""" Copyright [2022-2023] Victor C Hall Licensed under the GNU Affero General Public License; You may not use this code except in compliance with the License. You may obtain a copy of the License at https://www.gnu.org/licenses/agpl-3.0.en.html Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. """ import os import io import argparse import time import json import logging import re from typing import TYPE_CHECKING, Generator, Optional, List, Tuple, Dict, Any import torch from torchvision import transforms from PIL import Image import PIL.ImageOps as ImageOps from pynvml import * from transformers import AutoModelForCausalLM, LlamaTokenizer, PreTrainedTokenizer, BitsAndBytesConfig from transformers.modeling_outputs import BaseModelOutputWithPast from colorama import Fore, Style from plugins.caption_plugins import load_prompt_alteration_plugin from utils.patch_cog import patch_cog from data.gen_utils import image_generator, SUPPORTED_EXT IMAGE_SIZE: int = 490 PATCH_SIZE: int = 14 patch_cog() # fixes inv_freq key error with cogvlm, quantization, and newer transformers revisions def build_conversation_input_ids( tokenizer: PreTrainedTokenizer, *, query: str, history: Optional[List[Tuple[str, str]]] = None, images: Optional[List[Image.Image]] = None, starts_with: Optional[str] = None, ): # based on https://huggingface.co/THUDM/cogvlm-chat-hf/blob/main/modeling_cogvlm.py image_size: int = IMAGE_SIZE patch_size: int = PATCH_SIZE assert images is None or len(images) <= 1, f"not support multi images by now." history = history or [] text = f"Question: {query} Answer: " text += starts_with if starts_with is not None else "" input_ids = [tokenizer.bos_token_id] token_type_ids = [0] if images is not None and len(images) == 1: # vision transform = transforms.Compose( [ transforms.Resize( (image_size, image_size), interpolation=transforms.InterpolationMode.BICUBIC ), transforms.ToTensor(), transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711)), ] ) images = [transform(images[0])] vision_token_num = (image_size // patch_size) * (image_size // patch_size) + 2 input_ids += [tokenizer.pad_token_id] * vision_token_num token_type_ids += [1] * vision_token_num text_ids = tokenizer.encode(text, add_special_tokens=False) input_ids += text_ids token_type_ids += [0] * len(text_ids) attention_mask = [1] * len(input_ids) return { 'input_ids': torch.tensor(input_ids, dtype=torch.long), 'token_type_ids': torch.tensor(token_type_ids, dtype=torch.long), 'attention_mask': torch.tensor(attention_mask, dtype=torch.long), 'images': images, } def get_gpu_memory_map(): nvmlInit() handle = nvmlDeviceGetHandleByIndex(0) info = nvmlDeviceGetMemoryInfo(handle) nvmlShutdown() return info.used/1024/1024 def save_params(args, gen_kwargs): save_path = os.path.join(args.image_dir, "caption_cog_params.txt") args_dict = { "args": vars(args), "gen_kwargs": gen_kwargs, } pretty_print = json.dumps(args_dict, indent=4) with open(save_path, "w") as f: f.write(pretty_print) def create_bnb_config(args): return BitsAndBytesConfig( bnb_4bit_compute_dtype="float32", bnb_4bit_quant_type= "fp4", bnb_4bit_use_double_quant=False, llm_int8_enable_fp32_cpu_offload=False, llm_int8_has_fp16_weight=False, llm_int8_skip_modules=None, llm_int8_threshold= 6.0, load_in_4bit=True, load_in_8bit=False, quant_method="bitsandbytes" ) def main(args): prompt_plugin_fn = load_prompt_alteration_plugin(args.prompt_plugin, args=args) bnb_config = create_bnb_config(args) tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5') model = AutoModelForCausalLM.from_pretrained( 'THUDM/cogvlm-chat-hf', torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, trust_remote_code=True, # gee hope they don't get hacked or have a bad internal actor #revision=... # no one is actually doing this #load_in_4bit=not args.disable_4bit, quantization_config=bnb_config, ) do_sample = args.top_k is not None or args.top_p is not None or args.temp is not None if do_sample: args.top_k = args.top_k or 50 args.top_p = args.top_p or 1.0 args.temp = args.temp or 1.0 args.append = args.append or "" if len(args.append) > 0: args.append = " " + args.append.strip() gen_kwargs = { "max_length": args.max_length, "do_sample": do_sample, "length_penalty": args.length_penalty, "num_beams": args.num_beams, "temperature": args.temp, "top_k": args.top_k, "top_p": args.top_p, "repetition_penalty": args.repetition_penalty, "no_repeat_ngram_size": args.no_repeat_ngram_size, "min_new_tokens": args.min_new_tokens, "max_new_tokens": args.max_new_tokens, "length_penalty": args.length_penalty, } if args.max_new_tokens is not None: logging.info(f"** max_new_tokens set to {args.max_new_tokens}, ignoring max_length") del gen_kwargs["max_length"] if not do_sample: logging.info(f"** Using greedy sampling") del gen_kwargs["top_k"] del gen_kwargs["top_p"] del gen_kwargs["temperature"] else: logging.info(f"** Sampling enabled") force_words_ids = None if args.force_words is not None: force_words = args.force_words.split(",") if args.force_words is not None else [] logging.info(f"** force_words: {Fore.LIGHTGREEN_EX}{force_words}{Style.RESET_ALL}") force_words_ids = tokenizer(force_words, add_special_tokens=False)["input_ids"] if force_words else [] bad_words_ids = None if args.bad_words is not None: bad_words = args.bad_words.split(",") if args.bad_words is not None else [] logging.info(f"** bad_words: {Fore.LIGHTGREEN_EX}{bad_words}{Style.RESET_ALL}") bad_words_ids = tokenizer(bad_words, add_special_tokens=False)["input_ids"] if bad_words else [] logging.info(f"** gen_kwargs: \n{Fore.LIGHTGREEN_EX}{gen_kwargs}{Style.RESET_ALL}") save_params(args, gen_kwargs) total_start_time = time.time() i_processed = 0 starts_with = args.starts_with.strip() if args.starts_with is not None else "" for i, image_path in enumerate(image_generator(args.image_dir, do_recurse=not args.no_recurse)): candidate_caption_path = image_path.replace(os.path.splitext(image_path)[-1], ".txt") if args.no_overwrite and os.path.exists(candidate_caption_path): logging.warning(f"Skipping {image_path}, caption already exists.") continue cap_start_time = time.time() image = Image.open(image_path) try: image = image.convert('RGB') image = ImageOps.exif_transpose(image) except Exception as e: logging.warning(f"Non-fatal error processing {image_path}: {e}") continue logging.debug(f" __ Prompt before plugin: {Fore.LIGHTGREEN_EX}{args.prompt}{Style.RESET_ALL}") prompt = prompt_plugin_fn(image_path, args=args) logging.debug(f" __ Modified prompt after plugin: {Fore.LIGHTGREEN_EX}{prompt}{Style.RESET_ALL}") inputs = build_conversation_input_ids(tokenizer, query=prompt, history=[], images=[image], starts_with=args.starts_with) # chat mode inputs = { 'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'), 'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'), 'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'), 'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)] for _ in range(args.num_beams)], 'output_hidden_states': True, 'return_dict': True } # print(f"** inputs type: {type(inputs)}") # dict # print(f"** inputs len: {len(inputs)}") # 4 # print(f"** inputs keys: {inputs.keys()}") # dict_keys(['input_ids', 'token_type_ids', 'attention_mask', 'images']) # print(f"** inputs['images'] shape: {inputs['images'].shape}") # list has no shape # print(f"** image_path: {image_path}") with torch.no_grad(): #input_decoded = tokenizer.decode(inputs["input_ids"][0], skip_special_tokens=True) #logging.debug(f"inputs decoded: {input_decoded}") #print(f"calling generate with input shapes: {inputs['input_ids'].shape}, {inputs['token_type_ids'].shape}, {inputs['attention_mask'].shape}, {inputs['images'][0][0].shape}") #calling generate with input shapes: torch.Size([1, 1352]), torch.Size([1, 1352]), torch.Size([1, 1352]), torch.Size([3, 490, 490]) outputs = model.generate(**inputs, **gen_kwargs, force_words_ids=force_words_ids, bad_words_ids=bad_words_ids) print(f"type of outputs: {type(outputs)}, outputs shape: {outputs.shape}") #print(f"type of hidden_states: {type(hidden_states)}, outputs shape: {hidden_states.shape}") len_inputs = inputs['input_ids'].shape[1] outputs_without_prompt = outputs[:, len_inputs:] caption = tokenizer.decode(outputs_without_prompt[0], skip_special_tokens=True) if not args.remove_starts_with: # deal with caption starting with comma, etc if not re.match(r"^\W", caption): caption = starts_with + " " + caption else: caption = starts_with + caption caption += args.append with open(candidate_caption_path, "w") as f: f.write(caption) vram_gb = get_gpu_memory_map() elapsed_time = time.time() - cap_start_time logging.info(f"n:{i:05}, VRAM: {Fore.LIGHTYELLOW_EX}{vram_gb:0.1f} GB{Style.RESET_ALL}, elapsed: {Fore.LIGHTYELLOW_EX}{elapsed_time:0.1f}{Style.RESET_ALL} sec, Captioned {Fore.LIGHTYELLOW_EX}{image_path}{Style.RESET_ALL}: ") logging.info(f"{Fore.LIGHTCYAN_EX}{caption}{Style.RESET_ALL}") i_processed += 1 if i_processed == 0: logging.info(f"** No images found in {args.image_dir} with extension in {SUPPORTED_EXT} OR no images left to caption (did you use --no_overwrite?)") exit(1) total_elapsed_time = time.time() - total_start_time avg_time = total_elapsed_time / i_processed hh_mm_ss = time.strftime("%H:%M:%S", time.gmtime(total_elapsed_time)) logging.info(f"** Done captioning {args.image_dir} with prompt '{prompt}', total elapsed: {hh_mm_ss} (hh_mm_ss), avg: {avg_time:0.1f} sec/image") def configure_logging(args: argparse.Namespace): level = logging.INFO if not args.debug else logging.DEBUG filemode = "a" if args.append_log else "w" logging.basicConfig(filename="caption_cog.log", level=level, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", filemode=filemode) console = logging.StreamHandler() console.setLevel(level) console.setFormatter(logging.Formatter('%(message)s')) logging.getLogger('').addHandler(console) EXAMPLES = """ex. Basic example: python caption_cog.py --image_dir /mnt/mydata/kyrie/ --prompt 'Describe this image in detail, including the subject matter and medium of the artwork.' Use probabilistic sampling by using any of top_k, top_p, or temp: python caption_cog.py --image_dir \"c:/users/chadley/my documents/pictures\" --prompt \"What is this?\" --top_p 0.9 Use beam search and probabilistic sampling: python caption_cog.py --image_dir \"c:/users/chadley/my documents/pictures\" --prompt \"Write a description.\" --max_new_tokens 75 --num_beams 4 --temp 0.9 --top_k 3 --top_p 0.9 --repetition_penalty 1.0 --no_repeat_ngram_size 0 --min_new_tokens 5 Force "cat" and "dog" and disallow the word "depicts": python caption_cog.py --image_dir /mnt/lcl/nvme/mldata/test --num_beams 3 --force_words "cat,dog" --bad_words "depicts" Use a lot of beams and try to control the length with length_penalty: python caption_cog.py --image_dir /mnt/lcl/nvme/mldata/test --num_beams 8 --length_penalty 0.8 --prompt "Write a single sentence description." Notes: 1. Setting top_k, top_p, or temp enables probabilistic sampling (aka "do_sample"), otherwise greedy sampling is used. a. num_beams 1 and do_sample false uses "greedy decoding" b. num_beams 1 and do_sample true uses "multinomial sampling" c. num_beams > 1 and do_sample true uses "beam-search multinomial sampling" d. num_beams > 1 and do_sample false uses "beam-search decoding" 2. Max_length and max_new_tokens are mutually exclusive. If max_new_tokens is set, max_length is ignored. Default is max_length 2048 if nothing set. Using Max may abruptly end caption, consider modifying prompt or use length_penalty instead. Find more info on the Huggingface Transformers documentation: https://huggingface.co/docs/transformers/main_classes/text_generation Parameters definitions and use map directly to their API. """ DESCRIPTION = f"** {Fore.LIGHTBLUE_EX}CogVLM captioning script{Style.RESET_ALL} **\n Use --help for usage." if __name__ == "__main__": argparser = argparse.ArgumentParser() argparser.add_argument("--debug", action="store_true", help="Enable debug logging") argparser.add_argument("--disable_4bit", action="store_true", help="Disables 4bit inference for compatibility or experimentation. Bad for VRAM, fallback is bf16.") argparser.add_argument("--temp", type=float, default=None, help="Temperature for sampling") argparser.add_argument("--num_beams", type=int, default=2, help="Number of beams for beam search, default 1 (off)") argparser.add_argument("--top_k", type=int, default=None, help="Top-k, filter k highest probability tokens before sampling") argparser.add_argument("--top_p", type=float, default=None, help="Top-p, for sampling, selects from top tokens with cumulative probability >= p") argparser.add_argument("--repetition_penalty", type=float, default=1.0, help="Repetition penalty") argparser.add_argument("--no_repeat_ngram_size", type=int, default=0, help="No repetition n-gram size") argparser.add_argument("--min_new_tokens", type=int, default=5, help="Minimum number of tokens in returned caption.") argparser.add_argument("--max_new_tokens", type=int, default=None, help="Maximum number of tokens in returned caption.") argparser.add_argument("--max_length", type=int, default=2048, help="Alternate to max_new_tokens, limits context.") argparser.add_argument("--length_penalty", type=float, default=1.0, help="Length penalty, lower values encourage shorter captions.") argparser.add_argument("--prompt", type=str, default="Write a description.", help="Prompt that will guide captioning") argparser.add_argument("--image_dir", type=str, default=None, help="Path to folder of images to caption") argparser.add_argument("--no_overwrite", action="store_true", help="Skips captioning images that already have a caption file.") argparser.add_argument("--force_words", type=str, default=None, help="Forces the model to include these words in the caption, use CSV format.") argparser.add_argument("--bad_words", type=str, default=None, help="Words that will not be allowed, use CSV format.") argparser.add_argument("--append", type=str, default=None, help="Extra string to append to all captions. ex. 'painted by John Doe'") argparser.add_argument("--no_recurse", action="store_true", help="Do not recurse into subdirectories.") argparser.add_argument("--prompt_plugin", type=str, default=None, help="Function name to modify prompt, edit code to add plugins.") argparser.add_argument("--starts_with", type=str, default=None, help="Force start words on the output caption.") argparser.add_argument("--remove_starts_with", action="store_true", help="Removes the starts_with words from the output caption.") argparser.add_argument("--append_log", action="store_true", help="Sets logging to append mode.") args = argparser.parse_args() configure_logging(args) print(DESCRIPTION) print(EXAMPLES) if args.image_dir is None: logging.error(f"** {Fore.RED}Error: image_dir is required.{Style.RESET_ALL}") exit(1) if not os.path.exists(args.image_dir): logging.error(f"** {Fore.RED}Error: image_dir {args.image_dir} does not exist.{Style.RESET_ALL}") exit(1) startprint = f"** Running: {args.image_dir} with prompt '{args.prompt}" if args.starts_with is not None: startprint += f" {args.starts_with}'" else: startprint += "'" startprint += f" " if args.append is not None: startprint += f", and appending: {args.append}" logging.info(startprint) main(args)