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import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.checkpoint import checkpoint
NUM_FROM_TO = 4096
NUM_PROMO = 5
MAX_PLIES = 96
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-05):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
norm = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
return norm * self.weight
def parallel_scan(a: torch.Tensor, b: torch.Tensor) -> torch.Tensor:
L = a.shape[1]
d = 1
while d < L:
a_prev, b_prev = (a[:, :-d], b[:, :-d])
a_cur, b_cur = (a[:, d:], b[:, d:])
new_a = a_cur * a_prev
new_b = a_cur * b_prev + b_cur
a = torch.cat([a[:, :d], new_a], dim=1)
b = torch.cat([b[:, :d], new_b], dim=1)
d *= 2
return b
class S6Block(nn.Module):
def __init__(self, dim: int, state_dim: int = 16, expand: int = 2):
super().__init__()
inner_dim = dim * expand
self.dim = dim
self.inner_dim = inner_dim
self.state_dim = state_dim
self.in_proj = nn.Linear(dim, inner_dim * 2, bias=False)
self.x_proj = nn.Linear(inner_dim, state_dim * 2 + inner_dim, bias=False)
self.dt_bias = nn.Parameter(torch.zeros(inner_dim))
A = torch.arange(1, state_dim + 1, dtype=torch.float32).unsqueeze(0).repeat(inner_dim, 1)
self.A_log = nn.Parameter(torch.log(A))
self.D = nn.Parameter(torch.ones(inner_dim))
self.out_proj = nn.Linear(inner_dim, dim, bias=False)
def forward(self, x: torch.Tensor) -> torch.Tensor:
B_, L, _ = x.shape
xz = self.in_proj(x)
x_in, gate = xz.chunk(2, dim=-1)
x_in = F.silu(x_in)
x_dbl = self.x_proj(x_in)
Bmat, Cmat, delta_raw = torch.split(
x_dbl, [self.state_dim, self.state_dim, self.inner_dim], dim=-1
)
delta = F.softplus(delta_raw + self.dt_bias)
A = -torch.exp(self.A_log)
A_bar = torch.exp(delta.unsqueeze(-1) * A.view(1, 1, self.inner_dim, self.state_dim))
Bx = (delta * x_in).unsqueeze(-1) * Bmat.unsqueeze(2)
h = parallel_scan(A_bar, Bx)
y = (h * Cmat.unsqueeze(2)).sum(-1) + self.D * x_in
y = y * F.silu(gate)
return self.out_proj(y)
def step(
self, x_t: torch.Tensor, h_prev: torch.Tensor | None
) -> tuple[torch.Tensor, torch.Tensor]:
xz = self.in_proj(x_t)
x_in, gate = xz.chunk(2, dim=-1)
x_in = F.silu(x_in)
x_dbl = self.x_proj(x_in)
Bmat, Cmat, delta_raw = torch.split(
x_dbl, [self.state_dim, self.state_dim, self.inner_dim], dim=-1
)
delta = F.softplus(delta_raw + self.dt_bias)
A = -torch.exp(self.A_log)
A_bar = torch.exp(delta.unsqueeze(-1) * A.unsqueeze(0))
Bx = (delta * x_in).unsqueeze(-1) * Bmat.unsqueeze(1)
if h_prev is None:
h_prev = x_t.new_zeros(x_t.shape[0], self.inner_dim, self.state_dim)
h_new = A_bar * h_prev + Bx
y = (h_new * Cmat.unsqueeze(1)).sum(-1) + self.D * x_in
y = y * F.silu(gate)
return (self.out_proj(y), h_new)
class MambaBlock(nn.Module):
def __init__(self, dim: int, state_dim: int = 16, expand: int = 2):
super().__init__()
self.norm = RMSNorm(dim)
self.ssm = S6Block(dim, state_dim=state_dim, expand=expand)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.ssm(self.norm(x))
def step(
self, x_t: torch.Tensor, h_prev: torch.Tensor | None
) -> tuple[torch.Tensor, torch.Tensor]:
y, h_new = self.ssm.step(self.norm(x_t), h_prev)
return (x_t + y, h_new)
class ChessMamba(nn.Module):
def __init__(
self,
dim: int = 256,
depth: int = 8,
state_dim: int = 16,
expand: int = 2,
max_plies: int = MAX_PLIES,
use_checkpoint: bool = True,
):
super().__init__()
self.dim = dim
self.max_plies = max_plies
self.use_checkpoint = use_checkpoint
self.from_embed = nn.Embedding(64, dim)
self.to_embed = nn.Embedding(64, dim)
self.promo_embed = nn.Embedding(NUM_PROMO, dim)
self.pos_embed = nn.Embedding(max_plies + 1, dim)
self.start_token = nn.Parameter(torch.zeros(1, 1, dim))
self.blocks = nn.ModuleList([MambaBlock(dim, state_dim, expand) for _ in range(depth)])
self.norm_f = RMSNorm(dim)
self.policy_head = nn.Linear(dim, NUM_FROM_TO)
self.promo_head = nn.Linear(dim, NUM_PROMO)
self.value_head = nn.Linear(dim, 1)
nn.init.normal_(self.from_embed.weight, std=0.02)
nn.init.normal_(self.to_embed.weight, std=0.02)
nn.init.normal_(self.promo_embed.weight, std=0.02)
nn.init.normal_(self.pos_embed.weight, std=0.02)
def embed_moves(
self, from_ids: torch.Tensor, to_ids: torch.Tensor, promo_ids: torch.Tensor
) -> torch.Tensor:
B_ = from_ids.shape[0]
start = self.start_token.expand(B_, 1, -1)
if from_ids.shape[1] == 0:
tok = start
else:
mv = self.from_embed(from_ids) + self.to_embed(to_ids) + self.promo_embed(promo_ids)
tok = torch.cat([start, mv], dim=1)
positions = torch.arange(tok.shape[1], device=tok.device).unsqueeze(0)
return tok + self.pos_embed(positions)
def forward(
self,
from_ids: torch.Tensor,
to_ids: torch.Tensor,
promo_ids: torch.Tensor,
lengths: torch.Tensor | None = None,
):
x = self.embed_moves(from_ids, to_ids, promo_ids)
for block in self.blocks:
if self.use_checkpoint and self.training:
x = checkpoint(block, x, use_reentrant=False)
else:
x = block(x)
x = self.norm_f(x)
if lengths is None:
pooled = x[:, -1]
else:
idx = lengths.view(-1, 1, 1).expand(-1, 1, x.shape[-1])
pooled = x.gather(1, idx).squeeze(1)
policy_logits = self.policy_head(pooled)
promo_logits = self.promo_head(pooled)
value = torch.tanh(self.value_head(pooled))
return (policy_logits, promo_logits, value)
@torch.no_grad()
def init_incremental(self, device: torch.device | str = "cpu"):
pos = torch.zeros(1, dtype=torch.long, device=device)
x = self.start_token.view(1, self.dim).to(device) + self.pos_embed(pos)
block_states = []
for block in self.blocks:
x, s = block.step(x, None)
block_states.append(s)
x = self.norm_f(x)
outputs = self._heads(x)
return ((1, block_states), outputs)
@torch.no_grad()
def step_move(self, from_sq: int, to_sq: int, promo_id: int, state):
pos_idx, block_states = state
device = self.from_embed.weight.device
pos_idx_clamped = min(pos_idx, self.max_plies)
idx = lambda v: torch.tensor([v], device=device)
x = (
self.from_embed(idx(from_sq))
+ self.to_embed(idx(to_sq))
+ self.promo_embed(idx(promo_id))
+ self.pos_embed(idx(pos_idx_clamped))
)
new_block_states = []
for block, s in zip(self.blocks, block_states):
x, ns = block.step(x, s)
new_block_states.append(ns)
x = self.norm_f(x)
outputs = self._heads(x)
return ((pos_idx + 1, new_block_states), outputs)
@torch.no_grad()
def build_incremental_state(
self,
from_list: list[int],
to_list: list[int],
promo_list: list[int],
device: torch.device | str = "cpu",
):
state, outputs = self.init_incremental(device)
for f, t, p in zip(from_list, to_list, promo_list):
state, outputs = self.step_move(f, t, p, state)
return (state, outputs)
def _heads(self, x: torch.Tensor):
policy_logits = self.policy_head(x)
promo_logits = self.promo_head(x)
value = torch.tanh(self.value_head(x))
return (policy_logits, promo_logits, value)
def count_params(model: nn.Module) -> int:
return sum((p.numel() for p in model.parameters()))
if __name__ == "__main__":
m = ChessMamba(dim=256, depth=8)
print(f"params: {count_params(m):,}")
B_, L = (4, 20)
from_ids = torch.randint(0, 64, (B_, L))
to_ids = torch.randint(0, 64, (B_, L))
promo_ids = torch.zeros(B_, L, dtype=torch.long)
pl, pr, v = m(from_ids, to_ids, promo_ids)
print(pl.shape, pr.shape, v.shape)
|