def Tosa_SelectOp : Tosa_ElementwiseOp<"select"> {
}];
let arguments = (ins
- I1Tensor:$pred,
+ Tosa_I1Tensor:$pred,
Tosa_Tensor:$on_true,
Tosa_Tensor:$on_false
);
@@ -1249,7 +1244,7 @@ def Tosa_EqualOp : Tosa_ElementwiseOp<"equal", [
);
let results = (outs
- I1Tensor:$output
+ Tosa_I1Tensor:$output
);
let extraClassDeclaration = [{
@@ -1277,7 +1272,7 @@ def Tosa_GreaterOp : Tosa_ElementwiseOp<"greater", [SameOperandsElementType]> {
);
let results = (outs
- I1Tensor:$output
+ Tosa_I1Tensor:$output
);
let hasFolder = 1;
@@ -1300,7 +1295,7 @@ def Tosa_GreaterEqualOp : Tosa_ElementwiseOp<"greater_equal",
);
let results = (outs
- I1Tensor:$output
+ Tosa_I1Tensor:$output
);
let hasFolder = 1;
@@ -1721,7 +1716,7 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
let arguments = (ins
Tosa_Tensor:$input1,
- Tosa_Int32Or64Tensor:$perms
+ Tosa_Int32Tensor:$perms
);
let results = (
@@ -1729,7 +1724,7 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
);
let extraClassDeclaration = [{
- LogicalResult getConstantPerms(llvm::SmallVector<int64_t> &perms);
+ LogicalResult getConstantPerms(llvm::SmallVector<int32_t> &perms);
}];
let hasCanonicalizer = 1;
@@ -1755,7 +1750,7 @@ def Tosa_GatherOp : Tosa_InferShapedTypeOp<"gather"> {
let arguments = (ins
Tosa_Tensor3D:$values,
- 2DTensorOf<[Tosa_Int32]>:$indices
+ TosaTensorRankOf<[Tosa_Int32], [2]>:$indices
);
let results = (outs
@@ -1776,7 +1771,7 @@ def Tosa_ScatterOp : Tosa_InferShapedTypeOp<"scatter"> {
let arguments = (ins
Tosa_Tensor3D:$values_in,
- 2DTensorOf<[Tosa_Int32]>:$indices,
+ TosaTensorRankOf<[Tosa_Int32], [2]>:$indices,
Tosa_Tensor3D:$input
);
@@ -1947,10 +1942,11 @@ def Tosa_ConstOp : Tosa_Op<"const", [ConstantLike, Pure,
);
let results = (outs
- TensorOf<[AnyTypeOf<[Tosa_AnyNumber]>]>:$output
+ TosaTensorOf<[AnyTypeOf<[Tosa_AnyNumber]>]>:$output
);
let hasFolder = 1;
+ let hasVerifier = 1;
}
//===----------------------------------------------------------------------===//
@@ -2054,7 +2050,7 @@ def Tosa_IfOp : Tosa_Op<"cond_if",
}];
let arguments = (ins
- I1Tensor:$cond,
+ Tosa_I1Tensor:$cond,
Variadic<Tosa_Tensor>:$inputs
);
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
index 14fc9c7a6730cc..c3a0128e95a84b 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -82,58 +82,83 @@ def Tosa_AnyNumber : AnyTypeOf<[Tosa_Int, Tosa_QuantizedInt, AnyFloat],
def Tosa_Weight : AnyTypeOf<[Tosa_Int4, Tosa_Int8,
Tosa_QuantizedInt, AnyFloat]>;
+//===----------------------------------------------------------------------===//
+// TOSA Tensor Conformance
+//===----------------------------------------------------------------------===//
+
+def HasNo0Dimensions : And<[
+ IsRankedTensorTypePred,
+ CPred<"::llvm::all_of(::llvm::cast<::mlir::RankedTensorType>($_self).getShape(), [](auto v) { return v != 0; })">]>;
+
+class TosaTensorOf<
+ list<Type> allowedTypes, string summary = "tosa-conformant tensor">
+ : TensorOf<allowedTypes, [Or<[HasNo0Dimensions, IsUnrankedTensorTypePred]>], summary>;
+
+class TosaRankedTensorOf<
+ list<Type> allowedTypes, list<Pred> preds = [], string summary = "tosa-conformant ranked tensor">
+ : RankedTensorOf<allowedTypes, !listconcat([HasNo0Dimensions], preds), summary>;
+
+class TosaUnrankedTensorOf<list<Type> allowedTypes, list<Pred> preds = [], string summary = "tosa-conformant unranked tensor">
+ : UnrankedTensorOf<allowedTypes, preds, summary>;
+
+class TosaTensorRankOf<list<Type> allowedTypes, list<int> ranks>
+ : TosaRankedTensorOf<allowedTypes,
+ [HasAnyRankOfPred<ranks>],
+ !interleave(!foreach(rank, ranks, rank # "D"), "/") # " tensor">;
+
//===----------------------------------------------------------------------===//
// Tensor types
//===----------------------------------------------------------------------===//
-def Tosa_Int32Tensor : TensorOf<[Tosa_Int32]>;
-def Tosa_Int32Or64Tensor : TensorOf<[Tosa_Int32Or64]>;
+def Tosa_I1Tensor : TosaTensorOf<[I1]>;
+def Tosa_Int32Tensor : TosaTensorOf<[Tosa_Int32]>;
+def Tosa_Int32Or64Tensor :TosaTensorOf<[Tosa_Int32Or64]>;
-def Tosa_FloatTensor : TensorOf<[AnyFloat]>;
+def Tosa_FloatTensor : TosaTensorOf<[AnyFloat]>;
// Either ranked or unranked tensor of TOSA supported element types.
-def Tosa_Tensor : TensorOf<[Tosa_AnyNumber]>;
+def Tosa_Tensor : TosaTensorOf<[Tosa_AnyNumber]>;
// Must be ranked but no further constraints
-def Tosa_RankedTensor : RankedTensorOf<[Tosa_AnyNumber]>;
+def Tosa_RankedTensor : TosaRankedTensorOf<[Tosa_AnyNumber]>;
// Any tensor element type allowed in Tosa ops.
def Tosa_ElementType : Type<Or<[Tosa_Int.predicate, Tosa_QuantizedInt.predicate,
AnyFloat.predicate]>, "tosa.dtype">;
class Tosa_TensorOfOrNone<list<Type> allowedTypes, string description = ""> :
- AnyTypeOf<[TensorOf<allowedTypes>, NoneType], description>;
+ AnyTypeOf<[TosaTensorOf<allowedTypes>, NoneType], description>;
//===----------------------------------------------------------------------===//
// Tensor types with constrained ranks.
//===----------------------------------------------------------------------===//
// Rank-0 (scalar) tensor
-def Tosa_ScalarTensor : TensorRankOf<[Tosa_AnyNumber], [0]>;
+def Tosa_ScalarTensor : TosaTensorRankOf<[Tosa_AnyNumber], [0]>;
// We include unranked tensors as a supported type for all possible tosa
// Tensors as unranked does not guarantee invalid. If unranked tensors exist
// they should be shape propagate used Tosa's shape inference pass and verified
// to not include any remaining unranked tensors.
-def Tosa_UnrankedTensor : UnrankedTensorOf<[Tosa_AnyNumber]>;
+def Tosa_UnrankedTensor : TosaUnrankedTensorOf<[Tosa_AnyNumber]>;
-def Tosa_Tensor1D : AnyTypeOf<[Tosa_UnrankedTensor, 1DTensorOf<[Tosa_AnyNumber]>], "1-d tensor", "::mlir::TensorType">;
-def Tosa_Tensor2D : AnyTypeOf<[Tosa_UnrankedTensor, 2DTensorOf<[Tosa_AnyNumber]>], "2-d tensor", "::mlir::TensorType">;
-def Tosa_Tensor3D : AnyTypeOf<[Tosa_UnrankedTensor, 3DTensorOf<[Tosa_AnyNumber]>], "3-d tensor", "::mlir::TensorType">;
-def Tosa_Tensor4D : AnyTypeOf<[Tosa_UnrankedTensor, 4DTensorOf<[Tosa_AnyNumber]>], "4-d tensor", "::mlir::TensorType">;
-def Tosa_Tensor5D : AnyTypeOf<[Tosa_UnrankedTensor, TensorRankOf<[Tosa_AnyNumber], [5]>], "5-d tensor", "::mlir::TensorType">;
+def Tosa_Tensor1D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1]>], "1-d tosa-conformant tensor", "::mlir::TensorType">;
+def Tosa_Tensor2D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [2]>], "2-d tosa-conformant tensor", "::mlir::TensorType">;
+def Tosa_Tensor3D : AnyTypeOf<[Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [3]>], "3-d tosa-conformant tensor", "::mlir::TensorType">;
+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">;
// Ranked tensors up to given rank.
def Tosa_Tensor1Dto4D : AnyTypeOf<[
- Tosa_UnrankedTensor, TensorRankOf<[Tosa_AnyNumber], [1,2,3,4]>]>;
+ Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4]>]>;
def Tosa_Tensor1Dto6D : AnyTypeOf<[
- Tosa_UnrankedTensor, TensorRankOf<[Tosa_AnyNumber], [1,2,3,4,5,6]>]>;
+ Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4,5,6]>]>;
def Tosa_TensorUpto4D : AnyTypeOf<[
- Tosa_UnrankedTensor, TensorRankOf<[Tosa_AnyNumber], [0,1,2,3,4]>]>;
+ Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [0,1,2,3,4]>]>;
def Tosa_Int32TensorUpto4D : AnyTypeOf<[
- Tosa_UnrankedTensor, TensorRankOf<[Tosa_Int32], [0,1,2,3,4]>]>;
+ Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_Int32], [0,1,2,3,4]>]>;
//===----------------------------------------------------------------------===//
// Generic scalar, vector, or tensor of a particular type.
@@ -142,7 +167,7 @@ def Tosa_Int32TensorUpto4D : AnyTypeOf<[
class Tosa_TypeLike<list<Type> types, string description = ""> : TypeConstraint<Or<[
AnyTypeOf<types>.predicate,
VectorOf<types>.predicate,
- TensorOf<types>.predicate]>,
+ TosaTensorOf<types>.predicate]>,
description>;
def Tosa_IntLike : Tosa_TypeLike<[Tosa_Int], "signless-integer-like">;
diff --git a/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h b/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
index ef40b348ab5499..90fea1f68beb58 100644
--- a/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
+++ b/mlir/include/mlir/Dialect/Tosa/Utils/ConversionUtils.h
@@ -216,6 +216,19 @@ TosaOp CreateOpAndInferShape(PatternRewriter &rewriter, Location loc,
return CreateOpAndInferShape<TosaOp>(builder, resultTy, args...);
}
+// Apply an int32_t permutation to some input, that should be of the same
+// size as perms. Perms should contain some permutation of 0 - perms.size() - 1.
+template <typename T>
+SmallVector<T> applyTOSAPermutation(ArrayRef<T> input,
+ ArrayRef<int32_t> perms) {
+ SmallVector<T> permuted;
+ size_t N = input.size();
+ permuted.resize_for_overwrite(N);
+ for (size_t i = 0; i < N; i++)
+ permuted[i] = input[perms[i]];
+ return permuted;
+}
+
} // namespace tosa
} // namespace mlir
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index 77c3d2e8757910..fe53b499674324 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -313,7 +313,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
// convolution operation.
// TODO(suderman): See if this can be efficiently folded - check whether
// the input is used anywhere else, if not fold the constant.
- SmallVector<int64_t> weightPerm;
+ SmallVector<int32_t> weightPerm;
for (int i = 1; i < resultTy.getRank(); i++)
weightPerm.push_back(i);
weightPerm.push_back(0);
@@ -321,7 +321,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
SmallVector<int64_t> newWeightShape;
for (auto dim : weightPerm)
newWeightShape.push_back(weightShape[dim]);
- auto weightPermAttr = rewriter.getI64TensorAttr(weightPerm);
+ auto weightPermAttr = rewriter.getI32TensorAttr(weightPerm);
Value weightPermValue =
rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
Type newWeightTy =
@@ -337,7 +337,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
if (5 == inputTy.getRank()) {
// TODO(suderman): See if this can be efficiently folded - check whether
// the input is used anywhere else, if not fold the constant.
- SmallVector<int64_t> weightPerm;
+ SmallVector<int32_t> weightPerm;
for (int i = 1; i < resultTy.getRank(); i++)
weightPerm.push_back(i);
weightPerm.push_back(0);
@@ -345,7 +345,7 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
SmallVector<int64_t> newWeightShape;
for (auto dim : weightPerm)
newWeightShape.push_back(weightShape[dim]);
- auto weightPermAttr = rewriter.getI64TensorAttr(weightPerm);
+ auto weightPermAttr = rewriter.getI32TensorAttr(weightPerm);
Value weightPermValue =
rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
Type newWeightTy =
@@ -1040,22 +1040,25 @@ class TransposeConverter : public OpRewritePattern<tosa::TransposeOp> {
LogicalResult matchAndRewrite(tosa::TransposeOp op,
PatternRewriter &rewriter) const final {
- SmallVector<int64_t> constantPerms;
+ SmallVector<int32_t> constantPerms;
if (failed(op.getConstantPerms(constantPerms)))
return failure();
Location loc = op.getLoc();
- // The verifier should have made sure we have a valid permutation tensor.
- assert(isPermutationVector(constantPerms) && "Expected valid permutation");
+ // The verifier should have made sure we have a valid TOSA permutation
+ // tensor. isPermutationVector doesn't actually check the TOSA perms we
+ // expect.
SmallVector<OpFoldResult> inputSizes =
tensor::getMixedSizes(rewriter, loc, op.getInput1());
auto permutedSizes =
- applyPermutation<OpFoldResult>(inputSizes, constantPerms);
+ applyTOSAPermutation<OpFoldResult>(inputSizes, constantPerms);
auto permutedInit = rewriter.create<tensor::EmptyOp>(
loc, permutedSizes, op.getInput1().getType().getElementType());
rewriter.replaceOpWithNewOp<linalg::TransposeOp>(
- op, op.getInput1(), permutedInit, constantPerms);
+ op, op.getInput1(), permutedInit,
+ llvm::to_vector(llvm::map_range(
+ constantPerms, [](int32_t v) -> int64_t { return v; })));
return success();
}
};
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index da9a93feac4d65..03876a7c64d07c 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -88,7 +88,7 @@ struct ConsolidateTransposeOptimization
return rewriter.notifyMatchFailure(transposeOp,
"input must be transpose operation");
- SmallVector<int64_t> transposePerms, innerTransposePerms;
+ SmallVector<int32_t> transposePerms, innerTransposePerms;
if (transposeOp.getConstantPerms(transposePerms).failed())
return rewriter.notifyMatchFailure(transposeOp,
"transpose perms must be constant");
@@ -497,8 +497,10 @@ OpFoldResult AddOp::fold(FoldAdaptor adaptor) {
return {};
auto resultETy = resultTy.getElementType();
- auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
- auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
+ auto lhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
+ auto rhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
if (lhsTy == resultTy && isSplatZero(resultETy, rhsAttr))
return getInput1();
@@ -536,8 +538,10 @@ OpFoldResult IntDivOp::fold(FoldAdaptor adaptor) {
// IntDivOp inputs must be integer type, no need to check for quantized type
auto resultETy = resultTy.getElementType();
- auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
- auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
+ auto lhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
+ auto rhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
if (lhsAttr && lhsAttr.isSplat()) {
if (llvm::isa<IntegerType>(resultETy) &&
lhsAttr.getSplatValue<APInt>().isZero())
@@ -605,10 +609,13 @@ OpFoldResult MulOp::fold(FoldAdaptor adaptor) {
return {};
auto resultETy = resultTy.getElementType();
- auto lhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
- auto rhsAttr = llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
+ auto lhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput1());
+ auto rhsAttr =
+ llvm::dyn_cast_if_present<DenseElementsAttr>(adaptor.getInput2());
const int64_t shift = llvm::isa<IntegerType>(resultETy) ? getShift() : 0;
+
if (rhsTy == resultTy) {
if (isSplatZero(resultETy, lhsAttr))
return lhsAttr.resizeSplat(resultTy);
@@ -638,8 +645,10 @@ OpFoldResult SubOp::fold(FoldAdaptor adaptor) {
return {};
auto resultETy = resultTy....
[truncated]
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eric-k256
approved these changes
Sep 11, 2024
|
@arteen1000 Congratulations on having your first Pull Request (PR) merged into the LLVM Project! Your changes will be combined with recent changes from other authors, then tested by our build bots. If there is a problem with a build, you may receive a report in an email or a comment on this PR. Please check whether problems have been caused by your change specifically, as the builds can include changes from many authors. It is not uncommon for your change to be included in a build that fails due to someone else's changes, or infrastructure issues. How to do this, and the rest of the post-merge process, is covered in detail here. If your change does cause a problem, it may be reverted, or you can revert it yourself. This is a normal part of LLVM development. You can fix your changes and open a new PR to merge them again. If you don't get any reports, no action is required from you. Your changes are working as expected, well done! |
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LLVM Buildbot has detected a new failure on builder Full details are available at: https://lab.llvm.org/buildbot/#/builders/41/builds/1969 Here is the relevant piece of the build log for the reference |
VitaNuo
pushed a commit
to VitaNuo/llvm-project
that referenced
this pull request
Sep 12, 2024
---------- Motivation: ---------- Spec conformance. Allows assumptions to be made in TOSA code. ------------ Changes Made: ------------ Add full permutation tensor verification to tosa.TRANSPOSE. Priorly would not verify that permuted values were between 0 - (rank - 1). Update tosa.TRANSPOSE perms data type to be strictly i32. Verify input/output shapes for tosa.TRANSPOSE. Add verifier to tosa.CONST, with consideration for quantization. Fix TOSA conformance of tensor type to disallow dimensions with size 0 for ranked tensors, per spec. This is not the same as rank 0 tensors. Here is an example of a disallowed tensor: tensor<3x0xi32>. Naturally, this means that the number of elements in a TOSA tensor will always be greater than 0. Signed-off-by: Arteen Abrishami <arteen.abrishami@arm.com>
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Motivation:
Spec conformance. Allows assumptions to be made in TOSA code.
Changes Made:
Add full permutation tensor verification to tosa.TRANSPOSE. Priorly would not verify that permuted values were between 0 - (rank - 1).
Update tosa.TRANSPOSE perms data type to be strictly i32.
Verify input/output shapes for tosa.TRANSPOSE.
Add verifier to tosa.CONST, with consideration for quantization.
Fix TOSA conformance of tensor type to disallow dimensions with size 0 for ranked tensors, per spec.
This is not the same as rank 0 tensors. Here is an example of a disallowed tensor: tensor<3x0xi32>. Naturally, this means that the number of elements in a TOSA tensor will always be greater than 0.