Delete gpt4_eval.py

gpt4_eval.py

@@ -1,170 +0,0 @@
-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]))