tests/collections/llm/recipes/test_gemma2_9b.py

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#
# 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
#
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import nemo_run as run
import pytest

from nemo.collections.llm.api import finetune, pretrain
from nemo.collections.llm.gpt.data.mock import MockDataModule
from nemo.collections.llm.gpt.data.squad import SquadDataModule
from nemo.collections.llm.peft.lora import LoRA
from nemo.collections.llm.recipes import gemma2_9b
from nemo.lightning import Trainer


class TestGemma2_9B:
    @pytest.fixture(scope="class")
    def recipe_module(self):
        return gemma2_9b

    def test_model(self, recipe_module):
        model_config = recipe_module.model()
        assert isinstance(model_config, run.Config)
        # Note: Actual model class assertions would depend on gemma2_model implementation

    def test_pretrain_recipe(self, recipe_module):
        recipe = recipe_module.pretrain_recipe()
        assert isinstance(recipe, run.Partial)
        assert recipe.__fn_or_cls__ == pretrain
        assert isinstance(recipe.trainer, run.Config)
        assert recipe.trainer.__fn_or_cls__ == Trainer
        assert isinstance(recipe.data, run.Config)
        assert recipe.data.__fn_or_cls__ == MockDataModule

        # Check default parallelism settings
        assert recipe.trainer.strategy.tensor_model_parallel_size == 8
        assert recipe.trainer.strategy.pipeline_model_parallel_size == 1
        assert recipe.trainer.strategy.sequence_parallel is False

        # Check training configurations
        assert recipe.trainer.max_steps == 1168251
        assert recipe.trainer.accumulate_grad_batches == 1

    def test_finetune_recipe(self, recipe_module):
        recipe = recipe_module.finetune_recipe()
        assert isinstance(recipe, run.Partial)
        assert recipe.__fn_or_cls__ == finetune
        assert isinstance(recipe.trainer, run.Config)
        assert recipe.trainer.__fn_or_cls__ == Trainer
        assert isinstance(recipe.data, run.Config)
        assert recipe.data.__fn_or_cls__ == SquadDataModule
        assert recipe.data.dataset_kwargs == {'add_bos': True}
        assert isinstance(recipe.peft, run.Config)
        assert recipe.peft.__fn_or_cls__ == LoRA
        assert recipe.optim.config.lr == 1e-4

    @pytest.mark.parametrize("num_nodes,num_gpus_per_node", [(1, 8), (2, 4), (4, 2)])
    def test_pretrain_recipe_with_different_configurations(self, recipe_module, num_nodes, num_gpus_per_node):
        recipe = recipe_module.pretrain_recipe(num_nodes=num_nodes, num_gpus_per_node=num_gpus_per_node)
        assert recipe.trainer.num_nodes == num_nodes
        assert recipe.trainer.devices == num_gpus_per_node

    def test_finetune_recipe_without_peft(self, recipe_module):
        recipe = recipe_module.finetune_recipe(peft_scheme=None)
        assert recipe.trainer.strategy.tensor_model_parallel_size == 4
        assert recipe.optim.config.lr == 5e-6
        assert not hasattr(recipe, 'peft') or recipe.peft is None

    def test_finetune_recipe_with_invalid_peft(self, recipe_module):
        with pytest.raises(ValueError, match="Unrecognized peft scheme: invalid_scheme"):
            recipe_module.finetune_recipe(peft_scheme="invalid_scheme")