mirror of
https://github.com/huggingface/text-generation-inference.git
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37104acd75
* feat(gaudi): add integration test * feat(test): add more models to integration tests * remove debug comments * fix typos
277 lines
11 KiB
Python
277 lines
11 KiB
Python
from typing import Any, Dict
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from text_generation import AsyncClient
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import pytest
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from Levenshtein import distance as levenshtein_distance
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# The "args" config is not optimized for speed but only check that the inference is working for the different models architectures
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TEST_CONFIGS = {
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"meta-llama/Llama-3.1-8B-Instruct-shared": {
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"model_id": "meta-llama/Llama-3.1-8B-Instruct",
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"input": "What is Deep Learning?",
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"expected_greedy_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use",
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"expected_batch_output": " A Beginner’s Guide\nDeep learning is a subset of machine learning that involves the use",
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"args": [
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"--sharded",
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"true",
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"--num-shard",
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"8",
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"8",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"meta-llama/Llama-3.1-8B-Instruct": {
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"model_id": "meta-llama/Llama-3.1-8B-Instruct",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"env_config": {},
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"meta-llama/Llama-2-7b-chat-hf": {
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"model_id": "meta-llama/Llama-2-7b-chat-hf",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"mistralai/Mistral-7B-Instruct-v0.3": {
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"model_id": "mistralai/Mistral-7B-Instruct-v0.3",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"bigcode/starcoder2-3b": {
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"model_id": "bigcode/starcoder2-3b",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"google/gemma-7b-it": {
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"model_id": "google/gemma-7b-it",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"Qwen/Qwen2-0.5B-Instruct": {
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"model_id": "Qwen/Qwen2-0.5B-Instruct",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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"--max-batch-prefill-tokens",
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"2048",
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],
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},
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"tiiuae/falcon-7b-instruct": {
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"model_id": "tiiuae/falcon-7b-instruct",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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],
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},
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"microsoft/phi-1_5": {
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"model_id": "microsoft/phi-1_5",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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],
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},
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"openai-community/gpt2": {
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"model_id": "openai-community/gpt2",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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],
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},
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"facebook/opt-125m": {
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"model_id": "facebook/opt-125m",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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],
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},
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"EleutherAI/gpt-j-6b": {
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"model_id": "EleutherAI/gpt-j-6b",
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"input": "What is Deep Learning?",
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"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",
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"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",
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"args": [
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"--max-input-tokens",
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"512",
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"--max-total-tokens",
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"1024",
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"--max-batch-size",
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"4",
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],
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},
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}
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print(f"Testing {len(TEST_CONFIGS)} models")
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@pytest.fixture(scope="module", params=TEST_CONFIGS.keys())
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def test_config(request) -> Dict[str, Any]:
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"""Fixture that provides model configurations for testing."""
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test_config = TEST_CONFIGS[request.param]
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test_config["test_name"] = request.param
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return test_config
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@pytest.fixture(scope="module")
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def model_id(test_config):
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yield test_config["model_id"]
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@pytest.fixture(scope="module")
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def test_name(test_config):
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yield test_config["test_name"]
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@pytest.fixture(scope="module")
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def expected_outputs(test_config):
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return {
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"greedy": test_config["expected_greedy_output"],
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# "sampling": model_config["expected_sampling_output"],
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"batch": test_config["expected_batch_output"],
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}
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@pytest.fixture(scope="module")
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def input(test_config):
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return test_config["input"]
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@pytest.fixture(scope="module")
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def tgi_service(launcher, model_id, test_name):
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with launcher(model_id, test_name) as tgi_service:
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yield tgi_service
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@pytest.fixture(scope="module")
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async def tgi_client(tgi_service) -> AsyncClient:
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await tgi_service.health(1000)
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return tgi_service.client
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@pytest.mark.asyncio
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async def test_model_single_request(
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tgi_client: AsyncClient, expected_outputs: Dict[str, Any], input: str
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):
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# Bounded greedy decoding without input
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response = await tgi_client.generate(
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input,
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max_new_tokens=32,
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)
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assert response.details.generated_tokens == 32
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assert response.generated_text == expected_outputs["greedy"]
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@pytest.mark.asyncio
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async def test_model_multiple_requests(
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tgi_client, generate_load, expected_outputs, input
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):
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num_requests = 4
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responses = await generate_load(
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tgi_client,
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input,
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max_new_tokens=32,
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n=num_requests,
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)
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assert len(responses) == 4
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expected = expected_outputs["batch"]
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for r in responses:
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assert r.details.generated_tokens == 32
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# Compute the similarity with the expectation using the levenshtein distance
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# We should not have more than two substitutions or additions
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assert levenshtein_distance(r.generated_text, expected) < 3
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