let class predicate specify quantization parameters (#1638)

python/mlx/nn/layers/quantized.py

@@ -1,7 +1,7 @@
 # Copyright © 2023-2024 Apple Inc.
 
 import math
-from typing import Callable, Optional
+from typing import Callable, Optional, Union
 
 import mlx.core as mx
 from mlx.nn.layers.base import Module
@@ -12,7 +12,7 @@ def quantize(
     model: Module,
     group_size: int = 64,
     bits: int = 4,
-    class_predicate: Optional[Callable] = None,
+    class_predicate: Optional[Callable[[str, Module], Union[bool, dict]]] = None,
 ):
     """Quantize the sub-modules of a module according to a predicate.
 
@@ -27,17 +27,26 @@ def quantize(
         bits (int): The number of bits per parameter (see
            :func:`mlx.core.quantize`). Default: ``4``.
         class_predicate (Optional[Callable]): A callable which receives the
-          :obj:`Module` path and :obj:`Module` itself and returns ``True`` if
-          it should be quantized and ``False`` otherwise. If ``None``, then
-          all layers that define a ``to_quantized(group_size, bits)`` method
-          are quantized. Default: ``None``.
+          :obj:`Module` path and :obj:`Module` itself and returns ``True`` or a
+          dict of params for `to_quantized` if it should be quantized and
+          ``False`` otherwise. If ``None``, then all layers that define a
+          ``to_quantized(group_size, bits)`` method are quantized.
+          Default: ``None``.
     """
     class_predicate = class_predicate or (lambda _, m: hasattr(m, "to_quantized"))
 
     def _maybe_quantize(path, m):
-        if class_predicate(path, m):
+        if bool_or_params := class_predicate(path, m):
             if hasattr(m, "to_quantized"):
-                return m.to_quantized(group_size, bits)
+                if isinstance(bool_or_params, bool):
+                    return m.to_quantized(group_size=group_size, bits=bits)
+                elif isinstance(bool_or_params, dict):
+                    return m.to_quantized(**bool_or_params)
+                else:
+                    raise ValueError(
+                        "``class_predicate`` must return a bool"
+                        " or a dict of parameters to pass to ``to_quantized``"
+                    )
             else:
                 raise ValueError(f"Unable to quantize model of type {type(m)}")
         else:
@@ -197,7 +206,7 @@ class QuantizedLinear(Module):
         out_dims, in_dims = self.weight.shape
         in_dims *= 32 // self.bits
         return (
-            f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self},"
+            f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self}, "
             f"group_size={self.group_size}, bits={self.bits}"
         )