Add quantized distributed layers

python/mlx/nn/layers/distributed.py

@@ -71,8 +71,10 @@ def __init__(
             )
 
     def _extra_repr(self) -> str:
+        out_dims, in_dims = self.weight.shape
         N = self.group.size()
-        return f"input_dims={self.weight.shape[1]}, output_dims={N * self.weight.shape[0]}, bias={'bias' in self}"
+        out_dims *= N
+        return f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self}"
 
     def __call__(self, x: mx.array) -> mx.array:
         # Aggregate the gradients coming from each shard
@@ -86,13 +88,35 @@ def __call__(self, x: mx.array) -> mx.array:
             x = x @ self["weight"].T
         return x
 
+    @classmethod
+    def from_linear(
+        cls, linear_layer: Module, group: Optional[mx.distributed.Group] = None
+    ):
+        group = group or mx.distributed.init()
+        N = group.size()
+        r = group.rank()
+        output_dims, input_dims = linear_layer.weight.shape
+        step = output_dims // N
+
+        sl = cls(input_dims, output_dims, False, group)
+        # The multiplication with 1.0 forces a copy, perhaps change to
+        # something better when available.
+        sl.weight = linear_layer.weight[r * step : (r + 1) * step] * 1
+        if "bias" in linear_layer:
+            sl.bias = linear_layer.bias[r * step : (r + 1) * step] * 1
+
+        return sl
+
 
 class ShardedToAllLinear(Module):
     """Each member of the group applies part of the affine transformation and
     then aggregates the results.
 
     All nodes will have the same exact result after this layer.
 
+    :class:`ShardedToAllLinear` provides a classmethod :meth:`from_linear` to
+    convert linear layers to sharded :obj:`ShardedToAllLinear` layers.
+
     Args:
         input_dims (int): The dimensionality of the input features
         output_dims (int): The dimensionality of the output features
@@ -136,7 +160,9 @@ def __init__(
 
     def _extra_repr(self) -> str:
         N = self.group.size()
-        return f"input_dims={N * self.weight.shape[1]}, output_dims={self.weight.shape[0]}, bias={'bias' in self}"
+        out_dims, in_dims = self.weight.shape
+        in_dims *= N
+        return f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self}"
 
     def __call__(self, x: mx.array) -> mx.array:
         if self.group.size() > 1:
@@ -154,3 +180,268 @@ def __call__(self, x: mx.array) -> mx.array:
             else:
                 x = x @ self["weight"].T
         return x
+
+    @classmethod
+    def from_linear(
+        cls, linear_layer: Module, group: Optional[mx.distributed.Group] = None
+    ):
+        group = group or mx.distributed.init()
+        N = group.size()
+        r = group.rank()
+        output_dims, input_dims = linear_layer.weight.shape
+        step = input_dims // N
+
+        sl = cls(input_dims, output_dims, False, group)
+        # The multiplication with 1.0 forces a copy, perhaps change to
+        # something better when available.
+        sl.weight = linear_layer.weight[:, r * step : (r + 1) * step] * 1
+        if "bias" in linear_layer:
+            sl.bias = linear_layer.bias
+
+        return sl
+
+
+class QuantizedAllToShardedLinear(Module):
+    """Each member of the group applies part of the affine transformation with
+    a quantized matrix such that the result is sharded across the group.
+
+    It is the quantized equivalent of :class:`mlx.nn.AllToShardedLinear`.
+    Similar to :class:`mlx.nn.QuantizedLinear` its parameters are frozen and
+    will not be included in any gradient computation.
+
+    Args:
+        input_dims (int): The dimensionality of the input features.
+        output_dims (int): The dimensionality of the output features.
+        bias (bool, optional): If set to ``False`` then the layer will not use
+            a bias. Default: ``True``.
+        group_size (int, optional): The group size to use for the quantized
+            weight. See :func:`~mlx.core.quantize`. Default: ``64``.
+        bits (int, optional): The bit width to use for the quantized weight.
+            See :func:`~mlx.core.quantize`. Default: ``4``.
+        group (mx.distributed.Group, optional): The sharding will happen across
+            this group. If not set then the global group is used. Default is
+            ``None``.
+    """
+
+    def __init__(
+        self,
+        input_dims: int,
+        output_dims: int,
+        bias: bool = True,
+        group_size: int = 64,
+        bits: int = 4,
+        group: Optional[mx.distributed.Group] = None,
+    ):
+        super().__init__()
+
+        # Quantization config
+        self.group_size = group_size
+        self.bits = bits
+
+        # Initialize the quantized weight
+        scale = math.sqrt(1.0 / input_dims)
+        self.group = group or mx.distributed.init()
+        N = self.group.size()
+
+        if (output_dims % N) != 0:
+            raise ValueError(
+                f"Cannot shard the output of size {output_dims} across {N} devices."
+            )
+
+        weight = mx.random.uniform(
+            low=-scale,
+            high=scale,
+            shape=(output_dims // N, input_dims),
+        )
+        self.weight, self.scales, self.biases = mx.quantize(weight, group_size, bits)
+
+        # And bias if needed
+        if bias:
+            self.bias = mx.zeros((output_dims // N,))
+
+        # Freeze this model's parameters
+        self.freeze()
+
+    def unfreeze(self, *args, **kwargs):
+        """Wrap unfreeze so that we unfreeze any layers we might contain but
+        our parameters will remain frozen."""
+        super().unfreeze(*args, **kwargs)
+        self.freeze(recurse=False)
+
+    def _extra_repr(self) -> str:
+        out_dims, in_dims = self.weight.shape
+        in_dims *= 32 // self.bits
+        out_dims *= self.group.size()
+        return (
+            f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self}, "
+            f"group_size={self.group_size}, bits={self.bits}"
+        )
+
+    def __call__(self, x: mx.array) -> mx.array:
+        # Aggregate the gradients coming from each shard
+        if self.group.size() > 1:
+            x = sum_gradients(self.group)(x)
+
+        x = mx.quantized_matmul(
+            x,
+            self["weight"],
+            scales=self["scales"],
+            biases=self["biases"],
+            transpose=True,
+            group_size=self.group_size,
+            bits=self.bits,
+        )
+        if "bias" in self:
+            x = x + self["bias"]
+        return x
+
+    @classmethod
+    def from_quantized_linear(
+        cls,
+        quantized_linear_layer: Module,
+        group: Optional[mx.distributed.Group] = None,
+    ):
+        group = group or mx.distributed.init()
+        N = group.size()
+        r = group.rank()
+        output_dims, input_dims = quantized_linear_layer.weight.shape
+        input_dims *= 32 // quantized_linear_layer.bits
+        step = output_dims // N
+
+        sl = cls(
+            input_dims,
+            output_dims,
+            False,
+            group_size=quantized_linear_layer.group_size,
+            bits=quantized_linear_layer.bits,
+            group=group,
+        )
+        sl.weight = quantized_linear_layer.weight[r : step : (r + 1) * step] * 1
+        sl.scales = quantized_linear_layer.scales[r : step : (r + 1) * step] * 1
+        sl.biases = quantized_linear_layer.biases[r : step : (r + 1) * step] * 1
+        if "bias" in quantized_linear_layer:
+            sl.bias = quantized_linear_layer.bias[r * step : (r + 1) * step] * 1
+
+        return sl
+
+
+class QuantizedShardedToAllLinear(Module):
+    """Each member of the group applies part of the affine transformation using
+    the quantized matrix and then aggregates the results.
+
+    All nodes will have the same exact result after this layer.
+
+    It is the quantized equivalent of :class:`mlx.nn.ShardedToAllLinear`.
+    Similar to :class:`mlx.nn.QuantizedLinear` its parameters are frozen and
+    will not be included in any gradient computation.
+
+    Args:
+        input_dims (int): The dimensionality of the input features.
+        output_dims (int): The dimensionality of the output features.
+        bias (bool, optional): If set to ``False`` then the layer will not use
+            a bias. Default: ``True``.
+        group_size (int, optional): The group size to use for the quantized
+            weight. See :func:`~mlx.core.quantize`. Default: ``64``.
+        bits (int, optional): The bit width to use for the quantized weight.
+            See :func:`~mlx.core.quantize`. Default: ``4``.
+        group (mx.distributed.Group, optional): The sharding will happen across
+            this group. If not set then the global group is used. Default is
+            ``None``.
+    """
+
+    def __init__(
+        self,
+        input_dims: int,
+        output_dims: int,
+        bias: bool = True,
+        group_size: int = 64,
+        bits: int = 4,
+        group: Optional[mx.distributed.Group] = None,
+    ):
+        super().__init__()
+
+        # Quantization config
+        self.group_size = group_size
+        self.bits = bits
+
+        # Initialize the quantized weight
+        scale = math.sqrt(1.0 / input_dims)
+        self.group = group or mx.distributed.init()
+        N = self.group.size()
+
+        if (input_dims % N) != 0:
+            raise ValueError(
+                f"The input of size {input_dims} cannot be sharded across {N} devices."
+            )
+
+        weight = mx.random.uniform(
+            low=-scale,
+            high=scale,
+            shape=(output_dims, input_dims // N),
+        )
+        self.weight, self.scales, self.biases = mx.quantize(weight, group_size, bits)
+
+        # And bias if needed
+        if bias:
+            self.bias = mx.zeros((output_dims,))
+
+        # Freeze this model's parameters
+        self.freeze()
+
+    def unfreeze(self, *args, **kwargs):
+        """Wrap unfreeze so that we unfreeze any layers we might contain but
+        our parameters will remain frozen."""
+        super().unfreeze(*args, **kwargs)
+        self.freeze(recurse=False)
+
+    def _extra_repr(self) -> str:
+        out_dims, in_dims = self.weight.shape
+        in_dims *= (32 // self.bits) * self.group.size()
+        return (
+            f"input_dims={in_dims}, output_dims={out_dims}, bias={'bias' in self}, "
+            f"group_size={self.group_size}, bits={self.bits}"
+        )
+
+    def __call__(self, x: mx.array) -> mx.array:
+        x = mx.quantized_matmul(
+            x,
+            self["weight"],
+            scales=self["scales"],
+            biases=self["biases"],
+            transpose=True,
+            group_size=self.group_size,
+            bits=self.bits,
+        )
+        if self.group.size() > 1:
+            x = mx.distributed.sum_all(x, group=group)
+        if "bias" in self:
+            x = x + self["bias"]
+        return x
+
+    @classmethod
+    def from_quantized_linear(
+        cls,
+        quantized_linear_layer: Module,
+        group: Optional[mx.distributed.Group] = None,
+    ):
+        group = group or mx.distributed.init()
+        N = group.size()
+        r = group.rank()
+        output_dims, input_dims = quantized_linear_layer.weight.shape
+        input_dims *= (32 // quantized_linear_layer.bits) * N
+
+        sl = cls(
+            input_dims,
+            output_dims,
+            False,
+            group_size=quantized_linear_layer.group_size,
+            bits=quantized_linear_layer.bits,
+            group=group,
+        )
+        sl.weight = quantized_linear_layer.weight[r : step : (r + 1) * step] * 1
+        sl.scales = quantized_linear_layer.scales[r : step : (r + 1) * step] * 1
+        sl.biases = quantized_linear_layer.biases[r : step : (r + 1) * step] * 1
+        if "bias" in quantized_linear_layer:
+            sl.bias = quantized_linear_layer.bias
+
+        return sl

python/mlx/nn/layers/quantized.py

@@ -197,7 +197,7 @@ def _extra_repr(self):
         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}"
         )