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# Copyright (c) 2025, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from unittest.mock import MagicMock, patch
import numpy as np
import pytest
from nemo.deploy.nlp.query_llm import NemoQueryLLM, NemoQueryLLMBase, NemoQueryLLMHF, NemoQueryLLMPyTorch
class TestNemoQueryLLMBase:
def test_base_initialization(self):
url = "localhost:8000"
model_name = "test-model"
query = NemoQueryLLMBase(url=url, model_name=model_name)
assert query.url == url
assert query.model_name == model_name
class TestNemoQueryLLMPyTorch:
@pytest.fixture
def query(self):
return NemoQueryLLMPyTorch(url="localhost:8000", model_name="test-model")
def test_initialization(self, query):
assert isinstance(query, NemoQueryLLMBase)
assert query.url == "localhost:8000"
assert query.model_name == "test-model"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_basic(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"sentences": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test basic query
response = query.query_llm(prompts=["test prompt"], max_length=100, temperature=0.7, top_k=1, top_p=0.9)
assert isinstance(response, dict)
assert "choices" in response
assert response["choices"][0]["text"] == "test response"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_with_logprobs(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {
"sentences": np.array([b"test response"]),
"log_probs": np.array([0.1, 0.2, 0.3]),
}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test query with logprobs
response = query.query_llm(prompts=["test prompt"], max_length=100, compute_logprob=True)
assert "logprobs" in response["choices"][0]
assert "token_logprobs" in response["choices"][0]["logprobs"]
class TestNemoQueryLLMHF:
@pytest.fixture
def query(self):
return NemoQueryLLMHF(url="localhost:8000", model_name="test-model")
def test_initialization(self, query):
assert isinstance(query, NemoQueryLLMBase)
assert query.url == "localhost:8000"
assert query.model_name == "test-model"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_basic(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"sentences": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test basic query
response = query.query_llm(prompts=["test prompt"], max_length=100, temperature=0.7, top_k=1, top_p=0.9)
assert isinstance(response, dict)
assert "choices" in response
assert response["choices"][0]["text"] == "test response"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_with_logits(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {
"sentences": np.array([b"test response"]),
"logits": np.array([[0.1, 0.2, 0.3]]),
}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test query with logits
response = query.query_llm(prompts=["test prompt"], max_length=100, output_logits=True)
assert "logits" in response
class TestNemoQueryLLM:
@pytest.fixture
def query(self):
return NemoQueryLLM(url="localhost:8000", model_name="test-model")
def test_initialization(self, query):
assert isinstance(query, NemoQueryLLMBase)
assert query.url == "localhost:8000"
assert query.model_name == "test-model"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_basic(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"outputs": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test basic query
response = query.query_llm(prompts=["test prompt"], max_output_len=100, temperature=0.7, top_k=1, top_p=0.9)
assert isinstance(response[0], str)
assert response[0] == "test response"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_openai_format(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"outputs": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test query with OpenAI format
response = query.query_llm(prompts=["test prompt"], max_output_len=100, openai_format_response=True)
assert isinstance(response, dict)
assert "choices" in response
assert response["choices"][0]["text"] == "test response"
@patch('nemo.deploy.nlp.query_llm.DecoupledModelClient')
def test_query_llm_streaming(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = [
{"outputs": np.array([b"test"])},
{"outputs": np.array([b" response"])},
]
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test streaming query
responses = list(query.query_llm_streaming(prompts=["test prompt"], max_output_len=100))
assert len(responses) == 2
assert responses[0] == "test"
assert responses[1] == " response"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_with_stop_words(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"outputs": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test query with stop words
response = query.query_llm(prompts=["test prompt"], max_output_len=100, stop_words_list=["stop"])
assert isinstance(response[0], str)
assert response[0] == "test response"
@patch('nemo.deploy.nlp.query_llm.ModelClient')
def test_query_llm_with_bad_words(self, mock_client, query):
# Setup mock
mock_instance = MagicMock()
mock_client.return_value.__enter__.return_value = mock_instance
mock_instance.infer_batch.return_value = {"outputs": np.array([b"test response"])}
mock_instance.model_config.outputs = [MagicMock(dtype=np.bytes_)]
# Test query with bad words
response = query.query_llm(prompts=["test prompt"], max_output_len=100, bad_words_list=["bad"])
assert isinstance(response[0], str)
assert response[0] == "test response"
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