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import json
import os
import glob
import sys
import time
from pathlib import Path
from typing import Tuple
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(
ckpt0_path: str,
ckpt1_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)
# To reduce memory usuage
ckpt0 = torch.load(ckpt0_path, map_location='cuda')
model.load_state_dict(ckpt0, strict=False)
del ckpt0
torch.cuda.empty_cache()
ckpt1 = torch.load(ckpt1_path, map_location='cuda')
model.load_state_dict(ckpt1, strict=False)
del ckpt1
torch.cuda.empty_cache()
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
def download_llama_adapter(instruct_adapter_path, caption_adapter_path):
if not os.path.exists(instruct_adapter_path):
os.system(
f"wget -q -O {instruct_adapter_path} https://github.com/ZrrSkywalker/LLaMA-Adapter/releases/download/v.1.0.0/llama_adapter_len10_layer30_release.pth")
if not os.path.exists(caption_adapter_path):
os.system(
f"wget -q -O {caption_adapter_path} https://github.com/ZrrSkywalker/LLaMA-Adapter/releases/download/v.1.0.0/llama_adapter_len10_layer30_caption_vit_l.pth")
# ckpt_path = "/data1/llma/7B/consolidated.00.pth"
# param_path = "/data1/llma/7B/params.json"
# tokenizer_path = "/data1/llma/tokenizer.model"
ckpt0_path = hf_hub_download(
repo_id="csuhan/llama_storage", filename="consolidated.00_part0.pth")
ckpt1_path = hf_hub_download(
repo_id="csuhan/llama_storage", filename="consolidated.00_part1.pth")
param_path = hf_hub_download(
repo_id="nyanko7/LLaMA-7B", filename="params.json")
tokenizer_path = hf_hub_download(
repo_id="nyanko7/LLaMA-7B", filename="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 = 1
# download models
# download_llama_adapter(instruct_adapter_path, caption_adapter_path)
local_rank, world_size = setup_model_parallel()
if local_rank > 0:
sys.stdout = open(os.devnull, "w")
generator = load(
ckpt0_path, ckpt1_path, param_path, tokenizer_path, instruct_adapter_path, caption_adapter_path, local_rank, world_size, max_seq_len, max_batch_size
)
def create_instruct_demo():
with gr.Blocks() as instruct_demo:
with gr.Row():
with gr.Column():
instruction = gr.Textbox(lines=2, label="Instruction")
input = gr.Textbox(
lines=2, label="Context input", placeholder='none')
max_len = gr.Slider(minimum=1, maximum=512,
value=128, label="Max length")
with gr.Accordion(label='Advanced options', open=False):
temp = gr.Slider(minimum=0, maximum=1,
value=0.1, label="Temperature")
top_p = gr.Slider(minimum=0, maximum=1,
value=0.75, label="Top p")
run_botton = gr.Button("Run")
with gr.Column():
outputs = gr.Textbox(lines=10, label="Output")
inputs = [instruction, input, max_len, temp, top_p]
examples = [
"Tell me about alpacas.",
"Write a Python program that prints the first 10 Fibonacci numbers.",
"Write a conversation between the sun and pluto.",
"Write a theory to explain why cat never existed",
]
examples = [
[x, "none", 128, 0.1, 0.75]
for x in examples]
gr.Examples(
examples=examples,
inputs=inputs,
outputs=outputs,
fn=instruct_generate,
cache_examples=os.getenv('SYSTEM') == 'spaces'
)
run_botton.click(fn=instruct_generate, inputs=inputs, outputs=outputs)
return instruct_demo
description = """
# TAPA: xxx
"""
with gr.Blocks(css='style.css') as demo:
gr.Markdown(description)
with gr.TabItem("Instruction-Following"):
create_instruct_demo()
demo.queue(api_open=True, concurrency_count=1).launch()
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