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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
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from nemo.deploy.nlp.hf_deployable import HuggingFaceLLMDeploy
@pytest.fixture
def mock_model():
model = MagicMock(spec=AutoModelForCausalLM)
model.generate = MagicMock()
model.generate.return_value = torch.tensor([[1, 2, 3]])
model.cuda = MagicMock(return_value=model)
return model
@pytest.fixture
def mock_tokenizer():
tokenizer = MagicMock(spec=AutoTokenizer)
tokenizer.pad_token = "[PAD]"
tokenizer.eos_token = "[EOS]"
tokenizer.batch_decode = MagicMock(return_value=["Generated text"])
tokenizer.return_value = {"input_ids": torch.tensor([[1, 2, 3]]), "attention_mask": torch.tensor([[1, 1, 1]])}
return tokenizer
@pytest.fixture
def mock_peft_model():
with patch("nemo.deploy.nlp.hf_deployable.PeftModel") as mock:
mock.from_pretrained.return_value = MagicMock()
yield mock
@pytest.fixture
def mock_distributed():
with patch("torch.distributed") as mock:
mock.is_initialized.return_value = True
mock.get_world_size.return_value = 2
mock.get_rank.return_value = 1
mock.broadcast = MagicMock(return_value=torch.tensor([0]))
yield mock
@pytest.fixture
def mock_torch_cuda():
with patch('torch.cuda.is_available', return_value=False):
with patch('torch.Tensor.cuda', return_value=torch.tensor([[1, 2, 3]])):
yield
class MockRequest:
def __init__(self, data):
self.data = data
self.span = None
def __getitem__(self, key):
return self.data[key]
def keys(self):
return self.data.keys()
def values(self):
return self.data.values()
class TestHuggingFaceLLMDeploy:
def test_initialization_invalid_task(self):
with pytest.raises(AssertionError):
HuggingFaceLLMDeploy(hf_model_id_path="test/model", task="invalid-task")
def test_initialization_no_model(self):
with pytest.raises(ValueError):
HuggingFaceLLMDeploy(task="text-generation")
def test_initialization_with_model_and_tokenizer(self):
model = MagicMock(spec=AutoModelForCausalLM)
tokenizer = MagicMock(spec=AutoTokenizer)
deployer = HuggingFaceLLMDeploy(model=model, tokenizer=tokenizer, task="text-generation")
assert deployer.model == model
assert deployer.tokenizer == tokenizer
assert deployer.task == "text-generation"
def test_initialization_with_model_path(self, mock_model, mock_tokenizer):
with (
patch("transformers.AutoModelForCausalLM.from_pretrained", return_value=mock_model),
patch("transformers.AutoTokenizer.from_pretrained", return_value=mock_tokenizer),
):
deployer = HuggingFaceLLMDeploy(hf_model_id_path="test/model", task="text-generation")
assert deployer.model == mock_model
assert deployer.tokenizer == mock_tokenizer
def test_initialization_with_peft_model(self, mock_model, mock_tokenizer, mock_peft_model):
with (
patch("transformers.AutoModelForCausalLM.from_pretrained", return_value=mock_model),
patch("transformers.AutoTokenizer.from_pretrained", return_value=mock_tokenizer),
):
deployer = HuggingFaceLLMDeploy(
hf_model_id_path="test/model", hf_peft_model_id_path="test/peft_model", task="text-generation"
)
assert deployer.model == mock_peft_model.from_pretrained.return_value
def test_triton_input_output_config(self):
deployer = HuggingFaceLLMDeploy(model=MagicMock(), tokenizer=MagicMock(), task="text-generation")
inputs = deployer.get_triton_input
outputs = deployer.get_triton_output
assert len(inputs) == 10 # Verify number of input tensors
assert len(outputs) == 3 # Verify number of output tensors
# Verify required input tensor names
assert any(tensor.name == "prompts" for tensor in inputs)
assert any(tensor.name == "max_length" for tensor in inputs)
# Verify output tensor names
assert any(tensor.name == "sentences" for tensor in outputs)
assert any(tensor.name == "logits" for tensor in outputs)
assert any(tensor.name == "scores" for tensor in outputs)
def test_generate_without_model(self):
deployer = HuggingFaceLLMDeploy(model=MagicMock(), tokenizer=MagicMock(), task="text-generation")
deployer.model = None
with pytest.raises(RuntimeError):
deployer.generate(text_inputs=["test prompt"])
def test_generate_with_model(self, mock_model, mock_tokenizer, mock_torch_cuda):
deployer = HuggingFaceLLMDeploy(model=mock_model, tokenizer=mock_tokenizer, task="text-generation")
output = deployer.generate(text_inputs=["test prompt"])
assert output == ["Generated text"]
mock_model.generate.assert_called_once()
mock_tokenizer.batch_decode.assert_called_once()
def test_generate_with_output_logits_and_scores(self, mock_model, mock_tokenizer, mock_torch_cuda):
mock_model.generate.return_value = {
"sequences": torch.tensor([[1, 2, 3]]),
"logits": torch.tensor([1.0]),
"scores": torch.tensor([0.5]),
}
deployer = HuggingFaceLLMDeploy(model=mock_model, tokenizer=mock_tokenizer, task="text-generation")
output = deployer.generate(
text_inputs=["test prompt"], output_logits=True, output_scores=True, return_dict_in_generate=True
)
assert isinstance(output, dict)
assert "sentences" in output
assert "logits" in output
assert "scores" in output
def test_triton_infer_fn(self, mock_model, mock_tokenizer):
deployer = HuggingFaceLLMDeploy(model=mock_model, tokenizer=mock_tokenizer, task="text-generation")
request_data = {
"prompts": np.array(["test prompt"]),
"temperature": np.array([[1.0]]),
"top_k": np.array([[1]]),
"top_p": np.array([[0.0]]),
"max_length": np.array([[10]]),
"output_logits": np.array([[False]]),
"output_scores": np.array([[False]]),
}
requests = [MockRequest(request_data)]
output = deployer.triton_infer_fn(requests)
assert "sentences" in output[0]
assert isinstance(output[0]["sentences"], np.ndarray)
def test_triton_infer_fn_with_error(self, mock_model, mock_tokenizer):
deployer = HuggingFaceLLMDeploy(model=mock_model, tokenizer=mock_tokenizer, task="text-generation")
mock_model.generate.side_effect = Exception("Test error")
request_data = {
"prompts": np.array(["test prompt"]),
"temperature": np.array([[1.0]]),
"top_k": np.array([[1]]),
"top_p": np.array([[0.0]]),
"max_length": np.array([[10]]),
"output_logits": np.array([[False]]),
"output_scores": np.array([[False]]),
}
requests = [MockRequest(request_data)]
output = deployer.triton_infer_fn(requests)
assert "sentences" in output[0]
assert "An error occurred" in str(output[0]["sentences"][0])
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