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Personal Note
Andrej rebuild gpt2 in pytorch.
Take Away Points
- Before entereing serious training, he use Shakespear’s work as a small debugging datset to see if a model can overfit. Overfitging is a should thing.
- If we use TF32 or BF32, (by default is FP32 in pytorch it takes more memory), gpu can do hundred times faster. Becuase the core is computing really fast, most time (more than 40%) it is waiting for the memory allocation/transfer… Every computation is done in a manner like breaking down to 4x4 matrix multiplication.
- when time a gpu programing, remember torch.cuda.synchronize()
- watch gpu: watch -n 0.1 nvidia-smi
- torch.set_float32_matmul_precision(‘high’) easily activate tf32 mode
- default is highest -> float 32
- High-> if avaible tensorflow32 (depends on GPU)
- simply use torch.set_float32_matmul_precision(‘high’) theoraticlaly should make us have 8x speed. However we only achieve 3x. Becuse we are still memory bound, moving data around still cost a lot.
- This can only be used in Amphere:
use torch.autocast(device_type=device,dtype=torch.bfloat16) to wrap the forward process. In this wrap, some CUDA ops can autocast to BF16, many other stays in float32. Matrix Multiplication will be BF16. - One debug technique: import code; code.interact(local=locals())
- torch.compile! Model = torch.compile(model)
- Flash Attention. Flash Attention2. Online softmax.
Use F.scale_dot_product_attention(q,k,v,is_causal=True) instead - Look for ugly numbers, make it to beautiful numbers. Any ugly numbers->increase it to have as much as 2 (Although flops will increase, time will decrease)
- ALL ABOVE CHANGES MAKE PRAGRAM TRAINING 10x FASTER!!
- Linear_warmup + cosine learning rate with minimum learning rate, see GPT3 paper for more details
- First stage of the training, the model is not differing each tokens, they are just learning which tokens can show up which are not and driving them probability to zero. It is the reason that why in the early training stage, a small batchsize will be OK, as the gradients will not behave different if you use full batchsize.
- parameters that should be weight decayed and should not be. WD: all weight tensors + embeddings (p.dim()>=2), NWD: all biaes, layernorms (p.dim()<2)
- AdamW’s use_fused configuration (accelarate training process)
- Model size up, lr down, batchsize up.
- Grad accumulation: Remember to normalize: loss /= grad_accum_steps
- when evaluation, use torch.Generator to create object used in torch.multinomial(xx,xx,generator=.), so that the generating process do not impact the global random number generator used for training.
- However, torch.compile must be banned, so that you can sample in the training process.
CODES FOR DDP (SAMPLE)
# torchrun --stand_alone --nproc_per_node=<num_gpu_per_node> <your_training_script.py> <script_arguments>
# Above only applies for single node training.# SETTINGS FOR EACH DIFFERENT RANK
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']) # It is a global rank, for each process it has a unique ddp_rankddp_local_rank = int(os.environ['LOCAL_RANK']) # It is a local rank in the local machine (node)ddp_world_size = int(os.environ['WORLD_SIZE']) # How many gpus (processes) in totaldevice = f'cuda:{ddp_local_rank}'torch.cuda.set_device(device)master_process = ddp_rank == 0
else:ddp_rank = 0ddp_local_rank = 0ddp_world_size = 1master_process = Truedevice = "cpu"if torhc.cuda.is_available():device = "cuda"elif hasattr(torch.backends,"mps") and torch.bakends.mps.is_available():device = "mps"print(f"using device:{device}")# IF YOU USE GRAD ACCUMULATION
total_batch_size = 524288 # batch size measured in token numbers
B = 16 # micro batch for each process
T = 1024 # sequence length
assert total_batch%(B * T * ddp_world_size) == 0
grad_accum_steps = total_batch_size // (B * T * ddp_world_size)# SET DATALOADER
Dataloader = DataLoader(*args, ddp_world_size, ddp_rank) # MUST! make each process deal with different part of datset# CREATE MODEL
model = createmodel()
model.to(device)
model = torch.compile(model)
if ddp:model = DDP(model,device_ids=[ddp_local_rank]) # this must be ddp_local_rank not ddp_rank
raw_model = model.module if ddp else model# FIX SEED
seed = 'YOUR LUCKY NUMBER'
torch.mannual_seed(seed)
if torch.cuda.is_available():torch.cuda.manual_seed(seed)# TRAIN
for step in range(max_steps):t0 = time.time() model.train()optimizer.zero_grad()loss_accum = 0.0for micro_step in range(grad_accum_steps):x,y = Dataloader.next_batch()x,y = x.to(device),y.to(device)with torch.autocast(device_type=device,dtype=torch.bfloat16):logits, loss = model(x,y)loss = loss / grad_accum_stepsloss_accum += loss.detach()if ddp:model.require_backward_grad_sync = (micro_step == grad_accum_steps - 1) # The ddp sync if applied to every micro step will be wasting time. So only the last backward in one accum cycle should be synchronized. See ddp.no_sync() contextmanager for official advice. Or use it in this way shown here.loss.backward() if ddp:torch.distributed.all_reduce(loss_accum,op=torch.distributed.ReduceOp.AVG)
norm = torch.nn.utils.clip_grad_norm_(model.parameters(),1.0)if step%100 == 0:# start evaluationmodel.eval()with torch.no_grad():# SOME EVALUATION CODE
if ddp:destroy_process_group()
)
