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WIP large refactor to make finetune script a little more manageable (#3)

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This commit is contained in:
Wing Lian
2023-04-18 14:01:38 -04:00
committed by GitHub
parent 81de0efc18
commit 6045345d6b
9 changed files with 524 additions and 398 deletions
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scripts/finetune.py
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@@ -1,223 +1,29 @@
import logging
import math
import os
import random
import signal
import sys
from hashlib import md5
from pathlib import Path
import bitsandbytes as bnb
import fire
import torch
import transformers
import yaml
from attrdict import AttrDefault
from datasets import load_dataset, IterableDataset, Dataset, load_from_disk
from torch import nn
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
LlamaForCausalLM,
LlamaTokenizer,
EarlyStoppingCallback,
GenerationConfig,
)
# add src to the pythonpath so we don't need to pip install this
from transformers.trainer_pt_utils import get_parameter_names
project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
src_dir = os.path.join(project_root, "src")
sys.path.insert(0, src_dir)
from axolotl.datasets import TokenizedPromptDataset, ConstantLengthDataset
from axolotl.prompt_tokenizers import (
AlpacaPromptTokenizingStrategy,
ShareGPTPromptTokenizingStrategy,
LLAMA_DEFAULT_PAD_TOKEN,
GPTeacherPromptTokenizingStrategy,
OpenAssistantPromptTokenizingStrategy, AlpacaReflectionPTStrategy,
)
from axolotl.prompters import AlpacaPrompter, GPTeacherPrompter, ShareGPTPrompter, ReflectAlpacaPrompter
from axolotl.utils.data import load_prepare_datasets
from axolotl.utils.models import load_model
from axolotl.utils.trainer import setup_trainer
from axolotl.utils.wandb import setup_wandb_env_vars
logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
DEFAULT_DATASET_PREPARED_PATH = "last_run_prepared"
def setup_wandb_env_vars(cfg):
if cfg.wandb_project and len(cfg.wandb_project) > 0:
os.environ["WANDB_PROJECT"] = cfg.wandb_project
cfg.use_wandb = True
if cfg.wandb_watch and len(cfg.wandb_watch) > 0:
os.environ["WANDB_WATCH"] = cfg.wandb_watch
if cfg.wandb_log_model and len(cfg.wandb_log_model) > 0:
os.environ["WANDB_LOG_MODEL"] = cfg.wandb_log_model
if cfg.wandb_run_id and len(cfg.wandb_run_id) > 0:
os.environ["WANDB_RUN_ID"] = cfg.wandb_run_id
def load_model(
base_model,
base_model_config,
model_type,
tokenizer_type,
cfg,
adapter="lora",
inference: bool = False,
):
# TODO refactor as a kwarg
load_in_8bit = cfg.load_in_8bit
tokenizer = None
is_llama_derived_model = "llama" in base_model or "llama" in cfg.model_type.lower()
if adapter != "lora":
raise NotImplementedError(f"{adapter} peft adapter not available")
if is_llama_derived_model and cfg.flash_attention:
if cfg.device not in ["mps", "cpu"] and inference is False:
from axolotl.flash_attn import replace_llama_attn_with_flash_attn
logging.info("patching with flash attention")
replace_llama_attn_with_flash_attn()
torch_dtype = (torch.float16 if cfg.load_in_8bit or cfg.fp16 else torch.float32,)
try:
if cfg.load_4bit:
from alpaca_lora_4bit.monkeypatch.peft_tuners_lora_monkey_patch import (
replace_peft_model_with_int4_lora_model,
)
replace_peft_model_with_int4_lora_model()
from peft import (
LoraConfig,
get_peft_model,
prepare_model_for_int8_training,
PeftModel,
)
except Exception as e:
logging.exception(e)
raise e
try:
if cfg.load_4bit and is_llama_derived_model:
from alpaca_lora_4bit.autograd_4bit import load_llama_model_4bit_low_ram
from huggingface_hub import snapshot_download
cache_model_path = Path(snapshot_download(base_model))
files = (
list(cache_model_path.glob("*.pt"))
+ list(cache_model_path.glob("*.safetensors"))
+ list(cache_model_path.glob("*.bin"))
)
if len(files) > 0:
model_path = str(files[0])
else:
logging.warning(
"unable to find a cached model file, this will likely fail..."
)
model_path = str(cache_model_path)
model, tokenizer = load_llama_model_4bit_low_ram(
base_model_config if base_model_config else base_model,
model_path,
device_map=cfg.device_map,
groupsize=cfg.gptq_groupsize if cfg.gptq_groupsize else -1,
is_v1_model=cfg.gptq_model_v1
if cfg.gptq_model_v1 is not None
else True,
)
load_in_8bit = False
elif is_llama_derived_model:
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=cfg.load_in_8bit,
torch_dtype=torch_dtype,
device_map=cfg.device_map,
)
else:
model = getattr(transformers, model_type).from_pretrained(
base_model,
load_in_8bit=cfg.load_in_8bit,
torch_dtype=torch_dtype,
device_map=cfg.device_map,
)
except Exception as e:
logging.error(
"Exception raised attempting to load model, retrying with AutoModelForCausalLM"
)
logging.exception(e)
model = AutoModelForCausalLM.from_pretrained(
base_model,
load_in_8bit=cfg.load_in_8bit,
torch_dtype=torch_dtype,
device_map=cfg.device_map,
)
if not tokenizer:
try:
if is_llama_derived_model:
tokenizer = LlamaTokenizer.from_pretrained(model)
else:
tokenizer = getattr(transformers, tokenizer_type).from_pretrained(model)
except:
tokenizer = AutoTokenizer.from_pretrained(base_model)
logging.debug(f"EOS: {tokenizer.eos_token_id} / {tokenizer.eos_token}")
logging.debug(f"BOS: {tokenizer.bos_token_id} / {tokenizer.bos_token}")
logging.debug(f"PAD: {tokenizer.pad_token_id} / {tokenizer.pad_token}")
logging.debug(f"UNK: {tokenizer.unk_token_id} / {tokenizer.unk_token}")
if tokenizer.__class__.__name__ in ["LlamaTokenizer", "LlamaTokenizerFast"]:
tokenizer.pad_token = LLAMA_DEFAULT_PAD_TOKEN
if tokenizer.__class__.__name__ == "GPTNeoXTokenizerFast":
tokenizer.add_special_tokens({"pad_token": "[PAD]"})
os.environ["TOKENIZERS_PARALLELISM"] = "false"
if load_in_8bit and not cfg.load_4bit:
logging.info("converting model w/ prepare_model_for_int8_training")
model = prepare_model_for_int8_training(model)
lora_config = LoraConfig(
r=cfg.lora_r,
lora_alpha=cfg.lora_alpha,
target_modules=cfg.lora_target_modules,
lora_dropout=cfg.lora_dropout,
fan_in_fan_out=cfg.lora_fan_in_fan_out,
bias="none",
task_type="CAUSAL_LM",
)
if cfg.lora_model_dir:
model = PeftModel.from_pretrained(
model,
cfg.lora_model_dir,
device_map=cfg.device_map,
torch_dtype=torch.float16,
)
else:
model = get_peft_model(model, lora_config)
if cfg.ddp:
model.to(f"cuda:{cfg.local_rank}")
if cfg.load_4bit:
# Scales to half
logging.info("Fitting 4bit scales and zeros to half")
for n, m in model.named_modules():
if "Autograd4bitQuantLinear" in str(type(m)) or "Linear4bitLt" in str(
type(m)
):
if hasattr(m, "is_v1_model") and m.is_v1_model:
m.zeros = m.zeros.half()
m.scales = m.scales.half()
m.bias = m.bias.half()
# TODO resume_from_checkpoint handling
model.print_trainable_parameters()
return model, tokenizer, lora_config
def choose_device(cfg):
def get_device():
if torch.cuda.is_available():
@@ -271,11 +77,13 @@ def do_inference(cfg, model, tokenizer):
tokenizer.add_special_tokens({"bos_token": "<s>"})
tokenizer.add_special_tokens({"eos_token": "</s>"})
instruction = "Tell me a joke about dromedaries."
input = ""
prompt = "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:\n".format(
instruction=instruction, input=input
from axolotl.prompters import ReflectAlpacaPrompter
instruction = str(input("Give me an instruction: "))
instruction = (
instruction if not instruction else "Tell me a joke about dromedaries."
)
prompt = ReflectAlpacaPrompter().build_prompt(instruction=instruction)
batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
model.eval()
@@ -324,98 +132,6 @@ def choose_config(path: Path):
return chosen_file
def setup_trainer(cfg, train_dataset, eval_dataset, model, tokenizer):
total_num_steps = int(
math.ceil(len(train_dataset) * cfg.num_epochs / cfg.batch_size)
)
warmup_steps = min(int(0.03 * total_num_steps), 100)
logging_steps = max(min(int(0.005 * total_num_steps), 10), 1)
save_steps = eval_steps = min(int(0.05 * total_num_steps), 200)
training_arguments_kwargs = {}
if cfg.bf16 == "full":
training_arguments_kwargs["bf16_full_eval"] = True
else:
training_arguments_kwargs["bf16"] = cfg.bf16
training_arguments_kwargs["tf32"] = cfg.tf32
training_arguments_kwargs["warmup_steps"] = warmup_steps
training_arguments_kwargs["logging_steps"] = logging_steps
if cfg.gradient_checkpointing is not None:
training_arguments_kwargs["gradient_checkpointing"] = cfg.gradient_checkpointing
training_args = transformers.TrainingArguments(
per_device_train_batch_size=cfg.micro_batch_size,
gradient_accumulation_steps=cfg.gradient_accumulation_steps,
num_train_epochs=cfg.num_epochs,
learning_rate=cfg.learning_rate,
evaluation_strategy="steps" if cfg.val_set_size > 0 else "no",
save_strategy="steps",
eval_steps=eval_steps if cfg.val_set_size > 0 else None,
save_steps=save_steps,
output_dir=cfg.output_dir,
save_total_limit=3,
load_best_model_at_end=True if cfg.val_set_size > 0 else False,
ddp_find_unused_parameters=False if cfg.ddp else None,
group_by_length=cfg.group_by_length,
report_to="wandb" if cfg.use_wandb else None,
run_name=cfg.wandb_run_id if cfg.use_wandb else None,
**training_arguments_kwargs,
)
decay_parameters = get_parameter_names(model, [nn.LayerNorm])
decay_parameters = [name for name in decay_parameters if "bias" not in name]
optimizer_grouped_parameters = [
{
"params": [p for n, p in model.named_parameters() if n in decay_parameters],
"weight_decay": training_args.weight_decay,
},
{
"params": [
p for n, p in model.named_parameters() if n not in decay_parameters
],
"weight_decay": 0.0,
},
]
trainer_kwargs = {}
if cfg.load_in_8bit and not cfg.load_4bit:
adam_bnb_optim = bnb.optim.Adam8bit(
optimizer_grouped_parameters,
betas=(training_args.adam_beta1, training_args.adam_beta2),
eps=training_args.adam_epsilon,
lr=training_args.learning_rate,
)
# TODO optionally use torch.optim.OneCycleLR
lr_scheduler = transformers.get_cosine_schedule_with_warmup(
adam_bnb_optim,
training_args.warmup_steps,
total_num_steps,
)
trainer_kwargs["optimizers"] = (adam_bnb_optim, lr_scheduler)
# TODO on_save callback to sync checkpoints to GCP/AWS in background
if cfg.early_stopping_patience:
early_stop_cb = EarlyStoppingCallback(
cfg.early_stopping_patience,
)
trainer_kwargs["callbacks"] = [early_stop_cb]
trainer = transformers.Trainer(
model=model,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
args=training_args,
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
**trainer_kwargs,
)
return trainer
def train(
config: Path = Path("configs/"),
prepare_ds_only: bool = False,
@@ -474,110 +190,13 @@ def train(
do_inference(cfg, model, tokenizer)
return
max_packed_sequence_len = (
cfg.max_packed_sequence_len if cfg.max_packed_sequence_len else cfg.sequence_len
)
max_packed_sequence_len = min(
max_packed_sequence_len, cfg.sequence_len
) # make sure we don't accidentally set it larger than sequence_len
ds_hash = str(
md5(
(
str(max_packed_sequence_len)
+ "@"
+ "|".join(sorted([f"{d.path}:{d.type}" for d in cfg.datasets]))
).encode("utf-8")
).hexdigest()
)
prepared_ds_path = (
Path(cfg.dataset_prepared_path) / ds_hash
if cfg.dataset_prepared_path
else Path(DEFAULT_DATASET_PREPARED_PATH) / ds_hash
train_dataset, eval_dataset = load_prepare_datasets(
tokenizer, cfg, DEFAULT_DATASET_PREPARED_PATH
)
if any(prepared_ds_path.glob("*")):
logging.info("Loading prepared dataset from disk...")
dataset = load_from_disk(str(prepared_ds_path))
logging.info("Prepared dataset loaded from disk...")
else:
logging.info("Loading raw datasets...")
datasets = []
for d in cfg.datasets:
ds_from_hub = False
try:
load_dataset(d.path, streaming=True)
ds_from_hub = True
except FileNotFoundError:
pass
# prefer local dataset, even if hub exists
if Path(d.path).exists():
ds: IterableDataset = load_dataset(
"json", data_files=d.path, streaming=True, split=None
)
elif ds_from_hub:
ds = load_dataset(d.path, streaming=True)
else:
raise Exception("unhandled dataset load")
if d.type == "alpaca":
ds_strategy = AlpacaPromptTokenizingStrategy(
AlpacaPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d.type == "oasst":
ds_strategy = OpenAssistantPromptTokenizingStrategy(
AlpacaPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d.type == "gpteacher":
ds_strategy = GPTeacherPromptTokenizingStrategy(
GPTeacherPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d.type == "reflection":
ds_strategy = AlpacaReflectionPTStrategy(
ReflectAlpacaPrompter(),
tokenizer,
cfg.train_on_inputs,
cfg.sequence_len,
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
elif d.type == "sharegpt":
ds_strategy = ShareGPTPromptTokenizingStrategy(
ShareGPTPrompter(), tokenizer, cfg.train_on_inputs, cfg.sequence_len
)
ds_wrapper = TokenizedPromptDataset(ds_strategy, ds["train"])
datasets.append(ds_wrapper)
else:
logging.error(f"unhandled prompt tokenization strategy: {d.type}")
constant_len_dataset = ConstantLengthDataset(
tokenizer,
datasets,
seq_length=max_packed_sequence_len,
)
logging.info("merging, packing, shuffling, and splitting master dataset")
dataset = Dataset.from_list([_ for _ in constant_len_dataset]).train_test_split(
test_size=cfg.val_set_size, shuffle=True, seed=42
)
if cfg.local_rank == 0:
logging.info(f"Saving prepared dataset to disk... {prepared_ds_path}")
dataset.save_to_disk(prepared_ds_path)
if prepare_ds_only:
logging.info("Finished preparing dataset. Exiting...")
return
train_dataset = dataset["train"]
eval_dataset = dataset["test"]
if prepare_ds_only:
logging.info("Finished preparing dataset. Exiting...")
return
if cfg.debug:
check_dataset_labels(
@@ -594,8 +213,9 @@ def train(
model = torch.compile(model)
# go ahead and presave, so we have the adapter config available to inspect
logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
lora_config.save_pretrained(cfg.output_dir)
if lora_config:
logging.info(f"Pre-saving adapter config to {cfg.output_dir}")
lora_config.save_pretrained(cfg.output_dir)
# In case we want to stop early with ctrl+c, this is a nice to have to save the pretrained model
if cfg.local_rank == 0: