update trainer/lora.py and adding DepthWiseConv1d because mlx 0.18.0 doesnt axepts groups parameter

llms/mlx_lm/models/mamba2.py

@@ -74,6 +74,46 @@ class MambaRMSNormGated(nn.Module):
         variance = mx.mean(hidden_states ** 2, axis=-1, keepdims=True)
         hidden_states = hidden_states * mx.rsqrt(variance + self.variance_epsilon)
         return self.weight * hidden_states
+    
+
+class DepthWiseConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
+        super().__init__()
+        assert in_channels == out_channels, "For depthwise conv, in_channels must equal out_channels"
+        self.channels = in_channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+        
+        # For depthwise conv, we use groups equal to the number of channels
+        self.groups = self.channels if groups is None else groups
+        assert self.groups == self.channels, "For depthwise conv, groups must equal the number of channels"
+
+        # Weight shape: (channels, 1, kernel_size) for depthwise conv
+        self.weight = mx.random.normal((self.channels, 1, kernel_size))
+        self.bias = mx.zeros((self.channels,)) if bias else None
+
+    def __call__(self, x, cache=None):
+        B, L, C = x.shape
+        K = self.kernel_size
+
+        if cache is not None:
+            x = mx.concatenate([cache, x], axis=1)
+        else:
+            x = mx.pad(x, [(0, 0), (self.padding, 0), (0, 0)])
+
+        # Reshape for depthwise convolution
+        x = x.transpose(0, 2, 1)  # (B, C, L)
+        
+        # Perform depthwise convolution
+        y = mx.conv(x, self.weight, groups=self.groups)
+        
+        # Reshape back
+        y = y.transpose(0, 2, 1)  # (B, L, C)
+
+        if self.bias is not None:
+            y = y + self.bias
+
+        return y, x.transpose(0, 2, 1)[:, -K:, :]
 
 
 class Mamba2Mixer(nn.Module):
@@ -97,7 +137,7 @@ class Mamba2Mixer(nn.Module):
         )
 
         self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
-        self.conv1d = nn.Conv1d(
+        self.conv1d = DepthWiseConv1d(
             in_channels=self.conv_dim,
             out_channels=self.conv_dim,
             bias=args.use_conv_bias,

llms/mlx_lm/tuner/utils.py

@@ -143,6 +143,11 @@ def linear_to_lora_layers(
                 "mixer.out_proj",
             ]
         )
+    elif model.model_type == "mamba2":
+        keys = set(
+            [
+            ]
+        )
     else:
         raise ValueError(f"Lora does not support {model.model_type}")