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Use correct shapes in unit tests; remove use of unsupported params

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This commit is contained in:
Jash Gulabrai 2025-04-25 15:52:12 -04:00
parent 26c10b5ab5
commit bb142435db
3 changed files with 68 additions and 51 deletions
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47
tests/unit/providers/nvidia/test_supervised_fine_tuning.py
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@ -10,13 +10,17 @@ import warnings
from unittest.mock import patch
import pytest
from llama_stack_client.types.algorithm_config_param import LoraFinetuningConfig, QatFinetuningConfig
from llama_stack_client.types.post_training_supervised_fine_tune_params import (
TrainingConfig,
TrainingConfigDataConfig,
TrainingConfigOptimizerConfig,
)
from llama_stack.apis.post_training.post_training import (
DataConfig,
DatasetFormat,
LoraFinetuningConfig,
OptimizerConfig,
OptimizerType,
QATFinetuningConfig,
TrainingConfig,
)
from llama_stack.distribution.library_client import convert_pydantic_to_json_value
from llama_stack.providers.remote.post_training.nvidia.post_training import (
ListNvidiaPostTrainingJobs,
NvidiaPostTrainingAdapter,
@ -105,7 +109,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
"batch_size": 16,
"epochs": 2,
"learning_rate": 0.0001,
"lora": {"adapter_dim": 16, "adapter_dropout": 0.1},
"lora": {"alpha": 16},
},
"output_model": "default/job-1234",
"status": "created",
@ -116,8 +120,6 @@ class TestNvidiaPostTraining(unittest.TestCase):
algorithm_config = LoraFinetuningConfig(
type="LoRA",
adapter_dim=16,
adapter_dropout=0.1,
apply_lora_to_mlp=True,
apply_lora_to_output=True,
alpha=16,
@ -125,10 +127,15 @@ class TestNvidiaPostTraining(unittest.TestCase):
lora_attn_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
)
data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
data_config = DataConfig(
dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
)
optimizer_config = TrainingConfigOptimizerConfig(
optimizer_config = OptimizerConfig(
optimizer_type=OptimizerType.adam,
lr=0.0001,
weight_decay=0.01,
num_warmup_steps=100,
)
training_config = TrainingConfig(
@ -145,7 +152,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
model="meta-llama/Llama-3.1-8B-Instruct",
checkpoint_dir="",
algorithm_config=algorithm_config,
training_config=training_config,
training_config=convert_pydantic_to_json_value(training_config),
logger_config={},
hyperparam_search_config={},
)
@ -169,16 +176,22 @@ class TestNvidiaPostTraining(unittest.TestCase):
"epochs": 2,
"batch_size": 16,
"learning_rate": 0.0001,
"lora": {"alpha": 16, "adapter_dim": 16, "adapter_dropout": 0.1},
"weight_decay": 0.01,
"lora": {"alpha": 16},
},
},
)
def test_supervised_fine_tune_with_qat(self):
algorithm_config = QatFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
data_config = TrainingConfigDataConfig(dataset_id="sample-basic-test", batch_size=16)
optimizer_config = TrainingConfigOptimizerConfig(
algorithm_config = QATFinetuningConfig(type="QAT", quantizer_name="quantizer_name", group_size=1)
data_config = DataConfig(
dataset_id="sample-basic-test", batch_size=16, shuffle=False, data_format=DatasetFormat.instruct
)
optimizer_config = OptimizerConfig(
optimizer_type=OptimizerType.adam,
lr=0.0001,
weight_decay=0.01,
num_warmup_steps=100,
)
training_config = TrainingConfig(
n_epochs=2,
@ -193,7 +206,7 @@ class TestNvidiaPostTraining(unittest.TestCase):
model="meta-llama/Llama-3.1-8B-Instruct",
checkpoint_dir="",
algorithm_config=algorithm_config,
training_config=training_config,
training_config=convert_pydantic_to_json_value(training_config),
logger_config={},
hyperparam_search_config={},
)