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

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feat(ci): llama3 test working
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from typing import Any, Dict
from text_generation import AsyncClient
import pytest
wip(ci): rerun ci to debug
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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.
feat(ci): llama3 test working
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TEST_CONFIGS = {
wip(ci): debug the ci
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# "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 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",
# "args": [
# "--sharded",
# "true",
# "--num-shard",
# "8",
# "--max-input-tokens",
# "512",
# "--max-total-tokens",
# "1024",
# "--max-batch-size",
# "8",
# "--max-batch-prefill-tokens",
# "2048",
# ],
# },
feat(ci): llama3 test working
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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 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",
],
},
wip(ci): debug the ci
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# "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",
# ],
# },
feat(ci): llama3 test working
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}
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(gaudi_launcher, model_id, test_name):
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
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, gaudi_generate_load, expected_outputs, input
):
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
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