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GAMA / peft /tests /test_encoder_decoder_models.py

bug fix

fa57c60 over 2 years ago

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# coding=utf-8
# Copyright 2023-present the HuggingFace Inc. team.
#
# 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.
import unittest

import torch
from parameterized import parameterized
from transformers import AutoModelForSeq2SeqLM

from .testing_common import PeftCommonTester, PeftTestConfigManager


PEFT_ENCODER_DECODER_MODELS_TO_TEST = [
    "ybelkada/tiny-random-T5ForConditionalGeneration-calibrated",
    "hf-internal-testing/tiny-random-BartForConditionalGeneration",
]

FULL_GRID = {"model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST, "task_type": "SEQ_2_SEQ_LM"}


def skip_non_lora_or_pt(test_list):
    r"""
    Skip tests that are not lora or prefix tuning
    """
    return [test for test in test_list if ("lora" in test[0] or "prefix_tuning" in test[0])]


class PeftEncoderDecoderModelTester(unittest.TestCase, PeftCommonTester):
    r"""
    Test if the PeftModel behaves as expected. This includes:
    - test if the model has the expected methods
    We use parametrized.expand for debugging purposes to test each model individually.
    """
    transformers_class = AutoModelForSeq2SeqLM

    def prepare_inputs_for_testing(self):
        input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
        decoder_input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
        attention_mask = torch.tensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)

        input_dict = {
            "input_ids": input_ids,
            "decoder_input_ids": decoder_input_ids,
            "attention_mask": attention_mask,
        }

        return input_dict

    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
    def test_attributes_parametrized(self, test_name, model_id, config_cls, config_kwargs):
        self._test_model_attr(model_id, config_cls, config_kwargs)

    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
    def test_prepare_for_training_parametrized(self, test_name, model_id, config_cls, config_kwargs):
        self._test_prepare_for_training(model_id, config_cls, config_kwargs)

    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
    def test_save_pretrained(self, test_name, model_id, config_cls, config_kwargs):
        self._test_save_pretrained(model_id, config_cls, config_kwargs)

    @parameterized.expand(
        PeftTestConfigManager.get_grid_parameters(
            {
                "model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST,
                "lora_kwargs": {"init_lora_weights": [False]},
                "task_type": "SEQ_2_SEQ_LM",
            },
        )
    )
    def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
        self._test_merge_layers(model_id, config_cls, config_kwargs)

    # skip non lora models - generate does not work for prefix tuning, prompt tuning
    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_non_lora_or_pt))
    def test_generate(self, test_name, model_id, config_cls, config_kwargs):
        self._test_generate(model_id, config_cls, config_kwargs)

    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
    def test_generate_half_prec(self, test_name, model_id, config_cls, config_kwargs):
        self._test_generate_half_prec(model_id, config_cls, config_kwargs)

    @parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
    def test_training_encoder_decoders(self, test_name, model_id, config_cls, config_kwargs):
        self._test_training(model_id, config_cls, config_kwargs)

	# coding=utf-8
	# Copyright 2023-present the HuggingFace Inc. team.
	#
	# 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.
	import unittest

	import torch
	from parameterized import parameterized
	from transformers import AutoModelForSeq2SeqLM

	from .testing_common import PeftCommonTester, PeftTestConfigManager


	PEFT_ENCODER_DECODER_MODELS_TO_TEST = [
	"ybelkada/tiny-random-T5ForConditionalGeneration-calibrated",
	"hf-internal-testing/tiny-random-BartForConditionalGeneration",
	]

	FULL_GRID = {"model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST, "task_type": "SEQ_2_SEQ_LM"}


	def skip_non_lora_or_pt(test_list):
	r"""
	Skip tests that are not lora or prefix tuning
	"""
	return [test for test in test_list if ("lora" in test[0] or "prefix_tuning" in test[0])]


	class PeftEncoderDecoderModelTester(unittest.TestCase, PeftCommonTester):
	r"""
	Test if the PeftModel behaves as expected. This includes:
	- test if the model has the expected methods

	We use parametrized.expand for debugging purposes to test each model individually.
	"""
	transformers_class = AutoModelForSeq2SeqLM

	def prepare_inputs_for_testing(self):
	input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
	decoder_input_ids = torch.tensor([[1, 1, 1], [1, 2, 1]]).to(self.torch_device)
	attention_mask = torch.tensor([[1, 1, 1], [1, 0, 1]]).to(self.torch_device)

	input_dict = {
	"input_ids": input_ids,
	"decoder_input_ids": decoder_input_ids,
	"attention_mask": attention_mask,
	}

	return input_dict

	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
	def test_attributes_parametrized(self, test_name, model_id, config_cls, config_kwargs):
	self._test_model_attr(model_id, config_cls, config_kwargs)

	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
	def test_prepare_for_training_parametrized(self, test_name, model_id, config_cls, config_kwargs):
	self._test_prepare_for_training(model_id, config_cls, config_kwargs)

	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
	def test_save_pretrained(self, test_name, model_id, config_cls, config_kwargs):
	self._test_save_pretrained(model_id, config_cls, config_kwargs)

	@parameterized.expand(
	PeftTestConfigManager.get_grid_parameters(
	{
	"model_ids": PEFT_ENCODER_DECODER_MODELS_TO_TEST,
	"lora_kwargs": {"init_lora_weights": [False]},
	"task_type": "SEQ_2_SEQ_LM",
	},
	)
	)
	def test_merge_layers(self, test_name, model_id, config_cls, config_kwargs):
	self._test_merge_layers(model_id, config_cls, config_kwargs)

	# skip non lora models - generate does not work for prefix tuning, prompt tuning
	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID, filter_params_func=skip_non_lora_or_pt))
	def test_generate(self, test_name, model_id, config_cls, config_kwargs):
	self._test_generate(model_id, config_cls, config_kwargs)

	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
	def test_generate_half_prec(self, test_name, model_id, config_cls, config_kwargs):
	self._test_generate_half_prec(model_id, config_cls, config_kwargs)

	@parameterized.expand(PeftTestConfigManager.get_grid_parameters(FULL_GRID))
	def test_training_encoder_decoders(self, test_name, model_id, config_cls, config_kwargs):
	self._test_training(model_id, config_cls, config_kwargs)