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feat(ci): llama3 test working

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baptiste 2025-04-10 08:32:28 +00:00
parent 23fe77f059
commit e024f1dd22
4 changed files with 11 additions and 279 deletions
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2
backends/gaudi/Makefile
View File

@ -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:

6
integration-tests/conftest.py
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@ -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 (

9
integration-tests/fixtures/gaudi/service.py
View File

@ -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]:

273
integration-tests/gaudi/test_model.py
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@ -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