mirror of
https://github.com/meta-llama/llama-stack.git
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381 lines
15 KiB
Python
381 lines
15 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 os
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import unittest
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from unittest.mock import AsyncMock, MagicMock, patch
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import warnings
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import pytest
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from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig, QatFinetuningConfig
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from llama_stack_client.types.post_training.job_status_response import JobStatusResponse
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from llama_stack_client.types.post_training_job import PostTrainingJob
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from llama_stack_client.types.post_training_supervised_fine_tune_params import (
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TrainingConfig,
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TrainingConfigDataConfig,
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TrainingConfigOptimizerConfig,
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)
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from llama_stack.distribution.library_client import LlamaStackAsLibraryClient
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from .mock_llama_stack_client import MockLlamaStackClient
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class TestNvidiaPostTraining(unittest.TestCase):
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# ToDo: add tests for env variables, models supported.
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def setUp(self):
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os.environ["NVIDIA_BASE_URL"] = "http://nemo.test" # needed for llm inference
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os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test" # needed for nemo customizer
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os.environ["LLAMA_STACK_BASE_URL"] = "http://localhost:5002" # mocking llama stack base url
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with patch("llama_stack.distribution.library_client.LlamaStackAsLibraryClient", MockLlamaStackClient):
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self.llama_stack_client = LlamaStackAsLibraryClient("nvidia")
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_ = self.llama_stack_client.initialize()
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def _assert_request(self, mock_call, expected_method, expected_path, expected_params=None, expected_json=None):
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"""Helper method to verify request details in mock calls."""
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call_args = mock_call.call_args
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if expected_method and expected_path:
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if isinstance(call_args[0], tuple) and len(call_args[0]) == 2:
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assert call_args[0] == (expected_method, expected_path)
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else:
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assert call_args[1]["method"] == expected_method
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assert call_args[1]["path"] == expected_path
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if expected_params:
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assert call_args[1]["params"] == expected_params
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if expected_json:
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for key, value in expected_json.items():
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assert call_args[1]["json"][key] == value
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@patch("llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request")
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def test_supervised_fine_tune(self, mock_make_request):
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"""Test the supervised fine-tuning API call.
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ToDo: add tests for env variables."""
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mock_make_request.return_value = {
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"id": "cust-JGTaMbJMdqjJU8WbQdN9Q2",
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"created_at": "2024-12-09T04:06:28.542884",
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"updated_at": "2024-12-09T04:06:28.542884",
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"config": {
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"schema_version": "1.0",
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"id": "af783f5b-d985-4e5b-bbb7-f9eec39cc0b1",
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"created_at": "2024-12-09T04:06:28.542657",
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"updated_at": "2024-12-09T04:06:28.569837",
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"custom_fields": {},
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"name": "meta-llama/Llama-3.1-8B-Instruct",
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"base_model": "meta-llama/Llama-3.1-8B-Instruct",
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"model_path": "llama-3_1-8b-instruct",
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"training_types": [],
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"finetuning_types": ["lora"],
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"precision": "bf16",
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"num_gpus": 4,
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"num_nodes": 1,
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"micro_batch_size": 1,
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"tensor_parallel_size": 1,
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"max_seq_length": 4096,
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},
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"dataset": {
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"schema_version": "1.0",
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"id": "dataset-XU4pvGzr5tvawnbVxeJMTb",
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"created_at": "2024-12-09T04:06:28.542657",
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"updated_at": "2024-12-09T04:06:28.542660",
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"custom_fields": {},
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"name": "sample-basic-test",
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"version_id": "main",
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"version_tags": [],
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},
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"hyperparameters": {
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"finetuning_type": "lora",
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"training_type": "sft",
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"batch_size": 16,
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"epochs": 2,
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"learning_rate": 0.0001,
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"lora": {"adapter_dim": 16, "adapter_dropout": 0.1},
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},
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"output_model": "default/job-1234",
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"status": "created",
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"project": "default",
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"custom_fields": {},
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"ownership": {"created_by": "me", "access_policies": {}},
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}
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algorithm_config = LoraFinetuningConfig(
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type="LoRA",
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adapter_dim=16,
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adapter_dropout=0.1,
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apply_lora_to_mlp=True,
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apply_lora_to_output=True,
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alpha=16,
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rank=16,
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lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
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)
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data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
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optimizer_config = TrainingConfigOptimizerConfig(
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lr=0.0001,
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)
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training_config = TrainingConfig(
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n_epochs=2,
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data_config=data_config,
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optimizer_config=optimizer_config,
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)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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training_job = self.llama_stack_client.post_training.supervised_fine_tune(
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job_uuid="1234",
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model="meta-llama/Llama-3.1-8B-Instruct",
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checkpoint_dir="",
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algorithm_config=algorithm_config,
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training_config=training_config,
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logger_config={},
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hyperparam_search_config={},
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)
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# required lora config unsupported parameters warnings
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fields = [
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"apply_lora_to_mlp",
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"rank",
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"use_dora",
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"lora_attn_modules",
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"quantize_base",
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"apply_lora_to_output",
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]
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for field in fields:
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assert any(field in str(warning.message) for warning in w)
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# check the output is a PostTrainingJob
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# Note: Although the type is PostTrainingJob: llama_stack.apis.post_training.PostTrainingJob,
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# post llama_stack_client initialization it gets translated to llama_stack_client.types.post_training_job.PostTrainingJob
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assert isinstance(training_job, PostTrainingJob)
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assert training_job.job_uuid == "cust-JGTaMbJMdqjJU8WbQdN9Q2"
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mock_make_request.assert_called_once()
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self._assert_request(
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mock_make_request,
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"POST",
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"/v1/customization/jobs",
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expected_json={
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"config": "meta/llama-3.1-8b-instruct",
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"dataset": {"name": "sample-basic-test", "namespace": "default"},
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"hyperparameters": {
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"training_type": "sft",
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"finetuning_type": "lora",
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"epochs": 2,
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"batch_size": 16,
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"learning_rate": 0.0001,
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"lora": {"alpha": 16, "adapter_dim": 16, "adapter_dropout": 0.1},
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},
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},
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)
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def test_supervised_fine_tune_with_qat(self):
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algorithm_config = QatFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
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data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
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optimizer_config = TrainingConfigOptimizerConfig(
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lr=0.0001,
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)
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training_config = TrainingConfig(
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n_epochs=2,
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data_config=data_config,
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optimizer_config=optimizer_config,
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)
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# This will raise NotImplementedError since QAT is not supported
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with self.assertRaises(NotImplementedError):
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self.llama_stack_client.post_training.supervised_fine_tune(
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job_uuid="1234",
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model="meta-llama/Llama-3.1-8B-Instruct",
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checkpoint_dir="",
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algorithm_config=algorithm_config,
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training_config=training_config,
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logger_config={},
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hyperparam_search_config={},
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)
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@patch("llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request")
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def test_get_job_status(self, mock_make_request):
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mock_make_request.return_value = {
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"created_at": "2024-12-09T04:06:28.580220",
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"updated_at": "2024-12-09T04:21:19.852832",
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"status": "completed",
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"steps_completed": 1210,
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"epochs_completed": 2,
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"percentage_done": 100.0,
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"best_epoch": 2,
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"train_loss": 1.718016266822815,
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"val_loss": 1.8661999702453613,
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}
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job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
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status = self.llama_stack_client.post_training.job.status(job_uuid=job_id)
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assert isinstance(status, JobStatusResponse)
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assert status.status == "completed"
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assert status.steps_completed == 1210
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assert status.epochs_completed == 2
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assert status.percentage_done == 100.0
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assert status.best_epoch == 2
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assert status.train_loss == 1.718016266822815
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assert status.val_loss == 1.8661999702453613
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mock_make_request.assert_called_once()
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self._assert_request(
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mock_make_request, "GET", f"/v1/customization/jobs/{job_id}/status", expected_params={"job_id": job_id}
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)
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@patch("llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request")
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def test_get_job(self, mock_make_request):
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job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
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mock_make_request.return_value = {
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"data": [
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{
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"id": job_id,
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"created_at": "2024-12-09T04:06:28.542884",
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"updated_at": "2024-12-09T04:21:19.852832",
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"config": {
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"name": "meta-llama/Llama-3.1-8B-Instruct",
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"base_model": "meta-llama/Llama-3.1-8B-Instruct",
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},
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"dataset": {"name": "default/sample-basic-test"},
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"hyperparameters": {
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"finetuning_type": "lora",
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"training_type": "sft",
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"batch_size": 16,
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"epochs": 2,
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"learning_rate": 0.0001,
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"lora": {"adapter_dim": 16, "adapter_dropout": 0.1},
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},
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"output_model": "default/job-1234",
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"status": "completed",
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"project": "default",
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}
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]
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}
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jobs = self.llama_stack_client.post_training.job.list()
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assert isinstance(jobs, list)
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assert len(jobs) == 1
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job = jobs[0]
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assert job.job_uuid == job_id
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assert job.status == "completed"
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mock_make_request.assert_called_once()
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self._assert_request(
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mock_make_request,
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"GET",
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"/v1/customization/jobs",
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expected_params={"page": 1, "page_size": 10, "sort": "created_at"},
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)
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@patch("llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request")
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def test_cancel_job(self, mock_make_request):
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mock_make_request.return_value = {} # Empty response for successful cancellation
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job_id = "cust-JGTaMbJMdqjJU8WbQdN9Q2"
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result = self.llama_stack_client.post_training.job.cancel(job_uuid=job_id)
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assert result is None
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# Verify the correct request was made
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mock_make_request.assert_called_once()
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self._assert_request(
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mock_make_request, "POST", f"/v1/customization/jobs/{job_id}/cancel", expected_params={"job_id": job_id}
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)
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@pytest.mark.asyncio
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@patch("llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request")
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async def test_async_supervised_fine_tune(self, mock_make_request):
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mock_make_request.return_value = {
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"id": "cust-JGTaMbJMdqjJU8WbQdN9Q2",
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"status": "created",
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"created_at": "2024-12-09T04:06:28.542884",
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"updated_at": "2024-12-09T04:06:28.542884",
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"model": "meta-llama/Llama-3.1-8B-Instruct",
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"dataset_id": "sample-basic-test",
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"output_model": "default/job-1234",
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}
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algorithm_config = LoraFinetuningConfig(
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alpha=16,
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rank=16,
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type="LoRA",
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adapter_dim=16,
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adapter_dropout=0.1,
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apply_lora_to_mlp=True,
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apply_lora_to_output=True,
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)
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data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
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optimizer_config = TrainingConfigOptimizerConfig(
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lr=0.0001,
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)
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training_config = TrainingConfig(
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n_epochs=2,
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data_config=data_config,
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optimizer_config=optimizer_config,
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)
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training_job = await self.llama_stack_client.post_training.supervised_fine_tune_async(
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job_uuid="1234",
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model="meta-llama/Llama-3.1-8B-Instruct",
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checkpoint_dir="",
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algorithm_config=algorithm_config,
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training_config=training_config,
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logger_config={},
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hyperparam_search_config={},
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)
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assert training_job["job_uuid"] == "cust-JGTaMbJMdqjJU8WbQdN9Q2"
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assert training_job["status"] == "created"
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mock_make_request.assert_called_once()
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call_args = mock_make_request.call_args
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assert call_args[1]["method"] == "POST"
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assert call_args[1]["path"] == "/v1/customization/jobs"
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@pytest.mark.asyncio
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@patch("aiohttp.ClientSession.post")
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async def test_inference_with_fine_tuned_model(self, mock_post):
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mock_response = MagicMock()
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mock_response.status = 200
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mock_response.json = AsyncMock(
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return_value={
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"id": "cmpl-123456",
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"object": "text_completion",
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"created": 1677858242,
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"model": "job-1234",
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"choices": [
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{
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"text": "The next GTC will take place in the middle of March, 2023.",
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"index": 0,
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"logprobs": None,
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"finish_reason": "stop",
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}
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],
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"usage": {"prompt_tokens": 100, "completion_tokens": 12, "total_tokens": 112},
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}
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)
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mock_post.return_value.__aenter__.return_value = mock_response
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response = await self.llama_stack_client.inference.completion(
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content="When is the upcoming GTC event? GTC 2018 attracted over 8,400 attendees. Due to the COVID pandemic of 2020, GTC 2020 was converted to a digital event and drew roughly 59,000 registrants. The 2021 GTC keynote, which was streamed on YouTube on April 12, included a portion that was made with CGI using the Nvidia Omniverse real-time rendering platform. This next GTC will take place in the middle of March, 2023. Answer: ",
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stream=False,
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model_id="job-1234",
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sampling_params={
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"max_tokens": 128,
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},
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)
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assert response["model"] == "job-1234"
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assert response["choices"][0]["text"] == "The next GTC will take place in the middle of March, 2023."
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if __name__ == "__main__":
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unittest.main()
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