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Gpt_2.py
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Gpt_2.py
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from dataclasses import dataclass
import torch
import torch.nn as nn
from torch.nn import functional as F
from datasets import load_dataset
import math
import inspect
import time
import os
import numpy as np
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, config.n_embd * 3)
self.c_proj = nn.Linear(config.n_embd, config.n_embd)
self.n_head = config.n_head
self.n_embd = config.n_embd
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, nT, C = x.shape
x = self.c_attn(x)
q, k, v = x.split(self.n_embd, dim = 2)
q = q.view(B, nT, self.n_head, C // self.n_head).transpose(1, 2)
k = k.view(B, nT, self.n_head, C // self.n_head).transpose(1, 2)
v = v.view(B, nT, self.n_head, C // self.n_head).transpose(1, 2)
attn = (q @ k.transpose(-2, -1)) * (1.0 /math.sqrt(k.size(-1)))
attn = torch.masked_fill(attn, self.bias[:, :, :nT, :nT] == 0, float('-inf'))
attn = F.softmax(attn, dim = -1)
x = attn @ v
x = x.transpose(1, 2).contiguous().view(B, nT, C)
x = self.c_proj(x)
return x
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)
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 = CausalSelfAttention(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
vocab_size: int = 50257
n_layer: int = 12
n_head: int = 12
n_embd: int = 768
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):
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):
B, T = idx.size()
assert T <= self.config.block_size, f"Cannot forward sequence of length {T}"
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)
loss = None
if targets is not None:
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
return logits, loss
@classmethod
def from_pretrained(cls, model_type):
assert model_type == "gpt2"
from transformers import GPT2LMHeadModel
print("loads weights from pretrained gpt: %s" % model_type)
config_args = {
'gpt2': dict(n_layer = 12, n_head = 12, n_embd = 768)
}[model_type]
config_args['vocab_size'] = 50257
config_args['block_size'] = 1024
config = GPTConfig(**config_args)
model = GPT(config)
sd = model.state_dict()
sd_keys = sd.keys()
sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')]
model_hf = GPT2LMHeadModel.from_pretrained(model_type)
sd_hf = model_hf.state_dict()
sd_keys_hf = sd_hf.keys()
sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')]
sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')]
transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
assert len(sd_keys_hf) == len(sd_keys), f"mismatch keys: {len(sd_keys_hf)} != {len(sd_keys)}"
for k in sd_keys_hf:
if any(k.endswith(w) for w in transposed):
assert sd_hf[k].shape[::-1] == sd[k].shape, f"{k} shape mismatch"
with torch.no_grad():
sd[k].copy_(sd_hf[k].t())
else:
assert sd_hf[k].shape == sd[k].shape, f"{k} shape mismatch"
with torch.no_grad():
sd[k].copy_(sd_hf[k])
return model
def configure_optimizers(self, weight_decay, learning_rate, device):
"this function defines which kind of parameters are doing weight decay and which are not"
#basic we dont perform weight decay on 1d tensors like nn.linear, and do it on other tensors
#start with all of the candidate parameters which required grad
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}
#create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
#i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms dont
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)
print(f"number decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
print(f"num non_decayed parameter tensor: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
#create AdamW optimizer and use the fused version if it is available
fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
use_fused = fused_available and 'cuda' in device
print(f"using fused AdamW: {use_fused}")
optimizer = torch.optim.AdamW(optim_groups, lr = learning_rate, betas= (0.9, 0.95), eps = 1e-8)
return optimizer
def load_tokens(filename):
npt = np.load(filename)
npt_convert= npt.astype(np.int64)
ptt = torch.tensor(npt_convert, dtype=torch.long)
return ptt
class DataloaderLite:
def __init__(self, B, T, process_rank, num_processes, split):
self.B = B
self.T = T
self.process_rank = process_rank
self.num_processes = num_processes
assert split in {"train", "val"}
data_root = "/Users/eddy/Documents/Build_from_scratch/edu_fineweb10BT"
shards = os.listdir(data_root)
shards = [s for s in shards if split in s]
shards = sorted(shards)
shards = [os.path.join(data_root, s) for s in shards]
self.shards = shards
assert len(shards) > 0, f"no shards found for split {split}"
if master_process:
print(f"found {len(shards)} shards for split {split}")
self.reset()
def reset(self):
self.current_shard = 0
self.tokens = load_tokens(self.shards[self.current_shard])
self.current_position = self.B * self.T * self.process_rank
def next_batch(self):
B, T = self.B, self.T
buf = self.tokens[self.current_position: self.current_position+B*T+1]
x = (buf[:-1]).view(B, T)
y = (buf[1:]).view(B, T)
self.current_position += B * T * self.num_processes
if self.current_position + (B * T * self.num_processes + 1) > len(self.tokens):
self.current_position = self.B * self.T * self.processes
return x, y
#--------------------------------------------------------------------------------
#inference
#num_return_sequences = 5
#max_length = 30
#model = GPT.from_pretrained('gpt2')
#
#model.eval()
#
#import tiktoken
#enc = tiktoken.get_encoding("gpt2")
#tokens = enc.encode("Hello, I'm a language model,")
#tokens = torch.tensor(tokens, dtype = torch.long)
#x = tokens.unsqueeze(0).repeat(num_return_sequences, 1)
#
#torch.manual_seed(44)
#
#while x.size(1) < max_length:
# with torch.no_grad():
# logits = model(x)
# logits = logits[:, -1, :]
# probs = F.softmax(logits, dim = -1)
# topk_probs, topk_indices = torch.topk(probs, 50, dim = -1)
# ix = torch.multinomial(topk_probs, 1)
# xcol = torch.gather(topk_indices, -1, ix)
# x = torch.cat((x, xcol), dim = 1)
#
#
#for i in range(num_return_sequences):
# tokens = x[i, :max_length].tolist()
# decoded = enc.decode(tokens)
# print(">", decoded)
#------------------------------------------------------------------------------------------
#------------------------------------------------------------------------------------------
#training
from torch.distributed import init_process_group, destroy_process_group
from torch.nn.parallel import DistributedDataParallel as DDP
ddp = int(os.environ.get('RANK', -1)) != -1
if ddp:
assert torch.cuda.is_available()
init_process_group(backend='nccl')
ddp_rank = int(os.environ['RANK'])
ddp_local_rank = int(os.environ['LOCAL_RANK'])
ddp_world_size = int(os.environ['WORLD_SIZE'])
device = f'cuda: {ddp_local_rank}'
torch.cuda.set_device(device)
master_process = ddp_rank == 0 #this process will do logging, checking etc
else:
ddp_rank = 0
ddp_local_rank = 0
ddp_world_size = 1
master_process = True
device = "cpu"
if torch.cuda.is_available():
device = "cuda"
elif hasattr(torch.backends, 'mps') and torch.backends.mps.is_available():
device = 'mps'
print(f"----using device----: {device}")
import tiktoken
B = 4
T = 512
total_batch_size = 524488 #not any more
grad_accum_steps = total_batch_size // (B * T * ddp_world_size)
if master_process:
print(f"total desired batch size; {total_batch_size}")
print(f"=> calculated gradient accumulation steps: {grad_accum_steps}")
train_loader = DataloaderLite(B, T, process_rank = ddp_rank, num_processes = ddp_world_size, split = 'train')
val_loader = DataloaderLite(B, T, process_rank = ddp_rank, num_processes = ddp_world_size, split = 'val')
model = GPT(GPTConfig())
model.to(device)
#model = torch.compile(model) #mps not supported
if ddp:
model = DDP(model, device_ids=[ddp_local_rank])
raw_model = model.module if ddp else model
max_lr = 6e-4
min_lr = max_lr * 0.1
warmup_steps = 10
max_step = 50
def get_lr(it):
# 1) linear warmup for warmup_iters steps
if it < warmup_steps:
return max_lr * (it + 1) /warmup_steps
# 2) if it > lr_decay_iters, return min learning rate
if it > max_step:
return min_lr
decay_ratio = (it - warmup_steps)/(max_step - warmup_steps)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
return min_lr + coeff * (max_lr - min_lr)
#optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4, betas = (0.9, 0.95), eps = 1e-8)
optimizer = model.configure_optimizers(weight_decay = 0.1, learning_rate = 6e-4, device=device)
for step in range(max_step):
t0 = time.time()
#evaluate model once in a while
if step%100 == 0:
model.eval()
val_loader.reset()
with torch.no_grad():
val_loss_accum = 0.0
val_loss_steps = 20
for _ in range(val_loss_steps):
x, y = val_loader.next_batch()
x, y = x.to(device), y.to(device)
logits, loss = model(x, y)
loss = loss / val_loss_steps
val_loss_accum += loss.detach()
if ddp:
dist.all_reduce(val_loss_accum, op = dist.ReduceOp.AVG)
if master_process:
print(f"validation loss:{val_loss_accum.item():.4f}")
#training loop
x, y = train_loader.next_batch()
x, y = x.to(device), y.to(device)
optimizer.zero_grad() #make sure clean gradient first
logits, loss = model(x, y) #compute loss and logits
if ddp:
model.require_backward_grad_sync = (step == grad_accum_steps - 1)
loss.backward() #backward the loss
if ddp:
dist.all_reduce(loss_accum, op = dist.ReduceOp.AVG)
norm = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
lr = get_lr(step)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
optimizer.step() #compute the new parameters
#torch.cuda.synchronize() #wait for the GPU to finish work
torch.mps.synchronize() #mps for macbook
t1 = time.time()
dt = t1 - t0
if master_process:
print(f"step {step}, loss: {loss.item()} | dt: {dt * 1000} ms")
if ddp:
destroy_process_group()