[bc breaking] unify filtering functions

README.md

@@ -43,8 +43,19 @@ from float8_experimental.fsdp_utils import precompute_float8_dynamic_scale_for_f
 # create model
 m = Model(...)
 
+# optional: filter modules from being eligible for float8 conversion
+def module_filter_fn(fqn: str, mod: torch.nn.Module):
+    # don't convert the output module
+    if fqn == "output":
+        return False
+    # don't convert linear modules with weight dimensions not divisible by 16
+    if isinstance(mod, torch.nn.Linear):
+        if mod.in_features % 16 != 0 or mod.out_features % 16 != 0:
+            return False
+    return True
+
 # convert all `torch.nn.Linear` modules to `Float8Linear`
-swap_linear_with_float8_linear(m)
+swap_linear_with_float8_linear(m, module_filter_fn=module_filter_fn)
 
 # optional: use FSDP
 model = FSDP(model, use_orig_params=True)

float8_experimental/float8_linear_utils.py

@@ -59,26 +59,11 @@ def _update_history_stack(
     amax_history_stack.copy_(new_amax_history_stack)
 
 
-def filter_out_small_unaligned_layers(size_limit: int) -> Callable[[nn.Linear], bool]:
-    """
-    Returns a callable that filters out small (dimensions less than the given `size_limit`)
-        and unaligned (dimenstions not divisible by 16) layers.
-    It can be passed as the `linear_layer_filter` argument to `swap_linear_with_float8_linear`.
-    """
-    return (
-        lambda linear_layer: linear_layer.in_features >= size_limit
-        and linear_layer.out_features >= size_limit
-        and linear_layer.in_features % 16 == 0
-        and linear_layer.out_features % 16 == 0
-    )
-
-
 def swap_linear_layers(
     module: nn.Module,
     from_float_func: Callable[[nn.Linear], nn.Linear],
     *,
-    skip_fqn_list: Optional[List[str]] = None,
-    linear_layer_filter: Optional[Callable[[nn.Linear], bool]] = None,
+    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
 ) -> Optional[nn.Module]:
     """
     Generic function to swap linear layers in a module with a new type of linear layer.
@@ -90,18 +75,15 @@ def swap_linear_layers(
     Args:
         module: Module to modify.
         from_float_func: Function that accepts a linear layer and returns a new type of linear layer.
-        skip_fqn_list: If specified, a list of module FQNs to skip.
-        linear_layer_filter: If specified, only the linear layers
-            that pass the filter function will be swapped.
-        from_float_kwargs: Additional keyword arguments for from_float_func.
+        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.
 
     Returns:
      nn.Module: The modified module with swapped linear layers.
     """
-    module_names_to_skip = set(skip_fqn_list or [])
-
     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("", module)
     ):
         if len(list(module.children())) > 0:
             raise AssertionError(
@@ -112,43 +94,44 @@ def swap_linear_layers(
         )
 
     root_module = module
-    visited_modules = {root_module}
-
-    for module_name, module in root_module.named_modules():
-        if module_name in module_names_to_skip:
-            visited_modules.add(module)
 
     def post_order_traversal(
-        module: nn.Module, module_name: str, parent_module: Optional[nn.Module]
+        module: nn.Module,
+        cur_fqn: Optional[str] = None,
+        parent_module: Optional[nn.Module] = None,
     ):
-        nonlocal visited_modules
+        if cur_fqn is None:
+            cur_fqn = ""
+
         for child_module_name, child_module in module.named_children():
-            if child_module not in visited_modules:
-                visited_modules.add(child_module)
-                post_order_traversal(child_module, child_module_name, module)
+            if cur_fqn == "":
+                new_fqn = child_module_name
+            else:
+                new_fqn = f"{cur_fqn}.{child_module_name}"
+
+            post_order_traversal(child_module, new_fqn, module)
 
         if isinstance(module, nn.Linear) and (
-            linear_layer_filter is None or linear_layer_filter(module)
+            # linear_layer_filter is None or linear_layer_filter(module)
+            module_filter_fn is None
+            or module_filter_fn(cur_fqn, module)
         ):
             assert (
                 parent_module is not None
             ), f"Linear root module should return early: {module}"
             new_linear_module = from_float_func(module)
-            setattr(parent_module, module_name, new_linear_module)
+            cur_module_name = cur_fqn.split(".")[-1]
+            setattr(parent_module, cur_module_name, new_linear_module)
 
-    post_order_traversal(root_module, "", None)
-    # Without this explicit `del`, this set only gets deleted upon an explicit
-    # garbage collection (not from when its refcount hits zero)
-    del visited_modules
+    post_order_traversal(root_module)
     return root_module
 
 
 def swap_linear_with_float8_linear(
     module: nn.Module,
     *,
-    skip_fqn_list: Optional[List[str]] = None,
     emulate: bool = False,
-    linear_layer_filter: Optional[Callable[[nn.Linear], bool]] = None,
+    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
     scaling_type_x: TensorScalingType = TensorScalingType.DYNAMIC,
     scaling_type_w: TensorScalingType = TensorScalingType.DYNAMIC,
     scaling_type_dL_dY: TensorScalingType = TensorScalingType.DYNAMIC,
@@ -158,10 +141,10 @@ def swap_linear_with_float8_linear(
 
     Args:
         module: Module to modify.
-        skip_fqn_list: If specified, a list of module FQNs to skip.
         emulate: If True, emulation is used instead of hardware accelerated gemm
-        linear_layer_filter: If specified, only the linear layers
-            that pass the filter function will be swapped.
+        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.
         scaling_type_x (TensorScalingType): scaling type for `x`
         scaling_type_w (TensorScalingType): scaling type for `w`
         scaling_type_dL_dY (TensorScalingType): scaling type for `dL_dY`
@@ -179,8 +162,7 @@ def swap_linear_with_float8_linear(
     return swap_linear_layers(
         module,
         from_float,
-        skip_fqn_list=skip_fqn_list,
-        linear_layer_filter=linear_layer_filter,
+        module_filter_fn=module_filter_fn,
     )
 
 

float8_experimental/inference.py

@@ -10,7 +10,7 @@
 from dataclasses import dataclass
 
 from enum import auto, Enum
-from typing import List, Optional
+from typing import Callable, List, Optional
 
 import float8_experimental.config as config
 
@@ -209,7 +209,7 @@ def quantize_to_float8(
     module: nn.Module,
     quant_config: QuantConfig,
     *,
-    skip_fqn_list: Optional[List[str]] = None,
+    module_filter_fn: Optional[Callable[[str, nn.Module], bool]] = None,
     use_fast_accum: bool = True,
 ) -> Optional[nn.Module]:
     """
@@ -222,7 +222,9 @@ def quantize_to_float8(
     Args:
         module (nn.Module): The module to modify.
         quant_config (QuantConfig): Quantization configuration for Float8 conversion.
-        skip_fqn_list (List[str], optional): List of module FQNs to skip during 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.
         use_fast_accum : Whether to enable fast accumulation for the Float8InferenceLinear. Defaults to True.
 
     Returns:
@@ -234,5 +236,5 @@ def quantize_to_float8(
     return swap_linear_layers(
         module,
         lambda m: Float8InferenceLinear.from_float(m, quant_config, use_fast_accum),
-        skip_fqn_list=skip_fqn_list,
+        module_filter_fn=module_filter_fn,
     )

test/test_base.py

@@ -18,7 +18,6 @@
 
 from float8_experimental.float8_linear import Float8Linear, TensorScalingType
 from float8_experimental.float8_linear_utils import (
-    filter_out_small_unaligned_layers,
     linear_requires_sync,
     swap_linear_with_float8_linear,
     sync_float8_amax_and_scale_history,
@@ -631,24 +630,34 @@ def __init__(self, dim: int):
                 self.lin1 = nn.Linear(dim, 4 * dim)
                 self.lin2 = nn.Linear(4 * dim, 4 * dim)
 
-        for emulate in [True, False]:
-            model = nn.Sequential(MLP(8), nn.Linear(32, 32), MLP(40))
-            # filter out the linear layers whose shape is smaller than 32 or non-divisible by 16.
-            model = swap_linear_with_float8_linear(
-                model,
-                emulate=emulate,
-                linear_layer_filter=filter_out_small_unaligned_layers(32),
+        model = nn.Sequential(MLP(8), nn.Linear(32, 32), MLP(40))
+        # filter out the linear layers whose shape is smaller than 32 or non-divisible by 16.
+
+        size_limit = 32
+
+        def module_filter_fn(fqn, mod):
+            return (
+                mod.in_features >= size_limit
+                and mod.out_features >= size_limit
+                and mod.in_features % 16 == 0
+                and mod.out_features % 16 == 0
             )
-            # in_features=8, out_features=32, 8 is less than 32.
-            self.assertNotIsInstance(model[0].lin1, Float8Linear)
-            # in_features=32, out_features=32,
-            self.assertIsInstance(model[0].lin2, Float8Linear)
-            # in_features=32, out_features=32,
-            self.assertIsInstance(model[1], Float8Linear)
-            # in_features=40, out_features=160, 40 is not divisible by 16.
-            self.assertNotIsInstance(model[2].lin1, Float8Linear)
-            # in_features=160, out_features=160,
-            self.assertIsInstance(model[2].lin2, Float8Linear)
+
+        model = swap_linear_with_float8_linear(
+            model,
+            emulate=True,
+            module_filter_fn=module_filter_fn,
+        )
+        # in_features=8, out_features=32, 8 is less than 32.
+        self.assertNotIsInstance(model[0].lin1, Float8Linear)
+        # in_features=32, out_features=32,
+        self.assertIsInstance(model[0].lin2, Float8Linear)
+        # in_features=32, out_features=32,
+        self.assertIsInstance(model[1], Float8Linear)
+        # in_features=40, out_features=160, 40 is not divisible by 16.
+        self.assertNotIsInstance(model[2].lin1, Float8Linear)
+        # in_features=160, out_features=160,
+        self.assertIsInstance(model[2].lin2, Float8Linear)
 
     def test_swap_submodule_linears_with_skip(self):
         class MLP(nn.Module):
@@ -657,20 +666,21 @@ def __init__(self, dim: int):
                 self.lin1 = nn.Linear(dim, 4 * dim)
                 self.lin2 = nn.Linear(4 * dim, dim)
 
-        for emulate in [True, False]:
-            model = nn.Sequential(MLP(3), nn.Linear(3, 3), MLP(3))
-            skip_fqn_list = ["2", "0.lin2"]
-            model = swap_linear_with_float8_linear(
-                model, emulate=emulate, skip_fqn_list=skip_fqn_list
-            )
-            self.assertIsInstance(model[0].lin1, Float8Linear)
-            self.assertNotIsInstance(model[0].lin2, Float8Linear)
-            self.assertIsInstance(model[0].lin2, nn.Linear)
-            self.assertIsInstance(model[1], Float8Linear)
-            self.assertNotIsInstance(model[2].lin2, Float8Linear)
-            self.assertNotIsInstance(model[2].lin2, Float8Linear)
-            self.assertIsInstance(model[2].lin1, nn.Linear)
-            self.assertIsInstance(model[2].lin2, nn.Linear)
+        model = nn.Sequential(MLP(3), nn.Linear(3, 3), MLP(3))
+        module_filter_fn = lambda fqn, mod: fqn not in [
+            "0.lin2",
+            "2.lin1",
+        ]
+        model = swap_linear_with_float8_linear(
+            model,
+            emulate=True,
+            module_filter_fn=module_filter_fn,
+        )
+        self.assertTrue(type(model[0].lin1) is Float8Linear)
+        self.assertTrue(type(model[0].lin2) is nn.Linear)
+        self.assertTrue(type(model[1]) is Float8Linear)
+        self.assertTrue(type(model[2].lin1) is nn.Linear)
+        self.assertTrue(type(model[2].lin2) is Float8Linear)
 
     def test_fp8_tensor_statistics(self):
         hp_dtypes = (torch.float32, torch.float16, torch.bfloat16)