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FastBaseVisionModel

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This commit is contained in:
Daniel Han 2024-11-21 02:12:45 -08:00
commit 528b7f58af
3 changed files with 37 additions and 75 deletions
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2
unsloth/models/__init__.py
View file

@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from .loader import FastLanguageModel
from .loader import FastLanguageModel, FastVisionModel
from .llama import FastLlamaModel
from .mistral import FastMistralModel
from .qwen2 import FastQwen2Model

77
unsloth/models/loader.py
View file

@ -329,8 +329,10 @@ from ..kernels import (
patch_loss_functions,
post_patch_loss_function,
)
from .vision import FastBaseVisionModel
class FastVisionModel:
class FastVisionModel(FastBaseVisionModel):
@staticmethod
def from_pretrained(
model_name = "unsloth/Llama-3.2-11B-Vision-Instruct-bnb-4bit",
@ -469,71 +471,6 @@ class FastVisionModel:
if not was_disabled: enable_progress_bars()
model_type = model_config.model_type
if model_type == "llama":
scaling_type = None
if getattr(model_config, "rope_scaling", None) is not None:
scaling_type1 = model_config.rope_scaling.get("type", None)
scaling_type2 = model_config.rope_scaling.get("rope_type", None)
scaling_type = scaling_type1 if scaling_type1 is not None else scaling_type2
pass
if scaling_type == "llama3" and not SUPPORTS_LLAMA31:
raise ImportError(
f"Unsloth: Your transformers version of {transformers_version} does not support Llama 3.1.\n"\
f"The minimum required version is 4.43.2\n"\
f'Try `pip install --upgrade "transformers>=4.43.2"`\n'\
f"to obtain the latest transformers build, then restart this session."\
)
dispatch_model = FastLlamaModel
elif model_type == "mistral": dispatch_model = FastMistralModel
elif model_type == "gemma":
if not SUPPORTS_GEMMA:
raise ImportError(
f"Unsloth: Your transformers version of {transformers_version} does not support Gemma.\n"\
f"The minimum required version is 4.38.\n"\
f'Try `pip install --upgrade "transformers>=4.38"`\n'\
f"to obtain the latest transformers build, then restart this session."\
)
dispatch_model = FastGemmaModel
elif model_type == "gemma2":
if not SUPPORTS_GEMMA2:
raise ImportError(
f"Unsloth: Your transformers version of {transformers_version} does not support Gemma2.\n"\
f"The minimum required version is 4.42.3.\n"\
f'Try `pip install --upgrade "transformers>=4.42.3"`\n'\
f"to obtain the latest transformers build, then restart this session."\
)
# Also check for softcapping support in flash-attn which is faster!
if is_bfloat16_supported() and not HAS_FLASH_ATTENTION:
print(
"Unsloth: If you want to finetune Gemma 2, install flash-attn to make it faster!\n"\
"To install flash-attn, do the below:\n"\
'\npip install --no-deps --upgrade "flash-attn>=2.6.3"'
)
elif HAS_FLASH_ATTENTION and not HAS_FLASH_ATTENTION_SOFTCAPPING:
print(
"Unsloth: If you want to finetune Gemma 2, upgrade flash-attn to version 2.6.3 or higher!\n"\
"Newer versions support faster and less memory usage kernels for Gemma 2's attention softcapping!\n"\
"To update flash-attn, do the below:\n"\
'\npip install --no-deps --upgrade "flash-attn>=2.6.3"'
)
dispatch_model = FastGemma2Model
elif model_type == "qwen2":
dispatch_model = FastQwen2Model
elif model_type == "cohere":
dispatch_model = FastCohereModel
else:
raise NotImplementedError(
f"Unsloth: {model_name} not supported yet!\n"\
"Make an issue to https://github.com/unslothai/unsloth!",
)
pass
# Check if this is local model since the tokenizer gets overwritten
if os.path.exists(os.path.join(old_model_name, "tokenizer_config.json")) and \
os.path.exists(os.path.join(old_model_name, "tokenizer.json")) and \
@ -544,17 +481,13 @@ class FastVisionModel:
tokenizer_name = None
pass
model, tokenizer = dispatch_model.from_pretrained(
model, tokenizer = FastBaseVisionModel.from_pretrained(
model_name = model_name,
max_seq_length = max_seq_length,
dtype = _get_dtype(dtype),
load_in_4bit = load_in_4bit,
token = token,
device_map = device_map,
rope_scaling = rope_scaling,
fix_tokenizer = fix_tokenizer,
model_patcher = dispatch_model,
tokenizer_name = tokenizer_name,
trust_remote_code = trust_remote_code,
revision = revision if not is_peft else None,
*args, **kwargs,
@ -604,7 +537,7 @@ class FastVisionModel:
trust_remote_code = trust_remote_code,
)
# Patch it as well!
model = dispatch_model.patch_peft_model(model, use_gradient_checkpointing)
model = FastBaseVisionModel.patch_peft_model(model, use_gradient_checkpointing)
pass
return model, tokenizer
pass

33
unsloth/models/vision.py
View file

@ -31,6 +31,10 @@ from unsloth_zoo.peft_utils import (
merge_and_overwrite_lora,
)
__all__ = [
"FastBaseVisionModel",
]
class FastBaseVisionModel:
@ -234,9 +238,35 @@ class FastBaseVisionModel:
use_gradient_checkpointing = use_gradient_checkpointing,
)
model = get_peft_model(model, lora_config)
model = FastBaseVisionModel.patch_peft_model(model, use_gradient_checkpointing)
# Clear deleted GPU items
for _ in range(3):
gc.collect()
torch.cuda.empty_cache()
pass
patch_saving_functions(model, vision = True)
return model
pass
@staticmethod
def patch_peft_model(
model,
use_gradient_checkpointing = True,
):
if not isinstance(model, PeftModelForCausalLM):
raise TypeError(
"Unsloth: Your model needs to call `.get_peft_model` first!"
)
pass
model = prepare_model_for_kbit_training(
model,
use_gradient_checkpointing = use_gradient_checkpointing,
use_reentrant = True,
)
from transformers.trainer import Trainer
@ -248,6 +278,7 @@ class FastBaseVisionModel:
'Thank you for your understanding and we appreciate it immensely!'
)
pass
patch_saving_functions(model, vision = True)
# Patch tokenizer to pad to the right
internal_model = model
@ -266,8 +297,6 @@ class FastBaseVisionModel:
gc.collect()
torch.cuda.empty_cache()
pass
patch_saving_functions(model, vision = True)
return model
pass