text-generation-inference/integration-tests/gaudi/test_gaudi_generate.py

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from typing import Any, Dict, Generator
from _pytest.fixtures import SubRequest
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from text_generation import AsyncClient
import pytest
def pytest_configure(config):
config.addinivalue_line(
"markers", "gaudi_all_models: mark test to run with all models"
)
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# The "args" values in TEST_CONFIGS are not optimized for speed but only check that the inference is working for the different models architectures.
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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 Beginners 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 Beginners 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",
"args": [
"--sharded",
"true",
"--num-shard",
"8",
"--max-input-tokens",
"512",
"--max-total-tokens",
"1024",
"--max-batch-size",
"8",
"--max-batch-prefill-tokens",
"2048",
],
"run_by_default": True,
},
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"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 Beginners 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 Beginners 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",
],
"run_by_default": True,
},
"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",
],
},
"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",
],
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},
}
def pytest_generate_tests(metafunc):
if "test_config" in metafunc.fixturenames:
if metafunc.config.getoption("--gaudi-all-models"):
models = list(TEST_CONFIGS.keys())
else:
models = [
name
for name, config in TEST_CONFIGS.items()
if config.get("run_by_default", False)
]
print(f"Testing {len(models)} models")
metafunc.parametrize("test_config", models, indirect=True)
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@pytest.fixture(scope="module")
def test_config(request: SubRequest) -> Dict[str, Any]:
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"""Fixture that provides model configurations for testing."""
model_name = request.param
test_config = TEST_CONFIGS[model_name]
test_config["test_name"] = model_name
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return test_config
@pytest.fixture(scope="module")
def model_id(test_config: Dict[str, Any]) -> Generator[str, None, None]:
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yield test_config["model_id"]
@pytest.fixture(scope="module")
def test_name(test_config: Dict[str, Any]) -> Generator[str, None, None]:
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yield test_config["test_name"]
@pytest.fixture(scope="module")
def expected_outputs(test_config: Dict[str, Any]) -> Dict[str, str]:
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return {
"greedy": test_config["expected_greedy_output"],
"batch": test_config["expected_batch_output"],
}
@pytest.fixture(scope="module")
def input(test_config: Dict[str, Any]) -> str:
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return test_config["input"]
@pytest.fixture(scope="module")
def tgi_service(gaudi_launcher, model_id: str, test_name: str):
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with gaudi_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
@pytest.mark.all_models
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async def test_model_single_request(
tgi_client: AsyncClient, expected_outputs: Dict[str, str], input: str
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):
# 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
@pytest.mark.all_models
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async def test_model_multiple_requests(
tgi_client: AsyncClient,
gaudi_generate_load,
expected_outputs: Dict[str, str],
input: str,
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):
num_requests = 4
responses = await gaudi_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