update

pages/4_LLM.py

@@ -1,277 +0,0 @@
-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("mpi")
-    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 caption_generate(
-    img: str,
-    max_gen_len=512,
-    temperature: float = 0.1,
-    top_p: float = 0.75,
-):
-    imgs = [Image.open(img).convert('RGB')]
-    prompts = ["Generate caption of this image :",] * len(imgs)
-
-    results = generator.generate(
-        prompts, imgs=imgs, 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
-
-
-def create_caption_demo():
-    with gr.Blocks() as instruct_demo:
-        with gr.Row():
-            with gr.Column():
-                img = gr.Image(label='Input', type='filepath')
-                max_len = gr.Slider(minimum=1, maximum=512,
-                                    value=64, 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 = [img, max_len, temp, top_p]
-
-        examples = glob.glob("caption_demo/*.jpg")
-        examples = [
-            [x, 64, 0.1, 0.75]
-            for x in examples]
-
-        gr.Examples(
-            examples=examples,
-            inputs=inputs,
-            outputs=outputs,
-            fn=caption_generate,
-            cache_examples=os.getenv('SYSTEM') == 'spaces'
-        )
-        run_botton.click(fn=caption_generate, inputs=inputs, outputs=outputs)
-    return instruct_demo
-
-
-description = """
-# LLaMA-Adapter🚀
-The official demo for **LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention**.
-Please refer to our [arXiv paper](https://arxiv.org/abs/2303.16199) and [github](https://github.com/ZrrSkywalker/LLaMA-Adapter) for more details.
-"""
-
-with gr.Blocks(css='style.css') as demo:
-    gr.Markdown(description)
-    with gr.TabItem("Instruction-Following"):
-        create_instruct_demo()
-    with gr.TabItem("Image Captioning"):
-        create_caption_demo()
-
-demo.queue(api_open=True, concurrency_count=1).launch()