Update on "[2/x] clean up casting functions: delayed scaling"

Summary:

Removes delayed scaling from `float8_tensor.py`. After this PR, the
invariant is that everything in `float8_tensor.py` requires the scale to
be calculated elsewhere. This moves the codebase towards separation of
concerns for calculating the scale (via various scaling strategies),
separated from creating an instance of `Float8Tensor`.

Note that stateful delayed scaling is the reason we need this separation.

Test Plan:

```
./test/test_everything.sh
```

Reviewers:

Subscribers:

Tasks:

Tags:

[ghstack-poisoned]

float8_experimental/float8_tensor.py

@@ -207,7 +207,6 @@ def forward(
         tensor: torch.Tensor,
         scale: torch.Tensor,
         float8_dtype=e4m3_dtype,
-        # amax_buffer: Optional[torch.Tensor] = None,
         linear_mm_config: Optional[LinearMMConfig] = None,
         gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
     ):
@@ -216,11 +215,8 @@ def forward(
             tensor: the tensor to convert
             scale: the scale to use to convert the tensor
             float8_dtype: the float8 dtype either, torch.float8_e4m3fn or torch.float8_e5m2fn
-            amax_buffer: an Optional buffer buffer to store the amax value in prior to conversion
             emulate: whether to emulate the matmuls in fp32
         """
-        # if amax_buffer is not None:
-        #     amax_buffer.fill_(tensor_to_amax(tensor))
 
         return to_fp8_no_autograd(
             tensor,