Address feedback: fix header indentation and code block language

rfcs/20230622-quantized-reduction.md

@@ -2,12 +2,13 @@
 
 Status: Review<br/>
 Initial version: 06/22/2023<br/>
-Last updated: 07/02/2023<br/>
+Last updated: 07/13/2023<br/>
 Discussion thread: [GitHub](https://github.com/openxla/stablehlo/pull/1664)
 
 ## Version log
 
 * 06/22/2023: Initial version.
+* 07/13/2023: Fixed typo in code blocks, header indentation.
 
 ## Introduction
 
@@ -128,7 +129,7 @@ be applied to the non-leaf nodes of the schedule tree.
 The `output_conversion` block is applied just after the `result` for a particular
 index is computed as shown in the above diagram.
 
-Please refer to the [formal spec](#specification-of-reduce-op) of the proposed
+Please refer to the [formal spec](#revised-specification-of-reduce-op) of the proposed
 reduce op.
 
 ### Implementation details
@@ -206,7 +207,7 @@ generic printing,
 **even if the explicitly provided conversion functions are identity function**:
 To avoid identification of identity functions which could be tricky in general.
 
-#### Tradeoffs
+### Tradeoffs
 
 * (+) Enables programmers to program at (almost) baremetal. If the hardware
   can support reduction computation in wider type (e.g. in the SIMD
@@ -248,13 +249,10 @@ type.
   backend matches the pattern.
   [for more information](https://github.com/openxla/stablehlo/pull/1538#issuecomment-1599476906)
 
-## Other options considered
-
-There is another option considered which did not fly well because of limited
-extensibility. Adding it just for completeness purposes.
-
-### Option 3: allow accumulator type to be different from input type
+## Option 3: allow accumulator type to be different from input type
 
+This is another option we considered which does not fly well because of limited
+expressibility. Adding it just for completeness purposes.
 The idea here is to convey the accumulator type using the `init_value` operand
 of `reduce` op. The code snippet for `reduce` looks like:
 
@@ -284,7 +282,7 @@ from the input type. The first argument of the compute block is fixed for the
 traversed element and the second argument is fixed for the intermediate
 (accumulation) result.
 
-#### Tradeoffs
+### Tradeoffs
 
 * (+) Make the accumulation type explicit in the IR.
 * (-) This representation imposes a limitation on the evaluation order.
@@ -355,9 +353,8 @@ types.
 To provide an estimate of specification changes needed to implement option #1
 I have attempted to provide the blueprint here.
 
-### Specification of reduce op
+### Revised specification of reduce op
 
-```python
 #### Semantics
 
 Applies a reduction functions `input_conversion`, `body`, and
@@ -426,15 +423,13 @@ map(output_conversion, reduce(input_slices_converted))` where:
        element_type(input_types(output_conversion)...)`.
 * (C?) `shape(results...) = shape(inputs...)` except that the dimension
   sizes of `inputs...` corresponding to `dimensions` are not included.
-```
 
 The above specification of `reduce` op can be used to define the specification
 of other ops as shown below. For brevity, we are only presenting the relevant
 portions of the spec which needs modification.
 
-### reduce_window
+### Revised specification of  reduce_window op
 
-```python
 #### Semantics
 
 Applies a reduction functions `input_conversion`, `body`, and
@@ -471,17 +466,15 @@ where:
        `E'i = element_type(results[i])`.
 * (C?) `element_type(output_types(input_conversion)...) =
        element_type(input_types(output_conversion)...)`.
-```
 
-### select_and_scatter
+### Revised specification of select_and_scatter op
 
 This op originally takes two function arguments `select` and `scatter`. As the
 `select` function is supposed to perform a non-accumulative operation, we may
 not need additional conversion functions associated with `select`. But the
 `scatter` function needs be accompanied with `input_conversion` and
 `output_conversion` functions.
 
-```python
 #### Semantics
 
 Scatters the values from the `source` tensor using `scatter` based on the
@@ -490,6 +483,7 @@ a `result` tensor.
 
 More formally:
 ...
+
 * `result[result_index] = reduce([source_values], [init_value], [0],
         input_conversion, scatter, output_conversion)`
  where:
@@ -519,4 +513,3 @@ More formally:
        element_type(input_types(output_conversion))`.
 * (C11) `shape(operand) = shape(result)`.
 <!-- markdownlint-enable line-length -->
-```