forked from phoenix-oss/llama-stack-mirror
feat: Add nemo customizer (#1448)
# What does this PR do? This PR adds support for NVIDIA's NeMo Customizer API to the Llama Stack post-training module. The integration enables users to fine-tune models using NVIDIA's cloud-based customization service through a consistent Llama Stack interface. [//]: # (If resolving an issue, uncomment and update the line below) [//]: # (Closes #[issue-number]) ## Test Plan [Describe the tests you ran to verify your changes with result summaries. *Provide clear instructions so the plan can be easily re-executed.*] Yet to be done Things pending under this PR: - [x] Integration of fine-tuned model(new checkpoint) for inference with nvidia llm distribution - [x] distribution integration of API - [x] Add test cases for customizer(In Progress) - [x] Documentation ``` LLAMA_STACK_BASE_URL=http://localhost:5002 pytest -v tests/client-sdk/post_training/test_supervised_fine_tuning.py ============================================================================================================================================================================ test session starts ============================================================================================================================================================================= platform linux -- Python 3.10.0, pytest-8.3.4, pluggy-1.5.0 -- /home/ubuntu/llama-stack/.venv/bin/python cachedir: .pytest_cache metadata: {'Python': '3.10.0', 'Platform': 'Linux-6.8.0-1021-gcp-x86_64-with-glibc2.35', 'Packages': {'pytest': '8.3.4', 'pluggy': '1.5.0'}, 'Plugins': {'nbval': '0.11.0', 'metadata': '3.1.1', 'anyio': '4.8.0', 'html': '4.1.1', 'asyncio': '0.25.3'}} rootdir: /home/ubuntu/llama-stack configfile: pyproject.toml plugins: nbval-0.11.0, metadata-3.1.1, anyio-4.8.0, html-4.1.1, asyncio-0.25.3 asyncio: mode=strict, asyncio_default_fixture_loop_scope=None collected 2 items tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_post_training_provider_registration[txt=8B] PASSED [ 50%] tests/client-sdk/post_training/test_supervised_fine_tuning.py::test_list_training_jobs[txt=8B] PASSED [100%] ======================================================================================================================================================================== 2 passed, 1 warning in 0.10s ======================================================================================================================================================================== ``` cc: @mattf @dglogo @sumitb --------- Co-authored-by: Ubuntu <ubuntu@llama-stack-customizer-dev-inst-2tx95fyisatvlic4we8hidx5tfj.us-central1-a.c.brevdevprod.internal>
This commit is contained in:
Rashmi Pawar
GitHub
parent
ba14552a32
commit
1a73f8305b
26 changed files with 1571 additions and 8 deletions
271
tests/unit/providers/nvidia/test_parameters.py
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271
tests/unit/providers/nvidia/test_parameters.py
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# 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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import warnings
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from unittest.mock import patch
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import pytest
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from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig
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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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TrainingConfigEfficiencyConfig,
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)
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from llama_stack.providers.remote.post_training.nvidia.post_training import (
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NvidiaPostTrainingAdapter,
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NvidiaPostTrainingConfig,
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)
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class TestNvidiaParameters(unittest.TestCase):
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def setUp(self):
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os.environ["NVIDIA_BASE_URL"] = "http://nemo.test"
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os.environ["NVIDIA_CUSTOMIZER_URL"] = "http://nemo.test"
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config = NvidiaPostTrainingConfig(
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base_url=os.environ["NVIDIA_BASE_URL"], customizer_url=os.environ["NVIDIA_CUSTOMIZER_URL"], api_key=None
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)
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self.adapter = NvidiaPostTrainingAdapter(config)
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self.make_request_patcher = patch(
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"llama_stack.providers.remote.post_training.nvidia.post_training.NvidiaPostTrainingAdapter._make_request"
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)
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self.mock_make_request = self.make_request_patcher.start()
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self.mock_make_request.return_value = {
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"id": "job-123",
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"status": "created",
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"created_at": "2025-03-04T13:07:47.543605",
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"updated_at": "2025-03-04T13:07:47.543605",
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}
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def tearDown(self):
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self.make_request_patcher.stop()
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def _assert_request_params(self, expected_json):
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"""Helper method to verify parameters in the request JSON."""
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call_args = self.mock_make_request.call_args
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actual_json = call_args[1]["json"]
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for key, value in expected_json.items():
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if isinstance(value, dict):
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for nested_key, nested_value in value.items():
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assert actual_json[key][nested_key] == nested_value
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else:
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assert actual_json[key] == value
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@pytest.fixture(autouse=True)
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def inject_fixtures(self, run_async):
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self.run_async = run_async
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def test_customizer_parameters_passed(self):
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"""Test scenario 1: When an optional parameter is passed and value is correctly set."""
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custom_adapter_dim = 32 # Different from default of 8
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algorithm_config = LoraFinetuningConfig(
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type="LoRA",
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adapter_dim=custom_adapter_dim,
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adapter_dropout=0.2,
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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="test-dataset", batch_size=16)
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optimizer_config = TrainingConfigOptimizerConfig(lr=0.0002)
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training_config = TrainingConfig(
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n_epochs=3,
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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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self.run_async(
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self.adapter.supervised_fine_tune(
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job_uuid="test-job",
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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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)
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warning_texts = [str(warning.message) for warning in w]
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fields = [
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"apply_lora_to_output",
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"lora_attn_modules",
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"apply_lora_to_mlp",
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]
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for field in fields:
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assert any(field in text for text in warning_texts)
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self._assert_request_params(
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{
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"hyperparameters": {
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"lora": {"adapter_dim": custom_adapter_dim, "adapter_dropout": 0.2, "alpha": 16},
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"epochs": 3,
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"learning_rate": 0.0002,
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"batch_size": 16,
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}
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}
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)
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def test_required_parameters_passed(self):
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"""Test scenario 2: When required parameters are passed."""
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required_model = "meta-llama/Llama-3.1-8B-Instruct"
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required_dataset_id = "required-dataset"
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required_job_uuid = "required-job"
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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(
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dataset_id=required_dataset_id, # Required parameter
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batch_size=8,
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)
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optimizer_config = TrainingConfigOptimizerConfig(lr=0.0001)
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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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)
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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self.run_async(
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self.adapter.supervised_fine_tune(
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job_uuid=required_job_uuid, # Required parameter
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model=required_model, # Required parameter
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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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)
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warning_texts = [str(warning.message) for warning in w]
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fields = [
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"rank",
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"apply_lora_to_output",
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"lora_attn_modules",
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"apply_lora_to_mlp",
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]
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for field in fields:
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assert any(field in text for text in warning_texts)
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self.mock_make_request.assert_called_once()
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call_args = self.mock_make_request.call_args
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assert call_args[1]["json"]["config"] == "meta/llama-3.1-8b-instruct"
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assert call_args[1]["json"]["dataset"]["name"] == required_dataset_id
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def test_unsupported_parameters_warning(self):
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"""Test that warnings are raised for unsupported parameters."""
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data_config = TrainingConfigDataConfig(
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dataset_id="test-dataset",
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batch_size=8,
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# Unsupported parameters
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shuffle=True,
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data_format="instruct",
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validation_dataset_id="val-dataset",
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)
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optimizer_config = TrainingConfigOptimizerConfig(
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lr=0.0001,
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weight_decay=0.01,
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# Unsupported parameters
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optimizer_type="adam",
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num_warmup_steps=100,
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)
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efficiency_config = TrainingConfigEfficiencyConfig(
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enable_activation_checkpointing=True # Unsupported parameter
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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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# Unsupported parameters
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efficiency_config=efficiency_config,
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max_steps_per_epoch=1000,
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gradient_accumulation_steps=4,
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max_validation_steps=100,
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dtype="bf16",
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)
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# Capture warnings
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with warnings.catch_warnings(record=True) as w:
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warnings.simplefilter("always")
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self.run_async(
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self.adapter.supervised_fine_tune(
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job_uuid="test-job",
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model="meta-llama/Llama-3.1-8B-Instruct",
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checkpoint_dir="test-dir", # Unsupported parameter
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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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training_config=training_config,
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logger_config={"test": "value"}, # Unsupported parameter
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hyperparam_search_config={"test": "value"}, # Unsupported parameter
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)
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)
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assert len(w) >= 4
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warning_texts = [str(warning.message) for warning in w]
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fields = [
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"checkpoint_dir",
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"hyperparam_search_config",
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"logger_config",
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"TrainingConfig",
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"DataConfig",
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"OptimizerConfig",
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"max_steps_per_epoch",
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"gradient_accumulation_steps",
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"max_validation_steps",
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"dtype",
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# required unsupported parameters
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"rank",
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"apply_lora_to_output",
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"lora_attn_modules",
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"apply_lora_to_mlp",
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]
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for field in fields:
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assert any(field in text for text in warning_texts)
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if __name__ == "__main__":
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unittest.main()
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