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15ddd55 | 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 | import torch
import math
import inspect
from dataclasses import dataclass
import torch.nn as nn
from torch.nn import functional as F
import tiktoken
enc = tiktoken.get_encoding("gpt2")
print("tiktoken cached ✅")
@dataclass
class GPTConfig:
block_size:int=512
vocab_size:int=50304
n_layer:int=10
n_head:int=8
n_embd:int=256
dropout:float=0.0
bias:bool=False
class LayerNorm(nn.Module):
def __init__(self,ndim,bias):
super().__init__()
self.weight=nn.Parameter(torch.ones(ndim))
self.bias=nn.Parameter(torch.zeros(ndim)) if bias else None
def forward(self,input):
return F.layer_norm(input,self.weight.shape, self.weight,self.bias,1e-5)
class CausalSelfAttention(nn.Module):
def __init__(self,config):
super().__init__()
assert config.n_embd % config.n_head==0
self.c_attn=nn.Linear(config.n_embd,3*config.n_embd,bias= config.bias)
self.c_proj=nn.Linear(config.n_embd,config.n_embd,bias=config.bias)
self.attn_dropout=nn.Dropout(config.dropout)
self.resid_dropout = nn.Dropout(config.dropout)
self.n_head = config.n_head
self.n_embd = config.n_embd
self.dropout = config.dropout
self.flash=hasattr(torch.nn.functional,'scaled_dot_product_attention')
if not self.flash:
print("WARNING using slow attention")
self.register_buffer("bias",torch.tril(torch.ones(config.block_size,config.block_size)).view(1,1,config.block_size,config.block_size))
def forward(self,x):
B,T,C=x.size()
q, k, v=self.c_attn(x).split(self.n_embd,dim=2)
k=k.view(B,T,self.n_head, C //self.n_head).transpose(1,2)
q=q.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)
if self.flash:
y=torch.nn.functional.scaled_dot_product_attention(q, k, v,attn_mask=None, dropout_p=self.dropout if self.training else 0,is_causal=True)
else:
att=(q@k.transpose(-2,-1))*(1.0/math.sqrt(k.size(-1)))
att=att.masked_fill(self.bias[:,:,:T,:T]==0,float('-inf'))
att=F.softmax(att,dim=-1)
att=self.attn_dropout(att)
y=att@v
y=y.transpose(1,2).contiguous().view(B, T, C)
y=self.resid_dropout(self.c_proj(y))
return y
class MLP(nn.Module):
def __init__(self,config):
super().__init__()
self.c_fc=nn.Linear(config.n_embd,4*config.n_embd,bias=config.bias)
self.gelu=nn.GELU()
self.c_proj=nn.Linear(4*config.n_embd,config.n_embd,bias=config.bias)
self.dropout=nn.Dropout(config.dropout)
def forward(self,x):
x=self.c_fc(x)
x=self.gelu(x)
x=self.c_proj(x)
x=self.dropout(x)
return x
class Block(nn.Module):
def __init__(self,config):
super().__init__()
self.ln_1=LayerNorm(config.n_embd,bias=config.bias)
self.attn=CausalSelfAttention(config)
self.ln_2=LayerNorm(config.n_embd,bias=config.bias)
self.mlp=MLP(config)
def forward(self,x):
x=x+self.attn(self.ln_1(x))
x=x+self.mlp(self.ln_2(x))
return x
class GPT(nn.Module):
def __init__(self,config):
super().__init__()
assert config.vocab_size is not None
assert config.block_size is not None
self.config=config
self.transformer = nn.ModuleDict(dict(
wte=nn.Embedding(config.vocab_size,config.n_embd),
wpe=nn.Embedding(config.block_size, config.n_embd),
drop=nn.Dropout(config.dropout),
h=nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f=LayerNorm(config.n_embd, bias=config.bias),
))
self.lm_head=nn.Linear(config.n_embd, config.vocab_size, bias=False)
self.transformer.wte.weight=self.lm_head.weight
self.apply(self._init_weights)
for pn,p in self.named_parameters():
if pn.endswith('c_proj.weight'):
torch.nn.init.normal_(p,mean=0.0,std=0.02/math.sqrt(2*config.n_layer))
print("Number of Parameters: %.2fM" % (self.get_num_params()/1e6))
def get_num_params(self,non_embedding=True):
n_params=sum(p.numel() for p in self.parameters())
if non_embedding:
n_params-=self.transformer.wpe.weight.numel()
return n_params
def _init_weights(self,module):
if isinstance(module,nn.Linear):
torch.nn.init.normal_(module.weight, mean=0.0,std=0.02)
if module.bias is not None:
torch.nn.init.zeros_(module.bias)
elif isinstance(module,nn.Embedding):
torch.nn.init.normal_(module.weight,mean=0.0,std=0.02)
def forward(self,idx,targets=None):
device =idx.device
b,t=idx.size()
assert t<=self.config.block_size,f"Cannot forward Sequence of Length {t} block size is only {self.config.block_size}"
pos=torch.arange(0,t,dtype=torch.long, device=device)
tok_emb=self.transformer.wte(idx)
pos_emb=self.transformer.wpe(pos)
x=self.transformer.drop(tok_emb + pos_emb)
for block in self.transformer.h:
x=block(x)
x=self.transformer.ln_f(x)
if targets is not None:
logits=self.lm_head(x)
loss=F.cross_entropy(logits.view(-1,logits.size(-1)),targets.view(-1),ignore_index=-1)
else:
logits=self.lm_head(x[:,[-1],:])
loss=None
return logits,loss
def configure_optimizers(self, weight_decay,learning_rate,betas,device_type):
param_dict={pn:p for pn, p in self.named_parameters()}
param_dict={pn:p for pn, p in param_dict.items() if p.requires_grad}
decay_params=[p for n,p in param_dict.items() if p.dim()>=2]
ndecay_params=[p for n, p in param_dict.items() if p.dim()<2]
optim_grps=[
{'params':decay_params,'weight_decay':weight_decay},
{'params':ndecay_params,'weight_decay':0.0}
]
num_decay_params=sum(p.numel() for p in decay_params)
num_nodecay_params=sum(p.numel() for p in ndecay_params)
print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non-decayed parameter tensors: {len(ndecay_params)}, with {num_nodecay_params:,} parameters")
fused_available='fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type=='cuda'
extra_args=dict(fused=True) if use_fused else dict()
optimizer = torch.optim.AdamW(optim_grps,lr=learning_rate,betas=betas, **extra_args)
print(f"Using Fused AdamW: {use_fused}")
return optimizer
@torch.no_grad()
def generate(self,idx,max_new_tokens,temperature=1.0,top_k=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)
idx_next=torch.multinomial(probs,num_samples=1)
if idx_next.item()==50256:
break
idx =torch.cat((idx,idx_next),dim=1)
return idx
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