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

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.

Author: lhutton1

mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td

@@ -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

mlir/test/Dialect/Tosa/invalid.mlir

@@ -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>

mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir

@@ -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>