mirror of
https://github.com/KoboldAI/KoboldAI-Client.git
synced 2025-06-05 21:59:24 +02:00
Moved model backends to separate folders
added some model backend settings save/load
This commit is contained in:
ebolam
856
modeling/inference_models/hf_torch.py
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856
modeling/inference_models/hf_torch.py
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@@ -0,0 +1,856 @@
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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 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 breakmodel
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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
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+ 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 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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return AutoModelForCausalLM.from_pretrained(location, **tf_kwargs)
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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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def _move_to_devices(self) -> None:
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for key, value in self.model.state_dict().items():
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target_dtype = (
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torch.float32 if breakmodel.primary_device == "cpu" else torch.float16
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)
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if value.dtype is not target_dtype:
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accelerate.utils.set_module_tensor_to_device(
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self.model,
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tensor_name=key,
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device=torch.device(value.device),
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value=value,
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dtype=target_dtype,
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)
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disk_blocks = breakmodel.disk_blocks
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gpu_blocks = breakmodel.gpu_blocks
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ram_blocks = len(utils.layers_module_names) - sum(gpu_blocks)
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cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
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device_map = {}
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for name in utils.layers_module_names:
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layer = int(name.rsplit(".", 1)[1])
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device = (
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("disk" if layer < disk_blocks else "cpu")
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if layer < ram_blocks
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else bisect.bisect_right(cumulative_gpu_blocks, layer - ram_blocks)
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)
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device_map[name] = device
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for name in utils.get_missing_module_names(self.model, list(device_map.keys())):
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device_map[name] = breakmodel.primary_device
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breakmodel.dispatch_model_ex(
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self.model,
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device_map,
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main_device=breakmodel.primary_device,
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offload_buffers=True,
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offload_dir="accelerate-disk-cache",
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)
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gc.collect()
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return
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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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def _get_lazy_load_callback(self, n_layers: int, convert_to_float16: bool = True):
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if not self.lazy_load:
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return
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if utils.args.breakmodel_disklayers is not None:
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breakmodel.disk_blocks = utils.args.breakmodel_disklayers
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disk_blocks = breakmodel.disk_blocks
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gpu_blocks = breakmodel.gpu_blocks
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ram_blocks = ram_blocks = n_layers - sum(gpu_blocks)
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cumulative_gpu_blocks = tuple(itertools.accumulate(gpu_blocks))
|
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|
||||
def lazy_load_callback(
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model_dict: Dict[str, Union[lazy_loader.LazyTensor, torch.Tensor]],
|
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f,
|
||||
is_safetensors: bool = False,
|
||||
**_,
|
||||
):
|
||||
if lazy_load_callback.nested:
|
||||
return
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lazy_load_callback.nested = True
|
||||
|
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device_map: Dict[str, Union[str, int]] = {}
|
||||
|
||||
@functools.lru_cache(maxsize=None)
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def get_original_key(key) -> Optional[str]:
|
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key_candidates = [
|
||||
original_key
|
||||
for original_key in utils.module_names
|
||||
if original_key.endswith(key)
|
||||
]
|
||||
|
||||
if not key_candidates:
|
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logger.debug(f"!!! No key candidates for {key}")
|
||||
return None
|
||||
|
||||
return max(key_candidates, key=len)
|
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|
||||
for key, value in model_dict.items():
|
||||
original_key = get_original_key(key)
|
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|
||||
if not original_key:
|
||||
continue
|
||||
|
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if isinstance(value, lazy_loader.LazyTensor) and not any(
|
||||
original_key.startswith(n) for n in utils.layers_module_names
|
||||
):
|
||||
device_map[key] = (
|
||||
utils.koboldai_vars.gpu_device
|
||||
if utils.koboldai_vars.hascuda and self.usegpu
|
||||
else "cpu"
|
||||
if not utils.koboldai_vars.hascuda
|
||||
or not self.breakmodel
|
||||
else breakmodel.primary_device
|
||||
)
|
||||
else:
|
||||
layer = int(
|
||||
max(
|
||||
(
|
||||
n
|
||||
for n in utils.layers_module_names
|
||||
if original_key.startswith(n)
|
||||
),
|
||||
key=len,
|
||||
).rsplit(".", 1)[1]
|
||||
)
|
||||
device = (
|
||||
utils.koboldai_vars.gpu_device
|
||||
if utils.koboldai_vars.hascuda and self.usegpu
|
||||
else "disk"
|
||||
if layer < disk_blocks and layer < ram_blocks
|
||||
else "cpu"
|
||||
if not utils.koboldai_vars.hascuda
|
||||
or not self.breakmodel
|
||||
else "shared"
|
||||
if layer < ram_blocks
|
||||
else bisect.bisect_right(
|
||||
cumulative_gpu_blocks, layer - ram_blocks
|
||||
)
|
||||
)
|
||||
device_map[key] = device
|
||||
|
||||
if utils.num_shards is None or utils.current_shard == 0:
|
||||
utils.offload_index = {}
|
||||
if os.path.isdir("accelerate-disk-cache"):
|
||||
# Delete all of the files in the disk cache folder without deleting the folder itself to allow people to create symbolic links for this folder
|
||||
# (the folder doesn't contain any subfolders so os.remove will do just fine)
|
||||
for filename in os.listdir("accelerate-disk-cache"):
|
||||
try:
|
||||
os.remove(os.path.join("accelerate-disk-cache", filename))
|
||||
except OSError:
|
||||
pass
|
||||
os.makedirs("accelerate-disk-cache", exist_ok=True)
|
||||
if utils.num_shards is not None:
|
||||
num_tensors = len(
|
||||
utils.get_sharded_checkpoint_num_tensors(
|
||||
utils.from_pretrained_model_name,
|
||||
utils.from_pretrained_index_filename,
|
||||
is_safetensors=is_safetensors,
|
||||
**utils.from_pretrained_kwargs,
|
||||
)
|
||||
)
|
||||
else:
|
||||
num_tensors = len(device_map)
|
||||
print(flush=True)
|
||||
utils.koboldai_vars.status_message = "Loading model"
|
||||
utils.koboldai_vars.total_layers = num_tensors
|
||||
utils.koboldai_vars.loaded_layers = 0
|
||||
utils.bar = tqdm(
|
||||
total=num_tensors,
|
||||
desc="Loading model tensors",
|
||||
file=utils.UIProgressBarFile(),
|
||||
position=1
|
||||
)
|
||||
|
||||
if not is_safetensors:
|
||||
# Torch lazyload
|
||||
with zipfile.ZipFile(f, "r") as z:
|
||||
try:
|
||||
last_storage_key = None
|
||||
zipfolder = os.path.basename(os.path.normpath(f)).split(".")[0]
|
||||
f = None
|
||||
current_offset = 0
|
||||
able_to_pin_layers = True
|
||||
if utils.num_shards is not None:
|
||||
utils.current_shard += 1
|
||||
for key in sorted(
|
||||
device_map.keys(),
|
||||
key=lambda k: (
|
||||
model_dict[k].key,
|
||||
model_dict[k].seek_offset,
|
||||
),
|
||||
):
|
||||
storage_key = model_dict[key].key
|
||||
if (
|
||||
storage_key != last_storage_key
|
||||
or model_dict[key].seek_offset < current_offset
|
||||
):
|
||||
last_storage_key = storage_key
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
try:
|
||||
f = z.open(f"archive/data/{storage_key}")
|
||||
except:
|
||||
f = z.open(f"{zipfolder}/data/{storage_key}")
|
||||
current_offset = 0
|
||||
if current_offset != model_dict[key].seek_offset:
|
||||
f.read(model_dict[key].seek_offset - current_offset)
|
||||
current_offset = model_dict[key].seek_offset
|
||||
device = device_map[key]
|
||||
size = functools.reduce(
|
||||
lambda x, y: x * y, model_dict[key].shape, 1
|
||||
)
|
||||
dtype = model_dict[key].dtype
|
||||
nbytes = (
|
||||
size
|
||||
if dtype is torch.bool
|
||||
else size
|
||||
* (
|
||||
(
|
||||
torch.finfo
|
||||
if dtype.is_floating_point
|
||||
else torch.iinfo
|
||||
)(dtype).bits
|
||||
>> 3
|
||||
)
|
||||
)
|
||||
# print(f"Transferring <{key}> to {f'({device.upper()})' if isinstance(device, str) else '[device ' + str(device) + ']'} ... ", end="", flush=True)
|
||||
model_dict[key] = model_dict[key].materialize(
|
||||
f, map_location="cpu"
|
||||
)
|
||||
if model_dict[key].dtype is torch.float32:
|
||||
utils.koboldai_vars.fp32_model = True
|
||||
if (
|
||||
convert_to_float16
|
||||
and breakmodel.primary_device != "cpu"
|
||||
and utils.koboldai_vars.hascuda
|
||||
and (
|
||||
self.breakmodel
|
||||
or self.usegpu
|
||||
)
|
||||
and model_dict[key].dtype is torch.float32
|
||||
):
|
||||
model_dict[key] = model_dict[key].to(torch.float16)
|
||||
if breakmodel.primary_device == "cpu" or (
|
||||
not self.usegpu
|
||||
and not self.breakmodel
|
||||
and model_dict[key].dtype is torch.float16
|
||||
):
|
||||
model_dict[key] = model_dict[key].to(torch.float32)
|
||||
if device == "shared":
|
||||
model_dict[key] = model_dict[key].to("cpu").detach_()
|
||||
if able_to_pin_layers:
|
||||
try:
|
||||
model_dict[key] = model_dict[key].pin_memory()
|
||||
except:
|
||||
able_to_pin_layers = False
|
||||
elif device == "disk":
|
||||
accelerate.utils.offload_weight(
|
||||
model_dict[key],
|
||||
get_original_key(key),
|
||||
"accelerate-disk-cache",
|
||||
index=utils.offload_index,
|
||||
)
|
||||
model_dict[key] = model_dict[key].to("meta")
|
||||
else:
|
||||
model_dict[key] = model_dict[key].to(device)
|
||||
# print("OK", flush=True)
|
||||
current_offset += nbytes
|
||||
utils.bar.update(1)
|
||||
utils.koboldai_vars.loaded_layers += 1
|
||||
finally:
|
||||
if (
|
||||
utils.num_shards is None
|
||||
or utils.current_shard >= utils.num_shards
|
||||
):
|
||||
if utils.offload_index:
|
||||
for name, tensor in utils.named_buffers:
|
||||
dtype = tensor.dtype
|
||||
if (
|
||||
convert_to_float16
|
||||
and breakmodel.primary_device != "cpu"
|
||||
and utils.koboldai_vars.hascuda
|
||||
and (
|
||||
self.breakmodel
|
||||
or self.usegpu
|
||||
)
|
||||
):
|
||||
dtype = torch.float16
|
||||
if breakmodel.primary_device == "cpu" or (
|
||||
not self.usegpu
|
||||
and not self.breakmodel
|
||||
):
|
||||
dtype = torch.float32
|
||||
if (
|
||||
name in model_dict
|
||||
and model_dict[name].dtype is not dtype
|
||||
):
|
||||
model_dict[name] = model_dict[name].to(dtype)
|
||||
if tensor.dtype is not dtype:
|
||||
tensor = tensor.to(dtype)
|
||||
if name not in utils.offload_index:
|
||||
accelerate.utils.offload_weight(
|
||||
tensor,
|
||||
name,
|
||||
"accelerate-disk-cache",
|
||||
index=utils.offload_index,
|
||||
)
|
||||
accelerate.utils.save_offload_index(
|
||||
utils.offload_index, "accelerate-disk-cache"
|
||||
)
|
||||
utils.bar.close()
|
||||
utils.bar = None
|
||||
utils.koboldai_vars.status_message = ""
|
||||
lazy_load_callback.nested = False
|
||||
if isinstance(f, zipfile.ZipExtFile):
|
||||
f.close()
|
||||
else:
|
||||
# Loading with safetensors
|
||||
try:
|
||||
able_to_pin_layers = True
|
||||
|
||||
if utils.num_shards is not None:
|
||||
utils.current_shard += 1
|
||||
|
||||
for key in sorted(
|
||||
device_map.keys(),
|
||||
key=lambda k: model_dict[k].key,
|
||||
):
|
||||
storage_key = model_dict[key].key
|
||||
|
||||
device = device_map[key]
|
||||
|
||||
# print(f"Transferring <{key}> to {f'({device.upper()})' if isinstance(device, str) else '[device ' + str(device) + ']'} ... ", end="", flush=True)
|
||||
|
||||
model_dict[key] = model_dict[key].materialize(
|
||||
f, map_location="cpu"
|
||||
)
|
||||
|
||||
if model_dict[key].dtype is torch.float32:
|
||||
utils.koboldai_vars.fp32_model = True
|
||||
|
||||
if (
|
||||
convert_to_float16
|
||||
and breakmodel.primary_device != "cpu"
|
||||
and utils.koboldai_vars.hascuda
|
||||
and (
|
||||
self.breakmodel
|
||||
or self.usegpu
|
||||
)
|
||||
and model_dict[key].dtype is torch.float32
|
||||
):
|
||||
model_dict[key] = model_dict[key].to(torch.float16)
|
||||
|
||||
if breakmodel.primary_device == "cpu" or (
|
||||
not self.usegpu
|
||||
and not self.breakmodel
|
||||
and model_dict[key].dtype is torch.float16
|
||||
):
|
||||
model_dict[key] = model_dict[key].to(torch.float32)
|
||||
|
||||
if device == "shared":
|
||||
model_dict[key] = model_dict[key].to("cpu").detach_()
|
||||
if able_to_pin_layers:
|
||||
try:
|
||||
model_dict[key] = model_dict[key].pin_memory()
|
||||
except:
|
||||
able_to_pin_layers = False
|
||||
elif device == "disk":
|
||||
accelerate.utils.offload_weight(
|
||||
model_dict[key],
|
||||
get_original_key(key),
|
||||
"accelerate-disk-cache",
|
||||
index=utils.offload_index,
|
||||
)
|
||||
model_dict[key] = model_dict[key].to("meta")
|
||||
else:
|
||||
model_dict[key] = model_dict[key].to(device)
|
||||
|
||||
utils.bar.update(1)
|
||||
utils.koboldai_vars.loaded_layers += 1
|
||||
|
||||
finally:
|
||||
if (
|
||||
utils.num_shards is None
|
||||
or utils.current_shard >= utils.num_shards
|
||||
):
|
||||
if utils.offload_index:
|
||||
for name, tensor in utils.named_buffers:
|
||||
dtype = tensor.dtype
|
||||
if (
|
||||
convert_to_float16
|
||||
and breakmodel.primary_device != "cpu"
|
||||
and utils.koboldai_vars.hascuda
|
||||
and (
|
||||
self.breakmodel
|
||||
or self.usegpu
|
||||
)
|
||||
):
|
||||
dtype = torch.float16
|
||||
if breakmodel.primary_device == "cpu" or (
|
||||
not self.usegpu
|
||||
and not self.breakmodel
|
||||
):
|
||||
dtype = torch.float32
|
||||
if (
|
||||
name in model_dict
|
||||
and model_dict[name].dtype is not dtype
|
||||
):
|
||||
model_dict[name] = model_dict[name].to(dtype)
|
||||
if tensor.dtype is not dtype:
|
||||
tensor = tensor.to(dtype)
|
||||
if name not in utils.offload_index:
|
||||
accelerate.utils.offload_weight(
|
||||
tensor,
|
||||
name,
|
||||
"accelerate-disk-cache",
|
||||
index=utils.offload_index,
|
||||
)
|
||||
accelerate.utils.save_offload_index(
|
||||
utils.offload_index, "accelerate-disk-cache"
|
||||
)
|
||||
utils.bar.close()
|
||||
utils.bar = None
|
||||
utils.koboldai_vars.status_message = ""
|
||||
|
||||
lazy_load_callback.nested = False
|
||||
|
||||
lazy_load_callback.nested = False
|
||||
return lazy_load_callback
|
||||
|
||||
@contextlib.contextmanager
|
||||
def _maybe_use_float16(self, always_use: bool = False):
|
||||
if always_use or (
|
||||
utils.koboldai_vars.hascuda
|
||||
and self.low_mem
|
||||
and (self.usegpu or self.breakmodel)
|
||||
):
|
||||
original_dtype = torch.get_default_dtype()
|
||||
torch.set_default_dtype(torch.float16)
|
||||
yield True
|
||||
torch.set_default_dtype(original_dtype)
|
||||
else:
|
||||
yield False
|
||||
|
||||
def breakmodel_device_list(self, n_layers, primary=None, selected=None):
|
||||
# TODO: Find a better place for this or rework this
|
||||
|
||||
device_count = torch.cuda.device_count()
|
||||
if device_count < 2:
|
||||
primary = None
|
||||
logger.debug("n_layers: {}".format(n_layers))
|
||||
gpu_blocks = breakmodel.gpu_blocks + (
|
||||
device_count - len(breakmodel.gpu_blocks)
|
||||
) * [0]
|
||||
print(f"{Colors.YELLOW} DEVICE ID | LAYERS | DEVICE NAME{Colors.END}")
|
||||
for i in range(device_count):
|
||||
name = torch.cuda.get_device_name(i)
|
||||
if len(name) > 47:
|
||||
name = "..." + name[-44:]
|
||||
row_color = Colors.END
|
||||
sep_color = Colors.YELLOW
|
||||
print(
|
||||
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}"
|
||||
)
|
||||
row_color = Colors.END
|
||||
sep_color = Colors.YELLOW
|
||||
print(
|
||||
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}"
|
||||
)
|
||||
print(
|
||||
f"{row_color} {' '*9} N/A {sep_color}|{row_color} {n_layers:3} {sep_color}|{row_color} (CPU){Colors.END}"
|
||||
)
|
||||
|
||||
def breakmodel_device_config(self, config):
|
||||
# TODO: Find a better place for this or rework this
|
||||
|
||||
global breakmodel, generator
|
||||
import breakmodel
|
||||
|
||||
n_layers = utils.num_layers(config)
|
||||
|
||||
if utils.args.cpu:
|
||||
breakmodel.gpu_blocks = [0] * n_layers
|
||||
return
|
||||
|
||||
elif breakmodel.gpu_blocks != []:
|
||||
logger.info("Breakmodel not specified, assuming GPU 0")
|
||||
breakmodel.gpu_blocks = [n_layers]
|
||||
n_layers = 0
|
||||
|
||||
logger.init_ok("Final device configuration:", status="Info")
|
||||
self.breakmodel_device_list(n_layers, primary=breakmodel.primary_device)
|
||||
with open("settings/{}.breakmodel".format(self.model_name.replace("/", "_")), "w") as file:
|
||||
file.write("{}\n{}".format(",".join(map(str, breakmodel.gpu_blocks)), breakmodel.disk_blocks))
|
||||
|
||||
# If all layers are on the same device, use the old GPU generation mode
|
||||
while len(breakmodel.gpu_blocks) and breakmodel.gpu_blocks[-1] == 0:
|
||||
breakmodel.gpu_blocks.pop()
|
||||
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")
|
||||
import breakmodel
|
||||
|
||||
breakmodel.primary_device = "cpu"
|
||||
self.breakmodel = False
|
||||
self.usegpu = False
|
||||
return
|
||||
Reference in New Issue
Block a user