save push

llms/mlx_lm/models/mamba2.py

@@ -1,7 +1,6 @@
 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
 
@@ -28,18 +27,17 @@ class ModelArgs(BaseModelArgs):
     time_step_max: float
     time_step_floor: float
     rescale_prenorm_residual: bool
-    use_cache: bool
     rms_norm: bool
     chunk_size: int
     tie_word_embeddings: bool
-    intermediate_size: int = None
+    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):
-        self.intermediate_size = int(self.expand * self.hidden_size) # E*D = ED
-
+        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":
@@ -49,256 +47,241 @@ class ModelArgs(BaseModelArgs):
 class MambaRMSNormGated(nn.Module):
     def __init__(self, hidden_size, eps=1e-6):
         super().__init__()
-        self.weight = mx.ones(hidden_size)
+        self.weight = mx.ones((hidden_size,))
         self.variance_epsilon = eps
 
-    def forward(self, hidden_states, gate=None):
-        input_dtype = hidden_states.dtype
-        hidden_states = hidden_states.to(mx.float32)
-
+    def __call__(self, hidden_states, gate=None):
         if gate is not None:
-            hidden_states = hidden_states * nn.functional.silu(gate.to(mx.float32))
-        variance = hidden_states.pow(2).mean(-1, keepdim=True)
-        hidden_states = hidden_states * math.rsqrt(variance + self.variance_epsilon)
-        return self.weight * hidden_states.to(input_dtype)
-    
+            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 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__()
-        # Model dimensions
-        self.hidden_size = args.hidden_size
-        self.num_heads = args.num_heads
-        self.head_dim = args.head_dim
-        self.ssm_state_size = args.state_size
-        self.n_groups = args.n_groups
-        self.intermediate_size = int(args.expand * args.hidden_size)
-        
-        # Convolution parameters
-        self.conv_kernel = args.conv_kernel
-        self.use_conv_bias = args.use_conv_bias
-        
-        # Time step parameters
-        self.time_step_rank = int(args.time_step_rank)
-        self.time_step_min = args.time_step_min
-        self.time_step_max = args.time_step_max
-        
-        # Processing parameters
+        self.args = args
+
         self.chunk_size = args.chunk_size
-        self.layer_norm_epsilon = args.layer_norm_epsilon
-        
-        # Calculate dimensions
-        self.conv_dim = (self.intermediate_size + 
-                        2 * self.n_groups * self.ssm_state_size)
-        projection_size = (self.intermediate_size + 
-                         self.conv_dim + 
-                         self.num_heads)
-        
-        # Initialize layers
-        self.in_proj = nn.Linear(
-            self.hidden_size,
-            projection_size,
-            bias=args.use_bias
-        )
-        
-        self.conv1d = nn.Conv1d(
+
+        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=self.conv_kernel,
+            kernel_size=args.conv_kernel,
             groups=self.conv_dim,
-            padding=self.conv_kernel - 1,
-            bias=self.use_conv_bias
+            bias=args.use_conv_bias,
+            padding=args.conv_kernel - 1
         )
 
-        # Initialize parameters
-        self.dt_bias = mx.ones(self.num_heads)
-        A = mx.arange(1, self.num_heads + 1)
-        self.A_log = mx.log(A)
-        self.D = mx.ones(self.num_heads)
-        
-        # Output layers
-        self.norm = MambaRMSNormGated(
-            self.intermediate_size,
-            eps=self.layer_norm_epsilon
-        )
-        self.out_proj = nn.Linear(
-            self.intermediate_size,
-            self.hidden_size,
-            bias=args.use_bias
-        )
+        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
 
-    def reshape_into_chunks(self, tensor, pad_size, chunk_size):
-        if pad_size > 0:
-            pad_shape = list(tensor.shape)
-            pad_shape[1] = pad_size
-            padding = mx.zeros(pad_shape, dtype=tensor.dtype)
-            tensor = mx.concatenate([tensor, padding], axis=1)
-        
-        chunk_shape = list(tensor.shape)
-        chunk_shape[1] = -1
-        chunk_shape.insert(2, chunk_size)
-        return tensor.reshape(chunk_shape)
+        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)
 
-    def segment_sum(self, x):
-        return mx.cumsum(x, axis=-1)
+        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 process_single_token(self, hidden_states, B, C, dt, cache):
-        batch_size = hidden_states.shape[0]
-        
-        # Process convolution state
-        if cache is not None:
-            conv_state = cache.conv_states
-            # Roll the conv state and update the last position
-            conv_state = mx.roll(conv_state, shift=-1, axis=-1)
-            # Create new conv state with updated last position
-            new_conv_state = mx.array(conv_state)
-            new_conv_state = new_conv_state.at[:, :, -1].add(hidden_states)
-            conv_state = new_conv_state
-            
-            # Compute convolution
-            conv_out = mx.sum(conv_state * self.conv1d.weight[:, 0, :], axis=-1)
-            if self.use_conv_bias:
-                conv_out = conv_out + self.conv1d.bias
-            
-            # Apply SiLU activation
-            conv_out = mx.sigmoid(conv_out) * conv_out
-            
-        else:
-            # Initialize new cache
-            conv_state = mx.zeros((batch_size, self.conv_dim, self.conv_kernel - 1))
-            conv_out = self.conv1d(hidden_states)
-            conv_out = mx.sigmoid(conv_out) * conv_out
-        
-        # Process SSM
+    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.time_step_min,
-            self.time_step_max
+            self.args.time_step_min,
+            self.args.time_step_max
         )
-        
-        A = -mx.exp(self.A_log)
-        dA = mx.exp(dt * A[None, :])
-        
-        if cache is not None:
-            ssm_state = cache.ssm_states
-        else:
-            ssm_state = mx.zeros(
-                (batch_size, self.num_heads, self.head_dim, self.ssm_state_size)
-            )
-        
-        # Compute SSM updates
-        dBx = mx.einsum('bh,bhs,bhd->bhds', dt, B, hidden_states)
-        next_state = ssm_state * dA[:, :, None, None] + dBx
-        y = mx.einsum('bhds,bhs->bhd', next_state, C)
-        
-        # Add skip connection
-        y = y + hidden_states * self.D[None, :, None]
-        
-        return y, conv_state, next_state
+        dt = mx.maximum(dt, self.args.time_step_floor)
 
-    def process_long_sequence(self, hidden_states, B, C, dt, ssm_state):
-        batch_size, seq_len = hidden_states.shape[:2]
-        pad_size = self.chunk_size - (seq_len % self.chunk_size)
-        
-        # Reshape into chunks
-        x_chunks = self.reshape_into_chunks(hidden_states, pad_size, self.chunk_size)
-        B_chunks = self.reshape_into_chunks(B, pad_size, self.chunk_size)
-        C_chunks = self.reshape_into_chunks(C, pad_size, self.chunk_size)
-        
-        # Process time steps
-        dt = nn.softplus(dt + self.dt_bias)
-        dt = mx.clip(dt, self.time_step_min)
-        
-        # Prepare matrices
+        # 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)
-        A = A * dt[:, None]
-        
-        # Process chunks
-        A_chunks = self.reshape_into_chunks(
-            mx.broadcast_to(A, (batch_size, seq_len + pad_size, self.num_heads)),
-            pad_size,
-            self.chunk_size
-        )
-        
-        # Compute cumulative sums
-        A_cumsum = mx.cumsum(A_chunks, axis=-1)
-        L = mx.exp(self.segment_sum(A_chunks))
-        
-        # Process diagonal blocks
-        G = mx.einsum('...lhn,...shn->...lsh', C_chunks, B_chunks)
-        M = G * L[..., None, :]
-        Y_diag = mx.einsum('...lsh,...sh->...lh', M, x_chunks)
-        
-        # Process off-diagonal blocks
-        decay_states = mx.exp(A_cumsum[..., -1:] - A_cumsum)
-        B_decay = B_chunks * decay_states[..., None]
-        states = mx.einsum('...shn,...sh->...hn', B_decay, x_chunks)
-        
-        # Combine results
-        y = Y_diag + states
-        
-        # Remove padding if necessary
+        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]
-        
-        return y, ssm_state
+            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)
 
-    def __call__(self, x: mx.array, cache: Optional[Mamba2Cache] = None) -> mx.array:
-        batch_size, seq_len, _ = x.shape
-        
-        # Project input
-        projected_states = self.in_proj(x.squeeze(1))
-        
-        # Calculate d_mlp based on projection size
-        d_mlp = (projected_states.shape[-1] - 2 * self.intermediate_size - 2 *
-                self.n_groups * self.ssm_state_size - self.num_heads) // 2
-        
-        # Split projections with corrected dimensions
-        splits = [
-            d_mlp,                      # z0
-            d_mlp,                      # x0
-            self.intermediate_size,     # gate
-            self.conv_dim,             # hidden_states
-            self.num_heads             # dt
-        ]
-        
-        z0, x0, x1, gate, hidden_states, dt = projected_states.split(splits, axis=-1)
-        
-        # Split hidden states into components
-        x_conv, BC = mx.split(hidden_states, [self.intermediate_size], axis=-1)
-        B, C = mx.split(BC, [self.n_groups * self.ssm_state_size], axis=-1)
-        
-        # Process based on sequence length
-        if seq_len > 1 and cache is None:
-            y, next_state = self.process_long_sequence(
-                x_conv, B, C, dt,
-                mx.zeros((batch_size, self.num_heads, self.head_dim, self.ssm_state_size))
-            )
-        else:
-            # Reshape for single token processing
-            x_conv = x_conv.reshape(batch_size, -1, self.head_dim)
-            B = B.reshape(batch_size, self.num_heads, -1)
-            C = C.reshape(batch_size, self.num_heads, -1)
-            y, conv_state, next_state = self.process_single_token(x_conv, B, C, dt, cache)
-            
-            if cache is not None:
-                cache.update(conv_state, next_state)
-        
-        # Apply normalization and final projection
-        y = self.norm(y) * gate
         return self.out_proj(y)
-    
+
 
 class ResidualBlock(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
-        self.mixer = Mamba2Mixer(args)
+        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: Optional[Mamba2Cache] = None) -> mx.array:
+    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 Mamba2Model(nn.Module):
+
+class Mamba2(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
         self.args = args
@@ -306,26 +289,27 @@ class Mamba2Model(nn.Module):
         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=None) -> mx.array:
+    def __call__(self, x: mx.array, cache):
         x = self.embeddings(x)
-
         if cache is None:
             cache = [None] * len(self.layers)
-
-        for layer, layer_cache in zip(self.layers, cache):
-            x = layer(x, layer_cache)
-
+        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.backbone = Mamba2Model(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) -> mx.array:
+    def __call__(self, inputs: mx.array, cache=None):
         B, T = inputs.shape
 
         x = self.backbone(inputs, cache)
@@ -336,26 +320,24 @@ class Model(nn.Module):
             logits = self.lm_head(x)
 
         return logits
-    
+
     def make_cache(self, batch_size=1):
-        return [
-            Mamba2Cache(
-                batch_size=batch_size,
-                conv_dim=self.args.intermediate_size + 2 * self.args.n_groups * self.args.state_size,
-                kernel_size=self.args.conv_kernel,
-                num_heads=self.args.num_heads,
-                head_dim=self.args.head_dim,
-                state_size=self.args.state_size
-            ) 
-            for _ in range(len(self.backbone.layers))
-        ]
-    
+        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 and v.ndim == 3:
-                weights[k] = v.moveaxis(2, 1)
-        return weights
-    
+            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