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[utils] add merge_lora utility function (#227)

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* add merge_lora utility function

* forward contrib credits from original script

* some changes

* make style

* fix tets

* finally fix tests

* Update tests/test_peft_model.py

* adapt from suggestions

* adapt

* Update src/peft/tuners/lora.py

Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>

* fix 8bit

* Update src/peft/tuners/lora.py

Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>

---------

Co-authored-by: edbeeching <edbeeching@users.noreply.github.com>
Co-authored-by: Sourab Mangrulkar <13534540+pacman100@users.noreply.github.com>
This commit is contained in:
Younes Belkada
2023-03-30 13:45:37 +02:00
committed by GitHub
co-authored by Sourab Mangrulkar edbeeching
parent 542f2470e7
commit 8f63f565c6
3 changed files with 151 additions and 42 deletions
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tests/testing_common.py
+38 -25
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@@ -71,34 +71,47 @@ class ClassInstantier(OrderedDict):
return super().__getitem__(key, *args, **kwargs)
def get_grid_parameters(self, model_list):
def get_grid_parameters(self, grid_parameters, filter_params_func=None):
r"""
Returns a list of all possible combinations of the parameters in the config classes.
"""
grid_parameters = []
for model_tuple in model_list:
model_id, lora_kwargs, prefix_tuning_kwargs, prompt_encoder_kwargs, prompt_tuning_kwargs = model_tuple
for key, value in self.items():
peft_method = value[1].copy()
if key == "lora":
# update value[1] if necessary
if lora_kwargs is not None:
peft_method.update(lora_kwargs)
elif key == "prefix_tuning":
# update value[1] if necessary
if prefix_tuning_kwargs is not None:
peft_method.update(prefix_tuning_kwargs)
elif key == "prompt_encoder":
# update value[1] if necessary
if prompt_encoder_kwargs is not None:
peft_method.update(prompt_encoder_kwargs)
else:
# update value[1] if necessary
if prompt_tuning_kwargs is not None:
peft_method.update(prompt_tuning_kwargs)
grid_parameters.append((f"test_{model_id}_{key}", model_id, value[0], peft_method))
return grid_parameters
Args:
grid_parameters (`dict`):
A dictionary containing the parameters to be tested. There should be at least the key "model_ids" which
contains a list of model ids to be tested. The other keys should be the name of the config class
post-fixed with "_kwargs" and the value should be a dictionary containing the parameters to be tested
for that config class.
filter_params_func (`callable`, `optional`):
A function that takes a list of tuples and returns a list of tuples. This function is used to filter
out the tests that needs for example to be skipped.
Returns:
generated_tests (`list`):
A list of tuples containing the name of the test, the model id, the config class and the config class
kwargs.
"""
generated_tests = []
model_list = grid_parameters["model_ids"]
for model_id in model_list:
for key, value in self.items():
if "{}_kwargs".format(key) in grid_parameters:
peft_configs = []
current_peft_config = value[1].copy()
for current_key, current_value in grid_parameters[f"{key}_kwargs"].items():
for kwarg in current_value:
current_peft_config.update({current_key: kwarg})
peft_configs.append(current_peft_config)
else:
peft_configs = [value[1].copy()]
for peft_config in peft_configs:
generated_tests.append((f"test_{model_id}_{key}", model_id, value[0], peft_config))
if filter_params_func is not None:
generated_tests = filter_params_func(generated_tests)
return generated_tests
PeftTestConfigManager = ClassInstantier(CLASSES_MAPPING)