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MicroGLM: Tiny GLM-style model in PyTorch.
GLM (General Language Model) uses a prefix-LM architecture:
bidirectional attention on a prefix span + autoregressive generation on the rest.
This is a minimal implementation that fits in 6GB VRAM.
Reference: "GLM: General Language Model Pretraining with Autoregressive Blank Infilling" (Du et al., 2021)
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class RMSNorm(nn.Module):
def __init__(self, dim, eps=1e-5):
super().__init__()
self.weight = nn.Parameter(torch.ones(dim))
self.eps = eps
def forward(self, x):
return x * torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + self.eps) * self.weight
class GLMAttention(nn.Module):
"""
Attention with support for 2D attention masks (for prefix-LM).
The mask has shape (B, 1, T, T) where:
- 0 means attend (bidirectional / causal allowed)
- -inf means blocked
"""
def __init__(self, n_embd, n_head, block_size, dropout):
super().__init__()
assert n_embd % n_head == 0
self.n_head = n_head
self.head_dim = n_embd // n_head
self.qkv = nn.Linear(n_embd, n_embd * 3, bias=False)
self.proj = nn.Linear(n_embd, n_embd, bias=False)
self.attn_drop = nn.Dropout(dropout)
self.resid_drop = nn.Dropout(dropout)
def forward(self, x, attn_mask=None):
"""
x: (B, T, C)
attn_mask: (B, 1, T, T) or None (uses default causal mask)
"""
B, T, C = x.shape
qkv = self.qkv(x).reshape(B, T, 3, self.n_head, self.head_dim).permute(2, 0, 3, 1, 4)
q, k, v = qkv[0], qkv[1], qkv[2]
att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
if attn_mask is not None:
# attn_mask: 0 = attend, -inf = block
att = att + attn_mask
else:
# Default causal mask
mask = torch.tril(torch.ones(T, T, device=x.device)).view(1, 1, T, T)
att = att.masked_fill(mask == 0, float('-inf'))
att = F.softmax(att, dim=-1)
att = self.attn_drop(att)
y = att @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
return self.resid_drop(self.proj(y))
class GLMMLP(nn.Module):
def __init__(self, n_embd, dropout):
super().__init__()
self.fc = nn.Linear(n_embd, 4 * n_embd, bias=False)
self.proj = nn.Linear(4 * n_embd, n_embd, bias=False)
self.drop = nn.Dropout(dropout)
def forward(self, x):
x = F.gelu(self.fc(x))
x = self.proj(x)
return self.drop(x)
class GLMBlock(nn.Module):
def __init__(self, n_embd, n_head, block_size, dropout):
super().__init__()
self.ln1 = RMSNorm(n_embd)
self.attn = GLMAttention(n_embd, n_head, block_size, dropout)
self.ln2 = RMSNorm(n_embd)
self.mlp = GLMMLP(n_embd, dropout)
def forward(self, x, attn_mask=None):
x = x + self.attn(self.ln1(x), attn_mask)
x = x + self.mlp(self.ln2(x))
return x
class MicroGLM(nn.Module):
"""
Micro GLM: Prefix-LM decoder.
Supports two attention modes:
- Causal LM (default): standard autoregressive generation
- Prefix LM: bidirectional attention on first `prefix_len` tokens, causal on the rest
This is controlled by passing a custom attention mask during forward().
For training, we create a 2D mask where the prefix region is bidirectional
and the suffix region is causal.
"""
def __init__(self, vocab_size, block_size, n_layer=2, n_head=4, n_embd=128, dropout=0.1):
super().__init__()
self.block_size = block_size
self.wte = nn.Embedding(vocab_size, n_embd)
self.wpe = nn.Embedding(block_size, n_embd)
self.blocks = nn.ModuleList([GLMBlock(n_embd, n_head, block_size, dropout) for _ in range(n_layer)])
self.ln_f = RMSNorm(n_embd)
self.lm_head = nn.Linear(n_embd, vocab_size, bias=False)
self.lm_head.weight = self.wte.weight
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=0.02)
def _build_prefix_lm_mask(self, T, prefix_len, device):
"""
Build a 2D attention mask for prefix-LM.
- For positions i < prefix_len and j < prefix_len: bidirectional (0)
- For positions i >= prefix_len: causal (attend only to j <= i)
- All other positions: -inf
Returns: (1, 1, T, T) mask where 0 = allowed, -inf = blocked
"""
# Start with causal mask
mask = torch.tril(torch.ones(T, T, device=device))
# Set the prefix block to be fully connected (bidirectional)
mask[:, :prefix_len] = 1.0
# Convert to float mask: 0 = attend, -inf = blocked
mask = mask.view(1, 1, T, T)
mask = mask.masked_fill(mask == 0, float('-inf'))
mask = mask.masked_fill(mask == 1.0, 0.0)
return mask
def forward(self, idx, targets=None, prefix_len=0):
"""
idx: (B, T) input tokens
targets: (B, T) target tokens (shifted for loss)
prefix_len: number of prefix tokens to use bidirectional attention
"""
B, T = idx.shape
if T > self.block_size:
raise ValueError(f'block size exceeded: {T} > {self.block_size}')
pos = torch.arange(T, device=idx.device)
x = self.wte(idx) + self.wpe(pos)
if prefix_len > 0:
attn_mask = self._build_prefix_lm_mask(T, prefix_len, idx.device)
else:
attn_mask = None
for block in self.blocks:
x = block(x, attn_mask)
x = self.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.reshape(-1, logits.size(-1)), targets.reshape(-1))
return logits, loss
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=40):
"""Standard causal generation (no prefix)."""
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size:]
logits, _ = self(idx_cond, prefix_len=0)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, idx_next], dim=1)
return idx
@torch.no_grad()
def generate_with_prefix(self, idx, prefix_len, max_new_tokens, temperature=1.0, top_k=40):
"""
Generate tokens where the first `prefix_len` tokens use bidirectional attention
and the rest are autoregressive.
"""
self.eval()
for _ in range(max_new_tokens):
idx_cond = idx[:, -self.block_size:]
T = idx_cond.shape[1]
prefix = min(prefix_len, T)
logits, _ = self(idx_cond, prefix_len=prefix)
logits = logits[:, -1, :] / temperature
if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = -float('inf')
probs = F.softmax(logits, dim=-1)
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat([idx, idx_next], dim=1)
return idx |