[cleanup][4/x] unify weight casting

Summary:

Not ready for review yet, performance regression because tensorwise
abs+max and weight casting is happening twice between fwd and bwd.
Limitation of something in PT2 stack?

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 27996f8401a77ca2fc5fdf1bb2b200d3b9fd41a7
ghstack-comment-id: 2568319095
Pull Request resolved: #1481

torchao/float8/float8_linear.py

@@ -36,25 +36,30 @@ def _get_weight_scale(
     if tensor_already_casted_to_fp8(weight):
         return None
     assert scaling_type_weight is ScalingType.DYNAMIC
-    return tensor_to_scale(weight, config.cast_config_weight.target_dtype)
+    # inductor kernels for tensorwise max are faster when `weight` is 
+    # contiguous.
+    # context: https://github.com/pytorch/pytorch/issues/144431
+    weight_t = weight.t()
+    assert weight_t.is_contiguous()
+    return tensor_to_scale(weight_t, config.cast_config_weight.target_dtype)
 
 
-def _cast_weight_to_float8_t(
+def _cast_weight_to_float8(
     weight: torch.Tensor,
     config: Float8LinearConfig,
     linear_mm_config: LinearMMConfig,
     weight_scale: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
     if tensor_already_casted_to_fp8(weight):
-        return weight.t()
+        return weight
     weight_fp8 = hp_tensor_and_scale_to_float8(
         weight,
         weight_scale,
         config.cast_config_weight.target_dtype,
         linear_mm_config,
         gemm_input_role=GemmInputRole.WEIGHT,
     )
-    return weight_fp8.t()
+    return weight_fp8
 
 
 @torch._dynamo.allow_in_graph
@@ -103,16 +108,43 @@ def forward(
         elif c.cast_config_weight.scaling_type is ScalingType.DISABLED:
             weight_maybe_fp8_t = weight_hp_t
         else:
-            weight_maybe_fp8_t = hp_tensor_to_float8_dynamic(
-                weight_hp_t,
-                c.cast_config_weight.target_dtype,
-                linear_mm_config,
-                gemm_input_role=GemmInputRole.WEIGHT,
-                scaling_granularity=c.cast_config_weight.scaling_granularity,
-                axiswise_dim=get_maybe_axiswise_dim(
-                    0, c.cast_config_weight.scaling_granularity
-                ),
-            )
+            # non-axiswise
+            if (
+                config.cast_config_weight.scaling_granularity
+                is ScalingGranularity.TENSORWISE
+            ):
+                # If force_recompute_fp8_weight_in_bwd, we only recompute the fp8 weight,
+                # weight_scale should be saved.
+                weight_scale = _get_weight_scale(
+                    weight_hp_t, config.cast_config_weight.scaling_type, config
+                )
+
+                if config.force_recompute_fp8_weight_in_bwd:
+                    weight_maybe_fp8_t = checkpoint.checkpoint(
+                        _cast_weight_to_float8,
+                        weight_hp_t,
+                        config,
+                        linear_mm_config,
+                        weight_scale,
+                    )
+                else:
+                    weight_maybe_fp8_t = _cast_weight_to_float8(
+                        weight_hp_t,
+                        config,
+                        linear_mm_config,
+                        weight_scale,
+                    )
+            else:
+                weight_maybe_fp8_t = hp_tensor_to_float8_dynamic(
+                    weight_hp_t,
+                    c.cast_config_weight.target_dtype,
+                    linear_mm_config,
+                    gemm_input_role=GemmInputRole.WEIGHT,
+                    scaling_granularity=c.cast_config_weight.scaling_granularity,
+                    axiswise_dim=get_maybe_axiswise_dim(
+                        0, c.cast_config_weight.scaling_granularity
+                    ),
+                )
 
         # the reshapes are needed in order to make the shapes compatible with
         # torch.mm
@@ -310,7 +342,7 @@ def forward(self, input: torch.Tensor) -> torch.Tensor:
 
         # TODO(future PR): check for axiswise scaling for input, weight,
         # grad_output separately instead of together
-        if not has_any_axiswise_scaling:
+        if not has_any_axiswise_scaling and False:
             # If force_recompute_fp8_weight_in_bwd, we only recompute the fp8 weight,
             # weight_scale should be saved.
             weight_scale = _get_weight_scale(