fixbug, ensure FP32 accumulation for dW in Llama-mode RMSNorm backward (#950)

## Summary

This PR fixes a precision issue in `_block_rms_norm_backward_kernel`
when running in `_CASTING_MODE_LLAMA`.
It enforces FP32 accumulation for the weight gradient (`dW`), aligning
its behavior with the existing `_rms_norm_backward_kernel`.

* In `_rms_norm_backward_kernel` (Row-wise): The gradient `dW_row` is
initialized as `float32`. When iterating through elements, the term
`dY_row * (X_row * rstd_row).to(X_dtype)` (which is `bfloat16`) is added
to `dW_row`. This operation performs an implicit cast to FP32 during the
addition `dW_row += val`, effectively accumulating in high precision.

* In `_block_rms_norm_backward_kernel`(Block-wise - The Bug): The code
uses `tl.sum` for reduction:

```Python
# dY_row * (...) is bfloat16
dW_row += tl.sum((dY_row * (X_row * rstd_row[:, None]).to(X_dtype)), 0)
```

Here, `tl.sum` receives a `bfloat16` tensor. Consequently, the reduction
itself is performed in `bfloat16`. The precision loss occurs inside the
reduction before the result is added to the FP32 `dW_row`. This leads to
significant numerical errors for both small and large shapes due to the
limited mantissa of `bfloat16`.

## Testing Done


- Hardware Type: A100-SXM4-80GB
- [x] run `make test` to ensure correctness
- [x] run `make checkstyle` to ensure code style
- [x] run `make test-convergence` to ensure convergence

---------

Author: niyunsheng

src/liger_kernel/ops/rms_norm.py

@@ -349,7 +349,8 @@ def _block_rms_norm_backward_kernel(
 
         # calculate the gradient of W
         if casting_mode == _CASTING_MODE_LLAMA:
-            dW_row += tl.sum(dY_row * (X_row * rstd_row[:, None]).to(X_dtype), 0)
+			# TODO(tcc): use tl.sum(..., dtype=tl.float32) once we upgrade to triton>=3.3.0
+            dW_row += tl.sum((dY_row * (X_row * rstd_row[:, None]).to(X_dtype)).to(tl.float32), 0)
         else:
             # here X_row is already in fp32 (see previous if block)
             dW_row += tl.sum(dY_row * (X_row * rstd_row[:, None]), 0)