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| # Copyright The Caikit Authors | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
|
||
| from enum import Enum | ||
|
|
||
| import os | ||
|
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||
| from caikit import get_config | ||
|
|
||
| from ...resources.pretrained_model import ( | ||
| PretrainedModelBase, | ||
| ) | ||
|
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| from ...toolkit.verbalizer_utils import is_valid_verbalizer, render_verbalizer | ||
| from ...toolkit.data_type_utils import get_torch_dtype, str_to_torch_dtype | ||
|
|
||
| from peft import ( | ||
| MultitaskPromptTuningConfig, | ||
| MultitaskPromptTuningInit, | ||
| ) | ||
|
|
||
| from ...data_model import ( | ||
| PromptOutputModelType, | ||
| ) | ||
| from transformers import ( | ||
| AutoConfig | ||
| ) | ||
|
|
||
| # NOTE: We do not allow all the methods exposed by MPT / PT, such as `EXACT_SOURCE_TASK` | ||
| # since those are for experimental use and would not be useful / applicable | ||
| # for end-user use-cases | ||
| allowed_tuning_init_methods = [ | ||
| "TEXT", | ||
| "RANDOM", | ||
| "ONLY_SOURCE_SHARED", | ||
| "AVERAGE_SOURCE_TASKS", | ||
| ] | ||
|
|
||
|
|
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| class TuningType(str, Enum): | ||
| PROMPT_TUNING = "PROMPT_TUNING" | ||
| MULTITASK_PROMPT_TUNING = "MULTITASK_PROMPT_TUNING" | ||
| # MULTITASK_PREFIX_TUNING = "MULTITASK_PREFIX_TUNING" | ||
| # P_TUNING = "P_TUNING" | ||
| # PREFIX_TUNING = "PREFIX_TUNING" | ||
| # LORA = "LORA" | ||
|
|
||
|
|
||
| def validate_peft_config(tuning_type, | ||
| tuning_config, | ||
| error, | ||
| log, | ||
| base_model, | ||
| cls, | ||
| torch_dtype, | ||
| verbalizer): | ||
| # TODO: Move all of the validation into a separate function | ||
|
|
||
| if tuning_type not in TuningType._member_names_: | ||
| raise NotImplementedError( | ||
| "{} tuning type not supported!".format(tuning_type) | ||
| ) | ||
|
|
||
| if tuning_config.prompt_tuning_init_method: | ||
| # NOTE: GK-APR-5-2023 | ||
| # MultitaskPromptTuningInit and MultitaskPrefixTuningInit are same at the | ||
| # time of writing, which is a superset of PromptTuningInit | ||
| init_method = tuning_config.prompt_tuning_init_method | ||
|
|
||
| error.value_check( | ||
| "<NLP11848053E>", | ||
| init_method in allowed_tuning_init_methods, | ||
| f"Init method [{init_method}] not in allowed init methods: " | ||
| f"[{allowed_tuning_init_methods}]", | ||
| ) | ||
|
|
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| init_method = MultitaskPromptTuningInit(init_method) | ||
| log.info("Using initialization method [%s]", init_method) | ||
|
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||
| # If init method provided relates to one that requires source model, | ||
| # make sure the source prompt model is provided. | ||
| if init_method in [ | ||
| MultitaskPromptTuningInit.AVERAGE_SOURCE_TASKS, | ||
| MultitaskPromptTuningInit.ONLY_SOURCE_SHARED, | ||
| ]: | ||
| # NOTE: prompt_tuning_init_source_model is currently a path. In future | ||
| # we will replace this with caikit.resources to properly cataloging these | ||
| error.type_check( | ||
| "<NLP89108490E>", | ||
| str, | ||
| prompt_tuning_init_source_model=tuning_config.prompt_tuning_init_source_model, | ||
| ) | ||
| tuning_config.prompt_tuning_init_source_model = os.path.join( | ||
| get_config().source_prompt_base, | ||
| tuning_config.prompt_tuning_init_source_model, | ||
| ) | ||
|
|
||
| error.file_check( | ||
| "<NLP96030210E>", tuning_config.prompt_tuning_init_source_model | ||
| ) | ||
| log.debug( | ||
| "Validated tuning source prompt [%s]", | ||
| tuning_config.prompt_tuning_init_source_model, | ||
| ) | ||
|
|
||
| if isinstance(base_model, str): | ||
| model_config = AutoConfig.from_pretrained( | ||
| base_model, local_files_only=not get_config().allow_downloads | ||
| ) | ||
|
|
||
| resource_type = None | ||
| for resource in cls.supported_resources: | ||
| if model_config.model_type in resource.SUPPORTED_MODEL_TYPES: | ||
| resource_type = resource | ||
| break | ||
|
|
||
| if not resource_type: | ||
| error( | ||
| "<NLP61784225E>", | ||
| "{} model type is not supported currently!".format( | ||
| model_config.model_type | ||
| ), | ||
| ) | ||
| log.debug("Bootstrapping base resource [%s]", base_model) | ||
| base_model = resource_type.bootstrap(base_model, torch_dtype=torch_dtype) | ||
| error.type_check("<NLP65714919E>", PretrainedModelBase, base_model=base_model) | ||
|
|
||
| # Validate if tuned output model type is compatible with base model or not | ||
| if not tuning_config.output_model_types: | ||
| output_model_types = base_model.PROMPT_OUTPUT_TYPES | ||
| else: | ||
| # If the first element is not PromptOutputModelType, assume the entire list | ||
| # isn't and convert | ||
| if not isinstance( | ||
| tuning_config.output_model_types[0], PromptOutputModelType | ||
| ): | ||
| output_model_types = [] | ||
| for output_type in tuning_config.output_model_types: | ||
| output_model_types.append(PromptOutputModelType(output_type)) | ||
| else: | ||
| output_model_types = tuning_config.output_model_types | ||
| error.value_check( | ||
| "<NLP36947542E>", | ||
| all( | ||
| output_type in base_model.PROMPT_OUTPUT_TYPES | ||
| for output_type in output_model_types | ||
| ), | ||
| "{} not supported for base model type {}".format( | ||
| output_model_types, base_model.MODEL_TYPE | ||
| ), | ||
| ) | ||
|
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||
| error.value_check( | ||
| "<NLP30542004E>", | ||
| len(output_model_types) <= base_model.MAX_NUM_TRANSFORMERS, | ||
| f"Too many output model types. Got {len(output_model_types)}, " | ||
| f"maximum {base_model.MAX_NUM_TRANSFORMERS}", | ||
| ) | ||
| # Ensure that our verbalizer is a string and will not render to a hardcoded string | ||
| error.value_check( | ||
| "<NLP83837412E>", | ||
| is_valid_verbalizer(verbalizer), | ||
| "Provided verbalizer is an invalid type or has no renderable placeholders", | ||
| ) | ||
|
|
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| # NOTE: Base model is a resource at this point | ||
| task_type = base_model.TASK_TYPE | ||
|
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|
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| if isinstance(tuning_type, str): | ||
| error.value_check( | ||
| "<NLP65714994E>", | ||
| tuning_type in TuningType._member_names_, | ||
| f"Invalid tuning type [{tuning_type}]. Allowed types: " | ||
| f"[{TuningType._member_names_}]", | ||
| ) | ||
| tuning_type = TuningType(tuning_type) | ||
| error.type_check("<NLP65714993E>", TuningType, tuning_type=tuning_type) | ||
|
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||
| # Coerce the passed model into a resource; if we have one, this is a noop | ||
| # TODO: When splitting up this mono-module, use the configured resource | ||
| # type of the concrete class to bootstrap | ||
| torch_dtype = get_torch_dtype(torch_dtype) | ||
|
|
||
| return task_type, output_model_types | ||
|
|
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