[bc-breaking] switch argument order to module_filter_fn

README.md

@@ -44,7 +44,7 @@ from float8_experimental.fsdp_utils import precompute_float8_dynamic_scale_for_f
 m = Model(...)
 
 # optional: filter modules from being eligible for float8 conversion
-def module_filter_fn(fqn: str, mod: torch.nn.Module):
+def module_filter_fn(mod: torch.nn.Module, fqn: str):
     # don't convert the output module
     if fqn == "output":
         return False
@@ -91,9 +91,9 @@ from float8_experimental.float8_linear import TensorScalingType
 # create model
 m = Model(...)
 
-# optional: configure for compatibility with FSDP. Note that workarounds 
+# optional: configure for compatibility with FSDP. Note that workarounds
 # gated with config.enable_amax_init and
-# config.enable_pre_and_post_forward are needed for 
+# config.enable_pre_and_post_forward are needed for
 # autocast + compile + FSDP + float8 to work
 from float8_experimental import Float8LinearConfig, TensorScalingType, Float8TensorCastConfig
 config = Float8LinearConfig(

float8_experimental/float8_linear_utils.py

@@ -60,7 +60,7 @@ def swap_linear_layers(
     module: nn.Module,
     from_float_func: Callable[[nn.Linear], nn.Linear],
     *,
-    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
+    module_filter_fn: Optional[Callable[[nn.Module, str], bool]] = None,
 ) -> Optional[nn.Module]:
     """
     Generic function to swap linear layers in a module with a new type of linear layer.
@@ -74,13 +74,13 @@ def swap_linear_layers(
         from_float_func: Function that accepts a linear layer and returns a new type of linear layer.
         module_filter_fn: If specified, only the `torch.nn.Linear` subclasses that
             that pass the filter function will be swapped. The inputs to the
-            filter function are the FQN and module instance.
+            filter function are the module instance, and the FQN.
 
     Returns:
      nn.Module: The modified module with swapped linear layers.
     """
     if isinstance(module, nn.Linear) and (
-        module_filter_fn is None or module_filter_fn("", module)
+        module_filter_fn is None or module_filter_fn(module, "")
     ):
         if len(list(module.children())) > 0:
             raise AssertionError(
@@ -109,9 +109,7 @@ def post_order_traversal(
             post_order_traversal(child_module, new_fqn, module)
 
         if isinstance(module, nn.Linear) and (
-            # linear_layer_filter is None or linear_layer_filter(module)
-            module_filter_fn is None
-            or module_filter_fn(cur_fqn, module)
+            module_filter_fn is None or module_filter_fn(module, cur_fqn)
         ):
             assert (
                 parent_module is not None
@@ -127,7 +125,7 @@ def post_order_traversal(
 def swap_linear_with_float8_linear(
     module: nn.Module,
     *,
-    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
+    module_filter_fn: Optional[Callable[[nn.Module, str], bool]] = None,
     config: Float8LinearConfig = None,
 ) -> Optional[nn.Module]:
     """
@@ -137,7 +135,7 @@ def swap_linear_with_float8_linear(
         module: Module to modify.
         module_filter_fn: If specified, only the `torch.nn.Linear` subclasses that
             that pass the filter function will be swapped. The inputs to the
-            filter function are the FQN and module instance.
+            filter function are the module instance and the FQN.
         config (Float8LinearConfig): configuration for conversion to float8
 
     Returns:

float8_experimental/inference.py

@@ -213,7 +213,7 @@ def quantize_to_float8(
     module: nn.Module,
     quant_config: QuantConfig,
     *,
-    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
+    module_filter_fn: Optional[Callable[[nn.Module, str], bool]] = None,
     use_fast_accum: bool = True,
 ) -> Optional[nn.Module]:
     """
@@ -228,7 +228,7 @@ def quantize_to_float8(
         quant_config (QuantConfig): Quantization configuration for Float8 conversion.
         module_filter_fn: If specified, only the `torch.nn.Linear` subclasses that
             that pass the filter function will be swapped. The inputs to the
-            filter function are the FQN and module instance.
+            filter function are the module instance and the FQN.
         use_fast_accum : Whether to enable fast accumulation for the Float8InferenceLinear. Defaults to True.
 
     Returns:

test/test_base.py

@@ -649,7 +649,7 @@ def __init__(self, dim: int):
 
         size_limit = 32
 
-        def module_filter_fn(fqn, mod):
+        def module_filter_fn(mod, fqn):
             return (
                 mod.in_features >= size_limit
                 and mod.out_features >= size_limit
@@ -682,7 +682,7 @@ def __init__(self, dim: int):
                 self.lin2 = nn.Linear(4 * dim, dim)
 
         model = nn.Sequential(MLP(3), nn.Linear(3, 3), MLP(3))
-        module_filter_fn = lambda fqn, mod: fqn not in [
+        module_filter_fn = lambda mod, fqn: fqn not in [
             "0.lin2",
             "2.lin1",
         ]