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"""
TinyLLM Model Architecture
A small transformer language model (~54.93M parameters) trained from scratch.
Architecture:
- 12 layers
- 512 hidden size
- 8 attention heads
- 1408 intermediate (FFN)
- 32000 vocab size
- 512 max sequence length
- RoPE position encoding
- RMSNorm
- SwiGLU activation
- Weight tying
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from typing import Dict, Any, Optional
# Model configuration
MODEL_CONFIG = {
"vocab_size": 32000,
"hidden_size": 512,
"num_layers": 12,
"num_heads": 8,
"intermediate_size": 1408,
"max_position_embeddings": 512,
"dropout": 0.0,
"tie_weights": True,
}
class RMSNorm(nn.Module):
"""Root Mean Square Layer Normalization."""
def __init__(self, dim: int, eps: float = 1e-6):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def forward(self, x):
return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
class RotaryEmbedding(nn.Module):
"""Rotary Position Embedding (RoPE)."""
def __init__(self, dim: int, max_seq_len: int = 512, base: int = 10000):
super().__init__()
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self.max_seq_len = max_seq_len
def forward(self, seq_len: int, device):
t = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
freqs = torch.outer(t, self.inv_freq)
emb = torch.cat((freqs, freqs), dim=-1)
return emb.cos(), emb.sin()
def rotate_half(x):
"""Rotate half the hidden dims of the input."""
x1, x2 = x[..., :x.shape[-1]//2], x[..., x.shape[-1]//2:]
return torch.cat((-x2, x1), dim=-1)
def apply_rotary_pos_emb(q, k, cos, sin):
"""Apply rotary positional embeddings to query and key tensors."""
cos = cos.unsqueeze(0).unsqueeze(0)
sin = sin.unsqueeze(0).unsqueeze(0)
q_embed = (q * cos) + (rotate_half(q) * sin)
k_embed = (k * cos) + (rotate_half(k) * sin)
return q_embed, k_embed
class Attention(nn.Module):
"""Multi-head attention with RoPE."""
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.hidden_size = config["hidden_size"]
self.num_heads = config["num_heads"]
self.head_dim = self.hidden_size // self.num_heads
self.q_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.k_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.v_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.o_proj = nn.Linear(self.hidden_size, self.hidden_size, bias=False)
self.rotary = RotaryEmbedding(self.head_dim, config["max_position_embeddings"])
self.dropout = nn.Dropout(config.get("dropout", 0.0))
def forward(self, x, attention_mask=None):
B, T, C = x.shape
q = self.q_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
k = self.k_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
v = self.v_proj(x).view(B, T, self.num_heads, self.head_dim).transpose(1, 2)
cos, sin = self.rotary(T, x.device)
q, k = apply_rotary_pos_emb(q, k, cos, sin)
# Scaled dot-product attention
attn_weights = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
# Causal mask
causal_mask = torch.triu(torch.ones(T, T, device=x.device, dtype=torch.bool), diagonal=1)
attn_weights.masked_fill_(causal_mask, float('-inf'))
if attention_mask is not None:
attn_weights = attn_weights + attention_mask
attn_weights = F.softmax(attn_weights, dim=-1, dtype=torch.float32).to(x.dtype)
attn_weights = self.dropout(attn_weights)
out = torch.matmul(attn_weights, v)
out = out.transpose(1, 2).contiguous().view(B, T, C)
return self.o_proj(out)
class FFN(nn.Module):
"""Feed-forward network with SwiGLU activation."""
def __init__(self, config: Dict[str, Any]):
super().__init__()
# SwiGLU: w1=gate, w2=down, w3=up
self.w1 = nn.Linear(config["hidden_size"], config["intermediate_size"], bias=False)
self.w2 = nn.Linear(config["intermediate_size"], config["hidden_size"], bias=False)
self.w3 = nn.Linear(config["hidden_size"], config["intermediate_size"], bias=False)
def forward(self, x):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class TransformerBlock(nn.Module):
"""Transformer block with pre-norm architecture."""
def __init__(self, config: Dict[str, Any]):
super().__init__()
self.norm1 = RMSNorm(config["hidden_size"])
self.attn = Attention(config)
self.norm2 = RMSNorm(config["hidden_size"])
self.ffn = FFN(config)
def forward(self, x, attention_mask=None):
x = x + self.attn(self.norm1(x), attention_mask)
x = x + self.ffn(self.norm2(x))
return x
class TinyLLM(nn.Module):
"""
TinyLLM: A small decoder-only transformer language model.
Args:
config: Dictionary containing model configuration
"""
def __init__(self, config: Dict[str, Any] = None):
super().__init__()
self.config = config or MODEL_CONFIG
self.embed_tokens = nn.Embedding(self.config["vocab_size"], self.config["hidden_size"])
self.layers = nn.ModuleList([
TransformerBlock(self.config)
for _ in range(self.config["num_layers"])
])
self.norm = RMSNorm(self.config["hidden_size"])
self.lm_head = nn.Linear(self.config["hidden_size"], self.config["vocab_size"], bias=False)
# Tie embeddings if configured
if self.config.get("tie_weights", True):
self.lm_head.weight = self.embed_tokens.weight
# Register causal mask buffer
max_len = self.config["max_position_embeddings"]
self.register_buffer("causal_mask",
torch.triu(torch.ones(max_len, max_len, dtype=torch.bool), diagonal=1))
def forward(self, input_ids, attention_mask=None, labels=None):
x = self.embed_tokens(input_ids)
for layer in self.layers:
x = layer(x, attention_mask)
x = self.norm(x)
logits = self.lm_head(x)
loss = None
if labels is not None:
shift_logits = logits[..., :-1, :].contiguous()
shift_labels = labels[..., 1:].contiguous()
loss = F.cross_entropy(
shift_logits.view(-1, self.config["vocab_size"]),
shift_labels.view(-1),
ignore_index=-100
)
return {"logits": logits, "loss": loss}
@torch.no_grad()
def generate(
self,
input_ids: torch.Tensor,
max_new_tokens: int = 100,
temperature: float = 0.8,
top_p: float = 0.9,
top_k: int = 50,
eos_token_id: Optional[int] = None,
repetition_penalty: float = 1.0,
) -> torch.Tensor:
"""
Generate text autoregressively.
Args:
input_ids: Input token IDs [batch_size, seq_len]
max_new_tokens: Maximum number of tokens to generate
temperature: Sampling temperature (higher = more random)
top_p: Nucleus sampling threshold
top_k: Top-k sampling threshold
eos_token_id: Token ID that signals end of generation
repetition_penalty: Penalty for repeating tokens
Returns:
Generated token IDs including the prompt
"""
self.eval()
for _ in range(max_new_tokens):
# Truncate if needed
if input_ids.size(1) >= self.config["max_position_embeddings"]:
input_ids = input_ids[:, -self.config["max_position_embeddings"]+1:]
outputs = self(input_ids)
logits = outputs["logits"][:, -1, :]
# Apply repetition penalty
if repetition_penalty != 1.0:
for token_id in set(input_ids[0].tolist()):
logits[0, token_id] /= repetition_penalty
# Apply temperature
logits = logits / temperature
# Top-k filtering
if top_k > 0:
indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
logits[indices_to_remove] = float('-inf')
# Top-p (nucleus) filtering
sorted_logits, sorted_indices = torch.sort(logits, descending=True)
cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
sorted_indices_to_remove = cumulative_probs > top_p
sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
sorted_indices_to_remove[..., 0] = 0
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
logits[indices_to_remove] = float('-inf')
# Sample
probs = F.softmax(logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
input_ids = torch.cat([input_ids, next_token], dim=1)
# Check for EOS
if eos_token_id is not None and next_token.item() == eos_token_id:
break
return input_ids
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