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4a9e56a | 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 | from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
import math
def precompute(head_dim : int, seq_len : int, device : str, base : float = 10000.0):
assert head_dim % 2 == 0
theta_pair = torch.arange(0,head_dim,2).float()
theta = 1.0 / (base ** (theta_pair / head_dim)).to(device)
m = torch.arange(seq_len,device=device)
freqs = torch.outer(m,theta).float()
freqs_complex = torch.polar(torch.ones_like(freqs),freqs)
return freqs_complex
def apply_rope(x : torch.tensor, freqs_complex : torch.tensor, device:str):
x_complex = torch.view_as_complex(x.float().reshape(*x.shape[:-1],-1,2))
freqs_complex = freqs_complex.unsqueeze(0).unsqueeze(0)
x_rotated = x_complex * freqs_complex
x_out = torch.view_as_real(x_rotated)
x_out = x_out.reshape(*x.shape)
return x_out.type_as(x)
class CasualSelfAttention(nn.Module):
def __init__(self,config):
super().__init__()
self.c_attn = nn.Linear(config.n_embd, config.n_embd * 3,bias=False)
self.c_proj = nn.Linear(config.n_embd,config.n_embd,bias=False)
self.n_head = config.n_head
self.n_embd = config.n_embd
def forward(self,x,freqs):
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, C // self.n_head).transpose(1,2)
k = k.view(B,T, self.n_head, C // self.n_head).transpose(1,2)
v = v.view(B,T , self.n_head, C // self.n_head).transpose(1,2)
q = apply_rope(q, freqs, x.device)
k = apply_rope(k, freqs, x.device)
y = F.scaled_dot_product_attention(q,k,v,is_causal=True)
y = y.transpose(1,2).contiguous().view(B,T,C)
y = self.c_proj(y)
return y
class FeedForward(nn.Module):
def __init__(self,config):
super().__init__()
hidden_dim = int(2 * config.n_embd / 3)
hidden_dim = config.multiple_of * ((hidden_dim + config.multiple_of - 1) // config.multiple_of)
self.w1 = nn.Linear(config.n_embd,hidden_dim, bias=False)
self.w2 = nn.Linear(hidden_dim,config.n_embd, bias = False)
self.w3 = nn.Linear(config.n_embd, hidden_dim, bias= False)
def forward(self,x : torch.tensor):
return self.w2(F.silu(self.w1(x)) * self.w3(x))
class Block(nn.Module):
def __init__(self,config):
super().__init__()
self.sa = CasualSelfAttention(config)
self.mlp = FeedForward(config)
self.ln1 = nn.RMSNorm(config.n_embd,eps=1e-05)
self.ln2 = nn.RMSNorm(config.n_embd,eps =1e-05)
def forward(self,x,freqs):
x = x + self.sa(self.ln1(x),freqs)
x = x + self.mlp(self.ln2(x))
return x
class Model(nn.Module):
def __init__(self,config):
super().__init__()
self.register_buffer(
'freqs_complex',precompute(
config.n_embd // config.n_head,
config.block_size,
device=config.device
)
)
self.config = config
self.transformer = nn.ModuleDict(dict(
wte = nn.Embedding(config.vocab_size,config.n_embd),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f = nn.RMSNorm(config.n_embd,eps = 1e-05)
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size,bias=False)
self.transformer.wte.weight = self.lm_head.weight
def forward(self,idx,targets=None):
B,T = idx.size()
x = self.transformer.wte(idx)
for block in self.transformer.h:
x = block(x,self.freqs_complex[:T])
x = self.transformer.ln_f(x)
logits = self.lm_head(x)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1,logits.size(-1)),targets.view(-1))
return logits,loss
def sample_top_p(self,probs,p):
probs_sort, probs_idx = torch.sort(probs,dim=-1,descending=True)
probs_sum = torch.cumsum(probs_sort,dim=-1)
mask = probs_sum - probs_sort > p
probs_sort[mask] = 0.0
probs_sort.div_(probs_sort.sum(dim=-1,keepdim=True))
next_token = torch.multinomial(probs_sort,num_samples=1)
next_token = torch.gather(probs_idx,-1,next_token)
return next_token
@torch.no_grad()
def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None,top_p = None):
for _ in range(max_new_tokens):
idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
logits, _ = self(idx_cond)
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)
if top_p is not None:
idx_next = self.sample_top_p(probs,top_p)
else:
idx_next = torch.multinomial(probs, num_samples=1)
idx = torch.cat((idx, idx_next), dim=1)
return idx |