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import math
import os
import sys
import fire
from tqdm import tqdm
import pandas as pd
import torch
import transformers
from peft import PeftModel
import datasets
from transformers import GenerationConfig, LlamaForCausalLM, LlamaTokenizer
from utils.callbacks import Iteratorize, Stream
from utils.prompter import Prompter
device = "cuda"
def main(
load_8bit: bool = True,
base_model: str = "decapoda-research/llama-7b-hf",
lora_weights: str = "./lora-alpaca",
data_path: str = "./data",
output_path: str = "./output",
eval_rate: float = 0.1,
batch_size: int = 32,
# The prompt template to use, will default to alpaca.
prompt_template: str = "alpaca",
):
base_model = base_model or os.environ.get("BASE_MODEL", "")
assert (base_model), "Please specify a --base_model, e.g. --base_model='huggyllama/llama-7b'"
prompter = Prompter(prompt_template)
tokenizer = LlamaTokenizer.from_pretrained(base_model)
if device == "cuda":
model = LlamaForCausalLM.from_pretrained(
base_model,
load_in_8bit=load_8bit,
torch_dtype=torch.float16,
device_map="auto",
)
model = PeftModel.from_pretrained(
model,
lora_weights,
torch_dtype=torch.float16,
)
# unwind broken decapoda-research config
model.config.pad_token_id = tokenizer.pad_token_id = 0 # unk
model.config.bos_token_id = 1
model.config.eos_token_id = 2
if not load_8bit:
model.half() # seems to fix bugs for some users.
model.eval()
if torch.__version__ >= "2" and sys.platform != "win32":
model = torch.compile(model)
def evaluate_one(
instruction,
input=None,
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=2,
max_new_tokens=128,
**kwargs,
):
prompt = prompter.generate_prompt(instruction, input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
**kwargs,
)
# Without streaming
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=True,
max_new_tokens=max_new_tokens,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
return prompter.get_response(output)
def evaluate_all():
# data = datasets.load_dataset("json", data_files=data_path)
# data = data["train"]
# df = data.to_pandas()
df = pd.read_json(data_path, orient='records')
print(df.info())
# 计算准确率
correct = 0
total = 0
total_step = len(df)
pbar = tqdm(total=total_step, unit='batch')
error = []
for i in range(total_step):
instruction = df['instruction'].iloc[i]
input = df['input'].iloc[i]
label = df['output'].iloc[i]
pred = evaluate_one(instruction=instruction, input=input)
if pred == label:
correct += 1
else:
error.append((label, pred))
total += 1
acc = correct / total
# 更新进度条
# Update the progress bar
pbar.set_description(
f"Testing: Sample [{total}/{total_step}] Acc: {acc :.4f}")
pbar.update(1)
for e in error:
print(e)
def evaluate_by_batch(
temperature=0.1,
top_p=0.75,
top_k=40,
num_beams=1,
max_new_tokens=32
):
df = pd.read_json(data_path, orient='records')
# df = df.sample(frac=eval_rate).reset_index(drop=True)
df['prompt'] = df.apply(lambda x: prompter.generate_prompt(
x['instruction'], x['input']), axis=1)
tokenizer.padding_side = "left" # Allow batched inference
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams
)
outputs = []
total = 0
total_step = math.ceil(len(df) / batch_size)
pbar = tqdm(total=total_step, unit='batch')
# 计算准确率
with torch.no_grad():
for i in range(total_step):
batch = df.iloc[i*batch_size:(i+1)*batch_size]
inputs = tokenizer(batch['prompt'].tolist(), return_tensors="pt", padding=True)[
'input_ids'].to(device)
generation_outputs = model.generate(
input_ids=inputs,
generation_config=generation_config,
max_new_tokens=max_new_tokens,
pad_token_id=tokenizer.pad_token_id
)
for g in generation_outputs:
decoded_item = tokenizer.decode(
g, skip_special_tokens=True)
try:
output = prompter.get_response(decoded_item)
except:
output = decoded_item
outputs.append(output)
total += 1
# 更新进度条
pbar.set_description(f"Testing: Sample [{total}/{len(df)}] ")
pbar.update(1)
df['pred'] = outputs
df['pred'].to_csv(output_path, index=False)
evaluate_by_batch()
if __name__ == "__main__":
# fire.Fire(main)
import yaml
dataset_param = sys.argv[1]
with open("./configs/evaluate_params.yaml", "r") as stream:
# try:
params = yaml.safe_load(stream)
print('=' * 80)
print(params[dataset_param])
print('=' * 80)
# fire.Fire(train)
main(**params[dataset_param])
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