mirror of
https://github.com/meta-llama/llama-stack.git
synced 2025-08-02 08:44:44 +00:00
Use correct shapes in unit tests; remove use of unsupported params
This commit is contained in:
Jash Gulabrai
parent
26c10b5ab5
commit
bb142435db
3 changed files with 68 additions and 51 deletions
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@ -245,6 +245,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
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Supported models:
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- meta/llama-3.1-8b-instruct
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- meta/llama-3.2-1b-instruct
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Supported algorithm configs:
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- LoRA, SFT
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@ -290,10 +291,6 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
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- LoRA config:
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## NeMo customizer specific LoRA parameters
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- adapter_dim: int - Adapter dimension
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Default: 8 (supports powers of 2)
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- adapter_dropout: float - Adapter dropout
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Default: None (0.0-1.0)
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- alpha: int - Scaling factor for the LoRA update
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Default: 16
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Note:
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@ -336,7 +333,7 @@ class NvidiaPostTrainingAdapter(ModelRegistryHelper):
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},
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"data_config": {"dataset_id", "batch_size"},
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"optimizer_config": {"lr", "weight_decay"},
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"lora_config": {"type", "adapter_dim", "adapter_dropout", "alpha"},
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"lora_config": {"type", "alpha"},
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}
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# Validate all parameters at once
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@ -10,14 +10,17 @@ 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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TrainingConfigEfficiencyConfig,
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TrainingConfigOptimizerConfig,
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)
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from llama_stack.apis.post_training.post_training import (
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DataConfig,
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DatasetFormat,
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EfficiencyConfig,
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LoraFinetuningConfig,
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OptimizerConfig,
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OptimizerType,
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TrainingConfig,
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)
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from llama_stack.distribution.library_client import convert_pydantic_to_json_value
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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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@ -66,11 +69,8 @@ class TestNvidiaParameters(unittest.TestCase):
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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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@ -78,8 +78,15 @@ class TestNvidiaParameters(unittest.TestCase):
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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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data_config = DataConfig(
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dataset_id="test-dataset", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
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)
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optimizer_config = OptimizerConfig(
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optimizer_type=OptimizerType.adam,
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lr=0.0002,
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weight_decay=0.01,
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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=3,
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data_config=data_config,
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@ -95,7 +102,7 @@ class TestNvidiaParameters(unittest.TestCase):
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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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training_config=convert_pydantic_to_json_value(training_config),
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logger_config={},
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hyperparam_search_config={},
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)
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@ -114,7 +121,7 @@ class TestNvidiaParameters(unittest.TestCase):
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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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"lora": {"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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@ -130,8 +137,6 @@ class TestNvidiaParameters(unittest.TestCase):
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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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@ -139,12 +144,16 @@ class TestNvidiaParameters(unittest.TestCase):
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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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data_config = DataConfig(
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dataset_id=required_dataset_id, batch_size=8, shuffle=False, data_format=DatasetFormat.instruct
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)
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optimizer_config = TrainingConfigOptimizerConfig(lr=0.0001)
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optimizer_config = OptimizerConfig(
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optimizer_type=OptimizerType.adam,
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lr=0.0001,
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weight_decay=0.01,
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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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@ -161,7 +170,7 @@ class TestNvidiaParameters(unittest.TestCase):
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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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training_config=convert_pydantic_to_json_value(training_config),
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logger_config={},
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hyperparam_search_config={},
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)
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@ -186,24 +195,24 @@ class TestNvidiaParameters(unittest.TestCase):
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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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data_config = DataConfig(
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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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data_format=DatasetFormat.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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optimizer_config = OptimizerConfig(
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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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optimizer_type=OptimizerType.adam,
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num_warmup_steps=100,
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)
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efficiency_config = TrainingConfigEfficiencyConfig(
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efficiency_config = EfficiencyConfig(
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enable_activation_checkpointing=True # Unsupported parameter
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)
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@ -230,15 +239,13 @@ class TestNvidiaParameters(unittest.TestCase):
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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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training_config=convert_pydantic_to_json_value(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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@ -10,13 +10,17 @@ 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, QatFinetuningConfig
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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.apis.post_training.post_training import (
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DataConfig,
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DatasetFormat,
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LoraFinetuningConfig,
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OptimizerConfig,
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OptimizerType,
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QATFinetuningConfig,
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TrainingConfig,
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)
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from llama_stack.distribution.library_client import convert_pydantic_to_json_value
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from llama_stack.providers.remote.post_training.nvidia.post_training import (
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ListNvidiaPostTrainingJobs,
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NvidiaPostTrainingAdapter,
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@ -105,7 +109,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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"lora": {"alpha": 16},
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},
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"output_model": "default/job-1234",
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"status": "created",
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@ -116,8 +120,6 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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@ -125,10 +127,15 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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data_config = DataConfig(
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dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
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)
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optimizer_config = TrainingConfigOptimizerConfig(
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optimizer_config = OptimizerConfig(
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optimizer_type=OptimizerType.adam,
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lr=0.0001,
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weight_decay=0.01,
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num_warmup_steps=100,
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)
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training_config = TrainingConfig(
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@ -145,7 +152,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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training_config=convert_pydantic_to_json_value(training_config),
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logger_config={},
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hyperparam_search_config={},
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)
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@ -169,16 +176,22 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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"weight_decay": 0.01,
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"lora": {"alpha": 16},
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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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algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
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data_config = DataConfig(
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dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
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)
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optimizer_config = OptimizerConfig(
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optimizer_type=OptimizerType.adam,
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lr=0.0001,
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weight_decay=0.01,
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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=2,
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@ -193,7 +206,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
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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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training_config=convert_pydantic_to_json_value(training_config),
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logger_config={},
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hyperparam_search_config={},
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)
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