[6/x] clean up casting: rename delayed and dynamic casting functions

float8_experimental/float8_linear.py

@@ -18,8 +18,8 @@
 
 from float8_experimental.float8_scaling_utils import (
     _maybe_initialize_amaxes_scales_for_float8_cast,
-    cast_to_float8_delayed,
-    cast_to_float8_dynamic,
+    hp_tensor_to_float8_delayed,
+    hp_tensor_to_float8_dynamic,
     NoopFwToFloat8E5M2BwDelayed,
     NoopFwToFloat8E5M2BwDynamic,
 )
@@ -260,7 +260,7 @@ def cast_input_to_float8(
                 is_amax_initialized,
                 reduce_amax=True,
             )
-            input_fp8 = cast_to_float8_delayed(
+            input_fp8 = hp_tensor_to_float8_delayed(
                 input,
                 self.fp8_scale_input,
                 e4m3_dtype,
@@ -270,7 +270,9 @@ def cast_input_to_float8(
             )
         else:
             assert self.scaling_type_input is ScalingType.DYNAMIC
-            input_fp8 = cast_to_float8_dynamic(input, e4m3_dtype, self.linear_mm_config)
+            input_fp8 = hp_tensor_to_float8_dynamic(
+                input, e4m3_dtype, self.linear_mm_config
+            )
         return input_fp8
 
     def cast_weight_to_float8(
@@ -292,7 +294,7 @@ def cast_weight_to_float8(
                     reduce_amax=False,
                 )
 
-                weight_fp8 = cast_to_float8_delayed(
+                weight_fp8 = hp_tensor_to_float8_delayed(
                     weight,
                     self.fp8_scale_weight,
                     e4m3_dtype,
@@ -305,7 +307,7 @@ def cast_weight_to_float8(
             if isinstance(self.weight, Float8Tensor):  # cast by FSDP
                 weight_fp8 = self.weight
             else:
-                weight_fp8 = cast_to_float8_dynamic(
+                weight_fp8 = hp_tensor_to_float8_dynamic(
                     self.weight,
                     e4m3_dtype,
                     self.linear_mm_config,

float8_experimental/float8_scaling_utils.py

@@ -30,37 +30,66 @@
 )
 
 
-def cast_to_float8_dynamic(
-    inpt_tensor: torch.Tensor,
+def hp_tensor_to_float8_dynamic(
+    hp_tensor: torch.Tensor,
     float8_dtype: torch.dtype,
     linear_mm_config: LinearMMConfig,
     reduce_amax: bool = False,
     gemm_input_role: GemmInputRole = GemmInputRole.INPUT,
 ) -> Float8Tensor:
-    if tensor_already_casted_to_fp8(inpt_tensor):
-        return inpt_tensor
-    scale = tensor_to_scale(inpt_tensor, float8_dtype, reduce_amax)
+    """
+    Given a high precision tensor `hp_tensor`,
+    scales `hp_tensor` dynamically and returns a `Float8Tensor` of the result.
+
+    Args:
+        hp_tensor: the tensor to convert
+        float8_dtype: the float8 dtype to use
+        linear_mm_config: Defines the configuration for the scaled_mm for
+          the 3 fwd/bwd gemms of linear
+        reduce_amax: whether to reduce the max(abs(hp_tensor)) value across distributed ranks
+        gemm_input_role: Defines the role of this tensor (input, weight or grad_output) in
+          the 3 fwd/bwd gemms of linear
+    """
+    if tensor_already_casted_to_fp8(hp_tensor):
+        return hp_tensor
+    scale = tensor_to_scale(hp_tensor, float8_dtype, reduce_amax)
     return hp_tensor_and_scale_to_float8(
-        inpt_tensor,
+        hp_tensor,
         scale,
         float8_dtype,
         linear_mm_config,
         gemm_input_role,
     )
 
 
-def cast_to_float8_delayed(
-    tensor: torch.Tensor,
-    scale: torch.Tensor,
+def hp_tensor_to_float8_delayed(
+    hp_tensor: torch.Tensor,
+    s: torch.Tensor,
     float8_dtype: torch.dtype,
     amax_buffer: torch.Tensor,
     linear_mm_config: Optional[LinearMMConfig] = None,
     gemm_input_role: Optional[GemmInputRole] = GemmInputRole.INPUT,
-):
-    amax_buffer.fill_(tensor_to_amax(tensor))
+) -> Float8Tensor:
+    """
+    Given a high precision tensor `hp_tensor` and relevant metadata, scales it using
+    delayed scaling and returns a `Float8Tensor` of the result. Specifically:
+    1. calculates max(abs(hp_tensor)) and stores the result in `amax_buffer`, inplace
+    2. scales `hp_tensor` by `s` and returns the result wrapped in Float8Tensor
+
+    Args:
+        hp_tensor: the tensor to convert
+        s: the scale to use to convert the tensor
+        float8_dtype: the float8 dtype to use
+        amax_buffer: the buffer to modify inplace with max(abs(hp_tensor))
+        linear_mm_config: Defines the configuration for the scaled_mm for
+          the 3 fwd/bwd gemms of linear
+        gemm_input_role: Defines the role of this tensor (input, weight or grad_output) in
+          the 3 fwd/bwd gemms of linear
+    """
+    amax_buffer.fill_(tensor_to_amax(hp_tensor))
     return hp_tensor_and_scale_to_float8(
-        tensor,
-        scale,
+        hp_tensor,
+        s,
         float8_dtype,
         linear_mm_config,
         gemm_input_role,

float8_experimental/float8_tensor_parallel.py

@@ -2,7 +2,7 @@
 import torch.nn as nn
 from float8_experimental.config import ScalingType
 from float8_experimental.float8_scaling_utils import (
-    cast_to_float8_dynamic,
+    hp_tensor_to_float8_dynamic,
     NoopFwToFloat8E5M2BwDynamic,
 )
 from float8_experimental.float8_tensor import GemmInputRole
@@ -46,7 +46,7 @@ def _prepare_input_fn(
                 input_tensor, device_mesh, input_layouts, run_check=False
             )
 
-        input_tensor = cast_to_float8_dynamic(
+        input_tensor = hp_tensor_to_float8_dynamic(
             input_tensor,
             e4m3_dtype,
             mod.linear_mm_config,
@@ -100,7 +100,7 @@ def _prepare_input_fn(
                 input_tensor, device_mesh, input_layouts, run_check=False
             )
 
-        input_tensor = cast_to_float8_dynamic(
+        input_tensor = hp_tensor_to_float8_dynamic(
             input_tensor,
             e4m3_dtype,
             mod.linear_mm_config,
@@ -199,7 +199,7 @@ def _prepare_input_arg(self, input, mesh, input_layout, desired_layout):
                     input, mesh, (input_layout,), run_check=False
                 )
 
-            dt_inp = cast_to_float8_dynamic(
+            dt_inp = hp_tensor_to_float8_dynamic(
                 dt_inp,
                 e4m3_dtype,
                 self.linear_mm_config,

float8_experimental/fsdp_utils.py

@@ -11,8 +11,8 @@
 import torch.nn as nn
 import torch.utils._pytree as pytree
 from float8_experimental.float8_scaling_utils import (
-    cast_to_float8_delayed,
-    cast_to_float8_dynamic,
+    hp_tensor_to_float8_delayed,
+    hp_tensor_to_float8_dynamic,
 )
 
 from float8_experimental.float8_tensor import (
@@ -175,7 +175,7 @@ def fsdp_pre_all_gather(self, mesh):
                 GemmInputRole.WEIGHT,
             )
         else:
-            float8_tensor = cast_to_float8_dynamic(
+            float8_tensor = hp_tensor_to_float8_dynamic(
                 self._tensor,
                 e4m3_dtype,
                 self._linear_mm_config,
@@ -355,7 +355,7 @@ def fsdp_pre_all_gather(self, mesh):
             )
             self.is_amax_initialized = True
 
-        float8_tensor = cast_to_float8_delayed(
+        float8_tensor = hp_tensor_to_float8_delayed(
             self._tensor,
             self._scale_buffer,
             e4m3_dtype,

test/test_compile.py

@@ -20,7 +20,7 @@
     get_float8_layers,
     sync_float8_amax_and_scale_history,
 )
-from float8_experimental.float8_scaling_utils import cast_to_float8_delayed
+from float8_experimental.float8_scaling_utils import hp_tensor_to_float8_delayed
 from float8_experimental.float8_tensor import LinearMMConfig
 from float8_experimental.float8_utils import e4m3_dtype
 
@@ -179,7 +179,7 @@ def __init__(self, graph_break: bool):
             self.graph_break = graph_break
 
         def forward(self, x):
-            x_fp8 = cast_to_float8_delayed(
+            x_fp8 = hp_tensor_to_float8_delayed(
                 x,
                 self.fp8_scale_x,
                 e4m3_dtype,