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finetune_llama.py
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finetune_llama.py
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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
"""Finetune LLAMA, Modified from pretrain_gpt.py"""
import torch
import math
from functools import partial
from megatron import get_args
from megatron import print_rank_0
from megatron import get_timers
from megatron import get_tokenizer
from megatron.core import mpu, tensor_parallel
from megatron.core.enums import ModelType
from megatron.data.gpt_dataset import build_train_valid_test_datasets
from megatron.data.prompt_dataset import SupervisedDataset
from megatron.model import GPTModel, GPTModelPipe
from megatron.training import pretrain
from megatron.utils import get_ltor_masks_and_position_ids
from megatron.utils import average_losses_across_data_parallel_group, update_rotary_pos_emb
from megatron.arguments import core_transformer_config_from_args
import deepspeed
from deepspeed.runtime.utils import see_memory_usage
from deepspeed.accelerator.real_accelerator import get_accelerator
import os
import subprocess
from torch import nn
import torch.nn.functional as F
from transformers import AutoTokenizer
def model_provider(pre_process=True, post_process=True):
"""Build the model."""
print_rank_0('building GPT model ...')
see_memory_usage(f"Before Building Model", force=True)
args = get_args()
config = core_transformer_config_from_args(args)
with deepspeed.zero.Init(sequence_data_parallel_group=mpu.get_sequence_data_parallel_group(),
remote_device=None if args.remote_device == 'none' else args.remote_device,
config_dict_or_path=args.deepspeed_config,
enabled=args.zero_stage == 3,
mpu=mpu):
if args.deepspeed and not args.no_pipeline_parallel:
model = GPTModelPipe(
config=config,
num_tokentypes=0,
parallel_output=True
)
# This is a hack to give us a reference to get_batch_pipe from within training.py
# We need to call model.set_batch_fn after deepspeed.initialize
model._megatron_batch_fn = get_batch_pipe
# Predompute the attention mask and store it in args. This avoids having to
# pipeline it as an activation during training. The mask is constant, and thus
# we can reuse it.
attention_mask = torch.tril(torch.ones(
(1, args.seq_length, args.seq_length), device=get_accelerator().current_device_name())).view(
1, 1, args.seq_length, args.seq_length)
# Convert attention mask to binary:
attention_mask = (attention_mask < 0.5)
if args.fp16:
attention_mask = attention_mask.half()
elif args.bf16:
attention_mask = attention_mask.bfloat16()
# Attention mask must be bool.
args.attn_mask = attention_mask.to(torch.bool)
# For prertaining, since sequence length is fixed, cache rotary embedding in args, to avoid communicating around
if args.use_rotary_position_embeddings:
update_rotary_pos_emb(args.seq_length)
else:
model = GPTModel(
config=config,
num_tokentypes=0,
parallel_output=True,
pre_process=pre_process,
post_process=post_process
)
see_memory_usage(f"After Building Model", force=True)
return model
def get_batch(data_iterator):
"""Generate a batch"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['text']
datatype = torch.int64
# Broadcast data.
if data_iterator is not None:
data = next(data_iterator)
else:
data = None
data_b = tensor_parallel.broadcast_data(keys, data, datatype)
# Unpack.
tokens_ = data_b['text'].long()
labels = tokens_[:, 1:].contiguous()
tokens = tokens_[:, :-1].contiguous()
# Get the masks and postition ids.
skip_mask = args.use_flash_attn or args.use_flash_attn_triton
attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss,
skip_mask)
# For DS's sequence parallel
seq_parallel_world_size = mpu.get_sequence_parallel_world_size()
seq_parallel_world_rank = mpu.get_sequence_parallel_rank()
# For Megatron's sequence parallel
if args.sequence_parallel:
seq_parallel_world_size = mpu.get_tensor_model_parallel_world_size()
seq_parallel_world_rank = mpu.get_tensor_model_parallel_rank()
seq_length = tokens.size(1)
assert seq_length % seq_parallel_world_size == 0
sub_seq_length = seq_length // seq_parallel_world_size
sub_seq_start = seq_parallel_world_rank * sub_seq_length
sub_seq_end = (seq_parallel_world_rank + 1) * sub_seq_length
tokens = tokens[:, sub_seq_start:sub_seq_end]
position_ids = position_ids[:, sub_seq_start:sub_seq_end]
# For DS's sequence parallel
if mpu.get_sequence_parallel_world_size() > 1:
labels = labels[:, sub_seq_start:sub_seq_end]
return tokens, labels, loss_mask, attention_mask, position_ids
def data_post_process(data, data_sampler_state_dict):
args = get_args()
if args.data_efficiency_curriculum_learning:
if 'seqlen_truncate' in data_sampler_state_dict['current_difficulties']:
args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_truncate'
current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_truncate']
if current_seqlen < args.seq_length:
data['text'] = data['text'][:, :(current_seqlen+1)].contiguous()
elif 'seqlen_reshape' in data_sampler_state_dict['current_difficulties']:
args.data_efficiency_curriculum_learning_seqlen_type = 'seqlen_reshape'
current_seqlen = data_sampler_state_dict['current_difficulties']['seqlen_reshape']
if current_seqlen < args.seq_length:
orig_num_token = torch.numel(data['text'])
reshape_len = (data['text'].size()[1] // (current_seqlen+1)) * (current_seqlen+1)
data['text'] = torch.cat((data['text'][:, :reshape_len].contiguous().view(-1, current_seqlen+1),
data['text'][:, -(current_seqlen+1):]), 0).contiguous()
num_row = math.ceil(orig_num_token / (current_seqlen+1))
num_row = min(num_row, data['text'].size()[0])
if num_row > 1 and num_row % 2 != 0:
num_row -= 1
data['text'] = data['text'][:num_row, :].contiguous()
else:
args.data_efficiency_curriculum_learning_seqlen_type = None
return data
def get_batch_pipe(data):
"""Modification of `get_batch` to work on `next(data_iterator)` instead of `data_iterator`"""
args = get_args()
tokenizer = get_tokenizer()
# Items and their type.
keys = ['input_ids','labels']
datatype = torch.int64
# Broadcast data.
data_b = tensor_parallel.broadcast_data(keys, data, datatype)
# Unpack.
# HF will automatically handle tokens alignment for labels, while in Megatron, we need to manually adjust it.
labels = data_b['labels'].long()[:,1:].contiguous()
tokens = data_b['input_ids'].long()[:,:-1].contiguous()
# Get the masks and postition ids.
attention_mask, _, position_ids = get_ltor_masks_and_position_ids(
tokens,
tokenizer.eod,
args.reset_position_ids,
args.reset_attention_mask,
args.eod_mask_loss)
# mask loss for SFT training
# we use padding to fill the prompt in the labels
loss_mask = labels.ne(tokenizer.pad)
if args.curriculum_learning_legacy and args.curriculum_seqlen < tokens.size()[1]:
# seqlen-based curriculum learning
# tokens, position_ids, labels, loss_mask have size [batch size, seqlen]
tokens = tokens[:, :args.curriculum_seqlen].contiguous()
position_ids = position_ids[:, :args.curriculum_seqlen].contiguous()
if labels is not None:
labels = labels[:, :args.curriculum_seqlen].contiguous()
loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()
return (tokens, position_ids, attention_mask), (labels, loss_mask)
def loss_func(loss_mask, moe_loss, mos_loss, output_tensor):
args = get_args()
losses = output_tensor.float()
loss_mask = loss_mask.view(-1).float()
loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum()
# Reduce loss for logging.
averaged_loss = average_losses_across_data_parallel_group([loss])
if args.mos or args.kd:
# assert max(args.num_experts) >= 1
loss = loss + moe_loss + mos_loss
if args.mos:
return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'mos loss': mos_loss}
elif args.kd:
return loss, {'total loss': loss, 'lm loss': averaged_loss[0], 'moe loss': moe_loss, 'kd loss': mos_loss}
print_rank_0('>>> total loss: {}, lm loss {}, kd loss {}'.format(loss, averaged_loss[0], mos_loss))
else:
if max(args.num_experts) <= 1:
return loss, {'lm loss': averaged_loss[0]}
else:
loss = loss + moe_loss
return loss, {'lm loss': averaged_loss[0], 'moe loss': moe_loss}
def calculate_mos_loss(args, stu_output, teacher_model, tokens, position_ids, attention_mask):
mos_loss = 0
alpha = args.kd_alpha_ce
beta = args.kd_beta_ce
kd_temp = args.kd_temp
if teacher_model:
with torch.no_grad():
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
assert args.curriculum_seqlen is not None
curriculum_seqlen = args.curriculum_seqlen
tokens = tokens[:, :curriculum_seqlen].contiguous()
position_ids = position_ids[:, :curriculum_seqlen].contiguous()
attention_mask = attention_mask[:, :, :curriculum_seqlen, :curriculum_seqlen].contiguous()
# No need to truncate labels as we do not need it for the teacher logits
tea_output, tea_other_losses = teacher_model(tokens, position_ids, attention_mask)
assert stu_output.size() == tea_output.size(), 'teacher and student output should match in size. Student: {}, Teacher: {}, CL seq length {}'.format(stu_output.size(), tea_output.size(), args.curriculum_seqlen)
student_logits = F.log_softmax(stu_output / kd_temp, dim=2)
tea_logits = F.softmax(tea_output / kd_temp, dim=2) # The target logits is expected to be probabilities. If we use log_softmax, then we need to set target_log to true when initializing the KLDivLoss.
mos_loss = kd_temp * kd_temp * nn.KLDivLoss(reduction='batchmean')(student_logits, tea_logits)
mos_loss = mos_loss.div(args.seq_length) * beta
return mos_loss
def forward_step(data_iterator, model):
"""Forward step."""
args = get_args()
timers = get_timers()
# Get the batch.
timers('batch-generator', log_level=2).start()
tokens, labels, loss_mask, attention_mask, position_ids = get_batch(
data_iterator)
timers('batch-generator').stop()
if args.data_efficiency_curriculum_learning:
args.curriculum_seqlen = tokens.size()[1]
if hasattr(args, 'data_efficiency_curriculum_learning_seqlen_type') and \
args.data_efficiency_curriculum_learning_seqlen_type == 'seqlen_reshape':
args.data_efficiency_curriculum_learning_numel = torch.numel(tokens)
if args.mos or args.kd:
# The forward func can return either the loss or the logits, depending on whether passing in the labels or not.
stu_output, other_losses = model(tokens, position_ids, attention_mask)
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
assert args.curriculum_seqlen is not None
labels = labels[:, :args.curriculum_seqlen].contiguous()
output_tensor = tensor_parallel.vocab_parallel_cross_entropy(stu_output.contiguous().float(), labels)
else:
output_tensor, other_losses = model(tokens, position_ids, attention_mask,
labels=labels)
if args.curriculum_learning_legacy and args.curriculum_seqlen < args.seq_length:
loss_mask = loss_mask[:, :args.curriculum_seqlen].contiguous()
moe_losses = []
for moe_loss in other_losses:
if moe_loss is not None:
moe_losses.append(moe_loss)
moe_loss = sum(moe_losses) * args.moe_loss_coeff
mos_loss = 0
if args.mos or args.kd:
assert model.training
if args.teacher_forward and args.teacher_model is not None:
mos_loss = calculate_mos_loss(args, stu_output,
args.teacher_model[0], tokens, position_ids, attention_mask)
# Output_tensor stores the standard loss, loos_func calculates the total loss.
return output_tensor, partial(loss_func, loss_mask, moe_loss, mos_loss)
def prompt_train_valid_test_datasets_provider(train_val_test_num_samples):
"""Build train, valid, and test datasets."""
args = get_args()
print_rank_0('> building finetune prompt datasets '
'for llama ...')
tokenizer = get_tokenizer()
# The finetune dataset is not large and defaults to using one file
train_ds = SupervisedDataset(args.data_path[0],tokenizer)
return train_ds, None ,None
def command_exists(cmd):
result = subprocess.Popen(f'type {cmd}', stdout=subprocess.PIPE, shell=True)
return result.wait() == 0
def git_ds_info():
from deepspeed.env_report import main as ds_report
ds_report()
# Write out version/git info
git_hash_cmd = "git rev-parse --short HEAD"
git_branch_cmd = "git rev-parse --abbrev-ref HEAD"
if command_exists('git'):
try:
result = subprocess.check_output(git_hash_cmd, shell=True)
git_hash = result.decode('utf-8').strip()
result = subprocess.check_output(git_branch_cmd, shell=True)
git_branch = result.decode('utf-8').strip()
except subprocess.CalledProcessError:
git_hash = "unknown"
git_branch = "unknown"
else:
git_hash = "unknown"
git_branch = "unknown"
print(f'**** Git info for Megatron: git_hash={git_hash} git_branch={git_branch} ****')
if __name__ == "__main__":
git_ds_info()
pretrain(prompt_train_valid_test_datasets_provider,
model_provider,
ModelType.encoder_or_decoder,
forward_step,
data_post_process=data_post_process)