diff --git a/backends/gaudi/Makefile b/backends/gaudi/Makefile index bae0cdad3..cf739cf57 100644 --- a/backends/gaudi/Makefile +++ b/backends/gaudi/Makefile @@ -52,7 +52,7 @@ local-dev-install: install-dependencies run-integration-tests: DOCKER_VOLUME=${root_dir}/data \ HF_TOKEN=`cat ${HOME}/.cache/huggingface/token` \ - pytest --durations=0 -s -vv integration-tests --gaudi + pytest --durations=0 -s -vv ${root_dir}/integration-tests --gaudi # This is used to capture the expected outputs for the integration tests offering an easy way to add more models to the integration tests capture-expected-outputs-for-integration-tests: diff --git a/integration-tests/conftest.py b/integration-tests/conftest.py index 84d246374..594ffd495 100644 --- a/integration-tests/conftest.py +++ b/integration-tests/conftest.py @@ -1,4 +1,8 @@ -pytest_plugins = ["fixtures.neuron.service", "fixtures.neuron.export_models"] +pytest_plugins = [ + "fixtures.neuron.service", + "fixtures.neuron.export_models", + "fixtures.gaudi.service", +] # ruff: noqa: E402 from _pytest.fixtures import SubRequest from huggingface_hub.inference._generated.types.chat_completion import ( diff --git a/integration-tests/fixtures/gaudi/service.py b/integration-tests/fixtures/gaudi/service.py index 6b39a1e66..44c7f9993 100644 --- a/integration-tests/fixtures/gaudi/service.py +++ b/integration-tests/fixtures/gaudi/service.py @@ -15,9 +15,10 @@ import pytest from aiohttp import ClientConnectorError, ClientOSError, ServerDisconnectedError from docker.errors import NotFound import logging -from gaudi.test_generate import TEST_CONFIGS +from gaudi.test_gaudi_generate import TEST_CONFIGS from text_generation import AsyncClient from text_generation.types import Response +import huggingface_hub logging.basicConfig( level=logging.INFO, @@ -29,7 +30,7 @@ logger = logging.getLogger(__file__) # Use the latest image from the local docker build DOCKER_IMAGE = os.getenv("DOCKER_IMAGE", "tgi-gaudi") DOCKER_VOLUME = os.getenv("DOCKER_VOLUME", None) -HF_TOKEN = os.getenv("HF_TOKEN", None) +HF_TOKEN = huggingface_hub.get_token() assert ( HF_TOKEN is not None @@ -152,7 +153,7 @@ def data_volume(): @pytest.fixture(scope="module") -def launcher(data_volume): +def gaudi_launcher(event_loop): @contextlib.contextmanager def docker_launcher( model_id: str, @@ -272,7 +273,7 @@ def launcher(data_volume): @pytest.fixture(scope="module") -def generate_load(): +def gaudi_generate_load(): async def generate_load_inner( client: AsyncClient, prompt: str, max_new_tokens: int, n: int ) -> List[Response]: diff --git a/integration-tests/gaudi/test_model.py b/integration-tests/gaudi/test_model.py deleted file mode 100644 index cfdb05544..000000000 --- a/integration-tests/gaudi/test_model.py +++ /dev/null @@ -1,273 +0,0 @@ -from typing import Any, Dict - -from text_generation import AsyncClient -import pytest - -# The "args" config is not optimized for speed but only check that the inference is working for the different models architectures -TEST_CONFIGS = { - "meta-llama/Llama-3.1-8B-Instruct-shared": { - "model_id": "meta-llama/Llama-3.1-8B-Instruct", - "input": "What is Deep Learning?", - "expected_greedy_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use", - "expected_batch_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use", - "args": [ - "--sharded", - "true", - "--num-shard", - "8", - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "8", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "meta-llama/Llama-3.1-8B-Instruct": { - "model_id": "meta-llama/Llama-3.1-8B-Instruct", - "input": "What is Deep Learning?", - "expected_greedy_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use of artificial neural networks to analyze and interpret data. It is a type of", - "expected_batch_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use of artificial neural networks to analyze and interpret data. It is a type of", - "env_config": {}, - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "meta-llama/Llama-2-7b-chat-hf": { - "model_id": "meta-llama/Llama-2-7b-chat-hf", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning (also known as deep structured learning) is part of a broader family of machine learning techniques based on artificial neural networks\u2014specific", - "expected_batch_output": "\n\nDeep learning (also known as deep structured learning) is part of a broader family of machine learning techniques based on artificial neural networks\u2014specific", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "mistralai/Mistral-7B-Instruct-v0.3": { - "model_id": "mistralai/Mistral-7B-Instruct-v0.3", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured", - "expected_batch_output": "\n\nDeep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "bigcode/starcoder2-3b": { - "model_id": "bigcode/starcoder2-3b", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning is a subset of machine learning that uses artificial neural networks to perform tasks.\n\nNeural networks are a type of machine learning algorithm that", - "expected_batch_output": "\n\nDeep learning is a subset of machine learning that uses artificial neural networks to perform tasks.\n\nNeural networks are a type of machine learning algorithm that", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "google/gemma-7b-it": { - "model_id": "google/gemma-7b-it", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from large amounts of data. Neural networks are inspired by the structure and function of", - "expected_batch_output": "\n\nDeep learning is a subset of machine learning that uses artificial neural networks to learn from large amounts of data. Neural networks are inspired by the structure and function of", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "Qwen/Qwen2-0.5B-Instruct": { - "model_id": "Qwen/Qwen2-0.5B-Instruct", - "input": "What is Deep Learning?", - "expected_greedy_output": " Deep Learning is a type of machine learning that is based on the principles of artificial neural networks. It is a type of machine learning that is used to train models", - "expected_batch_output": " Deep Learning is a type of machine learning that is based on the principles of artificial neural networks. It is a type of machine learning that is used to train models", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - "--max-batch-prefill-tokens", - "2048", - ], - }, - "tiiuae/falcon-7b-instruct": { - "model_id": "tiiuae/falcon-7b-instruct", - "input": "What is Deep Learning?", - "expected_greedy_output": "\nDeep learning is a branch of machine learning that uses artificial neural networks to learn and make decisions. It is based on the concept of hierarchical learning, where a", - "expected_batch_output": "\nDeep learning is a branch of machine learning that uses artificial neural networks to learn and make decisions. It is based on the concept of hierarchical learning, where a", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - ], - }, - "microsoft/phi-1_5": { - "model_id": "microsoft/phi-1_5", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep Learning is a subfield of Machine Learning that focuses on building neural networks with multiple layers of interconnected nodes. These networks are designed to learn from large", - "expected_batch_output": "\n\nDeep Learning is a subfield of Machine Learning that focuses on building neural networks with multiple layers of interconnected nodes. These networks are designed to learn from large", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - ], - }, - "openai-community/gpt2": { - "model_id": "openai-community/gpt2", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning is a new field of research that has been around for a long time. It is a new field of research that has been around for a", - "expected_batch_output": "\n\nDeep learning is a new field of research that has been around for a long time. It is a new field of research that has been around for a", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - ], - }, - "facebook/opt-125m": { - "model_id": "facebook/opt-125m", - "input": "What is Deep Learning?", - "expected_greedy_output": "\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout", - "expected_batch_output": "\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout the Author\n\nAbout", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - ], - }, - "EleutherAI/gpt-j-6b": { - "model_id": "EleutherAI/gpt-j-6b", - "input": "What is Deep Learning?", - "expected_greedy_output": "\n\nDeep learning is a subset of machine learning that is based on the idea of neural networks. Neural networks are a type of artificial intelligence that is inspired by", - "expected_batch_output": "\n\nDeep learning is a subset of machine learning that is based on the idea of neural networks. Neural networks are a type of artificial intelligence that is inspired by", - "args": [ - "--max-input-tokens", - "512", - "--max-total-tokens", - "1024", - "--max-batch-size", - "4", - ], - }, -} - -print(f"Testing {len(TEST_CONFIGS)} models") - - -@pytest.fixture(scope="module", params=TEST_CONFIGS.keys()) -def test_config(request) -> Dict[str, Any]: - """Fixture that provides model configurations for testing.""" - test_config = TEST_CONFIGS[request.param] - test_config["test_name"] = request.param - return test_config - - -@pytest.fixture(scope="module") -def model_id(test_config): - yield test_config["model_id"] - - -@pytest.fixture(scope="module") -def test_name(test_config): - yield test_config["test_name"] - - -@pytest.fixture(scope="module") -def expected_outputs(test_config): - return { - "greedy": test_config["expected_greedy_output"], - # "sampling": model_config["expected_sampling_output"], - "batch": test_config["expected_batch_output"], - } - - -@pytest.fixture(scope="module") -def input(test_config): - return test_config["input"] - - -@pytest.fixture(scope="module") -def tgi_service(launcher, model_id, test_name): - with launcher(model_id, test_name) as tgi_service: - yield tgi_service - - -@pytest.fixture(scope="module") -async def tgi_client(tgi_service) -> AsyncClient: - await tgi_service.health(1000) - return tgi_service.client - - -@pytest.mark.asyncio -async def test_model_single_request( - tgi_client: AsyncClient, expected_outputs: Dict[str, Any], input: str -): - # Bounded greedy decoding without input - response = await tgi_client.generate( - input, - max_new_tokens=32, - ) - assert response.details.generated_tokens == 32 - assert response.generated_text == expected_outputs["greedy"] - - -@pytest.mark.asyncio -async def test_model_multiple_requests( - tgi_client, generate_load, expected_outputs, input -): - num_requests = 4 - responses = await generate_load( - tgi_client, - input, - max_new_tokens=32, - n=num_requests, - ) - - assert len(responses) == 4 - expected = expected_outputs["batch"] - for r in responses: - assert r.details.generated_tokens == 32 - assert r.generated_text == expected