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[mlir][tosa] Allow zero-points to be unranked (#143770)

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This commit allows zero-points used by a number of tosa operations to be
unranked. This allows the shape inference pass to propagate shape
information.
This commit is contained in:
Luke Hutton
2025-06-23 09:38:39 +01:00
committed by GitHub
parent b31413a966
commit 98a6fed096
3 changed files with 13 additions and 2 deletions
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2
mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
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@@ -152,7 +152,7 @@ def Tosa_Rank0Tensor : TosaTensorRankOf<[Tosa_AnyNumber], [0]>;
def Tosa_ScalarTensor : TosaScalarTensorOf<[Tosa_AnyNumber], [1]>;
def Tosa_ScalarInt8Tensor : TosaScalarTensorOf<[Tosa_Int8], [1]>;
def Tosa_ScalarIntOrFloatTensor : TosaScalarTensorOf<[Tosa_Int, AnyFloat], [1]>;
def Tosa_ScalarIntOrFloatTensor : AnyTypeOf<[TosaUnrankedTensorOf<[Tosa_Int, AnyFloat]>, TosaScalarTensorOf<[Tosa_Int, AnyFloat], [1]>]>;
// We include unranked tensors as a supported type for all possible tosa
// Tensors as unranked does not guarantee invalid. If unranked tensors exist

2
mlir/test/Dialect/Tosa/invalid.mlir
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@@ -1007,7 +1007,7 @@ 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@+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>'}}
// expected-error@+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>'}}
%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>

11
mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
View File

@@ -333,6 +333,17 @@ func.func @test_dynamic_mixed_matmul(%arg0 : tensor<?x3x?xi32>, %arg1 : tensor<?
// -----
// CHECK-LABEL: @test_unranked_zero_points_matmul
func.func @test_unranked_zero_points_matmul(%arg0: tensor<1x2x3xf32>, %arg1: tensor<1x3x4xf32>, %zero_point: tensor<1xf32>) -> tensor<1x2x4xf32> {
// CHECK: %[[ZP:.*]] = tosa.cast %arg2 : (tensor<1xf32>) -> tensor<1xf32>
%zero_point_unranked = "tosa.cast"(%zero_point) : (tensor<1xf32>) -> tensor<*xf32>
// CHECK: tosa.matmul %arg0, %arg1, %[[ZP]], %[[ZP]] : (tensor<1x2x3xf32>, tensor<1x3x4xf32>, tensor<1xf32>, tensor<1xf32>) -> tensor<1x2x4xf32>
%0 = tosa.matmul %arg0, %arg1, %zero_point_unranked, %zero_point_unranked : (tensor<1x2x3xf32>, tensor<1x3x4xf32>, tensor<*xf32>, tensor<*xf32>) -> tensor<1x2x4xf32>
return %0 : tensor<1x2x4xf32>
}
// -----
// CHECK-LABEL: @test_table_static
func.func @test_table_static(%arg0 : tensor<4x5xi16>, %arg1 : tensor<513xi16>) -> () {
// CHECK:tosa.table %arg0, %arg1 : (tensor<4x5xi16>, tensor<513xi16>) -> tensor<4x5xi16>