do less in the backwards

float8_experimental/float8_ops.py

@@ -8,7 +8,7 @@
 import torch
 
 from float8_experimental.float8_python_api import addmm_float8_unwrapped
-from float8_experimental.float8_tensor import Float8Tensor
+from float8_experimental.float8_tensor import Float8Tensor, re_construct_float8_weight
 from float8_experimental.float8_utils import is_row_major
 from torch.utils._pytree import tree_map
 
@@ -192,7 +192,7 @@ def forward(
             # This should be set to True when using traditional fsdp to avoid
             # saving the unsharded weight for backwards
             ctx.save_for_backward(
-                x_fp8, original_weight, weight_scale, weight_amax_buffer
+                x_fp8, original_weight, weight_scale
             )
         else:
             # Does this interact properly with activation checkpointing?
@@ -211,19 +211,13 @@ def forward(
     @staticmethod
     def backward(ctx, go_fp8: torch.Tensor):
         if ctx.recompute_float8_weight:
-            x_fp8, original_weight, weight_scale, weight_amax_buffer = ctx.saved_tensors
-            w_fp8 = Float8Tensor.to_float8(
-                original_weight,
-                weight_scale,
-                torch.float8_e4m3fn,
-                weight_amax_buffer,
-                emulate=ctx.emulate,
-            )
+            x_fp8, original_weight, weight_scale = ctx.saved_tensors
+            w_fp8 = re_construct_float8_weight(original_weight, weight_scale, torch.float8_e4m3fn, emulate=ctx.emulate)
         else:
             x_fp8, w_fp8 = ctx.saved_tensors
 
         # calculate dL/dX
-        go_fp8_reshaped = go_fp8.view(-1, go_fp8.size(-1))
+        go_fp8_reshaped = go_fp8.reshape(-1, go_fp8.size(-1))
         w_fp8_t_c_t = w_fp8.t().contiguous().t()
         dL_dX = float8_mm_helper(go_fp8_reshaped, w_fp8_t_c_t)
         dL_dX = dL_dX.view(*go_fp8.shape[:-1], dL_dX.size(-1))

float8_experimental/float8_tensor.py

@@ -42,6 +42,22 @@ def backward(ctx, g):
             return g, None, None, None, None
 
 
+@torch._dynamo.allow_in_graph
+def re_construct_float8_weight(tensor: torch.Tensor, scale: torch.Tensor, float8_dtype, emulate: bool = False):
+    """ In the backwards of float8_linear we don't need to fill the amax buffer
+    for the weight tensor since that was done during the forward and we just need to
+    recast the orignal precision tensor using the scale from the forward
+
+    Args:
+        tensor: the tensor to convert
+        scale: the scale to use to convert the tensor, from the forward
+        float8_dtype: the float8 dtype to use
+        emulate: if true using fp32 emulation for the matmuls, helpful
+            if you don't have access to h100 hardware.
+    """
+    tensor_scaled = tensor * scale
+    bits_fp8 = to_fp8_saturated(tensor_scaled, float8_dtype)
+    return Float8Tensor(bits_fp8, scale, tensor.dtype, emulate=emulate)
 @torch._dynamo.allow_in_graph
 class FromFloat8ConstrFunc(torch.autograd.Function):
     """