Update docstring

bitsandbytes/functional.py

@@ -2429,7 +2429,32 @@ def get_colrow_absmax(
     nnz_block_ptr: Optional[torch.Tensor] = None,
     threshold=0.0,
 ) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
-    # Note: prior impl only works with fp16
+    """ "Determine the quantization statistics for input matrix `A` in accordance to the `LLM.int8()` algorithm.
+
+    The row-wise and column-wise absmax values are determined.
+
+    For more information, see the [LLM.int8() paper](https://arxiv.org/abs/2208.07339).
+
+    <Tip>
+    This function is useful for training, but for inference it is advised to use [`get_row_absmax`] instead.
+    The column-wise quantization scales are not typically needed in inference scenarios.
+    </Tip>
+
+    Args:
+        A (`torch.Tensor` with dtype `torch.float16`): Input tensor.
+        row_stats (`torch.Tensor`, *optional*): If provided, calculation of row statistics is skipped.
+        col_stats (`torch.Tensor`, *optional*): If provided, calculation of column statistics is skipped.
+        nnz_block_ptr (`torch.Tensor`, *optional*): Not used.
+        threshold (`float`, `optional`):
+            An optional threshold for sparse decomposition of outlier features.
+            No outliers are held back when 0.0. Defaults to 0.0.
+
+    Returns:
+        `Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]`: A tuple containing quantization statistics.
+        - `torch.Tensor` with dtype `torch.float32`: The row-wise quantization statistics.
+        - `torch.Tensor` with dtype `torch.float32`: The column-wise quantization statistics.
+        - `torch.Tensor` with dtype `torch.bool`, *optional*: A mask indicating the locations of outliers in the input tensor.
+    """
     assert A.is_floating_point()
 
     outlier_mask = None