305 lines
13 KiB
Python
305 lines
13 KiB
Python
"""Training script for LLaMA model.
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torchrun --nproc_per_node 1 --master_addr localhost --master_port 25500 train.py --use_wandb
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torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --tp_size 2
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torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --pp_size 2
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torchrun --nproc_per_node 2 --master_addr localhost --master_port 25500 train.py --pp_size 1 --dp_size 2
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CUDA_DEVICE_MAX_CONNECTIONS=1 debugpy-run -p 5678 -m torch.distributed.run -- --nproc_per_node=2 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 train.py --pp_size 2
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CUDA_DEVICE_MAX_CONNECTIONS=1 torchrun --nproc_per_node=4 --nnodes=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:29400 --max_restarts=0 --tee=3 train.py
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#VERBOSE=0 torchrun --nproc_per_node 4 --master_addr localhost --master_port 25500 train.py --pp_size 2 --dp_size 2
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"""
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import os
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import numpy as np
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import torch.nn.functional as F
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import torch, torch.distributed as dist
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from torch.optim import AdamW
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from transformers import AutoConfig
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from transformers import AutoTokenizer
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from torch.utils.data import DataLoader, DistributedSampler
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from datasets import load_dataset,Features, Sequence, Value
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import argparse
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from datasets import Features, Sequence, Value
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import numpy as np
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from src.parallel.tensor_parallel.tensor_parallel import TensorParallel
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import src.distributed.process_group_manager as pgm
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from utils import set_all_seed, print
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from src.distributed.process_group_manager import setup_process_group_manager
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from src.parallel.pipeline_parallel import train_step_pipeline_1f1b, train_step_pipeline_afab, PipelineParallel
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from src.parallel.data_parallel.data_parallel_bucket import DataParallel
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from src.parallel.context_parallel import ContextParallel
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from model import Llama
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import wandb
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from src.distributed.distributed_primtives import all_reduce_loss_across_dp_ranks
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class MicroBatchDataLoader(DataLoader):
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def __init__(self, global_batch_size, micro_batch_size, seq_length, dataset_name, tokenizer_name, num_workers, num_proc, grad_acc=1, split="train", num_samples=None):
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self.global_batch_size = global_batch_size
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self.micro_batch_size = micro_batch_size
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self.seq_length = seq_length
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self.local_batch_size = self.global_batch_size // pgm.process_group_manager.dp_world_size # each DP rank gets a local batch
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self.num_local_micro_batches = self.local_batch_size // self.micro_batch_size
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self.num_global_micro_batches = self.global_batch_size // self.micro_batch_size
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self.grad_acc = grad_acc
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self.seq_length_per_gpu = seq_length // pgm.process_group_manager.cp_world_size
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self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_name)
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self.dataset = load_dataset(dataset_name, split=split)
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if num_samples:
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self.dataset = self.dataset.select(range(min(num_samples, len(self.dataset))))
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dist.barrier()
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# Tokenize and chunk the dataset
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self.tokenized_dataset = self.tokenize_dataset(self.dataset, "text", self.seq_length, num_proc)
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self.sampler = DistributedSampler(
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self.tokenized_dataset,
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num_replicas=pgm.process_group_manager.dp_world_size,
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rank=pgm.process_group_manager.dp_rank,
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shuffle=False
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)
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super().__init__(
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self.tokenized_dataset,
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batch_size=micro_batch_size if pgm.process_group_manager.pp_world_size > 1 else self.local_batch_size, # in PP we split a single batch into multiple micro-batches
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collate_fn=self.collate_batch,
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pin_memory=True,
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num_workers=num_workers,
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sampler=self.sampler,
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shuffle=False
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)
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def tokenize_dataset(self, dataset, text_column_name, sequence_length, num_proc):
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def _tokenizer_group_text(texts):
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tokenized_text_batch = self.tokenizer.batch_encode_plus(
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texts,
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return_attention_mask=False,
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return_token_type_ids=False,
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return_tensors='np'
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)
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concatenated_tokens = {'input_ids': np.concatenate(tokenized_text_batch['input_ids'])}
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total_length = len(concatenated_tokens['input_ids'])
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if total_length >= sequence_length + 1:
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total_length = ((total_length - 1) // sequence_length) * sequence_length + 1
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result = {
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'input_ids': [
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concatenated_tokens['input_ids'][i : i + sequence_length + 1]
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for i in range(0, total_length - sequence_length, sequence_length)
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]
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}
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return result
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tokenized_dataset = dataset.map(
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_tokenizer_group_text,
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input_columns=text_column_name,
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remove_columns=dataset.column_names,
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features=Features({"input_ids": Sequence(feature=Value(dtype="int64"), length=sequence_length + 1)}),
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batched=True,
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num_proc=num_proc, # Adjust this based on your system capabilities
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load_from_cache_file=True,
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desc=f"Grouping texts in chunks of {sequence_length+1}",
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)
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return tokenized_dataset
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def collate_batch(self, batch):
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batch_input_ids = torch.stack([torch.tensor(item['input_ids']) for item in batch])
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batch_size = batch_input_ids.size(0)
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start_idx = pgm.process_group_manager.cp_rank * self.seq_length_per_gpu
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end_idx = start_idx + self.seq_length_per_gpu
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input_ids = batch_input_ids[:, start_idx:end_idx].contiguous()
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target_ids = batch_input_ids[:, start_idx+1:end_idx+1].contiguous()
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position_ids = torch.arange(start_idx, end_idx, dtype=torch.long).unsqueeze(0).expand(batch_size, -1).contiguous()
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local_attn_mask = torch.tril(torch.ones((self.seq_length_per_gpu, self.seq_length_per_gpu), dtype=torch.bool))
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attn_mask = local_attn_mask.unsqueeze(0).expand(batch_size, -1, -1).contiguous()
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return {
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"input_ids": input_ids,
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"target_ids": target_ids,
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"position_ids": position_ids,
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"attn_mask": attn_mask,
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"hidden_states": None
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}
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def __iter__(self):
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if self._iterator is None:
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self._iterator = super().__iter__()
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return self
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def __next__(self):
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if self._iterator is None:
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self._iterator = super().__iter__()
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try:
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batch = next(self._iterator)
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except StopIteration:
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self._iterator = None
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raise StopIteration
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return batch
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def train_step(model, data_loader, device):
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acc_loss = 0.0
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# get the next batch
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batch = next(data_loader)
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input_ids = batch["input_ids"].to(device)
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target_ids = batch["target_ids"].to(device)
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for i in range(data_loader.grad_acc):
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outputs = model(input_ids=input_ids)
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# compute the loss
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batch_size, seq_len = input_ids.shape
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target_ids = target_ids.reshape(-1)
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outputs = outputs.view(seq_len*batch_size, -1)
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loss = F.cross_entropy(outputs, target_ids, reduction='mean')
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loss.backward()
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acc_loss += loss.item()
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acc_loss /= data_loader.grad_acc
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return acc_loss
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--tp_size", type=int, default=1)
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parser.add_argument("--cp_size", type=int, default=1)
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parser.add_argument("--pp_size", type=int, default=1)
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parser.add_argument("--dp_size", type=int, default=1)
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parser.add_argument("--use_wandb", action="store_true", default=False)
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parser.add_argument("--use_cpu", action="store_true", default=False)
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parser.add_argument("--master_addr", type=str, default="localhost")
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parser.add_argument("--master_port", type=int, default=29500)
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parser.add_argument("--load_path", type=str, default="smollm.pth")
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args = parser.parse_args()
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os.environ["OMP_NUM_THREADS"] = "1"
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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dtype = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.is_bf16_supported() else torch.float32
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os.environ["DTYPE"] = "bfloat16" if dtype == torch.bfloat16 else "float32"
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os.environ["FLASH_ATTEN"] = "1" # Use cuda kernels from flash attention repo to accelerate the training. Model dtype should be torch.float16!
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assert (dtype == torch.bfloat16 and os.getenv("FLASH_ATTEN") == "1") or os.getenv("FLASH_ATTEN") != "1", "Kernel operations requires dtype=torch.bfloat16"
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local_rank = int(os.environ["LOCAL_RANK"])
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world_size = int(os.environ["WORLD_SIZE"])
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host = os.environ["MASTER_ADDR"]
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port = int(os.environ["MASTER_PORT"])
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# SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 10, 6, 2, 1e-4, 20, 1800, 42
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## hyperparameters
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SEQ_LEN, GLOBAL_BATCH_SIZE, MICRO_BATCH_SIZE, LEARNING_RATE, NUM_SAMPLES, MAX_TOKENS, SEED = 1024, 16, 4, 3e-4, 100000, int(10e8), 42
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grad_acc = 16
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assert SEQ_LEN % args.cp_size == 0, "SEQ_LEN must be divisible by cp_size for Context Parallelism"
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backend = "gloo" if args.use_cpu else "nccl"
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if backend == "nccl":
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torch.cuda.set_device(local_rank)
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device = torch.device("cuda", local_rank)
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else:
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device = torch.device("cpu")
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dist.init_process_group(rank=local_rank, world_size=world_size, backend=backend, init_method=f"tcp://{host}:{port}")
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setup_process_group_manager(tp_size=args.tp_size, cp_size=args.cp_size, pp_size=args.pp_size, dp_size=args.dp_size)
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# if pgm.process_group_manager.global_rank == 0:
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# display_4D_parallelism_grid()
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set_all_seed(SEED)
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dataset_name = "roneneldan/TinyStories"
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model_name = "HuggingFaceTB/SmolLM-360M-Instruct"
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config = AutoConfig.from_pretrained(model_name)
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config.num_attention_heads = 16
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config.num_key_value_heads = 4
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model = Llama(config=config)
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.init(
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project="picotron",
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name=f"test_convergence_{pgm.process_group_manager}",
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config={
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"tensor_parallel_size": pgm.process_group_manager.tp_size,
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"pipeline_parallel_size": pgm.process_group_manager.pp_size,
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"data_parallel_size": pgm.process_group_manager.dp_size,
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"model": model_name,
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"dataset": dataset_name,
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"max_tokens": MAX_TOKENS,
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"learning_rate": LEARNING_RATE,
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"seed": SEED,
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"micro_batch_size": MICRO_BATCH_SIZE,
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"global_batch_size": GLOBAL_BATCH_SIZE,
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},
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)
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if pgm.process_group_manager.tp_world_size > 1:
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TensorParallel(model)
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# if pgm.process_group_manager.cp_size > 1:
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#TODO: do at the very end when we have fix convergence issue
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# model = ContextParallel(model, config)
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if pgm.process_group_manager.pp_world_size > 1:
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model = PipelineParallel(model, config)
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if pgm.process_group_manager.dp_world_size > 1:
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model = DataParallel(model)
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model.to(dtype).to(device)
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model.train()
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data_loader = MicroBatchDataLoader(global_batch_size=GLOBAL_BATCH_SIZE, micro_batch_size=MICRO_BATCH_SIZE, seq_length=SEQ_LEN, dataset_name=dataset_name, tokenizer_name=model_name, grad_acc = grad_acc,num_workers=4, num_proc=4, num_samples=NUM_SAMPLES)
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tensor_shapes = (data_loader.micro_batch_size, data_loader.seq_length_per_gpu, config.hidden_size)
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optimizer = AdamW(model.parameters(), lr=LEARNING_RATE)
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trained_tokens, step = 0, 0
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tokens_per_step = data_loader.num_global_micro_batches * data_loader.micro_batch_size * SEQ_LEN * grad_acc
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dist.barrier()
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#TODO: Double-check consumed tokens after each steps (for example, MICRO_BATCH_SIZE=2 and using only dp_size=4, num_local_micro_batches=0 => division by 0)
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#TODO: Check convergence
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#TODO: Try multi-nodes
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#TODO: Add activation checkpointing
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#TODO: add gradient accumulation
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while trained_tokens < MAX_TOKENS:
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#TODO: Add epoch support
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# data_loader.set_epoch(step)
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optimizer.zero_grad()
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if pgm.process_group_manager.pp_world_size > 1:
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loss = train_step_pipeline_afab(model, data_loader, tensor_shapes, device)
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else:
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loss = train_step(model, data_loader, device)
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loss = all_reduce_loss_across_dp_ranks(loss, device)
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optimizer.step()
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trained_tokens += tokens_per_step
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step += 1
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# In DDP implementation I need to reset the gradient buffers
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if hasattr(model, 'reset'):
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model.reset()
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if pgm.process_group_manager.tp_rank == 0 and pgm.process_group_manager.dp_rank == 0 and pgm.process_group_manager.pp_is_last_stage:
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print(f"[rank {pgm.process_group_manager.global_rank}] Step: {step}, Loss: {loss:.4f}, "
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f"Global batch size: {tokens_per_step}, "
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f"Tokens: {trained_tokens}/{MAX_TOKENS}"
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)
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.log({"loss": loss, "trained_tokens": trained_tokens})
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if pgm.process_group_manager.global_rank == 0 and args.use_wandb:
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wandb.finish()
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dist.destroy_process_group()
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