import gc import json import os import pathlib import subprocess from unittest.mock import MagicMock, patch import openai import pytest import torch from tensorizer import EncryptionParams from aphrodite import SamplingParams from aphrodite.engine.args_tools import EngineArgs # yapf: disable from aphrodite.modeling.model_loader.tensorizer import ( TensorizerConfig, TensorSerializer, is_aphrodite_tensorized, load_with_tensorizer, open_stream, serialize_aphrodite_model, tensorize_aphrodite_model) from ..conftest import AphroditeRunner from ..utils import RemoteOpenAIServer from .conftest import retry_until_skip # yapf conflicts with isort for this docstring prompts = [ "Hello, my name is", "The president of the United States is", "The capital of France is", "The future of AI is", ] # Create a sampling params object. sampling_params = SamplingParams(temperature=0.8, top_p=0.95, seed=0) model_ref = "facebook/opt-125m" tensorize_model_for_testing_script = os.path.join( os.path.dirname(__file__), "tensorize_aphrodite_model_for_testing.py") def is_curl_installed(): try: subprocess.check_call(['curl', '--version']) return True except (subprocess.CalledProcessError, FileNotFoundError): return False def get_torch_model(aphrodite_runner: AphroditeRunner): return aphrodite_runner \ .model \ .llm_engine \ .model_executor \ .driver_worker \ .model_runner \ .model def write_keyfile(keyfile_path: str): encryption_params = EncryptionParams.random() pathlib.Path(keyfile_path).parent.mkdir(parents=True, exist_ok=True) with open(keyfile_path, 'wb') as f: f.write(encryption_params.key) @patch('aphrodite.modeling.model_loader.tensorizer.TensorizerAgent') def test_load_with_tensorizer(mock_agent, tensorizer_config): mock_linear_method = MagicMock() mock_agent_instance = mock_agent.return_value mock_agent_instance.deserialize.return_value = MagicMock() result = load_with_tensorizer(tensorizer_config, quant_method=mock_linear_method) mock_agent.assert_called_once_with(tensorizer_config, quant_method=mock_linear_method) mock_agent_instance.deserialize.assert_called_once() assert result == mock_agent_instance.deserialize.return_value @pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed") def test_can_deserialize_s3(aphrodite_runner): model_ref = "EleutherAI/pythia-1.4b" tensorized_path = f"s3://tensorized/{model_ref}/fp16/model.tensors" with aphrodite_runner(model_ref, load_format="tensorizer", model_loader_extra_config=TensorizerConfig( tensorizer_uri=tensorized_path, num_readers=1, s3_endpoint="object.ord1.coreweave.com", )) as loaded_hf_model: deserialized_outputs = loaded_hf_model.generate(prompts, sampling_params) # noqa: E501 assert deserialized_outputs @pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed") def test_deserialized_encrypted_aphrodite_model_has_same_outputs( aphrodite_runner, tmp_path): with aphrodite_runner(model_ref) as aphrodite_model: model_path = tmp_path / (model_ref + ".tensors") key_path = tmp_path / (model_ref + ".key") write_keyfile(key_path) outputs = aphrodite_model.generate(prompts, sampling_params) config_for_serializing = TensorizerConfig( tensorizer_uri=model_path, encryption_keyfile=key_path ) serialize_aphrodite_model(get_torch_model(aphrodite_model), config_for_serializing) config_for_deserializing = TensorizerConfig(tensorizer_uri=model_path, encryption_keyfile=key_path) with aphrodite_runner( model_ref, load_format="tensorizer", model_loader_extra_config=config_for_deserializing) as loaded_aphrodite_model: # noqa: E501 deserialized_outputs = loaded_aphrodite_model.generate(prompts, sampling_params) # noqa: E501 assert outputs == deserialized_outputs def test_deserialized_hf_model_has_same_outputs(hf_runner, aphrodite_runner, tmp_path): with hf_runner(model_ref) as hf_model: model_path = tmp_path / (model_ref + ".tensors") max_tokens = 50 outputs = hf_model.generate_greedy(prompts, max_tokens=max_tokens) with open_stream(model_path, "wb+") as stream: serializer = TensorSerializer(stream) serializer.write_module(hf_model.model) with aphrodite_runner(model_ref, load_format="tensorizer", model_loader_extra_config=TensorizerConfig( tensorizer_uri=model_path, num_readers=1, )) as loaded_hf_model: deserialized_outputs = loaded_hf_model.generate_greedy( prompts, max_tokens=max_tokens) assert outputs == deserialized_outputs def test_aphrodite_model_can_load_with_lora(aphrodite_runner, tmp_path): from huggingface_hub import snapshot_download from examples.offline_inference.lora_aphrodite_engine import ( create_test_prompts, process_requests) model_ref = "meta-llama/Llama-2-7b-hf" lora_path = snapshot_download(repo_id="yard1/llama-2-7b-sql-lora-test") test_prompts = create_test_prompts(lora_path) # Serialize model before deserializing and binding LoRA adapters with aphrodite_runner(model_ref, ) as aphrodite_model: model_path = tmp_path / (model_ref + ".tensors") serialize_aphrodite_model(get_torch_model(aphrodite_model), TensorizerConfig(tensorizer_uri=model_path)) with aphrodite_runner( model_ref, load_format="tensorizer", model_loader_extra_config=TensorizerConfig( tensorizer_uri=model_path, num_readers=1, ), enable_lora=True, max_loras=1, max_lora_rank=8, max_cpu_loras=2, max_num_seqs=50, max_model_len=1000, ) as loaded_aphrodite_model: process_requests(loaded_aphrodite_model.model.llm_engine, test_prompts) assert loaded_aphrodite_model def test_load_without_tensorizer_load_format(aphrodite_runner): model = None with pytest.raises(ValueError): model = aphrodite_runner( model_ref, model_loader_extra_config=TensorizerConfig(tensorizer_uri="test")) del model gc.collect() torch.cuda.empty_cache() @pytest.mark.skipif(not is_curl_installed(), reason="cURL is not installed") def test_openai_apiserver_with_tensorizer(aphrodite_runner, tmp_path): ## Serialize model with aphrodite_runner(model_ref, ) as aphrodite_model: model_path = tmp_path / (model_ref + ".tensors") serialize_aphrodite_model(get_torch_model(aphrodite_model), TensorizerConfig(tensorizer_uri=model_path)) model_loader_extra_config = { "tensorizer_uri": str(model_path), } ## Start OpenAI API server openai_args = [ "--dtype", "float16", "--load-format", "tensorizer", "--model-loader-extra-config", json.dumps(model_loader_extra_config), ] with RemoteOpenAIServer(model_ref, openai_args) as server: print("Server ready.") client = server.get_client() completion = client.completions.create(model=model_ref, prompt="Hello, my name is", max_tokens=5, temperature=0.0) assert completion.id is not None assert len(completion.choices) == 1 assert len(completion.choices[0].text) >= 5 assert completion.choices[0].finish_reason == "length" assert completion.usage == openai.types.CompletionUsage( completion_tokens=5, prompt_tokens=6, total_tokens=11) def test_raise_value_error_on_invalid_load_format(aphrodite_runner): model = None with pytest.raises(ValueError): model = aphrodite_runner( model_ref, load_format="safetensors", model_loader_extra_config=TensorizerConfig(tensorizer_uri="test")) del model gc.collect() torch.cuda.empty_cache() @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 GPUs") def test_tensorizer_with_tp_path_without_template(aphrodite_runner): with pytest.raises(ValueError): model_ref = "EleutherAI/pythia-1.4b" tensorized_path = f"s3://tensorized/{model_ref}/fp16/model.tensors" aphrodite_runner( model_ref, load_format="tensorizer", model_loader_extra_config=TensorizerConfig( tensorizer_uri=tensorized_path, num_readers=1, s3_endpoint="object.ord1.coreweave.com", ), tensor_parallel_size=2, disable_custom_all_reduce=True, ) @pytest.mark.skipif(torch.cuda.device_count() < 2, reason="Requires 2 GPUs") def test_deserialized_encrypted_aphrodite_model_with_tp_has_same_outputs( aphrodite_runner, tmp_path): model_ref = "EleutherAI/pythia-1.4b" # record outputs from un-sharded un-tensorized model with aphrodite_runner( model_ref, disable_custom_all_reduce=True, enforce_eager=True, ) as base_model: outputs = base_model.generate(prompts, sampling_params) base_model.model.llm_engine.model_executor.shutdown() # load model with two shards and serialize with encryption model_path = str(tmp_path / (model_ref + "-%02d.tensors")) key_path = tmp_path / (model_ref + ".key") tensorizer_config = TensorizerConfig( tensorizer_uri=model_path, encryption_keyfile=key_path, ) tensorize_aphrodite_model( engine_args=EngineArgs( model=model_ref, tensor_parallel_size=2, disable_custom_all_reduce=True, enforce_eager=True, ), tensorizer_config=tensorizer_config, ) assert os.path.isfile(model_path % 0), "Serialization subprocess failed" assert os.path.isfile(model_path % 1), "Serialization subprocess failed" with aphrodite_runner( model_ref, tensor_parallel_size=2, load_format="tensorizer", disable_custom_all_reduce=True, enforce_eager=True, model_loader_extra_config=tensorizer_config ) as loaded_aphrodite_model: deserialized_outputs = loaded_aphrodite_model.generate(prompts, sampling_params) assert outputs == deserialized_outputs @retry_until_skip(3) def test_aphrodite_tensorized_model_has_same_outputs( aphrodite_runner, tmp_path): gc.collect() torch.cuda.empty_cache() model_ref = "facebook/opt-125m" model_path = tmp_path / (model_ref + ".tensors") config = TensorizerConfig(tensorizer_uri=str(model_path)) with aphrodite_runner(model_ref) as aphrodite_model: outputs = aphrodite_model.generate(prompts, sampling_params) serialize_aphrodite_model(get_torch_model(aphrodite_model), config) assert is_aphrodite_tensorized(config) with aphrodite_runner(model_ref, load_format="tensorizer", model_loader_extra_config=config ) as loaded_aphrodite_model: deserialized_outputs = loaded_aphrodite_model.generate(prompts, sampling_params) # noqa: E501 assert outputs == deserialized_outputs