Merge pull request #59 from EvolvingLMMs-Lab/add_idefics2

add idefics2

lmms_eval/models/__init__.py

@@ -9,6 +9,7 @@
     "gpt4v": "GPT4V",
     "instructblip": "InstructBLIP",
     "minicpm_v": "MiniCPM_V",
+    "idefics2": "Idefics2",
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():

lmms_eval/models/idefics2.py

@@ -0,0 +1,223 @@
+import torch
+import logging
+from tqdm import tqdm
+from lmms_eval import utils
+from lmms_eval.api.instance import Instance
+from lmms_eval.api.model import lmms
+from lmms_eval.api.registry import register_model
+from accelerate import Accelerator, DistributedType
+from accelerate.state import AcceleratorState
+from typing import List, Optional, Union, Tuple
+from transformers import Idefics2ForConditionalGeneration, AutoProcessor
+
+import warnings
+
+warnings.filterwarnings("ignore")
+
+eval_logger = logging.getLogger("lmms-eval")
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+try: 
+    import flash_attn
+    best_fit_attn_implementation = "flash_attention_2"
+except ImportError:
+    best_fit_attn_implementation = "eager"
+
+@register_model("idefics2")
+class Idefics2(lmms):
+    """
+    Idefics2 Model for Hugging Face Transformers: https://github.com/huggingface/transformers/blob/main/src/transformers/models/idefics2/modeling_idefics2.py
+
+    Example usage:
+
+    accelerate launch --num_processes=8 -m lmms_eval \
+        --model idefics2 \
+        --model_args pretrained=HuggingFaceM4/idefics2-8b \
+        --tasks mme \
+        --batch_size 1 \
+        --output_path ./logs/ \
+        --log_samples
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "HuggingFaceM4/idefics2-8b",
+        revision: str = "main",
+        device: str = "cuda",
+        dtype: Optional[Union[str, torch.dtype]] = "float16",
+        batch_size: int = 1,
+        trust_remote_code: Optional[bool] = False,
+        attn_implementation: Optional[str] = best_fit_attn_implementation,
+        device_map: str = "",
+        use_cache: bool = True,
+        do_image_splitting: bool =False,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        accelerator = Accelerator()
+        if accelerator.num_processes > 1 and device_map == "":
+            self._device = torch.device(f"cuda:{accelerator.local_process_index}")
+            self.device_map = f"cuda:{accelerator.local_process_index}"
+        else:
+            self._device = torch.device(device)
+            self.device_map = device_map
+        if isinstance(dtype, str) and dtype != "auto":
+            dtype = getattr(torch, dtype)
+        self._model = Idefics2ForConditionalGeneration.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+        self._processor = AutoProcessor.from_pretrained(pretrained, do_image_splitting=do_image_splitting, revision=revision, trust_remote_code=trust_remote_code)
+
+        self._tokenizer = self._processor.tokenizer
+        self._config = self._model.config
+        self.batch_size_per_gpu = int(batch_size)
+        self.use_cache = use_cache
+        if accelerator.num_processes > 1 and device_map == "":
+            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 pipeline 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._word_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 max_length(self):
+        return self._max_length
+
+    @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 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 tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        raise NotImplementedError("Loglikelihood is not implemented for Idefics2 model")
+
+    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[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]
+
+        # we group requests by their generation_kwargs,
+        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
+        # in the same batch.
+        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_visuals, doc_id, tasks, splits = zip(*chunk)
+            visuals = [doc_to_visual(self.task_dict[task][split][ids]) for ids, task, split, doc_to_visual in zip(doc_id, tasks, splits, doc_to_visuals)]
+            # we assume all gen kwargs in the batch are the same
+            # this is safe to assume because the `grouper` object ensures it.
+            gen_kwargs = all_gen_kwargs[0]
+            # 
+            until = gen_kwargs.pop("until", None)
+            prompts = []
+            for context, visual in zip(contexts, visuals):
+                content = []
+                if DEFAULT_IMAGE_TOKEN not in context:
+                    for image in visual:
+                        content.append({"type": "image"})
+                content.append({"type": "text", "text": context})
+                message = [{"role": "user", "content": content}]
+                prompt = self._processor.apply_chat_template(message, add_generation_prompt=True)
+                prompts.append(prompt)
+            inputs = self._processor(text=prompts, images=visuals, padding=True, return_tensors="pt")
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}
+            output_ids = self.model.generate(**inputs, **gen_kwargs)
+            # only retain the generated text
+            for output_id, input_id in zip(output_ids, inputs["input_ids"]):
+                generated_id = output_id[len(input_id):]
+                generated_text = self.tokenizer.decode(generated_id, skip_special_tokens=True)
+
+                res.append(generated_text)
+            pbar.update(1)
+        # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res

lmms_eval/tasks/mmmu/mmmu_val.yaml

@@ -10,7 +10,9 @@ process_results: !function utils.mmmu_process_results
 # Note that the metric name can be either a registed metric function (such as the case for GQA) or a key name returned by process_results
 generation_kwargs:
   max_new_tokens: 16
-  image_aspect_ratio: original
+model_specific_generation_kwargs:
+  llava:
+    image_aspect_ratio: original
 metric_list:
   - metric: mmmu_acc
     aggregation: !function utils.mmmu_aggregate_results

lmms_eval/tasks/scienceqa/scienceqa_img.yaml

@@ -29,6 +29,10 @@ model_specific_prompt_kwargs:
     post_prompt: "\nAnswer with the option's letter from the given choices directly."
   qwen_vl:
     format: qwen_vl
+  idefics2:
+    format: default
+    pre_prompt: ""
+    post_prompt: "\nAnswer:"
 model_specific_generation_kwargs:
   llava:
     image_aspect_ratio: original