support cogvlm2 model

lmms_eval/models/__init__.py

@@ -13,6 +13,7 @@
 AVAILABLE_MODELS = {
     "batch_gpt4": "BatchGPT4",
     "claude": "Claude",
+    "cogvlm2": "CogVLM2",
     "from_log": "FromLog",
     "fuyu": "Fuyu",
     "gemini_api": "GeminiAPI",

lmms_eval/models/cogvlm2.py

@@ -0,0 +1,226 @@
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from accelerate import Accelerator, DistributedType
+from accelerate.state import AcceleratorState
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+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
+
+warnings.filterwarnings("ignore")
+
+from loguru import logger as eval_logger
+
+
+@register_model("cogvlm2")
+class CogVLM2(lmms):
+    """
+    CogVLM2 Model
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "THUDM/cogvlm2-llama3-chinese-chat-19B",
+        device: Optional[str] = "cuda",
+        dtype: Optional[Union[str, torch.dtype]] = torch.bfloat16,
+        batch_size: Optional[Union[int, str]] = 1,
+        trust_remote_code: 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}")
+        else:
+            self._device = device
+        self.dtype = dtype
+        self._model = AutoModelForCausalLM.from_pretrained(pretrained, trust_remote_code=trust_remote_code, torch_dtype=dtype, device_map=self._device)
+        self._tokenizer = AutoTokenizer.from_pretrained(pretrained, trust_remote_code=trust_remote_code)
+        self._config = self._model.config
+        self.model.eval()
+        self.model.tie_weights()
+        self.batch_size_per_gpu = int(batch_size)
+        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
+        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):
+        # 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]]:
+        # TODO
+        assert False, "We have not implemented this function for CogVLM2 yet"
+
+    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_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)
+            # 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]
+
+            # Set default values for until and max_new_tokens
+            until = [self.tok_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)}")
+            assert self.batch_size_per_gpu == 1, "Do not support batch_size_per_gpu > 1 for now"
+            assert len(visuals) == 1, "CogVLM2 interface does not support bn_image > 1 for now"
+            context = contexts[0]
+            if "<image>" in context:
+                context = context.replace("<image>", "")
+
+            if "max_new_tokens" not in gen_kwargs:
+                gen_kwargs["max_new_tokens"] = 1024
+            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
+
+            image = visuals[0]
+            input_by_model = self.model.build_conversation_input_ids(self.tokenizer, query=context, history=[], images=[image])
+
+            inputs = {
+                "input_ids": input_by_model["input_ids"].unsqueeze(0).to(self.device),
+                "token_type_ids": input_by_model["token_type_ids"].unsqueeze(0).to(self.device),
+                "attention_mask": input_by_model["attention_mask"].unsqueeze(0).to(self.device),
+                "images": [[input_by_model["images"][0].to(self.device).to(self.dtype)]],
+            }
+            if "cross_images" in input_by_model and input_by_model["cross_images"]:
+                inputs["cross_images"] = [[input_by_model["cross_images"][0].to(self.device).to(self.dtype)]]
+
+            try:
+                outputs = self.model.generate(**inputs, **gen_kwargs)
+                outputs = outputs[:, inputs["input_ids"].shape[1] :]
+                response = self.tokenizer.decode(outputs[0])
+                response = response.split("</s>")[0]
+                response = response.split("<|end_of_text|>")[0]
+
+                context = [{"role": "user", "content": context}, {"role": "assistant", "content": response}]
+            except Exception as e:
+                eval_logger.error(f"Error {e} in generating")
+                cont = ""
+            res.append(response)
+            self.cache_hook.add_partial("generate_until", (context, gen_kwargs), response)
+            pbar.update(1)
+            # reorder this group of results back to original unsorted form
+        res = re_ords.get_original(res)
+
+        pbar.close()
+        return res