ceos/unsloth
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions

Allow bf16 full finetuning for FORCE_FLOAT32 models on bf16 GPUs

Browse source 
FORCE_FLOAT32 models (Gemma3, gpt_oss, qwen3_5) cannot use float16. Forcing
float32 is required on GPUs without bf16 (V100/T4) so they never autocast to
fp16, but on a bf16-capable GPU full finetuning can use bf16 autocast (the
master weights stay float32), which is faster and uses less memory. Gate the
force-float32 switch to skip full finetuning when bf16 is supported; LoRA/QLoRA
still go to float32 when forced.
This commit is contained in:
Daniel Han 2026-05-31 13:53:37 +00:00
commit e0becf489f
1 changed files with 12 additions and 9 deletions
Download patch file Download diff file Expand all files Collapse all files

21
unsloth/models/rl.py
View file

@ -1027,21 +1027,24 @@ def _patch_trl_rl_trainers_impl(trainer_file = "grpo_trainer"):
"use_fp16 = getattr(args, 'fp16', False)\n"
"if type(use_fp16) is not bool: use_fp16 = False\n"
"force_float32 = False\n"
# FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) cannot use float16; keep
# them in float32 even for full finetuning so V100/T4 never autocast to fp16.
"if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1':\n"
" print('Unsloth: Switching to float32 training since model cannot work with float16')\n"
" force_float32 = True\n"
"mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')\n"
"dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)\n"
"if dtype is None: dtype = model.get_input_embeddings().weight.dtype\n"
"from unsloth_zoo.utils import _get_dtype\n"
# device-aware bf16 check (CUDA/XPU/HIP), so V100/T4 never pick bf16
# but AMD/Intel are unaffected; fall back on older unsloth_zoo.
"try:\n"
" from unsloth_zoo.device_type import device_is_bf16_supported as _bf16_supported\n"
"except Exception:\n"
" _bf16_supported = torch.cuda.is_bf16_supported\n"
# FORCE_FLOAT32 models (Gemma3, gpt_oss, ...) cannot use float16. On a GPU without
# bf16 (V100/T4) keep them in float32 so they never autocast to fp16. On a bf16 GPU,
# full finetuning can still use bf16 autocast (master weights stay float32), which is
# faster and uses less memory; LoRA/QLoRA keep float32 when forced.
"full_finetuning = os.environ.get('UNSLOTH_ENABLE_FULL_FINETUNING', '0') == '1'\n"
"if os.environ.get('UNSLOTH_FORCE_FLOAT32', '0') == '1' and not (full_finetuning and _bf16_supported()):\n"
" print('Unsloth: Switching to float32 training since model cannot work with float16')\n"
" force_float32 = True\n"
"mixed_precision_dtype = os.environ.get('UNSLOTH_MIXED_PRECISION', 'float32')\n"
"dtype = getattr(model.config, 'dtype', None) or getattr(model.config, 'torch_dtype', None)\n"
"if dtype is None: dtype = model.get_input_embeddings().weight.dtype\n"
"from unsloth_zoo.utils import _get_dtype\n"
"dtype = _get_dtype(dtype)\n"
"float16 = dtype == torch.float16\n"
"bfloat16 = dtype == torch.bfloat16\n"