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# Copied from src/microreason/model.py (github.com/arpjw/microreason), with project-only helpers
# removed. Requires torch and transformers.
"""MicroReason decoder-only Transformer (spec §3).
Pre-norm blocks with RMSNorm, rotary position embeddings on queries and keys, SwiGLU MLPs,
PyTorch scaled-dot-product attention, no biases, and input/output embeddings tied by reusing
the embedding matrix as the output projection. The same class builds the full and smoke models.
It is wrapped as a Hugging Face PreTrainedModel so save_pretrained / from_pretrained work.
"""
from __future__ import annotations
import math
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutput
IGNORE_INDEX = -100
class MicroReasonConfig(PretrainedConfig):
model_type = "microreason"
def __init__(
self,
name: str = "microreason",
n_layers: int = 12,
d_model: int = 512,
n_heads: int = 8,
n_kv_heads: int = 8,
head_dim: int = 64,
mlp_hidden: int = 1408,
vocab_size: int = 8192,
max_seq_len: int = 2048,
rope_theta: float = 10000.0,
norm_eps: float = 1e-5,
init_std: float = 0.02,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
**kwargs,
):
self.name = name
self.n_layers = n_layers
self.d_model = d_model
self.n_heads = n_heads
self.n_kv_heads = n_kv_heads
self.head_dim = head_dim
self.mlp_hidden = mlp_hidden
self.vocab_size = vocab_size
self.max_seq_len = max_seq_len
self.rope_theta = rope_theta
self.norm_eps = norm_eps
self.init_std = init_std
kwargs.setdefault("tie_word_embeddings", False) # tying is done by hand, see forward
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x: torch.Tensor) -> torch.Tensor:
xf = x.float()
xf = xf * torch.rsqrt(xf.pow(2).mean(-1, keepdim=True) + self.eps)
return xf.to(x.dtype) * self.weight
def rope_cache(seq_len: int, head_dim: int, theta: float, device=None):
inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2, device=device).float() / head_dim))
t = torch.arange(seq_len, device=device).float()
freqs = torch.outer(t, inv_freq) # (T, D/2)
emb = torch.cat([freqs, freqs], dim=-1) # (T, D): pairs dimension i with i + D/2
return emb.cos(), emb.sin()
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1, x2 = x.chunk(2, dim=-1)
return torch.cat([-x2, x1], dim=-1)
def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
"""x: (B, H, T, D); cos/sin: (B, 1, T, D) gathered at each token's position."""
return x * cos + rotate_half(x) * sin
class KVCache:
"""Per-layer key/value buffers for incremental decoding, preallocated to `max_len` keys.
Buffers are created on first write with the dtype the keys arrive in (bf16 under autocast).
`length` counts filled key positions; the model advances it after every layer has written.
"""
def __init__(self, n_layers: int, max_len: int):
self.max_len = max_len
self.length = 0
self.k: list[torch.Tensor | None] = [None] * n_layers
self.v: list[torch.Tensor | None] = [None] * n_layers
def write(self, layer: int, k: torch.Tensor, v: torch.Tensor):
b, h, t, d = k.shape
end = self.length + t
if end > self.max_len:
raise ValueError(f"KV cache overflow: {end} > {self.max_len}")
if self.k[layer] is None:
self.k[layer] = k.new_zeros((b, h, self.max_len, d))
self.v[layer] = v.new_zeros((b, h, self.max_len, d))
self.k[layer][:, :, self.length : end] = k
self.v[layer][:, :, self.length : end] = v
return self.k[layer][:, :, :end], self.v[layer][:, :, :end]
class Attention(nn.Module):
def __init__(self, cfg: MicroReasonConfig):
super().__init__()
self.n_heads, self.n_kv, self.hd = cfg.n_heads, cfg.n_kv_heads, cfg.head_dim
self.q = nn.Linear(cfg.d_model, cfg.n_heads * cfg.head_dim, bias=False)
self.k = nn.Linear(cfg.d_model, cfg.n_kv_heads * cfg.head_dim, bias=False)
self.v = nn.Linear(cfg.d_model, cfg.n_kv_heads * cfg.head_dim, bias=False)
self.o = nn.Linear(cfg.n_heads * cfg.head_dim, cfg.d_model, bias=False)
def forward(self, x, cos, sin, attn_mask, cache: KVCache | None = None, layer: int = 0):
b, t, _ = x.shape
q = self.q(x).view(b, t, self.n_heads, self.hd).transpose(1, 2)
k = self.k(x).view(b, t, self.n_kv, self.hd).transpose(1, 2)
v = self.v(x).view(b, t, self.n_kv, self.hd).transpose(1, 2)
q, k = apply_rope(q, cos, sin), apply_rope(k, cos, sin)
if cache is not None:
k, v = cache.write(layer, k, v)
if self.n_kv != self.n_heads:
rep = self.n_heads // self.n_kv
k, v = k.repeat_interleave(rep, dim=1), v.repeat_interleave(rep, dim=1)
if attn_mask is None:
y = F.scaled_dot_product_attention(q, k, v, is_causal=True)
else:
y = F.scaled_dot_product_attention(q, k, v, attn_mask=attn_mask)
return self.o(y.transpose(1, 2).reshape(b, t, self.n_heads * self.hd))
class SwiGLU(nn.Module):
def __init__(self, cfg: MicroReasonConfig):
super().__init__()
self.gate = nn.Linear(cfg.d_model, cfg.mlp_hidden, bias=False)
self.up = nn.Linear(cfg.d_model, cfg.mlp_hidden, bias=False)
self.down = nn.Linear(cfg.mlp_hidden, cfg.d_model, bias=False)
def forward(self, x):
return self.down(F.silu(self.gate(x)) * self.up(x))
class Block(nn.Module):
def __init__(self, cfg: MicroReasonConfig):
super().__init__()
self.attn_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
self.attn = Attention(cfg)
self.mlp_norm = RMSNorm(cfg.d_model, cfg.norm_eps)
self.mlp = SwiGLU(cfg)
def forward(self, x, cos, sin, attn_mask, cache=None, layer=0):
x = x + self.attn(self.attn_norm(x), cos, sin, attn_mask, cache, layer)
return x + self.mlp(self.mlp_norm(x))
class MicroReasonForCausalLM(PreTrainedModel):
config_class = MicroReasonConfig
base_model_prefix = "microreason"
_no_split_modules = ["Block"]
def __init__(self, config: MicroReasonConfig):
super().__init__(config)
self.embed = nn.Embedding(config.vocab_size, config.d_model)
self.blocks = nn.ModuleList(Block(config) for _ in range(config.n_layers))
self.norm = RMSNorm(config.d_model, config.norm_eps)
# RoPE tables are derived from the config, not stored: Transformers 5 materializes
# models from a meta device, which would leave non-persistent buffers uninitialized.
self._rope: dict[torch.device, tuple[torch.Tensor, torch.Tensor]] = {}
self.post_init()
def rope_tables(self, device: torch.device) -> tuple[torch.Tensor, torch.Tensor]:
if device not in self._rope:
c = self.config
self._rope[device] = rope_cache(c.max_seq_len, c.head_dim, c.rope_theta, device)
return self._rope[device]
# Hugging Face hooks -------------------------------------------------------------------
def _init_weights(self, module: nn.Module) -> None:
std = self.config.init_std
if isinstance(module, nn.Linear):
# Residual output projections get a depth-scaled init (GPT-2 style).
scale = getattr(module, "_residual_scale", 1.0)
nn.init.normal_(module.weight, mean=0.0, std=std * scale)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=std)
elif isinstance(module, RMSNorm):
nn.init.ones_(module.weight)
def post_init(self) -> None:
scale = 1.0 / math.sqrt(2 * self.config.n_layers)
for block in self.blocks:
block.attn.o._residual_scale = scale
block.mlp.down._residual_scale = scale
super().post_init()
def get_input_embeddings(self) -> nn.Embedding:
return self.embed
def set_input_embeddings(self, value: nn.Embedding) -> None:
self.embed = value
# Forward ------------------------------------------------------------------------------
@staticmethod
def build_attention_mask(attention_mask: torch.Tensor) -> torch.Tensor:
"""(B, T) key mask of 1/0 -> (B, 1, T, T) boolean causal mask.
Every query may always attend to itself, so fully padded rows stay finite; their
outputs are never used because their labels are ignored.
"""
b, t = attention_mask.shape
causal = torch.ones(t, t, dtype=torch.bool, device=attention_mask.device).tril()
keys = attention_mask.bool()[:, None, None, :]
eye = torch.eye(t, dtype=torch.bool, device=attention_mask.device)
return (causal & keys) | eye
@staticmethod
def build_cached_attention_mask(attention_mask: torch.Tensor, start: int, t: int):
"""Mask for `t` new queries at key positions [start, start + t) over all keys so far.
attention_mask is the (B, start + t) key mask. Rows equal the last `t` rows of
build_attention_mask(attention_mask), so cached and uncached decoding see the same keys.
"""
dev = attention_mask.device
q_idx = torch.arange(start, start + t, device=dev)[:, None]
k_idx = torch.arange(attention_mask.shape[1], device=dev)[None, :]
keys = attention_mask.bool()[:, None, None, :]
return ((k_idx <= q_idx) & keys) | (k_idx == q_idx)
def forward(
self,
input_ids: torch.Tensor,
attention_mask: torch.Tensor | None = None,
position_ids: torch.Tensor | None = None,
labels: torch.Tensor | None = None,
cache: KVCache | None = None,
**_: object,
) -> CausalLMOutput:
"""With `cache`, input_ids holds only the new tokens and attention_mask covers every key
so far (cached plus new); positions come from the full mask, as without a cache."""
b, t = input_ids.shape
start = 0 if cache is None else cache.length
if start + t > self.config.max_seq_len:
raise ValueError(
f"sequence length {start + t} exceeds max_seq_len {self.config.max_seq_len}"
)
if cache is not None and attention_mask is None:
raise ValueError("cached decoding needs the full attention mask")
if position_ids is None:
if attention_mask is None:
position_ids = torch.arange(t, device=input_ids.device).expand(b, t)
else:
position_ids = (attention_mask.long().cumsum(-1) - 1).clamp(min=0)[:, -t:]
cos_t, sin_t = self.rope_tables(input_ids.device)
cos = cos_t[position_ids].unsqueeze(1)
sin = sin_t[position_ids].unsqueeze(1)
if attention_mask is None:
mask = None
elif cache is None:
mask = self.build_attention_mask(attention_mask)
else:
mask = self.build_cached_attention_mask(attention_mask, start, t)
x = self.embed(input_ids)
cos, sin = cos.to(x.dtype), sin.to(x.dtype)
for i, block in enumerate(self.blocks):
x = block(x, cos, sin, mask, cache, i)
if cache is not None:
cache.length = start + t
x = self.norm(x)
logits = F.linear(x, self.embed.weight) # tied output projection
loss = None
if labels is not None:
loss = F.cross_entropy(
logits[:, :-1].reshape(-1, logits.size(-1)).float(),
labels[:, 1:].reshape(-1),
ignore_index=IGNORE_INDEX,
)
return CausalLMOutput(loss=loss, logits=logits)
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