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import torch
import torch.nn as nn
import torch.nn.functional as F
from dataclasses import dataclass
import inspect
from .attention import CasualSelfAttention
class MLP(nn.Module):
def __init__(self, config):
super().__init__()
self.c_fc = nn.Linear(config.n_embd, 4 * config.n_embd)
self.gelu = nn.GELU(approximate='tanh')
self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
self.c_proj.NANOGPT_SCALE_INIT = 1
def forward(self, x):
x = self.c_fc(x)
x = self.gelu(x)
x = self.c_proj(x)
return x
class Block(nn.Module):
def __init__(self, config):
super().__init__()
self.ln_1 = nn.LayerNorm(config.n_embd)
self.attn = CasualSelfAttention(config)
self.ln_2 = nn.LayerNorm(config.n_embd)
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
@dataclass
class GPTConfig:
block_size: int = 1024 #max sequence length
vocab_size: int = 50257 #number of tokens: 50000 BPE merges + 256 byte tokens +1 special token which is endoftext
n_layer: int = 12 #number of layers
n_head: int = 12 #number of heads
n_embd: int = 768 #embedding dimensions
class GPT(nn.Module):
def __init__(self, config):
super().__init__()
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),
h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
ln_f = nn.LayerNorm(config.n_embd),
))
self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
#Weight sharing scheme
self.transformer.wte.weight = self.lm_head.weight
# init params
self.apply(self._init_weights)
def _init_weights(self, module):
if isinstance(module, nn.Linear):
std = 0.02
if hasattr(module, 'NANOGPT_SCALE_INIT'):
std *= (2 * self.config.n_layer) ** -0.5
torch.nn.init.normal_(module.weight, mean = 0.0, 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=0.0, std=0.02)
def forward(self, idx, targets=None):
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=idx.device)
pos_emb = self.transformer.wpe(pos)
tok_emb = self.transformer.wte(idx)
x = tok_emb + pos_emb
for block in self.transformer.h:
x = block(x)
x = self.transformer.ln_f(x)
logits = self.lm_head(x) #(B, T, vocab_size)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
def configure_optimizers(self, weight_decay, learning_rate, device_type, master_process):
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]
nodecay_params = [p for n, 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 decayed parameters tensors: {len(decay_params)}, with{num_decay_params}:parameters")
print(f"num non-decayed 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: {use_fused}")
optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=(0.9,0.95), eps=1e-8, fused=use_fused)
return optimizer
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