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train.py
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train.py
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"""
Read our announcement blog post: https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html.
This script trains a model using FSDP with LoRA & QLoRA. It pulls inspiration from
- llama-recipes (https://github.com/facebookresearch/llama-recipes/blob/main/src/llama_recipes/finetuning.py)
- PyTorch FSDP docs (https://pytorch.org/tutorials/intermediate/FSDP_tutorial.html)
- bitsandbytes (https://github.com/TimDettmers/bitsandbytes)
For information on the different arguments, run `python train.py --help`
You should treat this script as an alpha/preview release. If you're not comfortable with testing and debugging
models, we'd suggest holding off for a few months while the community more fully tests the approach.
"""
# Imports
# General
import copy
import functools
import gc
import math
import os
import sys
import time
import types
from contextlib import nullcontext
from glob import glob
from pathlib import Path
from typing import Dict, List
import bitsandbytes as bnb
import safetensors
import torch
import torch.distributed as dist
import torch.multiprocessing as mp
import torch.optim as optim
from accelerate import init_empty_weights
from accelerate.utils import set_seed
# Model loading
from bitsandbytes.nn import Linear4bit, Params4bit
from fastcore.parallel import parallel
# Argument parsing
from fastcore.script import Param, bool_arg, call_parse
from packaging.version import parse
from safetensors.torch import save_file
# Torch + distributed training
from torch import Tensor, nn
from torch.distributed.algorithms._checkpoint.checkpoint_wrapper import (
CheckpointImpl,
apply_activation_checkpointing,
checkpoint_wrapper,
offload_wrapper,
)
# FSDP
from torch.distributed.fsdp import FullStateDictConfig, MixedPrecision, StateDictType
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.api import BackwardPrefetch, CPUOffload, ShardingStrategy
from torch.distributed.fsdp.sharded_grad_scaler import ShardedGradScaler
from torch.distributed.fsdp.wrap import (
_or_policy,
lambda_auto_wrap_policy,
transformer_auto_wrap_policy,
)
from torch.nn.utils.rnn import pad_sequence
from torch.optim.lr_scheduler import LambdaLR
from torch.profiler import ProfilerActivity, profile, record_function
from torch.utils.data import DataLoader, Dataset, DistributedSampler
from tqdm.auto import tqdm
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer
from transformers.optimization import get_linear_schedule_with_warmup
from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
from transformers.utils import SAFE_WEIGHTS_INDEX_NAME, SAFE_WEIGHTS_NAME, hub
try:
from hqq.core.quantize import BaseQuantizeConfig, HQQBackend, HQQLinear
except ImportError:
HQQLinear = None
pass
# To add a new model, import the transformer, attention, & MLP layers
# for the wrapping policy and `check_fn` in activation checkpointing
from transformers.models.llama.modeling_llama import (
LLAMA_ATTENTION_CLASSES,
LlamaDecoderLayer,
LlamaMLP,
)
from transformers.models.mistral.modeling_mistral import (
MISTRAL_ATTENTION_CLASSES,
MistralDecoderLayer,
MistralMLP,
)
# To get rid of tokenizers warnings for now
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# For logging things during training
try:
import wandb
except ImportError:
pass
# LoRA and DORA modules
sys.path.append("./scripts")
from dora import BNBDORA, HQQDORA, DORALayer, MagnitudeLayer
from lora import LORA
from profiling_utils import profiling_context
class Logger:
def __init__(self, args, log_to="stdout", project_name="fsdp_qlora", entity=None, group=None, name=None, rank=0):
# self.log_every_n_steps = log_every_n_steps TODO: add this back as an option
self.log_to = log_to
if self.log_to == "wandb" and rank==0:
import wandb
wandb.init(project=project_name, entity=entity, group=group, name=name, config=args)
def log(self, d:Dict, rank:int):
if rank != 0: return
if self.log_to == "tqdm":
for k,v in d.items():
tqdm.write(f'{k}: {v}')
elif self.log_to == "wandb":
wandb.log(d)
elif self.log_to == "stdout":
for k,v in d.items():
print(f'{k}: {v}')
def finish(self, rank=0):
if self.log_to == "wandb" and rank==0: wandb.finish()
def update_progress_bar(progress_bar:tqdm, epoch:int, log_loss:float, log_lr:float, rank:int):
"""Updates the progress bar with the current epoch, loss, and learning rate"""
if rank == 0:
if log_lr >=0:
progress_bar.set_description(f"Epoch {epoch}, Loss {log_loss:.3f}, LR {log_lr:.2e}", refresh=True)
else:
progress_bar.set_description(f"Epoch {epoch}, Loss {log_loss:.3f}", refresh=True)
def n_loading_workers(quant_method:str, param_count:float):
devprops = torch.cuda.get_device_properties(torch.cuda.current_device())
left = int(os.cpu_count()/torch.cuda.device_count())
right = int((4 if quant_method == "hqq" else 8) * (devprops.total_memory/1e9/40) * (70/(param_count/1e9)))
return min(left, right)
# Utilities related to model loading
def replace_linear(model:nn.Module, linear_replacement:nn.Module, quant_config:dict|None=None,
skip_modules:List[str]=["lm_head"], **kwargs):
"""
Replace linear modules with a new Linear module.
Parameters:
model (`torch.nn.Module`):
Input model or `torch.nn.Module` as the function is run recursively.
linear_replacement (`torch.nn.Module`):
The linear module that replaces the old one. Only expects standard arguments.
If other arguments need to be passed, use a lambda.
skip_modules (`List[str]`, *optional*, defaults to `lm_head`):
List of modules names not to convert. Defaults to `lm_head`.
"""
for name, module in model.named_children():
if name in skip_modules:
continue
if len(list(module.children())) > 0:
replace_linear(module, linear_replacement, quant_config, skip_modules, **kwargs)
if isinstance(module, torch.nn.Linear):
if issubclass(linear_replacement, Linear4bit):
model._modules[name] = linear_replacement(
module.in_features,
module.out_features,
module.bias is not None,
**kwargs
)
elif issubclass(linear_replacement, HQQLinear):
model._modules[name] = linear_replacement(module, quant_config, **kwargs)
else:
raise ValueError(f"Unsupported linear replacement: {type(linear_replacement)}")
return model
def setup_quantized_meta_for_peft(model:nn.Module):
"""Replaces `quant_state.to` with a dummy function to prevent PEFT from moving `quant_state` to meta device"""
def temp_to_method(self, *args, **kwargs):
return self
for param in model.parameters():
if isinstance(param, Params4bit):
param.quant_state._orig_to = param.quant_state.to
param.quant_state.to = types.MethodType(temp_to_method, param.quant_state)
def setup_quantized_peft_meta_for_training(model:nn.Module):
"""Replaces dummy `quant_state.to` method with the original function to allow training to continue"""
for param in model.parameters():
if isinstance(param, Params4bit) and hasattr(param.quant_state, '_orig_to'):
param.quant_state.to = param.quant_state._orig_to
param.quant_state._orig_to = None
def load_and_quantize(module:nn.Module, name:str, value:Tensor, device:torch.device=None, dtype:torch.dtype=None,
skip_names:list[str]=[], to_cpu:bool=False, to_meta:bool=False, verbose:bool=False, quant_method:str='bnb',
is_dora:bool=False):
"""
Loads `value` tensor into submodule of `module`, optionally skipping `skip_names` and converting to `dtype`.
Quantizes `Params4bit` on `device` then places on "cpu" if to_cpu=True or "meta" if to_meta=True.
"""
def place_on_device(value):
if to_meta:
device = 'meta'
elif to_cpu:
device = 'cpu'
return value.to(device=device, dtype=dtype)
if any([skip_name in name for skip_name in skip_names]):
if verbose:
print(f"Skipping {name} because it is in skip_names")
return
module_key, _, value_key = name.rpartition('.')
try:
submodule = module.get_submodule(module_key)
except AttributeError as e:
print(f"Module {module_key} not found:\n{e}")
return
try:
if quant_method=='bnb':
param = submodule.get_parameter(value_key)
if isinstance(param, Params4bit):
# With `sync_module_states=True`, a meta device Params4bit needs to be the same
# shape as the quantized Params4bit with an initialized quant_state. However,
# FSDP only syncs parameters and buffers, so the quant_state isn't copied. This
# workaround quantizes Params4bit to initialize quant_state on all ranks, then
# replaces Params4bit's data with a meta tensor to free memory on non-rank 0.
if is_dora:
setattr(submodule, "dora_scale", value.norm(p=2, dim=1).to(dtype=dtype).to("cpu"))
value = type(param)(value.to(device=device, dtype=dtype).data, **param.__dict__).cuda(device)
if to_meta:
value = type(param)(value.data.to("meta"), **value.__dict__)
elif to_cpu:
value = type(param)(value.data.to("cpu"), **value.__dict__)
else:
value = type(param)(place_on_device(value).data)
elif quant_method=='hqq':
if isinstance(submodule, HQQLinear):
if value_key == "weight":
# Like `Params4bit`, this workaround quantizes `HQQLinear`` per device so the quantization
# meta dictionary is created on all ranks, before converting to meta on non-rank 0.
submodule.linear_layer.to_empty(device=device)
submodule.linear_layer.weight.data.copy_(value.to(device=device, dtype=dtype))
if is_dora:
setattr(submodule, "dora_scale", value.norm(p=2, dim=1).to(dtype=dtype).to("cpu"))
submodule.initialize()
if to_meta:
setattr(submodule, "W_q", nn.Parameter(submodule.W_q.to("meta")))
elif to_cpu:
setattr(submodule, "W_q", nn.Parameter(submodule.W_q.to("cpu")))
submodule.in_gpu = False
if value_key == "bias":
raise ValueError("Bias not supported in HQQLinear yet!")
else:
param = submodule.get_parameter(value_key)
value = type(param)(place_on_device(value).data)
except AttributeError:
# it's a buffer
value = place_on_device(value)
pass
if HQQLinear is None or not isinstance(submodule, HQQLinear):
setattr(submodule, value_key, value)
# DATASET + DATALOADERS (modified from llama recipes)
# Formatting prompts in alpaca
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
# Dataset class
class InstructionDataset(Dataset):
def __init__(self, dataset, tokenizer, style="alpaca"):
self.dataset = dataset
self.tokenizer = tokenizer
self.style = style
def __len__(self):
return len(self.dataset)
def __getitem__(self, index):
IGNORE_INDEX = -100 # The default setting in CrossEntropyLoss
if self.style == "guanaco":
prompt = self.dataset[index]["text"].split("### Assistant: ")[0]
example = self.dataset[index]["text"]
elif self.style == "qna":
prompt_template = "###Context:\n{context}\n###Question:\n{question}\n###Answer:\n"
sample = self.dataset[index]
prompt = prompt_template.format_map(sample)
example = prompt + sample['answer']
elif self.style == "qna_no_ctx":
prompt_template = "###Question:\n{question}\n###Answer:\n"
sample = self.dataset[index]
prompt = prompt_template.format_map(sample)
example = prompt + sample['answer']
else: # Alpaca
ann = self.dataset[index]
if ann.get("input", "") == "":
prompt = PROMPT_DICT["prompt_no_input"].format_map(ann)
else:
prompt = PROMPT_DICT["prompt_input"].format_map(ann)
example = prompt + ann["output"]
prompt = torch.tensor(
self.tokenizer.encode(prompt), dtype=torch.int64
)
example = self.tokenizer.encode(example)
example.append(self.tokenizer.eos_token_id)
example = torch.tensor(
example, dtype=torch.int64
)
labels = copy.deepcopy(example)
labels[: len(prompt)] = -1
example_mask = example.ge(0)
label_mask = labels.ge(0)
example[~example_mask] = 0
labels[~label_mask] = IGNORE_INDEX
return {
"input_ids": example.tolist(),
"labels": labels.tolist(),
"attention_mask":example_mask.tolist(),
}
# And to get the dataloader
def get_dataloader(tokenizer:PreTrainedTokenizerFast, args:Dict):
"""Creates a dataset and appropriate dataloader with distributed sampler."""
# Importing here rather than at the start to avoid multiprocessing issues
from datasets import Dataset, load_dataset
# Load the source dataset
if args["dataset"] == "alpaca":
dataset = load_dataset("yahma/alpaca-cleaned")['train']
elif args["dataset"] == "alpaca_sample":
dataset = load_dataset("yahma/alpaca-cleaned", split=f"train[:{args['dataset_samples']}]")
elif args["dataset"] == "dummy":
dataset = Dataset.from_dict({
'instruction': ["instruction"]*args["dataset_samples"],
'input': ["input"]*args["dataset_samples"],
'output': ["output"*args["context_length"]*2]*args["dataset_samples"]} # A long output to test memory usage (gets truncated)
)
elif args["dataset"] == "guanaco":
dataset = load_dataset("timdettmers/openassistant-guanaco", split="train")
elif args["dataset"] == "sql":
dataset = load_dataset("knowrohit07/know_sql")['validation']
dataset = dataset.shuffle(seed=args["seed"])
dataset = dataset.select(range(1000,len(dataset)))
elif args["dataset"] == "orca_math":
dataset = load_dataset("microsoft/orca-math-word-problems-200k")['train'].shuffle(seed=42)
# train with 10k for starters. Then 100k.
dataset = dataset.select(range(0,args['dataset_samples']))
# truncate dataset so it's evenly divisible by grad_accumulation_steps
dataset = dataset.select(range(0, len(dataset)-len(dataset)%(args["batch_size"]*args["gradient_accumulation_steps"])))
# # Create the InstructionDataset
if args["dataset"] == "guanaco":
dataset = InstructionDataset(dataset, tokenizer, style="guanaco")
elif args["dataset"] == "sql":
dataset = InstructionDataset(dataset, tokenizer, style="qna")
elif args["dataset"] == "orca_math":
dataset = InstructionDataset(dataset, tokenizer, style="qna_no_ctx")
else: # (w/ alpaca prompt formatting)
dataset = InstructionDataset(dataset, tokenizer, style="alpaca")
# Collate function
def collate_fn(batch, with_attention_mask=False):
# To list of tensors
input_ids = [torch.tensor(item['input_ids']) for item in batch]
attention_masks = [torch.tensor(item['attention_mask']) for item in batch]
labels = [torch.tensor(item['labels']) for item in batch]
# Pad + truncate
input_ids = pad_sequence(input_ids, batch_first=True, padding_value=tokenizer.pad_token_id)[:, :args["context_length"]]
if with_attention_mask:
attention_masks = pad_sequence(attention_masks, batch_first=True, padding_value=0)[:, :args["context_length"]]
else:
attention_masks = None
labels = pad_sequence(labels, batch_first=True, padding_value=-100)[:, :args["context_length"]]
# Return dict
return {'input_ids': input_ids, 'attention_mask': attention_masks, 'labels': labels}
# For distributed training, use DistributedSampler
sampler = DistributedSampler(dataset, seed=args["seed"])
# Use the custom collate function in DataLoader
dataloader = DataLoader(dataset, batch_size=args["batch_size"], collate_fn=collate_fn, sampler=sampler)
return dataloader
# LR scheduler.
def _get_cosine_one_cycle_lr_lambda(
current_step: int, *, num_warmup_steps: int, num_training_steps: int, min_lr_fraction = 0.1,
):
if current_step < num_warmup_steps:
return float(current_step) / float(max(1, num_warmup_steps))
scale_term = (1 - min_lr_fraction)
progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps))
return (math.cos(math.pi * progress)+1) * 0.5 * scale_term + min_lr_fraction
def get_cosine_one_cycle_scheduler(optimizer:optim.Optimizer, num_warmup_steps:int, num_training_steps:int, min_lr_fraction:float=0.1):
"A more general cosine scheduler with to control the minimum learning rate"
lr_lambda = functools.partial(
_get_cosine_one_cycle_lr_lambda,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
min_lr_fraction=min_lr_fraction
)
return LambdaLR(optimizer, lr_lambda, last_epoch=-1)
def get_lr_scheduler(optimizer:optim.Optimizer, dataloader:DataLoader, gradient_accumulation_steps:int, args:Dict):
"""Returns linear, cosine, or constant learning rate scheduler"""
num_training_steps = args['num_epochs'] * len(dataloader) // gradient_accumulation_steps
num_warmup_steps = int(num_training_steps * 0.1)
if args['lr_scheduler'] == "linear":
lr_scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps)
elif args['lr_scheduler'] == "cosine":
lr_scheduler = get_cosine_one_cycle_scheduler(optimizer, num_warmup_steps, num_training_steps, min_lr_fraction=0.1)
elif args['lr_scheduler'] == "constant":
lr_scheduler = None
else:
raise NotImplementedError(f"{args['lr_scheduler']} LR scheduler not implemented yet")
return lr_scheduler, num_training_steps
# Optimizer
def get_optimizer(model:nn.Module, args:Dict):
"""Returns an optimizer. We can add more options here if needed."""
if args["optimizer"] in ["adam", "fused_adam"]:
return optim.Adam(model.parameters(), lr=args['lr'], fused=args["optimizer"]=="fused_adam")
elif args["optimizer"] == "sgd":
return optim.SGD(model.parameters(), lr=args['lr'])
elif args["optimizer"] == "adadelta":
return optim.Adadelta(model.parameters(), lr=args['lr'])
elif args["optimizer"] in ["adamw", "fused_adamw"]:
return torch.optim.AdamW(model.parameters(), lr=args['lr'], betas=(0.9,0.95),
eps=1e-5, weight_decay=args['wd'], fused=args["optimizer"]=="fused_adamw")
else:
raise ValueError("Invalid optimizer")
# Wrap the model using LoRA policy from llama-recipes or custom policy:
# This checks for lora layers (has weight and requires_grad)
def get_wrapping_policy(custom_policy:bool=False, vanilla_policy:bool=False):
from peft.tuners import PrefixEncoder, PromptEmbedding, PromptEncoder
if custom_policy:
def lambda_policy_fn(module):
# LoRA and DoRA trainable layers.
return (isinstance(module, nn.Sequential) and all(m.weight.requires_grad for m in module)) or (isinstance(module, (DORALayer, MagnitudeLayer)))
else:
def lambda_policy_fn(module):
return (
len(list(module.named_children())) == 0
and getattr(module, "weight", None) is not None
and module.weight.requires_grad
)
def self_attn_policy_fn(module):
# Check module name is self_attn.
return isinstance(module, tuple((*LLAMA_ATTENTION_CLASSES.values(), *MISTRAL_ATTENTION_CLASSES.values())))
def mlp_policy_fn(module):
# Check module name is self_attn.
return isinstance(module, (LlamaMLP, MistralMLP))
lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)
self_attn_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=self_attn_policy_fn)
mlp_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=mlp_policy_fn)
transformer_wrap_policy = functools.partial(
transformer_auto_wrap_policy,
transformer_layer_cls=(LlamaDecoderLayer, MistralDecoderLayer),
)
if vanilla_policy:
return transformer_wrap_policy
policies=[lambda_policy, transformer_wrap_policy]
if custom_policy:
policies.extend([self_attn_policy, mlp_policy])
return functools.partial(_or_policy, policies=policies)
# Main function, run on each process
def fsdp_main(local_rank:int, world_size:int, args:Dict):
# Setup and initialize the process group
os.environ['MASTER_ADDR'] = args["master_addr"]
os.environ['MASTER_PORT'] = args["master_port"]
if 'SLURM_PROCID' in os.environ:
# assumes same number of GPUs per node.
rank = int(os.environ['SLURM_PROCID']) * torch.cuda.device_count() + local_rank
else:
rank = local_rank
dist.init_process_group("nccl", rank=rank, world_size=world_size)
torch.cuda.set_device(local_rank)
if args["use_cpu_offload"]:
torch.set_num_threads(os.cpu_count()//(min(world_size, torch.cuda.device_count())))
# Start logging
logger = Logger(args, log_to=args["log_to"], project_name=args["project_name"],
entity=args["entity"], group=args["group"], name=args["name"], rank=rank)
# Timing stuff
init_start_event = torch.cuda.Event(enable_timing=True)
init_end_event = torch.cuda.Event(enable_timing=True)
# model precision, qlora compute precison, and FSDP mixed precision policy.
# The Linear4Bit quant_storage dtype should always match the FSDP param_dtype. The compute_dtype should match the AMP compute dtype.
# MixedPrecision(param_dtype=fp32, reduce_dtype=fp32, buffer_dtype=fp32) uses `torch.amp.autocast` to control precision.
# limited qlora testing shows that fp16 only works with autocast while bf16 trains with both pure and autocast modes.
# TODO: test how often this holds for mp_fp16
mp_policy = None
load_param_skip_names = []
if args["precision"] == "bf16":
torch_dtype, compute_dtype = torch.bfloat16, torch.bfloat16
elif args["precision"] == "fp32":
torch_dtype, compute_dtype = torch.float32, torch.float16
elif args["precision"] == "fp16_autocast":
compute_dtype, torch_dtype = torch.float16, torch.float32
mp_policy = MixedPrecision(param_dtype=torch.float32, reduce_dtype=torch.float32, buffer_dtype=torch.float32)
elif args["precision"] == "bf16_autocast":
compute_dtype, torch_dtype = torch.bfloat16, torch.float32
mp_policy = MixedPrecision(param_dtype=torch.float32, reduce_dtype=torch.float32, buffer_dtype=torch.float32)
elif args["precision"] == "bf16_buffers_autocast":
compute_dtype, torch_dtype = torch.bfloat16, torch.bfloat16
mp_policy = MixedPrecision(param_dtype=torch.bfloat16, reduce_dtype=torch.bfloat16, buffer_dtype=torch.float32)
load_param_skip_names = ['inv_freq']
else:
raise ValueError("Invalid precision")
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(args["model_name"])
tokenizer.pad_token_id = tokenizer.eos_token_id # TODO check if it exists first
# Set up dataloader
dataloader = get_dataloader(tokenizer, args)
# Create model
cfg = None
attn_impl = "sdpa" # torch 2.2 sdpa uses flash attn 2
if rank == 0 or args['verbose']:
print("Creating model", rank)
if args["train_type"] in ["full", "lora", "custom_lora"]:
if (args["low_memory"] and rank == 0) or (not args["low_memory"]):
model = AutoModelForCausalLM.from_pretrained(
args["model_name"],
use_cache=False,
torch_dtype=torch_dtype,
_attn_implementation=attn_impl
)
dtype = torch_dtype if args["precision"] == "bf16" else None
model.to(dtype=dtype, device="cpu" if args["low_memory"] else rank)
else:
cfg = AutoConfig.from_pretrained(args["model_name"])
cfg.use_cache = False
cfg._attn_implementation = attn_impl
with init_empty_weights():
model = AutoModelForCausalLM.from_config(cfg, torch_dtype=torch_dtype)
if args["precision"] == "bf16":
model.to(torch_dtype)
elif args["train_type"] in ["qlora", "custom_qlora", "hqq_lora", "hqq_dora", "bnb_dora", "bnb_llama_pro", "hqq_llama_pro"]: # Our custom loading
cfg = AutoConfig.from_pretrained(args["model_name"])
cfg.use_cache = False
cfg._attn_implementation = attn_impl
skip_modules = ["lm_head"]
if args["train_type"] in ["bnb_llama_pro", "hqq_llama_pro"]:
llama_pro_path = Path(args["llama_pro_path"])
num_original_layers, num_expanded_layers = llama_pro_path.name.split("blk_exp-")[1].split("-")
num_original_layers, num_expanded_layers = int(num_original_layers), int(num_expanded_layers)
total_new_layers = num_expanded_layers - num_original_layers
split = int(num_original_layers / (num_expanded_layers - num_original_layers))
new_layer_ids = [split+(split+1)*n for n in range(total_new_layers)]
new_layer_names = [f"layers.{i}" for i in new_layer_ids]
skip_modules += [str(lid) for lid in new_layer_ids]
cfg.num_hidden_layers = num_expanded_layers
# load model on meta device without calling init and replace nn.Linear with Linear4bit
with init_empty_weights():
model = AutoModelForCausalLM.from_config(cfg)
if args["train_type"] in ["hqq_lora", "hqq_dora", "hqq_llama_pro"]:
# TODO: Tune BaseQuantizeConfig.
quant_config = BaseQuantizeConfig(nbits=int(args["n_bits"]), group_size=64, quant_zero=True,
quant_scale=True, offload_meta=True, view_as_float=True)
model.model = replace_linear(model.model, HQQLinear, quant_config, device=rank,
compute_dtype=compute_dtype, del_orig=True, initialize=False, skip_modules=skip_modules)
HQQLinear.set_backend(HQQBackend.ATEN_BACKPROP)
else:
model.model = replace_linear(model.model, Linear4bit, compute_dtype=compute_dtype,
quant_type='nf4', quant_storage=torch_dtype, skip_modules=skip_modules)
model.is_loaded_in_4bit = True
# Grab the safetensors files that hold the weights
if args["train_type"] in ["bnb_llama_pro", "hqq_llama_pro"]:
files = glob(str(llama_pro_path/"*.safetensors"))
else:
try:
idx = hub.cached_file(args["model_name"], SAFE_WEIGHTS_INDEX_NAME)
files, _ = hub.get_checkpoint_shard_files(args["model_name"], idx)
except OSError:
try:
# This means the model doesn't have a model.safetensors.index.json because it is not sharded
files = []
files.append(hub.cached_file(args["model_name"], SAFE_WEIGHTS_NAME))
except OSError as e:
# This means the model probably doesn't have a safetensors file
raise e
# Load in the weights, using our custom load_and_quantize method which quantizes Params4bit on the fly
# and then places each layer on CPU or meta if using low_memory to minimize GPU memory usage
def load_and_quantize_parallel(name_param, model, **kwargs):
name, param = name_param
load_and_quantize(model, name, param, **kwargs)
quant_method = "hqq" if args["train_type"] in ["hqq_lora", "hqq_dora", "hqq_llama_pro"] else "bnb"
param_count = sum((p.numel() for n,p in model.named_parameters()))
if rank == 0 or args['verbose']:
print("Loading model", rank)
if rank == 0 and args['verbose']:
print(f"Total model params: {param_count}")
n_workers = n_loading_workers(quant_method, param_count) if args["loading_workers"]==-1 else args["loading_workers"]
if rank == 0 and args['verbose']:
print(f"Using n_workers: {n_workers} for loading")
start = time.time()
for filename in tqdm(files, desc="Loading & Quantizing Model Shards", disable=rank!=0, position=0):
weights = safetensors.torch.load_file(filename)
parallel(load_and_quantize_parallel, iter(weights.items()), n_workers=n_workers, threadpool=True,
model=model, dtype=torch_dtype, device=local_rank, skip_names=load_param_skip_names,
to_cpu=(args["low_memory"] and rank==0), to_meta=(args["low_memory"] and rank!=0),
verbose=args["verbose"], quant_method=quant_method, is_dora=(args["train_type"] in ["hqq_dora", "bnb_dora"]))
if rank == 0 and args["verbose"]:
print(f"Loaded model weights in {time.time()-start:.3f} seconds")
# cleanup any extra memory usage from parallel loading
torch.cuda.empty_cache()
if rank == 0 or args['verbose']:
print(f"Rank {rank}: Model created: {torch.cuda.memory_reserved(local_rank)/2**30:.3f} GiB")
# PEFT setup (LoRA and QLoRA)
if args["train_type"] in ["lora", "qlora"]:
from peft import LoraConfig, TaskType, get_peft_model
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM, inference_mode=False,
r=args["lora_rank"],
lora_alpha=args["lora_alpha"],
lora_dropout=args["lora_dropout"],
target_modules=args["lora_target_modules"],
)
# PEFT will move quant_state to meta device, so this method prevents that
# from happening by replacing quant_state.to with a dummy function
if rank!=0 and args["low_memory"]:
setup_quantized_meta_for_peft(model)
model = get_peft_model(model, peft_config)
if rank==0:
model.print_trainable_parameters()
elif args['low_memory']:
# And then setup_quantized_peft_meta_for_training sets quant_state.to back to normal
setup_quantized_peft_meta_for_training(model)
elif args["train_type"] in ["custom_qlora", "custom_lora", "hqq_lora", "hqq_dora", "bnb_dora"]:
if args["train_type"] == "hqq_dora":
print("Using HQQDORA", rank)
lora_cls = HQQDORA
elif args["train_type"] == "bnb_dora":
print("Using BNB DORA", rank)
lora_cls = BNBDORA
else:
print("Using LORA", rank)
lora_cls = LORA
# Create LORA layers.
for name, _ in model.named_modules():
module_key, _, value_key = name.rpartition('.')
if value_key in args['lora_target_modules']:
m = model.get_submodule(name)
qlora_layer = lora_cls(m, args["lora_rank"], args["lora_alpha"], args["lora_dropout"])
parent_module = model.get_submodule(module_key)
setattr(parent_module, value_key, qlora_layer)
for n,p in model.named_parameters():
if any([lora_name in n for lora_name in ['lora_AB', 'lora_A', 'lora_B', 'magnitude']]):
p.requires_grad = True
if args['verbose']:
print("Trainable LORA layer", n)
else:
p.requires_grad = False
if rank == 0 or args['verbose']:
print(f"Rank {rank}: LoRA layers added: {torch.cuda.memory_reserved(local_rank)/2**30:.3f} GiB")
elif args["train_type"] in ["bnb_llama_pro", "hqq_llama_pro"]:
for n,p in model.named_parameters():
if any([layer_name in n for layer_name in new_layer_names]):
p.requires_grad = True
if args['verbose']:
print("Trainable Llama-Pro layer", n)
else:
p.requires_grad = False
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_after_model_created": torch.cuda.memory_allocated(local_rank)}, rank)
logger.log({"memory/reserved_after_model_creation": torch.cuda.memory_reserved(local_rank)}, rank)
# Wrap model with llama-recipies or custom LoRA policy
my_auto_wrap_policy = get_wrapping_policy(custom_policy=args["train_type"] in ["custom_qlora", "hqq_lora", "hqq_dora", "bnb_dora"],
vanilla_policy=args["train_type"] in ["full", "bnb_llama_pro", "hqq_llama_pro"])
if rank == 0 or args['verbose']:
print("Wrapping model w/ FSDP", rank)
if args["sharding_strategy"] == "full_shard":
sharding_strategy = ShardingStrategy.FULL_SHARD
elif args["sharding_strategy"] == "shard_grad_op":
sharding_strategy = ShardingStrategy.SHARD_GRAD_OP
elif args["sharding_strategy"] == "ddp":
sharding_strategy = ShardingStrategy.NO_SHARD
elif args["sharding_strategy"] == "hybrid_full_shard":
sharding_strategy = ShardingStrategy.HYBRID_SHARD
elif args["sharding_strategy"] == "hybrid_shard_grad_op":
sharding_strategy = ShardingStrategy._HYBRID_SHARD_ZERO2
else:
raise ValueError("Invalid FSDP sharding strategy")
model = FSDP(
model,
sharding_strategy=sharding_strategy,
auto_wrap_policy=my_auto_wrap_policy,
# backward_prefetch=None, #BackwardPrefetch.BACKWARD_PRE
use_orig_params=False,
cpu_offload=CPUOffload(offload_params=True) if args["use_cpu_offload"] else None,
limit_all_gathers=True, # See https://github.com/pytorch/pytorch/issues/91165
device_id=torch.cuda.current_device(),
sync_module_states=args["low_memory"],
param_init_fn=lambda module: module.to_empty(device=torch.device("cuda"), recurse=False)
if (rank!=0 and args["low_memory"]) else None, # TODO note about meta device and why we need this
mixed_precision=mp_policy,
)
if rank == 0 or args['verbose']:
print(f"Rank {rank}: Wrapped model: {torch.cuda.memory_reserved(local_rank)/2**30:.3f} GiB")
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_after_model_wrap": torch.cuda.memory_allocated(local_rank)}, rank)
logger.log({"memory/reserved_after_model_wrap": torch.cuda.memory_reserved(local_rank)}, rank)
# Synchronize at the start
dist.barrier()
# Apply activation checkpointing
if args["use_gradient_checkpointing"]:
if args['reentrant_checkpointing']:
model.enable_input_require_grads()
non_reentrant_wrapper = functools.partial(
checkpoint_wrapper,
checkpoint_impl=CheckpointImpl.REENTRANT if args['reentrant_checkpointing'] else CheckpointImpl.NO_REENTRANT,
)
check_fn = lambda submodule: isinstance(submodule, (LlamaDecoderLayer, MistralDecoderLayer))
if rank == 0 or args['verbose']:
print("Applying activation checkpointing", rank)
apply_activation_checkpointing(
model, checkpoint_wrapper_fn=non_reentrant_wrapper, check_fn=check_fn
)
if args["use_activation_cpu_offload"]:
if rank == 0 or args['verbose']:
print("Applying activation offloading", rank)
model = offload_wrapper(model)
if rank == 0 and args['verbose']:
print("Config:")
print(cfg)
print("Model:")
print(model)
print("Starting training")
# Create the optimizer
optimizer = get_optimizer(model, args)
# LR scheduler.
gradient_accumulation_steps = max(1, args['gradient_accumulation_steps'])
lr_scheduler, num_training_steps = get_lr_scheduler(optimizer, dataloader, gradient_accumulation_steps, args)
# Sanity check: see what parameters the optimizer has and which require grad:
if rank == 0 and args['verbose']:
print("Optimizer params:")
for group in optimizer.param_groups:
for param in group['params']:
print(f"Shape: {param.shape}, Requires Grad: {param.requires_grad}, Dtype: {param.dtype}")
# Autocast for mixed precision with fp16/bf16 compute types with fp32 params
if args["precision"] in ["fp16_autocast", "bf16_autocast", "bf16_buffers_autocast"]:
autocast = torch.cuda.amp.autocast(enabled=True, dtype=compute_dtype)
else:
autocast = nullcontext()
scaler = ShardedGradScaler() if args["precision"] == "fp16_autocast" else None
scale_grads = scaler is not None
if rank == 0:
print("Total Training Steps:", num_training_steps)
memory_stats = []
progress_bar = tqdm(range(num_training_steps), disable=rank != 0)
init_start_event.record()
log_loss, log_lr = 0.0, -1
# Reset peak memory to track that
torch.cuda.reset_peak_memory_stats(local_rank)
with profiling_context(args, rank=rank) as prof:
for epoch in range(args['num_epochs']):
update_progress_bar(progress_bar, epoch, log_loss, log_lr, rank)
model.train()
ddp_loss = torch.zeros(2).to(local_rank)
for batch_idx, batch in enumerate(dataloader):
accumulate_grads = (batch_idx+1) % gradient_accumulation_steps == 0
# Prevent gradient syncing until update step if using no_sync option.
# Documentation states this should only be used on the root FSDP instance
# We assume this is a one-node setup
if args['no_sync'] and not accumulate_grads:
sync_context = model.no_sync()
else:
sync_context = nullcontext()
# Start logging memory (first iter) if requested
if args['profile_memory'] and batch_idx==0 and rank == 0 and epoch == 0:
torch.cuda.memory._record_memory_history()
# Log memory usage
if batch_idx == 0 and epoch == 0 and (rank == 0 or args['verbose']):
reserved_before_forward = torch.cuda.memory_reserved(local_rank)
memory_stats.append(f"Rank {rank}: Before forward: {reserved_before_forward/2**30:.2f} GiB")
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_before_forward": torch.cuda.memory_allocated(local_rank)}, rank)
logger.log({"memory/reserved_before_forward": reserved_before_forward}, rank)
# Forward pass
with sync_context:
with autocast:
output = model(
batch['input_ids'].to(local_rank),
labels=batch['labels'].to(local_rank),
attention_mask=None,
)
loss = output.loss
# Scale loss for gradient accumulation
loss = loss / gradient_accumulation_steps
# Log memory usage
if batch_idx == 0 and epoch == 0 and (rank == 0 or args['verbose']):
reserved_after_forward = torch.cuda.memory_reserved(local_rank)
memory_stats.append(f"Rank {rank}: After forward: {reserved_after_forward/2**30:.2f} GiB")
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_after_forward": torch.cuda.memory_allocated(local_rank)}, rank)
logger.log({"memory/reserved_after_forward": reserved_after_forward}, rank)
# Backward pass
if scale_grads:
scaler.scale(loss).backward()
else:
loss.backward()
# Record loss
bs = batch['input_ids'].shape[0]
ddp_loss[0] += loss.item() * bs * gradient_accumulation_steps
ddp_loss[1] += bs
# Step the optimizer (w/ gradient accumulation)
if accumulate_grads:
if args['apply_gradient_clipping'] and (args['grad_norm'] is not None):
model.clip_grad_norm_(args['grad_norm'], norm_type=2.0)
if scale_grads:
scaler.step(optimizer)
scaler.update()
else:
optimizer.step()
optimizer.zero_grad()
# avoid overhead when lr is constant.
if lr_scheduler is not None:
lr_scheduler.step()
progress_bar.update(1)
# Log memory usage after backward
if batch_idx == 0 and epoch == 0 and (rank == 0 or args['verbose']):
reserved_after_backward = torch.cuda.memory_reserved(local_rank)
memory_stats.append(f"Rank {rank}: After backward: {reserved_after_backward/2**30:.2f} GiB")
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_after_backward": torch.cuda.memory_allocated(local_rank)}, rank)
logger.log({"memory/reserved_after_backward": reserved_after_backward}, rank)
# Delete the output so more memory frees up before the next forward pass
output = None
loss = None
# Stop logging memory (first iter)
if args['profile_memory'] and batch_idx==0 and rank == 0 and epoch == 0:
torch.cuda.memory._dump_snapshot("memory_snapshot.pickle")
torch.cuda.memory._record_memory_history(enabled=None) # Stop recording
# Log loss every gradient update steps
if accumulate_grads:
dist.all_reduce(ddp_loss, op=dist.ReduceOp.SUM)
if rank == 0:
log_loss = ddp_loss[0] / ddp_loss[1]
if lr_scheduler is not None:
log_lr = lr_scheduler.get_last_lr()[0]
else:
log_lr = args["lr"]
update_progress_bar(progress_bar, epoch, log_loss, log_lr, rank)
if args["log_to"] == 'wandb':
logger.log({"loss": log_loss, "lr": log_lr}, rank)
ddp_loss = torch.zeros(2).to(local_rank)
if rank == 0 and args['verbose']:
print(f"Batch idx {batch_idx}")
prof.step()
#Primarily for debugging
if args["max_steps"] > 0 and batch_idx > args["max_steps"]:
if rank == 0:
print("Max steps reached, skipping rest of epoch")
break
# Print + log peak memory usage for the whole fourth step of training
if epoch == 0 and (rank == 0 or args['verbose']):
peak_allocated_memory = torch.cuda.max_memory_allocated(local_rank)
peak_reserved_memory = torch.cuda.max_memory_reserved(local_rank)
memory_stats.append(f"Rank {rank}: Peak allocated memory: {peak_allocated_memory/2**30:.2f} GiB")
memory_stats.append(f"Rank {rank}: Peak reserved memory: {peak_reserved_memory/2**30:.2f} GiB")
if args["log_to"] == 'wandb':
logger.log({"memory/allocated_peak": peak_allocated_memory}, rank)
logger.log({"memory/reserved_peak": peak_reserved_memory}, rank)
# Synchronize at the end and record time
init_end_event.record()
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
print("Finished training", rank)
# Print time, model, & memory stats
time_taken = init_start_event.elapsed_time(init_end_event) / 1000
dist.barrier()
torch.cuda.synchronize()
if rank == 0:
print(f"CUDA event elapsed time: {time_taken} sec")
logger.log({"time_taken": time_taken}, rank)
for line in memory_stats:
print(line)
# End logging
logger.finish(rank=rank)
# Save model - ref: https://github.com/pytorch/pytorch/issues/98823
# HQQLinear custom state_dict() method causes issues when saving.
# Model is saved fine when `state_dict()` method is removed.
# Non param/buffer types are not saved with FSDP.
# It might be better to just save the trained lora layers.
# summon_full_params on lora layers and save.
if args["save_model"]:
if rank == 0:
os.makedirs(args["output_dir"], exist_ok=True)
dist.barrier()
save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True)
if args["train_type"] in ["custom_lora", "custom_qlora", "hqq_lora", "hqq_dora", "bnb_dora", "bnb_llama_pro", "hqq_llama_pro"]: