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3920b5f | 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 | import torch
import torch.nn as nn
import torch.nn.functional as f
from dataclasses import dataclass
import inspect
@dataclass
class Config:
context_length : int = 1024
vocab_size: int = 50257
num_layers : int = 12
embedding_dim : int = 768
num_heads: int = 12
class MultiHeadAttention(nn.Module):
def __init__(self,config : Config,masked=False):
super(MultiHeadAttention,self).__init__()
self.num_heads = config.num_heads
self.masked = masked
self.embedding_dim = config.embedding_dim
self.c_attention = nn.Linear(config.embedding_dim,3*config.embedding_dim)
self.c_projection = nn.Linear(config.embedding_dim,config.embedding_dim)
self.c_projection.SCALE_INIT = 1.0
def forward(self,x):
B, T, C = x.shape
QKV = self.c_attention(x)
Query_q,Key_k,Value_v = QKV.split(self.embedding_dim,dim=-1)
Query_q = Query_q.view(B,T,self.num_heads,self.embedding_dim//self.num_heads).transpose(1,2)
Key_k = Key_k.view(B,T,self.num_heads,self.embedding_dim//self.num_heads).transpose(1,2)
Value_v = Value_v.view(B,T,self.num_heads,self.embedding_dim//self.num_heads).transpose(1,2)
# out = f.scaled_dot_product_attention(Query_q,Key_k,Value_v,is_causal=True)
if self.masked:
out = f.scaled_dot_product_attention(Query_q,Key_k,Value_v,is_causal=True)
else:
out = f.scaled_dot_product_attention(Query_q,Key_k,Value_v,is_causal=False)
out = out.transpose(1,2).contiguous().view(B,T,C)
return self.c_projection(out)
class MLP(nn.Module):
def __init__(self,config : Config):
super(MLP,self).__init__()
self.c_fc = nn.Linear(config.embedding_dim,4*config.embedding_dim)
self.gelu = nn.GELU(approximate='tanh')
self.c_projection = nn.Linear(4*config.embedding_dim,config.embedding_dim)
self.c_projection.SCALE_INIT = 1.0
def forward(self,x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_projection(x)
return x
class DecoderBlock(nn.Module):
def __init__(self,config : Config):
"""Decoder block without the encoder output"""
super(DecoderBlock,self).__init__()
self.masked_attention = MultiHeadAttention(config,masked=True)
self.layer_norm1 = nn.LayerNorm(config.embedding_dim)
# self.attention = MultiHeadAttention(config,masked=False)
# self.layer_norm2 = nn.LayerNorm(config.embedding_dim)
self.mlp = MLP(config)
self.layer_norm3 = nn.LayerNorm(config.embedding_dim)
def forward(self,x):
x = x + self.masked_attention(self.layer_norm1(x))
# x = x + self.attention(self.layer_norm2(x))
x = x + self.mlp(self.layer_norm3(x))
return x
class TransformerDecoder(nn.Module):
def __init__(self,config : Config):
super(TransformerDecoder,self).__init__()
self.config = config
self.word_token_embedding = nn.Embedding(self.config.vocab_size,self.config.embedding_dim)
self.word_position_embedding = nn.Embedding(self.config.context_length,self.config.embedding_dim)
layers = [DecoderBlock(config) for _ in range(config.num_layers)]
self.hidden_layers = nn.Sequential(*layers)
self.layer_norm = nn.LayerNorm(self.config.embedding_dim)
def forward(self,idx):
B,T = idx.shape
pos = torch.arange(0,T,dtype=torch.long,device=idx.device)
pos_embed = self.word_position_embedding(pos)
token_embed = self.word_token_embedding(idx)
x = pos_embed + token_embed
x = self.hidden_layers(x)
x = self.layer_norm(x)
return x
class GPT(nn.Module):
def __init__(self,config : Config):
super(GPT,self).__init__()
self.config=config
self.transformerDecoder = TransformerDecoder(config)
self.language_modeling_head = nn.Linear(config.embedding_dim,config.vocab_size,bias=False)
self.transformerDecoder.word_token_embedding.weight = self.language_modeling_head.weight
self.apply(self._init_weights)
def _init_weights(self,module):
if isinstance(module,nn.Linear):
std=0.02
if hasattr(module,'SCALE_INIT'):
std /= (2*self.config.num_layers)**0.5
torch.nn.init.normal_(module.weight,mean=0,std=std)
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,std=0.02)
def forward(self,idx,targets=None):
x = self.transformerDecoder(idx)
logits = self.language_modeling_head(x)
loss = None
if targets is not None:
loss = f.cross_entropy(logits.view(-1,logits.shape[-1]),targets.view(-1))
return logits,loss
@torch.no_grad()
def generate(self, idx, max_new_tokens=50, temperature=0.8, top_k=None, do_sample=False, eos_token_id=None):
self.eval()
B, T = idx.shape
device = idx.device
context_len = self.config.context_length
if T > context_len:
idx = idx[:, -context_len:]
T = idx.shape[1]
generated = idx.clone()
for _ in range(max_new_tokens):
input_ids = generated[:, -context_len:]
logits, _ = self.forward(input_ids, targets=None)
next_logits = logits[:, -1, :]
if temperature != 1.0 and temperature > 0.0:
next_logits = next_logits / temperature
if do_sample:
if top_k is not None and top_k > 0:
vals, idxs = next_logits.topk(top_k, dim=-1)
min_vals = vals[:, -1].unsqueeze(-1)
mask = next_logits < min_vals
next_logits = next_logits.masked_fill(mask, float('-inf'))
probs = torch.softmax(next_logits, dim=-1)
next_token = torch.multinomial(probs, num_samples=1)
else:
next_token = torch.argmax(next_logits, dim=-1, keepdim=True)
generated = torch.cat([generated, next_token], dim=1)
if eos_token_id is not None:
if (generated == eos_token_id).any(dim=1).all():
break
return generated
def configure_optimizer(self,weight_decay,lr,device_type,master_process):
param_dict = {pn:p for pn, p in self.named_parameters() if p.requires_grad}
decay_params = [p for pn, p in param_dict.items() if p.dim() >=2]
nodecay_params = [p for pn, p in param_dict.items() if p.dim() < 2]
optim_groups = [
{'params':decay_params,'weight_decay':weight_decay},
{'params':nodecay_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 nodecay_params)
if master_process:
print(f'num decay parameter tensors: {len(decay_params)} with {num_decay_params:,} parameters')
print(f'num nodecay parameter tensors: {len(nodecay_params)} with {num_nodecay_params:,} parameters')
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and device_type == 'cuda'
if master_process:
print(f'using fused AdamW optimizer: {use_fused}')
optimizer = torch.optim.AdamW(optim_groups, lr=lr, betas=(0.9, 0.95), eps=1e-8, fused=use_fused)
return optimizer
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