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import json
import os
import glob
import sys
import time
from pathlib import Path
from typing import Tuple
import shortuuid
# from huggingface_hub import hf_hub_download
from PIL import Image
import gradio as gr
import torch
from fairscale.nn.model_parallel.initialize import initialize_model_parallel
from llama import LLaMA, ModelArgs, Tokenizer, Transformer, VisionModel
os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
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:"
),
}
def setup_model_parallel() -> Tuple[int, int]:
os.environ['RANK'] = '0'
os.environ['WORLD_SIZE'] = '1'
os.environ['MP'] = '1'
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '2223'
local_rank = int(os.environ.get("LOCAL_RANK", -1))
world_size = int(os.environ.get("WORLD_SIZE", -1))
torch.distributed.init_process_group("nccl")
initialize_model_parallel(world_size)
torch.cuda.set_device(local_rank)
# seed must be the same in all processes
torch.manual_seed(1)
return local_rank, world_size
def load(
ckpt_path: str,
param_path: str,
tokenizer_path: str,
instruct_adapter_path: str,
caption_adapter_path: str,
local_rank: int,
world_size: int,
max_seq_len: int,
max_batch_size: int,
) -> LLaMA:
start_time = time.time()
print("Loading")
instruct_adapter_checkpoint = torch.load(
instruct_adapter_path, map_location="cpu")
caption_adapter_checkpoint = torch.load(
caption_adapter_path, map_location="cpu")
with open(param_path, "r") as f:
params = json.loads(f.read())
model_args: ModelArgs = ModelArgs(
max_seq_len=max_seq_len, max_batch_size=max_batch_size, **params
)
model_args.adapter_layer = int(
instruct_adapter_checkpoint['adapter_query.weight'].shape[0] / model_args.adapter_len)
model_args.cap_adapter_layer = int(
caption_adapter_checkpoint['cap_adapter_query.weight'].shape[0] / model_args.cap_adapter_len)
tokenizer = Tokenizer(model_path=tokenizer_path)
model_args.vocab_size = tokenizer.n_words
torch.set_default_tensor_type(torch.cuda.HalfTensor)
model = Transformer(model_args)
ckpt = torch.load(ckpt_path, map_location='cuda')
model.load_state_dict(ckpt, strict=False)
vision_model = VisionModel(model_args)
torch.set_default_tensor_type(torch.FloatTensor)
model.load_state_dict(instruct_adapter_checkpoint, strict=False)
model.load_state_dict(caption_adapter_checkpoint, strict=False)
vision_model.load_state_dict(caption_adapter_checkpoint, strict=False)
generator = LLaMA(model, tokenizer, vision_model)
print(f"Loaded in {time.time() - start_time:.2f} seconds")
return generator
def instruct_generate(
instruct: str,
input: str = 'none',
max_gen_len=512,
temperature: float = 0.1,
top_p: float = 0.75,
):
if input == 'none':
prompt = PROMPT_DICT['prompt_no_input'].format_map(
{'instruction': instruct, 'input': ''})
else:
prompt = PROMPT_DICT['prompt_input'].format_map(
{'instruction': instruct, 'input': input})
results = generator.generate(
[prompt], max_gen_len=max_gen_len, temperature=temperature, top_p=top_p
)
result = results[0].strip()
# print(result)
return result
ckpt_path = "/data1/llma/7B/consolidated.00.pth"
param_path = "/data1/llma/7B/params.json"
tokenizer_path = "/data1/llma/tokenizer.model"
instruct_adapter_path = "llama_adapter_len10_layer30_release.pth"
caption_adapter_path = "llama_adapter_len10_layer30_caption_vit_l.pth"
max_seq_len = 512
max_batch_size = 32
local_rank, world_size = setup_model_parallel()
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
generator = load(
ckpt_path, param_path, tokenizer_path, instruct_adapter_path, caption_adapter_path, local_rank, world_size, max_seq_len, max_batch_size
)
answer_data = []
for line in open('question.jsonl').readlines():
line = json.loads(line)
question_text = line["text"]
answer = {
"answer_id": shortuuid.uuid(),
"model_id": "LLaMA-Adapter",
"question_id": line["question_id"],
"question_text": question_text,
"text": '',
"metadata": {}
}
answer_data.append(answer)
prompts = [PROMPT_DICT['prompt_no_input'].format_map({'instruction': x['question_text']}) for x in answer_data]
results = []
result = generator.generate(prompts[:32], max_gen_len=512, temperature=0.1, top_p=0.75)
results.extend(result)
result = generator.generate(prompts[32:64], max_gen_len=512, temperature=0.1, top_p=0.75)
results.extend(result)
result = generator.generate(prompts[64:], max_gen_len=512, temperature=0.1, top_p=0.75)
results.extend(result)
for i in range(len(answer_data)):
answer_i = answer_data[i]
answer_i['text'] = results[i].strip()
del answer_i['question_text']
answer_data[i] = answer_i
with open('llama_adapter_7b.json', 'w') as f:
f.write("\n".join([json.dumps(x) for x in answer_data])) |