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import base64
import json
import os
import time
from concurrent.futures import ThreadPoolExecutor, as_completed
from io import BytesIO
from typing import List, Tuple, Union
from urllib.parse import unquote
import numpy as np
from accelerate import Accelerator, DistributedType
from tqdm import tqdm
from lmms_eval.api.instance import Instance
from lmms_eval.api.model import lmms
from lmms_eval.api.registry import register_model
try:
from decord import VideoReader, cpu
except ImportError:
pass
from dotenv import load_dotenv
from loguru import logger as eval_logger
from openai import AzureOpenAI, DefaultHttpxClient, OpenAI
from PIL import Image
load_dotenv(verbose=True)
@register_model("openai_compatible")
class OpenAICompatible(lmms):
def __init__(
self,
model_version: str = "grok-2-latest",
base_url: str = None,
api_key: str = None,
timeout: int = 10,
max_retries: int = 5,
max_size_in_mb: int = 20,
continual_mode: bool = False,
response_persistent_folder: str = None,
azure_openai: bool = False,
max_frames_num: int = 10,
httpx_trust_env: bool = True,
batch_size: int = 64,
**kwargs,
) -> None:
"""
:param httpx_trust_env: bool
httpx.Client used by openai-python has trust_env set to True by default. A
False value of this param constructs a httpx.Client with trust_env set to
False. Such a httpx.Client ignores environment variables (HTTP_PROXY,
HTTPS_PROXY, ALL_PROXY) and macOS proxy server settings.
"""
super().__init__()
self.model_version = model_version
self.timeout = timeout
self.max_retries = max_retries
self.max_size_in_mb = max_size_in_mb # some models have a limit on the size of the image
self.continual_mode = continual_mode
self.max_frames_num = max_frames_num
if self.continual_mode:
if response_persistent_folder is None:
raise ValueError("Continual mode requires a persistent path for the response. Please provide a valid path.")
os.makedirs(response_persistent_folder, exist_ok=True)
self.response_persistent_folder = response_persistent_folder
self.response_persistent_file = os.path.join(self.response_persistent_folder, f"{self.model_version}_response.json")
if os.path.exists(self.response_persistent_file):
with open(self.response_persistent_file, "r") as f:
self.response_cache = json.load(f)
self.cache_mode = "resume"
else:
self.response_cache = {}
self.cache_mode = "start"
# In China mainland, people usually use a VPN client to access international web
# sites such as Google. Such a client usually configures macOS proxy server
# settings. openai-python uses a httpx.Client with trust_env set to True. Such a
# httpx.Client uses macOS proxy server settings. Adding httpx_trust_env option
# allows httpx to ignore proxy server settings set by VPN clients.
http_client = DefaultHttpxClient(trust_env=httpx_trust_env) if not httpx_trust_env else None
# Use provided parameters or fall back to environment variables
api_key = api_key or os.getenv("OPENAI_API_KEY")
base_url = base_url or os.getenv("OPENAI_API_BASE")
# Fix URL encoding issue - decode if it's URL encoded
if base_url and "%" in base_url:
base_url = unquote(base_url)
# Remove trailing slash if present
if base_url and base_url.endswith("/"):
base_url = base_url.rstrip("/")
self.client = (
OpenAI(api_key=api_key, base_url=base_url, http_client=http_client)
if not azure_openai
else AzureOpenAI(api_key=os.getenv("AZURE_OPENAI_API_KEY"), azure_endpoint=os.getenv("AZURE_OPENAI_API_BASE"), api_version=os.getenv("AZURE_OPENAI_API_VERSION"), http_client=http_client)
)
accelerator = Accelerator()
# assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue."
if accelerator.num_processes > 1:
assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported."
self.accelerator = accelerator
if self.accelerator.is_local_main_process:
eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism")
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
else:
self.accelerator = accelerator
self._rank = self.accelerator.local_process_index
self._world_size = self.accelerator.num_processes
self.device = self.accelerator.device
self.batch_size_per_gpu = int(batch_size)
@property
def batch_size(self):
return self.batch_size_per_gpu
def tok_encode(self, string: str):
return list(string.encode("utf-8"))
def tok_decode(self, tokens):
return ""
@property
def eot_token_id(self):
return 0
@property
def rank(self):
return self._rank
# Function to encode the image
def encode_image(self, image: Union[Image.Image, str]):
max_size = self.max_size_in_mb * 1024 * 1024 # 20MB in bytes
if isinstance(image, str):
img = Image.open(image).convert("RGB")
else:
img = image.copy()
output_buffer = BytesIO()
img.save(output_buffer, format="PNG")
byte_data = output_buffer.getvalue()
# If image is too large, resize it while maintaining aspect ratio
while len(byte_data) > max_size and img.size[0] > 100 and img.size[1] > 100:
new_size = (int(img.size[0] * 0.75), int(img.size[1] * 0.75))
img = img.resize(new_size, Image.Resampling.LANCZOS)
output_buffer = BytesIO()
img.save(output_buffer, format="PNG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
return base64_str
# Function to encode the video
def encode_video(self, video_path, for_get_frames_num):
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, for_get_frames_num, dtype=int)
# Ensure the last frame is included
if total_frame_num - 1 not in uniform_sampled_frames:
uniform_sampled_frames = np.append(uniform_sampled_frames, total_frame_num - 1)
frame_idx = uniform_sampled_frames.tolist()
frames = vr.get_batch(frame_idx).asnumpy()
base64_frames = []
for frame in frames:
img = Image.fromarray(frame)
output_buffer = BytesIO()
img.save(output_buffer, format="PNG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
base64_frames.append(base64_str)
return base64_frames
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def generate_until(self, requests) -> List[str]:
res = []
def _collate(x):
toks = self.tok_encode(x[0])
return -len(toks), x[0]
from lmms_eval import utils
re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True)
chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None)
num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1
pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding")
for chunk in chunks:
contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk)
gen_kwargs = all_gen_kwargs[0]
task = task[0]
split = split[0]
batch_payloads = []
batch_doc_uuids = []
batch_responses = []
for i, (context, doc_id_single) in enumerate(zip(contexts, doc_id)):
doc_uuid = f"{task}___{split}___{doc_id_single}"
batch_doc_uuids.append(doc_uuid)
if self.continual_mode is True and self.cache_mode == "resume":
if doc_uuid in self.response_cache:
response_text = self.response_cache[doc_uuid]
if response_text:
batch_responses.append(response_text)
continue
visuals = [doc_to_visual[i](self.task_dict[task][split][doc_id_single])]
if None in visuals:
visuals = []
imgs = []
else:
visuals = self.flatten(visuals)
imgs = []
for visual in visuals:
if isinstance(visual, str) and (".mp4" in visual or ".avi" in visual or ".mov" in visual or ".flv" in visual or ".wmv" in visual):
frames = self.encode_video(visual, self.max_frames_num)
imgs.extend(frames)
elif isinstance(visual, str) and (".jpg" in visual or ".jpeg" in visual or ".png" in visual or ".gif" in visual or ".bmp" in visual or ".tiff" in visual or ".webp" in visual):
img = self.encode_image(visual)
imgs.append(img)
elif isinstance(visual, Image.Image):
img = self.encode_image(visual)
imgs.append(img)
payload = {"messages": []}
payload["model"] = self.model_version
payload["messages"].append({"role": "user", "content": []})
payload["messages"][0]["content"].append({"type": "text", "text": context})
for img in imgs:
payload["messages"][0]["content"].append({"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img}"}})
if "max_new_tokens" not in gen_kwargs:
gen_kwargs["max_new_tokens"] = 1024
if gen_kwargs["max_new_tokens"] > 4096:
gen_kwargs["max_new_tokens"] = 4096
if "temperature" not in gen_kwargs:
gen_kwargs["temperature"] = 0
if "top_p" not in gen_kwargs:
gen_kwargs["top_p"] = None
if "num_beams" not in gen_kwargs:
gen_kwargs["num_beams"] = 1
payload["max_tokens"] = gen_kwargs["max_new_tokens"]
payload["temperature"] = gen_kwargs["temperature"]
if "o1" in self.model_version or "o3" in self.model_version:
del payload["temperature"]
payload["reasoning_effort"] = "medium"
payload["response_format"] = {"type": "text"}
payload.pop("max_tokens")
payload["max_completion_tokens"] = gen_kwargs["max_new_tokens"]
batch_payloads.append(payload)
batch_responses.append(None)
def process_single_request(payload, i):
if batch_responses[i] is not None:
return batch_responses[i], i
for attempt in range(self.max_retries):
try:
response = self.client.chat.completions.create(**payload)
response_text = response.choices[0].message.content
return response_text, i
except Exception as e:
error_msg = str(e)
eval_logger.info(f"Attempt {attempt + 1}/{self.max_retries} failed with error: {error_msg}")
if attempt == self.max_retries - 1:
eval_logger.error(f"All {self.max_retries} attempts failed. Last error: {error_msg}")
return "", i
else:
time.sleep(self.timeout)
return "", i
tasks_to_run = [(payload, i) for i, payload in enumerate(batch_payloads) if batch_responses[i] is None]
if tasks_to_run:
max_workers = min(len(tasks_to_run), 32)
with ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_index = {executor.submit(process_single_request, payload, i): i for payload, i in tasks_to_run}
for future in as_completed(future_to_index):
response_text, i = future.result()
batch_responses[i] = response_text
if self.continual_mode is True:
for doc_uuid, response_text in zip(batch_doc_uuids, batch_responses):
if response_text is not None:
self.response_cache[doc_uuid] = response_text
with open(self.response_persistent_file, "w") as f:
json.dump(self.response_cache, f)
res.extend([r for r in batch_responses if r is not None])
pbar.update(1)
res = re_ords.get_original(res)
pbar.close()
return res
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for OpenAI compatible models")
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
raise NotImplementedError("TODO: Implement loglikelihood for OpenAI compatible models")
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