File size: 11,014 Bytes
b0c0df0 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 |
import base64
import json
import os
import time
from copy import deepcopy
from io import BytesIO
from typing import List, Tuple, Union
from accelerate import Accelerator, DistributedType
from PIL import Image
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
NUM_SECONDS_TO_SLEEP = 5
from loguru import logger
eval_logger = logger
try:
import anthropic
import numpy as np
from decord import VideoReader, cpu
except Exception as e:
eval_logger.warning(f"Error importing claude: {e}")
API_URL = os.getenv("ANTHROPIC_API_URL", "https://api.anthropic.com/v1/complete")
API_KEY = os.getenv("ANTHROPIC_API_KEY", "YOUR_API_KEY")
@register_model("claude")
class Claude(lmms):
def __init__(
self,
model_version: str = "claude-3-opus-20240229",
image_token: str = "<image>", # Use to separate interleaved image and text
system_prompt: str = "", # Whether you want some special system prompt here
modality: str = "image",
max_frames_num: int = 10,
continual_mode: bool = False,
response_persistent_folder: str = None,
**kwargs,
) -> None:
super().__init__()
self.model_version = model_version
self.image_token = image_token
self.system_prompt = system_prompt
self.modality = modality
self.max_frames_num = max_frames_num
self.continual_mode = continual_mode
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"
accelerator = Accelerator()
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
def encode_image(self, image):
output_buffer = BytesIO()
image.save(output_buffer, format="JPEG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
return base64_str
def flatten(self, input):
new_list = []
for i in input:
for j in i:
new_list.append(j)
return new_list
def get_image_size(self, image):
# Create a BytesIO object to store the image bytes
img_byte_array = BytesIO()
# Save the image to the BytesIO object
image.save(img_byte_array, format="PNG")
# Get the size of the BytesIO object
img_size = img_byte_array.tell()
return img_size
# The max file size is 5MB for claude
def shrink_image_to_file_size(self, img: Image, max_file_size=4838990) -> Image:
# Get the current size of the image
original_size = self.get_image_size(img)
# If the image size is already smaller than the desired size, return
if original_size <= max_file_size:
return img
# Calculate the ratio to shrink the image
# Somehow I found out sqrt ratio is not enough to shrink the image
# below threshold, so I guess we do more
shrink_ratio = min(0.9, max_file_size / original_size)
# Resize the image with the calculated ratio
new_width = int(img.width * shrink_ratio)
new_height = int(img.height * shrink_ratio)
img = img.resize((new_width, new_height), Image.LANCZOS)
return self.shrink_image_to_file_size(img, max_file_size)
def encode_video(self, video_path):
vr = VideoReader(video_path, ctx=cpu(0))
total_frame_num = len(vr)
uniform_sampled_frames = np.linspace(0, total_frame_num - 1, self.max_frames_num, dtype=int)
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="JPEG")
byte_data = output_buffer.getvalue()
base64_str = base64.b64encode(byte_data).decode("utf-8")
base64_frames.append(f"{base64_str}")
return base64_frames
def generate_until(self, requests) -> List[str]:
client = anthropic.Anthropic()
res = []
pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
empty_image_block = {
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
},
}
empty_text_block = {"type": "text"}
empty_messages = [
{
"role": "user",
"content": [],
}
]
for contexts, gen_kwargs, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
###################### CONTINUAL MODE ######################
if self.continual_mode is True and self.cache_mode == "resume":
doc_uuid = f"{task}___{split}___{doc_id}"
if doc_uuid in self.response_cache:
response_text = self.response_cache[doc_uuid]
if response_text:
res.append(response_text)
pbar.update(1)
continue
visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
visuals = self.flatten(visuals)
imgs = []
for visual in visuals:
if isinstance(visual, str) and os.path.exists(visual): # Assuming visual is a path to a video
visual = self.encode_video(visual)
for img in visual:
imgs.append(img)
else:
visual = self.shrink_image_to_file_size(visual)
img = self.encode_image(visual)
imgs.append(img)
messages = deepcopy(empty_messages)
if self.image_token not in contexts:
for img in imgs:
image_block = deepcopy(empty_image_block)
image_block["source"]["data"] = img
messages[0]["content"].append(image_block)
text_block = deepcopy(empty_text_block)
text_block["text"] = contexts
messages[0]["content"].append(text_block)
else:
contexts = contexts.split(self.image_token)
for idx, img in enumerate(imgs):
text_block = deepcopy(empty_text_block)
image_block = deepcopy(empty_image_block)
text_block["text"] = contexts
messages[0]["content"].append(text_block)
image_block["source"]["data"] = img
messages[0]["content"].append(image_block)
# If n image tokens are in the contexts
# contexts will be splitted into n+1 chunks
# Manually add it into the messages
text_block = deepcopy(empty_text_block)
text_block["text"] = contexts
messages["content"].append(text_block)
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 or gen_kwargs["top_p"] is None:
gen_kwargs["top_p"] = 1
if "num_beams" not in gen_kwargs:
gen_kwargs["num_beams"] = 1
for attempt in range(5):
retry_flag = True
try:
message = client.messages.create(model=self.model_version, max_tokens=gen_kwargs["max_new_tokens"], system=self.system_prompt, temperature=gen_kwargs["temperature"], top_p=gen_kwargs["top_p"], messages=messages)
retry_flag = False
except Exception as e:
eval_logger.info(f"Attempt {attempt + 1} failed with error: {str(e)}")
if attempt < 5 - 1: # If we have retries left, sleep and then continue to next attempt
time.sleep(NUM_SECONDS_TO_SLEEP)
else: # If this was the last attempt, log and return empty
eval_logger.error(f"All 5 attempts failed. Last error message: {str(e)}")
res.append("")
pbar.update(1)
continue
if not retry_flag:
break
eval_logger.info("Retrying...")
response_text = message.content[0].text
res.append(message.content[0].text)
pbar.update(1)
###################### CONTINUAL MODE ######################
if self.continual_mode is True: # Cache the response
response_text = message.content[0].text
doc_uuid = f"{task}___{split}___{doc_id}"
self.response_cache[doc_uuid] = response_text
with open(self.response_persistent_file, "w") as f:
json.dump(self.response_cache, f, indent=4)
pbar.close()
return res
def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
assert False, "Not supported for claude"
def generate_until_multi_round(self, requests) -> List[str]:
raise NotImplementedError("TODO: Implement multi-round generation for Claude")
|