mirror of
https://github.com/deepseek-ai/ESFT.git
synced 2025-05-23 02:37:07 -04:00
259 lines
11 KiB
Python
259 lines
11 KiB
Python
import argparse
|
|
import json
|
|
import yaml
|
|
import os
|
|
import random
|
|
import torch
|
|
import torch.distributed as dist
|
|
from types import MethodType
|
|
from torch.utils.data import TensorDataset
|
|
from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, logging
|
|
|
|
from benchmarks import *
|
|
from utils import get_formatted_input_and_target, get_examples_from_buffer_pad, init_parallel_groups
|
|
from esft import to_esft
|
|
from deepseek.modeling_deepseek import DeepseekV2ForCausalLM
|
|
import time
|
|
|
|
os.environ["TOKENIZERS_PARALLELISM"] = "false"
|
|
os.environ["NCCL_AVOID_RECORD_STREAMS"] = "1"
|
|
logging.set_verbosity_error()
|
|
|
|
|
|
def main():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument("--base_model_path", type=str, required=True)
|
|
parser.add_argument("--expert_config", type=str, required=True)
|
|
parser.add_argument("--train_dataset", type=str, required=True)
|
|
parser.add_argument("--output_dir", type=str, required=True)
|
|
parser.add_argument("--train_config", type=str, required=True)
|
|
parser.add_argument("--wandb_api_key", type=str, required=False)
|
|
args = parser.parse_args()
|
|
|
|
expert_config = json.load(open(args.expert_config))
|
|
output_dir = args.output_dir
|
|
base_model_path = args.base_model_path
|
|
config = yaml.safe_load(open(args.train_config))
|
|
os.makedirs(args.output_dir, exist_ok=True)
|
|
|
|
seed = config['seed']
|
|
torch.manual_seed(seed)
|
|
torch.cuda.manual_seed(seed)
|
|
random.seed(seed)
|
|
|
|
if args.wandb_api_key is not None:
|
|
import wandb
|
|
wandb.login(key=args.wandb_api_key)
|
|
|
|
ep_size = config.get("ep_size", 1)
|
|
world_size, local_rank, ep_group, edp_group = init_parallel_groups(ep_size)
|
|
edp_size = world_size // ep_size
|
|
|
|
# Prepare data
|
|
tokenizer = AutoTokenizer.from_pretrained(base_model_path)
|
|
samples = [json.loads(i) for i in open(f"datasets/train/{args.train_dataset}.jsonl").readlines()]
|
|
buffer = []
|
|
for instance in samples:
|
|
input_ids, target_ids = get_formatted_input_and_target(instance['messages'], tokenizer, -100)
|
|
buffer.append((input_ids, target_ids))
|
|
seq_length = config['seq_length']
|
|
random_concat_ratio = config['random_concat_ratio']
|
|
concated_examples = get_examples_from_buffer_pad(buffer, seq_length, tokenizer, random_concat_ratio)
|
|
|
|
dataset = TensorDataset(concated_examples['input_ids'], concated_examples['labels'])
|
|
train_dataset, valid_dataset = torch.utils.data.random_split(dataset, [int(len(dataset) * 0.98), len(dataset) - int(len(dataset) * 0.98)])
|
|
|
|
# Training arguments
|
|
training_args = TrainingArguments(
|
|
output_dir=output_dir,
|
|
max_steps=config['steps'],
|
|
per_device_train_batch_size=config['per_device_batch_size'],
|
|
per_device_eval_batch_size=config['per_device_batch_size'],
|
|
warmup_steps=config['warmup_steps'],
|
|
weight_decay=config['weight_decay'],
|
|
logging_dir=f"{output_dir}/logs",
|
|
logging_steps=config['logging_steps'],
|
|
save_steps=config['save_steps'],
|
|
eval_strategy="steps",
|
|
eval_steps=config['eval_steps'],
|
|
gradient_accumulation_steps=config['gradient_accumulation_steps'],
|
|
load_best_model_at_end=True,
|
|
metric_for_best_model="loss",
|
|
greater_is_better=False,
|
|
bf16=True,
|
|
lr_scheduler_type='constant',
|
|
save_total_limit=5,
|
|
learning_rate=config['learning_rate'],
|
|
optim=config['optim'],
|
|
adam_beta1=config['adam_beta1'],
|
|
adam_beta2=config['adam_beta2'],
|
|
disable_tqdm=False,
|
|
gradient_checkpointing=config['gradient_checkpointing'],
|
|
gradient_checkpointing_kwargs={"use_reentrant": False} if config['gradient_checkpointing'] else {}, # if set to True, backward will raise bug
|
|
)
|
|
|
|
def data_collator(data):
|
|
input_ids = torch.stack([item[0] for item in data])
|
|
labels = torch.stack([item[1] for item in data])
|
|
return {"input_ids": input_ids, "labels": labels}
|
|
|
|
|
|
model = DeepseekV2ForCausalLM.from_pretrained(base_model_path, trust_remote_code=True, torch_dtype=torch.bfloat16,
|
|
ep_size=ep_size, attn_implementation="flash_attention_2")
|
|
model._ddp_params_and_buffers_to_ignore = [n for n, _ in model.named_parameters() if ".expert" in n] # we manage grad synchronization of expert parameters
|
|
to_esft(model, expert_config)
|
|
model.dummy = torch.nn.Parameter(torch.zeros(1, dtype=model.dtype)) # prevent DDP from having no trainable parameters
|
|
model._keys_to_ignore_on_save = ["dummy"]
|
|
expert_params = [p for n, p in model.named_parameters() if p.requires_grad and ".expert" in n]
|
|
for layer in model.model.layers:
|
|
if type(layer.mlp).__name__ != "DeepseekV2MoE":
|
|
continue
|
|
layer.mlp.ep_group = ep_group
|
|
# Force all2all backward the same number of times
|
|
if ep_size > 1 and not expert_config["non_expert_modules"]:
|
|
min_layer_id = min(int(k) for k, v in expert_config["experts"].items() if v)
|
|
mlp = model.model.layers[min_layer_id].mlp
|
|
forward = mlp.forward
|
|
def custom_forward(self, hidden_states: torch.Tensor):
|
|
return forward(hidden_states.requires_grad_(torch.is_grad_enabled()))
|
|
mlp.forward = MethodType(custom_forward, mlp)
|
|
|
|
# Initialize Trainer
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=train_dataset,
|
|
eval_dataset=valid_dataset,
|
|
data_collator=data_collator,
|
|
)
|
|
|
|
original_save_model = trainer.save_model
|
|
def custom_save_model(self, output_dir=None, _internal_call=False):
|
|
if output_dir is None:
|
|
output_dir = self.args.output_dir
|
|
|
|
# Ensure all ranks participate in saving
|
|
self._save(output_dir)
|
|
dist.barrier()
|
|
|
|
trainer.save_model = MethodType(custom_save_model, trainer)
|
|
|
|
original_save = trainer._save
|
|
def custom_save(self, output_dir=None, state_dict=None):
|
|
ep_rank = ep_group.rank()
|
|
edp_rank = edp_group.rank()
|
|
os.makedirs(output_dir, exist_ok=True)
|
|
|
|
if local_rank < ep_size and edp_rank == 0:
|
|
# Save expert model state
|
|
expert_state = {k: v for k, v in self.model.state_dict().items() if ".expert" in k}
|
|
expert_save_path = os.path.join(output_dir, f"expert_state_{ep_rank}.bin")
|
|
|
|
# Save expert optimizer state using parameter names instead of ids
|
|
optimizer = self.optimizer
|
|
opt_state_dict = optimizer.state_dict()
|
|
|
|
# Create a mapping from parameter id to parameter name
|
|
id_to_name = {}
|
|
for name, param in self.model.named_parameters():
|
|
if ".expert" in name:
|
|
id_to_name[id(param)] = name
|
|
|
|
# Get the mapping from optimizer state index to parameter
|
|
param_to_idx = {param: idx for idx, param in enumerate(optimizer.param_groups[0]['params'], 1)}
|
|
|
|
# Save optimizer state using parameter names as keys
|
|
expert_opt_state = {'state': {}, 'param_groups': opt_state_dict['param_groups']}
|
|
for param, idx in param_to_idx.items():
|
|
if id(param) in id_to_name:
|
|
param_name = id_to_name[id(param)]
|
|
if idx in opt_state_dict['state']:
|
|
expert_opt_state['state'][param_name] = opt_state_dict['state'][idx]
|
|
|
|
expert_opt_path = os.path.join(output_dir, f"expert_optimizer_{ep_rank}.bin")
|
|
|
|
# Save both states atomically
|
|
temp_expert_path = expert_save_path + ".tmp"
|
|
temp_opt_path = expert_opt_path + ".tmp"
|
|
torch.save(expert_state, temp_expert_path)
|
|
torch.save(expert_opt_state, temp_opt_path)
|
|
os.sync()
|
|
os.replace(temp_expert_path, expert_save_path)
|
|
os.replace(temp_opt_path, expert_opt_path)
|
|
|
|
dist.barrier()
|
|
|
|
if local_rank == 0:
|
|
original_state = self.model.state_dict()
|
|
optimizer_state = self.optimizer.state_dict()
|
|
|
|
# Create a mapping from parameter name to optimizer index for the current session
|
|
name_to_idx = {}
|
|
for name, param in self.model.named_parameters():
|
|
if ".expert" in name:
|
|
idx = next((i for i, p in enumerate(self.optimizer.param_groups[0]['params'], 1) if id(p) == id(param)), None)
|
|
if idx is not None:
|
|
name_to_idx[name] = idx
|
|
|
|
time.sleep(1)
|
|
|
|
for rank in range(1, ep_size):
|
|
expert_path = os.path.join(output_dir, f"expert_state_{rank}.bin")
|
|
opt_path = os.path.join(output_dir, f"expert_optimizer_{rank}.bin")
|
|
max_retries = 3
|
|
for retry in range(max_retries):
|
|
try:
|
|
expert_state = torch.load(expert_path)
|
|
expert_opt_state = torch.load(opt_path)
|
|
|
|
# Update model state
|
|
original_state.update(expert_state)
|
|
|
|
# Convert parameter names back to indices for the optimizer state
|
|
for param_name, state in expert_opt_state['state'].items():
|
|
if param_name in name_to_idx:
|
|
idx = name_to_idx[param_name]
|
|
optimizer_state['state'][idx] = state
|
|
|
|
break
|
|
except Exception as e:
|
|
if retry == max_retries - 1:
|
|
raise
|
|
time.sleep(1)
|
|
|
|
original_save(output_dir, state_dict=original_state)
|
|
# Save complete optimizer state
|
|
opt_save_path = os.path.join(output_dir, "optimizer.pt")
|
|
torch.save(optimizer_state, opt_save_path)
|
|
# remove those intermediate .bin files
|
|
for rank in range(1, ep_size):
|
|
os.remove(os.path.join(output_dir, f"expert_state_{rank}.bin"))
|
|
os.remove(os.path.join(output_dir, f"expert_optimizer_{rank}.bin"))
|
|
|
|
dist.barrier()
|
|
tokenizer.save_pretrained(output_dir)
|
|
|
|
|
|
trainer._save = MethodType(custom_save, trainer)
|
|
|
|
accelerator = trainer.accelerator
|
|
backward = accelerator.backward
|
|
def custom_backward(self, loss, **kwargs):
|
|
backward(loss, **kwargs)
|
|
if not self.sync_gradients or edp_size == 1:
|
|
return
|
|
for p in expert_params:
|
|
g = p.grad if p.grad is not None else torch.zeros_like(p)
|
|
dist.all_reduce(g, op=dist.ReduceOp.AVG, group=edp_group)
|
|
if p.grad is not g:
|
|
p.grad = g
|
|
accelerator.backward = MethodType(custom_backward, accelerator)
|
|
|
|
trainer.train()
|
|
|
|
print("Training complete")
|
|
|
|
if __name__ == "__main__":
|
|
main()
|