[Mlir-commits] [mlir] [mlir][tosa][WIP] More informative error messages on block scaled types (PR #209736)
Luke Hutton
llvmlistbot at llvm.org
Wed Jul 15 04:47:36 PDT 2026
https://github.com/lhutton1 created https://github.com/llvm/llvm-project/pull/209736
This draft PR demonstrates how error messages can be added to block scaled types. This aims to restore previous functionality that was reverted by https://github.com/llvm/llvm-project/pull/207995, but uses the ODS string interpolation mechanism instead to prevent incompatibility with PDLL.
I plan to create separate PR's for the 3 commits in this PR to make it easier for reviewers.
>From 82a25847092d71d6ae16f9a2ac456b2d3b8e7111 Mon Sep 17 00:00:00 2001
From: Luke Hutton <luke.hutton at arm.com>
Date: Thu, 9 Jul 2026 01:00:27 +0100
Subject: [PATCH 1/3] [mlir][tosa] Combine unranked/ranked tensor types into
single type
This commit refactors the defined TOSA types to combine unranked and
ranked tensor types into a single type `TosaTensorOf`. This helps
simplify the type definitions and allows all tensor types to support
both unranked and ranked tensors.
Change-Id: I9866f2e26db44401cbb0e99ce1dbaad131ab574e
---
.../mlir/Dialect/Tosa/IR/TosaTypesBase.td | 108 +++++++-----------
.../TosaToLinalg/tosa-to-linalg-pipeline.mlir | 2 +-
mlir/test/Dialect/Tosa/invalid.mlir | 40 +++----
mlir/test/Dialect/Tosa/verifier.mlir | 10 +-
4 files changed, 66 insertions(+), 94 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
index 1518024ecaa19..2ab135befaa96 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -174,33 +174,28 @@ def AtLeastRankOne : And<[
def IsValidBlockScaledTensorType
: CPred<"::mlir::succeeded(::mlir::tosa::verifyBlockScaledTensorType($_self, false))">;
+// 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 propagated using Tosa's shape inference pass and verified
+// to not include any remaining unranked tensors.
class TosaTensorOf<list<Type> allowedTypes,
+ list<Pred> extraRankedTensorPreds = [],
string summary = "tosa-conformant tensor">
: TensorOf<allowedTypes,
- [Or<[HasNo0Dimensions, IsUnrankedTensorTypePred]>,
+ [Or<[
+ IsUnrankedTensorTypePred,
+ And<!listconcat([IsRankedTensorTypePred, HasNo0Dimensions], extraRankedTensorPreds)>
+ ]>,
IsValidBlockScaledTensorType],
summary>;
-class TosaRankedTensorOf<list<Type> allowedTypes, list<Pred> preds = [],
- string summary = "tosa-conformant ranked tensor">
- : RankedTensorOf<
- allowedTypes,
- !listconcat([HasNo0Dimensions, IsValidBlockScaledTensorType], preds),
- summary>;
-
-class TosaUnrankedTensorOf<list<Type> allowedTypes, list<Pred> preds = [],
- string summary = "tosa-conformant unranked tensor">
- : UnrankedTensorOf<allowedTypes,
- !listconcat([IsValidBlockScaledTensorType], preds),
- summary>;
-
class TosaTensorRankOf<list<Type> allowedTypes, list<int> ranks>
- : TosaRankedTensorOf<allowedTypes,
+ : TosaTensorOf<allowedTypes,
[HasAnyRankOfPred<ranks>],
- !interleave(!foreach(rank, ranks, rank # "D"), "/") # " tensor">;
+ !interleave(!foreach(rank, ranks, rank # "D"), "/") # " tosa-conformant tensor">;
class TosaScalarTensorOf<list<Type> allowedTypes, list<int> ranks>
- : TosaRankedTensorOf<allowedTypes,
+ : TosaTensorOf<allowedTypes,
[HasAnyRankOfPred<ranks>, AllDimensionsAreSizeOne],
"tosa-conformant scalar tensor">;
@@ -217,9 +212,6 @@ def Tosa_FloatTensor : TosaTensorOf<[AnyFloat]>;
// Either ranked or unranked tensor of TOSA supported element types.
def Tosa_Tensor : TosaTensorOf<[Tosa_AnyNumber]>;
-// Must be ranked but no further constraints
-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">;
@@ -231,68 +223,48 @@ class Tosa_TensorOfOrNone<list<Type> allowedTypes, string description = ""> :
// Tensor types with constrained ranks.
//===----------------------------------------------------------------------===//
-def Tosa_Rank0Tensor : TosaTensorRankOf<[Tosa_AnyNumber], [0]>;
-
-def Tosa_ScalarTensor : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_Int, AnyFloat]>, TosaScalarTensorOf<[Tosa_AnyNumber], [1]>]>;
-def Tosa_ScalarInt8Tensor : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_Int8]>, TosaScalarTensorOf<[Tosa_Int8], [1]>]>;
-def Tosa_ScalarIntOrFloatTensor : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_Int, AnyFloat]>, TosaScalarTensorOf<[Tosa_Int, AnyFloat], [1]>]>;
-def Tosa_ScalarInt32Tensor : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_Int32]>, TosaScalarTensorOf<[Tosa_Int32], [1]>]>;
-
-// 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 : TosaUnrankedTensorOf<[Tosa_AnyNumber]>;
+def Tosa_ScalarTensor : TosaScalarTensorOf<[Tosa_AnyNumber], [1]>;
+def Tosa_ScalarInt8Tensor : TosaScalarTensorOf<[Tosa_Int8], [1]>;
+def Tosa_ScalarIntOrFloatTensor : TosaScalarTensorOf<[Tosa_Int, AnyFloat], [1]>;
+def Tosa_ScalarInt32Tensor : TosaScalarTensorOf<[Tosa_Int32], [1]>;
-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">;
+def Tosa_Tensor1D : TosaTensorRankOf<[Tosa_AnyNumber], [1]>;
+def Tosa_Tensor2D : TosaTensorRankOf<[Tosa_AnyNumber], [2]>;
+def Tosa_Tensor3D : TosaTensorRankOf<[Tosa_AnyNumber], [3]>;
+def Tosa_Tensor4D : TosaTensorRankOf<[Tosa_AnyNumber], [4]>;
+def Tosa_Tensor5D : TosaTensorRankOf<[Tosa_AnyNumber], [5]>;
// 1D tensor of specific types
-def Tosa_1DInt8Tensor : 1DTensorOf<[Tosa_Int8]>;
-def Tosa_1DInt16Or32Tensor : 1DTensorOf<[Tosa_Int16Or32]>;
+def Tosa_1DInt8Tensor : TosaTensorRankOf<[Tosa_Int8], [1]>;
+def Tosa_1DInt16Or32Tensor : TosaTensorRankOf<[Tosa_Int16Or32], [1]>;
-// Ranked tensors up to given rank.
-def Tosa_Tensor1Dto4D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4]>]>;
-def Tosa_Tensor1Dto6D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4,5,6]>]>;
+// Tensors up to a given rank. Unranked tensors are accepted.
+def Tosa_Tensor1Dto4D : TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4]>;
+def Tosa_Tensor1Dto6D : TosaTensorRankOf<[Tosa_AnyNumber], [1,2,3,4,5,6]>;
-def Tosa_TensorUpto4D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_AnyNumber], [0,1,2,3,4]>]>;
+def Tosa_TensorUpto4D : TosaTensorRankOf<[Tosa_AnyNumber], [0,1,2,3,4]>;
-def Tosa_IndexTensor1D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_Int32, Tosa_Int64], [1]>]>;
-def Tosa_IndexTensor2D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaTensorRankOf<[Tosa_Int32, Tosa_Int64], [2]>]>;
+def Tosa_IndexTensor1D : TosaTensorRankOf<[Tosa_Int32, Tosa_Int64], [1]>;
+def Tosa_IndexTensor2D : TosaTensorRankOf<[Tosa_Int32, Tosa_Int64], [2]>;
-def Tosa_TensorAtLeast1D : AnyTypeOf<[
- Tosa_UnrankedTensor, TosaRankedTensorOf<[Tosa_AnyNumber], [AtLeastRankOne]>], "tosa-conformant tensor of at least rank 1", "::mlir::TensorType">;
+def Tosa_TensorAtLeast1D
+ : TosaTensorOf<[Tosa_AnyNumber], [AtLeastRankOne],
+ "tosa-conformant tensor of at least rank 1">;
def Tosa_MXFPDataTensor3D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPValue]>,
- TosaTensorRankOf<[Tosa_MXFPValue], [3]>]>;
+ : TosaTensorRankOf<[Tosa_MXFPValue], [3]>;
def Tosa_MXFPScaleTensor3D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPScale]>,
- TosaTensorRankOf<[Tosa_MXFPScale], [3]>]>;
+ : TosaTensorRankOf<[Tosa_MXFPScale], [3]>;
def Tosa_MXFPDataTensor4D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPValue]>,
- TosaTensorRankOf<[Tosa_MXFPValue], [4]>]>;
+ : TosaTensorRankOf<[Tosa_MXFPValue], [4]>;
def Tosa_MXFPScaleTensor4D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPScale]>,
- TosaTensorRankOf<[Tosa_MXFPScale], [4]>]>;
+ : TosaTensorRankOf<[Tosa_MXFPScale], [4]>;
def Tosa_MXFPDataTensorAtLeast1D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPValue]>,
- TosaRankedTensorOf<[Tosa_MXFPValue], [AtLeastRankOne]>],
- "tosa-conformant tensor of at least rank 1",
- "::mlir::TensorType">;
+ : TosaTensorOf<[Tosa_MXFPValue], [AtLeastRankOne],
+ "tosa-conformant tensor of at least rank 1">;
def Tosa_MXFPScaleTensorAtLeast1D
- : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_MXFPScale]>,
- TosaRankedTensorOf<[Tosa_MXFPScale], [AtLeastRankOne]>],
- "tosa-conformant tensor of at least rank 1",
- "::mlir::TensorType">;
+ : TosaTensorOf<[Tosa_MXFPScale], [AtLeastRankOne],
+ "tosa-conformant tensor of at least rank 1">;
//===----------------------------------------------------------------------===//
// Generic scalar, vector, or tensor of a particular type.
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
index 67b6aa63f2293..373b5b71cb68b 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-pipeline.mlir
@@ -14,7 +14,7 @@ func.func @tensor_with_unknown_rank(%arg0: tensor<*xi32>) -> tensor<*xi32> {
// check that tosa verify kick in
func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
- // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x0x?x9xf32>'}}
+ // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x0x?x9xf32>'}}
%0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
: (tensor<1x0x?x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index d0336da15cee8..0e66cfa02b67f 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -317,7 +317,7 @@ func.func @test_concat_input_output_rank_mismatch(%arg0: tensor<2x2xf32>, %arg1:
func.func @test_pad_invalid_padConst_rank(%arg0: tensor<13x21xf32>) {
%0 = tosa.const_shape {values = dense<1> : tensor<4xindex>} : () -> !tosa.shape<4>
%1 = "tosa.const"() {values = dense<3.14> : tensor<2xf32>} : () -> tensor<2xf32>
- // expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant unranked tensor of unsigned integer or signless integer or floating-point values or tosa-conformant scalar tensor of number values, but got 'tensor<2xf32>'}}
+ // expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant scalar tensor of number values, but got 'tensor<2xf32>'}}
%2 = tosa.pad %arg0, %0, %1 : (tensor<13x21xf32>, !tosa.shape<4>, tensor<2xf32>) -> tensor<13x21xf32>
return
}
@@ -543,7 +543,7 @@ func.func @test_const_attribute_type_mismatch() -> tensor<100x100xf32> {
func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- // expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x29x0x4xf32>'}}
+ // expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x29x0x4xf32>'}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
@@ -554,7 +554,7 @@ func.func @test_conv2d_static_zero_dim_input(%arg0: tensor<1x29x0x4xf32>, %arg1:
func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<16x3x3x4xf32>, %arg2: tensor<16xf32>) -> tensor<1x27x27x16xf32> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi32>}> : () -> tensor<1xi32>
- // expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x?x0x4xf32>'}}
+ // expected-error at +1 {{'tosa.conv2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x?x0x4xf32>'}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x?x0x4xf32>, tensor<16x3x3x4xf32>, tensor<16xf32>, tensor<1xi32>, tensor<1xi32>) -> tensor<1x27x27x16xf32>
return %0 : tensor<1x27x27x16xf32>
@@ -564,7 +564,7 @@ func.func @test_conv2d_zero_dim_input(%arg0: tensor<1x?x0x4xf32>, %arg1: tensor<
// -----
func.func @test_avg_pool2d_static_zero_dim_input(%arg0: tensor<1x0x7x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
- // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x0x7x9xf32>'}}
+ // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x0x7x9xf32>'}}
%0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
: (tensor<1x0x7x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
@@ -573,7 +573,7 @@ func.func @test_avg_pool2d_static_zero_dim_input(%arg0: tensor<1x0x7x9xf32>, %ar
// -----
func.func @test_avg_pool2d_zero_dim_input(%arg0: tensor<1x0x?x9xf32>, %arg1: tensor<1xf32>, %arg2: tensor<1xf32>) -> tensor<1x7x7x9xf32> {
- // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4-d tosa-conformant tensor, but got 'tensor<1x0x?x9xf32>'}}
+ // expected-error at +1 {{'tosa.avg_pool2d' op operand #0 must be 4D tosa-conformant tensor of number values, but got 'tensor<1x0x?x9xf32>'}}
%0 = "tosa.avg_pool2d"(%arg0, %arg1, %arg2) {acc_type = f32, kernel = array<i64: 2, 2>, pad = array<i64: 0, 1, 0, 1>, stride = array<i64: 1, 1>}
: (tensor<1x0x?x9xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x7x7x9xf32>
return %0 : tensor<1x7x7x9xf32>
@@ -673,7 +673,7 @@ func.func @test_tile_io_rank_mismatch() {
// CHECK-LABEL: test_table_rank0_table
func.func @test_table_rank0_table(%arg0: tensor<64xi16>, %arg1: tensor<i16>) {
- // expected-error at +1 {{'tosa.table' op operand #1 must be 1-d tosa-conformant tensor, but got 'tensor<i16>'}}
+ // expected-error at +1 {{'tosa.table' op operand #1 must be 1D tosa-conformant tensor of number values, but got 'tensor<i16>'}}
%0 = tosa.table %arg0, %arg1 : (tensor<64xi16>, tensor<i16>) -> tensor<64xi16>
return
}
@@ -1011,7 +1011,7 @@ func.func @test_non_tosa_ops() {
func.func @test_pad_rank0_pad_const(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<13x21x3xf8E5M2> {
%padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
%cst = "tosa.const"() { values = dense<-0.0> : tensor<f8E4M3FN> } : () -> tensor<f8E4M3FN>
- // expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant unranked tensor of unsigned integer or signless integer or floating-point values or tosa-conformant scalar tensor of number values, but got 'tensor<f8E4M3FN>'}}
+ // expected-error at +1 {{'tosa.pad' op operand #2 must be tosa-conformant scalar tensor of number values, but got 'tensor<f8E4M3FN>'}}
%0 = tosa.pad %arg0, %padding, %cst : (tensor<13x21x3xf8E4M3FN>, !tosa.shape<6>, tensor<f8E4M3FN>) -> tensor<13x21x3xf8E5M2>
return %0 : tensor<13x21x3xf8E5M2>
}
@@ -1021,7 +1021,7 @@ func.func @test_pad_rank0_pad_const(%arg0: tensor<13x21x3xf8E4M3FN>) -> tensor<1
func.func @test_conv2d_rank0_zp(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi32> {
%input_zp = "tosa.const"() <{values = dense<0> : tensor<i8>}> : () -> tensor<i8>
%weight_zp = "tosa.const"() <{values = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
- // expected-error at +1 {{'tosa.conv2d' op operand #3 must be tosa-conformant unranked tensor of unsigned integer or signless integer or floating-point values or tosa-conformant scalar tensor of unsigned integer or signless integer or floating-point values, but got 'tensor<i8>'}}
+ // expected-error at +1 {{'tosa.conv2d' op operand #3 must be tosa-conformant scalar tensor of unsigned integer or signless integer or floating-point values, but got 'tensor<i8>'}}
%0 = tosa.conv2d %arg0, %arg1, %arg2, %input_zp, %weight_zp {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
: (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>, tensor<i8>, tensor<1xi8>) -> tensor<1x27x27x16xi32>
return %0 : tensor<1x27x27x16xi32>
@@ -1130,7 +1130,7 @@ func.func @test_sub_with_unequal_result_ranks(%arg0: tensor<1x21x3xf32>, %arg1:
// CHECK-LABEL: test_mul_non_scalar_shift_2d
func.func @test_mul_non_scalar_shift_2d(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
%shift = "tosa.const"() <{values = dense<0> : tensor<1x1xi8>}> : () -> tensor<1x1xi8>
- // expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant unranked tensor of 8-bit signless integer values or tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
+ // expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<1x1xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1139,7 +1139,7 @@ func.func @test_mul_non_scalar_shift_2d(%arg0: tensor<13x21x3xf32>, %arg1: tenso
// CHECK-LABEL: test_mul_non_scalar_shift_1d
func.func @test_mul_non_scalar_shift_1d(%arg0: tensor<13x21x3xf32>, %arg1: tensor<13x1x3xf32>) -> tensor<13x21x3xf32> {
%shift = "tosa.const"() <{values = dense<0> : tensor<2xi8>}> : () -> tensor<2xi8>
- // expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant unranked tensor of 8-bit signless integer values or tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<2xi8>'}}
+ // expected-error at +1 {{'tosa.mul' op operand #2 must be tosa-conformant scalar tensor of 8-bit signless integer values, but got 'tensor<2xi8>'}}
%0 = tosa.mul %arg0, %arg1, %shift : (tensor<13x21x3xf32>, tensor<13x1x3xf32>, tensor<2xi8>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1500,7 +1500,7 @@ func.func @test_rescale_invalid_multiplier_type(%arg0: tensor<13x21x3xi32>) -> t
%shift = "tosa.const"() {values = dense<30> : tensor<1xi16> } : () -> tensor<1xi16>
%input_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- // 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>'}}
+ // expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tosa-conformant tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1xi48>'}}
%0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi48>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1512,7 +1512,7 @@ func.func @test_rescale_invalid_shift_type(%arg0: tensor<13x21x3xi32>) -> tensor
%shift = "tosa.const"() {values = dense<30> : tensor<1xi16> } : () -> tensor<1xi16>
%input_zp = "tosa.const"() {values = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
%output_zp = "tosa.const"() {values = dense<0> : tensor<1xi32>} : () -> tensor<1xi32>
- // expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tensor of 8-bit signless integer values, but got 'tensor<1xi16>'}}
+ // expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tosa-conformant tensor of 8-bit signless integer values, but got 'tensor<1xi16>'}}
%0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = false} : (tensor<13x21x3xi32>, tensor<1xi32>, tensor<1xi16>, tensor<1xi32>, tensor<1xi32>) -> tensor<13x21x3xf32>
return %0 : tensor<13x21x3xf32>
}
@@ -1621,7 +1621,7 @@ func.func @test_rescale_invalid_multiplier_rank(%arg0: tensor<13x21x3xi16>) -> t
%shift = "tosa.const"() {values = dense<30> : tensor<1xi8> } : () -> tensor<1xi8>
%input_zp = "tosa.const"() {values = dense<0> : tensor<1xi16>} : () -> tensor<1xi16>
%output_zp = "tosa.const"() {values = dense<0> : 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<1x1xi32>'}}
+ // expected-error at +1 {{'tosa.rescale' op operand #1 must be 1D tosa-conformant tensor of 16-bit signless integer or 32-bit signless integer values, but got 'tensor<1x1xi32>'}}
%0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1x1xi32>, tensor<1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -1633,7 +1633,7 @@ func.func @test_rescale_invalid_shift_rank(%arg0: tensor<13x21x3xi16>) -> tensor
%shift = "tosa.const"() {values = dense<30> : tensor<1x1xi8> } : () -> tensor<1x1xi8>
%input_zp = "tosa.const"() {values = dense<1> : tensor<1xi16>} : () -> tensor<1xi16>
%output_zp = "tosa.const"() {values = dense<0> : 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<1x1xi8>'}}
+ // expected-error at +1 {{'tosa.rescale' op operand #2 must be 1D tosa-conformant tensor of 8-bit signless integer values, but got 'tensor<1x1xi8>'}}
%0 = tosa.rescale %arg0, %multiplier, %shift, %input_zp, %output_zp {rounding_mode = SINGLE_ROUND, per_channel = false, scale32 = true, input_unsigned = false, output_unsigned = true} : (tensor<13x21x3xi16>, tensor<1xi32>, tensor<1x1xi8>, tensor<1xi16>, tensor<1xi16>) -> tensor<13x21x3xi16>
return %0 : tensor<13x21x3xi16>
}
@@ -2057,7 +2057,7 @@ func.func @test_maxpool2d_unexpected_output_width(%arg0: tensor<1x32x32x8xf32>)
// -----
func.func @test_scalar_argmax(%arg0: tensor<i32>) -> tensor<i32> {
- // expected-error at +1 {{'tosa.argmax' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<i32>'}}
+ // expected-error at +1 {{'tosa.argmax' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i32>'}}
%0 = tosa.argmax %arg0 {axis = 0 : i32} : (tensor<i32>) -> tensor<i32>
return %0 : tensor<i32>
}
@@ -2065,7 +2065,7 @@ func.func @test_scalar_argmax(%arg0: tensor<i32>) -> tensor<i32> {
// -----
func.func @test_scalar_reduce_all(%arg0: tensor<i1>) -> tensor<i1> {
- // expected-error at +1 {{'tosa.reduce_all' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<i1>'}}
+ // expected-error at +1 {{'tosa.reduce_all' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i1>'}}
%0 = tosa.reduce_all %arg0 {axis = 0 : i32} : (tensor<i1>) -> tensor<i1>
return %0 : tensor<i1>
}
@@ -2073,7 +2073,7 @@ func.func @test_scalar_reduce_all(%arg0: tensor<i1>) -> tensor<i1> {
// -----
func.func @test_scalar_inputs_concat(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<2xf32> {
- // expected-error at +1 {{'tosa.concat' op operand #0 must be variadic of tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.concat' op operand #0 must be variadic of tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%0 = tosa.concat %arg0, %arg1 {axis = 0 : i32} : (tensor<f32>, tensor<f32>) -> tensor<2xf32>
return %0 : tensor<2xf32>
}
@@ -2083,7 +2083,7 @@ func.func @test_scalar_inputs_concat(%arg0: tensor<f32>, %arg1: tensor<f32>) ->
func.func @test_scalar_pad(%arg0: tensor<f32>) -> tensor<f32> {
%0 = "tosa.const"() {values = dense<3.14> : tensor<1xf32>} : () -> tensor<1xf32>
%padding = tosa.const_shape {values = dense<0> : tensor<6xindex>} : () -> !tosa.shape<6>
- // expected-error at +1 {{'tosa.pad' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.pad' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%1 = tosa.pad %arg0, %padding, %0 : (tensor<f32>, !tosa.shape<6>, tensor<1xf32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -2091,7 +2091,7 @@ func.func @test_scalar_pad(%arg0: tensor<f32>) -> tensor<f32> {
// -----
func.func @test_scalar_reverse(%arg0: tensor<f32>) -> tensor<f32> {
- // expected-error at +1 {{'tosa.reverse' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.reverse' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%0 = tosa.reverse %arg0 {axis = 0: i32} : (tensor<f32>) -> tensor<f32>
return %arg0 : tensor<f32>
}
@@ -2100,7 +2100,7 @@ func.func @test_scalar_reverse(%arg0: tensor<f32>) -> tensor<f32> {
func.func @test_scalar_tile(%arg0: tensor<f32>) -> tensor<*xf32> {
%cst = tosa.const_shape { values = dense<[]> : tensor<0xindex> } : () -> !tosa.shape<0>
- // expected-error at +1 {{'tosa.tile' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.tile' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%0 = tosa.tile %arg0, %cst: (tensor<f32>, !tosa.shape<0>) -> tensor<*xf32>
return %0 : tensor<*xf32>
}
diff --git a/mlir/test/Dialect/Tosa/verifier.mlir b/mlir/test/Dialect/Tosa/verifier.mlir
index c31effeb6a1d2..2fda4a966ec9b 100644
--- a/mlir/test/Dialect/Tosa/verifier.mlir
+++ b/mlir/test/Dialect/Tosa/verifier.mlir
@@ -120,7 +120,7 @@ func.func @test_large_constant_permutation() {
// -----
func.func @test_scalar_output_transpose(%arg0: tensor<*xf32>) -> tensor<f32> {
- // expected-error at +1 {{'tosa.transpose' op result #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.transpose' op result #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<*xf32>) -> tensor<f32>
return %1 : tensor<f32>
}
@@ -187,7 +187,7 @@ func.func @test_slice_invalid_size() {
func.func @test_scalar_slice(%arg0: tensor<f32>) -> tensor<f32> {
%0 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
%1 = tosa.const_shape {values = dense<[]> : tensor<0xindex>} : () -> !tosa.shape<0>
- // expected-error at +1 {{'tosa.slice' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.slice' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%2 = tosa.slice %arg0, %0, %1 : (tensor<f32>, !tosa.shape<0>, !tosa.shape<0>) -> tensor<f32>
return %2 : tensor<f32>
}
@@ -1563,7 +1563,7 @@ func.func @cast_from_block_scaled_incompatible_input_output_shape(%arg0: tensor<
// -----
func.func @cast_from_block_scaled_not_scalar(%arg0: tensor<f4E2M1FN>, %arg1: tensor<f8E8M0FNU>) -> tensor<f32> {
- // expected-error at +1 {{'tosa.cast_from_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f4E2M1FN>'}}
+ // expected-error at +1 {{'tosa.cast_from_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1 of micro-scaling format number values, but got 'tensor<f4E2M1FN>'}}
%0 = tosa.cast_from_block_scaled %arg0, %arg1 {block_size = #tosa.block_size<BLOCK_SIZE_32> : i32} : (tensor<f4E2M1FN>, tensor<f8E8M0FNU>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -1611,7 +1611,7 @@ func.func @test_cast_to_block_scaled_incompatible_input_output_shape(%arg0: tens
// -----
func.func @test_cast_to_block_scaled_not_scalar(%arg0: tensor<f32>) -> (tensor<f4E2M1FN>, tensor<f8E8M0FNU>) {
- // expected-error at +1 {{'tosa.cast_to_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<f32>'}}
+ // expected-error at +1 {{'tosa.cast_to_block_scaled' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<f32>'}}
%0:2 = tosa.cast_to_block_scaled %arg0 {block_size = #tosa.block_size<BLOCK_SIZE_32>} : (tensor<f32>) -> (tensor<f4E2M1FN>, tensor<f8E8M0FNU>)
return %0#0, %0#1 : tensor<f4E2M1FN>, tensor<f8E8M0FNU>
}
@@ -1731,7 +1731,7 @@ func.func @test_dim_invalid_axis(%arg0: tensor<1x2x3xi32>) -> !tosa.shape<1> {
// -----
func.func @test_dim_scalar(%arg0: tensor<i32>) -> !tosa.shape<1> {
- // expected-error at +1 {{'tosa.dim' op operand #0 must be tosa-conformant tensor of at least rank 1, but got 'tensor<i32>'}}
+ // expected-error at +1 {{'tosa.dim' op operand #0 must be tosa-conformant tensor of at least rank 1 of number values, but got 'tensor<i32>'}}
%0 = tosa.dim %arg0 {axis = 4 : i32} : (tensor<i32>) -> !tosa.shape<1>
return %0 : !tosa.shape<1>
}
>From 9608c88559b4ad29e4debe6e40df922d31c9f2b9 Mon Sep 17 00:00:00 2001
From: Luke Hutton <luke.hutton at arm.com>
Date: Thu, 9 Jul 2026 23:18:38 +0100
Subject: [PATCH 2/3] [mlir][ods] Fix string interpolation at end of
description
Updates the error streaming string logic to handle the case
where string interpolation used at the end of the description.
Previously this could generate malformed code that would not
compile e.g.:
```
"' failed to satisfy constraint: another attribute " << reformat(attr)";
```
With this change the above example would now generate:
```
"' failed to satisfy constraint: another attribute " << reformat(attr) << "";
```
Change-Id: Iea9bd52064b5e19a9a258f955b3b70eb01f26415
---
mlir/lib/TableGen/CodeGenHelpers.cpp | 14 ++++++--------
mlir/test/mlir-tblgen/constraint-unique.td | 4 ++--
2 files changed, 8 insertions(+), 10 deletions(-)
diff --git a/mlir/lib/TableGen/CodeGenHelpers.cpp b/mlir/lib/TableGen/CodeGenHelpers.cpp
index c8c419669f3d4..19439eddcb333 100644
--- a/mlir/lib/TableGen/CodeGenHelpers.cpp
+++ b/mlir/lib/TableGen/CodeGenHelpers.cpp
@@ -157,18 +157,16 @@ std::string mlir::tblgen::buildErrorStreamingString(
os << " << " << tgfmt(var, &ctx);
- if (rest.empty())
- break;
-
- split = rest.split("{{");
- if (split.second.empty() &&
- errorStreamType == ErrorStreamType::InsideOpError) {
+ if (errorStreamType == ErrorStreamType::InsideOpError) {
// To enable having part of string post, this adds a parenthesis before
// the last string segment to match the existing one.
- os << " << (\"" << split.first;
+ os << " << (\"";
} else {
- os << " << \"" << split.first;
+ os << " << \"";
}
+
+ split = rest.split("{{");
+ os << split.first;
msg = split.second;
}
diff --git a/mlir/test/mlir-tblgen/constraint-unique.td b/mlir/test/mlir-tblgen/constraint-unique.td
index 5fdaf368c323a..55d6a56e3fc80 100644
--- a/mlir/test/mlir-tblgen/constraint-unique.td
+++ b/mlir/test/mlir-tblgen/constraint-unique.td
@@ -17,7 +17,7 @@ def OtherType : Type<ATypePred, "another type">;
def AnAttrPred : CPred<"attrPred($_self, $_op)">;
def AnAttr : Attr<AnAttrPred, "an attribute (got {{reformat($_self)}})">;
-def OtherAttr : Attr<AnAttrPred, "another attribute">;
+def OtherAttr : Attr<AnAttrPred, "another attribute {{reformat($_self)}}">;
def ASuccessorPred : CPred<"successorPred($_self, $_op)">;
def ASuccessor : Successor<ASuccessorPred, "a successor">;
@@ -81,7 +81,7 @@ def OpC : NS_Op<"op_c"> {
// CHECK: static ::llvm::LogicalResult [[$O_ATTR_CONSTRAINT:__mlir_ods_local_attr_constraint.*]](
// CHECK: if (attr && !((attrPred(attr, *op))))
// CHECK-NEXT: return emitError() << "attribute '" << attrName
-// CHECK-NEXT: << "' failed to satisfy constraint: another attribute";
+// CHECK-NEXT: << "' failed to satisfy constraint: another attribute " << reformat(attr) << "";
/// Test that a successor contraint was generated.
// CHECK: static ::llvm::LogicalResult [[$A_SUCCESSOR_CONSTRAINT:__mlir_ods_local_successor_constraint.*]](
>From 8990668f2ccc5f2e48b9bc84a0f7bc225aac12a5 Mon Sep 17 00:00:00 2001
From: Luke Hutton <luke.hutton at arm.com>
Date: Wed, 15 Jul 2026 11:31:41 +0100
Subject: [PATCH 3/3] [mlir][tosa] Add more informative error messages to block
scaled types
This commit improves block scaled tensor type verification to provide
a specific reason about why type verification failed. Previously the
error message was a very generic
"must be tosa-conformant tensor of number values".
This commit resores previous functionality that was reverted by
https://github.com/llvm/llvm-project/pull/207995, but uses the ODS
string interpolation mechanism instead to prevent incompatibility
with PDLL.
Change-Id: I07f74efe8da9b3bdbae46fd14704d47ab96ec174
---
mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h | 13 ++++-
.../mlir/Dialect/Tosa/IR/TosaTypesBase.td | 22 +++++--
mlir/lib/Dialect/Tosa/IR/TosaOps.cpp | 58 ++++++++++++++++---
mlir/test/Dialect/Tosa/verifier.mlir | 10 ++--
4 files changed, 81 insertions(+), 22 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
index b1404d9c700ae..2c4d3b7446544 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.h
@@ -25,6 +25,8 @@
#include "mlir/Interfaces/SideEffectInterfaces.h"
#include "mlir/Interfaces/VectorInterfaces.h"
+#include <string>
+
//===----------------------------------------------------------------------===//
// TOSA dialect and structs includes.
//===----------------------------------------------------------------------===//
@@ -141,9 +143,14 @@ Type getStorageElementTypeOrSelf(Type type);
// Returns the storage element type for a given value
Type getStorageElementTypeOrSelf(Value value);
-// Verify a block scaled tensor type is valid
-LogicalResult verifyBlockScaledTensorType(mlir::Type type,
- bool allowScaleValues);
+// Verify that a given type is a valid block scaled tensor type
+LogicalResult verifyBlockScaledTensorType(
+ mlir::Type type,
+ llvm::function_ref<mlir::InFlightDiagnostic()> emitError = nullptr,
+ bool allowScaleValues = false);
+
+// Collect error messages for a given type
+std::string getTosaTensorTypeErrorMessage(mlir::Type type);
} // namespace tosa
} // namespace mlir
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
index 2ab135befaa96..0ab0da14e2ba2 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -172,7 +172,19 @@ def AtLeastRankOne : And<[
CPred<"::llvm::cast<::mlir::RankedTensorType>($_self).getRank() >= 1">]>;
def IsValidBlockScaledTensorType
- : CPred<"::mlir::succeeded(::mlir::tosa::verifyBlockScaledTensorType($_self, false))">;
+ : CPred<"::mlir::succeeded(::mlir::tosa::"
+ "verifyBlockScaledTensorType($_self))">;
+
+// Analogous to ShapedContainerType, but with additional 'summary' information.
+// This is used to provide more informative error messages when a type predicate
+// is false.
+class TosaShapedContainerType<list<Type> allowedTypes, Pred containerPred, string descr, string cppType = "::mlir::Type"> :
+ Type<And<[containerPred,
+ Concat<"[](::mlir::Type elementType) { return ",
+ SubstLeaves<"$_self", "elementType",
+ AnyTypeOf<allowedTypes>.predicate>,
+ "; }(::llvm::cast<::mlir::ShapedType>($_self).getElementType())">]>,
+ descr # " of " # AnyTypeOf<allowedTypes>.summary # " values" # "{{::mlir::tosa::getTosaTensorTypeErrorMessage($_self)}}", cppType>;
// We include unranked tensors as a supported type for all possible tosa
// Tensors as unranked does not guarantee invalid. If unranked tensors exist
@@ -181,13 +193,13 @@ def IsValidBlockScaledTensorType
class TosaTensorOf<list<Type> allowedTypes,
list<Pred> extraRankedTensorPreds = [],
string summary = "tosa-conformant tensor">
- : TensorOf<allowedTypes,
- [Or<[
+ : TosaShapedContainerType<allowedTypes,
+ And<[Or<[
IsUnrankedTensorTypePred,
And<!listconcat([IsRankedTensorTypePred, HasNo0Dimensions], extraRankedTensorPreds)>
]>,
- IsValidBlockScaledTensorType],
- summary>;
+ IsValidBlockScaledTensorType]>,
+ summary, "::mlir::TensorType">;
class TosaTensorRankOf<list<Type> allowedTypes, list<int> ranks>
: TosaTensorOf<allowedTypes,
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index bf9d98ffe35a0..215366ade3aba 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -741,22 +741,31 @@ LogicalResult mlir::tosa::mxint8Type::convertFromAttribute(
// TOSA block scaling utilities.
//===----------------------------------------------------------------------===//
-LogicalResult mlir::tosa::verifyBlockScaledTensorType(mlir::Type type,
- bool allowScaleValues) {
+LogicalResult mlir::tosa::verifyBlockScaledTensorType(
+ mlir::Type type, llvm::function_ref<mlir::InFlightDiagnostic()> emitError,
+ bool allowScaleValues) {
const auto tensorType = llvm::cast<ShapedType>(type);
const BlockScaledType elemType =
llvm::dyn_cast<BlockScaledType>(tensorType.getElementType());
if (!elemType)
return success();
- if (!allowScaleValues && elemType.hasScaleValues())
+ if (!allowScaleValues && elemType.hasScaleValues()) {
+ if (emitError)
+ emitError()
+ << "block scaled tensor type with scale values is not allowed";
return failure();
+ }
if (!tensorType.hasRank())
return success();
- if (tensorType.getRank() == 0)
+ if (tensorType.getRank() == 0) {
+ if (emitError)
+ emitError() << "block scaled tensor type must have rank greater than "
+ "zero";
return failure();
+ }
const ArrayRef<int64_t> tensorShape = tensorType.getShape();
const uint32_t blockSize =
@@ -765,19 +774,47 @@ LogicalResult mlir::tosa::verifyBlockScaledTensorType(mlir::Type type,
if (allowScaleValues && elemType.hasScaleValues() &&
tensorType.hasStaticShape()) {
const size_t numBlocks = tensorType.getNumElements() / blockSize;
- if (elemType.getScaleValues().size() != numBlocks)
+ if (elemType.getScaleValues().size() != numBlocks) {
+ if (emitError)
+ emitError() << "block scaled tensor type with scale values must have "
+ "scale values for each block, expected "
+ << numBlocks << ", got "
+ << elemType.getScaleValues().size();
return failure();
+ }
}
const int64_t blockedDimension = tensorShape.back();
if (ShapedType::isDynamic(blockedDimension))
return success();
- if (blockedDimension % blockSize != 0)
+
+ if (blockedDimension % blockSize != 0) {
+ if (emitError)
+ emitError() << "last dimension of block scaled tensor type ("
+ << blockedDimension << ") must be divisible by block size ("
+ << blockSize << ")";
+
return failure();
+ }
return success();
}
+std::string mlir::tosa::getTosaTensorTypeErrorMessage(mlir::Type type) {
+ MLIRContext *ctx = type.getContext();
+ std::string message;
+ ScopedDiagnosticHandler handler(
+ ctx, [&](Diagnostic &diag) { message = diag.str(); });
+
+ if (failed(verifyBlockScaledTensorType(
+ type, [ctx] { return emitError(UnknownLoc::get(ctx)); })) &&
+ !message.empty()) {
+ return ": " + message;
+ }
+
+ return "";
+}
+
static ParseResult parseScaleValues(AsmParser &parser,
SmallVector<Attribute> &scaleValues,
Type scaleType) {
@@ -959,9 +996,12 @@ LogicalResult tosa::ConstOp::verify() {
return op.emitOpError(
"attribute block scaled type must have scale values");
- if (failed(verifyBlockScaledTensorType(attrType, true)))
- return op.emitOpError("block scaled attribute type is not valid, got ")
- << attrType;
+ const auto emitAttributeError = [&op]() {
+ return op.emitOpError("attribute block scaled type is invalid ");
+ };
+
+ if (failed(verifyBlockScaledTensorType(attrType, emitAttributeError, true)))
+ return failure();
const BlockScaledType resultBlockScaledType =
llvm::dyn_cast<mlir::tosa::BlockScaledType>(resultElemType);
diff --git a/mlir/test/Dialect/Tosa/verifier.mlir b/mlir/test/Dialect/Tosa/verifier.mlir
index 2fda4a966ec9b..30eadf18dc620 100644
--- a/mlir/test/Dialect/Tosa/verifier.mlir
+++ b/mlir/test/Dialect/Tosa/verifier.mlir
@@ -1675,7 +1675,7 @@ func.func @test_cast_between_block_scaled(%arg0: tensor<4x32x!tosa.block_scaled<
// -----
func.func @test_block_scaled_cast_invalid_block_shape(%arg0: tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<1x16x31xf32> {
- // expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values, but got 'tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
+ // expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values: last dimension of block scaled tensor type (31) must be divisible by block size (32), but got 'tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
%0 = tosa.cast %arg0 : (tensor<1x16x31x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<1x16x31xf32>
return %0 : tensor<1x16x31xf32>
}
@@ -1683,7 +1683,7 @@ func.func @test_block_scaled_cast_invalid_block_shape(%arg0: tensor<1x16x31x!tos
// -----
func.func @test_block_scaled_cast_scalar(%arg0: tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<f32> {
- // expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values, but got 'tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
+ // expected-error at +1 {{'tosa.cast' op operand #0 must be tosa-conformant tensor of number values: block scaled tensor type must have rank greater than zero, but got 'tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>'}}
%0 = tosa.cast %arg0 : (tensor<!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN>>) -> tensor<f32>
return %0 : tensor<f32>
}
@@ -2316,7 +2316,7 @@ func.func @test_block_scaled_const_scale_value_non_float_explicit_type() -> tens
!mxint8_scale = !tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8, {1.0, 2.0, 4.0}>
func.func @test_block_scaled_const_invalid_num_scales() -> tensor<2x32x!mxint8> {
- // expected-error at +1 {{'tosa.const' op block scaled attribute type is not valid, got 'tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8, {1.000000e+00, 2.000000e+00, 4.000000e+00}>>'}}
+ // expected-error at +1 {{'tosa.const' op attribute block scaled type is invalid block scaled tensor type with scale values must have scale values for each block, expected 2, got 3}}
%0 = "tosa.const"() <{values = dense<tensor<2x32x!mxint8_scale> : 0 : i8>}> : () -> tensor<2x32x!mxint8>
return %0 : tensor<2x32x!mxint8>
}
@@ -2324,7 +2324,7 @@ func.func @test_block_scaled_const_invalid_num_scales() -> tensor<2x32x!mxint8>
// -----
func.func @test_block_scaled_const_invalid_num_scales_wide_inner_dim() -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>> {
- // expected-error at +1 {{'tosa.const' op block scaled attribute type is not valid, got 'tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8, {1.000000e+00, 2.000000e+00}>>'}}
+ // expected-error at +1 {{'tosa.const' op attribute block scaled type is invalid block scaled tensor type with scale values must have scale values for each block, expected 4, got 2}}
%0 = "tosa.const"() <{values = dense<tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8, {1.0, 2.0}>> : 0 : i8>}> : () -> tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
return %0 : tensor<2x64x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:!tosa.mxint8>>
}
@@ -2332,7 +2332,7 @@ func.func @test_block_scaled_const_invalid_num_scales_wide_inner_dim() -> tensor
// -----
func.func @test_block_scaled_const_cast_scale_values_propagate() -> tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.0, 4.0}>> {
- // expected-error at +1 {{'tosa.const' op result #0 must be tosa-conformant tensor of number values, but got 'tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.000000e+00, 4.000000e+00}>>'}}
+ // expected-error at +1 {{'tosa.const' op result #0 must be tosa-conformant tensor of number values: block scaled tensor type with scale values is not allowed, but got 'tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.000000e+00, 4.000000e+00}>>'}}
%0 = "tosa.const"() <{values = dense<tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.0, 4.0}>> : 0.0 : f8E4M3FN>}> : () -> tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.0, 4.0}>>
return %0 : tensor<2x32x!tosa.block_scaled<BLOCK_SHAPE_32:f8E8M0FNU:f8E4M3FN, {2.0, 4.0}>>
}
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