Added lora support for Phi-2 (#302)

* Added lora support for Phi-2

* Added Phi-2 support in fuse and convert

* format + readme

---------

Co-authored-by: Awni Hannun <awni@apple.com>

lora/models/lora.py

@@ -0,0 +1,86 @@
+import math
+
+import mlx.core as mx
+import mlx.nn as nn
+
+
+class LoRALinear(nn.Module):
+    @staticmethod
+    def from_linear(linear: nn.Linear, rank: int = 8):
+        # TODO remove when input_dims and output_dims are attributes
+        # on linear and quantized linear
+        output_dims, input_dims = linear.weight.shape
+        if isinstance(linear, nn.QuantizedLinear):
+            input_dims *= 32 // linear.bits
+        lora_lin = LoRALinear(input_dims, output_dims, rank)
+        lora_lin.linear = linear
+        return lora_lin
+
+    def to_linear(self):
+        linear = self.linear
+        bias = "bias" in linear
+        weight = linear.weight
+        is_quantized = isinstance(linear, nn.QuantizedLinear)
+
+        # Use the same type as the linear weight if not quantized
+        dtype = weight.dtype
+
+        if is_quantized:
+            dtype = mx.float16
+            weight = mx.dequantize(
+                weight,
+                linear.scales,
+                linear.biases,
+                linear.group_size,
+                linear.bits,
+            )
+        output_dims, input_dims = weight.shape
+        fused_linear = nn.Linear(input_dims, output_dims, bias=bias)
+
+        lora_b = (self.scale * self.lora_b.T).astype(dtype)
+        lora_a = self.lora_a.T.astype(dtype)
+        fused_linear.weight = weight + lora_b @ lora_a
+        if bias:
+            fused_linear.bias = linear.bias
+
+        if is_quantized:
+            fused_linear = nn.QuantizedLinear.from_linear(
+                fused_linear,
+                linear.group_size,
+                linear.bits,
+            )
+
+        return fused_linear
+
+    def __init__(
+        self,
+        input_dims: int,
+        output_dims: int,
+        lora_rank: int = 8,
+        bias: bool = False,
+        scale: float = 20.0,
+    ):
+        super().__init__()
+
+        # Regular linear layer weights
+        self.linear = nn.Linear(input_dims, output_dims, bias=bias)
+
+        # Scale for low-rank update
+        self.scale = scale
+
+        # Low rank lora weights
+        scale = 1 / math.sqrt(input_dims)
+        self.lora_a = mx.random.uniform(
+            low=-scale,
+            high=scale,
+            shape=(input_dims, lora_rank),
+        )
+        self.lora_b = mx.zeros(shape=(lora_rank, output_dims))
+
+    def __call__(self, x):
+        dtype = self.linear.weight.dtype
+        if isinstance(self.linear, nn.QuantizedLinear):
+            dtype = self.linear.scales.dtype
+        y = self.linear(x.astype(dtype))
+        z = (x @ self.lora_a) @ self.lora_b
+        return y + self.scale * z