[mlir][tosa] Spec v0.23 updates

Add pad_const field to tosa.pad.
Add builders to enable optional construction of pad_const in pad op.
Update documentation of tosa.clamp to match spec wording.

Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>

Reviewed By: rsuderman

Differential Revision: https://reviews.llvm.org/D113322

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

@@ -174,8 +174,8 @@ def Tosa_UnaryOpQuantInfoBuilder : OpBuilder<
     buildUnaryOpWithQuantInfo($_builder, $_state, outputType, input);
   }]>;
 
-// This builder is called on the TOSA pad operator that needs to create its own
-// OptionalAttr quantization_attr parameter to scale the padding values
+// These builders are called on the TOSA pad operator that needs to create its
+// own OptionalAttr quantization_attr parameter to scale the padding values
 // correctly.
 def Tosa_PadOpQuantInfoBuilder : OpBuilder<
   (ins "Type":$outputType, "Value":$input, "Value":$paddings),
@@ -184,6 +184,14 @@ def Tosa_PadOpQuantInfoBuilder : OpBuilder<
                             input, paddings);
   }]>;
 
+def Tosa_ExplicitValuePadOpQuantInfoBuilder : OpBuilder<
+  (ins "Type":$outputType, "Value":$input, "Value":$paddings,
+       "Value":$pad_value),
+  [{
+    buildExplicitValuePadOpWithQuantInfo($_builder, $_state, outputType,
+                                         input, paddings, pad_value);
+  }]>;
+
 //===----------------------------------------------------------------------===//
 // TOSA Operator.
 //===----------------------------------------------------------------------===//

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

@@ -321,9 +321,11 @@ def Tosa_ClampOp : Tosa_Op<"clamp", [
   let summary = "Computes clamp(features, min, max).";
 
   let description = [{
-    Clamp to an arbitrary minimum and maximum value. Note that the maximum and
-    minimum values are specified as signed quantized values, no scaling happens
-    before or after this operation.
+    Clamp to an arbitrary minimum and maximum value.
+    Maximum and minimum values are specified as values in the range of the 
+    input type.
+    No zero point subtraction is done to the values, thus to clamp to the zero 
+    point value, the zero point itself should be supplied as the minimum value.
   }];
 
   let arguments = (ins
@@ -1394,15 +1396,16 @@ def Tosa_PadOp : Tosa_Op<"pad", [
     DeclareOpInterfaceMethods<InferShapedTypeOpInterface,
                               ["inferReturnTypeComponents"]>,
     NoSideEffect]> {
-  let summary = "Pads a tensor with zeros.";
+  let summary = "Pads a tensor with value specified.";
 
   let description = [{
-    Zero-pads a tensor along borders of each dimension.
+    Pads a tensor along borders of each dimension with pad_value. 
   }];
 
   let arguments = (ins
     Tosa_RankedTensor:$input1,
     Tosa_Int32Or64Tensor:$padding,
+    Optional<Tosa_ScalarTensor>:$pad_const,
     OptionalAttr<Tosa_PadOpQuantizationAttr>:$quantization_info
   );
 
@@ -1410,7 +1413,8 @@ def Tosa_PadOp : Tosa_Op<"pad", [
     Tosa_RankedTensor:$output
   );
 
-  let builders = [Tosa_PadOpQuantInfoBuilder];
+  let builders = [Tosa_PadOpQuantInfoBuilder, 
+                  Tosa_ExplicitValuePadOpQuantInfoBuilder];
 }
 
 //===----------------------------------------------------------------------===//

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

@@ -117,6 +117,9 @@ class Tosa_TensorOfOrNone<list<Type> allowedTypes, string description = ""> :
 // Tensor types with constrained ranks.
 //===----------------------------------------------------------------------===//
 
+// Rank-0 (scalar) tensor
+def Tosa_ScalarTensor : TensorRankOf<[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

mlir/lib/Dialect/Tosa/IR/TosaOps.cpp

@@ -609,7 +609,7 @@ static void buildUnaryOpWithQuantInfo(OpBuilder &builder,
 
 /// This builder is called on TOSA pad operator that needs to create its own
 /// OptionalAttr quantization_attr parameter to scale the padding values
-/// correctly.
+/// correctly. No pad_const is interpreted as zero-padding.
 static void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
                                     Type outputType, Value input,
                                     Value paddings) {
@@ -620,6 +620,20 @@ static void buildPadOpWithQuantInfo(OpBuilder &builder, OperationState &result,
   result.types.push_back(outputType);
 }
 
+/// This builder is called on TOSA pad operator when an explicit pad_const
+/// value is passed in. It also optionally constructs quantization_attr.
+static void buildExplicitValuePadOpWithQuantInfo(OpBuilder &builder,
+                                                 OperationState &result,
+                                                 Type outputType, Value input,
+                                                 Value paddings,
+                                                 Value pad_const) {
+  result.addOperands({input, paddings, pad_const});
+  auto quantAttr = buildPadOpQuantizationAttr(builder, input);
+  if (quantAttr)
+    result.addAttribute("quantization_info", quantAttr);
+  result.types.push_back(outputType);
+}
+
 //===----------------------------------------------------------------------===//
 // TOSA Operator Return Type Inference.
 //===----------------------------------------------------------------------===//

mlir/test/Dialect/Tosa/ops.mlir

@@ -395,6 +395,14 @@ func @test_pad(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3x2xi32>) -> tensor<13x
   return %0 : tensor<13x21x3xf32>
 }
 
+// -----
+// CHECK-LABEL: pad_explicit_value
+func @test_pad_explicit_value(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3x2xi32>) -> tensor<13x21x3xf32> {
+  %0 = "tosa.const"() {value = dense<3.14> : tensor<f32>} : () -> tensor<f32>
+  %1 = "tosa.pad"(%arg0, %arg1, %0) : (tensor<13x21x3xf32>, tensor<3x2xi32>, tensor<f32>) -> tensor<13x21x3xf32>
+  return %1 : tensor<13x21x3xf32>
+}
+
 // -----
 // CHECK-LABEL: reshape
 func @test_reshape(%arg0: tensor<13x21x3xf32>) -> tensor<1x819xf32> {