## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect tensor indexing for small M values, using partial indices instead of full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily

(cherry picked from commit d667ffe)

## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs
when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect
tensor indexing for small M values, using partial indices instead of
full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily

(cherry picked from commit d667ffe)

## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs
when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect
tensor indexing for small M values, using partial indices instead of
full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily

(cherry picked from commit d667ffe)

Cherry-pick of #3090

Co-authored-by: Milica Stankovic <milica.stankovic@amd.com>

## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs
when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect
tensor indexing for small M values, using partial indices instead of
full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily

(cherry picked from commit d667ffe)

Cherry-pick of #3090

Co-authored-by: Milica Stankovic <milica.stankovic@amd.com>

## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs
when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect
tensor indexing for small M values, using partial indices instead of
full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily

(cherry picked from commit d667ffe)

## Summary

This PR fixes FP8 inductor test failures that occur on AMD RDNA4 GPUs when testing matrix multiplications with small M dimensions (M < 16).

## Problem

On gfx120x GPUs, FP8 scaled matrix multiplication tests fail with:
- 92.4% NaN outputs when M < BLOCK_M (typically 16)
- Large numerical mismatches between eager and compiled results
- Only occurs in `max-autotune` mode

**Root cause:** Autotuned Triton kernels on gfx120x generate incorrect tensor indexing for small M values, using partial indices instead of full computed indices in load/store operations.

## Solution

 - Added GPU-specific compile mode selection for small M values
 - gfx120x with M < 16: use `compile_mode="default"`
 - All other cases: use `compile_mode="max-autotune"`

Pull Request resolved: pytorch#174873
Approved by: https://github.com/jeffdaily