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[mlir][tosa] Avoid requirement to specify enum name for enum attributes
This commit updates the printer/parser of operations that use enum type attributes such that they aren't required to specify the full enum name e.g. `rounding_mode = #tosa.rounding_mode<SINGLE_ROUND>` becomes `rounding_mode = SINGLE_ROUND`. For compatibility, text with #tosa.rounding_mode<...> will still be accepted by the parser. Change-Id: Ic19c961069a297d442450fdab3dc727b0b64bd1a Signed-off-by: Tai Ly <tai.ly@arm.com>
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‎mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td

Lines changed: 8 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -391,22 +391,22 @@ def Tosa_RESIZE_BILINEAR : I32EnumAttrCase<"BILINEAR", 2>;
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def Tosa_ResizeModeAttr
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: Tosa_I32EnumAttr<"ResizeMode", "Supported resize/upsampling strategies", "resize_mode",
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[Tosa_RESIZE_NEAREST_NEIGHBOR, Tosa_RESIZE_BILINEAR]>;
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[Tosa_RESIZE_NEAREST_NEIGHBOR, Tosa_RESIZE_BILINEAR]>;
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def Tosa_NANPROPAGATION_PROPAGATE : I32EnumAttrCase<"PROPAGATE", 1>;
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def Tosa_NANPROPAGATION_IGNORE : I32EnumAttrCase<"IGNORE", 2>;
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def Tosa_NanPropagationModeAttr
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: Tosa_I32EnumAttr<"NanPropagationMode", "Supported NaN propagation strategies", "nan_mode",
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[Tosa_NANPROPAGATION_PROPAGATE, Tosa_NANPROPAGATION_IGNORE]>;
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[Tosa_NANPROPAGATION_PROPAGATE, Tosa_NANPROPAGATION_IGNORE]>;
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def Tosa_ROUNDING_SINGLE_ROUND : I32EnumAttrCase<"SINGLE_ROUND", 1>;
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def Tosa_ROUNDING_INEXACT_ROUND : I32EnumAttrCase<"INEXACT_ROUND", 2>;
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def Tosa_ROUNDING_DOUBLE_ROUND : I32EnumAttrCase<"DOUBLE_ROUND", 3>;
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def Tosa_RoundingModeAttr
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: Tosa_I32EnumAttr<"RoundingMode", "Supported rounding modes", "rounding_mode",
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[Tosa_ROUNDING_SINGLE_ROUND, Tosa_ROUNDING_INEXACT_ROUND, Tosa_ROUNDING_DOUBLE_ROUND]>;
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[Tosa_ROUNDING_SINGLE_ROUND, Tosa_ROUNDING_INEXACT_ROUND, Tosa_ROUNDING_DOUBLE_ROUND]>;
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//===----------------------------------------------------------------------===//
@@ -472,33 +472,26 @@ class Tosa_ElementwiseOp<string mnemonic, list<Trait> traits = []> :
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ResultsBroadcastableShape,
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TosaElementwiseOperator,
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SameOperandsAndResultRank,
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Pure])> {
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let assemblyFormat =
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"operands attr-dict `:` functional-type(operands, results)";
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}
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Pure])> {}
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class Tosa_ElementwiseUnaryOp<string mnemonic, list<Trait> traits = []> :
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Tosa_ElementwiseOp<mnemonic, !listconcat(traits, [
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SameOperandsAndResultShape,
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SameOperandsAndResultElementType])> {}
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class Tosa_InferTensorTypeOp<string mnemonic, list<Trait> traits = []>
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: Tosa_Op<mnemonic, !listconcat(traits, [InferTensorTypeAdaptor, Pure])> {
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let assemblyFormat =
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"operands attr-dict `:` functional-type(operands, results)";
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}
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: Tosa_Op<mnemonic, !listconcat(traits, [InferTensorTypeAdaptor, Pure])> {}
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class Tosa_InferShapedTypeOp<string mnemonic, list<Trait> traits = []>
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: Tosa_Op<mnemonic, !listconcat(traits, [InferShapedTypeOpAdaptor, Pure])> {
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let assemblyFormat =
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"operands attr-dict `:` functional-type(operands, results)";
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}
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: Tosa_Op<mnemonic, !listconcat(traits, [InferShapedTypeOpAdaptor, Pure])> {}
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// The "SameVariadicOperandSize" trait allows us to pass optional arguments
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// for multiple zero points in convolution ops.
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class Tosa_ConvOp<string mnemonic, list<Trait> traits = []>
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: Tosa_InferShapedTypeOp<mnemonic, !listconcat(traits,
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[SameVariadicOperandSize])> {
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let assemblyFormat =
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"operands attr-dict `:` functional-type(operands, results)";
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}
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#endif // TOSA_OP_BASE

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