fixing loading the model

llms/mamba2-130m-hf

@@ -0,0 +1 @@
+Subproject commit 05e8773fc4ac1cd067e8a18a5c45372ce5178405

llms/mlx_lm/models/mamba2 copy.py

@@ -0,0 +1,256 @@
+# Copyright © 2024 Apple Inc.
+
+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
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "mamba2"
+    num_heads: int = 128
+    head_dim: int = 64
+    vocab_size: int = 32768
+    hidden_size: int = 4096
+    state_size: int = 128
+    num_hidden_layers: int = 64
+    layer_norm_epsilon: float = 1e-5
+    pad_token_id: int = 1
+    bos_token_id: int = 0
+    eos_token_id: int = 2
+    expand: int = 2
+    conv_kernel: int = 4
+    n_groups: int = 8
+    use_bias: bool = False
+    use_conv_bias: bool = True
+    hidden_act: str = "silu"
+    initializer_range: float = 0.1
+    residual_in_fp32: bool = True
+    time_step_rank: Union[int, str] = "auto"
+    time_step_min: float = 0.001
+    time_step_max: float = 0.1
+    time_step_floor: float = 1e-4
+    time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
+    rescale_prenorm_residual: bool = False
+    use_cache: bool = True
+    rms_norm: bool = True
+    chunk_size: int = 256
+    tie_word_embeddings: bool = False
+
+    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 Mamba2Cache:
+    def __init__(self):
+        self.cache = [None, None]
+
+    def __setitem__(self, idx, value):
+        self.cache[idx] = value
+
+    def __getitem__(self, idx):
+        return self.cache[idx]
+
+    @property
+    def state(self):
+        return self.cache
+
+
+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
+
+class DepthWiseConv1d(nn.Module):
+    def __init__(self, channels, kernel_size, bias=True, groups=1, padding=0):
+        super().__init__()
+        self.channels = channels
+        self.kernel_size = kernel_size
+        self.padding = padding
+        self.groups = groups
+        self.weight = mx.random.normal((self.channels, kernel_size, 1))
+        self.bias = mx.zeros((channels,)) if bias else None
+
+    def __call__(self, x, cache=None):
+        B, L, C = x.shape
+        _, K, _ = self.weight.shape
+
+        if cache is not None:
+            x = mx.concatenate([cache, x], axis=1)
+        else:
+            x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
+
+        y = mx.conv_general(x, self.weight, groups=self.groups)
+
+        if self.bias is not None:
+            y = y + self.bias
+
+        return y, x[:, -K + 1 :, :]
+
+
+class Mamba2Mixer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.intermediate_size = args.intermediate_size
+        self.time_step_rank = args.time_step_rank
+        self.conv_kernel_size = args.conv_kernel
+        self.hidden_size = args.hidden_size
+        self.state_size = args.state_size
+        self.num_heads = args.num_heads
+        self.head_dim = args.head_dim
+        self.n_groups = args.n_groups
+
+        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
+        self.conv1d = DepthWiseConv1d(
+            channels=self.conv_dim,
+            kernel_size=self.conv_kernel_size,
+            bias=self.args.use_conv_bias,
+            groups=self.conv_dim,
+            padding=self.conv_kernel_size - 1,
+        )
+
+        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=args.use_bias
+        )
+
+        self.act = nn.SiLU()
+        self.dt_bias = mx.ones((self.num_heads,))
+        self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
+        self.D = mx.ones((self.num_heads,))
+
+        self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
+
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
+
+    def ssm_step(self, x, state=None):
+        A = -mx.exp(self.A_log)
+        D = self.D
+        deltaBC = self.x_proj(x)
+        delta, B, C = mx.split(
+            deltaBC,
+            indices_or_sections=[
+                self.time_step_rank,
+                self.time_step_rank + self.ssm_state_size,
+            ],
+            axis=-1,
+        )
+        delta = nn.softplus(self.dt_proj(delta))
+        new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
+        if state is not None:
+            new_state += state * mx.exp(mx.expand_dims(delta, -1) * A)
+        y = (new_state @ mx.expand_dims(C, -1)).squeeze(2)
+        y = y + D * x
+        return y, new_state
+
+    def __call__(self, x, cache):
+        B, T, D = x.shape
+        if cache is None:
+            cache = [None, None]
+
+        outputs = []
+        for t in range(T):
+            xt = x[:, t, :]
+            xz = self.in_proj(xt)
+            x_t, z_t = xz.split(indices_or_sections=2, axis=1)
+            conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
+            x_t = conv_out.squeeze(1)
+            x_t = nn.silu(x_t)
+            y_t, cache[1] = self.ssm_step(x_t, cache[1])
+            z_t = nn.silu(z_t)
+            output_t = y_t * z_t
+            output_t = self.out_proj(output_t)
+            outputs.append(output_t)
+        output = mx.stack(outputs, axis=1)
+        return output
+
+
+class Mamba2Block(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.mixer = Mamba2Mixer(args)
+        self.norm = nn.RMSNorm(args.hidden_size)
+
+    def __call__(self, x: mx.array, cache):
+        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 = [Mamba2Block(args) for idx in range(args.num_hidden_layers)]
+        self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None
+    ):
+        hidden_states = self.embeddings(inputs)
+        
+        if cache is None:
+            cache = Mamba2Cache(len(self.layers))
+
+        for i, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, cache[i])
+
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+
+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 sanitize(self, weights):
+        for k, v in weights.items():
+            if "conv1d.weight" in k and v.ndim == 3:
+                weights[k] = v.moveaxis(2, 1)
+        return weights
+
+    def make_cache(self, batch_size: int = 1):
+        return [Mamba2Cache() for _ in range(len(self.layers))]
+
+    @property
+    def layers(self):
+        return self.backbone.layers

llms/mlx_lm/models/mamba2-other.py

@@ -0,0 +1,275 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from dataclasses import dataclass, field
+from typing import Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "mamba2"
+    num_heads: int = 128
+    head_dim: int = 64
+    vocab_size: int = 32768
+    hidden_size: int = 4096
+    state_size: int = 128
+    num_hidden_layers: int = 64
+    layer_norm_epsilon: float = 1e-5
+    expand: int = 2
+    conv_kernel: int = 4
+    n_groups: int = 8
+    use_bias: bool = False
+    use_conv_bias: bool = True
+    initializer_range: float = 0.1
+    residual_in_fp32: bool = True
+    time_step_rank: Union[int, str] = "auto"
+    time_step_min: float = 0.001
+    time_step_max: float = 0.1
+    time_step_floor: float = 1e-4
+    time_step_limit: Tuple[float, float] = field(default_factory=lambda: (0.0, float("inf")))
+    rescale_prenorm_residual: bool = False
+    use_cache: bool = True
+    rms_norm: bool = True
+    chunk_size: int = 256
+    tie_word_embeddings: bool = False
+
+    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 Mamba2Cache:
+    def __init__(self, num_layers):
+        self.cache = [[None, None] for _ in range(num_layers)]
+
+    def __getitem__(self, idx):
+        return self.cache[idx]
+
+    def __setitem__(self, idx, value):
+        self.cache[idx] = value
+
+
+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
+
+
+class Mamba2Mixer(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.intermediate_size = args.intermediate_size
+        self.time_step_rank = args.time_step_rank
+        self.conv_kernel_size = args.conv_kernel
+        self.hidden_size = args.hidden_size
+        self.state_size = args.state_size
+        self.num_heads = args.num_heads
+        self.head_dim = args.head_dim
+        self.n_groups = args.n_groups
+
+        self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
+        self.conv1d = nn.Conv1d(
+            in_channels=self.conv_dim,
+            out_channels=self.conv_dim,
+            bias=args.use_conv_bias,
+            kernel_size=args.conv_kernel,
+            groups=self.conv_dim,
+            padding=args.conv_kernel - 1
+        )
+
+        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=args.use_bias
+        )
+
+        self.act = nn.SiLU()
+        self.dt_bias = mx.ones((self.num_heads,))
+        self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
+        self.D = mx.ones((self.num_heads,))
+
+        self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
+
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
+
+    def ssm_step(self, x, dt, state):
+        B, L, C = x.shape
+        print(f"x shape: {x.shape}")
+        projected_states = self.in_proj(x)
+        print(f"deltaBC shape: {projected_states.shape}")
+
+        d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
+
+        gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
+        conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
+        time_step = projected_states[:, :, -self.num_heads:]
+
+        print(f"conv_state shape before reshape: {conv_state.shape}")
+        print(f"self.conv_dim: {self.conv_dim}")
+        
+        # Reshape and handle the case where L=1
+        conv_state = conv_state.reshape(B, self.conv_dim, L)
+        if L == 1:
+            # If sequence length is 1, we need to pad to apply convolution
+            conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
+        
+        conv_out = self.conv1d(conv_state)
+        
+        # If we padded, we need to remove the padding
+        if L == 1:
+            conv_out = conv_out[:, :, :L]
+
+        # Reshape back to (B, L, C)
+        conv_out = conv_out.transpose(0, 2, 1)
+
+        x_and_conv_out, B, C = mx.split(
+            conv_out,
+            [self.intermediate_size, self.n_groups * self.state_size],
+            axis=-1
+        )
+
+        dt = nn.softplus(time_step + self.dt_bias)
+        dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
+
+        B = B.reshape(-1, self.num_heads, self.head_dim, self.state_size)
+        C = C.reshape(-1, self.num_heads, self.head_dim, self.state_size)
+
+        dA = mx.exp(dt[:, :, None, None] * A[None, :, None, None])
+        dB = dt[:, :, None, None] * B
+
+        new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
+        y = mx.sum(new_state * C, axis=-1)
+        y = y + C[None, :, None] * x_and_conv_out
+
+        y = self.norm(y.reshape(-1, self.intermediate_size), gate)
+        output = self.out_proj(y)
+
+        return output, new_state
+
+    def __call__(
+        self,
+        x: mx.array,
+        cache = None
+    ):
+        B, L, _ = x.shape
+
+        if cache[0] is not None:  # Using cached state
+            conv_state, ssm_state = cache
+            x = x[:, -1:]
+            output, new_ssm_state = self.ssm_step(x, None, ssm_state)
+            cache[1] = new_ssm_state  # Update SSM state in cache
+        else:
+            conv_state, ssm_state = None, None
+            outputs = []
+            for t in range(L):
+                x = x[:, t:t+1]
+                output, ssm_state = self.ssm_step(x, None, ssm_state)
+                outputs.append(output)
+            output = mx.concatenate(outputs, axis=1)
+            cache[1] = ssm_state  # Store final SSM state in cache
+
+        # Update conv state in cache
+        new_conv_state = x[:, -self.conv_kernel_size:]
+        cache[0] = new_conv_state
+        
+        return output
+
+
+class Mamba2Block(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.residual_in_fp32 = args.residual_in_fp32
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+        self.mixer = Mamba2Mixer(args)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        h = self.mixer(self.norm(inputs), cache=cache)
+        r = inputs + h
+        return r
+
+
+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 = [Mamba2Block(args) for idx in range(args.num_hidden_layers)]
+        self.norm_f = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None
+    ):
+        hidden_states = self.embeddings(inputs)
+        
+        if cache is None:
+            cache = Mamba2Cache(len(self.layers))
+
+        for i, layer in enumerate(self.layers):
+            hidden_states = layer(hidden_states, cache[i])
+
+        hidden_states = self.norm_f(hidden_states)
+        return hidden_states
+
+
+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 sanitize(self, weights):
+        for k, v in weights.items():
+            if "conv1d.weight" in k and v.ndim == 3:
+                weights[k] = v.moveaxis(2, 1)
+        return weights
+    
+    def make_cache(self, batch_size: int = 1):
+        return Mamba2Cache(len(self.backbone.layers))
+    
+    @property
+    def layers(self):
+        return self.backbone.layers

llms/mlx_lm/models/mamba2.py

@@ -2,7 +2,7 @@
 
 import math
 from dataclasses import dataclass, field
-from typing import Optional, Tuple, Union
+from typing import Tuple, Union
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -20,15 +20,11 @@ class ModelArgs(BaseModelArgs):
     state_size: int = 128
     num_hidden_layers: int = 64
     layer_norm_epsilon: float = 1e-5
-    pad_token_id: int = 1
-    bos_token_id: int = 0
-    eos_token_id: int = 2
     expand: int = 2
     conv_kernel: int = 4
     n_groups: int = 8
     use_bias: bool = False
     use_conv_bias: bool = True
-    hidden_act: str = "silu"
     initializer_range: float = 0.1
     residual_in_fp32: bool = True
     time_step_rank: Union[int, str] = "auto"
@@ -52,14 +48,18 @@ class ModelArgs(BaseModelArgs):
 
 
 class Mamba2Cache:
-    def __init__(self, num_layers):
+    def __init__(self):
-        self.cache = [[None, None] for _ in range(num_layers)]
+        self.cache = [None, None]
+
+    def __setitem__(self, idx, value):
+        self.cache[idx] = value
 
     def __getitem__(self, idx):
         return self.cache[idx]
 
-    def __setitem__(self, idx, value):
+    @property
-        self.cache[idx] = value
+    def state(self):
+        return self.cache
 
 
 class MambaRMSNormGated(nn.Module):
@@ -75,66 +75,53 @@ class MambaRMSNormGated(nn.Module):
         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.in_channels = in_channels
-        self.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
 
-        # For depthwise conv, we use groups equal to the number of channels
+        # Ensure in_channels and out_channels are the same for depthwise conv
-        self.groups = self.channels if groups is None else groups
+        assert in_channels == out_channels, "In and out channels must be the same for depthwise convolution"
-        assert self.groups == self.channels, "For depthwise conv, groups must equal the number of channels"
+        # Ensure groups is equal to in_channels for depthwise conv
+        assert self.groups == in_channels, "Groups must be equal to in_channels for depthwise convolution"
 
-        # Weight shape: (channels, 1, kernel_size) for depthwise conv
+        # Initialize weight with shape (out_channels, kernel_size, 1)
-        self.weight = mx.random.normal((self.channels, 1, kernel_size))
+        self.weight = mx.random.normal((out_channels, kernel_size, 1))
-        self.bias = mx.zeros((self.channels,)) if bias else None
+        self.bias = mx.zeros((out_channels,)) if bias else None
 
     def __call__(self, x, cache=None):
         B, L, C = x.shape
-        K = self.kernel_size
+        _, K, _ = self.weight.shape
 
         if cache is not None:
             x = mx.concatenate([cache, x], axis=1)
         else:
-            x = mx.pad(x, [(0, 0), (self.padding, 0), (0, 0)])
+            x = mx.pad(x, [(0, 0), (K - 1, 0), (0, 0)])
 
-        # Reshape for depthwise convolution
+        y = mx.conv_general(x, self.weight, groups=self.groups)
-        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:, :]
+        return y, x[:, -K + 1 :, :]
 
 
 class Mamba2Mixer(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
         self.args = args
-        self.hidden_size = args.hidden_size
         self.intermediate_size = args.intermediate_size
+        self.time_step_rank = args.time_step_rank
         self.conv_kernel_size = args.conv_kernel
+        self.hidden_size = args.hidden_size
         self.state_size = args.state_size
         self.num_heads = args.num_heads
         self.head_dim = args.head_dim
         self.n_groups = args.n_groups
-        self.time_step_rank = args.time_step_rank
-
-        projection_size = self.intermediate_size + self.intermediate_size + 2 * self.n_groups * self.state_size + self.num_heads
-        self.in_proj = nn.Linear(
-            self.hidden_size,
-            projection_size,
-            bias=args.use_bias
-        )
 
         self.conv_dim = self.intermediate_size + 2 * self.n_groups * self.state_size
         self.conv1d = DepthWiseConv1d(
@@ -143,32 +130,74 @@ class Mamba2Mixer(nn.Module):
             bias=args.use_conv_bias,
             kernel_size=args.conv_kernel,
             groups=self.conv_dim,
-            padding=args.conv_kernel - 1,
+            padding=args.conv_kernel - 1
+        )
+
+        projection_size = self.intermediate_size + self.conv_dim + self.num_heads
+        self.in_proj = nn.Linear(
+            self.hidden_size,
+            projection_size,
+            bias=args.use_bias
         )
 
         self.act = nn.SiLU()
         self.dt_bias = mx.ones((self.num_heads,))
-        self.A_log = mx.log(mx.arange(1, self.num_heads + 1, dtype=mx.float32))
+        self.A_log = mx.log(mx.arange(1, self.num_heads + 1))
         self.D = mx.ones((self.num_heads,))
 
-        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
         self.norm = MambaRMSNormGated(self.intermediate_size, eps=args.layer_norm_epsilon)
 
+        self.out_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=args.use_bias)
+
+    # def ssm_step(self, x, state=None):
+    #     A = -mx.exp(self.A_log)
+    #     D = self.D
+    #     deltaBC = self.x_proj(x)
+    #     delta, B, C = mx.split(
+    #         deltaBC,
+    #         indices_or_sections=[
+    #             self.time_step_rank,
+    #             self.time_step_rank + self.ssm_state_size,
+    #         ],
+    #         axis=-1,
+    #     )
+    #     delta = nn.softplus(self.dt_proj(delta))
+    #     new_state = mx.expand_dims(delta * x, -1) * mx.expand_dims(B, 1)
+    #     if state is not None:
+    #         new_state += state * mx.exp(mx.expand_dims(delta, -1) * A)
+    #     y = (new_state @ mx.expand_dims(C, -1)).squeeze(2)
+    #     y = y + D * x
+    #     return y, new_state
+
     def ssm_step(self, x, dt, state):
-        A = -mx.exp(self.A_log)
+        B, L, C = x.shape
-        D = self.D
+        print(f"x shape: {x.shape}")
+        projected_states = self.in_proj(x)
+        print(f"deltaBC shape: {projected_states.shape}")
 
-        deltaBC = self.in_proj(x)
+        d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 * self.n_groups * self.state_size - self.num_heads) // 2
-        gate, conv_state, time_step = mx.split(
+
-            deltaBC,
+        gate = projected_states[:, :, 2*d_mlp:2*d_mlp+self.intermediate_size]
-            [self.intermediate_size, self.intermediate_size + 2 * self.n_groups * self.state_size],
+        conv_state = projected_states[:, :, 2*d_mlp+self.intermediate_size:2*d_mlp+self.intermediate_size+self.conv_dim]
-            axis=-1
+        time_step = projected_states[:, :, -self.num_heads:]
-        )
+
+        print(f"conv_state shape before reshape: {conv_state.shape}")
+        print(f"self.conv_dim: {self.conv_dim}")
+        
+        # Reshape and handle the case where L=1
+        conv_state = conv_state.reshape(B, self.conv_dim, L)
+        if L == 1:
+            # If sequence length is 1, we need to pad to apply convolution
+            conv_state = mx.pad(conv_state, ((0, 0), (0, 0), (0, self.conv_kernel_size - 1)))
         
-        conv_state = conv_state.transpose(0, 2, 1)
         conv_out = self.conv1d(conv_state)
+        
+        # If we padded, we need to remove the padding
+        if L == 1:
+            conv_out = conv_out[:, :, :L]
+
+        # Reshape back to (B, L, C)
         conv_out = conv_out.transpose(0, 2, 1)
-        conv_out = self.act(conv_out)
 
         x_and_conv_out, B, C = mx.split(
             conv_out,
@@ -187,58 +216,47 @@ class Mamba2Mixer(nn.Module):
 
         new_state = state * dA + x_and_conv_out[:, :, None, None] * dB
         y = mx.sum(new_state * C, axis=-1)
-        y = y + D[None, :, None] * x_and_conv_out
+        y = y + C[None, :, None] * x_and_conv_out
 
         y = self.norm(y.reshape(-1, self.intermediate_size), gate)
         output = self.out_proj(y)
 
         return output, new_state
 
-    def __call__(
+    def __call__(self, x, cache):
-        self,
+        B, T, D = x.shape
-        x: mx.array,
+        if cache is None:
-        cache = None
+            cache = [None, None]
-    ):
-        B, L, _ = x.shape
 
-        if cache[0] is not None:  # Using cached state
-            conv_state, ssm_state = cache
-            x = x[:, -1:]
-            output, new_ssm_state = self.ssm_step(x, None, ssm_state)
-            cache[1] = new_ssm_state  # Update SSM state in cache
-        else:
-            conv_state, ssm_state = None, None
         outputs = []
-            for t in range(L):
+        for t in range(T):
-                x = x[:, t:t+1]
+            xt = x[:, t, :]
-                output, ssm_state = self.ssm_step(x, None, ssm_state)
+            xz = self.in_proj(xt)
-                outputs.append(output)
+            x_t, z_t = xz.split(indices_or_sections=2, axis=1)
-            output = mx.concatenate(outputs, axis=1)
-            cache[1] = ssm_state  # Store final SSM state in cache
 
-        # Update conv state in cache
+            if x_t.shape[-1] != self.conv_dim:
-        new_conv_state = x[:, -self.conv_kernel_size:]
+                raise ValueError(f"Expected conv input dim {self.conv_dim}, got {x_t.shape[-1]}")
-        cache[0] = new_conv_state
         
+            conv_out, cache[0] = self.conv1d(mx.expand_dims(x_t, 1), cache[0])
+            x_t = conv_out.squeeze(1)
+            x_t = nn.silu(x_t)
+            y_t, cache[1] = self.ssm_step(x_t, cache[1])
+            z_t = nn.silu(z_t)
+            output_t = y_t * z_t
+            output_t = self.out_proj(output_t)
+            outputs.append(output_t)
+        output = mx.stack(outputs, axis=1)
         return output
 
 
 class Mamba2Block(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
-        self.args = args
-        self.residual_in_fp32 = args.residual_in_fp32
-        self.norm = nn.RMSNorm(args.hidden_size, eps=args.layer_norm_epsilon)
         self.mixer = Mamba2Mixer(args)
+        self.norm = nn.RMSNorm(args.hidden_size)
 
-    def __call__(
+    def __call__(self, x: mx.array, cache):
-        self,
+        return self.mixer(self.norm(x), cache) + x
-        inputs: mx.array,
-        cache=None,
-    ):
-        h = self.mixer(self.norm(inputs), cache_params=cache)
-        r = inputs + h
-        return r
 
 
 class Mamba2(nn.Module):
@@ -275,11 +293,7 @@ class Model(nn.Module):
         if not args.tie_word_embeddings:
             self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
 
-    def __call__(
+    def __call__(self, inputs: mx.array, cache=None):
-        self,
-        inputs: mx.array,
-        cache=None
-    ):
         B, T = inputs.shape
 
         x = self.backbone(inputs, cache)
@@ -288,16 +302,17 @@ class Model(nn.Module):
             logits = self.backbone.embeddings.as_linear(x)
         else:
             logits = self.lm_head(x)
+
         return logits
 
-    def sanitize_mabey(self, weights):
+    def sanitize(self, weights):
         for k, v in weights.items():
             if "conv1d.weight" in k and v.ndim == 3:
                 weights[k] = v.moveaxis(2, 1)
         return weights
 
     def make_cache(self, batch_size: int = 1):
-        return Mamba2Cache(len(self.backbone.layers))
+        return [Mamba2Cache() for _ in range(len(self.layers))]
 
     @property
     def layers(self):