mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
520 lines
18 KiB
Python
520 lines
18 KiB
Python
from __future__ import annotations
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import gc
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import os
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import time
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import bisect
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import zipfile
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import functools
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import itertools
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import traceback
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import contextlib
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from accelerate.big_modeling import load_checkpoint_and_dispatch
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from accelerate.utils.modeling import infer_auto_device_map, load_checkpoint_in_model
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from tqdm.auto import tqdm
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from typing import Dict, List, Optional, Union
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import torch
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from torch.nn import Embedding
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import transformers
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from transformers import (
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StoppingCriteria,
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GPTNeoForCausalLM,
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GPT2LMHeadModel,
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AutoModelForCausalLM,
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LogitsProcessorList,
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)
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import utils
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import modeling.lazy_loader as lazy_loader
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from logger import logger, Colors
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from modeling import warpers
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from modeling.warpers import Warper
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from modeling.stoppers import Stoppers
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from modeling.post_token_hooks import PostTokenHooks
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from modeling.inference_models.hf import HFInferenceModel
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from modeling.inference_model import (
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GenerationResult,
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GenerationSettings,
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ModelCapabilities,
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use_core_manipulations,
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)
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try:
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import accelerate.utils
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except ModuleNotFoundError as e:
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if not utils.koboldai_vars.use_colab_tpu:
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raise e
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# When set to true, messages will appear in the console if samplers are not
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# changing the scores. Keep in mind some samplers don't always change the
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# scores for each token.
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LOG_SAMPLER_NO_EFFECT = False
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class HFTorchInferenceModel(HFInferenceModel):
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def __init__(self) -> None:
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super().__init__()
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self.hf_torch = True
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self.lazy_load = True
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self.low_mem = False
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self.nobreakmodel = False
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self.post_token_hooks = [
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PostTokenHooks.stream_tokens,
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]
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self.stopper_hooks = [
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Stoppers.core_stopper,
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Stoppers.dynamic_wi_scanner,
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Stoppers.singleline_stopper,
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Stoppers.chat_mode_stopper,
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Stoppers.stop_sequence_stopper,
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]
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self.capabilties = ModelCapabilities(
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embedding_manipulation=True,
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post_token_hooks=True,
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stopper_hooks=True,
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post_token_probs=True,
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)
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self._old_stopping_criteria = None
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def _apply_warpers(
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self, scores: torch.Tensor, input_ids: torch.Tensor
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) -> torch.Tensor:
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warpers.update_settings()
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if LOG_SAMPLER_NO_EFFECT:
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pre = torch.Tensor(scores)
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for sid in utils.koboldai_vars.sampler_order:
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warper = Warper.from_id(sid)
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if not warper.value_is_valid():
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continue
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if warper == warpers.RepetitionPenalty:
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# Rep pen needs more data than other samplers
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scores = warper.torch(scores, input_ids=input_ids)
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else:
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scores = warper.torch(scores)
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assert scores is not None, f"Scores are None; warper '{warper}' is to blame"
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if LOG_SAMPLER_NO_EFFECT:
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if torch.equal(pre, scores):
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logger.info(warper, "had no effect on the scores.")
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pre = torch.Tensor(scores)
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return scores
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def get_model_type(self) -> str:
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if not self.model_config:
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return "Read Only"
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if not isinstance(self.model_config, dict):
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return str(self.model_config.model_type)
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model_type = self.model_config.get("model_type")
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if model_type:
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return model_type
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if utils.koboldai_vars.mode.endswith("gpt2"):
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return "gpt2"
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else:
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return "Unknown"
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def _post_load(m_self) -> None:
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if not utils.koboldai_vars.model_type:
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utils.koboldai_vars.model_type = m_self.get_model_type()
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# Patch stopping_criteria
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class PTHStopper(StoppingCriteria):
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def __call__(
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hf_self,
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input_ids: torch.LongTensor,
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scores: torch.FloatTensor,
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) -> None:
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m_self._post_token_gen(input_ids)
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for stopper in m_self.stopper_hooks:
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do_stop = stopper(m_self, input_ids)
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if do_stop:
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return True
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return False
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old_gsc = transformers.GenerationMixin._get_stopping_criteria
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def _get_stopping_criteria(
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hf_self,
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*args,
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**kwargs,
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):
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stopping_criteria = old_gsc(hf_self, *args, **kwargs)
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stopping_criteria.insert(0, PTHStopper())
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return stopping_criteria
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use_core_manipulations.get_stopping_criteria = _get_stopping_criteria
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# Patch logitswarpers
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def new_get_logits_processor(*args, **kwargs) -> LogitsProcessorList:
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processors = new_get_logits_processor.old_get_logits_processor(
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*args, **kwargs
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)
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return processors
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use_core_manipulations.get_logits_processor = new_get_logits_processor
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new_get_logits_processor.old_get_logits_processor = (
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transformers.GenerationMixin._get_logits_processor
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)
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class KoboldLogitsWarperList(LogitsProcessorList):
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def __init__(self):
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pass
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def __call__(
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lw_self,
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input_ids: torch.LongTensor,
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scores: torch.FloatTensor,
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*args,
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**kwargs,
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):
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scores = m_self._apply_warpers(scores=scores, input_ids=input_ids)
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for processor in m_self.logits_processors:
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scores = processor(m_self, scores=scores, input_ids=input_ids)
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assert (
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scores is not None
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), f"Scores are None; processor '{processor}' is to blame"
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return scores
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def new_get_logits_warper(
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beams: int = 1,
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) -> LogitsProcessorList:
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return KoboldLogitsWarperList()
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def new_sample(self, *args, **kwargs):
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assert kwargs.pop("logits_warper", None) is not None
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kwargs["logits_warper"] = new_get_logits_warper(
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beams=1,
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)
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if utils.koboldai_vars.newlinemode in ["s", "ns"]:
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kwargs["eos_token_id"] = -1
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kwargs.setdefault("pad_token_id", 2)
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return new_sample.old_sample(self, *args, **kwargs)
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new_sample.old_sample = transformers.GenerationMixin.sample
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use_core_manipulations.sample = new_sample
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return super()._post_load()
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def _raw_generate(
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self,
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prompt_tokens: Union[List[int], torch.Tensor],
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max_new: int,
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gen_settings: GenerationSettings,
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single_line: bool = False,
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batch_count: int = 1,
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seed: Optional[int] = None,
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**kwargs,
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) -> GenerationResult:
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if not isinstance(prompt_tokens, torch.Tensor):
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gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
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else:
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gen_in = prompt_tokens
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device = utils.get_auxilary_device()
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gen_in = gen_in.to(device)
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additional_bad_words_ids = [self.tokenizer.encode("\n")] if single_line else []
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if seed is not None:
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torch.manual_seed(seed)
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with torch.no_grad():
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start_time = time.time()
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genout = self.model.generate(
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gen_in,
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do_sample=True,
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max_length=min(
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len(prompt_tokens) + max_new, utils.koboldai_vars.max_length
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),
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repetition_penalty=1.0,
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bad_words_ids=self.badwordsids + additional_bad_words_ids,
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use_cache=True,
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num_return_sequences=batch_count,
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)
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logger.debug(
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"torch_raw_generate: run generator {}s".format(time.time() - start_time)
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)
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return GenerationResult(
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self,
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out_batches=genout,
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prompt=prompt_tokens,
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is_whole_generation=False,
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output_includes_prompt=True,
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)
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def _get_model(self, location: str, tf_kwargs: Dict):
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tf_kwargs["revision"] = utils.koboldai_vars.revision
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tf_kwargs["cache_dir"] = "cache"
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if self.lazy_load:
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tf_kwargs.pop("low_cpu_mem_usage", None)
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# If we have model hints for legacy model, use them rather than fall back.
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try:
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if self.model_name == "GPT2Custom":
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return GPT2LMHeadModel.from_pretrained(location, **tf_kwargs)
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elif self.model_name == "NeoCustom":
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return GPTNeoForCausalLM.from_pretrained(location, **tf_kwargs)
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except Exception as e:
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logger.warning(f"{self.model_name} is a no-go; {e} - Falling back to auto.")
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# Try to determine model type from either AutoModel or falling back to legacy
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try:
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# with accelerate.init_empty_weights():
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# model = AutoModelForCausalLM.from_config(self.model_config)
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# print("[HUGE SKELETON] MAKING DEVICE MAP")
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# device_map = infer_auto_device_map(
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# model,
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# no_split_module_classes=model._no_split_modules,
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# max_memory={0: "10GiB", 1: "7GiB", "cpu": "20GiB"},
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# dtype=torch.float16,
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# )
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# # TODO: ??
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# print("[HUGE SKELETON] TYING WEIGHTS")
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# model.tie_weights()
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print("[HUGE SKELETON] LOADING FROM PRETRAINED")
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# model = load_checkpoint_and_dispatch(
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# model,
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# location + "/pytorch_model.bin",
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# device_map=device_map,
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# no_split_module_classes=model._no_split_modules,
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# dtype=torch.float16,
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# )
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with lazy_loader.use_lazy_load(
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enable=True,
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# dematerialized_modules=True,
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dematerialized_modules=False,
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):
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model = AutoModelForCausalLM.from_pretrained(
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location,
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device_map="auto",
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max_memory={0: "10GiB", 1: "7GiB", "cpu": "20GiB"},
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offload_folder="accelerate-disk-cache",
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torch_dtype=torch.float16,
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**tf_kwargs,
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)
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for name, value in list(model.named_parameters()) + list(
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model.named_buffers()
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):
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if value.device != torch.device("meta"):
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continue
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print(name, value, value.nelement())
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# try:
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# value.cpu()
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# except NotImplementedError:
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# # Can't be copied out of meta tensor, no data
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# print("Bad news at", name)
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# # setattr(model, name, torch.zeros(value.size()))
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return model
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except Exception as e:
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traceback_string = traceback.format_exc().lower()
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if "out of memory" in traceback_string:
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raise RuntimeError(
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"One of your GPUs ran out of memory when KoboldAI tried to load your model."
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)
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# Model corrupted or serious loading problem. Stop here.
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if "invalid load key" in traceback_string:
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logger.error("Invalid load key! Aborting.")
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raise
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logger.warning(f"Fell back to GPT2LMHeadModel due to {e}")
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logger.debug(traceback.format_exc())
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try:
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return GPT2LMHeadModel.from_pretrained(location, **tf_kwargs)
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except Exception as e:
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logger.warning(f"Fell back to GPTNeoForCausalLM due to {e}")
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logger.debug(traceback.format_exc())
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return GPTNeoForCausalLM.from_pretrained(location, **tf_kwargs)
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def get_hidden_size(self) -> int:
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return self.model.get_input_embeddings().embedding_dim
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def _will_load_with_safetensors(self) -> bool:
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path = self.get_local_model_path()
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# TODO: This might mess up download to run
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if not path:
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return False
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if not os.path.exists(os.path.join(path, "model.safetensors")):
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return False
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return True
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# Function to patch transformers to use our soft prompt
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def patch_embedding(self) -> None:
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if getattr(Embedding, "_koboldai_patch_causallm_model", None):
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Embedding._koboldai_patch_causallm_model = self.model
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return
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old_embedding_call = Embedding.__call__
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kai_model = self
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def new_embedding_call(self, input_ids, *args, **kwargs):
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# Don't touch embeddings for models other than the core inference model (that's us!)
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if (
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Embedding._koboldai_patch_causallm_model.get_input_embeddings()
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is not self
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):
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return old_embedding_call(self, input_ids, *args, **kwargs)
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assert input_ids is not None
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if utils.koboldai_vars.sp is not None:
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shifted_input_ids = input_ids - kai_model.model.config.vocab_size
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input_ids.clamp_(max=kai_model.model.config.vocab_size - 1)
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inputs_embeds = old_embedding_call(self, input_ids, *args, **kwargs)
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if utils.koboldai_vars.sp is not None:
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utils.koboldai_vars.sp = utils.koboldai_vars.sp.to(
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inputs_embeds.dtype
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).to(inputs_embeds.device)
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inputs_embeds = torch.where(
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(shifted_input_ids >= 0)[..., None],
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utils.koboldai_vars.sp[shifted_input_ids.clamp(min=0)],
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inputs_embeds,
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)
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return inputs_embeds
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Embedding.__call__ = new_embedding_call
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Embedding._koboldai_patch_causallm_model = self.model
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@contextlib.contextmanager
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def _maybe_use_float16(self, always_use: bool = False):
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if always_use or (
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utils.koboldai_vars.hascuda
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and self.low_mem
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and (self.usegpu or self.breakmodel)
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):
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original_dtype = torch.get_default_dtype()
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torch.set_default_dtype(torch.float16)
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yield True
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torch.set_default_dtype(original_dtype)
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else:
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yield False
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def breakmodel_device_list(self, n_layers, primary=None, selected=None):
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return
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# TODO: Find a better place for this or rework this
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device_count = torch.cuda.device_count()
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if device_count < 2:
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primary = None
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logger.debug("n_layers: {}".format(n_layers))
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logger.debug("gpu blocks: {}".format(breakmodel.gpu_blocks))
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gpu_blocks = breakmodel.gpu_blocks + (
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device_count - len(breakmodel.gpu_blocks)
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) * [0]
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print(f"{Colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{Colors.END}")
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for i in range(device_count):
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name = torch.cuda.get_device_name(i)
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if len(name) > 47:
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name = "..." + name[-44:]
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row_color = Colors.END
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sep_color = Colors.YELLOW
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print(
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f"{row_color}{Colors.YELLOW + '->' + row_color if i == selected else ' '} {'(primary)' if i == primary else ' '*9} {i:3} {sep_color}|{row_color} {gpu_blocks[i]:3} {sep_color}|{row_color} {name}{Colors.END}"
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)
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row_color = Colors.END
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sep_color = Colors.YELLOW
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print(
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f"{row_color}{Colors.YELLOW + '->' + row_color if -1 == selected else ' '} {' '*9} N/A {sep_color}|{row_color} {breakmodel.disk_blocks:3} {sep_color}|{row_color} (Disk cache){Colors.END}"
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)
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print(
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f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){Colors.END}"
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)
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def breakmodel_device_config(self, config):
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# TODO: Find a better place for this or rework this
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return
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global breakmodel, generator
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import breakmodel
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n_layers = utils.num_layers(config)
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logger.debug("gpu blocks before modification: {}".format(breakmodel.gpu_blocks))
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if utils.args.cpu:
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breakmodel.gpu_blocks = [0] * n_layers
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return
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elif breakmodel.gpu_blocks == []:
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logger.info("Breakmodel not specified, assuming GPU 0")
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breakmodel.gpu_blocks = [n_layers]
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n_layers = 0
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else:
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s = n_layers
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for i in range(len(breakmodel.gpu_blocks)):
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if breakmodel.gpu_blocks[i] <= -1:
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breakmodel.gpu_blocks[i] = s
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break
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else:
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s -= breakmodel.gpu_blocks[i]
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assert sum(breakmodel.gpu_blocks) <= n_layers
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n_layers -= sum(breakmodel.gpu_blocks)
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if breakmodel.disk_blocks is not None:
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assert breakmodel.disk_blocks <= n_layers
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n_layers -= breakmodel.disk_blocks
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logger.init_ok("Final device configuration:", status="Info")
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self.breakmodel_device_list(n_layers, primary=breakmodel.primary_device)
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with open(
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"settings/{}.breakmodel".format(self.model_name.replace("/", "_")), "w"
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) as file:
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file.write(
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"{}\n{}".format(
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",".join(map(str, breakmodel.gpu_blocks)), breakmodel.disk_blocks
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)
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)
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# If all layers are on the same device, use the old GPU generation mode
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while len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0:
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breakmodel.gpu_blocks.pop()
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self.breakmodel = True
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if len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] in (
|
|
-1,
|
|
utils.num_layers(config),
|
|
):
|
|
logger.debug("All layers on same GPU. Breakmodel disabled")
|
|
self.breakmodel = False
|
|
self.usegpu = True
|
|
utils.koboldai_vars.gpu_device = len(breakmodel.gpu_blocks) - 1
|
|
return
|
|
|
|
if not breakmodel.gpu_blocks:
|
|
logger.warning("Nothing assigned to a GPU, reverting to CPU only mode")
|
|
self.breakmodel = False
|
|
self.usegpu = False
|
|
return
|