When I used galore, the learning rate was set to 8e-6, but the training rate was 0.001 #31707
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Minami-su
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Minami-su
changed the title
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Hey @Minami-su, on which version of transformers are you? It reminds me of an older issue #30082 very similar to this which should have been fixed by #30085 (>= v4.40.0). Still pretty sure that it is more of a display issue. |
4.42.3.However, I found that lr and grad in print had problems. In fact, there were changes. |
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If you refer to changes, do you mean the actual display of lr/grad changed? I might look into it when I have time. Galore currently uses a lot of dummies to display things which might cause an issue here again (just my first intuition). |
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The lr shown is not changing,but the actual training lr is changing when I set lr to 1e-5 and 1e-2 |
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@Minami-su small update on my side. I could reproduce the issue with a somewhat shrinked variant of yours: import os
import torch
import transformers
from datasets import load_dataset
from transformers import AutoModelForCausalLM, AutoTokenizer, logging
logging.set_verbosity(logging.DEBUG)
os.environ["WANDB_DISABLED"] = "true"
# model/data params
base_model: str = "gpt2" # the only required argument
data_path: str = "yahma/alpaca-cleaned"
output_dir: str = "./lora-alpaca"
# training hyperparams
batch_size: int = 32
num_epochs: int = 3
learning_rate: float = 3e-4
cutoff_len: int = 256
val_set_size: int = 2000
# llm hyperparams
train_on_inputs: bool = True # if False, masks out inputs in loss
add_eos_token: bool = False
group_by_length: bool = False # faster, but produces an odd training loss curve
# wandb params
wandb_project: str = ""
wandb_run_name: str = ""
wandb_watch: str = "" # options: false | gradients | all
wandb_log_model: str = "" # options: false | true
resume_from_checkpoint: str = None # either training checkpoint or final adapter
prompt_template_name: str = "alpaca2" # The prompt template to use, will default to alpaca.
if int(os.environ.get("LOCAL_RANK", 0)) == 0:
print(
f"Training Alpaca-LoRA model with params:\n"
f"base_model: {base_model}\n"
f"data_path: {data_path}\n"
f"output_dir: {output_dir}\n"
f"batch_size: {batch_size}\n"
f"num_epochs: {num_epochs}\n"
f"learning_rate: {learning_rate}\n"
f"cutoff_len: {cutoff_len}\n"
f"val_set_size: {val_set_size}\n"
f"train_on_inputs: {train_on_inputs}\n"
f"add_eos_token: {add_eos_token}\n"
f"group_by_length: {group_by_length}\n"
f"wandb_project: {wandb_project}\n"
f"wandb_run_name: {wandb_run_name}\n"
f"wandb_watch: {wandb_watch}\n"
f"wandb_log_model: {wandb_log_model}\n"
f"resume_from_checkpoint: {resume_from_checkpoint or False}\n"
f"prompt template: {prompt_template_name}\n"
)
assert (
base_model
), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
# Check if parameter passed or if set within environ
use_wandb = len(wandb_project) > 0 or (
"WANDB_PROJECT" in os.environ and len(os.environ["WANDB_PROJECT"]) > 0
)
# Only overwrite environ if wandb param passed
if len(wandb_project) > 0:
os.environ["WANDB_PROJECT"] = wandb_project
if len(wandb_watch) > 0:
os.environ["WANDB_WATCH"] = wandb_watch
if len(wandb_log_model) > 0:
os.environ["WANDB_LOG_MODEL"] = wandb_log_model
tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
# needed for models like gpt2
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if base_model.find("qwen") != -1 or base_model.find("Qwen") != -1:
tokenizer.add_special_tokens({"bos_token": "<|im_start|>"})
tokenizer.add_special_tokens({"eos_token": "<|im_end|>"})
tokenizer.add_special_tokens({"pad_token": "<|endoftext|>"})
tokenizer.padding_side = "left" # Allow batched inference
def tokenize(prompt, add_eos_token=True):
# there's probably a way to do this with the tokenizer settings
# but again, gotta move fast
result = tokenizer(
prompt,
truncation=True,
max_length=cutoff_len,
padding=False,
return_tensors=None,
)
if (
result["input_ids"][-1] != tokenizer.eos_token_id
and len(result["input_ids"]) < cutoff_len
and add_eos_token
):
result["input_ids"].append(tokenizer.eos_token_id)
result["attention_mask"].append(1)
result["labels"] = result["input_ids"].copy()
return result
def generate_and_tokenize_prompt(data_point):
full_prompt =data_point["instruction"] + data_point["input"] + data_point["output"]
tokenized_full_prompt = tokenize(full_prompt)
return tokenized_full_prompt
print(tokenizer.pad_token_id)
print(tokenizer.pad_token)
print(tokenizer.bos_token_id)
print(tokenizer.bos_token)
print(tokenizer.eos_token_id)
print(tokenizer.eos_token)
if data_path.endswith(".json") or data_path.endswith(".jsonl"):
data = load_dataset("json", data_files=data_path)
else:
data = load_dataset(data_path)
if val_set_size > 0:
train_val = data["train"].train_test_split(
test_size=val_set_size, shuffle=True, seed=42
)
train_data = (
train_val["train"].shuffle().map(generate_and_tokenize_prompt)
)
val_data = (
train_val["test"].shuffle().map(generate_and_tokenize_prompt)
)
else:
train_data = data["train"].shuffle().map(generate_and_tokenize_prompt)
val_data = None
model = AutoModelForCausalLM.from_pretrained(base_model,
trust_remote_code=True,
torch_dtype=torch.bfloat16,
)
trainer = transformers.Trainer(
model=model,
train_dataset=train_data,
eval_dataset=val_data,
args=transformers.TrainingArguments(
per_device_train_batch_size=batch_size,
gradient_accumulation_steps=1,
warmup_steps=0,
num_train_epochs=num_epochs,
learning_rate=learning_rate,
bf16=True,
lr_scheduler_type="cosine",
optim = "galore_adamw_8bit_layerwise",
optim_target_modules=[
'q_proj', 'k_proj', 'down_proj', 'up_proj',
'gate_proj', 'v_proj', 'o_proj', 'lm_head'
],
optim_args="rank=1024, update_proj_gap=500, scale=0.25",
eval_strategy="steps" if val_set_size > 0 else "no",
save_strategy="steps",
logging_strategy="steps",
logging_steps=10,
eval_steps=100 if val_set_size > 0 else None,
save_steps=200,
output_dir=output_dir,
save_total_limit=2,
load_best_model_at_end=True if val_set_size > 0 else False,
group_by_length=group_by_length,
report_to="wandb" if use_wandb else None,
run_name=wandb_run_name if use_wandb else None,
),
data_collator=transformers.DataCollatorForSeq2Seq(
tokenizer, pad_to_multiple_of=8, return_tensors="pt", padding=True
),
)
trainer.train()
model.save_pretrained(output_dir)It is a display issue due to the (cosine) lr scheduler. Working on a fix that I'll submit in a PR. If you're interested why this happens: In short, galore work param-wise on each individually and to conform to this without interrupting it, dummy schedulers and optims are used as a global overhead. This is so that they don't interfer with the param-wise updates. In this case, the scheduler was the problem as it did not follow the scheduling as well as the param-wise learning rates were discarded in the process. To be clear tho, it's entirely a display issue. |
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@vasqu Thank you for your explanation,I figure out. |
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@Minami-su PR is up, and no problem! Small edit: You should also see the changes in the lr when using warmup steps. |
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