multible ssd step frunctions

llms/mlx_lm/models/mamba2.py

@@ -1,5 +1,5 @@
 import math
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Tuple, Union
 import mlx.core as mx
 import mlx.nn as nn
@@ -42,10 +42,6 @@ class ModelArgs(BaseModelArgs):
             self.time_step_rank = math.ceil(self.hidden_size / 16)
 
 
-def segsum(x):
-    return mx.cumsum(x, axis=-1).reshape(*x.shape[:-1], 1, x.shape[-1])
-
-
 class DepthWiseConv1d(nn.Module):
     def __init__(self, channels, kernel_size, bias=True, padding=0):
         super().__init__()
@@ -69,6 +65,10 @@ class DepthWiseConv1d(nn.Module):
         return y, x[:, -K + 1:, :]
 
 
+def segsum(x):
+    return mx.cumsum(x, axis=-1).reshape(*x.shape[:-1], 1, x.shape[-1])
+
+
 def ssd_forward_attn(
     x: mx.array,
     dt: mx.array,
@@ -137,6 +137,166 @@ def ssd_forward_attn(
     return y, next_state
 
 
+def ssd(x, A, B, C, chunk_size, initial_states=None):
+    """Structured State Space Duality (SSD) - the core of Mamba-2
+    
+    Arguments
+    x: (batch, seqlen, n_heads, d_head)
+    A: (batch, seqlen, n_heads)
+    B: (batch, seqlen, n_heads, d_state)
+    C: (batch, seqlen, n_heads, d_state)
+    
+    Return (y, final_state)
+    y: (batch, seqlen, n_heads, d_head)
+    final_state: final state for next inference step
+    """
+    # Verify sequence length is divisible by chunk_size
+    b, seqlen, h, dh = x.shape
+    assert seqlen % chunk_size == 0
+    
+    # Rearrange into chunks
+    num_chunks = seqlen // chunk_size
+    x_chunks = x.reshape(b, num_chunks, chunk_size, h, dh)
+    A_chunks = A.reshape(b, num_chunks, chunk_size, h)
+    B_chunks = B.reshape(b, num_chunks, chunk_size, -1, B.shape[-1])  # Account for groups
+    C_chunks = C.reshape(b, num_chunks, chunk_size, -1, C.shape[-1])
+    
+    # Transpose A for correct cumsum operation
+    A_chunks = mx.transpose(A_chunks, (0, 3, 1, 2))  # b h c l
+    A_cumsum = mx.cumsum(A_chunks, axis=-1)
+    
+    # 1. Compute output for each intra-chunk (diagonal blocks)
+    L = mx.exp(segsum(A_chunks))
+    
+    # Handle the dimensions for einsum
+    # "bclhn, bcshn, bhcls, bcshp -> bclhp"
+    C_expanded = mx.expand_dims(C_chunks, axis=3)  # b c l 1 h n
+    B_expanded = mx.expand_dims(B_chunks, axis=2)  # b c 1 s h n
+    L_reshaped = mx.transpose(L, (0, 2, 3, 1, 4))  # b h c l s -> b c l h s
+    x_reshaped = mx.transpose(x_chunks, (0, 1, 2, 3, 4))  # b c l h p
+    
+    # Perform the computation using manual broadcasting and reductions
+    # This is a manual implementation of the einsum from PyTorch
+    BC = mx.matmul(mx.transpose(C_expanded, (0, 1, 2, 4, 3)), 
+                  mx.transpose(B_expanded, (0, 1, 3, 4, 2)))  # b c l n n
+    L_x = mx.matmul(mx.transpose(L_reshaped, (0, 1, 2, 4, 3)), 
+                   mx.reshape(x_reshaped, (b, num_chunks, chunk_size, dh, 1)))  # b c l s 1
+    Y_diag = mx.matmul(BC, L_x)  # b c l h dh
+    Y_diag = mx.reshape(Y_diag, (b, num_chunks, chunk_size, h, dh))
+    
+    # 2. Compute state for each intra-chunk
+    decay_states = mx.exp(A_cumsum[:, :, :, -1:] - A_cumsum)
+    
+    # Compute states using matrix multiplications (replacing einsum)
+    # "bclhn, bhcl, bclhp -> bchpn"
+    B_decay = mx.matmul(B_chunks, 
+                       mx.reshape(decay_states, (b, h, num_chunks, chunk_size, 1)))
+    states = mx.matmul(B_decay, 
+                      mx.reshape(x_chunks, (b, num_chunks, chunk_size, h, dh, 1)))
+    states = mx.reshape(states, (b, num_chunks, h, dh, -1))  # b c h p n
+    
+    # 3. Compute inter-chunk recurrence
+    if initial_states is None:
+        initial_states = mx.zeros((b, 1, h, dh, B.shape[-1]))
+    
+    states = mx.concatenate([initial_states, states], axis=1)
+    
+    # Create padded A_cumsum for decay calculation
+    A_cumsum_last = A_cumsum[:, :, :, -1]
+    padded_A_cumsum = mx.pad(A_cumsum_last, [(0, 0), (0, 0), (1, 0)])
+    decay_chunk = mx.exp(segsum(padded_A_cumsum))
+    
+    # Compute new states (replacing einsum "bhzc, bchpn -> bzhpn")
+    decay_chunk_expanded = mx.reshape(decay_chunk, (b, h, -1, num_chunks+1, 1, 1))
+    states_expanded = mx.reshape(states, (b, 1, num_chunks+1, h, dh, -1))
+    new_states = decay_chunk_expanded * states_expanded
+    new_states = mx.sum(new_states, axis=2)
+    
+    states, final_state = new_states[:, :-1], new_states[:, -1]
+    
+    # 4. Compute state -> output conversion per chunk
+    state_decay_out = mx.exp(A_cumsum)
+    
+    # Compute Y_off (replacing einsum "bclhn, bchpn, bhcl -> bclhp")
+    state_decay_expanded = mx.reshape(state_decay_out, (b, h, num_chunks, chunk_size, 1))
+    states_reshaped = mx.reshape(states, (b, num_chunks, h, dh, -1))
+    C_states = mx.matmul(mx.transpose(C_chunks, (0, 1, 2, 4, 3)), 
+                         mx.transpose(states_reshaped, (0, 1, 3, 2, 4)))
+    Y_off = C_states * state_decay_expanded
+    Y_off = mx.sum(Y_off, axis=-1)
+    Y_off = mx.reshape(Y_off, (b, num_chunks, chunk_size, h, dh))
+    
+    # Add diagonal and off-diagonal contributions
+    Y = Y_diag + Y_off
+    Y = mx.reshape(Y, (b, seqlen, h, dh))
+    
+    return Y, final_state
+
+def ssd_inference_step(x, A, B, C, prev_state=None):
+    """Simple inference step for Mamba-2
+    
+    Works with:
+    - x: (batch, seqlen, n_heads, d_head)
+    - A: (n_heads,) - scalar values
+    - B: (batch, seqlen, n_groups, d_state)
+    - C: (batch, seqlen, n_groups, d_state)
+    """
+    # Extract dimensions
+    b, seqlen, h, dh = x.shape
+    _, _, g, d_state = B.shape
+    
+    # Compute decay factor
+    dA = mx.exp(A)  # (n_heads,)
+    
+    # Output container
+    outputs = []
+    
+    # Final state to return
+    final_state = prev_state
+    
+    # For each position in the sequence
+    for t in range(seqlen):
+        # Get current values
+        xt = x[:, t]  # (batch, n_heads, d_head)
+        Bt = B[:, t]  # (batch, n_groups, d_state)
+        Ct = C[:, t]  # (batch, n_groups, d_state)
+        
+        # Handle groups vs heads if they differ
+        if g < h:
+            repeat_factor = h // g
+            Bt = mx.repeat(Bt, repeat_factor, axis=1)  # (batch, n_heads, d_state)
+            Ct = mx.repeat(Ct, repeat_factor, axis=1)  # (batch, n_heads, d_state)
+        
+        # Reshape for matrix operations
+        xt = mx.reshape(xt, (b, h, dh, 1))
+        Bt = mx.reshape(Bt, (b, h, 1, d_state))
+        
+        # Compute B·x
+        dBx = mx.matmul(xt, Bt)  # (batch, n_heads, d_head, d_state)
+        
+        # Update state
+        if final_state is not None:
+            dA_expanded = mx.reshape(dA, (1, h, 1, 1))
+            new_state = final_state * dA_expanded + dBx
+        else:
+            new_state = dBx
+        
+        # Compute output
+        Ct = mx.reshape(Ct, (b, h, d_state, 1))
+        yt = mx.matmul(new_state, Ct)  # (batch, n_heads, d_head, 1)
+        yt = mx.reshape(yt, (b, h, dh))
+        
+        # Add to outputs
+        outputs.append(mx.expand_dims(yt, 1))
+        
+        # Update state for next position
+        final_state = new_state
+    
+    # Combine all outputs
+    y = mx.concatenate(outputs, axis=1)
+    
+    return y, final_state
+
 class Mamba2Block(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
@@ -170,57 +330,70 @@ class Mamba2Block(nn.Module):
 
     def __call__(self, u: mx.array, cache=None):
         batch_size, seq_len, _ = u.shape
-        
         if cache is None:
             cache = [None, None]
         else:
             conv_state, ssm_state = cache
-
+        
         zxBCdt = self.in_proj(u)
-
+        
+        # Split the projection into components
         z, xBC, dt = mx.split(
             zxBCdt,
-            [self.d_inner, 2 * self.d_inner + 2 * self.n_groups * self.d_state], 
+            [self.d_inner, 2*self.d_inner + 2*self.n_groups*self.d_state],
             axis=-1
         )
-
+        
+        # Apply convolution and gating
         xBC, conv_state = self.conv1d(xBC, conv_state)
         xBC = xBC * mx.sigmoid(xBC)
         xBC = xBC[:, :seq_len, :]
-
+        
+        # Split into the various components
         x, B, C = mx.split(
-            xBC, 
+            xBC,
-            [self.d_inner, self.d_inner + self.d_state * self.n_groups], 
+            [self.d_inner, self.d_inner + self.d_state*self.n_groups],
             axis=-1
         )
-
+        
+        # Reshape for SSM computation
         x = mx.reshape(x, (batch_size, seq_len, self.n_heads, self.d_head))
         B = mx.reshape(B, (batch_size, seq_len, self.n_groups, -1))
         C = mx.reshape(C, (batch_size, seq_len, self.n_groups, -1))
-
+        
-        A = -mx.exp(self.A_log)
+        # Process dt - similar to your ssd_forward_attn function
+        dt = mx.reshape(dt, (batch_size, seq_len, self.n_heads))
+        dt = dt + self.dt_bias.reshape(1, 1, -1)  # Apply bias
+        dt = nn.softplus(dt)  # Ensure positive time steps
+        dt = mx.clip(dt, self.args.time_step_min, self.args.time_step_max)
+        
+        # For inference, we use ssd_forward_attn which you already know works
         y, next_ssm_state = ssd_forward_attn(
             x=x,
             dt=dt,
-            A=-mx.exp(self.A_log),
+            A=self.A_log,  # Use A_log directly, the function will process it
             B=B,
             C=C,
             D=self.D,
-            dt_bias=self.dt_bias,
+            dt_bias=None,  # We already applied dt_bias above
             dt_min=self.args.time_step_min,
             dt_max=self.args.time_step_max,
             prev_state=ssm_state
         )
-
+        
+        # Reshape output
+        y = mx.reshape(y, (batch_size, seq_len, self.d_inner))
+        
+        # Apply normalization and gating
         if self.args.norm_before_gate:
             y = self.norm(y)
             y = y * nn.silu(z)
         else:
             y = y * nn.silu(z)
             y = self.norm(y)
-
+        
         y = self.out_proj(y)
-
+        
         cache[0] = conv_state
         cache[1] = next_ssm_state
         return y