[Mlir-commits] [mlir] [mlir][tosa] Combine unranked/ranked tensor types into single type (PR #209737)
Luke Hutton
llvmlistbot at llvm.org
Wed Jul 15 04:48:53 PDT 2026
https://github.com/lhutton1 created https://github.com/llvm/llvm-project/pull/209737
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.
>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] [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>
}
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