File size: 9,833 Bytes
eec13c0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 | """
CodeLLM - Custom Decoder-only Transformer Architecture
Built from scratch for code generation.
Architecture: GPT-style, 125M parameters
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
from typing import Optional, Tuple
@dataclass
class CodeLLMConfig:
vocab_size: int = 50257
n_positions: int = 2048
n_embd: int = 768
n_layer: int = 12
n_head: int = 12
n_inner: int = 3072
dropout: float = 0.1
layer_norm_epsilon: float = 1e-5
initializer_range: float = 0.02
use_cache: bool = True
pad_token_id: int = 50256
bos_token_id: int = 50256
eos_token_id: int = 50256
tie_word_embeddings: bool = True
@property
def num_parameters(self):
embed = self.vocab_size * self.n_embd
attn = self.n_layer * (4 * self.n_embd * self.n_embd)
ffn = self.n_layer * (2 * self.n_embd * self.n_inner)
return embed + attn + ffn
class RotaryEmbedding(nn.Module):
def __init__(self, dim: int, max_seq_len: int = 2048, base: int = 10000):
super().__init__()
self.dim = dim
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
self.register_buffer("inv_freq", inv_freq)
self._build_cache(max_seq_len)
def _build_cache(self, seq_len: int):
t = torch.arange(seq_len, device=self.inv_freq.device).float()
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
emb = torch.cat([freqs, freqs], dim=-1)
self.register_buffer("cos_cache", emb.cos()[None, None, :, :])
self.register_buffer("sin_cache", emb.sin()[None, None, :, :])
def forward(self, q: torch.Tensor, k: torch.Tensor, seq_len: int):
if seq_len > self.cos_cache.shape[2]:
self._build_cache(seq_len)
cos = self.cos_cache[:, :, :seq_len, :]
sin = self.sin_cache[:, :, :seq_len, :]
return apply_rotary(q, cos, sin), apply_rotary(k, cos, sin)
def rotate_half(x: torch.Tensor) -> torch.Tensor:
x1, x2 = x[..., : x.shape[-1] // 2], x[..., x.shape[-1] // 2 :]
return torch.cat([-x2, x1], dim=-1)
def apply_rotary(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
return (x * cos) + (rotate_half(x) * sin)
class CausalSelfAttention(nn.Module):
def __init__(self, config: CodeLLMConfig):
super().__init__()
assert config.n_embd % config.n_head == 0
self.n_head = config.n_head
self.n_embd = config.n_embd
self.head_dim = config.n_embd // config.n_head
self.dropout = config.dropout
self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=False)
self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=False)
self.attn_drop = nn.Dropout(config.dropout)
self.resid_drop = nn.Dropout(config.dropout)
self.rotary = RotaryEmbedding(self.head_dim, max_seq_len=config.n_positions)
self.register_buffer(
"bias",
torch.tril(torch.ones(config.n_positions, config.n_positions))
.view(1, 1, config.n_positions, config.n_positions),
)
def forward(self, x, attention_mask=None, past_key_value=None, use_cache=False):
B, T, C = x.size()
qkv = self.c_attn(x)
q, k, v = qkv.split(self.n_embd, dim=2)
q = q.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
k = k.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
v = v.view(B, T, self.n_head, self.head_dim).transpose(1, 2)
q, k = self.rotary(q, k, seq_len=T)
if past_key_value is not None:
k = torch.cat([past_key_value[0], k], dim=2)
v = torch.cat([past_key_value[1], v], dim=2)
present = (k, v) if use_cache else None
if hasattr(F, "scaled_dot_product_attention"):
y = F.scaled_dot_product_attention(
q, k, v,
attn_mask=attention_mask,
dropout_p=self.dropout if self.training else 0.0,
is_causal=(past_key_value is None),
)
else:
scale = 1.0 / math.sqrt(self.head_dim)
attn = (q @ k.transpose(-2, -1)) * scale
kT = k.size(2)
causal_mask = self.bias[:, :, kT - T : kT, :kT]
attn = attn.masked_fill(causal_mask == 0, float("-inf"))
if attention_mask is not None:
attn = attn + attention_mask
attn = F.softmax(attn, dim=-1)
attn = self.attn_drop(attn)
y = attn @ v
y = y.transpose(1, 2).contiguous().view(B, T, C)
y = self.resid_drop(self.c_proj(y))
return y, present
class SwiGLUFFN(nn.Module):
def __init__(self, config: CodeLLMConfig):
super().__init__()
hidden = config.n_inner
self.w1 = nn.Linear(config.n_embd, hidden, bias=False)
self.w2 = nn.Linear(config.n_embd, hidden, bias=False)
self.w3 = nn.Linear(hidden, config.n_embd, bias=False)
self.drop = nn.Dropout(config.dropout)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.drop(self.w3(F.silu(self.w1(x)) * self.w2(x)))
class TransformerBlock(nn.Module):
def __init__(self, config: CodeLLMConfig):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.attn = CausalSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
self.ffn = SwiGLUFFN(config)
def forward(self, x, attention_mask=None, past_key_value=None, use_cache=False):
attn_out, present = self.attn(
self.ln_1(x),
attention_mask=attention_mask,
past_key_value=past_key_value,
use_cache=use_cache,
)
x = x + attn_out
x = x + self.ffn(self.ln_2(x))
return x, present
class CodeLLM(nn.Module):
def __init__(self, config: CodeLLMConfig):
super().__init__()
self.config = config
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size, config.n_embd),
drop = nn.Dropout(config.dropout),
h = nn.ModuleList([TransformerBlock(config) for _ in range(config.n_layer)]),
ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
if config.tie_word_embeddings:
self.lm_head.weight = self.transformer.wte.weight
self.apply(self._init_weights)
for name, p in self.named_parameters():
if name.endswith("c_proj.weight"):
nn.init.normal_(p, mean=0.0, std=config.initializer_range / math.sqrt(2 * config.n_layer))
print(f"CodeLLM initialized | params: {self.num_parameters:,}")
def _init_weights(self, module):
if isinstance(module, nn.Linear):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
if module.bias is not None:
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, mean=0.0, std=self.config.initializer_range)
@property
def num_parameters(self):
return sum(p.numel() for p in self.parameters() if p.requires_grad)
def forward(self, input_ids, attention_mask=None, labels=None, past_key_values=None, use_cache=False):
B, T = input_ids.size()
x = self.transformer.wte(input_ids)
x = self.transformer.drop(x)
presents = []
for i, block in enumerate(self.transformer.h):
past_kv = past_key_values[i] if past_key_values else None
x, present = block(x, attention_mask=attention_mask, past_key_value=past_kv, use_cache=use_cache)
if use_cache:
presents.append(present)
x = self.transformer.ln_f(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, shift_logits.size(-1)),
shift_labels.view(-1),
ignore_index=-100,
)
return {"loss": loss, "logits": logits, "past_key_values": presents if use_cache else None}
@torch.no_grad()
def generate(self, input_ids, max_new_tokens=256, temperature=0.8, top_k=50, top_p=0.95, eos_token_id=None):
self.eval()
past_key_values = None
eos = eos_token_id or self.config.eos_token_id
for _ in range(max_new_tokens):
input_slice = input_ids if past_key_values is None else input_ids[:, -1:]
out = self.forward(input_slice, past_key_values=past_key_values, use_cache=True)
past_key_values = out["past_key_values"]
logits = out["logits"][:, -1, :] / temperature
if top_k > 0:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
logits[logits < v[:, [-1]]] = float("-inf")
if top_p < 1.0:
sorted_logits, sorted_idx = torch.sort(logits, descending=True)
cumprobs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
remove = cumprobs - F.softmax(sorted_logits, dim=-1) > top_p
sorted_logits[remove] = float("-inf")
logits.scatter_(1, sorted_idx, sorted_logits)
probs = F.softmax(logits, dim=-1)
next_tok = torch.multinomial(probs, num_samples=1)
input_ids = torch.cat([input_ids, next_tok], dim=1)
if (next_tok == eos).all():
break
return input_ids |