|
| 1 | +from typing import List, Optional, Tuple, Union |
| 2 | + |
| 3 | +import torch |
| 4 | +from accelerate import Accelerator, DistributedType |
| 5 | +from lmms_eval import utils |
| 6 | +from lmms_eval.api.instance import Instance |
| 7 | +from lmms_eval.api.model import lmms |
| 8 | +from lmms_eval.api.registry import register_model |
| 9 | +from loguru import logger as eval_logger |
| 10 | +from omegaconf import OmegaConf |
| 11 | +from PIL import Image |
| 12 | +from tqdm import tqdm |
| 13 | + |
| 14 | +from apps.plm.generate import (PackedCausalTransformerGenerator, |
| 15 | + PackedCausalTransformerGeneratorArgs, |
| 16 | + load_consolidated_model_and_tokenizer) |
| 17 | +from core.args import dataclass_from_dict |
| 18 | +from core.transforms.image_transform import get_image_transform |
| 19 | +from core.transforms.video_transform import get_video_transform |
| 20 | + |
| 21 | + |
| 22 | +@register_model("plm") |
| 23 | +class PerceptionLM(lmms): |
| 24 | + """ |
| 25 | + Perception Lanugate Model (PLM) |
| 26 | + "Paste the paper link" |
| 27 | + "Paste the github link" |
| 28 | + "Paste the huggingface link" |
| 29 | + """ |
| 30 | + |
| 31 | + def __init__( |
| 32 | + self, |
| 33 | + pretrained: str = "facebook/Perception-LM-8B", |
| 34 | + device: Optional[str] = "cuda", |
| 35 | + batch_size: Optional[Union[int, str]] = 1, |
| 36 | + compile_prefilling=False, |
| 37 | + reduce_generation_overhead=False, |
| 38 | + max_tokens=11264, |
| 39 | + **kwargs, |
| 40 | + ) -> None: |
| 41 | + super().__init__() |
| 42 | + |
| 43 | + accelerator = Accelerator() |
| 44 | + self._device = torch.device(f"cuda:{accelerator.local_process_index}") |
| 45 | + |
| 46 | + # Collect all arguments into a dictionary |
| 47 | + args = { |
| 48 | + "pretrained": pretrained, |
| 49 | + "device": device, |
| 50 | + "batch_size": batch_size, |
| 51 | + "compile_prefilling": compile_prefilling, |
| 52 | + "reduce_generation_overhead": reduce_generation_overhead, |
| 53 | + "max_tokens": max_tokens, |
| 54 | + **kwargs, # Include any additional keyword arguments |
| 55 | + } |
| 56 | + # Convert the dictionary to a dotlist format |
| 57 | + dotlist = [f"{key}={value}" for key, value in args.items()] |
| 58 | + cfg = OmegaConf.from_dotlist(dotlist) |
| 59 | + gen_cfg = dataclass_from_dict(PackedCausalTransformerGeneratorArgs, cfg, strict=False) |
| 60 | + # Load PLM model |
| 61 | + eval_logger.info(f"Lodding PLM model from {cfg.pretrained}") |
| 62 | + model, tokenizer, config = load_consolidated_model_and_tokenizer(cfg.pretrained) |
| 63 | + |
| 64 | + # Create preprocessors (transforms) |
| 65 | + processor = {} |
| 66 | + vision_input_type = config.get("model").get("vision_input_type", "thumb+tile") |
| 67 | + max_num_tiles = config.get("model").get("max_num_tiles", 36) |
| 68 | + processor["image"] = get_image_transform(vision_input_type=vision_input_type, image_res=model.vision_model.image_size, max_num_tiles=max_num_tiles) |
| 69 | + processor["video"] = get_video_transform(image_res=model.vision_model.image_size) |
| 70 | + self._video_max_frames = config.get("model").get("video_max_frames", 32) |
| 71 | + |
| 72 | + # Create PLM generator |
| 73 | + eval_logger.info(f"Creating packed generator with gen_cfg: {gen_cfg}") |
| 74 | + generator = PackedCausalTransformerGenerator(gen_cfg, model, tokenizer) |
| 75 | + |
| 76 | + # Set the class variables |
| 77 | + self._tokenizer = tokenizer |
| 78 | + self._processor = processor |
| 79 | + self._model = model |
| 80 | + self._generator = generator |
| 81 | + self.batch_size_per_gpu = int(batch_size) |
| 82 | + |
| 83 | + if accelerator.num_processes > 1: |
| 84 | + assert accelerator.distributed_type in [ |
| 85 | + DistributedType.FSDP, |
| 86 | + DistributedType.MULTI_GPU, |
| 87 | + ], "Unsupported distributed type provided. Only DDP and FSDP are supported." |
| 88 | + if accelerator.distributed_type == DistributedType.FSDP: |
| 89 | + self._model = accelerator.prepare(self.model) |
| 90 | + else: |
| 91 | + self._model = accelerator.prepare_model(self.model, evaluation_mode=True) |
| 92 | + self.accelerator = accelerator |
| 93 | + if self.accelerator.is_local_main_process: |
| 94 | + eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") |
| 95 | + self._rank = self.accelerator.process_index |
| 96 | + self._world_size = self.accelerator.num_processes |
| 97 | + else: |
| 98 | + self._rank = 0 |
| 99 | + self._world_size = 1 |
| 100 | + |
| 101 | + @property |
| 102 | + def generator(self): |
| 103 | + return self._generator |
| 104 | + |
| 105 | + @property |
| 106 | + def tokenizer(self): |
| 107 | + return self._tokenizer |
| 108 | + |
| 109 | + @property |
| 110 | + def processor(self): |
| 111 | + return self._processor |
| 112 | + |
| 113 | + @property |
| 114 | + def model(self): |
| 115 | + # returns the model, unwrapping it if using Accelerate |
| 116 | + if hasattr(self, "accelerator"): |
| 117 | + return self.accelerator.unwrap_model(self._model) |
| 118 | + else: |
| 119 | + return self._model |
| 120 | + |
| 121 | + @property |
| 122 | + def eot_token_id(self): |
| 123 | + # we use EOT because end of text is more accurate for what we're doing than end of sentence |
| 124 | + return self.tokenizer.eos_token_id |
| 125 | + |
| 126 | + @property |
| 127 | + def batch_size(self): |
| 128 | + return self.batch_size_per_gpu |
| 129 | + |
| 130 | + @property |
| 131 | + def video_max_frames(self): |
| 132 | + return self._video_max_frames |
| 133 | + |
| 134 | + @property |
| 135 | + def device(self): |
| 136 | + return self._device |
| 137 | + |
| 138 | + @property |
| 139 | + def rank(self): |
| 140 | + return self._rank |
| 141 | + |
| 142 | + @property |
| 143 | + def world_size(self): |
| 144 | + return self._world_size |
| 145 | + |
| 146 | + def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: |
| 147 | + raise NotImplementedError("Loglikelihood is not implemented for PLM") |
| 148 | + |
| 149 | + def flatten(self, input): |
| 150 | + new_list = [] |
| 151 | + for i in input: |
| 152 | + for j in i: |
| 153 | + new_list.append(j) |
| 154 | + return new_list |
| 155 | + |
| 156 | + def generate_until(self, requests: List[Instance]) -> List[str]: |
| 157 | + res = [] |
| 158 | + |
| 159 | + def _collate(x): |
| 160 | + # the negative sign on len(toks) sorts descending - this has a few advantages: |
| 161 | + # - time estimates will always be over not underestimates, which is more useful for planning |
| 162 | + # - to know the size of a batch when going through the list, you know the first one is always the batch |
| 163 | + # padded context length. this is useful to simplify the batching logic and more importantly to make |
| 164 | + # automatic adaptive batches much much easier to implement |
| 165 | + # - any OOMs will happen right away rather than near the end |
| 166 | + toks = self.tokenizer.encode(x[0], add_bos=False, add_eos=False) |
| 167 | + return -len(toks), x[0] |
| 168 | + |
| 169 | + pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") |
| 170 | + # we group requests by their generation_kwargs, |
| 171 | + # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling |
| 172 | + # in the same batch. |
| 173 | + re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) |
| 174 | + chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) |
| 175 | + for chunk in chunks: |
| 176 | + contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) |
| 177 | + task = task[0] |
| 178 | + split = split[0] |
| 179 | + visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] |
| 180 | + visuals = self.flatten(visuals) |
| 181 | + |
| 182 | + messages = [] |
| 183 | + for i, context in enumerate(contexts): |
| 184 | + if len(visuals) > 0: |
| 185 | + visual = visuals[i] if i < len(visuals) else None |
| 186 | + if isinstance(visual, str) and visual.endswith((".mp4", ".avi", ".mov")): # Video file |
| 187 | + video_info = (visual, self.video_max_frames, None, None, None) |
| 188 | + visual, _ = self.processor["video"](video_info) |
| 189 | + message = (context, visual) |
| 190 | + elif isinstance(visual, Image.Image): # Single image |
| 191 | + visual = visual.convert("RGB") |
| 192 | + visual, _ = self.processor["image"](visual) |
| 193 | + message = (context, visual) |
| 194 | + elif isinstance(visual, (list, tuple)) and all(isinstance(v, Image.Image) for v in visual): # Multiple images or Video Frames |
| 195 | + visual = [image.convert("RGB") for image in visual] |
| 196 | + visual, _ = self.processor["video"]._process_multiple_images_pil(visual) |
| 197 | + message = (context, visual) |
| 198 | + else: |
| 199 | + # Text-only sample |
| 200 | + raise NotImplementedError("Text-only input is not yet supported.") |
| 201 | + else: |
| 202 | + # Text-only sample |
| 203 | + raise NotImplementedError("Text-only input is not yet supported.") |
| 204 | + |
| 205 | + messages.append(message) |
| 206 | + |
| 207 | + gen_kwargs = all_gen_kwargs[0] |
| 208 | + if "max_new_tokens" in gen_kwargs: |
| 209 | + self.generator.max_gen_len = gen_kwargs["max_new_tokens"] |
| 210 | + if "temperature" in gen_kwargs: |
| 211 | + self.generator.temperature = gen_kwargs["temperature"] |
| 212 | + # Default for PLM |
| 213 | + self.generator.top_p = None |
| 214 | + self.generator.top_k = 100 |
| 215 | + |
| 216 | + generation, loglikelihood, greedy = self.generator.generate(messages) |
| 217 | + |
| 218 | + for gen, context in zip(generation, contexts): |
| 219 | + if gen.endswith("."): |
| 220 | + gen = gen[:-1] |
| 221 | + res.append(gen) |
| 222 | + self.cache_hook.add_partial("generate_until", (context, gen_kwargs), gen) |
| 223 | + pbar.update(1) |
| 224 | + # reorder this group of results back to original unsorted form |
| 225 | + res = re_ords.get_original(res) |
| 226 | + |
| 227 | + pbar.close() |
| 228 | + return res |
| 229 | + |
| 230 | + def generate_until_multi_round(self, requests) -> List[str]: |
| 231 | + raise NotImplementedError("Multi-round generation is not implemented yet.") |
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