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# Copyright (c) Microsoft Corporation.
# SPDX-License-Identifier: Apache-2.0
# DeepSpeed Team
import argparse
import re
import torch
import logging
# import transformers # noqa: F401
from transformers import pipeline, set_seed
from transformers import AutoConfig, OPTForCausalLM, AutoTokenizer, LlamaForCausalLM, LlamaTokenizer
from datasets import load_dataset
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
from torch.utils.data import DataLoader
import deepspeed
import os
import pandas as pd
import time
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--path",
type=str,
help="Directory containing trained actor model")
parser.add_argument("--debug",
type=bool,
default=False,
help="whether debug mode is on")
parser.add_argument("--local_rank",
type=int,
help="local rank")
parser.add_argument(
"--max_new_tokens",
type=int,
default=128,
help="Maximum new tokens to generate per response",
)
args = parser.parse_args()
return args
def get_generator(path, local_rank):
if 'llama' in path:
tokenizer = LlamaTokenizer.from_pretrained(path, fast_tokenizer=True)
tokenizer.pad_token = tokenizer.eos_token
model_config = AutoConfig.from_pretrained(path)
model = LlamaForCausalLM.from_pretrained(path,
from_tf=bool(".ckpt" in path),
config=model_config,
# device_map='auto'
# , load_in_8bit=True
)
model.config.end_token_id = tokenizer.eos_token_id
model.config.pad_token_id = model.config.eos_token_id
model.resize_token_embeddings(len(tokenizer))
model.eval()
generator = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device=local_rank)
else:
tokenizer = AutoTokenizer.from_pretrained(path, fast_tokenizer=True)
tokenizer.pad_token = tokenizer.eos_token
model_config = AutoConfig.from_pretrained(path)
model = OPTForCausalLM.from_pretrained(path,
from_tf=bool(".ckpt" in path),
config=model_config,
# device_map='auto'
# , load_in_8bit=True
)
model.config.end_token_id = tokenizer.eos_token_id
model.config.pad_token_id = model.config.eos_token_id
model.resize_token_embeddings(len(tokenizer))
model.eval()
generator = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device=local_rank)
return generator
def process_response(response):
output = str(response[0]["generated_text"])
output = output.split("Assistant:")[1].strip()
# output = output.replace("<|endoftext|></s>", "")
output = output.replace("<|endoftext|>", "")
output = output.replace("</s>", "")
return output
def main(args):
world_size = int(os.getenv('WORLD_SIZE', '1'))
# world_size = torch.distributed.get_world_size()
# local_rank = int(os.getenv('LOCAL_RANK', args.local_rank))
# args.local_rank=int(os.getenv("LOCAL_RANK", -1))
# local_rank = args.local_rank
generator = get_generator(args.path, args.local_rank)
# generator.model = deepspeed.init_inference(generator.model,
# mp_size=world_size,
# dtype=torch.half,
# replace_with_kernel_inject=True)
set_seed(42)
# load huggingface finacial phrasebank dataset
# https://huggingface.co/datasets/financial_phrasebank
dataset_fpb = load_dataset("financial_phrasebank", "sentences_50agree")
label_mapping_fpb = {"negative": 0, "neutral": 1, "positive": 2}
text_inputs_fpb = dataset_fpb['train']['sentence']
labels_fpb = dataset_fpb['train']['label']
recons_fpb = {"name": "fpb", "sentence": text_inputs_fpb, "label": labels_fpb, "label_mapping": label_mapping_fpb}
dataset_fiqa = load_dataset("pauri32/fiqa-2018")
label_mapping_fiqa = {"negative": 2, "neutral": 1, "positive": 0}
text_inputs_fiqa = dataset_fiqa['test']['sentence']
labels_fiqa = dataset_fiqa['test']['label']
recons_fiqa = {"name": "fiqa", "sentence": text_inputs_fiqa, "label": labels_fiqa, "label_mapping": label_mapping_fiqa}
# dataset_fpb_num = pd.read_csv("data/FPB_filtered_number.csv")
# label_mapping_fpb_num = {"negative": 0, "neutral": 1, "positive": 2}
# text_inputs_fpb_num = dataset_fpb_num['sentence']
# labels_fpb_num = dataset_fpb_num['sentiment'].apply(lambda x: label_mapping_fpb_num[x])
# recons_fpb_num = {"name": "fpb_num", "sentence": text_inputs_fpb_num, "label": labels_fpb_num, "label_mapping": label_mapping_fpb_num}
dataset_twitter = load_dataset("zeroshot/twitter-financial-news-sentiment")
label_mapping_twitter = {"negative": 0, "neutral": 2, "positive": 1}
text_inputs_twitter = dataset_twitter['validation']['text']
labels_twitter = dataset_twitter['validation']['label']
recons_twitter = {"name": "twitter", "sentence": text_inputs_twitter, "label": labels_twitter, "label_mapping": label_mapping_twitter}
# dataset_list = [recons_fpb, recons_fiqa, recons_twitter]
dataset_list = [recons_twitter]
# dataset_list = [recons_fpb_num]
for dataset in dataset_list:
labels = []
preds = []
text_inputs = dataset['sentence']
process_inputs = [f"Human: Determine the sentiment of the financial news as negative, neutral or positive: {text_inputs[i]} Assistant: " for i in range(len(text_inputs))]
labels = dataset['label']
label_mapping = dataset['label_mapping']
if args.debug:
process_inputs = process_inputs[:100]
labels = labels[:100]
# response = generator(process_inputs, max_new_tokens=args.max_new_tokens, do_sample=True)
start = time.time()
response = generator(process_inputs, max_new_tokens=args.max_new_tokens)
end = time.time()
# print(response)
outputs = [process_response(response[i]) for i in range(len(response))]
for i in range(len(outputs)):
if "negative" in outputs[i]:
preds.append(label_mapping["negative"])
elif "neutral" in outputs[i]:
preds.append(label_mapping["neutral"])
elif "positive" in outputs[i]:
preds.append(label_mapping["positive"])
else:
preds.append(label_mapping["neutral"])
print(f"Dataset: {dataset['name']}")
print(f"Process time: {end-start}")
print(f"Accuracy: {accuracy_score(labels, preds)}")
print(f"F1: {f1_score(labels, preds, average='macro')}")
print(f"Confusion Matrix: {confusion_matrix(labels, preds)}")
if __name__ == "__main__":
# Silence warnings about `max_new_tokens` and `max_length` being set
logging.getLogger("transformers").setLevel(logging.ERROR)
args = parse_args()
print(args)
main(args)
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