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multible ssd step frunctions

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Goekdeniz-Guelmez 2025-03-12 17:33:53 +01:00
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215
llms/mlx_lm/models/mamba2.py
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@ -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,
[self.d_inner, self.d_inner + self.d_state * self.n_groups],
xBC,
[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