Merge pull request #15 from neulab/jean-llavaone-molmo-llamavision

Adding new models: llava-onevision, molmo, llama-3.2-vision
neulab · Oct 11, 2024 · b2c18ff · b2c18ff
2 parents 7ac093e + 6cb04d7
commit b2c18ff
Show file tree

Hide file tree

Showing 4 changed files with 1,154 additions and 0 deletions.
diff --git a/lmms_eval/models/__init__.py b/lmms_eval/models/__init__.py
@@ -45,6 +45,9 @@
     "cambrian1": "Cambrian1",
     "mblip": "MBlip",
     "paligemma": "PaliGemma",
+    "llava_onevision": "Llava_OneVision",
+    "molmo": "Molmo",
+    "llama_vision": "LlamaVision",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():

diff --git a/lmms_eval/models/llama_vision.py b/lmms_eval/models/llama_vision.py
@@ -0,0 +1,218 @@
+import warnings
+
+warnings.simplefilter("ignore", category=DeprecationWarning)
+warnings.filterwarnings("ignore")
+
+from transformers import MllamaForConditionalGeneration, AutoProcessor, AutoTokenizer
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+import torch
+from typing import List, Optional, Union, Tuple
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from tqdm import tqdm
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+
+from loguru import logger as eval_logger
+from datetime import timedelta
+
+@register_model("llama_vision")
+class LlamaVision(lmms):
+    """
+    LlamaVision Model
+    """
+    def __init__(
+        self,
+        pretrained: str = "meta-llama/Llama-3.2-11B-Vision-Instruct",
+        device: Optional[str] = "cuda",
+        device_map="cuda:0",
+        max_new_tokens: int = 256,
+        batch_size: Optional[Union[int, str]] = 1,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
+        accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
+        self.accelerator = accelerator
+        if accelerator.num_processes > 1:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            self._device = torch.device(device)
+            self.device_map = device_map
+        else:
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+
+        self._tokenizer = AutoTokenizer.from_pretrained(pretrained)
+        self._model = MllamaForConditionalGeneration.from_pretrained(pretrained, torch_dtype=torch.bfloat16, device_map=self.device_map)
+        self.model.eval()
+        self.model.tie_weights()
+        self._config = self.model.config
+        self.processor = AutoProcessor.from_pretrained(pretrained)
+        self.max_new_tokens = max_new_tokens
+        self.batch_size_per_gpu = int(batch_size)
+        assert self.batch_size_per_gpu == 1, "Batch size must be 1 for LlamaVision model"
+        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."
+            # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model
+            # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works
+            # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work.
+            if accelerator.distributed_type == DistributedType.DEEPSPEED:
+                kwargs = {
+                    "train_micro_batch_size_per_gpu": self.batch_size_per_gpu,
+                    "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes,
+                }
+                AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs)
+                eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0")
+
+            if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED:
+                self._model = accelerator.prepare(self.model)
+            else:
+                self._model = accelerator.prepare_model(self.model, evaluation_mode=True)
+            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
+        elif accelerator.num_processes == 1 and device_map == "auto":
+            eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism")
+            self._rank = 0
+            self._word_size = 1
+        else:
+            eval_logger.info(f"Using single device: {self._device}")
+            self.model.to(self._device)
+            self._rank = 0
+            self._world_size = 1
+
+    @property
+    def config(self):
+        # return the associated transformers.AutoConfig for the given pretrained model.
+        return self._config
+
+    @property
+    def tokenizer(self):
+        return self._tokenizer
+
+    @property
+    def model(self):
+        # returns the model, unwrapping it if using Accelerate
+        if hasattr(self, "accelerator"):
+            return self.accelerator.unwrap_model(self._model)
+        else:
+            return self._model
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def batch_size(self):
+        return self.batch_size_per_gpu
+
+    @property
+    def device(self):
+        return self._device
+
+    @property
+    def rank(self):
+        return self._rank
+
+    @property
+    def world_size(self):
+        return self._world_size
+
+    def flatten(self, input, only_get_first=False):
+        new_list = []
+        for i in input:
+            for j in i:
+                new_list.append(j)
+                if only_get_first:
+                    break
+        return new_list
+
+    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]:
+        """ """
+        add_special_tokens = False if add_special_tokens is None else add_special_tokens
+        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
+        if left_truncate_len:
+            encoding = encoding[-left_truncate_len:]
+        return encoding
+
+    def generate_until(self, requests: List[Instance]) -> List[str]:
+        res = []
+
+        def _collate(x):
+            # the negative sign on len(toks) sorts descending - this has a few advantages:
+            # - time estimates will always be over not underestimates, which is more useful for planning
+            # - to know the size of a batch when going through the list, you know the first one is always the batch
+            #   padded context length. this is useful to simplify the batching logic and more importantly to make
+            #   automatic adaptive batches much much easier to implement
+            # - any OOMs will happen right away rather than near the end
+            toks = self.tok_encode(x[0])
+            return -len(toks), x[0]
+
+        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)
+            task = task[0]
+            split = split[0]
+            visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id]
+            visuals = self.flatten(visuals)
+            gen_kwargs = all_gen_kwargs[0]
+            input_text = contexts[0]
+            image_list = [{"type": "image"} for _ in range(len(visuals))]
+            image_list.append({"type": "text", "text": input_text})
+            message = [
+                {"role": "user", "content": image_list}
+            ]
+
+            prompts = self.processor.apply_chat_template(message, add_generation_prompt=True)
+            model_inputs = self.processor(
+                images=visuals,
+                text=prompts,
+                add_special_tokens=False,
+                return_tensors="pt"
+            )
+            model_inputs = model_inputs.to(self._model.device)
+
+            # preconfigure gen_kwargs with defaults
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = self.max_new_tokens
+            if "max_length" in gen_kwargs and "max_new_tokens" in gen_kwargs:
+                gen_kwargs.pop("max_length")
+            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
+            if "until" in gen_kwargs:
+                gen_kwargs.pop("until")
+            if "do_sample" not in gen_kwargs:
+                gen_kwargs["do_sample"] = False
+
+            generation_output = self._model.generate(**model_inputs, **gen_kwargs)
+            generated_tokens = generation_output[0, model_inputs['input_ids'].size(1):]
+            response = self.processor.decode(generated_tokens, skip_special_tokens=True)
+            assert type(response) == str
+            res.append(response)
+            pbar.update(1)
+
+        # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+        pbar.close()
+        return res
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        assert False, "Not implemented yet."