forked from phoenix-oss/llama-stack-mirror
9e6561a1ec
# What does this PR do? The goal of this PR is code base modernization. Schema reflection code needed a minor adjustment to handle UnionTypes and collections.abc.AsyncIterator. (Both are preferred for latest Python releases.) Note to reviewers: almost all changes here are automatically generated by pyupgrade. Some additional unused imports were cleaned up. The only change worth of note can be found under `docs/openapi_generator` and `llama_stack/strong_typing/schema.py` where reflection code was updated to deal with "newer" types. Signed-off-by: Ihar Hrachyshka <ihar.hrachyshka@gmail.com>
100 lines
3.6 KiB
Python
100 lines
3.6 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the terms described in the LICENSE file in
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# the root directory of this source tree.
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import pytest
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from llama_stack.apis.common.job_types import JobStatus
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from llama_stack.apis.post_training import (
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Checkpoint,
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DataConfig,
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LoraFinetuningConfig,
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OptimizerConfig,
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PostTrainingJob,
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PostTrainingJobArtifactsResponse,
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PostTrainingJobStatusResponse,
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TrainingConfig,
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)
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# How to run this test:
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#
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# pytest llama_stack/providers/tests/post_training/test_post_training.py
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# -m "torchtune_post_training_huggingface_datasetio"
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# -v -s --tb=short --disable-warnings
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@pytest.mark.skip(reason="FIXME FIXME @yanxi0830 this needs to be migrated to use the API")
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class TestPostTraining:
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@pytest.mark.asyncio
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async def test_supervised_fine_tune(self, post_training_stack):
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algorithm_config = LoraFinetuningConfig(
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type="LoRA",
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lora_attn_modules=["q_proj", "v_proj", "output_proj"],
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apply_lora_to_mlp=True,
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apply_lora_to_output=False,
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rank=8,
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alpha=16,
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)
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data_config = DataConfig(
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dataset_id="alpaca",
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batch_size=1,
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shuffle=False,
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)
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optimizer_config = OptimizerConfig(
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optimizer_type="adamw",
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lr=3e-4,
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lr_min=3e-5,
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weight_decay=0.1,
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num_warmup_steps=100,
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)
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training_config = TrainingConfig(
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n_epochs=1,
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data_config=data_config,
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optimizer_config=optimizer_config,
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max_steps_per_epoch=1,
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gradient_accumulation_steps=1,
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)
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post_training_impl = post_training_stack
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response = await post_training_impl.supervised_fine_tune(
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job_uuid="1234",
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model="Llama3.2-3B-Instruct",
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algorithm_config=algorithm_config,
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training_config=training_config,
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hyperparam_search_config={},
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logger_config={},
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checkpoint_dir="null",
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)
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assert isinstance(response, PostTrainingJob)
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assert response.job_uuid == "1234"
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@pytest.mark.asyncio
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async def test_get_training_jobs(self, post_training_stack):
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post_training_impl = post_training_stack
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jobs_list = await post_training_impl.get_training_jobs()
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assert isinstance(jobs_list, list)
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assert jobs_list[0].job_uuid == "1234"
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@pytest.mark.asyncio
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async def test_get_training_job_status(self, post_training_stack):
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post_training_impl = post_training_stack
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job_status = await post_training_impl.get_training_job_status("1234")
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assert isinstance(job_status, PostTrainingJobStatusResponse)
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assert job_status.job_uuid == "1234"
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assert job_status.status == JobStatus.completed
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assert isinstance(job_status.checkpoints[0], Checkpoint)
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@pytest.mark.asyncio
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async def test_get_training_job_artifacts(self, post_training_stack):
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post_training_impl = post_training_stack
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job_artifacts = await post_training_impl.get_training_job_artifacts("1234")
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assert isinstance(job_artifacts, PostTrainingJobArtifactsResponse)
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assert job_artifacts.job_uuid == "1234"
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assert isinstance(job_artifacts.checkpoints[0], Checkpoint)
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assert job_artifacts.checkpoints[0].identifier == "Llama3.2-3B-Instruct-sft-0"
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assert job_artifacts.checkpoints[0].epoch == 0
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assert "/.llama/checkpoints/Llama3.2-3B-Instruct-sft-0" in job_artifacts.checkpoints[0].path
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