Basic 4-bit backend

modeling/inference_models/basic_hf_4bit/class.py

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+from __future__ import annotations
+
+import gc
+import os
+import shutil
+import time
+import warnings
+from typing import List, Optional, Union
+
+import torch
+import transformers
+from transformers import AutoConfig, AutoModelForCausalLM, LogitsProcessorList
+
+import utils
+from logger import logger
+import koboldai_settings
+from modeling import warpers
+from modeling.inference_model import (
+    GenerationResult,
+    GenerationSettings,
+    InferenceModel,
+    use_core_manipulations,
+)
+
+model_backend_name = "Basic Huggingface 4-bit"
+model_backend_type = "Huggingface"
+
+
+class model_backend(InferenceModel):
+    # Model backends must inherit from InferenceModel.
+
+    def __init__(self) -> None:
+        super().__init__()
+        import importlib
+        dependency_exists = importlib.util.find_spec("bitsandbytes")
+        if dependency_exists:        
+            self.model_name = "Basic Huggingface"
+            self.path = None
+        else:
+            logger.warning("Bitsandbytes is not installed, you can not use Huggingface models in 4-bit")
+            self.disable = True
+
+    def is_valid(self, model_name, model_path, menu_path):
+        try:
+            if model_path is not None and os.path.exists(model_path):
+                self.model_config = AutoConfig.from_pretrained(model_path)
+            elif os.path.exists("models/{}".format(model_name.replace("/", "_"))):
+                self.model_config = AutoConfig.from_pretrained(
+                    "models/{}".format(model_name.replace("/", "_")),
+                    revision=utils.koboldai_vars.revision,
+                    cache_dir="cache",
+                )
+            else:
+                self.model_config = AutoConfig.from_pretrained(
+                    model_name, revision=utils.koboldai_vars.revision, cache_dir="cache"
+                )
+            return True
+        except:
+            return False
+
+    def get_requested_parameters(
+        self, model_name: str, model_path: str, menu_path: str, parameters: dict = {}
+    ):
+        requested_parameters = []
+
+        if model_name == "customhuggingface":
+            requested_parameters.append(
+                {
+                    "uitype": "text",
+                    "unit": "text",
+                    "label": "Huggingface Model Name",
+                    "id": "custom_model_name",
+                    "default": parameters.get("custom_model_name", ""),
+                    "check": {"value": "", "check": "!="},
+                    "tooltip": "Model name from https://huggingface.co/",
+                    "menu_path": "",
+                    "refresh_model_inputs": True,
+                    "extra_classes": "",
+                }
+            )
+
+        if model_name != "customhuggingface" or "custom_model_name" in parameters:
+            model_name = parameters.get("custom_model_name", None) or model_name
+            alt_model_path = self.get_local_model_path()
+
+            if model_path and os.path.exists(model_path):
+                # Use passed model path
+                self.model_config = AutoConfig.from_pretrained(model_path)
+            elif alt_model_path:
+                # Use known model path
+                self.model_config = AutoConfig.from_pretrained(
+                    alt_model_path,
+                    revision=utils.koboldai_vars.revision,
+                    cache_dir="cache",
+                )
+            else:
+                # No model path locally, we'll probably have to download
+                self.model_config = AutoConfig.from_pretrained(
+                    model_name, revision=utils.koboldai_vars.revision, cache_dir="cache"
+                )
+
+        return requested_parameters
+
+    def set_input_parameters(self, parameters: dict):
+        self.model_name = parameters.get("custom_model_name", parameters["id"])
+        self.path = parameters.get("path", None)
+        logger.info(parameters)
+
+    def unload(self):
+        if hasattr(self, "model"):
+            self.model = None
+
+        if hasattr(self, "tokenizer"):
+            self.tokenizer = None
+
+        if hasattr(self, "model_config"):
+            self.model_config = None
+
+        with torch.no_grad():
+            with warnings.catch_warnings():
+                warnings.filterwarnings(
+                    "ignore", message="torch.distributed.reduce_op is deprecated"
+                )
+                for tensor in gc.get_objects():
+                    try:
+                        if torch.is_tensor(tensor):
+                            tensor.set_(
+                                torch.tensor(
+                                    (), device=tensor.device, dtype=tensor.dtype
+                                )
+                            )
+                    except:
+                        pass
+        gc.collect()
+
+        try:
+            with torch.no_grad():
+                torch.cuda.empty_cache()
+        except:
+            pass
+
+    def _load(self, save_model: bool, initial_load: bool) -> None:
+        utils.koboldai_vars.allowsp = False
+
+        if self.model_name == "NeoCustom":
+            self.model_name = os.path.basename(os.path.normpath(self.path))
+        utils.koboldai_vars.model = self.model_name
+
+        # If we specify a model and it's in the root directory, we need to move
+        # it to the models directory (legacy folder structure to new)
+        if self.get_local_model_path(legacy=True):
+            shutil.move(
+                self.get_local_model_path(legacy=True, ignore_existance=True),
+                self.get_local_model_path(ignore_existance=True),
+            )
+
+        self.init_model_config()
+
+        self.model = AutoModelForCausalLM.from_pretrained(
+            self.get_local_model_path(), low_cpu_mem_usage=True, device_map="auto", load_in_4bit=True
+        )
+
+        self.tokenizer = self._get_tokenizer(self.get_local_model_path())
+        self.model.kai_model = self
+        self.badwordsids = koboldai_settings.badwordsids_default
+        utils.koboldai_vars.modeldim = self.model.get_input_embeddings().embedding_dim
+
+        # Patch Huggingface stuff to use our samplers
+        class KoboldLogitsWarperList(LogitsProcessorList):
+            def __call__(
+                _self,  # Unused
+                input_ids: torch.LongTensor,
+                scores: torch.FloatTensor,
+                *args,
+                **kwargs,
+            ):
+                # Kobold sampling is done here.
+                scores = self._apply_warpers(scores=scores, input_ids=input_ids)
+
+                # Things like Lua integration, phrase bias, and probability visualization are done here.
+                for processor in self.logits_processors:
+                    scores = processor(self, scores=scores, input_ids=input_ids)
+                    assert (
+                        scores is not None
+                    ), f"Scores are None; processor '{processor}' is to blame"
+                return scores
+
+        def new_sample(self, *args, **kwargs):
+            assert kwargs.pop("logits_warper", None) is not None
+            kwargs["logits_warper"] = KoboldLogitsWarperList()
+
+            if utils.koboldai_vars.newlinemode in ["s", "ns"]:
+                kwargs["eos_token_id"] = -1
+                kwargs.setdefault("pad_token_id", 2)
+
+            return new_sample.old_sample(self, *args, **kwargs)
+
+        new_sample.old_sample = transformers.GenerationMixin.sample
+        use_core_manipulations.sample = new_sample
+
+    def _apply_warpers(
+        self, scores: torch.Tensor, input_ids: torch.Tensor
+    ) -> torch.Tensor:
+        """Applies samplers/warpers to the given scores, returning the altered scores.
+
+        Args:
+            scores (torch.Tensor): The original scores.
+            input_ids (torch.Tensor): The input token sequence.
+
+        Returns:
+            torch.Tensor: The altered scores.
+        """
+        warpers.update_settings()
+
+        for sid in utils.koboldai_vars.sampler_order:
+            warper = warpers.Warper.from_id(sid)
+
+            if not warper.value_is_valid():
+                continue
+
+            if warper == warpers.RepetitionPenalty:
+                # Rep pen needs access to input tokens to decide what to penalize
+                scores = warper.torch(scores, input_ids=input_ids)
+            else:
+                scores = warper.torch(scores)
+
+            assert scores is not None, f"Scores are None; warper '{warper}' is to blame"
+        return scores
+
+    def _raw_generate(
+        self,
+        prompt_tokens: Union[List[int], torch.Tensor],
+        max_new: int,
+        gen_settings: GenerationSettings,
+        single_line: bool = False,
+        batch_count: int = 1,
+        seed: Optional[int] = None,
+        **kwargs,
+    ) -> GenerationResult:
+        if not isinstance(prompt_tokens, torch.Tensor):
+            gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
+        else:
+            gen_in = prompt_tokens
+
+        device = self.get_auxilary_device()
+        gen_in = gen_in.to(device)
+
+        additional_bad_words_ids = [self.tokenizer.encode("\n")] if single_line else []
+
+        if seed is not None:
+            torch.manual_seed(seed)
+
+        with torch.no_grad():
+            start_time = time.time()
+            genout = self.model.generate(
+                gen_in,
+                do_sample=True,
+                max_length=min(
+                    len(prompt_tokens) + max_new, utils.koboldai_vars.max_length
+                ),
+                repetition_penalty=1.0,
+                bad_words_ids=self.badwordsids + additional_bad_words_ids,
+                use_cache=True,
+                num_return_sequences=batch_count,
+            )
+
+        logger.debug(
+            "torch_raw_generate: run generator {}s".format(time.time() - start_time)
+        )
+
+        return GenerationResult(
+            self,
+            out_batches=genout,
+            prompt=prompt_tokens,
+            is_whole_generation=False,
+            output_includes_prompt=True,
+        )
+
+    def get_local_model_path(
+        self, legacy: bool = False, ignore_existance: bool = False
+    ) -> Optional[str]:
+        """
+        Returns a string of the model's path locally, or None if it is not downloaded.
+        If ignore_existance is true, it will always return a path.
+        """
+        if self.path is not None:
+            if os.path.exists(self.path):
+                return self.path
+
+        if self.model_name in [
+            "NeoCustom",
+            "GPT2Custom",
+            "TPUMeshTransformerGPTJ",
+            "TPUMeshTransformerGPTNeoX",
+        ]:
+            model_path = self.path
+            assert model_path
+
+            # Path can be absolute or relative to models directory
+            if os.path.exists(model_path):
+                return model_path
+
+            model_path = os.path.join("models", model_path)
+
+            try:
+                assert os.path.exists(model_path)
+            except AssertionError:
+                logger.error(
+                    f"Custom model does not exist at '{utils.koboldai_vars.custmodpth}' or '{model_path}'."
+                )
+                raise
+
+            return model_path
+
+        basename = self.model_name.replace("/", "_")
+        if legacy:
+            ret = basename
+        else:
+            ret = os.path.join("models", basename)
+
+        if os.path.isdir(ret) or ignore_existance:
+            return ret
+        return None
+
+    def init_model_config(self) -> None:
+        # Get the model_type from the config or assume a model type if it isn't present
+        try:
+            self.model_config = AutoConfig.from_pretrained(
+                self.get_local_model_path() or self.model_name,
+                revision=utils.koboldai_vars.revision,
+                cache_dir="cache",
+            )
+            self.model_type = self.model_config.model_type
+        except ValueError:
+            self.model_type = {
+                "NeoCustom": "gpt_neo",
+                "GPT2Custom": "gpt2",
+            }.get(self.model)
+
+            if not self.model_type:
+                logger.warning(
+                    "No model type detected, assuming Neo (If this is a GPT2 model use the other menu option or --model GPT2Custom)"
+                )
+                self.model_type = "gpt_neo"