Spaces:
Build error
Build error
File size: 3,530 Bytes
0156aec ee71b10 0156aec ee71b10 0156aec |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 |
import os
import sys
import torch
from dotenv import find_dotenv, load_dotenv
found_dotenv = find_dotenv(".env")
if len(found_dotenv) == 0:
found_dotenv = find_dotenv(".env.example")
print(f"loading env vars from: {found_dotenv}")
load_dotenv(found_dotenv, override=False)
path = os.path.dirname(found_dotenv)
print(f"Adding {path} to sys.path")
sys.path.append(path)
from llm_toolkit.llm_utils import *
from llm_toolkit.translation_utils import *
device = check_gpu()
is_cuda = torch.cuda.is_available()
model_name = os.getenv("MODEL_NAME")
adapter_name_or_path = os.getenv("ADAPTER_NAME_OR_PATH")
load_in_4bit = os.getenv("LOAD_IN_4BIT") == "true"
data_path = os.getenv("DATA_PATH")
results_path = os.getenv("RESULTS_PATH")
batch_size = int(os.getenv("BATCH_SIZE", 1))
use_english_datasets = os.getenv("USE_ENGLISH_DATASETS") == "true"
max_new_tokens = int(os.getenv("MAX_NEW_TOKENS", 2048))
start_repetition_penalty = float(os.getenv("START_REPETITION_PENALTY", 1.0))
end_repetition_penalty = float(os.getenv("END_REPETITION_PENALTY", 1.3))
print(
model_name,
adapter_name_or_path,
load_in_4bit,
data_path,
results_path,
use_english_datasets,
max_new_tokens,
batch_size,
)
if is_cuda:
torch.cuda.empty_cache()
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(0) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
torch.cuda.empty_cache()
model, tokenizer = load_model(
model_name, load_in_4bit=load_in_4bit, adapter_name_or_path=adapter_name_or_path
)
if is_cuda:
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(2) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
datasets = load_translation_dataset(data_path, tokenizer)
if len(sys.argv) > 1:
num = int(sys.argv[1])
if num > 0:
print(f"--- evaluating {num} entries")
datasets["test"] = datasets["test"].select(range(num))
print_row_details(datasets["test"].to_pandas(), indices=[0, -1])
def on_repetition_penalty_step_completed(model_name, predictions):
save_results(
model_name,
results_path,
datasets["test"],
predictions,
)
metrics = calc_metrics(datasets["test"]["english"], predictions, debug=True)
print(f"{model_name} metrics: {metrics}")
if adapter_name_or_path is not None:
model_name += "/" + adapter_name_or_path.split("/")[-1]
evaluate_model_with_repetition_penalty(
model,
tokenizer,
model_name,
datasets["test"],
on_repetition_penalty_step_completed,
start_repetition_penalty=start_repetition_penalty,
end_repetition_penalty=end_repetition_penalty,
step_repetition_penalty=0.02,
batch_size=batch_size,
max_new_tokens=max_new_tokens,
device=device,
)
if is_cuda:
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"(3) GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
|