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train_alpaca_prompt.py
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train_alpaca_prompt.py
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# We modified the code based on Alpaca train.py. Author: Zheng Yuan, Hongyi Yuan
import logging
from dataclasses import dataclass, field
from typing import Optional, Dict, Sequence
import io
import torch
import torch.nn.functional as F
import transformers
from torch.utils.data import Dataset
from transformers import Trainer
import json
from peft import (
LoraConfig,
get_peft_model,
# prepare_model_for_int8_training,
TaskType,
PeftModel,
)
from transformers import (
LlamaForCausalLM,
LlamaTokenizer,
AutoTokenizer,
AutoModelForCausalLM,
)
import torch.nn as nn
from random import sample
import math
from accelerate import init_empty_weights, init_on_device
import os
from copy import deepcopy
# from flashatt import replace_llama_attn_with_flash_attn
# replace_llama_attn_with_flash_attn()
def _make_r_io_base(f, mode: str):
if not isinstance(f, io.IOBase):
f = open(f, mode=mode)
return f
def jload(f, mode="r"):
"""Load a .json file into a dictionary."""
f = _make_r_io_base(f, mode)
jdict = json.load(f)
f.close()
return jdict
IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "<unk>"
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:"
),
}
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data."}
)
stop_response: bool = field(default=False)
train_sample_num: int = field(default=2)
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={
"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
lire_weight: float = field(default=100.0)
length_penalty: float = field(default=1.0)
only_use_provide: bool = field(default=False)
only_use_sample: bool = field(default=False)
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
"""Collects the state dict and dump to disk."""
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def smart_tokenizer_and_embedding_resize(
special_tokens_dict: Dict,
tokenizer: transformers.PreTrainedTokenizer,
model: transformers.PreTrainedModel,
):
"""Resize tokenizer and embedding.
Note: This is the unoptimized version that may make your embedding size not be divisible by 64.
"""
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict)
model.resize_token_embeddings(len(tokenizer))
if num_new_tokens > 0:
input_embeddings = model.get_input_embeddings().weight.data
output_embeddings = model.get_output_embeddings().weight.data
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True
)
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(
dim=0, keepdim=True
)
input_embeddings[-num_new_tokens:] = input_embeddings_avg
output_embeddings[-num_new_tokens:] = output_embeddings_avg
class ScoreDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer):
super(ScoreDataset, self).__init__()
logging.warning("Loading data...")
with open(data_path, "r") as f:
lines = f.readlines()
#########
self.data = [json.loads(line.strip()) for line in lines]
def __len__(self):
return len(self.data)
def __getitem__(self, i):
return dict(input_ids=self.data[i])
def _single_tokenize(text, tokenizer, max_len=None):
if max_len is None:
max_len = tokenizer.model_max_length
toked = tokenizer(
text,
return_tensors="pt",
padding="longest",
max_length=max_len,
truncation=True,
)
return toked["input_ids"][0]
def stop_response(res):
stops = ["\n\nHuman:", "\n\nAssistant:", "\n\nhuman:", "\n\nassistant:"]
for stop in stops:
if res.find(stop) >= 0:
res = res[: res.find(stop)].strip()
return res
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
stop_response: bool
num: int
def __call__(self, instances):
idxs = []
all_scores = []
input_ids = []
score_mask = []
labels = []
query_list = []
for idx, ins in enumerate(instances):
ins = ins["input_ids"] # hack
query = ins["query"]
# # ######test for hh dataset ressponses
responses = ins["responses"][:self.num]
scores = ins["scores"][:self.num]
##################################
all_scores.append(scores)
idxs.append([idx] * len(scores))
prompt_input, prompt_no_input = (
PROMPT_DICT["prompt_input"],
PROMPT_DICT["prompt_no_input"],
)
def get_input(query):
if query.find("\n") == -1:
return ""
return "\n".join(query.split("\n")[1:])
example = {"instruction": query.split("\n")[0], "input": get_input(query)}
# example = {"instruction": query, "input": ""}
prompt_input = (
prompt_input.format_map(example)
if example.get("input", "") != ""
else prompt_no_input.format_map(example)
)
######keep end for prompt
self.tokenizer.truncation_side = "left"
query_input_ids = _single_tokenize(
prompt_input,
self.tokenizer,
max_len=int(self.tokenizer.model_max_length * 2 / 3),
)
query_target = torch.LongTensor(
[IGNORE_INDEX] * (query_input_ids.shape[0] - 1)
)
dummy_target = torch.LongTensor([IGNORE_INDEX])
##for responses, always keep start
self.tokenizer.padding_side = "right"
self.tokenizer.truncation_side = "right"
for res in responses:
if self.stop_response:
r = stop_response(res)
else:
r = res
res_input_ids = _single_tokenize(
r + self.tokenizer.eos_token,
self.tokenizer,
max_len=self.tokenizer.model_max_length - query_input_ids.shape[0],
) # eos here
input_ids.append(torch.cat((query_input_ids, res_input_ids), dim=0))
labels.append(
torch.cat((query_target, res_input_ids, dummy_target), dim=0)
)
query_list.append(query)
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = torch.nn.utils.rnn.pad_sequence(
labels, batch_first=True, padding_value=IGNORE_INDEX
)
return dict(
input_ids=input_ids,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
labels=labels,
idxs=torch.LongTensor(idxs),
scores=torch.FloatTensor(all_scores),
query=query_list,
)
def make_supervised_data_module(
tokenizer: transformers.PreTrainedTokenizer, data_args
) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
train_dataset = ScoreDataset(tokenizer=tokenizer, data_path=data_args.data_path)
data_collator = DataCollatorForSupervisedDataset(
tokenizer=tokenizer, stop_response=data_args.stop_response,num=data_args.train_sample_num
)
return dict(
train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator
)
class LIRETrainer(Trainer):
def gather_logits_labels(self, logits, labels_):
labels = labels_.clone()
mask = (labels != -100).float()
new_logits = logits.clone() # Create a copy to avoid in-place modification
labels[labels == -100] = 0
output = torch.gather(new_logits, dim=-1, index=labels.unsqueeze(-1)).squeeze(
-1
)
output = output * mask # B * L
return output
def get_score(self, logit_label, labels):
mask = (labels != -100).float()
length = mask.sum(-1)
scores = logit_label.sum(-1) / (length**self.args.length_penalty)
return scores
def rrhf_loss(self, scores, rw_scores):
cand = rw_scores.shape[1]
bz = rw_scores.shape[0]
new_scores = scores.reshape(-1, cand) # batch * cand
diff = new_scores.unsqueeze(1) - new_scores.unsqueeze(-1) # batch * cand * cand
rw_diff = rw_scores.unsqueeze(1) - rw_scores.unsqueeze(-1)
aval = torch.bitwise_and(rw_diff > 0, diff < 0)
return -diff[aval].sum()
def lire_loss(self, logit_label, rw_scores):
T = 2.0
cand = rw_scores.shape[1]
bz = rw_scores.shape[0]
logit_label_batch = torch.reshape(
logit_label, (-1, cand, logit_label.shape[-1])
) # batch * cand
summed_logit = logit_label_batch.sum(-1)
Q = (summed_logit / T).softmax(dim=-1)
J = torch.mul(Q, rw_scores.softmax(dim=-1))
return -J.sum() / bz
def sft_loss(self, logit_label, rw_scores):
cand = rw_scores.shape[1]
logit_label_batch = torch.reshape(
logit_label, (-1, cand, logit_label.shape[-1])
) # batch * cand * L
# expert_response_logit_label = torch.gather(logit_label_batch, dim=1, index=max_idx.view(-1, 1, 1).repeat(1, 1, logit_label_batch.size(-1))).squeeze() # batch * L
expert_response_logit_label = logit_label_batch[
torch.arange(rw_scores.shape[0]), -2
].squeeze()
return -expert_response_logit_label.mean()
def dpo_loss(self,logit_label, logit_label_base, rw_scores):
cand = rw_scores.shape[1]
bz = rw_scores.shape[0]
logit_label_batch = torch.reshape(
logit_label, (-1, cand, logit_label.shape[-1])
) # batch * cand
logit_label_base_batch = torch.reshape(
logit_label_base, (-1, cand, logit_label.shape[-1])
) # batch * cand
summed_logit = logit_label_batch.sum(-1)
summed_logit_base = logit_label_base_batch.sum(-1)
policy_chosen_logps = summed_logit[:,0]
policy_rejected_logps = summed_logit[:,1]
reference_chosen_logps = summed_logit_base[:,0]
reference_rejected_logps = summed_logit_base[:,1]
pi_logratios = policy_chosen_logps - policy_rejected_logps
ref_logratios = reference_chosen_logps - reference_rejected_logps
logits = pi_logratios - ref_logratios
losses = -F.logsigmoid(0.1 * logits)
return losses.sum() / bz
def slic_loss(self,logit_label, logit_label_base, rw_scores):
cand = rw_scores.shape[1]
bz = rw_scores.shape[0]
logit_label_batch = torch.reshape(
logit_label, (-1, cand, logit_label.shape[-1])
) # batch * cand
logit_label_base_batch = torch.reshape(
logit_label_base, (-1, cand, logit_label.shape[-1])
) # batch * cand
summed_logit = logit_label_batch.sum(-1)
summed_logit_base = logit_label_base_batch.sum(-1)
policy_chosen_logps = summed_logit[:,0]
policy_rejected_logps = summed_logit[:,1]
reference_chosen_logps = summed_logit_base[:,0]
reference_rejected_logps = summed_logit_base[:,1]
pi_logratios = policy_chosen_logps - policy_rejected_logps
ref_logratios = reference_chosen_logps - reference_rejected_logps
# logits = pi_logratios - ref_logratios
losses = torch.clamp(policy_rejected_logps-policy_chosen_logps+1.0,min=0) ###-reference_chosen_logps
return losses.sum() / bz
def load_ref_model(self,model):
# # add LoRA adaptor
lora_model = deepcopy(model)
lora_model.config.use_cache = False
self.base_model = lora_model
self.base_model.eval()
return None
def compute_loss(self, model, inputs, return_outputs=False):
if self.args.only_use_provide:
inputs["input_ids"] = inputs["input_ids"][-2:]
inputs["attention_mask"] = inputs["attention_mask"][-2:]
inputs["labels"] = inputs["labels"][-2:]
inputs["idxs"] = inputs["idxs"][:, -2:]
inputs["scores"] = inputs["scores"][:, -2:]
if self.args.only_use_sample:
inputs["input_ids"] = inputs["input_ids"][:-2]
inputs["attention_mask"] = inputs["attention_mask"][:-2]
inputs["labels"] = inputs["labels"][:-2]
inputs["idxs"] = inputs["idxs"][:, :-2]
inputs["scores"] = inputs["scores"][:, :-2]
logits = model(
input_ids=inputs.get("input_ids"),
attention_mask=inputs.get("attention_mask"),
)[
0
] # (batch * cand) * L * V
logits = F.log_softmax(logits, dim=-1)
logit_label = self.gather_logits_labels(logits, inputs.get("labels"))
######add ref model for dpo loss or other loss that require the reference model##################
# self.base_model = self.base_model.to(model.device)
# with torch.no_grad():
# logits_base = self.base_model(
# input_ids=inputs.get("input_ids"),
# attention_mask=inputs.get("attention_mask"),
# )[0]
# logits_base_ = F.log_softmax(logits_base, dim=-1)
# logit_label_base = self.gather_logits_labels(logits_base_, inputs.get("labels"))
########################################################################
# # scores = self.get_score(logit_label, inputs.get("labels"))
# rrhf_loss = self.rrhf_loss(scores, inputs.get("scores"))
lire_loss = self.lire_loss(logit_label, inputs.get("scores"))
# dpo_loss = self.slic_loss(logit_label,logit_label_base,inputs.get("scores"))
# sft_loss = self.sft_loss(logit_label, inputs.get("scores"))
loss = self.args.lire_weight * lire_loss
return loss
def disable_dropout(model: torch.nn.Module):
"""Disable dropout in a model."""
for module in model.modules():
if isinstance(module, torch.nn.Dropout):
module.p = 0
def train():
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments)
)
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
# "/data/zhumingye/RRHF/result/alpaca-sft-chosen/checkpoint-595",
cache_dir=training_args.cache_dir,
torch_dtype=torch.float16,
)
# model.config.use_cache = False
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
if tokenizer.pad_token is None:
smart_tokenizer_and_embedding_resize(
special_tokens_dict=dict(pad_token=DEFAULT_PAD_TOKEN),
tokenizer=tokenizer,
model=model,
)
##### apply lora to llama
# Define LoRA Config
lora_config = LoraConfig(
bias="none",
task_type="CAUSAL_LM",
inference_mode=False,
target_modules=["q_proj", "v_proj"],
r=64,
lora_alpha=8,
lora_dropout=0.0,
)
# # add LoRA adaptor
model = get_peft_model(model, lora_config)
model.print_trainable_parameters()
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
trainer = LIRETrainer(
model=model, tokenizer=tokenizer, args=training_args, **data_module
)
####if DPO loss is applied, initialize the reference model
# trainer.load_ref_model(model=model)
##############################################################
trainer.train()
# trainer.train(resume_from_checkpoint="/private/home/zhumingye/code/RRHF/results/rso_4res_e2_i2/checkpoint-588")
trainer.save_state()
safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
if __name__ == "__main__":
import numpy as np
import random
seed = 0
torch.manual_seed(seed)
np.random.seed(seed)
train()