save checkpoint

llms/mlx_lm/models/cache.py

@@ -321,25 +321,34 @@ class RotatingKVCache(_BaseCache):
         return n
 
 
-class MambaCache(_BaseCache):
+class MambaCache:
     def __init__(self):
+        # cache[0] is conv state, cache[1] is ssm state
         self.cache = [None, None]
         self.offset = 0
-
+        
     def __setitem__(self, idx, value):
         self.cache[idx] = value
-
+        
     def __getitem__(self, idx):
         return self.cache[idx]
-
+        
     @property
     def state(self):
         return self.cache
-
+        
     @state.setter
     def state(self, v):
         self.cache = v
 
+    @property
+    def conv_states(self):
+        return [self.cache[0]]
+    
+    @property
+    def ssm_states(self):
+        return [self.cache[1]]
+
 
 
 class Mamba2Cache(_BaseCache):

llms/mlx_lm/models/mamba2.py

@@ -1,11 +1,11 @@
 import math
 from dataclasses import dataclass, field
-from typing import Tuple, Union
+from typing import Tuple, Union, Optional
 import mlx.core as mx
 import mlx.nn as nn
 
 from .base import BaseModelArgs
-from .cache import Mamba2Cache
+from .cache import MambaCache
 
 @dataclass
 class ModelArgs(BaseModelArgs):
@@ -61,8 +61,9 @@ class MambaRMSNormGated(nn.Module):
 def silu(x):
     return x * mx.sigmoid(x)
 
+
 def ssd(x, A, B, C, chunk_size):
-    # Replace einsum operations with explicit reshape and matrix multiply
+    # Not getting used
     batch, seqlen, nheads, dim = x.shape
     B = mx.expand_dims(B, axis=2)
     C = mx.expand_dims(C, axis=2)
@@ -91,179 +92,134 @@ def ssd(x, A, B, C, chunk_size):
     return mx.concatenate(outputs, axis=1), state
 
 
-class DepthWiseConv1d(nn.Module):
-    def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
-        super().__init__()
-        self.in_channels = in_channels
-        self.out_channels = out_channels
-        self.kernel_size = kernel_size
-        self.padding = padding
-        self.groups = groups if groups is not None else in_channels
-
-        assert in_channels == out_channels, "In and out channels must be same for depthwise convolution"
-        assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
-
-        # Initialize weight with correct shape [C_out, 1, kernel_size]
-        self.weight = mx.random.normal((out_channels, 1, kernel_size))
-        self.bias = mx.zeros((out_channels,)) if bias else None
-
-    def __call__(self, x: mx.array, cache=None) -> mx.array:
-        B, L, C = x.shape
-        K = self.kernel_size
-
-        assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
-
-        # Handle caching for sequential processing
-        if cache is not None and cache.conv_states[0] is not None:
-            if isinstance(cache.conv_states[0], type(None)):
-                cache.conv_states[0] = mx.zeros((B, K-1, C))
-            x = mx.concatenate([cache.conv_states[0], x], axis=1)
-
-        # Process each channel independently
-        outputs = []
-        for c in range(C):
-            # Extract and reshape the channel
-            x_c = x[:, :, c]  # [B, L]
-            x_c = mx.expand_dims(x_c, axis=1)  # [B, 1, L]
-
-            # Get weight for this channel - already in correct shape [1, 1, K]
-            w_c = mx.expand_dims(self.weight[c], axis=0)  # Ensure [1, 1, K]
-
-            # Apply convolution
-            y_c = mx.conv_general(
-                x_c,
-                w_c,
-                stride=1,
-                padding=self.padding
-            )
-
-            if self.bias is not None:
-                y_c = y_c + self.bias[c]
-
-            outputs.append(mx.squeeze(y_c, axis=1))
-
-        y = mx.stack(outputs, axis=-1)
-
-        # Update cache
-        if cache is not None:
-            cache.conv_states[0] = x[:, -K+1:, :] if x.shape[1] >= K else x
-
-        return y
-
-
 class Mamba2Block(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
         self.args = args
-
         self.chunk_size = args.chunk_size
-
+        
         d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
         self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
-
+        
         self.conv_dim = args.intermediate_size + 2 * args.state_size
-        self.conv1d = DepthWiseConv1d(
+        
+        # Replace DepthWiseConv1d with grouped nn.Conv1d
+        self.conv1d = nn.Conv1d(
             in_channels=self.conv_dim,
             out_channels=self.conv_dim,
             kernel_size=args.conv_kernel,
-            groups=self.conv_dim,
+            groups=self.conv_dim,  # Makes it depthwise
             bias=args.use_conv_bias,
-            padding=args.conv_kernel - 1
+            padding=0  # We'll handle padding via cache
         )
-
+        
         self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
         self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
         self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
-
+        
         self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
         self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
-
+        
         if args.rescale_prenorm_residual:
             layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
             self.out_proj.weight = self.out_proj.weight * layer_scale
-
-    def __call__(self, u: mx.array, cache=None):
-        # Expect input shape: [batch_size, 1, hidden_size]
+            
+    def __call__(self, u: mx.array, cache: Optional[MambaCache] = None):
         batch_size, seq_len, _ = u.shape
         pad_size = self.chunk_size - (seq_len % self.chunk_size)
-
+        
+        # Initialize cache if needed
+        if cache is None:
+            cache = MambaCache()
+            
         # Initialize states if needed
-        if cache.conv_states[0] is None:
-            cache.conv_states[0] = mx.zeros((
+        if cache[0] is None:  # conv state
+            cache[0] = mx.zeros((
                 batch_size,
                 self.args.conv_kernel - 1,
                 self.conv_dim
             ))
-
-        if cache.ssm_states[0] is None:
-            cache.ssm_states[0] = mx.zeros((
+            
+        if cache[1] is None:  # ssm state
+            cache[1] = mx.zeros((
                 batch_size,
                 self.args.num_heads,
                 self.args.head_dim,
                 self.args.state_size
             ))
-
+            
         # Project input
         zxbcdt = self.in_proj(u)
-
+        
         # Split projections
         z = zxbcdt[:, :, :self.args.intermediate_size]
         xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
         dt = zxbcdt[:, :, -(self.args.num_heads):]
-
+        
         # Process delta time
         dt = mx.reshape(dt, (batch_size, seq_len, self.args.num_heads))
-        dt = mx.squeeze(dt, axis=0)  # Remove sequence dimension for single token
+        dt = mx.squeeze(dt, axis=0)
         dt = mx.clip(
             nn.softplus(dt + self.dt_bias),
             self.args.time_step_min,
             self.args.time_step_max
         )
         dt = mx.maximum(dt, self.args.time_step_floor)
-
-        # Convolution step
-        xBC = self.conv1d(xBC, cache=cache)
+        
+        # Handle convolution caching and padding
+        conv_state = cache[0]
+        if conv_state is not None:
+            xBC = mx.concatenate([conv_state, xBC], axis=1)
+        
+        # Prepare input for conv1d: [B, C, L]
+        xBC = mx.transpose(xBC, [0, 2, 1])
+        
+        # Apply convolution
+        xBC = self.conv1d(xBC)
+        
+        # Update cache state
+        cache[0] = mx.transpose(xBC, [0, 2, 1])[:, -self.args.conv_kernel+1:, :]
+        
+        # Return to [B, L, C] format
+        xBC = mx.transpose(xBC, [0, 2, 1])
         xBC = silu(xBC)
-
+        
         # Split conv output
         x = xBC[:, :, :self.args.intermediate_size]
         B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
         C = xBC[:, :, -self.args.state_size:]
-
+        
         # Reshape for SSM
-        x = mx.reshape(x, (batch_size, 1, self.args.num_heads, self.args.head_dim))
-        x = mx.squeeze(x, axis=1)
-
-        B = mx.reshape(B, (batch_size, 1, self.args.state_size))
+        x = mx.reshape(x, (batch_size, seq_len, self.args.num_heads, self.args.head_dim))
+        
+        B = mx.reshape(B, (batch_size, seq_len, self.args.state_size))
         B = mx.broadcast_to(B, (batch_size, self.args.num_heads, self.args.state_size))
-        B = mx.expand_dims(B, axis=2)
-
-        C = mx.reshape(C, (batch_size, 1, self.args.state_size))
+        
+        C = mx.reshape(C, (batch_size, seq_len, self.args.state_size))
         C = mx.broadcast_to(C, (batch_size, self.args.num_heads, self.args.state_size))
-        C = mx.expand_dims(C, axis=3)
-
+        
         # SSM state update
+        ssm_state = cache[1]
         A = -mx.exp(self.A_log)
         dA = mx.exp(dt * mx.expand_dims(A, 0))
-        dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
-
-        x = mx.expand_dims(x, axis=3)
-        dBx = mx.matmul(x, B)
-
-        cache.ssm_states[0] = cache.ssm_states[0] * dA + dBx
-
+        
+        x = mx.expand_dims(x, axis=-1)
+        dBx = mx.matmul(x, mx.expand_dims(B, axis=-2))
+        
+        new_ssm_state = ssm_state * mx.expand_dims(dA, -1) + dBx
+        cache[1] = new_ssm_state
+        
         # Output computation
-        y = mx.matmul(cache.ssm_states[0], C)
+        y = mx.matmul(new_ssm_state, mx.expand_dims(C, axis=-1))
         y = mx.squeeze(y, axis=-1)
-
-        # y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
+        
         if pad_size > 0:
             y = y[:, :seq_len, :, :]
-
+            
         # Final reshape and projections
-        y = mx.reshape(y, (batch_size, 1, self.args.num_heads * self.args.head_dim))
+        y = mx.reshape(y, (batch_size, seq_len, -1))
         y = self.norm(y + z)
-
+        
         return self.out_proj(y)
 
 
@@ -322,21 +278,13 @@ class Model(nn.Module):
         return logits
 
     def make_cache(self, batch_size=1):
-        return [Mamba2Cache(batch_size, self.args.conv_kernel) for _ in range(len(self.layers))]
+        return [MambaCache() for _ in range(len(self.backbone.layers))]
 
     def sanitize(self, weights):
-        sanitized = {}
         for k, v in weights.items():
-            if "conv1d.weight" in k:
-                # Ensure weights are in correct shape (channels, 1, kernel_size)
-                if v.ndim == 2:
-                    v = mx.expand_dims(v, axis=1)
-                elif v.ndim == 1:
-                    v = mx.expand_dims(mx.expand_dims(v, axis=0), axis=0)
-                sanitized[k] = v
-            else:
-                sanitized[k] = v
-        return sanitized
+            if "conv1d.weight" in k and v.shape[-1] != 1:
+                weights[k] = v.moveaxis(2, 1)
+        return weights
 
     @property
     def layers(self):

llms/mlx_lm/models/s.py

@@ -0,0 +1,343 @@
+import math
+from dataclasses import dataclass, field
+from typing import Tuple, Union
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+from .cache import Mamba2Cache
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    num_heads: int
+    head_dim: int
+    vocab_size: int
+    hidden_size: int
+    state_size: int
+    num_hidden_layers: int
+    layer_norm_epsilon: float
+    expand: int
+    conv_kernel: int
+    n_groups: int
+    use_bias: bool
+    use_conv_bias: bool
+    initializer_range: float
+    residual_in_fp32: bool
+    time_step_min: float
+    time_step_max: float
+    time_step_floor: float
+    rescale_prenorm_residual: bool
+    rms_norm: bool
+    chunk_size: int
+    tie_word_embeddings: bool
+    use_cache: bool = True
+    time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
+    time_step_rank: Union[int, str] = "auto"
+    model_type: str = "mamba2"
+
+    def __post_init__(self):
+        if not hasattr(self, "intermediate_size"):
+            self.intermediate_size = int(self.expand * self.hidden_size)
+        if not hasattr(self, "head_dim"):
+            self.head_dim = self.hidden_size // self.num_heads
+        if self.time_step_rank == "auto":
+            self.time_step_rank = math.ceil(self.hidden_size / 16)
+
+
+class MambaRMSNormGated(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        super().__init__()
+        self.weight = mx.ones((hidden_size,))
+        self.variance_epsilon = eps
+
+    def __call__(self, hidden_states, gate=None):
+        if gate is not None:
+            hidden_states = hidden_states * nn.silu(gate)
+        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
+
+
+def silu(x):
+    return x * mx.sigmoid(x)
+
+def ssd(x, A, B, C, chunk_size):
+    # Replace einsum operations with explicit reshape and matrix multiply
+    batch, seqlen, nheads, dim = x.shape
+    B = mx.expand_dims(B, axis=2)
+    C = mx.expand_dims(C, axis=2)
+
+    state = mx.zeros((batch, nheads, dim, B.shape[-1]))
+    outputs = []
+
+    for i in range(0, seqlen, chunk_size):
+        chunk = slice(i, min(i + chunk_size, seqlen))
+        dA = mx.exp(mx.expand_dims(A[chunk], axis=0))
+
+        # Replace einsum with explicit operations
+        x_chunk = x[:, chunk]  # [batch, chunk_size, nheads, dim]
+        x_chunk = mx.transpose(x_chunk, [0, 2, 3, 1])  # [batch, nheads, dim, chunk_size]
+        B_chunk = B[:, chunk]  # [batch, chunk_size, state_size]
+        dBx = mx.matmul(x_chunk, B_chunk)  # [batch, nheads, dim, state_size]
+
+        state = state * mx.expand_dims(dA, axis=-1) + dBx
+
+        # Replace einsum with explicit operations
+        C_chunk = C[:, chunk]  # [batch, chunk_size, state_size]
+        y = mx.matmul(state, mx.transpose(C_chunk, [0, 2, 1]))  # [batch, nheads, dim, chunk_size]
+        y = mx.transpose(y, [0, 3, 1, 2])  # [batch, chunk_size, nheads, dim]
+        outputs.append(y)
+
+    return mx.concatenate(outputs, axis=1), state
+
+
+class DepthWiseConv1d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, bias=True, groups=None, padding=0):
+        super().__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+        self.groups = groups if groups is not None else in_channels
+
+        assert in_channels == out_channels, "In and out channels must be same for depthwise convolution"
+        assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
+
+        # Initialize weight with correct shape [C_out, 1, kernel_size]
+        self.weight = mx.random.normal((out_channels, 1, kernel_size))
+        self.bias = mx.zeros((out_channels,)) if bias else None
+
+    def __call__(self, x: mx.array, cache=None) -> mx.array:
+        B, L, C = x.shape
+        K = self.kernel_size
+
+        assert C == self.in_channels, f"Input channels {C} doesn't match expected {self.in_channels}"
+
+        # Handle caching for sequential processing
+        if cache is not None and cache.conv_states[0] is not None:
+            if isinstance(cache.conv_states[0], type(None)):
+                cache.conv_states[0] = mx.zeros((B, K-1, C))
+            x = mx.concatenate([cache.conv_states[0], x], axis=1)
+
+        # Process each channel independently
+        outputs = []
+        for c in range(C):
+            # Extract and reshape the channel
+            x_c = x[:, :, c]  # [B, L]
+            x_c = mx.expand_dims(x_c, axis=1)  # [B, 1, L]
+
+            # Get weight for this channel - already in correct shape [1, 1, K]
+            w_c = mx.expand_dims(self.weight[c], axis=0)  # Ensure [1, 1, K]
+
+            # Apply convolution
+            y_c = mx.conv_general(
+                x_c,
+                w_c,
+                stride=1,
+                padding=self.padding
+            )
+
+            if self.bias is not None:
+                y_c = y_c + self.bias[c]
+
+            outputs.append(mx.squeeze(y_c, axis=1))
+
+        y = mx.stack(outputs, axis=-1)
+
+        # Update cache
+        if cache is not None:
+            cache.conv_states[0] = x[:, -K+1:, :] if x.shape[1] >= K else x
+
+        return y
+
+
+class Mamba2Block(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+
+        self.chunk_size = args.chunk_size
+
+        d_in_proj = 2 * args.intermediate_size + 2 * args.state_size + args.num_heads
+        self.in_proj = nn.Linear(args.hidden_size, d_in_proj, bias=args.use_bias)
+
+        self.conv_dim = args.intermediate_size + 2 * args.state_size
+        self.conv1d = DepthWiseConv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            kernel_size=args.conv_kernel,
+            groups=self.conv_dim,
+            bias=args.use_conv_bias,
+            padding=args.conv_kernel - 1
+        )
+
+        self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
+        self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
+        self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
+
+        self.norm = MambaRMSNormGated(args.intermediate_size, eps=args.layer_norm_epsilon)
+        self.out_proj = nn.Linear(args.intermediate_size, args.hidden_size, bias=args.use_bias)
+
+        if args.rescale_prenorm_residual:
+            layer_scale = math.sqrt(1.0 / args.num_hidden_layers)
+            self.out_proj.weight = self.out_proj.weight * layer_scale
+
+    def __call__(self, u: mx.array, cache=None):
+        # Expect input shape: [batch_size, 1, hidden_size]
+        batch_size, seq_len, _ = u.shape
+        pad_size = self.chunk_size - (seq_len % self.chunk_size)
+
+        # Initialize states if needed
+        if cache.conv_states[0] is None:
+            cache.conv_states[0] = mx.zeros((
+                batch_size,
+                self.args.conv_kernel - 1,
+                self.conv_dim
+            ))
+
+        if cache.ssm_states[0] is None:
+            cache.ssm_states[0] = mx.zeros((
+                batch_size,
+                self.args.num_heads,
+                self.args.head_dim,
+                self.args.state_size
+            ))
+
+        # Project input
+        zxbcdt = self.in_proj(u)
+
+        # Split projections
+        z = zxbcdt[:, :, :self.args.intermediate_size]
+        xBC = zxbcdt[:, :, self.args.intermediate_size:self.args.intermediate_size + 2*self.args.state_size + self.args.intermediate_size]
+        dt = zxbcdt[:, :, -(self.args.num_heads):]
+
+        # Process delta time
+        dt = mx.reshape(dt, (batch_size, seq_len, self.args.num_heads))
+        dt = mx.squeeze(dt, axis=0)  # Remove sequence dimension for single token
+        dt = mx.clip(
+            nn.softplus(dt + self.dt_bias),
+            self.args.time_step_min,
+            self.args.time_step_max
+        )
+        dt = mx.maximum(dt, self.args.time_step_floor)
+
+        # Convolution step
+        xBC = self.conv1d(xBC, cache=cache)
+        xBC = silu(xBC)
+
+        # Split conv output
+        x = xBC[:, :, :self.args.intermediate_size]
+        B = xBC[:, :, self.args.intermediate_size:self.args.intermediate_size + self.args.state_size]
+        C = xBC[:, :, -self.args.state_size:]
+
+        # Reshape for SSM
+        x = mx.reshape(x, (batch_size, 1, self.args.num_heads, self.args.head_dim))
+        x = mx.squeeze(x, axis=1)
+
+        B = mx.reshape(B, (batch_size, 1, self.args.state_size))
+        B = mx.broadcast_to(B, (batch_size, self.args.num_heads, self.args.state_size))
+        B = mx.expand_dims(B, axis=2)
+
+        C = mx.reshape(C, (batch_size, 1, self.args.state_size))
+        C = mx.broadcast_to(C, (batch_size, self.args.num_heads, self.args.state_size))
+        C = mx.expand_dims(C, axis=3)
+
+        # SSM state update
+        A = -mx.exp(self.A_log)
+        dA = mx.exp(dt * mx.expand_dims(A, 0))
+        dA = mx.expand_dims(mx.expand_dims(dA, -1), -1)
+
+        x = mx.expand_dims(x, axis=3)
+        dBx = mx.matmul(x, B)
+
+        cache.ssm_states[0] = cache.ssm_states[0] * dA + dBx
+
+        # Output computation
+        y = mx.matmul(cache.ssm_states[0], C)
+        y = mx.squeeze(y, axis=-1)
+
+        # y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
+        if pad_size > 0:
+            y = y[:, :seq_len, :, :]
+
+        # Final reshape and projections
+        y = mx.reshape(y, (batch_size, 1, self.args.num_heads * self.args.head_dim))
+        y = self.norm(y + z)
+
+        return self.out_proj(y)
+
+
+class ResidualBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.residual_in_fp32 = args.residual_in_fp32
+
+        self.mixer = Mamba2Block(args)
+        self.norm = nn.RMSNorm(args.hidden_size)
+
+    def __call__(self, x: mx.array, cache):
+        if self.residual_in_fp32:
+            x = x.astype(mx.float32)
+        return self.mixer(self.norm(x), cache) + x
+
+
+class Mamba2(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.embeddings = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [ResidualBlock(args) for _ in range(args.num_hidden_layers)]
+        self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+
+    def __call__(self, x: mx.array, cache):
+        x = self.embeddings(x)
+        if cache is None:
+            cache = [None] * len(self.layers)
+        for layer, c in zip(self.layers, cache):
+            x = layer(x, c)
+        return self.norm_f(x)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+
+        self.backbone = Mamba2(args)
+
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(self, inputs: mx.array, cache=None):
+        B, T = inputs.shape
+
+        x = self.backbone(inputs, cache)
+
+        if self.args.tie_word_embeddings:
+            logits = self.backbone.embeddings.as_linear(x)
+        else:
+            logits = self.lm_head(x)
+
+        return logits
+
+    def make_cache(self, batch_size=1):
+        return [Mamba2Cache(batch_size, self.args.conv_kernel) for _ in range(len(self.layers))]
+
+    def sanitize(self, weights):
+        sanitized = {}
+        for k, v in weights.items():
+            if "conv1d.weight" in k:
+                # Ensure weights are in correct shape (channels, 1, kernel_size)
+                if v.ndim == 2:
+                    v = mx.expand_dims(v, axis=1)
+                elif v.ndim == 1:
+                    v = mx.expand_dims(mx.expand_dims(v, axis=0), axis=0)
+                sanitized[k] = v
+            else:
+                sanitized[k] = v
+        return sanitized
+
+    @property
+    def layers(self):
+        return self.backbone.layers