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import shutil
from dataclasses import dataclass, field
from typing import Optional
import evaluate
import nltk
import torch
from accelerate import Accelerator
from datasets import load_dataset
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
default_data_collator,
)
import wandb
shutil.disk_usage = lambda x: shutil._ntuple_diskusage(1, 1, 1)
@dataclass
class ScriptArguments:
model_name: Optional[str] = field(default="EleutherAI/pythia-6.9b-deduped", metadata={"help": "the model name"})
tokenizer_name: Optional[str] = field(default=None, metadata={"help": "the tokenizer name"})
dataset_name: Optional[str] = field(
default="CarperAI/openai_summarize_tldr", metadata={"help": "the dataset name"}
)
split: Optional[str] = field(default="valid[:20]", metadata={"help": "the dataset name"})
dataset_text_field: Optional[str] = field(default="prompt")
dataset_label_field: Optional[str] = field(default="label")
load_in_8bit: Optional[bool] = field(default=True, metadata={"help": "load the model in 8 bits precision"})
load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
use_peft: Optional[bool] = field(default=True, metadata={"help": "Wether to use PEFT or not to train adapters"})
seed: Optional[int] = field(default=0)
batch_size: Optional[int] = field(default=1)
bf16: Optional[bool] = field(default=False)
fp16: Optional[bool] = field(default=False)
seq_length: Optional[int] = field(default=512, metadata={"help": "Input sequence length"})
max_new_tokens: Optional[int] = field(default=48, metadata={"help": "Max new tokens to generate"})
num_logged_samples: int = field(default=100, metadata={"help": "Max samples to log to wandb"})
temperature: Optional[float] = field(default=0.0)
sample: Optional[bool] = field(default=False)
strip: Optional[bool] = field(default=False)
log_with: Optional[str] = field(default="wandb")
parser = HfArgumentParser(ScriptArguments)
args = parser.parse_args_into_dataclasses()[0]
print("Loading the model")
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
elif args.load_in_8bit or args.load_in_4bit:
quantization_config = BitsAndBytesConfig(load_in_8bit=args.load_in_8bit, load_in_4bit=args.load_in_4bit)
device_map = {"": Accelerator().local_process_index}
else:
device_map = None
quantization_config = None
model = AutoModelForCausalLM.from_pretrained(
args.model_name,
torch_dtype=torch.bfloat16 if args.bf16 else None,
)
print("Loading dataset")
tokenizer = AutoTokenizer.from_pretrained(
args.model_name if args.tokenizer_name is None else args.tokenizer_name, padding_side="left"
)
if getattr(tokenizer, "pad_token", None) is None:
tokenizer.pad_token = tokenizer.eos_token
def create_dataset(tokenizer, args):
eval_data = load_dataset(
args.dataset_name,
split=args.split,
)
padding = "max_length"
max_source_length = args.seq_length
max_target_length = 52
def preprocess_function(example):
inputs = example[args.dataset_text_field]
targets = example[args.dataset_label_field]
if args.strip:
inputs = inputs.strip()
targets = targets.strip()
model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)
# Tokenize targets with the `text_target` keyword argument
labels = tokenizer(text_target=targets, max_length=max_target_length, padding=padding, truncation=True)
model_inputs["labels"] = labels["input_ids"]
return model_inputs
eval_dataset = eval_data.map(
preprocess_function,
remove_columns=eval_data.column_names,
)
return eval_dataset
eval_dataset = create_dataset(tokenizer, args)
rouge = evaluate.load("rouge")
def postprocess_text(preds, labels):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
# rougeLSum expects newline after each sentence
preds = ["\n".join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ["\n".join(nltk.sent_tokenize(label)) for label in labels]
return preds, labels
accelerator = Accelerator()
eval_dataloader = DataLoader(eval_dataset, collate_fn=default_data_collator, batch_size=args.batch_size)
model, eval_dataloader = accelerator.prepare(model, eval_dataloader)
model.eval()
gen_kwargs = {
"max_new_tokens": args.max_new_tokens,
"pad_token_id": tokenizer.pad_token_id,
"eos_token_id": tokenizer.eos_token_id,
"do_sample": args.sample,
}
wandb.init(project="trl")
table = wandb.Table(columns=["prompt", "prediction", "label"])
for batch in tqdm(eval_dataloader):
with torch.no_grad():
output_tokens = accelerator.unwrap_model(model).generate(
batch["input_ids"],
attention_mask=batch["attention_mask"],
**gen_kwargs,
)
# get just the generated tokens
generated_tokens = output_tokens[:, batch["input_ids"].shape[1] :]
generated_tokens = accelerator.pad_across_processes(generated_tokens, dim=1, pad_index=tokenizer.pad_token_id)
labels = batch["labels"]
# if not args.pad_to_max_length:
# # If we did not pad to max length, we need to pad the labels too
# labels = accelerator.pad_across_processes(batch["labels"], dim=1, pad_index=tokenizer.pad_token_id)
generated_tokens, labels = accelerator.gather_for_metrics((generated_tokens, labels))
generated_tokens = generated_tokens.cpu().numpy()
labels = labels.cpu().numpy()
# if args.ignore_pad_token_for_loss:
# # Replace -100 in the labels as we can't decode them.
# labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
if isinstance(generated_tokens, tuple):
generated_tokens = generated_tokens[0]
decoded_preds = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
decoded_preds, decoded_labels = postprocess_text(decoded_preds, decoded_labels)
# print(f"Label {decoded_labels}")
# print(f"Pred {decoded_preds}")
rouge.add_batch(
predictions=decoded_preds,
references=decoded_labels,
)
# log samples
if accelerator.is_main_process:
if len(table.data) < args.num_logged_samples:
num_samples_to_add = min(args.num_logged_samples - len(table.data), len(batch["input_ids"]))
for i in range(num_samples_to_add):
table.add_data(
tokenizer.decode(batch["input_ids"][i], skip_special_tokens=True),
decoded_preds[i],
decoded_labels[i],
)
if len(table.data) == args.num_logged_samples:
if args.log_with == "wandb":
wandb.log({"examples": table})
else:
for row in table.iterrows():
print("PROMPT")
print(row[1][0])
print("\n")
print("PRED")
print(row[1][1])
print("\n")
print("LABEL")
print(row[1][2])
print("\n")
print("\n")
result = rouge.compute()
print(result)
for key, value in result.items():
wandb.run.summary[key] = value
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