diff --git a/colossalai/quantization/fp8.py b/colossalai/quantization/fp8.py
index d405de2de..051ecb45a 100644
--- a/colossalai/quantization/fp8.py
+++ b/colossalai/quantization/fp8.py
@@ -1,8 +1,10 @@
+from typing import Any, Callable, List, Optional, Tuple, Union
+
 import torch
 import torch.distributed as dist
 
 
-def cast_to_fp8(inp: torch.Tensor, scale=None, fp8_format="e4m3") -> (torch.Tensor, torch.Tensor):
+def cast_to_fp8(inp: torch.Tensor, fp8_format="e4m3") -> (torch.Tensor, torch.Tensor):
     r"""
     casting torch Tensor into specified fp8 tensor with per-channel scaling or per-tensor scaling.
     Args:
@@ -14,28 +16,28 @@ def cast_to_fp8(inp: torch.Tensor, scale=None, fp8_format="e4m3") -> (torch.Tens
     Returns:
         Tuples: A tuple (fp8_tensor, scale)
     """
-    if inp.dtype in [torch.FloatTensor, torch.HalfTensor, torch.BFloat16Tensor]:
-        return inp
+
+    if inp.dtype not in [torch.float32, torch.float16, torch.bfloat16]:
+        raise TypeError("Only float16, bfloat16, and float32 are allowed.")
+
     fp8_type = torch.float8_e4m3fn if fp8_format == "e4m3" else torch.float8_e5m2
+    fp8_max = torch.finfo(fp8_type).max
 
     if inp.dim() == 2:
-        if scale is None:
-            per_channel_max = inp.abs().max(dim=-1).values
-            scale = per_channel_max
-        scale_inv = 1.0 / scale
-        scale_inv = scale_inv[:, None]
-        ret = (scale_inv * inp).to(fp8_type)
+        per_channel_max = inp.abs().max(dim=-1).values.float()
+        per_channel_max = torch.where(per_channel_max > 0, per_channel_max, 1.0)
+        scale = fp8_max / per_channel_max[:, None]
     else:
-        if scale is None:
-            per_tensor_max = inp.abs().max()
-            scale = per_tensor_max
-        scale_inv = 1.0 / scale
-        ret = (scale_inv * inp).to(fp8_type)
+        per_tensor_max = inp.abs().max().float()
+        per_tensor_max = torch.where(per_tensor_max > 0, per_tensor_max, 1.0)
+        scale = fp8_max / per_tensor_max
 
-    return ret, scale
+    scale_inv = 1.0 / scale
+    ret = (scale * inp.float()).to(fp8_type)
+    return ret, scale_inv
 
 
-def cast_from_fp8(inp: torch.Tensor, scale: torch.Tensor, ret_type: torch.dtype) -> torch.Tensor:
+def cast_from_fp8(inp: torch.Tensor, scale_inv: torch.Tensor, ret_type: torch.dtype) -> torch.Tensor:
     r"""
 
     Args:
@@ -47,12 +49,13 @@ def cast_from_fp8(inp: torch.Tensor, scale: torch.Tensor, ret_type: torch.dtype)
         torch.Tensor
     """
     if inp.dtype not in [torch.float8_e4m3fn, torch.float8_e5m2]:
-        return inp
+        raise TypeError("Only float8_e4m3fn and float8_e5m2 are allowed.")
+
     if inp.dim() == 2:
-        ret = scale[:, None] * inp.to(ret_type)
+        ret = scale_inv[:, None] * inp.float()
     else:
-        ret = scale * inp.to(ret_type)
-    return ret
+        ret = scale_inv * inp.float()
+    return ret.to(ret_type)
 
 
 def all_reduce_fp8(tensor: torch.Tensor, fp8_format="e4m3") -> None:
@@ -69,7 +72,6 @@ def all_reduce_fp8(tensor: torch.Tensor, fp8_format="e4m3") -> None:
     """
 
     world_size = dist.get_world_size()
-    rank = dist.get_rank()
     input_type = tensor.dtype
     input_shape = tensor.shape
     input_device = tensor.device