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This commit is contained in:
Daniel Han 2025-03-25 23:17:19 -07:00
commit 0a8f92d6f7
2 changed files with 55 additions and 55 deletions
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4
unsloth/models/loader.py
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@ -481,8 +481,8 @@ class FastModel(FastBaseModel):
dtype = torch.float16
assert(dtype in (torch.float16, torch.bfloat16, torch.float32))
# patch_compiled_autograd()
# patch_compiling_bitsandbytes()
patch_compiled_autograd()
patch_compiling_bitsandbytes()
if full_finetuning and (load_in_4bit or load_in_8bit):
print("Unsloth: You selected full finetuning support, but 4bit / 8bit is enabled - disabling LoRA / QLoRA.")

106
unsloth/models/vision.py
View file

@ -362,8 +362,8 @@ class FastBaseModel:
# quantization_config = bnb_config,
token = token,
trust_remote_code = trust_remote_code,
# attn_implementation = attn_implementation,
# **kwargs,
attn_implementation = attn_implementation,
**kwargs,
)
# Return old flag
os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = old_hf_transfer
@ -373,62 +373,62 @@ class FastBaseModel:
auto_processor = AutoProcessor if auto_model is AutoModelForVision2Seq else AutoTokenizer
tokenizer = auto_processor.from_pretrained(
tokenizer_name,
# padding_side = "right",
padding_side = "right",
token = token,
)
# if hasattr(tokenizer, "tokenizer"):
# __tokenizer = tokenizer.tokenizer
# # Add padding side as well
# __tokenizer.padding_side = "right"
# # Check bos, eos, pad tokens
# if hasattr(__tokenizer, "bos_token"):
# tokenizer.bos_token = __tokenizer.bos_token
# tokenizer.bos_token_id = __tokenizer.bos_token_id
# if hasattr(__tokenizer, "eos_token"):
# tokenizer.eos_token = __tokenizer.eos_token
# tokenizer.eos_token_id = __tokenizer.eos_token_id
# if hasattr(__tokenizer, "pad_token"):
# tokenizer.pad_token = __tokenizer.pad_token
# tokenizer.pad_token_id = __tokenizer.pad_token_id
# pass
if hasattr(tokenizer, "tokenizer"):
__tokenizer = tokenizer.tokenizer
# Add padding side as well
__tokenizer.padding_side = "right"
# Check bos, eos, pad tokens
if hasattr(__tokenizer, "bos_token"):
tokenizer.bos_token = __tokenizer.bos_token
tokenizer.bos_token_id = __tokenizer.bos_token_id
if hasattr(__tokenizer, "eos_token"):
tokenizer.eos_token = __tokenizer.eos_token
tokenizer.eos_token_id = __tokenizer.eos_token_id
if hasattr(__tokenizer, "pad_token"):
tokenizer.pad_token = __tokenizer.pad_token
tokenizer.pad_token_id = __tokenizer.pad_token_id
pass
# Fix other stuff like BnB compute data types
# model, tokenizer = patch_model_and_tokenizer(
# model,
# tokenizer,
# downcast_rope = False,
# fix_embeddings = False,
# do_forced_float32 = do_forced_float32,
# )
# model, tokenizer = patch_tokenizer(model, tokenizer)
# model = post_patch_loss_function(model)
model, tokenizer = patch_model_and_tokenizer(
model,
tokenizer,
downcast_rope = False,
fix_embeddings = False,
do_forced_float32 = do_forced_float32,
)
model, tokenizer = patch_tokenizer(model, tokenizer)
model = post_patch_loss_function(model)
# Log Unsloth version for future fastpaths for inference
# if hasattr(model, "config"):
# model.config.update({"unsloth_version" : __version__})
# pass
# patch_saving_functions(model, vision = True)
# patch_saving_functions(tokenizer, vision = True)
if hasattr(model, "config"):
model.config.update({"unsloth_version" : __version__})
pass
patch_saving_functions(model, vision = True)
patch_saving_functions(tokenizer, vision = True)
# Fix gradient accumulation
# from transformers.trainer import Trainer
# patch_gradient_accumulation_fix(Trainer)
from transformers.trainer import Trainer
patch_gradient_accumulation_fix(Trainer)
# Save tokenizer for inference purposes
# tokenizer.padding_side = "left" # Force inference
# if hasattr(tokenizer, "tokenizer"):
# tokenizer.tokenizer.padding_side = "left" # Force inference
# m = model
# while hasattr(m, "model"):
# m.max_seq_length = max_seq_length
# m._saved_temp_tokenizer = tokenizer
# # 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
# m.max_seq_length = max_seq_length
# m._saved_temp_tokenizer = tokenizer
# # Also set is_loaded_in_8bit to disable incorrect DDP
# m.is_loaded_in_8bit = True if not full_finetuning else False
tokenizer.padding_side = "left" # Force inference
if hasattr(tokenizer, "tokenizer"):
tokenizer.tokenizer.padding_side = "left" # Force inference
m = model
while hasattr(m, "model"):
m.max_seq_length = max_seq_length
m._saved_temp_tokenizer = tokenizer
# 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
m.max_seq_length = max_seq_length
m._saved_temp_tokenizer = tokenizer
# Also set is_loaded_in_8bit to disable incorrect DDP
m.is_loaded_in_8bit = True if not full_finetuning else False
# Patch generate
if os.environ.get("UNSLOTH_DISABLE_FAST_GENERATION", "0") == "0":
@ -438,10 +438,10 @@ class FastBaseModel:
model.generate = types.MethodType(unsloth_base_fast_generate, model)
pass
# Post patches
# model = FastBaseModel.post_patch_model(
# model,
# use_gradient_checkpointing = use_gradient_checkpointing,
# )
model = FastBaseModel.post_patch_model(
model,
use_gradient_checkpointing = use_gradient_checkpointing,
)
# Clear deleted GPU items
for _ in range(3):
gc.collect()