Merge pull request #162 from baichuanzhou/main

Add model Mantis to the LMMs-Eval supported model list

lmms_eval/models/__init__.py

@@ -41,6 +41,7 @@
     "llava_hf": "LlavaHf",
     "longva": "LongVA",
     "vila": "VILA",
+    "mantis": "Mantis"
 }
 
 for model_name, model_class in AVAILABLE_MODELS.items():

lmms_eval/models/mantis.py

@@ -0,0 +1,311 @@
+import torch
+
+torch.backends.cuda.matmul.allow_tf32 = True
+
+
+import copy
+from tqdm import tqdm
+from datetime import timedelta
+
+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 lmms_eval.utils import stop_sequences_criteria
+
+from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs
+from accelerate.state import AcceleratorState
+from typing import List, Optional, Union, Tuple
+from packaging import version
+import warnings
+
+from loguru import logger as eval_logger
+
+warnings.filterwarnings("ignore")
+
+try:
+    from mantis.models.mllava import LlavaForConditionalGeneration, MLlavaProcessor
+    from mantis.models.mfuyu import MFuyuForCausalLM, MFuyuProcessor
+    from mantis.models.conversation import conv_mllava_v1 as default_conv, conv_templates
+
+except Exception as e:
+    eval_logger.debug("Mantis is not installed. Please install Mantis to use this model.\nError: %s" % e)
+
+try:
+    from transformers import AutoModelForVision2Seq, AutoProcessor
+except Exception as e:
+    eval_logger.debug("Upgrade transformers to use Mantis's idefics model.\nError: %s" % e)
+
+# inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5
+# if is_flash_attn_2_available:
+#     best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating
+
+try:
+    import flash_attn
+
+    best_fit_attn_implementation = "flash_attention_2"
+except ImportError:
+    best_fit_attn_implementation = "eager"
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+
+@register_model("mantis")
+class Mantis(lmms):
+    """
+    Mantis Model
+    This implementation is adpated from the Llava model from llava.py and the Idefics model from idefics.py
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "TIGER-Lab/Mantis-8B-siglip-llama3",
+        truncation: Optional[bool] = True,
+        device: Optional[str] = "cuda:0",
+        dtype: Optional[Union[str, torch.dtype]] = "float16",
+        batch_size: Optional[Union[int, str]] = 1,
+        attn_implementation=best_fit_attn_implementation,
+        device_map="cuda:0",
+        use_cache=True,
+        truncate_context=False,  # whether to truncate the context in generation, set it False for LLaVA-1.6
+        **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])
+        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._is_idefics = "idefics" in pretrained.lower()
+        if isinstance(dtype, str) and dtype != "auto":
+            dtype = getattr(torch, dtype)
+
+        # Here we load the "non-idefics" Mantis model.
+        if not self._is_idefics:
+            if 'fuyu' in pretrained.lower():
+                self._processor = MFuyuProcessor.from_pretrained(pretrained)
+                self._model = MFuyuForCausalLM.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype)
+            else:
+                self._processor = MLlavaProcessor.from_pretrained(pretrained)
+                self._model = LlavaForConditionalGeneration.from_pretrained(pretrained, device_map=self.device_map, attn_implementation=attn_implementation, torch_dtype=dtype)
+
+        else:
+            self._processor = AutoProcessor.from_pretrained(pretrained)
+            self._model = AutoModelForVision2Seq.from_pretrained(pretrained, device_map=self.device_map, torch_dtype=dtype)
+        eval_logger.info(f"Using {type(self._model)} to instantiate the Mantis model.")
+
+        self._tokenizer = self._processor.tokenizer
+
+        self._config = self._model.config
+        self.model.eval()
+        self.model.tie_weights()
+        self.truncation = truncation
+        self.batch_size_per_gpu = int(batch_size)
+        self.use_cache = use_cache
+        self.truncate_context = truncate_context
+
+        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 max_length(self):
+        return self._max_length
+
+    def pad_sequence(self, input_ids, batch_first, padding_value):
+        if self.tokenizer.padding_side == "left":
+            input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids]
+        input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value)
+        if self.tokenizer.padding_side == "left":
+            input_ids = torch.flip(input_ids, [1])
+        return input_ids
+
+    @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):
+        try:
+            return self.tokenizer.decode(tokens)
+        except:
+            return self.tokenizer.decode([tokens])
+
+    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        raise NotImplementedError
+
+    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)
+            image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio", None)
+
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            if "temperature" not in gen_kwargs:
+                gen_kwargs["temperature"] = 0
+
+            # prompts_input = contexts[0]
+
+            prompts = []
+            for visual, context in zip(visuals, contexts):
+                if self._is_idefics:
+                    # Follow the idefics implementation: 
+                    content = []
+                    if DEFAULT_IMAGE_TOKEN not in context:
+                        for _ 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)
+                else:
+                    # We follow the Mantis code base: https://github.com/TIGER-AI-Lab/Mantis/blob/main/mantis/models/mllava/utils.py#L33 to make sure they are consistent
+                    # Users don't need to define chat template as it is done here
+                    if "llama-3" in self._model.language_model.name_or_path.lower():
+                        conv = conv_templates['llama_3']
+                        terminators = [
+                            self._processor.tokenizer.eos_token_id,
+                            self._processor.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+                        ]
+                    else:
+                        conv = default_conv
+                        terminators = None
+
+                    gen_kwargs["eos_token_id"] = terminators
+
+                    conv = conv.copy()
+                    conv.append_message(conv.roles[0], context)
+                    conv.append_message(conv.roles[1], "")
+                    prompt = conv.get_prompt()
+                    prompts.append(prompt)
+            inputs = self._processor(images=visuals, text=prompts, return_tensors="pt", truncation=True)
+            if "image_patches" in inputs.keys():
+                inputs["image_patches"] = inputs["image_patches"][0] # FIXME: Fuyu model would return a list instead of a pytorch tensor. This weird behavior needs fixing.
+            inputs = {k: v.to(self.device) for k, v in inputs.items()}    
+
+            output_ids = self.model.generate(**inputs, **gen_kwargs)
+            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)
+
+            # self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_outputs)
+            pbar.update(1)
+            # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res