Add sae hooked llava

lmms_eval/models/llava_sae_hooked.py

@@ -0,0 +1,376 @@
+import os
+import warnings
+from typing import List, Optional, Tuple, Union
+
+import torch
+from accelerate import Accelerator, DistributedType
+from accelerate.state import AcceleratorState
+from sae import Sae
+from tqdm import tqdm
+from transformers import (
+    AutoConfig,
+    AutoProcessor,
+    LlavaForConditionalGeneration,
+    LlavaNextForConditionalGeneration,
+)
+
+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
+
+DEFAULT_IMAGE_TOKEN = "<image>"
+
+# Default chat for llava-hf/llava-1.5 models: https://huggingface.co/collections/llava-hf/llava-15-65f762d5b6941db5c2ba07e0
+VICUNA_CHAT_TEMPLATE = "{% for message in messages %}{% if loop.index0 == 0 %}A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {{ message['content'] }} {% elif message['role'] == 'user' %}USER: {{ message['content'] }} {% else %} ASSISTANT: {{ message['content'] }}{{ eos_token }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}"
+
+model_map = {
+    "llava": LlavaForConditionalGeneration,
+    "llava_next": LlavaNextForConditionalGeneration,
+}
+
+
+@register_model("llava_sae_hooked")
+class LlavaSaeHooked(lmms):
+    """
+    Llava Model for Hugging Face Transformers: https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/llava
+
+    Adapted from the InstructBLIP model in lmms_eval/models/instructblip.py
+
+    Example usage:
+
+    accelerate launch --num_processes=8 --main_process_port 12345 -m lmms_eval \
+        --model llava_hf \
+        --model_args pretrained=llava-hf/llava-1.5-7b-hf \
+        --tasks seedbench \
+        --batch_size 1 \
+        --output_path ./logs/ \
+        --log_samples
+    """
+
+    def __init__(
+        self,
+        pretrained: str = "llava-hf/llava-1.5-7b-hf",
+        revision: str = "main",
+        device: str = "cuda",
+        dtype: Optional[Union[str, torch.dtype]] = "auto",
+        batch_size: int = 1,
+        trust_remote_code: Optional[bool] = False,
+        attn_implementation: Optional[str] = None,
+        device_map: str = "",
+        chat_template: Optional[str] = None,
+        use_cache: bool = True,
+        specified_eot_token_id: Optional[int] = None,
+        sae_path: Optional[str] = None,
+        **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)
+
+        config = AutoConfig.from_pretrained(pretrained)
+        model_type = getattr(config, "model_type", "llava")
+        model_type = model_map[model_type]
+        self._model = model_type.from_pretrained(pretrained, revision=revision, torch_dtype=dtype, device_map=self.device_map, trust_remote_code=trust_remote_code, attn_implementation=attn_implementation)
+        if sae_path is not None:
+            self.module_dict = Sae.load_many(sae_path, local=True if os.path.exists(sae_path) else False, device=self._device)
+        else:
+            self.module_dict = None
+        self.name_to_module = {name: self.model.language_model.get_submodule(name) for name in self.module_dict.keys()}
+        self.module_to_name = {v: k for k, v in self.name_to_module.items()}
+
+        self.pretrained = pretrained
+        self._image_processor = AutoProcessor.from_pretrained(pretrained, revision=revision, trust_remote_code=trust_remote_code)
+        # Pad from left for batched generation: https://huggingface.co/docs/transformers/v4.39.3/en/model_doc/llava#usage-tips
+        self._image_processor.tokenizer.padding_side = "left"
+        self._tokenizer = self._image_processor.tokenizer
+        self._config = self._model.config
+        self.batch_size_per_gpu = int(batch_size)
+        self.chat_template = chat_template
+        self.use_cache = use_cache
+        self.specified_eot_token_id = specified_eot_token_id
+        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.module_dict = {k: accelerator.prepare_model(v) for k, v in self.module_dict.items()}
+            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
+        self.accelerator = accelerator
+
+    @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]]:
+        res = []
+        pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding")
+
+        for context, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]:
+            # encode, pad, and truncate contexts for this batch
+            if type(doc_to_target) == str:
+                continuation = doc_to_target
+            else:
+                continuation = doc_to_target(self.task_dict[task][split][doc_id])
+            visuals = [doc_to_visual(self.task_dict[task][split][doc_id])]
+            visuals = self.flatten(visuals)
+
+            image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
+            image_tokens = " ".join(image_tokens)
+            context = f"{image_tokens}\n{context}"
+            # Apply chat template
+            messages = [{"role": "user", "content": context}, {"role": "assistant", "content": continuation}]
+            if self.chat_template is not None:
+                self.tokenizer.chat_template = self.chat_template
+                prompt = self.tokenizer.apply_chat_template(messages[:-1], tokenize=False, add_generation_prompt=True)
+                prompt_and_continuation = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+            elif self.tokenizer.chat_template is not None:
+                prompt = self.tokenizer.apply_chat_template(messages[:-1], tokenize=False, add_generation_prompt=True)
+                prompt_and_continuation = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+            else:
+                self.tokenizer.chat_template = VICUNA_CHAT_TEMPLATE
+                prompt = self.tokenizer.apply_chat_template(messages[:-1], tokenize=False, add_generation_prompt=True)
+                prompt_and_continuation = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
+
+            formatted_contexts = [prompt]
+            formatted_continuation = [prompt_and_continuation]
+            model_inputs = self._image_processor(text=formatted_continuation, images=visuals).to(self._device, self.model.dtype)
+            labels = model_inputs["input_ids"].clone()
+            contxt_id = self._image_processor(text=formatted_contexts, return_tensors="pt")["input_ids"]
+            labels[: len(contxt_id)] = -100
+
+            if self.accelerator.is_main_process and doc_id % 100 == 0:
+                eval_logger.debug(f"Prompt for doc ID {doc_id}:\n\n{formatted_contexts[0]}\n")
+                eval_logger.debug(f"Prompt and continuation for doc ID {doc_id}:\n\n{formatted_continuation[0]}\n")
+
+            with torch.inference_mode():
+                outputs = self.model(**model_inputs, labels=labels)
+            loss = outputs["loss"]
+            logits = outputs["logits"]
+            greedy_tokens = logits.argmax(dim=-1)
+            cont_toks = model_inputs["input_ids"][:, contxt_id.shape[1] :]  # [1, seq]
+            greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : model_inputs["input_ids"].shape[1]]  # [1, seq]
+            max_equal = (greedy_tokens == cont_toks).all()
+            res.append((float(loss.item()), bool(max_equal)))
+            pbar.update(1)
+
+        pbar.close()
+        return res
+
+    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"
+            context = contexts[0]
+
+            # Some benchmarks like MME do not contain image tokens, so we prepend them to the prompt.
+            if DEFAULT_IMAGE_TOKEN not in context:
+                image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals)
+                image_tokens = " ".join(image_tokens)
+                context = f"{image_tokens}\n{context}"
+            # Apply chat template
+            messages = [{"role": "user", "content": context}]
+            if self.chat_template is not None:
+                self.tokenizer.chat_template = self.chat_template
+                text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            elif self.tokenizer.chat_template is not None:
+                text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+            else:
+                self.tokenizer.chat_template = VICUNA_CHAT_TEMPLATE
+                text = self.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+            if self.accelerator.is_main_process and doc_id[0] % 100 == 0:
+                eval_logger.debug(f"Prompt for doc ID {doc_id[0]}:\n\n{text}\n")
+
+            inputs = self._image_processor(images=visuals, text=text, return_tensors="pt").to(self._device, self.model.dtype)
+
+            gen_kwargs["image_sizes"] = [visuals[idx].size for idx in range(len(visuals))]
+            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
+
+            def hook(module: torch.nn.Module, _, outputs):
+                # Maybe unpack tuple outputs
+                if isinstance(outputs, tuple):
+                    unpack_outputs = list(outputs)
+                else:
+                    unpack_outputs = list(outputs)
+                name = self.module_to_name[module]
+                sae = self.module_dict[name]
+                sae_out = sae(unpack_outputs[0][0]).sae_out.unsqueeze(0).to(torch.float16)
+                unpack_outputs[0] = sae_out
+                if isinstance(outputs, tuple):
+                    outputs = tuple(unpack_outputs)
+                else:
+                    outputs = unpack_outputs[0]
+                return outputs
+
+            handles = [mod.register_forward_hook(hook) for mod in self.name_to_module.values()]
+            try:
+                cont = self.model.generate(
+                    **inputs,
+                    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,
+                    pad_token_id=self.tokenizer.eos_token_id,
+                    eos_token_id=self.specified_eot_token_id,
+                )
+                cont = cont[:, inputs["input_ids"].shape[-1] :]
+            except Exception as e:
+                eval_logger.error(f"Error {e} in generating")
+                cont = ""
+            finally:
+                for handle in handles:
+                    handle.remove()
+            text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0]
+            if self.accelerator.is_main_process and doc_id[0] % 100 == 0:
+                eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{text_outputs}\n")
+
+            res.append(text_outputs)
+            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