fixed inference slowness but it cant handle multible Token inputs and is generateing gibberish

llms/mlx_lm/models/cache.py

@@ -350,28 +350,11 @@ class MambaCache:
         return [self.cache[1]]
 
 
-
+class Mamba2Cache:
-class Mamba2Cache(_BaseCache):
+    def __init__(self):
-    def __init__(
+        self.conv_states = [None]  # Initialize as None, will be set on first use
-            self,
+        self.ssm_states = [None]   # Initialize as None, will be set on first use
-            batch_size,
-            conv_kernel
-        ):
-        self.conv_kernel: mx.array = conv_kernel
-        self.conv_states: mx.array = [None]
-        self.ssm_states = [None]
-        self.seqlen_offset = 0
         
-    def reset(self):
+    @property
-        self.conv_states = None
+    def state(self):
-        self.ssm_state = None
+        return [self.conv_states[0], self.ssm_states[0]]
-        
-    def update(self, layer_idx: int, new_conv_state: mx.array, cache_position: mx.array) -> mx.array:
-        conv_state = self.conv_states[layer_idx]
-        cache_position = cache_position.clamp(0, self.conv_kernel - 1)
-
-        conv_state = conv_state.roll(shifts=-1, dims=-1)
-        conv_state[:, :, cache_position] = new_conv_state
-        self.conv_states[layer_idx].zero_()
-        self.conv_states[layer_idx] += conv_state
-        return self.conv_states[layer_idx]

llms/mlx_lm/models/mamba2.py

@@ -1,11 +1,11 @@
 import math
 from dataclasses import dataclass, field
-from typing import Tuple, Union, Optional
+from typing import Tuple, Union
 import mlx.core as mx
 import mlx.nn as nn
 
 from .base import BaseModelArgs
-from .cache import MambaCache
+from .cache import Mamba2Cache
 
 @dataclass
 class ModelArgs(BaseModelArgs):
@@ -56,186 +56,217 @@ 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
-
+    
 
 def silu(x):
     return x * mx.sigmoid(x)
 
-
 def ssd(x, A, B, C, chunk_size):
-    # Not getting used
+    # 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"
+        
+        self.weight = mx.random.normal((in_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}"
+        
+        if cache 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)
+
+        outputs = []
+        for c in range(C):
+            # Input prep debug
+            x_c = x[:, :, c]
+            x_c = mx.expand_dims(x_c, axis=1)
+
+            # Weight prep debug
+            w_c = self.weight[c]
+            if w_c.ndim == 2:
+                w_c = mx.expand_dims(w_c, axis=0)
+            elif w_c.ndim == 1:
+                w_c = mx.expand_dims(mx.expand_dims(w_c, axis=0), axis=0)
+
+            y_c = mx.conv_general(
+                x_c,
+                w_c,
+                stride=1,
+                padding=0
+            )
+            if self.bias is not None:
+                y_c = y_c + self.bias[c]
+            
+            y_c = mx.squeeze(y_c, axis=1)
+            outputs.append(y_c)
+        
+        # Output statistics
+        y = mx.stack(outputs, axis=-1)
+
+        # Cache update debug
+        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
+        conv_dim = args.intermediate_size + 2 * args.state_size
-        
+        self.conv1d = DepthWiseConv1d(
-        # Replace DepthWiseConv1d with grouped nn.Conv1d
+            in_channels=conv_dim,
-        self.conv1d = nn.Conv1d(
+            out_channels=conv_dim,
-            in_channels=self.conv_dim,
-            out_channels=self.conv_dim,
             kernel_size=args.conv_kernel,
-            groups=self.conv_dim,  # Makes it depthwise
+            groups=conv_dim,
             bias=args.use_conv_bias,
-            padding=0  # We'll handle padding via cache
+            padding=args.conv_kernel - 1
         )
-        
+
-        self.dt_bias = mx.random.normal((args.num_heads,)) * args.initializer_range
+        # Initialize parameters
-        self.A_log = mx.random.normal((args.num_heads,)) * args.initializer_range
+        self.dt_bias = mx.ones(args.num_heads)
-        self.D = mx.random.normal((args.num_heads,)) * args.initializer_range
+        A = mx.arange(1, args.num_heads + 1)
-        
+        self.A_log = mx.log(A)
+        self.D = mx.ones(args.num_heads)
+
         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: Optional[MambaCache] = None):
+    def __call__(self, u: mx.array, cache=None):
-        batch_size, seq_len, _ = u.shape
+        # Expect input to be shape [batch_size, 1, dim]
-        pad_size = self.chunk_size - (seq_len % self.chunk_size)
+        batch_size, seq_len, dimension = u.shape
-        
+        assert seq_len == 1, "Input should be a single token"
+
         # Initialize cache if needed
-        if cache is None:
+        if cache.conv_states[0] is None:
-            cache = MambaCache()
+            conv_dim = self.args.intermediate_size + 2 * self.args.state_size
-            
+            cache.conv_states[0] = mx.zeros((batch_size, self.args.conv_kernel - 1, conv_dim))
-        # Initialize states if needed
+
-        if cache[0] is None:  # conv state
+        if cache.ssm_states[0] is None:
-            cache[0] = mx.zeros((
+            cache.ssm_states[0] = mx.zeros((
-                batch_size,
-                self.args.conv_kernel - 1,
-                self.conv_dim
-            ))
-            
-        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
+        n_heads = self.args.num_heads
         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):]
+        dt = zxbcdt[:, :, -(n_heads):]
-        
+
-        # Process delta time
+        # Time steps
-        dt = mx.reshape(dt, (batch_size, seq_len, self.args.num_heads))
+        dt = mx.reshape(dt, (batch_size, n_heads))
-        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.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)
-        
+
-        # Handle convolution caching and padding
+        # Convolution
-        conv_state = cache[0]
+        xBC = self.conv1d(xBC, cache=cache)
-        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
+        # Split states
         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, seq_len, self.args.num_heads, self.args.head_dim))
+        x = mx.reshape(x, (batch_size, 1, n_heads, self.args.head_dim))
-        
+        x = mx.squeeze(x, axis=1)
-        B = mx.reshape(B, (batch_size, seq_len, self.args.state_size))
+        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.broadcast_to(B, (batch_size, n_heads, self.args.state_size))
-        
+        B = mx.expand_dims(B, axis=2)
-        C = mx.reshape(C, (batch_size, seq_len, self.args.state_size))
+        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.broadcast_to(C, (batch_size, n_heads, self.args.state_size))
-        
+        C = mx.expand_dims(C, axis=3)
-        # SSM state update
+
-        ssm_state = cache[1]
+        # SSM updates
         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=-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(new_ssm_state, mx.expand_dims(C, axis=-1))
-        y = mx.squeeze(y, axis=-1)
-        
-        if pad_size > 0:
-            y = y[:, :seq_len, :, :]
-            
-        # Final reshape and projections
-        y = mx.reshape(y, (batch_size, seq_len, -1))
-        y = self.norm(y + z)
-        
-        return self.out_proj(y)
 
+        # Update state
+        x = mx.expand_dims(x, axis=3)
+        dBx = mx.matmul(x, B)
+        cache.ssm_states[0] = cache.ssm_states[0] * dA + dBx
+
+        # Compute output
+        y = mx.matmul(cache.ssm_states[0], C)
+        y = mx.squeeze(y, axis=-1)
+        y = y + x[:, :, :, 0] * mx.expand_dims(self.D, -1)
+        y = mx.reshape(y, (batch_size, 1, n_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
+        normed = self.norm(x)
-
+        output = self.mixer(normed, cache)
+        return output + x
 
 class Mamba2(nn.Module):
     def __init__(self, args: ModelArgs):
@@ -249,9 +280,11 @@ class Mamba2(nn.Module):
         x = self.embeddings(x)
         if cache is None:
             cache = [None] * len(self.layers)
+        
+        hidden = x
         for layer, c in zip(self.layers, cache):
-            x = layer(x, c)
+            hidden = layer(hidden, c)
-        return self.norm_f(x)
+        return self.norm_f(hidden)
 
 
 class Model(nn.Module):
@@ -259,33 +292,24 @@ class Model(nn.Module):
         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
+        hidden = self.backbone(inputs, cache)
-
+        
-        x = self.backbone(inputs, cache)
-
         if self.args.tie_word_embeddings:
-            logits = self.backbone.embeddings.as_linear(x)
+            logits = self.backbone.embeddings.as_linear(hidden)
         else:
-            logits = self.lm_head(x)
+            logits = self.lm_head(hidden)
-
+        
         return logits
 
-    def make_cache(self, batch_size=1):
+    def make_cache(self):
-        return [MambaCache() for _ in range(len(self.backbone.layers))]
+        return [Mamba2Cache() for _ in range(len(self.layers))]
-
-    def sanitize(self, weights):
-        for k, v in weights.items():
-            if "conv1d.weight" in k and v.shape[-1] != 1:
-                weights[k] = v.moveaxis(2, 1)
-        return weights
 
     @property
     def layers(self):
-        return self.backbone.layers
+        return self.backbone.layers