Adding example to scaling_extf description

Signed-off-by: Muzammiluddin Syed <[email protected]>

Author: Muzammiluddin-Syed-ECE

mlir/include/mlir/Dialect/Arith/IR/ArithOps.td

@@ -1229,25 +1229,25 @@ def Arith_ScalingExtFOp
   let summary = "Upcasts input floats using provided scales values following "
                 "OCP MXFP Spec";
   let description = [{
-  This operation upcasts input floating-point values using provided scale
-  values. It expects both scales and the input operand to be of the same shape,
-  making the operation elementwise. Scales are usually calculated per block
-  following the OCP MXFP spec as described in https://arxiv.org/abs/2310.10537.
-
-  If scales are calculated per block where blockSize != 1, then scales may
-  require broadcasting to make this operation elementwise. For example, let's
-  say the input is of shape `<dim1 x dim2 x ... dimN>`. Given blockSize != 1 and
-  assuming quantization happens on the last axis, the input can be reshaped to
-  `<dim1 x dim2 x ... (dimN/blockSize) x blockSize>`. Scales will be calculated
-  per block on the last axis. Therefore, scales will be of shape
-  `<dim1 x dim2 x ... (dimN/blockSize) x 1>`. Scales could also be of some other
-  shape as long as it is broadcast compatible with the input, e.g.,
-  `<1 x 1 x ... (dimN/blockSize) x 1>`.
-
-  In this example, before calling into `arith.scaling_extf`, scales must be
-  broadcasted to `<dim1 x dim2 x dim3 ... (dimN/blockSize) x blockSize>`. Note
-  that there could be multiple quantization axes. Internally,
-  `arith.scaling_extf` would perform the following:
+    This operation upcasts input floating-point values using provided scale
+    values. It expects both scales and the input operand to be of the same shape,
+    making the operation elementwise. Scales are usually calculated per block
+    following the OCP MXFP spec as described in https://arxiv.org/abs/2310.10537.
+
+    If scales are calculated per block where blockSize != 1, then scales may
+    require broadcasting to make this operation elementwise. For example, let's
+    say the input is of shape `<dim1 x dim2 x ... dimN>`. Given blockSize != 1 and
+    assuming quantization happens on the last axis, the input can be reshaped to
+    `<dim1 x dim2 x ... (dimN/blockSize) x blockSize>`. Scales will be calculated
+    per block on the last axis. Therefore, scales will be of shape
+    `<dim1 x dim2 x ... (dimN/blockSize) x 1>`. Scales could also be of some other
+    shape as long as it is broadcast compatible with the input, e.g.,
+    `<1 x 1 x ... (dimN/blockSize) x 1>`.
+
+    In this example, before calling into `arith.scaling_extf`, scales must be
+    broadcasted to `<dim1 x dim2 x dim3 ... (dimN/blockSize) x blockSize>`. Note
+    that there could be multiple quantization axes. Internally,
+    `arith.scaling_extf` would perform the following:
 
     ```
     resultTy = get_type(result)
@@ -1260,6 +1260,17 @@ def Arith_ScalingExtFOp
     ```
     It propagates NaN values. Therefore, if either scale or the input element
     contains NaN, then the output element value will also be a NaN.
+    
+    Example:
+
+    ```mlir
+    // Upcast from f4E2M1FN to f32.
+    %a = arith.scaling_extf %b, %c : f4E2M1FN, f8E8M0FNU to f32
+
+    // Broadcasting to perform eltwise upcasting (Block size = 32)
+    %f = vector.broadcast %g : vector<1xf8E8M0FNU> to vector<32xf8E8M0FNU>
+    %h = arith.scaling_extf %i, %f : vector<32xf4E2M1FN>, vector<32xf8E8M0FNU> to vector<32xbf16>
+    ```
   }];
   let hasVerifier = 1;
   let assemblyFormat =