QQ linear

python/mlx/nn/layers/__init__.py

@@ -87,7 +87,12 @@
     MaxPool3d,
 )
 from mlx.nn.layers.positional_encoding import ALiBi, RoPE, SinusoidalPositionalEncoding
-from mlx.nn.layers.quantized import QuantizedEmbedding, QuantizedLinear, quantize
+from mlx.nn.layers.quantized import (
+    QQLinear,
+    QuantizedEmbedding,
+    QuantizedLinear,
+    quantize,
+)
 from mlx.nn.layers.recurrent import GRU, LSTM, RNN
 from mlx.nn.layers.transformer import (
     MultiHeadAttention,

python/mlx/nn/layers/base.py

@@ -559,6 +559,9 @@ def _unfreeze_impl(_, m):
             _unfreeze_impl("", self)
         return self
 
+    def _set_training_mode(self, mode: bool) -> None:
+        self._training = mode
+
     def train(self, mode: bool = True) -> Module:
         """Set the model in or out of training mode.
 
@@ -573,10 +576,8 @@ def train(self, mode: bool = True) -> Module:
             The module instance after updating the training mode.
         """
 
-        def _set_train(_, m):
-            m._training = mode
+        self.apply_to_modules(lambda _, m: m._set_training_mode(mode))
 
-        self.apply_to_modules(_set_train)
         return self
 
     def eval(self) -> Module:

python/mlx/nn/layers/quantized.py

@@ -277,3 +277,128 @@ def from_linear(
             ql.bias = linear_layer.bias
 
         return ql
+
+
+class QQLinear(Module):
+    """Quantizes the input and applies an affine transformation using quantized weights.
+
+    Two use cases are supported:
+
+    1) **Eval**:  The weights are frozen and stored in quantized form together with
+       their scales (``self.weight`` is quantized and ``self.scales`` is provided).
+    2) **Train**: The weights are stored in higher precision and are quantized on
+         the fly during computation so that gradients with respect to the weights
+         can be computed.
+
+    To switch between the two cases, use ``layer.eval()`` and ``layer.train()`` respectively.
+
+    Compared to the :class:`mlx.nn.QuantizedLinear` layer, this layer
+    quantizes the input as well and includes weights in gradient computations.
+
+    :obj:`QQLinear` also provides:
+     -  the class method :meth:`from_linear` to convert :class:`mlx.nn.Linear`
+     layers to :obj:`QQLinear` layers.
+
+    Note: This layer does not support a bias term yet.
+
+    Args:
+        input_dims (int): The dimensionality of the input features.
+        output_dims (int): The dimensionality of the output features.
+        group_size (Optional[int]): The group size to use for the quantized weight.
+            See :func:`~mlx.core.quantize`. Default: ``None``.
+        bits (Optional[int]): The bit width to use for the quantized weight.
+            See :func:`~mlx.core.quantize`. Default: ``None``.
+        mode (Optional[str]): The quantization method to use (see
+            :func:`mlx.core.quantize`). Currently, only ``"nvfp4"`` and ``"mxfp8"``
+            are supported. Default: ``"nvfp4"``.
+    """
+
+    def __init__(
+        self,
+        input_dims: int,
+        output_dims: int,
+        group_size: int = None,
+        bits: int = None,
+        mode: str = "nvfp4",
+    ):
+        super().__init__()
+
+        # Quantization config
+        self.group_size, self.bits = _defaults_for_mode(mode, group_size, bits)
+        self.mode = mode
+
+        scale = math.sqrt(1 / input_dims)
+        self.weight = mx.random.uniform(
+            low=-scale,
+            high=scale,
+            shape=(output_dims, input_dims),
+        )
+        self._quantized = False
+
+    def _extra_repr(self):
+        out_dims, in_dims = self.weight.shape
+        if self.weight.dtype == mx.uint32:
+            in_dims *= 32 // self.bits
+        return (
+            f"input_dims={in_dims}, output_dims={out_dims}, "
+            f"group_size={self.group_size}, bits={self.bits}, mode={self.mode}"
+        )
+
+    def quantize(self):
+        if not self._quantized:
+            self.weight, self.scales = mx.quantize(
+                self.weight,
+                self.group_size,
+                self.bits,
+                mode=self.mode,
+            )
+            self._quantized = True
+
+    def dequantize(self):
+        if self._quantized:
+            self.weight = mx.dequantize(
+                self.weight,
+                scales=self.scales,
+                group_size=self.group_size,
+                bits=self.bits,
+                mode=self.mode,
+            )
+            self.__delattr__("scales")
+            self._quantized = False
+
+    def _set_training_mode(self, mode: bool):
+        super()._set_training_mode(mode)
+
+        if self._training:
+            self.dequantize()
+        else:
+            self.quantize()
+
+    def __call__(self, x):
+        x = mx.qqmm(
+            x,
+            self["weight"],
+            scales=self.get("scales"),
+            group_size=self.group_size,
+            bits=self.bits,
+            mode=self.mode,
+        )
+        return x
+
+    @classmethod
+    def from_linear(
+        cls,
+        linear_layer: Module,
+        group_size: int = None,
+        bits: int = None,
+        mode: str = "nvfp4",
+    ):
+        """Create a :obj:`QQLinear` layer from a :obj:`Linear` layer."""
+        output_dims, input_dims = linear_layer.weight.shape  # (N,K)
+        if linear_layer.get("bias") is not None:
+            raise NotImplementedError("QQLinear does not support bias yet.")
+        ql = cls(input_dims, output_dims, group_size, bits, mode=mode)
+        ql.weight = linear_layer.weight
+        ql.train(linear_layer.training)
+
+        return ql