678 lines
26 KiB
Python
678 lines
26 KiB
Python
# Copyright 2023-present Daniel Han-Chen & the Unsloth team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from transformers import (
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BitsAndBytesConfig,
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AutoProcessor,
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AutoTokenizer,
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AutoModelForCausalLM,
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)
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try:
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from transformers import AutoModelForImageTextToText
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AutoModelForVision2Seq = AutoModelForImageTextToText
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except:
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from transformers import AutoModelForVision2Seq
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pass
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from ..kernels import (
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post_patch_loss_function,
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)
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from ._utils import __version__
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from ._utils import *
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from ..save import patch_saving_functions
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from peft import LoraConfig, TaskType, get_peft_model as _get_peft_model
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from peft import PeftModelForCausalLM
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from transformers import set_seed as transformers_set_seed
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from unsloth_zoo.peft_utils import (
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get_peft_regex,
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SKIP_QUANTIZATION_MODULES,
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requires_grad_for_gradient_checkpointing,
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)
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from transformers.models.llama.modeling_llama import logger
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from transformers import __version__ as transformers_version
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from triton import __version__ as triton_version
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from unsloth_zoo.utils import _get_dtype
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from unsloth_zoo.patching_utils import patch_model_and_tokenizer
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from unsloth_zoo.training_utils import prepare_model_for_training
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import types
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import functools
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import os
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import gc
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import math
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import functools
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from typing import Optional, Tuple, List, Union
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import re, inspect, sys
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import contextlib
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import types
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try:
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from huggingface_hub.utils import get_token
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except:
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# Old HF Hub versions <= 0.0.25
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from huggingface_hub.utils._token import get_token
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pass
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__all__ = [
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"FastBaseModel",
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]
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global FORCE_EAGER_ATTENTION
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FORCE_EAGER_ATTENTION = [
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"pixtral", # Pixtral SDPA not implemented
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]
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global NUM_LOGITS_TO_KEEP
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NUM_LOGITS_TO_KEEP = dict()
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global PROMPT_LOOPKUP
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PROMPT_LOOPKUP = dict()
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from transformers import GenerationConfig, CompileConfig, HybridCache
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_compile_config = CompileConfig(
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fullgraph = False,
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dynamic = None,
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mode = "reduce-overhead",
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)
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_compile_config.disable = True # Must set manually
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from unsloth_zoo.vllm_utils import (
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convert_lora_modules,
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return_lora_modules,
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)
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def unsloth_base_fast_generate(
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self,
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*args,
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**kwargs,
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):
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if len(args) != 0:
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input_ids = args[0]
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elif "input_ids" in kwargs:
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input_ids = kwargs["input_ids"]
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elif "input" in kwargs:
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input_ids = kwargs["input_ids"]
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else:
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raise TypeError("Unsloth: You need to pass in input_ids to .generate!")
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assert(type(input_ids) is torch.Tensor)
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bsz = input_ids.shape[0]
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FastBaseModel.for_inference(self)
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dtype = _get_dtype(self.config.torch_dtype)
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# Check if VLM
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is_vlm = any(
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x.endswith(("ForConditionalGeneration", "ForVisionText2Text"))
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for x in self.config.architectures
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)
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is_vlm = is_vlm or hasattr(self.config, "vision_config")
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arch = self.config.architectures[0]
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# Remove token_type_ids - WRONG for Gemma 3 since bidirectional attention
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# kwargs.pop("token_type_ids", None)
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# VLMs do not allow logits_to_keep
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global NUM_LOGITS_TO_KEEP
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if arch not in NUM_LOGITS_TO_KEEP:
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m = self
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# Find which is needed ie
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# num_logits_to_keep or logits_to_keep
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while hasattr(m, "model"):
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if hasattr(m, "forward"):
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keys = inspect.signature(m.forward).parameters.keys()
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if "num_logits_to_keep" in keys:
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NUM_LOGITS_TO_KEEP[arch] = "num_logits_to_keep"
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break
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elif "logits_to_keep" in keys:
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NUM_LOGITS_TO_KEEP[arch] = "logits_to_keep"
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break
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m = m.model
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pass
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if arch not in NUM_LOGITS_TO_KEEP:
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NUM_LOGITS_TO_KEEP[arch] = None
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pass
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pass
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key = NUM_LOGITS_TO_KEEP[arch]
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if key is not None and key not in kwargs:
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kwargs[key] = 1
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global PROMPT_LOOPKUP
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if arch not in PROMPT_LOOPKUP:
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PROMPT_LOOPKUP[arch] = True
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if bsz == 1 and PROMPT_LOOPKUP[arch]:
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kwargs["prompt_lookup_num_tokens"] = 3
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# Check pad_token
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model_eos_token_id = getattr(self.config, "eos_token_id", None)
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if model_eos_token_id is not None and hasattr(model_eos_token_id, "__iter__"):
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model_eos_token_id = model_eos_token_id[0]
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kwargs["pad_token_id"] = kwargs.pop("pad_token_id", model_eos_token_id)
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# Get pixel values for VLMs
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try: kwargs["pixel_values"] = kwargs["pixel_values"].to(dtype)
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except: pass
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# Mixed precision autocast
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if os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
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autocaster = torch.autocast(device_type = "cuda", dtype = torch.float16)
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dtype = torch.float16
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else:
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autocaster = torch.autocast(device_type = "cuda", dtype = dtype)
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# Prepare LoRA
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# state_dict = convert_lora_modules(self, dtype = dtype)
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# Set compile dynamic shapes
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torch._dynamo.mark_static(input_ids, 0)
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torch._dynamo.mark_dynamic(input_ids, 1)
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if "attention_mask" in kwargs:
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torch._dynamo.mark_static(kwargs["attention_mask"], 0)
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torch._dynamo.mark_dynamic(kwargs["attention_mask"], 1)
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if "token_type_ids" in kwargs:
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torch._dynamo.mark_static(kwargs["token_type_ids"], 0)
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torch._dynamo.mark_dynamic(kwargs["token_type_ids"], 1)
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# Fix generation_config
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# Use hybrid if sliding window seen, otherwise try static
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cache_implementation = getattr(self.config, "cache_implementation", None)
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if getattr(self, "_supports_static_cache", True):
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cache_implementation = "static"
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else:
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cache_implementation = None
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if cache_implementation is not None:
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swa = getattr(getattr(self.config, "text_config", self.config), "sliding_window", None)
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if swa == 0 or type(swa) is not int:
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cache_implementation = "static"
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else:
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cache_implementation = "hybrid"
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if "generation_config" in kwargs:
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kwargs["generation_config"].cache_implementation = cache_implementation
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kwargs["generation_config"].compile_config = _compile_config
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else:
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kwargs["cache_implementation"] = cache_implementation
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kwargs["compile_config"] = _compile_config
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pass
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import pprint
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pprint.pprint(args, kwargs)
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try:
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with torch.inference_mode(), autocaster:
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output = self._old_generate(*args, **kwargs)
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except:
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PROMPT_LOOPKUP[arch] = False
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kwargs.pop("prompt_lookup_num_tokens", None)
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with torch.inference_mode(), autocaster:
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output = self._old_generate(*args, **kwargs)
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finally:
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pass
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# return_lora_modules(self, state_dict, torch.float32)
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pass
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FastBaseModel.for_training(self)
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return output
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pass
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class FastBaseModel:
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@staticmethod
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def from_pretrained(
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model_name = "unsloth/Llama-3.2-1B-Instruct",
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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load_in_8bit = False,
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full_finetuning = False,
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token = None,
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device_map = "sequential",
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trust_remote_code = False,
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model_types = None,
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tokenizer_name = None,
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auto_model = AutoModelForVision2Seq,
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use_gradient_checkpointing = "unsloth",
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**kwargs,
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):
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os.environ["UNSLOTH_USE_NEW_MODEL"] = "1"
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if trust_remote_code:
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print(
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"Unsloth: WARNING `trust_remote_code` is True.\n"\
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"Are you certain you want to do remote code execution?"
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)
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pass
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if token is None: token = get_token()
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SUPPORTS_BFLOAT16 = is_bfloat16_supported()
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gpu_stats = torch.cuda.get_device_properties(0)
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max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
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from importlib.metadata import version as importlib_version
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try: vllm_version = f" vLLM: {importlib_version('vllm')}."
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except: vllm_version = ""
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model_type_arch = model_types[0]
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if model_type_arch == "siglip":
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for model_type_arch in model_types:
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if model_type_arch != "siglip": break
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statistics = \
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f"==((====))== Unsloth {__version__}: Fast {model_type_arch.title()} patching. Transformers: {transformers_version}.{vllm_version}\n"\
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f" {chr(92)}{chr(92)} /| {gpu_stats.name}. Num GPUs = {torch.cuda.device_count()}. Max memory: {max_memory} GB. Platform: {platform_system}.\n"\
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f"O^O/ {chr(92)}_/ {chr(92)} Torch: {torch.__version__}. CUDA: {gpu_stats.major}.{gpu_stats.minor}. CUDA Toolkit: {torch.version.cuda}. Triton: {triton_version}\n"\
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f"{chr(92)} / Bfloat16 = {str(SUPPORTS_BFLOAT16).upper()}. FA [Xformers = {xformers_version}. FA2 = {HAS_FLASH_ATTENTION}]\n"\
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f' "-____-" Free license: http://github.com/unslothai/unsloth'
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print(statistics)
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# Warn about fast transfers
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if "HF_HUB_ENABLE_HF_TRANSFER" in os.environ:
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old_hf_transfer = os.environ["HF_HUB_ENABLE_HF_TRANSFER"]
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if old_hf_transfer in ("False", "false"): old_hf_transfer = "0"
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if old_hf_transfer in ("True", "true" ): old_hf_transfer = "1"
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else:
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old_hf_transfer = "0"
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if old_hf_transfer == "1":
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print("Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!")
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pass
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if old_hf_transfer != "0": os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
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get_statistics() # For debugging - we use a download counter to see if environments are not breaking
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if dtype is None:
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dtype = torch.float16 if not SUPPORTS_BFLOAT16 else torch.bfloat16
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elif os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
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if dtype == torch.float16: dtype = torch.bfloat16
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elif dtype == torch.bfloat16 and not SUPPORTS_BFLOAT16:
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logger.warning_once("Device does not support bfloat16. Will change to float16.")
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dtype = torch.float16
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pass
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assert(dtype in (torch.float16, torch.bfloat16, torch.float32))
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bnb_compute_dtype = dtype
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do_forced_float32 = False
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if os.environ.get("UNSLOTH_FORCE_FLOAT32", "0") == "1":
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print(f"Unsloth: Using float16 precision for {model_type_arch} won't work! Using float32.")
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bnb_compute_dtype = torch.float16
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do_forced_float32 = True
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pass
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global FORCE_EAGER_ATTENTION
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attn_implementation = "sdpa"
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for disable_name in FORCE_EAGER_ATTENTION:
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if (disable_name.lower() == model_type_arch.lower() or \
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disable_name.lower() in model_name.lower()):
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print(f"Unsloth: {model_type_arch} does not support SDPA - switching to eager!")
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attn_implementation = "eager"
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break
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pass
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bnb_config = None
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if full_finetuning and (load_in_4bit or load_in_8bit):
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print("Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA.")
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load_in_4bit = False
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load_in_8bit = False
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pass
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if load_in_4bit and load_in_8bit:
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raise RuntimeError("Unsloth: Can only load in 4bit or 8bit, not both!")
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if load_in_4bit:
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bnb_config = BitsAndBytesConfig(
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load_in_4bit = True,
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bnb_4bit_use_double_quant = True,
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bnb_4bit_quant_type = "nf4",
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bnb_4bit_compute_dtype = bnb_compute_dtype,
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llm_int8_skip_modules = SKIP_QUANTIZATION_MODULES.copy(),
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)
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elif load_in_8bit:
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bnb_config = BitsAndBytesConfig(
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load_in_8bit = True,
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llm_int8_skip_modules = SKIP_QUANTIZATION_MODULES.copy(),
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)
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elif not load_in_4bit and not load_in_8bit and not full_finetuning:
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print("Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.")
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pass
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if full_finetuning:
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os.environ["UNSLOTH_ENABLE_FULL_FINETUNING"] = "1"
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if dtype == torch.bfloat16:
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print("Unsloth: Using bfloat16 full finetuning which cuts memory usage by 50%.")
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else:
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print("Unsloth: Float16 full finetuning uses more memory since we upcast weights to float32.")
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else:
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os.environ["UNSLOTH_ENABLE_FULL_FINETUNING"] = "0"
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pass
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kwargs.pop("attn_implementation", None); # No need since we auto call it
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# Cannot be None, since HF now checks for the config
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if load_in_4bit: kwargs["quantization_config"] = bnb_config
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# Check if using forced float32 - we load it in bfloat16, then cast to float16!
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torch_dtype = dtype
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if do_forced_float32: torch_dtype = torch.bfloat16
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model = auto_model.from_pretrained(
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model_name,
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device_map = device_map,
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torch_dtype = torch_dtype,
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# quantization_config = bnb_config,
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token = token,
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trust_remote_code = trust_remote_code,
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attn_implementation = attn_implementation,
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**kwargs,
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)
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# Return old flag
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os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
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# Counteract saved tokenizers
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tokenizer_name = model_name if tokenizer_name is None else tokenizer_name
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auto_processor = AutoProcessor if auto_model is AutoModelForVision2Seq else AutoTokenizer
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tokenizer = auto_processor.from_pretrained(
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tokenizer_name,
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padding_side = "right",
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token = token,
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)
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if hasattr(tokenizer, "tokenizer"):
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__tokenizer = tokenizer.tokenizer
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# Add padding side as well
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__tokenizer.padding_side = "right"
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# Check bos, eos, pad tokens
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if hasattr(__tokenizer, "bos_token"):
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tokenizer.bos_token = __tokenizer.bos_token
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tokenizer.bos_token_id = __tokenizer.bos_token_id
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if hasattr(__tokenizer, "eos_token"):
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tokenizer.eos_token = __tokenizer.eos_token
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tokenizer.eos_token_id = __tokenizer.eos_token_id
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if hasattr(__tokenizer, "pad_token"):
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tokenizer.pad_token = __tokenizer.pad_token
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tokenizer.pad_token_id = __tokenizer.pad_token_id
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pass
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# Fix other stuff like BnB compute data types
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model, tokenizer = patch_model_and_tokenizer(
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model,
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tokenizer,
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downcast_rope = False,
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fix_embeddings = False,
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do_forced_float32 = do_forced_float32,
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)
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model, tokenizer = patch_tokenizer(model, tokenizer)
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model = post_patch_loss_function(model)
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# Log Unsloth version for future fastpaths for inference
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if hasattr(model, "config"):
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model.config.update({"unsloth_version" : __version__})
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pass
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patch_saving_functions(model, vision = True)
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patch_saving_functions(tokenizer, vision = True)
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# Fix gradient accumulation
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from transformers.trainer import Trainer
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patch_gradient_accumulation_fix(Trainer)
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# Save tokenizer for inference purposes
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tokenizer.padding_side = "left" # Force inference
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if hasattr(tokenizer, "tokenizer"):
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tokenizer.tokenizer.padding_side = "left" # Force inference
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m = model
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while hasattr(m, "model"):
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m.max_seq_length = max_seq_length
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m._saved_temp_tokenizer = tokenizer
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# Also set is_loaded_in_8bit to disable incorrect DDP
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m.is_loaded_in_8bit = True if not full_finetuning else False
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m = m.model
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pass
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m.max_seq_length = max_seq_length
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m._saved_temp_tokenizer = tokenizer
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# Also set is_loaded_in_8bit to disable incorrect DDP
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m.is_loaded_in_8bit = True if not full_finetuning else False
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# Patch generate
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if os.environ.get("UNSLOTH_DISABLE_FAST_GENERATION", "0") == "0":
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if model.generate.__name__ != "unsloth_base_fast_generate":
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model._old_generate = model.generate
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unsloth_base_fast_generate.__doc__ = model._old_generate.__doc__
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model.generate = types.MethodType(unsloth_base_fast_generate, model)
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pass
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# Post patches
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model = FastBaseModel.post_patch_model(
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model,
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use_gradient_checkpointing = use_gradient_checkpointing,
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)
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# Clear deleted GPU items
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for _ in range(3):
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gc.collect()
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torch.cuda.empty_cache()
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pass
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return model, tokenizer
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pass
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@staticmethod
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def get_peft_model(
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model,
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r = 16,
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target_modules = None,
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lora_alpha = 16,
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|
lora_dropout = 0,
|
|
bias = "none",
|
|
finetune_vision_layers = True,
|
|
finetune_language_layers = True,
|
|
finetune_attention_modules = True,
|
|
finetune_mlp_modules = True,
|
|
layers_to_transform = None,
|
|
layers_pattern = None,
|
|
use_gradient_checkpointing = True,
|
|
random_state = 3407,
|
|
max_seq_length = 2048, # not used anymore
|
|
use_rslora = False,
|
|
modules_to_save = None,
|
|
init_lora_weights = True,
|
|
loftq_config = {},
|
|
temporary_location = "_unsloth_temporary_saved_buffers",
|
|
**kwargs,
|
|
):
|
|
if os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1":
|
|
print("Unsloth: Full finetuning is enabled, so .get_peft_model has no effect")
|
|
return model
|
|
pass
|
|
transformers_set_seed(random_state)
|
|
|
|
if type(r) is not int:
|
|
raise TypeError(f"Unsloth: Rank of {str(r)} must be an integer.")
|
|
if r <= 0:
|
|
raise TypeError(f"Unsloth: Rank of {str(r)} must be larger than 0.")
|
|
|
|
if isinstance(model, PeftModelForCausalLM):
|
|
raise RuntimeError("Unsloth: You already added LoRA adapters to your model!")
|
|
|
|
if target_modules == "all-linear":
|
|
finetune_vision_layers = True
|
|
finetune_language_layers = True
|
|
finetune_attention_modules = True
|
|
finetune_mlp_modules = True
|
|
pass
|
|
if target_modules is None:
|
|
target_modules = get_peft_regex(
|
|
model,
|
|
finetune_vision_layers = finetune_vision_layers,
|
|
finetune_language_layers = finetune_language_layers,
|
|
finetune_attention_modules = finetune_attention_modules,
|
|
finetune_mlp_modules = finetune_mlp_modules,
|
|
)
|
|
else:
|
|
assert(type(target_modules) in (list, tuple,))
|
|
pass
|
|
|
|
# Clear deleted GPU items
|
|
for _ in range(3):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
pass
|
|
max_seq_length = model.max_seq_length
|
|
lora_config = LoraConfig(
|
|
r = r,
|
|
lora_alpha = lora_alpha,
|
|
target_modules = target_modules,
|
|
lora_dropout = lora_dropout,
|
|
bias = bias,
|
|
task_type = TaskType.CAUSAL_LM,
|
|
)
|
|
model = prepare_model_for_kbit_training(
|
|
model,
|
|
use_gradient_checkpointing = use_gradient_checkpointing,
|
|
)
|
|
model = _get_peft_model(model, lora_config)
|
|
# Enable gradients on modules which are trainable
|
|
requires_grad_for_gradient_checkpointing(model)
|
|
|
|
model = FastBaseModel.post_patch_model(model, use_gradient_checkpointing)
|
|
model.max_seq_length = max_seq_length
|
|
|
|
# Clear deleted GPU items
|
|
for _ in range(3):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
pass
|
|
patch_saving_functions(model, vision = True)
|
|
|
|
# Add for_inference and for_training
|
|
model.for_training = functools.partial(FastBaseModel.for_training, model)
|
|
model.for_inference = functools.partial(FastBaseModel.for_inference, model)
|
|
return model
|
|
pass
|
|
|
|
|
|
@staticmethod
|
|
def post_patch_model(
|
|
model,
|
|
use_gradient_checkpointing = True,
|
|
):
|
|
full_finetuning = os.environ.get("UNSLOTH_ENABLE_FULL_FINETUNING", "0") == "1"
|
|
|
|
float32_mixed_precision = True
|
|
if _get_dtype(model.config.torch_dtype) == torch.bfloat16 and full_finetuning:
|
|
# Use bfloat16 precision for full finetuning
|
|
float32_mixed_precision = False
|
|
|
|
model = prepare_model_for_training(
|
|
model,
|
|
use_gradient_checkpointing = use_gradient_checkpointing,
|
|
use_reentrant = True,
|
|
full_finetuning = full_finetuning,
|
|
train_layernorms = full_finetuning,
|
|
train_embedding = full_finetuning,
|
|
train_lm_head = full_finetuning,
|
|
float32_mixed_precision = float32_mixed_precision,
|
|
)
|
|
|
|
from transformers.trainer import Trainer
|
|
if Trainer._inner_training_loop.__name__ != "_fast_inner_training_loop":
|
|
raise RuntimeError('Unsloth: Unsuccessfully patched inner_training_loop')
|
|
pass
|
|
patch_saving_functions(model, vision = True)
|
|
|
|
# Patch tokenizer to pad to the right
|
|
m = model
|
|
while hasattr(m, "model"):
|
|
if hasattr(m, "_saved_temp_tokenizer"):
|
|
if hasattr(m._saved_temp_tokenizer, "tokenizer"):
|
|
m._saved_temp_tokenizer.tokenizer.padding_side = "right"
|
|
pass
|
|
# Also set is_loaded_in_8bit to disable incorrect DDP
|
|
m.is_loaded_in_8bit = True if not full_finetuning else False
|
|
m = m.model
|
|
pass
|
|
if hasattr(m, "_saved_temp_tokenizer"):
|
|
if hasattr(m._saved_temp_tokenizer, "tokenizer"):
|
|
m._saved_temp_tokenizer.tokenizer.padding_side = "right"
|
|
pass
|
|
# Also set is_loaded_in_8bit to disable incorrect DDP
|
|
m.is_loaded_in_8bit = True if not full_finetuning else False
|
|
|
|
# Clear deleted GPU items
|
|
for _ in range(3):
|
|
gc.collect()
|
|
torch.cuda.empty_cache()
|
|
pass
|
|
# Add for_inference and for_training
|
|
model.for_training = functools.partial(FastBaseModel.for_training, model)
|
|
model.for_inference = functools.partial(FastBaseModel.for_inference, model)
|
|
return model
|
|
pass
|
|
|
|
|
|
@staticmethod
|
|
def for_inference(model):
|
|
if not hasattr(model, "parameters"):
|
|
raise TypeError("Unsloth: I think you're passing a tokenizer, not the model to for_inference!")
|
|
|
|
def _for_inference(m):
|
|
if hasattr(m, "gradient_checkpointing"): m.gradient_checkpointing = False
|
|
if hasattr(m, "training"): m.training = False
|
|
# Pad tokenizer to the left
|
|
if hasattr(m, "_saved_temp_tokenizer"): m._saved_temp_tokenizer.padding_side = "left"
|
|
# Set a flag for generation!
|
|
m._flag_for_generation = True
|
|
pass
|
|
m = model
|
|
while hasattr(m, "model"):
|
|
_for_inference(m)
|
|
m = m.model
|
|
_for_inference(m)
|
|
|
|
# Also disable training for embeddings for NEFTune
|
|
if hasattr(model, "get_input_embeddings"):
|
|
embeddings = model.get_input_embeddings()
|
|
if hasattr(embeddings, "training"): embeddings.training = False
|
|
pass
|
|
if hasattr(model, "get_output_embeddings"):
|
|
embeddings = model.get_output_embeddings()
|
|
if hasattr(embeddings, "training"): embeddings.training = False
|
|
pass
|
|
return model
|
|
pass
|
|
|
|
|
|
@staticmethod
|
|
def for_training(model, use_gradient_checkpointing = True):
|
|
if not hasattr(model, "parameters"):
|
|
raise TypeError("Unsloth: I think you're passing a tokenizer, not the model to for_training!")
|
|
|
|
# Delete all fast inference loras
|
|
for param in model.parameters():
|
|
if hasattr(param, "_fast_lora"):
|
|
del param._fast_lora
|
|
pass
|
|
|
|
def _for_training(m):
|
|
if hasattr(m, "gradient_checkpointing"): m.gradient_checkpointing = use_gradient_checkpointing
|
|
if hasattr(m, "training"): m.training = True
|
|
# Pad tokenizer to the left
|
|
if hasattr(m, "_saved_temp_tokenizer"): m._saved_temp_tokenizer.padding_side = "right"
|
|
# Set a flag for generation!
|
|
if hasattr(m, "_flag_for_generation"): del m._flag_for_generation
|
|
pass
|
|
m = model
|
|
while hasattr(m, "model"):
|
|
_for_training(m)
|
|
m = m.model
|
|
_for_training(m)
|
|
|
|
# Also re-enable training for embeddings for NEFTune
|
|
if hasattr(model, "get_input_embeddings"):
|
|
embeddings = model.get_input_embeddings()
|
|
if hasattr(embeddings, "training"): embeddings.training = True
|
|
pass
|
|
if hasattr(model, "get_output_embeddings"):
|
|
embeddings = model.get_output_embeddings()
|
|
if hasattr(embeddings, "training"): embeddings.training = True
|
|
pass
|
|
return model
|
|
pass
|
|
pass
|