[Model] support Qwen2 VL (#268)

* add qwen2-vl

* qwen2 vl (black isort)

* qwen2 vl black

* black

* without qwen vl utils and temp images

* black

* isort

* qwen2 vl batch generate

* remove unused import

* remove unreferenced

lmms_eval/models/__init__.py

@@ -35,6 +35,7 @@
     "mplug_owl_video": "mplug_Owl",
     "phi3v": "Phi3v",
     "qwen_vl": "Qwen_VL",
+    "qwen2_vl": "Qwen2_VL",
     "qwen_vl_api": "Qwen_VL_API",
     "reka": "Reka",
     "srt_api": "SRT_API",

lmms_eval/models/qwen2_vl.py

@@ -0,0 +1,243 @@
+from typing import List, Optional, Tuple, Union
+
+import torch
+from accelerate import Accelerator, DistributedType
+from loguru import logger as eval_logger
+from tqdm import tqdm
+from transformers import AutoProcessor, AutoTokenizer, Qwen2VLForConditionalGeneration
+
+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
+
+
+@register_model("qwen2_vl")
+class Qwen2_VL(lmms):
+    """
+    Qwen2_VL Model
+    "https://github.com/QwenLM/Qwen2-VL"
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "Qwen/Qwen2-VL-7B-Instruct",
+        device: Optional[str] = "cuda",
+        device_map: Optional[str] = "cuda",
+        batch_size: Optional[Union[int, str]] = 1,
+        use_cache=True,
+        use_flash_attention_2: Optional[bool] = True,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        # Do not use kwargs for now
+        assert kwargs == {}, f"Unexpected kwargs: {kwargs}"
+
+        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}"
+
+        if use_flash_attention_2:
+            self._model = Qwen2VLForConditionalGeneration.from_pretrained(
+                pretrained,
+                torch_dtype="auto",
+                device_map=self.device_map,
+                attn_implementation="flash_attention_2",
+            ).eval()
+        else:
+            self._model = Qwen2VLForConditionalGeneration.from_pretrained(pretrained, torch_dtype="auto", device_map=self.device_map).eval()
+        self.processor = AutoProcessor.from_pretrained(pretrained)
+        self._tokenizer = AutoTokenizer.from_pretrained(pretrained)
+
+        self._config = self.model.config
+        self.batch_size_per_gpu = int(batch_size)
+        self.use_cache = use_cache
+
+        if accelerator.num_processes > 1:
+            assert accelerator.distributed_type in [
+                DistributedType.FSDP,
+                DistributedType.MULTI_GPU,
+            ], "Unsupported distributed type provided. Only DDP and FSDP are supported."
+            if accelerator.distributed_type == DistributedType.FSDP:
+                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
+        else:
+            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):
+        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 loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
+        raise NotImplementedError("Loglikelihood is not implemented for Qwen2_VL")
+
+    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.tokenizer.encode(x[0])
+            return -len(toks), x[0]
+
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+        # 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)
+        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]
+
+            # Set default values for until and max_new_tokens
+            until = [self.tokenizer.decode(self.eot_token_id)]
+
+            # Update values from gen_kwargs if present
+            if "until" in gen_kwargs:
+                until = gen_kwargs.pop("until")
+                if isinstance(until, str):
+                    until = [until]
+                elif not isinstance(until, list):
+                    raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}")
+
+            if isinstance(contexts, tuple):
+                contexts = list(contexts)
+
+            for i in range(len(contexts)):
+                if "<image>" in contexts[i]:
+                    contexts[i] = contexts[i].replace("<image>", "")
+
+            messages = []
+
+            if len(visuals) == 0:
+                for context in contexts:
+                    message = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": [{"type": "text", "text": context}]}]
+                    messages.append(message)
+            else:
+                for _, context in zip(visuals, contexts):
+                    message = [{"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": [{"type": "image"}, {"type": "text", "text": context}]}]
+                    messages.append(message)
+
+            texts = [self.processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True) for msg in messages]
+            inputs = self.processor(text=texts, images=[visuals], padding=True, return_tensors="pt")
+
+            if self.device_map == "auto":
+                inputs = inputs.to("cuda")
+            else:
+                inputs = inputs.to(self.device)
+
+            # preconfigure gen_kwargs with defaults
+            if "image_sizes" not in gen_kwargs:
+                try:
+                    gen_kwargs["image_sizes"] = [visuals[0].size]
+                except:
+                    gen_kwargs["image_sizes"] = None
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 128
+            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
+
+            pad_token_id = self.tokenizer.pad_token_id
+
+            cont = self.model.generate(
+                **inputs,
+                eos_token_id=self.tokenizer.eos_token_id,
+                pad_token_id=pad_token_id,
+                do_sample=True if gen_kwargs["temperature"] > 0 else False,
+                temperature=gen_kwargs["temperature"],
+                top_p=gen_kwargs["top_p"],
+                num_beams=gen_kwargs["num_beams"],
+                max_new_tokens=gen_kwargs["max_new_tokens"],
+                use_cache=self.use_cache,
+                # kwargs=gen_kwargs
+            )
+
+            generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, cont)]
+            answers = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+            for i, ans in enumerate(answers):
+                for term in until:
+                    if len(term) > 0:
+                        ans = ans.split(term)[0]
+                answers[i] = ans
+
+            for ans, context in zip(answers, contexts):
+                res.append(ans)
+                self.cache_hook.add_partial("generate_until", (context, gen_kwargs), ans)
+                pbar.update(1)
+            # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res